From 7e42badcf7fee5cb4035f0a96ea0fac471839cf1 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sat, 18 Apr 2026 15:31:15 +0300 Subject: [PATCH 001/120] Link llvm dynamicly & implement memo popultaion --- Makefile | 3 + cmake/SetupLLVM.cmake | 2 - flake.nix | 2 + .../stewkk/sql/logic/optimizer/optimizer.hpp | 12 ++ src/stewkk/sql/CMakeLists.txt | 7 +- src/stewkk/sql/logic/optimizer/optimizer.cpp | 15 ++ .../sql/logic/optimizer/optimizer_test.cpp | 136 ++++++++++++++++++ test/CMakeLists.txt | 8 +- 8 files changed, 178 insertions(+), 7 deletions(-) create mode 100644 include/stewkk/sql/logic/optimizer/optimizer.hpp create mode 100644 src/stewkk/sql/logic/optimizer/optimizer.cpp create mode 100644 src/stewkk/sql/logic/optimizer/optimizer_test.cpp diff --git a/Makefile b/Makefile index b7b89d1..0dca4cf 100644 --- a/Makefile +++ b/Makefile @@ -5,6 +5,9 @@ PARSER_SOURCE_DIR := $(CURRENT_DIR)/src/stewkk/sql/logic/parser build: cmake --build build -- -j 6 +sanitize: + cmake --build build-sanitizers -- -j 8 + codegen: @antlr -Dlanguage=Cpp -visitor -o $(CODEGEN_DIR) -package stewkk::sql::codegen $(PARSER_SOURCE_DIR)/PostgreSQLParser.g4 $(PARSER_SOURCE_DIR)/PostgreSQLLexer.g4 diff --git a/cmake/SetupLLVM.cmake b/cmake/SetupLLVM.cmake index a61d32d..0364d84 100644 --- a/cmake/SetupLLVM.cmake +++ b/cmake/SetupLLVM.cmake @@ -1,6 +1,4 @@ find_package(ZLIB QUIET) - -set(LLVM_DIR "/home/st/c/llvm-project/build/lib/cmake/llvm") find_package(LLVM REQUIRED CONFIG) message(STATUS "Found LLVM ${LLVM_PACKAGE_VERSION}") diff --git a/flake.nix b/flake.nix index d3566d8..0dae742 100644 --- a/flake.nix +++ b/flake.nix @@ -42,6 +42,8 @@ tex plantuml inkscape + llvmPackages_21.llvm + llvmPackages_21.llvm.dev ]; nativeBuildInputs = [ diff --git a/include/stewkk/sql/logic/optimizer/optimizer.hpp b/include/stewkk/sql/logic/optimizer/optimizer.hpp new file mode 100644 index 0000000..287782d --- /dev/null +++ b/include/stewkk/sql/logic/optimizer/optimizer.hpp @@ -0,0 +1,12 @@ +#pragma once + +#include + +namespace stewkk::sql { + +class Optimizer { + public: + Operator Optimize(Operator expression); +}; + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/CMakeLists.txt b/src/stewkk/sql/CMakeLists.txt index be6ee2a..2826b7b 100644 --- a/src/stewkk/sql/CMakeLists.txt +++ b/src/stewkk/sql/CMakeLists.txt @@ -23,6 +23,7 @@ add_library(libsql models/executor/tuple.cpp logic/executor/materialization.cpp logic/executor/llvm.cpp + logic/optimizer/optimizer.cpp ) add_library(stewkk::libsql ALIAS libsql) target_compile_features(libsql PUBLIC cxx_std_23) @@ -43,8 +44,12 @@ target_include_directories( ) target_compile_options(libsql PRIVATE ${BASE_COMPILE_FLAGS}) target_link_options(libsql PRIVATE ${BASE_LINK_FLAGS}) -llvm_map_components_to_libnames(llvm_libs Support Core IRReader OrcJIT X86CodeGen X86AsmParser X86Desc X86Info) target_link_directories(libsql PUBLIC ${LLVM_LIBRARY_DIRS}) +if(TARGET LLVM) + set(llvm_libs LLVM) +else() + llvm_map_components_to_libnames(llvm_libs Support Core IRReader OrcJIT X86CodeGen X86AsmParser X86Desc X86Info) +endif() target_link_libraries(libsql PRIVATE antlr4_static ${llvm_libs} diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp new file mode 100644 index 0000000..e213ec4 --- /dev/null +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -0,0 +1,15 @@ +#include + +namespace stewkk::sql { + +Operator Optimizer::Optimize(Operator expression) { + return expression; +} + +/* +** нам нужно как-то маппить логическое выражение в его группу. +** для этого нужно хотя бы научиться проверять эквивалентны ли выражения. +** +*/ + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp new file mode 100644 index 0000000..68c4053 --- /dev/null +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -0,0 +1,136 @@ +#include + +#include +#include + +#include +#include +#include + +using ::testing::Eq; + +namespace stewkk::sql { + +TEST(OptimizerTest, Simple) { + std::stringstream s{"SELECT * FROM users;"}; + Operator op = GetAST(s).value(); + Optimizer optimizer; + + auto got = optimizer.Optimize(op); + + ASSERT_EQ(got, op); +} + +template struct Overloaded : Ts... { using Ts::operator()...; }; + +using GroupKey = std::string; + +namespace logical { + +using Table = Table; + +struct Filter { + GroupKey source; + Expression predicate; +}; + +struct Projection { + GroupKey source; + std::vector expressions; +}; + +struct CrossJoin { + GroupKey lhs; + GroupKey rhs; +}; + +struct Join { + GroupKey lhs; + GroupKey rhs; + using Type = JoinType; + Type type; + Expression qual; +}; + +} // namespace logical + + +using LogicalExpr = std::variant; + +class Group { + public: + explicit Group(LogicalExpr expr) : logical_exprs_({std::move(expr)}) {} + private: + std::vector logical_exprs_; +}; + +class Memo { + public: + explicit Memo(const Operator& expr) : mapping_(), root_(AddGroup(expr)) {} + + private: + + GroupKey AddGroup(const Operator& expr) { + return std::visit(Overloaded{ + [this](const Table& t) { + auto key = std::format("Table({})", t.name); + mapping_.insert_or_assign(key, Group(logical::Table(t))); + return key; + }, + [this](const Filter& f) { + auto source_group = AddGroup(*f.source); + auto key = std::format("Filter({}, {})", source_group, ToString(f.expr)); + mapping_.insert_or_assign(key, Group(logical::Filter(source_group, f.expr))); + return key; + }, + [this](const Projection& p) { + auto source_group = AddGroup(*p.source); + auto exprs = p.expressions | std::views::transform([](const Expression& e) { return ToString(e); }) | std::views::join_with(',') | std::ranges::to(); + auto key = std::format("Projection({}, {})", source_group, exprs); + mapping_.insert_or_assign(key, Group(logical::Projection(source_group, p.expressions))); + return key; + }, + [this](const CrossJoin& j) { + auto lhs_group = AddGroup(*j.lhs); + auto rhs_group = AddGroup(*j.rhs); + auto key = std::format("CrossJoin({}, {})", lhs_group, rhs_group); + mapping_.insert_or_assign(key, Group(logical::CrossJoin(lhs_group, rhs_group))); + return key; + }, + [this](const Join& j) { + auto lhs_group = AddGroup(*j.lhs); + auto rhs_group = AddGroup(*j.rhs); + auto key = std::format("Join({}, {}, {}, {})", lhs_group, rhs_group, ToString(j.type), ToString(j.qual)); + mapping_.insert_or_assign(key, Group(logical::Join(lhs_group, rhs_group, j.type, j.qual))); + return key; + }, + }, expr); + } + + std::unordered_map mapping_; + GroupKey root_; +}; + +// DSU data-structure needed? + +TEST(OptimizerTest, GetGroup) { + Operator join_ab = Join{ + .type = JoinType::kInner, + .qual = Literal::kTrue, + .lhs = std::make_shared(Table{.name = "A"}), + .rhs = std::make_shared(Table{.name = "B"}), + }; + + Memo memo(join_ab); + + // Operator join_ba = Join{ + // .type = JoinType::kInner, + // .qual = Literal::kTrue, + // .lhs = std::make_shared(Table{.name = "B"}), + // .rhs = std::make_shared(Table{.name = "A"}), + // }; + + // ASSERT_EQ(*memo.GetGroup(join_ab), *memo.GetGroup(join_ba)); +} + +} // namespace stewkk::sql diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt index 65187f8..face239 100644 --- a/test/CMakeLists.txt +++ b/test/CMakeLists.txt @@ -5,6 +5,7 @@ add_executable(unittests) target_sources(unittests PRIVATE ${PROJECT_SOURCE_DIR}/src/stewkk/sql/logic/parser/parser_test.cpp ${PROJECT_SOURCE_DIR}/src/stewkk/sql/logic/executor/executor_test.cpp + ${PROJECT_SOURCE_DIR}/src/stewkk/sql/logic/optimizer/optimizer_test.cpp ) target_compile_features(unittests PRIVATE cxx_std_23) set_target_properties(unittests PROPERTIES @@ -20,12 +21,11 @@ target_include_directories( ) target_compile_options(unittests PRIVATE ${BASE_COMPILE_FLAGS}) target_link_options(unittests PRIVATE ${BASE_COMPILE_FLAGS}) -llvm_map_components_to_libnames(llvm_libs Support Core IRReader OrcJIT X86CodeGen X86AsmParser X86Desc X86Info) -target_link_directories(libsql PUBLIC ${LLVM_LIBRARY_DIRS}) -target_link_libraries(unittests PRIVATE gmock_main libsql antlr4_static +target_link_libraries(unittests PRIVATE + gmock_main + libsql Boost::asio Boost::thread Boost::filesystem - ${llvm_libs} ) gtest_discover_tests(unittests) From 893e292bf09076bc5d0777eab967c2da113dfc55 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sun, 19 Apr 2026 17:59:12 +0300 Subject: [PATCH 002/120] Impl search algorithm --- .../stewkk/sql/logic/optimizer/optimizer.hpp | 4 - src/stewkk/sql/logic/optimizer/optimizer.cpp | 10 - .../sql/logic/optimizer/optimizer_test.cpp | 365 ++++++++++++++++-- 3 files changed, 326 insertions(+), 53 deletions(-) diff --git a/include/stewkk/sql/logic/optimizer/optimizer.hpp b/include/stewkk/sql/logic/optimizer/optimizer.hpp index 287782d..f889486 100644 --- a/include/stewkk/sql/logic/optimizer/optimizer.hpp +++ b/include/stewkk/sql/logic/optimizer/optimizer.hpp @@ -4,9 +4,5 @@ namespace stewkk::sql { -class Optimizer { - public: - Operator Optimize(Operator expression); -}; } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index e213ec4..f1e70bb 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -2,14 +2,4 @@ namespace stewkk::sql { -Operator Optimizer::Optimize(Operator expression) { - return expression; -} - -/* -** нам нужно как-то маппить логическое выражение в его группу. -** для этого нужно хотя бы научиться проверять эквивалентны ли выражения. -** -*/ - } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index 68c4053..ef9652f 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -1,7 +1,9 @@ #include +#include #include #include +#include #include #include @@ -11,16 +13,6 @@ using ::testing::Eq; namespace stewkk::sql { -TEST(OptimizerTest, Simple) { - std::stringstream s{"SELECT * FROM users;"}; - Operator op = GetAST(s).value(); - Optimizer optimizer; - - auto got = optimizer.Optimize(op); - - ASSERT_EQ(got, op); -} - template struct Overloaded : Ts... { using Ts::operator()...; }; using GroupKey = std::string; @@ -54,60 +46,195 @@ struct Join { } // namespace logical - using LogicalExpr = std::variant; +using RuleNumber = size_t; + +class Memo; + +class Rule { + public: + virtual bool IsApplicable(LogicalExpr expr, Memo& memo) = 0; + virtual bool IsTransformationRule() const = 0; + virtual LogicalExpr Apply(const LogicalExpr& expr, Memo& memo) = 0; + virtual ~Rule() = default; +}; + +class Rules { + public: + + explicit Rules(std::vector> rules) : rules_(std::move(rules)) {} + + size_t Count() const { + return rules_.size(); + } + + LogicalExpr Apply(const LogicalExpr& expr, RuleNumber rule, Memo& memo) const { + return rules_[rule]->Apply(expr, memo); + } + + bool IsApplicable(RuleNumber rule, const LogicalExpr& expr, Memo& memo) const { + return rules_[rule]->IsApplicable(expr, memo); + } + + bool IsTransformationRule(RuleNumber rule) const { + return rules_[rule]->IsTransformationRule(); + } + + private: + std::vector> rules_; +}; + +class ExpressionRulesApplier { + public: + ExpressionRulesApplier(LogicalExpr&& expr, const Rules& rules, GroupKey group) + : expr_(std::move(expr)), + rules_(rules), + is_applied_(rules_.Count(), false), + group_(std::move(group)) {} + + ExpressionRulesApplier ApplyRule(RuleNumber rule, Memo& memo) { + is_applied_[rule] = true; + return ExpressionRulesApplier(rules_.Apply(expr_, rule, memo), rules_, group_); + } + + bool IsApplicable(RuleNumber rule, Memo& memo, bool transformation_rules_only=false) const { + return !IsApplied(rule) && (!transformation_rules_only || rules_.IsTransformationRule(rule)) && rules_.IsApplicable(rule, expr_, memo); + } + + bool IsTransformationRule(RuleNumber rule) const { + return rules_.IsTransformationRule(rule); + } + + GroupKey GetGroup() const { return group_; } + const LogicalExpr& GetExpr() const { return expr_; } + + std::vector GetChildren() const { + return std::visit(Overloaded{ + [](const logical::Table&) -> std::vector { return {}; }, + [](const logical::Filter& f) -> std::vector { return {f.source}; }, + [](const logical::Projection& p) -> std::vector { return {p.source}; }, + [](const logical::CrossJoin& j) -> std::vector { return {j.lhs, j.rhs}; }, + [](const logical::Join& j) -> std::vector { return {j.lhs, j.rhs}; }, + }, expr_); + } + + private: + bool IsApplied(RuleNumber rule) const { return is_applied_[rule]; } + + const LogicalExpr expr_; + const Rules& rules_; + GroupKey group_; + std::vector is_applied_; +}; + class Group { public: - explicit Group(LogicalExpr expr) : logical_exprs_({std::move(expr)}) {} + Group(GroupKey key, ExpressionRulesApplier expr) : key_(std::move(key)), logical_exprs_({std::move(expr)}) {} + const GroupKey& GetKey() const { + return key_; + } + std::span GetExpressions() { + return logical_exprs_; + } + ExpressionRulesApplier& AddExpression(ExpressionRulesApplier expr) { + logical_exprs_.emplace_back(std::move(expr)); + return logical_exprs_.back(); + } private: - std::vector logical_exprs_; + const GroupKey key_; + std::vector logical_exprs_; }; class Memo { public: - explicit Memo(const Operator& expr) : mapping_(), root_(AddGroup(expr)) {} + explicit Memo(const Operator& expr, const Rules& rules) : mapping_(), rules_(rules), root_(AddGroup(expr, rules)) {} + + Group& GetRoot() { + return mapping_.find(root_)->second; + } + + GroupKey AddGroup(const LogicalExpr& expr) { + return AddGroup(expr, rules_); + } + + GroupKey AddGroup(const LogicalExpr& expr, const Rules& rules) { + return std::visit(Overloaded{ + [this, &rules](const logical::Table& t) { + auto key = std::format("Table({})", t.name); + mapping_.emplace(key, Group(key, ExpressionRulesApplier(t, rules, key))); + return key; + }, + [this, &rules](const logical::Filter& f) { + auto key = std::format("Filter({}, {})", f.source, ToString(f.predicate)); + mapping_.emplace(key, Group(key, ExpressionRulesApplier(f, rules, key))); + return key; + }, + [this, &rules](const logical::Projection& p) { + auto exprs = p.expressions | std::views::transform([](const Expression& e) { return ToString(e); }) | std::views::join_with(',') | std::ranges::to(); + auto key = std::format("Projection({}, {})", p.source, exprs); + mapping_.emplace(key, Group(key, ExpressionRulesApplier(p, rules, key))); + return key; + }, + [this, &rules](const logical::CrossJoin& j) { + auto key = std::format("CrossJoin({}, {})", j.lhs, j.rhs); + mapping_.emplace(key, Group(key, ExpressionRulesApplier(j, rules, key))); + return key; + }, + [this, &rules](const logical::Join& j) { + auto key = std::format("Join({}, {}, {}, {})", j.lhs, j.rhs, ToString(j.type), ToString(j.qual)); + mapping_.emplace(key, Group(key, ExpressionRulesApplier(j, rules, key))); + return key; + }, + }, expr); + } + + // TODO: replace with raw ptr (or some non-null wrapper) + Group& GetGroup(GroupKey key) { + return mapping_.find(key)->second; + } private: - GroupKey AddGroup(const Operator& expr) { + GroupKey AddGroup(const Operator& expr, const Rules& rules) { return std::visit(Overloaded{ - [this](const Table& t) { + [this, &rules](const Table& t) { auto key = std::format("Table({})", t.name); - mapping_.insert_or_assign(key, Group(logical::Table(t))); + mapping_.emplace(key, Group(key, ExpressionRulesApplier(logical::Table(t), rules, key))); return key; }, - [this](const Filter& f) { - auto source_group = AddGroup(*f.source); + [this, &rules](const Filter& f) { + auto source_group = AddGroup(*f.source, rules); auto key = std::format("Filter({}, {})", source_group, ToString(f.expr)); - mapping_.insert_or_assign(key, Group(logical::Filter(source_group, f.expr))); + mapping_.emplace(key, Group(key, ExpressionRulesApplier(logical::Filter(source_group, f.expr), rules, key))); return key; }, - [this](const Projection& p) { - auto source_group = AddGroup(*p.source); + [this, &rules](const Projection& p) { + auto source_group = AddGroup(*p.source, rules); auto exprs = p.expressions | std::views::transform([](const Expression& e) { return ToString(e); }) | std::views::join_with(',') | std::ranges::to(); auto key = std::format("Projection({}, {})", source_group, exprs); - mapping_.insert_or_assign(key, Group(logical::Projection(source_group, p.expressions))); + mapping_.emplace(key, Group(key, ExpressionRulesApplier(logical::Projection(source_group, p.expressions), rules, key))); return key; }, - [this](const CrossJoin& j) { - auto lhs_group = AddGroup(*j.lhs); - auto rhs_group = AddGroup(*j.rhs); + [this, &rules](const CrossJoin& j) { + auto lhs_group = AddGroup(*j.lhs, rules); + auto rhs_group = AddGroup(*j.rhs, rules); auto key = std::format("CrossJoin({}, {})", lhs_group, rhs_group); - mapping_.insert_or_assign(key, Group(logical::CrossJoin(lhs_group, rhs_group))); + mapping_.emplace(key, Group(key, ExpressionRulesApplier(logical::CrossJoin(lhs_group, rhs_group), rules, key))); return key; }, - [this](const Join& j) { - auto lhs_group = AddGroup(*j.lhs); - auto rhs_group = AddGroup(*j.rhs); + [this, &rules](const Join& j) { + auto lhs_group = AddGroup(*j.lhs, rules); + auto rhs_group = AddGroup(*j.rhs, rules); auto key = std::format("Join({}, {}, {}, {})", lhs_group, rhs_group, ToString(j.type), ToString(j.qual)); - mapping_.insert_or_assign(key, Group(logical::Join(lhs_group, rhs_group, j.type, j.qual))); + mapping_.emplace(key, Group(key, ExpressionRulesApplier(logical::Join(lhs_group, rhs_group, j.type, j.qual), rules, key))); return key; }, }, expr); } std::unordered_map mapping_; + const Rules& rules_; GroupKey root_; }; @@ -121,16 +248,176 @@ TEST(OptimizerTest, GetGroup) { .rhs = std::make_shared(Table{.name = "B"}), }; - Memo memo(join_ab); + Memo memo(join_ab, Rules{{}}); +} + +class Optimizer { + public: + + explicit Optimizer(const Operator& expr, const Rules& rules) : memo_(expr, rules), rules_count_(rules.Count()) {} + + bool IsExplored(const Group& group) const { + return explored_groups_.contains(group.GetKey()); + } + + void SetExplored(const Group& group) { + explored_groups_.insert(group.GetKey()); + } + + void OptimizeInputs(const ExpressionRulesApplier& expr) { + } + + void ApplyRule(ExpressionRulesApplier& expr, RuleNumber rule) { + auto new_expr = expr.ApplyRule(rule, memo_); + auto group = memo_.GetGroup(new_expr.GetGroup()); + auto new_expr_ref = group.AddExpression(std::move(new_expr)); + if (new_expr.IsTransformationRule(rule)) { + tasks_.emplace([this, &new_expr_ref]() { ExploreExpression(new_expr_ref); }); + } else { + tasks_.emplace([this, &new_expr_ref]() { OptimizeInputs(new_expr_ref); }); + } + } + + void OptimizeExpression(ExpressionRulesApplier& expr) { + for (RuleNumber rule = 0; rule < rules_count_; rule++) { + if (!expr.IsApplicable(rule, memo_)) { + continue; + } + tasks_.emplace([this, &expr, rule]() { + ApplyRule(expr, rule); + }); + } - // Operator join_ba = Join{ - // .type = JoinType::kInner, - // .qual = Literal::kTrue, - // .lhs = std::make_shared(Table{.name = "B"}), - // .rhs = std::make_shared(Table{.name = "A"}), - // }; + for (auto child : expr.GetChildren()) { + auto& group = memo_.GetGroup(child); + if (IsExplored(group)) { + continue; + } + tasks_.emplace([this, &group]() { + ExploreGroup(group); + }); + } + } + + void ExploreExpression(ExpressionRulesApplier& expr) { + for (RuleNumber rule = 0; rule < rules_count_; rule++) { + bool transformation_rules_only = true; + if (!expr.IsApplicable(rule, memo_, transformation_rules_only)) { + continue; + } + tasks_.emplace([this, &expr, rule]() { + ApplyRule(expr, rule); + }); + } + + for (auto child : expr.GetChildren()) { + auto& group = memo_.GetGroup(child); + if (IsExplored(group)) { + continue; + } + tasks_.emplace([this, &group]() { + ExploreGroup(group); + }); + } + } + + void ExploreGroup(Group& group) { + SetExplored(group); + for (auto& expr : group.GetExpressions()) { + tasks_.emplace([this, &expr]() { + ExploreExpression(expr); + }); + } + } + + void OptimizeGroup(Group& group) { + if (!IsExplored(group)) { + tasks_.emplace([this, &group](){ + OptimizeGroup(group); + }); + tasks_.emplace([this, &group](){ + ExploreGroup(group); + }); + return; + } + + for (auto& expr : group.GetExpressions()) { + tasks_.emplace([this, &expr](){ + OptimizeExpression(expr); + }); + } + } + + Operator Optimize() { + tasks_.emplace([this]() { + OptimizeGroup(memo_.GetRoot()); + }); + while (!tasks_.empty()) { + auto next_task = std::move(tasks_.top()); + tasks_.pop(); + next_task(); + } + return Table{"A"}; + } + + private: + Memo memo_; + std::stack> tasks_; + std::unordered_set explored_groups_; + const size_t rules_count_; +}; + +class JoinCommutativity : public Rule { + public: + bool IsApplicable(LogicalExpr expr, Memo&) override { + return std::holds_alternative(expr); + } + + bool IsTransformationRule() const override { return true; } + + LogicalExpr Apply(const LogicalExpr& expr, Memo&) override { + auto join = std::get(expr); + std::swap(join.lhs, join.rhs); + return join; + } +}; + +class JoinAssociativity : public Rule { + public: + bool IsApplicable(LogicalExpr expr, Memo& memo) override { + if (!std::holds_alternative(expr)) return false; + const auto& outer = std::get(expr); + for (auto& applier : memo.GetGroup(outer.lhs).GetExpressions()) { + if (!std::holds_alternative(applier.GetExpr())) return false; + } + return true; + } + + bool IsTransformationRule() const override { return true; } + + LogicalExpr Apply(const LogicalExpr& expr, Memo& memo) override { + const auto& outer = std::get(expr); + for (auto& applier : memo.GetGroup(outer.lhs).GetExpressions()) { + const auto& inner = std::get(applier.GetExpr()); + auto combined_qual = Expression{BinaryExpression{ + std::make_shared(inner.qual), + BinaryOp::kAnd, + std::make_shared(outer.qual), + }}; + auto new_rhs = memo.AddGroup(logical::Join{inner.rhs, outer.rhs, outer.type, Literal::kTrue}); + return logical::Join{inner.lhs, new_rhs, inner.type, combined_qual}; + } + return expr; + } +}; + + +TEST(OptimizerTest, Simple) { + std::stringstream s{"SELECT * FROM users;"}; + Operator op = GetAST(s).value(); + Optimizer optimizer(op, Rules{{}}); - // ASSERT_EQ(*memo.GetGroup(join_ab), *memo.GetGroup(join_ba)); + auto got = optimizer.Optimize(); } } // namespace stewkk::sql From 8fe07e491225fe5fea635f2e329b66a8efbe9e68 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sun, 19 Apr 2026 20:15:23 +0300 Subject: [PATCH 003/120] Refactoring --- include/stewkk/sql/logic/optimizer/group.hpp | 41 +++++ .../sql/logic/optimizer/logical_expr.hpp | 44 +++++ include/stewkk/sql/logic/optimizer/memo.hpp | 24 +++ .../sql/logic/optimizer/physical_expr.hpp | 17 ++ include/stewkk/sql/logic/optimizer/rule.hpp | 26 +++ include/stewkk/sql/utils/not_null.hpp | 25 +++ include/stewkk/sql/utils/overloaded.hpp | 7 + .../sql/logic/optimizer/optimizer_test.cpp | 154 +----------------- src/stewkk/sql/logic/optimizer/rule_test.cpp | 122 ++++++++++++++ src/stewkk/sql/utils/non_null.cpp | 1 + test/CMakeLists.txt | 1 + 11 files changed, 310 insertions(+), 152 deletions(-) create mode 100644 include/stewkk/sql/logic/optimizer/group.hpp create mode 100644 include/stewkk/sql/logic/optimizer/logical_expr.hpp create mode 100644 include/stewkk/sql/logic/optimizer/memo.hpp create mode 100644 include/stewkk/sql/logic/optimizer/physical_expr.hpp create mode 100644 include/stewkk/sql/logic/optimizer/rule.hpp create mode 100644 include/stewkk/sql/utils/not_null.hpp create mode 100644 include/stewkk/sql/utils/overloaded.hpp create mode 100644 src/stewkk/sql/logic/optimizer/rule_test.cpp create mode 100644 src/stewkk/sql/utils/non_null.cpp diff --git a/include/stewkk/sql/logic/optimizer/group.hpp b/include/stewkk/sql/logic/optimizer/group.hpp new file mode 100644 index 0000000..d24436c --- /dev/null +++ b/include/stewkk/sql/logic/optimizer/group.hpp @@ -0,0 +1,41 @@ +#pragma once + +#include +#include +#include + +#include +#include + +namespace stewkk::sql { + +using LogicalOperator = decltype(LogicalExpr::root_operator); + +class Group { + public: + using LogicalExprPtr = std::unique_ptr; + using PhysicalExprPtr = std::unique_ptr; + + utils::NotNull AddLogicalExpr(LogicalOperator root_operator) { + auto& ptr = logical_exprs_.emplace_back( + std::make_unique(std::move(root_operator), this)); + return ptr.get(); + } + + utils::NotNull AddPhysicalExpr() { + auto& ptr = physical_exprs_.emplace_back( + std::make_unique(this)); + return ptr.get(); + } + + std::span GetLogicalExprs() const { return logical_exprs_; } + + private: + friend class Memo; + Group() = default; + + std::vector logical_exprs_; + std::vector physical_exprs_; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/logical_expr.hpp b/include/stewkk/sql/logic/optimizer/logical_expr.hpp new file mode 100644 index 0000000..cac6bc0 --- /dev/null +++ b/include/stewkk/sql/logic/optimizer/logical_expr.hpp @@ -0,0 +1,44 @@ +#pragma once + +#include +#include + +namespace stewkk::sql { + +struct Group; + +namespace logical { + +using Table = Table; + +struct Filter { + utils::NotNull source; + Expression predicate; +}; + +struct Projection { + utils::NotNull source; + std::vector expressions; +}; + +struct CrossJoin { + utils::NotNull lhs; + utils::NotNull rhs; +}; + +struct Join { + utils::NotNull lhs; + utils::NotNull rhs; + using Type = JoinType; + Type type; + Expression qual; +}; + +} // namespace logical + +struct LogicalExpr { + std::variant root_operator; + utils::NotNull group; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/memo.hpp b/include/stewkk/sql/logic/optimizer/memo.hpp new file mode 100644 index 0000000..08e1746 --- /dev/null +++ b/include/stewkk/sql/logic/optimizer/memo.hpp @@ -0,0 +1,24 @@ +#pragma once + +#include +#include + +#include + +namespace stewkk::sql { + +class Memo { + public: + size_t GroupCount() const { return groups_.size(); } + + utils::NotNull AddGroup(LogicalOperator root_operator) { + auto& ptr = groups_.emplace_back(new Group()); + ptr->AddLogicalExpr(std::move(root_operator)); + return ptr.get(); + } + + private: + std::vector> groups_; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/physical_expr.hpp b/include/stewkk/sql/logic/optimizer/physical_expr.hpp new file mode 100644 index 0000000..bec27bf --- /dev/null +++ b/include/stewkk/sql/logic/optimizer/physical_expr.hpp @@ -0,0 +1,17 @@ +#pragma once + +#include + +namespace stewkk::sql { + +struct Group; + +namespace physical { + +} // namespace physical + +struct PhysicalExpr { + utils::NotNull group; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/rule.hpp b/include/stewkk/sql/logic/optimizer/rule.hpp new file mode 100644 index 0000000..f46a07f --- /dev/null +++ b/include/stewkk/sql/logic/optimizer/rule.hpp @@ -0,0 +1,26 @@ +#pragma once + +#include +#include + +namespace stewkk::sql { + +class Memo; + +class TransformationRule { + public: + virtual bool IsApplicable(utils::NotNull expr) = 0; + virtual utils::NotNull Apply(utils::NotNull expr, Memo& memo) = 0; + virtual ~TransformationRule() = default; +}; + +class ImplementationRule { + public: + virtual bool IsApplicable(utils::NotNull expr) = 0; + virtual utils::NotNull Apply(utils::NotNull, Memo& memo) = 0; + virtual ~ImplementationRule() = default; +}; + +// FIXME: enforcer rules? + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/utils/not_null.hpp b/include/stewkk/sql/utils/not_null.hpp new file mode 100644 index 0000000..72f70bd --- /dev/null +++ b/include/stewkk/sql/utils/not_null.hpp @@ -0,0 +1,25 @@ +#pragma once + +#include +#include + +namespace stewkk::sql::utils { + +template + requires std::is_pointer_v +class NotNull { + public: + NotNull(T ptr) : ptr_(ptr) { assert(ptr_ != nullptr); } // NOLINT(google-explicit-constructor) + NotNull(std::nullptr_t) = delete; + + T get() const { return ptr_; } + auto& operator*() const { return *ptr_; } + T operator->() const { return ptr_; } + + operator T() const { return ptr_; } // NOLINT(google-explicit-constructor) + + private: + T ptr_; +}; + +} // namespace stewkk::sql::utils diff --git a/include/stewkk/sql/utils/overloaded.hpp b/include/stewkk/sql/utils/overloaded.hpp new file mode 100644 index 0000000..9eb1b19 --- /dev/null +++ b/include/stewkk/sql/utils/overloaded.hpp @@ -0,0 +1,7 @@ +#pragma once + +namespace stewkk::sql::utils { + +template struct Overloaded : Ts... { using Ts::operator()...; }; + +} // namespace stewkk::sql::utils diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index ef9652f..5f0483a 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -7,84 +7,14 @@ #include #include +#include #include +#include using ::testing::Eq; namespace stewkk::sql { -template struct Overloaded : Ts... { using Ts::operator()...; }; - -using GroupKey = std::string; - -namespace logical { - -using Table = Table; - -struct Filter { - GroupKey source; - Expression predicate; -}; - -struct Projection { - GroupKey source; - std::vector expressions; -}; - -struct CrossJoin { - GroupKey lhs; - GroupKey rhs; -}; - -struct Join { - GroupKey lhs; - GroupKey rhs; - using Type = JoinType; - Type type; - Expression qual; -}; - -} // namespace logical - -using LogicalExpr = std::variant; - -using RuleNumber = size_t; - -class Memo; - -class Rule { - public: - virtual bool IsApplicable(LogicalExpr expr, Memo& memo) = 0; - virtual bool IsTransformationRule() const = 0; - virtual LogicalExpr Apply(const LogicalExpr& expr, Memo& memo) = 0; - virtual ~Rule() = default; -}; - -class Rules { - public: - - explicit Rules(std::vector> rules) : rules_(std::move(rules)) {} - - size_t Count() const { - return rules_.size(); - } - - LogicalExpr Apply(const LogicalExpr& expr, RuleNumber rule, Memo& memo) const { - return rules_[rule]->Apply(expr, memo); - } - - bool IsApplicable(RuleNumber rule, const LogicalExpr& expr, Memo& memo) const { - return rules_[rule]->IsApplicable(expr, memo); - } - - bool IsTransformationRule(RuleNumber rule) const { - return rules_[rule]->IsTransformationRule(); - } - - private: - std::vector> rules_; -}; - class ExpressionRulesApplier { public: ExpressionRulesApplier(LogicalExpr&& expr, const Rules& rules, GroupKey group) @@ -102,10 +32,6 @@ class ExpressionRulesApplier { return !IsApplied(rule) && (!transformation_rules_only || rules_.IsTransformationRule(rule)) && rules_.IsApplicable(rule, expr_, memo); } - bool IsTransformationRule(RuleNumber rule) const { - return rules_.IsTransformationRule(rule); - } - GroupKey GetGroup() const { return group_; } const LogicalExpr& GetExpr() const { return expr_; } @@ -128,24 +54,6 @@ class ExpressionRulesApplier { std::vector is_applied_; }; -class Group { - public: - Group(GroupKey key, ExpressionRulesApplier expr) : key_(std::move(key)), logical_exprs_({std::move(expr)}) {} - const GroupKey& GetKey() const { - return key_; - } - std::span GetExpressions() { - return logical_exprs_; - } - ExpressionRulesApplier& AddExpression(ExpressionRulesApplier expr) { - logical_exprs_.emplace_back(std::move(expr)); - return logical_exprs_.back(); - } - private: - const GroupKey key_; - std::vector logical_exprs_; -}; - class Memo { public: explicit Memo(const Operator& expr, const Rules& rules) : mapping_(), rules_(rules), root_(AddGroup(expr, rules)) {} @@ -238,19 +146,6 @@ class Memo { GroupKey root_; }; -// DSU data-structure needed? - -TEST(OptimizerTest, GetGroup) { - Operator join_ab = Join{ - .type = JoinType::kInner, - .qual = Literal::kTrue, - .lhs = std::make_shared(Table{.name = "A"}), - .rhs = std::make_shared(Table{.name = "B"}), - }; - - Memo memo(join_ab, Rules{{}}); -} - class Optimizer { public: @@ -367,51 +262,6 @@ class Optimizer { const size_t rules_count_; }; -class JoinCommutativity : public Rule { - public: - bool IsApplicable(LogicalExpr expr, Memo&) override { - return std::holds_alternative(expr); - } - - bool IsTransformationRule() const override { return true; } - - LogicalExpr Apply(const LogicalExpr& expr, Memo&) override { - auto join = std::get(expr); - std::swap(join.lhs, join.rhs); - return join; - } -}; - -class JoinAssociativity : public Rule { - public: - bool IsApplicable(LogicalExpr expr, Memo& memo) override { - if (!std::holds_alternative(expr)) return false; - const auto& outer = std::get(expr); - for (auto& applier : memo.GetGroup(outer.lhs).GetExpressions()) { - if (!std::holds_alternative(applier.GetExpr())) return false; - } - return true; - } - - bool IsTransformationRule() const override { return true; } - - LogicalExpr Apply(const LogicalExpr& expr, Memo& memo) override { - const auto& outer = std::get(expr); - for (auto& applier : memo.GetGroup(outer.lhs).GetExpressions()) { - const auto& inner = std::get(applier.GetExpr()); - auto combined_qual = Expression{BinaryExpression{ - std::make_shared(inner.qual), - BinaryOp::kAnd, - std::make_shared(outer.qual), - }}; - auto new_rhs = memo.AddGroup(logical::Join{inner.rhs, outer.rhs, outer.type, Literal::kTrue}); - return logical::Join{inner.lhs, new_rhs, inner.type, combined_qual}; - } - return expr; - } -}; - - TEST(OptimizerTest, Simple) { std::stringstream s{"SELECT * FROM users;"}; Operator op = GetAST(s).value(); diff --git a/src/stewkk/sql/logic/optimizer/rule_test.cpp b/src/stewkk/sql/logic/optimizer/rule_test.cpp new file mode 100644 index 0000000..173e5e2 --- /dev/null +++ b/src/stewkk/sql/logic/optimizer/rule_test.cpp @@ -0,0 +1,122 @@ +#include + +#include +#include + +namespace stewkk::sql { + +class JoinCommutativity : public TransformationRule { + public: + bool IsApplicable(utils::NotNull expr) override { + return std::holds_alternative(expr->root_operator); + } + + utils::NotNull Apply(utils::NotNull expr, Memo&) override { + auto join = std::get(expr->root_operator); + std::swap(join.lhs, join.rhs); + return expr->group->AddLogicalExpr(std::move(join)); + } +}; + +class JoinCommutativityTest : public ::testing::Test { + protected: + void SetUp() override { + a = memo.AddGroup(logical::Table{"a"}); + b = memo.AddGroup(logical::Table{"b"}); + } + + Memo memo; + Group* a = nullptr; + Group* b = nullptr; + JoinCommutativity rule; +}; + +TEST_F(JoinCommutativityTest, ReturnsSwappedJoin) { + auto join_group = memo.AddGroup(logical::Join{a, b, JoinType::kInner, Literal::kTrue}); + auto* expr = join_group->GetLogicalExprs()[0].get(); + + auto result = rule.Apply(expr, memo); + + const auto& join = std::get(result->root_operator); + EXPECT_EQ(join.lhs.get(), b); + EXPECT_EQ(join.rhs.get(), a); +} + +TEST_F(JoinCommutativityTest, AddsNewJoinIntoGroup) { + auto join_group = memo.AddGroup(logical::Join{a, b, JoinType::kInner, Literal::kTrue}); + auto* expr = join_group->GetLogicalExprs()[0].get(); + + rule.Apply(expr, memo); + + EXPECT_EQ(join_group->GetLogicalExprs().size(), 2u); + const auto& new_join = std::get(join_group->GetLogicalExprs()[1]->root_operator); + EXPECT_EQ(new_join.lhs.get(), b); + EXPECT_EQ(new_join.rhs.get(), a); +} + +class JoinAssociativity : public TransformationRule { + public: + bool IsApplicable(utils::NotNull expr) override { + if (!std::holds_alternative(expr->root_operator)) return false; + const auto& outer = std::get(expr->root_operator); + for (const auto& inner_expr : outer.lhs->GetLogicalExprs()) { + if (std::holds_alternative(inner_expr->root_operator)) return true; + } + return false; + } + + utils::NotNull Apply(utils::NotNull expr, Memo& memo) override { + const auto& outer = std::get(expr->root_operator); + for (const auto& inner_expr : outer.lhs->GetLogicalExprs()) { + if (!std::holds_alternative(inner_expr->root_operator)) continue; + const auto& inner = std::get(inner_expr->root_operator); + auto combined_qual = Expression{BinaryExpression{ + std::make_shared(inner.qual), + BinaryOp::kAnd, + std::make_shared(outer.qual), + }}; + auto new_rhs = memo.AddGroup(logical::Join{inner.rhs, outer.rhs, outer.type, Literal::kTrue}); + return expr->group->AddLogicalExpr(logical::Join{inner.lhs, new_rhs, inner.type, combined_qual}); + } + return expr; + } +}; + +class JoinAssociativityTest : public ::testing::Test { + protected: + void SetUp() override { + a = memo.AddGroup(logical::Table{"a"}); + b = memo.AddGroup(logical::Table{"b"}); + c = memo.AddGroup(logical::Table{"c"}); + ab = memo.AddGroup(logical::Join{a, b, JoinType::kInner, Literal::kTrue}); + abc = memo.AddGroup(logical::Join{ab, c, JoinType::kInner, Literal::kFalse}); + } + + Memo memo; + Group* a = nullptr; + Group* b = nullptr; + Group* c = nullptr; + Group* ab = nullptr; + Group* abc = nullptr; + JoinAssociativity rule; +}; + +TEST_F(JoinAssociativityTest, CreatesNewGroup) { + rule.Apply(abc->GetLogicalExprs()[0].get(), memo); + + EXPECT_EQ(memo.GroupCount(), 6u); +} + +TEST_F(JoinAssociativityTest, ReturnsCorrectExpression) { + auto result = rule.Apply(abc->GetLogicalExprs()[0].get(), memo); + + const auto& outer = std::get(result->root_operator); + EXPECT_EQ(outer.lhs.get(), a); + EXPECT_EQ(outer.type, JoinType::kInner); + const auto& inner = std::get(outer.rhs->GetLogicalExprs()[0]->root_operator); + EXPECT_EQ(inner.lhs.get(), b); + EXPECT_EQ(inner.rhs.get(), c); + EXPECT_EQ(inner.qual, Expression{Literal::kTrue}); +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/utils/non_null.cpp b/src/stewkk/sql/utils/non_null.cpp new file mode 100644 index 0000000..9b62def --- /dev/null +++ b/src/stewkk/sql/utils/non_null.cpp @@ -0,0 +1 @@ +#include diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt index face239..092cfbd 100644 --- a/test/CMakeLists.txt +++ b/test/CMakeLists.txt @@ -5,6 +5,7 @@ add_executable(unittests) target_sources(unittests PRIVATE ${PROJECT_SOURCE_DIR}/src/stewkk/sql/logic/parser/parser_test.cpp ${PROJECT_SOURCE_DIR}/src/stewkk/sql/logic/executor/executor_test.cpp + ${PROJECT_SOURCE_DIR}/src/stewkk/sql/logic/optimizer/rule_test.cpp ${PROJECT_SOURCE_DIR}/src/stewkk/sql/logic/optimizer/optimizer_test.cpp ) target_compile_features(unittests PRIVATE cxx_std_23) From 1495fe389188fda0aeeaa6e7ce0b6abd48fc75a2 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sun, 19 Apr 2026 21:23:28 +0300 Subject: [PATCH 004/120] Refactoring --- include/stewkk/sql/logic/optimizer/group.hpp | 17 +- include/stewkk/sql/logic/optimizer/memo.hpp | 9 +- include/stewkk/sql/logic/optimizer/rules.hpp | 20 + .../sql/logic/optimizer/rules_applier.hpp | 20 + .../join_associativity.hpp | 14 + .../join_commutativity.hpp | 14 + src/stewkk/sql/CMakeLists.txt | 5 + src/stewkk/sql/logic/optimizer/group.cpp | 21 + src/stewkk/sql/logic/optimizer/memo.cpp | 15 + .../sql/logic/optimizer/optimizer_test.cpp | 508 +++++++++--------- src/stewkk/sql/logic/optimizer/rule_test.cpp | 47 +- src/stewkk/sql/logic/optimizer/rules.cpp | 15 + .../logic/optimizer/rules_applier_test.cpp | 10 + .../join_associativity.cpp | 33 ++ .../join_commutativity.cpp | 15 + test/CMakeLists.txt | 1 + 16 files changed, 447 insertions(+), 317 deletions(-) create mode 100644 include/stewkk/sql/logic/optimizer/rules.hpp create mode 100644 include/stewkk/sql/logic/optimizer/rules_applier.hpp create mode 100644 include/stewkk/sql/logic/transformation_rules/join_associativity.hpp create mode 100644 include/stewkk/sql/logic/transformation_rules/join_commutativity.hpp create mode 100644 src/stewkk/sql/logic/optimizer/group.cpp create mode 100644 src/stewkk/sql/logic/optimizer/memo.cpp create mode 100644 src/stewkk/sql/logic/optimizer/rules.cpp create mode 100644 src/stewkk/sql/logic/optimizer/rules_applier_test.cpp create mode 100644 src/stewkk/sql/logic/transformation_rules/join_associativity.cpp create mode 100644 src/stewkk/sql/logic/transformation_rules/join_commutativity.cpp diff --git a/include/stewkk/sql/logic/optimizer/group.hpp b/include/stewkk/sql/logic/optimizer/group.hpp index d24436c..0b15171 100644 --- a/include/stewkk/sql/logic/optimizer/group.hpp +++ b/include/stewkk/sql/logic/optimizer/group.hpp @@ -10,25 +10,16 @@ namespace stewkk::sql { using LogicalOperator = decltype(LogicalExpr::root_operator); +using RuleId = size_t; class Group { public: using LogicalExprPtr = std::unique_ptr; using PhysicalExprPtr = std::unique_ptr; - utils::NotNull AddLogicalExpr(LogicalOperator root_operator) { - auto& ptr = logical_exprs_.emplace_back( - std::make_unique(std::move(root_operator), this)); - return ptr.get(); - } - - utils::NotNull AddPhysicalExpr() { - auto& ptr = physical_exprs_.emplace_back( - std::make_unique(this)); - return ptr.get(); - } - - std::span GetLogicalExprs() const { return logical_exprs_; } + utils::NotNull AddLogicalExpr(LogicalOperator root_operator); + utils::NotNull AddPhysicalExpr(); + std::span GetLogicalExprs() const; private: friend class Memo; diff --git a/include/stewkk/sql/logic/optimizer/memo.hpp b/include/stewkk/sql/logic/optimizer/memo.hpp index 08e1746..2e582cd 100644 --- a/include/stewkk/sql/logic/optimizer/memo.hpp +++ b/include/stewkk/sql/logic/optimizer/memo.hpp @@ -9,13 +9,8 @@ namespace stewkk::sql { class Memo { public: - size_t GroupCount() const { return groups_.size(); } - - utils::NotNull AddGroup(LogicalOperator root_operator) { - auto& ptr = groups_.emplace_back(new Group()); - ptr->AddLogicalExpr(std::move(root_operator)); - return ptr.get(); - } + size_t GroupCount() const; + utils::NotNull AddGroup(LogicalOperator root_operator); private: std::vector> groups_; diff --git a/include/stewkk/sql/logic/optimizer/rules.hpp b/include/stewkk/sql/logic/optimizer/rules.hpp new file mode 100644 index 0000000..c0c7a7b --- /dev/null +++ b/include/stewkk/sql/logic/optimizer/rules.hpp @@ -0,0 +1,20 @@ +#pragma once + +#include +#include + +#include +#include +#include + +namespace stewkk::sql { + +template +struct Rules { + std::array, NTransformation> transformation_rules; + std::array, NImplementation> implementation_rules; +}; + +Rules<2, 0> MakeMainRules(); + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/rules_applier.hpp b/include/stewkk/sql/logic/optimizer/rules_applier.hpp new file mode 100644 index 0000000..9d9ad9f --- /dev/null +++ b/include/stewkk/sql/logic/optimizer/rules_applier.hpp @@ -0,0 +1,20 @@ +#pragma once + +#include +#include + +#include + +namespace stewkk::sql { + +template +class RulesApplier { +public: + + +private: + Rules rules_; + std::unordered_map> applied_transformation_rules_; +}; + +} // stewkk::sql \ No newline at end of file diff --git a/include/stewkk/sql/logic/transformation_rules/join_associativity.hpp b/include/stewkk/sql/logic/transformation_rules/join_associativity.hpp new file mode 100644 index 0000000..bbf46f6 --- /dev/null +++ b/include/stewkk/sql/logic/transformation_rules/join_associativity.hpp @@ -0,0 +1,14 @@ +#pragma once + +#include +#include + +namespace stewkk::sql { + +class JoinAssociativity : public TransformationRule { + public: + bool IsApplicable(utils::NotNull expr) override; + utils::NotNull Apply(utils::NotNull expr, Memo& memo) override; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/transformation_rules/join_commutativity.hpp b/include/stewkk/sql/logic/transformation_rules/join_commutativity.hpp new file mode 100644 index 0000000..dbfb986 --- /dev/null +++ b/include/stewkk/sql/logic/transformation_rules/join_commutativity.hpp @@ -0,0 +1,14 @@ +#pragma once + +#include +#include + +namespace stewkk::sql { + +class JoinCommutativity : public TransformationRule { + public: + bool IsApplicable(utils::NotNull expr) override; + utils::NotNull Apply(utils::NotNull expr, Memo& memo) override; +}; + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/CMakeLists.txt b/src/stewkk/sql/CMakeLists.txt index 2826b7b..090a2d6 100644 --- a/src/stewkk/sql/CMakeLists.txt +++ b/src/stewkk/sql/CMakeLists.txt @@ -24,6 +24,11 @@ add_library(libsql logic/executor/materialization.cpp logic/executor/llvm.cpp logic/optimizer/optimizer.cpp + logic/optimizer/group.cpp + logic/optimizer/memo.cpp + logic/optimizer/rules.cpp + logic/transformation_rules/join_commutativity.cpp + logic/transformation_rules/join_associativity.cpp ) add_library(stewkk::libsql ALIAS libsql) target_compile_features(libsql PUBLIC cxx_std_23) diff --git a/src/stewkk/sql/logic/optimizer/group.cpp b/src/stewkk/sql/logic/optimizer/group.cpp new file mode 100644 index 0000000..3f15873 --- /dev/null +++ b/src/stewkk/sql/logic/optimizer/group.cpp @@ -0,0 +1,21 @@ +#include + +namespace stewkk::sql { + +utils::NotNull Group::AddLogicalExpr(LogicalOperator root_operator) { + auto& ptr = logical_exprs_.emplace_back( + std::make_unique(std::move(root_operator), this)); + return ptr.get(); +} + +utils::NotNull Group::AddPhysicalExpr() { + auto& ptr = physical_exprs_.emplace_back( + std::make_unique(this)); + return ptr.get(); +} + +std::span Group::GetLogicalExprs() const { + return logical_exprs_; +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/memo.cpp b/src/stewkk/sql/logic/optimizer/memo.cpp new file mode 100644 index 0000000..2afade3 --- /dev/null +++ b/src/stewkk/sql/logic/optimizer/memo.cpp @@ -0,0 +1,15 @@ +#include + +namespace stewkk::sql { + +size_t Memo::GroupCount() const { + return groups_.size(); +} + +utils::NotNull Memo::AddGroup(LogicalOperator root_operator) { + auto& ptr = groups_.emplace_back(new Group()); + ptr->AddLogicalExpr(std::move(root_operator)); + return ptr.get(); +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index 5f0483a..7eb2b1b 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -15,259 +15,259 @@ using ::testing::Eq; namespace stewkk::sql { -class ExpressionRulesApplier { - public: - ExpressionRulesApplier(LogicalExpr&& expr, const Rules& rules, GroupKey group) - : expr_(std::move(expr)), - rules_(rules), - is_applied_(rules_.Count(), false), - group_(std::move(group)) {} - - ExpressionRulesApplier ApplyRule(RuleNumber rule, Memo& memo) { - is_applied_[rule] = true; - return ExpressionRulesApplier(rules_.Apply(expr_, rule, memo), rules_, group_); - } - - bool IsApplicable(RuleNumber rule, Memo& memo, bool transformation_rules_only=false) const { - return !IsApplied(rule) && (!transformation_rules_only || rules_.IsTransformationRule(rule)) && rules_.IsApplicable(rule, expr_, memo); - } - - GroupKey GetGroup() const { return group_; } - const LogicalExpr& GetExpr() const { return expr_; } - - std::vector GetChildren() const { - return std::visit(Overloaded{ - [](const logical::Table&) -> std::vector { return {}; }, - [](const logical::Filter& f) -> std::vector { return {f.source}; }, - [](const logical::Projection& p) -> std::vector { return {p.source}; }, - [](const logical::CrossJoin& j) -> std::vector { return {j.lhs, j.rhs}; }, - [](const logical::Join& j) -> std::vector { return {j.lhs, j.rhs}; }, - }, expr_); - } - - private: - bool IsApplied(RuleNumber rule) const { return is_applied_[rule]; } - - const LogicalExpr expr_; - const Rules& rules_; - GroupKey group_; - std::vector is_applied_; -}; - -class Memo { - public: - explicit Memo(const Operator& expr, const Rules& rules) : mapping_(), rules_(rules), root_(AddGroup(expr, rules)) {} - - Group& GetRoot() { - return mapping_.find(root_)->second; - } - - GroupKey AddGroup(const LogicalExpr& expr) { - return AddGroup(expr, rules_); - } - - GroupKey AddGroup(const LogicalExpr& expr, const Rules& rules) { - return std::visit(Overloaded{ - [this, &rules](const logical::Table& t) { - auto key = std::format("Table({})", t.name); - mapping_.emplace(key, Group(key, ExpressionRulesApplier(t, rules, key))); - return key; - }, - [this, &rules](const logical::Filter& f) { - auto key = std::format("Filter({}, {})", f.source, ToString(f.predicate)); - mapping_.emplace(key, Group(key, ExpressionRulesApplier(f, rules, key))); - return key; - }, - [this, &rules](const logical::Projection& p) { - auto exprs = p.expressions | std::views::transform([](const Expression& e) { return ToString(e); }) | std::views::join_with(',') | std::ranges::to(); - auto key = std::format("Projection({}, {})", p.source, exprs); - mapping_.emplace(key, Group(key, ExpressionRulesApplier(p, rules, key))); - return key; - }, - [this, &rules](const logical::CrossJoin& j) { - auto key = std::format("CrossJoin({}, {})", j.lhs, j.rhs); - mapping_.emplace(key, Group(key, ExpressionRulesApplier(j, rules, key))); - return key; - }, - [this, &rules](const logical::Join& j) { - auto key = std::format("Join({}, {}, {}, {})", j.lhs, j.rhs, ToString(j.type), ToString(j.qual)); - mapping_.emplace(key, Group(key, ExpressionRulesApplier(j, rules, key))); - return key; - }, - }, expr); - } - - // TODO: replace with raw ptr (or some non-null wrapper) - Group& GetGroup(GroupKey key) { - return mapping_.find(key)->second; - } - - private: - - GroupKey AddGroup(const Operator& expr, const Rules& rules) { - return std::visit(Overloaded{ - [this, &rules](const Table& t) { - auto key = std::format("Table({})", t.name); - mapping_.emplace(key, Group(key, ExpressionRulesApplier(logical::Table(t), rules, key))); - return key; - }, - [this, &rules](const Filter& f) { - auto source_group = AddGroup(*f.source, rules); - auto key = std::format("Filter({}, {})", source_group, ToString(f.expr)); - mapping_.emplace(key, Group(key, ExpressionRulesApplier(logical::Filter(source_group, f.expr), rules, key))); - return key; - }, - [this, &rules](const Projection& p) { - auto source_group = AddGroup(*p.source, rules); - auto exprs = p.expressions | std::views::transform([](const Expression& e) { return ToString(e); }) | std::views::join_with(',') | std::ranges::to(); - auto key = std::format("Projection({}, {})", source_group, exprs); - mapping_.emplace(key, Group(key, ExpressionRulesApplier(logical::Projection(source_group, p.expressions), rules, key))); - return key; - }, - [this, &rules](const CrossJoin& j) { - auto lhs_group = AddGroup(*j.lhs, rules); - auto rhs_group = AddGroup(*j.rhs, rules); - auto key = std::format("CrossJoin({}, {})", lhs_group, rhs_group); - mapping_.emplace(key, Group(key, ExpressionRulesApplier(logical::CrossJoin(lhs_group, rhs_group), rules, key))); - return key; - }, - [this, &rules](const Join& j) { - auto lhs_group = AddGroup(*j.lhs, rules); - auto rhs_group = AddGroup(*j.rhs, rules); - auto key = std::format("Join({}, {}, {}, {})", lhs_group, rhs_group, ToString(j.type), ToString(j.qual)); - mapping_.emplace(key, Group(key, ExpressionRulesApplier(logical::Join(lhs_group, rhs_group, j.type, j.qual), rules, key))); - return key; - }, - }, expr); - } - - std::unordered_map mapping_; - const Rules& rules_; - GroupKey root_; -}; - -class Optimizer { - public: - - explicit Optimizer(const Operator& expr, const Rules& rules) : memo_(expr, rules), rules_count_(rules.Count()) {} - - bool IsExplored(const Group& group) const { - return explored_groups_.contains(group.GetKey()); - } - - void SetExplored(const Group& group) { - explored_groups_.insert(group.GetKey()); - } - - void OptimizeInputs(const ExpressionRulesApplier& expr) { - } - - void ApplyRule(ExpressionRulesApplier& expr, RuleNumber rule) { - auto new_expr = expr.ApplyRule(rule, memo_); - auto group = memo_.GetGroup(new_expr.GetGroup()); - auto new_expr_ref = group.AddExpression(std::move(new_expr)); - if (new_expr.IsTransformationRule(rule)) { - tasks_.emplace([this, &new_expr_ref]() { ExploreExpression(new_expr_ref); }); - } else { - tasks_.emplace([this, &new_expr_ref]() { OptimizeInputs(new_expr_ref); }); - } - } - - void OptimizeExpression(ExpressionRulesApplier& expr) { - for (RuleNumber rule = 0; rule < rules_count_; rule++) { - if (!expr.IsApplicable(rule, memo_)) { - continue; - } - tasks_.emplace([this, &expr, rule]() { - ApplyRule(expr, rule); - }); - } - - for (auto child : expr.GetChildren()) { - auto& group = memo_.GetGroup(child); - if (IsExplored(group)) { - continue; - } - tasks_.emplace([this, &group]() { - ExploreGroup(group); - }); - } - } - - void ExploreExpression(ExpressionRulesApplier& expr) { - for (RuleNumber rule = 0; rule < rules_count_; rule++) { - bool transformation_rules_only = true; - if (!expr.IsApplicable(rule, memo_, transformation_rules_only)) { - continue; - } - tasks_.emplace([this, &expr, rule]() { - ApplyRule(expr, rule); - }); - } - - for (auto child : expr.GetChildren()) { - auto& group = memo_.GetGroup(child); - if (IsExplored(group)) { - continue; - } - tasks_.emplace([this, &group]() { - ExploreGroup(group); - }); - } - } - - void ExploreGroup(Group& group) { - SetExplored(group); - for (auto& expr : group.GetExpressions()) { - tasks_.emplace([this, &expr]() { - ExploreExpression(expr); - }); - } - } - - void OptimizeGroup(Group& group) { - if (!IsExplored(group)) { - tasks_.emplace([this, &group](){ - OptimizeGroup(group); - }); - tasks_.emplace([this, &group](){ - ExploreGroup(group); - }); - return; - } - - for (auto& expr : group.GetExpressions()) { - tasks_.emplace([this, &expr](){ - OptimizeExpression(expr); - }); - } - } - - Operator Optimize() { - tasks_.emplace([this]() { - OptimizeGroup(memo_.GetRoot()); - }); - while (!tasks_.empty()) { - auto next_task = std::move(tasks_.top()); - tasks_.pop(); - next_task(); - } - return Table{"A"}; - } - - private: - Memo memo_; - std::stack> tasks_; - std::unordered_set explored_groups_; - const size_t rules_count_; -}; - -TEST(OptimizerTest, Simple) { - std::stringstream s{"SELECT * FROM users;"}; - Operator op = GetAST(s).value(); - Optimizer optimizer(op, Rules{{}}); - - auto got = optimizer.Optimize(); -} +// class ExpressionRulesApplier { +// public: +// ExpressionRulesApplier(LogicalExpr&& expr, const Rules& rules, GroupKey group) +// : expr_(std::move(expr)), +// rules_(rules), +// is_applied_(rules_.Count(), false), +// group_(std::move(group)) {} + +// ExpressionRulesApplier ApplyRule(RuleNumber rule, Memo& memo) { +// is_applied_[rule] = true; +// return ExpressionRulesApplier(rules_.Apply(expr_, rule, memo), rules_, group_); +// } + +// bool IsApplicable(RuleNumber rule, Memo& memo, bool transformation_rules_only=false) const { +// return !IsApplied(rule) && (!transformation_rules_only || rules_.IsTransformationRule(rule)) && rules_.IsApplicable(rule, expr_, memo); +// } + +// GroupKey GetGroup() const { return group_; } +// const LogicalExpr& GetExpr() const { return expr_; } + +// std::vector GetChildren() const { +// return std::visit(Overloaded{ +// [](const logical::Table&) -> std::vector { return {}; }, +// [](const logical::Filter& f) -> std::vector { return {f.source}; }, +// [](const logical::Projection& p) -> std::vector { return {p.source}; }, +// [](const logical::CrossJoin& j) -> std::vector { return {j.lhs, j.rhs}; }, +// [](const logical::Join& j) -> std::vector { return {j.lhs, j.rhs}; }, +// }, expr_); +// } + +// private: +// bool IsApplied(RuleNumber rule) const { return is_applied_[rule]; } + +// const LogicalExpr expr_; +// const Rules& rules_; +// GroupKey group_; +// std::vector is_applied_; +// }; + +// class Memo { +// public: +// explicit Memo(const Operator& expr, const Rules& rules) : mapping_(), rules_(rules), root_(AddGroup(expr, rules)) {} + +// Group& GetRoot() { +// return mapping_.find(root_)->second; +// } + +// GroupKey AddGroup(const LogicalExpr& expr) { +// return AddGroup(expr, rules_); +// } + +// GroupKey AddGroup(const LogicalExpr& expr, const Rules& rules) { +// return std::visit(Overloaded{ +// [this, &rules](const logical::Table& t) { +// auto key = std::format("Table({})", t.name); +// mapping_.emplace(key, Group(key, ExpressionRulesApplier(t, rules, key))); +// return key; +// }, +// [this, &rules](const logical::Filter& f) { +// auto key = std::format("Filter({}, {})", f.source, ToString(f.predicate)); +// mapping_.emplace(key, Group(key, ExpressionRulesApplier(f, rules, key))); +// return key; +// }, +// [this, &rules](const logical::Projection& p) { +// auto exprs = p.expressions | std::views::transform([](const Expression& e) { return ToString(e); }) | std::views::join_with(',') | std::ranges::to(); +// auto key = std::format("Projection({}, {})", p.source, exprs); +// mapping_.emplace(key, Group(key, ExpressionRulesApplier(p, rules, key))); +// return key; +// }, +// [this, &rules](const logical::CrossJoin& j) { +// auto key = std::format("CrossJoin({}, {})", j.lhs, j.rhs); +// mapping_.emplace(key, Group(key, ExpressionRulesApplier(j, rules, key))); +// return key; +// }, +// [this, &rules](const logical::Join& j) { +// auto key = std::format("Join({}, {}, {}, {})", j.lhs, j.rhs, ToString(j.type), ToString(j.qual)); +// mapping_.emplace(key, Group(key, ExpressionRulesApplier(j, rules, key))); +// return key; +// }, +// }, expr); +// } + +// // TODO: replace with raw ptr (or some non-null wrapper) +// Group& GetGroup(GroupKey key) { +// return mapping_.find(key)->second; +// } + +// private: + +// GroupKey AddGroup(const Operator& expr, const Rules& rules) { +// return std::visit(Overloaded{ +// [this, &rules](const Table& t) { +// auto key = std::format("Table({})", t.name); +// mapping_.emplace(key, Group(key, ExpressionRulesApplier(logical::Table(t), rules, key))); +// return key; +// }, +// [this, &rules](const Filter& f) { +// auto source_group = AddGroup(*f.source, rules); +// auto key = std::format("Filter({}, {})", source_group, ToString(f.expr)); +// mapping_.emplace(key, Group(key, ExpressionRulesApplier(logical::Filter(source_group, f.expr), rules, key))); +// return key; +// }, +// [this, &rules](const Projection& p) { +// auto source_group = AddGroup(*p.source, rules); +// auto exprs = p.expressions | std::views::transform([](const Expression& e) { return ToString(e); }) | std::views::join_with(',') | std::ranges::to(); +// auto key = std::format("Projection({}, {})", source_group, exprs); +// mapping_.emplace(key, Group(key, ExpressionRulesApplier(logical::Projection(source_group, p.expressions), rules, key))); +// return key; +// }, +// [this, &rules](const CrossJoin& j) { +// auto lhs_group = AddGroup(*j.lhs, rules); +// auto rhs_group = AddGroup(*j.rhs, rules); +// auto key = std::format("CrossJoin({}, {})", lhs_group, rhs_group); +// mapping_.emplace(key, Group(key, ExpressionRulesApplier(logical::CrossJoin(lhs_group, rhs_group), rules, key))); +// return key; +// }, +// [this, &rules](const Join& j) { +// auto lhs_group = AddGroup(*j.lhs, rules); +// auto rhs_group = AddGroup(*j.rhs, rules); +// auto key = std::format("Join({}, {}, {}, {})", lhs_group, rhs_group, ToString(j.type), ToString(j.qual)); +// mapping_.emplace(key, Group(key, ExpressionRulesApplier(logical::Join(lhs_group, rhs_group, j.type, j.qual), rules, key))); +// return key; +// }, +// }, expr); +// } + +// std::unordered_map mapping_; +// const Rules& rules_; +// GroupKey root_; +// }; + +// class Optimizer { +// public: + +// explicit Optimizer(const Operator& expr, const Rules& rules) : memo_(expr, rules), rules_count_(rules.Count()) {} + +// bool IsExplored(const Group& group) const { +// return explored_groups_.contains(group.GetKey()); +// } + +// void SetExplored(const Group& group) { +// explored_groups_.insert(group.GetKey()); +// } + +// void OptimizeInputs(const ExpressionRulesApplier& expr) { +// } + +// void ApplyRule(ExpressionRulesApplier& expr, RuleNumber rule) { +// auto new_expr = expr.ApplyRule(rule, memo_); +// auto group = memo_.GetGroup(new_expr.GetGroup()); +// auto new_expr_ref = group.AddExpression(std::move(new_expr)); +// if (new_expr.IsTransformationRule(rule)) { +// tasks_.emplace([this, &new_expr_ref]() { ExploreExpression(new_expr_ref); }); +// } else { +// tasks_.emplace([this, &new_expr_ref]() { OptimizeInputs(new_expr_ref); }); +// } +// } + +// void OptimizeExpression(ExpressionRulesApplier& expr) { +// for (RuleNumber rule = 0; rule < rules_count_; rule++) { +// if (!expr.IsApplicable(rule, memo_)) { +// continue; +// } +// tasks_.emplace([this, &expr, rule]() { +// ApplyRule(expr, rule); +// }); +// } + +// for (auto child : expr.GetChildren()) { +// auto& group = memo_.GetGroup(child); +// if (IsExplored(group)) { +// continue; +// } +// tasks_.emplace([this, &group]() { +// ExploreGroup(group); +// }); +// } +// } + +// void ExploreExpression(ExpressionRulesApplier& expr) { +// for (RuleNumber rule = 0; rule < rules_count_; rule++) { +// bool transformation_rules_only = true; +// if (!expr.IsApplicable(rule, memo_, transformation_rules_only)) { +// continue; +// } +// tasks_.emplace([this, &expr, rule]() { +// ApplyRule(expr, rule); +// }); +// } + +// for (auto child : expr.GetChildren()) { +// auto& group = memo_.GetGroup(child); +// if (IsExplored(group)) { +// continue; +// } +// tasks_.emplace([this, &group]() { +// ExploreGroup(group); +// }); +// } +// } + +// void ExploreGroup(Group& group) { +// SetExplored(group); +// for (auto& expr : group.GetExpressions()) { +// tasks_.emplace([this, &expr]() { +// ExploreExpression(expr); +// }); +// } +// } + +// void OptimizeGroup(Group& group) { +// if (!IsExplored(group)) { +// tasks_.emplace([this, &group](){ +// OptimizeGroup(group); +// }); +// tasks_.emplace([this, &group](){ +// ExploreGroup(group); +// }); +// return; +// } + +// for (auto& expr : group.GetExpressions()) { +// tasks_.emplace([this, &expr](){ +// OptimizeExpression(expr); +// }); +// } +// } + +// Operator Optimize() { +// tasks_.emplace([this]() { +// OptimizeGroup(memo_.GetRoot()); +// }); +// while (!tasks_.empty()) { +// auto next_task = std::move(tasks_.top()); +// tasks_.pop(); +// next_task(); +// } +// return Table{"A"}; +// } + +// private: +// Memo memo_; +// std::stack> tasks_; +// std::unordered_set explored_groups_; +// const size_t rules_count_; +// }; + +// TEST(OptimizerTest, Simple) { +// std::stringstream s{"SELECT * FROM users;"}; +// Operator op = GetAST(s).value(); +// Optimizer optimizer(op, Rules{{}}); + +// auto got = optimizer.Optimize(); +// } } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/rule_test.cpp b/src/stewkk/sql/logic/optimizer/rule_test.cpp index 173e5e2..0699ab9 100644 --- a/src/stewkk/sql/logic/optimizer/rule_test.cpp +++ b/src/stewkk/sql/logic/optimizer/rule_test.cpp @@ -2,22 +2,11 @@ #include #include +#include +#include namespace stewkk::sql { -class JoinCommutativity : public TransformationRule { - public: - bool IsApplicable(utils::NotNull expr) override { - return std::holds_alternative(expr->root_operator); - } - - utils::NotNull Apply(utils::NotNull expr, Memo&) override { - auto join = std::get(expr->root_operator); - std::swap(join.lhs, join.rhs); - return expr->group->AddLogicalExpr(std::move(join)); - } -}; - class JoinCommutativityTest : public ::testing::Test { protected: void SetUp() override { @@ -49,39 +38,11 @@ TEST_F(JoinCommutativityTest, AddsNewJoinIntoGroup) { rule.Apply(expr, memo); EXPECT_EQ(join_group->GetLogicalExprs().size(), 2u); - const auto& new_join = std::get(join_group->GetLogicalExprs()[1]->root_operator); + const auto& new_join = std::get(join_group->GetLogicalExprs()[1].get()->root_operator); EXPECT_EQ(new_join.lhs.get(), b); EXPECT_EQ(new_join.rhs.get(), a); } -class JoinAssociativity : public TransformationRule { - public: - bool IsApplicable(utils::NotNull expr) override { - if (!std::holds_alternative(expr->root_operator)) return false; - const auto& outer = std::get(expr->root_operator); - for (const auto& inner_expr : outer.lhs->GetLogicalExprs()) { - if (std::holds_alternative(inner_expr->root_operator)) return true; - } - return false; - } - - utils::NotNull Apply(utils::NotNull expr, Memo& memo) override { - const auto& outer = std::get(expr->root_operator); - for (const auto& inner_expr : outer.lhs->GetLogicalExprs()) { - if (!std::holds_alternative(inner_expr->root_operator)) continue; - const auto& inner = std::get(inner_expr->root_operator); - auto combined_qual = Expression{BinaryExpression{ - std::make_shared(inner.qual), - BinaryOp::kAnd, - std::make_shared(outer.qual), - }}; - auto new_rhs = memo.AddGroup(logical::Join{inner.rhs, outer.rhs, outer.type, Literal::kTrue}); - return expr->group->AddLogicalExpr(logical::Join{inner.lhs, new_rhs, inner.type, combined_qual}); - } - return expr; - } -}; - class JoinAssociativityTest : public ::testing::Test { protected: void SetUp() override { @@ -113,7 +74,7 @@ TEST_F(JoinAssociativityTest, ReturnsCorrectExpression) { const auto& outer = std::get(result->root_operator); EXPECT_EQ(outer.lhs.get(), a); EXPECT_EQ(outer.type, JoinType::kInner); - const auto& inner = std::get(outer.rhs->GetLogicalExprs()[0]->root_operator); + const auto& inner = std::get(outer.rhs->GetLogicalExprs()[0].get()->root_operator); EXPECT_EQ(inner.lhs.get(), b); EXPECT_EQ(inner.rhs.get(), c); EXPECT_EQ(inner.qual, Expression{Literal::kTrue}); diff --git a/src/stewkk/sql/logic/optimizer/rules.cpp b/src/stewkk/sql/logic/optimizer/rules.cpp new file mode 100644 index 0000000..961f528 --- /dev/null +++ b/src/stewkk/sql/logic/optimizer/rules.cpp @@ -0,0 +1,15 @@ +#include + +namespace stewkk::sql { + +Rules<2, 0> MakeMainRules() { + return { + .transformation_rules = { + std::make_unique(), + std::make_unique(), + }, + .implementation_rules = {}, + }; +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/rules_applier_test.cpp b/src/stewkk/sql/logic/optimizer/rules_applier_test.cpp new file mode 100644 index 0000000..c59feb2 --- /dev/null +++ b/src/stewkk/sql/logic/optimizer/rules_applier_test.cpp @@ -0,0 +1,10 @@ +#include + +#include + +namespace stewkk::sql { + +TEST(RulesApplierTest, Placeholder) { +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp b/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp new file mode 100644 index 0000000..fea44bf --- /dev/null +++ b/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp @@ -0,0 +1,33 @@ +#include + +#include + +namespace stewkk::sql { + +bool JoinAssociativity::IsApplicable(utils::NotNull expr) { + if (!std::holds_alternative(expr->root_operator)) return false; + const auto& outer = std::get(expr->root_operator); + for (const auto& inner_expr : outer.lhs->GetLogicalExprs()) { + if (std::holds_alternative(inner_expr->root_operator)) return true; + } + return false; +} + +// (A ⋈₁ B) ⋈₂ C → A ⋈₁∧₂ (B ⋈₂ C) +utils::NotNull JoinAssociativity::Apply(utils::NotNull expr, Memo& memo) { + const auto& outer = std::get(expr->root_operator); + for (const auto& inner_expr : outer.lhs->GetLogicalExprs()) { + if (!std::holds_alternative(inner_expr->root_operator)) continue; + const auto& inner = std::get(inner_expr->root_operator); + auto combined_qual = Expression{BinaryExpression{ + std::make_shared(inner.qual), + BinaryOp::kAnd, + std::make_shared(outer.qual), + }}; + auto new_rhs = memo.AddGroup(logical::Join{inner.rhs, outer.rhs, outer.type, Literal::kTrue}); + return expr->group->AddLogicalExpr(logical::Join{inner.lhs, new_rhs, inner.type, combined_qual}); + } + return expr; +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/transformation_rules/join_commutativity.cpp b/src/stewkk/sql/logic/transformation_rules/join_commutativity.cpp new file mode 100644 index 0000000..fece6b8 --- /dev/null +++ b/src/stewkk/sql/logic/transformation_rules/join_commutativity.cpp @@ -0,0 +1,15 @@ +#include + +namespace stewkk::sql { + +bool JoinCommutativity::IsApplicable(utils::NotNull expr) { + return std::holds_alternative(expr->root_operator); +} + +utils::NotNull JoinCommutativity::Apply(utils::NotNull expr, Memo&) { + auto join = std::get(expr->root_operator); + std::swap(join.lhs, join.rhs); + return expr->group->AddLogicalExpr(std::move(join)); +} + +} // namespace stewkk::sql diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt index 092cfbd..0413bc6 100644 --- a/test/CMakeLists.txt +++ b/test/CMakeLists.txt @@ -6,6 +6,7 @@ target_sources(unittests PRIVATE ${PROJECT_SOURCE_DIR}/src/stewkk/sql/logic/parser/parser_test.cpp ${PROJECT_SOURCE_DIR}/src/stewkk/sql/logic/executor/executor_test.cpp ${PROJECT_SOURCE_DIR}/src/stewkk/sql/logic/optimizer/rule_test.cpp + ${PROJECT_SOURCE_DIR}/src/stewkk/sql/logic/optimizer/rules_applier_test.cpp ${PROJECT_SOURCE_DIR}/src/stewkk/sql/logic/optimizer/optimizer_test.cpp ) target_compile_features(unittests PRIVATE cxx_std_23) From d2d6074664aff4b82bd43e5db613ba1b8afd26bc Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sun, 19 Apr 2026 22:04:02 +0300 Subject: [PATCH 005/120] Implement RulesApplier --- include/stewkk/sql/logic/optimizer/rule.hpp | 6 +++++- .../sql/logic/optimizer/rules_applier.hpp | 20 +++++++++++++++---- .../join_associativity.hpp | 2 +- .../join_commutativity.hpp | 2 +- src/stewkk/sql/CMakeLists.txt | 1 + src/stewkk/sql/logic/optimizer/rule.cpp | 9 +++++++++ .../logic/optimizer/rules_applier_test.cpp | 14 ++++++++++++- .../join_associativity.cpp | 7 ++++--- .../join_commutativity.cpp | 4 ++-- 9 files changed, 52 insertions(+), 13 deletions(-) create mode 100644 src/stewkk/sql/logic/optimizer/rule.cpp diff --git a/include/stewkk/sql/logic/optimizer/rule.hpp b/include/stewkk/sql/logic/optimizer/rule.hpp index f46a07f..3960ecb 100644 --- a/include/stewkk/sql/logic/optimizer/rule.hpp +++ b/include/stewkk/sql/logic/optimizer/rule.hpp @@ -2,6 +2,7 @@ #include #include +#include namespace stewkk::sql { @@ -10,8 +11,11 @@ class Memo; class TransformationRule { public: virtual bool IsApplicable(utils::NotNull expr) = 0; - virtual utils::NotNull Apply(utils::NotNull expr, Memo& memo) = 0; + utils::NotNull Apply(utils::NotNull expr, Memo& memo); virtual ~TransformationRule() = default; + + private: + virtual LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo) = 0; }; class ImplementationRule { diff --git a/include/stewkk/sql/logic/optimizer/rules_applier.hpp b/include/stewkk/sql/logic/optimizer/rules_applier.hpp index 9d9ad9f..b6ff5e3 100644 --- a/include/stewkk/sql/logic/optimizer/rules_applier.hpp +++ b/include/stewkk/sql/logic/optimizer/rules_applier.hpp @@ -4,17 +4,29 @@ #include #include +#include namespace stewkk::sql { template class RulesApplier { -public: + public: + explicit RulesApplier(Rules rules) + : rules_(std::move(rules)) {} - -private: + bool IsApplicable(RuleId rule, utils::NotNull expr) { + return !applied_transformation_rules_[expr.get()][rule] && + rules_.transformation_rules[rule]->IsApplicable(expr); + } + + utils::NotNull Apply(RuleId rule, utils::NotNull expr, Memo& memo) { + applied_transformation_rules_[expr.get()][rule] = 1; + return rules_.transformation_rules[rule]->Apply(expr, memo); + } + + private: Rules rules_; std::unordered_map> applied_transformation_rules_; }; -} // stewkk::sql \ No newline at end of file +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/transformation_rules/join_associativity.hpp b/include/stewkk/sql/logic/transformation_rules/join_associativity.hpp index bbf46f6..b4a2281 100644 --- a/include/stewkk/sql/logic/transformation_rules/join_associativity.hpp +++ b/include/stewkk/sql/logic/transformation_rules/join_associativity.hpp @@ -8,7 +8,7 @@ namespace stewkk::sql { class JoinAssociativity : public TransformationRule { public: bool IsApplicable(utils::NotNull expr) override; - utils::NotNull Apply(utils::NotNull expr, Memo& memo) override; + LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo) override; }; } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/transformation_rules/join_commutativity.hpp b/include/stewkk/sql/logic/transformation_rules/join_commutativity.hpp index dbfb986..ccaf13b 100644 --- a/include/stewkk/sql/logic/transformation_rules/join_commutativity.hpp +++ b/include/stewkk/sql/logic/transformation_rules/join_commutativity.hpp @@ -8,7 +8,7 @@ namespace stewkk::sql { class JoinCommutativity : public TransformationRule { public: bool IsApplicable(utils::NotNull expr) override; - utils::NotNull Apply(utils::NotNull expr, Memo& memo) override; + LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo) override; }; } // namespace stewkk::sql diff --git a/src/stewkk/sql/CMakeLists.txt b/src/stewkk/sql/CMakeLists.txt index 090a2d6..0190406 100644 --- a/src/stewkk/sql/CMakeLists.txt +++ b/src/stewkk/sql/CMakeLists.txt @@ -27,6 +27,7 @@ add_library(libsql logic/optimizer/group.cpp logic/optimizer/memo.cpp logic/optimizer/rules.cpp + logic/optimizer/rule.cpp logic/transformation_rules/join_commutativity.cpp logic/transformation_rules/join_associativity.cpp ) diff --git a/src/stewkk/sql/logic/optimizer/rule.cpp b/src/stewkk/sql/logic/optimizer/rule.cpp new file mode 100644 index 0000000..13ad570 --- /dev/null +++ b/src/stewkk/sql/logic/optimizer/rule.cpp @@ -0,0 +1,9 @@ +#include + +namespace stewkk::sql { + +utils::NotNull TransformationRule::Apply(utils::NotNull expr, Memo& memo) { +return expr->group->AddLogicalExpr(ApplyImpl(expr, memo)); +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/rules_applier_test.cpp b/src/stewkk/sql/logic/optimizer/rules_applier_test.cpp index c59feb2..9023624 100644 --- a/src/stewkk/sql/logic/optimizer/rules_applier_test.cpp +++ b/src/stewkk/sql/logic/optimizer/rules_applier_test.cpp @@ -4,7 +4,19 @@ namespace stewkk::sql { -TEST(RulesApplierTest, Placeholder) { +TEST(RulesApplierTest, ChecksThatRuleAlreadyApplied) { + Memo memo; + auto a = memo.AddGroup(logical::Table{"a"}); + auto b = memo.AddGroup(logical::Table{"b"}); + auto join_group = memo.AddGroup(logical::Join{a, b, JoinType::kInner, Literal::kTrue}); + auto* expr = join_group->GetLogicalExprs()[0].get(); + + RulesApplier applier(MakeMainRules()); + constexpr RuleId kJoinCommutativity = 0; + + EXPECT_TRUE(applier.IsApplicable(kJoinCommutativity, expr)); + applier.Apply(kJoinCommutativity, expr, memo); + EXPECT_FALSE(applier.IsApplicable(kJoinCommutativity, expr)); } } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp b/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp index fea44bf..0544238 100644 --- a/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp +++ b/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp @@ -14,7 +14,7 @@ bool JoinAssociativity::IsApplicable(utils::NotNull expr) { } // (A ⋈₁ B) ⋈₂ C → A ⋈₁∧₂ (B ⋈₂ C) -utils::NotNull JoinAssociativity::Apply(utils::NotNull expr, Memo& memo) { +LogicalOperator JoinAssociativity::ApplyImpl(utils::NotNull expr, Memo& memo) { const auto& outer = std::get(expr->root_operator); for (const auto& inner_expr : outer.lhs->GetLogicalExprs()) { if (!std::holds_alternative(inner_expr->root_operator)) continue; @@ -25,9 +25,10 @@ utils::NotNull JoinAssociativity::Apply(utils::NotNull(outer.qual), }}; auto new_rhs = memo.AddGroup(logical::Join{inner.rhs, outer.rhs, outer.type, Literal::kTrue}); - return expr->group->AddLogicalExpr(logical::Join{inner.lhs, new_rhs, inner.type, combined_qual}); + // FIXME: should return more than one operators!!! + return logical::Join{inner.lhs, new_rhs, inner.type, combined_qual}; } - return expr; + throw std::runtime_error{"cant perform JoinAssociativity"}; } } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/transformation_rules/join_commutativity.cpp b/src/stewkk/sql/logic/transformation_rules/join_commutativity.cpp index fece6b8..18bff95 100644 --- a/src/stewkk/sql/logic/transformation_rules/join_commutativity.cpp +++ b/src/stewkk/sql/logic/transformation_rules/join_commutativity.cpp @@ -6,10 +6,10 @@ bool JoinCommutativity::IsApplicable(utils::NotNull expr) { return std::holds_alternative(expr->root_operator); } -utils::NotNull JoinCommutativity::Apply(utils::NotNull expr, Memo&) { +LogicalOperator JoinCommutativity::ApplyImpl(utils::NotNull expr, Memo&) { auto join = std::get(expr->root_operator); std::swap(join.lhs, join.rhs); - return expr->group->AddLogicalExpr(std::move(join)); + return join; } } // namespace stewkk::sql From 28cec906ce1b4e5acbf3f20826810ea65fc2a219 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 20 Apr 2026 01:15:25 +0300 Subject: [PATCH 006/120] Add deduptication in Memo --- include/stewkk/sql/logic/optimizer/group.hpp | 5 +- .../sql/logic/optimizer/logical_expr.hpp | 8 +++ include/stewkk/sql/logic/optimizer/memo.hpp | 3 + include/stewkk/sql/utils/not_null.hpp | 2 + src/stewkk/sql/logic/optimizer/group.cpp | 4 ++ src/stewkk/sql/logic/optimizer/memo.cpp | 38 +++++++++++- src/stewkk/sql/logic/optimizer/memo_test.cpp | 29 +++++++++ .../sql/logic/optimizer/optimizer_test.cpp | 62 ------------------- test/CMakeLists.txt | 1 + 9 files changed, 88 insertions(+), 64 deletions(-) create mode 100644 src/stewkk/sql/logic/optimizer/memo_test.cpp diff --git a/include/stewkk/sql/logic/optimizer/group.hpp b/include/stewkk/sql/logic/optimizer/group.hpp index 0b15171..78d06e7 100644 --- a/include/stewkk/sql/logic/optimizer/group.hpp +++ b/include/stewkk/sql/logic/optimizer/group.hpp @@ -21,10 +21,13 @@ class Group { utils::NotNull AddPhysicalExpr(); std::span GetLogicalExprs() const; + size_t id() const; + private: friend class Memo; - Group() = default; + explicit Group(size_t id) : id_(id) {} + size_t id_; std::vector logical_exprs_; std::vector physical_exprs_; }; diff --git a/include/stewkk/sql/logic/optimizer/logical_expr.hpp b/include/stewkk/sql/logic/optimizer/logical_expr.hpp index cac6bc0..6a74cf2 100644 --- a/include/stewkk/sql/logic/optimizer/logical_expr.hpp +++ b/include/stewkk/sql/logic/optimizer/logical_expr.hpp @@ -14,16 +14,22 @@ using Table = Table; struct Filter { utils::NotNull source; Expression predicate; + + bool operator==(const Filter&) const = default; }; struct Projection { utils::NotNull source; std::vector expressions; + + bool operator==(const Projection&) const = default; }; struct CrossJoin { utils::NotNull lhs; utils::NotNull rhs; + + bool operator==(const CrossJoin&) const = default; }; struct Join { @@ -32,6 +38,8 @@ struct Join { using Type = JoinType; Type type; Expression qual; + + bool operator==(const Join&) const = default; }; } // namespace logical diff --git a/include/stewkk/sql/logic/optimizer/memo.hpp b/include/stewkk/sql/logic/optimizer/memo.hpp index 2e582cd..dd01cce 100644 --- a/include/stewkk/sql/logic/optimizer/memo.hpp +++ b/include/stewkk/sql/logic/optimizer/memo.hpp @@ -2,6 +2,8 @@ #include #include +#include +#include #include @@ -14,6 +16,7 @@ class Memo { private: std::vector> groups_; + std::unordered_map expr_index_; }; } // namespace stewkk::sql diff --git a/include/stewkk/sql/utils/not_null.hpp b/include/stewkk/sql/utils/not_null.hpp index 72f70bd..d48d54f 100644 --- a/include/stewkk/sql/utils/not_null.hpp +++ b/include/stewkk/sql/utils/not_null.hpp @@ -18,6 +18,8 @@ class NotNull { operator T() const { return ptr_; } // NOLINT(google-explicit-constructor) + bool operator==(const NotNull& other) const = default; + private: T ptr_; }; diff --git a/src/stewkk/sql/logic/optimizer/group.cpp b/src/stewkk/sql/logic/optimizer/group.cpp index 3f15873..6395d92 100644 --- a/src/stewkk/sql/logic/optimizer/group.cpp +++ b/src/stewkk/sql/logic/optimizer/group.cpp @@ -18,4 +18,8 @@ std::span Group::GetLogicalExprs() const { return logical_exprs_; } +size_t Group::id() const { + return id_; +} + } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/memo.cpp b/src/stewkk/sql/logic/optimizer/memo.cpp index 2afade3..40a3c3d 100644 --- a/src/stewkk/sql/logic/optimizer/memo.cpp +++ b/src/stewkk/sql/logic/optimizer/memo.cpp @@ -1,14 +1,50 @@ #include +#include namespace stewkk::sql { +namespace { + +std::string ToKey(const LogicalOperator& op) { + return std::visit(utils::Overloaded{ + [](const logical::Table& t) { + return "Table(" + t.name + ")"; + }, + [](const logical::Filter& f) { + return "Filter(" + ToString(f.predicate) + "," + std::to_string(f.source->id()) + ")"; + }, + [](const logical::Projection& p) { + std::string exprs; + for (const auto& e : p.expressions) { + exprs += ToString(e) + ","; + } + return "Projection(" + exprs + std::to_string(p.source->id()) + ")"; + }, + [](const logical::CrossJoin& j) { + return "CrossJoin(" + std::to_string(j.lhs->id()) + "," + std::to_string(j.rhs->id()) + ")"; + }, + [](const logical::Join& j) { + return "Join(" + ToString(j.type) + "," + ToString(j.qual) + "," + + std::to_string(j.lhs->id()) + "," + std::to_string(j.rhs->id()) + ")"; + }, + }, op); +} + +} // namespace + size_t Memo::GroupCount() const { return groups_.size(); } utils::NotNull Memo::AddGroup(LogicalOperator root_operator) { - auto& ptr = groups_.emplace_back(new Group()); + auto key = ToKey(root_operator); + auto it = expr_index_.find(key); + if (it != expr_index_.end()) { + return it->second; + } + auto& ptr = groups_.emplace_back(new Group(groups_.size())); ptr->AddLogicalExpr(std::move(root_operator)); + expr_index_[key] = ptr.get(); return ptr.get(); } diff --git a/src/stewkk/sql/logic/optimizer/memo_test.cpp b/src/stewkk/sql/logic/optimizer/memo_test.cpp new file mode 100644 index 0000000..923eb5b --- /dev/null +++ b/src/stewkk/sql/logic/optimizer/memo_test.cpp @@ -0,0 +1,29 @@ +#include + +#include + +namespace stewkk::sql { + +TEST(MemoTest, AddGroup) { + Memo memo; + EXPECT_EQ(memo.GroupCount(), 0); + + auto group = memo.AddGroup(logical::Table{"t"}); + EXPECT_EQ(memo.GroupCount(), 1); + + auto exprs = group->GetLogicalExprs(); + ASSERT_EQ(exprs.size(), 1); + EXPECT_TRUE(std::holds_alternative(exprs[0]->root_operator)); + EXPECT_EQ(std::get(exprs[0]->root_operator).name, "t"); +} + +TEST(MemoTest, AddSameGroupSecondReturnsIt) { + Memo memo; + auto first = memo.AddGroup(logical::Table{"t"}); + auto second = memo.AddGroup(logical::Table{"t"}); + + EXPECT_EQ(first, second); + EXPECT_EQ(memo.GroupCount(), 1); +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index 7eb2b1b..bd84ccd 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -15,57 +15,6 @@ using ::testing::Eq; namespace stewkk::sql { -// class ExpressionRulesApplier { -// public: -// ExpressionRulesApplier(LogicalExpr&& expr, const Rules& rules, GroupKey group) -// : expr_(std::move(expr)), -// rules_(rules), -// is_applied_(rules_.Count(), false), -// group_(std::move(group)) {} - -// ExpressionRulesApplier ApplyRule(RuleNumber rule, Memo& memo) { -// is_applied_[rule] = true; -// return ExpressionRulesApplier(rules_.Apply(expr_, rule, memo), rules_, group_); -// } - -// bool IsApplicable(RuleNumber rule, Memo& memo, bool transformation_rules_only=false) const { -// return !IsApplied(rule) && (!transformation_rules_only || rules_.IsTransformationRule(rule)) && rules_.IsApplicable(rule, expr_, memo); -// } - -// GroupKey GetGroup() const { return group_; } -// const LogicalExpr& GetExpr() const { return expr_; } - -// std::vector GetChildren() const { -// return std::visit(Overloaded{ -// [](const logical::Table&) -> std::vector { return {}; }, -// [](const logical::Filter& f) -> std::vector { return {f.source}; }, -// [](const logical::Projection& p) -> std::vector { return {p.source}; }, -// [](const logical::CrossJoin& j) -> std::vector { return {j.lhs, j.rhs}; }, -// [](const logical::Join& j) -> std::vector { return {j.lhs, j.rhs}; }, -// }, expr_); -// } - -// private: -// bool IsApplied(RuleNumber rule) const { return is_applied_[rule]; } - -// const LogicalExpr expr_; -// const Rules& rules_; -// GroupKey group_; -// std::vector is_applied_; -// }; - -// class Memo { -// public: -// explicit Memo(const Operator& expr, const Rules& rules) : mapping_(), rules_(rules), root_(AddGroup(expr, rules)) {} - -// Group& GetRoot() { -// return mapping_.find(root_)->second; -// } - -// GroupKey AddGroup(const LogicalExpr& expr) { -// return AddGroup(expr, rules_); -// } - // GroupKey AddGroup(const LogicalExpr& expr, const Rules& rules) { // return std::visit(Overloaded{ // [this, &rules](const logical::Table& t) { @@ -96,12 +45,6 @@ namespace stewkk::sql { // }, // }, expr); // } - -// // TODO: replace with raw ptr (or some non-null wrapper) -// Group& GetGroup(GroupKey key) { -// return mapping_.find(key)->second; -// } - // private: // GroupKey AddGroup(const Operator& expr, const Rules& rules) { @@ -141,11 +84,6 @@ namespace stewkk::sql { // }, expr); // } -// std::unordered_map mapping_; -// const Rules& rules_; -// GroupKey root_; -// }; - // class Optimizer { // public: diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt index 0413bc6..0f6c674 100644 --- a/test/CMakeLists.txt +++ b/test/CMakeLists.txt @@ -8,6 +8,7 @@ target_sources(unittests PRIVATE ${PROJECT_SOURCE_DIR}/src/stewkk/sql/logic/optimizer/rule_test.cpp ${PROJECT_SOURCE_DIR}/src/stewkk/sql/logic/optimizer/rules_applier_test.cpp ${PROJECT_SOURCE_DIR}/src/stewkk/sql/logic/optimizer/optimizer_test.cpp + ${PROJECT_SOURCE_DIR}/src/stewkk/sql/logic/optimizer/memo_test.cpp ) target_compile_features(unittests PRIVATE cxx_std_23) set_target_properties(unittests PROPERTIES From a62c354a6a0cc1f89b590ac81fb7be5d7492cd30 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 20 Apr 2026 01:35:56 +0300 Subject: [PATCH 007/120] Impl Memo population --- include/stewkk/sql/logic/optimizer/memo.hpp | 3 +- src/stewkk/sql/logic/optimizer/memo.cpp | 34 ++++++++++++++++--- src/stewkk/sql/logic/optimizer/memo_test.cpp | 22 ++++++++++-- src/stewkk/sql/logic/optimizer/rule_test.cpp | 18 +++++----- .../logic/optimizer/rules_applier_test.cpp | 6 ++-- .../join_associativity.cpp | 2 +- 6 files changed, 64 insertions(+), 21 deletions(-) diff --git a/include/stewkk/sql/logic/optimizer/memo.hpp b/include/stewkk/sql/logic/optimizer/memo.hpp index dd01cce..e188f26 100644 --- a/include/stewkk/sql/logic/optimizer/memo.hpp +++ b/include/stewkk/sql/logic/optimizer/memo.hpp @@ -12,7 +12,8 @@ namespace stewkk::sql { class Memo { public: size_t GroupCount() const; - utils::NotNull AddGroup(LogicalOperator root_operator); + utils::NotNull AddGroup(LogicalOperator root_operator); + utils::NotNull Populate(const Operator& op); private: std::vector> groups_; diff --git a/src/stewkk/sql/logic/optimizer/memo.cpp b/src/stewkk/sql/logic/optimizer/memo.cpp index 40a3c3d..687e866 100644 --- a/src/stewkk/sql/logic/optimizer/memo.cpp +++ b/src/stewkk/sql/logic/optimizer/memo.cpp @@ -36,16 +36,42 @@ size_t Memo::GroupCount() const { return groups_.size(); } -utils::NotNull Memo::AddGroup(LogicalOperator root_operator) { +utils::NotNull Memo::AddGroup(LogicalOperator root_operator) { auto key = ToKey(root_operator); auto it = expr_index_.find(key); if (it != expr_index_.end()) { - return it->second; + return it->second->GetLogicalExprs()[0].get(); } auto& ptr = groups_.emplace_back(new Group(groups_.size())); - ptr->AddLogicalExpr(std::move(root_operator)); + auto expr = ptr->AddLogicalExpr(std::move(root_operator)); expr_index_[key] = ptr.get(); - return ptr.get(); + return expr; +} + +utils::NotNull Memo::Populate(const Operator& op) { + return std::visit(utils::Overloaded{ + [this](const Table& t) { + return AddGroup(logical::Table{t.name}); + }, + [this](const Filter& f) { + auto source = Populate(*f.source); + return AddGroup(logical::Filter{source->group, f.expr}); + }, + [this](const Projection& p) { + auto source = Populate(*p.source); + return AddGroup(logical::Projection{source->group, p.expressions}); + }, + [this](const CrossJoin& j) { + auto lhs = Populate(*j.lhs); + auto rhs = Populate(*j.rhs); + return AddGroup(logical::CrossJoin{lhs->group, rhs->group}); + }, + [this](const Join& j) { + auto lhs = Populate(*j.lhs); + auto rhs = Populate(*j.rhs); + return AddGroup(logical::Join{lhs->group, rhs->group, j.type, j.qual}); + }, + }, op); } } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/memo_test.cpp b/src/stewkk/sql/logic/optimizer/memo_test.cpp index 923eb5b..e5348c4 100644 --- a/src/stewkk/sql/logic/optimizer/memo_test.cpp +++ b/src/stewkk/sql/logic/optimizer/memo_test.cpp @@ -1,6 +1,9 @@ #include +#include + #include +#include namespace stewkk::sql { @@ -8,10 +11,10 @@ TEST(MemoTest, AddGroup) { Memo memo; EXPECT_EQ(memo.GroupCount(), 0); - auto group = memo.AddGroup(logical::Table{"t"}); + auto expr = memo.AddGroup(logical::Table{"t"}); EXPECT_EQ(memo.GroupCount(), 1); - auto exprs = group->GetLogicalExprs(); + auto exprs = expr->group->GetLogicalExprs(); ASSERT_EQ(exprs.size(), 1); EXPECT_TRUE(std::holds_alternative(exprs[0]->root_operator)); EXPECT_EQ(std::get(exprs[0]->root_operator).name, "t"); @@ -22,8 +25,21 @@ TEST(MemoTest, AddSameGroupSecondReturnsIt) { auto first = memo.AddGroup(logical::Table{"t"}); auto second = memo.AddGroup(logical::Table{"t"}); - EXPECT_EQ(first, second); + EXPECT_EQ(first->group, second->group); EXPECT_EQ(memo.GroupCount(), 1); } +TEST(MemoTest, PopulateWithWholeQuery) { + std::stringstream s{"SELECT * FROM users, orders;"}; + Operator op = GetAST(s).value(); + Memo memo; + + auto root = memo.Populate(op); + + ASSERT_EQ(memo.GroupCount(), 3); + const auto& cross_join = std::get(root->root_operator); + EXPECT_EQ(std::get(cross_join.lhs->GetLogicalExprs()[0]->root_operator).name, "users"); + EXPECT_EQ(std::get(cross_join.rhs->GetLogicalExprs()[0]->root_operator).name, "orders"); +} + } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/rule_test.cpp b/src/stewkk/sql/logic/optimizer/rule_test.cpp index 0699ab9..700b8cc 100644 --- a/src/stewkk/sql/logic/optimizer/rule_test.cpp +++ b/src/stewkk/sql/logic/optimizer/rule_test.cpp @@ -10,8 +10,8 @@ namespace stewkk::sql { class JoinCommutativityTest : public ::testing::Test { protected: void SetUp() override { - a = memo.AddGroup(logical::Table{"a"}); - b = memo.AddGroup(logical::Table{"b"}); + a = memo.AddGroup(logical::Table{"a"})->group; + b = memo.AddGroup(logical::Table{"b"})->group; } Memo memo; @@ -21,7 +21,7 @@ class JoinCommutativityTest : public ::testing::Test { }; TEST_F(JoinCommutativityTest, ReturnsSwappedJoin) { - auto join_group = memo.AddGroup(logical::Join{a, b, JoinType::kInner, Literal::kTrue}); + auto join_group = memo.AddGroup(logical::Join{a, b, JoinType::kInner, Literal::kTrue})->group; auto* expr = join_group->GetLogicalExprs()[0].get(); auto result = rule.Apply(expr, memo); @@ -32,7 +32,7 @@ TEST_F(JoinCommutativityTest, ReturnsSwappedJoin) { } TEST_F(JoinCommutativityTest, AddsNewJoinIntoGroup) { - auto join_group = memo.AddGroup(logical::Join{a, b, JoinType::kInner, Literal::kTrue}); + auto join_group = memo.AddGroup(logical::Join{a, b, JoinType::kInner, Literal::kTrue})->group; auto* expr = join_group->GetLogicalExprs()[0].get(); rule.Apply(expr, memo); @@ -46,11 +46,11 @@ TEST_F(JoinCommutativityTest, AddsNewJoinIntoGroup) { class JoinAssociativityTest : public ::testing::Test { protected: void SetUp() override { - a = memo.AddGroup(logical::Table{"a"}); - b = memo.AddGroup(logical::Table{"b"}); - c = memo.AddGroup(logical::Table{"c"}); - ab = memo.AddGroup(logical::Join{a, b, JoinType::kInner, Literal::kTrue}); - abc = memo.AddGroup(logical::Join{ab, c, JoinType::kInner, Literal::kFalse}); + a = memo.AddGroup(logical::Table{"a"})->group; + b = memo.AddGroup(logical::Table{"b"})->group; + c = memo.AddGroup(logical::Table{"c"})->group; + ab = memo.AddGroup(logical::Join{a, b, JoinType::kInner, Literal::kTrue})->group; + abc = memo.AddGroup(logical::Join{ab, c, JoinType::kInner, Literal::kFalse})->group; } Memo memo; diff --git a/src/stewkk/sql/logic/optimizer/rules_applier_test.cpp b/src/stewkk/sql/logic/optimizer/rules_applier_test.cpp index 9023624..7ec6be9 100644 --- a/src/stewkk/sql/logic/optimizer/rules_applier_test.cpp +++ b/src/stewkk/sql/logic/optimizer/rules_applier_test.cpp @@ -6,9 +6,9 @@ namespace stewkk::sql { TEST(RulesApplierTest, ChecksThatRuleAlreadyApplied) { Memo memo; - auto a = memo.AddGroup(logical::Table{"a"}); - auto b = memo.AddGroup(logical::Table{"b"}); - auto join_group = memo.AddGroup(logical::Join{a, b, JoinType::kInner, Literal::kTrue}); + auto a = memo.AddGroup(logical::Table{"a"})->group; + auto b = memo.AddGroup(logical::Table{"b"})->group; + auto join_group = memo.AddGroup(logical::Join{a, b, JoinType::kInner, Literal::kTrue})->group; auto* expr = join_group->GetLogicalExprs()[0].get(); RulesApplier applier(MakeMainRules()); diff --git a/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp b/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp index 0544238..d6cf49c 100644 --- a/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp +++ b/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp @@ -24,7 +24,7 @@ LogicalOperator JoinAssociativity::ApplyImpl(utils::NotNull expr, BinaryOp::kAnd, std::make_shared(outer.qual), }}; - auto new_rhs = memo.AddGroup(logical::Join{inner.rhs, outer.rhs, outer.type, Literal::kTrue}); + auto new_rhs = memo.AddGroup(logical::Join{inner.rhs, outer.rhs, outer.type, Literal::kTrue})->group; // FIXME: should return more than one operators!!! return logical::Join{inner.lhs, new_rhs, inner.type, combined_qual}; } From c1657a75fa7203ab47726f912456036d2031ad3d Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 20 Apr 2026 02:19:53 +0300 Subject: [PATCH 008/120] Impl implementation rules handling in RulesApplier --- include/stewkk/sql/logic/optimizer/group.hpp | 1 - include/stewkk/sql/logic/optimizer/rule.hpp | 2 + .../sql/logic/optimizer/rules_applier.hpp | 28 +- src/stewkk/sql/CMakeLists.txt | 1 + .../sql/logic/optimizer/optimizer_test.cpp | 345 ++++++++---------- .../sql/logic/optimizer/rules_applier.cpp | 33 ++ .../logic/optimizer/rules_applier_test.cpp | 6 +- 7 files changed, 209 insertions(+), 207 deletions(-) create mode 100644 src/stewkk/sql/logic/optimizer/rules_applier.cpp diff --git a/include/stewkk/sql/logic/optimizer/group.hpp b/include/stewkk/sql/logic/optimizer/group.hpp index 78d06e7..d5c6b7a 100644 --- a/include/stewkk/sql/logic/optimizer/group.hpp +++ b/include/stewkk/sql/logic/optimizer/group.hpp @@ -10,7 +10,6 @@ namespace stewkk::sql { using LogicalOperator = decltype(LogicalExpr::root_operator); -using RuleId = size_t; class Group { public: diff --git a/include/stewkk/sql/logic/optimizer/rule.hpp b/include/stewkk/sql/logic/optimizer/rule.hpp index 3960ecb..a764cce 100644 --- a/include/stewkk/sql/logic/optimizer/rule.hpp +++ b/include/stewkk/sql/logic/optimizer/rule.hpp @@ -1,5 +1,7 @@ #pragma once +#include + #include #include #include diff --git a/include/stewkk/sql/logic/optimizer/rules_applier.hpp b/include/stewkk/sql/logic/optimizer/rules_applier.hpp index b6ff5e3..b57b4d2 100644 --- a/include/stewkk/sql/logic/optimizer/rules_applier.hpp +++ b/include/stewkk/sql/logic/optimizer/rules_applier.hpp @@ -8,21 +8,25 @@ namespace stewkk::sql { + +struct TransformationRuleId { + size_t value; +}; + +struct ImplementationRuleId { + size_t value; +}; + template class RulesApplier { public: - explicit RulesApplier(Rules rules) - : rules_(std::move(rules)) {} - - bool IsApplicable(RuleId rule, utils::NotNull expr) { - return !applied_transformation_rules_[expr.get()][rule] && - rules_.transformation_rules[rule]->IsApplicable(expr); - } - - utils::NotNull Apply(RuleId rule, utils::NotNull expr, Memo& memo) { - applied_transformation_rules_[expr.get()][rule] = 1; - return rules_.transformation_rules[rule]->Apply(expr, memo); - } + explicit RulesApplier(Rules rules); + + bool IsApplicable(TransformationRuleId rule, utils::NotNull expr); + utils::NotNull Apply(TransformationRuleId rule, utils::NotNull expr, Memo& memo); + + bool IsApplicable(ImplementationRuleId rule, utils::NotNull expr); + utils::NotNull Apply(ImplementationRuleId rule, utils::NotNull expr, Memo& memo); private: Rules rules_; diff --git a/src/stewkk/sql/CMakeLists.txt b/src/stewkk/sql/CMakeLists.txt index 0190406..5cbde49 100644 --- a/src/stewkk/sql/CMakeLists.txt +++ b/src/stewkk/sql/CMakeLists.txt @@ -27,6 +27,7 @@ add_library(libsql logic/optimizer/group.cpp logic/optimizer/memo.cpp logic/optimizer/rules.cpp + logic/optimizer/rules_applier.cpp logic/optimizer/rule.cpp logic/transformation_rules/join_commutativity.cpp logic/transformation_rules/join_associativity.cpp diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index bd84ccd..ec4a896 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -10,202 +10,161 @@ #include #include #include +#include +#include +#include using ::testing::Eq; namespace stewkk::sql { - -// GroupKey AddGroup(const LogicalExpr& expr, const Rules& rules) { -// return std::visit(Overloaded{ -// [this, &rules](const logical::Table& t) { -// auto key = std::format("Table({})", t.name); -// mapping_.emplace(key, Group(key, ExpressionRulesApplier(t, rules, key))); -// return key; -// }, -// [this, &rules](const logical::Filter& f) { -// auto key = std::format("Filter({}, {})", f.source, ToString(f.predicate)); -// mapping_.emplace(key, Group(key, ExpressionRulesApplier(f, rules, key))); -// return key; -// }, -// [this, &rules](const logical::Projection& p) { -// auto exprs = p.expressions | std::views::transform([](const Expression& e) { return ToString(e); }) | std::views::join_with(',') | std::ranges::to(); -// auto key = std::format("Projection({}, {})", p.source, exprs); -// mapping_.emplace(key, Group(key, ExpressionRulesApplier(p, rules, key))); -// return key; -// }, -// [this, &rules](const logical::CrossJoin& j) { -// auto key = std::format("CrossJoin({}, {})", j.lhs, j.rhs); -// mapping_.emplace(key, Group(key, ExpressionRulesApplier(j, rules, key))); -// return key; -// }, -// [this, &rules](const logical::Join& j) { -// auto key = std::format("Join({}, {}, {}, {})", j.lhs, j.rhs, ToString(j.type), ToString(j.qual)); -// mapping_.emplace(key, Group(key, ExpressionRulesApplier(j, rules, key))); -// return key; -// }, -// }, expr); -// } -// private: - -// GroupKey AddGroup(const Operator& expr, const Rules& rules) { -// return std::visit(Overloaded{ -// [this, &rules](const Table& t) { -// auto key = std::format("Table({})", t.name); -// mapping_.emplace(key, Group(key, ExpressionRulesApplier(logical::Table(t), rules, key))); -// return key; -// }, -// [this, &rules](const Filter& f) { -// auto source_group = AddGroup(*f.source, rules); -// auto key = std::format("Filter({}, {})", source_group, ToString(f.expr)); -// mapping_.emplace(key, Group(key, ExpressionRulesApplier(logical::Filter(source_group, f.expr), rules, key))); -// return key; -// }, -// [this, &rules](const Projection& p) { -// auto source_group = AddGroup(*p.source, rules); -// auto exprs = p.expressions | std::views::transform([](const Expression& e) { return ToString(e); }) | std::views::join_with(',') | std::ranges::to(); -// auto key = std::format("Projection({}, {})", source_group, exprs); -// mapping_.emplace(key, Group(key, ExpressionRulesApplier(logical::Projection(source_group, p.expressions), rules, key))); -// return key; -// }, -// [this, &rules](const CrossJoin& j) { -// auto lhs_group = AddGroup(*j.lhs, rules); -// auto rhs_group = AddGroup(*j.rhs, rules); -// auto key = std::format("CrossJoin({}, {})", lhs_group, rhs_group); -// mapping_.emplace(key, Group(key, ExpressionRulesApplier(logical::CrossJoin(lhs_group, rhs_group), rules, key))); -// return key; -// }, -// [this, &rules](const Join& j) { -// auto lhs_group = AddGroup(*j.lhs, rules); -// auto rhs_group = AddGroup(*j.rhs, rules); -// auto key = std::format("Join({}, {}, {}, {})", lhs_group, rhs_group, ToString(j.type), ToString(j.qual)); -// mapping_.emplace(key, Group(key, ExpressionRulesApplier(logical::Join(lhs_group, rhs_group, j.type, j.qual), rules, key))); -// return key; -// }, -// }, expr); -// } - -// class Optimizer { -// public: - -// explicit Optimizer(const Operator& expr, const Rules& rules) : memo_(expr, rules), rules_count_(rules.Count()) {} - -// bool IsExplored(const Group& group) const { -// return explored_groups_.contains(group.GetKey()); -// } - -// void SetExplored(const Group& group) { -// explored_groups_.insert(group.GetKey()); -// } - -// void OptimizeInputs(const ExpressionRulesApplier& expr) { -// } - -// void ApplyRule(ExpressionRulesApplier& expr, RuleNumber rule) { -// auto new_expr = expr.ApplyRule(rule, memo_); -// auto group = memo_.GetGroup(new_expr.GetGroup()); -// auto new_expr_ref = group.AddExpression(std::move(new_expr)); -// if (new_expr.IsTransformationRule(rule)) { -// tasks_.emplace([this, &new_expr_ref]() { ExploreExpression(new_expr_ref); }); -// } else { -// tasks_.emplace([this, &new_expr_ref]() { OptimizeInputs(new_expr_ref); }); -// } -// } - -// void OptimizeExpression(ExpressionRulesApplier& expr) { -// for (RuleNumber rule = 0; rule < rules_count_; rule++) { -// if (!expr.IsApplicable(rule, memo_)) { -// continue; -// } -// tasks_.emplace([this, &expr, rule]() { -// ApplyRule(expr, rule); -// }); -// } - -// for (auto child : expr.GetChildren()) { -// auto& group = memo_.GetGroup(child); -// if (IsExplored(group)) { -// continue; -// } -// tasks_.emplace([this, &group]() { -// ExploreGroup(group); -// }); -// } -// } - -// void ExploreExpression(ExpressionRulesApplier& expr) { -// for (RuleNumber rule = 0; rule < rules_count_; rule++) { -// bool transformation_rules_only = true; -// if (!expr.IsApplicable(rule, memo_, transformation_rules_only)) { -// continue; -// } -// tasks_.emplace([this, &expr, rule]() { -// ApplyRule(expr, rule); -// }); -// } - -// for (auto child : expr.GetChildren()) { -// auto& group = memo_.GetGroup(child); -// if (IsExplored(group)) { -// continue; -// } -// tasks_.emplace([this, &group]() { -// ExploreGroup(group); -// }); -// } -// } - -// void ExploreGroup(Group& group) { -// SetExplored(group); -// for (auto& expr : group.GetExpressions()) { -// tasks_.emplace([this, &expr]() { -// ExploreExpression(expr); -// }); -// } -// } - -// void OptimizeGroup(Group& group) { -// if (!IsExplored(group)) { -// tasks_.emplace([this, &group](){ -// OptimizeGroup(group); -// }); -// tasks_.emplace([this, &group](){ -// ExploreGroup(group); -// }); -// return; -// } - -// for (auto& expr : group.GetExpressions()) { -// tasks_.emplace([this, &expr](){ -// OptimizeExpression(expr); -// }); -// } -// } - -// Operator Optimize() { -// tasks_.emplace([this]() { -// OptimizeGroup(memo_.GetRoot()); -// }); -// while (!tasks_.empty()) { -// auto next_task = std::move(tasks_.top()); -// tasks_.pop(); -// next_task(); -// } -// return Table{"A"}; -// } - -// private: -// Memo memo_; -// std::stack> tasks_; -// std::unordered_set explored_groups_; -// const size_t rules_count_; -// }; - -// TEST(OptimizerTest, Simple) { -// std::stringstream s{"SELECT * FROM users;"}; -// Operator op = GetAST(s).value(); -// Optimizer optimizer(op, Rules{{}}); - -// auto got = optimizer.Optimize(); -// } + +// FIXME: проверить, не возникает ли висячих ссылок нигде? + +std::vector> GetChildren(utils::NotNull expr) { + return std::visit(utils::Overloaded{ + [](const logical::Table&) -> std::vector> { + return {}; + }, + [](const logical::Filter& f) -> std::vector> { + return {f.source}; + }, + [](const logical::Projection& p) -> std::vector> { + return {p.source}; + }, + [](const logical::CrossJoin& j) -> std::vector> { + return {j.lhs, j.rhs}; + }, + [](const logical::Join& j) -> std::vector> { + return {j.lhs, j.rhs}; + }, + }, expr->root_operator); +} + +template +class Optimizer { + public: + Optimizer(const Operator& expr, Rules&& rules) + : memo_(), rules_applier_(std::move(rules)), root_(memo_.Populate(expr)) { + } + +private: + bool IsExplored(utils::NotNull group) const { + return explored_groups_.contains(group.get()); + } + + void SetExplored(utils::NotNull group) { + explored_groups_.insert(group); + } + + void OptimizeInputs() { + } + + // void ApplyRule(ExpressionRulesApplier& expr, RuleNumber rule) { + // auto new_expr = expr.ApplyRule(rule, memo_); + // auto group = memo_.GetGroup(new_expr.GetGroup()); + // auto new_expr_ref = group.AddExpression(std::move(new_expr)); + // if (new_expr.IsTransformationRule(rule)) { + // tasks_.emplace([this, &new_expr_ref]() { ExploreExpression(new_expr_ref); }); + // } else { + // tasks_.emplace(root->root_operator[this, &new_expr_ref]() { + // OptimizeInputs(new_expr_ref); }); + // } + // } + + void OptimizeExpression(utils::NotNull expr) { + for (size_t rule = 0; rule < NImplementation; rule++) { + if (!rules_applier_.IsApplicable(ImplementationRuleId{rule}, expr)) { + continue; + } + tasks_.emplace([this, expr, rule]() { + rules_applier_.Apply(ImplementationRuleId{rule}, expr, memo_); + }); + } + + for (auto child : GetChildren(expr)) { + if (IsExplored(child)) { + continue; + } + tasks_.emplace([this, child]() { + ExploreGroup(child); + }); + } + } + + void ExploreExpression(utils::NotNull expr) { + for (size_t rule = 0; rule < NTransformation; rule++) { + if (!rules_applier_.IsApplicable(TransformationRuleId{rule}, expr)) { + continue; + } + tasks_.emplace([this, expr, rule]() { + rules_applier_.Apply(TransformationRuleId{rule}, expr, memo_); + }); + } + + for (auto child : GetChildren(expr)) { + if (IsExplored(child)) { + continue; + } + tasks_.emplace([this, child]() { + ExploreGroup(child); + }); + } + } + + void ExploreGroup(utils::NotNull group) { + SetExplored(group); + for (auto& expr : group->GetLogicalExprs()) { + tasks_.emplace([this, expr = expr.get()]() { + ExploreExpression(expr); + }); + } + } + + void OptimizeGroup(utils::NotNull group) { + if (!IsExplored(group)) { + tasks_.emplace([this, group](){ + OptimizeGroup(group); + }); + tasks_.emplace([this, group](){ + ExploreGroup(group); + }); + return; + } + + for (auto& expr : group->GetLogicalExprs()) { + tasks_.emplace([this, expr = expr.get()](){ + OptimizeExpression(expr); + }); + } + } + + public: + Operator Optimize() { + tasks_.emplace([this]() { + OptimizeGroup(root_->group); + }); + while (!tasks_.empty()) { + auto next_task = std::move(tasks_.top()); + tasks_.pop(); + next_task(); + } + return Table{"A"}; + } + + private: + Memo memo_; + RulesApplier rules_applier_; + std::stack> tasks_; + std::unordered_set explored_groups_; + utils::NotNull root_; +}; + +TEST(OptimizerTest, Simple) { + std::stringstream s{"SELECT * FROM users;"}; + Operator op = GetAST(s).value(); + Optimizer optimizer(op, MakeMainRules()); + + auto got = optimizer.Optimize(); +} } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/rules_applier.cpp b/src/stewkk/sql/logic/optimizer/rules_applier.cpp new file mode 100644 index 0000000..3a43141 --- /dev/null +++ b/src/stewkk/sql/logic/optimizer/rules_applier.cpp @@ -0,0 +1,33 @@ +#include + +namespace stewkk::sql { + +template +RulesApplier::RulesApplier(Rules rules) + : rules_(std::move(rules)) {} + +template +bool RulesApplier::IsApplicable(TransformationRuleId rule, utils::NotNull expr) { + return !applied_transformation_rules_[expr.get()][rule.value] && + rules_.transformation_rules[rule.value]->IsApplicable(expr); +} + +template +utils::NotNull RulesApplier::Apply(TransformationRuleId rule, utils::NotNull expr, Memo& memo) { + applied_transformation_rules_[expr.get()][rule.value] = 1; + return rules_.transformation_rules[rule.value]->Apply(expr, memo); +} + +template +bool RulesApplier::IsApplicable(ImplementationRuleId rule, utils::NotNull expr) { + return rules_.implementation_rules[rule.value]->IsApplicable(expr); +} + +template +utils::NotNull RulesApplier::Apply(ImplementationRuleId rule, utils::NotNull expr, Memo& memo) { + return rules_.implementation_rules[rule.value]->Apply(expr, memo); +} + +template class RulesApplier<2, 0>; + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/rules_applier_test.cpp b/src/stewkk/sql/logic/optimizer/rules_applier_test.cpp index 7ec6be9..6be13d4 100644 --- a/src/stewkk/sql/logic/optimizer/rules_applier_test.cpp +++ b/src/stewkk/sql/logic/optimizer/rules_applier_test.cpp @@ -12,11 +12,15 @@ TEST(RulesApplierTest, ChecksThatRuleAlreadyApplied) { auto* expr = join_group->GetLogicalExprs()[0].get(); RulesApplier applier(MakeMainRules()); - constexpr RuleId kJoinCommutativity = 0; + constexpr TransformationRuleId kJoinCommutativity{0}; EXPECT_TRUE(applier.IsApplicable(kJoinCommutativity, expr)); applier.Apply(kJoinCommutativity, expr, memo); EXPECT_FALSE(applier.IsApplicable(kJoinCommutativity, expr)); } +TEST(RulesApplierTest, ApplyImplementationRule) { + // FIXME +} + } // namespace stewkk::sql From 20286694ea11ff185da807806e285e19bfc7a1dc Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 20 Apr 2026 13:33:39 +0300 Subject: [PATCH 009/120] Refactor --- include/stewkk/sql/logic/optimizer/group.hpp | 23 ++++---- include/stewkk/sql/logic/optimizer/memo.hpp | 7 ++- src/stewkk/sql/logic/optimizer/group.cpp | 14 ++--- src/stewkk/sql/logic/optimizer/memo.cpp | 16 +++--- .../sql/logic/optimizer/optimizer_test.cpp | 53 ++++++++++++------- src/stewkk/sql/logic/optimizer/rule_test.cpp | 12 ++--- .../logic/optimizer/rules_applier_test.cpp | 2 +- .../join_associativity.cpp | 4 +- 8 files changed, 74 insertions(+), 57 deletions(-) diff --git a/include/stewkk/sql/logic/optimizer/group.hpp b/include/stewkk/sql/logic/optimizer/group.hpp index d5c6b7a..e269a15 100644 --- a/include/stewkk/sql/logic/optimizer/group.hpp +++ b/include/stewkk/sql/logic/optimizer/group.hpp @@ -1,8 +1,7 @@ #pragma once -#include -#include -#include +#include +#include #include #include @@ -12,23 +11,29 @@ namespace stewkk::sql { using LogicalOperator = decltype(LogicalExpr::root_operator); class Group { + private: + struct ToNotNull { + utils::NotNull operator()(LogicalExpr& e) const { return &e; } + }; + public: - using LogicalExprPtr = std::unique_ptr; - using PhysicalExprPtr = std::unique_ptr; + using LogicalExprs = std::ranges::transform_view< + std::ranges::ref_view>, + ToNotNull>; utils::NotNull AddLogicalExpr(LogicalOperator root_operator); utils::NotNull AddPhysicalExpr(); - std::span GetLogicalExprs() const; + LogicalExprs GetLogicalExprs(); - size_t id() const; + size_t GetId() const; private: friend class Memo; explicit Group(size_t id) : id_(id) {} size_t id_; - std::vector logical_exprs_; - std::vector physical_exprs_; + std::deque logical_exprs_; + std::deque physical_exprs_; }; } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/memo.hpp b/include/stewkk/sql/logic/optimizer/memo.hpp index e188f26..6e41920 100644 --- a/include/stewkk/sql/logic/optimizer/memo.hpp +++ b/include/stewkk/sql/logic/optimizer/memo.hpp @@ -1,7 +1,6 @@ #pragma once -#include -#include +#include #include #include @@ -16,8 +15,8 @@ class Memo { utils::NotNull Populate(const Operator& op); private: - std::vector> groups_; - std::unordered_map expr_index_; + std::deque groups_; + std::unordered_map expr_index_; }; } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/group.cpp b/src/stewkk/sql/logic/optimizer/group.cpp index 6395d92..9a7478a 100644 --- a/src/stewkk/sql/logic/optimizer/group.cpp +++ b/src/stewkk/sql/logic/optimizer/group.cpp @@ -3,22 +3,18 @@ namespace stewkk::sql { utils::NotNull Group::AddLogicalExpr(LogicalOperator root_operator) { - auto& ptr = logical_exprs_.emplace_back( - std::make_unique(std::move(root_operator), this)); - return ptr.get(); + return &logical_exprs_.emplace_back(std::move(root_operator), this); } utils::NotNull Group::AddPhysicalExpr() { - auto& ptr = physical_exprs_.emplace_back( - std::make_unique(this)); - return ptr.get(); + return &physical_exprs_.emplace_back(this); } -std::span Group::GetLogicalExprs() const { - return logical_exprs_; +Group::LogicalExprs Group::GetLogicalExprs() { + return std::views::transform(logical_exprs_, ToNotNull{}); } -size_t Group::id() const { +size_t Group::GetId() const { return id_; } diff --git a/src/stewkk/sql/logic/optimizer/memo.cpp b/src/stewkk/sql/logic/optimizer/memo.cpp index 687e866..89e48c5 100644 --- a/src/stewkk/sql/logic/optimizer/memo.cpp +++ b/src/stewkk/sql/logic/optimizer/memo.cpp @@ -11,21 +11,21 @@ std::string ToKey(const LogicalOperator& op) { return "Table(" + t.name + ")"; }, [](const logical::Filter& f) { - return "Filter(" + ToString(f.predicate) + "," + std::to_string(f.source->id()) + ")"; + return "Filter(" + ToString(f.predicate) + "," + std::to_string(f.source->GetId()) + ")"; }, [](const logical::Projection& p) { std::string exprs; for (const auto& e : p.expressions) { exprs += ToString(e) + ","; } - return "Projection(" + exprs + std::to_string(p.source->id()) + ")"; + return "Projection(" + exprs + std::to_string(p.source->GetId()) + ")"; }, [](const logical::CrossJoin& j) { - return "CrossJoin(" + std::to_string(j.lhs->id()) + "," + std::to_string(j.rhs->id()) + ")"; + return "CrossJoin(" + std::to_string(j.lhs->GetId()) + "," + std::to_string(j.rhs->GetId()) + ")"; }, [](const logical::Join& j) { return "Join(" + ToString(j.type) + "," + ToString(j.qual) + "," + - std::to_string(j.lhs->id()) + "," + std::to_string(j.rhs->id()) + ")"; + std::to_string(j.lhs->GetId()) + "," + std::to_string(j.rhs->GetId()) + ")"; }, }, op); } @@ -40,11 +40,11 @@ utils::NotNull Memo::AddGroup(LogicalOperator root_operator) { auto key = ToKey(root_operator); auto it = expr_index_.find(key); if (it != expr_index_.end()) { - return it->second->GetLogicalExprs()[0].get(); + return it->second; } - auto& ptr = groups_.emplace_back(new Group(groups_.size())); - auto expr = ptr->AddLogicalExpr(std::move(root_operator)); - expr_index_[key] = ptr.get(); + auto& group = groups_.emplace_back(Group(groups_.size())); + auto expr = group.AddLogicalExpr(std::move(root_operator)); + expr_index_[key] = expr; return expr; } diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index ec4a896..0cdc198 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -19,6 +19,9 @@ using ::testing::Eq; namespace stewkk::sql { // FIXME: проверить, не возникает ли висячих ссылок нигде? +// FIXME: branch and bound +// FIXME: физические операторы! +// FIXME: сделать API в виде DoStep(), которое возвращает какой-то внутренний стейт оптимизатора std::vector> GetChildren(utils::NotNull expr) { return std::visit(utils::Overloaded{ @@ -40,6 +43,10 @@ std::vector> GetChildren(utils::NotNull exp }, expr->root_operator); } +std::vector> GetChildren(utils::NotNull expr) { + return {}; +} + template class Optimizer { public: @@ -56,20 +63,30 @@ class Optimizer { explored_groups_.insert(group); } - void OptimizeInputs() { + void OptimizeInputs(utils::NotNull expr, size_t child_index = 0) { + auto children = GetChildren(expr); + if (child_index >= children.size()) { + // FIXME: update best plan + return; + } + tasks_.emplace([this, expr, child_index]() { + OptimizeInputs(expr, child_index+1); + }); + tasks_.emplace([this, expr]() { + OptimizeGroup(expr->group); + }); + } + + void ApplyRule(TransformationRuleId rule, utils::NotNull expr) { + auto new_expr = rules_applier_.Apply(rule, expr, memo_); + tasks_.emplace([this, new_expr]() { ExploreExpression(new_expr); }); } - // void ApplyRule(ExpressionRulesApplier& expr, RuleNumber rule) { - // auto new_expr = expr.ApplyRule(rule, memo_); - // auto group = memo_.GetGroup(new_expr.GetGroup()); - // auto new_expr_ref = group.AddExpression(std::move(new_expr)); - // if (new_expr.IsTransformationRule(rule)) { - // tasks_.emplace([this, &new_expr_ref]() { ExploreExpression(new_expr_ref); }); - // } else { - // tasks_.emplace(root->root_operator[this, &new_expr_ref]() { - // OptimizeInputs(new_expr_ref); }); - // } - // } + void ApplyRule(ImplementationRuleId rule, utils::NotNull expr) { + auto new_expr = rules_applier_.Apply(rule, expr, memo_); + tasks_.emplace( + [this, new_expr]() { OptimizeInputs(new_expr); }); + } void OptimizeExpression(utils::NotNull expr) { for (size_t rule = 0; rule < NImplementation; rule++) { @@ -77,7 +94,7 @@ class Optimizer { continue; } tasks_.emplace([this, expr, rule]() { - rules_applier_.Apply(ImplementationRuleId{rule}, expr, memo_); + ApplyRule(ImplementationRuleId{rule}, expr); }); } @@ -97,7 +114,7 @@ class Optimizer { continue; } tasks_.emplace([this, expr, rule]() { - rules_applier_.Apply(TransformationRuleId{rule}, expr, memo_); + ApplyRule(TransformationRuleId{rule}, expr); }); } @@ -113,8 +130,8 @@ class Optimizer { void ExploreGroup(utils::NotNull group) { SetExplored(group); - for (auto& expr : group->GetLogicalExprs()) { - tasks_.emplace([this, expr = expr.get()]() { + for (auto expr : group->GetLogicalExprs()) { + tasks_.emplace([this, expr]() { ExploreExpression(expr); }); } @@ -131,8 +148,8 @@ class Optimizer { return; } - for (auto& expr : group->GetLogicalExprs()) { - tasks_.emplace([this, expr = expr.get()](){ + for (auto expr : group->GetLogicalExprs()) { + tasks_.emplace([this, expr](){ OptimizeExpression(expr); }); } diff --git a/src/stewkk/sql/logic/optimizer/rule_test.cpp b/src/stewkk/sql/logic/optimizer/rule_test.cpp index 700b8cc..572e4bb 100644 --- a/src/stewkk/sql/logic/optimizer/rule_test.cpp +++ b/src/stewkk/sql/logic/optimizer/rule_test.cpp @@ -22,7 +22,7 @@ class JoinCommutativityTest : public ::testing::Test { TEST_F(JoinCommutativityTest, ReturnsSwappedJoin) { auto join_group = memo.AddGroup(logical::Join{a, b, JoinType::kInner, Literal::kTrue})->group; - auto* expr = join_group->GetLogicalExprs()[0].get(); + auto expr = join_group->GetLogicalExprs()[0]; auto result = rule.Apply(expr, memo); @@ -33,12 +33,12 @@ TEST_F(JoinCommutativityTest, ReturnsSwappedJoin) { TEST_F(JoinCommutativityTest, AddsNewJoinIntoGroup) { auto join_group = memo.AddGroup(logical::Join{a, b, JoinType::kInner, Literal::kTrue})->group; - auto* expr = join_group->GetLogicalExprs()[0].get(); + auto expr = join_group->GetLogicalExprs()[0]; rule.Apply(expr, memo); EXPECT_EQ(join_group->GetLogicalExprs().size(), 2u); - const auto& new_join = std::get(join_group->GetLogicalExprs()[1].get()->root_operator); + const auto& new_join = std::get(join_group->GetLogicalExprs()[1]->root_operator); EXPECT_EQ(new_join.lhs.get(), b); EXPECT_EQ(new_join.rhs.get(), a); } @@ -63,18 +63,18 @@ class JoinAssociativityTest : public ::testing::Test { }; TEST_F(JoinAssociativityTest, CreatesNewGroup) { - rule.Apply(abc->GetLogicalExprs()[0].get(), memo); + rule.Apply(abc->GetLogicalExprs()[0], memo); EXPECT_EQ(memo.GroupCount(), 6u); } TEST_F(JoinAssociativityTest, ReturnsCorrectExpression) { - auto result = rule.Apply(abc->GetLogicalExprs()[0].get(), memo); + auto result = rule.Apply(abc->GetLogicalExprs()[0], memo); const auto& outer = std::get(result->root_operator); EXPECT_EQ(outer.lhs.get(), a); EXPECT_EQ(outer.type, JoinType::kInner); - const auto& inner = std::get(outer.rhs->GetLogicalExprs()[0].get()->root_operator); + const auto& inner = std::get(outer.rhs->GetLogicalExprs()[0]->root_operator); EXPECT_EQ(inner.lhs.get(), b); EXPECT_EQ(inner.rhs.get(), c); EXPECT_EQ(inner.qual, Expression{Literal::kTrue}); diff --git a/src/stewkk/sql/logic/optimizer/rules_applier_test.cpp b/src/stewkk/sql/logic/optimizer/rules_applier_test.cpp index 6be13d4..d49d40e 100644 --- a/src/stewkk/sql/logic/optimizer/rules_applier_test.cpp +++ b/src/stewkk/sql/logic/optimizer/rules_applier_test.cpp @@ -9,7 +9,7 @@ TEST(RulesApplierTest, ChecksThatRuleAlreadyApplied) { auto a = memo.AddGroup(logical::Table{"a"})->group; auto b = memo.AddGroup(logical::Table{"b"})->group; auto join_group = memo.AddGroup(logical::Join{a, b, JoinType::kInner, Literal::kTrue})->group; - auto* expr = join_group->GetLogicalExprs()[0].get(); + auto expr = join_group->GetLogicalExprs()[0]; RulesApplier applier(MakeMainRules()); constexpr TransformationRuleId kJoinCommutativity{0}; diff --git a/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp b/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp index d6cf49c..915ad0f 100644 --- a/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp +++ b/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp @@ -7,7 +7,7 @@ namespace stewkk::sql { bool JoinAssociativity::IsApplicable(utils::NotNull expr) { if (!std::holds_alternative(expr->root_operator)) return false; const auto& outer = std::get(expr->root_operator); - for (const auto& inner_expr : outer.lhs->GetLogicalExprs()) { + for (auto inner_expr : outer.lhs->GetLogicalExprs()) { if (std::holds_alternative(inner_expr->root_operator)) return true; } return false; @@ -16,7 +16,7 @@ bool JoinAssociativity::IsApplicable(utils::NotNull expr) { // (A ⋈₁ B) ⋈₂ C → A ⋈₁∧₂ (B ⋈₂ C) LogicalOperator JoinAssociativity::ApplyImpl(utils::NotNull expr, Memo& memo) { const auto& outer = std::get(expr->root_operator); - for (const auto& inner_expr : outer.lhs->GetLogicalExprs()) { + for (auto inner_expr : outer.lhs->GetLogicalExprs()) { if (!std::holds_alternative(inner_expr->root_operator)) continue; const auto& inner = std::get(inner_expr->root_operator); auto combined_qual = Expression{BinaryExpression{ From 6f6c19919107837c5b34ad57390e1a629a5d4b17 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 20 Apr 2026 23:04:41 +0300 Subject: [PATCH 010/120] Update flake --- flake.lock | 11 ++++++----- flake.nix | 19 +++++++++++++++++-- 2 files changed, 23 insertions(+), 7 deletions(-) diff --git a/flake.lock b/flake.lock index 1ff1a60..b75d055 100644 --- a/flake.lock +++ b/flake.lock @@ -36,15 +36,16 @@ }, "nixpkgs": { "locked": { - "lastModified": 1760027740, - "narHash": "sha256-53qTARbdnAAmHU29yOivHQCh4Ir1orhhOBtJdol23xk=", - "owner": "NixOS", + "lastModified": 1776548001, + "narHash": "sha256-ZSK0NL4a1BwVbbTBoSnWgbJy9HeZFXLYQizjb2DPF24=", + "owner": "nixos", "repo": "nixpkgs", - "rev": "4b3a7fbda14c1310e607b79caefe103cbffb1fa9", + "rev": "b12141ef619e0a9c1c84dc8c684040326f27cdcc", "type": "github" }, "original": { - "owner": "NixOS", + "owner": "nixos", + "ref": "nixos-unstable", "repo": "nixpkgs", "type": "github" } diff --git a/flake.nix b/flake.nix index 0dae742..2d2563e 100644 --- a/flake.nix +++ b/flake.nix @@ -2,7 +2,7 @@ description = "An example project using flutter"; inputs.nixpkgs = { - url = "github:NixOS/nixpkgs"; + url = "github:nixos/nixpkgs/nixos-unstable"; }; inputs.flake-utils.url = "github:numtide/flake-utils"; inputs.flake-compat = { @@ -22,6 +22,9 @@ ps.pip ps.virtualenv ps.pygments + ps.jupyter + ps.ipykernel + ps.notebook ]); tex = (pkgs.texlive.combine { inherit (pkgs.texlive) scheme-full @@ -30,7 +33,16 @@ in { devShells.default = pkgs.mkShell.override {stdenv = pkgs.llvmPackages_21.stdenv;} { buildInputs = with pkgs; [ - code-cursor-fhs + (pkgs.vscode-with-extensions.override { + vscode = pkgs.vscode-fhs; + vscodeExtensions = with pkgs.vscode-extensions; [ + ms-python.python + ms-python.vscode-pylance + ms-toolsai.jupyter + anthropic.claude-code + ms-vscode.cpptools + ]; + }) pythonEnv antlr cmake @@ -44,6 +56,7 @@ inkscape llvmPackages_21.llvm llvmPackages_21.llvm.dev + nodejs_20 ]; nativeBuildInputs = [ @@ -74,6 +87,8 @@ pip install -r requirements.txt fi + python -m ipykernel install --user --name iu9-sql-compiler --display-name "Python (iu9-sql-compiler)" + python --version ''; }; From 8898de918927070fcf1a3d36c0cf9038eb87d051 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Wed, 22 Apr 2026 23:25:43 +0300 Subject: [PATCH 011/120] Add limit passing --- .../sql/logic/optimizer/optimizer_test.cpp | 62 ++++++++++--------- 1 file changed, 33 insertions(+), 29 deletions(-) diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index 0cdc198..48c0267 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -43,6 +43,8 @@ std::vector> GetChildren(utils::NotNull exp }, expr->root_operator); } +using Limit = std::optional; + std::vector> GetChildren(utils::NotNull expr) { return {}; } @@ -63,38 +65,40 @@ class Optimizer { explored_groups_.insert(group); } - void OptimizeInputs(utils::NotNull expr, size_t child_index = 0) { + void OptimizeInputs(utils::NotNull expr, Limit limit, size_t child_index = 0) { auto children = GetChildren(expr); if (child_index >= children.size()) { // FIXME: update best plan return; } - tasks_.emplace([this, expr, child_index]() { - OptimizeInputs(expr, child_index+1); + tasks_.emplace([this, expr, child_index, limit]() { + OptimizeInputs(expr, limit, child_index+1); }); - tasks_.emplace([this, expr]() { - OptimizeGroup(expr->group); + // FIXME: UpdateCostLimit + tasks_.emplace([this, expr, limit]() { + OptimizeGroup(expr->group, limit); }); } - void ApplyRule(TransformationRuleId rule, utils::NotNull expr) { + void ApplyRule(TransformationRuleId rule, utils::NotNull expr, Limit limit) { auto new_expr = rules_applier_.Apply(rule, expr, memo_); - tasks_.emplace([this, new_expr]() { ExploreExpression(new_expr); }); + tasks_.emplace([this, new_expr, limit]() { ExploreExpression(new_expr, limit); }); } - void ApplyRule(ImplementationRuleId rule, utils::NotNull expr) { + void ApplyRule(ImplementationRuleId rule, utils::NotNull expr, Limit limit) { auto new_expr = rules_applier_.Apply(rule, expr, memo_); + // FIXME: UpdateCostLimit! tasks_.emplace( - [this, new_expr]() { OptimizeInputs(new_expr); }); + [this, new_expr, limit]() { OptimizeInputs(new_expr, limit); }); } - void OptimizeExpression(utils::NotNull expr) { + void OptimizeExpression(utils::NotNull expr, Limit limit) { for (size_t rule = 0; rule < NImplementation; rule++) { if (!rules_applier_.IsApplicable(ImplementationRuleId{rule}, expr)) { continue; } - tasks_.emplace([this, expr, rule]() { - ApplyRule(ImplementationRuleId{rule}, expr); + tasks_.emplace([this, expr, rule, limit]() { + ApplyRule(ImplementationRuleId{rule}, expr, limit); }); } @@ -102,19 +106,19 @@ class Optimizer { if (IsExplored(child)) { continue; } - tasks_.emplace([this, child]() { - ExploreGroup(child); + tasks_.emplace([this, child, limit]() { + ExploreGroup(child, limit); }); } } - void ExploreExpression(utils::NotNull expr) { + void ExploreExpression(utils::NotNull expr, Limit limit) { for (size_t rule = 0; rule < NTransformation; rule++) { if (!rules_applier_.IsApplicable(TransformationRuleId{rule}, expr)) { continue; } - tasks_.emplace([this, expr, rule]() { - ApplyRule(TransformationRuleId{rule}, expr); + tasks_.emplace([this, expr, rule, limit]() { + ApplyRule(TransformationRuleId{rule}, expr, limit); }); } @@ -122,35 +126,35 @@ class Optimizer { if (IsExplored(child)) { continue; } - tasks_.emplace([this, child]() { - ExploreGroup(child); + tasks_.emplace([this, child, limit]() { + ExploreGroup(child, limit); }); } } - void ExploreGroup(utils::NotNull group) { + void ExploreGroup(utils::NotNull group, Limit limit) { SetExplored(group); for (auto expr : group->GetLogicalExprs()) { - tasks_.emplace([this, expr]() { - ExploreExpression(expr); + tasks_.emplace([this, expr, limit]() { + ExploreExpression(expr, limit); }); } } - void OptimizeGroup(utils::NotNull group) { + void OptimizeGroup(utils::NotNull group, Limit limit=std::nullopt) { if (!IsExplored(group)) { - tasks_.emplace([this, group](){ - OptimizeGroup(group); + tasks_.emplace([this, group, limit](){ + OptimizeGroup(group, limit); }); - tasks_.emplace([this, group](){ - ExploreGroup(group); + tasks_.emplace([this, group, limit](){ + ExploreGroup(group, limit); }); return; } for (auto expr : group->GetLogicalExprs()) { - tasks_.emplace([this, expr](){ - OptimizeExpression(expr); + tasks_.emplace([this, expr, limit](){ + OptimizeExpression(expr, limit); }); } } From 3d998588932c63c92322fdea74dbc4953b28df1a Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Thu, 23 Apr 2026 22:57:00 +0300 Subject: [PATCH 012/120] Implement branch & bound --- .gitignore | 2 + .gitmodules | 6 + flake.nix | 2 + .../implement_cross_join.hpp | 13 + .../implementation_rules/implement_filter.hpp | 13 + .../implementation_rules/implement_join.hpp | 13 + .../implement_projection.hpp | 13 + .../implementation_rules/implement_table.hpp | 13 + include/stewkk/sql/logic/optimizer/group.hpp | 3 +- .../sql/logic/optimizer/physical_expr.hpp | 42 ++ include/stewkk/sql/logic/optimizer/rules.hpp | 7 +- research/datasets | 1 + research/docker-compose.yaml | 12 + research/extractor.py | 16 + research/imdb-sqlserver | 1 + research/ms-sql-rules-list.txt | 448 ++++++++++++++++++ research/ms_sql_server_extractor.py | 171 +++++++ research/research.ipynb | 101 ++++ research/show_rules.sql | 13 + research/tmp.py | 23 + src/stewkk/sql/CMakeLists.txt | 5 + .../implement_cross_join.cpp | 14 + .../implementation_rules/implement_filter.cpp | 14 + .../implementation_rules/implement_join.cpp | 15 + .../implement_projection.cpp | 14 + .../implementation_rules/implement_table.cpp | 14 + src/stewkk/sql/logic/optimizer/group.cpp | 4 +- .../sql/logic/optimizer/optimizer_test.cpp | 132 +++++- src/stewkk/sql/logic/optimizer/rules.cpp | 10 +- .../sql/logic/optimizer/rules_applier.cpp | 1 + 30 files changed, 1115 insertions(+), 21 deletions(-) create mode 100644 .gitmodules create mode 100644 include/stewkk/sql/logic/implementation_rules/implement_cross_join.hpp create mode 100644 include/stewkk/sql/logic/implementation_rules/implement_filter.hpp create mode 100644 include/stewkk/sql/logic/implementation_rules/implement_join.hpp create mode 100644 include/stewkk/sql/logic/implementation_rules/implement_projection.hpp create mode 100644 include/stewkk/sql/logic/implementation_rules/implement_table.hpp create mode 160000 research/datasets create mode 100644 research/docker-compose.yaml create mode 100644 research/extractor.py create mode 160000 research/imdb-sqlserver create mode 100644 research/ms-sql-rules-list.txt create mode 100644 research/ms_sql_server_extractor.py create mode 100644 research/research.ipynb create mode 100644 research/show_rules.sql create mode 100644 research/tmp.py create mode 100644 src/stewkk/sql/logic/implementation_rules/implement_cross_join.cpp create mode 100644 src/stewkk/sql/logic/implementation_rules/implement_filter.cpp create mode 100644 src/stewkk/sql/logic/implementation_rules/implement_join.cpp create mode 100644 src/stewkk/sql/logic/implementation_rules/implement_projection.cpp create mode 100644 src/stewkk/sql/logic/implementation_rules/implement_table.cpp diff --git a/.gitignore b/.gitignore index b5c418b..2ebf72d 100644 --- a/.gitignore +++ b/.gitignore @@ -10,3 +10,5 @@ FlameGraph/ /perf.data.old **/.auctex-auto/ **/build/ +/.odbc/ +**/__pycache__ diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..68eabaa --- /dev/null +++ b/.gitmodules @@ -0,0 +1,6 @@ +[submodule "research/datasets"] + path = research/datasets + url = git@github.com:ronaldbradford/data.git +[submodule "research/imdb-sqlserver"] + path = research/imdb-sqlserver + url = git@github.com:sqlsunday/imdb-to-sqlserver.git diff --git a/flake.nix b/flake.nix index 2d2563e..da6ce7d 100644 --- a/flake.nix +++ b/flake.nix @@ -25,6 +25,7 @@ ps.jupyter ps.ipykernel ps.notebook + ps.pymssql ]); tex = (pkgs.texlive.combine { inherit (pkgs.texlive) scheme-full @@ -41,6 +42,7 @@ ms-toolsai.jupyter anthropic.claude-code ms-vscode.cpptools + asvetliakov.vscode-neovim ]; }) pythonEnv diff --git a/include/stewkk/sql/logic/implementation_rules/implement_cross_join.hpp b/include/stewkk/sql/logic/implementation_rules/implement_cross_join.hpp new file mode 100644 index 0000000..fd0bd40 --- /dev/null +++ b/include/stewkk/sql/logic/implementation_rules/implement_cross_join.hpp @@ -0,0 +1,13 @@ +#pragma once + +#include + +namespace stewkk::sql { + +class ImplementCrossJoin : public ImplementationRule { + public: + bool IsApplicable(utils::NotNull expr) override; + utils::NotNull Apply(utils::NotNull expr, Memo& memo) override; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/implementation_rules/implement_filter.hpp b/include/stewkk/sql/logic/implementation_rules/implement_filter.hpp new file mode 100644 index 0000000..ff48d4a --- /dev/null +++ b/include/stewkk/sql/logic/implementation_rules/implement_filter.hpp @@ -0,0 +1,13 @@ +#pragma once + +#include + +namespace stewkk::sql { + +class ImplementFilter : public ImplementationRule { + public: + bool IsApplicable(utils::NotNull expr) override; + utils::NotNull Apply(utils::NotNull expr, Memo& memo) override; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/implementation_rules/implement_join.hpp b/include/stewkk/sql/logic/implementation_rules/implement_join.hpp new file mode 100644 index 0000000..7838bef --- /dev/null +++ b/include/stewkk/sql/logic/implementation_rules/implement_join.hpp @@ -0,0 +1,13 @@ +#pragma once + +#include + +namespace stewkk::sql { + +class ImplementJoin : public ImplementationRule { + public: + bool IsApplicable(utils::NotNull expr) override; + utils::NotNull Apply(utils::NotNull expr, Memo& memo) override; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/implementation_rules/implement_projection.hpp b/include/stewkk/sql/logic/implementation_rules/implement_projection.hpp new file mode 100644 index 0000000..f0c2716 --- /dev/null +++ b/include/stewkk/sql/logic/implementation_rules/implement_projection.hpp @@ -0,0 +1,13 @@ +#pragma once + +#include + +namespace stewkk::sql { + +class ImplementProjection : public ImplementationRule { + public: + bool IsApplicable(utils::NotNull expr) override; + utils::NotNull Apply(utils::NotNull expr, Memo& memo) override; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/implementation_rules/implement_table.hpp b/include/stewkk/sql/logic/implementation_rules/implement_table.hpp new file mode 100644 index 0000000..1d554f9 --- /dev/null +++ b/include/stewkk/sql/logic/implementation_rules/implement_table.hpp @@ -0,0 +1,13 @@ +#pragma once + +#include + +namespace stewkk::sql { + +class ImplementTable : public ImplementationRule { + public: + bool IsApplicable(utils::NotNull expr) override; + utils::NotNull Apply(utils::NotNull expr, Memo& memo) override; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/group.hpp b/include/stewkk/sql/logic/optimizer/group.hpp index e269a15..d66c54e 100644 --- a/include/stewkk/sql/logic/optimizer/group.hpp +++ b/include/stewkk/sql/logic/optimizer/group.hpp @@ -9,6 +9,7 @@ namespace stewkk::sql { using LogicalOperator = decltype(LogicalExpr::root_operator); +using PhysicalOperator = decltype(PhysicalExpr::root_operator); class Group { private: @@ -22,7 +23,7 @@ class Group { ToNotNull>; utils::NotNull AddLogicalExpr(LogicalOperator root_operator); - utils::NotNull AddPhysicalExpr(); + utils::NotNull AddPhysicalExpr(PhysicalOperator root_operator); LogicalExprs GetLogicalExprs(); size_t GetId() const; diff --git a/include/stewkk/sql/logic/optimizer/physical_expr.hpp b/include/stewkk/sql/logic/optimizer/physical_expr.hpp index bec27bf..7e4af52 100644 --- a/include/stewkk/sql/logic/optimizer/physical_expr.hpp +++ b/include/stewkk/sql/logic/optimizer/physical_expr.hpp @@ -1,6 +1,10 @@ #pragma once +#include +#include + #include +#include namespace stewkk::sql { @@ -8,9 +12,47 @@ struct Group; namespace physical { +struct SeqScan { + std::string table; + + bool operator==(const SeqScan&) const = default; +}; + +struct Projection { + utils::NotNull source; + std::vector expressions; + + bool operator==(const Projection&) const = default; +}; + +struct Filter { + utils::NotNull source; + Expression predicate; + + bool operator==(const Filter&) const = default; +}; + +struct NestedLoopJoin { + utils::NotNull lhs; + utils::NotNull rhs; + JoinType type; + Expression qual; + + bool operator==(const NestedLoopJoin&) const = default; +}; + +struct NestedLoopCrossJoin { + utils::NotNull lhs; + utils::NotNull rhs; + + bool operator==(const NestedLoopCrossJoin&) const = default; +}; + } // namespace physical struct PhysicalExpr { + std::variant root_operator; utils::NotNull group; }; diff --git a/include/stewkk/sql/logic/optimizer/rules.hpp b/include/stewkk/sql/logic/optimizer/rules.hpp index c0c7a7b..a4b3174 100644 --- a/include/stewkk/sql/logic/optimizer/rules.hpp +++ b/include/stewkk/sql/logic/optimizer/rules.hpp @@ -6,6 +6,11 @@ #include #include #include +#include +#include +#include +#include +#include namespace stewkk::sql { @@ -15,6 +20,6 @@ struct Rules { std::array, NImplementation> implementation_rules; }; -Rules<2, 0> MakeMainRules(); +Rules<2, 5> MakeMainRules(); } // namespace stewkk::sql diff --git a/research/datasets b/research/datasets new file mode 160000 index 0000000..e761ca3 --- /dev/null +++ b/research/datasets @@ -0,0 +1 @@ +Subproject commit e761ca32458a9d6640e5127a6c23b2cfd8e46118 diff --git a/research/docker-compose.yaml b/research/docker-compose.yaml new file mode 100644 index 0000000..51933a8 --- /dev/null +++ b/research/docker-compose.yaml @@ -0,0 +1,12 @@ +services: + mssql: + image: mcr.microsoft.com/mssql/server:2025-latest + container_name: research-mssql + environment: + ACCEPT_EULA: "Y" + MSSQL_SA_PASSWORD: "Password123!" + MSSQL_PID: Developer + ports: + - "1433:1433" + volumes: + - ./datasets:/datasets:ro diff --git a/research/extractor.py b/research/extractor.py new file mode 100644 index 0000000..769d85c --- /dev/null +++ b/research/extractor.py @@ -0,0 +1,16 @@ +from abc import ABC, abstractmethod + + +class PlanExtractor(ABC): + def __init__(self, dataset: str): + self.dataset = dataset + self.load_dataset() + + @abstractmethod + def load_dataset(self) -> None: + pass + + @abstractmethod + def extract(self, request) -> str: + pass + diff --git a/research/imdb-sqlserver b/research/imdb-sqlserver new file mode 160000 index 0000000..876c8f4 --- /dev/null +++ b/research/imdb-sqlserver @@ -0,0 +1 @@ +Subproject commit 876c8f4a2261c1dac8edf90813ddeec88e15d7f8 diff --git a/research/ms-sql-rules-list.txt b/research/ms-sql-rules-list.txt new file mode 100644 index 0000000..348588b --- /dev/null +++ b/research/ms-sql-rules-list.txt @@ -0,0 +1,448 @@ +JNtoNL +LOJNtoNL +LSJNtoNL +LASJNtoNL +JNtoSM +FOJNtoSM +LOJNtoSM +ROJNtoSM +LSJNtoSM +RSJNtoSM +LASJNtoSM +RASJNtoSM +IdxJNtoSM +FOJnoneqToSM +JNtoHS +FOJNtoHS +LOJNtoHS +ROJNtoHS +LSJNtoHS +RSJNtoHS +LASJNtoHS +RASJNtoHS +IdxJNtoHS +PSJNtoHS +HJwBMtoHS +JoinCommute +JoinSwitch +JoinLinearize +JoinLeftAssociate +JoinRightAssociate +JoinExchange +ColocatedJoin +StarJoinToIdxStrategy +StarSelJoinToIdxStrategy +StarJoinToHashJoinsWithBitmap +StarSelJoinToHashJoinsWithBitmap +GbTopToGbAgg +GbTopAfterJoin +GenGbApplySimple +GenKeyBatch +GenGbApplyAgg +GbApplyAfterJoin +CommFOJN +CommLOJN +CommLSJN +CommLASJN +CommROJN +CommRSJN +CommRASJN +CommPSJN +JoinOJSwitch +OJJoinSwitch +SelOJJoinSwitch +OJOJSwitch +SelOJOJSwitch +ReorderLOJN +LSJNtoDistOnJN +LSJNtoJNonDist +SubLSJNtoJNonDist +LASJNtoLASJNonDist +LSJOnLclDist +LASJOnLclDist +LSJIsNullToDist +LASJIsNullToDist +FOJNtoLSJNandLASJN +DiscardLSJN +DiscardRSJN +PullJoinAboveLSJN +PullJoinAboveLASJN +PullJoinAboveApply +PullJoinAboveSelApply +JNtoJNSELNotNull +JNonPrjLeft +JNonPrjRight +RedundantLOJN +RedundantROJN +RedundantApplyOJ +SimplifyJoinWithCTG +JoinWithCTGToSel +ExpandDistinctGbAgg +ReduceGbAgg +GbAggWithNoReqdCol +NormalizeGbAgg +NormalizeTop +GbAggOnPrj +GbAggOnRestrRemap +SelOnGbAgg +SelOnSeqPrj +GbAggToPrj +GbAggSmpfyEmptyT +ReduceGbAggExpr +GbAggToStrm +GbAggToHS +GbAggToSort +GbAggToUde +GbAggAfterJoin +GbAggAfterJoinSel +GbAggAfterLOJ +GbAggBeforeJoin +GbAggUnderTopBeforeJoin +GbAggUnderTopJoinBeforeJoin +GbAggBeforeLOJ +ScalarGbAggToTop +GbAggToConstScanOrTop +GbAggJNtoLSJN +GbAggIFFPredicateUnderJoin +GenLGAgg +GenLGTop +ReduceForDistinctAggs +LocalAggBelowJoin +LocalAggBelowPrjJoin +LocalAggBelowLOJ +LocalAggBelowFOJ +PushLocalAgg +LocalAggBelowUniAll +GbAggBelowUniAll +JoinOnGbAgg +RmtEmptyVectorAgg +DiscardPrj +ReducePrjExpr +SelOnPrj +TOpOnPrj +PrjOnConstTbl +AddCompSca +AddCCPrjToGet +AddCCPrjToInsert +AddCCPrjToUpdate +AddCCPrjToDelete +SelPredNorm +JoinPredNorm +ImpliedPredInnerAndAllLeftJn +SimplifyMultiColumnJoinPred +EnforceSort +OrderByToNOP +SELonUNI +SELonUNIA +ReduceUnionCol +ReduceUnionAllCol +ReduceSwitchUnionCol +UNIAtoCON +SWUtoSWU +UNIAtoMERGE +UNItoDISonUNIA +UNItoMERGE +UNItoHASH +CollapseUNI +CollapseUNIA +JNonUNIA +LOJonUNIA +LSJonUNIA +LASJonUNIA +CorrelateJNonUNIA +JNonUNIAUNIA +LOJonUNIAUNIA +LSJonUNIAUNIA +LASJonUNIAUNIA +UNIAReorderInputs +SplitUniIntoDistCompat +SelOnRestrRemap +SELonJN +CleanupNaryJoin +SELonNaryJoin +SELonLOJ +SELonROJ +SELonSJ +SELonApply +SimplifyLOJN +SimplifyROJN +SimplifyFOJN +LOJToLASJ +ROJToLASJ +SelOnTFP +ApplyToNL +ApplyCnstToNL +RemoveSubqInSel +RemoveSubqInPrj +RemoveSubqInGbAgg +RemoveSubqInRollup +RemoveSubqInCube +RemoveSubqInJN +RemoveSubqInLOJN +RemoveSubqInLSJN +RemoveSubqInLASJN +RemoveSubqInFOJN +RemoveSubqInTop +RemoveSubqInSwitchUN +RemoveSubqInTFP +RemoveSubqInTVF +RemoveSubqInSTVF +FOJNtoUnionAll +ApplyHandler +SelApplyHandler +PrjApplyHandler +ApplyUAtoUniSJ +LSJNtoApply +LASJNtoApply +LOJNtoApply +JNtoApplySTVF +LOJPrjGetToApply +LOJSelPrjGetToApply +SplitLASJN +SplitApplyLASJN +LSJNtoAgg +LASJNtoAgg +PushApplyBelowJN +PullApplyOverJoin +PullSelApplyOverJoin +PullLSJOverJoin +PullLASJOverJoin +PullColumnRestrPrjOverJoin +PullColumnRestrPrjOverJoinSel +LASJDistincttoLASJ +LSJDistincttoLSJ +PushTFPBelowJoin +GetToScan +GetToAssert +SelectToFilter +ConstGetToConstScan +ImplRestrRemap +ProjectToComputeScalar +ReduceSequenceProjectExpr +ImplementSequenceProject +ImplementLocalSequenceProject +ImplementGlobalSequenceProject +SplitSequenceProject +EnforceBatch +EnforceRow +FetchToApply +SelToIdxStrategy +SelSTVFToSTVF +SelSeqPrjToTop +SelSeqPrjToAnyAgg +SelToTrivialFilter +GetToTrivialScan +SelPrjGetToTrivialScan +GetIdxToRng +GetToIdxScan +SelResToFilter +AppIdxToApp +SelIdxToRng +CrsFtchToUnionAll +JNtoIdxLookup +LeftSideJNtoIdxLookup +PSJNtoIdxLookup +WCJNonSELtoIdxLookup +PSJNonSELtoIdxLookup +SelToIndexOnTheFly +JoinToIndexOnTheFly +SelIterToIdxOnFly +JoinIterToIdxOnFly +SelOnFetch +JNFetchGetToApply +JNSelFetchGetToApply +RemoveViewAnchor +AnyOnEmptyTrivial +RmtUpdateOnEmpty +RmtDeleteOnEmpty +RmtInsertOnEmpty +SelectOnEmpty +GbAggOnEmpty +RollupOnEmpty +CubeOnEmpty +PrjOnEmpty +SpoolOnEmpty +UpdateOnEmpty +DeleteOnEmpty +InsertOnEmpty +ApplyOnEmpty +NaryJoinOnEmpty +IJOnEmpty +LSJOnEmpty +RSJOnEmpty +LASJOnEmpty +RASJOnEmpty +LOJOnEmpty +ROJOnEmpty +FOJOnEmpty +LASJOnEmptyRight +RASJOnEmptyLeft +LOJOnEmptyRight +ROJOnEmptyLeft +FOJOnOneEmpty +SELonTrue +TopOnEmpty +DiscardTop +EmptyIterator +SelToLSJ +SelToLASJ +SelInToJoinGb +ImplementFastFwd +ImplementFastFwdAsInsert +EnforceHPandAccCard +InsertSpoolForPlanForcing +BuildSpool +AddNOPToCSRootSpool +ScrollLockAsFetch +ScrollLockAsDynamic +SpoolGetToGet +UpdateToStreamUpdate +DeleteToStreamUpdate +InsertToStreamUpdate +PutToPhysicalPut +ExpandUpdateCons +ExpandDeleteCons +ExpandInsertCons +ExpandUpdateImplicitAssignments +ExpandInsertImplicitAssignments +ExpandInsertToPut +ExpandPtnViewIns +ExpandPtnViewUpd +ExpandPtnViewDel +AssertToStreamCheck +ExpandVerifyCnst +BuildSplit +BuildCollapse +BuildSequence +IndexCreate +IndexCreateOnPrj +SplitDictionaryBuild +ExpandInsteadOfTriggerIns +ExpandInsteadOfTriggerUpd +ExpandInsteadOfTriggerDel +ColocatedInsert +ResumableIndexBuild +MatchAggGraphOp +MatchJoin +MatchPrjJoin +MatchSelectGet +MatchPrjSelectGet +MatchGet +MatchPrjGet +MatchGbNAryJoin +MatchGbJoin +CubeSimpleAgg +RollupSimpleAgg +CubeToRollup +CubeNaive +CubeGbAgg +CubeNoGbAgg +ReduceRollupExpr +ReduceCubeExpr +RollupGbAgg +RollupToStrm +BuildTop +BuildGlobalTop +BuildLocalTop +TopRowcountGbPrj +BuildGbTop +BuildGbApply +BuildKeyBatchApply +BuildUde +BuildBsl +BuildTFP +IJtoIJSEL +LSJtoLSJSEL +RSJtoRSJSEL +LASJtoLASJSEL +RASJtoRASJSEL +LOJtoLOJSEL +ROJtoROJSEL +ApplyToSM +ApplyCnstToSM +PushImpliedIsNotNull +ExpandNAryJoinNoSnowflake +ExpandNAryJoinWithSnowflake +GatherDistrCompatJoins +ExpandNAryJoinToBatchSnowflake +ExpandPivot +ExpandUnpivot +ExpandPivotLOJ +SelOnPivot +SpoolOnIterator +IterateToDepthFirst +RecRefToConstTble +IteratorToAnchor +SelOnIterator +LogTVFToPhyTVF +LogSTVFToPhySTVF +STVFOrderCheck +JoinToStarJoin +JoinOnStarJoin +JoinOnSelStarJoin +GetToRmtScan +SpoolOverRmtScan +BuildRmtQuery +SpoolOverRmtQuery +LogRmtQToPhyRmtQ +RmtInsertToRmtModify +RmtUpdateToRmtModify +RmtDeleteToRmtModify +ParametrizeRmtUpdate +ParametrizeRmtUpdateNoPrj +ParametrizeRmtDelete +ParametrizeRmtDeleteNoPrj +GetIdxToRmtRange +SelIdxToRmtRange +FtchToRmtFtch +RemoteHint +JoinToApply +NAryJoinToApply +SplitSelects +CollapseSelects +TopOnRmtGet +TopOnRmtQuery +OffsetOnRmtGet +OffsetOnRmtQuery +SpatialIntersectFilterOverGridIndex +JoinExtToPrimaryFilter +JoinExtToPrimaryFilterOverSelect +SpatialJointoApply +SpatialNearestNeighbor +SelSTVFToIdxOnFly +SelSTVFToSTVFExp +NormalizeSequenceProject +BuildNOP +BuildExternalComputation +SpoolOverExtStrTable +RefIntegrityMaintainer +CollapseIdenticalScalarSubquery +GetToExtExtractScan +SelOnChoose +GraphIterateToDepthFirst +GraphIterateWithOneJoin +GenLGSequenceProject +LocalGlobalCube +SinglePassCube +SplitSemiApplyUnionAll +ExpCubeToRollup +PutSemijoinUnderGbApply +GraphIterateOnGraphIterator +GbAggSplitToRanges +SelOnGbAggSplitToRanges +BuildRemotePut +EnforceDistribution +ImplementDistribution +BuildDataExportPut +SelectToFilteredExternalGet +LogicalToPhysicalSort +RedundantCseSpool +SelOnPrjGet +JsonFilterToJsonIndex +SpoolDecorrelation +BuildANN +SimplifyApplyOJ +TightenRestrictsUnderChoose +ApplyOpenjsonToJsonIndex +ApplyOpenjsonFilterAboveToJsonIndex +BuildBitmapProcessor diff --git a/research/ms_sql_server_extractor.py b/research/ms_sql_server_extractor.py new file mode 100644 index 0000000..2889812 --- /dev/null +++ b/research/ms_sql_server_extractor.py @@ -0,0 +1,171 @@ +import re +from pathlib import Path + +import pymssql + +from extractor import PlanExtractor + +_SUBMODULE = Path(__file__).parent / "imdb-sqlserver" + +# (tsv filename, raw staging table, column count) +_RAW_FILES = [ + ("name.basics.tsv", "[Raw].[name.basics.tsv.gz]", 6), + ("title.basics.tsv", "[Raw].[title.basics.tsv.gz]", 9), + ("title.akas.tsv", "[Raw].[title.akas.tsv.gz]", 8), + ("title.crew.tsv", "[Raw].[title.crew.tsv.gz]", 3), + ("title.episode.tsv", "[Raw].[title.episode.tsv.gz]", 4), + ("title.principals.tsv", "[Raw].[title.principals.tsv.gz]", 6), + ("title.ratings.tsv", "[Raw].[title.ratings.tsv.gz]", 3), +] + + +def _run_sql_file(cursor, path: Path) -> None: + sql = path.read_text(encoding="utf-8") + for stmt in re.split(r'^\s*GO\s*$', sql, flags=re.MULTILINE | re.IGNORECASE): + stmt = stmt.strip() + if stmt: + cursor.execute(stmt) + + +def _drop_raw_pks(cursor) -> None: + cursor.execute(""" + SELECT kc.name, t.name + FROM sys.key_constraints kc + JOIN sys.tables t ON t.object_id = kc.parent_object_id + JOIN sys.schemas s ON s.schema_id = t.schema_id + WHERE s.name = 'Raw' AND kc.type = 'PK' + """) + rows = cursor.fetchall() + for pk_name, table_name in rows: + cursor.execute(f"ALTER TABLE [Raw].[{table_name}] DROP CONSTRAINT [{pk_name}]") + + +def _get_raw_columns(cursor, raw_table_name: str) -> list[str]: + cursor.execute(""" + SELECT COLUMN_NAME + FROM INFORMATION_SCHEMA.COLUMNS + WHERE TABLE_SCHEMA = 'Raw' AND TABLE_NAME = %s + ORDER BY ORDINAL_POSITION + """, (raw_table_name,)) + return [row[0] for row in cursor.fetchall()] + + +def _load_raw_table(cursor, tsv_path: Path, raw_table: str, n_cols: int, server_path: str, max_rows: int | None = None) -> None: + if not tsv_path.exists(): + print(f" skipping {tsv_path.name} (not found)") + return + + # raw_table looks like [Raw].[name.basics.tsv.gz] — extract just the table name + raw_table_name = raw_table[len("[Raw].["):-1] + stage_table = f"[Stage].[{raw_table_name}]" + + col_defs = ", ".join(f"c{i} varchar(max) NULL" for i in range(1, n_cols + 1)) + cursor.execute(f"CREATE TABLE {stage_table} ({col_defs})") + + lastrow_clause = f"LASTROW = {max_rows + 1}," if max_rows is not None else "" + cursor.execute(f""" + BULK INSERT {stage_table} + FROM '{server_path}' + WITH ( + FIRSTROW = 2, + {lastrow_clause} + FIELDTERMINATOR = '\\t', + ROWTERMINATOR = '0x0a', + TABLOCK + ) + """) + + col_names = _get_raw_columns(cursor, raw_table_name) + raw_cols = ", ".join(f"[{c}]" for c in col_names) + nullif_sel = ", ".join(f"NULLIF(c{i}, '\\N')" for i in range(1, n_cols + 1)) + + cursor.execute(f""" + INSERT INTO {raw_table} WITH (TABLOCK) ({raw_cols}) + SELECT {nullif_sel} + FROM {stage_table} + """) + + cursor.execute(f"DROP TABLE {stage_table}") + + +class MsSqlServerExtractor(PlanExtractor): + def __init__( + self, + dataset: str, + host: str, + port: int, + user: str, + password: str, + server_dataset: str = "/datasets/mysql-data/imdb", + max_rows: int | None = None, + ): + self._host = host + self._port = port + self._user = user + self._password = password + self._server_dataset = server_dataset.rstrip("/") + self._max_rows = max_rows + super().__init__(dataset) + + def _conn(self, database="master"): + c = pymssql.connect( + server=self._host, + port=str(self._port), + user=self._user, + password=self._password, + database=database, + autocommit=True, + ) + return c + + def load_dataset(self) -> None: + dataset_dir = Path(self.dataset) + + print("Recreating database...") + with self._conn("master") as conn: + cur = conn.cursor() + cur.execute("SELECT COUNT(*) FROM sys.databases WHERE name=N'imdb'") + row = cur.fetchone() + if row and row[0]: + cur.execute("ALTER DATABASE imdb SET SINGLE_USER WITH ROLLBACK IMMEDIATE") + cur.execute("DROP DATABASE imdb") + cur.execute("CREATE DATABASE imdb") + + with self._conn("imdb") as conn: + cur = conn.cursor() + + print("Creating schema and tables...") + _run_sql_file(cur, _SUBMODULE / "Create IMDB-schema.sql") + + cur.execute("CREATE SCHEMA [Stage]") + _drop_raw_pks(cur) + + for tsv_name, raw_table, n_cols in _RAW_FILES: + print(f"Loading {tsv_name}...") + server_path = f"{self._server_dataset}/{tsv_name}" + _load_raw_table(cur, dataset_dir / tsv_name, raw_table, n_cols, server_path, self._max_rows) + + cur.execute("DROP SCHEMA [Stage]") + + print("Transforming into relational tables...") + if self._max_rows is not None: + cur.execute(""" + DECLARE @sql NVARCHAR(MAX) = ''; + SELECT @sql += 'ALTER TABLE [dbo].[' + t.name + '] NOCHECK CONSTRAINT ALL; ' + FROM sys.tables t JOIN sys.schemas s ON s.schema_id = t.schema_id WHERE s.name = 'dbo'; + EXEC(@sql) + """) + _run_sql_file(cur, _SUBMODULE / "Load IMDB relational tables.sql") + + print("Dataset loaded.") + + def extract(self, request: str) -> str: + with self._conn("imdb") as conn: + cur = conn.cursor() + cur.execute("SET STATISTICS XML ON") + cur.execute(request) + while cur.nextset(): + row = cur.fetchone() + if row and isinstance(row[0], str) and row[0].startswith("here for more info. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mCanceled future for execute_request message before replies were done" + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mCanceled future for execute_request message before replies were done. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "import extractor, ms_sql_server_extractor\n", + "\n", + "from ms_sql_server_extractor import MsSqlServerExtractor\n", + "\n", + "extractor_obj = MsSqlServerExtractor(\n", + " dataset=\"/home/st/c/iu9-sql-compiler/research/datasets/mysql-data/imdb\",\n", + " host=\"localhost\",\n", + " port=1433,\n", + " user=\"sa\",\n", + " password=\"Password123!\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "70ba9fa9", + "metadata": {}, + "outputs": [], + "source": [ + "query = \"\"\"\n", + "SELECT p.primaryName, t.averageRating\n", + "FROM dbo.TitlePrincipals tp\n", + "JOIN dbo.Principals p ON p.principalId = tp.principalId\n", + "JOIN dbo.Titles t ON t.titleId = tp.titleId\n", + "WHERE t.averageRating IS NOT NULL\n", + "\"\"\"\n", + "\n", + "plan = extractor_obj.extract(query)\n", + "print(plan)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/research/show_rules.sql b/research/show_rules.sql new file mode 100644 index 0000000..b832053 --- /dev/null +++ b/research/show_rules.sql @@ -0,0 +1,13 @@ +USE master; +GO + +DBCC TRACEON(3604); +GO + +PRINT '=== ENABLED RULES ==='; +DBCC SHOWONRULES; +GO + +PRINT '=== DISABLED RULES ==='; +DBCC SHOWOFFRULES; +GO diff --git a/research/tmp.py b/research/tmp.py new file mode 100644 index 0000000..c6c559b --- /dev/null +++ b/research/tmp.py @@ -0,0 +1,23 @@ +import extractor, ms_sql_server_extractor + +from ms_sql_server_extractor import MsSqlServerExtractor + +extractor_obj = MsSqlServerExtractor( + dataset="/home/st/c/iu9-sql-compiler/research/datasets/mysql-data/imdb", + host="localhost", + port=1433, + user="sa", + password="Password123!", + max_rows=10_000, +) + +query = """ +SELECT p.primaryName, t.averageRating +FROM dbo.TitlePrincipals tp +JOIN dbo.Principals p ON p.principalId = tp.principalId +JOIN dbo.Titles t ON t.titleId = tp.titleId +WHERE t.averageRating IS NOT NULL +""" + +plan = extractor_obj.extract(query) +print(plan) \ No newline at end of file diff --git a/src/stewkk/sql/CMakeLists.txt b/src/stewkk/sql/CMakeLists.txt index 5cbde49..f9b2e17 100644 --- a/src/stewkk/sql/CMakeLists.txt +++ b/src/stewkk/sql/CMakeLists.txt @@ -31,6 +31,11 @@ add_library(libsql logic/optimizer/rule.cpp logic/transformation_rules/join_commutativity.cpp logic/transformation_rules/join_associativity.cpp + logic/implementation_rules/implement_table.cpp + logic/implementation_rules/implement_filter.cpp + logic/implementation_rules/implement_projection.cpp + logic/implementation_rules/implement_join.cpp + logic/implementation_rules/implement_cross_join.cpp ) add_library(stewkk::libsql ALIAS libsql) target_compile_features(libsql PUBLIC cxx_std_23) diff --git a/src/stewkk/sql/logic/implementation_rules/implement_cross_join.cpp b/src/stewkk/sql/logic/implementation_rules/implement_cross_join.cpp new file mode 100644 index 0000000..c9a2677 --- /dev/null +++ b/src/stewkk/sql/logic/implementation_rules/implement_cross_join.cpp @@ -0,0 +1,14 @@ +#include + +namespace stewkk::sql { + +bool ImplementCrossJoin::IsApplicable(utils::NotNull expr) { + return std::holds_alternative(expr->root_operator); +} + +utils::NotNull ImplementCrossJoin::Apply(utils::NotNull expr, Memo&) { + auto& cj = std::get(expr->root_operator); + return expr->group->AddPhysicalExpr(physical::NestedLoopCrossJoin{cj.lhs, cj.rhs}); +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/implementation_rules/implement_filter.cpp b/src/stewkk/sql/logic/implementation_rules/implement_filter.cpp new file mode 100644 index 0000000..96b8b21 --- /dev/null +++ b/src/stewkk/sql/logic/implementation_rules/implement_filter.cpp @@ -0,0 +1,14 @@ +#include + +namespace stewkk::sql { + +bool ImplementFilter::IsApplicable(utils::NotNull expr) { + return std::holds_alternative(expr->root_operator); +} + +utils::NotNull ImplementFilter::Apply(utils::NotNull expr, Memo&) { + auto& filter = std::get(expr->root_operator); + return expr->group->AddPhysicalExpr(physical::Filter{filter.source, filter.predicate}); +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/implementation_rules/implement_join.cpp b/src/stewkk/sql/logic/implementation_rules/implement_join.cpp new file mode 100644 index 0000000..d7110f9 --- /dev/null +++ b/src/stewkk/sql/logic/implementation_rules/implement_join.cpp @@ -0,0 +1,15 @@ +#include + +namespace stewkk::sql { + +bool ImplementJoin::IsApplicable(utils::NotNull expr) { + return std::holds_alternative(expr->root_operator); +} + +utils::NotNull ImplementJoin::Apply(utils::NotNull expr, Memo&) { + auto& join = std::get(expr->root_operator); + return expr->group->AddPhysicalExpr( + physical::NestedLoopJoin{join.lhs, join.rhs, join.type, join.qual}); +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/implementation_rules/implement_projection.cpp b/src/stewkk/sql/logic/implementation_rules/implement_projection.cpp new file mode 100644 index 0000000..b447330 --- /dev/null +++ b/src/stewkk/sql/logic/implementation_rules/implement_projection.cpp @@ -0,0 +1,14 @@ +#include + +namespace stewkk::sql { + +bool ImplementProjection::IsApplicable(utils::NotNull expr) { + return std::holds_alternative(expr->root_operator); +} + +utils::NotNull ImplementProjection::Apply(utils::NotNull expr, Memo&) { + auto& proj = std::get(expr->root_operator); + return expr->group->AddPhysicalExpr(physical::Projection{proj.source, proj.expressions}); +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/implementation_rules/implement_table.cpp b/src/stewkk/sql/logic/implementation_rules/implement_table.cpp new file mode 100644 index 0000000..b164175 --- /dev/null +++ b/src/stewkk/sql/logic/implementation_rules/implement_table.cpp @@ -0,0 +1,14 @@ +#include + +namespace stewkk::sql { + +bool ImplementTable::IsApplicable(utils::NotNull expr) { + return std::holds_alternative(expr->root_operator); +} + +utils::NotNull ImplementTable::Apply(utils::NotNull expr, Memo&) { + auto& table = std::get(expr->root_operator); + return expr->group->AddPhysicalExpr(physical::SeqScan{table.name}); +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/group.cpp b/src/stewkk/sql/logic/optimizer/group.cpp index 9a7478a..d896259 100644 --- a/src/stewkk/sql/logic/optimizer/group.cpp +++ b/src/stewkk/sql/logic/optimizer/group.cpp @@ -6,8 +6,8 @@ utils::NotNull Group::AddLogicalExpr(LogicalOperator root_operator return &logical_exprs_.emplace_back(std::move(root_operator), this); } -utils::NotNull Group::AddPhysicalExpr() { - return &physical_exprs_.emplace_back(this); +utils::NotNull Group::AddPhysicalExpr(PhysicalOperator root_operator) { + return &physical_exprs_.emplace_back(std::move(root_operator), this); } Group::LogicalExprs Group::GetLogicalExprs() { diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index 48c0267..f8969d8 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -18,10 +18,9 @@ using ::testing::Eq; namespace stewkk::sql { -// FIXME: проверить, не возникает ли висячих ссылок нигде? // FIXME: branch and bound -// FIXME: физические операторы! // FIXME: сделать API в виде DoStep(), которое возвращает какой-то внутренний стейт оптимизатора +// FIXME: применение правила (по крайней мере трансформации), должно создавать несколько выражений std::vector> GetChildren(utils::NotNull expr) { return std::visit(utils::Overloaded{ @@ -46,14 +45,88 @@ std::vector> GetChildren(utils::NotNull exp using Limit = std::optional; std::vector> GetChildren(utils::NotNull expr) { - return {}; + return std::visit(utils::Overloaded{ + [](const physical::SeqScan&) -> std::vector> { + return {}; + }, + [](const physical::Filter& f) -> std::vector> { + return {f.source}; + }, + [](const physical::Projection& p) -> std::vector> { + return {p.source}; + }, + [](const physical::NestedLoopJoin& j) -> std::vector> { + return {j.lhs, j.rhs}; + }, + [](const physical::NestedLoopCrossJoin& j) -> std::vector> { + return {j.lhs, j.rhs}; + }, + }, expr->root_operator); +} + +class CardinalityEstimates { +public: + int64_t GetCardinality(utils::NotNull group) { + if (auto it = cache_.find(group.get()); it != cache_.end()) { + return it->second; + } + auto cardinality = GetCardinality(group->GetLogicalExprs().front()->root_operator); + cache_[group.get()] = cardinality; + return cardinality; + } + + private: + int64_t GetCardinality(const LogicalOperator& op) { + return std::visit(utils::Overloaded{ + [](const logical::Table&) -> int64_t { + return 10; + }, + [this](const logical::Filter& f) -> int64_t { + return GetCardinality(f.source); + }, + [this](const logical::Projection& p) -> int64_t { + return GetCardinality(p.source); + }, + [this](const logical::CrossJoin& j) -> int64_t { + return GetCardinality(j.lhs) * GetCardinality(j.rhs); + }, + [this](const logical::Join& j) -> int64_t { + return GetCardinality(j.lhs) * GetCardinality(j.rhs); + }, + }, op); + } + + std::unordered_map cache_; +}; + +int64_t CalcCost(utils::NotNull expr, CardinalityEstimates& cardinality) { + return std::visit(utils::Overloaded{ + [&](const physical::SeqScan&) -> int64_t { + return cardinality.GetCardinality(expr->group); + }, + [&](const physical::Filter&) -> int64_t { + return cardinality.GetCardinality(expr->group); + }, + [&](const physical::Projection&) -> int64_t { + return cardinality.GetCardinality(expr->group); + }, + [&](const physical::NestedLoopJoin& j) -> int64_t { + return 3 * cardinality.GetCardinality(j.lhs) * cardinality.GetCardinality(j.rhs) + + cardinality.GetCardinality(expr->group); + }, + [&](const physical::NestedLoopCrossJoin& j) -> int64_t { + return 3 * cardinality.GetCardinality(j.lhs) * cardinality.GetCardinality(j.rhs) + + cardinality.GetCardinality(expr->group); + }, + }, expr->root_operator); } template class Optimizer { public: Optimizer(const Operator& expr, Rules&& rules) - : memo_(), rules_applier_(std::move(rules)), root_(memo_.Populate(expr)) { + : memo_(), rules_applier_(std::move(rules)), root_(memo_.Populate(expr)), + cardinality_() { } private: @@ -68,15 +141,28 @@ class Optimizer { void OptimizeInputs(utils::NotNull expr, Limit limit, size_t child_index = 0) { auto children = GetChildren(expr); if (child_index >= children.size()) { - // FIXME: update best plan + int64_t total = local_cost_[expr.get()] + accum_child_cost_[expr.get()]; + auto* g = expr->group.get(); + if (!best_cost_.contains(g) || total < best_cost_.at(g)) { + best_cost_[g] = total; + best_plan_[g] = expr.get(); + } return; } - tasks_.emplace([this, expr, child_index, limit]() { - OptimizeInputs(expr, limit, child_index+1); + + auto child = children[child_index]; + + tasks_.emplace([this, expr, child, child_index, limit]() { + if (!best_cost_.contains(child.get())) return; + auto cc = best_cost_.at(child.get()); + accum_child_cost_[expr.get()] += cc; + Limit next = limit ? std::optional{*limit - local_cost_[expr.get()] - accum_child_cost_[expr.get()]} : std::nullopt; + if (next && *next < 0) return; + OptimizeInputs(expr, next, child_index + 1); }); - // FIXME: UpdateCostLimit - tasks_.emplace([this, expr, limit]() { - OptimizeGroup(expr->group, limit); + + tasks_.emplace([this, child, limit]() { + OptimizeGroup(child, limit); }); } @@ -87,9 +173,15 @@ class Optimizer { void ApplyRule(ImplementationRuleId rule, utils::NotNull expr, Limit limit) { auto new_expr = rules_applier_.Apply(rule, expr, memo_); - // FIXME: UpdateCostLimit! - tasks_.emplace( - [this, new_expr, limit]() { OptimizeInputs(new_expr, limit); }); + // FIXME: missing physical properties + auto lc = CalcCost(new_expr, cardinality_); + local_cost_[new_expr.get()] = lc; + accum_child_cost_[new_expr.get()] = 0; + + if (limit && lc >= *limit) return; + + Limit child_limit = limit ? std::optional{*limit - lc} : std::nullopt; + tasks_.emplace([this, new_expr, child_limit]() { OptimizeInputs(new_expr, child_limit); }); } void OptimizeExpression(utils::NotNull expr, Limit limit) { @@ -142,6 +234,10 @@ class Optimizer { } void OptimizeGroup(utils::NotNull group, Limit limit=std::nullopt) { + if (auto it = best_cost_.find(group.get()); it != best_cost_.end()) { + if (!limit || it->second < *limit) return; + } + if (!IsExplored(group)) { tasks_.emplace([this, group, limit](){ OptimizeGroup(group, limit); @@ -160,7 +256,7 @@ class Optimizer { } public: - Operator Optimize() { + PhysicalExpr* Optimize() { tasks_.emplace([this]() { OptimizeGroup(root_->group); }); @@ -169,7 +265,8 @@ class Optimizer { tasks_.pop(); next_task(); } - return Table{"A"}; + auto it = best_plan_.find(root_->group.get()); + return it != best_plan_.end() ? it->second : nullptr; } private: @@ -178,6 +275,11 @@ class Optimizer { std::stack> tasks_; std::unordered_set explored_groups_; utils::NotNull root_; + CardinalityEstimates cardinality_; + std::unordered_map local_cost_; + std::unordered_map accum_child_cost_; + std::unordered_map best_cost_; + std::unordered_map best_plan_; }; TEST(OptimizerTest, Simple) { diff --git a/src/stewkk/sql/logic/optimizer/rules.cpp b/src/stewkk/sql/logic/optimizer/rules.cpp index 961f528..c0ace93 100644 --- a/src/stewkk/sql/logic/optimizer/rules.cpp +++ b/src/stewkk/sql/logic/optimizer/rules.cpp @@ -2,13 +2,19 @@ namespace stewkk::sql { -Rules<2, 0> MakeMainRules() { +Rules<2, 5> MakeMainRules() { return { .transformation_rules = { std::make_unique(), std::make_unique(), }, - .implementation_rules = {}, + .implementation_rules = { + std::make_unique(), + std::make_unique(), + std::make_unique(), + std::make_unique(), + std::make_unique(), + }, }; } diff --git a/src/stewkk/sql/logic/optimizer/rules_applier.cpp b/src/stewkk/sql/logic/optimizer/rules_applier.cpp index 3a43141..3f3b659 100644 --- a/src/stewkk/sql/logic/optimizer/rules_applier.cpp +++ b/src/stewkk/sql/logic/optimizer/rules_applier.cpp @@ -29,5 +29,6 @@ utils::NotNull RulesApplier::Ap } template class RulesApplier<2, 0>; +template class RulesApplier<2, 5>; } // namespace stewkk::sql From ecdcca72112e414547c86e237f77778876a180a9 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Fri, 1 May 2026 00:55:39 +0300 Subject: [PATCH 013/120] Add report wip --- report/Makefile | 2 +- report/header.tex | 483 ++++++++++++ report/thesis.tex | 714 ++++++++++++++++++ .../sql/logic/optimizer/optimizer_test.cpp | 1 + 4 files changed, 1199 insertions(+), 1 deletion(-) create mode 100644 report/header.tex create mode 100644 report/thesis.tex diff --git a/report/Makefile b/report/Makefile index bca1c92..4694b53 100644 --- a/report/Makefile +++ b/report/Makefile @@ -3,7 +3,7 @@ all: build build: - @latexmk -f -pdf -output-directory=build -shell-escape ./report.tex + @latexmk -f -pdf -output-directory=build -shell-escape ./thesis.tex bib: @cp ./biblio.bib ./build diff --git a/report/header.tex b/report/header.tex new file mode 100644 index 0000000..b279b1a --- /dev/null +++ b/report/header.tex @@ -0,0 +1,483 @@ +\let\counterwithout\relax +\let\counterwithin\relax +\usepackage{float} +\usepackage{xcolor} +\usepackage{subfig} +\usepackage[export]{adjustbox} +\usepackage{tocvsec2} % возможность менять учитываемую глубину разделов в оглавлении +\usepackage[subfigure]{tocloft} +\usepackage[newfloat]{minted} +\captionsetup[listing]{position=top} + +\makeatletter +\AddToHook{begindocument/before}{\@ifpackageloaded{minted}{\removefromtoclist[float]{lol}}{}} +\makeatother + +\AtBeginEnvironment{figure}{\vspace{0.5cm}} +\AtBeginEnvironment{table}{\vspace{0.5cm}} +\AtBeginEnvironment{listing}{\vspace{0.5cm}} +\AtBeginEnvironment{algorithm}{\vspace{0.5cm}} +\AtBeginEnvironment{minted}{\vspace{-0.5cm}} + +\usepackage{fancyvrb} +\usepackage{ulem,bm,mathrsfs,ifsym} %зачеркивания, особо жирный стиль и RSFS начертание +\usepackage{sectsty} % переопределение стилей подразделов +%%%%%%%%%%%%%%%%%%%%%%% + +%%% Поля и разметка страницы %%% +\usepackage{pdflscape} % Для включения альбомных страниц +\usepackage{geometry} % Для последующего задания полей +\geometry{a4paper,tmargin=2cm,bmargin=2cm,lmargin=3cm,rmargin=1cm,includefoot} % тоже самое, но лучше + +%%% Математические пакеты %%% +\usepackage{amsthm,amsfonts,amsmath,amssymb,amscd} % Математические дополнения от AMS +\usepackage{mathtools} % Добавляет окружение multlined +\usepackage[perpage]{footmisc} +%\usepackage{times} + +%%%% Установки для размера шрифта 14 pt %%%% +%% Формирование переменных и констант для сравнения (один раз для всех подключаемых файлов)%% +%% должно располагаться до вызова пакета fontspec или polyglossia, потому что они сбивают его работу +%\newlength{\curtextsize} +%\newlength{\bigtextsize} +%\setlength{\bigtextsize}{13pt} +\KOMAoptions{fontsize=14pt} + +\makeatletter +\def\showfontsize{\f@size{} point} +\makeatother + +\makeatletter +\setlength{\@fptop}{0pt} +\makeatother + +%\makeatletter +%\show\f@size % неплохо для отслеживания, но вызывает стопорение процесса, если документ компилируется без команды -interaction=nonstopmode +%\setlength{\curtextsize}{\f@size pt} +%\makeatother + +%шрифт times +\usepackage{tempora} +%\usepackage{pscyr} +%\setmainfont[Ligatures={TeX,Historic}]{Times New Roman} + +%%% Решение проблемы копирования текста в буфер кракозябрами +% \input glyphtounicode.tex +% \input glyphtounicode-cmr.tex %from pdfx package +% \pdfgentounicode=1 +\usepackage{cmap} % Улучшенный поиск русских слов в полученном pdf-файле +\usepackage[T2A]{fontenc} % Поддержка русских букв +\usepackage[utf8]{inputenc} % Кодировка utf8 +\usepackage[english, main=russian]{babel} % Языки: русский, английский +% \IfFileExists{pscyr.sty}{\usepackage{pscyr}}{} % Красивые русские шрифты +%\renewcommand{\rmdefault}{ftm} +%%% Оформление абзацев %%% +\usepackage{indentfirst} % Красная строка +%\usepackage{eskdpz} + +%%% Таблицы %%% +\usepackage{longtable} % Длинные таблицы +\usepackage{multirow,makecell,array} % Улучшенное форматирование таблиц +\usepackage{booktabs} % Возможность оформления таблиц в классическом книжном стиле (при правильном использовании не противоречит ГОСТ) + +%%% Общее форматирование +\usepackage{soulutf8} % Поддержка переносоустойчивых подчёркиваний и зачёркиваний +\usepackage{icomma} % Запятая в десятичных дробях + +\usepackage{tikz} +\usetikzlibrary{automata,patterns, positioning, arrows,shadows,shapes,datavisualization} +\usetikzlibrary{decorations.pathreplacing} +\usetikzlibrary{arrows.meta,patterns.meta,graphs} + + +%%% Изображения %%% +\usepackage{graphicx} % Подключаем пакет работы с графикой +\usepackage{wrapfig} + +%%% Списки %%% +\usepackage{enumitem} + +%%% Подписи %%% +\usepackage{caption} % Для управления подписями (рисунков и таблиц) % Может управлять номерами рисунков и таблиц с caption %Иногда может управлять заголовками в списках рисунков и таблиц +%% Использование: +%\begin{table}[h!]\ContinuedFloat - чтобы не переключать счетчик +%\captionsetup{labelformat=continued}% должен стоять до самого caption +%\caption{} +% либо ручками \caption*{Продолжение таблицы~\ref{...}.} :) + +%%% Интервалы %%% +\addto\captionsrussian{% + \renewcommand{\listingname}{Листинг}% +} +%%% Счётчики %%% +\usepackage[figure,table,section]{totalcount} % Счётчик рисунков и таблиц +\DeclareTotalCounter{lstlisting} +\usepackage{totcount} % Пакет создания счётчиков на основе последнего номера подсчитываемого элемента (может требовать дважды компилировать документ) +\usepackage{totpages} % Счётчик страниц, совместимый с hyperref (ссылается на номер последней страницы). Желательно ставить последним пакетом в преамбуле + +%%% Продвинутое управление групповыми ссылками (пока только формулами) %%% +%% Кодировки и шрифты %%% + +% \newfontfamily{\cyrillicfont}{Times New Roman} +% \newfontfamily{\cyrillicfonttt}{CMU Typewriter Text} +%\setmainfont{Times New Roman} +%\newfontfamily\cyrillicfont{Times New Roman} +%\setsansfont{Times New Roman} %% задаёт шрифт без засечек +% \setmonofont{Liberation Mono} %% задаёт моноширинный шрифт +% \IfFileExists{pscyr.sty}{\renewcommand{\rmdefault}{ftm}}{} +%%% Интервалы %%% +%linespread-реализация ближе к реализации полуторного интервала в ворде. +%setspace реализация заточена под шрифты 10, 11, 12pt, под остальные кегли хуже, но всё же ближе к типографской классике. +\linespread{1.3} % Полуторный интервал (ГОСТ Р 7.0.11-2011, 5.3.6) +%\renewcommand{\@biblabel}[1]{#1} + +%%% Гиперссылки %%% +\usepackage{hyperref} + +%%% Выравнивание и переносы %%% +\sloppy % Избавляемся от переполнений +\clubpenalty=10000 % Запрещаем разрыв страницы после первой строки абзаца +\widowpenalty=10000 % Запрещаем разрыв страницы после последней строки абзаца + +\makeatletter % малые заглавные, small caps shape +\let\@@scshape=\scshape +\renewcommand{\scshape}{% + \ifnum\strcmp{\f@series}{bx}=\z@ + \usefont{T1}{cmr}{bx}{sc}% + \else + \ifnum\strcmp{\f@shape}{it}=\z@ + \fontshape{scsl}\selectfont + \else + \@@scshape + \fi + \fi} +\makeatother + +%%% Подписи %%% +%\captionsetup{% +%singlelinecheck=off, % Многострочные подписи, например у таблиц +%skip=2pt, % Вертикальная отбивка между подписью и содержимым рисунка или таблицы определяется ключом +%justification=centering, % Центрирование подписей, заданных командой \caption +%} +%%% Подключение пакетов %%% +\usepackage{ifthen} % добавляет ifthenelse +%%% Инициализирование переменных, не трогать! %%% +\newcounter{intvl} +\newcounter{otstup} +\newcounter{contnumeq} +\newcounter{contnumfig} +\newcounter{contnumtab} +\newcounter{pgnum} +\newcounter{bibliosel} +\newcounter{chapstyle} +\newcounter{headingdelim} +\newcounter{headingalign} +\newcounter{headingsize} +\newcounter{tabcap} +\newcounter{tablaba} +\newcounter{tabtita} +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Область упрощённого управления оформлением %%% + +%% Интервал между заголовками и между заголовком и текстом +% Заголовки отделяют от текста сверху и снизу тремя интервалами (ГОСТ Р 7.0.11-2011, 5.3.5) +\setcounter{intvl}{3} % Коэффициент кратности к размеру шрифта + +%% Отступы у заголовков в тексте +\setcounter{otstup}{0} % 0 --- без отступа; 1 --- абзацный отступ + +%% Нумерация формул, таблиц и рисунков +\setcounter{contnumeq}{1} % Нумерация формул: 0 --- пораздельно (во введении подряд, без номера раздела); 1 --- сквозная нумерация по всей диссертации +\setcounter{contnumfig}{1} % Нумерация рисунков: 0 --- пораздельно (во введении подряд, без номера раздела); 1 --- сквозная нумерация по всей диссертации +\setcounter{contnumtab}{1} % Нумерация таблиц: 0 --- пораздельно (во введении подряд, без номера раздела); 1 --- сквозная нумерация по всей диссертации + +%% Оглавление +\setcounter{pgnum}{0} % 0 --- номера страниц никак не обозначены; 1 --- Стр. над номерами страниц (дважды компилировать после изменения) + +%% Библиография +\setcounter{bibliosel}{1} % 0 --- встроенная реализация с загрузкой файла через движок bibtex8; 1 --- реализация пакетом biblatex через движок biber + +%% Текст и форматирование заголовков +\setcounter{chapstyle}{1} % 0 --- разделы только под номером; 1 --- разделы с названием "Глава" перед номером +\setcounter{headingdelim}{1} % 0 --- номер отделен пропуском в 1em или \quad; 1 --- номера разделов и приложений отделены точкой с пробелом, подразделы пропуском без точки; 2 --- номера разделов, подразделов и приложений отделены точкой с пробелом. + +%% Выравнивание заголовков в тексте +\setcounter{headingalign}{0} % 0 --- по центру; 1 --- по левому краю + +%% Размеры заголовков в тексте +\setcounter{headingsize}{0} % 0 --- по ГОСТ, все всегда 14 пт; 1 --- пропорционально изменяющийся размер в зависимости от базового шрифта + +%% Подпись таблиц +\setcounter{tabcap}{0} % 0 --- по ГОСТ, номер таблицы и название разделены тире, выровнены по левому краю, при необходимости на нескольких строках; 1 --- подпись таблицы не по ГОСТ, на двух и более строках, дальнейшие настройки: +%Выравнивание первой строки, с подписью и номером +\setcounter{tablaba}{2} % 0 --- по левому краю; 1 --- по центру; 2 --- по правому краю +%Выравнивание строк с самим названием таблицы +\setcounter{tabtita}{1} % 0 --- по левому краю; 1 --- по центру; 2 --- по правому краю + +%%% Рисунки %%% +\DeclareCaptionLabelSeparator*{emdash}{~--- } % (ГОСТ 2.105, 4.3.1) +\captionsetup[figure]{labelsep=emdash,font=onehalfspacing,position=bottom} + +%%% Таблицы %%% +\ifthenelse{\equal{\thetabcap}{0}}{% + \newcommand{\tabcapalign}{\raggedright} % по левому краю страницы или аналога parbox +} + +\ifthenelse{\equal{\thetablaba}{0} \AND \equal{\thetabcap}{1}}{% + \newcommand{\tabcapalign}{\raggedright} % по левому краю страницы или аналога parbox +} + +\ifthenelse{\equal{\thetablaba}{1} \AND \equal{\thetabcap}{1}}{% + \newcommand{\tabcapalign}{\centering} % по центру страницы или аналога parbox +} + +\ifthenelse{\equal{\thetablaba}{2} \AND \equal{\thetabcap}{1}}{% + \newcommand{\tabcapalign}{\raggedleft} % по правому краю страницы или аналога parbox +} + +\ifthenelse{\equal{\thetabtita}{0} \AND \equal{\thetabcap}{1}}{% + \newcommand{\tabtitalign}{\raggedright} % по левому краю страницы или аналога parbox +} + +\ifthenelse{\equal{\thetabtita}{1} \AND \equal{\thetabcap}{1}}{% + \newcommand{\tabtitalign}{\centering} % по центру страницы или аналога parbox +} + +\ifthenelse{\equal{\thetabtita}{2} \AND \equal{\thetabcap}{1}}{% + \newcommand{\tabtitalign}{\raggedleft} % по правому краю страницы или аналога parbox +} + +\DeclareCaptionFormat{tablenocaption}{\tabcapalign #1\strut} % Наименование таблицы отсутствует +\ifthenelse{\equal{\thetabcap}{0}}{% + \DeclareCaptionFormat{tablecaption}{\tabcapalign #1#2#3} + \captionsetup[table]{labelsep=emdash} % тире как разделитель идентификатора с номером от наименования +}{% + \DeclareCaptionFormat{tablecaption}{\tabcapalign #1#2\par% % Идентификатор таблицы на отдельной строке + \tabtitalign{#3}} % Наименование таблицы строкой ниже + \captionsetup[table]{labelsep=space} % пробельный разделитель идентификатора с номером от наименования +} +\captionsetup[table]{format=tablecaption,singlelinecheck=off,font=onehalfspacing,position=top,skip=-5pt} % многострочные наименования и прочее +\DeclareCaptionLabelFormat{continued}{Продолжение таблицы~#2} +\setlength{\belowcaptionskip}{.2cm} +\setlength{\intextsep}{0ex} + +%%% Подписи подрисунков %%% +\renewcommand{\thesubfigure}{\asbuk{subfigure}} % Буквенные номера подрисунков +\captionsetup[subfigure]{font={normalsize}, % Шрифт подписи названий подрисунков (не отличается от основного) + labelformat=brace, % Формат обозначения подрисунка + justification=centering, % Выключка подписей (форматирование), один из вариантов +} +%\DeclareCaptionFont{font12pt}{\fontsize{12pt}{13pt}\selectfont} % объявляем шрифт 12pt для использования в подписях, тут же надо интерлиньяж объявлять, если не наследуется +%\captionsetup[subfigure]{font={font12pt}} % Шрифт подписи названий подрисунков (всегда 12pt) + +%%% Настройки гиперссылок %%% + +\definecolor{linkcolor}{rgb}{0.0,0,0} +\definecolor{citecolor}{rgb}{0,0.0,0} +\definecolor{urlcolor}{rgb}{0,0,0} + +\hypersetup{ + linktocpage=true, % ссылки с номера страницы в оглавлении, списке таблиц и списке рисунков + % linktoc=all, % both the section and page part are links + % pdfpagelabels=false, % set PDF page labels (true|false) + plainpages=true, % Forces page anchors to be named by the Arabic form of the page number, rather than the formatted form + colorlinks, % ссылки отображаются раскрашенным текстом, а не раскрашенным прямоугольником, вокруг текста + linkcolor={linkcolor}, % цвет ссылок типа ref, eqref и подобных + citecolor={citecolor}, % цвет ссылок-цитат + urlcolor={urlcolor}, % цвет гиперссылок + pdflang={ru}, +} +\urlstyle{same} +%%% Шаблон %%% +%\DeclareRobustCommand{\todo}{\textcolor{red}} % решаем проблему превращения названия цвета в результате \MakeUppercase, http://tex.stackexchange.com/a/187930/79756 , \DeclareRobustCommand protects \todo from expanding inside \MakeUppercase +\setlength{\parindent}{2.5em} % Абзацный отступ. Должен быть одинаковым по всему тексту и равен пяти знакам (ГОСТ Р 7.0.11-2011, 5.3.7). + +%%% Списки %%% +% Используем дефис для ненумерованных списков (ГОСТ 2.105-95, 4.1.7) +%\renewcommand{\labelitemi}{\normalfont\bfseries~{---}} +\renewcommand{\labelitemi}{\bfseries~{---}} +\setlist{nosep,% % Единый стиль для всех списков (пакет enumitem), без дополнительных интервалов. + labelindent=\parindent,leftmargin=*% % Каждый пункт, подпункт и перечисление записывают с абзацного отступа (ГОСТ 2.105-95, 4.1.8) +} +%%%%%%%%%%%%%%%%%%%%%% +%\usepackage{xltxtra} % load xunicode + +\usepackage{ragged2e} +\usepackage[explicit]{titlesec} +\usepackage{placeins} +\usepackage{xparse} +\usepackage{csquotes} + +\usepackage{listingsutf8} +\usepackage{url} %пакеты расширений +\usepackage{algorithm, algorithmicx} +\usepackage[noend]{algpseudocode} +\usepackage{blkarray} +\usepackage{chngcntr} +\usepackage{tabularx} +\usepackage[backend=biber, + bibstyle=gost-numeric, + citestyle=nature]{biblatex} +\newcommand*\template[1]{\text{<}#1\text{>}} + +\titleformat{name=\section,numberless}[block]{\normalfont\Large\centering}{}{0em}{#1} +\titleformat{\section}[block]{\normalfont\Large\bfseries\raggedright}{}{0em}{\thesection\hspace{0.25em}#1} +\titleformat{\subsection}[block]{\normalfont\Large\bfseries\raggedright}{}{0em}{\thesubsection\hspace{0.25em}#1} +\titleformat{\subsubsection}[block]{\normalfont\large\bfseries\raggedright}{}{0em}{\thesubsubsection\hspace{0.25em}#1} + +\newcounter{subsubsubsection}[subsubsection] +\renewcommand{\thesubsubsubsection}{\thesubsubsection.\arabic{subsubsubsection}} +\setcounter{secnumdepth}{4} +\setcounter{tocdepth}{4} +\titleclass{\subsubsubsection}{straight}[\subsubsection] +\titleformat{\subsubsubsection}[block]{\normalfont\normalsize\bfseries\raggedright}{}{0em}{\thesubsubsubsection\hspace{0.25em}#1} +\titlespacing*{\subsubsubsection}{0pt}{3.25ex plus 1ex minus .2ex}{1.5ex plus .2ex} +\makeatletter +\newcommand*\l@subsubsubsection{\@dottedtocline{4}{0pt}{5em}} +\makeatother + +\let\Algorithm\algorithm +\renewcommand\algorithm[1][]{\Algorithm[#1]\setstretch{1.5}} +%\renewcommand{\listingscaption}{Листинг} + +\usepackage{pifont} +\usepackage{calc} +\usepackage{suffix} +\usepackage{csquotes} +\DeclareQuoteStyle{russian} +{\guillemotleft}{\guillemotright}[0.025em] +{\quotedblbase}{\textquotedblleft} +\ExecuteQuoteOptions{style=russian} +\newcommand{\enq}[1]{\enquote{#1}} +\newcommand{\eng}[1]{\begin{english}#1\end{english}} +% Подчиненные счетчики в окружениях http://old.kpfu.ru/journals/izv_vuz/arch/sample1251.tex +\newcounter{cTheorem} +\newcounter{cDefinition} +\newcounter{cConsequent} +\newcounter{cExample} +\newcounter{cLemma} +\newcounter{cConjecture} +\newtheorem{Theorem}{Теорема}[cTheorem] +\newtheorem{Definition}{Определение}[cDefinition] +\newtheorem{Consequent}{Следствие}[cConsequent] +\newtheorem{Example}{Пример}[cExample] +\newtheorem{Lemma}{Лемма}[cLemma] +\newtheorem{Conjecture}{Гипотеза}[cConjecture] + +\renewcommand{\theTheorem}{\arabic{Theorem}} +\renewcommand{\theDefinition}{\arabic{Definition}} +\renewcommand{\theConsequent}{\arabic{Consequent}} +\renewcommand{\theExample}{\arabic{Example}} +\renewcommand{\theLemma}{\arabic{Lemma}} +\renewcommand{\theConjecture}{\arabic{Conjecture}} +%\makeatletter +\NewDocumentCommand{\Newline}{}{\text{\\}} +\newcommand{\sequence}[2]{\ensuremath \left(#1,\ \dots,\ #2\right)} + +\definecolor{mygreen}{rgb}{0,0.6,0} +\definecolor{mygray}{rgb}{0.5,0.5,0.5} +\definecolor{mymauve}{rgb}{0.58,0,0.82} +\renewcommand{\listalgorithmname}{Список алгоритмов} +\floatname{algorithm}{Листинг} +\renewcommand{\lstlistingname}{Листинг} +\renewcommand{\thealgorithm}{\arabic{algorithm}} + +\newcommand{\refAlgo}[1]{(листинг \ref{#1})} +\newcommand{\refImage}[1]{(рисунок \ref{#1})} + +\renewcommand{\theenumi}{\arabic{enumi}.}% Меняем везде перечисления на цифра.цифра +\renewcommand{\labelenumi}{\arabic{enumi}.}% Меняем везде перечисления на цифра.цифра +\renewcommand{\theenumii}{\arabic{enumii}}% Меняем везде перечисления на цифра.цифра +\renewcommand{\labelenumii}{(\arabic{enumii})}% Меняем везде перечисления на цифра.цифра +\renewcommand{\theenumiii}{\roman{enumiii}}% Меняем везде перечисления на цифра.цифра +\renewcommand{\labelenumiii}{(\roman{enumiii})}% Меняем везде перечисления на цифра.цифра +\renewcommand{\labelitemi}{---} +\renewcommand{\labelitemii}{---} + +\makeatletter +\def\p@subsection{} +\def\p@subsubsection{\thesection\,\thesubsection\,} +\makeatother +\newcommand{\anonsection}[1]{\cleardoublepage + \phantomsection + \addcontentsline{toc}{section}{\protect\numberline{}#1} + \section*{#1}\vspace*{2.5ex} % По госту положены 3 пустые строки после заголовка ненумеруемого раздела +} +\newcommand{\sectionbreak}{\clearpage} +\renewcommand{\sectionfont}{\normalsize} % Сбиваем стиль оглавления в стандартный +\renewcommand{\cftsecleader}{\cftdotfill{\cftdotsep}} % Точки в оглавлении напротив разделов + +\renewcommand{\cftsecfont}{\normalfont\large} % Переключение на times в содержании +\renewcommand{\cftsubsecfont}{\normalfont\large} % Переключение на times в содержании + +\setlength{\cftsecindent}{0pt}% Убираем отступы в содержании для \section +\setlength{\cftsubsecindent}{0pt}% Убираем отступы в содержании для \subsection +\setlength{\cftsubsubsecindent}{0pt}% Убираем отступы в содержании для \subsubsection + +\usepackage{caption} +%\captionsetup[table]{justification=raggedleft} +%\captionsetup[figure]{justification=centering,labelsep=endash} +\usepackage{amsmath} % \bar (матрицы и проч. ...) +\usepackage{amsfonts} % \mathbb (символ для множества действительных чисел и проч. ...) +\usepackage{mathtools} % \abs, \norm +\DeclarePairedDelimiter\abs{\lvert}{\rvert} % операция модуля +\DeclarePairedDelimiter\norm{\lVert}{\rVert} % операция нормы +\DeclareTextCommandDefault{\textvisiblespace}{% + \mbox{\kern.06em\vrule \@height.3ex}% + \vbox{\hrule \@width.3em}% + \hbox{\vrule \@height.3ex}} +\newsavebox{\spacebox} +\begin{lrbox}{\spacebox} + \verb*! ! +\end{lrbox} +\newcommand{\aspace}{\usebox{\spacebox}} +\DeclareTotalCounter{listing} +\def\figurename{Рисунок} + +\makeatletter +\renewcommand*{\p@subsubsection}{} +\makeatother + +\newcommand{\bmstuheader} +{\vspace*{-30pt} + \hspace{-45pt} + \begin{minipage}{0.17\textwidth} + \hspace*{-20pt}\centering + \includegraphics[width=1.3\textwidth]{emblem.png} + \end{minipage} + \begin{minipage}{0.82\textwidth}\small \textbf{ + \vspace*{-0.7ex} + \hspace*{-10pt}\centerline{Министерство науки и высшего образования Российской Федерации} + \vspace*{-0.7ex} + \centerline{Федеральное государственное автономное образовательное учреждение } + \vspace*{-0.7ex} + \centerline{высшего образования} + \vspace*{-0.7ex} + \centerline{<<Московский государственный технический университет} + \vspace*{-0.7ex} + \centerline{имени Н.Э. Баумана} + \vspace*{-0.7ex} + \centerline{(национальный исследовательский университет)>>} + \vspace*{-0.7ex} + \centerline{(МГТУ им. Н.Э. Баумана)}} + \end{minipage} + + \vspace{-2pt} + \hspace{-34.5pt}\rule{\textwidth}{0.5pt} + + \vspace*{-18.3pt} + \hspace{-34.5pt}\rule{\textwidth}{2.5pt} + + \vspace{0.5ex} + \noindent \small ФАКУЛЬТЕТ\hspace{80pt} <<Информатика и системы управления>> + + \vspace*{-16pt} + \hspace{35pt}\rule{0.855\textwidth}{0.4pt} + + \vspace{0.5ex} + \noindent \small КАФЕДРА\hspace{50pt} <<Теоретическая информатика и компьютерные технологии>> + + \vspace*{-16pt} + \hspace{25pt}\rule{0.875\textwidth}{0.4pt} +} diff --git a/report/thesis.tex b/report/thesis.tex new file mode 100644 index 0000000..0f3728c --- /dev/null +++ b/report/thesis.tex @@ -0,0 +1,714 @@ +% !TeX TXS-program:bibliography = txs:///biber +\documentclass[fontsize=14pt, russian]{scrartcl} +\input{header.tex} +\addbibresource{biblio.bib} + +\begin{document} +\sloppy + +\def\figurename{Рисунок} + +\begin{titlepage} + \thispagestyle{empty} + \newpage + + \bmstuheader + + \vspace{3em} + + \begin{center} + {\Large\bfseries РАСЧЕТНО-ПОЯСНИТЕЛЬНАЯ ЗАПИСКА\\\textbf{\textit{К НАУЧНО-ИССЛЕДОВАТЕЛЬСКОЙ РАБОТЕ\\НА ТЕМУ:}} \\} + \end{center} + + \vspace*{-6ex} + \begin{center} + \Large{\textit{\textbf{<<}}} + + \vspace*{-3ex} + \rule{1\textwidth}{1.2pt} + + \vspace*{-0.2ex} + + \Large{\textit{\textbf{>>}}} + + \vspace*{-3ex} + \rule{1\textwidth}{1.2pt} + + \vspace*{-0.2ex} + \rule{1\textwidth}{1.2pt} + + \vspace*{-0.2ex} + \rule{1\textwidth}{1.2pt} + \end{center} + + \vspace{\fill} + + + \newlength{\ML} + \settowidth{\ML}{«\underline{\hspace{0.7cm}}» \underline{\hspace{2cm}}} + + \noindent Студент \underline{\hspace{1.5cm}} \hfill \underline{\hspace{4cm}}\quad + \underline{\hspace{4cm}} + + \vspace{-2.1ex} + \noindent\hspace{9ex}\scriptsize{(Группа)}\normalsize\hspace{170pt}\hspace{2ex}\scriptsize{(Подпись, дата)}\normalsize\hspace{30pt}\hspace{6ex}\scriptsize{(И.О. Фамилия)}\normalsize + + \bigskip + + \noindent Руководитель НИР \hfill \underline{\hspace{4cm}}\quad + \underline{\hspace{4cm}} + + \vspace{-2ex} + \noindent\hspace{13.5ex}\normalsize\hspace{170pt}\hspace{2ex}\scriptsize{(Подпись, дата)}\normalsize\hspace{30pt}\hspace{6ex}\scriptsize{(И.О. Фамилия)}\normalsize + \bigskip + + \noindent Нормоконтролер \hfill \underline{\hspace{4cm}}\quad + \underline{\hspace{4cm}} + + \vspace{-2ex} + \noindent\hspace{13.5ex}\normalsize\hspace{170pt}\hspace{2ex}\scriptsize{(Подпись, дата)}\normalsize\hspace{30pt}\hspace{6ex}\scriptsize{(И.О. Фамилия)}\normalsize + \vfill + + \begin{center} + \textsl{\the\year{} г.} + \end{center} +\end{titlepage} + +\setlength{\tabcolsep}{3pt} +\newpage +\setcounter{page}{2} +%---------------------------------------------------------------------------- +% ОТСЮДА --- СОБСТВЕННО ТЕКСТ +%---------------------------------------------------------------------------- + +\newpage +\renewcommand\contentsname{\hfill{\normalfont{СОДЕРЖАНИЕ}}\hfill} +\tableofcontents +\newpage +\anonsection{ВВЕДЕНИЕ} + +В эпоху стремительного роста объемов данных системы управления базами данных +(СУБД) являются ключевой частью информационных систем. По данным аналитических +исследований, объем мирового рынка СУБД ежегодно увеличивается, что обусловлено +как расширением круга решаемых задач, так и усложнением структуры обрабатываемых +данных. Реляционные СУБД, работающие с различными диалектами языка SQL, +продолжают занимать доминирующее положение в сегменте обработки транзакций и +аналитических запросов. + +Одним из определяющих факторов конкурентоспособности между различными СУБД +является качество оптимизатора запросов. Оптимизатор представляет собой +компонент, ответственный за преобразование декларативного SQL-запроса в +эффективный физический план исполнения. Именно от качества работы оптимизатора в +значительной степени зависит производительность системы: выбор неоптимального +плана исполнения может привести к увеличению времени выполнения запроса в +десятки раз. + +Также оптимизатор запросов считается наиболее сложной составной частью любой +СУБД. Задача нахождения оптимального плана является NP-полной в общем случае, +что обусловливает необходимость применения эвристических методов и алгоритмов +ограниченного перебора. Современные коммерческие и открытые СУБД используют +различные архитектуры оптимизаторов, среди них наиболее популярны устоявшиеся и +проверенные временем архитектуры Cascades и Volcano. + +Архитектура Cascades используется в таких промышленных системах, как Microsoft +SQL Server, Apache Orca, а также в ряде исследовательских проектов, включая +Columbia и CockroachDB. Преимущества этой архитектуры заключаются в +расширяемости, модульности, а также понятном и эффективном алгоритме поиска, +допускающим параллельность исполнения. + +Целью данной работы является разработка и реализация оптимизатора подмножества +SQL-запросов на основе архитектуры Cascades. + +{\color{red} TODO: переформулировать} +Для достижения поставленной цели необходимо решить следующие задачи: +\begin{enumerate} + \item провести обзор существующих архитектур оптимизаторов запросов, + включая System~R, Volcano и Cascades; + \item формализовать подмножество реляционной алгебры, подлежащее + оптимизации; + \item разработать структуру данных Memo и алгоритм поиска оптимального + плана на основе архитектуры Cascades; + \item реализовать набор правил трансформации и реализации для + основных операторов реляционной алгебры; + \item реализовать метод ветвей и границ для эффективного отсечения + неоптимальных планов; + \item предложить метод дифференциального анализа физических планов + для автоматизированного сравнения с существующими СУБД. +\end{enumerate} + +\section{Оптимизатор как составная часть СУБД} + +\subsection{Архитектура системы управления базами данных} + +Система управления базами данных представляет собой программный комплекс, +обеспечивающий хранение, извлечение и модификацию структурированных +данных. Несмотря на разнообразие существующих реализаций, архитектура +большинства реляционных СУБД включает ряд типовых компонентов, +взаимодействие которых обеспечивает выполнение пользовательских запросов. + +К основным компонентам СУБД относятся: транспортный уровень, принимающий +клиентские подключения и запросы; синтаксический анализатор (парсер), +выполняющий разбор текста SQL-запроса и построение синтаксического дерева; +оптимизатор запросов, преобразующий логическое представление запроса в +эффективный физический план исполнения; исполнитель, непосредственно реализующий +физический план; менеджер хранения данных, управляющий размещением данных на +физических носителях; менеджер транзакций и менеджер блокировок, обеспечивающие +гарантии транзакций; а также менеджер страниц памяти, отвечающий за кэширование +страниц данных в оперативной памяти~\refImage{fig::dbms_arch}. + +{\color{red} TODO: диаграмму можно поинтереснее} + +\begin{figure}[!htb]\centering + \begin{tikzpicture}[ + layer/.style={rectangle, draw, minimum width=10cm, minimum height=0.8cm, align=center, font=\small}, + node distance=0.15cm + ] + \node[layer] (net) {Сетевой протокол и менеджер соединений}; + \node[layer, below=of net] (parse) {Парсер и семантический анализатор SQL}; + \node[layer, below=of parse, fill=gray!20] (opt) {\textbf{Оптимизатор запросов}}; + \node[layer, below=of opt] (exec) {Исполнитель физического плана}; + \node[layer, below=of exec] (tx) {Менеджер транзакций}; + \node[layer, below=of tx] (buf) {Менеджер страниц памяти}; + \node[layer, below=of buf] (acc) {Методы доступа: индексы, кучи}; + \node[layer, below=of acc] (disk) {Дисковая подсистема хранения}; + \end{tikzpicture} + \caption{Архитектура реляционной системы управления базами данных} + \label{fig::dbms_arch} +\end{figure} + +\subsection{Процесс исполнения SQL-запроса} + +Процесс исполнения SQL-запроса в реляционной СУБД проходит несколько +последовательных этапов. + +\begin{enumerate} + \item \emph{Синтаксический анализ} --- текст SQL-запроса подвергается + лексическому и синтаксическому разбору. Результатом данного этапа + является синтаксическое дерево, узлы которого соответствуют конструкциям + языка SQL: операторам \texttt{SELECT}, \texttt{FROM}, \texttt{WHERE}, + \texttt{JOIN}, \texttt{GROUP BY}, \texttt{ORDER BY} и другим. На этом же + этапе выполняется семантический анализ: проверяется существование + указанных таблиц и столбцов, разрешаются имена объектов и определяются + типы выражений. + + \item \emph{Оптимизация} --- синтаксическое дерево преобразуется в логический + план запроса, выраженный в терминах реляционной алгебры. Оптимизатор + исследует пространство эквивалентных логических планов и для каждого из + них рассматривает возможные физические реализации операторов. С помощью + модели стоимости оптимизатор оценивает затраты на выполнение каждого + плана и выбирает план с наименьшей оценочной стоимостью. Результатом + этого этапа является физический план исполнения --- дерево физических + операторов с указанием конкретных алгоритмов соединения, методов доступа + к данным и порядка операций. + + \item \emph{Исполнение физического плана} --- движок выполнения реализует + физический план, порождая потоки кортежей. Данные извлекаются из + хранилища, обрабатываются операторами плана и формируют результирующий + набор, возвращаемый клиенту. +\end{enumerate} + + +\section{Реляционная алгебра} + +\subsection{Определение реляционной алгебры} + +Реляционная алгебра представляет собой формальный язык для описания операций над +отношениями реляционной базы данных. В отличие от декларативного языка SQL, +реляционная алгебра является процедурным языком: она определяет конкретную +последовательность операций, необходимых для получения результата. + +Отношение определяется как конечное множество кортежей, каждый из которых +представляет собой упорядоченный набор значений атрибутов: + +\[ + R \subseteq \text{dom}(A_1) \times \text{dom}(A_2) \times \ldots \times \text{dom}(A_n). +\] + +\subsection{Основные операторы реляционной алгебры} + +Среди основных операторов реляционной алгебры выделяют следующие. + +\begin{enumerate} + \item \emph{Фильтрация} \(\sigma_p(R)\) --- возвращает подмножество кортежей + отношения \(R\), удовлетворяющих предикату \(p\). Например, + \(\sigma_{\text{age} > 30}(\text{Employee})\) вернет всех + сотрудников старше 30 лет. + + \item \emph{Проекция} \(\pi_{A_1, \ldots, A_k}(R)\) --- формирует новое + отношение, содержащее только указанные атрибуты. + + \item \emph{Декартово произведение} \(R \times S\) --- формирует отношение, + каждый кортеж которого является конкатенацией кортежа из \(R\) и кортежа + из \(S\), при этом в результате получается + \(|R \times S| = |R| \cdot |S|\) кортежей. + + \item \emph{Соединение} \(R \bowtie_p S\) --- комбинирует кортежи двух + отношений на основании предиката \(p\). Внутреннее соединение + определяется как + \(R \bowtie_{R.a = S.b} S = \sigma_{R.a = S.b}(R \times S)\). + + \item \emph{Объединение} \(R \cup S\) --- возвращает все кортежи, + принадлежащие хотя бы одному из двух совместимых по схеме отношений. + + \item \emph{Разность} \(R - S\) --- выбирает кортежи, принадлежащие \(R\), но + отсутствующие в \(S\). + + \item \emph{Агрегация} \(\gamma_{G, f}(R)\) --- разбивает отношение \(R\) на + непересекающиеся множества по множеству атрибутов \(G\) и вычисляет + агрегированные значения функции \(f\) (такие как \texttt{COUNT}, + \texttt{SUM}, \texttt{AVG}, \texttt{MIN}, \texttt{MAX}) по каждому + такому множеству. +\end{enumerate} + +\subsection{Преобразование синтаксического дерева к реляционной алгебре} + +Полученное после лексического и синтаксического разбора абстракное +синтаксическое дерево обычно преобразовывают в форму операторов реляционной +алгебры. + +Это преобразование выполняется по фиксированному +набору правил, отражающих семантику конструкций SQL: блок +\texttt{SELECT--FROM--WHERE} превращается в проекцию над селекцией над +произведением соединяемых таблиц; конструкция \texttt{GROUP BY} +порождает оператор группировки; \texttt{ORDER BY} --- запрос на +требуемое физическое свойство сортированности; вложенные подзапросы --- +оператор Apply, который впоследствии может быть удалён процедурой +декорреляции. + +Данное преобразование необходимо по нескольким причинам. Во-первых, +реляционная алгебра обладает формализованным набором правил эквивалентности, которые позволяют оптимизатору корректно +преобразовывать план запроса без изменения семантики. Во-вторых, +алгебраическое представление позволяет единообразно обрабатывать +различные синтаксические формы SQL-запросов, порождающие одинаковые +логические планы. Наконец, разделение на логические и физические +операторы позволяет оптимизатору независимо исследовать порядок +операций и алгоритмы их реализации. + + +\section{Цель оптимизатора} + +Сформулируем задачу, которую решает оптимизатор: по запросу, представленному в +форме дерева операторов реляционной алгебры, построить эквивалентный и +оптимальный, с точки зрения необходимых для исполнения ресурсов, план +исполнения. Итоговый план является деревом, узлы которого помечены конкретными +указаниями на алгоритмы, реализующими один или несколько операторов реляционной +алгебры. Эквивалентность означает, что итоговый физический план и исходный +запрос при исполнении на любом наборе данных порождают одинаковые отношения с +точностью до требуемых свойств, таких как сортировка. + +Запрос в форме реляционной алгебры также называют логическим планом, а операторы +реляционной алгебры --- логическими операторами. + +Для каждого логического оператора существует несколько возможных физических +реализаций. Например, оператор соединения \(\bowtie\) может быть реализован как +соединение вложенными циклами, хеш-соединение или соединение слиянием. +Аналогично, оператор доступа к данным может быть реализован как последовательное +сканирование таблицы или сканирование индекса. Еще одним важным примером +является эквивалентность сканирования индекса с предикатом и двух логических +операторов, примененных последовательно: доступ к данным и фильтрация по тому же +предикату. + +Эквивалентность планов обеспечивается свойствами реляционной алгебры. Например, +коммутативность и ассоциативность соединения: +\[ + R \bowtie S = S \bowtie R, \qquad (R \bowtie S) \bowtie T = R \bowtie (S \bowtie T). +\] + +Оптимальность запроса оценивается с помощью стоимостной функции +\(\mathrm{cost} \colon \mathcal{P}_L \to \mathbb{R}_{\ge 0}\), где +\(\mathcal{P}_{L}\) --- множество эквивалентных физических планов. Это +отображение оценивает затраты на исполнение плана исходя из модели работы +операторов, а также основываясь на эвристиках и статистике о данных в +отношениях. Другими словами, цель оптимизатора состоит в том, чтобы для входного +запроса найти план \(P^* \in \mathcal{P}_L\), минимизирующий \(\mathrm{cost}\): +\[ + P^* = \arg\min_{P \in \mathcal{P}_L} \mathrm{cost}(P). +\] + +Одной из подзадач рамках построения оптимального плана является определение +порядка выполнения соединений. Для соединения \(n\) отношений количество +возможных порядков соединения выражается как \(O((n-1)! \cdot C_{n-1})\), где +\(C_k\) --- \(k\)-е число Каталана. Таким образом, пространство поиска растет +экспоненциально с увеличением числа соединяемых таблиц. + +{\color{red}: TODO: ссылка на источник} + +Поскольку полный перебор \(\mathcal{P}_L\) в общем случае невозможен из-за +экспоненциального роста числа альтернатив, на практике речь идёт о поиске плана +с приемлемо малой стоимостью в определенном подмножестве пространства поиска. +Именно эта задача и определяет архитектуру современных оптимизаторов: +расширяемое описание пространства поиска через правила преобразования, +эффективный алгоритм его исследования и стоимостная модель, позволяющая отсекать +заведомо неоптимальные ветви. + +Выбор плана исполнения запроса может оказывать кардинальное влияние на объем +требуемых вычислительных ресурсов. В качестве примера оценим количества +страничных чтений с диска для различных физических операторов. Пусть имеется три отношения: таблица +\texttt{customer} с \(|C| = 10^5\) строк, таблица \texttt{orders} с +\(|O| = 10^7\) строк и таблица \texttt{lineitem} с \(|L| = 5 \cdot 10^7\) +строк. Размер страницы --- \(8\) КБ; в одну страницу укладывается +\(100\) строк \texttt{customer}, \(80\) строк \texttt{orders} и \(50\) +строк \texttt{lineitem}. Соответственно, общее число страниц составляет +\(P_C = 10^3\), \(P_O = 1{,}25 \cdot 10^5\), \(P_L = 10^6\). + +Запрос состоит в соединении трёх таблиц по внешним ключам с +последующей агрегацией. Рассмотрим два альтернативных плана. + +\begin{itemize} + \item План \(P_1\): + \((\texttt{customer} \bowtie \texttt{orders}) \bowtie \texttt{lineitem}\) + с реализацией обоих соединений через Hash Join, для чего сначала + полностью считываются \(P_C + P_O = 1{,}26 \cdot 10^5\) страниц для + построения хеш-таблицы, после чего промежуточный результат размером + \(10^7\) строк (равный \(|O|\)) соединяется с \texttt{lineitem}, требуя + считывания ещё \(P_L = 10^6\) страниц. В сумме --- порядка + \(1{,}126 \cdot 10^6\) операций чтения с диска. + \item План \(P_2\): тот же запрос, но реализованный как соединение с помощью + вложенных циклов без использования индексов: для каждой строки + \texttt{customer} полностью просматривается \texttt{orders}, для каждой + результирующей пары --- \texttt{lineitem}. Совокупный объём ввода-вывода + оценивается величиной + \(P_C \cdot P_O \cdot P_L \approx 1{,}25 \cdot 10^{14}\) операций + чтения, что на восемь порядков превышает \(P_1\). +\end{itemize} + +{\color{red} TODO: пример посложнее} + +Различие на восемь порядков иллюстрирует, что разница между удачным и неудачным +планами может составлять сотни миллионов раз. Если исполнение \(P_1\) на +современной системе занимает порядка секунд, то наивный \(P_2\) --- десятки лет +реального времени. Более того, если в плане \(P_1\) дополнительно использовать +индекс по внешнему ключу при соединении с \texttt{lineitem}, стоимость может +снизиться ещё в несколько раз. + +Этот пример наглядно демонстрирует, что оптимизатор отвечает не за доли +процентов производительности, а за принципиальную возможность исполнения сложных +аналитических запросов в приемлемое время. Именно поэтому разработка +качественного оптимизатора занимает центральное место в проектировании любой +реляционной СУБД. + +\section{Обзор архитектур оптимизаторов} + +\subsection{System R} + +Оптимизатор System~R, разработанный в IBM Research в конце 1970-х годов, стал +первым стоимостным оптимизатором запросов и заложил фундаментальные принципы, +используемые во всех последующих системах. + +Оптимизатор, построенный по архитектуре System~R, разбивает запрос на блоки, +каждый из которых оптимизируется индивидуально. Блок запроса состоит из +конструкции \texttt{SELECT}, конструкции \texttt{FROM}, ссылающейся на одну или +несколько таблиц, и дерева предикатов \texttt{WHERE}. Несколько блоков +появляется в случае запроса с вложенными подзапросами. + +Для каждого блока оптимизатор выполняет два основных действия. Во-первых, для +запросов к одному отношению выбирается наилучший метод доступа на основании +стоимостной модели. Во-вторых, для запросов с несколькими отношениями +определяется оптимальный порядок соединений методом восходящего динамического +программирования: сначала выбираются оптимальные планы доступа к отдельным +таблицам, затем перебираются двухтабличные соединения, трехтабличные и так +далее. + +Стоимость плана вычисляется по формуле +\(\text{COST} = \text{PageFetches} + w \cdot \text{RSICalls}\), учитывающей +ожидаемый объем операций ввода-вывода и потребление ресурсов процессора, где +\(w\) --- весовой коэффициент. + +Важным нововведением System~R стало понятие интересных порядков. Стоимость плана +зависит не только от непосредственных затрат на выполнение оператора, но и от +того, обеспечивает ли он упорядоченность результата, требуемую вышестоящими +операторами. Например, использование соединения слиянием вместо хеш-соединения +может быть выгодно, если результат должен быть отсортирован для последующей +операции \texttt{ORDER BY}. + +К ограничениям архитектуры System~R относятся: + +\begin{itemize} + \item рассмотрение только левосторонних + деревьев соединений; + \item независимая оптимизация вложенных подзапросов; + \item невозможность гибкого расширения набора правил преобразования, что + необходимо при добавлении новых операторов языка SQL. +\end{itemize} + +Эти ограничения послужили мотивацией для создания расширяемых архитектур +оптимизаторов. + +Архитектуру, аналогичную System~R, используют PostgreSQL и ранние версии IBM +DB2. + +\subsection{Volcano} + +Volcano --- расширяемый, основанный на правилах, стоимостной +оптимизатор запросов, разработанный Грефе и Маккенной в 1993 году. +Volcano представил ряд концепций, ставших основой для последующих +расширяемых оптимизаторов: физические свойства выражений, +энфорсеры (обобщение интересных порядков System~R), структуру данных +Memo и нисходящий алгоритм поиска на основе динамического +программирования. + +Volcano разделяет поиск на две фазы: + +\begin{itemize} + \item \emph{фаза генерации} --- оптимизатор применяет правила трансформации + для порождения всех эквивалентных логических выражений. Применяется + рекурсивный нисходящий обход дерева: для каждого выражения сначала + исследуются дочерние выражения, затем применяются правила трансформации + к текущему выражению. Все порожденные выражения кэшируются в структуре + данных Memo для предотвращения дублирования. + + \item \emph{фаза стоимостного анализа} --- по завершении генерации всех + эквивалентных логических выражений оптимизатор ищет наилучший физический + план. Для каждого логического выражения применяются правила реализации, + порождающие физические операторы. Стоимость каждого физического плана + оценивается рекурсивно, и сохраняется наилучший найденный план. +\end{itemize} + +Поиск осуществляется нисходящим динамическим программированием с использованием +стратегии ветвей и границ: текущая верхняя граница стоимости передается при +рекурсивных вызовах и используется для отсечения заведомо неоптимальных планов. + +Основным недостаткам Volcano является полное раскрытие всех классов +эквивалентности на фазе генерации до начала стоимостного анализа, что приводит к +избыточным вычислениям. + +\section{Архитектура Cascades} + +\subsection{Основные понятия Cascades} + +Cascades --- это расширяемая архитектура оптимизатора запросов, являющаяся +логическим продолжением идеи Volcano. + +Основными объектами Cascades являются: + +\begin{itemize} + \item выражение --- дерево или фрагмент дерева, корнем которого является + логический либо физический оператор; + \item групповое выражение --- выражение, дочерние узлы которого являются + ссылками на группы Memo; + \item группа --- множество логически эквивалентных выражений, возвращающих + один и тот же результат; + \item правило трансформации --- правило, преобразующее логическое выражение в + логически эквивалентное выражение; + \item правило реализации --- правило, преобразующее логический оператор в + физический оператор; + \item физическое свойство --- требование относительно порядка, распределения, + разбиения или уникальности результата; + \item задача --- элементарная единица работы оптимизатора, например + исследование группы, применение правила или оптимизация выражения под + заданное свойство. +\end{itemize} + +Важным принципом Cascades является то, что правила являются объектами. Они +определяются условиями применимости и собственно алгоритмом применения. +Предполагается, что система легко расширяется добавленимем новых правил, потому +что все правила наследуются от общего интерфейса. + +\subsection{Группы эквивалентности и групповые выражения} + +Группа в Memo представляет собой множество выражений, которые эквивалентны с +точки зрения логического результата. Например, для трех отношений \(A\), \(B\) и +\(C\) выражения + +\[ + A \bowtie (B \bowtie C) = (A \bowtie B) \bowtie C +\] + +при выполнении условий ассоциативности соединения принадлежат одной группе, +поскольку возвращают одинаковый результат. Причем физические реализации этих +выражений могут отличаться, а стоимость может зависеть от кардинальности +промежуточных результатов. + +Групповое выражение хранит оператор и ссылки на дочерние группы. Например, +выражение \(Join(G_1, G_2)\) не указывает конкретное дерево для левого и правого +входов, а ссылается на группы \(G_1\) и \(G_2\), каждая из которых может +содержать множество альтернатив. Поэтому одно групповое выражение кодирует +комбинацию многих конкретных деревьев, храня эту информацию компактно. + +\subsection{Структура Memo} + +Memo является основной структурой данных Cascades. Оно выполняет сразу +несколько функций: устраняет дублирующиеся выражения, хранит альтернативы, +фиксирует результаты оптимизации под разными физическими свойствами и обеспечивает +точку синхронизации для задач поиска. + +Структура группы Memo включает следующие поля: + +\begin{itemize} + \item уникальный идентификатор группы; + \item список логических групповых выражений; + \item список физических групповых выражений; + \item логические свойства группы; + \item статистику и оценку кардинальности; + \item множество примененных правил, предотвращающее повторное применение + одного и того же правила к одному и тому же выражению; + \item состояние исследования группы. +\end{itemize} + +Групповое выражение, в свою очередь, содержит оператор, массив идентификаторов +дочерних групп, локальные свойства оператора, рассчитанную стоимость самого +оператора и служебные признаки. Для физического выражения дополнительно могут +храниться предоставляемые физические свойства, например порядок выходного потока +или способ распределения данных. + +На рисунке~\ref{fig:memo-structure} показан пример Memo для запроса с тремя +отношениями. Группа \(G_5\) представляет результат соединения трех отношений, а +внутри нее находятся два логически эквивалентных выражения с разной +ассоциацией соединений. Дочерние выражения ссылаются на группы, поэтому общие +подпланы не дублируются. + +{\color{red} TODO: поправить схему} +\begin{figure}[H] + \centering + \begin{tikzpicture}[ + group/.style={draw, rounded corners, align=left, minimum width=4.0cm, minimum height=1.5cm}, + expr/.style={draw, align=center, fill=white, minimum width=3.3cm, minimum height=0.6cm}, + arrow/.style={-{Stealth[length=2mm]}, thick} + ] + \node[group] (g1) at (0,0) {\textbf{Группа G1}\\Scan(A)}; + \node[group] (g2) at (5,0) {\textbf{Группа G2}\\Scan(B)}; + \node[group] (g3) at (10,0) {\textbf{Группа G3}\\Scan(C)}; + \node[group] (g4) at (2.5,-3) {\textbf{Группа G4}\\Join(G1,G2)\\HashJoin(G1,G2)}; + \node[group] (g6) at (7.5,-3) {\textbf{Группа G6}\\Join(G2,G3)\\HashJoin(G2,G3)}; + \node[group] (g5) at (5,-6) {\textbf{Группа G5}\\Join(G4,G3)\\Join(G1,G6)}; + + \draw[arrow] (g4) -- (g1); + \draw[arrow] (g4) -- (g2); + \draw[arrow] (g6) -- (g2); + \draw[arrow] (g6) -- (g3); + \draw[arrow] (g5) -- (g4); + \draw[arrow] (g5) -- (g6); + \draw[arrow] (g5) -- (g3); + \draw[arrow] (g5) -- (g1); + \end{tikzpicture} + \caption{Пример структуры Memo с группами эквивалентных выражений} + \label{fig:memo-structure} +\end{figure} + +При добавлении нового выражения Memo выполняет нормализацию и проверку на +дубликаты. Если выражение уже существует в некоторой группе, оно не добавляется +повторно. Если выражение обнаруживает эквивалентность двух ранее различных +групп, группы могут быть объединены. Операция объединения требует аккуратного +обновления ссылок, таблиц победителей и состояния правил, поэтому в практической +реализации она является одной из наиболее сложных частей структуры Memo. + +\subsection{Задачи и взаимодействие компонентов поиска} + +Cascades организует оптимизацию как выполнение набора задач. Задача может +исследовать группу, исследовать выражение, применить правило, реализовать +логический оператор, оптимизировать дочернюю группу под заданное физическое +свойство или построить вспомогательный оператор для обеспечения свойства. Такой +подход позволяет гибко управлять порядком работы и откладывать дорогостоящие +действия до тех пор, пока они не станут необходимыми. + +На рисунке~\ref{fig:cascades-tasks} показана схема взаимодействия задач в +оптимизаторе. Все подписи намеренно приведены на русском языке, поскольку схема +является частью русскоязычного отчета. + +\begin{figure}[H] + \centering + \begin{tikzpicture}[ + node distance=0.9cm and 1.1cm, + block/.style={draw, rounded corners, align=center, minimum width=3.4cm, minimum height=0.9cm}, + small/.style={draw, rounded corners, align=center, minimum width=2.9cm, minimum height=0.8cm}, + arrow/.style={-{Stealth[length=2mm]}, thick} + ] + \node[block] (root) {запрос\\к оптимизации}; + \node[block, right=of root] (memo) {вставка\\в Memo}; + \node[block, right=of memo] (optgroup) {задача\\оптимизации группы}; + \node[small, below left=of optgroup] (explore) {исследовать\\группу}; + \node[small, below=of optgroup] (implement) {реализовать\\выражение}; + \node[small, below right=of optgroup] (enforce) {обеспечить\\свойство}; + \node[small, below=of explore] (rules) {применить\\правила}; + \node[small, below=of implement] (cost) {оценить\\стоимость}; + \node[small, below=of enforce] (winner) {обновить\\победителя}; + + \draw[arrow] (root) -- (memo); + \draw[arrow] (memo) -- (optgroup); + \draw[arrow] (optgroup) -- (explore); + \draw[arrow] (optgroup) -- (implement); + \draw[arrow] (optgroup) -- (enforce); + \draw[arrow] (explore) -- (rules); + \draw[arrow] (rules) -| (memo); + \draw[arrow] (implement) -- (cost); + \draw[arrow] (cost) -- (winner); + \draw[arrow] (enforce) -- (winner); + \draw[arrow] (winner) -| (optgroup); + \end{tikzpicture} + \caption{Взаимодействие задач в оптимизаторе Cascades} + \label{fig:cascades-tasks} +\end{figure} + +В практической реализации задачи могут храниться в стеке, очереди с приоритетом +или специализированном планировщике. Приоритет задачи определяется ожидаемой +полезностью, ограничением стоимости, глубиной поиска и типом правила. Например, +применение правила проталкивания предиката обычно имеет высокий приоритет, +поскольку оно может уменьшить кардинальности и тем самым усилить последующее +отсечение ветвей. + +\subsection{Псевдокод алгоритма поиска} + +Ниже приведен упрощенный псевдокод поиска физического плана в архитектуре +Cascades. + +\begin{algorithm}[H] + \caption{Top-down Cascades search} + \label{alg:cascades-search} + \begin{algorithmic}[1] + \Function{Optimize}{$rootExpr, requiredProps$} + \State $rootGroup \gets Memo.Insert(rootExpr)$ + \State $upperBound \gets +\infty$ + \State \Return \Call{OptimizeGroup}{$rootGroup, requiredProps, upperBound$} + \EndFunction + \Statex + \Function{OptimizeGroup}{$group, props, bound$} + \If{$group$ has winner for $props$ with cost $\le bound$} + \State \Return stored winner + \EndIf + \State \Call{ExploreGroup}{$group$} + \State $bestPlan \gets null$ + \State $bestCost \gets bound$ + \ForAll{$expr \in group.expressions$} + \State $candidate \gets$ \Call{OptimizeExpression}{$expr, props, bestCost$} + \If{$candidate.cost < bestCost$} + \State $bestPlan \gets candidate$ + \State $bestCost \gets candidate.cost$ + \EndIf + \EndFor + \State \Call{StoreWinner}{$group, props, bestPlan, bestCost$} + \State \Return $bestPlan$ + \EndFunction + \Statex + \Function{ExploreGroup}{$group$} + \ForAll{$expr \in group.logicalExpressions$} + \ForAll{$rule \in MatchingRules(expr)$} + \If{\Call{IsApplicable}{$rule, expr$}} + \State $newExpr \gets$ \Call{ApplyRule}{$rule, expr$} + \State \Call{Memo.InsertIntoEquivalentGroup}{$newExpr, group$} + \EndIf + \EndFor + \EndFor + \EndFunction + \Statex + \Function{OptimizeExpression}{$expr, props, bound$} + \If{$expr$ is logical} + \State generate physical expressions using implementation rules + \EndIf + \State optimize children with derived required properties + \State prune expression if lower bound exceeds $bound$ + \State add local operator cost and enforcer cost if needed + \State \Return physical candidate + \EndFunction + \end{algorithmic} +\end{algorithm} + + + +\renewcommand\refname{СПИСОК ИСПОЛЬЗОВАННЫХ ИСТОЧНИКОВ} +\clearpage +{\catcode`"\active\def"{\relax} + \addcontentsline{toc}{section}{\protect\numberline{}\refname}% + \printbibliography +} +\newpage +\end{document} diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index f8969d8..8a972eb 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -256,6 +256,7 @@ class Optimizer { } public: + // FIXME: сделать генерацию лучшего плана в виде просто дерева. PhysicalExpr* Optimize() { tasks_.emplace([this]() { OptimizeGroup(root_->group); From 93ae0890e3a52b4cb65554d5b3e8cd96eed0af8b Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sat, 2 May 2026 19:46:50 +0300 Subject: [PATCH 014/120] wip --- report/thesis.tex | 714 +++++++++++++++++++++++++++++++++------------- 1 file changed, 523 insertions(+), 191 deletions(-) diff --git a/report/thesis.tex b/report/thesis.tex index 0f3728c..c49cc15 100644 --- a/report/thesis.tex +++ b/report/thesis.tex @@ -22,14 +22,14 @@ \vspace*{-6ex} \begin{center} - \Large{\textit{\textbf{<<}}} + \Large{\textit{\textbf{}}} \vspace*{-3ex} \rule{1\textwidth}{1.2pt} \vspace*{-0.2ex} - \Large{\textit{\textbf{>>}}} + \Large{\textit{\textbf{}}} \vspace*{-3ex} \rule{1\textwidth}{1.2pt} @@ -91,22 +91,22 @@ (СУБД) являются ключевой частью информационных систем. По данным аналитических исследований, объем мирового рынка СУБД ежегодно увеличивается, что обусловлено как расширением круга решаемых задач, так и усложнением структуры обрабатываемых -данных. Реляционные СУБД, работающие с различными диалектами языка SQL, -продолжают занимать доминирующее положение в сегменте обработки транзакций и -аналитических запросов. - -Одним из определяющих факторов конкурентоспособности между различными СУБД -является качество оптимизатора запросов. Оптимизатор представляет собой -компонент, ответственный за преобразование декларативного SQL-запроса в -эффективный физический план исполнения. Именно от качества работы оптимизатора в -значительной степени зависит производительность системы: выбор неоптимального -плана исполнения может привести к увеличению времени выполнения запроса в -десятки раз. +данных и увеличением их объема. Реляционные базы данных, работающие с различными +диалектами языка SQL, продолжают занимать доминирующее положение в сегменте +обработки транзакций и аналитических запросов. + +Одним из определяющих факторов конкурентоспособности между различными +реализациями СУБД является качество оптимизатора запросов. Оптимизатор +представляет собой компонент, ответственный за преобразование декларативного +SQL-запроса в эффективный физический план исполнения. Именно от качества работы +оптимизатора в значительной степени зависит производительность всей системы: +выбор неоптимального плана исполнения может привести к увеличению времени +выполнения запроса в десятки раз относительно оптимального случая. Также оптимизатор запросов считается наиболее сложной составной частью любой СУБД. Задача нахождения оптимального плана является NP-полной в общем случае, -что обусловливает необходимость применения эвристических методов и алгоритмов -ограниченного перебора. Современные коммерческие и открытые СУБД используют +что обуславливает необходимость применения эвристических методов и алгоритмов +ограниченного перебора. Современные коммерческие и открытые системы используют различные архитектуры оптимизаторов, среди них наиболее популярны устоявшиеся и проверенные временем архитектуры Cascades и Volcano. @@ -117,28 +117,26 @@ допускающим параллельность исполнения. Целью данной работы является разработка и реализация оптимизатора подмножества -SQL-запросов на основе архитектуры Cascades. +SQL-запросов на основе архитектуры Cascades, а также создание фреймворка для его +итеративного улучшения путем сравнения с промышленными реализациями. -{\color{red} TODO: переформулировать} Для достижения поставленной цели необходимо решить следующие задачи: -\begin{enumerate} - \item провести обзор существующих архитектур оптимизаторов запросов, - включая System~R, Volcano и Cascades; - \item формализовать подмножество реляционной алгебры, подлежащее - оптимизации; - \item разработать структуру данных Memo и алгоритм поиска оптимального - плана на основе архитектуры Cascades; - \item реализовать набор правил трансформации и реализации для - основных операторов реляционной алгебры; - \item реализовать метод ветвей и границ для эффективного отсечения - неоптимальных планов; - \item предложить метод дифференциального анализа физических планов - для автоматизированного сравнения с существующими СУБД. -\end{enumerate} - -\section{Оптимизатор как составная часть СУБД} +\begin{itemize} + \item изучить существующие архитектуры оптимизаторов запросов, включая + System~R, Volcano и Cascades; + \item формализовать подмножество SQL и соответствующее подмножество + реляционной алгебры; + \item реализовать структуру данных Memo и алгоритм поиска оптимального плана + на основе архитектуры Cascades; + \item реализовать набор правил трансформации и реализации для основных + операторов реляционной алгебры; + \item реализовать метод ветвей и границ для эффективного отсечения + неоптимальных планов; + \item реализовать метод дифференциального анализа физических планов для + автоматизированного поиска примеров неоптимальных планов. +\end{itemize} -\subsection{Архитектура системы управления базами данных} +\section{Архитектура СУБД} Система управления базами данных представляет собой программный комплекс, обеспечивающий хранение, извлечение и модификацию структурированных @@ -146,32 +144,67 @@ \subsection{Архитектура системы управления база большинства реляционных СУБД включает ряд типовых компонентов, взаимодействие которых обеспечивает выполнение пользовательских запросов. -К основным компонентам СУБД относятся: транспортный уровень, принимающий -клиентские подключения и запросы; синтаксический анализатор (парсер), -выполняющий разбор текста SQL-запроса и построение синтаксического дерева; -оптимизатор запросов, преобразующий логическое представление запроса в -эффективный физический план исполнения; исполнитель, непосредственно реализующий -физический план; менеджер хранения данных, управляющий размещением данных на -физических носителях; менеджер транзакций и менеджер блокировок, обеспечивающие -гарантии транзакций; а также менеджер страниц памяти, отвечающий за кэширование -страниц данных в оперативной памяти~\refImage{fig::dbms_arch}. +К основным компонентам СУБД относятся~\refImage{fig::dbms_arch}: -{\color{red} TODO: диаграмму можно поинтереснее} +\begin{itemize} + \item транспортный уровень, принимающий клиентские подключения и запросы; + \item синтаксический анализатор (парсер), выполняющий разбор текста +SQL-запроса и построение синтаксического дерева; + \item оптимизатор запросов, преобразующий логическое представление запроса в +эффективный физический план исполнения; + \item исполнитель, непосредственно реализующий физический план; + \item менеджер хранения данных, управляющий размещением данных на физических +носителях; + \item менеджер транзакций и менеджер блокировок, обеспечивающие гарантии +транзакций; + \item менеджер страниц памяти, отвечающий за кэширование страниц данных в +оперативной памяти. +\end{itemize} \begin{figure}[!htb]\centering - \begin{tikzpicture}[ - layer/.style={rectangle, draw, minimum width=10cm, minimum height=0.8cm, align=center, font=\small}, - node distance=0.15cm - ] - \node[layer] (net) {Сетевой протокол и менеджер соединений}; - \node[layer, below=of net] (parse) {Парсер и семантический анализатор SQL}; - \node[layer, below=of parse, fill=gray!20] (opt) {\textbf{Оптимизатор запросов}}; - \node[layer, below=of opt] (exec) {Исполнитель физического плана}; - \node[layer, below=of exec] (tx) {Менеджер транзакций}; - \node[layer, below=of tx] (buf) {Менеджер страниц памяти}; - \node[layer, below=of buf] (acc) {Методы доступа: индексы, кучи}; - \node[layer, below=of acc] (disk) {Дисковая подсистема хранения}; - \end{tikzpicture} +\usetikzlibrary{fit} +\begin{tikzpicture}[ + box/.style={rectangle, draw, minimum width=4.1cm, minimum height=1cm, + align=center, font=\small}, + wbox/.style={rectangle, draw, minimum width=9.1cm, minimum height=1cm, + align=center, font=\small}, + grp/.style={draw, dashed, inner sep=0.3cm}, + lbl/.style={font=\small}, +] + \node[box] (clust) at (-2.5, 0) {Кластерные\\соединения}; + \node[box] (client) at ( 2.5, 0) {Клиентские\\соединения}; + \node[grp, fit=(clust)(client)] (TG) {}; + \node[lbl, anchor=south west] at (TG.north west) {Транспорт}; + + \node[wbox] (parser) at (0, -2.8) {Синтаксический анализатор}; + \node[wbox, fill=gray!20] (optim) at (0, -4.05) {\textbf{Оптимизатор запросов}}; + \node[grp, fit=(parser)(optim)] (QPG) {}; + \node[lbl, anchor=south west] at (QPG.north west) {Обработчик запросов}; + + \node[box] (remote) at (-2.5, -6.85) {Удалённое\\исполнение}; + \node[box] (local) at ( 2.5, -6.85) {Локальное\\исполнение}; + \node[grp, fit=(remote)(local)] (EEG) {}; + \node[lbl, anchor=south west] at (EEG.north west) {Исполнитель}; + + \node[box] (txmgr) at (-2.5, -9.65) {Менеджер\\транзакций}; + \node[box] (lockmgr) at ( 2.5, -9.65) {Менеджер\\блокировок}; + \node[wbox] (access) at ( 0, -10.9) {Методы доступа}; + \node[box] (bufmgr) at (-2.5, -12.15) {Менеджер\\страниц}; + \node[box] (recmgr) at ( 2.5, -12.15) {Менеджер\\восстановления}; + \node[grp, fit=(txmgr)(lockmgr)(access)(bufmgr)(recmgr)] (SEG) {}; + \node[lbl, anchor=south west] at (SEG.north west) {Подсистема хранения}; + + \draw[<->, thick, >=stealth] + ([xshift=0.4cm]TG.south east) -- ([xshift=0.4cm]QPG.north east); + \draw[<->, thick, >=stealth] + ([xshift=0.4cm]QPG.south east) -- ([xshift=0.4cm]EEG.north east); + \draw[<->, thick, >=stealth] + ([xshift=0.4cm]EEG.south east) -- ([xshift=0.4cm]SEG.north east); + + \draw[thick] ([xshift=-0.5cm]clust.west) -- ([xshift=-0.5cm]remote.west); + \draw[->, thick, >=stealth] ([xshift=-0.5cm]clust.west) -- (clust.west); + \draw[->, thick, >=stealth] ([xshift=-0.5cm]remote.west) -- (remote.west); +\end{tikzpicture} \caption{Архитектура реляционной системы управления базами данных} \label{fig::dbms_arch} \end{figure} @@ -182,8 +215,7 @@ \subsection{Процесс исполнения SQL-запроса} последовательных этапов. \begin{enumerate} - \item \emph{Синтаксический анализ} --- текст SQL-запроса подвергается - лексическому и синтаксическому разбору. Результатом данного этапа + \item \emph{Синтаксический анализ} --- запроса проходит лексический и синтаксический разбор. Результатом данного этапа является синтаксическое дерево, узлы которого соответствуют конструкциям языка SQL: операторам \texttt{SELECT}, \texttt{FROM}, \texttt{WHERE}, \texttt{JOIN}, \texttt{GROUP BY}, \texttt{ORDER BY} и другим. На этом же @@ -197,31 +229,29 @@ \subsection{Процесс исполнения SQL-запроса} них рассматривает возможные физические реализации операторов. С помощью модели стоимости оптимизатор оценивает затраты на выполнение каждого плана и выбирает план с наименьшей оценочной стоимостью. Результатом - этого этапа является физический план исполнения --- дерево физических + этого этапа является физический план исполнения. Под этим понимается дерево физических операторов с указанием конкретных алгоритмов соединения, методов доступа к данным и порядка операций. \item \emph{Исполнение физического плана} --- движок выполнения реализует физический план, порождая потоки кортежей. Данные извлекаются из хранилища, обрабатываются операторами плана и формируют результирующий - набор, возвращаемый клиенту. + набор, возвращаемый пользователю. \end{enumerate} \section{Реляционная алгебра} -\subsection{Определение реляционной алгебры} - Реляционная алгебра представляет собой формальный язык для описания операций над -отношениями реляционной базы данных. В отличие от декларативного языка SQL, -реляционная алгебра является процедурным языком: она определяет конкретную +отношениями реляционной базы данных. В отличие от декларативного SQL, +реляционная алгебра процедурной, так как она определяет конкретную последовательность операций, необходимых для получения результата. Отношение определяется как конечное множество кортежей, каждый из которых представляет собой упорядоченный набор значений атрибутов: \[ - R \subseteq \text{dom}(A_1) \times \text{dom}(A_2) \times \ldots \times \text{dom}(A_n). + R \subseteq \text{dom}(A_1) \times \text{dom}(A_2) \times \ldots \times \text{dom}(A_n). \] \subsection{Основные операторы реляционной алгебры} @@ -231,30 +261,24 @@ \subsection{Основные операторы реляционной алге \begin{enumerate} \item \emph{Фильтрация} \(\sigma_p(R)\) --- возвращает подмножество кортежей отношения \(R\), удовлетворяющих предикату \(p\). Например, - \(\sigma_{\text{age} > 30}(\text{Employee})\) вернет всех - сотрудников старше 30 лет. - + \(\sigma_{\text{age} > 30}(\text{Employee})\) вернет все кортежи из + отношения Employee, для которых значение атрибута age больше 30. \item \emph{Проекция} \(\pi_{A_1, \ldots, A_k}(R)\) --- формирует новое - отношение, содержащее только указанные атрибуты. - + отношение, содержащее только перечисленные атрибуты. \item \emph{Декартово произведение} \(R \times S\) --- формирует отношение, каждый кортеж которого является конкатенацией кортежа из \(R\) и кортежа - из \(S\), при этом в результате получается - \(|R \times S| = |R| \cdot |S|\) кортежей. - + из \(S\), при этом результат содержит \(|R \times S| = |R| \cdot |S|\) + элементов. \item \emph{Соединение} \(R \bowtie_p S\) --- комбинирует кортежи двух отношений на основании предиката \(p\). Внутреннее соединение определяется как \(R \bowtie_{R.a = S.b} S = \sigma_{R.a = S.b}(R \times S)\). - \item \emph{Объединение} \(R \cup S\) --- возвращает все кортежи, принадлежащие хотя бы одному из двух совместимых по схеме отношений. - \item \emph{Разность} \(R - S\) --- выбирает кортежи, принадлежащие \(R\), но отсутствующие в \(S\). - \item \emph{Агрегация} \(\gamma_{G, f}(R)\) --- разбивает отношение \(R\) на - непересекающиеся множества по множеству атрибутов \(G\) и вычисляет + непересекающиеся множества по набору атрибутов \(G\) и вычисляет агрегированные значения функции \(f\) (такие как \texttt{COUNT}, \texttt{SUM}, \texttt{AVG}, \texttt{MIN}, \texttt{MAX}) по каждому такому множеству. @@ -266,35 +290,38 @@ \subsection{Преобразование синтаксического дере синтаксическое дерево обычно преобразовывают в форму операторов реляционной алгебры. -Это преобразование выполняется по фиксированному -набору правил, отражающих семантику конструкций SQL: блок -\texttt{SELECT--FROM--WHERE} превращается в проекцию над селекцией над -произведением соединяемых таблиц; конструкция \texttt{GROUP BY} -порождает оператор группировки; \texttt{ORDER BY} --- запрос на -требуемое физическое свойство сортированности; вложенные подзапросы --- -оператор Apply, который впоследствии может быть удалён процедурой -декорреляции. - -Данное преобразование необходимо по нескольким причинам. Во-первых, -реляционная алгебра обладает формализованным набором правил эквивалентности, которые позволяют оптимизатору корректно -преобразовывать план запроса без изменения семантики. Во-вторых, -алгебраическое представление позволяет единообразно обрабатывать -различные синтаксические формы SQL-запросов, порождающие одинаковые -логические планы. Наконец, разделение на логические и физические -операторы позволяет оптимизатору независимо исследовать порядок -операций и алгоритмы их реализации. +Это преобразование выполняется по фиксированному набору правил, отражающих +семантику конструкций SQL: + +\begin{itemize} + \item блок \texttt{SELECT-FROM-WHERE} превращается в проекцию над селекцией + над декартовым произведением соединяемых таблиц; + \item конструкция \texttt{GROUP BY} порождает оператор группировки; + \item \texttt{ORDER BY} --- запрос на требуемое физическое свойство + сортированности; + \item вложенные подзапросы --- оператор Apply, который впоследствии может быть + удалён процедурой декорреляции. +\end{itemize} +Данное преобразование необходимо по нескольким причинам. Во-первых, реляционная +алгебра обладает формализованным набором правил эквивалентности, которые +позволяют оптимизатору корректно преобразовывать план запроса без изменения +семантики. Во-вторых, алгебраическое представление позволяет единообразно +обрабатывать различные синтаксические формы SQL-запросов, порождающие одинаковые +логические планы. Наконец, разделение на логические и физические операторы +позволяет оптимизатору независимо исследовать порядок операций и алгоритмы их +реализации. \section{Цель оптимизатора} Сформулируем задачу, которую решает оптимизатор: по запросу, представленному в форме дерева операторов реляционной алгебры, построить эквивалентный и оптимальный, с точки зрения необходимых для исполнения ресурсов, план -исполнения. Итоговый план является деревом, узлы которого помечены конкретными -указаниями на алгоритмы, реализующими один или несколько операторов реляционной -алгебры. Эквивалентность означает, что итоговый физический план и исходный -запрос при исполнении на любом наборе данных порождают одинаковые отношения с -точностью до требуемых свойств, таких как сортировка. +исполнения. Итоговый план является деревом, вершины которого помечены +конкретными указаниями на алгоритмы, реализующими один или несколько операторов +реляционной алгебры. Эквивалентность означает, что итоговый физический план и +исходный запрос при исполнении на любом наборе данных порождают одинаковые +отношения с точностью до требуемых свойств, таких как сортировка. Запрос в форме реляционной алгебры также называют логическим планом, а операторы реляционной алгебры --- логическими операторами. @@ -308,17 +335,27 @@ \section{Цель оптимизатора} операторов, примененных последовательно: доступ к данным и фильтрация по тому же предикату. -Эквивалентность планов обеспечивается свойствами реляционной алгебры. Например, -коммутативность и ассоциативность соединения: -\[ - R \bowtie S = S \bowtie R, \qquad (R \bowtie S) \bowtie T = R \bowtie (S \bowtie T). -\] +Эквивалентность планов обеспечивается свойствами реляционной алгебры. + +\begin{Example} + Коммутативность соединения: + \[ + R \bowtie S \equiv S \bowtie R. + \] +\end{Example} -Оптимальность запроса оценивается с помощью стоимостной функции +\begin{Example} + Ассоциативность соединения: + \[ + (R \bowtie S) \bowtie T = R \bowtie (S \bowtie T). + \] +\end{Example} + +Оптимальность запроса оценивается с помощью функции стоимости \(\mathrm{cost} \colon \mathcal{P}_L \to \mathbb{R}_{\ge 0}\), где \(\mathcal{P}_{L}\) --- множество эквивалентных физических планов. Это -отображение оценивает затраты на исполнение плана исходя из модели работы -операторов, а также основываясь на эвристиках и статистике о данных в +отображение оценивает затраты на исполнение плана исходя из конкретных +реализаций алгоритмов, а также основываясь на эвристиках и статистике о данных в отношениях. Другими словами, цель оптимизатора состоит в том, чтобы для входного запроса найти план \(P^* \in \mathcal{P}_L\), минимизирующий \(\mathrm{cost}\): \[ @@ -331,70 +368,79 @@ \section{Цель оптимизатора} \(C_k\) --- \(k\)-е число Каталана. Таким образом, пространство поиска растет экспоненциально с увеличением числа соединяемых таблиц. -{\color{red}: TODO: ссылка на источник} - -Поскольку полный перебор \(\mathcal{P}_L\) в общем случае невозможен из-за -экспоненциального роста числа альтернатив, на практике речь идёт о поиске плана -с приемлемо малой стоимостью в определенном подмножестве пространства поиска. -Именно эта задача и определяет архитектуру современных оптимизаторов: -расширяемое описание пространства поиска через правила преобразования, -эффективный алгоритм его исследования и стоимостная модель, позволяющая отсекать -заведомо неоптимальные ветви. - -Выбор плана исполнения запроса может оказывать кардинальное влияние на объем -требуемых вычислительных ресурсов. В качестве примера оценим количества -страничных чтений с диска для различных физических операторов. Пусть имеется три отношения: таблица -\texttt{customer} с \(|C| = 10^5\) строк, таблица \texttt{orders} с -\(|O| = 10^7\) строк и таблица \texttt{lineitem} с \(|L| = 5 \cdot 10^7\) -строк. Размер страницы --- \(8\) КБ; в одну страницу укладывается -\(100\) строк \texttt{customer}, \(80\) строк \texttt{orders} и \(50\) -строк \texttt{lineitem}. Соответственно, общее число страниц составляет +{\color{red}: TODO: ссылка на статью есть в лекциях Andy} + +Поскольку полный перебор \(\mathcal{P}_L\) для достаточно больших \(n\) +невозможен, на практике речь идёт о поиске плана с приемлемо малой стоимостью в +определенном подмножестве пространства поиска. Именно эта задача и определяет +архитектуру современных оптимизаторов: расширяемое описание пространства поиска +через правила преобразования, эффективный алгоритм его исследования и +стоимостная модель, позволяющая отсекать заведомо неоптимальные ветви. + +Выбор плана исполнения запроса может оказывать огромное влияние на объем +требуемых вычислительных ресурсов. В качестве примера оценим количество чтений +страниц с диска для различных физических операторов. Пусть имеется три +отношения: таблица \texttt{customer} с \(|C| = 10^5\) строк, таблица +\texttt{orders} с \(|O| = 10^7\) строк и таблица \texttt{lineitem} с +\(|L| = 5 \cdot 10^7\) строк. Размер страницы --- \(8\) КБ. В одну страницу +помещается \(100\) строк \texttt{customer}, \(80\) строк \texttt{orders} и +\(50\) строк \texttt{lineitem}. Соответственно, общее число страниц составляет \(P_C = 10^3\), \(P_O = 1{,}25 \cdot 10^5\), \(P_L = 10^6\). Запрос состоит в соединении трёх таблиц по внешним ключам с последующей агрегацией. Рассмотрим два альтернативных плана. +{\color{red} TODO: визуализация планов (tikz) в виде дерева с кардинальностью + отношений на ребрах} + \begin{itemize} \item План \(P_1\): \((\texttt{customer} \bowtie \texttt{orders}) \bowtie \texttt{lineitem}\) с реализацией обоих соединений через Hash Join, для чего сначала полностью считываются \(P_C + P_O = 1{,}26 \cdot 10^5\) страниц для построения хеш-таблицы, после чего промежуточный результат размером - \(10^7\) строк (равный \(|O|\)) соединяется с \texttt{lineitem}, требуя - считывания ещё \(P_L = 10^6\) страниц. В сумме --- порядка - \(1{,}126 \cdot 10^6\) операций чтения с диска. + \(10^7\) строк соединяется с \texttt{lineitem}, требуя считывания ещё % + \(P_L = 10^6\) страниц. В сумме --- порядка \(1{,}126 \cdot 10^6\) + операций чтения с диска. \item План \(P_2\): тот же запрос, но реализованный как соединение с помощью вложенных циклов без использования индексов: для каждой строки - \texttt{customer} полностью просматривается \texttt{orders}, для каждой + \texttt{customer} полностью читается таблица \texttt{orders}, для каждой результирующей пары --- \texttt{lineitem}. Совокупный объём ввода-вывода оценивается величиной \(P_C \cdot P_O \cdot P_L \approx 1{,}25 \cdot 10^{14}\) операций чтения, что на восемь порядков превышает \(P_1\). \end{itemize} -{\color{red} TODO: пример посложнее} +{\color{red} TODO: пример с логическими трансформациями, аналогично визуализация + планов (tikz). Адаптировать абзац ниже.} -Различие на восемь порядков иллюстрирует, что разница между удачным и неудачным -планами может составлять сотни миллионов раз. Если исполнение \(P_1\) на -современной системе занимает порядка секунд, то наивный \(P_2\) --- десятки лет -реального времени. Более того, если в плане \(P_1\) дополнительно использовать -индекс по внешнему ключу при соединении с \texttt{lineitem}, стоимость может -снизиться ещё в несколько раз. +Различие на восемь порядков в первом случае показывает, что разница между +удачным и неудачным планами может составлять сотни миллионов раз. Если +исполнение \(P_1\) на современной системе занимает порядка секунд, то наивный +\(P_2\) будет завершен через десятки лет. Более того, если в плане \(P_1\) +дополнительно использовать индекс по внешнему ключу при соединении с +\texttt{lineitem}, стоимость может снизиться ещё в несколько раз. Этот пример наглядно демонстрирует, что оптимизатор отвечает не за доли процентов производительности, а за принципиальную возможность исполнения сложных -аналитических запросов в приемлемое время. Именно поэтому разработка +аналитических запросов за приемлемое время. Именно поэтому разработка качественного оптимизатора занимает центральное место в проектировании любой реляционной СУБД. \section{Обзор архитектур оптимизаторов} +Рассмотрим наиболее популярные архитектуры оптимизаторов. + \subsection{System R} +{\color{red} TODO: ссылка на статью по System R. Есть в лекциях Andy} + Оптимизатор System~R, разработанный в IBM Research в конце 1970-х годов, стал первым стоимостным оптимизатором запросов и заложил фундаментальные принципы, используемые во всех последующих системах. +Архитектуру, аналогичную System~R, реализуют PostgreSQL и ранние версии IBM DB2. + Оптимизатор, построенный по архитектуре System~R, разбивает запрос на блоки, каждый из которых оптимизируется индивидуально. Блок запроса состоит из конструкции \texttt{SELECT}, конструкции \texttt{FROM}, ссылающейся на одну или @@ -434,18 +480,16 @@ \subsection{System R} Эти ограничения послужили мотивацией для создания расширяемых архитектур оптимизаторов. -Архитектуру, аналогичную System~R, используют PostgreSQL и ранние версии IBM -DB2. - \subsection{Volcano} -Volcano --- расширяемый, основанный на правилах, стоимостной -оптимизатор запросов, разработанный Грефе и Маккенной в 1993 году. -Volcano представил ряд концепций, ставших основой для последующих -расширяемых оптимизаторов: физические свойства выражений, -энфорсеры (обобщение интересных порядков System~R), структуру данных -Memo и нисходящий алгоритм поиска на основе динамического -программирования. +Volcano --- расширяемый, основанный на правилах, стоимостной оптимизатор +запросов, разработанный Грефе в 1993 году. Volcano ввел ряд концепций, ставших +основой для последующих расширяемых оптимизаторов: физические свойства +выражений, обеспечивающие правила, структуру данных Memo и нисходящий алгоритм +поиска на основе динамического программирования. + +{color{red} TODO: ссылка на статью (взять из лекций Andy). Добавить системы + использующие Volcano} Volcano разделяет поиск на две фазы: @@ -474,8 +518,6 @@ \subsection{Volcano} \section{Архитектура Cascades} -\subsection{Основные понятия Cascades} - Cascades --- это расширяемая архитектура оптимизатора запросов, являющаяся логическим продолжением идеи Volcano. @@ -499,19 +541,20 @@ \subsection{Основные понятия Cascades} заданное свойство. \end{itemize} -Важным принципом Cascades является то, что правила являются объектами. Они -определяются условиями применимости и собственно алгоритмом применения. -Предполагается, что система легко расширяется добавленимем новых правил, потому -что все правила наследуются от общего интерфейса. +Важным принципом данной архитектуры является то, что правила являются объектами +первого класса. Они определяются условиями применимости и собственно алгоритмом +применения. Предполагается, что система легко расширяется добавленимем новых +правил, потому что все правила наследуются от общего интерфейса и имеют +одинаковую структуру. -\subsection{Группы эквивалентности и групповые выражения} +\subsection{Группы эквивалентности и выражения} Группа в Memo представляет собой множество выражений, которые эквивалентны с -точки зрения логического результата. Например, для трех отношений \(A\), \(B\) и +точки зрения логического результата. Например, для отношений \(A\), \(B\) и \(C\) выражения \[ - A \bowtie (B \bowtie C) = (A \bowtie B) \bowtie C + A \bowtie (B \bowtie C) ; \qquad{} (A \bowtie B) \bowtie C \] при выполнении условий ассоциативности соединения принадлежат одной группе, @@ -532,24 +575,21 @@ \subsection{Структура Memo} фиксирует результаты оптимизации под разными физическими свойствами и обеспечивает точку синхронизации для задач поиска. -Структура группы Memo включает следующие поля: +Группа в Memo включает следующую информацию: \begin{itemize} - \item уникальный идентификатор группы; - \item список логических групповых выражений; - \item список физических групповых выражений; - \item логические свойства группы; - \item статистику и оценку кардинальности; - \item множество примененных правил, предотвращающее повторное применение - одного и того же правила к одному и тому же выражению; - \item состояние исследования группы. + \item уникальный идентификатор группы; + \item список логических выражений; + \item список физических выражений; + \item логические свойства группы; + \item статистику и оценку кардинальности; + \item состояние исследования группы. \end{itemize} -Групповое выражение, в свою очередь, содержит оператор, массив идентификаторов -дочерних групп, локальные свойства оператора, рассчитанную стоимость самого -оператора и служебные признаки. Для физического выражения дополнительно могут -храниться предоставляемые физические свойства, например порядок выходного потока -или способ распределения данных. +Выражение, в свою очередь, содержит сам оператор, массив идентификаторов +дочерних групп. Для физического выражения дополнительно могут храниться +предоставляемые физические свойства, например сортировка выходного потока, и +рассчитанная стоимость исполнения этого оператора. На рисунке~\ref{fig:memo-structure} показан пример Memo для запроса с тремя отношениями. Группа \(G_5\) представляет результат соединения трех отношений, а @@ -558,6 +598,7 @@ \subsection{Структура Memo} подпланы не дублируются. {\color{red} TODO: поправить схему} +{\color{red} TODO: привести сам запрос} \begin{figure}[H] \centering \begin{tikzpicture}[ @@ -585,25 +626,27 @@ \subsection{Структура Memo} \label{fig:memo-structure} \end{figure} -При добавлении нового выражения Memo выполняет нормализацию и проверку на -дубликаты. Если выражение уже существует в некоторой группе, оно не добавляется -повторно. Если выражение обнаруживает эквивалентность двух ранее различных -групп, группы могут быть объединены. Операция объединения требует аккуратного -обновления ссылок, таблиц победителей и состояния правил, поэтому в практической -реализации она является одной из наиболее сложных частей структуры Memo. +При добавлении нового выражения Memo выполняет проверку на дубликаты. Если +выражение уже существует в некоторой группе, оно не добавляется повторно. Если +алгоритм в процессе работы обнаруживает эквивалентность двух ранее различных +групп, то они могут быть объединены. При этом требуется обновление ссылок и +состояния правил. -\subsection{Задачи и взаимодействие компонентов поиска} +\subsection{Алгоритм поиска} Cascades организует оптимизацию как выполнение набора задач. Задача может исследовать группу, исследовать выражение, применить правило, реализовать логический оператор, оптимизировать дочернюю группу под заданное физическое свойство или построить вспомогательный оператор для обеспечения свойства. Такой подход позволяет гибко управлять порядком работы и откладывать дорогостоящие -действия до тех пор, пока они не станут необходимыми. +действия до тех пор, пока они не станут необходимыми, а также разбивать поиск на +несколько этапов и распараллеливать исполнение независимых веток. При этом +однопоточные реализации хранят задачи в обычной очереди. На рисунке~\ref{fig:cascades-tasks} показана схема взаимодействия задач в -оптимизаторе. Все подписи намеренно приведены на русском языке, поскольку схема -является частью русскоязычного отчета. +оптимизаторе. + +{\color{red} TODO: поправить рисунок} \begin{figure}[H] \centering @@ -639,17 +682,10 @@ \subsection{Задачи и взаимодействие компонентов \label{fig:cascades-tasks} \end{figure} -В практической реализации задачи могут храниться в стеке, очереди с приоритетом -или специализированном планировщике. Приоритет задачи определяется ожидаемой -полезностью, ограничением стоимости, глубиной поиска и типом правила. Например, -применение правила проталкивания предиката обычно имеет высокий приоритет, -поскольку оно может уменьшить кардинальности и тем самым усилить последующее -отсечение ветвей. +На листинге~\ref{alg:cascades-search} приведен псевдокод алгоритма поиска в +архитектуре Cascades. -\subsection{Псевдокод алгоритма поиска} - -Ниже приведен упрощенный псевдокод поиска физического плана в архитектуре -Cascades. +{color{red} TODO: поправить листинг, убрать в приложение} \begin{algorithm}[H] \caption{Top-down Cascades search} @@ -702,7 +738,303 @@ \subsection{Псевдокод алгоритма поиска} \end{algorithmic} \end{algorithm} +{color{red} TODO: добавить описание алгоритма} + +\section{Правила трансформации и реализации} + +Правило в оптимизаторе Cascades состоит из образца, условия применимости и +алгоритма применения. Образец описывает форму логического выражения, к которому +может быть применено правило. Условие применимости проверяет дополнительные +требования: отсутствие внешних ссылок, тип соединения, сохранение семантики +\texttt{NULL}, совместимость свойств и другие ограничения. + +Формально правило можно представить как отображение: + +\[ + r : pattern(e) \land condition(e) \rightarrow substitute(e), +\] + +где \(e\) --- исходное выражение, \(pattern\) --- структурный образец, +\(condition\) --- предикат применимости, а \(substitute\) --- выражение-замена. +Для правил трансформации результатом применения является новое логическое +выражение в группе, а для правил реализации --- физическое выражение в той же +группе. + +\subsection{Логические правила трансформации} + +Наиболее важные правила трансформации направлены на уменьшение промежуточных +кардинальностей, удаление лишних атрибутов и исследование новых порядков +выполнения соединения. + +Правило проталкивания предиката перемещает выборку ниже оператора соединения, +если предикат обращается только к атрибутам одного входа. Для внутреннего +соединения это правило можно записать следующим образом: + +\[ + \sigma_p(R \bowtie_q S) \Rightarrow (\sigma_p(R)) \bowtie_q S, + \quad attrs(p) \subseteq attrs(R). +\] + +Условие активации требует, чтобы предикат не зависел от атрибутов второго входа +и чтобы его перемещение не меняло семантику внешнего соединения. Для левого +внешнего соединения проталкивание предиката в правый вход может быть +некорректным, если предикат фильтрует строки, которые должны быть дополнены +значениями \texttt{NULL}. + +Правило проталкивания проекции уменьшает ширину промежуточных кортежей. Если +родительские операторы используют только часть атрибутов входа, оптимизатор +может удалить ненужные столбцы раньше, снизив объем памяти и стоимость передачи +данных. Условие активации требует сохранить все атрибуты, необходимые для +предикатов, соединений, группировок и последующих вычисляемых выражений. + +Правила коммутативности и ассоциативности соединений исследуют пространство +возможных порядков применения соединения: + +\[ + R \bowtie S \Rightarrow S \bowtie R, +\] + +\[ + R \bowtie (S \bowtie T) \Rightarrow (R \bowtie S) \bowtie T. +\] + +Эти правила безопасны для внутренних соединений при корректном переносе +предикатов, однако для внешних соединений их условия применимости значительно +сложнее. Поэтому промышленные системы часто используют специальные алгоритмы, +учитывающие зависимости в предикатах с помощью представлений в виде графов. + +Правило объединения выборок заменяет два последовательных фильтра одним с +конъюнкцией предикатов: + +\[ + \sigma_p(\sigma_q(R)) \Rightarrow \sigma_{p \land q}(R). +\] + +Такое преобразование упрощает дальнейший анализ селективности и позволяет +сопоставить составной предикат с составным индексом. Обратное правило также +может быть полезно, если оптимизатору нужно разнести конъюнкты по разным ветвям +плана. + +Правило удаления лишнего соединения применимо, если соединяемая таблица не +вносит атрибутов в результат, а ограничения ключей и внешних ключей гарантируют, +что соединение не изменяет множество строк. Например, соединение таблицы заказов +с таблицей клиентов может быть удалено, если результат использует только +атрибуты заказов, а ограничение внешнего ключа гарантирует существование +соответствующего клиента. + +{color{red}: дописать, мб примеры?} + +\subsection{Правила реализации} + +Правила реализации переводят логические операторы в физические. Например, для +логического оператора чтения таблицы могут быть применены правила: + +\begin{itemize} + \item \texttt{LogicalGet} \(\Rightarrow\) \texttt{SeqScan}; + \item \texttt{LogicalGet} \(\Rightarrow\) \texttt{IndexScan}, если имеется + индекс, совместимый с предикатом или требуемым порядком. +\end{itemize} + +Для логического соединения могут быть созданы физические операторы +\texttt{NestedLoopJoin}, \texttt{HashJoin} и \texttt{MergeJoin}. Хеш-соединение +обычно требует предиката равенства по ключам, поскольку именно по ним строится +хеш-таблица. Сортировочное соединение требует, чтобы оба входа были +отсортированы по ключам соединения, либо чтобы оптимизатор мог добавить +сортировки как обеспечивающие операторы. Вложенные циклы применимы всегда, но +стоимость этого оператора может быть высокой без доступа к внутреннему отношению +с использованием индекса. + +Правила реализации агрегации выбирают между хеш-агрегацией и потоковой +агрегацией. Хеш-агрегация не требует предварительного порядка, но потребляет +память пропорционально числу групп. Потоковая агрегация эффективна при уже +отсортированном входе, поскольку каждая группа обрабатывается последовательно. +Если требуемый порядок нужен также родительскому оператору, потоковая агрегация +может оказаться выгодной даже учитывая затраты на сортировку. + +{color{red} TODO: псевдокод алгоритмов} + +\subsection{Обеспечивающие операторы} + +Физические свойства в Cascades обрабатываются через требуемые и предоставляемые +свойства. Если родительский оператор требует вход, отсортированный по атрибуту +\(a\), а выбранный дочерний план такого порядка не предоставляет, оптимизатор +может добавить обеспечивающий оператор \texttt{Sort}. + +Обеспечивающие операторы не изменяют логический результат, но меняют физические +свойства и увеличивают стоимость. Поэтому они рассматриваются наравне с другими +физическими альтернативами. Например, план с хеш-соединением и последующей +сортировкой может конкурировать с планом на основе соединения слиянием, которое +сохраняет порядок результата. + +\section{Метод ветвей и границ} + +Метод ветвей и границ используется для сокращения пространства поиска и, как +следствие, неасимптотическим улучшением скорости работы алгоритма. В контексте +Cascades ветвями являются альтернативные выражения и комбинации физических +реализаций дочерних групп, а границей является текущая лучшая стоимость плана +для заданной группы и требуемых физических свойств. Если нижняя оценка стоимости +частично построенного плана уже превышает текущую лучшую, дальнейшее +исследование по этой ветке не имеет смысла. + +Пусть для группы \(G\) и требуемого свойства \(P\) уже найден план стоимости +\(C^*\). Рассматривается физическое выражение \(e\), имеющее локальную стоимость +\(C_{local}(e)\) и дочерние группы \(G_1, \ldots, G_k\). Если даже минимально +возможная оценка дочерних групп приводит к неравенству + +\[ + C_{local}(e) + \sum_{i=1}^{k} LB(G_i, P_i) \geq C^*, +\] + +то выражение \(e\) может быть отсечено. Здесь \(LB\) обозначает нижнюю границу +стоимости оптимизации дочерней группы под требуемым свойством \(P_i\). Чем +точнее нижняя граница, тем эффективнее отсечение. + +\subsection{Оценка кардинальности} + +Оценка кардинальности является процессом предсказания числа кортежей, +возвращаемых оператором. + +Для выборки используется селективность предиката \(sel(p)\), после чего +кардинальность результата оценивается как + +\[ + |\sigma_p(R)| = |R| \cdot sel(p). +\] + +Для соединения на основе предиката равенства независимых атрибутов часто +применяется эвристика: + +\[ + |R \bowtie_{R.a = S.b} S| \approx + \frac{|R| \cdot |S|}{\max(NDV(R.a), NDV(S.b))}, +\] + +где \(NDV\) --- число различных значений атрибута. Такая оценка удобна, но может +быть грубой, если данные имеют корреляции, сильный перекос или сложные +зависимости между атрибутами. Экспериментальные исследования показывают, что +ошибки кардинальности часто являются одной из основных причин выбора неудачных +планов~\cite{leis2015}. + +Для повышения качества оценок используются гистограммы, статистики по +многоколоночным ключам, сведения об уникальности, функциональные зависимости, +ограничения на схему, а в современных исследованиях также модели машинного +обучения. + +\subsection{Оценка стоимости} + +Функция стоимости сопоставляет физическому плану численную оценку затрат. В +простейшем случае стоимость можно представить как взвешенную сумму операций +чтения страниц, операций записи, вычислительных операций и сетевой передачи: + +\[ + Cost(p) = w_{io} \cdot IO(p) + w_{cpu} \cdot CPU(p) + + w_{mem} \cdot MEM(p) + w_{net} \cdot NET(p). +\] + +Коэффициенты \(w_{io}\), \(w_{cpu}\), \(w_{mem}\) и \(w_{net}\) зависят от +конкретной аппаратной конфигурации. + +Стоимость физического выражения обычно вычисляется рекурсивно: + +\[ + Cost(node) = LocalCost(node) + \sum_{child \in children(node)} Cost(child). +\] + +При этом локальная стоимость зависит от кардинальности входов и физических +свойств, а также конкретного физического оператора. + +{\color{red} TODO: табличка формул для разных операторов} + +\section{Дифференциальный анализ физических планов} + +Множество правил трансформации и реализации, используемых в +оптимизаторах запросов, изучено довольно хорошо и описано в +академической литературе. Вместе с тем условия активации правил +существенно различаются от системы к системе. Многие реализации +оптимизаторов упускают возможности для оптимизации из-за слишком +строгих условий активации правил или отсутствия определенных правил +в их наборе. + +{\color{red} TODO: почему нет формализации?} + +Данная проблема особенно актуальна при разработке нового оптимизатора: после +реализации базового набора правил возникает вопрос о полноте и корректности +этого набора по сравнению с устоявшимися и проверенными временем промышленными +системами. Такие системы аккумулировали обратную связь от пользователей и +уточняли условия активации своих правил в течение многих лет. Не имея такого +преимущества сложно добиться отличного качества построенных планов. + +\subsection{Предлагаемый метод} + +Для поиска несоответствий между поведением реализуемой системы и внешней +эталонной системы предлагается применить дифференциальный анализ планов. + +Первым шагом нужно сформировать набор данных состоящий из схем таблиц и данных. +Далее, по заданным схемам генерируются случайные SQL-запросы различной структуры +и сложности. Такой подход исключает тривиальные планы и приближает условия +работы оптимизатора к реальным нагрузкам при дальнейшем построении планов +запросов. + +Следующим шагом сгенерированные запросы транспилируются в диалекты SQL целевых +промышленных СУБД: Oracle, Microsoft SQL Server, PostgreSQL и т.д. Затем для +получения физических планов исполняется аналог \texttt{EXPLAIN ANALYZE}. + +Полученные физические планы промышленных СУБД конвертируются в формат +сериализованного плана разрабатываемого оптимизатора. Для этого необходимо +создать набор отображений между физическими операторами различных систем. +Например, \texttt{Hash Match} в SQL Server отображается в \texttt{HashJoin} в +разрабатываемой системе, \texttt{Clustered Index Scan} --- в \texttt{IndexScan} +и так далее. + +Корректность конвертации плана можно проверить путем исполнения исходого плана +на внешней системе и полученного плана на разрабатываемой системе и сравнения +полученных результатов. + +Конвертированные планы сравниваются с планами, порождаемыми разрабатываемым +оптимизатором на исходных запросах, с целью определить, мог ли существующий +набор правил разрабатываемого оптимизатора породить план, эквивалентный плану +промышленной системы. Для корректности сравнения необходимо полное исследование +пространства эквивалентных планов. Если разрабатываемая система не может +породить ``эталонный'' план, то условия активации или набор правил требует +уточнения. Определение требуемых изменений лежит на разработчике системы, +поэтому найденный запрос и примеры планов сохраняются для последующего анализа. +В дальнейшем, полученные после конвертации ``эталонные'' планы можно +использовать как тесты для оптимизатора, которые будут успешно выполняться после +уточнения соответвующих правил активации. + +\subsection{Поиск ближайшего плана} + +В случае невозможности построить ``эталонный'' план разрабатываемым +оптимизатором, предлагается искать ближайший план, т.е. план из пространства +поиска разрабатываемого оптимизатора, максимально близкий к плану промышленной +системы. При этом метрика расстояния между планами может быть определена как +расстояние редактирования деревьев. + +Анализ ближайшего плана может помочь упростить выявление трансформаций, которые +разрабатываемый план по какой-то причине не применил. + +\subsection{Область применения} + +Предлагаемый метод дифференциального анализа применим не только для сравнения с +проприетарными системами, но и с СУБД с открытым исходным кодом, позволяя +уточнять условия применения правил. + +Систематическое применение этого подхода позволяет построить итеративный процесс +совершенствования оптимизатора: + +\begin{itemize} + \item генерация запросов; + \item запуск и сбор планов; + \item приведение к единому виду, сравнение и выявление расхождений; + \item добавление или корректировка правил; + \item повторное сравнение. +\end{itemize} + +Таким образом, дифференциальный анализ обеспечивает управляемое и измеримое +улучшение качества оптимизатора и позволяет новым или открытым оптимизаторам +быстрее достигать качества устоявшихся промышленных оптимизаторов. +{\color{red} TODO: описать как нормализовать и т.п.} \renewcommand\refname{СПИСОК ИСПОЛЬЗОВАННЫХ ИСТОЧНИКОВ} \clearpage From ea46c0e2f8cf2146275a3016cc501ba93a122e09 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Tue, 5 May 2026 01:25:41 +0300 Subject: [PATCH 015/120] Add pdf --- report/thesis.pdf | Bin 0 -> 573217 bytes report/thesis.tex | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) create mode 100644 report/thesis.pdf diff --git a/report/thesis.pdf b/report/thesis.pdf new file mode 100644 index 0000000000000000000000000000000000000000..c5566253fd684f5173cb6d41d3f1ea5bbc35f367 GIT binary patch literal 573217 zcmeFYWmH?;x;9*;Ek)W=q!eqRxCQr=QrwCLr$~U{4nZqO@fHdcx1zxvf|cMF+ylkk zT@t>~{p|BT@AsZD&iS+Vubq*Ql{MFzbKY{@x_I^OtrQ!8ogepA|8VO#E`WxU#?IIh zS40GtLji1S?rcFL0N|(j#|xK33S#XHcBJ8uvNm!CzXhAvnSyb}#BiOQ9l=I6xbBlS z8j5!FoR8{PK<0k>gTC%mf&ahVhlmjD>*xsg`w=*T-=%hy23*EUGBHYoPHE@j}oQG1xR{(>i 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zk_bI7NyC&)aK0LsCV@X5wM#D3+R5-C>J?i1{FdP=tsN98QBo2-6+zPLw07L2L~qdA z_qzt9BnTB>x89_+Wo*DEizGg3pZyl?EpU>3@X%2S*mXImfDQ1F05>h?A%%5+$e;k0 z(>-8+n`=-BMPRw*DyW84sDWDG-OBsG5_S#hp#ki3X@n+Nfo5m{cCFkBZLkdOPy)8N zydMrg2Xw+gC_@)?!y)K_)6feO&4j{1i zfxQoGdtloGJ095az-EUw$v%A;pZmqI`ybz0>$?hNZe(+Ga%Ev{3T1AWZMX^#4K+70 NIW;y4B_%~qMha(@n05dF diff --git a/report/thesis.tex b/report/thesis.tex index b3315ea..c1ef71f 100644 --- a/report/thesis.tex +++ b/report/thesis.tex @@ -822,7 +822,22 @@ \subsection{Алгоритм поиска} однопоточные реализации хранят задачи в обычной очереди. На рисунке~\ref{fig:cascades-tasks} показана схема взаимодействия задач в -оптимизаторе. +оптимизаторе, а на +листингах~\ref{alg:cascades-search}-\ref{alg:cascades-search-3} приведен +псевдокод алгоритма поиска в архитектуре Cascades~\cite{DingNarasayya2024}. + +Алгоритм реализуется в виде управляемого стеком поиска в пространстве +эквивалентных планов: сначала запускается оптимизация корневой группы, +\textsc{OptimizeGroup} в свою очередь исследует группу, а затем оптимизирует +каждое ее логическое выражение. Исследование выполняется через +\textsc{ExploreGroup} и \textsc{ExploreExpression}, которые сохраняют в Memo +новые логические выражения и группы с помощью правил преобразования. После этого +\textsc{OptimizeExpression} применяет правила реализации, превращая логические +выражения в физические. Для каждого физического варианта +\textsc{ApplyImplementation} проверяет локальную стоимость и передает план в +\textsc{OptimizeInputs}, где последовательно оптимизируются дочерние группы, +накапливается полная стоимость и обновляется информация о лучшем плане. Параметр +\texttt{limit} используется для отсечения заведомо дорогих вариантов. \begin{figure}[H] \centering @@ -831,10 +846,6 @@ \subsection{Алгоритм поиска} \label{fig:cascades-tasks} \end{figure} -На листингах~\ref{alg:cascades-search}-\ref{alg:cascades-search-3} приведен псевдокод алгоритма поиска в -архитектуре Cascades. - - \section{Правила трансформации и реализации} Правило в оптимизаторе Cascades состоит из образца, условия применимости и @@ -855,7 +866,7 @@ \section{Правила трансформации и реализации} выражение в группе, а для правил реализации --- физическое выражение в той же группе. -\subsection{Логические правила трансформации} +\subsection{Правила трансформации} Наиболее важные правила трансформации направлены на уменьшение промежуточных кардинальностей, удаление лишних атрибутов и исследование новых порядков @@ -917,7 +928,8 @@ \subsection{Логические правила трансформации} атрибуты заказов, а ограничение внешнего ключа гарантирует существование соответствующего клиента. -{\color{red} TODO: дописать, мб примеры?} +{\color{red} TODO: вообще плохо написан раздел про трансформации. Расширить и + более четко описать} \subsection{Правила реализации} From 470d60e60057652e0c67c37b3116f516d5463713 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Thu, 7 May 2026 19:23:40 +0300 Subject: [PATCH 020/120] Fix --- report/thesis.pdf | Bin 702601 -> 720779 bytes report/thesis.tex | 266 ++++++++++++++++++++++++++++++++++++++-------- 2 files changed, 224 insertions(+), 42 deletions(-) diff --git a/report/thesis.pdf b/report/thesis.pdf index fe3913a016f2138fd34ee6ff852a04458b96ab1a..c2c815f89219d33f57f495c0c64233b5a7a548b7 100644 GIT binary patch delta 131789 zcmZs?Q*FbaRJ8IQ#J}dnj z2E8uiDuLz-;qn)K8bZ6z85QCV@Tky-%)(Pz?Fvo{P%ZTxzOnXsFX4$OF_9Kmg`(K^0nYCD#V;?Bg(}kBA;98aHfv+ zp??iR{w#TPp_QA!1j>NkOHIQN;EadJjJRkT1?ZI#g{^-!>W+p0D4!l=SL>|ZS-=Wv zE^%9Q3fOJsMoQt+e~p{;rhKIBTO8f10J8{_C4up9@UvH^bZ6brkqJgQOtdM^m`N+N zgCblW>vXx#OH3RqV8}uNE{zMs1eXI?FOpIL4F2yPvD%9ziy`?%z^kbn0EWL^f~Th9 zsnP;(7HZ4_Fa5;PttoU4BkoGT7JlogNF5kZ%AQk7&rHsHE`A$ce0$kxWVx$((sKd< 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zArF-iQbodFV?tf8A%GwIQbHjD`LA83W=>G59!EMCdvjTC^j^T_)L|coTS&Q>kd6tjs0bln7K!RMNk;mk zDP>&)pCZZOe*wjpU&@Dlt<4k-<)eoQ+B2GC8?m}M%Dv7Cz-WSW%bT6rREjv2k?|RQ zOTJe^;$DY~$KZSEfXH!|fGANdVIbj9m-;7Jnz$N>GGj=&{du|9t)&)P)iWfZcf(PpT-EwOcRW8^kY46#WbGrGR=O5Ufv-e^g} zr@fvrOGc-8VbJR~d~9GpIKwk^+<6(lLPMow9oBwS(Tp^>>qxex`CaSsU9UaU66?=! zHi3lm6mX+`WhW^QOIQ>KMbb&%qG86jQ&wtbe?66(tJAAA(Wn=SsT4_Y77D-~%Frl@ zd7+IZBj`LGr9S8geKEce>tXZy{y+MPxudBqoU5COi`!2Z3rjd|7FJeHPB= Date: Sat, 9 May 2026 18:51:39 +0300 Subject: [PATCH 021/120] Impl building optimal plan --- include/stewkk/sql/logic/executor/plan.hpp | 55 ++++++++++ .../stewkk/sql/models/parser/expression.hpp | 78 ++++++++++++++ .../stewkk/sql/models/parser/join_type.hpp | 16 +++ .../models/parser/relational_algebra_ast.hpp | 80 +------------- src/stewkk/sql/CMakeLists.txt | 3 + src/stewkk/sql/logic/executor/plan.cpp | 21 ++++ .../sql/logic/optimizer/optimizer_test.cpp | 50 ++++++++- src/stewkk/sql/models/parser/expression.cpp | 95 ++++++++++++++++ src/stewkk/sql/models/parser/join_type.cpp | 18 ++++ .../models/parser/relational_algebra_ast.cpp | 101 ------------------ 10 files changed, 335 insertions(+), 182 deletions(-) create mode 100644 include/stewkk/sql/logic/executor/plan.hpp create mode 100644 include/stewkk/sql/models/parser/expression.hpp create mode 100644 include/stewkk/sql/models/parser/join_type.hpp create mode 100644 src/stewkk/sql/logic/executor/plan.cpp create mode 100644 src/stewkk/sql/models/parser/expression.cpp create mode 100644 src/stewkk/sql/models/parser/join_type.cpp diff --git a/include/stewkk/sql/logic/executor/plan.hpp b/include/stewkk/sql/logic/executor/plan.hpp new file mode 100644 index 0000000..6d84d9e --- /dev/null +++ b/include/stewkk/sql/logic/executor/plan.hpp @@ -0,0 +1,55 @@ +#include +#include +#include + +#include +#include +#include + +namespace stewkk::sql { + +struct SeqScan; +struct PhysicalProjection; +struct PhysicalFilter; +struct NestedLoopJoin; +struct NestedLoopCrossJoin; + +using PhysicalPlanNode = std::variant; + +struct SeqScan { + std::string table; + + bool operator==(const SeqScan&) const = default; +}; + +struct PhysicalProjection { + std::shared_ptr source; + std::vector expressions; + + bool operator==(const PhysicalProjection&) const; +}; + +struct PhysicalFilter { + std::shared_ptr source; + Expression predicate; + + bool operator==(const PhysicalFilter&) const; +}; + +struct NestedLoopJoin { + std::shared_ptr lhs; + std::shared_ptr rhs; + JoinType type; + Expression qual; + + bool operator==(const NestedLoopJoin&) const; +}; + +struct NestedLoopCrossJoin { + std::shared_ptr lhs; + std::shared_ptr rhs; + + bool operator==(const NestedLoopCrossJoin&) const; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/models/parser/expression.hpp b/include/stewkk/sql/models/parser/expression.hpp new file mode 100644 index 0000000..21006f9 --- /dev/null +++ b/include/stewkk/sql/models/parser/expression.hpp @@ -0,0 +1,78 @@ +#pragma once + +#include +#include +#include +#include + +namespace stewkk::sql { + +struct Attribute { + std::string table; + std::string name; + + auto operator<=>(const Attribute& other) const = default; +}; + +std::string ToString(const Attribute& attr); + +using IntConst = std::int64_t; + +enum class Literal { + kNull, + kTrue, + kFalse, + kUnknown, +}; + +std::string ToString(Literal literal); + +enum class BinaryOp { + kGt, + kLt, + kLe, + kGe, + kNotEq, + kEq, + kOr, + kAnd, + kPlus, + kMinus, + kMul, + kDiv, + kMod, + kPow, +}; + +std::string ToString(BinaryOp binop); + +enum class UnaryOp { + kNot, + kMinus, +}; + +std::string ToString(UnaryOp op); + +struct BinaryExpression; +struct UnaryExpression; + +using Expression = std::variant; + +std::string ToString(const Expression& expr); + +struct BinaryExpression { + std::shared_ptr lhs; + BinaryOp binop; + std::shared_ptr rhs; + + bool operator==(const BinaryExpression& other) const; +}; + +struct UnaryExpression { + UnaryOp op; + std::shared_ptr child; + + bool operator==(const UnaryExpression& other) const; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/models/parser/join_type.hpp b/include/stewkk/sql/models/parser/join_type.hpp new file mode 100644 index 0000000..c0eeae9 --- /dev/null +++ b/include/stewkk/sql/models/parser/join_type.hpp @@ -0,0 +1,16 @@ +#pragma once + +#include + +namespace stewkk::sql { + +enum class JoinType { + kInner, + kFull, + kLeft, + kRight, +}; + +std::string ToString(JoinType type); + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/models/parser/relational_algebra_ast.hpp b/include/stewkk/sql/models/parser/relational_algebra_ast.hpp index 0f8ea9d..8c66a27 100644 --- a/include/stewkk/sql/models/parser/relational_algebra_ast.hpp +++ b/include/stewkk/sql/models/parser/relational_algebra_ast.hpp @@ -5,21 +5,13 @@ #include #include +#include +#include + namespace stewkk::sql { constexpr static std::string kEmptyTableName = "_EMPTY_TABLE_"; -struct Attribute { - std::string table; - std::string name; - - auto operator<=>(const Attribute& other) const = default; -}; - -std::string ToString(const Attribute& attr); - -using IntConst = std::int64_t; - struct Table; struct Projection; struct Filter; @@ -34,22 +26,6 @@ struct Table { auto operator<=>(const Table& other) const = default; }; -struct BinaryExpression; -struct UnaryExpression; - -enum class Literal { - kNull, - kTrue, - kFalse, - kUnknown, -}; - -std::string ToString(Literal literal); - -using Expression = std::variant; - -std::string ToString(const Expression& expr); - struct Projection { std::vector expressions; std::shared_ptr source; @@ -57,47 +33,6 @@ struct Projection { bool operator==(const Projection& other) const; }; -enum class BinaryOp { - kGt, - kLt, - kLe, - kGe, - kNotEq, - kEq, - kOr, - kAnd, - kPlus, - kMinus, - kMul, - kDiv, - kMod, - kPow, -}; - -std::string ToString(BinaryOp binop); - -struct BinaryExpression { - std::shared_ptr lhs; - BinaryOp binop; - std::shared_ptr rhs; - - bool operator==(const BinaryExpression& other) const; -}; - -enum class UnaryOp { - kNot, - kMinus, -}; - -std::string ToString(UnaryOp op); - -struct UnaryExpression { - UnaryOp op; - std::shared_ptr child; - - bool operator==(const UnaryExpression& other) const; -}; - struct Filter { Expression expr; std::shared_ptr source; @@ -112,15 +47,6 @@ struct CrossJoin { bool operator==(const CrossJoin& other) const; }; -enum class JoinType { - kInner, - kFull, - kLeft, - kRight, -}; - -std::string ToString(JoinType type); - struct Join { JoinType type; Expression qual; diff --git a/src/stewkk/sql/CMakeLists.txt b/src/stewkk/sql/CMakeLists.txt index f9b2e17..1bfa039 100644 --- a/src/stewkk/sql/CMakeLists.txt +++ b/src/stewkk/sql/CMakeLists.txt @@ -15,9 +15,12 @@ add_library(libsql logic/parser/PostgreSQLParserBase.cpp logic/parser/parser.cpp logic/parser/visitor.cpp + models/parser/expression.cpp + models/parser/join_type.cpp models/parser/relational_algebra_ast.cpp logic/result/error.cpp logic/result/result.cpp + logic/executor/plan.cpp logic/executor/executor.cpp logic/executor/sequential_scan.cpp models/executor/tuple.cpp diff --git a/src/stewkk/sql/logic/executor/plan.cpp b/src/stewkk/sql/logic/executor/plan.cpp new file mode 100644 index 0000000..3649726 --- /dev/null +++ b/src/stewkk/sql/logic/executor/plan.cpp @@ -0,0 +1,21 @@ +#include + +namespace stewkk::sql { + +bool PhysicalProjection::operator==(const PhysicalProjection& other) const { + return *source == *other.source && expressions == other.expressions; +} + +bool PhysicalFilter::operator==(const PhysicalFilter& other) const { + return *source == *other.source && predicate == other.predicate; +} + +bool NestedLoopJoin::operator==(const NestedLoopJoin& other) const { + return *lhs == *other.lhs && *rhs == *other.rhs && type == other.type && qual == other.qual; +} + +bool NestedLoopCrossJoin::operator==(const NestedLoopCrossJoin& other) const { + return *lhs == *other.lhs && *rhs == *other.rhs; +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index 1119055..ba26055 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -13,6 +13,7 @@ #include #include #include +#include using ::testing::Eq; @@ -255,10 +256,52 @@ class Optimizer { }); } } + + PhysicalPlanNode BuildOptimalPlan(Group* group) { + auto best_expr = best_plan_[group]; + if (!best_expr) { + throw std::runtime_error{"no optimal plan for group"}; + } + return std::visit( + utils::Overloaded{ + [](const physical::SeqScan& op) -> PhysicalPlanNode { + return SeqScan{ + .table = op.table, + }; + }, + [this](const physical::Projection& op) -> PhysicalPlanNode { + return PhysicalProjection{ + .source + = std::make_shared(BuildOptimalPlan(op.source.get())), + .expressions = op.expressions, + }; + }, + [this](const physical::Filter& op) -> PhysicalPlanNode { + return PhysicalFilter{ + .source = std::make_shared(BuildOptimalPlan(op.source.get())), + .predicate = op.predicate, + }; + }, + [this](const physical::NestedLoopJoin& op) -> PhysicalPlanNode { + return NestedLoopJoin{ + .lhs = std::make_shared(BuildOptimalPlan(op.lhs.get())), + .rhs = std::make_shared(BuildOptimalPlan(op.rhs.get())), + .type = op.type, + .qual = op.qual, + }; + }, + [this](const physical::NestedLoopCrossJoin& op) -> PhysicalPlanNode { + return NestedLoopCrossJoin{ + .lhs = std::make_shared(BuildOptimalPlan(op.lhs.get())), + .rhs = std::make_shared(BuildOptimalPlan(op.rhs.get())), + }; + }, + }, + best_expr->root_operator); + } public: - // FIXME: сделать генерацию лучшего плана в виде просто дерева. - PhysicalExpr* Optimize() { + PhysicalPlanNode Optimize() { tasks_.emplace([this]() { OptimizeGroup(root_->group); }); @@ -267,8 +310,7 @@ class Optimizer { tasks_.pop(); next_task(); } - auto it = best_plan_.find(root_->group.get()); - return it != best_plan_.end() ? it->second : nullptr; + return BuildOptimalPlan(root_->group.get()); } private: diff --git a/src/stewkk/sql/models/parser/expression.cpp b/src/stewkk/sql/models/parser/expression.cpp new file mode 100644 index 0000000..090ccb7 --- /dev/null +++ b/src/stewkk/sql/models/parser/expression.cpp @@ -0,0 +1,95 @@ +#include + +#include + +namespace stewkk::sql { + +bool BinaryExpression::operator==(const BinaryExpression& other) const { + return *lhs == *other.lhs && binop == other.binop && *rhs == *other.rhs; +} + +bool UnaryExpression::operator==(const UnaryExpression& other) const { + return op == other.op && *child == *other.child; +} + +std::string ToString(const Attribute& attr) { + return std::format("{}.{}", attr.table, attr.name); +} + +std::string ToString(Literal literal) { + switch (literal) { + case Literal::kNull: + return "NULL"; + case Literal::kTrue: + return "TRUE"; + case Literal::kFalse: + return "FALSE"; + case Literal::kUnknown: + return "UNKNOWN"; + } +} + +std::string ToString(BinaryOp binop) { + switch (binop) { + case BinaryOp::kGt: + return ">"; + case BinaryOp::kOr: + return "or"; + case BinaryOp::kAnd: + return "and"; + case BinaryOp::kEq: + return "="; + case BinaryOp::kPlus: + return "+"; + case BinaryOp::kMinus: + return "-"; + case BinaryOp::kMul: + return "*"; + case BinaryOp::kDiv: + return "/"; + case BinaryOp::kMod: + return "%"; + case BinaryOp::kPow: + return "^"; + case BinaryOp::kLt: + return "<"; + case BinaryOp::kLe: + return "<="; + case BinaryOp::kGe: + return ">="; + case BinaryOp::kNotEq: + return "!="; + } +} + +std::string ToString(UnaryOp op) { + switch (op) { + case UnaryOp::kNot: + return "not"; + case UnaryOp::kMinus: + return "-"; + } +} + +std::string ToString(const Expression& expr) { + struct Formatter { + std::string operator()(const BinaryExpression& expr) { + return std::format("{} {} {}", ToString(*expr.lhs), ToString(expr.binop), ToString(*expr.rhs)); + } + std::string operator()(const Attribute& expr) { + return ToString(expr); + } + std::string operator()(const IntConst& expr) { + return std::to_string(expr); + } + std::string operator()(const UnaryExpression& expr) { + return std::format("{} {}", ToString(expr.op), ToString(*expr.child)); + } + std::string operator()(const Literal& expr) { + return ToString(expr); + } + }; + return std::visit(Formatter{}, expr); +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/models/parser/join_type.cpp b/src/stewkk/sql/models/parser/join_type.cpp new file mode 100644 index 0000000..9d47aff --- /dev/null +++ b/src/stewkk/sql/models/parser/join_type.cpp @@ -0,0 +1,18 @@ +#include + +namespace stewkk::sql { + +std::string ToString(JoinType type) { + switch (type) { + case JoinType::kInner: + return "⋈"; + case JoinType::kFull: + return "⟗"; + case JoinType::kLeft: + return "⟕"; + case JoinType::kRight: + return "⟖"; + } +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/models/parser/relational_algebra_ast.cpp b/src/stewkk/sql/models/parser/relational_algebra_ast.cpp index 1518f35..9fadfcf 100644 --- a/src/stewkk/sql/models/parser/relational_algebra_ast.cpp +++ b/src/stewkk/sql/models/parser/relational_algebra_ast.cpp @@ -10,14 +10,6 @@ bool Filter::operator==(const Filter& other) const { return expr == other.expr && *source == *other.source; } -bool BinaryExpression::operator==(const BinaryExpression& other) const { - return *lhs == *other.lhs && binop == other.binop && *rhs == *other.rhs; -} - -bool UnaryExpression::operator==(const UnaryExpression& other) const { - return op == other.op && *child == *other.child; -} - bool CrossJoin::operator==(const CrossJoin& other) const { return *lhs == *other.lhs && *rhs == *other.rhs; } @@ -26,97 +18,4 @@ bool Join::operator==(const Join& other) const { return type == other.type && qual == other.qual && *lhs == *other.lhs && *rhs == *other.rhs; } -std::string ToString(BinaryOp binop) { - switch (binop) { - case BinaryOp::kGt: - return ">"; - case BinaryOp::kOr: - return "or"; - case BinaryOp::kAnd: - return "and"; - case BinaryOp::kEq: - return "="; - case BinaryOp::kPlus: - return "+"; - case BinaryOp::kMinus: - return "-"; - case BinaryOp::kMul: - return "*"; - case BinaryOp::kDiv: - return "/"; - case BinaryOp::kMod: - return "%"; - case BinaryOp::kPow: - return "^"; - case BinaryOp::kLt: - return "<"; - case BinaryOp::kLe: - return "<="; - case BinaryOp::kGe: - return ">="; - case BinaryOp::kNotEq: - return "!="; - } -} - -std::string ToString(const Attribute& attr) { - return std::format("{}.{}", attr.table, attr.name); -} - -std::string ToString(UnaryOp op) { - switch (op) { - case UnaryOp::kNot: - return "not"; - case UnaryOp::kMinus: - return "-"; - } -} - -std::string ToString(Literal literal) { - switch (literal) { - case Literal::kNull: - return "NULL"; - case Literal::kTrue: - return "TRUE"; - case Literal::kFalse: - return "FALSE"; - case Literal::kUnknown: - return "UNKNOWN"; - } -} - -std::string ToString(const Expression& expr) { - struct Formatter { - std::string operator()(const BinaryExpression& expr) { - return std::format("{} {} {}", ToString(*expr.lhs), ToString(expr.binop), ToString(*expr.rhs)); - } - std::string operator()(const Attribute& expr) { - return ToString(expr); - } - std::string operator()(const IntConst& expr) { - return std::to_string(expr); - } - std::string operator()(const UnaryExpression& expr) { - return std::format("{} {}", ToString(expr.op), ToString(*expr.child)); - } - std::string operator()(const Literal& expr) { - return ToString(expr); - } - }; - return std::visit(Formatter{}, expr); -} - -std::string ToString(JoinType type) { - switch (type) { - case JoinType::kInner: - return "⋈"; - case JoinType::kFull: - return "⟗"; - case JoinType::kLeft: - return "⟕"; - case JoinType::kRight: - return "⟖"; - } -} - } // namespace stewkk::sql From 095da0a183e4223809eb77008ab7db233ada0b86 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sat, 9 May 2026 23:24:16 +0300 Subject: [PATCH 022/120] Impl plan serializer --- include/stewkk/sql/logic/executor/plan.hpp | 2 + .../sql/logic/executor/plan_serializer.hpp | 15 + src/stewkk/sql/CMakeLists.txt | 1 + .../sql/logic/executor/plan_serializer.cpp | 385 ++++++++++++++++++ .../logic/executor/plan_serializer_test.cpp | 180 ++++++++ .../sql/logic/optimizer/optimizer_test.cpp | 3 + test/CMakeLists.txt | 1 + 7 files changed, 587 insertions(+) create mode 100644 include/stewkk/sql/logic/executor/plan_serializer.hpp create mode 100644 src/stewkk/sql/logic/executor/plan_serializer.cpp create mode 100644 src/stewkk/sql/logic/executor/plan_serializer_test.cpp diff --git a/include/stewkk/sql/logic/executor/plan.hpp b/include/stewkk/sql/logic/executor/plan.hpp index 6d84d9e..a708649 100644 --- a/include/stewkk/sql/logic/executor/plan.hpp +++ b/include/stewkk/sql/logic/executor/plan.hpp @@ -1,3 +1,5 @@ +#pragma once + #include #include #include diff --git a/include/stewkk/sql/logic/executor/plan_serializer.hpp b/include/stewkk/sql/logic/executor/plan_serializer.hpp new file mode 100644 index 0000000..2c6c4e9 --- /dev/null +++ b/include/stewkk/sql/logic/executor/plan_serializer.hpp @@ -0,0 +1,15 @@ +#pragma once + +#include +#include + +#include + +namespace stewkk::sql { + +std::string Serialize(const PhysicalPlanNode& node); +PhysicalPlanNode Deserialize(std::string_view text); + +std::string SerializeDot(const PhysicalPlanNode& node); + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/CMakeLists.txt b/src/stewkk/sql/CMakeLists.txt index 1bfa039..102883f 100644 --- a/src/stewkk/sql/CMakeLists.txt +++ b/src/stewkk/sql/CMakeLists.txt @@ -21,6 +21,7 @@ add_library(libsql logic/result/error.cpp logic/result/result.cpp logic/executor/plan.cpp + logic/executor/plan_serializer.cpp logic/executor/executor.cpp logic/executor/sequential_scan.cpp models/executor/tuple.cpp diff --git a/src/stewkk/sql/logic/executor/plan_serializer.cpp b/src/stewkk/sql/logic/executor/plan_serializer.cpp new file mode 100644 index 0000000..3290d55 --- /dev/null +++ b/src/stewkk/sql/logic/executor/plan_serializer.cpp @@ -0,0 +1,385 @@ +#include + +#include +#include +#include +#include +#include +#include +#include +#include + +namespace stewkk::sql { + +namespace { + +std::string SerializeExpr(const Expression& expr); +std::string SerializeNode(const PhysicalPlanNode& node); + +std::string SerializeJoinType(JoinType t) { + switch (t) { + case JoinType::kInner: return "Inner"; + case JoinType::kFull: return "Full"; + case JoinType::kLeft: return "Left"; + case JoinType::kRight: return "Right"; + } +} + +std::string SerializeBinaryOp(BinaryOp op) { + switch (op) { + case BinaryOp::kGt: return ">"; + case BinaryOp::kLt: return "<"; + case BinaryOp::kLe: return "<="; + case BinaryOp::kGe: return ">="; + case BinaryOp::kNotEq: return "!="; + case BinaryOp::kEq: return "="; + case BinaryOp::kOr: return "or"; + case BinaryOp::kAnd: return "and"; + case BinaryOp::kPlus: return "+"; + case BinaryOp::kMinus: return "-"; + case BinaryOp::kMul: return "*"; + case BinaryOp::kDiv: return "/"; + case BinaryOp::kMod: return "%"; + case BinaryOp::kPow: return "^"; + } +} + +std::string SerializeUnaryOp(UnaryOp op) { + switch (op) { + case UnaryOp::kNot: return "not"; + case UnaryOp::kMinus: return "uminus"; + } +} + +std::string SerializeExpr(const Expression& expr) { + struct Visitor { + std::string operator()(const BinaryExpression& e) const { + return std::format("({} {} {})", SerializeBinaryOp(e.binop), + SerializeExpr(*e.lhs), SerializeExpr(*e.rhs)); + } + std::string operator()(const Attribute& a) const { + return std::format("(attr {} {})", a.table, a.name); + } + std::string operator()(IntConst n) const { + return std::to_string(n); + } + std::string operator()(const UnaryExpression& e) const { + return std::format("({} {})", SerializeUnaryOp(e.op), SerializeExpr(*e.child)); + } + std::string operator()(Literal l) const { + switch (l) { + case Literal::kNull: return "NULL"; + case Literal::kTrue: return "TRUE"; + case Literal::kFalse: return "FALSE"; + case Literal::kUnknown: return "UNKNOWN"; + } + } + }; + return std::visit(Visitor{}, expr); +} + +std::string SerializeNode(const PhysicalPlanNode& node) { + struct Visitor { + std::string operator()(const SeqScan& n) const { + return std::format("(SeqScan {})", n.table); + } + std::string operator()(const PhysicalFilter& n) const { + return std::format("(PhysicalFilter {} {})", + SerializeExpr(n.predicate), SerializeNode(*n.source)); + } + std::string operator()(const PhysicalProjection& n) const { + std::string exprs; + for (const auto& e : n.expressions) { + if (!exprs.empty()) exprs += ' '; + exprs += SerializeExpr(e); + } + return std::format("(PhysicalProjection (exprs {}) {})", + exprs, SerializeNode(*n.source)); + } + std::string operator()(const NestedLoopCrossJoin& n) const { + return std::format("(NestedLoopCrossJoin {} {})", + SerializeNode(*n.lhs), SerializeNode(*n.rhs)); + } + std::string operator()(const NestedLoopJoin& n) const { + return std::format("(NestedLoopJoin {} {} {} {})", + SerializeJoinType(n.type), SerializeExpr(n.qual), + SerializeNode(*n.lhs), SerializeNode(*n.rhs)); + } + }; + return std::visit(Visitor{}, node); +} + +enum class TokenKind { LParen, RParen, Atom }; + +struct Token { + TokenKind kind; + std::string text; +}; + +std::vector Tokenize(std::string_view input) { + std::vector tokens; + size_t i = 0; + while (i < input.size()) { + if (std::isspace(static_cast(input[i]))) { ++i; continue; } + if (input[i] == '(') { tokens.push_back({TokenKind::LParen, "("}); ++i; continue; } + if (input[i] == ')') { tokens.push_back({TokenKind::RParen, ")"}); ++i; continue; } + size_t j = i; + while (j < input.size() + && !std::isspace(static_cast(input[j])) + && input[j] != '(' && input[j] != ')') + ++j; + tokens.push_back({TokenKind::Atom, std::string(input.substr(i, j - i))}); + i = j; + } + return tokens; +} + +struct ParseState { + const std::vector& tokens; + size_t pos = 0; + + const Token& Peek() const { + if (pos >= tokens.size()) + throw std::runtime_error("unexpected end of input"); + return tokens[pos]; + } + + const Token& Consume() { + if (pos >= tokens.size()) + throw std::runtime_error("unexpected end of input"); + return tokens[pos++]; + } + + void ExpectLParen() { + const auto& t = Consume(); + if (t.kind != TokenKind::LParen) + throw std::runtime_error(std::format("expected '(' but got '{}'", t.text)); + } + + void ExpectRParen() { + const auto& t = Consume(); + if (t.kind != TokenKind::RParen) + throw std::runtime_error(std::format("expected ')' but got '{}'", t.text)); + } + + std::string ExpectAtom() { + const auto& t = Consume(); + if (t.kind != TokenKind::Atom) + throw std::runtime_error(std::format("expected atom but got '{}'", t.text)); + return t.text; + } +}; + +const std::unordered_map kBinaryOps = { + {">", BinaryOp::kGt}, {"<", BinaryOp::kLt}, {"<=", BinaryOp::kLe}, + {">=", BinaryOp::kGe}, {"!=", BinaryOp::kNotEq}, {"=", BinaryOp::kEq}, + {"or", BinaryOp::kOr}, {"and", BinaryOp::kAnd}, {"+", BinaryOp::kPlus}, + {"-", BinaryOp::kMinus}, {"*", BinaryOp::kMul}, {"/", BinaryOp::kDiv}, + {"%", BinaryOp::kMod}, {"^", BinaryOp::kPow}, +}; + +const std::unordered_map kUnaryOps = { + {"not", UnaryOp::kNot}, {"uminus", UnaryOp::kMinus}, +}; + +const std::unordered_map kJoinTypes = { + {"Inner", JoinType::kInner}, {"Full", JoinType::kFull}, + {"Left", JoinType::kLeft}, {"Right", JoinType::kRight}, +}; + +const std::unordered_map kLiterals = { + {"NULL", Literal::kNull}, {"TRUE", Literal::kTrue}, + {"FALSE", Literal::kFalse}, {"UNKNOWN", Literal::kUnknown}, +}; + +Expression ParseExpr(ParseState& s); +PhysicalPlanNode ParseNode(ParseState& s); + +Expression ParseExpr(ParseState& s) { + const auto& t = s.Peek(); + + if (t.kind == TokenKind::Atom) { + auto text = s.ExpectAtom(); + try { + size_t idx; + IntConst n = std::stoll(text, &idx); + if (idx == text.size()) return n; + } catch (...) {} + if (auto it = kLiterals.find(text); it != kLiterals.end()) return it->second; + throw std::runtime_error(std::format("unexpected atom in expression: '{}'", text)); + } + + if (t.kind == TokenKind::LParen) { + s.ExpectLParen(); + auto head = s.ExpectAtom(); + + if (head == "attr") { + auto table = s.ExpectAtom(); + auto name = s.ExpectAtom(); + s.ExpectRParen(); + return Attribute{std::move(table), std::move(name)}; + } + if (auto it = kBinaryOps.find(head); it != kBinaryOps.end()) { + auto lhs = ParseExpr(s); + auto rhs = ParseExpr(s); + s.ExpectRParen(); + return BinaryExpression{ + std::make_shared(std::move(lhs)), + it->second, + std::make_shared(std::move(rhs)), + }; + } + if (auto it = kUnaryOps.find(head); it != kUnaryOps.end()) { + auto child = ParseExpr(s); + s.ExpectRParen(); + return UnaryExpression{it->second, std::make_shared(std::move(child))}; + } + throw std::runtime_error(std::format("unknown expression head: '{}'", head)); + } + + throw std::runtime_error("expected expression"); +} + +PhysicalPlanNode ParseNode(ParseState& s) { + s.ExpectLParen(); + auto head = s.ExpectAtom(); + + if (head == "SeqScan") { + auto table = s.ExpectAtom(); + s.ExpectRParen(); + return SeqScan{std::move(table)}; + } + if (head == "PhysicalFilter") { + auto pred = ParseExpr(s); + auto source = ParseNode(s); + s.ExpectRParen(); + return PhysicalFilter{ + std::make_shared(std::move(source)), + std::move(pred), + }; + } + if (head == "PhysicalProjection") { + s.ExpectLParen(); + auto kw = s.ExpectAtom(); + if (kw != "exprs") + throw std::runtime_error(std::format("expected 'exprs' but got '{}'", kw)); + std::vector exprs; + while (s.Peek().kind != TokenKind::RParen) + exprs.push_back(ParseExpr(s)); + s.ExpectRParen(); + auto source = ParseNode(s); + s.ExpectRParen(); + return PhysicalProjection{ + std::make_shared(std::move(source)), + std::move(exprs), + }; + } + if (head == "NestedLoopCrossJoin") { + auto lhs = ParseNode(s); + auto rhs = ParseNode(s); + s.ExpectRParen(); + return NestedLoopCrossJoin{ + std::make_shared(std::move(lhs)), + std::make_shared(std::move(rhs)), + }; + } + if (head == "NestedLoopJoin") { + auto type_str = s.ExpectAtom(); + auto it = kJoinTypes.find(type_str); + if (it == kJoinTypes.end()) + throw std::runtime_error(std::format("unknown join type: '{}'", type_str)); + auto qual = ParseExpr(s); + auto lhs = ParseNode(s); + auto rhs = ParseNode(s); + s.ExpectRParen(); + return NestedLoopJoin{ + std::make_shared(std::move(lhs)), + std::make_shared(std::move(rhs)), + it->second, + std::move(qual), + }; + } + + throw std::runtime_error(std::format("unknown plan node: '{}'", head)); +} + +std::string EscapeLabel(std::string_view s) { + std::string out; + out.reserve(s.size()); + for (char c : s) { + if (c == '"' || c == '\\') out += '\\'; + out += c; + } + return out; +} + +struct DotBuilder { + std::ostringstream os; + int next_id = 0; + + int Emit(std::string_view label) { + int id = next_id++; + os << std::format(" n{} [label=\"{}\"]\n", id, EscapeLabel(label)); + return id; + } + + void EmitEdge(int from, int to) { + os << std::format(" n{} -> n{}\n", from, to); + } + + int operator()(const SeqScan& n) { + return Emit(std::format("SeqScan\\n{}", n.table)); + } + int operator()(const PhysicalFilter& n) { + int src = std::visit(*this, *n.source); + int id = Emit(std::format("σ {}", ToString(n.predicate))); + EmitEdge(src, id); + return id; + } + int operator()(const PhysicalProjection& n) { + int src = std::visit(*this, *n.source); + auto exprs = n.expressions + | std::views::transform([](const Expression& e) { return ToString(e); }) + | std::views::join_with(std::string(", ")) + | std::ranges::to(); + int id = Emit(std::format("π {}", exprs)); + EmitEdge(src, id); + return id; + } + int operator()(const NestedLoopCrossJoin& n) { + int lhs = std::visit(*this, *n.lhs); + int rhs = std::visit(*this, *n.rhs); + int id = Emit("×"); + EmitEdge(lhs, id); + EmitEdge(rhs, id); + return id; + } + int operator()(const NestedLoopJoin& n) { + int lhs = std::visit(*this, *n.lhs); + int rhs = std::visit(*this, *n.rhs); + int id = Emit(std::format("{}\\nON {}", ToString(n.type), ToString(n.qual))); + EmitEdge(lhs, id); + EmitEdge(rhs, id); + return id; + } +}; + +} // namespace + +std::string Serialize(const PhysicalPlanNode& node) { + return SerializeNode(node); +} + +PhysicalPlanNode Deserialize(std::string_view text) { + auto tokens = Tokenize(text); + ParseState s{tokens}; + return ParseNode(s); +} + +std::string SerializeDot(const PhysicalPlanNode& node) { + DotBuilder b; + std::visit(b, node); + return std::format("digraph G {{ rankdir=BT;\n{}}}\n", b.os.str()); +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/executor/plan_serializer_test.cpp b/src/stewkk/sql/logic/executor/plan_serializer_test.cpp new file mode 100644 index 0000000..6bc63da --- /dev/null +++ b/src/stewkk/sql/logic/executor/plan_serializer_test.cpp @@ -0,0 +1,180 @@ +#include + +#include + +using ::testing::Eq; + +namespace stewkk::sql { + +namespace { + +PhysicalPlanNode RoundTrip(const PhysicalPlanNode& node) { + return Deserialize(Serialize(node)); +} + +} // namespace + +TEST(PlanSerializerTest, SeqScan) { + PhysicalPlanNode plan = SeqScan{"users"}; + EXPECT_THAT(RoundTrip(plan), Eq(plan)); + EXPECT_THAT(Serialize(plan), Eq("(SeqScan users)")); +} + +TEST(PlanSerializerTest, PhysicalFilter) { + PhysicalPlanNode plan = PhysicalFilter{ + std::make_shared(SeqScan{"users"}), + BinaryExpression{ + std::make_shared(Attribute{"users", "age"}), + BinaryOp::kLt, + std::make_shared(IntConst{10}), + }, + }; + EXPECT_THAT(RoundTrip(plan), Eq(plan)); +} + +TEST(PlanSerializerTest, PhysicalProjection) { + PhysicalPlanNode plan = PhysicalProjection{ + std::make_shared(SeqScan{"users"}), + {Attribute{"users", "id"}, Attribute{"users", "age"}}, + }; + EXPECT_THAT(RoundTrip(plan), Eq(plan)); +} + +TEST(PlanSerializerTest, PhysicalProjectionEmpty) { + PhysicalPlanNode plan = PhysicalProjection{ + std::make_shared(SeqScan{"t"}), + {}, + }; + EXPECT_THAT(RoundTrip(plan), Eq(plan)); +} + +TEST(PlanSerializerTest, NestedLoopCrossJoin) { + PhysicalPlanNode plan = NestedLoopCrossJoin{ + std::make_shared(SeqScan{"users"}), + std::make_shared(SeqScan{"books"}), + }; + EXPECT_THAT(RoundTrip(plan), Eq(plan)); +} + +TEST(PlanSerializerTest, NestedLoopJoinInner) { + PhysicalPlanNode plan = NestedLoopJoin{ + std::make_shared(SeqScan{"a"}), + std::make_shared(SeqScan{"b"}), + JoinType::kInner, + BinaryExpression{ + std::make_shared(Attribute{"a", "x"}), + BinaryOp::kEq, + std::make_shared(Attribute{"b", "x"}), + }, + }; + EXPECT_THAT(RoundTrip(plan), Eq(plan)); +} + +TEST(PlanSerializerTest, NestedLoopJoinAllTypes) { + for (auto type : {JoinType::kInner, JoinType::kFull, JoinType::kLeft, JoinType::kRight}) { + PhysicalPlanNode plan = NestedLoopJoin{ + std::make_shared(SeqScan{"a"}), + std::make_shared(SeqScan{"b"}), + type, + Literal::kTrue, + }; + EXPECT_THAT(RoundTrip(plan), Eq(plan)); + } +} + +TEST(PlanSerializerTest, ExprIntConst) { + PhysicalPlanNode plan = PhysicalFilter{ + std::make_shared(SeqScan{"t"}), + IntConst{-42}, + }; + EXPECT_THAT(RoundTrip(plan), Eq(plan)); +} + +TEST(PlanSerializerTest, ExprLiterals) { + for (auto lit : {Literal::kNull, Literal::kTrue, Literal::kFalse, Literal::kUnknown}) { + PhysicalPlanNode plan = PhysicalFilter{ + std::make_shared(SeqScan{"t"}), + lit, + }; + EXPECT_THAT(RoundTrip(plan), Eq(plan)); + } +} + +TEST(PlanSerializerTest, ExprUnaryNot) { + PhysicalPlanNode plan = PhysicalFilter{ + std::make_shared(SeqScan{"t"}), + UnaryExpression{UnaryOp::kNot, std::make_shared(Attribute{"t", "x"})}, + }; + EXPECT_THAT(RoundTrip(plan), Eq(plan)); +} + +TEST(PlanSerializerTest, ExprUnaryMinus) { + PhysicalPlanNode plan = PhysicalFilter{ + std::make_shared(SeqScan{"t"}), + UnaryExpression{UnaryOp::kMinus, std::make_shared(IntConst{5})}, + }; + EXPECT_THAT(RoundTrip(plan), Eq(plan)); +} + +TEST(PlanSerializerTest, ExprAllBinaryOps) { + for (auto op : {BinaryOp::kGt, BinaryOp::kLt, BinaryOp::kLe, BinaryOp::kGe, + BinaryOp::kNotEq, BinaryOp::kEq, BinaryOp::kOr, BinaryOp::kAnd, + BinaryOp::kPlus, BinaryOp::kMinus, BinaryOp::kMul, BinaryOp::kDiv, + BinaryOp::kMod, BinaryOp::kPow}) { + PhysicalPlanNode plan = PhysicalFilter{ + std::make_shared(SeqScan{"t"}), + BinaryExpression{ + std::make_shared(IntConst{1}), + op, + std::make_shared(IntConst{2}), + }, + }; + EXPECT_THAT(RoundTrip(plan), Eq(plan)); + } +} + +TEST(PlanSerializerTest, DeepNestedPlan) { + PhysicalPlanNode plan = PhysicalProjection{ + std::make_shared(PhysicalFilter{ + std::make_shared(NestedLoopJoin{ + std::make_shared(SeqScan{"employees"}), + std::make_shared(SeqScan{"departments"}), + JoinType::kLeft, + BinaryExpression{ + std::make_shared(Attribute{"employees", "dept_id"}), + BinaryOp::kEq, + std::make_shared(Attribute{"departments", "id"}), + }, + }), + BinaryExpression{ + std::make_shared(Attribute{"departments", "id"}), + BinaryOp::kGt, + std::make_shared(IntConst{3}), + }, + }), + {Attribute{"employees", "id"}, Attribute{"departments", "id"}}, + }; + EXPECT_THAT(RoundTrip(plan), Eq(plan)); +} + +TEST(PlanSerializerDotTest, SmokeTest) { + PhysicalPlanNode plan = NestedLoopJoin{ + std::make_shared(SeqScan{"a"}), + std::make_shared(SeqScan{"b"}), + JoinType::kInner, + BinaryExpression{ + std::make_shared(Attribute{"a", "x"}), + BinaryOp::kEq, + std::make_shared(Attribute{"b", "x"}), + }, + }; + auto dot = SerializeDot(plan); + EXPECT_THAT(dot, ::testing::HasSubstr("digraph G")); + EXPECT_THAT(dot, ::testing::HasSubstr("rankdir=BT")); + EXPECT_THAT(dot, ::testing::HasSubstr("SeqScan")); + EXPECT_THAT(dot, ::testing::HasSubstr("ON a.x = b.x")); + EXPECT_THAT(dot, ::testing::HasSubstr("n0 -> n2")); + EXPECT_THAT(dot, ::testing::HasSubstr("n1 -> n2")); +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index ba26055..94636d0 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -14,6 +14,7 @@ #include #include #include +#include using ::testing::Eq; @@ -332,6 +333,8 @@ TEST(OptimizerTest, Simple) { Optimizer optimizer(op, MakeMainRules()); auto got = optimizer.Optimize(); + + ASSERT_THAT(SerializeDot(got), Eq("digraph G { rankdir=BT;\n n0 [label=\"SeqScan\\\\nusers\"]\n}\n")); } } // namespace stewkk::sql diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt index 0f6c674..f13e258 100644 --- a/test/CMakeLists.txt +++ b/test/CMakeLists.txt @@ -5,6 +5,7 @@ add_executable(unittests) target_sources(unittests PRIVATE ${PROJECT_SOURCE_DIR}/src/stewkk/sql/logic/parser/parser_test.cpp ${PROJECT_SOURCE_DIR}/src/stewkk/sql/logic/executor/executor_test.cpp + ${PROJECT_SOURCE_DIR}/src/stewkk/sql/logic/executor/plan_serializer_test.cpp ${PROJECT_SOURCE_DIR}/src/stewkk/sql/logic/optimizer/rule_test.cpp ${PROJECT_SOURCE_DIR}/src/stewkk/sql/logic/optimizer/rules_applier_test.cpp ${PROJECT_SOURCE_DIR}/src/stewkk/sql/logic/optimizer/optimizer_test.cpp From af7fe0d4a7290a0eea1938a15ada64933a86b07b Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 11 May 2026 14:16:53 +0300 Subject: [PATCH 023/120] Impl execution of physical plan --- benchmarks/main.cpp | 44 ++++++++- .../stewkk/sql/logic/executor/executor.hpp | 52 ++++++----- .../sql/logic/executor/materialization.hpp | 1 + src/stewkk/sql/logic/executor/executor.cpp | 2 +- .../sql/logic/executor/executor_test.cpp | 90 ++++++++++++++++--- .../sql/logic/executor/materialization.cpp | 5 ++ .../sql/logic/optimizer/optimizer_test.cpp | 29 ++++-- 7 files changed, 177 insertions(+), 46 deletions(-) diff --git a/benchmarks/main.cpp b/benchmarks/main.cpp index 4356209..0e2d2bd 100644 --- a/benchmarks/main.cpp +++ b/benchmarks/main.cpp @@ -11,9 +11,49 @@ #include #include #include +#include +#include namespace stewkk::sql { +namespace { + +PhysicalPlanNode ToPhysicalPlan(const Operator& op) { + return std::visit(utils::Overloaded{ + [](const Table& t) -> PhysicalPlanNode { + return SeqScan{.table = t.name}; + }, + [](const Projection& p) -> PhysicalPlanNode { + return PhysicalProjection{ + .source = std::make_shared(ToPhysicalPlan(*p.source)), + .expressions = p.expressions, + }; + }, + [](const Filter& f) -> PhysicalPlanNode { + return PhysicalFilter{ + .source = std::make_shared(ToPhysicalPlan(*f.source)), + .predicate = f.expr, + }; + }, + [](const CrossJoin& j) -> PhysicalPlanNode { + return NestedLoopCrossJoin{ + .lhs = std::make_shared(ToPhysicalPlan(*j.lhs)), + .rhs = std::make_shared(ToPhysicalPlan(*j.rhs)), + }; + }, + [](const Join& j) -> PhysicalPlanNode { + return NestedLoopJoin{ + .lhs = std::make_shared(ToPhysicalPlan(*j.lhs)), + .rhs = std::make_shared(ToPhysicalPlan(*j.rhs)), + .type = j.type, + .qual = j.qual, + }; + }, + }, op); +} + +} // namespace + const static std::string kProjectDir = std::getenv("PWD"); static constexpr char kSimpleSelectSmall[]{"SELECT users.id FROM users;"}; @@ -35,7 +75,7 @@ void BM_SQL(benchmark::State& state) { ctx, [&state]() -> boost::asio::awaitable { std::stringstream s{Query}; - Operator op = GetAST(s).value(); + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value()); CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; Executor executor(std::move(seq_scan), co_await boost::asio::this_coro::executor); @@ -88,7 +128,7 @@ void BM_SQL_Multithreaded(benchmark::State& state) { [&state]() -> boost::asio::awaitable { boost::asio::thread_pool pool{4}; std::stringstream s{Query}; - Operator op = GetAST(s).value(); + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value()); CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; Executor executor(std::move(seq_scan), pool.executor()); diff --git a/include/stewkk/sql/logic/executor/executor.hpp b/include/stewkk/sql/logic/executor/executor.hpp index 8cb3765..af2c88a 100644 --- a/include/stewkk/sql/logic/executor/executor.hpp +++ b/include/stewkk/sql/logic/executor/executor.hpp @@ -17,9 +17,11 @@ #include #include #include -#include +#include #include +// FIXME: add explicit instantiations and move implementations to .cpp + namespace stewkk::sql { using ExecExpression = std::function; @@ -54,20 +56,20 @@ class Executor { const std::string& table_name, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan)>; Executor(SequentialScan seq_scan, boost::asio::any_io_executor executor); - boost::asio::awaitable> Execute(const Operator& op); + boost::asio::awaitable> Execute(const PhysicalPlanNode& op); private: - boost::asio::awaitable Execute(const Operator& op, AttributesInfoChannel& attr_chan, + boost::asio::awaitable Execute(const PhysicalPlanNode& op, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan); - boost::asio::awaitable ExecuteProjection(const Projection& proj, AttributesInfoChannel& attr_chan, + boost::asio::awaitable ExecuteProjection(const PhysicalProjection& proj, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan); - boost::asio::awaitable ExecuteFilter(const Filter& filter, AttributesInfoChannel& attr_chan, + boost::asio::awaitable ExecuteFilter(const PhysicalFilter& filter, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan); - boost::asio::awaitable ExecuteCrossJoin(const CrossJoin& cross_join, + boost::asio::awaitable ExecuteCrossJoin(const NestedLoopCrossJoin& cross_join, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan); - boost::asio::awaitable ExecuteJoin(const Join& join, AttributesInfoChannel& attr_chan, + boost::asio::awaitable ExecuteJoin(const NestedLoopJoin& join, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan); - boost::asio::awaitable SpawnExecutor(const Operator& op, AttributesInfoChannel& attr_chan, TuplesChannel& tuple_chan); + boost::asio::awaitable SpawnExecutor(const PhysicalPlanNode& op, AttributesInfoChannel& attr_chan, TuplesChannel& tuple_chan); private: SequentialScan sequential_scan_; @@ -76,7 +78,7 @@ class Executor { Type GetExpressionType(const Expression& expr, const AttributesInfo& available_attrs); Type GetExpressionTypeUnchecked(const Expression& expr, const AttributesInfo& available_attrs); -AttributesInfo GetAttributesAfterProjection(const AttributesInfo& attrs, const Projection& proj); +AttributesInfo GetAttributesAfterProjection(const AttributesInfo& attrs, const PhysicalProjection& proj); template requires std::invocable @@ -118,7 +120,7 @@ Executor::Executor(SequentialScan seq_scan, boost::asio::any : sequential_scan_(std::move(seq_scan)), expression_executor_(executor) {} template -boost::asio::awaitable> Executor::Execute(const Operator& op) { +boost::asio::awaitable> Executor::Execute(const PhysicalPlanNode& op) { auto [attr_chan, tuples_chan] = co_await GetChannels(); co_await SpawnExecutor(op, attr_chan, tuples_chan); @@ -146,27 +148,27 @@ boost::asio::awaitable> Executor::Execute(c } template -boost::asio::awaitable Executor::Execute(const Operator& op, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan) { +boost::asio::awaitable Executor::Execute(const PhysicalPlanNode& op, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan) { struct ExecuteVisitor{ - boost::asio::awaitable operator()(const Table& table) { - co_await executor.sequential_scan_(table.name, attr_chan, tuples_chan); + boost::asio::awaitable operator()(const SeqScan& seq_scan) { + co_await executor.sequential_scan_(seq_scan.table, attr_chan, tuples_chan); co_return; } - boost::asio::awaitable operator()(const Projection& projection) { + boost::asio::awaitable operator()(const PhysicalProjection& projection) { // NOTE: We are using multiset relational algebra projection (i.e. not // eleminating duplicate tuples) co_await executor.ExecuteProjection(projection, attr_chan, tuples_chan); co_return; } - boost::asio::awaitable operator()(const Filter& filter) { + boost::asio::awaitable operator()(const PhysicalFilter& filter) { co_await executor.ExecuteFilter(filter, attr_chan, tuples_chan); co_return; } - boost::asio::awaitable operator()(const Join& join) { + boost::asio::awaitable operator()(const NestedLoopJoin& join) { co_await executor.ExecuteJoin(join, attr_chan, tuples_chan); co_return; } - boost::asio::awaitable operator()(const CrossJoin& cross_join) { + boost::asio::awaitable operator()(const NestedLoopCrossJoin& cross_join) { co_await executor.ExecuteCrossJoin(cross_join, attr_chan, tuples_chan); co_return; } @@ -180,7 +182,7 @@ boost::asio::awaitable Executor::Execute(const Operato } template -boost::asio::awaitable Executor::ExecuteProjection(const Projection& proj, +boost::asio::awaitable Executor::ExecuteProjection(const PhysicalProjection& proj, AttributesInfoChannel& out_attr_chan, TuplesChannel& out_tuples_chan) { #ifdef DEBUG @@ -219,7 +221,7 @@ boost::asio::awaitable Executor::ExecuteProjection(con } template -boost::asio::awaitable Executor::ExecuteFilter(const Filter& filter, +boost::asio::awaitable Executor::ExecuteFilter(const PhysicalFilter& filter, AttributesInfoChannel& out_attr_chan, TuplesChannel& out_tuples_chan) { #ifdef DEBUG @@ -233,7 +235,7 @@ boost::asio::awaitable Executor::ExecuteFilter(const F std::clog << "Filter received attrs\n"; #endif - if (GetExpressionType(filter.expr, attrs) != Type::kBool) { + if (GetExpressionType(filter.predicate, attrs) != Type::kBool) { throw std::logic_error{"filter expr should return bool"}; } @@ -246,7 +248,7 @@ boost::asio::awaitable Executor::ExecuteFilter(const F std::clog << "Filter sent attrs\n"; #endif - auto filter_executor = co_await expression_executor_.GetExpressionExecutor(filter.expr, attrs); + auto filter_executor = co_await expression_executor_.GetExpressionExecutor(filter.predicate, attrs); Tuples output_buf; output_buf.reserve(kBufSize); @@ -287,7 +289,7 @@ boost::asio::awaitable Executor::ExecuteFilter(const F } template -boost::asio::awaitable Executor::ExecuteCrossJoin(const CrossJoin& cross_join, +boost::asio::awaitable Executor::ExecuteCrossJoin(const NestedLoopCrossJoin& cross_join, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan) { std::clog << "Executing cross join\n"; @@ -314,6 +316,7 @@ boost::asio::awaitable Executor::ExecuteCrossJoin(cons } std::clog << std::format("Received {} tuples in cross join as rhs\n", buf_rhs.size()); + reader.Rewind(); for (;;) { auto buf_lhs = reader.Read(); if (buf_lhs.empty()) { @@ -340,7 +343,7 @@ boost::asio::awaitable Executor::ExecuteCrossJoin(cons } template -boost::asio::awaitable Executor::ExecuteJoin(const Join& join, +boost::asio::awaitable Executor::ExecuteJoin(const NestedLoopJoin& join, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan) { #ifdef DEBUG @@ -384,6 +387,7 @@ boost::asio::awaitable Executor::ExecuteJoin(const Joi #endif std::vector used(buf_rhs.size(), false); + reader.Rewind(); for (;;) { auto buf_lhs = reader.Read(); if (buf_lhs.empty()) { @@ -443,7 +447,7 @@ boost::asio::awaitable Executor::ExecuteJoin(const Joi } template -boost::asio::awaitable Executor::SpawnExecutor(const Operator& op, +boost::asio::awaitable Executor::SpawnExecutor(const PhysicalPlanNode& op, AttributesInfoChannel& attr_chan, TuplesChannel& tuple_chan) { auto executor = co_await boost::asio::this_coro::executor; diff --git a/include/stewkk/sql/logic/executor/materialization.hpp b/include/stewkk/sql/logic/executor/materialization.hpp index e8a3387..75ed8ce 100644 --- a/include/stewkk/sql/logic/executor/materialization.hpp +++ b/include/stewkk/sql/logic/executor/materialization.hpp @@ -17,6 +17,7 @@ class DiskFileReader { DiskFileReader(DiskFileReader&& other) = default; DiskFileReader& operator=(DiskFileReader&& other) = default; Tuples Read(); + void Rewind(); private: fs::path path_; std::ifstream f_; diff --git a/src/stewkk/sql/logic/executor/executor.cpp b/src/stewkk/sql/logic/executor/executor.cpp index 322a550..1d304e2 100644 --- a/src/stewkk/sql/logic/executor/executor.cpp +++ b/src/stewkk/sql/logic/executor/executor.cpp @@ -113,7 +113,7 @@ Type GetExpressionTypeUnchecked(const Expression& expr, const AttributesInfo& av return std::visit(ExpressionTypeVisitor{available_attrs}, expr); } -AttributesInfo GetAttributesAfterProjection(const AttributesInfo& attrs, const Projection& proj) { +AttributesInfo GetAttributesAfterProjection(const AttributesInfo& attrs, const PhysicalProjection& proj) { AttributesInfo result_attributes; result_attributes.reserve(proj.expressions.size()); for (const auto& target : proj.expressions) { diff --git a/src/stewkk/sql/logic/executor/executor_test.cpp b/src/stewkk/sql/logic/executor/executor_test.cpp index 73e182e..2cc8314 100644 --- a/src/stewkk/sql/logic/executor/executor_test.cpp +++ b/src/stewkk/sql/logic/executor/executor_test.cpp @@ -10,11 +10,51 @@ #include #include #include +#include +#include using ::testing::Eq; namespace stewkk::sql { +namespace { + +PhysicalPlanNode ToPhysicalPlan(const Operator& op) { + return std::visit(utils::Overloaded{ + [](const Table& t) -> PhysicalPlanNode { + return SeqScan{.table = t.name}; + }, + [](const Projection& p) -> PhysicalPlanNode { + return PhysicalProjection{ + .source = std::make_shared(ToPhysicalPlan(*p.source)), + .expressions = p.expressions, + }; + }, + [](const Filter& f) -> PhysicalPlanNode { + return PhysicalFilter{ + .source = std::make_shared(ToPhysicalPlan(*f.source)), + .predicate = f.expr, + }; + }, + [](const CrossJoin& j) -> PhysicalPlanNode { + return NestedLoopCrossJoin{ + .lhs = std::make_shared(ToPhysicalPlan(*j.lhs)), + .rhs = std::make_shared(ToPhysicalPlan(*j.rhs)), + }; + }, + [](const Join& j) -> PhysicalPlanNode { + return NestedLoopJoin{ + .lhs = std::make_shared(ToPhysicalPlan(*j.lhs)), + .rhs = std::make_shared(ToPhysicalPlan(*j.rhs)), + .type = j.type, + .qual = j.qual, + }; + }, + }, op); +} + +} // namespace + const static std::string kProjectDir = std::getenv("PWD"); namespace { @@ -39,7 +79,7 @@ TEST(ExecutorTest, SimpleSelect) { ctx, []() -> boost::asio::awaitable> { std::stringstream s{"SELECT * FROM users;"}; - Operator op = GetAST(s).value(); + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value()); CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; Executor executor(std::move(seq_scan), co_await boost::asio::this_coro::executor); @@ -65,7 +105,7 @@ TEST(ExecutorTest, SimpleSelectWithParallelism) { pool, []() -> boost::asio::awaitable> { std::stringstream s{"SELECT * FROM users;"}; - Operator op = GetAST(s).value(); + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value()); CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; Executor executor(std::move(seq_scan), co_await boost::asio::this_coro::executor); @@ -91,7 +131,7 @@ TYPED_TEST_P(ExecutorTest, Projection) { pool, []() -> boost::asio::awaitable> { std::stringstream s{"SELECT users.id FROM users;"}; - Operator op = GetAST(s).value(); + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value()); CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; Executor executor(std::move(seq_scan), co_await boost::asio::this_coro::executor); @@ -117,7 +157,7 @@ TYPED_TEST_P(ExecutorTest, Filter) { pool, []() -> boost::asio::awaitable> { std::stringstream s{"SELECT users.id FROM users WHERE users.age < 10;"}; - Operator op = GetAST(s).value(); + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value()); CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; Executor executor(std::move(seq_scan), co_await boost::asio::this_coro::executor); @@ -143,7 +183,7 @@ TYPED_TEST_P(ExecutorTest, FilterMany) { pool, []() -> boost::asio::awaitable> { std::stringstream s{"SELECT users.id FROM users WHERE users.age > 10;"}; - Operator op = GetAST(s).value(); + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value()); CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; Executor executor(std::move(seq_scan), co_await boost::asio::this_coro::executor); @@ -169,7 +209,7 @@ TYPED_TEST_P(ExecutorTest, CrossJoin) { pool, []() -> boost::asio::awaitable> { std::stringstream s{"SELECT * FROM users, books WHERE users.age < 10;"}; - Operator op = GetAST(s).value(); + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value()); CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; Executor executor(std::move(seq_scan), co_await boost::asio::this_coro::executor); @@ -199,7 +239,7 @@ TYPED_TEST_P(ExecutorTest, InnerJoin) { pool, []() -> boost::asio::awaitable> { std::stringstream s{"SELECT * FROM employees JOIN departments ON employees.department_id = departments.id;"}; - Operator op = GetAST(s).value(); + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value()); CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; Executor executor(std::move(seq_scan), co_await boost::asio::this_coro::executor); @@ -228,7 +268,7 @@ TYPED_TEST_P(ExecutorTest, LeftJoin) { pool, []() -> boost::asio::awaitable> { std::stringstream s{"SELECT * FROM employees LEFT OUTER JOIN departments ON employees.department_id = departments.id;"}; - Operator op = GetAST(s).value(); + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value()); CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; Executor executor(std::move(seq_scan), co_await boost::asio::this_coro::executor); @@ -257,7 +297,7 @@ TYPED_TEST_P(ExecutorTest, RightJoin) { pool, []() -> boost::asio::awaitable> { std::stringstream s{"SELECT * FROM employees RIGHT JOIN departments ON employees.department_id = departments.id;"}; - Operator op = GetAST(s).value(); + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value()); CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; Executor executor(std::move(seq_scan), co_await boost::asio::this_coro::executor); @@ -286,7 +326,7 @@ TYPED_TEST_P(ExecutorTest, ComplexJoin) { pool, []() -> boost::asio::awaitable> { std::stringstream s{"SELECT departments.id*2, employees.id+1 FROM employees RIGHT JOIN departments ON employees.department_id = departments.id AND departments.id > 3 AND departments.id*2*2/2 < 30;"}; - Operator op = GetAST(s).value(); + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value()); CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; Executor executor(std::move(seq_scan), co_await boost::asio::this_coro::executor); @@ -308,6 +348,36 @@ TYPED_TEST_P(ExecutorTest, ComplexJoin) { pool.join(); } +TEST(ExecutorTest, InnerJoinLargeRhsBug) { + boost::asio::thread_pool pool{4}; + boost::asio::co_spawn( + pool, + []() -> boost::asio::awaitable> { + Expression qual{BinaryExpression{ + .lhs = std::make_shared(Attribute{"employees", "id"}), + .binop = BinaryOp::kGt, + .rhs = std::make_shared(IntConst{0}), + }}; + PhysicalPlanNode op = NestedLoopJoin{ + .lhs = std::make_shared(SeqScan{.table = "employees"}), + .rhs = std::make_shared(SeqScan{.table = "departments_4000"}), + .type = JoinType::kInner, + .qual = std::move(qual), + }; + CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; + Executor executor(std::move(seq_scan), co_await boost::asio::this_coro::executor); + + auto got = co_await executor.Execute(op); + + co_return got; + }(), + [](std::exception_ptr p, Result got) { + if (p) std::rethrow_exception(p); + ASSERT_THAT(got.value().tuples.size(), Eq(44000u)); + }); + pool.join(); +} + REGISTER_TYPED_TEST_SUITE_P(ExecutorTest, Projection, Filter, FilterMany, CrossJoin, InnerJoin, LeftJoin, RightJoin, ComplexJoin); using ExecutorTypes = ::testing::Types; INSTANTIATE_TYPED_TEST_SUITE_P(TypedExecutorTest, ExecutorTest, ExecutorTypes); diff --git a/src/stewkk/sql/logic/executor/materialization.cpp b/src/stewkk/sql/logic/executor/materialization.cpp index 3dcbfe0..e1018e2 100644 --- a/src/stewkk/sql/logic/executor/materialization.cpp +++ b/src/stewkk/sql/logic/executor/materialization.cpp @@ -34,6 +34,11 @@ DiskFileReader::~DiskFileReader() { fs::remove(path_); } +void DiskFileReader::Rewind() { + f_.clear(); + f_.seekg(0, std::ios::beg); +} + Tuples DiskFileReader::Read() { Tuples buf; buf.reserve(kBufSize); diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index 94636d0..c881338 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -14,6 +14,7 @@ #include #include #include +#include #include using ::testing::Eq; @@ -67,7 +68,10 @@ std::vector> GetChildren(utils::NotNull ex } class CardinalityEstimates { -public: +public: + CardinalityEstimates(std::unordered_map table_sizes = {}) + : table_sizes_(std::move(table_sizes)) {} + int64_t GetCardinality(utils::NotNull group) { if (auto it = cache_.find(group.get()); it != cache_.end()) { return it->second; @@ -76,11 +80,14 @@ class CardinalityEstimates { cache_[group.get()] = cardinality; return cardinality; } - + private: int64_t GetCardinality(const LogicalOperator& op) { return std::visit(utils::Overloaded{ - [](const logical::Table&) -> int64_t { + [this](const logical::Table& t) -> int64_t { + if (auto it = table_sizes_.find(t.name); it != table_sizes_.end()) { + return it->second; + } return 10; }, [this](const logical::Filter& f) -> int64_t { @@ -98,6 +105,7 @@ class CardinalityEstimates { }, op); } + std::unordered_map table_sizes_; std::unordered_map cache_; }; @@ -113,12 +121,14 @@ int64_t CalcCost(utils::NotNull expr, CardinalityEstimates& cardi return cardinality.GetCardinality(expr->group); }, [&](const physical::NestedLoopJoin& j) -> int64_t { - return 3 * cardinality.GetCardinality(j.lhs) * cardinality.GetCardinality(j.rhs) - + cardinality.GetCardinality(expr->group); + auto p_l = (cardinality.GetCardinality(j.lhs) + kBufSize - 1) / kBufSize; + auto p_r = (cardinality.GetCardinality(j.rhs) + kBufSize - 1) / kBufSize; + return p_l * (1 + p_r); }, [&](const physical::NestedLoopCrossJoin& j) -> int64_t { - return 3 * cardinality.GetCardinality(j.lhs) * cardinality.GetCardinality(j.rhs) - + cardinality.GetCardinality(expr->group); + auto p_l = (cardinality.GetCardinality(j.lhs) + kBufSize - 1) / kBufSize; + auto p_r = (cardinality.GetCardinality(j.rhs) + kBufSize - 1) / kBufSize; + return p_l * (1 + p_r); }, }, expr->root_operator); } @@ -126,9 +136,10 @@ int64_t CalcCost(utils::NotNull expr, CardinalityEstimates& cardi template class Optimizer { public: - Optimizer(const Operator& expr, Rules&& rules) + Optimizer(const Operator& expr, Rules&& rules, + CardinalityEstimates cardinality = {}) : memo_(), rules_applier_(std::move(rules)), root_(memo_.Populate(expr)), - cardinality_() { + cardinality_(std::move(cardinality)) { } private: From 5708c10689edd37f4ced9162c4da018430ca5fe8 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 11 May 2026 14:17:34 +0300 Subject: [PATCH 024/120] Add test --- src/stewkk/sql/logic/optimizer/optimizer_test.cpp | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index c881338..ecd1cda 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -348,4 +348,17 @@ TEST(OptimizerTest, Simple) { ASSERT_THAT(SerializeDot(got), Eq("digraph G { rankdir=BT;\n n0 [label=\"SeqScan\\\\nusers\"]\n}\n")); } +TEST(OptimizerTest, JoinCommutativity) { + std::stringstream s{"SELECT * FROM users JOIN orders ON users.id = orders.user_id;"}; + Operator op = GetAST(s).value(); + Optimizer optimizer(op, MakeMainRules(), CardinalityEstimates({ + {"users", 10000}, + {"orders", 100}, + })); + + auto got = optimizer.Optimize(); + + ASSERT_THAT(Serialize(got), ::testing::HasSubstr("(SeqScan orders) (SeqScan users)")); +} + } // namespace stewkk::sql From aadd4973f81ace3976f0938f182f5e09096b164d Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 11 May 2026 14:47:31 +0300 Subject: [PATCH 025/120] Fix review issues --- report/thesis.tex | 195 +++++++++++++++++----------------------------- 1 file changed, 73 insertions(+), 122 deletions(-) diff --git a/report/thesis.tex b/report/thesis.tex index f35aae0..432bc1f 100644 --- a/report/thesis.tex +++ b/report/thesis.tex @@ -1,4 +1,5 @@ % !TeX TXS-program:bibliography = txs:///biber +% chktex-file 1 \documentclass[fontsize=14pt, russian]{scrartcl} \input{header.tex} \addbibresource{biblio.bib} @@ -10,74 +11,29 @@ \def\figurename{Рисунок} -\begin{titlepage} - \thispagestyle{empty} - \newpage - - \bmstuheader - - \vspace{3em} - - \begin{center} - {\Large\bfseries РАСЧЕТНО-ПОЯСНИТЕЛЬНАЯ ЗАПИСКА\\\textbf{\textit{К НАУЧНО-ИССЛЕДОВАТЕЛЬСКОЙ РАБОТЕ\\НА ТЕМУ:}} \\} - \end{center} - - \vspace*{-6ex} - \begin{center} - \Large{\textit{\textbf{}}} - - \vspace*{-3ex} - \rule{1\textwidth}{1.2pt} - - \vspace*{-0.2ex} - - \Large{\textit{\textbf{}}} - - \vspace*{-3ex} - \rule{1\textwidth}{1.2pt} - - \vspace*{-0.2ex} - \rule{1\textwidth}{1.2pt} - - \vspace*{-0.2ex} - \rule{1\textwidth}{1.2pt} - \end{center} - - \vspace{\fill} - - - \newlength{\ML} - \settowidth{\ML}{«\underline{\hspace{0.7cm}}» \underline{\hspace{2cm}}} - - \noindent Студент \underline{\hspace{1.5cm}} \hfill \underline{\hspace{4cm}}\quad - \underline{\hspace{4cm}} - - \vspace{-2.1ex} - \noindent\hspace{9ex}\scriptsize{(Группа)}\normalsize\hspace{170pt}\hspace{2ex}\scriptsize{(Подпись, дата)}\normalsize\hspace{30pt}\hspace{6ex}\scriptsize{(И.О. Фамилия)}\normalsize - - \bigskip - - \noindent Руководитель НИР \hfill \underline{\hspace{4cm}}\quad - \underline{\hspace{4cm}} - - \vspace{-2ex} - \noindent\hspace{13.5ex}\normalsize\hspace{170pt}\hspace{2ex}\scriptsize{(Подпись, дата)}\normalsize\hspace{30pt}\hspace{6ex}\scriptsize{(И.О. Фамилия)}\normalsize - \bigskip - - \noindent Нормоконтролер \hfill \underline{\hspace{4cm}}\quad - \underline{\hspace{4cm}} - - \vspace{-2ex} - \noindent\hspace{13.5ex}\normalsize\hspace{170pt}\hspace{2ex}\scriptsize{(Подпись, дата)}\normalsize\hspace{30pt}\hspace{6ex}\scriptsize{(И.О. Фамилия)}\normalsize - \vfill - - \begin{center} - \textsl{\the\year{} г.} - \end{center} -\end{titlepage} +\newcommand{\sqlkw}[1]{\texttt{#1}} +\newcommand{\SELECT}{\sqlkw{SELECT}} +\newcommand{\FROM}{\sqlkw{FROM}} +\newcommand{\WHERE}{\sqlkw{WHERE}} +\newcommand{\JOIN}{\sqlkw{JOIN}} +\newcommand{\GROUPBY}{\sqlkw{GROUP BY}} +\newcommand{\ORDERBY}{\sqlkw{ORDER BY}} + +\newcommand{\COUNT}{\sqlkw{COUNT}} +\newcommand{\SUM}{\sqlkw{SUM}} +\newcommand{\AVG}{\sqlkw{AVG}} +\newcommand{\MIN}{\sqlkw{MIN}} +\newcommand{\MAX}{\sqlkw{MAX}} + +\newcommand{\LogicalGet}{\sqlkw{LogicalGet}} +\newcommand{\SeqScan}{\sqlkw{SeqScan}} +\newcommand{\IndexScan}{\sqlkw{IndexScan}} +\newcommand{\NestedLoopJoin}{\sqlkw{NestedLoopJoin}} +\newcommand{\HashJoin}{\sqlkw{HashJoin}} +\newcommand{\MergeJoin}{\sqlkw{MergeJoin}} +\newcommand{\Sort}{\sqlkw{Sort}} \setlength{\tabcolsep}{3pt} -\newpage \setcounter{page}{2} %---------------------------------------------------------------------------- % ОТСЮДА --- СОБСТВЕННО ТЕКСТ @@ -90,7 +46,7 @@ \anonsection{ВВЕДЕНИЕ} В эпоху стремительного роста объемов данных системы управления базами данных -(СУБД) являются ключевой частью информационных систем. По данным аналитических +(СУБД) являются важной частью информационных систем. По данным аналитических исследований, объем мирового рынка СУБД ежегодно увеличивается, что обусловлено как расширением круга решаемых задач, так и усложнением структуры обрабатываемых данных и увеличением их объема. Реляционные базы данных, работающие с различными @@ -114,12 +70,12 @@ Архитектура Cascades используется в таких промышленных системах, как Microsoft SQL Server, Apache Orca, а также в ряде исследовательских проектов, включая -Columbia и CockroachDB. Преимущества этой архитектуры заключаются в +Columbia и CockroachDB\@. Преимущества этой архитектуры заключаются в расширяемости, модульности, а также понятном и эффективном алгоритме поиска, допускающим параллельность исполнения. Целью данной работы является разработка и реализация оптимизатора подмножества -SQL-запросов на основе архитектуры Cascades, а также создание фреймворка для его +SQL-запросов на основе архитектуры Cascades, а также создание каркаса для его итеративного улучшения путем сравнения с промышленными реализациями. Для достижения поставленной цели необходимо решить следующие задачи: @@ -207,7 +163,7 @@ \section{Архитектура СУБД} \draw[->, thick, >=stealth] ([xshift=-0.5cm]clust.west) -- (clust.west); \draw[->, thick, >=stealth] ([xshift=-0.5cm]remote.west) -- (remote.west); \end{tikzpicture} - \caption{Архитектура реляционной системы управления базами данных} + \caption{Архитектура реляционной системы управления базами данных}% \label{fig::dbms_arch} \end{figure} @@ -217,10 +173,10 @@ \subsection{Процесс исполнения SQL-запроса} последовательных этапов. \begin{enumerate} - \item \emph{Синтаксический анализ} --- запроса проходит лексический и синтаксический разбор. Результатом данного этапа - является синтаксическое дерево, узлы которого соответствуют конструкциям - языка SQL: операторам \texttt{SELECT}, \texttt{FROM}, \texttt{WHERE}, - \texttt{JOIN}, \texttt{GROUP BY}, \texttt{ORDER BY} и другим. На этом же + \item \emph{Синтаксический анализ} --- запроса проходит лексический и + синтаксический разбор. Результатом данного этапа является синтаксическое + дерево, узлы которого соответствуют конструкциям языка SQL: операторам + \SELECT, \FROM, \WHERE, \JOIN, \GROUPBY, \ORDERBY{} и другим. На этом же этапе выполняется семантический анализ: проверяется существование указанных таблиц и столбцов, разрешаются имена объектов и определяются типы выражений. @@ -231,9 +187,9 @@ \subsection{Процесс исполнения SQL-запроса} них рассматривает возможные физические реализации операторов. С помощью модели стоимости оптимизатор оценивает затраты на выполнение каждого плана и выбирает план с наименьшей оценочной стоимостью. Результатом - этого этапа является физический план исполнения. Под этим понимается дерево физических - операторов с указанием конкретных алгоритмов соединения, методов доступа - к данным и порядка операций. + этого этапа является физический план исполнения. Под этим понимается + дерево физических операторов с указанием конкретных алгоритмов + соединения, методов доступа к данным и порядка операций. \item \emph{Исполнение физического плана} --- движок выполнения реализует физический план, порождая потоки кортежей. Данные извлекаются из @@ -246,7 +202,7 @@ \section{Реляционная алгебра} Реляционная алгебра представляет собой формальный язык для описания операций над отношениями реляционной базы данных. В отличие от декларативного SQL, -реляционная алгебра процедурной, так как она определяет конкретную +реляционная алгебра является процедурной, так как она определяет конкретную последовательность операций, необходимых для получения результата. Отношение определяется как конечное множество кортежей, каждый из которых @@ -281,8 +237,8 @@ \subsection{Основные операторы реляционной алге отсутствующие в \(S\). \item \emph{Агрегация} \(\gamma_{G, f}(R)\) --- разбивает отношение \(R\) на непересекающиеся множества по набору атрибутов \(G\) и вычисляет - агрегированные значения функции \(f\) (такие как \texttt{COUNT}, - \texttt{SUM}, \texttt{AVG}, \texttt{MIN}, \texttt{MAX}) по каждому + агрегированные значения функции \(f\) (такие как \COUNT, + \SUM, \AVG, \MIN, \MAX) по каждому такому множеству. \end{enumerate} @@ -298,8 +254,8 @@ \subsection{Преобразование синтаксического дере \begin{itemize} \item блок \texttt{SELECT-FROM-WHERE} превращается в проекцию над селекцией над декартовым произведением соединяемых таблиц; - \item конструкция \texttt{GROUP BY} порождает оператор группировки; - \item \texttt{ORDER BY} --- запрос на требуемое физическое свойство + \item конструкция \GROUPBY{} порождает оператор группировки; + \item \ORDERBY{} --- запрос на требуемое физическое свойство сортированности; \item вложенные подзапросы --- оператор Apply, который впоследствии может быть удалён процедурой декорреляции. @@ -432,7 +388,7 @@ \section{Цель оптимизатора} \draw[->] (p2o) -- (p2j1); \node[font=\small\bfseries] at (4.4, 4.0) {План \(P_2\)}; \end{tikzpicture} -\caption{Сравнение планов для запроса из листинга~\ref{lst:join_query}.} +\caption{Сравнение планов для запроса из листинга~\ref{lst:join_query}.}% \label{fig::join_plan_comparison} \end{figure} @@ -507,7 +463,7 @@ \section{Цель оптимизатора} \draw[->] (p2dept) -- (p2sig); \end{tikzpicture} \caption{Сравнение планов для запроса из - листинга~\ref{lst:logical_transform_query}.} + листинга~\ref{lst:logical_transform_query}.}% \label{fig::logical_transform_plans} \end{figure} @@ -537,14 +493,14 @@ \section{Цель оптимизатора} \] \end{itemize} -Оба приведенных примера наглядно демонстрируют, что оптимизатор отвечает не за -доли процентов производительности, а за принципиальную возможность исполнения -сложных аналитических запросов за приемлемое время. В первом случае разница -между физическими планами составляет восемь порядков: если \(P_1\) завершается -за секунды, то наивный \(P_2\) потребует десятков лет. Во втором случае даже -единственное логическое преобразование, в виде проталкивания предиката, снижает -число чтений с диска в \(1{,}900\) раз. Именно поэтому качественный оптимизатор -является конкурентным преимуществом и одной из самых важных частей СУБД. +Оба приведенных примера наглядно демонстрируют, что оптимизатор отвечает за +принципиальную возможность исполнения сложных аналитических запросов за +приемлемое время. В первом случае разница между физическими планами составляет +восемь порядков: если \(P_1\) завершается за секунды, то наивный \(P_2\) +потребует десятков лет. Во втором случае даже единственное логическое +преобразование, в виде проталкивания предиката, снижает число чтений с диска в +\(1{,}900\) раз. Именно поэтому качественный оптимизатор является конкурентным +преимуществом и одной из самых важных частей СУБД. \section{Обзор архитектур оптимизаторов} @@ -560,8 +516,8 @@ \subsection{System R} Оптимизатор, построенный по архитектуре System~R, разбивает запрос на блоки, каждый из которых оптимизируется индивидуально. Блок запроса состоит из -конструкции \texttt{SELECT}, конструкции \texttt{FROM}, ссылающейся на одну или -несколько таблиц, и дерева предикатов \texttt{WHERE}. Несколько блоков +конструкции \SELECT, конструкции \FROM, ссылающейся на одну или +несколько таблиц, и дерева предикатов \WHERE. Несколько блоков появляется в случае запроса с вложенными подзапросами. Для каждого блока оптимизатор выполняет два основных действия. Во-первых, для @@ -578,11 +534,11 @@ \subsection{System R} \(w\) --- весовой коэффициент. Важным нововведением System~R стало понятие интересных порядков. Стоимость плана -зависит не только от непосредственных затрат на выполнение оператора, но и от +зависит от непосредственных затрат на выполнение оператора и от того, обеспечивает ли он упорядоченность результата, требуемую вышестоящими операторами. Например, использование соединения слиянием вместо хеш-соединения может быть выгодно, если результат должен быть отсортирован для последующей -операции \texttt{ORDER BY}. +операции \ORDERBY. К ограничениям архитектуры System~R относятся: @@ -591,7 +547,7 @@ \subsection{System R} деревьев соединений; \item независимая оптимизация вложенных подзапросов; \item невозможность гибкого расширения набора правил преобразования, что - необходимо при добавлении новых операторов языка SQL. + необходимо при добавлении новых операторов языка SQL\@. \end{itemize} Эти ограничения послужили мотивацией для создания расширяемых архитектур @@ -657,7 +613,7 @@ \section{Архитектура Cascades} Важным принципом данной архитектуры является то, что правила являются объектами первого класса. Они определяются условиями применимости и собственно алгоритмом -применения. Предполагается, что система легко расширяется добавленимем новых +применения. Предполагается, что система легко расширяется добавлением новых правил, потому что все правила наследуются от общего интерфейса и имеют одинаковую структуру. @@ -665,12 +621,7 @@ \subsection{Группы эквивалентности и выражения} Группа в Memo представляет собой множество выражений, которые эквивалентны с точки зрения логического результата. Например, для отношений \(A\), \(B\) и -\(C\) выражения - -\[ - A \bowtie (B \bowtie C) ; \qquad{} (A \bowtie B) \bowtie C -\] - +\(C\) выражения \( A \bowtie (B \bowtie C) ; \qquad{} (A \bowtie B) \bowtie C \) при выполнении условий ассоциативности соединения принадлежат одной группе, поскольку возвращают одинаковый результат. Причем физические реализации этих выражений могут отличаться, а стоимость может зависеть от кардинальности @@ -717,7 +668,7 @@ \subsection{Структура Memo} листинга~\ref{lst:query}. Например, группа \(G_5\) представляет результат соединения трех отношений, в ней находятся два логически эквивалентных выражения с разной ассоциацией соединений и четыре физические реализации для них. Входными -данными для операторов являются группы группы, поэтому общие подвыражения не +данными для операторов являются группы, поэтому общие подвыражения не дублируются. \begin{figure}[H] @@ -800,7 +751,7 @@ \subsection{Структура Memo} \draw[arrow] (g6) -- (g2); \draw[arrow] (g6) -- (g3); \end{tikzpicture} - \caption{Пример структуры Memo.} + \caption{Пример структуры Memo.}% \label{fig:memo-structure} \end{figure} @@ -842,7 +793,7 @@ \subsection{Алгоритм поиска} \begin{figure}[H] \centering \includegraphics[width=0.8\textwidth]{cascades.pdf} -\caption{Схема взаимодействия задач в оптимизаторе Cascades} +\caption{Схема взаимодействия задач в оптимизаторе Cascades}% \label{fig:cascades-tasks} \end{figure} @@ -1072,8 +1023,8 @@ \subsection{Правила трансформации} где \(G\) --- ключи группировки, \(F\) --- набор агрегатных функций, \(G_R = G \cap attrs(R)\), \(F_R\) --- частичные агрегаты по атрибутам \(R\), а -\(F'\) --- финальные комбинаторы. Например, \texttt{SUM} раскладывается в сумму -частичных сумм, а \texttt{AVG} --- в пару \texttt{SUM} и \texttt{COUNT}. +\(F'\) --- финальные комбинаторы. Например, \SUM{} раскладывается в сумму +частичных сумм, а \AVG{} --- в пару \SUM{} и \COUNT{}. Условиями активации проталкивания агрегации ниже соединения являются: \begin{itemize} @@ -1119,13 +1070,13 @@ \subsection{Правила реализации} логического оператора чтения таблицы могут быть применены правила: \begin{itemize} - \item \texttt{LogicalGet} \(\Rightarrow\) \texttt{SeqScan}; - \item \texttt{LogicalGet} \(\Rightarrow\) \texttt{IndexScan}, если имеется + \item \LogicalGet{} \(\Rightarrow\) \SeqScan; + \item \LogicalGet{} \(\Rightarrow\) \IndexScan, если имеется индекс, совместимый с предикатом или требуемым порядком. \end{itemize} Для логического соединения могут быть созданы физические операторы -\texttt{NestedLoopJoin}, \texttt{HashJoin} и \texttt{MergeJoin}. Хеш-соединение +\NestedLoopJoin, \HashJoin{} и \MergeJoin. Хеш-соединение обычно требует предиката равенства по ключам, поскольку именно по ним строится хеш-таблица. Сортировочное соединение требует, чтобы оба входа были отсортированы по ключам соединения, либо чтобы оптимизатор мог добавить @@ -1147,7 +1098,7 @@ \subsection{Обеспечивающие операторы} Физические свойства в Cascades обрабатываются через требуемые и предоставляемые свойства. Если родительский оператор требует вход, отсортированный по атрибуту \(a\), а выбранный дочерний план такого порядка не предоставляет, оптимизатор -может добавить обеспечивающий оператор \texttt{Sort}. +может добавить обеспечивающий оператор \Sort. Обеспечивающие операторы не изменяют логический результат, но меняют физические свойства и увеличивают стоимость. Поэтому они рассматриваются наравне с другими @@ -1185,7 +1136,7 @@ \section{Метод ветвей и границ} стоимость служит нижней границей. Если же группа \(G_i\) под свойством \(P_i\) ещё не рассматривалась, используется тривиальная граница \(LB = 0\), или, при условии наличия минимальной стоимости на кортеж, можно принимать -\(LB(G_i, P_i) = |G_i| \cdot min_cost_per_row\). +\(LB(G_i, P_i) = |G_i| \cdot \mathit{minCostPerRow}\). Требуемые свойства дочерних групп \(P_i\) определяются оператором выражения \(e\) совместно с требованием родителя \(P\). Каждый физический оператор описывает, какие @@ -1286,9 +1237,9 @@ \subsection{Предлагаемый метод} Полученные физические планы промышленных СУБД конвертируются в формат сериализованного плана разрабатываемого оптимизатора. Для этого необходимо создать набор отображений между физическими операторами различных систем. -Например, \texttt{Hash Match} в SQL Server отображается в \texttt{HashJoin} в -разрабатываемой системе, \texttt{Clustered Index Scan} --- в \texttt{IndexScan} -и так далее. +Например, \texttt{Hash Match} в SQL Server отображается в \HashJoin{} в +разрабатываемой системе, \texttt{Clustered Index Scan} --- в \IndexScan{} и так +далее. Корректность конвертации плана можно проверить путем исполнения исходого плана на внешней системе и полученного плана на разрабатываемой системе и сравнения @@ -1319,8 +1270,8 @@ \subsection{Поиск ближайшего плана} \subsection{Область применения} -Предлагаемый метод дифференциального анализа применим не только для сравнения с -проприетарными системами, но и с СУБД с открытым исходным кодом, позволяя +Предлагаемый метод дифференциального анализа применим для сравнения как с +проприетарными системами, так и с СУБД с открытым исходным кодом, позволяя уточнять условия применения правил. Систематическое применение этого подхода позволяет построить итеративный процесс @@ -1353,7 +1304,7 @@ \subsection{Область применения} \vspace{-30pt} \begin{algorithm}[H] - \caption{Алгоритм поиска (часть 1).} + \caption{Алгоритм поиска (часть 1).}% \label{alg:cascades-search} \begin{algorithmic}[1] @@ -1389,7 +1340,7 @@ \subsection{Область применения} \end{algorithm} \begin{algorithm}[H] - \caption{Алгоритм поиска (часть 2).} + \caption{Алгоритм поиска (часть 2).}% \label{alg:cascades-search-2} \begin{algorithmic}[1] \Procedure{ExploreExpression}{$expr, limit$} @@ -1432,7 +1383,7 @@ \subsection{Область применения} \end{algorithm} \begin{algorithm}[H] - \caption{Алгоритм поиска (часть 3).} + \caption{Алгоритм поиска (часть 3).}% \label{alg:cascades-search-3} \begin{algorithmic}[1] \Procedure{OptimizeInputs}{$expr, limit, i$} From d164eaa0992ede7a9d71b1f926784779f072e7ef Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 11 May 2026 15:53:34 +0300 Subject: [PATCH 026/120] Fix --- report/thesis.tex | 168 ++++++++++++++++++++++++---------------------- 1 file changed, 89 insertions(+), 79 deletions(-) diff --git a/report/thesis.tex b/report/thesis.tex index 432bc1f..750ace7 100644 --- a/report/thesis.tex +++ b/report/thesis.tex @@ -33,6 +33,20 @@ \newcommand{\MergeJoin}{\sqlkw{MergeJoin}} \newcommand{\Sort}{\sqlkw{Sort}} +\newcommand{\SelOp}[1]{\sigma_{#1}} +\newcommand{\ProjectionOp}[1]{\pi_{#1}} + +\newcommand{\Sel}[2]{\sigma_{#1}(#2)} +\newcommand{\Projection}[2]{\pi_{#1}(#2)} +\newcommand{\Agg}[3]{\gamma_{#1;\,#2}(#3)} +\newcommand{\AggP}[3]{\gamma_{#1;\,#2}^{partial}(#3)} +\newcommand{\NJoin}[2]{#1 \bowtie #2} +\newcommand{\InnerJoin}[3]{#1 \bowtie_{#2} #3} +\newcommand{\CProd}[2]{#1 \times #2} +\newcommand{\LOJ}[3]{#1 \ltimes_{#2} #3} +\newcommand{\ROJ}[3]{#1 \rtimes_{#2} #3} +\newcommand{\FOJ}[3]{#1 \mathbin{\ltimes\!\!\!\rtimes}_{#2} #3} + \setlength{\tabcolsep}{3pt} \setcounter{page}{2} %---------------------------------------------------------------------------- @@ -198,6 +212,27 @@ \subsection{Процесс исполнения SQL-запроса} \end{enumerate} +Сформулируем задачу, которую решает оптимизатор: по запросу, представленному в +форме дерева операторов реляционной алгебры, построить эквивалентный и +оптимальный, с точки зрения необходимых для исполнения ресурсов, план +исполнения. Итоговый план является деревом, вершины которого помечены +конкретными указаниями на алгоритмы, реализующими один или несколько операторов +реляционной алгебры. Эквивалентность означает, что итоговый физический план и +исходный запрос при исполнении на любом наборе данных порождают одинаковые +отношения с точностью до требуемых свойств, таких как сортировка. + +Запрос в форме реляционной алгебры также называют логическим планом, а операторы +реляционной алгебры --- логическими операторами. + +Для каждого логического оператора существует несколько возможных физических +реализаций. Например, оператор соединения \(\bowtie\) может быть реализован как +соединение вложенными циклами, хеш-соединение или соединение слиянием. +Аналогично, оператор доступа к данным может быть реализован как последовательное +сканирование таблицы или сканирование индекса. Еще одним важным примером +является эквивалентность сканирования индекса с предикатом и двух логических +операторов, примененных последовательно: доступ к данным и фильтрация по тому же +условию. + \section{Реляционная алгебра} Реляционная алгебра представляет собой формальный язык для описания операций над @@ -217,25 +252,25 @@ \subsection{Основные операторы реляционной алге Среди основных операторов реляционной алгебры выделяют следующие. \begin{enumerate} - \item \emph{Фильтрация} \(\sigma_p(R)\) --- возвращает подмножество кортежей + \item \emph{Фильтрация} \(\Sel{p}{R}\) --- возвращает подмножество кортежей отношения \(R\), удовлетворяющих предикату \(p\). Например, - \(\sigma_{\text{age} > 30}(\text{Employee})\) вернет все кортежи из + \(\Sel{\text{age} > 30}{\text{Employee}}\) вернет все кортежи из отношения Employee, для которых значение атрибута age больше 30. - \item \emph{Проекция} \(\pi_{A_1, \ldots, A_k}(R)\) --- формирует новое + \item \emph{Проекция} \(\Projection{A_1, \ldots, A_k}{R}\) --- формирует новое отношение, содержащее только перечисленные атрибуты. - \item \emph{Декартово произведение} \(R \times S\) --- формирует отношение, + \item \emph{Декартово произведение} \(\CProd{R}{S}\) --- формирует отношение, каждый кортеж которого является конкатенацией кортежа из \(R\) и кортежа - из \(S\), при этом результат содержит \(|R \times S| = |R| \cdot |S|\) + из \(S\), при этом результат содержит \(|\CProd{R}{S}| = |R| \cdot |S|\) элементов. - \item \emph{Соединение} \(R \bowtie_p S\) --- комбинирует кортежи двух + \item \emph{Соединение} \(\InnerJoin{R}{p}{S}\) --- комбинирует кортежи двух отношений на основании предиката \(p\). Внутреннее соединение определяется как - \(R \bowtie_{R.a = S.b} S = \sigma_{R.a = S.b}(R \times S)\). + \(\InnerJoin{R}{R.a = S.b}{S} = \Sel{R.a = S.b}{\CProd{R}{S}}\). \item \emph{Объединение} \(R \cup S\) --- возвращает все кортежи, принадлежащие хотя бы одному из двух совместимых по схеме отношений. \item \emph{Разность} \(R - S\) --- выбирает кортежи, принадлежащие \(R\), но отсутствующие в \(S\). - \item \emph{Агрегация} \(\gamma_{G, f}(R)\) --- разбивает отношение \(R\) на + \item \emph{Агрегация} \(\Agg{G}{f}{R}\) --- разбивает отношение \(R\) на непересекающиеся множества по набору атрибутов \(G\) и вычисляет агрегированные значения функции \(f\) (такие как \COUNT, \SUM, \AVG, \MIN, \MAX) по каждому @@ -254,7 +289,7 @@ \subsection{Преобразование синтаксического дере \begin{itemize} \item блок \texttt{SELECT-FROM-WHERE} превращается в проекцию над селекцией над декартовым произведением соединяемых таблиц; - \item конструкция \GROUPBY{} порождает оператор группировки; + \item конструкция \GROUPBY{} порождает оператор агрегации \(\Agg{G}{f}{R}\); \item \ORDERBY{} --- запрос на требуемое физическое свойство сортированности; \item вложенные подзапросы --- оператор Apply, который впоследствии может быть @@ -270,42 +305,21 @@ \subsection{Преобразование синтаксического дере позволяет оптимизатору независимо исследовать порядок операций и алгоритмы их реализации. -\section{Цель оптимизатора} - -Сформулируем задачу, которую решает оптимизатор: по запросу, представленному в -форме дерева операторов реляционной алгебры, построить эквивалентный и -оптимальный, с точки зрения необходимых для исполнения ресурсов, план -исполнения. Итоговый план является деревом, вершины которого помечены -конкретными указаниями на алгоритмы, реализующими один или несколько операторов -реляционной алгебры. Эквивалентность означает, что итоговый физический план и -исходный запрос при исполнении на любом наборе данных порождают одинаковые -отношения с точностью до требуемых свойств, таких как сортировка. - -Запрос в форме реляционной алгебры также называют логическим планом, а операторы -реляционной алгебры --- логическими операторами. - -Для каждого логического оператора существует несколько возможных физических -реализаций. Например, оператор соединения \(\bowtie\) может быть реализован как -соединение вложенными циклами, хеш-соединение или соединение слиянием. -Аналогично, оператор доступа к данным может быть реализован как последовательное -сканирование таблицы или сканирование индекса. Еще одним важным примером -является эквивалентность сканирования индекса с предикатом и двух логических -операторов, примененных последовательно: доступ к данным и фильтрация по тому же -условию. +\section{Поиск оптимального плана} Эквивалентность планов обеспечивается свойствами реляционной алгебры. \begin{Example} Коммутативность соединения: \[ - R \bowtie S \equiv S \bowtie R. + \NJoin{R}{S} \equiv \NJoin{S}{R}. \] \end{Example} \begin{Example} Ассоциативность соединения: \[ - (R \bowtie S) \bowtie T \equiv R \bowtie (S \bowtie T). + \NJoin{(\NJoin{R}{S})}{T} \equiv \NJoin{R}{(\NJoin{S}{T})}. \] \end{Example} @@ -394,7 +408,7 @@ \section{Цель оптимизатора} \begin{itemize} \item План \(P_1\): - \((\texttt{customer} \bowtie \texttt{orders}) \bowtie \texttt{lineitem}\) + \(\NJoin{(\NJoin{\texttt{customer}}{\texttt{orders}})}{\texttt{lineitem}}\) с реализацией обоих соединений через Hash Join, для чего сначала полностью считываются \(P_C + P_O = 1{,}26 \cdot 10^5\) страниц для построения хеш-таблицы, после чего промежуточный результат размером @@ -437,10 +451,10 @@ \section{Цель оптимизатора} minimum height=1.2cm, align=center, font=\small} ] \node[font=\small\bfseries] at (-3.5, 8.8) {План \(P_1\)}; - \node[op] (p1proj) at (-3.5, 7.7) {\(\pi_{\texttt{ename}}\)}; - \node[op] (p1sig2) at (-3.5, 6.2) {\(\sigma_{\texttt{dname}=\texttt{'Toy'}}\)}; - \node[op] (p1sig1) at (-3.5, 4.7) {\(\sigma_{\texttt{Emp.did}=\texttt{Dept.did}}\)}; - \node[op] (p1cp) at (-3.5, 3.2) {\(\texttt{Emp} \times \texttt{Dept}\)\\\scriptsize \(50{,}050\) чтений}; + \node[op] (p1proj) at (-3.5, 7.7) {\(\ProjectionOp{\texttt{ename}}\)}; + \node[op] (p1sig2) at (-3.5, 6.2) {\(\SelOp{\texttt{dname}=\texttt{'Toy'}}\)}; + \node[op] (p1sig1) at (-3.5, 4.7) {\(\SelOp{\texttt{Emp.did}=\texttt{Dept.did}}\)}; + \node[op] (p1cp) at (-3.5, 3.2) {\(\CProd{\texttt{Emp}}{\texttt{Dept}}\)\\\scriptsize \(50{,}050\) чтений}; \node[rel] (p1emp) at (-5.2, 1.7) {\texttt{Emp}}; \node[rel] (p1dept) at (-1.8, 1.7) {\texttt{Dept}}; @@ -451,10 +465,10 @@ \section{Цель оптимизатора} \draw[->] (p1sig2) -- (p1proj); \node[font=\small\bfseries] at (3.5, 8.8) {План \(P_2\)}; - \node[op] (p2proj) at (3.5, 7.7) {\(\pi_{\texttt{ename}}\)}; + \node[op] (p2proj) at (3.5, 7.7) {\(\ProjectionOp{\texttt{ename}}\)}; \node[op] (p2join) at (3.5, 5.5) {Index NL Join\\\(\texttt{Emp.did}=\texttt{Dept.did}\)\\\scriptsize \(23\) чтения}; \node[rel] (p2emp) at (1.6, 3.5) {\texttt{Emp}}; - \node[op] (p2sig) at (5.4, 3.5) {\(\sigma_{\texttt{dname}=\texttt{'Toy'}}\)\\\scriptsize \(3\) чтения}; + \node[op] (p2sig) at (5.4, 3.5) {\(\SelOp{\texttt{dname}=\texttt{'Toy'}}\)\\\scriptsize \(3\) чтения}; \node[rel] (p2dept) at (5.4, 1.8) {\texttt{Dept}}; \draw[->] (p2emp) -- (p2join); @@ -621,7 +635,7 @@ \subsection{Группы эквивалентности и выражения} Группа в Memo представляет собой множество выражений, которые эквивалентны с точки зрения логического результата. Например, для отношений \(A\), \(B\) и -\(C\) выражения \( A \bowtie (B \bowtie C) ; \qquad{} (A \bowtie B) \bowtie C \) +\(C\) выражения \( \NJoin{A}{(\NJoin{B}{C})} ; \qquad{} \NJoin{(\NJoin{A}{B})}{C} \) при выполнении условий ассоциативности соединения принадлежат одной группе, поскольку возвращают одинаковый результат. Причем физические реализации этих выражений могут отличаться, а стоимость может зависеть от кардинальности @@ -828,9 +842,9 @@ \subsection{Правила трансформации} вычисления и выбрать тот, при котором стоимость физического плана меньше. \[ - R \bowtie_{\theta} S + \InnerJoin{R}{\theta}{S} \Rightarrow - S \bowtie_{\theta} R. + \InnerJoin{S}{\theta}{R}. \] Это правило применимо для внутренних и полных внешних соединений. @@ -841,9 +855,9 @@ \subsection{Правила трансформации} выбрать тот, при котором промежуточные результаты имеют наименьший размер. \[ - (R \bowtie_{\theta_1} S) \bowtie_{\theta_2} T + \InnerJoin{(\InnerJoin{R}{\theta_1}{S})}{\theta_2}{T} \Rightarrow - R \bowtie_{\theta'_1} (S \bowtie_{\theta'_2} T), + \InnerJoin{R}{\theta'_1}{(\InnerJoin{S}{\theta'_2}{T})}, \] где \(\theta'_1\) и \(\theta'_2\) --- результат перераспределения конъюнктов @@ -865,16 +879,16 @@ \subsection{Правила трансформации} кардинальность входа. \[ - \sigma_p(R \bowtie_{\theta} S) + \Sel{p}{\InnerJoin{R}{\theta}{S}} \Rightarrow - \sigma_p(R) \bowtie_{\theta} S, + \InnerJoin{\Sel{p}{R}}{\theta}{S}, \quad attrs(p) \subseteq attrs(R), \] \[ - \sigma_p(R \bowtie_{\theta} S) + \Sel{p}{\InnerJoin{R}{\theta}{S}} \Rightarrow - R \bowtie_{\theta} \sigma_p(S), + \InnerJoin{R}{\theta}{\Sel{p}{S}}, \quad attrs(p) \subseteq attrs(S). \] @@ -892,18 +906,18 @@ \subsection{Правила трансформации} хеш-соединение или соединение слиянием. \[ - \sigma_p(R \bowtie_{\theta} S) + \Sel{p}{\InnerJoin{R}{\theta}{S}} \Rightarrow - R \bowtie_{\theta \land p} S. + \InnerJoin{R}{\theta \land p}{S}. \] Частный случай для декартова произведения превращает его в полноценное соединение с предикатом: \[ - \sigma_p(R \times S) + \Sel{p}{\CProd{R}{S}} \Rightarrow - R \bowtie_p S. + \InnerJoin{R}{p}{S}. \] Условиями активации переноса фильтрации в предикат соединения являются: @@ -918,9 +932,9 @@ \subsection{Правила трансформации} или открывают другие возможности применения правил. \[ - \sigma_p(R) \bowtie_{\theta} S + \InnerJoin{\Sel{p}{R}}{\theta}{S} \Rightarrow - \sigma_p(R \bowtie_{\theta} S), + \Sel{p}{\InnerJoin{R}{\theta}{S}}, \quad attrs(p) \subseteq attrs(R). \] @@ -939,9 +953,9 @@ \subsection{Правила трансформации} удален. \[ - \sigma_{p_1 \land p_2 \land \ldots \land p_n}(R) + \Sel{p_1 \land p_2 \land \ldots \land p_n}{R} \Rightarrow - \sigma_{p_1}(\sigma_{p_2}(\ldots\sigma_{p_n}(R)\ldots)). + \Sel{p_1}{\Sel{p_2}{\ldots\Sel{p_n}{R}\ldots}}. \] Правило проталкивания проекции добавляет на каждый из входов соединения @@ -950,13 +964,11 @@ \subsection{Правила трансформации} кортежей и сокращают объем данных, передаваемых между операторами. \[ - \pi_L(R \bowtie_{\theta} S) + \Projection{L}{\InnerJoin{R}{\theta}{S}} \Rightarrow - \pi_L\bigl( - \pi_{L_R \cup J_R}(R) - \bowtie_{\theta} - \pi_{L_S \cup J_S}(S) - \bigr), + \Projection{L}{ + \InnerJoin{\Projection{L_R \cup J_R}{R}}{\theta}{\Projection{L_S \cup J_S}{S}} + }, \] где \(L_R = L \cap attrs(R)\), \(L_S = L \cap attrs(S)\), @@ -975,21 +987,21 @@ \subsection{Правила трансформации} ассоциативность, проталкивание фильтрации и другие. \[ - \sigma_p(R \operatorname{LOJ}_{\theta} S) + \Sel{p}{\LOJ{R}{\theta}{S}} \Rightarrow - \sigma_p(R \bowtie_{\theta} S), + \Sel{p}{\InnerJoin{R}{\theta}{S}}, \] \[ - \sigma_p(R \operatorname{ROJ}_{\theta} S) + \Sel{p}{\ROJ{R}{\theta}{S}} \Rightarrow - \sigma_p(R \bowtie_{\theta} S), + \Sel{p}{\InnerJoin{R}{\theta}{S}}, \] \[ - \sigma_p(R \operatorname{FOJ}_{\theta} S) + \Sel{p}{\FOJ{R}{\theta}{S}} \Rightarrow - \sigma_p(R \bowtie_{\theta} S). + \Sel{p}{\InnerJoin{R}{\theta}{S}}. \] Условиями активации преобразования внешнего соединения во внутреннее являются: @@ -1012,13 +1024,11 @@ \subsection{Правила трансформации} часть нагрузки с финальной агрегации. \[ - \gamma_{G; F}(R \bowtie_{R.k = S.k} S) + \Agg{G}{F}{\InnerJoin{R}{R.k = S.k}{S}} \Rightarrow - \gamma_{G; F'}\bigl( - \gamma_{G_R \cup \{R.k\}; F_R}^{partial}(R) - \bowtie_{R.k = S.k} - S - \bigr), + \Agg{G}{F'}{ + \InnerJoin{\AggP{G_R \cup \{R.k\}}{F_R}{R}}{R.k = S.k}{S} + }, \] где \(G\) --- ключи группировки, \(F\) --- набор агрегатных функций, @@ -1043,9 +1053,9 @@ \subsection{Правила трансформации} теряет строки агрегируемой стороны. \[ - \gamma_{G; F_R}(R \bowtie_{R.k = S.k} S) + \Agg{G}{F_R}{\InnerJoin{R}{R.k = S.k}{S}} \Rightarrow - \gamma_{G; F_R}(R) \bowtie_{R.k = S.k} S. + \InnerJoin{\Agg{G}{F_R}{R}}{R.k = S.k}{S}. \] Условиями активации перестановки агрегации и соединения являются: @@ -1155,14 +1165,14 @@ \subsection{Оценка кардинальности} кардинальность результата оценивается как \[ - |\sigma_p(R)| = |R| \cdot sel(p). + |\Sel{p}{R}| = |R| \cdot sel(p). \] Для соединения на основе предиката равенства независимых атрибутов часто применяется эвристика: \[ - |R \bowtie_{R.a = S.b} S| \approx + |\InnerJoin{R}{R.a = S.b}{S}| \approx \frac{|R| \cdot |S|}{\max(NDV(R.a), NDV(S.b))}, \] From 456171fdfc7263bc145189a5371b12d7fbdd6097 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 11 May 2026 17:01:11 +0300 Subject: [PATCH 027/120] Fix --- report/thesis.tex | 69 +++++++++++++++++++++++++++++++++++------------ 1 file changed, 52 insertions(+), 17 deletions(-) diff --git a/report/thesis.tex b/report/thesis.tex index 750ace7..ff01f7e 100644 --- a/report/thesis.tex +++ b/report/thesis.tex @@ -46,6 +46,8 @@ \newcommand{\LOJ}[3]{#1 \ltimes_{#2} #3} \newcommand{\ROJ}[3]{#1 \rtimes_{#2} #3} \newcommand{\FOJ}[3]{#1 \mathbin{\ltimes\!\!\!\rtimes}_{#2} #3} +\newcommand{\Tr}[1]{[\![ #1 ]\!]} +\newcommand{\Apply}[2]{#1 \mathbin{\mathcal{A}} #2} \setlength{\tabcolsep}{3pt} \setcounter{page}{2} @@ -272,29 +274,62 @@ \subsection{Основные операторы реляционной алге отсутствующие в \(S\). \item \emph{Агрегация} \(\Agg{G}{f}{R}\) --- разбивает отношение \(R\) на непересекающиеся множества по набору атрибутов \(G\) и вычисляет - агрегированные значения функции \(f\) (такие как \COUNT, - \SUM, \AVG, \MIN, \MAX) по каждому - такому множеству. + агрегированные значения функции \(f\) по каждому такому множеству. + Примеры функций: \COUNT, \SUM, \AVG, \MIN, \MAX. + \item \emph{Apply} \(\Apply{R}{S(t)}\) --- для каждого кортежа \(t\) + отношения \(R\) вычисляет параметризованное им отношение \(S(t)\) и + конкатенирует \(t\) с каждым кортежем результата: + \[ + \Apply{R}{S(t)} = \bigcup_{t \in R} \{t\} \times S(t). + \] + В отличие от соединения, правая сторона зависит от текущего кортежа + левой и используется для представления коррелированных подзапросов. \end{enumerate} \subsection{Преобразование синтаксического дерева к реляционной алгебре} -Полученное после лексического и синтаксического разбора абстракное -синтаксическое дерево обычно преобразовывают в форму операторов реляционной -алгебры. +Полученное после лексического и синтаксического разбора абстрактное +синтаксическое дерево преобразуется в выражение реляционной алгебры. Этот +процесс определяется структурной индукцией по дереву запроса. Введём оператор +конверсии \(\Tr{\cdot}\), сопоставляющий узлу синтаксического дерева его +алгебраическое представление. Тогда правила трансляции записываются в виде +правил вывода. Если для всех поддеревьев известны алгебраические представления, +то алгебраическое представление составной конструкции выражается через них. -Это преобразование выполняется по фиксированному набору правил, отражающих -семантику конструкций SQL: - -\begin{itemize} - \item блок \texttt{SELECT-FROM-WHERE} превращается в проекцию над селекцией - над декартовым произведением соединяемых таблиц; - \item конструкция \GROUPBY{} порождает оператор агрегации \(\Agg{G}{f}{R}\); - \item \ORDERBY{} --- запрос на требуемое физическое свойство - сортированности; - \item вложенные подзапросы --- оператор Apply, который впоследствии может быть +\begin{enumerate} + \item \emph{Базовый случай.} Для ссылки на таблицу \(T\) выполняется + \(\Tr{T} = T\). + \item \emph{Блок \texttt{SELECT-FROM-WHERE}.} Пусть \(\bar{T} = T_1, \ldots, + T_n\) --- ссылки на таблицы или вложенные подзапросы, \(p\) --- + предикат фильтрации, \(\bar{a} = a_1, \ldots, a_k\) --- список + выражений проекции. Тогда + \[ + \frac{\Tr{T_1} = E_1 \quad \cdots \quad \Tr{T_n} = E_n} + {\Tr{\SELECT\ \bar{a}\ \FROM\ \bar{T}\ \WHERE\ p} + = \Projection{\bar{a}}{\Sel{p}{E_1 \times \cdots \times E_n}}}. + \] + \item \emph{Группировка.} Пусть \(\bar{g} = g_1, \ldots, g_m\) --- список + группирующих атрибутов, \(\bar{f} = f_1, \ldots, f_l\) --- список + агрегирующих функций. Тогда + \[ + \frac{\Tr{T_1} = E_1 \quad \cdots \quad \Tr{T_n} = E_n} + {\Tr{\SELECT\ \bar{g}, \bar{f}\ \FROM\ \bar{T}\ \WHERE\ p\ \GROUPBY\ \bar{g}} + = \Agg{\bar{g}}{\bar{f}}{\Sel{p}{E_1 \times \cdots \times E_n}}}. + \] + \item \emph{Вложенные подзапросы.} Пусть \(Q\) --- внешний блок, а \(Q'(t)\) + --- коррелированный подзапрос, зависящий от кортежа \(t\) из + \(\Tr{Q}\). Тогда + \[ + \frac{\Tr{Q} = E \quad \Tr{Q'(t)} = E'(t)} + {\Tr{Q\ \text{с подзапросом}\ Q'} = \Apply{E}{E'(t)}}, + \] + где \(\mathcal{A}\) --- оператор Apply, который впоследствии может быть удалён процедурой декорреляции. -\end{itemize} + \item \emph{Сортировка.} Конструкция \ORDERBY{} не выражается в виде + операторов реляционной алгебры. Вместо этого она задает требуемое + физическое свойство упорядоченности результата, учитываемое на этапе + выбора физического плана. +\end{enumerate} Данное преобразование необходимо по нескольким причинам. Во-первых, реляционная алгебра обладает формализованным набором правил эквивалентности, которые From ca548b38d6e7e75707c8792ead86e5f93416ddec Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 11 May 2026 17:18:36 +0300 Subject: [PATCH 028/120] Add example --- report/thesis.tex | 89 ++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 81 insertions(+), 8 deletions(-) diff --git a/report/thesis.tex b/report/thesis.tex index ff01f7e..7784cbc 100644 --- a/report/thesis.tex +++ b/report/thesis.tex @@ -331,14 +331,87 @@ \subsection{Преобразование синтаксического дере выбора физического плана. \end{enumerate} -Данное преобразование необходимо по нескольким причинам. Во-первых, реляционная -алгебра обладает формализованным набором правил эквивалентности, которые -позволяют оптимизатору корректно преобразовывать план запроса без изменения -семантики. Во-вторых, алгебраическое представление позволяет единообразно -обрабатывать различные синтаксические формы SQL-запросов, порождающие одинаковые -логические планы. Наконец, разделение на логические и физические операторы -позволяет оптимизатору независимо исследовать порядок операций и алгоритмы их -реализации. +Приведём пример применения этих правил к SQL-запросу из +листинга~\ref{lst:correlated_subquery}. Трансляция этого запроса в реляционную +алгебру выполняется по следующим шагам: + +\begin{listing}[H] + \caption{Запрос с коррелированным подзапросом.} + \label{lst:correlated_subquery} + \begin{minted}[style=bw, breaklines, frame=single, fontsize = \footnotesize, linenos=false, xleftmargin = 1.5em]{SQL} + SELECT e.name + FROM Employee e + WHERE e.salary > ( + SELECT AVG(salary) + FROM Employee + WHERE dept = e.dept + ) + \end{minted} +\end{listing} + +\begin{enumerate} + \item По базовому правилу оба вхождения таблицы остаются как есть + \(\Tr{\text{Employee}} = \text{Employee}\). + \item Внутренний подзапрос + \(Q'(e) = (\SELECT\ \AVG(\text{salary})\ \FROM\ \ldots \WHERE\ \ldots\)) + транслируется по правилу группировки, где \(\bar{g} = \varnothing\), + \(\bar{f} = \AVG(\text{salary})\): + \[ + \Tr{Q'(e)} = \Agg{\varnothing}{\AVG(\text{salary})}{\Sel{\text{dept} = e.\text{dept}}{\text{Employee}}}. + \] + Подзапрос коррелирован, так как зависит от атрибута \(e.\text{dept}\) + внешнего кортежа. + \item По правилу вложенных подзапросов результат \(\Tr{Q'(e)}\) присоединяется + к внешнему блоку оператором \(\mathcal{A}\), добавляя к каждому кортежу + столбец \(c\) с вычисленным значением средней зарплаты. + \item Оставшаяся часть внешнего запроса переводится по правилу + \texttt{SELECT-FROM-WHERE} с \(\bar{a} = (e.\text{name})\) и + \(p = (e.\text{salary} > c)\). +\end{enumerate} + +Итоговое алгебраическое выражение имеет вид +\[ + \Projection{e.\text{name}}{\Sel{e.\text{salary} > c}{\Apply{\text{Employee}\;e}{\Agg{\varnothing}{\text{AVG}(\text{salary}) \to c}{\Sel{\text{dept} = e.\text{dept}}{\text{Employee}}}}}}. +\] +Соответствующее дерево операторов изображено на +рисунке~\ref{fig:correlated_subquery_tree}. + +\begin{figure}[!htb]\centering +\begin{tikzpicture}[ + rel/.style={rectangle, draw, rounded corners=2pt, minimum width=2.2cm, + minimum height=0.8cm, align=center, font=\small}, + op/.style={rectangle, draw, fill=gray!12, minimum width=2.6cm, + minimum height=0.9cm, align=center, font=\small}, + level distance=1.15cm +] + \node[op] (proj) at (0, 6.5) {\(\Projection{e.\text{name}}{}\)}; + \node[op] (sel1) at (0, 5.2) {\(\Sel{e.\text{salary} > c}{}\)}; + \node[op] (apply) at (0, 3.9) {\(\mathcal{A}\)}; + \node[rel] (emp1) at (-2.8, 2.6) {\texttt{Employee} \(e\)}; + \node[op] (agg) at (2.8, 2.6) {\(\Agg{\varnothing}{\text{AVG}(\text{salary}) \to c}{}\)}; + \node[op] (sel2) at (2.8, 1.3) {\(\Sel{\text{dept} = e.\text{dept}}{}\)}; + \node[rel] (emp2) at (2.8, 0) {\texttt{Employee}}; + + \draw[->] (sel1) -- (proj); + \draw[->] (apply) -- (sel1); + \draw[->] (emp1) -- (apply); + \draw[->] (agg) -- (apply); + \draw[->] (sel2) -- (agg); + \draw[->] (emp2) -- (sel2); +\end{tikzpicture} +\caption{Дерево реляционной алгебры для запроса из +листинга~\ref{lst:correlated_subquery}.}% +\label{fig:correlated_subquery_tree} +\end{figure} + +Преобразование в форму реляционной алгебры необходимо по нескольким причинам. +Во-первых, это позволяет использовать формализованный набор правил +эквивалентности, который позволяют оптимизатору корректно преобразовывать план +запроса без изменения семантики. Во-вторых, алгебраическое представление +позволяет единообразно обрабатывать различные синтаксические формы SQL-запросов, +порождающие одинаковые логические планы. Наконец, разделение на логические и +физические операторы позволяет оптимизатору независимо исследовать порядок +операций и алгоритмы их реализации. \section{Поиск оптимального плана} From bb99b46dad6004e05d0c8aa70f6b6d4624c8f338 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 11 May 2026 17:28:27 +0300 Subject: [PATCH 029/120] Fix --- report/thesis.tex | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/report/thesis.tex b/report/thesis.tex index 7784cbc..a30b6d4 100644 --- a/report/thesis.tex +++ b/report/thesis.tex @@ -415,16 +415,22 @@ \subsection{Преобразование синтаксического дере \section{Поиск оптимального плана} -Эквивалентность планов обеспечивается свойствами реляционной алгебры. - -\begin{Example} +Эквивалентность планов, исследуемых в процессе поиска оптимального, +обеспечивается применением алгебраических тождеств +(пример~\ref{ex:join_comm},~\ref{ex:join_ass}). Подробнее правила +эквивалентности реляционной алгебры рассматриваются в +разделе~\ref{sec:transformation_rules}. + +\begin{Example}% + \label{ex:join_comm} Коммутативность соединения: \[ \NJoin{R}{S} \equiv \NJoin{S}{R}. \] \end{Example} -\begin{Example} +\begin{Example}% + \label{ex:join_ass} Ассоциативность соединения: \[ \NJoin{(\NJoin{R}{S})}{T} \equiv \NJoin{R}{(\NJoin{S}{T})}. @@ -919,7 +925,7 @@ \subsection{Алгоритм поиска} \label{fig:cascades-tasks} \end{figure} -\section{Правила трансформации и реализации} +\section{Правила трансформации и реализации}\label{sec:transformation_rules} Правило в оптимизаторе Cascades состоит из образца, условия применимости и алгоритма применения. Образец описывает форму логического выражения, к которому From dae5cac79879358552201acd374a00636471305e Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 11 May 2026 17:33:45 +0300 Subject: [PATCH 030/120] Fix --- report/thesis.tex | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/report/thesis.tex b/report/thesis.tex index a30b6d4..45ba780 100644 --- a/report/thesis.tex +++ b/report/thesis.tex @@ -437,9 +437,9 @@ \section{Поиск оптимального плана} \] \end{Example} -Оптимальность запроса оценивается с помощью функции стоимости -\(\mathrm{cost} \colon \mathcal{P}_L \to \mathbb{R}_{\ge 0}\), где -\(\mathcal{P}_{L}\) --- множество эквивалентных физических планов. Это +Оптимальность запроса принято оценивать с помощью функции стоимости +\(\mathrm{cost} \colon \mathcal{P}_L \to \mathbb{R}_{\ge 0}\)~\cite{Selinger1979}, +где \(\mathcal{P}_{L}\) --- множество эквивалентных физических планов. Это отображение оценивает затраты на исполнение плана исходя из конкретных реализаций алгоритмов, а также основываясь на эвристиках и статистике о данных в отношениях. Другими словами, цель оптимизатора состоит в том, чтобы для входного From 2795f12aa3a155e09e8826a4eacd5249031e8bb6 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 11 May 2026 17:43:17 +0300 Subject: [PATCH 031/120] Fix --- report/thesis.tex | 39 ++++++++++++++------------------------- 1 file changed, 14 insertions(+), 25 deletions(-) diff --git a/report/thesis.tex b/report/thesis.tex index 45ba780..f40837c 100644 --- a/report/thesis.tex +++ b/report/thesis.tex @@ -286,7 +286,7 @@ \subsection{Основные операторы реляционной алге левой и используется для представления коррелированных подзапросов. \end{enumerate} -\subsection{Преобразование синтаксического дерева к реляционной алгебре} +\subsection{Преобразование синтаксического дерева к реляционной алгебре}\label{sec:translation} Полученное после лексического и синтаксического разбора абстрактное синтаксическое дерево преобразуется в выражение реляционной алгебры. Этот @@ -416,26 +416,9 @@ \subsection{Преобразование синтаксического дере \section{Поиск оптимального плана} Эквивалентность планов, исследуемых в процессе поиска оптимального, -обеспечивается применением алгебраических тождеств -(пример~\ref{ex:join_comm},~\ref{ex:join_ass}). Подробнее правила -эквивалентности реляционной алгебры рассматриваются в -разделе~\ref{sec:transformation_rules}. - -\begin{Example}% - \label{ex:join_comm} - Коммутативность соединения: - \[ - \NJoin{R}{S} \equiv \NJoin{S}{R}. - \] -\end{Example} - -\begin{Example}% - \label{ex:join_ass} - Ассоциативность соединения: - \[ - \NJoin{(\NJoin{R}{S})}{T} \equiv \NJoin{R}{(\NJoin{S}{T})}. - \] -\end{Example} +обеспечивается применением алгебраических тождеств, например ассоциативности и +коммутативности соединений. Правила эквивалентности реляционной алгебры подробно +рассматриваются в разделе~\ref{sec:transformation_rules}. Оптимальность запроса принято оценивать с помощью функции стоимости \(\mathrm{cost} \colon \mathcal{P}_L \to \mathbb{R}_{\ge 0}\)~\cite{Selinger1979}, @@ -448,9 +431,14 @@ \section{Поиск оптимального плана} P^* = \arg\min_{P \in \mathcal{P}_L} \mathrm{cost}(P). \] -Одной из подзадач рамках построения оптимального плана является определение -порядка выполнения соединений. Для соединения \(n\) отношений количество -возможных порядков их выполнения выражается как +Одной из подзадач в рамках построения оптимального плана является определение +порядка выполнения соединений. Благодаря правилам эквивалентости, таким как +ассоциативность и коммутативность соединения +(раздел~\ref{sec:transformation_rules}), одно и то же множество соединяемых +отношений допускает большое количество эквивалентных порядков вычисления. +Различные порядки имеют кардинально разные стоимости, ввиду особенностей +алгоритмов, реализующих соединение. Для \(n\) отношений количество таких +порядков выражается как \(O(n! \cdot C_{n-1}) = O(\frac{(2n-2)!}{(n-1)!})\)~\cite{IntroToTheJoinOrderingProblem}, где \(C_k\) --- \(k\)-е число Каталана. Таким образом, пространство поиска растет экспоненциально с увеличением числа соединяемых таблиц. @@ -599,7 +587,8 @@ \section{Поиск оптимального плана} \begin{itemize} \item План \(P_1\) соответствует прямому преобразованию запроса в реляционную - алгебру. Если рассматривать стоимость исполнения такого плана в модели + алгебру, выполненному по алгоритму из раздела~\ref{sec:translation}. + Если рассматривать стоимость исполнения такого плана в модели итераторов, то каждый оператор запрашивает у дочернего следующий кортеж через вызов \texttt{next()}, поэтому \(\sigma\) и~\(\pi\) работают в режиме конвейера и не инициируют обращений к диску. Вся стоимость From 3c571b997c8f035f7bfc7278ba5586431b85b1ff Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 11 May 2026 20:42:13 +0300 Subject: [PATCH 032/120] Fix --- report/thesis.tex | 442 ++++++++++++++++++---------------------------- 1 file changed, 176 insertions(+), 266 deletions(-) diff --git a/report/thesis.tex b/report/thesis.tex index f40837c..20bd546 100644 --- a/report/thesis.tex +++ b/report/thesis.tex @@ -48,6 +48,15 @@ \newcommand{\FOJ}[3]{#1 \mathbin{\ltimes\!\!\!\rtimes}_{#2} #3} \newcommand{\Tr}[1]{[\![ #1 ]\!]} \newcommand{\Apply}[2]{#1 \mathbin{\mathcal{A}} #2} +\newcommand{\TransRule}[3]{% + \ensuremath{% + \begin{array}{c} + #1 \\[1pt] + \xRightarrow{\,#2\,} \\[1pt] + #3 + \end{array}% + }% +} \setlength{\tabcolsep}{3pt} \setcounter{page}{2} @@ -922,289 +931,45 @@ \section{Правила трансформации и реализации}\labe требования: отсутствие внешних ссылок, тип соединения, сохранение семантики \texttt{NULL}, совместимость свойств и другие ограничения. -Формально правило можно представить как отображение: - +Формально правило записывается в виде \[ - r : pattern(e) \land condition(e) \rightarrow substitute(e), + r : \TransRule{pattern(e)}{condition(e)}{substitute(e)}, \] - где \(e\) --- исходное выражение, \(pattern\) --- структурный образец, -\(condition\) --- предикат применимости, а \(substitute\) --- выражение-замена. +\(condition\) --- предикат применимости, \(substitute\) --- выражение-замена. Для правил трансформации результатом применения является новое логическое -выражение в группе, а для правил реализации --- физическое выражение в той же +выражение в группе, а для правил реализации физическое выражение в той же группе. \subsection{Правила трансформации} -Правила трансформации применяются для исследования пространства эквивалентных -планов. Каждое из них задается тремя частями: паттерном, выражением-заменой и -условием активации. - -Правило коммутативности соединения переставляет местами левый и правый входы -соединения. Эти преобразования позволяют оптимизатору рассмотреть оба порядка -вычисления и выбрать тот, при котором стоимость физического плана меньше. - -\[ - \InnerJoin{R}{\theta}{S} - \Rightarrow - \InnerJoin{S}{\theta}{R}. -\] - -Это правило применимо для внутренних и полных внешних соединений. - -Правило ассоциативности соединения изменяет группировку цепочки соединений и -перераспределяет конъюнкты предикатов по ее узлам. Эти преобразования также -позволяют оптимизатору исследовать различные порядки выполнения соединений и -выбрать тот, при котором промежуточные результаты имеют наименьший размер. - -\[ - \InnerJoin{(\InnerJoin{R}{\theta_1}{S})}{\theta_2}{T} - \Rightarrow - \InnerJoin{R}{\theta'_1}{(\InnerJoin{S}{\theta'_2}{T})}, -\] - -где \(\theta'_1\) и \(\theta'_2\) --- результат перераспределения конъюнктов -исходных предикатов \(\theta_1 \land \theta_2\) по тем узлам, в схемы которых -попадают все необходимые им атрибуты. - -Условиями активации ассоциативности соединения являются: -\begin{itemize} - \item оба соединения являются внутренними; - \item конъюнкты предиката \(\theta_1 \land \theta_2\) можно перераспределить - так, что атрибуты каждого конъюнкта в \(\theta'_2\) принадлежат - \(attrs(S) \cup attrs(T)\), конъюнкты, ссылающиеся на \(attrs(R)\), - остаются в \(\theta'_1\). -\end{itemize} - -Правило проталкивания фильтрации через соединение перемещает фильтрующий -предикат над соединением в один из его входов. Эти преобразования позволяют -применить селективный предикат до соединения и тем самым уменьшить -кардинальность входа. - -\[ - \Sel{p}{\InnerJoin{R}{\theta}{S}} - \Rightarrow - \InnerJoin{\Sel{p}{R}}{\theta}{S}, - \quad attrs(p) \subseteq attrs(R), -\] - -\[ - \Sel{p}{\InnerJoin{R}{\theta}{S}} - \Rightarrow - \InnerJoin{R}{\theta}{\Sel{p}{S}}, - \quad attrs(p) \subseteq attrs(S). -\] - -Условиями активации проталкивания фильтрации через соединение являются: -\begin{itemize} - \item атрибуты предиката \(p\) принадлежат только одному из входов соединения; - \item соединение является внутренним, либо проталкивание выполняется в - сохраняющий вход внешнего соединения. -\end{itemize} - -Правило переноса фильтрации в предикат соединения объединяет фильтр над -соединением и предикат соединения в одно условие. Эти преобразования раскрывают -дополнительные равенства внутри предиката соединения, по которым физические -правила реализации могут построить эффективный алгоритм соединения, например -хеш-соединение или соединение слиянием. - -\[ - \Sel{p}{\InnerJoin{R}{\theta}{S}} - \Rightarrow - \InnerJoin{R}{\theta \land p}{S}. -\] - -Частный случай для декартова произведения превращает его в полноценное -соединение с предикатом: - -\[ - \Sel{p}{\CProd{R}{S}} - \Rightarrow - \InnerJoin{R}{p}{S}. -\] - -Условиями активации переноса фильтрации в предикат соединения являются: -\begin{itemize} - \item соединение является внутренним; - \item атрибуты предиката \(p\) принадлежат \(attrs(R) \cup attrs(S)\). -\end{itemize} - -Правило поднятия фильтрации над соединением переносит фильтр из одного из входов -соединения на уровень над самим соединением. Эти преобразования позволяют -объединить локальный фильтр с другими предикатами, расположенными выше по плану, -или открывают другие возможности применения правил. - -\[ - \InnerJoin{\Sel{p}{R}}{\theta}{S} - \Rightarrow - \Sel{p}{\InnerJoin{R}{\theta}{S}}, - \quad attrs(p) \subseteq attrs(R). -\] - -Условиями активации поднятия фильтрации над соединением являются: -\begin{itemize} - \item атрибуты предиката \(p\) принадлежат только той ветви, из которой он - поднимается; - \item соединение является внутренним, либо подъем выполняется с сохраняющей - стороны внешнего соединения. -\end{itemize} - -Правило разбиения выборки на конъюнкты заменяет фильтр с конъюнктивным -предикатом на цепочку фильтров с отдельными подвыражениями. Эти преобразования -дают оптимизатору большую гранулярность для последующих трансформаций: каждый -конъюнкт может быть независимо протолкнут в нужную ветвь плана, переписан или -удален. - -\[ - \Sel{p_1 \land p_2 \land \ldots \land p_n}{R} - \Rightarrow - \Sel{p_1}{\Sel{p_2}{\ldots\Sel{p_n}{R}\ldots}}. -\] - -Правило проталкивания проекции добавляет на каждый из входов соединения -проекцию, удаляющую атрибуты, не используемые ни предикатом соединения, ни -операторами выше по плану. Эти преобразования уменьшают ширину промежуточных -кортежей и сокращают объем данных, передаваемых между операторами. - -\[ - \Projection{L}{\InnerJoin{R}{\theta}{S}} - \Rightarrow - \Projection{L}{ - \InnerJoin{\Projection{L_R \cup J_R}{R}}{\theta}{\Projection{L_S \cup J_S}{S}} - }, -\] - -где \(L_R = L \cap attrs(R)\), \(L_S = L \cap attrs(S)\), -\(J_R = attrs(\theta) \cap attrs(R)\), \(J_S = attrs(\theta) \cap attrs(S)\). - -Условием активации проталкивания проекции является нетривиальность хотя бы -одной из внутренних проекций: \(L_R \cup J_R \subsetneq attrs(R)\) или -\(L_S \cup J_S \subsetneq attrs(S)\). Иначе правило не уменьшает ширину -кортежей и его применение бесполезно. - -Правило преобразования внешнего соединения во внутреннее заменяет левое и -правое, а также полное внешнее соединение на внутреннее соединение, если -расположенный над ними фильтр гарантированно отбрасывает все строки с -\texttt{NULL}-дополнением. Эти преобразования открывают для подплана с внешним -соединением правила, ограниченные внутренним соединением: коммутативность, -ассоциативность, проталкивание фильтрации и другие. - -\[ - \Sel{p}{\LOJ{R}{\theta}{S}} - \Rightarrow - \Sel{p}{\InnerJoin{R}{\theta}{S}}, -\] - -\[ - \Sel{p}{\ROJ{R}{\theta}{S}} - \Rightarrow - \Sel{p}{\InnerJoin{R}{\theta}{S}}, -\] - -\[ - \Sel{p}{\FOJ{R}{\theta}{S}} - \Rightarrow - \Sel{p}{\InnerJoin{R}{\theta}{S}}. -\] - -Условиями активации преобразования внешнего соединения во внутреннее являются: -\begin{itemize} - \item для левого внешнего соединения предикат \(p\) является - null-отбрасывающим относительно атрибутов \(S\); - \item для правого внешнего соединения предикат \(p\) является - null-отбрасывающим относительно атрибутов \(R\); - \item для полного внешнего соединения предикат \(p\) является - null-отбрасывающим относительно атрибутов как \(R\), так и \(S\). -\end{itemize} +Правила трансформации задают эквивалентные преобразования логических выражений и +тем самым определяют пространство планов, исследуемое оптимизатором. Наиболее +важные из них представлены в таблице~\ref{tbl:transformation_rules}. +\begin{Definition} Null-отбрасывающим называется предикат, который при замене всех атрибутов соответствующей стороны на \texttt{NULL} принимает значение, отличное от \texttt{TRUE}. +\end{Definition} -Правило проталкивания агрегации ниже соединения добавляет частичную группировку -на одном из входов соединения, оставляя финальную комбинацию на верхнем уровне. -Эти преобразования уменьшают число строк, участвующих в соединении, и снимают -часть нагрузки с финальной агрегации. - -\[ - \Agg{G}{F}{\InnerJoin{R}{R.k = S.k}{S}} - \Rightarrow - \Agg{G}{F'}{ - \InnerJoin{\AggP{G_R \cup \{R.k\}}{F_R}{R}}{R.k = S.k}{S} - }, -\] - -где \(G\) --- ключи группировки, \(F\) --- набор агрегатных функций, -\(G_R = G \cap attrs(R)\), \(F_R\) --- частичные агрегаты по атрибутам \(R\), а -\(F'\) --- финальные комбинаторы. Например, \SUM{} раскладывается в сумму -частичных сумм, а \AVG{} --- в пару \SUM{} и \COUNT{}. +\begin{Definition} +Конъюнкты \(\theta_1 \land \theta_2\) называются перераспределяемыми, если их +можно разбить на две группы \(\theta'_1\) и \(\theta'_2\) такие, что атрибуты +\(\theta'_2\) принадлежат \(attrs(S) \cup attrs(T)\), а конъюнкты с атрибутами +из \(attrs(R)\) остаются в \(\theta'_1\). +\end{Definition} -Условиями активации проталкивания агрегации ниже соединения являются: -\begin{itemize} - \item каждая агрегатная функция в \(F\) представима композицией частичной - агрегации и финального комбинатора; - \item соединение является внутренним эквивалентным соединением по - \(R.k = S.k\); - \item частичная агрегация группирует по объединению ключей группировки стороны - \(R\) и ключа соединения \(R.k\). -\end{itemize} - -Правило перестановки агрегации и соединения полностью выполняет агрегацию до -соединения. От проталкивания агрегации ниже соединения этот случай отличается -тем, что финальная комбинация после соединения не требуется. Это возможно -тогда, когда соединение не меняет результат агрегации: оно не дублирует и не -теряет строки агрегируемой стороны. - -\[ - \Agg{G}{F_R}{\InnerJoin{R}{R.k = S.k}{S}} - \Rightarrow - \InnerJoin{\Agg{G}{F_R}{R}}{R.k = S.k}{S}. -\] - -Условиями активации перестановки агрегации и соединения являются: -\begin{itemize} - \item соединение является внутренним с предикатом равенства \(R.k = S.k\); - \item агрегатные функции \(F_R\) вычисляются только по атрибутам \(R\), что - позволяет выполнить агрегацию до того, как станут доступны атрибуты - \(S\); - \item каждой строке \(R\) при соединении соответствует ровно одна строка - \(S\) с равным значением ключа, то есть \(S.k\) уникален и каждое - значение \(R.k\) присутствует в \(S\). При нарушении уникальности - соединение размножит строки \(R\), при нарушении ссылочной целостности - часть строк \(R\) исчезнет; - \item ключи группировки \(G\) содержат \(R.k\), то есть \(R.k \in G\). - Без этого после агрегации значения \(R.k\) теряются и предикат - соединения нельзя вычислить. -\end{itemize} +\begin{Definition} +Функция агрегации называется разделяемой, если она представима композицией +частичного агрегата и финального комбинатора. Например, \SUM{} раскладывается в +сумму частичных сумм, \AVG{} --- в пару \SUM{} и \COUNT{}. +\end{Definition} \subsection{Правила реализации} -Правила реализации переводят логические операторы в физические. Например, для -логического оператора чтения таблицы могут быть применены правила: - -\begin{itemize} - \item \LogicalGet{} \(\Rightarrow\) \SeqScan; - \item \LogicalGet{} \(\Rightarrow\) \IndexScan, если имеется - индекс, совместимый с предикатом или требуемым порядком. -\end{itemize} - -Для логического соединения могут быть созданы физические операторы -\NestedLoopJoin, \HashJoin{} и \MergeJoin. Хеш-соединение -обычно требует предиката равенства по ключам, поскольку именно по ним строится -хеш-таблица. Сортировочное соединение требует, чтобы оба входа были -отсортированы по ключам соединения, либо чтобы оптимизатор мог добавить -сортировки как обеспечивающие операторы. Вложенные циклы применимы всегда, но -стоимость этого оператора может быть высокой без доступа к внутреннему отношению -с использованием индекса. - -Правила реализации агрегации выбирают между хеш-агрегацией и потоковой -агрегацией. Хеш-агрегация не требует предварительного порядка, но потребляет -память пропорционально числу групп. Потоковая агрегация эффективна при уже -отсортированном входе, поскольку каждая группа обрабатывается последовательно. -Если требуемый порядок нужен также родительскому оператору, потоковая агрегация -может оказаться выгодной даже учитывая затраты на сортировку. - -% {\color{red} TODO: псевдокод алгоритмов} +Правила реализации переводят логические операторы в физические. Используемые в +настоящей работе правила собраны в таблице~\ref{tbl:implementation_rules}. \subsection{Обеспечивающие операторы} @@ -1516,4 +1281,149 @@ \subsection{Область применения} \end{algorithmic} \end{algorithm} +\newpage{} + +\begin{scriptsize} +\begin{longtable}{|c|p{2.6cm}|c|p{4.2cm}|} + \caption{Правила трансформации.}\label{tbl:transformation_rules}\\ + \hline + № & Название & Правило & Комментарий \\ + \hline + \endfirsthead + \multicolumn{4}{l}{\small Продолжение таблицы~\ref{tbl:transformation_rules}}\\ + \hline + № & Название & Правило & Комментарий \\ + \hline + \endhead + \hline + \endfoot + \hline + \endlastfoot + + 1 & Коммутативность соединения & + \(\TransRule{\InnerJoin{R}{\theta}{S}}{\text{внутреннее или полное внешнее соединение}}{\InnerJoin{S}{\theta}{R}}\) & + Перестановка входов соединения. Позволяет рассмотреть оба порядка вычисления. \\ + \hline + + 2 & Ассоциативность соединения & + \(\TransRule{\InnerJoin{(\InnerJoin{R}{\theta_1}{S})}{\theta_2}{T}}{\substack{\text{оба соединения внутренние;}\\ \theta_1 \land \theta_2 \text{ перераспределяемы}}}{\InnerJoin{R}{\theta'_1}{(\InnerJoin{S}{\theta'_2}{T})}}\) & + Изменяет группировку цепочки соединений. Открывает альтернативные порядки и уменьшает размер промежуточных результатов. \\ + \hline + + 3 & Проталкивание фильтра в левый вход & + \(\TransRule{\Sel{p}{\InnerJoin{R}{\theta}{S}}}{\substack{attrs(p) \subseteq attrs(R); \\ \text{внутреннее соединение или сохраняющий вход}}}{\InnerJoin{\Sel{p}{R}}{\theta}{S}}\) & + Применяет селективный предикат до соединения, уменьшая кардинальность входа. \\ + \hline + + 4 & Проталкивание фильтра в правый вход & + \(\TransRule{\Sel{p}{\InnerJoin{R}{\theta}{S}}}{\substack{attrs(p) \subseteq attrs(S); \\ \text{внутреннее соединение или сохраняющий вход}}}{\InnerJoin{R}{\theta}{\Sel{p}{S}}}\) & + Симметричный случай для правого входа. \\ + \hline + + 5 & Перенос фильтра в предикат соединения & + \(\TransRule{\Sel{p}{\InnerJoin{R}{\theta}{S}}}{\substack{\text{соединение внутреннее;} \\ attrs(p) \subseteq attrs(R) \cup attrs(S)}}{\InnerJoin{R}{\theta \land p}{S}}\) & + Объединяет фильтр и предикат соединения. Может позволить применить хеш-соединение или соединение слиянием. \\ + \hline + + 6 & Превращение декартова произведения в соединение & + \(\TransRule{\Sel{p}{\CProd{R}{S}}}{\top}{\InnerJoin{R}{p}{S}}\) & + Частный случай правила~5. \\ + \hline + + 7 & Поднятие фильтра над соединением & + \(\TransRule{\InnerJoin{\Sel{p}{R}}{\theta}{S}}{\substack{attrs(p) \subseteq attrs(R); \\ \text{внутреннее соединение или сохраняющая сторона}}}{\Sel{p}{\InnerJoin{R}{\theta}{S}}}\) & + Переносит фильтр выше соединения для объединения с предикатами родительских операторов. \\ + \hline + + 8 & Разбиение фильтра на конъюнкты & + \(\TransRule{\Sel{p_1 \land \ldots \land p_n}{R}}{\top}{\Sel{p_1}{\ldots \Sel{p_n}{R} \ldots}}\) & + Позволяет обрабатывать каждый конъюнкт независимо. \\ + \hline + + 9 & Проталкивание проекции через соединение & + \(\TransRule{\Projection{L}{\InnerJoin{R}{\theta}{S}}}{\substack{L_R \cup J_R \subsetneq attrs(R) \\ \text{или}\ L_S \cup J_S \subsetneq attrs(S)}}{\Projection{L}{\InnerJoin{\Projection{L_R \cup J_R}{R}}{\theta}{\Projection{L_S \cup J_S}{S}}}}\) & + Удаляет неиспользуемые атрибуты до соединения, сокращая ширину кортежей. \\ + \hline + + 10 & Левое внешнее соединение во внутреннее & + \(\TransRule{\Sel{p}{\LOJ{R}{\theta}{S}}}{p\ \text{null-отбрасывающий по}\ attrs(S)}{\Sel{p}{\InnerJoin{R}{\theta}{S}}}\) & + Открывает для подплана правила, ограниченные внутренним соединением. \\ + \hline + + 11 & Правое внешнее соединение во внутреннее & + \(\TransRule{\Sel{p}{\ROJ{R}{\theta}{S}}}{p\ \text{null-отбрасывающий по}\ attrs(R)}{\Sel{p}{\InnerJoin{R}{\theta}{S}}}\) & + Симметричный случай. \\ + \hline + + 12 & Полное внешнее соединение во внутреннее & + \(\TransRule{\Sel{p}{\FOJ{R}{\theta}{S}}}{p\ \text{null-отбрасывающий по}\ attrs(R)\ \text{и}\ attrs(S)}{\Sel{p}{\InnerJoin{R}{\theta}{S}}}\) & + Требует null-отбрасывания с обеих сторон. \\ + \hline + + 13 & Проталкивание агрегации ниже соединения & + \(\TransRule{\Agg{G}{F}{\InnerJoin{R}{R.k = S.k}{S}}}{\substack{F\text{ разделяема;}\\ \text{внутреннее соединение по равенству}}}{\Agg{G}{F'}{\InnerJoin{\AggP{G_R \cup \{R.k\}}{F_R}{R}}{R.k=S.k}{S}}}\) & + Снижает кардинальность входа частичной группировкой. \(G_R = G \cap attrs(R)\); \(F_R\) --- частичные агрегаты по атрибутам \(R\); \(F'\) --- финальные комбинаторы. \\ + \hline + + 14 & Перестановка агрегации и соединения & + \(\TransRule{\Agg{G}{F_R}{\InnerJoin{R}{R.k=S.k}{S}}}{\substack{\text{внутреннее соединение по равенству;}\\ F_R\subseteq attrs(R);\ R.k \in G; \\ S.k\text{ уникален и полон}}}{\InnerJoin{\Agg{G}{F_R}{R}}{R.k=S.k}{S}}\) & + Полностью переносит агрегацию до соединения. \(S.k\) уникален: соединение не дублирует строки \(R\). \(S.k\) полон относительно \(R.k\): соединение не теряет строки \(R\). \\ + \hline +\end{longtable} + +\newpage{} + +\begin{longtable}{|c|p{2.8cm}|c|p{4.5cm}|} + \caption{Правила реализации.}\label{tbl:implementation_rules}\\ + \hline + № & Название & Правило & Комментарий \\ + \hline + \endfirsthead + \multicolumn{4}{l}{\small Продолжение таблицы~\ref{tbl:implementation_rules}}\\ + \hline + № & Название & Правило & Комментарий \\ + \hline + \endhead + \hline + \endfoot + \hline + \endlastfoot + + 1 & Последовательное сканирование & + \(\TransRule{\LogicalGet}{\top}{\SeqScan}\) & + Применимо к любому отношению. \\ + \hline + + 2 & Сканирование по индексу & + \(\TransRule{\LogicalGet}{\text{есть совместимый индекс}}{\IndexScan}\) & + Индекс должен быть совместим с предикатом или требуемым порядком. \\ + \hline + + 3 & Соединение вложенными циклами & + \(\TransRule{\InnerJoin{R}{\theta}{S}}{\top}{\NestedLoopJoin}\) & + Применимо при любом виде предиката. Стоимость высока без доступа по индексу к внутреннему отношению. \\ + \hline + + 4 & Хеш-соединение & + \(\TransRule{\InnerJoin{R}{\theta}{S}}{\theta\ \text{содержит равенство по ключам}}{\HashJoin}\) & + Хеш-таблица строится по ключам равенства. \\ + \hline + + 5 & Соединение слиянием & + \(\TransRule{\InnerJoin{R}{\theta}{S}}{\text{входы упорядочены по ключам соединения}}{\MergeJoin}\) & + Требуемый порядок может быть обеспечен явным оператором \Sort{}. \\ + \hline + + 6 & Хеш-агрегация & + \(\TransRule{\Agg{G}{F}{R}}{\top}{\sqlkw{HashAggregate}}\) & + Не требует упорядоченности входа. Потребляет память пропорционально числу групп. \\ + \hline + + 7 & Потоковая агрегация & + \(\TransRule{\Agg{G}{F}{R}}{\text{вход упорядочен по } G}{\sqlkw{StreamAggregate}}\) & + Эффективна при уже отсортированном входе. Может быть выгодна, если требуемый порядок нужен и родительскому оператору. \\ + \hline +\end{longtable} +\end{scriptsize} + \end{document} From 38c53e90a0744a37b28445439ccc138f39672536 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 11 May 2026 20:44:26 +0300 Subject: [PATCH 033/120] Add todos --- report/thesis.tex | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/report/thesis.tex b/report/thesis.tex index 20bd546..d02ee14 100644 --- a/report/thesis.tex +++ b/report/thesis.tex @@ -986,6 +986,9 @@ \subsection{Обеспечивающие операторы} \section{Метод ветвей и границ} +{\color{red} FIXME: Тут, конечно, маловато конкретики - хотелось бы видеть, + какую стоимостную модель вы выбрали для своей системы} + Метод ветвей и границ используется для сокращения пространства поиска и, как следствие, неасимптотического улучшения скорости работы алгоритма. В контексте Cascades ветвями являются альтернативные выражения и комбинации физических @@ -1082,6 +1085,13 @@ \subsection{Оценка стоимости} \section{Дифференциальный анализ физических планов} +{\color{red} FIXME: Тут крайне мало конкретики: пока что раздел выглядит как "за + всё хорошее и против всего плохого". Непонятно, какие конкретно системы + считаются эталонами, как конкретно вы собираетесь конвертировать извлеченный + физический план в свою модель, и не получится ли после этого преобразования + совсем другая производительность, а также почему эквивалентность планов + достаточно проверить на единственном наборе таблиц.} + Множество правил трансформации и реализации, используемых в оптимизаторах запросов, изучено довольно хорошо и описано в академической литературе. Вместе с тем условия активации правил From 84bea1fd399c50da71f0d6e0a5669170f3a3f43d Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 11 May 2026 21:08:53 +0300 Subject: [PATCH 034/120] Refactor --- benchmarks/main.cpp | 1 + .../stewkk/sql/logic/executor/executor.hpp | 376 +----------------- .../sql/logic/optimizer/cardinality.hpp | 25 ++ .../stewkk/sql/logic/optimizer/optimizer.hpp | 54 ++- include/stewkk/sql/utils/log.hpp | 21 + src/stewkk/sql/CMakeLists.txt | 1 + src/stewkk/sql/logic/executor/executor.cpp | 335 ++++++++++++++++ .../sql/logic/optimizer/cardinality.cpp | 42 ++ src/stewkk/sql/logic/optimizer/optimizer.cpp | 264 ++++++++++++ .../sql/logic/optimizer/optimizer_test.cpp | 328 +-------------- 10 files changed, 745 insertions(+), 702 deletions(-) create mode 100644 include/stewkk/sql/logic/optimizer/cardinality.hpp create mode 100644 include/stewkk/sql/utils/log.hpp create mode 100644 src/stewkk/sql/logic/optimizer/cardinality.cpp diff --git a/benchmarks/main.cpp b/benchmarks/main.cpp index 0e2d2bd..3b60b8d 100644 --- a/benchmarks/main.cpp +++ b/benchmarks/main.cpp @@ -1,6 +1,7 @@ #include #include +#include #include #include diff --git a/include/stewkk/sql/logic/executor/executor.hpp b/include/stewkk/sql/logic/executor/executor.hpp index af2c88a..9added1 100644 --- a/include/stewkk/sql/logic/executor/executor.hpp +++ b/include/stewkk/sql/logic/executor/executor.hpp @@ -1,18 +1,12 @@ #pragma once -#include -#include -#include -#include #include #include +#include -#include -#include #include #include -#include #include #include #include @@ -20,8 +14,6 @@ #include #include -// FIXME: add explicit instantiations and move implementations to .cpp - namespace stewkk::sql { using ExecExpression = std::function; @@ -80,32 +72,6 @@ Type GetExpressionType(const Expression& expr, const AttributesInfo& available_a Type GetExpressionTypeUnchecked(const Expression& expr, const AttributesInfo& available_attrs); AttributesInfo GetAttributesAfterProjection(const AttributesInfo& attrs, const PhysicalProjection& proj); -template - requires std::invocable - && std::same_as, Ret> -Value ApplyIntegersOperator(Value lhs, Value rhs) { - if (lhs.is_null || rhs.is_null) { - return Value{true}; - } - return Value{false, Op{}(lhs.value.int_value, rhs.value.int_value)}; -} - -template - requires std::invocable - && std::same_as, bool> -Value ApplyBooleanOperator(Value lhs, Value rhs) { - if (lhs.is_null || rhs.is_null) { - return Value{true}; - } - return Value{false, Op{}(lhs.value.bool_value, rhs.value.bool_value)}; -} - -struct IntPow { - int64_t operator()(int64_t base, int64_t exp) const { - return static_cast(std::pow(base, exp)); - } -}; - Value CalcExpression(const Tuple& source, const AttributesInfo& source_attrs, const Expression& expr); Tuple ApplyProjection(const Tuple& source, const AttributesInfo& source_attrs, const std::vector& expressions); bool ApplyFilter(const Tuple& source, const AttributesInfo& source_attrs, const ExecExpression& filter); @@ -115,344 +81,4 @@ boost::asio::awaitable ConcatAttrs(AttributesInfoChannel& lhs_at boost::asio::awaitable MaterializeChannel(TuplesChannel& tuples_chan); Tuple ConcatTuples(const Tuple& lhs, const Tuple& rhs); -template -Executor::Executor(SequentialScan seq_scan, boost::asio::any_io_executor executor) - : sequential_scan_(std::move(seq_scan)), expression_executor_(executor) {} - -template -boost::asio::awaitable> Executor::Execute(const PhysicalPlanNode& op) { - auto [attr_chan, tuples_chan] = co_await GetChannels(); - co_await SpawnExecutor(op, attr_chan, tuples_chan); - - auto attrs = co_await attr_chan.async_receive(boost::asio::use_awaitable); -#ifdef DEBUG - std::clog << "Received attrs in root\n"; -#endif - - Tuples result; - for (;;) { - auto buf = co_await ReceiveTuples(tuples_chan); - if (buf.empty()) { - break; - } -#ifdef DEBUG - std::clog << std::format("Received {} tuples in root\n", buf.size()); -#endif - std::move(buf.begin(), buf.end(), std::back_inserter(result)); - } - -#ifdef DEBUG - std::clog << std::format("Total {} tuples in root\n", result.size()); -#endif - co_return Ok(Relation{std::move(attrs), std::move(result)}); -} - -template -boost::asio::awaitable Executor::Execute(const PhysicalPlanNode& op, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan) { - struct ExecuteVisitor{ - boost::asio::awaitable operator()(const SeqScan& seq_scan) { - co_await executor.sequential_scan_(seq_scan.table, attr_chan, tuples_chan); - co_return; - } - boost::asio::awaitable operator()(const PhysicalProjection& projection) { - // NOTE: We are using multiset relational algebra projection (i.e. not - // eleminating duplicate tuples) - co_await executor.ExecuteProjection(projection, attr_chan, tuples_chan); - co_return; - } - boost::asio::awaitable operator()(const PhysicalFilter& filter) { - co_await executor.ExecuteFilter(filter, attr_chan, tuples_chan); - co_return; - } - boost::asio::awaitable operator()(const NestedLoopJoin& join) { - co_await executor.ExecuteJoin(join, attr_chan, tuples_chan); - co_return; - } - boost::asio::awaitable operator()(const NestedLoopCrossJoin& cross_join) { - co_await executor.ExecuteCrossJoin(cross_join, attr_chan, tuples_chan); - co_return; - } - - AttributesInfoChannel& attr_chan; - TuplesChannel& tuples_chan; - Executor& executor; - }; - co_await std::visit(ExecuteVisitor{attr_chan, tuples_chan, *this}, op); - co_return; -} - -template -boost::asio::awaitable Executor::ExecuteProjection(const PhysicalProjection& proj, - AttributesInfoChannel& out_attr_chan, - TuplesChannel& out_tuples_chan) { -#ifdef DEBUG - std::clog << "Executing projection\n"; -#endif - auto [in_attrs_chan, in_tuples_chan] = co_await GetChannels(); - co_await SpawnExecutor(*proj.source, in_attrs_chan, in_tuples_chan); - - auto attrs = co_await in_attrs_chan.async_receive(boost::asio::use_awaitable); - auto attrs_after = GetAttributesAfterProjection(attrs, proj); - co_await out_attr_chan.async_send(boost::system::error_code{}, attrs_after, - boost::asio::use_awaitable); - out_attr_chan.close(); - - std::vector executors; - executors.reserve(proj.expressions.size()); - for (const auto& expr : proj.expressions) { - executors.push_back(co_await expression_executor_.GetExpressionExecutor(expr, attrs)); - } - - for (;;) { - auto buf = co_await ReceiveTuples(in_tuples_chan); - if (buf.empty()) { - break; - } -#ifdef DEBUG - std::clog << std::format("Received {} tuples in projection\n", buf.size()); -#endif - buf = buf | std::views::transform([&](const auto& tuple) { - return ApplyProjection(tuple, attrs, executors); - }) | std::ranges::to(); - co_await out_tuples_chan.async_send(boost::system::error_code{}, std::move(buf), - boost::asio::use_awaitable); - } - out_tuples_chan.close(); -} - -template -boost::asio::awaitable Executor::ExecuteFilter(const PhysicalFilter& filter, - AttributesInfoChannel& out_attr_chan, - TuplesChannel& out_tuples_chan) { -#ifdef DEBUG - std::clog << "Executing filter\n"; -#endif - auto [in_attrs_chan, in_tuples_chan] = co_await GetChannels(); - co_await SpawnExecutor(*filter.source, in_attrs_chan, in_tuples_chan); - - auto attrs = co_await in_attrs_chan.async_receive(boost::asio::use_awaitable); -#ifdef DEBUG - std::clog << "Filter received attrs\n"; -#endif - - if (GetExpressionType(filter.predicate, attrs) != Type::kBool) { - throw std::logic_error{"filter expr should return bool"}; - } - -#ifdef DEBUG - std::clog << "Filter sending attrs\n"; -#endif - co_await out_attr_chan.async_send(boost::system::error_code{}, attrs, boost::asio::use_awaitable); - out_attr_chan.close(); -#ifdef DEBUG - std::clog << "Filter sent attrs\n"; -#endif - - auto filter_executor = co_await expression_executor_.GetExpressionExecutor(filter.predicate, attrs); - - Tuples output_buf; - output_buf.reserve(kBufSize); - for (;;) { - auto input_buf = co_await ReceiveTuples(in_tuples_chan); - if (input_buf.empty()) { - break; - } -#ifdef DEBUG - std::clog << std::format("Received {} tuples in filter\n", input_buf.size()); -#endif - auto filtered_view = input_buf | std::views::filter([&](const auto& tuple) { - return ApplyFilter(tuple, attrs, filter_executor); - }) | std::views::as_rvalue; - for (auto&& tuple : filtered_view) { - output_buf.push_back(std::move(tuple)); - if (output_buf.size() == kBufSize) { -#ifdef DEBUG - std::clog << std::format("Sending {} tuples in filter\n", output_buf.size()); -#endif - co_await out_tuples_chan.async_send(boost::system::error_code{}, std::move(output_buf), - boost::asio::use_awaitable); - output_buf.clear(); - } - } - } -#ifdef DEBUG - std::clog << std::format("{} tuples left in output_buf\n", output_buf.size()); -#endif - if (!output_buf.empty()) { -#ifdef DEBUG - std::clog << std::format("Sending {} tuples in filter\n", output_buf.size()); -#endif - co_await out_tuples_chan.async_send(boost::system::error_code{}, std::move(output_buf), - boost::asio::use_awaitable); - } - out_tuples_chan.close(); -} - -template -boost::asio::awaitable Executor::ExecuteCrossJoin(const NestedLoopCrossJoin& cross_join, - AttributesInfoChannel& attr_chan, - TuplesChannel& tuples_chan) { - std::clog << "Executing cross join\n"; - auto [lhs_attrs_chan, lhs_tuples_chan] = co_await GetChannels(); - co_await SpawnExecutor(*cross_join.lhs, lhs_attrs_chan, lhs_tuples_chan); - auto [rhs_attrs_chan, rhs_tuples_chan] = co_await GetChannels(); - co_await SpawnExecutor(*cross_join.rhs, rhs_attrs_chan, rhs_tuples_chan); - - auto attrs = co_await ConcatAttrs(lhs_attrs_chan, rhs_attrs_chan); - std::clog << "Cross join received attrs\n"; - - std::clog << "Cross join sending attrs\n"; - co_await attr_chan.async_send(boost::system::error_code{}, attrs, boost::asio::use_awaitable); - attr_chan.close(); - std::clog << "Cross join sent attrs\n"; - - auto reader = co_await MaterializeChannel(lhs_tuples_chan); - std::clog << std::format("Materialized tuples in cross join\n"); - - for (;;) { - auto buf_rhs = co_await ReceiveTuples(rhs_tuples_chan); - if (buf_rhs.empty()) { - break; - } - std::clog << std::format("Received {} tuples in cross join as rhs\n", buf_rhs.size()); - - reader.Rewind(); - for (;;) { - auto buf_lhs = reader.Read(); - if (buf_lhs.empty()) { - break; - } - std::clog << std::format("Read {} tuples back from materialized form\n", buf_lhs.size()); - for (const auto& tuple_lhs : buf_lhs) { - Tuples buf_joined; - buf_joined.reserve(kBufSize); - // NOTE: not optimal if rhs is a small table (<< kBufSize tuples) - for (const auto& tuple_rhs : buf_rhs) { - auto joined_tuple = ConcatTuples(tuple_lhs, tuple_rhs); - buf_joined.push_back(std::move(joined_tuple)); - } -#ifdef DEBUG - std::clog << std::format("Sending {} tuples from cross join\n", buf_joined.size()); -#endif - co_await tuples_chan.async_send(boost::system::error_code{}, std::move(buf_joined), - boost::asio::use_awaitable); - } - } - } - tuples_chan.close(); -} - -template -boost::asio::awaitable Executor::ExecuteJoin(const NestedLoopJoin& join, - AttributesInfoChannel& attr_chan, - TuplesChannel& tuples_chan) { -#ifdef DEBUG - std::clog << "Executing join\n"; -#endif - if (join.type == JoinType::kFull) { - throw std::logic_error{"Full joins are not supported by executor"}; - } - if (join.type == JoinType::kLeft) { - std::swap(*join.lhs, *join.rhs); - } - auto [lhs_attrs_chan, lhs_tuples_chan] = co_await GetChannels(); - co_await SpawnExecutor(*join.lhs, lhs_attrs_chan, lhs_tuples_chan); - auto [rhs_attrs_chan, rhs_tuples_chan] = co_await GetChannels(); - co_await SpawnExecutor(*join.rhs, rhs_attrs_chan, rhs_tuples_chan); - - auto attrs = co_await ConcatAttrs(lhs_attrs_chan, rhs_attrs_chan); -#ifdef DEBUG - std::clog << "Join received attrs\n"; -#endif - -#ifdef DEBUG - std::clog << "Join sending attrs\n"; -#endif - co_await attr_chan.async_send(boost::system::error_code{}, attrs, boost::asio::use_awaitable); - attr_chan.close(); -#ifdef DEBUG - std::clog << "Join sent attrs\n"; -#endif - - auto qual_executor = co_await expression_executor_.GetExpressionExecutor(join.qual, attrs); - - auto reader = co_await MaterializeChannel(lhs_tuples_chan); - for (;;) { - auto buf_rhs = co_await ReceiveTuples(rhs_tuples_chan); - if (buf_rhs.empty()) { - break; - } -#ifdef DEBUG - std::clog << std::format("Received {} tuples in join as rhs\n", buf_rhs.size()); -#endif - - std::vector used(buf_rhs.size(), false); - reader.Rewind(); - for (;;) { - auto buf_lhs = reader.Read(); - if (buf_lhs.empty()) { - break; - } -#ifdef DEBUG - std::clog << std::format("Read {} tuples back from materialized form\n", buf_lhs.size()); -#endif - - for (const auto& tuple_lhs : buf_lhs) { - Tuples buf_res; - buf_res.reserve(kBufSize); - for (const auto& [rhs_index, tuple_rhs] : buf_rhs | std::views::enumerate) { - auto joined_tuple = ConcatTuples(tuple_lhs, tuple_rhs); - auto qual_expr_res = qual_executor(joined_tuple, attrs); - if (qual_expr_res.value.bool_value) { - buf_res.push_back(std::move(joined_tuple)); - used[rhs_index] = true; - } - } - if (!buf_res.empty()) { -#ifdef DEBUG - std::clog << std::format("Sending {} tuples from join\n", buf_res.size()); -#endif - co_await tuples_chan.async_send(boost::system::error_code{}, std::move(buf_res), - boost::asio::use_awaitable); - } - } - } - - if (join.type == JoinType::kRight || join.type == JoinType::kLeft) { - Tuples buf_res; - buf_res.reserve(kBufSize); - for (auto [rhs_index, is_used] : used | std::views::enumerate) { - if (is_used) { - continue; - } - - auto rhs_tuple = std::move(buf_rhs[rhs_index]); - - auto lhs_size = attrs.size() - rhs_tuple.size(); - Tuple lhs_tuple(lhs_size, Value{true}); - - auto joined_tuple = ConcatTuples(lhs_tuple, rhs_tuple); - buf_res.push_back(std::move(joined_tuple)); - } - if (!buf_res.empty()) { -#ifdef DEBUG - std::clog << std::format("Sending {} tuples from join\n", buf_res.size()); -#endif - co_await tuples_chan.async_send(boost::system::error_code{}, std::move(buf_res), - boost::asio::use_awaitable); - } - } - } - tuples_chan.close(); -} - -template -boost::asio::awaitable Executor::SpawnExecutor(const PhysicalPlanNode& op, - AttributesInfoChannel& attr_chan, - TuplesChannel& tuple_chan) { - auto executor = co_await boost::asio::this_coro::executor; - boost::asio::co_spawn(executor, Execute(op, attr_chan, tuple_chan), boost::asio::detached); -} - - } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/cardinality.hpp b/include/stewkk/sql/logic/optimizer/cardinality.hpp new file mode 100644 index 0000000..7be0dc3 --- /dev/null +++ b/include/stewkk/sql/logic/optimizer/cardinality.hpp @@ -0,0 +1,25 @@ +#pragma once + +#include +#include +#include + +#include +#include + +namespace stewkk::sql { + +class CardinalityEstimates { +public: + CardinalityEstimates(std::unordered_map table_sizes = {}); + + int64_t GetCardinality(utils::NotNull group); + +private: + int64_t GetCardinality(const LogicalOperator& op); + + std::unordered_map table_sizes_; + std::unordered_map cache_; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/optimizer.hpp b/include/stewkk/sql/logic/optimizer/optimizer.hpp index f889486..864e5d0 100644 --- a/include/stewkk/sql/logic/optimizer/optimizer.hpp +++ b/include/stewkk/sql/logic/optimizer/optimizer.hpp @@ -1,8 +1,60 @@ #pragma once -#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include namespace stewkk::sql { +std::vector> GetChildren(utils::NotNull expr); +std::vector> GetChildren(utils::NotNull expr); + +template +class Optimizer { +public: + Optimizer(const Operator& expr, Rules&& rules, + CardinalityEstimates cardinality = {}); + + PhysicalPlanNode Optimize(); + +private: + using Limit = std::optional; + + bool IsExplored(utils::NotNull group) const; + void SetExplored(utils::NotNull group); + + void OptimizeInputs(utils::NotNull expr, Limit limit, size_t child_index = 0); + + void ApplyRule(TransformationRuleId rule, utils::NotNull expr, Limit limit); + void ApplyRule(ImplementationRuleId rule, utils::NotNull expr, Limit limit); + + void OptimizeExpression(utils::NotNull expr, Limit limit); + void ExploreExpression(utils::NotNull expr, Limit limit); + void ExploreGroup(utils::NotNull group, Limit limit); + void OptimizeGroup(utils::NotNull group, Limit limit = std::nullopt); + + PhysicalPlanNode BuildOptimalPlan(Group* group); + + Memo memo_; + RulesApplier rules_applier_; + std::stack> tasks_; + std::unordered_set explored_groups_; + utils::NotNull root_; + CardinalityEstimates cardinality_; + std::unordered_map local_cost_; + std::unordered_map accum_child_cost_; + std::unordered_map best_cost_; + std::unordered_map best_plan_; +}; } // namespace stewkk::sql diff --git a/include/stewkk/sql/utils/log.hpp b/include/stewkk/sql/utils/log.hpp new file mode 100644 index 0000000..a0f3e1c --- /dev/null +++ b/include/stewkk/sql/utils/log.hpp @@ -0,0 +1,21 @@ +#pragma once + +#include + +namespace stewkk::sql { + +inline constexpr bool kDebug = +#ifdef DEBUG + true; +#else + false; +#endif + +template +void Log(std::format_string fmt, Args&&... args) { + if constexpr (kDebug) { + std::println(stderr, fmt, std::forward(args)...); + } +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/CMakeLists.txt b/src/stewkk/sql/CMakeLists.txt index 102883f..b06974f 100644 --- a/src/stewkk/sql/CMakeLists.txt +++ b/src/stewkk/sql/CMakeLists.txt @@ -28,6 +28,7 @@ add_library(libsql logic/executor/materialization.cpp logic/executor/llvm.cpp logic/optimizer/optimizer.cpp + logic/optimizer/cardinality.cpp logic/optimizer/group.cpp logic/optimizer/memo.cpp logic/optimizer/rules.cpp diff --git a/src/stewkk/sql/logic/executor/executor.cpp b/src/stewkk/sql/logic/executor/executor.cpp index 1d304e2..fc32211 100644 --- a/src/stewkk/sql/logic/executor/executor.cpp +++ b/src/stewkk/sql/logic/executor/executor.cpp @@ -1,7 +1,45 @@ #include +#include +#include +#include + +#include +#include +#include +#include + +#include +#include + namespace stewkk::sql { +template + requires std::invocable + && std::same_as, Ret> +Value ApplyIntegersOperator(Value lhs, Value rhs) { + if (lhs.is_null || rhs.is_null) { + return Value{true}; + } + return Value{false, Op{}(lhs.value.int_value, rhs.value.int_value)}; +} + +template + requires std::invocable + && std::same_as, bool> +Value ApplyBooleanOperator(Value lhs, Value rhs) { + if (lhs.is_null || rhs.is_null) { + return Value{true}; + } + return Value{false, Op{}(lhs.value.bool_value, rhs.value.bool_value)}; +} + +struct IntPow { + int64_t operator()(int64_t base, int64_t exp) const { + return static_cast(std::pow(base, exp)); + } +}; + Type GetExpressionType(const Expression& expr, const AttributesInfo& available_attrs) { struct ExpressionTypeVisitor { Type operator()(const BinaryExpression& binop) const { @@ -345,4 +383,301 @@ boost::asio::awaitable CachedJitCompiledExpressionExecutor::GetE CachedJitCompiledExpressionExecutor::CachedJitCompiledExpressionExecutor(boost::asio::any_io_executor executor) : compiler_(executor) {} +template +Executor::Executor(SequentialScan seq_scan, boost::asio::any_io_executor executor) + : sequential_scan_(std::move(seq_scan)), expression_executor_(executor) {} + +template +boost::asio::awaitable> Executor::Execute(const PhysicalPlanNode& op) { + auto [attr_chan, tuples_chan] = co_await GetChannels(); + co_await SpawnExecutor(op, attr_chan, tuples_chan); + + auto attrs = co_await attr_chan.async_receive(boost::asio::use_awaitable); + Log("Received attrs in root"); + + Tuples result; + for (;;) { + auto buf = co_await ReceiveTuples(tuples_chan); + if (buf.empty()) { + break; + } + Log("Received {} tuples in root", buf.size()); + std::move(buf.begin(), buf.end(), std::back_inserter(result)); + } + + Log("Total {} tuples in root", result.size()); + co_return Ok(Relation{std::move(attrs), std::move(result)}); +} + +template +boost::asio::awaitable Executor::Execute(const PhysicalPlanNode& op, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan) { + struct ExecuteVisitor{ + boost::asio::awaitable operator()(const SeqScan& seq_scan) { + co_await executor.sequential_scan_(seq_scan.table, attr_chan, tuples_chan); + co_return; + } + boost::asio::awaitable operator()(const PhysicalProjection& projection) { + // NOTE: We are using multiset relational algebra projection (i.e. not + // eleminating duplicate tuples) + co_await executor.ExecuteProjection(projection, attr_chan, tuples_chan); + co_return; + } + boost::asio::awaitable operator()(const PhysicalFilter& filter) { + co_await executor.ExecuteFilter(filter, attr_chan, tuples_chan); + co_return; + } + boost::asio::awaitable operator()(const NestedLoopJoin& join) { + co_await executor.ExecuteJoin(join, attr_chan, tuples_chan); + co_return; + } + boost::asio::awaitable operator()(const NestedLoopCrossJoin& cross_join) { + co_await executor.ExecuteCrossJoin(cross_join, attr_chan, tuples_chan); + co_return; + } + + AttributesInfoChannel& attr_chan; + TuplesChannel& tuples_chan; + Executor& executor; + }; + co_await std::visit(ExecuteVisitor{attr_chan, tuples_chan, *this}, op); + co_return; +} + +template +boost::asio::awaitable Executor::ExecuteProjection(const PhysicalProjection& proj, + AttributesInfoChannel& out_attr_chan, + TuplesChannel& out_tuples_chan) { + Log("Executing projection"); + auto [in_attrs_chan, in_tuples_chan] = co_await GetChannels(); + co_await SpawnExecutor(*proj.source, in_attrs_chan, in_tuples_chan); + + auto attrs = co_await in_attrs_chan.async_receive(boost::asio::use_awaitable); + auto attrs_after = GetAttributesAfterProjection(attrs, proj); + co_await out_attr_chan.async_send(boost::system::error_code{}, attrs_after, + boost::asio::use_awaitable); + out_attr_chan.close(); + + std::vector executors; + executors.reserve(proj.expressions.size()); + for (const auto& expr : proj.expressions) { + executors.push_back(co_await expression_executor_.GetExpressionExecutor(expr, attrs)); + } + + for (;;) { + auto buf = co_await ReceiveTuples(in_tuples_chan); + if (buf.empty()) { + break; + } + Log("Received {} tuples in projection", buf.size()); + buf = buf | std::views::transform([&](const auto& tuple) { + return ApplyProjection(tuple, attrs, executors); + }) | std::ranges::to(); + co_await out_tuples_chan.async_send(boost::system::error_code{}, std::move(buf), + boost::asio::use_awaitable); + } + out_tuples_chan.close(); +} + +template +boost::asio::awaitable Executor::ExecuteFilter(const PhysicalFilter& filter, + AttributesInfoChannel& out_attr_chan, + TuplesChannel& out_tuples_chan) { + Log("Executing filter"); + auto [in_attrs_chan, in_tuples_chan] = co_await GetChannels(); + co_await SpawnExecutor(*filter.source, in_attrs_chan, in_tuples_chan); + + auto attrs = co_await in_attrs_chan.async_receive(boost::asio::use_awaitable); + Log("Filter received attrs"); + + if (GetExpressionType(filter.predicate, attrs) != Type::kBool) { + throw std::logic_error{"filter expr should return bool"}; + } + + Log("Filter sending attrs"); + co_await out_attr_chan.async_send(boost::system::error_code{}, attrs, boost::asio::use_awaitable); + out_attr_chan.close(); + Log("Filter sent attrs"); + + auto filter_executor = co_await expression_executor_.GetExpressionExecutor(filter.predicate, attrs); + + Tuples output_buf; + output_buf.reserve(kBufSize); + for (;;) { + auto input_buf = co_await ReceiveTuples(in_tuples_chan); + if (input_buf.empty()) { + break; + } + Log("Received {} tuples in filter", input_buf.size()); + auto filtered_view = input_buf | std::views::filter([&](const auto& tuple) { + return ApplyFilter(tuple, attrs, filter_executor); + }) | std::views::as_rvalue; + for (auto&& tuple : filtered_view) { + output_buf.push_back(std::move(tuple)); + if (output_buf.size() == kBufSize) { + Log("Sending {} tuples in filter", output_buf.size()); + co_await out_tuples_chan.async_send(boost::system::error_code{}, std::move(output_buf), + boost::asio::use_awaitable); + output_buf.clear(); + } + } + } + Log("{} tuples left in output_buf", output_buf.size()); + if (!output_buf.empty()) { + Log("Sending {} tuples in filter", output_buf.size()); + co_await out_tuples_chan.async_send(boost::system::error_code{}, std::move(output_buf), + boost::asio::use_awaitable); + } + out_tuples_chan.close(); +} + +template +boost::asio::awaitable Executor::ExecuteCrossJoin(const NestedLoopCrossJoin& cross_join, + AttributesInfoChannel& attr_chan, + TuplesChannel& tuples_chan) { + Log("Executing cross join"); + auto [lhs_attrs_chan, lhs_tuples_chan] = co_await GetChannels(); + co_await SpawnExecutor(*cross_join.lhs, lhs_attrs_chan, lhs_tuples_chan); + auto [rhs_attrs_chan, rhs_tuples_chan] = co_await GetChannels(); + co_await SpawnExecutor(*cross_join.rhs, rhs_attrs_chan, rhs_tuples_chan); + + auto attrs = co_await ConcatAttrs(lhs_attrs_chan, rhs_attrs_chan); + Log("Cross join received attrs"); + Log("Cross join sending attrs"); + co_await attr_chan.async_send(boost::system::error_code{}, attrs, boost::asio::use_awaitable); + attr_chan.close(); + Log("Cross join sent attrs"); + + auto reader = co_await MaterializeChannel(lhs_tuples_chan); + Log("Materialized tuples in cross join"); + + for (;;) { + auto buf_rhs = co_await ReceiveTuples(rhs_tuples_chan); + if (buf_rhs.empty()) { + break; + } + Log("Received {} tuples in cross join as rhs", buf_rhs.size()); + + reader.Rewind(); + for (;;) { + auto buf_lhs = reader.Read(); + if (buf_lhs.empty()) { + break; + } + Log("Read {} tuples back from materialized form", buf_lhs.size()); + for (const auto& tuple_lhs : buf_lhs) { + Tuples buf_joined; + buf_joined.reserve(kBufSize); + // NOTE: not optimal if rhs is a small table (<< kBufSize tuples) + for (const auto& tuple_rhs : buf_rhs) { + auto joined_tuple = ConcatTuples(tuple_lhs, tuple_rhs); + buf_joined.push_back(std::move(joined_tuple)); + } + Log("Sending {} tuples from cross join", buf_joined.size()); + co_await tuples_chan.async_send(boost::system::error_code{}, std::move(buf_joined), + boost::asio::use_awaitable); + } + } + } + tuples_chan.close(); +} + +template +boost::asio::awaitable Executor::ExecuteJoin(const NestedLoopJoin& join, + AttributesInfoChannel& attr_chan, + TuplesChannel& tuples_chan) { + Log("Executing join"); + if (join.type == JoinType::kFull) { + throw std::logic_error{"Full joins are not supported by executor"}; + } + if (join.type == JoinType::kLeft) { + std::swap(*join.lhs, *join.rhs); + } + auto [lhs_attrs_chan, lhs_tuples_chan] = co_await GetChannels(); + co_await SpawnExecutor(*join.lhs, lhs_attrs_chan, lhs_tuples_chan); + auto [rhs_attrs_chan, rhs_tuples_chan] = co_await GetChannels(); + co_await SpawnExecutor(*join.rhs, rhs_attrs_chan, rhs_tuples_chan); + + auto attrs = co_await ConcatAttrs(lhs_attrs_chan, rhs_attrs_chan); + Log("Join received attrs"); + Log("Join sending attrs"); + co_await attr_chan.async_send(boost::system::error_code{}, attrs, boost::asio::use_awaitable); + attr_chan.close(); + Log("Join sent attrs"); + + auto qual_executor = co_await expression_executor_.GetExpressionExecutor(join.qual, attrs); + + auto reader = co_await MaterializeChannel(lhs_tuples_chan); + for (;;) { + auto buf_rhs = co_await ReceiveTuples(rhs_tuples_chan); + if (buf_rhs.empty()) { + break; + } + Log("Received {} tuples in join as rhs", buf_rhs.size()); + + std::vector used(buf_rhs.size(), false); + reader.Rewind(); + for (;;) { + auto buf_lhs = reader.Read(); + if (buf_lhs.empty()) { + break; + } + Log("Read {} tuples back from materialized form", buf_lhs.size()); + + for (const auto& tuple_lhs : buf_lhs) { + Tuples buf_res; + buf_res.reserve(kBufSize); + for (const auto& [rhs_index, tuple_rhs] : buf_rhs | std::views::enumerate) { + auto joined_tuple = ConcatTuples(tuple_lhs, tuple_rhs); + auto qual_expr_res = qual_executor(joined_tuple, attrs); + if (qual_expr_res.value.bool_value) { + buf_res.push_back(std::move(joined_tuple)); + used[rhs_index] = true; + } + } + if (!buf_res.empty()) { + Log("Sending {} tuples from join", buf_res.size()); + co_await tuples_chan.async_send(boost::system::error_code{}, std::move(buf_res), + boost::asio::use_awaitable); + } + } + } + + if (join.type == JoinType::kRight || join.type == JoinType::kLeft) { + Tuples buf_res; + buf_res.reserve(kBufSize); + for (auto [rhs_index, is_used] : used | std::views::enumerate) { + if (is_used) { + continue; + } + + auto rhs_tuple = std::move(buf_rhs[rhs_index]); + + auto lhs_size = attrs.size() - rhs_tuple.size(); + Tuple lhs_tuple(lhs_size, Value{true}); + + auto joined_tuple = ConcatTuples(lhs_tuple, rhs_tuple); + buf_res.push_back(std::move(joined_tuple)); + } + if (!buf_res.empty()) { + Log("Sending {} tuples from join", buf_res.size()); + co_await tuples_chan.async_send(boost::system::error_code{}, std::move(buf_res), + boost::asio::use_awaitable); + } + } + } + tuples_chan.close(); +} + +template +boost::asio::awaitable Executor::SpawnExecutor(const PhysicalPlanNode& op, + AttributesInfoChannel& attr_chan, + TuplesChannel& tuple_chan) { + auto executor = co_await boost::asio::this_coro::executor; + boost::asio::co_spawn(executor, Execute(op, attr_chan, tuple_chan), boost::asio::detached); +} + +template class Executor; +template class Executor; +template class Executor; + } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/cardinality.cpp b/src/stewkk/sql/logic/optimizer/cardinality.cpp new file mode 100644 index 0000000..1252680 --- /dev/null +++ b/src/stewkk/sql/logic/optimizer/cardinality.cpp @@ -0,0 +1,42 @@ +#include + +#include + +namespace stewkk::sql { + +CardinalityEstimates::CardinalityEstimates(std::unordered_map table_sizes) + : table_sizes_(std::move(table_sizes)) {} + +int64_t CardinalityEstimates::GetCardinality(utils::NotNull group) { + if (auto it = cache_.find(group.get()); it != cache_.end()) { + return it->second; + } + auto cardinality = GetCardinality(group->GetLogicalExprs().front()->root_operator); + cache_[group.get()] = cardinality; + return cardinality; +} + +int64_t CardinalityEstimates::GetCardinality(const LogicalOperator& op) { + return std::visit(utils::Overloaded{ + [this](const logical::Table& t) -> int64_t { + if (auto it = table_sizes_.find(t.name); it != table_sizes_.end()) { + return it->second; + } + return 10; + }, + [this](const logical::Filter& f) -> int64_t { + return GetCardinality(f.source); + }, + [this](const logical::Projection& p) -> int64_t { + return GetCardinality(p.source); + }, + [this](const logical::CrossJoin& j) -> int64_t { + return GetCardinality(j.lhs) * GetCardinality(j.rhs); + }, + [this](const logical::Join& j) -> int64_t { + return GetCardinality(j.lhs) * GetCardinality(j.rhs); + }, + }, op); +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index f1e70bb..36554a3 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -1,5 +1,269 @@ #include +#include + +#include +#include + namespace stewkk::sql { +std::vector> GetChildren(utils::NotNull expr) { + return std::visit(utils::Overloaded{ + [](const logical::Table&) -> std::vector> { + return {}; + }, + [](const logical::Filter& f) -> std::vector> { + return {f.source}; + }, + [](const logical::Projection& p) -> std::vector> { + return {p.source}; + }, + [](const logical::CrossJoin& j) -> std::vector> { + return {j.lhs, j.rhs}; + }, + [](const logical::Join& j) -> std::vector> { + return {j.lhs, j.rhs}; + }, + }, expr->root_operator); +} + +std::vector> GetChildren(utils::NotNull expr) { + return std::visit(utils::Overloaded{ + [](const physical::SeqScan&) -> std::vector> { + return {}; + }, + [](const physical::Filter& f) -> std::vector> { + return {f.source}; + }, + [](const physical::Projection& p) -> std::vector> { + return {p.source}; + }, + [](const physical::NestedLoopJoin& j) -> std::vector> { + return {j.lhs, j.rhs}; + }, + [](const physical::NestedLoopCrossJoin& j) -> std::vector> { + return {j.lhs, j.rhs}; + }, + }, expr->root_operator); +} + +static int64_t CalcCost(utils::NotNull expr, CardinalityEstimates& cardinality) { + return std::visit(utils::Overloaded{ + [&](const physical::SeqScan&) -> int64_t { + return cardinality.GetCardinality(expr->group); + }, + [&](const physical::Filter&) -> int64_t { + return cardinality.GetCardinality(expr->group); + }, + [&](const physical::Projection&) -> int64_t { + return cardinality.GetCardinality(expr->group); + }, + [&](const physical::NestedLoopJoin& j) -> int64_t { + auto p_l = (cardinality.GetCardinality(j.lhs) + kBufSize - 1) / kBufSize; + auto p_r = (cardinality.GetCardinality(j.rhs) + kBufSize - 1) / kBufSize; + return p_l * (1 + p_r); + }, + [&](const physical::NestedLoopCrossJoin& j) -> int64_t { + auto p_l = (cardinality.GetCardinality(j.lhs) + kBufSize - 1) / kBufSize; + auto p_r = (cardinality.GetCardinality(j.rhs) + kBufSize - 1) / kBufSize; + return p_l * (1 + p_r); + }, + }, expr->root_operator); +} + +template +Optimizer::Optimizer( + const Operator& expr, Rules&& rules, + CardinalityEstimates cardinality) + : memo_(), rules_applier_(std::move(rules)), root_(memo_.Populate(expr)), + cardinality_(std::move(cardinality)) {} + +template +bool Optimizer::IsExplored(utils::NotNull group) const { + return explored_groups_.contains(group.get()); +} + +template +void Optimizer::SetExplored(utils::NotNull group) { + explored_groups_.insert(group); +} + +template +void Optimizer::OptimizeInputs( + utils::NotNull expr, Limit limit, size_t child_index) { + auto children = GetChildren(expr); + if (child_index >= children.size()) { + int64_t total = local_cost_[expr.get()] + accum_child_cost_[expr.get()]; + auto* g = expr->group.get(); + if (!best_cost_.contains(g) || total < best_cost_.at(g)) { + best_cost_[g] = total; + best_plan_[g] = expr.get(); + } + return; + } + + auto child = children[child_index]; + + tasks_.emplace([this, expr, child, child_index, limit]() { + if (!best_cost_.contains(child.get())) return; + auto cc = best_cost_.at(child.get()); + accum_child_cost_[expr.get()] += cc; + Limit next = limit ? std::optional{*limit - local_cost_[expr.get()] - accum_child_cost_[expr.get()]} : std::nullopt; + if (next && *next < 0) return; + OptimizeInputs(expr, next, child_index + 1); + }); + + tasks_.emplace([this, child, limit]() { + OptimizeGroup(child, limit); + }); +} + +template +void Optimizer::ApplyRule( + TransformationRuleId rule, utils::NotNull expr, Limit limit) { + auto new_expr = rules_applier_.Apply(rule, expr, memo_); + tasks_.emplace([this, new_expr, limit]() { ExploreExpression(new_expr, limit); }); +} + +template +void Optimizer::ApplyRule( + ImplementationRuleId rule, utils::NotNull expr, Limit limit) { + auto new_expr = rules_applier_.Apply(rule, expr, memo_); + auto lc = CalcCost(new_expr, cardinality_); + local_cost_[new_expr.get()] = lc; + accum_child_cost_[new_expr.get()] = 0; + + if (limit && lc >= *limit) return; + + Limit child_limit = limit ? std::optional{*limit - lc} : std::nullopt; + tasks_.emplace([this, new_expr, child_limit]() { OptimizeInputs(new_expr, child_limit); }); +} + +template +void Optimizer::OptimizeExpression( + utils::NotNull expr, Limit limit) { + for (size_t rule = 0; rule < NImplementation; rule++) { + if (!rules_applier_.IsApplicable(ImplementationRuleId{rule}, expr)) { + continue; + } + tasks_.emplace([this, expr, rule, limit]() { + ApplyRule(ImplementationRuleId{rule}, expr, limit); + }); + } + + for (auto child : GetChildren(expr)) { + if (IsExplored(child)) { + continue; + } + tasks_.emplace([this, child, limit]() { + ExploreGroup(child, limit); + }); + } +} + +template +void Optimizer::ExploreExpression( + utils::NotNull expr, Limit limit) { + for (size_t rule = 0; rule < NTransformation; rule++) { + if (!rules_applier_.IsApplicable(TransformationRuleId{rule}, expr)) { + continue; + } + tasks_.emplace([this, expr, rule, limit]() { + ApplyRule(TransformationRuleId{rule}, expr, limit); + }); + } + + for (auto child : GetChildren(expr)) { + if (IsExplored(child)) { + continue; + } + tasks_.emplace([this, child, limit]() { + ExploreGroup(child, limit); + }); + } +} + +template +void Optimizer::ExploreGroup( + utils::NotNull group, Limit limit) { + SetExplored(group); + for (auto expr : group->GetLogicalExprs()) { + tasks_.emplace([this, expr, limit]() { + ExploreExpression(expr, limit); + }); + } +} + +template +void Optimizer::OptimizeGroup( + utils::NotNull group, Limit limit) { + if (auto it = best_cost_.find(group.get()); it != best_cost_.end()) { + if (!limit || it->second < *limit) return; + } + + if (!IsExplored(group)) { + tasks_.emplace([this, group, limit]() { OptimizeGroup(group, limit); }); + tasks_.emplace([this, group, limit]() { ExploreGroup(group, limit); }); + return; + } + + for (auto expr : group->GetLogicalExprs()) { + tasks_.emplace([this, expr, limit]() { OptimizeExpression(expr, limit); }); + } +} + +template +PhysicalPlanNode Optimizer::BuildOptimalPlan(Group* group) { + auto best_expr = best_plan_[group]; + if (!best_expr) { + throw std::runtime_error{"no optimal plan for group"}; + } + return std::visit( + utils::Overloaded{ + [](const physical::SeqScan& op) -> PhysicalPlanNode { + return SeqScan{.table = op.table}; + }, + [this](const physical::Projection& op) -> PhysicalPlanNode { + return PhysicalProjection{ + .source = std::make_shared(BuildOptimalPlan(op.source.get())), + .expressions = op.expressions, + }; + }, + [this](const physical::Filter& op) -> PhysicalPlanNode { + return PhysicalFilter{ + .source = std::make_shared(BuildOptimalPlan(op.source.get())), + .predicate = op.predicate, + }; + }, + [this](const physical::NestedLoopJoin& op) -> PhysicalPlanNode { + return NestedLoopJoin{ + .lhs = std::make_shared(BuildOptimalPlan(op.lhs.get())), + .rhs = std::make_shared(BuildOptimalPlan(op.rhs.get())), + .type = op.type, + .qual = op.qual, + }; + }, + [this](const physical::NestedLoopCrossJoin& op) -> PhysicalPlanNode { + return NestedLoopCrossJoin{ + .lhs = std::make_shared(BuildOptimalPlan(op.lhs.get())), + .rhs = std::make_shared(BuildOptimalPlan(op.rhs.get())), + }; + }, + }, + best_expr->root_operator); +} + +template +PhysicalPlanNode Optimizer::Optimize() { + tasks_.emplace([this]() { OptimizeGroup(root_->group); }); + while (!tasks_.empty()) { + auto next_task = std::move(tasks_.top()); + tasks_.pop(); + next_task(); + } + return BuildOptimalPlan(root_->group.get()); +} + +template class Optimizer<2, 5>; + } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index ecd1cda..ef38df2 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -1,343 +1,19 @@ #include -#include -#include -#include -#include - #include #include -#include -#include -#include -#include +#include #include -#include -#include -#include #include using ::testing::Eq; namespace stewkk::sql { - + // FIXME: branch and bound // FIXME: сделать API в виде DoStep(), которое возвращает какой-то внутренний стейт оптимизатора // FIXME: применение правила (по крайней мере трансформации), должно создавать несколько выражений -std::vector> GetChildren(utils::NotNull expr) { - return std::visit(utils::Overloaded{ - [](const logical::Table&) -> std::vector> { - return {}; - }, - [](const logical::Filter& f) -> std::vector> { - return {f.source}; - }, - [](const logical::Projection& p) -> std::vector> { - return {p.source}; - }, - [](const logical::CrossJoin& j) -> std::vector> { - return {j.lhs, j.rhs}; - }, - [](const logical::Join& j) -> std::vector> { - return {j.lhs, j.rhs}; - }, - }, expr->root_operator); -} - -using Limit = std::optional; - -std::vector> GetChildren(utils::NotNull expr) { - return std::visit(utils::Overloaded{ - [](const physical::SeqScan&) -> std::vector> { - return {}; - }, - [](const physical::Filter& f) -> std::vector> { - return {f.source}; - }, - [](const physical::Projection& p) -> std::vector> { - return {p.source}; - }, - [](const physical::NestedLoopJoin& j) -> std::vector> { - return {j.lhs, j.rhs}; - }, - [](const physical::NestedLoopCrossJoin& j) -> std::vector> { - return {j.lhs, j.rhs}; - }, - }, expr->root_operator); -} - -class CardinalityEstimates { -public: - CardinalityEstimates(std::unordered_map table_sizes = {}) - : table_sizes_(std::move(table_sizes)) {} - - int64_t GetCardinality(utils::NotNull group) { - if (auto it = cache_.find(group.get()); it != cache_.end()) { - return it->second; - } - auto cardinality = GetCardinality(group->GetLogicalExprs().front()->root_operator); - cache_[group.get()] = cardinality; - return cardinality; - } - - private: - int64_t GetCardinality(const LogicalOperator& op) { - return std::visit(utils::Overloaded{ - [this](const logical::Table& t) -> int64_t { - if (auto it = table_sizes_.find(t.name); it != table_sizes_.end()) { - return it->second; - } - return 10; - }, - [this](const logical::Filter& f) -> int64_t { - return GetCardinality(f.source); - }, - [this](const logical::Projection& p) -> int64_t { - return GetCardinality(p.source); - }, - [this](const logical::CrossJoin& j) -> int64_t { - return GetCardinality(j.lhs) * GetCardinality(j.rhs); - }, - [this](const logical::Join& j) -> int64_t { - return GetCardinality(j.lhs) * GetCardinality(j.rhs); - }, - }, op); - } - - std::unordered_map table_sizes_; - std::unordered_map cache_; -}; - -int64_t CalcCost(utils::NotNull expr, CardinalityEstimates& cardinality) { - return std::visit(utils::Overloaded{ - [&](const physical::SeqScan&) -> int64_t { - return cardinality.GetCardinality(expr->group); - }, - [&](const physical::Filter&) -> int64_t { - return cardinality.GetCardinality(expr->group); - }, - [&](const physical::Projection&) -> int64_t { - return cardinality.GetCardinality(expr->group); - }, - [&](const physical::NestedLoopJoin& j) -> int64_t { - auto p_l = (cardinality.GetCardinality(j.lhs) + kBufSize - 1) / kBufSize; - auto p_r = (cardinality.GetCardinality(j.rhs) + kBufSize - 1) / kBufSize; - return p_l * (1 + p_r); - }, - [&](const physical::NestedLoopCrossJoin& j) -> int64_t { - auto p_l = (cardinality.GetCardinality(j.lhs) + kBufSize - 1) / kBufSize; - auto p_r = (cardinality.GetCardinality(j.rhs) + kBufSize - 1) / kBufSize; - return p_l * (1 + p_r); - }, - }, expr->root_operator); -} - -template -class Optimizer { - public: - Optimizer(const Operator& expr, Rules&& rules, - CardinalityEstimates cardinality = {}) - : memo_(), rules_applier_(std::move(rules)), root_(memo_.Populate(expr)), - cardinality_(std::move(cardinality)) { - } - -private: - bool IsExplored(utils::NotNull group) const { - return explored_groups_.contains(group.get()); - } - - void SetExplored(utils::NotNull group) { - explored_groups_.insert(group); - } - - void OptimizeInputs(utils::NotNull expr, Limit limit, size_t child_index = 0) { - auto children = GetChildren(expr); - if (child_index >= children.size()) { - int64_t total = local_cost_[expr.get()] + accum_child_cost_[expr.get()]; - auto* g = expr->group.get(); - if (!best_cost_.contains(g) || total < best_cost_.at(g)) { - best_cost_[g] = total; - best_plan_[g] = expr.get(); - } - return; - } - - auto child = children[child_index]; - - tasks_.emplace([this, expr, child, child_index, limit]() { - if (!best_cost_.contains(child.get())) return; - auto cc = best_cost_.at(child.get()); - accum_child_cost_[expr.get()] += cc; - // FIXME add -LB(children[child_index+1..k]) - Limit next = limit ? std::optional{*limit - local_cost_[expr.get()] - accum_child_cost_[expr.get()]} : std::nullopt; - if (next && *next < 0) return; - OptimizeInputs(expr, next, child_index + 1); - }); - - tasks_.emplace([this, child, limit]() { - OptimizeGroup(child, limit); - }); - } - - void ApplyRule(TransformationRuleId rule, utils::NotNull expr, Limit limit) { - auto new_expr = rules_applier_.Apply(rule, expr, memo_); - tasks_.emplace([this, new_expr, limit]() { ExploreExpression(new_expr, limit); }); - } - - void ApplyRule(ImplementationRuleId rule, utils::NotNull expr, Limit limit) { - auto new_expr = rules_applier_.Apply(rule, expr, memo_); - // FIXME: missing physical properties - auto lc = CalcCost(new_expr, cardinality_); - local_cost_[new_expr.get()] = lc; - accum_child_cost_[new_expr.get()] = 0; - - if (limit && lc >= *limit) return; - - Limit child_limit = limit ? std::optional{*limit - lc} : std::nullopt; - tasks_.emplace([this, new_expr, child_limit]() { OptimizeInputs(new_expr, child_limit); }); - } - - void OptimizeExpression(utils::NotNull expr, Limit limit) { - for (size_t rule = 0; rule < NImplementation; rule++) { - if (!rules_applier_.IsApplicable(ImplementationRuleId{rule}, expr)) { - continue; - } - tasks_.emplace([this, expr, rule, limit]() { - ApplyRule(ImplementationRuleId{rule}, expr, limit); - }); - } - - for (auto child : GetChildren(expr)) { - if (IsExplored(child)) { - continue; - } - tasks_.emplace([this, child, limit]() { - ExploreGroup(child, limit); - }); - } - } - - void ExploreExpression(utils::NotNull expr, Limit limit) { - for (size_t rule = 0; rule < NTransformation; rule++) { - if (!rules_applier_.IsApplicable(TransformationRuleId{rule}, expr)) { - continue; - } - tasks_.emplace([this, expr, rule, limit]() { - ApplyRule(TransformationRuleId{rule}, expr, limit); - }); - } - - for (auto child : GetChildren(expr)) { - if (IsExplored(child)) { - continue; - } - tasks_.emplace([this, child, limit]() { - ExploreGroup(child, limit); - }); - } - } - - void ExploreGroup(utils::NotNull group, Limit limit) { - SetExplored(group); - for (auto expr : group->GetLogicalExprs()) { - tasks_.emplace([this, expr, limit]() { - ExploreExpression(expr, limit); - }); - } - } - - void OptimizeGroup(utils::NotNull group, Limit limit=std::nullopt) { - if (auto it = best_cost_.find(group.get()); it != best_cost_.end()) { - if (!limit || it->second < *limit) return; - } - - if (!IsExplored(group)) { - tasks_.emplace([this, group, limit](){ - OptimizeGroup(group, limit); - }); - tasks_.emplace([this, group, limit](){ - ExploreGroup(group, limit); - }); - return; - } - - for (auto expr : group->GetLogicalExprs()) { - tasks_.emplace([this, expr, limit](){ - OptimizeExpression(expr, limit); - }); - } - } - - PhysicalPlanNode BuildOptimalPlan(Group* group) { - auto best_expr = best_plan_[group]; - if (!best_expr) { - throw std::runtime_error{"no optimal plan for group"}; - } - return std::visit( - utils::Overloaded{ - [](const physical::SeqScan& op) -> PhysicalPlanNode { - return SeqScan{ - .table = op.table, - }; - }, - [this](const physical::Projection& op) -> PhysicalPlanNode { - return PhysicalProjection{ - .source - = std::make_shared(BuildOptimalPlan(op.source.get())), - .expressions = op.expressions, - }; - }, - [this](const physical::Filter& op) -> PhysicalPlanNode { - return PhysicalFilter{ - .source = std::make_shared(BuildOptimalPlan(op.source.get())), - .predicate = op.predicate, - }; - }, - [this](const physical::NestedLoopJoin& op) -> PhysicalPlanNode { - return NestedLoopJoin{ - .lhs = std::make_shared(BuildOptimalPlan(op.lhs.get())), - .rhs = std::make_shared(BuildOptimalPlan(op.rhs.get())), - .type = op.type, - .qual = op.qual, - }; - }, - [this](const physical::NestedLoopCrossJoin& op) -> PhysicalPlanNode { - return NestedLoopCrossJoin{ - .lhs = std::make_shared(BuildOptimalPlan(op.lhs.get())), - .rhs = std::make_shared(BuildOptimalPlan(op.rhs.get())), - }; - }, - }, - best_expr->root_operator); - } - - public: - PhysicalPlanNode Optimize() { - tasks_.emplace([this]() { - OptimizeGroup(root_->group); - }); - while (!tasks_.empty()) { - auto next_task = std::move(tasks_.top()); - tasks_.pop(); - next_task(); - } - return BuildOptimalPlan(root_->group.get()); - } - - private: - Memo memo_; - RulesApplier rules_applier_; - std::stack> tasks_; - std::unordered_set explored_groups_; - utils::NotNull root_; - CardinalityEstimates cardinality_; - std::unordered_map local_cost_; - std::unordered_map accum_child_cost_; - std::unordered_map best_cost_; - std::unordered_map best_plan_; -}; - TEST(OptimizerTest, Simple) { std::stringstream s{"SELECT * FROM users;"}; Operator op = GetAST(s).value(); From 847b183214f2aba6ea06d7d2e848948683d8fd83 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 11 May 2026 22:05:32 +0300 Subject: [PATCH 035/120] Refactor, add debug logs, fix bug --- CMakeLists.txt | 4 ++++ include/stewkk/sql/logic/optimizer/memo.hpp | 3 +++ include/stewkk/sql/utils/log.hpp | 2 +- src/stewkk/sql/logic/optimizer/memo.cpp | 14 +++++++++++++- src/stewkk/sql/logic/optimizer/optimizer.cpp | 12 ++++++++++++ src/stewkk/sql/logic/optimizer/rule.cpp | 10 +++++++++- 6 files changed, 42 insertions(+), 3 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 96a5572..3eefa3a 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -30,6 +30,10 @@ if (SANITIZE_THREADS) add_link_options(-fsanitize=thread) endif() +if(STEWKK_SQL_DEBUG) + add_definitions(-DSTEWKK_SQL_DEBUG) +endif() + set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${PROJECT_BINARY_DIR}/bin) set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${PROJECT_BINARY_DIR}/lib) set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${PROJECT_BINARY_DIR}/lib) diff --git a/include/stewkk/sql/logic/optimizer/memo.hpp b/include/stewkk/sql/logic/optimizer/memo.hpp index 6e41920..9518ef3 100644 --- a/include/stewkk/sql/logic/optimizer/memo.hpp +++ b/include/stewkk/sql/logic/optimizer/memo.hpp @@ -12,9 +12,12 @@ class Memo { public: size_t GroupCount() const; utils::NotNull AddGroup(LogicalOperator root_operator); + LogicalExpr* GetGroup(LogicalOperator root_operator) const; utils::NotNull Populate(const Operator& op); private: + LogicalExpr* GetGroup(const std::string& key) const; + std::deque groups_; std::unordered_map expr_index_; }; diff --git a/include/stewkk/sql/utils/log.hpp b/include/stewkk/sql/utils/log.hpp index a0f3e1c..ee99705 100644 --- a/include/stewkk/sql/utils/log.hpp +++ b/include/stewkk/sql/utils/log.hpp @@ -5,7 +5,7 @@ namespace stewkk::sql { inline constexpr bool kDebug = -#ifdef DEBUG +#ifdef STEWKK_SQL_DEBUG true; #else false; diff --git a/src/stewkk/sql/logic/optimizer/memo.cpp b/src/stewkk/sql/logic/optimizer/memo.cpp index 89e48c5..4627e04 100644 --- a/src/stewkk/sql/logic/optimizer/memo.cpp +++ b/src/stewkk/sql/logic/optimizer/memo.cpp @@ -36,12 +36,24 @@ size_t Memo::GroupCount() const { return groups_.size(); } -utils::NotNull Memo::AddGroup(LogicalOperator root_operator) { +LogicalExpr* Memo::GetGroup(LogicalOperator root_operator) const { auto key = ToKey(root_operator); + return GetGroup(key); +} + +LogicalExpr* Memo::GetGroup(const std::string& key) const { auto it = expr_index_.find(key); if (it != expr_index_.end()) { return it->second; } + return nullptr; +} + +utils::NotNull Memo::AddGroup(LogicalOperator root_operator) { + auto key = ToKey(root_operator); + if (auto g = GetGroup(key); g) { + return g; + } auto& group = groups_.emplace_back(Group(groups_.size())); auto expr = group.AddLogicalExpr(std::move(root_operator)); expr_index_[key] = expr; diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index 36554a3..8093109 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -3,6 +3,7 @@ #include #include +#include #include namespace stewkk::sql { @@ -96,6 +97,7 @@ void Optimizer::OptimizeInputs( int64_t total = local_cost_[expr.get()] + accum_child_cost_[expr.get()]; auto* g = expr->group.get(); if (!best_cost_.contains(g) || total < best_cost_.at(g)) { + Log("New best plan for group {} with cost {}", g->GetId(), total); best_cost_[g] = total; best_plan_[g] = expr.get(); } @@ -121,6 +123,7 @@ void Optimizer::OptimizeInputs( template void Optimizer::ApplyRule( TransformationRuleId rule, utils::NotNull expr, Limit limit) { + Log("Applying transformation rule {} to group {}", rule.value, expr->group->GetId()); auto new_expr = rules_applier_.Apply(rule, expr, memo_); tasks_.emplace([this, new_expr, limit]() { ExploreExpression(new_expr, limit); }); } @@ -128,8 +131,10 @@ void Optimizer::ApplyRule( template void Optimizer::ApplyRule( ImplementationRuleId rule, utils::NotNull expr, Limit limit) { + Log("Applying implementation rule {} to group {}", rule.value, expr->group->GetId()); auto new_expr = rules_applier_.Apply(rule, expr, memo_); auto lc = CalcCost(new_expr, cardinality_); + Log("Local cost for group {} expression: {}", new_expr->group->GetId(), lc); local_cost_[new_expr.get()] = lc; accum_child_cost_[new_expr.get()] = 0; @@ -142,6 +147,7 @@ void Optimizer::ApplyRule( template void Optimizer::OptimizeExpression( utils::NotNull expr, Limit limit) { + Log("Optimizing expression in group {}", expr->group->GetId()); for (size_t rule = 0; rule < NImplementation; rule++) { if (!rules_applier_.IsApplicable(ImplementationRuleId{rule}, expr)) { continue; @@ -164,6 +170,7 @@ void Optimizer::OptimizeExpression( template void Optimizer::ExploreExpression( utils::NotNull expr, Limit limit) { + Log("Exploring expression in group {}", expr->group->GetId()); for (size_t rule = 0; rule < NTransformation; rule++) { if (!rules_applier_.IsApplicable(TransformationRuleId{rule}, expr)) { continue; @@ -186,6 +193,7 @@ void Optimizer::ExploreExpression( template void Optimizer::ExploreGroup( utils::NotNull group, Limit limit) { + Log("Exploring group {}", group->GetId()); SetExplored(group); for (auto expr : group->GetLogicalExprs()) { tasks_.emplace([this, expr, limit]() { @@ -197,6 +205,7 @@ void Optimizer::ExploreGroup( template void Optimizer::OptimizeGroup( utils::NotNull group, Limit limit) { + Log("Optimizing group {}", group->GetId()); if (auto it = best_cost_.find(group.get()); it != best_cost_.end()) { if (!limit || it->second < *limit) return; } @@ -214,6 +223,7 @@ void Optimizer::OptimizeGroup( template PhysicalPlanNode Optimizer::BuildOptimalPlan(Group* group) { + Log("Building optimal plan for group {}", group->GetId()); auto best_expr = best_plan_[group]; if (!best_expr) { throw std::runtime_error{"no optimal plan for group"}; @@ -255,12 +265,14 @@ PhysicalPlanNode Optimizer::BuildOptimalPlan(G template PhysicalPlanNode Optimizer::Optimize() { + Log("Starting optimization"); tasks_.emplace([this]() { OptimizeGroup(root_->group); }); while (!tasks_.empty()) { auto next_task = std::move(tasks_.top()); tasks_.pop(); next_task(); } + Log("Optimization complete, building plan"); return BuildOptimalPlan(root_->group.get()); } diff --git a/src/stewkk/sql/logic/optimizer/rule.cpp b/src/stewkk/sql/logic/optimizer/rule.cpp index 13ad570..9961b89 100644 --- a/src/stewkk/sql/logic/optimizer/rule.cpp +++ b/src/stewkk/sql/logic/optimizer/rule.cpp @@ -1,9 +1,17 @@ #include +#include + namespace stewkk::sql { utils::NotNull TransformationRule::Apply(utils::NotNull expr, Memo& memo) { -return expr->group->AddLogicalExpr(ApplyImpl(expr, memo)); + // FIXME: maybe it should be Memo's responsibility to check for duplicates? + auto op = ApplyImpl(expr, memo); + auto existing = memo.GetGroup(op); + if (existing) { + return existing; + } + return expr->group->AddLogicalExpr(std::move(op)); } } // namespace stewkk::sql From 0ede4e040e7305fd3053bece3c971e074daaf28d Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Thu, 14 May 2026 00:22:31 +0300 Subject: [PATCH 036/120] Fix --- report/thesis.tex | 120 ++++++++++++++++++++++++++++++---------------- 1 file changed, 79 insertions(+), 41 deletions(-) diff --git a/report/thesis.tex b/report/thesis.tex index d02ee14..5c1fd68 100644 --- a/report/thesis.tex +++ b/report/thesis.tex @@ -29,9 +29,12 @@ \newcommand{\SeqScan}{\sqlkw{SeqScan}} \newcommand{\IndexScan}{\sqlkw{IndexScan}} \newcommand{\NestedLoopJoin}{\sqlkw{NestedLoopJoin}} +\newcommand{\NestedLoopCrossJoin}{\sqlkw{NestedLoopCrossJoin}} \newcommand{\HashJoin}{\sqlkw{HashJoin}} \newcommand{\MergeJoin}{\sqlkw{MergeJoin}} \newcommand{\Sort}{\sqlkw{Sort}} +\newcommand{\FilterOp}{\sqlkw{Filter}} +\newcommand{\ProjOp}{\sqlkw{Projection}} \newcommand{\SelOp}[1]{\sigma_{#1}} \newcommand{\ProjectionOp}[1]{\pi_{#1}} @@ -986,9 +989,6 @@ \subsection{Обеспечивающие операторы} \section{Метод ветвей и границ} -{\color{red} FIXME: Тут, конечно, маловато конкретики - хотелось бы видеть, - какую стоимостную модель вы выбрали для своей системы} - Метод ветвей и границ используется для сокращения пространства поиска и, как следствие, неасимптотического улучшения скорости работы алгоритма. В контексте Cascades ветвями являются альтернативные выражения и комбинации физических @@ -1060,38 +1060,59 @@ \subsection{Оценка кардинальности} \subsection{Оценка стоимости} -Функция стоимости сопоставляет физическому плану численную оценку затрат. В -простейшем случае стоимость можно представить как взвешенную сумму операций -чтения страниц, операций записи, вычислительных операций и сетевой передачи: +Функция стоимости сопоставляет физическому плану численную оценку затрат на его +исполнение. Классическим считается представление стоимости в виде взвешенной +суммы операций чтения и записи страниц на диск, а также числа обработанных +кортежей и операций над ними~\cite{GraefeMcKenna1993,Graefe1995}: \[ - Cost(p) = w_{io} \cdot IO(p) + w_{cpu} \cdot CPU(p) + - w_{mem} \cdot MEM(p) + w_{net} \cdot NET(p). + Cost(p) = w_{io} \cdot IO(p) + w_{cpu} \cdot CPU(p). \] -Коэффициенты \(w_{io}\), \(w_{cpu}\), \(w_{mem}\) и \(w_{net}\) зависят от -конкретной аппаратной конфигурации. +Коэффициенты \(w_{io}\), \(w_{cpu}\) зависят от конкретной аппаратной +конфигурации. -Стоимость физического выражения обычно вычисляется рекурсивно: +Стоимость является аддитивной и вычисляется рекурсивно по дереву физического +плана: \[ Cost(node) = LocalCost(node) + \sum_{child \in children(node)} Cost(child). \] -При этом локальная стоимость зависит от кардинальности входов и физических -свойств, а также конкретного физического оператора. +Локальная стоимость оператора определяется его типом и кардинальностью его +входов. Пусть \(|R|\) --- кардинальность отношения \(R\), \(b\) --- блочный +фактор (число кортежей на странице), тогда \(P(R) = \lceil|R|/b\rceil\) --- +число страниц, занимаемых \(R\); \(c_\theta\) --- стоимость вычисления +предиката \(\theta\) на одной паре кортежей; \(\sigma_\theta\) --- +селективность предиката \(\theta\) (доля кортежей входа, удовлетворяющих +\(\theta\)); \(M\) --- число страниц буфера, доступных оператору. Формулы +локальной стоимости физических операторов приведены в +таблице~\ref{tbl:local-cost}. + +\begin{longtable}{|l|c|c|} + \caption{Локальная стоимость физических операторов.}\label{tbl:local-cost}\\ + \hline + Оператор & \(IO(op)\) & \(CPU(op)\) \\ + \hline + \endfirsthead + \hline + \endfoot + + \(\SeqScan(T)\) & \(P(T)\) & \(|T|\) \\ \hline + \(\IndexScan(T, \theta)\) & \(\lceil \sigma_\theta \cdot P(T) \rceil\) & \(\sigma_\theta \cdot |T|\) \\ \hline + \(\FilterOp(R, \theta)\) & \(0\) & \(|R| \cdot c_\theta\) \\ \hline + \(\ProjOp(R)\) & \(0\) & \(|R|\) \\ \hline + \(\NestedLoopJoin(L, R, \theta)\) & \(P(L) \cdot (1 + P(R))\) & \(|L| \cdot |R| \cdot c_\theta\) \\ \hline + \(\NestedLoopCrossJoin(L, R)\) & \(P(L) \cdot (1 + P(R))\) & \(|L| \cdot |R|\) \\ \hline + \(\HashJoin(L, R, \theta)\) & \(P(L) + P(R)\) & \(|L| + |R| \cdot (1 + c_\theta)\) \\ \hline + \(\MergeJoin(L, R)\) & \(P(L) + P(R)\) & \(|L| + |R|\) \\ \hline + \(\Sort(R)\) & \(2 P(R) \cdot \lceil \log_M P(R) \rceil\) & \(|R| \cdot \log_2 |R|\) +\end{longtable} -% {\color{red} TODO: табличка формул для разных операторов} +Для операторов \(\FilterOp\) и \(\ProjOp\) стоимость ввода-вывода равна \(0\), потому что это конвейерные операторы, которые не используют диск. \section{Дифференциальный анализ физических планов} -{\color{red} FIXME: Тут крайне мало конкретики: пока что раздел выглядит как "за - всё хорошее и против всего плохого". Непонятно, какие конкретно системы - считаются эталонами, как конкретно вы собираетесь конвертировать извлеченный - физический план в свою модель, и не получится ли после этого преобразования - совсем другая производительность, а также почему эквивалентность планов - достаточно проверить на единственном наборе таблиц.} - Множество правил трансформации и реализации, используемых в оптимизаторах запросов, изучено довольно хорошо и описано в академической литературе. Вместе с тем условия активации правил @@ -1112,38 +1133,55 @@ \subsection{Предлагаемый метод} Для поиска несоответствий между поведением реализуемой системы и внешней эталонной системы предлагается применить дифференциальный анализ планов. -Первым шагом нужно сформировать набор данных состоящий из схем таблиц и данных. +Первым шагом нужно сформировать несколько наборов данных, состоящих из схем +таблиц и их наполнения. Разные данные позволяют всесторонне исследовать правила +активации, потому что они зависят от кардинальности отношений, наличия индексов +и т.п. + Далее, по заданным схемам генерируются случайные SQL-запросы различной структуры и сложности. Такой подход исключает тривиальные планы и приближает условия работы оптимизатора к реальным нагрузкам при дальнейшем построении планов запросов. Следующим шагом сгенерированные запросы транспилируются в диалекты SQL целевых -промышленных СУБД: Oracle, Microsoft SQL Server, PostgreSQL и т.д. Затем для -получения физических планов исполняется аналог \texttt{EXPLAIN ANALYZE}. +промышленных СУБД. В качестве эталонных систем рассматриваются PostgreSQL, +DuckDB, Umbra и Microsoft SQL Server. Эти системы являются зрелыми, имеют +документированный формат вывода планов и представляют разные архитектурные +подходы. Затем для получения физических планов исполняется аналог +\texttt{EXPLAIN ANALYZE}. Полученные физические планы промышленных СУБД конвертируются в формат -сериализованного плана разрабатываемого оптимизатора. Для этого необходимо -создать набор отображений между физическими операторами различных систем. -Например, \texttt{Hash Match} в SQL Server отображается в \HashJoin{} в -разрабатываемой системе, \texttt{Clustered Index Scan} --- в \IndexScan{} и так -далее. - -Корректность конвертации плана можно проверить путем исполнения исходого плана -на внешней системе и полученного плана на разрабатываемой системе и сравнения -полученных результатов. +сериализованного плана разрабатываемого оптимизатора. Современные СУБД +предоставляют вывод планов в виде XML или JSON, что позволяет легко реализовать +их разбор. Для конвертации используется отображение между физическими +операторами разрабатываемой системы и операторами эталонной СУБД. Например, +\texttt{Hash Match} в SQL Server отображается в \HashJoin{} в разрабатываемой +системе, \texttt{Clustered Index Scan} --- в \IndexScan{} и так далее. +Восстановление предикатов, ключей соединения и выражений проекции выполняется +путем разбора соответствующих полей плана, причем не нужно поддерживать весь +синтаксис предикатов, так как генератор запросов и схемы данных ограничивают +множество допустимых выражений в планах. + +Корректность конвертации выполняется по построению, но дополнительно проверяется +путем исполнения исходного плана на внешней системе и сконвертированного плана +на разрабатываемой системе с последующим сравнением полученных результатов при +фиксированной схеме и различных вариантах сгенерированных для нее данных. Конвертированные планы сравниваются с планами, порождаемыми разрабатываемым оптимизатором на исходных запросах, с целью определить, мог ли существующий набор правил разрабатываемого оптимизатора породить план, эквивалентный плану -промышленной системы. Для корректности сравнения необходимо полное исследование -пространства эквивалентных планов. Если разрабатываемая система не может -породить ``эталонный'' план, то условия активации или набор правил требует -уточнения. Определение требуемых изменений лежит на разработчике системы, -поэтому найденный запрос и примеры планов сохраняются для последующего анализа. -В дальнейшем, полученные после конвертации ``эталонные'' планы можно -использовать как тесты для оптимизатора, которые будут успешно выполняться после -уточнения соответвующих правил активации. +промышленной системы. Стоимостные модели и реализации операторов в разных СУБД +различаются, поэтому план, оптимальный для эталонной системы, не обязательно +будет оптимальным для разрабатываемой. По этоу причине сравнение направлено на +исследование полноты пространства поиска разрабатываемого оптимизатора, и не +предполагает сравнение эффективности планов. + +Для корректности структурного сравнения необходимо полное исследование +пространства эквивалентных планов. Определение требуемых изменений лежит на +разработчике системы, поэтому найденный запрос и примеры планов сохраняются для +последующего анализа. В дальнейшем, полученные после конвертации ``эталонные'' +планы можно использовать как тесты для оптимизатора, которые будут успешно +выполняться после уточнения соответвующих правил активации. \subsection{Поиск ближайшего плана} From 6c2d5f07ef551f7814934fd1063bbb4dfd6c4884 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Wed, 20 May 2026 18:24:55 +0300 Subject: [PATCH 037/120] Add lazy loading of dataset --- flake.nix | 1 + research/ms_sql_server_extractor.py | 19 +++++++++++++++++++ research/research.ipynb | 16 ++++++++++++++-- research/{tmp.py => run.py} | 0 research/test_converter.py | 4 ++++ .../sql/logic/optimizer/optimizer_test.cpp | 3 ++- 6 files changed, 40 insertions(+), 3 deletions(-) rename research/{tmp.py => run.py} (100%) create mode 100644 research/test_converter.py diff --git a/flake.nix b/flake.nix index 2e59869..ef3113a 100644 --- a/flake.nix +++ b/flake.nix @@ -26,6 +26,7 @@ ps.ipykernel ps.notebook ps.pymssql + ps.pytest ]); tex = (pkgs.texlive.combine { inherit (pkgs.texlive) scheme-full diff --git a/research/ms_sql_server_extractor.py b/research/ms_sql_server_extractor.py index 2889812..18771b7 100644 --- a/research/ms_sql_server_extractor.py +++ b/research/ms_sql_server_extractor.py @@ -118,7 +118,26 @@ def _conn(self, database="master"): ) return c + def _is_dataset_loaded(self) -> bool: + with self._conn("master") as conn: + cur = conn.cursor() + cur.execute("SELECT COUNT(*) FROM sys.databases WHERE name=N'imdb'") + if not cur.fetchone()[0]: + return False + with self._conn("imdb") as conn: + cur = conn.cursor() + cur.execute(""" + SELECT COUNT(*) FROM sys.tables t + JOIN sys.schemas s ON s.schema_id = t.schema_id + WHERE s.name = 'dbo' + """) + return cur.fetchone()[0] > 0 + def load_dataset(self) -> None: + if self._is_dataset_loaded(): + print("Dataset already loaded, skipping.") + return + dataset_dir = Path(self.dataset) print("Recreating database...") diff --git a/research/research.ipynb b/research/research.ipynb index 24db12a..3af90cd 100644 --- a/research/research.ipynb +++ b/research/research.ipynb @@ -59,10 +59,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "70ba9fa9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'extractor_obj' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 9\u001b[39m\n\u001b[32m 1\u001b[39m query = \u001b[33m\"\"\"\u001b[39m\n\u001b[32m 2\u001b[39m \u001b[33mSELECT p.primaryName, t.averageRating\u001b[39m\n\u001b[32m 3\u001b[39m \u001b[33mFROM dbo.TitlePrincipals tp\u001b[39m\n\u001b[32m (...)\u001b[39m\u001b[32m 6\u001b[39m \u001b[33mWHERE t.averageRating IS NOT NULL\u001b[39m\n\u001b[32m 7\u001b[39m \u001b[33m\"\"\"\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m9\u001b[39m plan = \u001b[43mextractor_obj\u001b[49m.extract(query)\n\u001b[32m 10\u001b[39m \u001b[38;5;28mprint\u001b[39m(plan)\n", + "\u001b[31mNameError\u001b[39m: name 'extractor_obj' is not defined" + ] + } + ], "source": [ "query = \"\"\"\n", "SELECT p.primaryName, t.averageRating\n", diff --git a/research/tmp.py b/research/run.py similarity index 100% rename from research/tmp.py rename to research/run.py diff --git a/research/test_converter.py b/research/test_converter.py new file mode 100644 index 0000000..2751330 --- /dev/null +++ b/research/test_converter.py @@ -0,0 +1,4 @@ +#!/usr/bin/env python3 + +def test_converter(): + assert 2 == 3 diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index ef38df2..ebb35b5 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -32,9 +32,10 @@ TEST(OptimizerTest, JoinCommutativity) { {"orders", 100}, })); + auto got = optimizer.Optimize(); - ASSERT_THAT(Serialize(got), ::testing::HasSubstr("(SeqScan orders) (SeqScan users)")); + ASSERT_THAT(Serialize(got), Eq("(NestedLoopJoin Inner (= (attr users id) (attr orders user_id)) (SeqScan orders) (SeqScan users))")); } } // namespace stewkk::sql From 6b292d7ab027432ba09ebbe3728b2dffb1f0b509 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Wed, 20 May 2026 19:27:46 +0300 Subject: [PATCH 038/120] Add missing physical ops --- include/stewkk/sql/logic/executor/plan.hpp | 22 ++++++++++- src/stewkk/sql/logic/executor/executor.cpp | 8 ++++ src/stewkk/sql/logic/executor/plan.cpp | 8 ++++ .../sql/logic/executor/plan_serializer.cpp | 38 +++++++++++++++++-- .../logic/executor/plan_serializer_test.cpp | 28 ++++++++++++++ 5 files changed, 99 insertions(+), 5 deletions(-) diff --git a/include/stewkk/sql/logic/executor/plan.hpp b/include/stewkk/sql/logic/executor/plan.hpp index a708649..25bfb9f 100644 --- a/include/stewkk/sql/logic/executor/plan.hpp +++ b/include/stewkk/sql/logic/executor/plan.hpp @@ -15,8 +15,10 @@ struct PhysicalProjection; struct PhysicalFilter; struct NestedLoopJoin; struct NestedLoopCrossJoin; +struct HashJoin; +struct MergeJoin; -using PhysicalPlanNode = std::variant; +using PhysicalPlanNode = std::variant; struct SeqScan { std::string table; @@ -54,4 +56,22 @@ struct NestedLoopCrossJoin { bool operator==(const NestedLoopCrossJoin&) const; }; +struct HashJoin { + std::shared_ptr lhs; + std::shared_ptr rhs; + JoinType type; + Expression qual; + + bool operator==(const HashJoin&) const; +}; + +struct MergeJoin { + std::shared_ptr lhs; + std::shared_ptr rhs; + JoinType type; + Expression qual; + + bool operator==(const MergeJoin&) const; +}; + } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/executor/executor.cpp b/src/stewkk/sql/logic/executor/executor.cpp index fc32211..9816729 100644 --- a/src/stewkk/sql/logic/executor/executor.cpp +++ b/src/stewkk/sql/logic/executor/executor.cpp @@ -434,6 +434,14 @@ boost::asio::awaitable Executor::Execute(const Physica co_await executor.ExecuteCrossJoin(cross_join, attr_chan, tuples_chan); co_return; } + boost::asio::awaitable operator()(const HashJoin&) { + throw std::runtime_error("HashJoin execution not implemented"); + co_return; + } + boost::asio::awaitable operator()(const MergeJoin&) { + throw std::runtime_error("MergeJoin execution not implemented"); + co_return; + } AttributesInfoChannel& attr_chan; TuplesChannel& tuples_chan; diff --git a/src/stewkk/sql/logic/executor/plan.cpp b/src/stewkk/sql/logic/executor/plan.cpp index 3649726..8029ac9 100644 --- a/src/stewkk/sql/logic/executor/plan.cpp +++ b/src/stewkk/sql/logic/executor/plan.cpp @@ -18,4 +18,12 @@ bool NestedLoopCrossJoin::operator==(const NestedLoopCrossJoin& other) const { return *lhs == *other.lhs && *rhs == *other.rhs; } +bool HashJoin::operator==(const HashJoin& other) const { + return *lhs == *other.lhs && *rhs == *other.rhs && type == other.type && qual == other.qual; +} + +bool MergeJoin::operator==(const MergeJoin& other) const { + return *lhs == *other.lhs && *rhs == *other.rhs && type == other.type && qual == other.qual; +} + } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/executor/plan_serializer.cpp b/src/stewkk/sql/logic/executor/plan_serializer.cpp index 3290d55..df91bcf 100644 --- a/src/stewkk/sql/logic/executor/plan_serializer.cpp +++ b/src/stewkk/sql/logic/executor/plan_serializer.cpp @@ -105,6 +105,16 @@ std::string SerializeNode(const PhysicalPlanNode& node) { SerializeJoinType(n.type), SerializeExpr(n.qual), SerializeNode(*n.lhs), SerializeNode(*n.rhs)); } + std::string operator()(const HashJoin& n) const { + return std::format("(HashJoin {} {} {} {})", + SerializeJoinType(n.type), SerializeExpr(n.qual), + SerializeNode(*n.lhs), SerializeNode(*n.rhs)); + } + std::string operator()(const MergeJoin& n) const { + return std::format("(MergeJoin {} {} {} {})", + SerializeJoinType(n.type), SerializeExpr(n.qual), + SerializeNode(*n.lhs), SerializeNode(*n.rhs)); + } }; return std::visit(Visitor{}, node); } @@ -283,7 +293,7 @@ PhysicalPlanNode ParseNode(ParseState& s) { std::make_shared(std::move(rhs)), }; } - if (head == "NestedLoopJoin") { + auto ParseJoinNode = [&]() -> JoinT { auto type_str = s.ExpectAtom(); auto it = kJoinTypes.find(type_str); if (it == kJoinTypes.end()) @@ -292,13 +302,17 @@ PhysicalPlanNode ParseNode(ParseState& s) { auto lhs = ParseNode(s); auto rhs = ParseNode(s); s.ExpectRParen(); - return NestedLoopJoin{ + return JoinT{ std::make_shared(std::move(lhs)), std::make_shared(std::move(rhs)), it->second, std::move(qual), }; - } + }; + + if (head == "NestedLoopJoin") return ParseJoinNode.template operator()(); + if (head == "HashJoin") return ParseJoinNode.template operator()(); + if (head == "MergeJoin") return ParseJoinNode.template operator()(); throw std::runtime_error(std::format("unknown plan node: '{}'", head)); } @@ -357,7 +371,23 @@ struct DotBuilder { int operator()(const NestedLoopJoin& n) { int lhs = std::visit(*this, *n.lhs); int rhs = std::visit(*this, *n.rhs); - int id = Emit(std::format("{}\\nON {}", ToString(n.type), ToString(n.qual))); + int id = Emit(std::format("NL {}\\nON {}", ToString(n.type), ToString(n.qual))); + EmitEdge(lhs, id); + EmitEdge(rhs, id); + return id; + } + int operator()(const HashJoin& n) { + int lhs = std::visit(*this, *n.lhs); + int rhs = std::visit(*this, *n.rhs); + int id = Emit(std::format("Hash {}\\nON {}", ToString(n.type), ToString(n.qual))); + EmitEdge(lhs, id); + EmitEdge(rhs, id); + return id; + } + int operator()(const MergeJoin& n) { + int lhs = std::visit(*this, *n.lhs); + int rhs = std::visit(*this, *n.rhs); + int id = Emit(std::format("Merge {}\\nON {}", ToString(n.type), ToString(n.qual))); EmitEdge(lhs, id); EmitEdge(rhs, id); return id; diff --git a/src/stewkk/sql/logic/executor/plan_serializer_test.cpp b/src/stewkk/sql/logic/executor/plan_serializer_test.cpp index 6bc63da..ad2af53 100644 --- a/src/stewkk/sql/logic/executor/plan_serializer_test.cpp +++ b/src/stewkk/sql/logic/executor/plan_serializer_test.cpp @@ -157,6 +157,34 @@ TEST(PlanSerializerTest, DeepNestedPlan) { EXPECT_THAT(RoundTrip(plan), Eq(plan)); } +TEST(PlanSerializerTest, HashJoin) { + PhysicalPlanNode plan = HashJoin{ + std::make_shared(SeqScan{"a"}), + std::make_shared(SeqScan{"b"}), + JoinType::kInner, + BinaryExpression{ + std::make_shared(Attribute{"a", "id"}), + BinaryOp::kEq, + std::make_shared(Attribute{"b", "id"}), + }, + }; + EXPECT_THAT(RoundTrip(plan), Eq(plan)); +} + +TEST(PlanSerializerTest, MergeJoin) { + PhysicalPlanNode plan = MergeJoin{ + std::make_shared(SeqScan{"a"}), + std::make_shared(SeqScan{"b"}), + JoinType::kLeft, + BinaryExpression{ + std::make_shared(Attribute{"a", "id"}), + BinaryOp::kEq, + std::make_shared(Attribute{"b", "id"}), + }, + }; + EXPECT_THAT(RoundTrip(plan), Eq(plan)); +} + TEST(PlanSerializerDotTest, SmokeTest) { PhysicalPlanNode plan = NestedLoopJoin{ std::make_shared(SeqScan{"a"}), From d4df2e5dc56ea6e80101c31f6544e2558837e284 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Wed, 20 May 2026 19:54:18 +0300 Subject: [PATCH 039/120] Implement converter --- include/stewkk/sql/logic/executor/plan.hpp | 10 +- research/converter.py | 309 ++++++++++++++++++ research/dump_plan.py | 20 ++ research/ms-server-plans/cross_join.xml | 71 ++++ research/ms-server-plans/filter_and.xml | 113 +++++++ research/ms-server-plans/filter_eq.xml | 83 +++++ research/ms-server-plans/filter_gt.xml | 95 ++++++ research/ms-server-plans/filter_lt.xml | 95 ++++++ research/ms-server-plans/filter_not.xml | 81 +++++ research/ms-server-plans/filter_or.xml | 95 ++++++ research/ms-server-plans/inner_join.xml | 121 +++++++ .../join_compound_predicate.xml | 137 ++++++++ research/ms-server-plans/join_with_filter.xml | 139 ++++++++ research/ms-server-plans/left_join.xml | 136 ++++++++ research/ms-server-plans/seq_scan.xml | 61 ++++ research/test_converter.py | 103 +++++- src/stewkk/sql/logic/executor/executor.cpp | 4 + src/stewkk/sql/logic/executor/plan.cpp | 4 + .../sql/logic/executor/plan_serializer.cpp | 12 + .../logic/executor/plan_serializer_test.cpp | 13 + 20 files changed, 1699 insertions(+), 3 deletions(-) create mode 100644 research/converter.py create mode 100644 research/dump_plan.py create mode 100644 research/ms-server-plans/cross_join.xml create mode 100644 research/ms-server-plans/filter_and.xml create mode 100644 research/ms-server-plans/filter_eq.xml create mode 100644 research/ms-server-plans/filter_gt.xml create mode 100644 research/ms-server-plans/filter_lt.xml create mode 100644 research/ms-server-plans/filter_not.xml create mode 100644 research/ms-server-plans/filter_or.xml create mode 100644 research/ms-server-plans/inner_join.xml create mode 100644 research/ms-server-plans/join_compound_predicate.xml create mode 100644 research/ms-server-plans/join_with_filter.xml create mode 100644 research/ms-server-plans/left_join.xml create mode 100644 research/ms-server-plans/seq_scan.xml diff --git a/include/stewkk/sql/logic/executor/plan.hpp b/include/stewkk/sql/logic/executor/plan.hpp index 25bfb9f..9e8bea8 100644 --- a/include/stewkk/sql/logic/executor/plan.hpp +++ b/include/stewkk/sql/logic/executor/plan.hpp @@ -17,8 +17,9 @@ struct NestedLoopJoin; struct NestedLoopCrossJoin; struct HashJoin; struct MergeJoin; +struct IndexSeek; -using PhysicalPlanNode = std::variant; +using PhysicalPlanNode = std::variant; struct SeqScan { std::string table; @@ -74,4 +75,11 @@ struct MergeJoin { bool operator==(const MergeJoin&) const; }; +struct IndexSeek { + std::string table; + Expression predicate; + + bool operator==(const IndexSeek&) const; +}; + } // namespace stewkk::sql diff --git a/research/converter.py b/research/converter.py new file mode 100644 index 0000000..32ac4d9 --- /dev/null +++ b/research/converter.py @@ -0,0 +1,309 @@ +#!/usr/bin/env python3 +""" +Converter from MS SQL Server ShowPlanXML to serialized physical plan format. + +Serialized format (s-expressions): + (SeqScan table) + (PhysicalFilter EXPR SOURCE) + (PhysicalProjection (exprs EXPR...) SOURCE) + (NestedLoopCrossJoin LHS RHS) + (NestedLoopJoin JoinType EXPR LHS RHS) JoinType: Inner | Full | Left | Right + +Expressions: + 42 NULL TRUE FALSE UNKNOWN + (attr table column) + (OP LHS RHS) OP: = != > < >= <= and or + - * / % ^ + (not EXPR) + (uminus EXPR) + +Known missing operators in the target format (raise NotImplementedError): + - IS NULL / IS NOT NULL (MS SQL CompareOp "IS" / "IS NOT") +""" + +import xml.etree.ElementTree as ET + +NS = "{http://schemas.microsoft.com/sqlserver/2004/07/showplan}" + +_JOIN_TYPES = { + "Inner Join": "Inner", + "Left Outer Join": "Left", + "Right Outer Join":"Right", + "Full Outer Join": "Full", +} + +_COMPARE_OPS = { + "EQ": "=", + "NE": "!=", + "GT": ">", + "LT": "<", + "GE": ">=", + "LE": "<=", +} + +_LOGICAL_OPS = { + "AND": "and", + "OR": "or", +} + +_ARITHMETIC_OPS = { + "ADD": "+", + "SUB": "-", + "MUL": "*", + "DIV": "/", + "MOD": "%", +} + + +def convert(plan_xml: str) -> str: + root = ET.fromstring(plan_xml) + top_relop = root.find(f".//{NS}RelOp") + if top_relop is None: + raise ValueError("no RelOp found in plan XML") + return _convert_relop(top_relop) + + +# ─── Operator conversion ───────────────────────────────────────────────────── + +def _convert_relop(relop: ET.Element) -> str: + phys_op = relop.get("PhysicalOp", "") + logical_op = relop.get("LogicalOp", "") + + if phys_op in ("Clustered Index Scan", "Index Scan", "Table Scan", + "Clustered Index Seek", "Index Seek"): + return _convert_scan(relop) + if phys_op == "Nested Loops": + return _convert_nested_loops(relop, logical_op) + if phys_op == "Hash Match": + return _convert_hash_match(relop, logical_op) + if phys_op == "Merge Join": + return _convert_merge_join(relop, logical_op) + + raise NotImplementedError(f"unhandled PhysicalOp: {phys_op!r} (LogicalOp={logical_op!r})") + + +_SEEK_SCAN_TYPES = {"GT": ">", "GE": ">=", "LT": "<", "LE": "<=", "EQ": "="} + + +def _convert_scan(relop: ET.Element) -> str: + phys_op = relop.get("PhysicalOp", "") + obj = relop.find(f".//{NS}IndexScan/{NS}Object") + if obj is None: + obj = relop.find(f".//{NS}TableScan/{NS}Object") + if obj is None: + raise ValueError("cannot find Object element in scan") + table = (obj.get("Alias") or obj.get("Table", "")).strip("[]") + + if "Seek" in phys_op: + seek_pred = _convert_seek_predicates(relop, table) + if seek_pred is None: + raise ValueError(f"Index Seek node has no SeekPredicates: {phys_op!r}") + base = f"(IndexSeek {seek_pred} {table})" + else: + base = f"(SeqScan {table})" + + # Residual predicate (pushed-down filter evaluated after the scan/seek) + pred_elem = relop.find(f".//{NS}IndexScan/{NS}Predicate/{NS}ScalarOperator") + if pred_elem is None: + pred_elem = relop.find(f".//{NS}TableScan/{NS}Predicate/{NS}ScalarOperator") + if pred_elem is not None: + return f"(PhysicalFilter {_convert_scalar(pred_elem)} {base})" + + return base + + +def _convert_seek_predicates(relop: ET.Element, table: str) -> str | None: + seek_preds = relop.find(f".//{NS}SeekPredicates") + if seek_preds is None: + return None + + conditions = [] + for range_elem in (seek_preds.findall(f".//{NS}StartRange") + + seek_preds.findall(f".//{NS}EndRange")): + op = _SEEK_SCAN_TYPES.get(range_elem.get("ScanType", "")) + if not op: + continue + col_refs = range_elem.findall(f"{NS}RangeColumns/{NS}ColumnReference") + expr_elems = range_elem.findall(f"{NS}RangeExpressions/{NS}ScalarOperator") + for col_ref, expr_elem in zip(col_refs, expr_elems): + col = col_ref.get("Column", "").strip("[]") + col_table = (col_ref.get("Alias") or col_ref.get("Table") or table).strip("[]") + conditions.append(f"({op} (attr {col_table} {col}) {_convert_scalar(expr_elem)})") + + if not conditions: + return None + result = conditions[0] + for c in conditions[1:]: + result = f"(and {result} {c})" + return result + + +def _convert_nested_loops(relop: ET.Element, logical_op: str) -> str: + nl = relop.find(f"{NS}NestedLoops") + if nl is None: + raise ValueError("NestedLoops element missing") + lhs, rhs = _expect_two_relops(nl, "NestedLoops") + + pred_elem = nl.find(f"{NS}Predicate/{NS}ScalarOperator") + if pred_elem is None: + return f"(NestedLoopCrossJoin {_convert_relop(lhs)} {_convert_relop(rhs)})" + + join_type = _JOIN_TYPES.get(logical_op, "Inner") + pred = _convert_scalar(pred_elem) + return f"(NestedLoopJoin {join_type} {pred} {_convert_relop(lhs)} {_convert_relop(rhs)})" + + +def _convert_hash_match(relop: ET.Element, logical_op: str) -> str: + hash_elem = relop.find(f"{NS}Hash") + if hash_elem is None: + raise ValueError("Hash element missing from Hash Match node") + build_relop, probe_relop = _expect_two_relops(hash_elem, "Hash") + + build_refs = hash_elem.findall(f"{NS}HashKeysBuild/{NS}ColumnReference") + probe_refs = hash_elem.findall(f"{NS}HashKeysProbe/{NS}ColumnReference") + if not build_refs or len(build_refs) != len(probe_refs): + raise ValueError("cannot reconstruct Hash Match predicate: key lists empty or mismatched") + + pairs = [f"(= {_col_ref_to_attr(b)} {_col_ref_to_attr(p)})" + for b, p in zip(build_refs, probe_refs)] + pred = pairs[0] + for p in pairs[1:]: + pred = f"(and {pred} {p})" + + join_type = _JOIN_TYPES.get(logical_op, "Inner") + return f"(HashJoin {join_type} {pred} {_convert_relop(build_relop)} {_convert_relop(probe_relop)})" + + +def _convert_merge_join(relop: ET.Element, logical_op: str) -> str: + merge = relop.find(f"{NS}Merge") + if merge is None: + raise ValueError("Merge element missing from Merge Join node") + outer_relop, inner_relop = _expect_two_relops(merge, "Merge") + + pred_elem = merge.find(f"{NS}Residual/{NS}ScalarOperator") + if pred_elem is None: + raise NotImplementedError("Merge Join with no Residual predicate") + + join_type = _JOIN_TYPES.get(logical_op, "Inner") + pred = _convert_scalar(pred_elem) + return f"(MergeJoin {join_type} {pred} {_convert_relop(outer_relop)} {_convert_relop(inner_relop)})" + + +def _expect_two_relops(parent: ET.Element, label: str) -> tuple[ET.Element, ET.Element]: + children = parent.findall(f"{NS}RelOp") + if len(children) != 2: + raise ValueError(f"{label} has {len(children)} RelOp children, expected 2") + return children[0], children[1] + + +def _col_ref_to_attr(cr: ET.Element) -> str: + table = (cr.get("Alias") or cr.get("Table", "")).strip("[]") + col = cr.get("Column", "").strip("[]") + return f"(attr {table} {col})" + + +# ─── Scalar expression conversion ──────────────────────────────────────────── + +def _convert_scalar(scalar: ET.Element) -> str: + convert = scalar.find(f"{NS}Convert") + if convert is not None: + child = convert.find(f"{NS}ScalarOperator") + if child is None: + raise ValueError("Convert has no child ScalarOperator") + return _convert_scalar(child) + + compare = scalar.find(f"{NS}Compare") + if compare is not None: + return _convert_compare(compare) + + logical = scalar.find(f"{NS}Logical") + if logical is not None: + return _convert_logical(logical) + + arithmetic = scalar.find(f"{NS}Arithmetic") + if arithmetic is not None: + return _convert_arithmetic(arithmetic) + + identifier = scalar.find(f"{NS}Identifier") + if identifier is not None: + return _convert_identifier(identifier) + + const = scalar.find(f"{NS}Const") + if const is not None: + return _convert_const(const) + + raise NotImplementedError(f"unhandled ScalarOperator children: {[c.tag.split('}')[1] for c in scalar]}") + + +def _convert_compare(compare: ET.Element) -> str: + op_str = compare.get("CompareOp", "") + + if op_str in ("IS", "IS NOT"): + raise NotImplementedError( + f"CompareOp={op_str!r} (IS NULL / IS NOT NULL) is not supported by the target plan format: " + "BinaryOp in the project has no kIsNull / kIsNotNull" + ) + + op = _COMPARE_OPS.get(op_str) + if op is None: + raise NotImplementedError(f"unhandled CompareOp: {op_str!r}") + + scalars = compare.findall(f"{NS}ScalarOperator") + if len(scalars) != 2: + raise ValueError(f"Compare has {len(scalars)} ScalarOperator children, expected 2") + return f"({op} {_convert_scalar(scalars[0])} {_convert_scalar(scalars[1])})" + + +def _convert_logical(logical: ET.Element) -> str: + op_str = logical.get("Operation", "").upper() + children = [_convert_scalar(s) for s in logical.findall(f"{NS}ScalarOperator")] + + if op_str == "NOT": + if len(children) != 1: + raise ValueError(f"NOT expects 1 child, got {len(children)}") + return f"(not {children[0]})" + + op = _LOGICAL_OPS.get(op_str) + if op is None: + raise NotImplementedError(f"unhandled Logical Operation: {op_str!r}") + # MS SQL AND/OR can be n-ary — fold left into binary pairs. + result = children[0] + for child in children[1:]: + result = f"({op} {result} {child})" + return result + + +def _convert_arithmetic(arithmetic: ET.Element) -> str: + op_str = arithmetic.get("Operation", "").upper() + op = _ARITHMETIC_OPS.get(op_str) + if op is None: + raise NotImplementedError(f"unhandled Arithmetic Operation: {op_str!r}") + scalars = arithmetic.findall(f"{NS}ScalarOperator") + if len(scalars) != 2: + raise ValueError(f"Arithmetic has {len(scalars)} ScalarOperator children, expected 2") + return f"({op} {_convert_scalar(scalars[0])} {_convert_scalar(scalars[1])})" + + +def _convert_identifier(identifier: ET.Element) -> str: + col_ref = identifier.find(f"{NS}ColumnReference") + if col_ref is None: + raise ValueError("Identifier has no ColumnReference") + column = col_ref.get("Column", "").strip("[]") + + if column.startswith("ConstExpr"): + child = col_ref.find(f"{NS}ScalarOperator") + if child is None: + raise ValueError(f"ConstExpr {column!r} has no child ScalarOperator") + return _convert_scalar(child) + + if column.startswith("@"): + raise NotImplementedError( + f"parameter reference {column!r} — use OPTION (RECOMPILE) to embed literal values in the plan" + ) + + table = (col_ref.get("Alias") or col_ref.get("Table", "")).strip("[]") + return f"(attr {table} {column})" + + +def _convert_const(const: ET.Element) -> str: + value = const.get("ConstValue", "").strip("()") + return value diff --git a/research/dump_plan.py b/research/dump_plan.py new file mode 100644 index 0000000..4ccd847 --- /dev/null +++ b/research/dump_plan.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python3 +"""Phase 0 helper: pretty-print an MS SQL Server execution plan XML.""" + +import sys +import xml.dom.minidom + +from ms_sql_server_extractor import MsSqlServerExtractor + +QUERY = sys.argv[1] if len(sys.argv) > 1 else "SELECT * FROM dbo.Titles WHERE isAdult = 1" + +extractor = MsSqlServerExtractor( + dataset="./datasets", + host="localhost", + port=1433, + user="sa", + password="Password123!", +) + +xml_str = extractor.extract(QUERY) +print(xml.dom.minidom.parseString(xml_str).toprettyxml(indent=" ")) diff --git a/research/ms-server-plans/cross_join.xml b/research/ms-server-plans/cross_join.xml new file mode 100644 index 0000000..c623c97 --- /dev/null +++ b/research/ms-server-plans/cross_join.xml @@ -0,0 +1,71 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/research/ms-server-plans/filter_and.xml b/research/ms-server-plans/filter_and.xml new file mode 100644 index 0000000..e2605cb --- /dev/null +++ b/research/ms-server-plans/filter_and.xml @@ -0,0 +1,113 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/research/ms-server-plans/filter_eq.xml b/research/ms-server-plans/filter_eq.xml new file mode 100644 index 0000000..97f6e2a --- /dev/null +++ b/research/ms-server-plans/filter_eq.xml @@ -0,0 +1,83 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/research/ms-server-plans/filter_gt.xml b/research/ms-server-plans/filter_gt.xml new file mode 100644 index 0000000..d7463d2 --- /dev/null +++ b/research/ms-server-plans/filter_gt.xml @@ -0,0 +1,95 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/research/ms-server-plans/filter_lt.xml b/research/ms-server-plans/filter_lt.xml new file mode 100644 index 0000000..13ac111 --- /dev/null +++ b/research/ms-server-plans/filter_lt.xml @@ -0,0 +1,95 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/research/ms-server-plans/filter_not.xml b/research/ms-server-plans/filter_not.xml new file mode 100644 index 0000000..a6c9cd8 --- /dev/null +++ b/research/ms-server-plans/filter_not.xml @@ -0,0 +1,81 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/research/ms-server-plans/filter_or.xml b/research/ms-server-plans/filter_or.xml new file mode 100644 index 0000000..bc4fc89 --- /dev/null +++ b/research/ms-server-plans/filter_or.xml @@ -0,0 +1,95 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/research/ms-server-plans/inner_join.xml b/research/ms-server-plans/inner_join.xml new file mode 100644 index 0000000..aa4ef91 --- /dev/null +++ b/research/ms-server-plans/inner_join.xml @@ -0,0 +1,121 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/research/ms-server-plans/join_compound_predicate.xml b/research/ms-server-plans/join_compound_predicate.xml new file mode 100644 index 0000000..4b7078f --- /dev/null +++ b/research/ms-server-plans/join_compound_predicate.xml @@ -0,0 +1,137 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/research/ms-server-plans/join_with_filter.xml b/research/ms-server-plans/join_with_filter.xml new file mode 100644 index 0000000..7657b97 --- /dev/null +++ b/research/ms-server-plans/join_with_filter.xml @@ -0,0 +1,139 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/research/ms-server-plans/left_join.xml b/research/ms-server-plans/left_join.xml new file mode 100644 index 0000000..2ba5d6b --- /dev/null +++ b/research/ms-server-plans/left_join.xml @@ -0,0 +1,136 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/research/ms-server-plans/seq_scan.xml b/research/ms-server-plans/seq_scan.xml new file mode 100644 index 0000000..42aeb63 --- /dev/null +++ b/research/ms-server-plans/seq_scan.xml @@ -0,0 +1,61 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/research/test_converter.py b/research/test_converter.py index 2751330..53d14a4 100644 --- a/research/test_converter.py +++ b/research/test_converter.py @@ -1,4 +1,103 @@ #!/usr/bin/env python3 -def test_converter(): - assert 2 == 3 +import pytest + +from ms_sql_server_extractor import MsSqlServerExtractor +from converter import convert + + +@pytest.fixture(scope="session") +def extractor(): + return MsSqlServerExtractor( + dataset="./datasets", + host="localhost", + port=1433, + user="sa", + password="Password123!", + ) + + +def extract_plan(extractor, sql: str) -> str: + """Extract plan with OPTION (RECOMPILE) so literal values appear in the XML instead of @N placeholders.""" + return extractor.extract(sql + " OPTION (RECOMPILE)") + + +def test_seq_scan(extractor): + plan = extract_plan(extractor, "SELECT * FROM Titles") + result = convert(plan) + assert result == "(SeqScan Titles)" + + +def test_filter_eq(extractor): + plan = extract_plan(extractor, "SELECT * FROM Titles WHERE Titles.isAdult = 1") + result = convert(plan) + assert result == "(PhysicalFilter (= (attr Titles isAdult) 1) (SeqScan Titles))" + + +def test_filter_gt(extractor): + plan = extract_plan(extractor, "SELECT * FROM Titles WHERE Titles.titleId > 5000") + result = convert(plan) + assert result == "(IndexSeek (> (attr Titles titleId) 5000) Titles)" + + +def test_filter_lt(extractor): + plan = extract_plan(extractor, "SELECT * FROM Titles WHERE Titles.titleId < 5000") + result = convert(plan) + assert result == "(IndexSeek (< (attr Titles titleId) 5000) Titles)" + + +def test_filter_and(extractor): + plan = extract_plan(extractor, "SELECT * FROM Titles WHERE Titles.isAdult = 1 AND Titles.titleId > 5000") + result = convert(plan) + assert result == "(PhysicalFilter (= (attr Titles isAdult) 1) (IndexSeek (> (attr Titles titleId) 5000) Titles))" + + +@pytest.mark.skip(reason="IS NULL not supported in target plan format (BinaryOp lacks kIsNull)") +def test_filter_or(extractor): + plan = extract_plan(extractor, "SELECT * FROM Titles WHERE Titles.isAdult = 0 OR Titles.endYear IS NULL") + result = convert(plan) + assert result == "" + + +def test_filter_not(extractor): + plan = extract_plan(extractor, "SELECT * FROM Titles WHERE NOT Titles.isAdult = 1") + result = convert(plan) + assert result == "(PhysicalFilter (!= (attr Titles isAdult) 1) (SeqScan Titles))" + + +def test_cross_join(extractor): + plan = extract_plan(extractor, "SELECT * FROM TitleTypes CROSS JOIN Genres") + result = convert(plan) + assert result == "(NestedLoopCrossJoin (SeqScan TitleTypes) (SeqScan Genres))" + + +def test_inner_join(extractor): + plan = extract_plan(extractor, + "SELECT * FROM TitlePrincipals JOIN Principals ON TitlePrincipals.principalId = Principals.principalId" + ) + result = convert(plan) + assert result == "(HashJoin Inner (= (attr Principals principalId) (attr TitlePrincipals principalId)) (SeqScan Principals) (SeqScan TitlePrincipals))" + + +def test_left_join(extractor): + plan = extract_plan(extractor, + "SELECT * FROM Titles LEFT OUTER JOIN Episodes ON Titles.titleId = Episodes.episodeId" + ) + result = convert(plan) + assert result == "(MergeJoin Left (= (attr Titles titleId) (attr Episodes episodeId)) (SeqScan Titles) (SeqScan Episodes))" + + +def test_join_with_filter(extractor): + plan = extract_plan(extractor, + "SELECT * FROM TitlePrincipals JOIN Principals ON TitlePrincipals.principalId = Principals.principalId WHERE TitlePrincipals.ordinal > 1" + ) + result = convert(plan) + assert result == "(HashJoin Inner (= (attr Principals principalId) (attr TitlePrincipals principalId)) (SeqScan Principals) (PhysicalFilter (> (attr TitlePrincipals ordinal) 1) (SeqScan TitlePrincipals)))" + + +def test_join_compound_predicate(extractor): + plan = extract_plan(extractor, + "SELECT * FROM TitlePrincipals JOIN Principals ON TitlePrincipals.principalId = Principals.principalId AND TitlePrincipals.ordinal = 1" + ) + result = convert(plan) + assert result == "(HashJoin Inner (= (attr TitlePrincipals principalId) (attr Principals principalId)) (PhysicalFilter (= (attr TitlePrincipals ordinal) 1) (SeqScan TitlePrincipals)) (SeqScan Principals))" diff --git a/src/stewkk/sql/logic/executor/executor.cpp b/src/stewkk/sql/logic/executor/executor.cpp index 9816729..044580e 100644 --- a/src/stewkk/sql/logic/executor/executor.cpp +++ b/src/stewkk/sql/logic/executor/executor.cpp @@ -442,6 +442,10 @@ boost::asio::awaitable Executor::Execute(const Physica throw std::runtime_error("MergeJoin execution not implemented"); co_return; } + boost::asio::awaitable operator()(const IndexSeek&) { + throw std::runtime_error("IndexSeek execution not implemented"); + co_return; + } AttributesInfoChannel& attr_chan; TuplesChannel& tuples_chan; diff --git a/src/stewkk/sql/logic/executor/plan.cpp b/src/stewkk/sql/logic/executor/plan.cpp index 8029ac9..e269a93 100644 --- a/src/stewkk/sql/logic/executor/plan.cpp +++ b/src/stewkk/sql/logic/executor/plan.cpp @@ -26,4 +26,8 @@ bool MergeJoin::operator==(const MergeJoin& other) const { return *lhs == *other.lhs && *rhs == *other.rhs && type == other.type && qual == other.qual; } +bool IndexSeek::operator==(const IndexSeek& other) const { + return table == other.table && predicate == other.predicate; +} + } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/executor/plan_serializer.cpp b/src/stewkk/sql/logic/executor/plan_serializer.cpp index df91bcf..e326353 100644 --- a/src/stewkk/sql/logic/executor/plan_serializer.cpp +++ b/src/stewkk/sql/logic/executor/plan_serializer.cpp @@ -105,6 +105,9 @@ std::string SerializeNode(const PhysicalPlanNode& node) { SerializeJoinType(n.type), SerializeExpr(n.qual), SerializeNode(*n.lhs), SerializeNode(*n.rhs)); } + std::string operator()(const IndexSeek& n) const { + return std::format("(IndexSeek {} {})", SerializeExpr(n.predicate), n.table); + } std::string operator()(const HashJoin& n) const { return std::format("(HashJoin {} {} {} {})", SerializeJoinType(n.type), SerializeExpr(n.qual), @@ -310,6 +313,12 @@ PhysicalPlanNode ParseNode(ParseState& s) { }; }; + if (head == "IndexSeek") { + auto pred = ParseExpr(s); + auto table = s.ExpectAtom(); + s.ExpectRParen(); + return IndexSeek{std::move(table), std::move(pred)}; + } if (head == "NestedLoopJoin") return ParseJoinNode.template operator()(); if (head == "HashJoin") return ParseJoinNode.template operator()(); if (head == "MergeJoin") return ParseJoinNode.template operator()(); @@ -376,6 +385,9 @@ struct DotBuilder { EmitEdge(rhs, id); return id; } + int operator()(const IndexSeek& n) { + return Emit(std::format("IndexSeek\\n{}\\n{}", n.table, ToString(n.predicate))); + } int operator()(const HashJoin& n) { int lhs = std::visit(*this, *n.lhs); int rhs = std::visit(*this, *n.rhs); diff --git a/src/stewkk/sql/logic/executor/plan_serializer_test.cpp b/src/stewkk/sql/logic/executor/plan_serializer_test.cpp index ad2af53..b617101 100644 --- a/src/stewkk/sql/logic/executor/plan_serializer_test.cpp +++ b/src/stewkk/sql/logic/executor/plan_serializer_test.cpp @@ -157,6 +157,19 @@ TEST(PlanSerializerTest, DeepNestedPlan) { EXPECT_THAT(RoundTrip(plan), Eq(plan)); } +TEST(PlanSerializerTest, IndexSeek) { + PhysicalPlanNode plan = IndexSeek{ + "users", + BinaryExpression{ + std::make_shared(Attribute{"users", "id"}), + BinaryOp::kGt, + std::make_shared(IntConst{42}), + }, + }; + EXPECT_THAT(RoundTrip(plan), Eq(plan)); + EXPECT_THAT(Serialize(plan), Eq("(IndexSeek (> (attr users id) 42) users)")); +} + TEST(PlanSerializerTest, HashJoin) { PhysicalPlanNode plan = HashJoin{ std::make_shared(SeqScan{"a"}), From 3157f2ec779c98ce55d077e739f3095a5575b423 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Wed, 20 May 2026 20:25:42 +0300 Subject: [PATCH 040/120] Add simple random query generator --- research/query_generator.py | 434 ++++++++++++++++++++++++++++++++++++ 1 file changed, 434 insertions(+) create mode 100644 research/query_generator.py diff --git a/research/query_generator.py b/research/query_generator.py new file mode 100644 index 0000000..9af297f --- /dev/null +++ b/research/query_generator.py @@ -0,0 +1,434 @@ +#!/usr/bin/env python3 +""" +Random SQL query generator. + +Builds queries in a strict SELECT-Project-Filter-Join (SPJ) subset that matches +what the C++ parser accepts, then renders them in pluggable SQL dialects. + +Currently supported dialects: + - PostgresSubsetDialect (the subset the C++ ANTLR visitor accepts) + - MsSqlDialect (T-SQL with bracket quoting and schema prefix) + +Usage: + python query_generator.py \\ + --data-dir ../test/static/executor/test_data \\ + [--seed 42] [--count 5] [--dialect pg|mssql] +""" +from __future__ import annotations + +import argparse +import csv +import re +import random +from dataclasses import dataclass +from pathlib import Path +from typing import Protocol + + +# --------------------------------------------------------------------------- +# Schema +# --------------------------------------------------------------------------- + +@dataclass +class Column: + name: str + type: str # "int" only in current C++ implementation + + +@dataclass +class TableSchema: + name: str + columns: list[Column] + sample_values: dict[str, list[int]] # col_name -> sampled actual values + + +@dataclass +class Schema: + tables: dict[str, TableSchema] + + +def load_schema(csv_dir: Path, max_sample: int = 200) -> Schema: + """ + Read all *.csv files from csv_dir. + Files whose stem ends in _ (e.g. departments_1000) are skipped — + they share a schema with the base table and are only used for benchmarks. + """ + tables: dict[str, TableSchema] = {} + for path in sorted(csv_dir.glob("*.csv")): + if re.search(r"_\d+$", path.stem): + continue + with path.open() as f: + reader = csv.reader(f) + header_row = next(reader) + columns = [] + for h in header_row: + col_name, col_type = h.strip().split(":") + columns.append(Column(col_name.strip(), col_type.strip())) + + sample_values: dict[str, list[int]] = {c.name: [] for c in columns} + for i, row in enumerate(reader): + if i >= max_sample: + break + for col, val in zip(columns, row): + v = val.strip() + if v != "NULL": + try: + sample_values[col.name].append(int(v)) + except ValueError: + pass + + tables[path.stem] = TableSchema(path.stem, columns, sample_values) + return Schema(tables) + + +# --------------------------------------------------------------------------- +# Query IR — only constructs that the C++ parser accepts +# --------------------------------------------------------------------------- + +@dataclass +class Attr: + table: str + column: str + + +@dataclass +class IntLit: + value: int + + +@dataclass +class NullLit: + pass + + +@dataclass +class BoolLit: + value: bool + + +@dataclass +class BinaryExpr: + # Comparison: "=", "!=", "<", ">", "<=", ">=" + # Logical: "and", "or" + # Arithmetic: "+", "-", "*" + op: str + lhs: "Expr" + rhs: "Expr" + + +@dataclass +class UnaryExpr: + op: str # "not" | "uminus" + child: "Expr" + + +@dataclass +class IsNullExpr: + child: "Expr" + negated: bool # False -> IS NULL, True -> IS NOT NULL + + +Expr = Attr | IntLit | NullLit | BoolLit | BinaryExpr | UnaryExpr | IsNullExpr + + +@dataclass +class TableScan: + name: str + + +@dataclass +class ExplicitJoin: + join_type: str # "INNER" | "LEFT" | "RIGHT" | "FULL" + lhs: "TableRef" + rhs: "TableRef" + on: Expr + + +@dataclass +class CrossJoin: + lhs: "TableRef" + rhs: "TableRef" + + +TableRef = TableScan | ExplicitJoin | CrossJoin + + +@dataclass +class SelectQuery: + targets: list[Attr] + from_: TableRef + where: Expr | None + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +def _tables_in_ref(ref: TableRef) -> list[str]: + if isinstance(ref, TableScan): + return [ref.name] + return _tables_in_ref(ref.lhs) + _tables_in_ref(ref.rhs) + + +def _available_attrs(schema: Schema, ref: TableRef) -> list[Attr]: + return [ + Attr(t, col.name) + for t in _tables_in_ref(ref) + if t in schema.tables + for col in schema.tables[t].columns + ] + + +# --------------------------------------------------------------------------- +# Random query builder +# --------------------------------------------------------------------------- + +_COMPARISON_OPS = ["=", "!=", "<", ">", "<=", ">="] +_LOGICAL_OPS = ["and", "or"] +_ARITHMETIC_OPS = ["+", "-", "*"] +_JOIN_TYPES = ["INNER", "LEFT", "RIGHT", "FULL"] + + +class QueryGenerator: + def __init__(self, schema: Schema, rng: random.Random): + self.schema = schema + self.rng = rng + + def generate(self) -> SelectQuery: + from_ = self._gen_from() + attrs = _available_attrs(self.schema, from_) + + n_targets = self.rng.randint(1, min(3, len(attrs))) + targets = self.rng.sample(attrs, n_targets) + + where = self._gen_predicate(attrs) if self.rng.random() < 0.6 else None + return SelectQuery(targets, from_, where) + + def _gen_from(self) -> TableRef: + names = list(self.schema.tables.keys()) + n = self.rng.randint(1, min(3, len(names))) + chosen = self.rng.sample(names, n) + + ref: TableRef = TableScan(chosen[0]) + for table_name in chosen[1:]: + rhs: TableRef = TableScan(table_name) + + # Try equi-join on a shared column name first. + lhs_col_names = { + c.name + for t in _tables_in_ref(ref) + for c in self.schema.tables[t].columns + } + rhs_col_names = {c.name for c in self.schema.tables[table_name].columns} + shared = sorted(lhs_col_names & rhs_col_names) + + if shared and self.rng.random() < 0.7: + col = self.rng.choice(shared) + # Pick a LHS table that actually has this column. + lhs_candidate = self.rng.choice([ + t for t in _tables_in_ref(ref) + if any(c.name == col for c in self.schema.tables[t].columns) + ]) + on: Expr = BinaryExpr("=", Attr(lhs_candidate, col), Attr(table_name, col)) + jtype = self.rng.choice(_JOIN_TYPES) + ref = ExplicitJoin(jtype, ref, rhs, on) + else: + ref = CrossJoin(ref, rhs) + + return ref + + def _gen_predicate(self, attrs: list[Attr], depth: int = 0) -> Expr: + # Deeper recursion becomes a leaf comparison to avoid runaway trees. + if depth >= 2 or self.rng.random() < 0.45: + return self._gen_comparison(attrs) + + op = self.rng.choice(_LOGICAL_OPS) + lhs = self._gen_predicate(attrs, depth + 1) + rhs = self._gen_predicate(attrs, depth + 1) + return BinaryExpr(op, lhs, rhs) + + def _gen_comparison(self, attrs: list[Attr]) -> Expr: + attr = self.rng.choice(attrs) + + if self.rng.random() < 0.12: + return IsNullExpr(attr, negated=self.rng.random() < 0.5) + + op = self.rng.choice(_COMPARISON_OPS) + rhs = self._gen_int_expr(attr) + return BinaryExpr(op, attr, rhs) + + def _gen_int_expr(self, hint_attr: Attr) -> Expr: + sample = self.schema.tables[hint_attr.table].sample_values.get(hint_attr.column, []) + if sample and self.rng.random() < 0.75: + return IntLit(self.rng.choice(sample)) + return IntLit(self.rng.randint(1, 100)) + + +# --------------------------------------------------------------------------- +# Dialect protocol +# --------------------------------------------------------------------------- + +class Dialect(Protocol): + def fmt_table(self, name: str) -> str: ... + def fmt_col_ref(self, table: str, col: str) -> str: ... + def fmt_not_equals(self) -> str: ... + def fmt_join_keyword(self, join_type: str) -> str: ... + + +class PostgresSubsetDialect: + """Plain identifiers, no schema prefix. Matches the C++ ANTLR visitor.""" + + def fmt_table(self, name: str) -> str: + return name + + def fmt_col_ref(self, table: str, col: str) -> str: + return f"{table}.{col}" + + def fmt_not_equals(self) -> str: + return "!=" + + def fmt_join_keyword(self, join_type: str) -> str: + return { + "INNER": "JOIN", + "LEFT": "LEFT JOIN", + "RIGHT": "RIGHT JOIN", + "FULL": "FULL JOIN", + }[join_type] + + +class MsSqlDialect: + """Bracket-quoted identifiers with an optional schema prefix.""" + + def __init__(self, schema_prefix: str = "dbo"): + self._schema = schema_prefix + + def fmt_table(self, name: str) -> str: + if self._schema: + return f"{self._schema}.{name}" + return name + + def fmt_col_ref(self, table: str, col: str) -> str: + return f"{table}.{col}" + + def fmt_not_equals(self) -> str: + return "<>" + + def fmt_join_keyword(self, join_type: str) -> str: + return { + "INNER": "INNER JOIN", + "LEFT": "LEFT OUTER JOIN", + "RIGHT": "RIGHT OUTER JOIN", + "FULL": "FULL OUTER JOIN", + }[join_type] + + +# --------------------------------------------------------------------------- +# Renderer +# --------------------------------------------------------------------------- + +def render_expr(expr: Expr, d: Dialect) -> str: + match expr: + case Attr(table, col): + return d.fmt_col_ref(table, col) + case IntLit(value): + return str(value) + case NullLit(): + return "NULL" + case BoolLit(value): + return "TRUE" if value else "FALSE" + case BinaryExpr(op, lhs, rhs): + l = render_expr(lhs, d) + r = render_expr(rhs, d) + if op == "!=": + op = d.fmt_not_equals() + elif op in ("and", "or"): + op = op.upper() + return f"({l} {op} {r})" + return f"{l} {op} {r}" + case UnaryExpr("not", child): + return f"NOT ({render_expr(child, d)})" + case UnaryExpr("uminus", child): + return f"-{render_expr(child, d)}" + case IsNullExpr(child, negated): + c = render_expr(child, d) + return f"{c} IS NOT NULL" if negated else f"{c} IS NULL" + raise ValueError(f"unhandled expr type: {type(expr)}") + + +def render_table_ref(ref: TableRef, d: Dialect) -> str: + match ref: + case TableScan(name): + return d.fmt_table(name) + case CrossJoin(lhs, rhs): + return f"{render_table_ref(lhs, d)} CROSS JOIN {render_table_ref(rhs, d)}" + case ExplicitJoin(jtype, lhs, rhs, on): + kw = d.fmt_join_keyword(jtype) + return ( + f"{render_table_ref(lhs, d)}" + f" {kw} {render_table_ref(rhs, d)}" + f" ON {render_expr(on, d)}" + ) + raise ValueError(f"unhandled ref type: {type(ref)}") + + +def render_query(query: SelectQuery, d: Dialect) -> str: + targets = ", ".join(d.fmt_col_ref(a.table, a.column) for a in query.targets) + from_clause = render_table_ref(query.from_, d) + sql = f"SELECT {targets}\nFROM {from_clause}" + if query.where is not None: + sql += f"\nWHERE {render_expr(query.where, d)}" + return sql + + +# --------------------------------------------------------------------------- +# Registry — add new dialects here +# --------------------------------------------------------------------------- + +DIALECTS: dict[str, Dialect] = { + "pg": PostgresSubsetDialect(), + "mssql": MsSqlDialect(), +} + + +# --------------------------------------------------------------------------- +# CLI +# --------------------------------------------------------------------------- + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--data-dir", + default="../test/static/executor/test_data", + help="Directory containing *.csv table files", + ) + parser.add_argument("--seed", type=int, default=None) + parser.add_argument("--count", type=int, default=5) + parser.add_argument( + "--dialect", + choices=list(DIALECTS), + default=None, + help="Print only one dialect. Omit to print all.", + ) + args = parser.parse_args() + + schema = load_schema(Path(args.data_dir)) + print(f"Loaded tables: {', '.join(schema.tables)}\n") + + rng = random.Random(args.seed) + gen = QueryGenerator(schema, rng) + + dialects_to_print: dict[str, Dialect] = ( + {args.dialect: DIALECTS[args.dialect]} if args.dialect else DIALECTS + ) + + for i in range(1, args.count + 1): + query = gen.generate() + print(f"-- Query {i} " + "-" * 60) + for name, dialect in dialects_to_print.items(): + print(f"-- [{name}]") + print(render_query(query, dialect)) + print() + + +if __name__ == "__main__": + main() From 6611f4f743e48cf4637617483f6d200e14c504d7 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Thu, 21 May 2026 22:06:27 +0300 Subject: [PATCH 041/120] Impl reachability check --- include/stewkk/sql/logic/optimizer/group.hpp | 7 ++ .../stewkk/sql/logic/optimizer/optimizer.hpp | 8 ++ .../sql/logic/optimizer/reachability.hpp | 24 ++++ src/stewkk/sql/CMakeLists.txt | 1 + src/stewkk/sql/logic/optimizer/group.cpp | 4 + src/stewkk/sql/logic/optimizer/optimizer.cpp | 17 ++- .../sql/logic/optimizer/optimizer_test.cpp | 80 +++++++++++++ .../sql/logic/optimizer/reachability.cpp | 107 ++++++++++++++++++ 8 files changed, 247 insertions(+), 1 deletion(-) create mode 100644 include/stewkk/sql/logic/optimizer/reachability.hpp create mode 100644 src/stewkk/sql/logic/optimizer/reachability.cpp diff --git a/include/stewkk/sql/logic/optimizer/group.hpp b/include/stewkk/sql/logic/optimizer/group.hpp index d66c54e..03ca40d 100644 --- a/include/stewkk/sql/logic/optimizer/group.hpp +++ b/include/stewkk/sql/logic/optimizer/group.hpp @@ -16,15 +16,22 @@ class Group { struct ToNotNull { utils::NotNull operator()(LogicalExpr& e) const { return &e; } }; + struct ToNotNullPhysical { + utils::NotNull operator()(PhysicalExpr& e) const { return &e; } + }; public: using LogicalExprs = std::ranges::transform_view< std::ranges::ref_view>, ToNotNull>; + using PhysicalExprs = std::ranges::transform_view< + std::ranges::ref_view>, + ToNotNullPhysical>; utils::NotNull AddLogicalExpr(LogicalOperator root_operator); utils::NotNull AddPhysicalExpr(PhysicalOperator root_operator); LogicalExprs GetLogicalExprs(); + PhysicalExprs GetPhysicalExprs(); size_t GetId() const; diff --git a/include/stewkk/sql/logic/optimizer/optimizer.hpp b/include/stewkk/sql/logic/optimizer/optimizer.hpp index 864e5d0..f69082c 100644 --- a/include/stewkk/sql/logic/optimizer/optimizer.hpp +++ b/include/stewkk/sql/logic/optimizer/optimizer.hpp @@ -27,7 +27,15 @@ class Optimizer { PhysicalPlanNode Optimize(); + // Runs exhaustive search (no cost limit), populating all physical_exprs_ in + // every reachable group. Invariant: must not be called with a top-level limit. + // Use GetRootGroup() afterward to drive IsReachable(). + void OptimizeExhaustive(); + + utils::NotNull GetRootGroup() const; + private: + void RunSearch(); using Limit = std::optional; bool IsExplored(utils::NotNull group) const; diff --git a/include/stewkk/sql/logic/optimizer/reachability.hpp b/include/stewkk/sql/logic/optimizer/reachability.hpp new file mode 100644 index 0000000..6bf5edc --- /dev/null +++ b/include/stewkk/sql/logic/optimizer/reachability.hpp @@ -0,0 +1,24 @@ +#pragma once + +#include +#include + +#include +#include +#include +#include + +namespace stewkk::sql { + +// FIXME: use std::expected +struct MatchResult { + bool reachable; + std::string mismatch; +}; + +MatchResult IsReachable(utils::NotNull root, const PhysicalPlanNode& target); + +MatchResult IsPlanReachable(std::istream& sql, const PhysicalPlanNode& target, + CardinalityEstimates cardinality = {}); + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/CMakeLists.txt b/src/stewkk/sql/CMakeLists.txt index b06974f..893c40c 100644 --- a/src/stewkk/sql/CMakeLists.txt +++ b/src/stewkk/sql/CMakeLists.txt @@ -28,6 +28,7 @@ add_library(libsql logic/executor/materialization.cpp logic/executor/llvm.cpp logic/optimizer/optimizer.cpp + logic/optimizer/reachability.cpp logic/optimizer/cardinality.cpp logic/optimizer/group.cpp logic/optimizer/memo.cpp diff --git a/src/stewkk/sql/logic/optimizer/group.cpp b/src/stewkk/sql/logic/optimizer/group.cpp index d896259..cfec2ad 100644 --- a/src/stewkk/sql/logic/optimizer/group.cpp +++ b/src/stewkk/sql/logic/optimizer/group.cpp @@ -14,6 +14,10 @@ Group::LogicalExprs Group::GetLogicalExprs() { return std::views::transform(logical_exprs_, ToNotNull{}); } +Group::PhysicalExprs Group::GetPhysicalExprs() { + return std::views::transform(physical_exprs_, ToNotNullPhysical{}); +} + size_t Group::GetId() const { return id_; } diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index 8093109..a25ae99 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -264,7 +264,7 @@ PhysicalPlanNode Optimizer::BuildOptimalPlan(G } template -PhysicalPlanNode Optimizer::Optimize() { +void Optimizer::RunSearch() { Log("Starting optimization"); tasks_.emplace([this]() { OptimizeGroup(root_->group); }); while (!tasks_.empty()) { @@ -272,10 +272,25 @@ PhysicalPlanNode Optimizer::Optimize() { tasks_.pop(); next_task(); } +} + +template +PhysicalPlanNode Optimizer::Optimize() { + RunSearch(); Log("Optimization complete, building plan"); return BuildOptimalPlan(root_->group.get()); } +template +void Optimizer::OptimizeExhaustive() { + RunSearch(); +} + +template +utils::NotNull Optimizer::GetRootGroup() const { + return root_->group; +} + template class Optimizer<2, 5>; } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index ebb35b5..34bacd8 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -4,9 +4,13 @@ #include #include #include +#include #include using ::testing::Eq; +using ::testing::IsTrue; +using ::testing::IsFalse; +using ::testing::HasSubstr; namespace stewkk::sql { @@ -38,4 +42,80 @@ TEST(OptimizerTest, JoinCommutativity) { ASSERT_THAT(Serialize(got), Eq("(NestedLoopJoin Inner (= (attr users id) (attr orders user_id)) (SeqScan orders) (SeqScan users))")); } +TEST(ReachabilityTest, SeqScanReachable) { + std::stringstream s{"SELECT * FROM users;"}; + auto result = IsPlanReachable(s, SeqScan{"users"}); + ASSERT_THAT(result.reachable, IsTrue()); +} + +TEST(ReachabilityTest, SeqScanWrongTable) { + std::stringstream s{"SELECT * FROM users;"}; + auto result = IsPlanReachable(s, SeqScan{"orders"}); + ASSERT_THAT(result.reachable, IsFalse()); + ASSERT_THAT(result.mismatch, HasSubstr("users")); +} + +TEST(ReachabilityTest, WrongOperatorType) { + std::stringstream s{"SELECT * FROM users;"}; + Expression qual = BinaryExpression{ + std::make_shared(Attribute{"users", "id"}), + BinaryOp::kEq, + std::make_shared(Attribute{"orders", "user_id"})}; + auto result = IsPlanReachable(s, HashJoin{ + std::make_shared(SeqScan{"users"}), + std::make_shared(SeqScan{"orders"}), + JoinType::kInner, + qual}); + ASSERT_THAT(result.reachable, IsFalse()); +} + +TEST(ReachabilityTest, BothJoinOrdersReachable) { + std::stringstream s1{"SELECT * FROM users JOIN orders ON users.id = orders.user_id;"}; + std::stringstream s2{"SELECT * FROM users JOIN orders ON users.id = orders.user_id;"}; + Expression qual = BinaryExpression{ + std::make_shared(Attribute{"users", "id"}), + BinaryOp::kEq, + std::make_shared(Attribute{"orders", "user_id"})}; + CardinalityEstimates cardinality({{"users", 10000}, {"orders", 100}}); + + auto optimal = IsPlanReachable(s1, NestedLoopJoin{ + std::make_shared(SeqScan{"orders"}), + std::make_shared(SeqScan{"users"}), + JoinType::kInner, qual}, cardinality); + ASSERT_THAT(optimal.reachable, IsTrue()); + + auto suboptimal = IsPlanReachable(s2, NestedLoopJoin{ + std::make_shared(SeqScan{"users"}), + std::make_shared(SeqScan{"orders"}), + JoinType::kInner, qual}, cardinality); + ASSERT_THAT(suboptimal.reachable, IsTrue()); +} + +TEST(ReachabilityTest, HashJoinNotReachable) { + std::stringstream s{"SELECT * FROM users JOIN orders ON users.id = orders.user_id;"}; + Expression qual = BinaryExpression{ + std::make_shared(Attribute{"users", "id"}), + BinaryOp::kEq, + std::make_shared(Attribute{"orders", "user_id"})}; + auto result = IsPlanReachable(s, HashJoin{ + std::make_shared(SeqScan{"orders"}), + std::make_shared(SeqScan{"users"}), + JoinType::kInner, qual}); + ASSERT_THAT(result.reachable, IsFalse()); +} + +TEST(ReachabilityTest, WrongJoinQual) { + std::stringstream s{"SELECT * FROM users JOIN orders ON users.id = orders.user_id;"}; + Expression wrong_qual = BinaryExpression{ + std::make_shared(Attribute{"users", "id"}), + BinaryOp::kEq, + std::make_shared(Attribute{"orders", "id"})}; + auto result = IsPlanReachable(s, NestedLoopJoin{ + std::make_shared(SeqScan{"orders"}), + std::make_shared(SeqScan{"users"}), + JoinType::kInner, wrong_qual}); + ASSERT_THAT(result.reachable, IsFalse()); + ASSERT_THAT(result.mismatch, HasSubstr("qual")); +} + } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/reachability.cpp b/src/stewkk/sql/logic/optimizer/reachability.cpp new file mode 100644 index 0000000..54fb95b --- /dev/null +++ b/src/stewkk/sql/logic/optimizer/reachability.cpp @@ -0,0 +1,107 @@ +#include + +#include + +#include +#include +#include +#include + +namespace stewkk::sql { + +namespace { + +struct InternalMatch { + bool ok; + int depth; + std::string reason; +}; + +InternalMatch MatchGroup(Group* group, const PhysicalPlanNode& target, int depth); + +InternalMatch TryMatchExpr(utils::NotNull pe, + const PhysicalPlanNode& target, int depth) { + return std::visit(utils::Overloaded{ + [&](const physical::SeqScan& op) -> InternalMatch { + const auto* t = std::get_if(&target); + if (!t) return {false, depth, "type mismatch: expected SeqScan"}; + if (op.table != t->table) + return {false, depth, + std::format("SeqScan table '{}' != '{}'", op.table, t->table)}; + return {true, depth + 1, {}}; + }, + [&](const physical::Filter& op) -> InternalMatch { + const auto* t = std::get_if(&target); + if (!t) return {false, depth, "type mismatch: expected Filter"}; + if (op.predicate != t->predicate) + return {false, depth, + std::format("Filter predicate '{}' != '{}'", + ToString(op.predicate), ToString(t->predicate))}; + auto child = MatchGroup(op.source.get(), *t->source, depth + 1); + if (!child.ok) child.reason = "Filter.source: " + child.reason; + return child; + }, + [&](const physical::Projection& op) -> InternalMatch { + const auto* t = std::get_if(&target); + if (!t) return {false, depth, "type mismatch: expected Projection"}; + if (op.expressions != t->expressions) + return {false, depth, "Projection expressions mismatch"}; + auto child = MatchGroup(op.source.get(), *t->source, depth + 1); + if (!child.ok) child.reason = "Projection.source: " + child.reason; + return child; + }, + [&](const physical::NestedLoopJoin& op) -> InternalMatch { + const auto* t = std::get_if(&target); + if (!t) return {false, depth, "type mismatch: expected NestedLoopJoin"}; + if (op.type != t->type) + return {false, depth, "NestedLoopJoin join type mismatch"}; + if (op.qual != t->qual) + return {false, depth, + std::format("NestedLoopJoin qual '{}' != '{}'", + ToString(op.qual), ToString(t->qual))}; + auto lhs = MatchGroup(op.lhs.get(), *t->lhs, depth + 1); + if (!lhs.ok) { lhs.reason = "NestedLoopJoin.lhs: " + lhs.reason; return lhs; } + auto rhs = MatchGroup(op.rhs.get(), *t->rhs, depth + 1); + if (!rhs.ok) { rhs.reason = "NestedLoopJoin.rhs: " + rhs.reason; return rhs; } + return {true, std::max(lhs.depth, rhs.depth), {}}; + }, + [&](const physical::NestedLoopCrossJoin& op) -> InternalMatch { + const auto* t = std::get_if(&target); + if (!t) return {false, depth, "type mismatch: expected NestedLoopCrossJoin"}; + auto lhs = MatchGroup(op.lhs.get(), *t->lhs, depth + 1); + if (!lhs.ok) { lhs.reason = "NestedLoopCrossJoin.lhs: " + lhs.reason; return lhs; } + auto rhs = MatchGroup(op.rhs.get(), *t->rhs, depth + 1); + if (!rhs.ok) { rhs.reason = "NestedLoopCrossJoin.rhs: " + rhs.reason; return rhs; } + return {true, std::max(lhs.depth, rhs.depth), {}}; + }, + }, pe->root_operator); +} + +InternalMatch MatchGroup(Group* group, const PhysicalPlanNode& target, int depth) { + InternalMatch best{false, -1, + std::format("group {}: no physical expressions generated", group->GetId())}; + for (auto pe : group->GetPhysicalExprs()) { + auto r = TryMatchExpr(pe, target, depth); + if (r.ok) return r; + if (r.depth > best.depth) best = r; + } + return best; +} + + +} // namespace + +MatchResult IsReachable(utils::NotNull root, const PhysicalPlanNode& target) { + auto r = MatchGroup(root.get(), target, 0); + return {r.ok, r.reason}; +} + +MatchResult IsPlanReachable(std::istream& sql, const PhysicalPlanNode& target, + CardinalityEstimates cardinality) { + auto ast = GetAST(sql).value(); + Optimizer optimizer(ast, MakeMainRules(), std::move(cardinality)); + optimizer.OptimizeExhaustive(); + return IsReachable(optimizer.GetRootGroup(), target); +} + +} // namespace stewkk::sql From 89f826fd6e77d45ff4ad7e4160b0c554804ce278 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Fri, 22 May 2026 22:55:42 +0300 Subject: [PATCH 042/120] Add sort order property --- include/stewkk/sql/logic/optimizer/group.hpp | 2 +- .../sql/logic/optimizer/physical_expr.hpp | 2 + .../optimizer/properties/property_set.hpp | 32 +++++++++ .../logic/optimizer/properties/sort_order.hpp | 40 +++++++++++ src/stewkk/sql/CMakeLists.txt | 2 + src/stewkk/sql/logic/optimizer/group.cpp | 4 +- .../optimizer/properties/properties_test.cpp | 67 +++++++++++++++++++ .../optimizer/properties/property_set.cpp | 33 +++++++++ .../logic/optimizer/properties/sort_order.cpp | 37 ++++++++++ test/CMakeLists.txt | 1 + 10 files changed, 217 insertions(+), 3 deletions(-) create mode 100644 include/stewkk/sql/logic/optimizer/properties/property_set.hpp create mode 100644 include/stewkk/sql/logic/optimizer/properties/sort_order.hpp create mode 100644 src/stewkk/sql/logic/optimizer/properties/properties_test.cpp create mode 100644 src/stewkk/sql/logic/optimizer/properties/property_set.cpp create mode 100644 src/stewkk/sql/logic/optimizer/properties/sort_order.cpp diff --git a/include/stewkk/sql/logic/optimizer/group.hpp b/include/stewkk/sql/logic/optimizer/group.hpp index 03ca40d..fbd33cf 100644 --- a/include/stewkk/sql/logic/optimizer/group.hpp +++ b/include/stewkk/sql/logic/optimizer/group.hpp @@ -29,7 +29,7 @@ class Group { ToNotNullPhysical>; utils::NotNull AddLogicalExpr(LogicalOperator root_operator); - utils::NotNull AddPhysicalExpr(PhysicalOperator root_operator); + utils::NotNull AddPhysicalExpr(PhysicalOperator root_operator, PropertySet delivered = PropertySet::Any()); LogicalExprs GetLogicalExprs(); PhysicalExprs GetPhysicalExprs(); diff --git a/include/stewkk/sql/logic/optimizer/physical_expr.hpp b/include/stewkk/sql/logic/optimizer/physical_expr.hpp index 7e4af52..d3b12dd 100644 --- a/include/stewkk/sql/logic/optimizer/physical_expr.hpp +++ b/include/stewkk/sql/logic/optimizer/physical_expr.hpp @@ -5,6 +5,7 @@ #include #include +#include namespace stewkk::sql { @@ -54,6 +55,7 @@ struct PhysicalExpr { std::variant root_operator; utils::NotNull group; + PropertySet delivered = PropertySet::Any(); }; } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/properties/property_set.hpp b/include/stewkk/sql/logic/optimizer/properties/property_set.hpp new file mode 100644 index 0000000..d092408 --- /dev/null +++ b/include/stewkk/sql/logic/optimizer/properties/property_set.hpp @@ -0,0 +1,32 @@ +#pragma once + +#include +#include + +#include + +namespace stewkk::sql { + +struct PropertySet { + std::optional sort; + + PropertySet() = default; + explicit PropertySet(std::optional sort); + + bool Satisfies(const PropertySet& required) const; + + static PropertySet Any(); + + bool operator==(const PropertySet&) const = default; +}; + +} // namespace stewkk::sql + +namespace std { + +template <> +struct hash { + size_t operator()(const stewkk::sql::PropertySet& ps) const noexcept; +}; + +} // namespace std diff --git a/include/stewkk/sql/logic/optimizer/properties/sort_order.hpp b/include/stewkk/sql/logic/optimizer/properties/sort_order.hpp new file mode 100644 index 0000000..39160a9 --- /dev/null +++ b/include/stewkk/sql/logic/optimizer/properties/sort_order.hpp @@ -0,0 +1,40 @@ +#pragma once + +#include +#include +#include + +namespace stewkk::sql { + +enum class Direction { kAsc, kDesc }; + +struct SortKey { + std::string column; + Direction dir; + + bool operator==(const SortKey&) const = default; +}; + +struct SortOrder { + std::vector keys; + + bool Satisfies(const SortOrder& required) const; + + bool operator==(const SortOrder&) const = default; +}; + +} // namespace stewkk::sql + +namespace std { + +template <> +struct hash { + size_t operator()(const stewkk::sql::SortKey& k) const noexcept; +}; + +template <> +struct hash { + size_t operator()(const stewkk::sql::SortOrder& o) const noexcept; +}; + +} // namespace std diff --git a/src/stewkk/sql/CMakeLists.txt b/src/stewkk/sql/CMakeLists.txt index 893c40c..4b09e16 100644 --- a/src/stewkk/sql/CMakeLists.txt +++ b/src/stewkk/sql/CMakeLists.txt @@ -42,6 +42,8 @@ add_library(libsql logic/implementation_rules/implement_projection.cpp logic/implementation_rules/implement_join.cpp logic/implementation_rules/implement_cross_join.cpp + logic/optimizer/properties/sort_order.cpp + logic/optimizer/properties/property_set.cpp ) add_library(stewkk::libsql ALIAS libsql) target_compile_features(libsql PUBLIC cxx_std_23) diff --git a/src/stewkk/sql/logic/optimizer/group.cpp b/src/stewkk/sql/logic/optimizer/group.cpp index cfec2ad..55c7aed 100644 --- a/src/stewkk/sql/logic/optimizer/group.cpp +++ b/src/stewkk/sql/logic/optimizer/group.cpp @@ -6,8 +6,8 @@ utils::NotNull Group::AddLogicalExpr(LogicalOperator root_operator return &logical_exprs_.emplace_back(std::move(root_operator), this); } -utils::NotNull Group::AddPhysicalExpr(PhysicalOperator root_operator) { - return &physical_exprs_.emplace_back(std::move(root_operator), this); +utils::NotNull Group::AddPhysicalExpr(PhysicalOperator root_operator, PropertySet delivered) { + return &physical_exprs_.emplace_back(std::move(root_operator), this, std::move(delivered)); } Group::LogicalExprs Group::GetLogicalExprs() { diff --git a/src/stewkk/sql/logic/optimizer/properties/properties_test.cpp b/src/stewkk/sql/logic/optimizer/properties/properties_test.cpp new file mode 100644 index 0000000..15942d1 --- /dev/null +++ b/src/stewkk/sql/logic/optimizer/properties/properties_test.cpp @@ -0,0 +1,67 @@ +#include + +#include +#include + +using ::testing::IsTrue; +using ::testing::IsFalse; + +namespace stewkk::sql { + +TEST(SortOrderTest, EmptyRequiredAlwaysSatisfied) { + SortOrder delivered{{{"a", Direction::kAsc}, {"b", Direction::kAsc}}}; + SortOrder required{}; + ASSERT_THAT(delivered.Satisfies(required), IsTrue()); +} + +TEST(SortOrderTest, ExactMatchSatisfied) { + SortOrder delivered{{{"a", Direction::kAsc}, {"b", Direction::kDesc}}}; + SortOrder required{{{"a", Direction::kAsc}, {"b", Direction::kDesc}}}; + ASSERT_THAT(delivered.Satisfies(required), IsTrue()); +} + +TEST(SortOrderTest, LongerDeliveredSatisfiesPrefix) { + SortOrder delivered{{{"a", Direction::kAsc}, {"b", Direction::kAsc}, {"c", Direction::kAsc}}}; + SortOrder required{{{"a", Direction::kAsc}, {"b", Direction::kAsc}}}; + ASSERT_THAT(delivered.Satisfies(required), IsTrue()); +} + +TEST(SortOrderTest, ShorterDeliveredDoesNotSatisfy) { + SortOrder delivered{{{"a", Direction::kAsc}}}; + SortOrder required{{{"a", Direction::kAsc}, {"b", Direction::kAsc}}}; + ASSERT_THAT(delivered.Satisfies(required), IsFalse()); +} + +TEST(SortOrderTest, WrongDirectionDoesNotSatisfy) { + SortOrder delivered{{{"a", Direction::kAsc}}}; + SortOrder required{{{"a", Direction::kDesc}}}; + ASSERT_THAT(delivered.Satisfies(required), IsFalse()); +} + +TEST(SortOrderTest, WrongColumnDoesNotSatisfy) { + SortOrder delivered{{{"b", Direction::kAsc}}}; + SortOrder required{{{"a", Direction::kAsc}}}; + ASSERT_THAT(delivered.Satisfies(required), IsFalse()); +} + +TEST(PropertySetTest, AnyAlwaysSatisfied) { + PropertySet delivered{SortOrder{{{"a", Direction::kAsc}}}}; + ASSERT_THAT(delivered.Satisfies(PropertySet::Any()), IsTrue()); +} + +TEST(PropertySetTest, AnySatisfiesAny) { + ASSERT_THAT(PropertySet::Any().Satisfies(PropertySet::Any()), IsTrue()); +} + +TEST(PropertySetTest, AnyDoesNotSatisfySortRequirement) { + PropertySet required{SortOrder{{{"a", Direction::kAsc}}}}; + ASSERT_THAT(PropertySet::Any().Satisfies(required), IsFalse()); +} + +TEST(PropertySetTest, SortSatisfiesSortPrefix) { + PropertySet delivered{SortOrder{{{"a", Direction::kAsc}, {"b", Direction::kAsc}}}}; + PropertySet required{SortOrder{{{"a", Direction::kAsc}}}}; + ASSERT_THAT(delivered.Satisfies(required), IsTrue()); +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/properties/property_set.cpp b/src/stewkk/sql/logic/optimizer/properties/property_set.cpp new file mode 100644 index 0000000..310c908 --- /dev/null +++ b/src/stewkk/sql/logic/optimizer/properties/property_set.cpp @@ -0,0 +1,33 @@ +#include + +#include + +namespace stewkk::sql { + +PropertySet::PropertySet(std::optional s) : sort(std::move(s)) { + // derive implied properties from sort here as new property types are added +} + +PropertySet PropertySet::Any() { return {}; } + +bool PropertySet::Satisfies(const PropertySet& required) const { + if (required.sort) { + if (!sort.has_value() || !sort->Satisfies(required.sort.value())) { + return false; + } + } + return true; +} + +} // namespace stewkk::sql + +namespace std { + +size_t hash::operator()(const stewkk::sql::PropertySet& ps) const noexcept { + size_t h = 0; + if (ps.sort) + boost::hash_combine(h, hash{}(*ps.sort)); + return h; +} + +} // namespace std diff --git a/src/stewkk/sql/logic/optimizer/properties/sort_order.cpp b/src/stewkk/sql/logic/optimizer/properties/sort_order.cpp new file mode 100644 index 0000000..e4c872f --- /dev/null +++ b/src/stewkk/sql/logic/optimizer/properties/sort_order.cpp @@ -0,0 +1,37 @@ +#include + +#include + +namespace stewkk::sql { + +bool SortOrder::Satisfies(const SortOrder& required) const { + if (required.keys.size() > keys.size()) { + return false; + } + for (size_t i = 0; i < required.keys.size(); ++i) { + if (keys[i] != required.keys[i]) { + return false; + } + } + return true; +} + +} // namespace stewkk::sql + +namespace std { + +size_t hash::operator()(const stewkk::sql::SortKey& k) const noexcept { + size_t h = hash{}(k.column); + boost::hash_combine(h, static_cast(k.dir)); + return h; +} + +size_t hash::operator()(const stewkk::sql::SortOrder& o) const noexcept { + size_t h = 0; + for (const auto& key : o.keys) { + boost::hash_combine(h, hash{}(key)); + } + return h; +} + +} // namespace std diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt index f13e258..99ea0ac 100644 --- a/test/CMakeLists.txt +++ b/test/CMakeLists.txt @@ -10,6 +10,7 @@ target_sources(unittests PRIVATE ${PROJECT_SOURCE_DIR}/src/stewkk/sql/logic/optimizer/rules_applier_test.cpp ${PROJECT_SOURCE_DIR}/src/stewkk/sql/logic/optimizer/optimizer_test.cpp ${PROJECT_SOURCE_DIR}/src/stewkk/sql/logic/optimizer/memo_test.cpp + ${PROJECT_SOURCE_DIR}/src/stewkk/sql/logic/optimizer/properties/properties_test.cpp ) target_compile_features(unittests PRIVATE cxx_std_23) set_target_properties(unittests PROPERTIES From 51fdf3bcd3a706ba06bd2a0b52baa899860fcf3e Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Fri, 22 May 2026 23:37:12 +0300 Subject: [PATCH 043/120] Add sort operator --- include/stewkk/sql/logic/executor/plan.hpp | 11 ++++- .../sql/logic/optimizer/physical_expr.hpp | 11 ++++- src/stewkk/sql/logic/executor/executor.cpp | 47 +++++++++++++++++++ src/stewkk/sql/logic/executor/plan.cpp | 4 ++ .../sql/logic/executor/plan_serializer.cpp | 41 ++++++++++++++++ src/stewkk/sql/logic/optimizer/optimizer.cpp | 15 ++++++ .../sql/logic/optimizer/reachability.cpp | 9 ++++ 7 files changed, 136 insertions(+), 2 deletions(-) diff --git a/include/stewkk/sql/logic/executor/plan.hpp b/include/stewkk/sql/logic/executor/plan.hpp index 9e8bea8..c07d412 100644 --- a/include/stewkk/sql/logic/executor/plan.hpp +++ b/include/stewkk/sql/logic/executor/plan.hpp @@ -7,6 +7,7 @@ #include #include #include +#include namespace stewkk::sql { @@ -18,8 +19,9 @@ struct NestedLoopCrossJoin; struct HashJoin; struct MergeJoin; struct IndexSeek; +struct PhysicalSort; -using PhysicalPlanNode = std::variant; +using PhysicalPlanNode = std::variant; struct SeqScan { std::string table; @@ -82,4 +84,11 @@ struct IndexSeek { bool operator==(const IndexSeek&) const; }; +struct PhysicalSort { + std::shared_ptr source; + SortOrder keys; + + bool operator==(const PhysicalSort&) const; +}; + } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/physical_expr.hpp b/include/stewkk/sql/logic/optimizer/physical_expr.hpp index d3b12dd..58cac10 100644 --- a/include/stewkk/sql/logic/optimizer/physical_expr.hpp +++ b/include/stewkk/sql/logic/optimizer/physical_expr.hpp @@ -6,6 +6,7 @@ #include #include #include +#include namespace stewkk::sql { @@ -49,11 +50,19 @@ struct NestedLoopCrossJoin { bool operator==(const NestedLoopCrossJoin&) const = default; }; +struct Sort { + utils::NotNull input; + SortOrder keys; + + bool operator==(const Sort&) const = default; +}; + } // namespace physical struct PhysicalExpr { std::variant root_operator; + physical::NestedLoopJoin, physical::NestedLoopCrossJoin, + physical::Sort> root_operator; utils::NotNull group; PropertySet delivered = PropertySet::Any(); }; diff --git a/src/stewkk/sql/logic/executor/executor.cpp b/src/stewkk/sql/logic/executor/executor.cpp index 044580e..5102c2a 100644 --- a/src/stewkk/sql/logic/executor/executor.cpp +++ b/src/stewkk/sql/logic/executor/executor.cpp @@ -446,6 +446,53 @@ boost::asio::awaitable Executor::Execute(const Physica throw std::runtime_error("IndexSeek execution not implemented"); co_return; } + // FIXME: that's in-memory sort + boost::asio::awaitable operator()(const PhysicalSort& sort) { + auto [in_attrs_chan, in_tuples_chan] = co_await GetChannels(); + co_await executor.SpawnExecutor(*sort.source, in_attrs_chan, in_tuples_chan); + + auto attrs = co_await in_attrs_chan.async_receive(boost::asio::use_awaitable); + co_await attr_chan.async_send(boost::system::error_code{}, attrs, boost::asio::use_awaitable); + attr_chan.close(); + + Tuples all_tuples; + for (;;) { + auto buf = co_await ReceiveTuples(in_tuples_chan); + if (buf.empty()) break; + std::move(buf.begin(), buf.end(), std::back_inserter(all_tuples)); + } + + std::vector> key_indices; + for (const auto& key : sort.keys.keys) { + auto it = std::find_if(attrs.begin(), attrs.end(), + [&](const AttributeInfo& a) { return a.name == key.column; }); + if (it == attrs.end()) + throw std::runtime_error{"sort key column not found: " + key.column}; + key_indices.push_back({static_cast(it - attrs.begin()), key.dir}); + } + + std::sort(all_tuples.begin(), all_tuples.end(), + [&](const Tuple& a, const Tuple& b) { + for (const auto& [idx, dir] : key_indices) { + const auto& va = a[idx]; + const auto& vb = b[idx]; + if (va.is_null && vb.is_null) continue; + if (va.is_null) return false; + if (vb.is_null) return true; + if (va.value.int_value != vb.value.int_value) + return dir == Direction::kAsc + ? va.value.int_value < vb.value.int_value + : va.value.int_value > vb.value.int_value; + } + return false; + }); + + if (!all_tuples.empty()) + co_await tuples_chan.async_send(boost::system::error_code{}, + std::move(all_tuples), boost::asio::use_awaitable); + tuples_chan.close(); + co_return; + } AttributesInfoChannel& attr_chan; TuplesChannel& tuples_chan; diff --git a/src/stewkk/sql/logic/executor/plan.cpp b/src/stewkk/sql/logic/executor/plan.cpp index e269a93..f7aa541 100644 --- a/src/stewkk/sql/logic/executor/plan.cpp +++ b/src/stewkk/sql/logic/executor/plan.cpp @@ -30,4 +30,8 @@ bool IndexSeek::operator==(const IndexSeek& other) const { return table == other.table && predicate == other.predicate; } +bool PhysicalSort::operator==(const PhysicalSort& other) const { + return *source == *other.source && keys == other.keys; +} + } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/executor/plan_serializer.cpp b/src/stewkk/sql/logic/executor/plan_serializer.cpp index e326353..e839d6e 100644 --- a/src/stewkk/sql/logic/executor/plan_serializer.cpp +++ b/src/stewkk/sql/logic/executor/plan_serializer.cpp @@ -118,6 +118,15 @@ std::string SerializeNode(const PhysicalPlanNode& node) { SerializeJoinType(n.type), SerializeExpr(n.qual), SerializeNode(*n.lhs), SerializeNode(*n.rhs)); } + std::string operator()(const PhysicalSort& n) const { + std::string keys; + for (const auto& k : n.keys.keys) { + if (!keys.empty()) keys += ' '; + keys += k.column; + keys += k.dir == Direction::kAsc ? " Asc" : " Desc"; + } + return std::format("(Sort (keys {}) {})", keys, SerializeNode(*n.source)); + } }; return std::visit(Visitor{}, node); } @@ -322,6 +331,26 @@ PhysicalPlanNode ParseNode(ParseState& s) { if (head == "NestedLoopJoin") return ParseJoinNode.template operator()(); if (head == "HashJoin") return ParseJoinNode.template operator()(); if (head == "MergeJoin") return ParseJoinNode.template operator()(); + if (head == "Sort") { + s.ExpectLParen(); + auto kw = s.ExpectAtom(); + if (kw != "keys") + throw std::runtime_error(std::format("expected 'keys' but got '{}'", kw)); + std::vector keys; + while (s.Peek().kind != TokenKind::RParen) { + auto col = s.ExpectAtom(); + auto dir_str = s.ExpectAtom(); + Direction dir = dir_str == "Asc" ? Direction::kAsc : Direction::kDesc; + keys.push_back({std::move(col), dir}); + } + s.ExpectRParen(); + auto source = ParseNode(s); + s.ExpectRParen(); + return PhysicalSort{ + std::make_shared(std::move(source)), + SortOrder{std::move(keys)}, + }; + } throw std::runtime_error(std::format("unknown plan node: '{}'", head)); } @@ -404,6 +433,18 @@ struct DotBuilder { EmitEdge(rhs, id); return id; } + int operator()(const PhysicalSort& n) { + int src = std::visit(*this, *n.source); + std::string keys; + for (const auto& k : n.keys.keys) { + if (!keys.empty()) keys += ", "; + keys += k.column; + keys += k.dir == Direction::kAsc ? " asc" : " desc"; + } + int id = Emit(std::format("Sort\\n{}", keys)); + EmitEdge(src, id); + return id; + } }; } // namespace diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index a25ae99..3bf1ef1 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -2,6 +2,8 @@ #include +#include + #include #include #include @@ -45,6 +47,9 @@ std::vector> GetChildren(utils::NotNull ex [](const physical::NestedLoopCrossJoin& j) -> std::vector> { return {j.lhs, j.rhs}; }, + [](const physical::Sort& s) -> std::vector> { + return {s.input}; + }, }, expr->root_operator); } @@ -69,6 +74,10 @@ static int64_t CalcCost(utils::NotNull expr, CardinalityEstimates auto p_r = (cardinality.GetCardinality(j.rhs) + kBufSize - 1) / kBufSize; return p_l * (1 + p_r); }, + [&](const physical::Sort& s) -> int64_t { + auto n = cardinality.GetCardinality(s.input); + return n > 1 ? n * static_cast(std::bit_width(static_cast(n))) : n; + }, }, expr->root_operator); } @@ -259,6 +268,12 @@ PhysicalPlanNode Optimizer::BuildOptimalPlan(G .rhs = std::make_shared(BuildOptimalPlan(op.rhs.get())), }; }, + [this](const physical::Sort& op) -> PhysicalPlanNode { + return PhysicalSort{ + .source = std::make_shared(BuildOptimalPlan(op.input.get())), + .keys = op.keys, + }; + }, }, best_expr->root_operator); } diff --git a/src/stewkk/sql/logic/optimizer/reachability.cpp b/src/stewkk/sql/logic/optimizer/reachability.cpp index 54fb95b..929d558 100644 --- a/src/stewkk/sql/logic/optimizer/reachability.cpp +++ b/src/stewkk/sql/logic/optimizer/reachability.cpp @@ -74,6 +74,15 @@ InternalMatch TryMatchExpr(utils::NotNull pe, if (!rhs.ok) { rhs.reason = "NestedLoopCrossJoin.rhs: " + rhs.reason; return rhs; } return {true, std::max(lhs.depth, rhs.depth), {}}; }, + [&](const physical::Sort& op) -> InternalMatch { + const auto* t = std::get_if(&target); + if (!t) return {false, depth, "type mismatch: expected Sort"}; + if (op.keys != t->keys) + return {false, depth, "Sort keys mismatch"}; + auto child = MatchGroup(op.input.get(), *t->source, depth + 1); + if (!child.ok) child.reason = "Sort.input: " + child.reason; + return child; + }, }, pe->root_operator); } From c1e55d1e762c3e308b7e058c009d17746312ccbd Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sat, 23 May 2026 00:56:58 +0300 Subject: [PATCH 044/120] Impl sort by support in optimizer --- include/stewkk/sql/logic/optimizer/group.hpp | 2 +- .../stewkk/sql/logic/optimizer/optimizer.hpp | 39 +- .../sql/logic/optimizer/physical_expr.hpp | 2 +- .../logic/optimizer/properties/sort_order.hpp | 1 + .../sql/logic/optimizer/rules_applier.hpp | 1 + .../sql/logic/optimizer/schema_catalog.hpp | 30 ++ .../stewkk/sql/logic/optimizer/winner_key.hpp | 26 ++ src/stewkk/sql/CMakeLists.txt | 2 + src/stewkk/sql/logic/executor/executor.cpp | 6 +- .../sql/logic/executor/plan_serializer.cpp | 13 +- src/stewkk/sql/logic/optimizer/group.cpp | 4 +- src/stewkk/sql/logic/optimizer/optimizer.cpp | 372 +++++++++++++----- .../sql/logic/optimizer/optimizer_test.cpp | 4 + .../optimizer/properties/properties_test.cpp | 30 +- .../logic/optimizer/properties/sort_order.cpp | 3 +- .../sql/logic/optimizer/rules_applier.cpp | 4 +- .../sql/logic/optimizer/schema_catalog.cpp | 61 +++ src/stewkk/sql/logic/optimizer/winner_key.cpp | 15 + 18 files changed, 468 insertions(+), 147 deletions(-) create mode 100644 include/stewkk/sql/logic/optimizer/schema_catalog.hpp create mode 100644 include/stewkk/sql/logic/optimizer/winner_key.hpp create mode 100644 src/stewkk/sql/logic/optimizer/schema_catalog.cpp create mode 100644 src/stewkk/sql/logic/optimizer/winner_key.cpp diff --git a/include/stewkk/sql/logic/optimizer/group.hpp b/include/stewkk/sql/logic/optimizer/group.hpp index fbd33cf..590aef9 100644 --- a/include/stewkk/sql/logic/optimizer/group.hpp +++ b/include/stewkk/sql/logic/optimizer/group.hpp @@ -29,7 +29,7 @@ class Group { ToNotNullPhysical>; utils::NotNull AddLogicalExpr(LogicalOperator root_operator); - utils::NotNull AddPhysicalExpr(PhysicalOperator root_operator, PropertySet delivered = PropertySet::Any()); + utils::NotNull AddPhysicalExpr(PhysicalOperator root_operator, bool is_enforcer = false); LogicalExprs GetLogicalExprs(); PhysicalExprs GetPhysicalExprs(); diff --git a/include/stewkk/sql/logic/optimizer/optimizer.hpp b/include/stewkk/sql/logic/optimizer/optimizer.hpp index f69082c..90587ea 100644 --- a/include/stewkk/sql/logic/optimizer/optimizer.hpp +++ b/include/stewkk/sql/logic/optimizer/optimizer.hpp @@ -12,6 +12,8 @@ #include #include #include +#include +#include #include namespace stewkk::sql { @@ -23,46 +25,55 @@ template class Optimizer { public: Optimizer(const Operator& expr, Rules&& rules, - CardinalityEstimates cardinality = {}); + CardinalityEstimates cardinality = {}, SchemaCatalog schema = {}); - PhysicalPlanNode Optimize(); + PhysicalPlanNode Optimize(PropertySet required = PropertySet::Any()); - // Runs exhaustive search (no cost limit), populating all physical_exprs_ in - // every reachable group. Invariant: must not be called with a top-level limit. - // Use GetRootGroup() afterward to drive IsReachable(). + // Runs exhaustive search, populating all physical_exprs_ in every reachable + // group. void OptimizeExhaustive(); utils::NotNull GetRootGroup() const; private: - void RunSearch(); using Limit = std::optional; + void RunSearch(PropertySet required, Limit limit); bool IsExplored(utils::NotNull group) const; void SetExplored(utils::NotNull group); - void OptimizeInputs(utils::NotNull expr, Limit limit, size_t child_index = 0); + void OptimizeInputs(utils::NotNull expr, PropertySet required, std::vector child_delivered, int64_t accum, Limit limit, size_t child_index = 0); + + // Property-independent admissible lower bound on the cheapest physical plan + // for this group: min over logical alternatives of (best-case local cost + + // sum of children's lower bounds). Cached; safe to memoize once Phase 1 + // exploration has populated the group's logical alternatives. + std::int64_t LowerBoundCost(utils::NotNull group); void ApplyRule(TransformationRuleId rule, utils::NotNull expr, Limit limit); - void ApplyRule(ImplementationRuleId rule, utils::NotNull expr, Limit limit); - void OptimizeExpression(utils::NotNull expr, Limit limit); + void TryRules(utils::NotNull expr, Limit limit); void ExploreExpression(utils::NotNull expr, Limit limit); void ExploreGroup(utils::NotNull group, Limit limit); - void OptimizeGroup(utils::NotNull group, Limit limit = std::nullopt); + void OptimizeGroup(utils::NotNull group, PropertySet required = PropertySet::Any(), Limit limit = std::nullopt); - PhysicalPlanNode BuildOptimalPlan(Group* group); + PhysicalPlanNode BuildOptimalPlan(Group* group, PropertySet required = PropertySet::Any()); Memo memo_; RulesApplier rules_applier_; std::stack> tasks_; std::unordered_set explored_groups_; + std::unordered_set explored_exprs_; utils::NotNull root_; CardinalityEstimates cardinality_; + SchemaCatalog schema_; std::unordered_map local_cost_; - std::unordered_map accum_child_cost_; - std::unordered_map best_cost_; - std::unordered_map best_plan_; + std::unordered_map best_cost_; + std::unordered_map best_plan_; + std::unordered_map best_delivered_; + std::unordered_set enforcers_added_; + std::unordered_map> group_parents_; + std::unordered_map lower_bounds_; }; } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/physical_expr.hpp b/include/stewkk/sql/logic/optimizer/physical_expr.hpp index 58cac10..6c4ebd7 100644 --- a/include/stewkk/sql/logic/optimizer/physical_expr.hpp +++ b/include/stewkk/sql/logic/optimizer/physical_expr.hpp @@ -64,7 +64,7 @@ struct PhysicalExpr { physical::NestedLoopJoin, physical::NestedLoopCrossJoin, physical::Sort> root_operator; utils::NotNull group; - PropertySet delivered = PropertySet::Any(); + bool is_enforcer = false; }; } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/properties/sort_order.hpp b/include/stewkk/sql/logic/optimizer/properties/sort_order.hpp index 39160a9..de68d97 100644 --- a/include/stewkk/sql/logic/optimizer/properties/sort_order.hpp +++ b/include/stewkk/sql/logic/optimizer/properties/sort_order.hpp @@ -9,6 +9,7 @@ namespace stewkk::sql { enum class Direction { kAsc, kDesc }; struct SortKey { + std::string table; std::string column; Direction dir; diff --git a/include/stewkk/sql/logic/optimizer/rules_applier.hpp b/include/stewkk/sql/logic/optimizer/rules_applier.hpp index b57b4d2..59aca4f 100644 --- a/include/stewkk/sql/logic/optimizer/rules_applier.hpp +++ b/include/stewkk/sql/logic/optimizer/rules_applier.hpp @@ -31,6 +31,7 @@ class RulesApplier { private: Rules rules_; std::unordered_map> applied_transformation_rules_; + std::unordered_map> applied_implementation_rules_; }; } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/schema_catalog.hpp b/include/stewkk/sql/logic/optimizer/schema_catalog.hpp new file mode 100644 index 0000000..93c0446 --- /dev/null +++ b/include/stewkk/sql/logic/optimizer/schema_catalog.hpp @@ -0,0 +1,30 @@ +#pragma once + +#include +#include +#include +#include + +#include +#include +#include + +namespace stewkk::sql { + +using Schema = std::vector; + +class SchemaCatalog { +public: + SchemaCatalog(std::unordered_map tables = {}); + + // Returns nullopt when any table in the subtree is unknown to the catalog. + std::optional GetSchema(utils::NotNull group); + +private: + std::optional Derive(const LogicalOperator& op); + + std::unordered_map tables_; + std::unordered_map> cache_; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/winner_key.hpp b/include/stewkk/sql/logic/optimizer/winner_key.hpp new file mode 100644 index 0000000..7c7f791 --- /dev/null +++ b/include/stewkk/sql/logic/optimizer/winner_key.hpp @@ -0,0 +1,26 @@ +#pragma once + +#include +#include + +#include +#include + +namespace stewkk::sql { + +struct WinnerKey { + Group* group; + PropertySet required; + bool operator==(const WinnerKey&) const = default; +}; + +} // namespace stewkk::sql + +namespace std { + +template <> +struct hash { + size_t operator()(const stewkk::sql::WinnerKey& k) const noexcept; +}; + +} // namespace std diff --git a/src/stewkk/sql/CMakeLists.txt b/src/stewkk/sql/CMakeLists.txt index 4b09e16..6b72238 100644 --- a/src/stewkk/sql/CMakeLists.txt +++ b/src/stewkk/sql/CMakeLists.txt @@ -30,6 +30,8 @@ add_library(libsql logic/optimizer/optimizer.cpp logic/optimizer/reachability.cpp logic/optimizer/cardinality.cpp + logic/optimizer/schema_catalog.cpp + logic/optimizer/winner_key.cpp logic/optimizer/group.cpp logic/optimizer/memo.cpp logic/optimizer/rules.cpp diff --git a/src/stewkk/sql/logic/executor/executor.cpp b/src/stewkk/sql/logic/executor/executor.cpp index 5102c2a..59f095d 100644 --- a/src/stewkk/sql/logic/executor/executor.cpp +++ b/src/stewkk/sql/logic/executor/executor.cpp @@ -465,9 +465,11 @@ boost::asio::awaitable Executor::Execute(const Physica std::vector> key_indices; for (const auto& key : sort.keys.keys) { auto it = std::find_if(attrs.begin(), attrs.end(), - [&](const AttributeInfo& a) { return a.name == key.column; }); + [&](const AttributeInfo& a) { + return a.table == key.table && a.name == key.column; + }); if (it == attrs.end()) - throw std::runtime_error{"sort key column not found: " + key.column}; + throw std::runtime_error{"sort key column not found: " + key.table + "." + key.column}; key_indices.push_back({static_cast(it - attrs.begin()), key.dir}); } diff --git a/src/stewkk/sql/logic/executor/plan_serializer.cpp b/src/stewkk/sql/logic/executor/plan_serializer.cpp index e839d6e..83e011c 100644 --- a/src/stewkk/sql/logic/executor/plan_serializer.cpp +++ b/src/stewkk/sql/logic/executor/plan_serializer.cpp @@ -122,6 +122,8 @@ std::string SerializeNode(const PhysicalPlanNode& node) { std::string keys; for (const auto& k : n.keys.keys) { if (!keys.empty()) keys += ' '; + keys += k.table; + keys += '.'; keys += k.column; keys += k.dir == Direction::kAsc ? " Asc" : " Desc"; } @@ -338,10 +340,15 @@ PhysicalPlanNode ParseNode(ParseState& s) { throw std::runtime_error(std::format("expected 'keys' but got '{}'", kw)); std::vector keys; while (s.Peek().kind != TokenKind::RParen) { - auto col = s.ExpectAtom(); + auto qualified = s.ExpectAtom(); + auto dot = qualified.find('.'); + if (dot == std::string::npos) + throw std::runtime_error("sort key must be table.column, got: " + qualified); + std::string table = qualified.substr(0, dot); + std::string col = qualified.substr(dot + 1); auto dir_str = s.ExpectAtom(); Direction dir = dir_str == "Asc" ? Direction::kAsc : Direction::kDesc; - keys.push_back({std::move(col), dir}); + keys.push_back({std::move(table), std::move(col), dir}); } s.ExpectRParen(); auto source = ParseNode(s); @@ -438,6 +445,8 @@ struct DotBuilder { std::string keys; for (const auto& k : n.keys.keys) { if (!keys.empty()) keys += ", "; + keys += k.table; + keys += '.'; keys += k.column; keys += k.dir == Direction::kAsc ? " asc" : " desc"; } diff --git a/src/stewkk/sql/logic/optimizer/group.cpp b/src/stewkk/sql/logic/optimizer/group.cpp index 55c7aed..9d63af6 100644 --- a/src/stewkk/sql/logic/optimizer/group.cpp +++ b/src/stewkk/sql/logic/optimizer/group.cpp @@ -6,8 +6,8 @@ utils::NotNull Group::AddLogicalExpr(LogicalOperator root_operator return &logical_exprs_.emplace_back(std::move(root_operator), this); } -utils::NotNull Group::AddPhysicalExpr(PhysicalOperator root_operator, PropertySet delivered) { - return &physical_exprs_.emplace_back(std::move(root_operator), this, std::move(delivered)); +utils::NotNull Group::AddPhysicalExpr(PhysicalOperator root_operator, bool is_enforcer) { + return &physical_exprs_.emplace_back(std::move(root_operator), this, is_enforcer); } Group::LogicalExprs Group::GetLogicalExprs() { diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index 3bf1ef1..5de52be 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -1,15 +1,111 @@ #include +#include #include +#include #include +#include + #include #include #include namespace stewkk::sql { +namespace { + +// Top-down: given `required` of this expr, what must child `child_index` deliver? +PropertySet RequiredInputProps(utils::NotNull expr, + PropertySet required, size_t child_index) { + return std::visit(utils::Overloaded{ + [&](const physical::SeqScan&) { return PropertySet::Any(); }, + [&](const physical::Filter&) { return required; }, + [&](const physical::Projection&) { return required; }, + [&](const physical::NestedLoopJoin&) -> PropertySet { + return child_index == 0 ? required : PropertySet::Any(); + }, + [&](const physical::NestedLoopCrossJoin&) -> PropertySet { + return child_index == 0 ? required : PropertySet::Any(); + }, + [&](const physical::Sort&) { return PropertySet::Any(); }, + }, expr->root_operator); +} + +// Bottom-up: given what children delivered, what does this expr deliver? +// Sort property is schema-blind — operators that need column access (sort +// enforcer placement, merge join applicability) check the schema separately. +PropertySet DeriveOutputProps(utils::NotNull expr, + const std::vector& child_delivered) { + return std::visit(utils::Overloaded{ + [&](const physical::SeqScan&) { return PropertySet::Any(); }, + [&](const physical::Filter&) { return child_delivered[0]; }, + [&](const physical::Projection&) { return child_delivered[0]; }, + [&](const physical::NestedLoopJoin&) { return child_delivered[0]; }, + [&](const physical::NestedLoopCrossJoin&) { return child_delivered[0]; }, + [&](const physical::Sort& s) { return PropertySet{s.keys}; }, + }, expr->root_operator); +} + +// Best-case local cost achievable by any physical impl of this logical +// operator, ignoring required physical properties. Used to compute group lower +// bounds for B&B pruning. Must remain ≤ every CalcCost over physical impls of +// the same logical alternative — update when adding cheaper physical impls. +int64_t LowerBoundLocalCost(utils::NotNull expr, CardinalityEstimates& cardinality) { + return std::visit(utils::Overloaded{ + [&](const logical::Table&) -> int64_t { + return cardinality.GetCardinality(expr->group); + }, + [&](const logical::Filter&) -> int64_t { + return cardinality.GetCardinality(expr->group); + }, + [&](const logical::Projection&) -> int64_t { + return cardinality.GetCardinality(expr->group); + }, + [&](const logical::CrossJoin& j) -> int64_t { + auto p_l = (cardinality.GetCardinality(j.lhs) + kBufSize - 1) / kBufSize; + auto p_r = (cardinality.GetCardinality(j.rhs) + kBufSize - 1) / kBufSize; + return p_l * (1 + p_r); + }, + [&](const logical::Join& j) -> int64_t { + auto p_l = (cardinality.GetCardinality(j.lhs) + kBufSize - 1) / kBufSize; + auto p_r = (cardinality.GetCardinality(j.rhs) + kBufSize - 1) / kBufSize; + return p_l * (1 + p_r); + }, + }, expr->root_operator); +} + +int64_t CalcCost(utils::NotNull expr, CardinalityEstimates& cardinality) { + return std::visit(utils::Overloaded{ + [&](const physical::SeqScan&) -> int64_t { + return cardinality.GetCardinality(expr->group); + }, + [&](const physical::Filter&) -> int64_t { + return cardinality.GetCardinality(expr->group); + }, + [&](const physical::Projection&) -> int64_t { + return cardinality.GetCardinality(expr->group); + }, + [&](const physical::NestedLoopJoin& j) -> int64_t { + auto p_l = (cardinality.GetCardinality(j.lhs) + kBufSize - 1) / kBufSize; + auto p_r = (cardinality.GetCardinality(j.rhs) + kBufSize - 1) / kBufSize; + return p_l * (1 + p_r); + }, + [&](const physical::NestedLoopCrossJoin& j) -> int64_t { + auto p_l = (cardinality.GetCardinality(j.lhs) + kBufSize - 1) / kBufSize; + auto p_r = (cardinality.GetCardinality(j.rhs) + kBufSize - 1) / kBufSize; + return p_l * (1 + p_r); + }, + [&](const physical::Sort& s) -> int64_t { + auto n = cardinality.GetCardinality(s.input); + return n > 1 ? n * static_cast(std::bit_width(static_cast(n))) : n; + }, + }, expr->root_operator); +} + +} // namespace + std::vector> GetChildren(utils::NotNull expr) { return std::visit(utils::Overloaded{ [](const logical::Table&) -> std::vector> { @@ -53,40 +149,36 @@ std::vector> GetChildren(utils::NotNull ex }, expr->root_operator); } -static int64_t CalcCost(utils::NotNull expr, CardinalityEstimates& cardinality) { - return std::visit(utils::Overloaded{ - [&](const physical::SeqScan&) -> int64_t { - return cardinality.GetCardinality(expr->group); - }, - [&](const physical::Filter&) -> int64_t { - return cardinality.GetCardinality(expr->group); - }, - [&](const physical::Projection&) -> int64_t { - return cardinality.GetCardinality(expr->group); - }, - [&](const physical::NestedLoopJoin& j) -> int64_t { - auto p_l = (cardinality.GetCardinality(j.lhs) + kBufSize - 1) / kBufSize; - auto p_r = (cardinality.GetCardinality(j.rhs) + kBufSize - 1) / kBufSize; - return p_l * (1 + p_r); - }, - [&](const physical::NestedLoopCrossJoin& j) -> int64_t { - auto p_l = (cardinality.GetCardinality(j.lhs) + kBufSize - 1) / kBufSize; - auto p_r = (cardinality.GetCardinality(j.rhs) + kBufSize - 1) / kBufSize; - return p_l * (1 + p_r); - }, - [&](const physical::Sort& s) -> int64_t { - auto n = cardinality.GetCardinality(s.input); - return n > 1 ? n * static_cast(std::bit_width(static_cast(n))) : n; - }, - }, expr->root_operator); -} - template Optimizer::Optimizer( const Operator& expr, Rules&& rules, - CardinalityEstimates cardinality) + CardinalityEstimates cardinality, SchemaCatalog schema) : memo_(), rules_applier_(std::move(rules)), root_(memo_.Populate(expr)), - cardinality_(std::move(cardinality)) {} + cardinality_(std::move(cardinality)), schema_(std::move(schema)) {} + +template +int64_t Optimizer::LowerBoundCost(utils::NotNull group) { + if (auto it = lower_bounds_.find(group.get()); it != lower_bounds_.end()) { + return it->second; + } + // Memo groups form a DAG (no cycles), so recursion terminates. + int64_t best = std::numeric_limits::max(); + for (auto expr : group->GetLogicalExprs()) { + int64_t local = LowerBoundLocalCost(expr, cardinality_); + int64_t children = 0; + bool overflow = false; + for (auto child : GetChildren(expr)) { + auto cb = LowerBoundCost(child); + if (children > std::numeric_limits::max() - cb) { overflow = true; break; } + children += cb; + } + if (overflow) continue; + if (local > std::numeric_limits::max() - children) continue; + best = std::min(best, local + children); + } + lower_bounds_[group.get()] = best; + return best; +} template bool Optimizer::IsExplored(utils::NotNull group) const { @@ -100,32 +192,50 @@ void Optimizer::SetExplored(utils::NotNull void Optimizer::OptimizeInputs( - utils::NotNull expr, Limit limit, size_t child_index) { + utils::NotNull expr, PropertySet required, + std::vector child_delivered, int64_t accum, Limit limit, size_t child_index) { + // accum = local_cost(expr) + cost of children processed so far. + // child_delivered[i] = what child i actually delivered (filled as we go). + WinnerKey self_key{expr->group.get(), required}; + if (auto it = best_cost_.find(self_key); it != best_cost_.end()) { + limit = limit ? Limit{std::min(*limit, it->second)} : Limit{it->second}; + } + if (limit && accum >= *limit) return; auto children = GetChildren(expr); if (child_index >= children.size()) { - int64_t total = local_cost_[expr.get()] + accum_child_cost_[expr.get()]; - auto* g = expr->group.get(); - if (!best_cost_.contains(g) || total < best_cost_.at(g)) { - Log("New best plan for group {} with cost {}", g->GetId(), total); - best_cost_[g] = total; - best_plan_[g] = expr.get(); + auto delivered = DeriveOutputProps(expr, child_delivered); + if (!delivered.Satisfies(required)) return; + WinnerKey key{expr->group.get(), required}; + if (!best_cost_.contains(key) || accum < best_cost_.at(key)) { + Log("New best plan for group {} with cost {}", expr->group->GetId(), accum); + best_cost_[key] = accum; + best_plan_[key] = expr.get(); + best_delivered_[key] = delivered; } return; } auto child = children[child_index]; - - tasks_.emplace([this, expr, child, child_index, limit]() { - if (!best_cost_.contains(child.get())) return; - auto cc = best_cost_.at(child.get()); - accum_child_cost_[expr.get()] += cc; - Limit next = limit ? std::optional{*limit - local_cost_[expr.get()] - accum_child_cost_[expr.get()]} : std::nullopt; - if (next && *next < 0) return; - OptimizeInputs(expr, next, child_index + 1); + auto child_required = RequiredInputProps(expr, required, child_index); + + tasks_.emplace([this, expr, child, child_index, required, child_delivered, accum, limit, child_required]() mutable { + WinnerKey child_key{child.get(), child_required}; + if (!best_cost_.contains(child_key)) return; + auto new_accum = accum + best_cost_.at(child_key); + if (limit && new_accum >= *limit) return; + child_delivered.push_back(best_delivered_.at(child_key)); + OptimizeInputs(expr, required, std::move(child_delivered), new_accum, limit, child_index + 1); }); - tasks_.emplace([this, child, limit]() { - OptimizeGroup(child, limit); + int64_t children_lb = 0; + for (size_t i = child_index + 1; i < children.size(); i++) { + auto cb = LowerBoundCost(children[i]); + if (children_lb > std::numeric_limits::max() - cb) { children_lb = std::numeric_limits::max(); break; } + children_lb += cb; + } + Limit child_limit = limit ? Limit{*limit - accum - children_lb} : std::nullopt; + tasks_.emplace([this, child, child_required, child_limit]() { + OptimizeGroup(child, child_required, child_limit); }); } @@ -135,43 +245,40 @@ void Optimizer::ApplyRule( Log("Applying transformation rule {} to group {}", rule.value, expr->group->GetId()); auto new_expr = rules_applier_.Apply(rule, expr, memo_); tasks_.emplace([this, new_expr, limit]() { ExploreExpression(new_expr, limit); }); + // A new logical alternative in new_expr->group may unlock parent-side rules + // whose patterns inspect child operators. Re-try rules on every parent; + // already-applied (expr, rule) pairs are no-ops thanks to RulesApplier gating. + if (auto it = group_parents_.find(new_expr->group.get()); it != group_parents_.end()) { + for (auto* parent : it->second) { + tasks_.emplace([this, parent, limit]() { + TryRules(utils::NotNull{parent}, limit); + }); + } + } } template -void Optimizer::ApplyRule( - ImplementationRuleId rule, utils::NotNull expr, Limit limit) { - Log("Applying implementation rule {} to group {}", rule.value, expr->group->GetId()); - auto new_expr = rules_applier_.Apply(rule, expr, memo_); - auto lc = CalcCost(new_expr, cardinality_); - Log("Local cost for group {} expression: {}", new_expr->group->GetId(), lc); - local_cost_[new_expr.get()] = lc; - accum_child_cost_[new_expr.get()] = 0; - - if (limit && lc >= *limit) return; - - Limit child_limit = limit ? std::optional{*limit - lc} : std::nullopt; - tasks_.emplace([this, new_expr, child_limit]() { OptimizeInputs(new_expr, child_limit); }); -} - -template -void Optimizer::OptimizeExpression( +void Optimizer::TryRules( utils::NotNull expr, Limit limit) { - Log("Optimizing expression in group {}", expr->group->GetId()); - for (size_t rule = 0; rule < NImplementation; rule++) { - if (!rules_applier_.IsApplicable(ImplementationRuleId{rule}, expr)) { + for (size_t rule = 0; rule < NTransformation; rule++) { + if (!rules_applier_.IsApplicable(TransformationRuleId{rule}, expr)) { continue; } tasks_.emplace([this, expr, rule, limit]() { - ApplyRule(ImplementationRuleId{rule}, expr, limit); + ApplyRule(TransformationRuleId{rule}, expr, limit); }); } - for (auto child : GetChildren(expr)) { - if (IsExplored(child)) { + for (size_t rule = 0; rule < NImplementation; rule++) { + if (!rules_applier_.IsApplicable(ImplementationRuleId{rule}, expr)) { continue; } - tasks_.emplace([this, child, limit]() { - ExploreGroup(child, limit); + tasks_.emplace([this, expr, rule]() { + Log("Applying implementation rule {} to group {}", rule, expr->group->GetId()); + auto new_expr = rules_applier_.Apply(ImplementationRuleId{rule}, expr, memo_); + auto lc = CalcCost(new_expr, cardinality_); + Log("Local cost for group {} expression: {}", new_expr->group->GetId(), lc); + local_cost_[new_expr.get()] = lc; }); } } @@ -180,16 +287,11 @@ template void Optimizer::ExploreExpression( utils::NotNull expr, Limit limit) { Log("Exploring expression in group {}", expr->group->GetId()); - for (size_t rule = 0; rule < NTransformation; rule++) { - if (!rules_applier_.IsApplicable(TransformationRuleId{rule}, expr)) { - continue; - } - tasks_.emplace([this, expr, rule, limit]() { - ApplyRule(TransformationRuleId{rule}, expr, limit); - }); - } + TryRules(expr, limit); + if (!explored_exprs_.insert(expr.get()).second) return; for (auto child : GetChildren(expr)) { + group_parents_[child.get()].push_back(expr.get()); if (IsExplored(child)) { continue; } @@ -213,64 +315,118 @@ void Optimizer::ExploreGroup( template void Optimizer::OptimizeGroup( - utils::NotNull group, Limit limit) { + utils::NotNull group, PropertySet required, Limit limit) { Log("Optimizing group {}", group->GetId()); - if (auto it = best_cost_.find(group.get()); it != best_cost_.end()) { - if (!limit || it->second < *limit) return; - } + WinnerKey key{group.get(), required}; + if (best_cost_.contains(key)) return; + + if (IsExplored(group) && limit && LowerBoundCost(group) >= *limit) return; if (!IsExplored(group)) { - tasks_.emplace([this, group, limit]() { OptimizeGroup(group, limit); }); + tasks_.emplace([this, group, required, limit]() { + OptimizeGroup(group, required, limit); + }); tasks_.emplace([this, group, limit]() { ExploreGroup(group, limit); }); return; } - for (auto expr : group->GetLogicalExprs()) { - tasks_.emplace([this, expr, limit]() { OptimizeExpression(expr, limit); }); + // Try every non-enforcer phys expr under `required`. Whether it actually + // serves `required` is decided at the bottom of OptimizeInputs once children + // have resolved and DeriveOutputProps can compute the true delivered. + for (auto phys_expr : group->GetPhysicalExprs()) { + if (phys_expr->is_enforcer) continue; + auto lc = local_cost_[phys_expr.get()]; + if (limit && lc >= *limit) continue; + tasks_.emplace([this, phys_expr, required, lc, limit]() { + OptimizeInputs(phys_expr, required, {}, lc, limit); + }); + } + + // Add Sort enforcer once per (group, required) if required asks for a sort, + // and schedule its OptimizeInputs. Enforcers are scheduled here only — the + // general scan skips them so the enforcer doesn't get picked up under + // required=Any (which would recurse into its own group infinitely). + // A Sort enforcer references the keys' columns by (table, name), so it is + // only placeable when those columns are still in this group's output schema. + // Unknown schema (catalog has no info) is treated permissively. + if (required.sort && !enforcers_added_.contains(key)) { + bool can_enforce = true; + if (auto schema = schema_.GetSchema(group)) { + for (const auto& sk : required.sort->keys) { + bool found = false; + for (const auto& a : *schema) { + if (a.table == sk.table && a.name == sk.column) { found = true; break; } + } + if (!found) { can_enforce = false; break; } + } + } + if (can_enforce) { + enforcers_added_.insert(key); + auto sort_expr = group->AddPhysicalExpr( + physical::Sort{group, *required.sort}, /*is_enforcer=*/true); + auto lc = CalcCost(sort_expr, cardinality_); + Log("Sort enforcer local cost for group {}: {}", group->GetId(), lc); + local_cost_[sort_expr.get()] = lc; + if (!limit || lc < *limit) { + tasks_.emplace([this, sort_expr, required, lc, limit]() { + OptimizeInputs(sort_expr, required, {}, lc, limit); + }); + } + } } } template -PhysicalPlanNode Optimizer::BuildOptimalPlan(Group* group) { +PhysicalPlanNode Optimizer::BuildOptimalPlan(Group* group, PropertySet required) { Log("Building optimal plan for group {}", group->GetId()); - auto best_expr = best_plan_[group]; - if (!best_expr) { + WinnerKey key{group, required}; + auto it = best_plan_.find(key); + if (it == best_plan_.end() || !it->second) { throw std::runtime_error{"no optimal plan for group"}; } + auto* best_expr = it->second; + utils::NotNull best_expr_nn{best_expr}; return std::visit( utils::Overloaded{ [](const physical::SeqScan& op) -> PhysicalPlanNode { return SeqScan{.table = op.table}; }, - [this](const physical::Projection& op) -> PhysicalPlanNode { + [this, best_expr_nn, required](const physical::Projection& op) -> PhysicalPlanNode { return PhysicalProjection{ - .source = std::make_shared(BuildOptimalPlan(op.source.get())), + .source = std::make_shared( + BuildOptimalPlan(op.source.get(), RequiredInputProps(best_expr_nn, required, 0))), .expressions = op.expressions, }; }, - [this](const physical::Filter& op) -> PhysicalPlanNode { + [this, best_expr_nn, required](const physical::Filter& op) -> PhysicalPlanNode { return PhysicalFilter{ - .source = std::make_shared(BuildOptimalPlan(op.source.get())), + .source = std::make_shared( + BuildOptimalPlan(op.source.get(), RequiredInputProps(best_expr_nn, required, 0))), .predicate = op.predicate, }; }, - [this](const physical::NestedLoopJoin& op) -> PhysicalPlanNode { + [this, best_expr_nn, required](const physical::NestedLoopJoin& op) -> PhysicalPlanNode { return NestedLoopJoin{ - .lhs = std::make_shared(BuildOptimalPlan(op.lhs.get())), - .rhs = std::make_shared(BuildOptimalPlan(op.rhs.get())), + .lhs = std::make_shared( + BuildOptimalPlan(op.lhs.get(), RequiredInputProps(best_expr_nn, required, 0))), + .rhs = std::make_shared( + BuildOptimalPlan(op.rhs.get(), RequiredInputProps(best_expr_nn, required, 1))), .type = op.type, .qual = op.qual, }; }, - [this](const physical::NestedLoopCrossJoin& op) -> PhysicalPlanNode { + [this, best_expr_nn, required](const physical::NestedLoopCrossJoin& op) -> PhysicalPlanNode { return NestedLoopCrossJoin{ - .lhs = std::make_shared(BuildOptimalPlan(op.lhs.get())), - .rhs = std::make_shared(BuildOptimalPlan(op.rhs.get())), + .lhs = std::make_shared( + BuildOptimalPlan(op.lhs.get(), RequiredInputProps(best_expr_nn, required, 0))), + .rhs = std::make_shared( + BuildOptimalPlan(op.rhs.get(), RequiredInputProps(best_expr_nn, required, 1))), }; }, - [this](const physical::Sort& op) -> PhysicalPlanNode { + [this, best_expr_nn, required](const physical::Sort& op) -> PhysicalPlanNode { return PhysicalSort{ - .source = std::make_shared(BuildOptimalPlan(op.input.get())), + .source = std::make_shared( + BuildOptimalPlan(op.input.get(), RequiredInputProps(best_expr_nn, required, 0))), .keys = op.keys, }; }, @@ -279,9 +435,9 @@ PhysicalPlanNode Optimizer::BuildOptimalPlan(G } template -void Optimizer::RunSearch() { +void Optimizer::RunSearch(PropertySet required, Limit limit) { Log("Starting optimization"); - tasks_.emplace([this]() { OptimizeGroup(root_->group); }); + tasks_.emplace([this, required, limit]() { OptimizeGroup(root_->group, required, limit); }); while (!tasks_.empty()) { auto next_task = std::move(tasks_.top()); tasks_.pop(); @@ -290,15 +446,15 @@ void Optimizer::RunSearch() { } template -PhysicalPlanNode Optimizer::Optimize() { - RunSearch(); +PhysicalPlanNode Optimizer::Optimize(PropertySet required) { + RunSearch(required, std::numeric_limits::max()); Log("Optimization complete, building plan"); - return BuildOptimalPlan(root_->group.get()); + return BuildOptimalPlan(root_->group.get(), required); } template void Optimizer::OptimizeExhaustive() { - RunSearch(); + RunSearch(PropertySet::Any(), std::nullopt); } template diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index 34bacd8..fba1769 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -42,6 +42,10 @@ TEST(OptimizerTest, JoinCommutativity) { ASSERT_THAT(Serialize(got), Eq("(NestedLoopJoin Inner (= (attr users id) (attr orders user_id)) (SeqScan orders) (SeqScan users))")); } +TEST(OptimizerTest, OrderBy) { + // TODO: SELECT a FROM T ORDER BY b +} + TEST(ReachabilityTest, SeqScanReachable) { std::stringstream s{"SELECT * FROM users;"}; auto result = IsPlanReachable(s, SeqScan{"users"}); diff --git a/src/stewkk/sql/logic/optimizer/properties/properties_test.cpp b/src/stewkk/sql/logic/optimizer/properties/properties_test.cpp index 15942d1..7263ca1 100644 --- a/src/stewkk/sql/logic/optimizer/properties/properties_test.cpp +++ b/src/stewkk/sql/logic/optimizer/properties/properties_test.cpp @@ -9,43 +9,43 @@ using ::testing::IsFalse; namespace stewkk::sql { TEST(SortOrderTest, EmptyRequiredAlwaysSatisfied) { - SortOrder delivered{{{"a", Direction::kAsc}, {"b", Direction::kAsc}}}; + SortOrder delivered{{{"t", "a", Direction::kAsc}, {"t", "b", Direction::kAsc}}}; SortOrder required{}; ASSERT_THAT(delivered.Satisfies(required), IsTrue()); } TEST(SortOrderTest, ExactMatchSatisfied) { - SortOrder delivered{{{"a", Direction::kAsc}, {"b", Direction::kDesc}}}; - SortOrder required{{{"a", Direction::kAsc}, {"b", Direction::kDesc}}}; + SortOrder delivered{{{"t", "a", Direction::kAsc}, {"t", "b", Direction::kDesc}}}; + SortOrder required{{{"t", "a", Direction::kAsc}, {"t", "b", Direction::kDesc}}}; ASSERT_THAT(delivered.Satisfies(required), IsTrue()); } TEST(SortOrderTest, LongerDeliveredSatisfiesPrefix) { - SortOrder delivered{{{"a", Direction::kAsc}, {"b", Direction::kAsc}, {"c", Direction::kAsc}}}; - SortOrder required{{{"a", Direction::kAsc}, {"b", Direction::kAsc}}}; + SortOrder delivered{{{"t", "a", Direction::kAsc}, {"t", "b", Direction::kAsc}, {"t", "c", Direction::kAsc}}}; + SortOrder required{{{"t", "a", Direction::kAsc}, {"t", "b", Direction::kAsc}}}; ASSERT_THAT(delivered.Satisfies(required), IsTrue()); } TEST(SortOrderTest, ShorterDeliveredDoesNotSatisfy) { - SortOrder delivered{{{"a", Direction::kAsc}}}; - SortOrder required{{{"a", Direction::kAsc}, {"b", Direction::kAsc}}}; + SortOrder delivered{{{"t", "a", Direction::kAsc}}}; + SortOrder required{{{"t", "a", Direction::kAsc}, {"t", "b", Direction::kAsc}}}; ASSERT_THAT(delivered.Satisfies(required), IsFalse()); } TEST(SortOrderTest, WrongDirectionDoesNotSatisfy) { - SortOrder delivered{{{"a", Direction::kAsc}}}; - SortOrder required{{{"a", Direction::kDesc}}}; + SortOrder delivered{{{"t", "a", Direction::kAsc}}}; + SortOrder required{{{"t", "a", Direction::kDesc}}}; ASSERT_THAT(delivered.Satisfies(required), IsFalse()); } TEST(SortOrderTest, WrongColumnDoesNotSatisfy) { - SortOrder delivered{{{"b", Direction::kAsc}}}; - SortOrder required{{{"a", Direction::kAsc}}}; + SortOrder delivered{{{"t", "b", Direction::kAsc}}}; + SortOrder required{{{"t", "a", Direction::kAsc}}}; ASSERT_THAT(delivered.Satisfies(required), IsFalse()); } TEST(PropertySetTest, AnyAlwaysSatisfied) { - PropertySet delivered{SortOrder{{{"a", Direction::kAsc}}}}; + PropertySet delivered{SortOrder{{{"t", "a", Direction::kAsc}}}}; ASSERT_THAT(delivered.Satisfies(PropertySet::Any()), IsTrue()); } @@ -54,13 +54,13 @@ TEST(PropertySetTest, AnySatisfiesAny) { } TEST(PropertySetTest, AnyDoesNotSatisfySortRequirement) { - PropertySet required{SortOrder{{{"a", Direction::kAsc}}}}; + PropertySet required{SortOrder{{{"t", "a", Direction::kAsc}}}}; ASSERT_THAT(PropertySet::Any().Satisfies(required), IsFalse()); } TEST(PropertySetTest, SortSatisfiesSortPrefix) { - PropertySet delivered{SortOrder{{{"a", Direction::kAsc}, {"b", Direction::kAsc}}}}; - PropertySet required{SortOrder{{{"a", Direction::kAsc}}}}; + PropertySet delivered{SortOrder{{{"t", "a", Direction::kAsc}, {"t", "b", Direction::kAsc}}}}; + PropertySet required{SortOrder{{{"t", "a", Direction::kAsc}}}}; ASSERT_THAT(delivered.Satisfies(required), IsTrue()); } diff --git a/src/stewkk/sql/logic/optimizer/properties/sort_order.cpp b/src/stewkk/sql/logic/optimizer/properties/sort_order.cpp index e4c872f..5c51bed 100644 --- a/src/stewkk/sql/logic/optimizer/properties/sort_order.cpp +++ b/src/stewkk/sql/logic/optimizer/properties/sort_order.cpp @@ -21,7 +21,8 @@ bool SortOrder::Satisfies(const SortOrder& required) const { namespace std { size_t hash::operator()(const stewkk::sql::SortKey& k) const noexcept { - size_t h = hash{}(k.column); + size_t h = hash{}(k.table); + boost::hash_combine(h, hash{}(k.column)); boost::hash_combine(h, static_cast(k.dir)); return h; } diff --git a/src/stewkk/sql/logic/optimizer/rules_applier.cpp b/src/stewkk/sql/logic/optimizer/rules_applier.cpp index 3f3b659..469a96c 100644 --- a/src/stewkk/sql/logic/optimizer/rules_applier.cpp +++ b/src/stewkk/sql/logic/optimizer/rules_applier.cpp @@ -20,11 +20,13 @@ utils::NotNull RulesApplier::App template bool RulesApplier::IsApplicable(ImplementationRuleId rule, utils::NotNull expr) { - return rules_.implementation_rules[rule.value]->IsApplicable(expr); + return !applied_implementation_rules_[expr.get()][rule.value] && + rules_.implementation_rules[rule.value]->IsApplicable(expr); } template utils::NotNull RulesApplier::Apply(ImplementationRuleId rule, utils::NotNull expr, Memo& memo) { + applied_implementation_rules_[expr.get()][rule.value] = 1; return rules_.implementation_rules[rule.value]->Apply(expr, memo); } diff --git a/src/stewkk/sql/logic/optimizer/schema_catalog.cpp b/src/stewkk/sql/logic/optimizer/schema_catalog.cpp new file mode 100644 index 0000000..d1d04f1 --- /dev/null +++ b/src/stewkk/sql/logic/optimizer/schema_catalog.cpp @@ -0,0 +1,61 @@ +#include + +#include + +namespace stewkk::sql { + +SchemaCatalog::SchemaCatalog(std::unordered_map tables) + : tables_(std::move(tables)) {} + +std::optional SchemaCatalog::GetSchema(utils::NotNull group) { + if (auto it = cache_.find(group.get()); it != cache_.end()) { + return it->second; + } + auto schema = Derive(group->GetLogicalExprs().front()->root_operator); + cache_[group.get()] = schema; + return schema; +} + +// TODO: refactor to remove duplicate logic: both executor and optimizer derive +// attributes +std::optional SchemaCatalog::Derive(const LogicalOperator& op) { + return std::visit(utils::Overloaded{ + [this](const logical::Table& t) -> std::optional { + auto it = tables_.find(t.name); + if (it == tables_.end()) return std::nullopt; + return it->second; + }, + [this](const logical::Filter& f) -> std::optional { + return GetSchema(f.source); + }, + [](const logical::Projection& p) -> std::optional { + // Schema after projection is exactly the Attributes named in its + // expression list. Computed (non-Attribute) expressions produce + // columns that can't be referenced by (table, name), so they are + // dropped from the schema — sorting on them is impossible anyway. + Schema out; + for (const auto& expr : p.expressions) { + if (const auto* a = std::get_if(&expr)) { + out.push_back(*a); + } + } + return out; + }, + [this](const logical::CrossJoin& j) -> std::optional { + auto l = GetSchema(j.lhs); + auto r = GetSchema(j.rhs); + if (!l || !r) return std::nullopt; + l->insert(l->end(), r->begin(), r->end()); + return l; + }, + [this](const logical::Join& j) -> std::optional { + auto l = GetSchema(j.lhs); + auto r = GetSchema(j.rhs); + if (!l || !r) return std::nullopt; + l->insert(l->end(), r->begin(), r->end()); + return l; + }, + }, op); +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/winner_key.cpp b/src/stewkk/sql/logic/optimizer/winner_key.cpp new file mode 100644 index 0000000..3861905 --- /dev/null +++ b/src/stewkk/sql/logic/optimizer/winner_key.cpp @@ -0,0 +1,15 @@ +#include + +#include + +#include + +namespace std { + +size_t hash::operator()(const stewkk::sql::WinnerKey& k) const noexcept { + size_t h = std::hash{}(k.group); + boost::hash_combine(h, std::hash{}(k.required)); + return h; +} + +} // namespace std From 25d1c2114a24dd3a3fe72b87fee6c6dc11a422b0 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sun, 24 May 2026 16:31:50 +0300 Subject: [PATCH 045/120] Fix use after free --- .../stewkk/sql/logic/executor/executor.hpp | 6 ++- src/stewkk/sql/logic/executor/executor.cpp | 42 +++++++++++++------ 2 files changed, 34 insertions(+), 14 deletions(-) diff --git a/include/stewkk/sql/logic/executor/executor.hpp b/include/stewkk/sql/logic/executor/executor.hpp index 9added1..94a2d79 100644 --- a/include/stewkk/sql/logic/executor/executor.hpp +++ b/include/stewkk/sql/logic/executor/executor.hpp @@ -6,6 +6,8 @@ #include #include +#include +#include #include #include @@ -61,7 +63,9 @@ class Executor { TuplesChannel& tuples_chan); boost::asio::awaitable ExecuteJoin(const NestedLoopJoin& join, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan); - boost::asio::awaitable SpawnExecutor(const PhysicalPlanNode& op, AttributesInfoChannel& attr_chan, TuplesChannel& tuple_chan); + boost::asio::experimental::promise SpawnExecutor( + boost::asio::any_io_executor exec, + const PhysicalPlanNode& op, AttributesInfoChannel& attr_chan, TuplesChannel& tuple_chan); private: SequentialScan sequential_scan_; diff --git a/src/stewkk/sql/logic/executor/executor.cpp b/src/stewkk/sql/logic/executor/executor.cpp index 59f095d..0135416 100644 --- a/src/stewkk/sql/logic/executor/executor.cpp +++ b/src/stewkk/sql/logic/executor/executor.cpp @@ -389,8 +389,9 @@ Executor::Executor(SequentialScan seq_scan, boost::asio::any template boost::asio::awaitable> Executor::Execute(const PhysicalPlanNode& op) { + auto exec = co_await boost::asio::this_coro::executor; auto [attr_chan, tuples_chan] = co_await GetChannels(); - co_await SpawnExecutor(op, attr_chan, tuples_chan); + auto task = SpawnExecutor(exec, op, attr_chan, tuples_chan); auto attrs = co_await attr_chan.async_receive(boost::asio::use_awaitable); Log("Received attrs in root"); @@ -405,6 +406,7 @@ boost::asio::awaitable> Executor::Execute(c std::move(buf.begin(), buf.end(), std::back_inserter(result)); } + co_await task(boost::asio::use_awaitable); Log("Total {} tuples in root", result.size()); co_return Ok(Relation{std::move(attrs), std::move(result)}); } @@ -448,8 +450,9 @@ boost::asio::awaitable Executor::Execute(const Physica } // FIXME: that's in-memory sort boost::asio::awaitable operator()(const PhysicalSort& sort) { + auto exec = co_await boost::asio::this_coro::executor; auto [in_attrs_chan, in_tuples_chan] = co_await GetChannels(); - co_await executor.SpawnExecutor(*sort.source, in_attrs_chan, in_tuples_chan); + auto task = executor.SpawnExecutor(exec, *sort.source, in_attrs_chan, in_tuples_chan); auto attrs = co_await in_attrs_chan.async_receive(boost::asio::use_awaitable); co_await attr_chan.async_send(boost::system::error_code{}, attrs, boost::asio::use_awaitable); @@ -492,6 +495,7 @@ boost::asio::awaitable Executor::Execute(const Physica if (!all_tuples.empty()) co_await tuples_chan.async_send(boost::system::error_code{}, std::move(all_tuples), boost::asio::use_awaitable); + co_await task(boost::asio::use_awaitable); tuples_chan.close(); co_return; } @@ -509,8 +513,9 @@ boost::asio::awaitable Executor::ExecuteProjection(con AttributesInfoChannel& out_attr_chan, TuplesChannel& out_tuples_chan) { Log("Executing projection"); + auto exec = co_await boost::asio::this_coro::executor; auto [in_attrs_chan, in_tuples_chan] = co_await GetChannels(); - co_await SpawnExecutor(*proj.source, in_attrs_chan, in_tuples_chan); + auto task = SpawnExecutor(exec, *proj.source, in_attrs_chan, in_tuples_chan); auto attrs = co_await in_attrs_chan.async_receive(boost::asio::use_awaitable); auto attrs_after = GetAttributesAfterProjection(attrs, proj); @@ -536,6 +541,7 @@ boost::asio::awaitable Executor::ExecuteProjection(con co_await out_tuples_chan.async_send(boost::system::error_code{}, std::move(buf), boost::asio::use_awaitable); } + co_await task(boost::asio::use_awaitable); out_tuples_chan.close(); } @@ -544,8 +550,9 @@ boost::asio::awaitable Executor::ExecuteFilter(const P AttributesInfoChannel& out_attr_chan, TuplesChannel& out_tuples_chan) { Log("Executing filter"); + auto exec = co_await boost::asio::this_coro::executor; auto [in_attrs_chan, in_tuples_chan] = co_await GetChannels(); - co_await SpawnExecutor(*filter.source, in_attrs_chan, in_tuples_chan); + auto task = SpawnExecutor(exec, *filter.source, in_attrs_chan, in_tuples_chan); auto attrs = co_await in_attrs_chan.async_receive(boost::asio::use_awaitable); Log("Filter received attrs"); @@ -588,6 +595,7 @@ boost::asio::awaitable Executor::ExecuteFilter(const P co_await out_tuples_chan.async_send(boost::system::error_code{}, std::move(output_buf), boost::asio::use_awaitable); } + co_await task(boost::asio::use_awaitable); out_tuples_chan.close(); } @@ -596,10 +604,11 @@ boost::asio::awaitable Executor::ExecuteCrossJoin(cons AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan) { Log("Executing cross join"); + auto exec = co_await boost::asio::this_coro::executor; auto [lhs_attrs_chan, lhs_tuples_chan] = co_await GetChannels(); - co_await SpawnExecutor(*cross_join.lhs, lhs_attrs_chan, lhs_tuples_chan); + auto lhs_task = SpawnExecutor(exec, *cross_join.lhs, lhs_attrs_chan, lhs_tuples_chan); auto [rhs_attrs_chan, rhs_tuples_chan] = co_await GetChannels(); - co_await SpawnExecutor(*cross_join.rhs, rhs_attrs_chan, rhs_tuples_chan); + auto rhs_task = SpawnExecutor(exec, *cross_join.rhs, rhs_attrs_chan, rhs_tuples_chan); auto attrs = co_await ConcatAttrs(lhs_attrs_chan, rhs_attrs_chan); Log("Cross join received attrs"); @@ -639,6 +648,8 @@ boost::asio::awaitable Executor::ExecuteCrossJoin(cons } } } + co_await lhs_task(boost::asio::use_awaitable); + co_await rhs_task(boost::asio::use_awaitable); tuples_chan.close(); } @@ -653,10 +664,11 @@ boost::asio::awaitable Executor::ExecuteJoin(const Nes if (join.type == JoinType::kLeft) { std::swap(*join.lhs, *join.rhs); } + auto exec = co_await boost::asio::this_coro::executor; auto [lhs_attrs_chan, lhs_tuples_chan] = co_await GetChannels(); - co_await SpawnExecutor(*join.lhs, lhs_attrs_chan, lhs_tuples_chan); + auto lhs_task = SpawnExecutor(exec, *join.lhs, lhs_attrs_chan, lhs_tuples_chan); auto [rhs_attrs_chan, rhs_tuples_chan] = co_await GetChannels(); - co_await SpawnExecutor(*join.rhs, rhs_attrs_chan, rhs_tuples_chan); + auto rhs_task = SpawnExecutor(exec, *join.rhs, rhs_attrs_chan, rhs_tuples_chan); auto attrs = co_await ConcatAttrs(lhs_attrs_chan, rhs_attrs_chan); Log("Join received attrs"); @@ -726,15 +738,19 @@ boost::asio::awaitable Executor::ExecuteJoin(const Nes } } } + co_await lhs_task(boost::asio::use_awaitable); + co_await rhs_task(boost::asio::use_awaitable); tuples_chan.close(); } template -boost::asio::awaitable Executor::SpawnExecutor(const PhysicalPlanNode& op, - AttributesInfoChannel& attr_chan, - TuplesChannel& tuple_chan) { - auto executor = co_await boost::asio::this_coro::executor; - boost::asio::co_spawn(executor, Execute(op, attr_chan, tuple_chan), boost::asio::detached); +boost::asio::experimental::promise +Executor::SpawnExecutor(boost::asio::any_io_executor exec, + const PhysicalPlanNode& op, + AttributesInfoChannel& attr_chan, + TuplesChannel& tuple_chan) { + return boost::asio::co_spawn(exec, Execute(op, attr_chan, tuple_chan), + boost::asio::experimental::use_promise); } template class Executor; From 618a2c5b9989846b0a6765dd610bfae6c5c6d3cf Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sun, 24 May 2026 17:19:24 +0300 Subject: [PATCH 046/120] Refactor --- .gitignore | 1 + .../stewkk/sql/logic/optimizer/optimizer.hpp | 10 +++++--- src/stewkk/sql/logic/optimizer/optimizer.cpp | 25 +++++++++---------- 3 files changed, 20 insertions(+), 16 deletions(-) diff --git a/.gitignore b/.gitignore index 2ebf72d..86a6b40 100644 --- a/.gitignore +++ b/.gitignore @@ -12,3 +12,4 @@ FlameGraph/ **/build/ /.odbc/ **/__pycache__ +/build-tsan/ diff --git a/include/stewkk/sql/logic/optimizer/optimizer.hpp b/include/stewkk/sql/logic/optimizer/optimizer.hpp index 90587ea..f6852be 100644 --- a/include/stewkk/sql/logic/optimizer/optimizer.hpp +++ b/include/stewkk/sql/logic/optimizer/optimizer.hpp @@ -59,6 +59,12 @@ class Optimizer { PhysicalPlanNode BuildOptimalPlan(Group* group, PropertySet required = PropertySet::Any()); + struct WinnerEntry { + int64_t cost; + PhysicalExpr* plan; + PropertySet delivered; + }; + Memo memo_; RulesApplier rules_applier_; std::stack> tasks_; @@ -68,9 +74,7 @@ class Optimizer { CardinalityEstimates cardinality_; SchemaCatalog schema_; std::unordered_map local_cost_; - std::unordered_map best_cost_; - std::unordered_map best_plan_; - std::unordered_map best_delivered_; + std::unordered_map winner_; std::unordered_set enforcers_added_; std::unordered_map> group_parents_; std::unordered_map lower_bounds_; diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index 5de52be..3cd06bc 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -197,8 +197,8 @@ void Optimizer::OptimizeInputs( // accum = local_cost(expr) + cost of children processed so far. // child_delivered[i] = what child i actually delivered (filled as we go). WinnerKey self_key{expr->group.get(), required}; - if (auto it = best_cost_.find(self_key); it != best_cost_.end()) { - limit = limit ? Limit{std::min(*limit, it->second)} : Limit{it->second}; + if (auto it = winner_.find(self_key); it != winner_.end()) { + limit = limit ? Limit{std::min(*limit, it->second.cost)} : Limit{it->second.cost}; } if (limit && accum >= *limit) return; auto children = GetChildren(expr); @@ -206,11 +206,9 @@ void Optimizer::OptimizeInputs( auto delivered = DeriveOutputProps(expr, child_delivered); if (!delivered.Satisfies(required)) return; WinnerKey key{expr->group.get(), required}; - if (!best_cost_.contains(key) || accum < best_cost_.at(key)) { + if (!winner_.contains(key) || accum < winner_.at(key).cost) { Log("New best plan for group {} with cost {}", expr->group->GetId(), accum); - best_cost_[key] = accum; - best_plan_[key] = expr.get(); - best_delivered_[key] = delivered; + winner_[key] = WinnerEntry{accum, expr.get(), delivered}; } return; } @@ -220,10 +218,11 @@ void Optimizer::OptimizeInputs( tasks_.emplace([this, expr, child, child_index, required, child_delivered, accum, limit, child_required]() mutable { WinnerKey child_key{child.get(), child_required}; - if (!best_cost_.contains(child_key)) return; - auto new_accum = accum + best_cost_.at(child_key); + auto child_it = winner_.find(child_key); + if (child_it == winner_.end()) return; + auto new_accum = accum + child_it->second.cost; if (limit && new_accum >= *limit) return; - child_delivered.push_back(best_delivered_.at(child_key)); + child_delivered.push_back(child_it->second.delivered); OptimizeInputs(expr, required, std::move(child_delivered), new_accum, limit, child_index + 1); }); @@ -318,7 +317,7 @@ void Optimizer::OptimizeGroup( utils::NotNull group, PropertySet required, Limit limit) { Log("Optimizing group {}", group->GetId()); WinnerKey key{group.get(), required}; - if (best_cost_.contains(key)) return; + if (winner_.contains(key)) return; if (IsExplored(group) && limit && LowerBoundCost(group) >= *limit) return; @@ -380,11 +379,11 @@ template PhysicalPlanNode Optimizer::BuildOptimalPlan(Group* group, PropertySet required) { Log("Building optimal plan for group {}", group->GetId()); WinnerKey key{group, required}; - auto it = best_plan_.find(key); - if (it == best_plan_.end() || !it->second) { + auto it = winner_.find(key); + if (it == winner_.end() || !it->second.plan) { throw std::runtime_error{"no optimal plan for group"}; } - auto* best_expr = it->second; + auto* best_expr = it->second.plan; utils::NotNull best_expr_nn{best_expr}; return std::visit( utils::Overloaded{ From bfff9d9ca96e6f4951ade8f435f31dac419bae75 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sun, 24 May 2026 23:12:16 +0300 Subject: [PATCH 047/120] Refactor --- .../stewkk/sql/logic/optimizer/enforcer.hpp | 22 +++++++ .../stewkk/sql/logic/optimizer/optimizer.hpp | 1 + .../logic/optimizer/properties/property.hpp | 31 ++++++++++ .../optimizer/properties/property_set.hpp | 48 +++++++++++++--- .../optimizer/properties/sort_property.hpp | 24 ++++++++ include/stewkk/sql/logic/optimizer/rule.hpp | 2 - .../sql/logic/optimizer/sort_enforcer.hpp | 21 +++++++ src/stewkk/sql/CMakeLists.txt | 2 + src/stewkk/sql/logic/optimizer/optimizer.cpp | 51 +++++++---------- .../optimizer/properties/properties_test.cpp | 9 +-- .../optimizer/properties/property_set.cpp | 57 ++++++++++++++++--- .../optimizer/properties/sort_property.cpp | 23 ++++++++ .../sql/logic/optimizer/sort_enforcer.cpp | 26 +++++++++ 13 files changed, 266 insertions(+), 51 deletions(-) create mode 100644 include/stewkk/sql/logic/optimizer/enforcer.hpp create mode 100644 include/stewkk/sql/logic/optimizer/properties/property.hpp create mode 100644 include/stewkk/sql/logic/optimizer/properties/sort_property.hpp create mode 100644 include/stewkk/sql/logic/optimizer/sort_enforcer.hpp create mode 100644 src/stewkk/sql/logic/optimizer/properties/sort_property.cpp create mode 100644 src/stewkk/sql/logic/optimizer/sort_enforcer.cpp diff --git a/include/stewkk/sql/logic/optimizer/enforcer.hpp b/include/stewkk/sql/logic/optimizer/enforcer.hpp new file mode 100644 index 0000000..4a7173d --- /dev/null +++ b/include/stewkk/sql/logic/optimizer/enforcer.hpp @@ -0,0 +1,22 @@ +#pragma once + +#include + +#include +#include +#include +#include + +namespace stewkk::sql { + +class Enforcer { +public: + virtual ~Enforcer() = default; + + virtual std::optional TryBuild( + utils::NotNull group, + const PropertySet& required, + SchemaCatalog& schema) const = 0; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/optimizer.hpp b/include/stewkk/sql/logic/optimizer/optimizer.hpp index f6852be..c91dc32 100644 --- a/include/stewkk/sql/logic/optimizer/optimizer.hpp +++ b/include/stewkk/sql/logic/optimizer/optimizer.hpp @@ -10,6 +10,7 @@ #include #include +#include #include #include #include diff --git a/include/stewkk/sql/logic/optimizer/properties/property.hpp b/include/stewkk/sql/logic/optimizer/properties/property.hpp new file mode 100644 index 0000000..425425f --- /dev/null +++ b/include/stewkk/sql/logic/optimizer/properties/property.hpp @@ -0,0 +1,31 @@ +#pragma once + +#include +#include + +namespace stewkk::sql { + +class Property { +public: + virtual ~Property() = default; + + virtual std::unique_ptr Clone() const = 0; + virtual bool Satisfies(const Property& required) const = 0; + virtual bool Equals(const Property& other) const = 0; + virtual std::size_t Hash() const = 0; +}; + +template +class PropertyBase : public Property { +public: + bool Satisfies(const Property& other) const final { + return static_cast(this)->SatisfiesTyped( + static_cast(other)); + } + bool Equals(const Property& other) const final { + return static_cast(this)->EqualsTyped( + static_cast(other)); + } +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/properties/property_set.hpp b/include/stewkk/sql/logic/optimizer/properties/property_set.hpp index d092408..c3a006c 100644 --- a/include/stewkk/sql/logic/optimizer/properties/property_set.hpp +++ b/include/stewkk/sql/logic/optimizer/properties/property_set.hpp @@ -1,23 +1,55 @@ #pragma once +#include #include -#include +#include +#include +#include +#include -#include +#include namespace stewkk::sql { -struct PropertySet { - std::optional sort; - +class PropertySet { +public: PropertySet() = default; - explicit PropertySet(std::optional sort); + PropertySet(const PropertySet& other); + PropertySet(PropertySet&&) noexcept = default; + PropertySet& operator=(const PropertySet& other); + PropertySet& operator=(PropertySet&&) noexcept = default; + ~PropertySet() = default; - bool Satisfies(const PropertySet& required) const; + template + requires (sizeof...(Ps) > 0) && + (std::derived_from, Property> && ...) + explicit PropertySet(Ps&&... ps) { + props_.reserve(sizeof...(Ps)); + (props_.push_back(std::make_unique>(std::forward(ps))), ...); + Normalize(); + } static PropertySet Any(); - bool operator==(const PropertySet&) const = default; + bool Satisfies(const PropertySet& required) const; + + template + const T* Get() const noexcept { + for (const auto& p : props_) { + const Property& prop = *p; + if (typeid(prop) == typeid(T)) return static_cast(&prop); + } + return nullptr; + } + + const std::vector>& Items() const noexcept; + + bool operator==(const PropertySet& other) const; + +private: + void Normalize(); + + std::vector> props_; }; } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/properties/sort_property.hpp b/include/stewkk/sql/logic/optimizer/properties/sort_property.hpp new file mode 100644 index 0000000..562a954 --- /dev/null +++ b/include/stewkk/sql/logic/optimizer/properties/sort_property.hpp @@ -0,0 +1,24 @@ +#pragma once + +#include +#include + +#include +#include + +namespace stewkk::sql { + +class SortProperty final : public PropertyBase { +public: + SortOrder order; + + SortProperty() = default; + explicit SortProperty(SortOrder o) : order(std::move(o)) {} + + std::unique_ptr Clone() const override; + bool SatisfiesTyped(const SortProperty& required) const; + bool EqualsTyped(const SortProperty& other) const; + std::size_t Hash() const override; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/rule.hpp b/include/stewkk/sql/logic/optimizer/rule.hpp index a764cce..8690454 100644 --- a/include/stewkk/sql/logic/optimizer/rule.hpp +++ b/include/stewkk/sql/logic/optimizer/rule.hpp @@ -27,6 +27,4 @@ class ImplementationRule { virtual ~ImplementationRule() = default; }; -// FIXME: enforcer rules? - } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/sort_enforcer.hpp b/include/stewkk/sql/logic/optimizer/sort_enforcer.hpp new file mode 100644 index 0000000..4273f3b --- /dev/null +++ b/include/stewkk/sql/logic/optimizer/sort_enforcer.hpp @@ -0,0 +1,21 @@ +#pragma once + +#include + +#include +#include +#include +#include +#include + +namespace stewkk::sql { + +class SortEnforcer final : public Enforcer { +public: + std::optional TryBuild( + utils::NotNull group, + const PropertySet& required, + SchemaCatalog& schema) const override; +}; + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/CMakeLists.txt b/src/stewkk/sql/CMakeLists.txt index 6b72238..daeda1c 100644 --- a/src/stewkk/sql/CMakeLists.txt +++ b/src/stewkk/sql/CMakeLists.txt @@ -32,6 +32,7 @@ add_library(libsql logic/optimizer/cardinality.cpp logic/optimizer/schema_catalog.cpp logic/optimizer/winner_key.cpp + logic/optimizer/sort_enforcer.cpp logic/optimizer/group.cpp logic/optimizer/memo.cpp logic/optimizer/rules.cpp @@ -45,6 +46,7 @@ add_library(libsql logic/implementation_rules/implement_join.cpp logic/implementation_rules/implement_cross_join.cpp logic/optimizer/properties/sort_order.cpp + logic/optimizer/properties/sort_property.cpp logic/optimizer/properties/property_set.cpp ) add_library(stewkk::libsql ALIAS libsql) diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index 3cd06bc..dc1f16b 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -11,11 +11,19 @@ #include #include #include +#include +#include namespace stewkk::sql { namespace { +static const std::vector> kEnforcers = [] { + std::vector> v; + v.push_back(std::make_unique()); + return v; +}(); + // Top-down: given `required` of this expr, what must child `child_index` deliver? PropertySet RequiredInputProps(utils::NotNull expr, PropertySet required, size_t child_index) { @@ -44,7 +52,7 @@ PropertySet DeriveOutputProps(utils::NotNull expr, [&](const physical::Projection&) { return child_delivered[0]; }, [&](const physical::NestedLoopJoin&) { return child_delivered[0]; }, [&](const physical::NestedLoopCrossJoin&) { return child_delivered[0]; }, - [&](const physical::Sort& s) { return PropertySet{s.keys}; }, + [&](const physical::Sort& s) { return PropertySet{SortProperty{s.keys}}; }, }, expr->root_operator); } @@ -154,7 +162,8 @@ Optimizer::Optimizer( const Operator& expr, Rules&& rules, CardinalityEstimates cardinality, SchemaCatalog schema) : memo_(), rules_applier_(std::move(rules)), root_(memo_.Populate(expr)), - cardinality_(std::move(cardinality)), schema_(std::move(schema)) {} + cardinality_(std::move(cardinality)), schema_(std::move(schema)) { +} template int64_t Optimizer::LowerBoundCost(utils::NotNull group) { @@ -341,34 +350,18 @@ void Optimizer::OptimizeGroup( }); } - // Add Sort enforcer once per (group, required) if required asks for a sort, - // and schedule its OptimizeInputs. Enforcers are scheduled here only — the - // general scan skips them so the enforcer doesn't get picked up under - // required=Any (which would recurse into its own group infinitely). - // A Sort enforcer references the keys' columns by (table, name), so it is - // only placeable when those columns are still in this group's output schema. - // Unknown schema (catalog has no info) is treated permissively. - if (required.sort && !enforcers_added_.contains(key)) { - bool can_enforce = true; - if (auto schema = schema_.GetSchema(group)) { - for (const auto& sk : required.sort->keys) { - bool found = false; - for (const auto& a : *schema) { - if (a.table == sk.table && a.name == sk.column) { found = true; break; } - } - if (!found) { can_enforce = false; break; } - } - } - if (can_enforce) { - enforcers_added_.insert(key); - auto sort_expr = group->AddPhysicalExpr( - physical::Sort{group, *required.sort}, /*is_enforcer=*/true); - auto lc = CalcCost(sort_expr, cardinality_); - Log("Sort enforcer local cost for group {}: {}", group->GetId(), lc); - local_cost_[sort_expr.get()] = lc; + if (!enforcers_added_.contains(key)) { + enforcers_added_.insert(key); + for (const auto& enforcer : kEnforcers) { + auto op = enforcer->TryBuild(group, required, schema_); + if (!op) continue; + auto enf_expr = group->AddPhysicalExpr(*op, /*is_enforcer=*/true); + auto lc = CalcCost(enf_expr, cardinality_); + Log("Enforcer local cost for group {}: {}", group->GetId(), lc); + local_cost_[enf_expr.get()] = lc; if (!limit || lc < *limit) { - tasks_.emplace([this, sort_expr, required, lc, limit]() { - OptimizeInputs(sort_expr, required, {}, lc, limit); + tasks_.emplace([this, enf_expr, required, lc, limit]() { + OptimizeInputs(enf_expr, required, {}, lc, limit); }); } } diff --git a/src/stewkk/sql/logic/optimizer/properties/properties_test.cpp b/src/stewkk/sql/logic/optimizer/properties/properties_test.cpp index 7263ca1..4d61bb4 100644 --- a/src/stewkk/sql/logic/optimizer/properties/properties_test.cpp +++ b/src/stewkk/sql/logic/optimizer/properties/properties_test.cpp @@ -2,6 +2,7 @@ #include #include +#include using ::testing::IsTrue; using ::testing::IsFalse; @@ -45,7 +46,7 @@ TEST(SortOrderTest, WrongColumnDoesNotSatisfy) { } TEST(PropertySetTest, AnyAlwaysSatisfied) { - PropertySet delivered{SortOrder{{{"t", "a", Direction::kAsc}}}}; + PropertySet delivered{SortProperty{SortOrder{{{"t", "a", Direction::kAsc}}}}}; ASSERT_THAT(delivered.Satisfies(PropertySet::Any()), IsTrue()); } @@ -54,13 +55,13 @@ TEST(PropertySetTest, AnySatisfiesAny) { } TEST(PropertySetTest, AnyDoesNotSatisfySortRequirement) { - PropertySet required{SortOrder{{{"t", "a", Direction::kAsc}}}}; + PropertySet required{SortProperty{SortOrder{{{"t", "a", Direction::kAsc}}}}}; ASSERT_THAT(PropertySet::Any().Satisfies(required), IsFalse()); } TEST(PropertySetTest, SortSatisfiesSortPrefix) { - PropertySet delivered{SortOrder{{{"t", "a", Direction::kAsc}, {"t", "b", Direction::kAsc}}}}; - PropertySet required{SortOrder{{{"t", "a", Direction::kAsc}}}}; + PropertySet delivered{SortProperty{SortOrder{{{"t", "a", Direction::kAsc}, {"t", "b", Direction::kAsc}}}}}; + PropertySet required{SortProperty{SortOrder{{{"t", "a", Direction::kAsc}}}}}; ASSERT_THAT(delivered.Satisfies(required), IsTrue()); } diff --git a/src/stewkk/sql/logic/optimizer/properties/property_set.cpp b/src/stewkk/sql/logic/optimizer/properties/property_set.cpp index 310c908..3286b0c 100644 --- a/src/stewkk/sql/logic/optimizer/properties/property_set.cpp +++ b/src/stewkk/sql/logic/optimizer/properties/property_set.cpp @@ -1,20 +1,58 @@ #include +#include +#include +#include + #include namespace stewkk::sql { -PropertySet::PropertySet(std::optional s) : sort(std::move(s)) { - // derive implied properties from sort here as new property types are added +PropertySet::PropertySet(const PropertySet& other) { + props_.reserve(other.props_.size()); + for (const auto& p : other.props_) props_.push_back(p->Clone()); +} + +PropertySet& PropertySet::operator=(const PropertySet& other) { + if (this == &other) return *this; + std::vector> copy; + copy.reserve(other.props_.size()); + for (const auto& p : other.props_) copy.push_back(p->Clone()); + props_ = std::move(copy); + return *this; } PropertySet PropertySet::Any() { return {}; } +const std::vector>& PropertySet::Items() const noexcept { return props_; } + +void PropertySet::Normalize() { + std::ranges::sort(props_, {}, [](const std::unique_ptr& p) { + const Property& prop = *p; + return std::type_index(typeid(prop)); + }); +} + bool PropertySet::Satisfies(const PropertySet& required) const { - if (required.sort) { - if (!sort.has_value() || !sort->Satisfies(required.sort.value())) { - return false; - } + for (const auto& req : required.props_) { + const Property& req_ref = *req; + auto it = std::ranges::find_if(props_, [&](const std::unique_ptr& d) { + const Property& d_ref = *d; + return typeid(d_ref) == typeid(req_ref); + }); + if (it == props_.end()) return false; + if (!(*it)->Satisfies(*req)) return false; + } + return true; +} + +bool PropertySet::operator==(const PropertySet& other) const { + if (props_.size() != other.props_.size()) return false; + for (std::size_t i = 0; i < props_.size(); ++i) { + const Property& a = *props_[i]; + const Property& b = *other.props_[i]; + if (typeid(a) != typeid(b)) return false; + if (!a.Equals(b)) return false; } return true; } @@ -25,8 +63,11 @@ namespace std { size_t hash::operator()(const stewkk::sql::PropertySet& ps) const noexcept { size_t h = 0; - if (ps.sort) - boost::hash_combine(h, hash{}(*ps.sort)); + for (const auto& p : ps.Items()) { + const stewkk::sql::Property& prop = *p; + boost::hash_combine(h, std::type_index(typeid(prop)).hash_code()); + boost::hash_combine(h, prop.Hash()); + } return h; } diff --git a/src/stewkk/sql/logic/optimizer/properties/sort_property.cpp b/src/stewkk/sql/logic/optimizer/properties/sort_property.cpp new file mode 100644 index 0000000..b0d671b --- /dev/null +++ b/src/stewkk/sql/logic/optimizer/properties/sort_property.cpp @@ -0,0 +1,23 @@ +#include + +#include + +namespace stewkk::sql { + +std::unique_ptr SortProperty::Clone() const { + return std::make_unique(*this); +} + +bool SortProperty::SatisfiesTyped(const SortProperty& required) const { + return order.Satisfies(required.order); +} + +bool SortProperty::EqualsTyped(const SortProperty& other) const { + return order == other.order; +} + +std::size_t SortProperty::Hash() const { + return std::hash{}(order); +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/sort_enforcer.cpp b/src/stewkk/sql/logic/optimizer/sort_enforcer.cpp new file mode 100644 index 0000000..0a183de --- /dev/null +++ b/src/stewkk/sql/logic/optimizer/sort_enforcer.cpp @@ -0,0 +1,26 @@ +#include + +#include +#include + +namespace stewkk::sql { + +std::optional SortEnforcer::TryBuild( + utils::NotNull group, + const PropertySet& required, + SchemaCatalog& schema) const { + const auto* req = required.Get(); + if (!req) return std::nullopt; + if (auto sch = schema.GetSchema(group)) { + for (const auto& sk : req->order.keys) { + bool found = false; + for (const auto& a : *sch) { + if (a.table == sk.table && a.name == sk.column) { found = true; break; } + } + if (!found) return std::nullopt; + } + } + return PhysicalOperator{physical::Sort{group, req->order}}; +} + +} // namespace stewkk::sql From 8e827c3c5f6fcf334983ea762593aab4db6b2db6 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sun, 24 May 2026 23:52:50 +0300 Subject: [PATCH 048/120] Add ORDER BY support to parser --- benchmarks/main.cpp | 4 +- .../stewkk/sql/logic/optimizer/optimizer.hpp | 8 +- include/stewkk/sql/logic/parser/parser.hpp | 9 +- .../sql/logic/executor/executor_test.cpp | 20 ++-- src/stewkk/sql/logic/optimizer/memo_test.cpp | 2 +- src/stewkk/sql/logic/optimizer/optimizer.cpp | 17 ++-- .../sql/logic/optimizer/optimizer_test.cpp | 20 +++- .../sql/logic/optimizer/reachability.cpp | 4 +- src/stewkk/sql/logic/parser/parser.cpp | 9 +- src/stewkk/sql/logic/parser/parser_test.cpp | 91 ++++++++++++++++--- src/stewkk/sql/logic/parser/visitor.cpp | 42 ++++++++- src/stewkk/sql/logic/parser/visitor.hpp | 8 ++ 12 files changed, 184 insertions(+), 50 deletions(-) diff --git a/benchmarks/main.cpp b/benchmarks/main.cpp index 3b60b8d..923eccf 100644 --- a/benchmarks/main.cpp +++ b/benchmarks/main.cpp @@ -76,7 +76,7 @@ void BM_SQL(benchmark::State& state) { ctx, [&state]() -> boost::asio::awaitable { std::stringstream s{Query}; - PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value()); + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value().op); CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; Executor executor(std::move(seq_scan), co_await boost::asio::this_coro::executor); @@ -129,7 +129,7 @@ void BM_SQL_Multithreaded(benchmark::State& state) { [&state]() -> boost::asio::awaitable { boost::asio::thread_pool pool{4}; std::stringstream s{Query}; - PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value()); + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value().op); CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; Executor executor(std::move(seq_scan), pool.executor()); diff --git a/include/stewkk/sql/logic/optimizer/optimizer.hpp b/include/stewkk/sql/logic/optimizer/optimizer.hpp index c91dc32..531d4f9 100644 --- a/include/stewkk/sql/logic/optimizer/optimizer.hpp +++ b/include/stewkk/sql/logic/optimizer/optimizer.hpp @@ -26,9 +26,10 @@ template class Optimizer { public: Optimizer(const Operator& expr, Rules&& rules, - CardinalityEstimates cardinality = {}, SchemaCatalog schema = {}); + CardinalityEstimates cardinality = {}, SchemaCatalog schema = {}, + PropertySet required = PropertySet::Any()); - PhysicalPlanNode Optimize(PropertySet required = PropertySet::Any()); + PhysicalPlanNode Optimize(); // Runs exhaustive search, populating all physical_exprs_ in every reachable // group. @@ -38,7 +39,7 @@ class Optimizer { private: using Limit = std::optional; - void RunSearch(PropertySet required, Limit limit); + void RunSearch(Limit limit); bool IsExplored(utils::NotNull group) const; void SetExplored(utils::NotNull group); @@ -74,6 +75,7 @@ class Optimizer { utils::NotNull root_; CardinalityEstimates cardinality_; SchemaCatalog schema_; + PropertySet required_; std::unordered_map local_cost_; std::unordered_map winner_; std::unordered_set enforcers_added_; diff --git a/include/stewkk/sql/logic/parser/parser.hpp b/include/stewkk/sql/logic/parser/parser.hpp index cf99d18..0ccc371 100644 --- a/include/stewkk/sql/logic/parser/parser.hpp +++ b/include/stewkk/sql/logic/parser/parser.hpp @@ -1,13 +1,20 @@ #pragma once #include +#include #include #include +#include namespace stewkk::sql { -Result GetAST(std::istream& in); +struct ParsedQuery { + Operator op; + std::optional required_order; +}; + +Result GetAST(std::istream& in); std::string GetDotRepresentation(const Operator& op); diff --git a/src/stewkk/sql/logic/executor/executor_test.cpp b/src/stewkk/sql/logic/executor/executor_test.cpp index 2cc8314..0d44949 100644 --- a/src/stewkk/sql/logic/executor/executor_test.cpp +++ b/src/stewkk/sql/logic/executor/executor_test.cpp @@ -79,7 +79,7 @@ TEST(ExecutorTest, SimpleSelect) { ctx, []() -> boost::asio::awaitable> { std::stringstream s{"SELECT * FROM users;"}; - PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value()); + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value().op); CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; Executor executor(std::move(seq_scan), co_await boost::asio::this_coro::executor); @@ -105,7 +105,7 @@ TEST(ExecutorTest, SimpleSelectWithParallelism) { pool, []() -> boost::asio::awaitable> { std::stringstream s{"SELECT * FROM users;"}; - PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value()); + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value().op); CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; Executor executor(std::move(seq_scan), co_await boost::asio::this_coro::executor); @@ -131,7 +131,7 @@ TYPED_TEST_P(ExecutorTest, Projection) { pool, []() -> boost::asio::awaitable> { std::stringstream s{"SELECT users.id FROM users;"}; - PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value()); + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value().op); CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; Executor executor(std::move(seq_scan), co_await boost::asio::this_coro::executor); @@ -157,7 +157,7 @@ TYPED_TEST_P(ExecutorTest, Filter) { pool, []() -> boost::asio::awaitable> { std::stringstream s{"SELECT users.id FROM users WHERE users.age < 10;"}; - PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value()); + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value().op); CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; Executor executor(std::move(seq_scan), co_await boost::asio::this_coro::executor); @@ -183,7 +183,7 @@ TYPED_TEST_P(ExecutorTest, FilterMany) { pool, []() -> boost::asio::awaitable> { std::stringstream s{"SELECT users.id FROM users WHERE users.age > 10;"}; - PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value()); + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value().op); CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; Executor executor(std::move(seq_scan), co_await boost::asio::this_coro::executor); @@ -209,7 +209,7 @@ TYPED_TEST_P(ExecutorTest, CrossJoin) { pool, []() -> boost::asio::awaitable> { std::stringstream s{"SELECT * FROM users, books WHERE users.age < 10;"}; - PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value()); + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value().op); CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; Executor executor(std::move(seq_scan), co_await boost::asio::this_coro::executor); @@ -239,7 +239,7 @@ TYPED_TEST_P(ExecutorTest, InnerJoin) { pool, []() -> boost::asio::awaitable> { std::stringstream s{"SELECT * FROM employees JOIN departments ON employees.department_id = departments.id;"}; - PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value()); + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value().op); CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; Executor executor(std::move(seq_scan), co_await boost::asio::this_coro::executor); @@ -268,7 +268,7 @@ TYPED_TEST_P(ExecutorTest, LeftJoin) { pool, []() -> boost::asio::awaitable> { std::stringstream s{"SELECT * FROM employees LEFT OUTER JOIN departments ON employees.department_id = departments.id;"}; - PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value()); + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value().op); CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; Executor executor(std::move(seq_scan), co_await boost::asio::this_coro::executor); @@ -297,7 +297,7 @@ TYPED_TEST_P(ExecutorTest, RightJoin) { pool, []() -> boost::asio::awaitable> { std::stringstream s{"SELECT * FROM employees RIGHT JOIN departments ON employees.department_id = departments.id;"}; - PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value()); + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value().op); CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; Executor executor(std::move(seq_scan), co_await boost::asio::this_coro::executor); @@ -326,7 +326,7 @@ TYPED_TEST_P(ExecutorTest, ComplexJoin) { pool, []() -> boost::asio::awaitable> { std::stringstream s{"SELECT departments.id*2, employees.id+1 FROM employees RIGHT JOIN departments ON employees.department_id = departments.id AND departments.id > 3 AND departments.id*2*2/2 < 30;"}; - PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value()); + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value().op); CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; Executor executor(std::move(seq_scan), co_await boost::asio::this_coro::executor); diff --git a/src/stewkk/sql/logic/optimizer/memo_test.cpp b/src/stewkk/sql/logic/optimizer/memo_test.cpp index e5348c4..0dc6af7 100644 --- a/src/stewkk/sql/logic/optimizer/memo_test.cpp +++ b/src/stewkk/sql/logic/optimizer/memo_test.cpp @@ -31,7 +31,7 @@ TEST(MemoTest, AddSameGroupSecondReturnsIt) { TEST(MemoTest, PopulateWithWholeQuery) { std::stringstream s{"SELECT * FROM users, orders;"}; - Operator op = GetAST(s).value(); + Operator op = GetAST(s).value().op; Memo memo; auto root = memo.Populate(op); diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index dc1f16b..a4f739d 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -160,9 +160,10 @@ std::vector> GetChildren(utils::NotNull ex template Optimizer::Optimizer( const Operator& expr, Rules&& rules, - CardinalityEstimates cardinality, SchemaCatalog schema) + CardinalityEstimates cardinality, SchemaCatalog schema, PropertySet required) : memo_(), rules_applier_(std::move(rules)), root_(memo_.Populate(expr)), - cardinality_(std::move(cardinality)), schema_(std::move(schema)) { + cardinality_(std::move(cardinality)), schema_(std::move(schema)), + required_(std::move(required)) { } template @@ -427,9 +428,9 @@ PhysicalPlanNode Optimizer::BuildOptimalPlan(G } template -void Optimizer::RunSearch(PropertySet required, Limit limit) { +void Optimizer::RunSearch(Limit limit) { Log("Starting optimization"); - tasks_.emplace([this, required, limit]() { OptimizeGroup(root_->group, required, limit); }); + tasks_.emplace([this, limit]() { OptimizeGroup(root_->group, required_, limit); }); while (!tasks_.empty()) { auto next_task = std::move(tasks_.top()); tasks_.pop(); @@ -438,15 +439,15 @@ void Optimizer::RunSearch(PropertySet required } template -PhysicalPlanNode Optimizer::Optimize(PropertySet required) { - RunSearch(required, std::numeric_limits::max()); +PhysicalPlanNode Optimizer::Optimize() { + RunSearch(std::numeric_limits::max()); Log("Optimization complete, building plan"); - return BuildOptimalPlan(root_->group.get(), required); + return BuildOptimalPlan(root_->group.get(), required_); } template void Optimizer::OptimizeExhaustive() { - RunSearch(PropertySet::Any(), std::nullopt); + RunSearch(std::nullopt); } template diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index fba1769..02517c9 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -5,6 +5,8 @@ #include #include #include +#include +#include #include using ::testing::Eq; @@ -20,17 +22,17 @@ namespace stewkk::sql { TEST(OptimizerTest, Simple) { std::stringstream s{"SELECT * FROM users;"}; - Operator op = GetAST(s).value(); + Operator op = GetAST(s).value().op; Optimizer optimizer(op, MakeMainRules()); auto got = optimizer.Optimize(); - + ASSERT_THAT(SerializeDot(got), Eq("digraph G { rankdir=BT;\n n0 [label=\"SeqScan\\\\nusers\"]\n}\n")); } TEST(OptimizerTest, JoinCommutativity) { std::stringstream s{"SELECT * FROM users JOIN orders ON users.id = orders.user_id;"}; - Operator op = GetAST(s).value(); + Operator op = GetAST(s).value().op; Optimizer optimizer(op, MakeMainRules(), CardinalityEstimates({ {"users", 10000}, {"orders", 100}, @@ -43,7 +45,17 @@ TEST(OptimizerTest, JoinCommutativity) { } TEST(OptimizerTest, OrderBy) { - // TODO: SELECT a FROM T ORDER BY b + std::stringstream s{"SELECT * FROM users ORDER BY users.id;"}; + auto parsed = GetAST(s).value(); + SchemaCatalog schema({{"users", {Attribute{"users", "id"}, Attribute{"users", "age"}}}}); + PropertySet required = parsed.required_order + ? PropertySet{SortProperty{*parsed.required_order}} + : PropertySet::Any(); + Optimizer optimizer(parsed.op, MakeMainRules(), {}, std::move(schema), std::move(required)); + + auto got = optimizer.Optimize(); + + ASSERT_THAT(Serialize(got), Eq("(Sort (keys users.id Asc) (SeqScan users))")); } TEST(ReachabilityTest, SeqScanReachable) { diff --git a/src/stewkk/sql/logic/optimizer/reachability.cpp b/src/stewkk/sql/logic/optimizer/reachability.cpp index 929d558..12ce20c 100644 --- a/src/stewkk/sql/logic/optimizer/reachability.cpp +++ b/src/stewkk/sql/logic/optimizer/reachability.cpp @@ -107,8 +107,8 @@ MatchResult IsReachable(utils::NotNull root, const PhysicalPlanNode& tar MatchResult IsPlanReachable(std::istream& sql, const PhysicalPlanNode& target, CardinalityEstimates cardinality) { - auto ast = GetAST(sql).value(); - Optimizer optimizer(ast, MakeMainRules(), std::move(cardinality)); + auto parsed = GetAST(sql).value(); + Optimizer optimizer(parsed.op, MakeMainRules(), std::move(cardinality)); optimizer.OptimizeExhaustive(); return IsReachable(optimizer.GetRootGroup(), target); } diff --git a/src/stewkk/sql/logic/parser/parser.cpp b/src/stewkk/sql/logic/parser/parser.cpp index 0615f49..e84a997 100644 --- a/src/stewkk/sql/logic/parser/parser.cpp +++ b/src/stewkk/sql/logic/parser/parser.cpp @@ -11,7 +11,7 @@ namespace stewkk::sql { -Result GetAST(std::istream& in) { +Result GetAST(std::istream& in) { antlr4::ANTLRInputStream antlr_input(in); codegen::PostgreSQLLexer lexer(&antlr_input); @@ -44,11 +44,12 @@ Result GetAST(std::istream& in) { return std::unexpected(ex); } - if (Operator* op = std::any_cast(&res)) { - return std::move(*op); + Operator op = Table{kEmptyTableName}; + if (Operator* p = std::any_cast(&res)) { + op = std::move(*p); } - return Table{kEmptyTableName}; + return ParsedQuery{std::move(op), visitor.GetRequiredOrder()}; } std::string GetDotRepresentation(const Expression& expr) { diff --git a/src/stewkk/sql/logic/parser/parser_test.cpp b/src/stewkk/sql/logic/parser/parser_test.cpp index bdb12f9..cc6afd3 100644 --- a/src/stewkk/sql/logic/parser/parser_test.cpp +++ b/src/stewkk/sql/logic/parser/parser_test.cpp @@ -2,12 +2,16 @@ #include #include +#include #include #include +#include using ::testing::Eq; using ::testing::IsTrue; +using ::testing::IsFalse; +using ::testing::Optional; using ::testing::VariantWith; namespace stewkk::sql { @@ -29,7 +33,7 @@ std::string ReadFromFile(std::filesystem::path path) { TEST(ParserTest, SelectAllFromSingleTable) { std::stringstream s{"SELECT * FROM users;"}; - Operator got = GetAST(s).value(); + Operator got = GetAST(s).value().op; ASSERT_THAT(got, VariantWith(Table{"users"})); } @@ -37,7 +41,7 @@ TEST(ParserTest, SelectAllFromSingleTable) { TEST(ParserTest, SelectSingleColumnFromSingleTable) { std::stringstream s{"SELECT users.id FROM users;"}; - Operator got = GetAST(s).value(); + Operator got = GetAST(s).value().op; ASSERT_THAT(got, VariantWith(Projection{std::vector{{Attribute{"users", "id"}}}, std::make_shared(Table{"users"})})); @@ -46,7 +50,7 @@ TEST(ParserTest, SelectSingleColumnFromSingleTable) { TEST(ParserTest, SelectMultipleColumnsFromSingleTable) { std::stringstream s{"SELECT users.id, users.email, users.phone FROM users;"}; - Operator got = GetAST(s).value(); + Operator got = GetAST(s).value().op; ASSERT_THAT(got, VariantWith(Projection{ @@ -57,7 +61,7 @@ TEST(ParserTest, SelectMultipleColumnsFromSingleTable) { TEST(ParserTest, SelectWithWhereClause) { std::stringstream s{"SELECT users.id FROM users WHERE users.age > 18;"}; - Operator got = GetAST(s).value(); + Operator got = GetAST(s).value().op; ASSERT_THAT(got, VariantWith(Projection{ std::vector{Attribute{"users", "id"}}, @@ -70,7 +74,7 @@ TEST(ParserTest, SelectWithWhereClause) { TEST(ParserTest, GetDotRepresentation) { std::stringstream s{"SELECT users.id FROM users WHERE users.age > 18;"}; - Operator op = GetAST(s).value(); + Operator op = GetAST(s).value().op; auto expected = ReadFromFile(kProjectDir + "/test/static/parser/expected.dot"); auto got = GetDotRepresentation(op); @@ -81,7 +85,7 @@ TEST(ParserTest, GetDotRepresentation) { TEST(ParserTest, SelectWithBooleanExpression) { std::stringstream s{"SELECT TRUE AND NULL OR FALSE AND NOT NULL;"}; - Operator got = GetAST(s).value(); + Operator got = GetAST(s).value().op; ASSERT_THAT(got, VariantWith(Projection{ {BinaryExpression{ @@ -104,7 +108,7 @@ TEST(ParserTest, SelectWithBooleanExpression) { TEST(ParserTest, SelectWithArithmeticalOperations) { std::stringstream s{"SELECT 1+2-3;"}; - Operator got = GetAST(s).value(); + Operator got = GetAST(s).value().op; ASSERT_THAT( got, VariantWith(Projection{{BinaryExpression{ @@ -122,7 +126,7 @@ TEST(ParserTest, SelectWithArithmeticalOperations) { TEST(ParserTest, GetDotRepresentationOfArithmeticalExpression) { std::stringstream s{"SELECT 1+2-3+4+5-6;"}; - Operator op = GetAST(s).value(); + Operator op = GetAST(s).value().op; auto expected = ReadFromFile(kProjectDir + "/test/static/parser/expected_arithmetical.dot"); auto got = GetDotRepresentation(op); @@ -151,7 +155,7 @@ TEST(ParserTest, NotSupportedError) { TEST(ParserTest, EmptyQuery) { std::stringstream s{""}; - Operator got = GetAST(s).value(); + Operator got = GetAST(s).value().op; ASSERT_THAT(got, VariantWith
(Table{"_EMPTY_TABLE_"})); } @@ -159,7 +163,7 @@ TEST(ParserTest, EmptyQuery) { TEST(ParserTest, SelectWithParens) { std::stringstream s{"((SELECT * FROM users));"}; - Operator got = GetAST(s).value(); + Operator got = GetAST(s).value().op; ASSERT_THAT(got, VariantWith
(Table{"users"})); } @@ -167,7 +171,7 @@ TEST(ParserTest, SelectWithParens) { TEST(ParserTest, EmptySelect) { std::stringstream s{"SELECT;"}; - Operator got = GetAST(s).value(); + Operator got = GetAST(s).value().op; ASSERT_THAT(got, VariantWith
(Table{"_EMPTY_TABLE_"})); } @@ -175,7 +179,7 @@ TEST(ParserTest, EmptySelect) { TEST(ParserTest, SelectWithJoinDot) { std::stringstream s{"SELECT * FROM users, books;"}; auto expected = ReadFromFile(kProjectDir + "/test/static/parser/expected_join.dot"); - Operator op = GetAST(s).value(); + Operator op = GetAST(s).value().op; auto got = GetDotRepresentation(op); @@ -185,15 +189,74 @@ TEST(ParserTest, SelectWithJoinDot) { TEST(ParserTest, SelectWithOuterJoinDot) { std::stringstream s{"SELECT * FROM users LEFT OUTER JOIN books ON users.book = books.id;"}; auto expected = ReadFromFile(kProjectDir + "/test/static/parser/expected_outer_join.dot"); - Operator op = GetAST(s).value(); + Operator op = GetAST(s).value().op; auto got = GetDotRepresentation(op); ASSERT_THAT(got, Eq(expected)); } +TEST(ParserTest, OrderByAscDefault) { + std::stringstream s{"SELECT * FROM users ORDER BY users.id;"}; + + auto parsed = GetAST(s).value(); + + ASSERT_THAT(parsed.op, VariantWith
(Table{"users"})); + ASSERT_THAT(parsed.required_order, Optional(SortOrder{{SortKey{"users", "id", Direction::kAsc}}})); +} + +TEST(ParserTest, OrderByDesc) { + std::stringstream s{"SELECT * FROM users ORDER BY users.id DESC;"}; + + auto parsed = GetAST(s).value(); + + ASSERT_THAT(parsed.required_order, Optional(SortOrder{{SortKey{"users", "id", Direction::kDesc}}})); +} + +TEST(ParserTest, OrderByMultiKey) { + std::stringstream s{"SELECT * FROM users ORDER BY users.id ASC, users.age DESC;"}; + + auto parsed = GetAST(s).value(); + + ASSERT_THAT(parsed.required_order, Optional(SortOrder{{ + SortKey{"users", "id", Direction::kAsc}, + SortKey{"users", "age", Direction::kDesc}, + }})); +} + +TEST(ParserTest, OrderByNoOrderByClause) { + std::stringstream s{"SELECT * FROM users;"}; + + auto parsed = GetAST(s).value(); + + ASSERT_THAT(parsed.required_order.has_value(), IsFalse()); +} + +TEST(ParserTest, OrderByIntOrdinalRejected) { + std::stringstream s{"SELECT * FROM users ORDER BY 1;"}; + + auto got = GetAST(s).error(); + + ASSERT_THAT(got.Wraps(ErrorType::kQueryNotSupported), IsTrue()); +} + +TEST(ParserTest, OrderByUnqualifiedColumnRejected) { + std::stringstream s{"SELECT * FROM users ORDER BY id;"}; + + auto got = GetAST(s).error(); + + ASSERT_THAT(got.Wraps(ErrorType::kQueryNotSupported), IsTrue()); +} + +TEST(ParserTest, OrderByNullsFirstRejected) { + std::stringstream s{"SELECT * FROM users ORDER BY users.id NULLS FIRST;"}; + + auto got = GetAST(s).error(); + + ASSERT_THAT(got.Wraps(ErrorType::kQueryNotSupported), IsTrue()); +} + /* -** ORDER BY ** aggregations: SELECT kind, sum(len) AS total FROM films GROUP BY kind; */ diff --git a/src/stewkk/sql/logic/parser/visitor.cpp b/src/stewkk/sql/logic/parser/visitor.cpp index 49b348f..78a787c 100644 --- a/src/stewkk/sql/logic/parser/visitor.cpp +++ b/src/stewkk/sql/logic/parser/visitor.cpp @@ -4,6 +4,7 @@ #include #include +#include namespace stewkk::sql { @@ -368,11 +369,50 @@ std::any Visitor::visitSelect_no_parens(codegen::PostgreSQLParser::Select_no_par auto select_clause = std::any_cast(visit(ctx->select_clause())); - // TODO: remaining clauses + if (ctx->sort_clause_()) { + auto* sortby_list = ctx->sort_clause_()->sort_clause()->sortby_list(); + std::vector keys; + for (auto* sortby : sortby_list->sortby()) { + keys.push_back(std::any_cast(visit(sortby))); + } + required_order_ = SortOrder{std::move(keys)}; + } return select_clause; } +std::any Visitor::visitSortby(codegen::PostgreSQLParser::SortbyContext *ctx) { + if (ctx->USING()) { + throw Error{ErrorType::kQueryNotSupported, "USING in ORDER BY is not supported"}; + } + if (ctx->nulls_order_()) { + throw Error{ErrorType::kQueryNotSupported, "NULLS FIRST/LAST in ORDER BY is not supported"}; + } + + auto expr = std::any_cast(visit(ctx->a_expr())); + auto* attr = std::get_if(&expr); + if (!attr) { + throw Error{ErrorType::kQueryNotSupported, "ORDER BY expression must be a qualified column reference"}; + } + if (attr->table.empty()) { + throw Error{ErrorType::kQueryNotSupported, "ORDER BY requires a qualified column reference (table.column)"}; + } + + Direction dir = Direction::kAsc; + if (ctx->asc_desc_()) { + dir = std::any_cast(visit(ctx->asc_desc_())); + } + + return SortKey{attr->table, attr->name, dir}; +} + +std::any Visitor::visitAsc_desc_(codegen::PostgreSQLParser::Asc_desc_Context *ctx) { + if (ctx->DESC()) { + return Direction::kDesc; + } + return Direction::kAsc; +} + std::any Visitor::visitA_expr_qual(codegen::PostgreSQLParser::A_expr_qualContext *ctx) { if (ctx->qual_op()) { throw Error{ErrorType::kQueryNotSupported, "qualified operators are not supported"}; diff --git a/src/stewkk/sql/logic/parser/visitor.hpp b/src/stewkk/sql/logic/parser/visitor.hpp index 85b5a9b..d7ab26e 100644 --- a/src/stewkk/sql/logic/parser/visitor.hpp +++ b/src/stewkk/sql/logic/parser/visitor.hpp @@ -1,6 +1,9 @@ #pragma once +#include + #include +#include namespace stewkk::sql { @@ -8,6 +11,8 @@ class Visitor : public codegen::PostgreSQLParserBaseVisitor { public: explicit Visitor(codegen::PostgreSQLParser* parser); virtual std::any visit(antlr4::tree::ParseTree *tree) override; + + std::optional GetRequiredOrder() const { return required_order_; } virtual std::any visitRoot(codegen::PostgreSQLParser::RootContext* ctx) override; virtual std::any visitStmt(codegen::PostgreSQLParser::StmtContext *ctx) override; virtual std::any visitStmtmulti(codegen::PostgreSQLParser::StmtmultiContext* ctx) override; @@ -62,9 +67,12 @@ class Visitor : public codegen::PostgreSQLParserBaseVisitor { virtual std::any visitQualified_name(codegen::PostgreSQLParser::Qualified_nameContext *ctx) override; virtual std::any visitJoin_type(codegen::PostgreSQLParser::Join_typeContext *ctx) override; virtual std::any visitJoin_qual(codegen::PostgreSQLParser::Join_qualContext *ctx) override; + virtual std::any visitSortby(codegen::PostgreSQLParser::SortbyContext *ctx) override; + virtual std::any visitAsc_desc_(codegen::PostgreSQLParser::Asc_desc_Context *ctx) override; private: codegen::PostgreSQLParser* parser_; + std::optional required_order_; }; From 0c158bdfaaf8a4ee8c955801d8bc51bb9001b48a Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 25 May 2026 03:32:55 +0300 Subject: [PATCH 049/120] wip: needs review --- benchmarks/generate-multiway-data.sh | 47 + benchmarks/main.cpp | 144 +- include/stewkk/sql/logic/optimizer/memo.hpp | 2 + .../sql/logic/optimizer/cardinality.cpp | 30 +- src/stewkk/sql/logic/optimizer/memo.cpp | 11 + .../sql/logic/optimizer/optimizer_test.cpp | 44 + src/stewkk/sql/logic/optimizer/rule.cpp | 8 +- src/stewkk/sql/logic/optimizer/rule_test.cpp | 3 +- .../join_associativity.cpp | 112 +- test/static/executor/test_data/customers.csv | 501 ++ test/static/executor/test_data/orders.csv | 5001 +++++++++++++++++ test/static/executor/test_data/regions.csv | 11 + 12 files changed, 5846 insertions(+), 68 deletions(-) create mode 100755 benchmarks/generate-multiway-data.sh create mode 100644 test/static/executor/test_data/customers.csv create mode 100644 test/static/executor/test_data/orders.csv create mode 100644 test/static/executor/test_data/regions.csv diff --git a/benchmarks/generate-multiway-data.sh b/benchmarks/generate-multiway-data.sh new file mode 100755 index 0000000..0c58b92 --- /dev/null +++ b/benchmarks/generate-multiway-data.sh @@ -0,0 +1,47 @@ +#!/usr/bin/env bash +# Generates three CSV tables for multi-way join benchmarking with skewed +# cardinalities: regions (10) << customers (500) << orders (5000). +# +# Schema (all int): +# regions(id) +# customers(id, region_id) -- region_id is FK into regions +# orders(id, customer_id) -- customer_id is FK into customers +# +# Idempotent: overwrites existing files at OUT_DIR. + +set -euo pipefail + +OUT_DIR="${1:-$(dirname "$0")/../test/static/executor/test_data}" + +n_regions=10 +n_customers=500 +n_orders=5000 + +regions="$OUT_DIR/regions.csv" +customers="$OUT_DIR/customers.csv" +orders="$OUT_DIR/orders.csv" + +{ + echo 'id:int' + seq 1 "$n_regions" +} > "$regions" + +{ + echo 'id:int,region_id:int' + awk -v n="$n_customers" -v r="$n_regions" 'BEGIN { + srand(1); + for (i = 1; i <= n; ++i) printf "%d,%d\n", i, 1 + int(rand() * r); + }' +} > "$customers" + +{ + echo 'id:int,customer_id:int' + awk -v n="$n_orders" -v c="$n_customers" 'BEGIN { + srand(2); + for (i = 1; i <= n; ++i) printf "%d,%d\n", i, 1 + int(rand() * c); + }' +} > "$orders" + +echo "wrote $regions ($(wc -l < "$regions") lines)" +echo "wrote $customers ($(wc -l < "$customers") lines)" +echo "wrote $orders ($(wc -l < "$orders") lines)" diff --git a/benchmarks/main.cpp b/benchmarks/main.cpp index 923eccf..8a5ed45 100644 --- a/benchmarks/main.cpp +++ b/benchmarks/main.cpp @@ -12,6 +12,9 @@ #include #include #include +#include +#include +#include #include #include @@ -19,6 +22,7 @@ namespace stewkk::sql { namespace { +// FIXME: ORDER BY support missing PhysicalPlanNode ToPhysicalPlan(const Operator& op) { return std::visit(utils::Overloaded{ [](const Table& t) -> PhysicalPlanNode { @@ -53,6 +57,37 @@ PhysicalPlanNode ToPhysicalPlan(const Operator& op) { }, op); } +CardinalityEstimates MakeBenchCardinality() { + return CardinalityEstimates({ + {"users", 17}, + {"employees", 11}, + {"employees_200", 200}, + {"departments", 5}, + {"departments_500", 1000}, + {"departments_1000", 1000}, + {"departments_2000", 2000}, + {"departments_4000", 4000}, + {"departments_8000", 8000}, + {"departments_16000", 16000}, + {"books", 3}, + {"regions", 10}, + {"customers", 500}, + {"orders", 5000}, + }); +} + +enum class PlannerMode { kNaive, kOptimized }; + +template +PhysicalPlanNode MakePlan(const Operator& op) { + if constexpr (Mode == PlannerMode::kNaive) { + return ToPhysicalPlan(op); + } else { + Optimizer optimizer(op, MakeMainRules(), MakeBenchCardinality()); + return optimizer.Optimize(); + } +} + } // namespace const static std::string kProjectDir = std::getenv("PWD"); @@ -67,7 +102,20 @@ static constexpr char kComplex4000[]{"SELECT departments_4000.id*2, employees_20 static constexpr char kComplex8000[]{"SELECT departments_8000.id*2, employees_200.id+1 FROM employees_200 RIGHT JOIN departments_8000 ON employees_200.department_id = departments_8000.id AND departments_8000.id > 3 AND departments_8000.id*2*2/2/2*2 < 30;"}; static constexpr char kComplex16000[]{"SELECT departments_16000.id*2, employees_200.id+1 FROM employees_200 RIGHT JOIN departments_16000 ON employees_200.department_id = departments_16000.id AND departments_16000.id > 3 AND departments_16000.id*2*2/2/2*2 < 30;"}; -template +// 3-way joins on skewed tables (regions=10, customers=500, orders=5000). +// Naive textual order (orders ⋈ customers) ⋈ regions builds a ~2.5M-tuple +// intermediate. JoinAssociativity + JoinCommutativity let the optimizer pick a +// shape with much smaller intermediates. +static constexpr char kMultiwayOCR[]{ + "SELECT orders.id, customers.id, regions.id FROM orders " + "JOIN customers ON orders.customer_id = customers.id " + "JOIN regions ON customers.region_id = regions.id;"}; +static constexpr char kMultiwayROC[]{ + "SELECT orders.id, customers.id, regions.id FROM regions " + "JOIN customers ON customers.region_id = regions.id " + "JOIN orders ON orders.customer_id = customers.id;"}; + +template void BM_SQL(benchmark::State& state) { std::ofstream nullstream("/dev/null"); std::clog.rdbuf(nullstream.rdbuf()); @@ -76,7 +124,7 @@ void BM_SQL(benchmark::State& state) { ctx, [&state]() -> boost::asio::awaitable { std::stringstream s{Query}; - PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value().op); + PhysicalPlanNode op = MakePlan(GetAST(s).value().op); CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; Executor executor(std::move(seq_scan), co_await boost::asio::this_coro::executor); @@ -92,34 +140,30 @@ void BM_SQL(benchmark::State& state) { ctx.run(); } -BENCHMARK(BM_SQL)->UseRealTime(); -BENCHMARK(BM_SQL)->UseRealTime(); - -BENCHMARK(BM_SQL)->UseRealTime(); -BENCHMARK(BM_SQL)->UseRealTime(); - -BENCHMARK(BM_SQL)->UseRealTime(); -BENCHMARK(BM_SQL)->UseRealTime(); - -BENCHMARK(BM_SQL)->UseRealTime(); -BENCHMARK(BM_SQL)->UseRealTime(); - -BENCHMARK(BM_SQL)->UseRealTime(); -BENCHMARK(BM_SQL)->UseRealTime(); - -BENCHMARK(BM_SQL)->UseRealTime(); -BENCHMARK(BM_SQL)->UseRealTime(); - -BENCHMARK(BM_SQL)->UseRealTime(); -BENCHMARK(BM_SQL)->UseRealTime(); - -BENCHMARK(BM_SQL)->UseRealTime(); -BENCHMARK(BM_SQL)->UseRealTime(); - -BENCHMARK(BM_SQL)->UseRealTime(); -BENCHMARK(BM_SQL)->UseRealTime(); - -template +#define REGISTER_BM_SQL(Query) \ + BENCHMARK(BM_SQL)->UseRealTime(); \ + BENCHMARK(BM_SQL) \ + ->UseRealTime(); \ + BENCHMARK(BM_SQL) \ + ->UseRealTime(); \ + BENCHMARK(BM_SQL) \ + ->UseRealTime(); + +REGISTER_BM_SQL(kSimpleSelectSmall) +REGISTER_BM_SQL(kJoinSmall) +REGISTER_BM_SQL(kComplex5) +REGISTER_BM_SQL(kComplex500) +REGISTER_BM_SQL(kComplex1000) +REGISTER_BM_SQL(kComplex2000) +REGISTER_BM_SQL(kComplex4000) +REGISTER_BM_SQL(kComplex8000) +REGISTER_BM_SQL(kComplex16000) +// FIXME: Optimizer search does not terminate within minutes on 3-way joins. +// Re-enable once that perf bug is fixed; see kMultiwayOCR/kMultiwayROC. +// REGISTER_BM_SQL(kMultiwayOCR) +// REGISTER_BM_SQL(kMultiwayROC) + +template void BM_SQL_Multithreaded(benchmark::State& state) { std::ofstream nullstream("/dev/null"); std::clog.rdbuf(nullstream.rdbuf()); @@ -129,7 +173,7 @@ void BM_SQL_Multithreaded(benchmark::State& state) { [&state]() -> boost::asio::awaitable { boost::asio::thread_pool pool{4}; std::stringstream s{Query}; - PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value().op); + PhysicalPlanNode op = MakePlan(GetAST(s).value().op); CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; Executor executor(std::move(seq_scan), pool.executor()); @@ -148,26 +192,24 @@ void BM_SQL_Multithreaded(benchmark::State& state) { ctx.run(); } -BENCHMARK(BM_SQL_Multithreaded)->UseRealTime(); -BENCHMARK(BM_SQL_Multithreaded)->UseRealTime(); - -BENCHMARK(BM_SQL_Multithreaded)->UseRealTime(); -BENCHMARK(BM_SQL_Multithreaded)->UseRealTime(); - -BENCHMARK(BM_SQL_Multithreaded)->UseRealTime(); -BENCHMARK(BM_SQL_Multithreaded)->UseRealTime(); - -BENCHMARK(BM_SQL_Multithreaded)->UseRealTime(); -BENCHMARK(BM_SQL_Multithreaded)->UseRealTime(); - -BENCHMARK(BM_SQL_Multithreaded)->UseRealTime(); -BENCHMARK(BM_SQL_Multithreaded)->UseRealTime(); - -BENCHMARK(BM_SQL_Multithreaded)->UseRealTime(); -BENCHMARK(BM_SQL_Multithreaded)->UseRealTime(); - -BENCHMARK(BM_SQL_Multithreaded)->UseRealTime(); -BENCHMARK(BM_SQL_Multithreaded)->UseRealTime(); +#define REGISTER_BM_SQL_MT(Query) \ + BENCHMARK(BM_SQL_Multithreaded) \ + ->UseRealTime(); \ + BENCHMARK(BM_SQL_Multithreaded) \ + ->UseRealTime(); \ + BENCHMARK(BM_SQL_Multithreaded) \ + ->UseRealTime(); \ + BENCHMARK( \ + BM_SQL_Multithreaded) \ + ->UseRealTime(); + +REGISTER_BM_SQL_MT(kComplex5) +REGISTER_BM_SQL_MT(kComplex500) +REGISTER_BM_SQL_MT(kComplex1000) +REGISTER_BM_SQL_MT(kComplex2000) +REGISTER_BM_SQL_MT(kComplex4000) +REGISTER_BM_SQL_MT(kComplex8000) +REGISTER_BM_SQL_MT(kComplex16000) } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/memo.hpp b/include/stewkk/sql/logic/optimizer/memo.hpp index 9518ef3..b62a57d 100644 --- a/include/stewkk/sql/logic/optimizer/memo.hpp +++ b/include/stewkk/sql/logic/optimizer/memo.hpp @@ -13,6 +13,8 @@ class Memo { size_t GroupCount() const; utils::NotNull AddGroup(LogicalOperator root_operator); LogicalExpr* GetGroup(LogicalOperator root_operator) const; + utils::NotNull AddLogicalExprToGroup(utils::NotNull group, + LogicalOperator root_operator); utils::NotNull Populate(const Operator& op); private: diff --git a/src/stewkk/sql/logic/optimizer/cardinality.cpp b/src/stewkk/sql/logic/optimizer/cardinality.cpp index 1252680..28c5a83 100644 --- a/src/stewkk/sql/logic/optimizer/cardinality.cpp +++ b/src/stewkk/sql/logic/optimizer/cardinality.cpp @@ -1,9 +1,33 @@ #include +#include + #include namespace stewkk::sql { +namespace { + +// System R: each `attr = attr` conjunct contributes 1/max(lhs_card, rhs_card); +// AND multiplies; anything else (including TRUE) contributes 1.0. +double JoinSelectivity(const Expression& qual, int64_t lhs_card, int64_t rhs_card) { + const auto* b = std::get_if(&qual); + if (!b) return 1.0; + if (b->binop == BinaryOp::kAnd) { + return JoinSelectivity(*b->lhs, lhs_card, rhs_card) + * JoinSelectivity(*b->rhs, lhs_card, rhs_card); + } + if (b->binop == BinaryOp::kEq + && std::holds_alternative(*b->lhs) + && std::holds_alternative(*b->rhs)) { + auto m = std::max(1, std::max(lhs_card, rhs_card)); + return 1.0 / static_cast(m); + } + return 1.0; +} + +} // namespace + CardinalityEstimates::CardinalityEstimates(std::unordered_map table_sizes) : table_sizes_(std::move(table_sizes)) {} @@ -34,7 +58,11 @@ int64_t CardinalityEstimates::GetCardinality(const LogicalOperator& op) { return GetCardinality(j.lhs) * GetCardinality(j.rhs); }, [this](const logical::Join& j) -> int64_t { - return GetCardinality(j.lhs) * GetCardinality(j.rhs); + auto lhs_c = GetCardinality(j.lhs); + auto rhs_c = GetCardinality(j.rhs); + auto out = static_cast(lhs_c) * static_cast(rhs_c) + * JoinSelectivity(j.qual, lhs_c, rhs_c); + return std::max(1, static_cast(out)); }, }, op); } diff --git a/src/stewkk/sql/logic/optimizer/memo.cpp b/src/stewkk/sql/logic/optimizer/memo.cpp index 4627e04..51653c7 100644 --- a/src/stewkk/sql/logic/optimizer/memo.cpp +++ b/src/stewkk/sql/logic/optimizer/memo.cpp @@ -60,6 +60,17 @@ utils::NotNull Memo::AddGroup(LogicalOperator root_operator) { return expr; } +utils::NotNull Memo::AddLogicalExprToGroup(utils::NotNull group, + LogicalOperator root_operator) { + auto key = ToKey(root_operator); + if (auto g = GetGroup(key); g) { + return g; + } + auto expr = group->AddLogicalExpr(std::move(root_operator)); + expr_index_[key] = expr; + return expr; +} + utils::NotNull Memo::Populate(const Operator& op) { return std::visit(utils::Overloaded{ [this](const Table& t) { diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index 02517c9..6381a6c 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -44,6 +44,50 @@ TEST(OptimizerTest, JoinCommutativity) { ASSERT_THAT(Serialize(got), Eq("(NestedLoopJoin Inner (= (attr users id) (attr orders user_id)) (SeqScan orders) (SeqScan users))")); } +TEST(OptimizerTest, MultiwayJoinOCR) { + std::stringstream s{ + "SELECT orders.id, customers.id, regions.id FROM orders " + "JOIN customers ON orders.customer_id = customers.id " + "JOIN regions ON customers.region_id = regions.id;"}; + Operator op = GetAST(s).value().op; + Optimizer optimizer(op, MakeMainRules(), CardinalityEstimates({ + {"regions", 10}, + {"customers", 500}, + {"orders", 5000}, + })); + + auto got = optimizer.Optimize(); + + ASSERT_THAT( + Serialize(got), + Eq("(PhysicalProjection (exprs (attr orders id) (attr customers id) (attr regions id))" + " (NestedLoopJoin Inner (= (attr customers region_id) (attr regions id))" + " (NestedLoopJoin Inner (= (attr orders customer_id) (attr customers id))" + " (SeqScan customers) (SeqScan orders)) (SeqScan regions)))")); +} + +TEST(OptimizerTest, MultiwayJoinROC) { + std::stringstream s{ + "SELECT orders.id, customers.id, regions.id FROM regions " + "JOIN customers ON customers.region_id = regions.id " + "JOIN orders ON orders.customer_id = customers.id;"}; + Operator op = GetAST(s).value().op; + Optimizer optimizer(op, MakeMainRules(), CardinalityEstimates({ + {"regions", 10}, + {"customers", 500}, + {"orders", 5000}, + })); + + auto got = optimizer.Optimize(); + + ASSERT_THAT( + Serialize(got), + Eq("(PhysicalProjection (exprs (attr orders id) (attr customers id) (attr regions id))" + " (NestedLoopJoin Inner (= (attr orders customer_id) (attr customers id))" + " (NestedLoopJoin Inner (= (attr customers region_id) (attr regions id))" + " (SeqScan customers) (SeqScan regions)) (SeqScan orders)))")); +} + TEST(OptimizerTest, OrderBy) { std::stringstream s{"SELECT * FROM users ORDER BY users.id;"}; auto parsed = GetAST(s).value(); diff --git a/src/stewkk/sql/logic/optimizer/rule.cpp b/src/stewkk/sql/logic/optimizer/rule.cpp index 9961b89..81c387a 100644 --- a/src/stewkk/sql/logic/optimizer/rule.cpp +++ b/src/stewkk/sql/logic/optimizer/rule.cpp @@ -5,13 +5,7 @@ namespace stewkk::sql { utils::NotNull TransformationRule::Apply(utils::NotNull expr, Memo& memo) { - // FIXME: maybe it should be Memo's responsibility to check for duplicates? - auto op = ApplyImpl(expr, memo); - auto existing = memo.GetGroup(op); - if (existing) { - return existing; - } - return expr->group->AddLogicalExpr(std::move(op)); + return memo.AddLogicalExprToGroup(expr->group, ApplyImpl(expr, memo)); } } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/rule_test.cpp b/src/stewkk/sql/logic/optimizer/rule_test.cpp index 572e4bb..da720d4 100644 --- a/src/stewkk/sql/logic/optimizer/rule_test.cpp +++ b/src/stewkk/sql/logic/optimizer/rule_test.cpp @@ -77,7 +77,8 @@ TEST_F(JoinAssociativityTest, ReturnsCorrectExpression) { const auto& inner = std::get(outer.rhs->GetLogicalExprs()[0]->root_operator); EXPECT_EQ(inner.lhs.get(), b); EXPECT_EQ(inner.rhs.get(), c); - EXPECT_EQ(inner.qual, Expression{Literal::kTrue}); + EXPECT_EQ(inner.qual, Expression{Literal::kFalse}); + EXPECT_EQ(outer.qual, Expression{Literal::kTrue}); } } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp b/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp index 915ad0f..d021c08 100644 --- a/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp +++ b/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp @@ -1,9 +1,87 @@ #include +#include #include +#include +#include + +#include namespace stewkk::sql { +namespace { + +bool IsTrue(const Expression& e) { + return std::holds_alternative(e) && std::get(e) == Literal::kTrue; +} + +void CollectConjuncts(const Expression& e, std::vector& out) { + if (IsTrue(e)) return; + if (const auto* b = std::get_if(&e); b && b->binop == BinaryOp::kAnd) { + CollectConjuncts(*b->lhs, out); + CollectConjuncts(*b->rhs, out); + return; + } + out.push_back(e); +} + +Expression AndConjuncts(const std::vector& conjs) { + if (conjs.empty()) return Literal::kTrue; + Expression acc = conjs[0]; + for (size_t i = 1; i < conjs.size(); i++) { + acc = BinaryExpression{ + std::make_shared(std::move(acc)), + BinaryOp::kAnd, + std::make_shared(conjs[i]), + }; + } + return acc; +} + +void CollectAttrTables(const Expression& e, std::unordered_set& out) { + std::visit(utils::Overloaded{ + [&](const Attribute& a) { out.insert(a.table); }, + [&](const BinaryExpression& b) { + CollectAttrTables(*b.lhs, out); + CollectAttrTables(*b.rhs, out); + }, + [&](const UnaryExpression& u) { CollectAttrTables(*u.child, out); }, + [&](const IntConst&) {}, + [&](const Literal&) {}, + }, e); +} + +// Equivalent logical exprs in a memo group all expose the same output tables, +// so one expression suffices. Recurses across child groups via their fronts. +void CollectGroupTables(utils::NotNull g, std::unordered_set& out, + std::unordered_set& seen) { + if (!seen.insert(g.get()).second) return; + auto exprs = g->GetLogicalExprs(); + if (exprs.empty()) return; + std::visit(utils::Overloaded{ + [&](const logical::Table& t) { out.insert(t.name); }, + [&](const logical::Filter& f) { CollectGroupTables(f.source, out, seen); }, + [&](const logical::Projection& p) { CollectGroupTables(p.source, out, seen); }, + [&](const logical::CrossJoin& j) { + CollectGroupTables(j.lhs, out, seen); + CollectGroupTables(j.rhs, out, seen); + }, + [&](const logical::Join& j) { + CollectGroupTables(j.lhs, out, seen); + CollectGroupTables(j.rhs, out, seen); + }, + }, (*exprs.begin())->root_operator); +} + +std::unordered_set GroupTables(utils::NotNull g) { + std::unordered_set out; + std::unordered_set seen; + CollectGroupTables(g, out, seen); + return out; +} + +} // namespace + bool JoinAssociativity::IsApplicable(utils::NotNull expr) { if (!std::holds_alternative(expr->root_operator)) return false; const auto& outer = std::get(expr->root_operator); @@ -13,20 +91,38 @@ bool JoinAssociativity::IsApplicable(utils::NotNull expr) { return false; } -// (A ⋈₁ B) ⋈₂ C → A ⋈₁∧₂ (B ⋈₂ C) +// (A ⋈_p1 B) ⋈_p2 C → A ⋈_pa (B ⋈_pbc C) +// where pbc collects every conjunct of (p1 ∧ p2) whose attributes lie inside +// B ∪ C, and pa keeps the rest. Avoids the degenerate ⋈_TRUE inner that drops +// otherwise-pushable predicates onto the outer join. LogicalOperator JoinAssociativity::ApplyImpl(utils::NotNull expr, Memo& memo) { const auto& outer = std::get(expr->root_operator); for (auto inner_expr : outer.lhs->GetLogicalExprs()) { if (!std::holds_alternative(inner_expr->root_operator)) continue; const auto& inner = std::get(inner_expr->root_operator); - auto combined_qual = Expression{BinaryExpression{ - std::make_shared(inner.qual), - BinaryOp::kAnd, - std::make_shared(outer.qual), - }}; - auto new_rhs = memo.AddGroup(logical::Join{inner.rhs, outer.rhs, outer.type, Literal::kTrue})->group; + + auto bc_tables = GroupTables(inner.rhs); + auto c_tables = GroupTables(outer.rhs); + bc_tables.insert(c_tables.begin(), c_tables.end()); + + std::vector conjs; + CollectConjuncts(inner.qual, conjs); + CollectConjuncts(outer.qual, conjs); + + std::vector inner_quals; + std::vector outer_quals; + for (auto& q : conjs) { + std::unordered_set q_tables; + CollectAttrTables(q, q_tables); + bool fits_inner = std::all_of(q_tables.begin(), q_tables.end(), + [&](const auto& t) { return bc_tables.contains(t); }); + (fits_inner ? inner_quals : outer_quals).push_back(std::move(q)); + } + + auto new_rhs = memo.AddGroup(logical::Join{ + inner.rhs, outer.rhs, outer.type, AndConjuncts(inner_quals)})->group; // FIXME: should return more than one operators!!! - return logical::Join{inner.lhs, new_rhs, inner.type, combined_qual}; + return logical::Join{inner.lhs, new_rhs, inner.type, AndConjuncts(outer_quals)}; } throw std::runtime_error{"cant perform JoinAssociativity"}; } diff --git a/test/static/executor/test_data/customers.csv b/test/static/executor/test_data/customers.csv new file mode 100644 index 0000000..4d48d7c --- /dev/null +++ b/test/static/executor/test_data/customers.csv @@ -0,0 +1,501 @@ +id:int,region_id:int +1,10 +2,6 +3,4 +4,6 +5,8 +6,8 +7,5 +8,4 +9,8 +10,2 +11,8 +12,9 +13,8 +14,5 +15,5 +16,10 +17,9 +18,8 +19,2 +20,2 +21,10 +22,9 +23,5 +24,8 +25,4 +26,7 +27,7 +28,4 +29,6 +30,10 +31,2 +32,8 +33,2 +34,6 +35,10 +36,9 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+4948,154 +4949,354 +4950,412 +4951,318 +4952,206 +4953,20 +4954,392 +4955,237 +4956,491 +4957,226 +4958,245 +4959,449 +4960,388 +4961,426 +4962,341 +4963,398 +4964,207 +4965,461 +4966,320 +4967,223 +4968,305 +4969,204 +4970,218 +4971,419 +4972,299 +4973,337 +4974,438 +4975,19 +4976,219 +4977,377 +4978,148 +4979,188 +4980,463 +4981,280 +4982,493 +4983,67 +4984,156 +4985,69 +4986,324 +4987,377 +4988,289 +4989,341 +4990,25 +4991,386 +4992,250 +4993,395 +4994,262 +4995,70 +4996,14 +4997,366 +4998,420 +4999,153 +5000,464 diff --git a/test/static/executor/test_data/regions.csv b/test/static/executor/test_data/regions.csv new file mode 100644 index 0000000..b9bdb04 --- /dev/null +++ b/test/static/executor/test_data/regions.csv @@ -0,0 +1,11 @@ +id:int +1 +2 +3 +4 +5 +6 +7 +8 +9 +10 From 0e99fd9911667d3c668db5a7c377d49cdd18ff6e Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 25 May 2026 04:16:41 +0300 Subject: [PATCH 050/120] wip: hash join support, needs review --- benchmarks/main.cpp | 6 +- .../stewkk/sql/logic/executor/executor.hpp | 2 + .../implement_hash_join.hpp | 13 ++ .../sql/logic/optimizer/physical_expr.hpp | 11 +- include/stewkk/sql/logic/optimizer/rules.hpp | 3 +- src/stewkk/sql/CMakeLists.txt | 1 + src/stewkk/sql/logic/executor/executor.cpp | 119 +++++++++++++++++- .../implement_hash_join.cpp | 33 +++++ src/stewkk/sql/logic/optimizer/optimizer.cpp | 42 +++++-- .../sql/logic/optimizer/optimizer_test.cpp | 16 +-- .../sql/logic/optimizer/reachability.cpp | 22 +++- src/stewkk/sql/logic/optimizer/rules.cpp | 3 +- .../sql/logic/optimizer/rules_applier.cpp | 2 +- 13 files changed, 246 insertions(+), 27 deletions(-) create mode 100644 include/stewkk/sql/logic/implementation_rules/implement_hash_join.hpp create mode 100644 src/stewkk/sql/logic/implementation_rules/implement_hash_join.cpp diff --git a/benchmarks/main.cpp b/benchmarks/main.cpp index 8a5ed45..101b6fc 100644 --- a/benchmarks/main.cpp +++ b/benchmarks/main.cpp @@ -158,10 +158,8 @@ REGISTER_BM_SQL(kComplex2000) REGISTER_BM_SQL(kComplex4000) REGISTER_BM_SQL(kComplex8000) REGISTER_BM_SQL(kComplex16000) -// FIXME: Optimizer search does not terminate within minutes on 3-way joins. -// Re-enable once that perf bug is fixed; see kMultiwayOCR/kMultiwayROC. -// REGISTER_BM_SQL(kMultiwayOCR) -// REGISTER_BM_SQL(kMultiwayROC) +REGISTER_BM_SQL(kMultiwayOCR) +REGISTER_BM_SQL(kMultiwayROC) template void BM_SQL_Multithreaded(benchmark::State& state) { diff --git a/include/stewkk/sql/logic/executor/executor.hpp b/include/stewkk/sql/logic/executor/executor.hpp index 94a2d79..2361967 100644 --- a/include/stewkk/sql/logic/executor/executor.hpp +++ b/include/stewkk/sql/logic/executor/executor.hpp @@ -63,6 +63,8 @@ class Executor { TuplesChannel& tuples_chan); boost::asio::awaitable ExecuteJoin(const NestedLoopJoin& join, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan); + boost::asio::awaitable ExecuteHashJoin(const HashJoin& join, AttributesInfoChannel& attr_chan, + TuplesChannel& tuples_chan); boost::asio::experimental::promise SpawnExecutor( boost::asio::any_io_executor exec, const PhysicalPlanNode& op, AttributesInfoChannel& attr_chan, TuplesChannel& tuple_chan); diff --git a/include/stewkk/sql/logic/implementation_rules/implement_hash_join.hpp b/include/stewkk/sql/logic/implementation_rules/implement_hash_join.hpp new file mode 100644 index 0000000..f2a11a3 --- /dev/null +++ b/include/stewkk/sql/logic/implementation_rules/implement_hash_join.hpp @@ -0,0 +1,13 @@ +#pragma once + +#include + +namespace stewkk::sql { + +class ImplementHashJoin : public ImplementationRule { + public: + bool IsApplicable(utils::NotNull expr) override; + utils::NotNull Apply(utils::NotNull expr, Memo& memo) override; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/physical_expr.hpp b/include/stewkk/sql/logic/optimizer/physical_expr.hpp index 6c4ebd7..4c6c43e 100644 --- a/include/stewkk/sql/logic/optimizer/physical_expr.hpp +++ b/include/stewkk/sql/logic/optimizer/physical_expr.hpp @@ -50,6 +50,15 @@ struct NestedLoopCrossJoin { bool operator==(const NestedLoopCrossJoin&) const = default; }; +struct HashJoin { + utils::NotNull lhs; + utils::NotNull rhs; + JoinType type; + Expression qual; + + bool operator==(const HashJoin&) const = default; +}; + struct Sort { utils::NotNull input; SortOrder keys; @@ -62,7 +71,7 @@ struct Sort { struct PhysicalExpr { std::variant root_operator; + physical::HashJoin, physical::Sort> root_operator; utils::NotNull group; bool is_enforcer = false; }; diff --git a/include/stewkk/sql/logic/optimizer/rules.hpp b/include/stewkk/sql/logic/optimizer/rules.hpp index a4b3174..f7ecb12 100644 --- a/include/stewkk/sql/logic/optimizer/rules.hpp +++ b/include/stewkk/sql/logic/optimizer/rules.hpp @@ -11,6 +11,7 @@ #include #include #include +#include namespace stewkk::sql { @@ -20,6 +21,6 @@ struct Rules { std::array, NImplementation> implementation_rules; }; -Rules<2, 5> MakeMainRules(); +Rules<2, 6> MakeMainRules(); } // namespace stewkk::sql diff --git a/src/stewkk/sql/CMakeLists.txt b/src/stewkk/sql/CMakeLists.txt index daeda1c..e74be8b 100644 --- a/src/stewkk/sql/CMakeLists.txt +++ b/src/stewkk/sql/CMakeLists.txt @@ -45,6 +45,7 @@ add_library(libsql logic/implementation_rules/implement_projection.cpp logic/implementation_rules/implement_join.cpp logic/implementation_rules/implement_cross_join.cpp + logic/implementation_rules/implement_hash_join.cpp logic/optimizer/properties/sort_order.cpp logic/optimizer/properties/sort_property.cpp logic/optimizer/properties/property_set.cpp diff --git a/src/stewkk/sql/logic/executor/executor.cpp b/src/stewkk/sql/logic/executor/executor.cpp index 0135416..acc02b8 100644 --- a/src/stewkk/sql/logic/executor/executor.cpp +++ b/src/stewkk/sql/logic/executor/executor.cpp @@ -3,6 +3,7 @@ #include #include #include +#include #include #include @@ -436,8 +437,8 @@ boost::asio::awaitable Executor::Execute(const Physica co_await executor.ExecuteCrossJoin(cross_join, attr_chan, tuples_chan); co_return; } - boost::asio::awaitable operator()(const HashJoin&) { - throw std::runtime_error("HashJoin execution not implemented"); + boost::asio::awaitable operator()(const HashJoin& join) { + co_await executor.ExecuteHashJoin(join, attr_chan, tuples_chan); co_return; } boost::asio::awaitable operator()(const MergeJoin&) { @@ -743,6 +744,120 @@ boost::asio::awaitable Executor::ExecuteJoin(const Nes tuples_chan.close(); } +namespace { + +size_t FindAttrIndex(const AttributesInfo& attrs, const Attribute& a) { + auto it = std::find_if(attrs.begin(), attrs.end(), [&](const AttributeInfo& ai) { + return ai.table == a.table && ai.name == a.name; + }); + if (it == attrs.end()) { + throw std::runtime_error{"hash join key attribute not found: " + a.table + "." + a.name}; + } + return static_cast(it - attrs.begin()); +} + +} // namespace + +// Inner equi-join: build a hash table on lhs.key, then probe with rhs. +// ImplementHashJoin gates applicability to `attr = attr` quals on Inner joins, +// so the only shapes that reach here are: lhs_attr == rhs_attr or rhs_attr == +// lhs_attr. We resolve which side owns each attribute via the lhs attribute +// stream; the remaining one must belong to rhs. +template +boost::asio::awaitable Executor::ExecuteHashJoin( + const HashJoin& join, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan) { + Log("Executing hash join"); + if (join.type != JoinType::kInner) { + throw std::logic_error{"HashJoin executor supports only Inner joins"}; + } + const auto* bin = std::get_if(&join.qual); + if (!bin || bin->binop != BinaryOp::kEq) { + throw std::logic_error{"HashJoin qual must be an equality"}; + } + const auto* a = std::get_if(bin->lhs.get()); + const auto* b = std::get_if(bin->rhs.get()); + if (!a || !b) { + throw std::logic_error{"HashJoin qual must be `attr = attr`"}; + } + + auto exec = co_await boost::asio::this_coro::executor; + auto [lhs_attrs_chan, lhs_tuples_chan] = co_await GetChannels(); + auto lhs_task = SpawnExecutor(exec, *join.lhs, lhs_attrs_chan, lhs_tuples_chan); + auto [rhs_attrs_chan, rhs_tuples_chan] = co_await GetChannels(); + auto rhs_task = SpawnExecutor(exec, *join.rhs, rhs_attrs_chan, rhs_tuples_chan); + + auto lhs_attrs = co_await lhs_attrs_chan.async_receive(boost::asio::use_awaitable); + auto rhs_attrs = co_await rhs_attrs_chan.async_receive(boost::asio::use_awaitable); + + // Pick the (lhs_key, rhs_key) pair regardless of which side the qual wrote first. + auto lhs_has = [&](const Attribute& attr) { + return std::any_of(lhs_attrs.begin(), lhs_attrs.end(), [&](const AttributeInfo& ai) { + return ai.table == attr.table && ai.name == attr.name; + }); + }; + const Attribute* lhs_attr = nullptr; + const Attribute* rhs_attr = nullptr; + if (lhs_has(*a)) { lhs_attr = a; rhs_attr = b; } + else if (lhs_has(*b)) { lhs_attr = b; rhs_attr = a; } + else { + throw std::runtime_error{"HashJoin qual: neither side of equality found in lhs attrs"}; + } + + size_t lhs_key_idx = FindAttrIndex(lhs_attrs, *lhs_attr); + size_t rhs_key_idx = FindAttrIndex(rhs_attrs, *rhs_attr); + + AttributesInfo out_attrs = lhs_attrs; + std::ranges::copy(rhs_attrs, std::back_inserter(out_attrs)); + co_await attr_chan.async_send(boost::system::error_code{}, out_attrs, + boost::asio::use_awaitable); + attr_chan.close(); + + // Build phase: collect all lhs tuples into a hash multimap keyed by lhs.key. + // NULL keys never match (SQL = on NULL is unknown), so drop them. + std::unordered_multimap build; + for (;;) { + auto buf = co_await ReceiveTuples(lhs_tuples_chan); + if (buf.empty()) break; + Log("HashJoin build received {} tuples", buf.size()); + for (auto& t : buf) { + const Value& k = t[lhs_key_idx]; + if (k.is_null) continue; + build.emplace(k.value.int_value, std::move(t)); + } + } + Log("HashJoin build phase done; {} entries", build.size()); + + // Probe phase: stream rhs, lookup matches, emit joined tuples in kBufSize chunks. + Tuples out_buf; + out_buf.reserve(kBufSize); + for (;;) { + auto buf = co_await ReceiveTuples(rhs_tuples_chan); + if (buf.empty()) break; + Log("HashJoin probe received {} tuples", buf.size()); + for (const auto& rt : buf) { + const Value& k = rt[rhs_key_idx]; + if (k.is_null) continue; + auto range = build.equal_range(k.value.int_value); + for (auto it = range.first; it != range.second; ++it) { + out_buf.push_back(ConcatTuples(it->second, rt)); + if (out_buf.size() == kBufSize) { + co_await tuples_chan.async_send(boost::system::error_code{}, std::move(out_buf), + boost::asio::use_awaitable); + out_buf.clear(); + out_buf.reserve(kBufSize); + } + } + } + } + if (!out_buf.empty()) { + co_await tuples_chan.async_send(boost::system::error_code{}, std::move(out_buf), + boost::asio::use_awaitable); + } + co_await lhs_task(boost::asio::use_awaitable); + co_await rhs_task(boost::asio::use_awaitable); + tuples_chan.close(); +} + template boost::asio::experimental::promise Executor::SpawnExecutor(boost::asio::any_io_executor exec, diff --git a/src/stewkk/sql/logic/implementation_rules/implement_hash_join.cpp b/src/stewkk/sql/logic/implementation_rules/implement_hash_join.cpp new file mode 100644 index 0000000..a207506 --- /dev/null +++ b/src/stewkk/sql/logic/implementation_rules/implement_hash_join.cpp @@ -0,0 +1,33 @@ +#include + +namespace stewkk::sql { + +namespace { + +// HashJoin currently handles only `attr_l = attr_r` quals on Inner joins. +// Outer-join semantics need a "right-side matched" bitmap during probe; not +// implemented yet. Composite/expression keys would require key extraction +// from arbitrary Expression nodes, also not done. +bool IsSimpleEquiJoin(const Expression& qual) { + const auto* bin = std::get_if(&qual); + if (!bin || bin->binop != BinaryOp::kEq) return false; + return std::holds_alternative(*bin->lhs) + && std::holds_alternative(*bin->rhs); +} + +} // namespace + +bool ImplementHashJoin::IsApplicable(utils::NotNull expr) { + if (!std::holds_alternative(expr->root_operator)) return false; + const auto& join = std::get(expr->root_operator); + if (join.type != JoinType::kInner) return false; + return IsSimpleEquiJoin(join.qual); +} + +utils::NotNull ImplementHashJoin::Apply(utils::NotNull expr, Memo&) { + auto& join = std::get(expr->root_operator); + return expr->group->AddPhysicalExpr( + physical::HashJoin{join.lhs, join.rhs, join.type, join.qual}); +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index a4f739d..380257b 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -37,6 +37,12 @@ PropertySet RequiredInputProps(utils::NotNull expr, [&](const physical::NestedLoopCrossJoin&) -> PropertySet { return child_index == 0 ? required : PropertySet::Any(); }, + [&](const physical::HashJoin&) -> PropertySet { + // HashJoin reorders output (hash-partitioned scan of build side, then + // probe-driven emit). It does not preserve any input sort order, so + // never ask children to satisfy `required`. + return PropertySet::Any(); + }, [&](const physical::Sort&) { return PropertySet::Any(); }, }, expr->root_operator); } @@ -52,6 +58,7 @@ PropertySet DeriveOutputProps(utils::NotNull expr, [&](const physical::Projection&) { return child_delivered[0]; }, [&](const physical::NestedLoopJoin&) { return child_delivered[0]; }, [&](const physical::NestedLoopCrossJoin&) { return child_delivered[0]; }, + [&](const physical::HashJoin&) { return PropertySet::Any(); }, [&](const physical::Sort& s) { return PropertySet{SortProperty{s.keys}}; }, }, expr->root_operator); } @@ -77,9 +84,12 @@ int64_t LowerBoundLocalCost(utils::NotNull expr, CardinalityEstima return p_l * (1 + p_r); }, [&](const logical::Join& j) -> int64_t { - auto p_l = (cardinality.GetCardinality(j.lhs) + kBufSize - 1) / kBufSize; - auto p_r = (cardinality.GetCardinality(j.rhs) + kBufSize - 1) / kBufSize; - return p_l * (1 + p_r); + // Must stay ≤ every physical impl. NLJ ~ n_l*n_r, HJ ~ n_l+n_r + // (equi only). When either side has 1 tuple, NLJ can beat HJ, so + // take the min so the bound is safe for both alternatives. + auto n_l = cardinality.GetCardinality(j.lhs); + auto n_r = cardinality.GetCardinality(j.rhs); + return std::min(n_l + n_r, n_l * n_r); }, }, expr->root_operator); } @@ -96,15 +106,20 @@ int64_t CalcCost(utils::NotNull expr, CardinalityEstimates& cardi return cardinality.GetCardinality(expr->group); }, [&](const physical::NestedLoopJoin& j) -> int64_t { - auto p_l = (cardinality.GetCardinality(j.lhs) + kBufSize - 1) / kBufSize; - auto p_r = (cardinality.GetCardinality(j.rhs) + kBufSize - 1) / kBufSize; - return p_l * (1 + p_r); + // Per-tuple work: each rhs tuple compared against every lhs tuple. + auto n_l = cardinality.GetCardinality(j.lhs); + auto n_r = cardinality.GetCardinality(j.rhs); + return n_l * n_r; }, [&](const physical::NestedLoopCrossJoin& j) -> int64_t { auto p_l = (cardinality.GetCardinality(j.lhs) + kBufSize - 1) / kBufSize; auto p_r = (cardinality.GetCardinality(j.rhs) + kBufSize - 1) / kBufSize; return p_l * (1 + p_r); }, + [&](const physical::HashJoin& j) -> int64_t { + // Build hash on lhs (n_l inserts), probe with rhs (n_r O(1) lookups). + return cardinality.GetCardinality(j.lhs) + cardinality.GetCardinality(j.rhs); + }, [&](const physical::Sort& s) -> int64_t { auto n = cardinality.GetCardinality(s.input); return n > 1 ? n * static_cast(std::bit_width(static_cast(n))) : n; @@ -151,6 +166,9 @@ std::vector> GetChildren(utils::NotNull ex [](const physical::NestedLoopCrossJoin& j) -> std::vector> { return {j.lhs, j.rhs}; }, + [](const physical::HashJoin& j) -> std::vector> { + return {j.lhs, j.rhs}; + }, [](const physical::Sort& s) -> std::vector> { return {s.input}; }, @@ -416,6 +434,16 @@ PhysicalPlanNode Optimizer::BuildOptimalPlan(G BuildOptimalPlan(op.rhs.get(), RequiredInputProps(best_expr_nn, required, 1))), }; }, + [this, best_expr_nn, required](const physical::HashJoin& op) -> PhysicalPlanNode { + return HashJoin{ + .lhs = std::make_shared( + BuildOptimalPlan(op.lhs.get(), RequiredInputProps(best_expr_nn, required, 0))), + .rhs = std::make_shared( + BuildOptimalPlan(op.rhs.get(), RequiredInputProps(best_expr_nn, required, 1))), + .type = op.type, + .qual = op.qual, + }; + }, [this, best_expr_nn, required](const physical::Sort& op) -> PhysicalPlanNode { return PhysicalSort{ .source = std::make_shared( @@ -455,6 +483,6 @@ utils::NotNull Optimizer::GetRootGroup return root_->group; } -template class Optimizer<2, 5>; +template class Optimizer<2, 6>; } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index 6381a6c..01c40fa 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -41,7 +41,7 @@ TEST(OptimizerTest, JoinCommutativity) { auto got = optimizer.Optimize(); - ASSERT_THAT(Serialize(got), Eq("(NestedLoopJoin Inner (= (attr users id) (attr orders user_id)) (SeqScan orders) (SeqScan users))")); + ASSERT_THAT(Serialize(got), Eq("(HashJoin Inner (= (attr users id) (attr orders user_id)) (SeqScan orders) (SeqScan users))")); } TEST(OptimizerTest, MultiwayJoinOCR) { @@ -61,9 +61,9 @@ TEST(OptimizerTest, MultiwayJoinOCR) { ASSERT_THAT( Serialize(got), Eq("(PhysicalProjection (exprs (attr orders id) (attr customers id) (attr regions id))" - " (NestedLoopJoin Inner (= (attr customers region_id) (attr regions id))" - " (NestedLoopJoin Inner (= (attr orders customer_id) (attr customers id))" - " (SeqScan customers) (SeqScan orders)) (SeqScan regions)))")); + " (HashJoin Inner (= (attr orders customer_id) (attr customers id))" + " (SeqScan orders) (HashJoin Inner (= (attr customers region_id) (attr regions id))" + " (SeqScan regions) (SeqScan customers))))")); } TEST(OptimizerTest, MultiwayJoinROC) { @@ -83,8 +83,8 @@ TEST(OptimizerTest, MultiwayJoinROC) { ASSERT_THAT( Serialize(got), Eq("(PhysicalProjection (exprs (attr orders id) (attr customers id) (attr regions id))" - " (NestedLoopJoin Inner (= (attr orders customer_id) (attr customers id))" - " (NestedLoopJoin Inner (= (attr customers region_id) (attr regions id))" + " (HashJoin Inner (= (attr orders customer_id) (attr customers id))" + " (HashJoin Inner (= (attr customers region_id) (attr regions id))" " (SeqScan customers) (SeqScan regions)) (SeqScan orders)))")); } @@ -151,7 +151,7 @@ TEST(ReachabilityTest, BothJoinOrdersReachable) { ASSERT_THAT(suboptimal.reachable, IsTrue()); } -TEST(ReachabilityTest, HashJoinNotReachable) { +TEST(ReachabilityTest, HashJoinReachable) { std::stringstream s{"SELECT * FROM users JOIN orders ON users.id = orders.user_id;"}; Expression qual = BinaryExpression{ std::make_shared(Attribute{"users", "id"}), @@ -161,7 +161,7 @@ TEST(ReachabilityTest, HashJoinNotReachable) { std::make_shared(SeqScan{"orders"}), std::make_shared(SeqScan{"users"}), JoinType::kInner, qual}); - ASSERT_THAT(result.reachable, IsFalse()); + ASSERT_THAT(result.reachable, IsTrue()); } TEST(ReachabilityTest, WrongJoinQual) { diff --git a/src/stewkk/sql/logic/optimizer/reachability.cpp b/src/stewkk/sql/logic/optimizer/reachability.cpp index 12ce20c..8f8101a 100644 --- a/src/stewkk/sql/logic/optimizer/reachability.cpp +++ b/src/stewkk/sql/logic/optimizer/reachability.cpp @@ -52,11 +52,14 @@ InternalMatch TryMatchExpr(utils::NotNull pe, }, [&](const physical::NestedLoopJoin& op) -> InternalMatch { const auto* t = std::get_if(&target); + // depth+1 once the operator type matches: a same-type partial + // mismatch is more informative than a foreign-type rejection, so + // it should outrank type-mismatch reports for the same group. if (!t) return {false, depth, "type mismatch: expected NestedLoopJoin"}; if (op.type != t->type) - return {false, depth, "NestedLoopJoin join type mismatch"}; + return {false, depth + 1, "NestedLoopJoin join type mismatch"}; if (op.qual != t->qual) - return {false, depth, + return {false, depth + 1, std::format("NestedLoopJoin qual '{}' != '{}'", ToString(op.qual), ToString(t->qual))}; auto lhs = MatchGroup(op.lhs.get(), *t->lhs, depth + 1); @@ -74,6 +77,21 @@ InternalMatch TryMatchExpr(utils::NotNull pe, if (!rhs.ok) { rhs.reason = "NestedLoopCrossJoin.rhs: " + rhs.reason; return rhs; } return {true, std::max(lhs.depth, rhs.depth), {}}; }, + [&](const physical::HashJoin& op) -> InternalMatch { + const auto* t = std::get_if(&target); + if (!t) return {false, depth, "type mismatch: expected HashJoin"}; + if (op.type != t->type) + return {false, depth + 1, "HashJoin join type mismatch"}; + if (op.qual != t->qual) + return {false, depth + 1, + std::format("HashJoin qual '{}' != '{}'", + ToString(op.qual), ToString(t->qual))}; + auto lhs = MatchGroup(op.lhs.get(), *t->lhs, depth + 1); + if (!lhs.ok) { lhs.reason = "HashJoin.lhs: " + lhs.reason; return lhs; } + auto rhs = MatchGroup(op.rhs.get(), *t->rhs, depth + 1); + if (!rhs.ok) { rhs.reason = "HashJoin.rhs: " + rhs.reason; return rhs; } + return {true, std::max(lhs.depth, rhs.depth), {}}; + }, [&](const physical::Sort& op) -> InternalMatch { const auto* t = std::get_if(&target); if (!t) return {false, depth, "type mismatch: expected Sort"}; diff --git a/src/stewkk/sql/logic/optimizer/rules.cpp b/src/stewkk/sql/logic/optimizer/rules.cpp index c0ace93..29606eb 100644 --- a/src/stewkk/sql/logic/optimizer/rules.cpp +++ b/src/stewkk/sql/logic/optimizer/rules.cpp @@ -2,7 +2,7 @@ namespace stewkk::sql { -Rules<2, 5> MakeMainRules() { +Rules<2, 6> MakeMainRules() { return { .transformation_rules = { std::make_unique(), @@ -14,6 +14,7 @@ Rules<2, 5> MakeMainRules() { std::make_unique(), std::make_unique(), std::make_unique(), + std::make_unique(), }, }; } diff --git a/src/stewkk/sql/logic/optimizer/rules_applier.cpp b/src/stewkk/sql/logic/optimizer/rules_applier.cpp index 469a96c..44f864f 100644 --- a/src/stewkk/sql/logic/optimizer/rules_applier.cpp +++ b/src/stewkk/sql/logic/optimizer/rules_applier.cpp @@ -31,6 +31,6 @@ utils::NotNull RulesApplier::Ap } template class RulesApplier<2, 0>; -template class RulesApplier<2, 5>; +template class RulesApplier<2, 6>; } // namespace stewkk::sql From 0ed62903918b32b8551c69d9e12a87ab7f3b8ae2 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Wed, 27 May 2026 03:26:44 +0300 Subject: [PATCH 051/120] Impl fuzzing, fix bugs --- cmake/FetchBoost.cmake | 2 +- .../stewkk/sql/models/parser/expression.hpp | 1 + research/fuzz/__init__.py | 0 research/fuzz/diff_fuzz.py | 158 +++++++++++ research/fuzz/mssql_runner.py | 130 +++++++++ src/stewkk/sql/CMakeLists.txt | 4 + src/stewkk/sql/logic/executor/executor.cpp | 150 ++++++++-- src/stewkk/sql/logic/executor/llvm.cpp | 28 +- .../sql/logic/executor/plan_serializer.cpp | 3 +- src/stewkk/sql/logic/parser/visitor.cpp | 148 ++++++---- .../join_commutativity.cpp | 5 + src/stewkk/sql/main.cpp | 265 +++++++++++++++++- src/stewkk/sql/models/parser/expression.cpp | 2 + 13 files changed, 807 insertions(+), 89 deletions(-) create mode 100644 research/fuzz/__init__.py create mode 100644 research/fuzz/diff_fuzz.py create mode 100644 research/fuzz/mssql_runner.py diff --git a/cmake/FetchBoost.cmake b/cmake/FetchBoost.cmake index 359c609..0d6274c 100644 --- a/cmake/FetchBoost.cmake +++ b/cmake/FetchBoost.cmake @@ -9,6 +9,6 @@ FetchContent_Declare( OVERRIDE_FIND_PACKAGE ) -set(BOOST_INCLUDE_LIBRARIES asio thread filesystem) +set(BOOST_INCLUDE_LIBRARIES asio thread filesystem scope) FetchContent_MakeAvailable(Boost) diff --git a/include/stewkk/sql/models/parser/expression.hpp b/include/stewkk/sql/models/parser/expression.hpp index 21006f9..e6e2763 100644 --- a/include/stewkk/sql/models/parser/expression.hpp +++ b/include/stewkk/sql/models/parser/expression.hpp @@ -49,6 +49,7 @@ std::string ToString(BinaryOp binop); enum class UnaryOp { kNot, kMinus, + kIsNull, }; std::string ToString(UnaryOp op); diff --git a/research/fuzz/__init__.py b/research/fuzz/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/research/fuzz/diff_fuzz.py b/research/fuzz/diff_fuzz.py new file mode 100644 index 0000000..622e2ed --- /dev/null +++ b/research/fuzz/diff_fuzz.py @@ -0,0 +1,158 @@ +""" +Differential fuzzer: generate a random SQL query, run it through our compiler +and MS SQL Server, stop on the first observable divergence. + +Compares result rows as multisets of tab-separated strings. Column names are +ignored (the two engines disagree on naming conventions), but column count +must match. + +Usage: + python -m research.fuzz.diff_fuzz \\ + --cli build/bin/sql \\ + --data-dir test/static/executor/test_data \\ + [--start-seed 0] +""" +from __future__ import annotations + +import argparse +import random +import subprocess +import sys +from itertools import count +from pathlib import Path + +from research.fuzz.mssql_runner import MsSqlRunner, RunResult +from research.query_generator import ( + DIALECTS, + QueryGenerator, + SelectQuery, + load_schema, + render_query, +) + + +def _value_to_canonical(v) -> str: + if v is None: + return "NULL" + if isinstance(v, bool): + return "1" if v else "0" + return str(v) + + +def _mssql_canonical_rows(res: RunResult) -> list[str]: + return ["\t".join(_value_to_canonical(v) for v in row) for row in res.rows] + + +def _ours_canonical_rows(stdout: str) -> tuple[int, list[str]]: + """Parse the CLI's output. Returns (column_count, row_strings).""" + lines = stdout.splitlines() + if not lines: + return 0, [] + header = lines[0] + col_count = len(header.split("\t")) + return col_count, lines[1:] + + +def _run_ours(cli: str, data_dir: str, query: str, jit: bool) -> subprocess.CompletedProcess: + cmd = [cli, "--data-dir", data_dir] + if jit: + cmd.append("--jit") + return subprocess.run( + cmd, + input=query, + capture_output=True, + text=True, + timeout=30, + ) + + +def _compare( + query: SelectQuery, + ours_proc: subprocess.CompletedProcess, + theirs: RunResult, +) -> str | None: + """Returns a diagnostic string on divergence, None on match.""" + if ours_proc.returncode != 0: + return f"our CLI exited {ours_proc.returncode}\nstderr: {ours_proc.stderr.strip()}" + if theirs.error is not None: + return f"MS SQL rejected the query: {theirs.error}" + + our_cols, our_rows = _ours_canonical_rows(ours_proc.stdout) + their_rows = _mssql_canonical_rows(theirs) + their_cols = len(theirs.columns) + + if our_cols != their_cols: + return f"column count: ours={our_cols} theirs={their_cols}" + + # Generator does not emit ORDER BY, so always compare as multisets. + if sorted(our_rows) != sorted(their_rows): + return "row contents differ" + + return None + + +def main() -> None: + ap = argparse.ArgumentParser(description=__doc__) + ap.add_argument("--cli", required=True, help="Path to our sql binary") + ap.add_argument("--data-dir", required=True, help="CSV table directory") + ap.add_argument("--start-seed", type=int, default=0) + ap.add_argument("--jit", action="store_true", help="Run our CLI with the JIT executor") + ap.add_argument("--mssql-host", default="localhost") + ap.add_argument("--mssql-port", type=int, default=1433) + ap.add_argument("--mssql-user", default="sa") + ap.add_argument("--mssql-password", default="Password123!") + ap.add_argument("--mssql-database", default="fuzz") + args = ap.parse_args() + + schema = load_schema(Path(args.data_dir)) + + mssql = MsSqlRunner( + host=args.mssql_host, + port=args.mssql_port, + user=args.mssql_user, + password=args.mssql_password, + database=args.mssql_database, + ) + print("Setting up MS SQL schema...", flush=True) + mssql.setup_schema(args.data_dir) + print("Schema ready. Starting fuzz loop.\n", flush=True) + + pg = DIALECTS["pg"] + ms = DIALECTS["mssql"] + + for seed in count(args.start_seed): + rng = random.Random(seed) + query = QueryGenerator(schema, rng).generate() + ours_sql = render_query(query, pg) + ";" + theirs_sql = render_query(query, ms) + ";" + + try: + ours_proc = _run_ours(args.cli, args.data_dir, ours_sql, args.jit) + except subprocess.TimeoutExpired: + print(f"DIVERGENCE seed={seed}: our CLI timed out\n--- query (ours):\n{ours_sql}") + sys.exit(1) + + theirs = mssql.run(theirs_sql) + + diag = _compare(query, ours_proc, theirs) + if diag is not None: + print(f"DIVERGENCE seed={seed}: {diag}") + print(f"\n--- query (ours):\n{ours_sql}") + print(f"\n--- query (mssql):\n{theirs_sql}") + print(f"\n--- ours stdout:\n{ours_proc.stdout}", end="") + if ours_proc.stderr: + print(f"\n--- ours stderr:\n{ours_proc.stderr}", end="") + print(f"\n--- mssql columns: {theirs.columns}") + print(f"--- mssql rows ({len(theirs.rows)}):") + for r in theirs.rows[:50]: + print(r) + if len(theirs.rows) > 50: + print(f"... ({len(theirs.rows) - 50} more)") + sys.exit(1) + + if seed % 25 == 0: + print(f"seed={seed} ok", flush=True) + + +if __name__ == "__main__": + main() diff --git a/research/fuzz/mssql_runner.py b/research/fuzz/mssql_runner.py new file mode 100644 index 0000000..2211da6 --- /dev/null +++ b/research/fuzz/mssql_runner.py @@ -0,0 +1,130 @@ +""" +Run queries against MS SQL Server for the differential fuzzer. + +Responsibilities: + - setup_schema(data_dir): drop+recreate the fuzzer schema, mirror every + *.csv in data_dir as a `dbo.
(... INT NULL)` table, and bulk-load + rows. + - run(query): execute one SELECT and return columns/rows or a structured + error. + +The connection is reused across calls so the fuzz loop pays one TCP/login cost +per session, not per query. +""" +from __future__ import annotations + +import csv +import re +from dataclasses import dataclass +from pathlib import Path + +import pymssql + + +_BENCH_RE = re.compile(r"_\d+$") + + +@dataclass +class RunResult: + columns: list[str] # column names in projection order + rows: list[tuple] # values as returned by the driver + error: str | None = None # set when execution failed; columns/rows empty + + +class MsSqlRunner: + def __init__( + self, + host: str = "localhost", + port: int = 1433, + user: str = "sa", + password: str = "Password123!", + database: str = "fuzz", + ): + self._host = host + self._port = port + self._user = user + self._password = password + self._database = database + self._conn = None # opened lazily after the database exists + + def _connect(self, database: str): + return pymssql.connect( + server=self._host, + port=str(self._port), + user=self._user, + password=self._password, + database=database, + autocommit=True, + ) + + def _ensure_database(self) -> None: + with self._connect("master") as conn: + cur = conn.cursor() + cur.execute("SELECT COUNT(*) FROM sys.databases WHERE name=%s", (self._database,)) + if cur.fetchone()[0] == 0: + cur.execute(f"CREATE DATABASE [{self._database}]") + + def setup_schema(self, data_dir: str | Path) -> None: + """ + Drop everything under dbo and recreate one INT-NULL table per *.csv + in data_dir (skipping the benchmark-only ``_`` siblings — they + share the base table's schema). + """ + data_dir = Path(data_dir) + self._ensure_database() + self._conn = self._connect(self._database) + cur = self._conn.cursor() + + # Drop everything we own so reruns are idempotent. + cur.execute(""" + DECLARE @sql NVARCHAR(MAX) = N''; + SELECT @sql += 'DROP TABLE [dbo].[' + t.name + ']; ' + FROM sys.tables t + JOIN sys.schemas s ON s.schema_id = t.schema_id + WHERE s.name = 'dbo'; + EXEC sp_executesql @sql; + """) + + for path in sorted(data_dir.glob("*.csv")): + if _BENCH_RE.search(path.stem): + continue + self._create_and_load(cur, path) + + def _create_and_load(self, cur, path: Path) -> None: + with path.open() as f: + reader = csv.reader(f) + header = next(reader) + cols = [] + for h in header: + name, type_ = (s.strip() for s in h.split(":")) + if type_ != "int": + raise ValueError(f"{path}: unsupported column type {type_!r}") + cols.append(name) + rows = [tuple(None if v.strip() == "NULL" else int(v) for v in r) for r in reader] + + col_defs = ", ".join(f"[{c}] INT NULL" for c in cols) + cur.execute(f"CREATE TABLE [dbo].[{path.stem}] ({col_defs})") + + if rows: + placeholders = ", ".join(["%s"] * len(cols)) + cur.executemany( + f"INSERT INTO [dbo].[{path.stem}] VALUES ({placeholders})", + rows, + ) + + def run(self, query: str) -> RunResult: + if self._conn is None: + self._conn = self._connect(self._database) + cur = self._conn.cursor() + try: + cur.execute(query) + rows = cur.fetchall() + columns = [d[0] for d in cur.description] if cur.description else [] + return RunResult(columns=columns, rows=rows) + except pymssql.Error as e: + return RunResult(columns=[], rows=[], error=str(e)) + + def close(self) -> None: + if self._conn is not None: + self._conn.close() + self._conn = None diff --git a/src/stewkk/sql/CMakeLists.txt b/src/stewkk/sql/CMakeLists.txt index e74be8b..7c1e315 100644 --- a/src/stewkk/sql/CMakeLists.txt +++ b/src/stewkk/sql/CMakeLists.txt @@ -81,6 +81,7 @@ target_link_libraries(libsql PRIVATE Boost::asio Boost::thread Boost::filesystem + Boost::scope ) add_executable(sql @@ -95,4 +96,7 @@ set_target_properties(sql PROPERTIES target_compile_options(sql PRIVATE ${BASE_COMPILE_FLAGS}) target_link_libraries(sql PRIVATE libsql + Boost::asio + Boost::thread + Boost::filesystem ) diff --git a/src/stewkk/sql/logic/executor/executor.cpp b/src/stewkk/sql/logic/executor/executor.cpp index acc02b8..168072e 100644 --- a/src/stewkk/sql/logic/executor/executor.cpp +++ b/src/stewkk/sql/logic/executor/executor.cpp @@ -9,6 +9,7 @@ #include #include #include +#include #include #include @@ -67,6 +68,9 @@ Type GetExpressionType(const Expression& expr, const AttributesInfo& available_a } return Type::kInt; } + if (unop.op == UnaryOp::kIsNull) { + return Type::kBool; + } if (child_type != Type::kBool) { throw std::logic_error{"types mismatch"}; } @@ -203,10 +207,20 @@ Value CalcExpression(const Tuple& source, const AttributesInfo& source_attrs, co return ApplyBooleanOperator>(std::move(lhs), std::move(rhs)); } return ApplyIntegersOperator, bool>(std::move(lhs), std::move(rhs)); - case BinaryOp::kOr: - return ApplyBooleanOperator>(std::move(lhs), std::move(rhs)); - case BinaryOp::kAnd: - return ApplyBooleanOperator>(std::move(lhs), std::move(rhs)); + case BinaryOp::kOr: { + bool lhs_true = !lhs.is_null && lhs.value.bool_value; + bool rhs_true = !rhs.is_null && rhs.value.bool_value; + if (lhs_true || rhs_true) return Value{false, true}; + if (lhs.is_null || rhs.is_null) return Value{true}; + return Value{false, false}; + } + case BinaryOp::kAnd: { + bool lhs_false = !lhs.is_null && !lhs.value.bool_value; + bool rhs_false = !rhs.is_null && !rhs.value.bool_value; + if (lhs_false || rhs_false) return Value{false, false}; + if (lhs.is_null || rhs.is_null) return Value{true}; + return Value{false, true}; + } case BinaryOp::kPlus: return ApplyIntegersOperator, int64_t>(std::move(lhs), std::move(rhs)); case BinaryOp::kMinus: @@ -238,6 +252,8 @@ Value CalcExpression(const Tuple& source, const AttributesInfo& source_attrs, co return Value{true}; } return Value{false, -child.value.int_value}; + case UnaryOp::kIsNull: + return Value{false, child.is_null}; } } Value operator()(const Attribute& expr) { @@ -394,20 +410,34 @@ boost::asio::awaitable> Executor::Execute(c auto [attr_chan, tuples_chan] = co_await GetChannels(); auto task = SpawnExecutor(exec, op, attr_chan, tuples_chan); - auto attrs = co_await attr_chan.async_receive(boost::asio::use_awaitable); - Log("Received attrs in root"); - + // If the child throws, its scope_fail closes both channels, which wakes + // our async_receive with channel_closed. We then await the task to surface + // the child's real exception instead of the channel_closed wrapper. + std::exception_ptr eptr; + AttributesInfo attrs; Tuples result; - for (;;) { - auto buf = co_await ReceiveTuples(tuples_chan); - if (buf.empty()) { - break; + try { + attrs = co_await attr_chan.async_receive(boost::asio::use_awaitable); + Log("Received attrs in root"); + for (;;) { + auto buf = co_await ReceiveTuples(tuples_chan); + if (buf.empty()) { + break; + } + Log("Received {} tuples in root", buf.size()); + std::move(buf.begin(), buf.end(), std::back_inserter(result)); } - Log("Received {} tuples in root", buf.size()); - std::move(buf.begin(), buf.end(), std::back_inserter(result)); + } catch (...) { + eptr = std::current_exception(); + } + try { + co_await task(boost::asio::use_awaitable); + } catch (...) { + eptr = std::current_exception(); + } + if (eptr) { + std::rethrow_exception(eptr); } - - co_await task(boost::asio::use_awaitable); Log("Total {} tuples in root", result.size()); co_return Ok(Relation{std::move(attrs), std::move(result)}); } @@ -451,6 +481,8 @@ boost::asio::awaitable Executor::Execute(const Physica } // FIXME: that's in-memory sort boost::asio::awaitable operator()(const PhysicalSort& sort) { + auto close_on_fail = boost::scope::make_scope_fail( + [this] { attr_chan.close(); tuples_chan.close(); }); auto exec = co_await boost::asio::this_coro::executor; auto [in_attrs_chan, in_tuples_chan] = co_await GetChannels(); auto task = executor.SpawnExecutor(exec, *sort.source, in_attrs_chan, in_tuples_chan); @@ -514,6 +546,8 @@ boost::asio::awaitable Executor::ExecuteProjection(con AttributesInfoChannel& out_attr_chan, TuplesChannel& out_tuples_chan) { Log("Executing projection"); + auto close_on_fail = boost::scope::make_scope_fail( + [&] { out_attr_chan.close(); out_tuples_chan.close(); }); auto exec = co_await boost::asio::this_coro::executor; auto [in_attrs_chan, in_tuples_chan] = co_await GetChannels(); auto task = SpawnExecutor(exec, *proj.source, in_attrs_chan, in_tuples_chan); @@ -551,6 +585,8 @@ boost::asio::awaitable Executor::ExecuteFilter(const P AttributesInfoChannel& out_attr_chan, TuplesChannel& out_tuples_chan) { Log("Executing filter"); + auto close_on_fail = boost::scope::make_scope_fail( + [&] { out_attr_chan.close(); out_tuples_chan.close(); }); auto exec = co_await boost::asio::this_coro::executor; auto [in_attrs_chan, in_tuples_chan] = co_await GetChannels(); auto task = SpawnExecutor(exec, *filter.source, in_attrs_chan, in_tuples_chan); @@ -605,6 +641,8 @@ boost::asio::awaitable Executor::ExecuteCrossJoin(cons AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan) { Log("Executing cross join"); + auto close_on_fail = boost::scope::make_scope_fail( + [&] { attr_chan.close(); tuples_chan.close(); }); auto exec = co_await boost::asio::this_coro::executor; auto [lhs_attrs_chan, lhs_tuples_chan] = co_await GetChannels(); auto lhs_task = SpawnExecutor(exec, *cross_join.lhs, lhs_attrs_chan, lhs_tuples_chan); @@ -659,9 +697,8 @@ boost::asio::awaitable Executor::ExecuteJoin(const Nes AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan) { Log("Executing join"); - if (join.type == JoinType::kFull) { - throw std::logic_error{"Full joins are not supported by executor"}; - } + auto close_on_fail = boost::scope::make_scope_fail( + [&] { attr_chan.close(); tuples_chan.close(); }); if (join.type == JoinType::kLeft) { std::swap(*join.lhs, *join.rhs); } @@ -680,6 +717,81 @@ boost::asio::awaitable Executor::ExecuteJoin(const Nes auto qual_executor = co_await expression_executor_.GetExpressionExecutor(join.qual, attrs); + if (join.type == JoinType::kFull) { + std::vector lhs_all; + { + auto full_reader = co_await MaterializeChannel(lhs_tuples_chan); + full_reader.Rewind(); + for (;;) { + auto buf = full_reader.Read(); + if (buf.empty()) break; + std::move(buf.begin(), buf.end(), std::back_inserter(lhs_all)); + } + } + std::vector lhs_matched(lhs_all.size(), false); + + for (;;) { + auto buf_rhs = co_await ReceiveTuples(rhs_tuples_chan); + if (buf_rhs.empty()) break; + Log("Received {} tuples in full join as rhs", buf_rhs.size()); + + for (const auto& tuple_rhs : buf_rhs) { + bool rhs_matched = false; + Tuples buf_res; + buf_res.reserve(kBufSize); + for (size_t i = 0; i < lhs_all.size(); ++i) { + auto joined = ConcatTuples(lhs_all[i], tuple_rhs); + auto qual_result = qual_executor(joined, attrs); + if (!qual_result.is_null && qual_result.value.bool_value) { + buf_res.push_back(std::move(joined)); + lhs_matched[i] = true; + rhs_matched = true; + if (buf_res.size() == kBufSize) { + co_await tuples_chan.async_send(boost::system::error_code{}, std::move(buf_res), + boost::asio::use_awaitable); + buf_res.clear(); + buf_res.reserve(kBufSize); + } + } + } + if (!rhs_matched) { + auto rhs_size = tuple_rhs.size(); + auto lhs_size = attrs.size() - rhs_size; + buf_res.push_back(ConcatTuples(Tuple(lhs_size, Value{true}), tuple_rhs)); + } + if (!buf_res.empty()) { + co_await tuples_chan.async_send(boost::system::error_code{}, std::move(buf_res), + boost::asio::use_awaitable); + } + } + } + + Tuples buf_unmatched; + buf_unmatched.reserve(kBufSize); + for (size_t i = 0; i < lhs_all.size(); ++i) { + if (!lhs_matched[i]) { + auto lhs_size = lhs_all[i].size(); + auto rhs_size = attrs.size() - lhs_size; + buf_unmatched.push_back(ConcatTuples(lhs_all[i], Tuple(rhs_size, Value{true}))); + if (buf_unmatched.size() == kBufSize) { + co_await tuples_chan.async_send(boost::system::error_code{}, std::move(buf_unmatched), + boost::asio::use_awaitable); + buf_unmatched.clear(); + buf_unmatched.reserve(kBufSize); + } + } + } + if (!buf_unmatched.empty()) { + co_await tuples_chan.async_send(boost::system::error_code{}, std::move(buf_unmatched), + boost::asio::use_awaitable); + } + + co_await lhs_task(boost::asio::use_awaitable); + co_await rhs_task(boost::asio::use_awaitable); + tuples_chan.close(); + co_return; + } + auto reader = co_await MaterializeChannel(lhs_tuples_chan); for (;;) { auto buf_rhs = co_await ReceiveTuples(rhs_tuples_chan); @@ -767,6 +879,8 @@ template boost::asio::awaitable Executor::ExecuteHashJoin( const HashJoin& join, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan) { Log("Executing hash join"); + auto close_on_fail = boost::scope::make_scope_fail( + [&] { attr_chan.close(); tuples_chan.close(); }); if (join.type != JoinType::kInner) { throw std::logic_error{"HashJoin executor supports only Inner joins"}; } diff --git a/src/stewkk/sql/logic/executor/llvm.cpp b/src/stewkk/sql/logic/executor/llvm.cpp index c807ff0..9783fd0 100644 --- a/src/stewkk/sql/logic/executor/llvm.cpp +++ b/src/stewkk/sql/logic/executor/llvm.cpp @@ -204,16 +204,23 @@ llvm::Function* JITCompiler::GenerateIR( { auto* trunc_lhs = builder.CreateTrunc(value_lhs, builder.getInt1Ty(), "i64_to_i1_trunc"); auto* trunc_rhs = builder.CreateTrunc(value_rhs, builder.getInt1Ty(), "i64_to_i1_trunc"); - res_value = builder.CreateLogicalOr(trunc_lhs, trunc_rhs); - res_value = builder.CreateZExt(res_value, builder.getInt64Ty(), "i1_to_i64_zext"); + auto* lhs_true = builder.CreateAnd(builder.CreateNot(is_null_lhs), trunc_lhs); + auto* rhs_true = builder.CreateAnd(builder.CreateNot(is_null_rhs), trunc_rhs); + auto* has_true = builder.CreateOr(lhs_true, rhs_true); + is_null = builder.CreateAnd(is_null, builder.CreateNot(has_true)); + res_value = builder.CreateZExt(has_true, builder.getInt64Ty(), "i1_to_i64_zext"); break; } case BinaryOp::kAnd: { auto* trunc_lhs = builder.CreateTrunc(value_lhs, builder.getInt1Ty(), "i64_to_i1_trunc"); auto* trunc_rhs = builder.CreateTrunc(value_rhs, builder.getInt1Ty(), "i64_to_i1_trunc"); - res_value = builder.CreateLogicalAnd(trunc_lhs, trunc_rhs); - res_value = builder.CreateZExt(res_value, builder.getInt64Ty(), "i1_to_i64_zext"); + auto* lhs_false = builder.CreateAnd(builder.CreateNot(is_null_lhs), builder.CreateNot(trunc_lhs)); + auto* rhs_false = builder.CreateAnd(builder.CreateNot(is_null_rhs), builder.CreateNot(trunc_rhs)); + auto* has_false = builder.CreateOr(lhs_false, rhs_false); + is_null = builder.CreateAnd(is_null, builder.CreateNot(has_false)); + auto* both_true = builder.CreateNot(has_false); + res_value = builder.CreateZExt(both_true, builder.getInt64Ty(), "i1_to_i64_zext"); break; } case BinaryOp::kPlus: @@ -267,9 +274,9 @@ llvm::Function* JITCompiler::GenerateIR( case UnaryOp::kMinus: { auto is_null = CheckNull(child); auto value = LoadValue(child); - + auto* negated = builder.CreateNeg(value); - + auto* select = builder.CreateSelect( is_null, llvm::ConstantStruct::get(static_cast(value_type), @@ -277,6 +284,15 @@ llvm::Function* JITCompiler::GenerateIR( negated); return select; } + case UnaryOp::kIsNull: { + auto* is_null = CheckNull(child); + auto* res_value + = builder.CreateZExt(is_null, builder.getInt64Ty(), "isnull_value"); + llvm::Value* result = llvm::UndefValue::get(value_type); + result = builder.CreateInsertValue(result, builder.getInt8(0), {0}); + result = builder.CreateInsertValue(result, res_value, {1}); + return result; + } } } llvm::Value* operator()(const IntConst& expr) { diff --git a/src/stewkk/sql/logic/executor/plan_serializer.cpp b/src/stewkk/sql/logic/executor/plan_serializer.cpp index 83e011c..5df35da 100644 --- a/src/stewkk/sql/logic/executor/plan_serializer.cpp +++ b/src/stewkk/sql/logic/executor/plan_serializer.cpp @@ -48,6 +48,7 @@ std::string SerializeUnaryOp(UnaryOp op) { switch (op) { case UnaryOp::kNot: return "not"; case UnaryOp::kMinus: return "uminus"; + case UnaryOp::kIsNull: return "isnull"; } } @@ -203,7 +204,7 @@ const std::unordered_map kBinaryOps = { }; const std::unordered_map kUnaryOps = { - {"not", UnaryOp::kNot}, {"uminus", UnaryOp::kMinus}, + {"not", UnaryOp::kNot}, {"uminus", UnaryOp::kMinus}, {"isnull", UnaryOp::kIsNull}, }; const std::unordered_map kJoinTypes = { diff --git a/src/stewkk/sql/logic/parser/visitor.cpp b/src/stewkk/sql/logic/parser/visitor.cpp index 78a787c..8a2c392 100644 --- a/src/stewkk/sql/logic/parser/visitor.cpp +++ b/src/stewkk/sql/logic/parser/visitor.cpp @@ -188,40 +188,27 @@ std::any Visitor::visitFrom_list(codegen::PostgreSQLParser::From_listContext *ct return result; } -std::any Visitor::visitTable_ref(codegen::PostgreSQLParser::Table_refContext *ctx) { +namespace { + +using TableRefCtx = codegen::PostgreSQLParser::Table_refContext; +using ChildIt = std::vector::const_iterator; + +Operator BuildTableRef(Visitor* v, TableRefCtx* ctx); + +// Returns the table_ref's atom (no joins) and an iterator pointing to its first +// post-atom child. The grammar's `(... join ...)*` tail is greedy on the rhs +// table_ref, so the caller is responsible for flattening that chain instead of +// visiting it whole. +std::pair ExtractAtom(Visitor* v, TableRefCtx* ctx) { if (ctx->xmltable()) { throw Error{ErrorType::kQueryNotSupported, "xmltable is not supported"}; } if (ctx->func_table()) { - // NOTE: may want to support throw Error{ErrorType::kQueryNotSupported, "func_table is not supported"}; } if (ctx->LATERAL_P()) { throw Error{ErrorType::kQueryNotSupported, "LATERAL clause is not supported"}; } - - auto children = ctx->children; - auto children_it = children.begin(); - - Operator res; - if (ctx->relation_expr()) { - auto table = std::any_cast(visit(ctx->relation_expr())); - children_it++; - res = Table{std::move(table)}; - } - if (ctx->select_with_parens()) { - res = std::any_cast(visit(ctx->select_with_parens())); - children_it++; - children_it++; - } - auto table_refs = ctx->table_ref(); - auto table_ref_it = table_refs.begin(); - if (ctx->OPEN_PAREN()) { - res = std::any_cast(visit(*table_ref_it)); - children_it++; - table_ref_it++; - } - if (ctx->alias_clause()) { throw Error{ErrorType::kQueryNotSupported, "alias_clause is not supported"}; } @@ -229,45 +216,74 @@ std::any Visitor::visitTable_ref(codegen::PostgreSQLParser::Table_refContext *ct throw Error{ErrorType::kQueryNotSupported, "tablesample_clause is not supported"}; } - for (; children_it != children.end(); children_it++) { - auto text = (*children_it)->getText(); + const auto& children = ctx->children; + auto it = children.cbegin(); + Operator res; + if (ctx->relation_expr()) { + res = Table{std::any_cast(v->visit(ctx->relation_expr()))}; + ++it; + } else if (ctx->select_with_parens()) { + res = std::any_cast(v->visit(ctx->select_with_parens())); + ++it; + } else if (ctx->OPEN_PAREN()) { + res = BuildTableRef(v, ctx->table_ref(0)); + it += 3; + } + return {std::move(res), it}; +} + +// Walks the join tail of `ctx` starting at `it`, descending into each rhs +// table_ref so that nested greedy joins become left-associative. +Operator ContinueChain(Visitor* v, Operator lhs, TableRefCtx* ctx, ChildIt it) { + const auto end = ctx->children.cend(); + while (it != end) { + const auto text = (*it)->getText(); if (text == "CROSS") { - // CROSS JOIN table_ref - children_it += 2; - auto rhs = std::any_cast(visit(*children_it)); - res = CrossJoin{ - std::make_shared(std::move(res)), - std::make_shared(std::move(rhs)), + it += 2; + auto* rhs_ctx = dynamic_cast(*it); + ++it; + auto [rhs_atom, rhs_post] = ExtractAtom(v, rhs_ctx); + lhs = CrossJoin{ + std::make_shared(std::move(lhs)), + std::make_shared(std::move(rhs_atom)), }; + lhs = ContinueChain(v, std::move(lhs), rhs_ctx, rhs_post); } else if (text == "NATURAL") { throw Error{ErrorType::kQueryNotSupported, "NATURAL clause is not supported"}; } else { - Operator rhs; - auto join_type = JoinType::kInner; - Expression qual_expression; + JoinType jt = JoinType::kInner; if (text == "JOIN") { - // JOIN table_ref join_qual - children_it++; - rhs = std::any_cast(visit(*children_it)); - children_it++; - qual_expression = std::any_cast(visit(*children_it)); + ++it; } else { - // join_type JOIN table_ref join_qual - join_type = std::any_cast(visit(*children_it)); - children_it += 2; - rhs = std::any_cast(visit(*children_it)); - children_it++; - qual_expression = std::any_cast(visit(*children_it)); + jt = std::any_cast(v->visit(*it)); + it += 2; } - res = Join{ - join_type, - std::move(qual_expression), - std::make_shared(std::move(res)), - std::make_shared(std::move(rhs)), + auto* rhs_ctx = dynamic_cast(*it); + ++it; + auto qual = std::any_cast(v->visit(*it)); + ++it; + auto [rhs_atom, rhs_post] = ExtractAtom(v, rhs_ctx); + lhs = Join{ + jt, + std::move(qual), + std::make_shared(std::move(lhs)), + std::make_shared(std::move(rhs_atom)), }; + lhs = ContinueChain(v, std::move(lhs), rhs_ctx, rhs_post); } } - return res; + return lhs; +} + +Operator BuildTableRef(Visitor* v, TableRefCtx* ctx) { + auto [atom, it] = ExtractAtom(v, ctx); + return ContinueChain(v, std::move(atom), ctx, it); +} + +} // namespace + +std::any Visitor::visitTable_ref(codegen::PostgreSQLParser::Table_refContext *ctx) { + return Operator{BuildTableRef(this, ctx)}; } std::any Visitor::visitJoin_type(codegen::PostgreSQLParser::Join_typeContext *ctx) { @@ -476,10 +492,10 @@ std::any Visitor::visitA_expr_unary_not(codegen::PostgreSQLParser::A_expr_unary_ std::any Visitor::visitA_expr_isnull(codegen::PostgreSQLParser::A_expr_isnullContext *ctx) { auto result = std::any_cast(visit(ctx->a_expr_is_not())); if (ctx->ISNULL()) { - result = BinaryExpression{std::make_shared(std::move(result)), BinaryOp::kEq, std::make_shared(Literal::kNull)}; + result = UnaryExpression{UnaryOp::kIsNull, std::make_shared(std::move(result))}; } if (ctx->NOTNULL()) { - result = BinaryExpression{std::make_shared(std::move(result)), BinaryOp::kEq, std::make_shared(Literal::kNull)}; + result = UnaryExpression{UnaryOp::kIsNull, std::make_shared(std::move(result))}; result = UnaryExpression{UnaryOp::kNot, std::make_shared(std::move(result))}; } return result; @@ -492,7 +508,7 @@ std::any Visitor::visitA_expr_is_not(codegen::PostgreSQLParser::A_expr_is_notCon } if (ctx->NULL_P()) { - result = BinaryExpression{std::make_shared(std::move(result)), BinaryOp::kEq, std::make_shared(Literal::kNull)}; + result = UnaryExpression{UnaryOp::kIsNull, std::make_shared(std::move(result))}; } else if (ctx->TRUE_P()) { result = BinaryExpression{std::make_shared(std::move(result)), BinaryOp::kEq, std::make_shared(Literal::kTrue)}; } else if (ctx->FALSE_P()) { @@ -526,11 +542,20 @@ std::any Visitor::visitA_expr_like(codegen::PostgreSQLParser::A_expr_likeContext std::any Visitor::visitA_expr_qual_op(codegen::PostgreSQLParser::A_expr_qual_opContext *ctx) { const auto& exprs = ctx->a_expr_unary_qualop(); - if (exprs.size() > 1) { - // NOTE: may want to implement - throw Error{ErrorType::kQueryNotSupported, "qual_op is not supported"}; + if (exprs.size() == 1) { + return visit(exprs.front()); } - return visit(exprs.front()); + // Postgres allows != as an alias for <>, which the lexer produces as Operator (qual_op). + if (exprs.size() == 2 && ctx->qual_op(0)->getText() == "!=") { + auto lhs = std::any_cast(visit(exprs[0])); + auto rhs = std::any_cast(visit(exprs[1])); + return Expression{BinaryExpression{ + std::make_shared(std::move(lhs)), + BinaryOp::kNotEq, + std::make_shared(std::move(rhs)), + }}; + } + throw Error{ErrorType::kQueryNotSupported, "qual_op is not supported"}; } std::any Visitor::visitA_expr_unary_qualop(codegen::PostgreSQLParser::A_expr_unary_qualopContext *ctx) { @@ -662,6 +687,9 @@ std::any Visitor::visitC_expr_expr(codegen::PostgreSQLParser::C_expr_exprContext if (ctx->aexprconst()) { return visit(ctx->aexprconst()); } + if (ctx->a_expr_in_parens != nullptr) { + return visit(ctx->a_expr_in_parens); + } // NOTE: may want to support throw Error{ErrorType::kQueryNotSupported, "c_expr is not fully supported"}; } diff --git a/src/stewkk/sql/logic/transformation_rules/join_commutativity.cpp b/src/stewkk/sql/logic/transformation_rules/join_commutativity.cpp index 18bff95..7776cb1 100644 --- a/src/stewkk/sql/logic/transformation_rules/join_commutativity.cpp +++ b/src/stewkk/sql/logic/transformation_rules/join_commutativity.cpp @@ -9,6 +9,11 @@ bool JoinCommutativity::IsApplicable(utils::NotNull expr) { LogicalOperator JoinCommutativity::ApplyImpl(utils::NotNull expr, Memo&) { auto join = std::get(expr->root_operator); std::swap(join.lhs, join.rhs); + if (join.type == JoinType::kLeft) { + join.type = JoinType::kRight; + } else if (join.type == JoinType::kRight) { + join.type = JoinType::kLeft; + } return join; } diff --git a/src/stewkk/sql/main.cpp b/src/stewkk/sql/main.cpp index 4d3a5ae..ab9299e 100644 --- a/src/stewkk/sql/main.cpp +++ b/src/stewkk/sql/main.cpp @@ -1,7 +1,266 @@ +#include +#include +#include +#include +#include #include +#include +#include +#include +#include +#include +#include -int main() { - std::cout << "Hello\n"; +#include - return 0; +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace stewkk::sql { + +namespace { + +constexpr int kOk = 0; +constexpr int kUsage = 64; +constexpr int kParseError = 1; +constexpr int kOptimizerError = 2; +constexpr int kRuntimeError = 3; + +struct Args { + std::string data_dir; + bool print_plan = false; + bool print_ast = false; + bool jit = false; +}; + +Args ParseArgs(int argc, char** argv) { + Args args; + for (int i = 1; i < argc; ++i) { + std::string a = argv[i]; + if (a == "--data-dir") { + if (i + 1 >= argc) { + std::cerr << "--data-dir requires an argument\n"; + std::exit(kUsage); + } + args.data_dir = argv[++i]; + } else if (a == "--print-plan") { + args.print_plan = true; + } else if (a == "--print-ast") { + args.print_ast = true; + } else if (a == "--jit") { + args.jit = true; + } else { + std::cerr << "unknown argument: " << a << "\n"; + std::exit(kUsage); + } + } + if (args.data_dir.empty()) { + std::cerr << "usage: sql --data-dir [--print-plan] [--jit] < query.sql\n"; + std::exit(kUsage); + } + return args; +} + +// Build a SchemaCatalog by reading the header of every
.csv in the +// directory. Mirrors the conventions used by the query generator and +// CsvDirSequentialScanner: header is "col:type,col:type,...", and files whose +// stem ends in _ are benchmark-only siblings of a base table and share +// its schema, so they are skipped. +SchemaCatalog LoadSchema(const std::string& dir) { + std::unordered_map tables; + static const std::regex kBench{R"(_\d+$)"}; + for (const auto& entry : std::filesystem::directory_iterator{dir}) { + if (entry.path().extension() != ".csv") continue; + auto stem = entry.path().stem().string(); + if (std::regex_search(stem, kBench)) continue; + + std::ifstream in{entry.path()}; + std::string header; + if (!std::getline(in, header)) continue; + + Schema schema; + for (const auto& part : header | std::views::split(',')) { + std::string token{part.begin(), part.end()}; + auto colon = token.find(':'); + if (colon == std::string::npos) continue; + schema.push_back(Attribute{stem, token.substr(0, colon)}); + } + tables.emplace(std::move(stem), std::move(schema)); + } + return SchemaCatalog{std::move(tables)}; +} + +std::string SerializeAst(const Operator& op) { + return std::visit([](auto&& node) -> std::string { + using T = std::decay_t; + if constexpr (std::is_same_v) { + return "(Table " + node.name + ")"; + } else if constexpr (std::is_same_v) { + return "(Filter " + ToString(node.expr) + " " + SerializeAst(*node.source) + ")"; + } else if constexpr (std::is_same_v) { + std::string exprs; + for (const auto& e : node.expressions) exprs += " " + ToString(e); + return "(Projection" + exprs + " " + SerializeAst(*node.source) + ")"; + } else if constexpr (std::is_same_v) { + return "(CrossJoin " + SerializeAst(*node.lhs) + " " + SerializeAst(*node.rhs) + ")"; + } else if constexpr (std::is_same_v) { + return "(Join " + ToString(node.type) + " " + ToString(node.qual) + + " " + SerializeAst(*node.lhs) + " " + SerializeAst(*node.rhs) + ")"; + } + return "?"; + }, op); +} + +std::string ValueToString(const Value& v, const AttributeInfo& attr) { + if (v.is_null) return "NULL"; + switch (attr.type) { + case Type::kInt: + return std::to_string(v.value.int_value); + case Type::kBool: + return v.value.bool_value ? "1" : "0"; + } + return "?"; +} + +std::string TypeName(Type t) { + switch (t) { + case Type::kInt: + return "int"; + case Type::kBool: + return "bool"; + } + return "?"; +} + +// Canonical text format the fuzzer compares against MS SQL Server output. +// First line: tab-separated "table.col:type" header (or just ":type" when the +// attribute has no name, e.g. an expression in the projection). +// Following lines: one row each, tab-separated values, "NULL" for nulls. +// Rows are sorted lexicographically unless the query had ORDER BY. +void PrintRelation(const Relation& rel, bool preserve_order) { + std::ostringstream header; + for (size_t i = 0; i < rel.attributes.size(); ++i) { + const auto& a = rel.attributes[i]; + if (i) header << '\t'; + if (!a.table.empty() || !a.name.empty()) { + header << a.table << '.' << a.name; + } + header << ':' << TypeName(a.type); + } + + std::vector rows; + rows.reserve(rel.tuples.size()); + for (const auto& tuple : rel.tuples) { + std::ostringstream line; + for (size_t i = 0; i < tuple.size(); ++i) { + if (i) line << '\t'; + line << ValueToString(tuple[i], rel.attributes[i]); + } + rows.push_back(std::move(line).str()); + } + if (!preserve_order) std::sort(rows.begin(), rows.end()); + + std::cout << header.str() << '\n'; + for (const auto& r : rows) std::cout << r << '\n'; +} + +// Coroutine declared as a free function (not a captureless lambda) so the +// awaitable holds its parameters by reference into main's stack, sidestepping +// the lifetime trap of an IIFE'd `[&]` lambda whose closure dies before the +// coroutine frame. +boost::asio::awaitable> RunQuery(const std::string& data_dir, + const PhysicalPlanNode& plan) { + CsvDirSequentialScanner seq_scan{data_dir}; + Executor executor(std::move(seq_scan), co_await boost::asio::this_coro::executor); + co_return co_await executor.Execute(plan); +} + +boost::asio::awaitable> RunQueryJit(const std::string& data_dir, + const PhysicalPlanNode& plan) { + CsvDirSequentialScanner seq_scan{data_dir}; + Executor executor( + std::move(seq_scan), co_await boost::asio::this_coro::executor); + co_return co_await executor.Execute(plan); +} + +} // namespace + +} // namespace stewkk::sql + +int main(int argc, char** argv) { + using namespace stewkk::sql; + + auto args = ParseArgs(argc, argv); + + std::ostringstream sql_buf; + sql_buf << std::cin.rdbuf(); + std::stringstream sql_stream{sql_buf.str()}; + + auto parsed_result = GetAST(sql_stream); + if (!parsed_result.has_value()) { + std::cerr << "parse error: " << What(parsed_result.error()) << "\n"; + return kParseError; + } + auto parsed = std::move(parsed_result).value(); + + if (args.print_ast) { + std::cerr << SerializeAst(parsed.op) << "\n"; + } + + PhysicalPlanNode plan; + try { + PropertySet required = parsed.required_order + ? PropertySet{SortProperty{*parsed.required_order}} + : PropertySet::Any(); + Optimizer optimizer(parsed.op, MakeMainRules(), {}, LoadSchema(args.data_dir), + std::move(required)); + plan = optimizer.Optimize(); + } catch (const std::exception& e) { + std::cerr << "optimizer error: " << e.what() << "\n"; + return kOptimizerError; + } + + if (args.print_plan) { + std::cerr << Serialize(plan) << "\n"; + } + + Result result; + try { + boost::asio::io_context ctx; + boost::asio::any_io_executor exec = ctx.get_executor(); + auto fut = args.jit + ? boost::asio::co_spawn(exec, RunQueryJit(args.data_dir, plan), + boost::asio::use_future) + : boost::asio::co_spawn(exec, RunQuery(args.data_dir, plan), + boost::asio::use_future); + ctx.run(); + result = fut.get(); + } catch (const std::exception& e) { + std::cerr << "runtime error: " << e.what() << "\n"; + return kRuntimeError; + } + + if (!result.has_value()) { + std::cerr << "runtime error: " << What(result.error()) << "\n"; + return kRuntimeError; + } + + PrintRelation(result.value(), parsed.required_order.has_value()); + return kOk; } diff --git a/src/stewkk/sql/models/parser/expression.cpp b/src/stewkk/sql/models/parser/expression.cpp index 090ccb7..cb37c9b 100644 --- a/src/stewkk/sql/models/parser/expression.cpp +++ b/src/stewkk/sql/models/parser/expression.cpp @@ -68,6 +68,8 @@ std::string ToString(UnaryOp op) { return "not"; case UnaryOp::kMinus: return "-"; + case UnaryOp::kIsNull: + return "isnull"; } } From 0ead6f91a9ee71dc9395a1ecb491c18d45d8a149 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Wed, 27 May 2026 03:44:21 +0300 Subject: [PATCH 052/120] Add some rules --- include/stewkk/sql/logic/optimizer/rules.hpp | 6 +- .../transformation_rules/filter_merge.hpp | 17 +++ .../filter_pushdown_through_join.hpp | 19 +++ .../filter_pushdown_through_projection.hpp | 19 +++ .../transformation_rules/filter_split.hpp | 17 +++ .../transformation_rules/predicate_utils.hpp | 25 ++++ src/stewkk/sql/CMakeLists.txt | 5 + src/stewkk/sql/logic/optimizer/optimizer.cpp | 2 +- src/stewkk/sql/logic/optimizer/rules.cpp | 6 +- .../sql/logic/optimizer/rules_applier.cpp | 2 +- .../transformation_rules/filter_merge.cpp | 44 +++++++ .../filter_pushdown_through_join.cpp | 121 ++++++++++++++++++ .../filter_pushdown_through_projection.cpp | 71 ++++++++++ .../transformation_rules/filter_split.cpp | 43 +++++++ .../join_associativity.cpp | 79 +----------- .../transformation_rules/predicate_utils.cpp | 89 +++++++++++++ 16 files changed, 484 insertions(+), 81 deletions(-) create mode 100644 include/stewkk/sql/logic/transformation_rules/filter_merge.hpp create mode 100644 include/stewkk/sql/logic/transformation_rules/filter_pushdown_through_join.hpp create mode 100644 include/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.hpp create mode 100644 include/stewkk/sql/logic/transformation_rules/filter_split.hpp create mode 100644 include/stewkk/sql/logic/transformation_rules/predicate_utils.hpp create mode 100644 src/stewkk/sql/logic/transformation_rules/filter_merge.cpp create mode 100644 src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_join.cpp create mode 100644 src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.cpp create mode 100644 src/stewkk/sql/logic/transformation_rules/filter_split.cpp create mode 100644 src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp diff --git a/include/stewkk/sql/logic/optimizer/rules.hpp b/include/stewkk/sql/logic/optimizer/rules.hpp index f7ecb12..d655cc7 100644 --- a/include/stewkk/sql/logic/optimizer/rules.hpp +++ b/include/stewkk/sql/logic/optimizer/rules.hpp @@ -6,6 +6,10 @@ #include #include #include +#include +#include +#include +#include #include #include #include @@ -21,6 +25,6 @@ struct Rules { std::array, NImplementation> implementation_rules; }; -Rules<2, 6> MakeMainRules(); +Rules<6, 6> MakeMainRules(); } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/transformation_rules/filter_merge.hpp b/include/stewkk/sql/logic/transformation_rules/filter_merge.hpp new file mode 100644 index 0000000..b45801c --- /dev/null +++ b/include/stewkk/sql/logic/transformation_rules/filter_merge.hpp @@ -0,0 +1,17 @@ +#pragma once + +#include +#include + +namespace stewkk::sql { + +// Filter(p_outer, Filter(p_inner, X)) → Filter(p_outer AND p_inner, X) +// Collapses adjacent filters so the merged predicate can be pushed down or +// matched against join quals as a single conjunction. +class FilterMerge : public TransformationRule { + public: + bool IsApplicable(utils::NotNull expr) override; + LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo) override; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/transformation_rules/filter_pushdown_through_join.hpp b/include/stewkk/sql/logic/transformation_rules/filter_pushdown_through_join.hpp new file mode 100644 index 0000000..b659e71 --- /dev/null +++ b/include/stewkk/sql/logic/transformation_rules/filter_pushdown_through_join.hpp @@ -0,0 +1,19 @@ +#pragma once + +#include +#include + +namespace stewkk::sql { + +// Filter(p, Join(L, R, T, q)) → [Filter(rest,)] Join(maybeFilter(p_L,L), maybeFilter(p_R,R), T, q) +// Conjuncts of p whose attributes lie entirely within one side are pushed +// into that side; conjuncts spanning both sides remain above the join. +// Outer-join safety: a conjunct may only be pushed into a *preserved* side — +// LEFT preserves lhs, RIGHT preserves rhs, INNER preserves both, FULL neither. +class FilterPushdownThroughJoin : public TransformationRule { + public: + bool IsApplicable(utils::NotNull expr) override; + LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo) override; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.hpp b/include/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.hpp new file mode 100644 index 0000000..8217370 --- /dev/null +++ b/include/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.hpp @@ -0,0 +1,19 @@ +#pragma once + +#include +#include + +namespace stewkk::sql { + +// Filter(p, Projection(exprs, X)) → Projection(exprs, Filter(p, X)) +// Safe only when every Attribute referenced by p appears as a bare Attribute +// in exprs (i.e. is passed through unchanged). Computed projections cannot be +// referenced by name in this AST, so the conservative bare-attribute check +// also matches the wider correctness condition. +class FilterPushdownThroughProjection : public TransformationRule { + public: + bool IsApplicable(utils::NotNull expr) override; + LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo) override; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/transformation_rules/filter_split.hpp b/include/stewkk/sql/logic/transformation_rules/filter_split.hpp new file mode 100644 index 0000000..51bc362 --- /dev/null +++ b/include/stewkk/sql/logic/transformation_rules/filter_split.hpp @@ -0,0 +1,17 @@ +#pragma once + +#include +#include + +namespace stewkk::sql { + +// Filter(p1 AND p2 AND ... AND pn, X) → Filter(p1, Filter(p2 AND ... AND pn, X)) +// Splits the first conjunct off; remaining conjuncts cascade-split when the +// rule fires on the inner Filter. +class FilterSplit : public TransformationRule { + public: + bool IsApplicable(utils::NotNull expr) override; + LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo) override; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/transformation_rules/predicate_utils.hpp b/include/stewkk/sql/logic/transformation_rules/predicate_utils.hpp new file mode 100644 index 0000000..a36f9ea --- /dev/null +++ b/include/stewkk/sql/logic/transformation_rules/predicate_utils.hpp @@ -0,0 +1,25 @@ +#pragma once + +#include +#include +#include + +#include +#include +#include + +namespace stewkk::sql { + +bool IsTrue(const Expression& e); + +void CollectConjuncts(const Expression& e, std::vector& out); + +Expression AndConjuncts(const std::vector& conjs); + +void CollectAttrTables(const Expression& e, std::unordered_set& out); + +std::unordered_set ExprTables(const Expression& e); + +std::unordered_set GroupTables(utils::NotNull g); + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/CMakeLists.txt b/src/stewkk/sql/CMakeLists.txt index 7c1e315..fea3108 100644 --- a/src/stewkk/sql/CMakeLists.txt +++ b/src/stewkk/sql/CMakeLists.txt @@ -38,8 +38,13 @@ add_library(libsql logic/optimizer/rules.cpp logic/optimizer/rules_applier.cpp logic/optimizer/rule.cpp + logic/transformation_rules/predicate_utils.cpp logic/transformation_rules/join_commutativity.cpp logic/transformation_rules/join_associativity.cpp + logic/transformation_rules/filter_split.cpp + logic/transformation_rules/filter_merge.cpp + logic/transformation_rules/filter_pushdown_through_projection.cpp + logic/transformation_rules/filter_pushdown_through_join.cpp logic/implementation_rules/implement_table.cpp logic/implementation_rules/implement_filter.cpp logic/implementation_rules/implement_projection.cpp diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index 380257b..ad7ac30 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -483,6 +483,6 @@ utils::NotNull Optimizer::GetRootGroup return root_->group; } -template class Optimizer<2, 6>; +template class Optimizer<6, 6>; } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/rules.cpp b/src/stewkk/sql/logic/optimizer/rules.cpp index 29606eb..3b4eed0 100644 --- a/src/stewkk/sql/logic/optimizer/rules.cpp +++ b/src/stewkk/sql/logic/optimizer/rules.cpp @@ -2,11 +2,15 @@ namespace stewkk::sql { -Rules<2, 6> MakeMainRules() { +Rules<6, 6> MakeMainRules() { return { .transformation_rules = { std::make_unique(), std::make_unique(), + std::make_unique(), + std::make_unique(), + std::make_unique(), + std::make_unique(), }, .implementation_rules = { std::make_unique(), diff --git a/src/stewkk/sql/logic/optimizer/rules_applier.cpp b/src/stewkk/sql/logic/optimizer/rules_applier.cpp index 44f864f..1ade608 100644 --- a/src/stewkk/sql/logic/optimizer/rules_applier.cpp +++ b/src/stewkk/sql/logic/optimizer/rules_applier.cpp @@ -31,6 +31,6 @@ utils::NotNull RulesApplier::Ap } template class RulesApplier<2, 0>; -template class RulesApplier<2, 6>; +template class RulesApplier<6, 6>; } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/transformation_rules/filter_merge.cpp b/src/stewkk/sql/logic/transformation_rules/filter_merge.cpp new file mode 100644 index 0000000..4411a3e --- /dev/null +++ b/src/stewkk/sql/logic/transformation_rules/filter_merge.cpp @@ -0,0 +1,44 @@ +#include + +#include +#include + +#include + +namespace stewkk::sql { + +namespace { + +const logical::Filter* FindInnerFilter(utils::NotNull source) { + for (auto inner_expr : source->GetLogicalExprs()) { + if (const auto* f = std::get_if(&inner_expr->root_operator)) { + return f; + } + } + return nullptr; +} + +} // namespace + +bool FilterMerge::IsApplicable(utils::NotNull expr) { + if (!std::holds_alternative(expr->root_operator)) return false; + const auto& outer = std::get(expr->root_operator); + return FindInnerFilter(outer.source) != nullptr; +} + +LogicalOperator FilterMerge::ApplyImpl(utils::NotNull expr, Memo&) { + const auto& outer = std::get(expr->root_operator); + const auto* inner = FindInnerFilter(outer.source); + if (inner == nullptr) { + throw std::runtime_error{"FilterMerge requires a nested filter"}; + } + + Expression merged = BinaryExpression{ + std::make_shared(outer.predicate), + BinaryOp::kAnd, + std::make_shared(inner->predicate), + }; + return logical::Filter{inner->source, std::move(merged)}; +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_join.cpp b/src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_join.cpp new file mode 100644 index 0000000..23fcb6b --- /dev/null +++ b/src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_join.cpp @@ -0,0 +1,121 @@ +#include + +#include +#include +#include +#include +#include + +#include + +namespace stewkk::sql { + +namespace { + +struct Sides { + bool push_left; + bool push_right; +}; + +Sides PushableSides(JoinType type) { + switch (type) { + case JoinType::kInner: return {true, true}; + case JoinType::kLeft: return {true, false}; + case JoinType::kRight: return {false, true}; + case JoinType::kFull: return {false, false}; + } + return {false, false}; +} + +struct Partition { + std::vector left; + std::vector right; + std::vector rest; +}; + +Partition PartitionConjuncts(const std::vector& conjs, + const std::unordered_set& l_tables, + const std::unordered_set& r_tables, + Sides sides) { + Partition out; + for (const auto& q : conjs) { + auto q_tables = ExprTables(q); + bool fits_left = !q_tables.empty() && std::all_of(q_tables.begin(), q_tables.end(), + [&](const auto& t) { return l_tables.contains(t); }); + bool fits_right = !q_tables.empty() && std::all_of(q_tables.begin(), q_tables.end(), + [&](const auto& t) { return r_tables.contains(t); }); + if (fits_left && sides.push_left) { + out.left.push_back(q); + } else if (fits_right && sides.push_right) { + out.right.push_back(q); + } else { + out.rest.push_back(q); + } + } + return out; +} + +const logical::Join* FindPushableJoin(utils::NotNull source, + const Expression& predicate) { + std::vector conjs; + CollectConjuncts(predicate, conjs); + if (conjs.empty()) return nullptr; + + for (auto inner_expr : source->GetLogicalExprs()) { + const auto* j = std::get_if(&inner_expr->root_operator); + if (j == nullptr) continue; + auto sides = PushableSides(j->type); + if (!sides.push_left && !sides.push_right) continue; + + auto l_tables = GroupTables(j->lhs); + auto r_tables = GroupTables(j->rhs); + auto parts = PartitionConjuncts(conjs, l_tables, r_tables, sides); + if (!parts.left.empty() || !parts.right.empty()) { + return j; + } + } + return nullptr; +} + +} // namespace + +bool FilterPushdownThroughJoin::IsApplicable(utils::NotNull expr) { + if (!std::holds_alternative(expr->root_operator)) return false; + const auto& f = std::get(expr->root_operator); + return FindPushableJoin(f.source, f.predicate) != nullptr; +} + +LogicalOperator FilterPushdownThroughJoin::ApplyImpl(utils::NotNull expr, + Memo& memo) { + const auto& f = std::get(expr->root_operator); + const auto* join = FindPushableJoin(f.source, f.predicate); + if (join == nullptr) { + throw std::runtime_error{"FilterPushdownThroughJoin requires a pushable join below"}; + } + + std::vector conjs; + CollectConjuncts(f.predicate, conjs); + auto sides = PushableSides(join->type); + auto l_tables = GroupTables(join->lhs); + auto r_tables = GroupTables(join->rhs); + auto parts = PartitionConjuncts(conjs, l_tables, r_tables, sides); + + utils::NotNull new_lhs = join->lhs; + if (!parts.left.empty()) { + new_lhs = memo.AddGroup(logical::Filter{join->lhs, AndConjuncts(parts.left)})->group; + } + utils::NotNull new_rhs = join->rhs; + if (!parts.right.empty()) { + new_rhs = memo.AddGroup(logical::Filter{join->rhs, AndConjuncts(parts.right)})->group; + } + + logical::Join new_join{new_lhs, new_rhs, join->type, join->qual}; + + if (parts.rest.empty()) { + return new_join; + } + auto new_join_group = memo.AddGroup(new_join)->group; + return logical::Filter{new_join_group, AndConjuncts(parts.rest)}; +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.cpp b/src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.cpp new file mode 100644 index 0000000..0a0a5d9 --- /dev/null +++ b/src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.cpp @@ -0,0 +1,71 @@ +#include + +#include +#include +#include + +#include +#include + +namespace stewkk::sql { + +namespace { + +void CollectAttributes(const Expression& e, std::vector& out) { + std::visit(utils::Overloaded{ + [&](const Attribute& a) { out.push_back(a); }, + [&](const BinaryExpression& b) { + CollectAttributes(*b.lhs, out); + CollectAttributes(*b.rhs, out); + }, + [&](const UnaryExpression& u) { CollectAttributes(*u.child, out); }, + [&](const IntConst&) {}, + [&](const Literal&) {}, + }, e); +} + +bool ProjectionPassesThrough(const std::vector& proj_exprs, + const std::vector& needed) { + return std::all_of(needed.begin(), needed.end(), [&](const Attribute& a) { + return std::any_of(proj_exprs.begin(), proj_exprs.end(), [&](const Expression& e) { + const auto* pa = std::get_if(&e); + return pa != nullptr && *pa == a; + }); + }); +} + +const logical::Projection* FindPushableProjection(utils::NotNull source, + const Expression& predicate) { + std::vector needed; + CollectAttributes(predicate, needed); + for (auto inner_expr : source->GetLogicalExprs()) { + if (const auto* p = std::get_if(&inner_expr->root_operator)) { + if (ProjectionPassesThrough(p->expressions, needed)) { + return p; + } + } + } + return nullptr; +} + +} // namespace + +bool FilterPushdownThroughProjection::IsApplicable(utils::NotNull expr) { + if (!std::holds_alternative(expr->root_operator)) return false; + const auto& f = std::get(expr->root_operator); + return FindPushableProjection(f.source, f.predicate) != nullptr; +} + +LogicalOperator FilterPushdownThroughProjection::ApplyImpl(utils::NotNull expr, + Memo& memo) { + const auto& f = std::get(expr->root_operator); + const auto* proj = FindPushableProjection(f.source, f.predicate); + if (proj == nullptr) { + throw std::runtime_error{"FilterPushdownThroughProjection requires a passthrough projection"}; + } + + auto new_filter_group = memo.AddGroup(logical::Filter{proj->source, f.predicate})->group; + return logical::Projection{new_filter_group, proj->expressions}; +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/transformation_rules/filter_split.cpp b/src/stewkk/sql/logic/transformation_rules/filter_split.cpp new file mode 100644 index 0000000..e61a54c --- /dev/null +++ b/src/stewkk/sql/logic/transformation_rules/filter_split.cpp @@ -0,0 +1,43 @@ +#include + +#include +#include + +#include + +namespace stewkk::sql { + +namespace { + +size_t ConjunctCount(const Expression& e) { + std::vector conjs; + CollectConjuncts(e, conjs); + return conjs.size(); +} + +} // namespace + +bool FilterSplit::IsApplicable(utils::NotNull expr) { + if (!std::holds_alternative(expr->root_operator)) return false; + const auto& f = std::get(expr->root_operator); + return ConjunctCount(f.predicate) >= 2; +} + +LogicalOperator FilterSplit::ApplyImpl(utils::NotNull expr, Memo& memo) { + const auto& f = std::get(expr->root_operator); + + std::vector conjs; + CollectConjuncts(f.predicate, conjs); + if (conjs.size() < 2) { + throw std::runtime_error{"FilterSplit requires a conjunction"}; + } + + Expression head = conjs.front(); + std::vector rest(conjs.begin() + 1, conjs.end()); + Expression rest_pred = AndConjuncts(rest); + + auto inner_group = memo.AddGroup(logical::Filter{f.source, std::move(rest_pred)})->group; + return logical::Filter{inner_group, std::move(head)}; +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp b/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp index d021c08..ac5de43 100644 --- a/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp +++ b/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp @@ -1,87 +1,13 @@ #include #include -#include #include #include -#include +#include namespace stewkk::sql { -namespace { - -bool IsTrue(const Expression& e) { - return std::holds_alternative(e) && std::get(e) == Literal::kTrue; -} - -void CollectConjuncts(const Expression& e, std::vector& out) { - if (IsTrue(e)) return; - if (const auto* b = std::get_if(&e); b && b->binop == BinaryOp::kAnd) { - CollectConjuncts(*b->lhs, out); - CollectConjuncts(*b->rhs, out); - return; - } - out.push_back(e); -} - -Expression AndConjuncts(const std::vector& conjs) { - if (conjs.empty()) return Literal::kTrue; - Expression acc = conjs[0]; - for (size_t i = 1; i < conjs.size(); i++) { - acc = BinaryExpression{ - std::make_shared(std::move(acc)), - BinaryOp::kAnd, - std::make_shared(conjs[i]), - }; - } - return acc; -} - -void CollectAttrTables(const Expression& e, std::unordered_set& out) { - std::visit(utils::Overloaded{ - [&](const Attribute& a) { out.insert(a.table); }, - [&](const BinaryExpression& b) { - CollectAttrTables(*b.lhs, out); - CollectAttrTables(*b.rhs, out); - }, - [&](const UnaryExpression& u) { CollectAttrTables(*u.child, out); }, - [&](const IntConst&) {}, - [&](const Literal&) {}, - }, e); -} - -// Equivalent logical exprs in a memo group all expose the same output tables, -// so one expression suffices. Recurses across child groups via their fronts. -void CollectGroupTables(utils::NotNull g, std::unordered_set& out, - std::unordered_set& seen) { - if (!seen.insert(g.get()).second) return; - auto exprs = g->GetLogicalExprs(); - if (exprs.empty()) return; - std::visit(utils::Overloaded{ - [&](const logical::Table& t) { out.insert(t.name); }, - [&](const logical::Filter& f) { CollectGroupTables(f.source, out, seen); }, - [&](const logical::Projection& p) { CollectGroupTables(p.source, out, seen); }, - [&](const logical::CrossJoin& j) { - CollectGroupTables(j.lhs, out, seen); - CollectGroupTables(j.rhs, out, seen); - }, - [&](const logical::Join& j) { - CollectGroupTables(j.lhs, out, seen); - CollectGroupTables(j.rhs, out, seen); - }, - }, (*exprs.begin())->root_operator); -} - -std::unordered_set GroupTables(utils::NotNull g) { - std::unordered_set out; - std::unordered_set seen; - CollectGroupTables(g, out, seen); - return out; -} - -} // namespace - bool JoinAssociativity::IsApplicable(utils::NotNull expr) { if (!std::holds_alternative(expr->root_operator)) return false; const auto& outer = std::get(expr->root_operator); @@ -112,8 +38,7 @@ LogicalOperator JoinAssociativity::ApplyImpl(utils::NotNull expr, std::vector inner_quals; std::vector outer_quals; for (auto& q : conjs) { - std::unordered_set q_tables; - CollectAttrTables(q, q_tables); + auto q_tables = ExprTables(q); bool fits_inner = std::all_of(q_tables.begin(), q_tables.end(), [&](const auto& t) { return bc_tables.contains(t); }); (fits_inner ? inner_quals : outer_quals).push_back(std::move(q)); diff --git a/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp b/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp new file mode 100644 index 0000000..4c73ee6 --- /dev/null +++ b/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp @@ -0,0 +1,89 @@ +#include + +#include +#include + +#include + +namespace stewkk::sql { + +bool IsTrue(const Expression& e) { + return std::holds_alternative(e) && std::get(e) == Literal::kTrue; +} + +void CollectConjuncts(const Expression& e, std::vector& out) { + if (IsTrue(e)) return; + if (const auto* b = std::get_if(&e); b && b->binop == BinaryOp::kAnd) { + CollectConjuncts(*b->lhs, out); + CollectConjuncts(*b->rhs, out); + return; + } + out.push_back(e); +} + +Expression AndConjuncts(const std::vector& conjs) { + if (conjs.empty()) return Literal::kTrue; + Expression acc = conjs[0]; + for (size_t i = 1; i < conjs.size(); i++) { + acc = BinaryExpression{ + std::make_shared(std::move(acc)), + BinaryOp::kAnd, + std::make_shared(conjs[i]), + }; + } + return acc; +} + +void CollectAttrTables(const Expression& e, std::unordered_set& out) { + std::visit(utils::Overloaded{ + [&](const Attribute& a) { out.insert(a.table); }, + [&](const BinaryExpression& b) { + CollectAttrTables(*b.lhs, out); + CollectAttrTables(*b.rhs, out); + }, + [&](const UnaryExpression& u) { CollectAttrTables(*u.child, out); }, + [&](const IntConst&) {}, + [&](const Literal&) {}, + }, e); +} + +std::unordered_set ExprTables(const Expression& e) { + std::unordered_set out; + CollectAttrTables(e, out); + return out; +} + +namespace { + +// Equivalent logical exprs in a memo group all expose the same output tables, +// so one expression suffices. Recurses across child groups via their fronts. +void CollectGroupTables(utils::NotNull g, std::unordered_set& out, + std::unordered_set& seen) { + if (!seen.insert(g.get()).second) return; + auto exprs = g->GetLogicalExprs(); + if (exprs.empty()) return; + std::visit(utils::Overloaded{ + [&](const logical::Table& t) { out.insert(t.name); }, + [&](const logical::Filter& f) { CollectGroupTables(f.source, out, seen); }, + [&](const logical::Projection& p) { CollectGroupTables(p.source, out, seen); }, + [&](const logical::CrossJoin& j) { + CollectGroupTables(j.lhs, out, seen); + CollectGroupTables(j.rhs, out, seen); + }, + [&](const logical::Join& j) { + CollectGroupTables(j.lhs, out, seen); + CollectGroupTables(j.rhs, out, seen); + }, + }, (*exprs.begin())->root_operator); +} + +} // namespace + +std::unordered_set GroupTables(utils::NotNull g) { + std::unordered_set out; + std::unordered_set seen; + CollectGroupTables(g, out, seen); + return out; +} + +} // namespace stewkk::sql From 7400561e586b132bf967606453bef0a4f6fd20fd Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Wed, 27 May 2026 04:00:17 +0300 Subject: [PATCH 053/120] Add reachability fuzzer --- research/converter.py | 22 +++-- research/fuzz/mssql_runner.py | 16 ++++ research/fuzz/reach_fuzz.py | 155 ++++++++++++++++++++++++++++++++++ src/stewkk/sql/main.cpp | 44 +++++++++- 4 files changed, 227 insertions(+), 10 deletions(-) create mode 100644 research/fuzz/reach_fuzz.py diff --git a/research/converter.py b/research/converter.py index 32ac4d9..16c029b 100644 --- a/research/converter.py +++ b/research/converter.py @@ -15,9 +15,7 @@ (OP LHS RHS) OP: = != > < >= <= and or + - * / % ^ (not EXPR) (uminus EXPR) - -Known missing operators in the target format (raise NotImplementedError): - - IS NULL / IS NOT NULL (MS SQL CompareOp "IS" / "IS NOT") + (isnull EXPR) """ import xml.etree.ElementTree as ET @@ -236,18 +234,26 @@ def _convert_scalar(scalar: ET.Element) -> str: def _convert_compare(compare: ET.Element) -> str: op_str = compare.get("CompareOp", "") + scalars = compare.findall(f"{NS}ScalarOperator") if op_str in ("IS", "IS NOT"): - raise NotImplementedError( - f"CompareOp={op_str!r} (IS NULL / IS NOT NULL) is not supported by the target plan format: " - "BinaryOp in the project has no kIsNull / kIsNotNull" - ) + # MS SQL emits "lhs IS NULL" / "lhs IS NOT NULL" as a Compare where the + # rhs is a NULL constant. Map to the project's (isnull lhs) / (not (isnull lhs)). + if len(scalars) != 2: + raise ValueError(f"IS/IS NOT Compare has {len(scalars)} ScalarOperators, expected 2") + lhs = _convert_scalar(scalars[0]) + rhs_const = scalars[1].find(f"{NS}Const") + if rhs_const is None or rhs_const.get("ConstValue", "").upper() not in ("NULL", "(NULL)"): + raise NotImplementedError( + f"CompareOp={op_str!r} with non-NULL rhs (IS DISTINCT FROM-like) is not supported" + ) + isnull = f"(isnull {lhs})" + return f"(not {isnull})" if op_str == "IS NOT" else isnull op = _COMPARE_OPS.get(op_str) if op is None: raise NotImplementedError(f"unhandled CompareOp: {op_str!r}") - scalars = compare.findall(f"{NS}ScalarOperator") if len(scalars) != 2: raise ValueError(f"Compare has {len(scalars)} ScalarOperator children, expected 2") return f"({op} {_convert_scalar(scalars[0])} {_convert_scalar(scalars[1])})" diff --git a/research/fuzz/mssql_runner.py b/research/fuzz/mssql_runner.py index 2211da6..f4f5b7d 100644 --- a/research/fuzz/mssql_runner.py +++ b/research/fuzz/mssql_runner.py @@ -124,6 +124,22 @@ def run(self, query: str) -> RunResult: except pymssql.Error as e: return RunResult(columns=[], rows=[], error=str(e)) + def get_plan(self, query: str) -> str: + """Return the ShowPlanXML for `query`. Use OPTION (RECOMPILE) to inline literals.""" + if self._conn is None: + self._conn = self._connect(self._database) + cur = self._conn.cursor() + cur.execute("SET STATISTICS XML ON") + try: + cur.execute(query) + while cur.nextset(): + row = cur.fetchone() + if row and isinstance(row[0], str) and row[0].startswith(" None: if self._conn is not None: self._conn.close() diff --git a/research/fuzz/reach_fuzz.py b/research/fuzz/reach_fuzz.py new file mode 100644 index 0000000..c2530d6 --- /dev/null +++ b/research/fuzz/reach_fuzz.py @@ -0,0 +1,155 @@ +""" +Reachability fuzzer: generate a random SQL query, ask MS SQL Server for its +execution plan, convert that plan to the project's serialized format, then +invoke our CLI in ``--check-reachable`` mode to confirm that our exhaustive +search would have considered it. Stops at the first plan our optimizer cannot +reach. + +The fuzzer only flags a divergence when the MS SQL plan converts cleanly but +our optimizer cannot reach it. Coverage gaps in the XML→s-expr converter +(unhandled physical operators or scalar shapes) are logged and skipped, since +they are converter limitations rather than optimizer bugs. + +Usage: + python -m research.fuzz.reach_fuzz \\ + --cli build/bin/sql \\ + --data-dir test/static/executor/test_data \\ + [--start-seed 0] +""" +from __future__ import annotations + +import argparse +import random +import subprocess +import sys +import tempfile +from itertools import count +from pathlib import Path + +from research.converter import convert as convert_plan +from research.fuzz.mssql_runner import MsSqlRunner +from research.query_generator import ( + DIALECTS, + QueryGenerator, + SelectQuery, + load_schema, + render_query, +) + + +def _wrap_projection(plan_sexpr: str, query: SelectQuery) -> str: + """Our parser always emits a Projection at the top of a SELECT with + targets. MS SQL Server's plans express projection implicitly via the + scan/join OutputList rather than as a node, so we add the wrapper here so + the shape matches what the optimizer's exhaustive search produces.""" + exprs = " ".join(f"(attr {a.table} {a.column})" for a in query.targets) + return f"(PhysicalProjection (exprs {exprs}) {plan_sexpr})" + + +def _run_reach_check(cli: str, sql: str, plan_sexpr: str) -> subprocess.CompletedProcess: + with tempfile.NamedTemporaryFile("w", suffix=".plan", delete=False) as f: + f.write(plan_sexpr) + plan_path = f.name + try: + return subprocess.run( + [cli, "--check-reachable", plan_path], + input=sql, + capture_output=True, + text=True, + timeout=60, + ) + finally: + Path(plan_path).unlink(missing_ok=True) + + +def main() -> None: + ap = argparse.ArgumentParser(description=__doc__) + ap.add_argument("--cli", required=True, help="Path to our sql binary") + ap.add_argument("--data-dir", required=True, help="CSV table directory (shared with MS SQL)") + ap.add_argument("--start-seed", type=int, default=0) + ap.add_argument("--mssql-host", default="localhost") + ap.add_argument("--mssql-port", type=int, default=1433) + ap.add_argument("--mssql-user", default="sa") + ap.add_argument("--mssql-password", default="Password123!") + ap.add_argument("--mssql-database", default="fuzz") + args = ap.parse_args() + + schema = load_schema(Path(args.data_dir)) + + mssql = MsSqlRunner( + host=args.mssql_host, + port=args.mssql_port, + user=args.mssql_user, + password=args.mssql_password, + database=args.mssql_database, + ) + print("Setting up MS SQL schema...", flush=True) + mssql.setup_schema(args.data_dir) + print("Schema ready. Starting reachability fuzz loop.\n", flush=True) + + pg = DIALECTS["pg"] + ms = DIALECTS["mssql"] + + skipped_convert = 0 + skipped_mssql = 0 + + for seed in count(args.start_seed): + rng = random.Random(seed) + query = QueryGenerator(schema, rng).generate() + ours_sql = render_query(query, pg) + ";" + # OPTION (RECOMPILE) embeds literal values in the plan so the converter + # sees Const nodes instead of @P parameter references. + theirs_sql = render_query(query, ms) + " OPTION (RECOMPILE);" + + try: + plan_xml = mssql.get_plan(theirs_sql) + except Exception as e: + skipped_mssql += 1 + if seed % 25 == 0: + print(f"seed={seed} mssql refused: {e}", flush=True) + continue + + try: + converted = convert_plan(plan_xml) + except NotImplementedError as e: + skipped_convert += 1 + if seed % 25 == 0: + print(f"seed={seed} converter skip: {e}", flush=True) + continue + except Exception as e: + print(f"seed={seed} converter error: {e}\n--- query:\n{theirs_sql}") + sys.exit(2) + + target_plan = _wrap_projection(converted, query) + + try: + proc = _run_reach_check(args.cli, ours_sql, target_plan) + except subprocess.TimeoutExpired: + print(f"DIVERGENCE seed={seed}: reachability check timed out") + print(f"\n--- query (ours):\n{ours_sql}") + print(f"\n--- target plan:\n{target_plan}") + sys.exit(1) + + if proc.returncode == 0: + if seed % 25 == 0: + print( + f"seed={seed} ok" + f" (skipped: {skipped_convert} convert, {skipped_mssql} mssql)", + flush=True, + ) + continue + + # Non-zero: either parse/optimizer error, or NOT REACHABLE. + print(f"DIVERGENCE seed={seed}: exit={proc.returncode}") + print(f"\n--- query (ours):\n{ours_sql}") + print(f"\n--- query (mssql):\n{theirs_sql}") + print(f"\n--- mssql plan (converted):\n{target_plan}") + if proc.stdout: + print(f"\n--- stdout:\n{proc.stdout}", end="") + if proc.stderr: + print(f"\n--- stderr:\n{proc.stderr}", end="") + sys.exit(1) + + +if __name__ == "__main__": + main() diff --git a/src/stewkk/sql/main.cpp b/src/stewkk/sql/main.cpp index ab9299e..6358eb9 100644 --- a/src/stewkk/sql/main.cpp +++ b/src/stewkk/sql/main.cpp @@ -25,6 +25,7 @@ #include #include #include +#include #include #include #include @@ -41,12 +42,14 @@ constexpr int kUsage = 64; constexpr int kParseError = 1; constexpr int kOptimizerError = 2; constexpr int kRuntimeError = 3; +constexpr int kNotReachable = 4; struct Args { std::string data_dir; bool print_plan = false; bool print_ast = false; bool jit = false; + std::string check_reachable_path; }; Args ParseArgs(int argc, char** argv) { @@ -65,13 +68,20 @@ Args ParseArgs(int argc, char** argv) { args.print_ast = true; } else if (a == "--jit") { args.jit = true; + } else if (a == "--check-reachable") { + if (i + 1 >= argc) { + std::cerr << "--check-reachable requires a plan file path\n"; + std::exit(kUsage); + } + args.check_reachable_path = argv[++i]; } else { std::cerr << "unknown argument: " << a << "\n"; std::exit(kUsage); } } - if (args.data_dir.empty()) { - std::cerr << "usage: sql --data-dir [--print-plan] [--jit] < query.sql\n"; + if (args.data_dir.empty() && args.check_reachable_path.empty()) { + std::cerr << "usage: sql --data-dir [--print-plan] [--jit] < query.sql\n" + << " sql --check-reachable < query.sql\n"; std::exit(kUsage); } return args; @@ -212,6 +222,36 @@ int main(int argc, char** argv) { sql_buf << std::cin.rdbuf(); std::stringstream sql_stream{sql_buf.str()}; + if (!args.check_reachable_path.empty()) { + std::ifstream plan_in{args.check_reachable_path}; + if (!plan_in) { + std::cerr << "cannot open plan file: " << args.check_reachable_path << "\n"; + return kUsage; + } + std::ostringstream plan_buf; + plan_buf << plan_in.rdbuf(); + PhysicalPlanNode target; + try { + target = Deserialize(plan_buf.str()); + } catch (const std::exception& e) { + std::cerr << "plan parse error: " << e.what() << "\n"; + return kParseError; + } + MatchResult mr; + try { + mr = IsPlanReachable(sql_stream, target); + } catch (const std::exception& e) { + std::cerr << "reachability error: " << e.what() << "\n"; + return kOptimizerError; + } + if (mr.reachable) { + std::cout << "REACHABLE\n"; + return kOk; + } + std::cout << "NOT REACHABLE: " << mr.mismatch << "\n"; + return kNotReachable; + } + auto parsed_result = GetAST(sql_stream); if (!parsed_result.has_value()) { std::cerr << "parse error: " << What(parsed_result.error()) << "\n"; From 30abea0fd4927d954fb8a2d9b5bec0d3118d73a9 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Fri, 29 May 2026 20:50:26 +0300 Subject: [PATCH 054/120] Fix bug in jit --- flake.lock | 12 ++++++------ flake.nix | 9 +++++---- src/stewkk/sql/logic/executor/llvm.cpp | 12 ++++++++++-- 3 files changed, 21 insertions(+), 12 deletions(-) diff --git a/flake.lock b/flake.lock index b75d055..7bda188 100644 --- a/flake.lock +++ b/flake.lock @@ -3,11 +3,11 @@ "flake-compat": { "flake": false, "locked": { - "lastModified": 1747046372, - "narHash": "sha256-CIVLLkVgvHYbgI2UpXvIIBJ12HWgX+fjA8Xf8PUmqCY=", + "lastModified": 1767039857, + "narHash": "sha256-vNpUSpF5Nuw8xvDLj2KCwwksIbjua2LZCqhV1LNRDns=", "owner": "edolstra", "repo": "flake-compat", - "rev": "9100a0f413b0c601e0533d1d94ffd501ce2e7885", + "rev": "5edf11c44bc78a0d334f6334cdaf7d60d732daab", "type": "github" }, "original": { @@ -36,11 +36,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1776548001, - "narHash": "sha256-ZSK0NL4a1BwVbbTBoSnWgbJy9HeZFXLYQizjb2DPF24=", + "lastModified": 1779560665, + "narHash": "sha256-tpyBcxPpcQb8ukyNF7DoCwfSY3VPsxHoYwj00Cayv5o=", "owner": "nixos", "repo": "nixpkgs", - "rev": "b12141ef619e0a9c1c84dc8c684040326f27cdcc", + "rev": "64c08a7ca051951c8eae34e3e3cb1e202fe36786", "type": "github" }, "original": { diff --git a/flake.nix b/flake.nix index ef3113a..e8162f2 100644 --- a/flake.nix +++ b/flake.nix @@ -41,9 +41,9 @@ ms-python.python ms-python.vscode-pylance ms-toolsai.jupyter - anthropic.claude-code - ms-vscode.cpptools - asvetliakov.vscode-neovim + anthropic.claude-code + ms-vscode.cpptools + asvetliakov.vscode-neovim ]; }) pythonEnv @@ -59,8 +59,9 @@ inkscape llvmPackages_21.llvm llvmPackages_21.llvm.dev - nodejs_20 + nodejs_24 graphviz + codex ]; nativeBuildInputs = [ diff --git a/src/stewkk/sql/logic/executor/llvm.cpp b/src/stewkk/sql/logic/executor/llvm.cpp index 9783fd0..f2dd0c1 100644 --- a/src/stewkk/sql/logic/executor/llvm.cpp +++ b/src/stewkk/sql/logic/executor/llvm.cpp @@ -264,11 +264,15 @@ llvm::Function* JITCompiler::GenerateIR( auto* logical_not = builder.CreateZExt(is_zero, value->getType(), "logical_not_result"); + llvm::Value* not_null_struct = llvm::UndefValue::get(value_type); + not_null_struct = builder.CreateInsertValue(not_null_struct, builder.getInt8(0), {0}); + not_null_struct = builder.CreateInsertValue(not_null_struct, logical_not, {1}); + auto* select = builder.CreateSelect( is_null, llvm::ConstantStruct::get(static_cast(value_type), {builder.getInt8(1), builder.getInt64(0)}), - logical_not); + not_null_struct); return select; } case UnaryOp::kMinus: { @@ -277,11 +281,15 @@ llvm::Function* JITCompiler::GenerateIR( auto* negated = builder.CreateNeg(value); + llvm::Value* not_null_struct = llvm::UndefValue::get(value_type); + not_null_struct = builder.CreateInsertValue(not_null_struct, builder.getInt8(0), {0}); + not_null_struct = builder.CreateInsertValue(not_null_struct, negated, {1}); + auto* select = builder.CreateSelect( is_null, llvm::ConstantStruct::get(static_cast(value_type), {builder.getInt8(1), builder.getInt64(0)}), - negated); + not_null_struct); return select; } case UnaryOp::kIsNull: { From 2d000456db28bdb8de68edc1864f5028f3590681 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Fri, 29 May 2026 21:22:17 +0300 Subject: [PATCH 055/120] Add SBB benchmark --- .gitignore | 2 + Makefile | 5 +- benchmarks/datasets/ssb/README.md | 43 ++++++ benchmarks/datasets/ssb/convert_tbl_to_csv.py | 137 ++++++++++++++++++ .../ssb/fixtures/expected_csv/customer.csv | 3 + .../ssb/fixtures/expected_csv/date.csv | 3 + .../ssb/fixtures/expected_csv/lineorder.csv | 3 + .../ssb/fixtures/expected_csv/part.csv | 3 + .../ssb/fixtures/expected_csv/supplier.csv | 3 + .../datasets/ssb/fixtures/tbl/customer.tbl | 2 + benchmarks/datasets/ssb/fixtures/tbl/date.tbl | 2 + .../datasets/ssb/fixtures/tbl/lineorder.tbl | 2 + benchmarks/datasets/ssb/fixtures/tbl/part.tbl | 2 + .../datasets/ssb/fixtures/tbl/supplier.tbl | 2 + benchmarks/datasets/ssb/schema.json | 87 +++++++++++ .../datasets/ssb/test_convert_tbl_to_csv.py | 82 +++++++++++ 16 files changed, 380 insertions(+), 1 deletion(-) create mode 100644 benchmarks/datasets/ssb/README.md create mode 100644 benchmarks/datasets/ssb/convert_tbl_to_csv.py create mode 100644 benchmarks/datasets/ssb/fixtures/expected_csv/customer.csv create mode 100644 benchmarks/datasets/ssb/fixtures/expected_csv/date.csv create mode 100644 benchmarks/datasets/ssb/fixtures/expected_csv/lineorder.csv create mode 100644 benchmarks/datasets/ssb/fixtures/expected_csv/part.csv create mode 100644 benchmarks/datasets/ssb/fixtures/expected_csv/supplier.csv create mode 100644 benchmarks/datasets/ssb/fixtures/tbl/customer.tbl create mode 100644 benchmarks/datasets/ssb/fixtures/tbl/date.tbl create mode 100644 benchmarks/datasets/ssb/fixtures/tbl/lineorder.tbl create mode 100644 benchmarks/datasets/ssb/fixtures/tbl/part.tbl create mode 100644 benchmarks/datasets/ssb/fixtures/tbl/supplier.tbl create mode 100644 benchmarks/datasets/ssb/schema.json create mode 100644 benchmarks/datasets/ssb/test_convert_tbl_to_csv.py diff --git a/.gitignore b/.gitignore index 86a6b40..7f94a71 100644 --- a/.gitignore +++ b/.gitignore @@ -13,3 +13,5 @@ FlameGraph/ /.odbc/ **/__pycache__ /build-tsan/ +/benchmarks/datasets/ssb/generated/ +/benchmarks/datasets/ssb/raw/ diff --git a/Makefile b/Makefile index 0dca4cf..f0e0c80 100644 --- a/Makefile +++ b/Makefile @@ -11,4 +11,7 @@ sanitize: codegen: @antlr -Dlanguage=Cpp -visitor -o $(CODEGEN_DIR) -package stewkk::sql::codegen $(PARSER_SOURCE_DIR)/PostgreSQLParser.g4 $(PARSER_SOURCE_DIR)/PostgreSQLLexer.g4 -.PHONY: codegen build +test-ssb-converter: + pytest benchmarks/datasets/ssb + +.PHONY: codegen build sanitize test-ssb-converter diff --git a/benchmarks/datasets/ssb/README.md b/benchmarks/datasets/ssb/README.md new file mode 100644 index 0000000..192b4f1 --- /dev/null +++ b/benchmarks/datasets/ssb/README.md @@ -0,0 +1,43 @@ +# Star Schema Benchmark data + +This directory contains the schema and conversion tooling for Star Schema +Benchmark data. It does not download or build an SSB data generator; generate +the `.tbl` files with an external SSB dbgen-compatible tool first. + +## Convert `.tbl` files + +Expected input files: + +- `lineorder.tbl` +- `customer.tbl` +- `supplier.tbl` +- `part.tbl` +- `date.tbl` + +Convert them to the CSV format used by this project: + +```sh +python3 benchmarks/datasets/ssb/convert_tbl_to_csv.py \ + --input-dir /path/to/ssb/tbl \ + --output-dir benchmarks/datasets/ssb/generated/sf1 +``` + +The converter reads `schema.json` for table and column definitions. Output CSV +headers use `column:type`, for example `lo_orderkey:int`. String columns are +preserved as `string` values in the generated CSV. + +Generated full-scale data should stay under `benchmarks/datasets/ssb/generated/` +or another ignored location. + +## Current engine limitation + +This first slice only adds data layout and conversion. The current C++ scanner +supports `int` and `NULL` values, so SSB CSV files containing `string` columns +are not executable until string type support is added to the parser, scanner, +executor, and output formatting. + +## Test the converter + +```sh +make test-ssb-converter +``` diff --git a/benchmarks/datasets/ssb/convert_tbl_to_csv.py b/benchmarks/datasets/ssb/convert_tbl_to_csv.py new file mode 100644 index 0000000..e7a6c7a --- /dev/null +++ b/benchmarks/datasets/ssb/convert_tbl_to_csv.py @@ -0,0 +1,137 @@ +#!/usr/bin/env python3 +"""Convert Star Schema Benchmark .tbl files to this project's CSV format.""" + +from __future__ import annotations + +import argparse +import csv +import json +import sys +from dataclasses import dataclass +from pathlib import Path +from typing import Iterable + + +SUPPORTED_TYPES = {"int", "string"} + + +@dataclass(frozen=True) +class Column: + name: str + type: str + + +@dataclass(frozen=True) +class Table: + name: str + columns: tuple[Column, ...] + + +class ConversionError(RuntimeError): + pass + + +def load_schema(path: Path) -> list[Table]: + with path.open(encoding="utf-8") as f: + raw = json.load(f) + + tables = [] + for table_raw in raw.get("tables", []): + columns = [] + for column_raw in table_raw.get("columns", []): + column_type = column_raw["type"] + if column_type not in SUPPORTED_TYPES: + raise ConversionError( + f"{path}: unsupported type {column_type!r} in table {table_raw['name']}" + ) + columns.append(Column(column_raw["name"], column_type)) + if not columns: + raise ConversionError(f"{path}: table {table_raw.get('name')!r} has no columns") + tables.append(Table(table_raw["name"], tuple(columns))) + + if not tables: + raise ConversionError(f"{path}: schema does not define any tables") + return tables + + +def parse_tbl_line(line: str) -> list[str]: + fields = line.rstrip("\r\n").split("|") + if fields and fields[-1] == "": + fields.pop() + return fields + + +def validate_row(table: Table, row: list[str], input_path: Path, line_no: int) -> None: + if len(row) != len(table.columns): + raise ConversionError( + f"{input_path}:{line_no}: expected {len(table.columns)} fields for " + f"{table.name}, got {len(row)}" + ) + + for value, column in zip(row, table.columns, strict=True): + if column.type == "int": + try: + int(value) + except ValueError as exc: + raise ConversionError( + f"{input_path}:{line_no}: {column.name} expects int, got {value!r}" + ) from exc + + +def header(table: Table) -> list[str]: + return [f"{column.name}:{column.type}" for column in table.columns] + + +def convert_table(table: Table, input_dir: Path, output_dir: Path) -> int: + input_path = input_dir / f"{table.name}.tbl" + output_path = output_dir / f"{table.name}.csv" + if not input_path.exists(): + raise ConversionError(f"missing input file: {input_path}") + + rows = 0 + with input_path.open(encoding="utf-8", newline="") as src, output_path.open( + "w", encoding="utf-8", newline="" + ) as dst: + writer = csv.writer(dst, lineterminator="\n") + writer.writerow(header(table)) + for line_no, line in enumerate(src, start=1): + row = parse_tbl_line(line) + validate_row(table, row, input_path, line_no) + writer.writerow(row) + rows += 1 + return rows + + +def convert_all(schema_path: Path, input_dir: Path, output_dir: Path) -> dict[str, int]: + tables = load_schema(schema_path) + output_dir.mkdir(parents=True, exist_ok=True) + return { + table.name: convert_table(table, input_dir, output_dir) + for table in tables + } + + +def parse_args(argv: Iterable[str]) -> argparse.Namespace: + default_schema = Path(__file__).with_name("schema.json") + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--input-dir", required=True, type=Path, help="Directory containing SSB .tbl files") + parser.add_argument("--output-dir", required=True, type=Path, help="Directory for generated CSV files") + parser.add_argument("--schema", default=default_schema, type=Path, help="Schema JSON path") + return parser.parse_args(argv) + + +def main(argv: Iterable[str] = sys.argv[1:]) -> int: + args = parse_args(argv) + try: + counts = convert_all(args.schema, args.input_dir, args.output_dir) + except ConversionError as exc: + print(f"error: {exc}", file=sys.stderr) + return 1 + + for table_name, rows in counts.items(): + print(f"wrote {args.output_dir / (table_name + '.csv')} ({rows} rows)") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/benchmarks/datasets/ssb/fixtures/expected_csv/customer.csv b/benchmarks/datasets/ssb/fixtures/expected_csv/customer.csv new file mode 100644 index 0000000..82689fb --- /dev/null +++ b/benchmarks/datasets/ssb/fixtures/expected_csv/customer.csv @@ -0,0 +1,3 @@ +c_custkey:int,c_name:string,c_address:string,c_city:string,c_nation:string,c_region:string,c_phone:string,c_mktsegment:string +10,Customer#000000010,Addr 10,UNITED KI1,UNITED KINGDOM,EUROPE,10-111-111-1111,BUILDING +20,Customer#000000020,"Addr, 20",BRAZIL 1,BRAZIL,AMERICA,20-222-222-2222,AUTOMOBILE diff --git a/benchmarks/datasets/ssb/fixtures/expected_csv/date.csv b/benchmarks/datasets/ssb/fixtures/expected_csv/date.csv new file mode 100644 index 0000000..f898834 --- /dev/null +++ b/benchmarks/datasets/ssb/fixtures/expected_csv/date.csv @@ -0,0 +1,3 @@ +d_datekey:int,d_date:string,d_dayofweek:string,d_month:string,d_year:int,d_yearmonthnum:int,d_yearmonth:string,d_daynuminweek:int,d_daynuminmonth:int,d_daynuminyear:int,d_monthnuminyear:int,d_weeknuminyear:int,d_sellingseason:string,d_lastdayinweekfl:int,d_lastdayinmonthfl:int,d_holidayfl:int,d_weekdayfl:int +19940101,"January 1, 1994",Saturday,January,1994,199401,Jan1994,6,1,1,1,1,Winter,0,0,1,0 +19940102,"January 2, 1994",Sunday,January,1994,199401,Jan1994,7,2,2,1,1,Winter,1,0,0,0 diff --git a/benchmarks/datasets/ssb/fixtures/expected_csv/lineorder.csv b/benchmarks/datasets/ssb/fixtures/expected_csv/lineorder.csv new file mode 100644 index 0000000..38f03a6 --- /dev/null +++ b/benchmarks/datasets/ssb/fixtures/expected_csv/lineorder.csv @@ -0,0 +1,3 @@ +lo_orderkey:int,lo_linenumber:int,lo_custkey:int,lo_partkey:int,lo_suppkey:int,lo_orderdate:int,lo_orderpriority:string,lo_shippriority:string,lo_quantity:int,lo_extendedprice:int,lo_ordtotalprice:int,lo_discount:int,lo_revenue:int,lo_supplycost:int,lo_tax:int,lo_commitdate:int,lo_shipmode:string +1,1,10,100,1000,19940101,1-URGENT,0,5,10000,12000,3,9700,6000,1,19940105,AIR +2,1,20,200,2000,19940102,2-HIGH,0,7,20000,23000,4,19200,12000,2,19940106,RAIL diff --git a/benchmarks/datasets/ssb/fixtures/expected_csv/part.csv b/benchmarks/datasets/ssb/fixtures/expected_csv/part.csv new file mode 100644 index 0000000..6c6dc3a --- /dev/null +++ b/benchmarks/datasets/ssb/fixtures/expected_csv/part.csv @@ -0,0 +1,3 @@ +p_partkey:int,p_name:string,p_mfgr:string,p_category:string,p_brand:string,p_color:string,p_type:string,p_size:int,p_container:string +100,almond antique,MFGR#1,MFGR#11,MFGR#111,almond,STANDARD ANODIZED TIN,3,SM CASE +200,green burnished,MFGR#2,MFGR#22,MFGR#222,green,PROMO POLISHED STEEL,7,LG BOX diff --git a/benchmarks/datasets/ssb/fixtures/expected_csv/supplier.csv b/benchmarks/datasets/ssb/fixtures/expected_csv/supplier.csv new file mode 100644 index 0000000..c5f58d1 --- /dev/null +++ b/benchmarks/datasets/ssb/fixtures/expected_csv/supplier.csv @@ -0,0 +1,3 @@ +s_suppkey:int,s_name:string,s_address:string,s_city:string,s_nation:string,s_region:string,s_phone:string +1000,Supplier#000001000,Supplier Addr 1000,RUSSIA 1,RUSSIA,EUROPE,30-333-333-3333 +2000,Supplier#000002000,Supplier Addr 2000,ARGENTINA1,ARGENTINA,AMERICA,40-444-444-4444 diff --git a/benchmarks/datasets/ssb/fixtures/tbl/customer.tbl b/benchmarks/datasets/ssb/fixtures/tbl/customer.tbl new file mode 100644 index 0000000..2f1c800 --- /dev/null +++ b/benchmarks/datasets/ssb/fixtures/tbl/customer.tbl @@ -0,0 +1,2 @@ +10|Customer#000000010|Addr 10|UNITED KI1|UNITED KINGDOM|EUROPE|10-111-111-1111|BUILDING| +20|Customer#000000020|Addr, 20|BRAZIL 1|BRAZIL|AMERICA|20-222-222-2222|AUTOMOBILE| diff --git a/benchmarks/datasets/ssb/fixtures/tbl/date.tbl b/benchmarks/datasets/ssb/fixtures/tbl/date.tbl new file mode 100644 index 0000000..99b880c --- /dev/null +++ b/benchmarks/datasets/ssb/fixtures/tbl/date.tbl @@ -0,0 +1,2 @@ +19940101|January 1, 1994|Saturday|January|1994|199401|Jan1994|6|1|1|1|1|Winter|0|0|1|0| +19940102|January 2, 1994|Sunday|January|1994|199401|Jan1994|7|2|2|1|1|Winter|1|0|0|0| diff --git a/benchmarks/datasets/ssb/fixtures/tbl/lineorder.tbl b/benchmarks/datasets/ssb/fixtures/tbl/lineorder.tbl new file mode 100644 index 0000000..8219280 --- /dev/null +++ b/benchmarks/datasets/ssb/fixtures/tbl/lineorder.tbl @@ -0,0 +1,2 @@ +1|1|10|100|1000|19940101|1-URGENT|0|5|10000|12000|3|9700|6000|1|19940105|AIR| +2|1|20|200|2000|19940102|2-HIGH|0|7|20000|23000|4|19200|12000|2|19940106|RAIL| diff --git a/benchmarks/datasets/ssb/fixtures/tbl/part.tbl b/benchmarks/datasets/ssb/fixtures/tbl/part.tbl new file mode 100644 index 0000000..064fd5e --- /dev/null +++ b/benchmarks/datasets/ssb/fixtures/tbl/part.tbl @@ -0,0 +1,2 @@ +100|almond antique|MFGR#1|MFGR#11|MFGR#111|almond|STANDARD ANODIZED TIN|3|SM CASE| +200|green burnished|MFGR#2|MFGR#22|MFGR#222|green|PROMO POLISHED STEEL|7|LG BOX| diff --git a/benchmarks/datasets/ssb/fixtures/tbl/supplier.tbl b/benchmarks/datasets/ssb/fixtures/tbl/supplier.tbl new file mode 100644 index 0000000..07a8143 --- /dev/null +++ b/benchmarks/datasets/ssb/fixtures/tbl/supplier.tbl @@ -0,0 +1,2 @@ +1000|Supplier#000001000|Supplier Addr 1000|RUSSIA 1|RUSSIA|EUROPE|30-333-333-3333| +2000|Supplier#000002000|Supplier Addr 2000|ARGENTINA1|ARGENTINA|AMERICA|40-444-444-4444| diff --git a/benchmarks/datasets/ssb/schema.json b/benchmarks/datasets/ssb/schema.json new file mode 100644 index 0000000..0bf0a2d --- /dev/null +++ b/benchmarks/datasets/ssb/schema.json @@ -0,0 +1,87 @@ +{ + "tables": [ + { + "name": "lineorder", + "columns": [ + {"name": "lo_orderkey", "type": "int"}, + {"name": "lo_linenumber", "type": "int"}, + {"name": "lo_custkey", "type": "int"}, + {"name": "lo_partkey", "type": "int"}, + {"name": "lo_suppkey", "type": "int"}, + {"name": "lo_orderdate", "type": "int"}, + {"name": "lo_orderpriority", "type": "string"}, + {"name": "lo_shippriority", "type": "string"}, + {"name": "lo_quantity", "type": "int"}, + {"name": "lo_extendedprice", "type": "int"}, + {"name": "lo_ordtotalprice", "type": "int"}, + {"name": "lo_discount", "type": "int"}, + {"name": "lo_revenue", "type": "int"}, + {"name": "lo_supplycost", "type": "int"}, + {"name": "lo_tax", "type": "int"}, + {"name": "lo_commitdate", "type": "int"}, + {"name": "lo_shipmode", "type": "string"} + ] + }, + { + "name": "customer", + "columns": [ + {"name": "c_custkey", "type": "int"}, + {"name": "c_name", "type": "string"}, + {"name": "c_address", "type": "string"}, + {"name": "c_city", "type": "string"}, + {"name": "c_nation", "type": "string"}, + {"name": "c_region", "type": "string"}, + {"name": "c_phone", "type": "string"}, + {"name": "c_mktsegment", "type": "string"} + ] + }, + { + "name": "supplier", + "columns": [ + {"name": "s_suppkey", "type": "int"}, + {"name": "s_name", "type": "string"}, + {"name": "s_address", "type": "string"}, + {"name": "s_city", "type": "string"}, + {"name": "s_nation", "type": "string"}, + {"name": "s_region", "type": "string"}, + {"name": "s_phone", "type": "string"} + ] + }, + { + "name": "part", + "columns": [ + {"name": "p_partkey", "type": "int"}, + {"name": "p_name", "type": "string"}, + {"name": "p_mfgr", "type": "string"}, + {"name": "p_category", "type": "string"}, + {"name": "p_brand", "type": "string"}, + {"name": "p_color", "type": "string"}, + {"name": "p_type", "type": "string"}, + {"name": "p_size", "type": "int"}, + {"name": "p_container", "type": "string"} + ] + }, + { + "name": "date", + "columns": [ + {"name": "d_datekey", "type": "int"}, + {"name": "d_date", "type": "string"}, + {"name": "d_dayofweek", "type": "string"}, + {"name": "d_month", "type": "string"}, + {"name": "d_year", "type": "int"}, + {"name": "d_yearmonthnum", "type": "int"}, + {"name": "d_yearmonth", "type": "string"}, + {"name": "d_daynuminweek", "type": "int"}, + {"name": "d_daynuminmonth", "type": "int"}, + {"name": "d_daynuminyear", "type": "int"}, + {"name": "d_monthnuminyear", "type": "int"}, + {"name": "d_weeknuminyear", "type": "int"}, + {"name": "d_sellingseason", "type": "string"}, + {"name": "d_lastdayinweekfl", "type": "int"}, + {"name": "d_lastdayinmonthfl", "type": "int"}, + {"name": "d_holidayfl", "type": "int"}, + {"name": "d_weekdayfl", "type": "int"} + ] + } + ] +} diff --git a/benchmarks/datasets/ssb/test_convert_tbl_to_csv.py b/benchmarks/datasets/ssb/test_convert_tbl_to_csv.py new file mode 100644 index 0000000..96d00d8 --- /dev/null +++ b/benchmarks/datasets/ssb/test_convert_tbl_to_csv.py @@ -0,0 +1,82 @@ +from __future__ import annotations + +import shutil +import subprocess +import sys +from pathlib import Path + + +SSB_DIR = Path(__file__).resolve().parent +CONVERTER = SSB_DIR / "convert_tbl_to_csv.py" +SCHEMA = SSB_DIR / "schema.json" +FIXTURE_TBL = SSB_DIR / "fixtures" / "tbl" +EXPECTED_CSV = SSB_DIR / "fixtures" / "expected_csv" + + +def run_converter(input_dir: Path, output_dir: Path) -> subprocess.CompletedProcess[str]: + return subprocess.run( + [ + sys.executable, + str(CONVERTER), + "--schema", + str(SCHEMA), + "--input-dir", + str(input_dir), + "--output-dir", + str(output_dir), + ], + text=True, + capture_output=True, + check=False, + ) + + +def test_converts_all_ssb_tables(tmp_path: Path) -> None: + output_dir = tmp_path / "out" + + result = run_converter(FIXTURE_TBL, output_dir) + + assert result.returncode == 0, result.stderr + expected_files = sorted(path.name for path in EXPECTED_CSV.glob("*.csv")) + actual_files = sorted(path.name for path in output_dir.glob("*.csv")) + assert actual_files == expected_files + for filename in expected_files: + assert (output_dir / filename).read_text() == (EXPECTED_CSV / filename).read_text() + + +def test_rejects_malformed_row_width(tmp_path: Path) -> None: + input_dir = tmp_path / "tbl" + shutil.copytree(FIXTURE_TBL, input_dir) + with (input_dir / "lineorder.tbl").open("a", encoding="utf-8") as f: + f.write("3|too-few-fields|\n") + + result = run_converter(input_dir, tmp_path / "out") + + assert result.returncode != 0 + assert "expected 17 fields for lineorder" in result.stderr + + +def test_rejects_missing_input_file(tmp_path: Path) -> None: + input_dir = tmp_path / "tbl" + shutil.copytree(FIXTURE_TBL, input_dir) + (input_dir / "supplier.tbl").unlink() + + result = run_converter(input_dir, tmp_path / "out") + + assert result.returncode != 0 + assert "missing input file" in result.stderr + assert "supplier.tbl" in result.stderr + + +def test_rejects_invalid_integer(tmp_path: Path) -> None: + input_dir = tmp_path / "tbl" + shutil.copytree(FIXTURE_TBL, input_dir) + (input_dir / "date.tbl").write_text( + "not-an-int|January 1, 1994|Saturday|January|1994|199401|Jan1994|6|1|1|1|1|Winter|0|0|1|0|\n", + encoding="utf-8", + ) + + result = run_converter(input_dir, tmp_path / "out") + + assert result.returncode != 0 + assert "d_datekey expects int" in result.stderr From 27111a9cfdadd78a37d54e2f7b9b09997eb61c47 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Fri, 29 May 2026 22:48:49 +0300 Subject: [PATCH 056/120] Add strings support --- benchmarks/main.cpp | 2 +- .../stewkk/sql/logic/executor/executor.hpp | 3 +- include/stewkk/sql/logic/executor/plan.hpp | 5 + .../sql/logic/executor/sequential_scan.hpp | 1 + .../sql/logic/optimizer/physical_expr.hpp | 2 + include/stewkk/sql/models/executor/tuple.hpp | 4 + .../stewkk/sql/models/parser/expression.hpp | 3 +- .../models/parser/relational_algebra_ast.hpp | 5 + research/converter.py | 24 +++-- research/test_converter.py | 92 +++++++++++++++++++ src/stewkk/sql/logic/executor/executor.cpp | 51 +++++++++- .../sql/logic/executor/executor_test.cpp | 59 +++++++++++- src/stewkk/sql/logic/executor/llvm.cpp | 3 + src/stewkk/sql/logic/executor/plan.cpp | 4 + .../sql/logic/executor/plan_serializer.cpp | 82 ++++++++++++++++- .../logic/executor/plan_serializer_test.cpp | 15 +++ .../sql/logic/executor/sequential_scan.cpp | 71 +++++++++++--- .../implementation_rules/implement_table.cpp | 2 +- src/stewkk/sql/logic/optimizer/memo.cpp | 4 +- src/stewkk/sql/logic/optimizer/optimizer.cpp | 2 +- .../sql/logic/optimizer/optimizer_test.cpp | 17 ++++ .../sql/logic/optimizer/reachability.cpp | 4 + .../sql/logic/optimizer/schema_catalog.cpp | 6 +- src/stewkk/sql/logic/parser/parser.cpp | 6 +- src/stewkk/sql/logic/parser/parser_test.cpp | 47 ++++++++++ src/stewkk/sql/logic/parser/visitor.cpp | 49 +++++++++- .../filter_pushdown_through_projection.cpp | 1 + .../transformation_rules/predicate_utils.cpp | 3 +- src/stewkk/sql/main.cpp | 7 +- src/stewkk/sql/models/executor/tuple.cpp | 51 ++++++++++ src/stewkk/sql/models/parser/expression.cpp | 11 +++ .../models/parser/relational_algebra_ast.cpp | 4 + test/static/executor/test_data/markets.csv | 4 + 33 files changed, 606 insertions(+), 38 deletions(-) create mode 100644 test/static/executor/test_data/markets.csv diff --git a/benchmarks/main.cpp b/benchmarks/main.cpp index 101b6fc..dfd9de0 100644 --- a/benchmarks/main.cpp +++ b/benchmarks/main.cpp @@ -26,7 +26,7 @@ namespace { PhysicalPlanNode ToPhysicalPlan(const Operator& op) { return std::visit(utils::Overloaded{ [](const Table& t) -> PhysicalPlanNode { - return SeqScan{.table = t.name}; + return SeqScan{.table = t.name, .alias = t.alias}; }, [](const Projection& p) -> PhysicalPlanNode { return PhysicalProjection{ diff --git a/include/stewkk/sql/logic/executor/executor.hpp b/include/stewkk/sql/logic/executor/executor.hpp index 2361967..7ca29ef 100644 --- a/include/stewkk/sql/logic/executor/executor.hpp +++ b/include/stewkk/sql/logic/executor/executor.hpp @@ -47,7 +47,8 @@ template class Executor { public: using SequentialScan = std::function>( - const std::string& table_name, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan)>; + const std::string& table_name, const std::string& output_table_name, + AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan)>; Executor(SequentialScan seq_scan, boost::asio::any_io_executor executor); boost::asio::awaitable> Execute(const PhysicalPlanNode& op); diff --git a/include/stewkk/sql/logic/executor/plan.hpp b/include/stewkk/sql/logic/executor/plan.hpp index c07d412..67fe7fb 100644 --- a/include/stewkk/sql/logic/executor/plan.hpp +++ b/include/stewkk/sql/logic/executor/plan.hpp @@ -1,6 +1,8 @@ #pragma once #include +#include +#include #include #include @@ -25,10 +27,13 @@ using PhysicalPlanNode = std::variant alias; bool operator==(const SeqScan&) const = default; }; +std::string_view OutputTable(const SeqScan& scan); + struct PhysicalProjection { std::shared_ptr source; std::vector expressions; diff --git a/include/stewkk/sql/logic/executor/sequential_scan.hpp b/include/stewkk/sql/logic/executor/sequential_scan.hpp index 51451e1..0ee7435 100644 --- a/include/stewkk/sql/logic/executor/sequential_scan.hpp +++ b/include/stewkk/sql/logic/executor/sequential_scan.hpp @@ -11,6 +11,7 @@ struct CsvDirSequentialScanner { std::string dir; boost::asio::awaitable> operator()(const std::string& table_name, + const std::string& output_table_name, AttributesInfoChannel& attrs_chan, TuplesChannel& tuples_chan) const; }; diff --git a/include/stewkk/sql/logic/optimizer/physical_expr.hpp b/include/stewkk/sql/logic/optimizer/physical_expr.hpp index 4c6c43e..6342731 100644 --- a/include/stewkk/sql/logic/optimizer/physical_expr.hpp +++ b/include/stewkk/sql/logic/optimizer/physical_expr.hpp @@ -2,6 +2,7 @@ #include #include +#include #include #include @@ -16,6 +17,7 @@ namespace physical { struct SeqScan { std::string table; + std::optional alias; bool operator==(const SeqScan&) const = default; }; diff --git a/include/stewkk/sql/models/executor/tuple.hpp b/include/stewkk/sql/models/executor/tuple.hpp index b7189a6..2e6544c 100644 --- a/include/stewkk/sql/models/executor/tuple.hpp +++ b/include/stewkk/sql/models/executor/tuple.hpp @@ -8,6 +8,7 @@ namespace stewkk::sql { enum class Type { kInt, kBool, + kString, }; std::string ToString(Type type); @@ -24,6 +25,7 @@ struct AttributeInfo { union NonNullValue { int64_t int_value; bool bool_value; + int64_t string_id; }; std::string ToString(bool v); @@ -37,6 +39,8 @@ struct Value { }; std::string ToString(Value v, const AttributeInfo& attr); +int64_t InternString(std::string value); +const std::string& GetInternedString(int64_t id); using Tuple = std::vector; using Tuples = std::vector; diff --git a/include/stewkk/sql/models/parser/expression.hpp b/include/stewkk/sql/models/parser/expression.hpp index e6e2763..2a33c02 100644 --- a/include/stewkk/sql/models/parser/expression.hpp +++ b/include/stewkk/sql/models/parser/expression.hpp @@ -17,6 +17,7 @@ struct Attribute { std::string ToString(const Attribute& attr); using IntConst = std::int64_t; +using StringConst = std::string; enum class Literal { kNull, @@ -57,7 +58,7 @@ std::string ToString(UnaryOp op); struct BinaryExpression; struct UnaryExpression; -using Expression = std::variant; +using Expression = std::variant; std::string ToString(const Expression& expr); diff --git a/include/stewkk/sql/models/parser/relational_algebra_ast.hpp b/include/stewkk/sql/models/parser/relational_algebra_ast.hpp index 8c66a27..70f9e3f 100644 --- a/include/stewkk/sql/models/parser/relational_algebra_ast.hpp +++ b/include/stewkk/sql/models/parser/relational_algebra_ast.hpp @@ -1,9 +1,11 @@ #pragma once #include +#include #include #include #include +#include #include #include @@ -22,10 +24,13 @@ using Operator = std::variant; struct Table { std::string name; + std::optional alias; auto operator<=>(const Table& other) const = default; }; +std::string_view VisibleName(const Table& table); + struct Projection { std::vector expressions; std::shared_ptr source; diff --git a/research/converter.py b/research/converter.py index 16c029b..aa46dce 100644 --- a/research/converter.py +++ b/research/converter.py @@ -4,6 +4,7 @@ Serialized format (s-expressions): (SeqScan table) + (SeqScan table alias) (PhysicalFilter EXPR SOURCE) (PhysicalProjection (exprs EXPR...) SOURCE) (NestedLoopCrossJoin LHS RHS) @@ -82,6 +83,16 @@ def _convert_relop(relop: ET.Element) -> str: _SEEK_SCAN_TYPES = {"GT": ">", "GE": ">=", "LT": "<", "LE": "<=", "EQ": "="} +def _strip_sql_name(value: str | None) -> str: + return (value or "").strip("[]") + + +def _object_table_and_alias(obj: ET.Element) -> tuple[str, str | None]: + table = _strip_sql_name(obj.get("Table")) + alias = _strip_sql_name(obj.get("Alias")) + return table, alias or None + + def _convert_scan(relop: ET.Element) -> str: phys_op = relop.get("PhysicalOp", "") obj = relop.find(f".//{NS}IndexScan/{NS}Object") @@ -89,15 +100,16 @@ def _convert_scan(relop: ET.Element) -> str: obj = relop.find(f".//{NS}TableScan/{NS}Object") if obj is None: raise ValueError("cannot find Object element in scan") - table = (obj.get("Alias") or obj.get("Table", "")).strip("[]") + table, alias = _object_table_and_alias(obj) + visible_table = alias or table if "Seek" in phys_op: - seek_pred = _convert_seek_predicates(relop, table) + seek_pred = _convert_seek_predicates(relop, visible_table) if seek_pred is None: raise ValueError(f"Index Seek node has no SeekPredicates: {phys_op!r}") base = f"(IndexSeek {seek_pred} {table})" else: - base = f"(SeqScan {table})" + base = f"(SeqScan {table} {alias})" if alias else f"(SeqScan {table})" # Residual predicate (pushed-down filter evaluated after the scan/seek) pred_elem = relop.find(f".//{NS}IndexScan/{NS}Predicate/{NS}ScalarOperator") @@ -124,7 +136,7 @@ def _convert_seek_predicates(relop: ET.Element, table: str) -> str | None: expr_elems = range_elem.findall(f"{NS}RangeExpressions/{NS}ScalarOperator") for col_ref, expr_elem in zip(col_refs, expr_elems): col = col_ref.get("Column", "").strip("[]") - col_table = (col_ref.get("Alias") or col_ref.get("Table") or table).strip("[]") + col_table = _strip_sql_name(col_ref.get("Alias") or col_ref.get("Table") or table) conditions.append(f"({op} (attr {col_table} {col}) {_convert_scalar(expr_elem)})") if not conditions: @@ -194,7 +206,7 @@ def _expect_two_relops(parent: ET.Element, label: str) -> tuple[ET.Element, ET.E def _col_ref_to_attr(cr: ET.Element) -> str: - table = (cr.get("Alias") or cr.get("Table", "")).strip("[]") + table = _strip_sql_name(cr.get("Alias") or cr.get("Table")) col = cr.get("Column", "").strip("[]") return f"(attr {table} {col})" @@ -306,7 +318,7 @@ def _convert_identifier(identifier: ET.Element) -> str: f"parameter reference {column!r} — use OPTION (RECOMPILE) to embed literal values in the plan" ) - table = (col_ref.get("Alias") or col_ref.get("Table", "")).strip("[]") + table = _strip_sql_name(col_ref.get("Alias") or col_ref.get("Table")) return f"(attr {table} {column})" diff --git a/research/test_converter.py b/research/test_converter.py index 53d14a4..15ee9d0 100644 --- a/research/test_converter.py +++ b/research/test_converter.py @@ -5,6 +5,26 @@ from ms_sql_server_extractor import MsSqlServerExtractor from converter import convert +NS = "http://schemas.microsoft.com/sqlserver/2004/07/showplan" + + +def showplan(relop: str) -> str: + return f""" + + + + + + + {relop} + + + + + + + """ + @pytest.fixture(scope="session") def extractor(): @@ -28,18 +48,90 @@ def test_seq_scan(extractor): assert result == "(SeqScan Titles)" +def test_aliased_seq_scan_from_xml(): + plan = showplan(""" + + + + + + """) + + result = convert(plan) + + assert result == "(SeqScan Titles t)" + + def test_filter_eq(extractor): plan = extract_plan(extractor, "SELECT * FROM Titles WHERE Titles.isAdult = 1") result = convert(plan) assert result == "(PhysicalFilter (= (attr Titles isAdult) 1) (SeqScan Titles))" +def test_aliased_seq_scan_filter_from_xml(): + plan = showplan(""" + + + + + + + + + + + + + + + + + + + + """) + + result = convert(plan) + + assert result == "(PhysicalFilter (= (attr t isAdult) 1) (SeqScan Titles t))" + + def test_filter_gt(extractor): plan = extract_plan(extractor, "SELECT * FROM Titles WHERE Titles.titleId > 5000") result = convert(plan) assert result == "(IndexSeek (> (attr Titles titleId) 5000) Titles)" +def test_aliased_index_seek_from_xml(): + plan = showplan(""" + + + + + + + + + + + + + + + + + + + + + + """) + + result = convert(plan) + + assert result == "(IndexSeek (> (attr t titleId) 5000) Titles)" + + def test_filter_lt(extractor): plan = extract_plan(extractor, "SELECT * FROM Titles WHERE Titles.titleId < 5000") result = convert(plan) diff --git a/src/stewkk/sql/logic/executor/executor.cpp b/src/stewkk/sql/logic/executor/executor.cpp index 168072e..8917912 100644 --- a/src/stewkk/sql/logic/executor/executor.cpp +++ b/src/stewkk/sql/logic/executor/executor.cpp @@ -58,6 +58,16 @@ Type GetExpressionType(const Expression& expr, const AttributesInfo& available_a } return Type::kInt; } + if (std::ranges::contains(std::vector{BinaryOp::kAnd, BinaryOp::kOr}, binop.binop)) { + if (lhs_type != Type::kBool) { + throw std::logic_error{"types mismatch"}; + } + return Type::kBool; + } + if (lhs_type == Type::kString + && !std::ranges::contains(std::vector{BinaryOp::kEq, BinaryOp::kNotEq}, binop.binop)) { + throw std::logic_error{"strings support only = and != operators"}; + } return Type::kBool; } Type operator()(const UnaryExpression& unop) const { @@ -79,6 +89,9 @@ Type GetExpressionType(const Expression& expr, const AttributesInfo& available_a Type operator()(const IntConst& iconst) const { return Type::kInt; } + Type operator()(const StringConst& sconst) const { + return Type::kString; + } Type operator()(const Literal& literal) const { // NOTE: we are using switch because compiler will remind about adding // type of new literal here @@ -128,6 +141,9 @@ Type GetExpressionTypeUnchecked(const Expression& expr, const AttributesInfo& av Type operator()(const IntConst& iconst) const { return Type::kInt; } + Type operator()(const StringConst& sconst) const { + return Type::kString; + } Type operator()(const Literal& literal) const { switch (literal) { case Literal::kNull: @@ -268,6 +284,9 @@ Value CalcExpression(const Tuple& source, const AttributesInfo& source_attrs, co Value operator()(const IntConst& expr) { return Value{false, expr}; } + Value operator()(const StringConst& expr) { + return Value{false, {.string_id = InternString(expr)}}; + } Value operator()(const Literal& expr) { switch (expr) { case Literal::kNull: @@ -346,6 +365,29 @@ Tuple ConcatTuples(const Tuple& lhs, const Tuple& rhs) { return joined_tuple; } +bool ContainsStringExpression(const Expression& expr, const AttributesInfo& attrs) { + struct Visitor { + bool operator()(const BinaryExpression& expr) const { + return std::visit(*this, *expr.lhs) || std::visit(*this, *expr.rhs); + } + bool operator()(const UnaryExpression& expr) const { + return std::visit(*this, *expr.child); + } + bool operator()(const Attribute& attr) const { + auto it = std::find_if(attrs.begin(), attrs.end(), [&](const AttributeInfo& attr_info) { + return attr_info.name == attr.name && attr_info.table == attr.table; + }); + return it != attrs.end() && it->type == Type::kString; + } + bool operator()(const StringConst&) const { return true; } + bool operator()(const IntConst&) const { return false; } + bool operator()(const Literal&) const { return false; } + + const AttributesInfo& attrs; + }; + return std::visit(Visitor{attrs}, expr); +} + boost::asio::awaitable InterpretedExpressionExecutor::GetExpressionExecutor(const Expression& expr, const AttributesInfo& attrs) { struct Executor { Value operator()(const Tuple& source, const AttributesInfo& source_attrs) { @@ -358,6 +400,9 @@ boost::asio::awaitable InterpretedExpressionExecutor::GetExpress } boost::asio::awaitable JitCompiledExpressionExecutor::GetExpressionExecutor(const Expression& expr, const AttributesInfo& attrs) { + if (ContainsStringExpression(expr, attrs)) { + throw std::logic_error{"string expressions are not supported by JIT"}; + } struct Executor { Value operator()(const Tuple& source, const AttributesInfo& source_attrs) { Value result; @@ -377,6 +422,9 @@ JitCompiledExpressionExecutor::JitCompiledExpressionExecutor(boost::asio::any_io InterpretedExpressionExecutor::InterpretedExpressionExecutor(boost::asio::any_io_executor executor) {} boost::asio::awaitable CachedJitCompiledExpressionExecutor::GetExpressionExecutor(const Expression& expr, const AttributesInfo& attrs) { + if (ContainsStringExpression(expr, attrs)) { + throw std::logic_error{"string expressions are not supported by JIT"}; + } auto expr_str = ToString(expr); if (auto it = cache_.find(expr_str); it != cache_.end()) { co_return it->second; @@ -446,7 +494,8 @@ template boost::asio::awaitable Executor::Execute(const PhysicalPlanNode& op, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan) { struct ExecuteVisitor{ boost::asio::awaitable operator()(const SeqScan& seq_scan) { - co_await executor.sequential_scan_(seq_scan.table, attr_chan, tuples_chan); + co_await executor.sequential_scan_(seq_scan.table, std::string{OutputTable(seq_scan)}, + attr_chan, tuples_chan); co_return; } boost::asio::awaitable operator()(const PhysicalProjection& projection) { diff --git a/src/stewkk/sql/logic/executor/executor_test.cpp b/src/stewkk/sql/logic/executor/executor_test.cpp index 0d44949..e191b26 100644 --- a/src/stewkk/sql/logic/executor/executor_test.cpp +++ b/src/stewkk/sql/logic/executor/executor_test.cpp @@ -2,6 +2,7 @@ #include #include +#include #include #include @@ -22,7 +23,7 @@ namespace { PhysicalPlanNode ToPhysicalPlan(const Operator& op) { return std::visit(utils::Overloaded{ [](const Table& t) -> PhysicalPlanNode { - return SeqScan{.table = t.name}; + return SeqScan{.table = t.name, .alias = t.alias}; }, [](const Projection& p) -> PhysicalPlanNode { return PhysicalProjection{ @@ -125,6 +126,62 @@ TEST(ExecutorTest, SimpleSelectWithParallelism) { pool.join(); } +TEST(ExecutorTest, StringFilterProjectionAndCsvQuotes) { + boost::asio::io_context ctx; + boost::asio::co_spawn( + ctx, + []() -> boost::asio::awaitable> { + std::stringstream s{"SELECT m.region, m.note FROM markets AS m WHERE m.region = 'AMERICA';"}; + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value().op); + CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; + Executor executor( + std::move(seq_scan), co_await boost::asio::this_coro::executor); + + auto got = co_await executor.Execute(op); + + co_return got; + }(), + [](std::exception_ptr p, Result got) { + if (p) std::rethrow_exception(p); + + ASSERT_THAT(got.value().attributes, + Eq(AttributesInfo{{"m", "region", Type::kString}, {"m", "note", Type::kString}})); + ASSERT_THAT(got.value().tuples.size(), Eq(2)); + ASSERT_THAT(GetInternedString(got.value().tuples[0][0].value.string_id), Eq("AMERICA")); + ASSERT_THAT(GetInternedString(got.value().tuples[0][1].value.string_id), Eq("North, South")); + ASSERT_THAT(GetInternedString(got.value().tuples[1][1].value.string_id), Eq("Fast lane")); + }); + + ctx.run(); +} + +TEST(ExecutorTest, JitRejectsStringExpressions) { + boost::asio::io_context ctx; + bool rejected = false; + boost::asio::co_spawn( + ctx, + [&rejected]() -> boost::asio::awaitable { + std::stringstream s{"SELECT m.id FROM markets AS m WHERE m.region = 'AMERICA';"}; + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value().op); + CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; + Executor executor( + std::move(seq_scan), co_await boost::asio::this_coro::executor); + + try { + (void) co_await executor.Execute(op); + } catch (const std::logic_error& e) { + rejected = std::string_view{e.what()}.find("string expressions are not supported by JIT") + != std::string_view::npos; + } + }(), + [](std::exception_ptr p) { + if (p) std::rethrow_exception(p); + }); + + ctx.run(); + ASSERT_THAT(rejected, Eq(true)); +} + TYPED_TEST_P(ExecutorTest, Projection) { boost::asio::thread_pool pool{4}; boost::asio::co_spawn( diff --git a/src/stewkk/sql/logic/executor/llvm.cpp b/src/stewkk/sql/logic/executor/llvm.cpp index f2dd0c1..12bb750 100644 --- a/src/stewkk/sql/logic/executor/llvm.cpp +++ b/src/stewkk/sql/logic/executor/llvm.cpp @@ -307,6 +307,9 @@ llvm::Function* JITCompiler::GenerateIR( return llvm::ConstantStruct::get(static_cast(value_type), {builder.getInt8(0), builder.getInt64(expr)}); } + llvm::Value* operator()(const StringConst&) { + throw std::logic_error{"string expressions are not supported by JIT"}; + } llvm::Value* operator()(const Literal& expr) { switch (expr) { case Literal::kNull: diff --git a/src/stewkk/sql/logic/executor/plan.cpp b/src/stewkk/sql/logic/executor/plan.cpp index f7aa541..cbba2f1 100644 --- a/src/stewkk/sql/logic/executor/plan.cpp +++ b/src/stewkk/sql/logic/executor/plan.cpp @@ -2,6 +2,10 @@ namespace stewkk::sql { +std::string_view OutputTable(const SeqScan& scan) { + return scan.alias ? std::string_view{*scan.alias} : std::string_view{scan.table}; +} + bool PhysicalProjection::operator==(const PhysicalProjection& other) const { return *source == *other.source && expressions == other.expressions; } diff --git a/src/stewkk/sql/logic/executor/plan_serializer.cpp b/src/stewkk/sql/logic/executor/plan_serializer.cpp index 5df35da..0480822 100644 --- a/src/stewkk/sql/logic/executor/plan_serializer.cpp +++ b/src/stewkk/sql/logic/executor/plan_serializer.cpp @@ -16,6 +16,48 @@ namespace { std::string SerializeExpr(const Expression& expr); std::string SerializeNode(const PhysicalPlanNode& node); +std::string QuoteString(std::string_view value) { + std::string out; + out.reserve(value.size() + 2); + out.push_back('"'); + for (char c : value) { + switch (c) { + case '\\': out += "\\\\"; break; + case '"': out += "\\\""; break; + case '\n': out += "\\n"; break; + case '\t': out += "\\t"; break; + default: out.push_back(c); break; + } + } + out.push_back('"'); + return out; +} + +std::string UnquoteString(std::string_view value) { + if (value.size() < 2 || value.front() != '"' || value.back() != '"') { + throw std::runtime_error("expected quoted string"); + } + std::string out; + for (size_t i = 1; i + 1 < value.size(); ++i) { + if (value[i] != '\\') { + out.push_back(value[i]); + continue; + } + if (i + 1 >= value.size() - 1) { + throw std::runtime_error("unterminated string escape"); + } + char escaped = value[++i]; + switch (escaped) { + case '\\': out.push_back('\\'); break; + case '"': out.push_back('"'); break; + case 'n': out.push_back('\n'); break; + case 't': out.push_back('\t'); break; + default: throw std::runtime_error(std::format("unsupported string escape: \\{}", escaped)); + } + } + return out; +} + std::string SerializeJoinType(JoinType t) { switch (t) { case JoinType::kInner: return "Inner"; @@ -64,6 +106,9 @@ std::string SerializeExpr(const Expression& expr) { std::string operator()(IntConst n) const { return std::to_string(n); } + std::string operator()(const StringConst& s) const { + return std::format("(str {})", QuoteString(s)); + } std::string operator()(const UnaryExpression& e) const { return std::format("({} {})", SerializeUnaryOp(e.op), SerializeExpr(*e.child)); } @@ -82,6 +127,9 @@ std::string SerializeExpr(const Expression& expr) { std::string SerializeNode(const PhysicalPlanNode& node) { struct Visitor { std::string operator()(const SeqScan& n) const { + if (n.alias) { + return std::format("(SeqScan {} {})", n.table, *n.alias); + } return std::format("(SeqScan {})", n.table); } std::string operator()(const PhysicalFilter& n) const { @@ -148,6 +196,26 @@ std::vector Tokenize(std::string_view input) { if (std::isspace(static_cast(input[i]))) { ++i; continue; } if (input[i] == '(') { tokens.push_back({TokenKind::LParen, "("}); ++i; continue; } if (input[i] == ')') { tokens.push_back({TokenKind::RParen, ")"}); ++i; continue; } + if (input[i] == '"') { + size_t j = i + 1; + bool escaped = false; + while (j < input.size()) { + if (escaped) { + escaped = false; + } else if (input[j] == '\\') { + escaped = true; + } else if (input[j] == '"') { + ++j; + break; + } + ++j; + } + if (j > input.size() || input[j - 1] != '"') + throw std::runtime_error("unterminated quoted string"); + tokens.push_back({TokenKind::Atom, std::string(input.substr(i, j - i))}); + i = j; + continue; + } size_t j = i; while (j < input.size() && !std::isspace(static_cast(input[j])) @@ -244,6 +312,11 @@ Expression ParseExpr(ParseState& s) { s.ExpectRParen(); return Attribute{std::move(table), std::move(name)}; } + if (head == "str") { + auto value = UnquoteString(s.ExpectAtom()); + s.ExpectRParen(); + return StringConst{std::move(value)}; + } if (auto it = kBinaryOps.find(head); it != kBinaryOps.end()) { auto lhs = ParseExpr(s); auto rhs = ParseExpr(s); @@ -271,8 +344,12 @@ PhysicalPlanNode ParseNode(ParseState& s) { if (head == "SeqScan") { auto table = s.ExpectAtom(); + std::optional alias; + if (s.Peek().kind != TokenKind::RParen) { + alias = s.ExpectAtom(); + } s.ExpectRParen(); - return SeqScan{std::move(table)}; + return SeqScan{std::move(table), std::move(alias)}; } if (head == "PhysicalFilter") { auto pred = ParseExpr(s); @@ -388,6 +465,9 @@ struct DotBuilder { } int operator()(const SeqScan& n) { + if (n.alias) { + return Emit(std::format("SeqScan\\n{} AS {}", n.table, *n.alias)); + } return Emit(std::format("SeqScan\\n{}", n.table)); } int operator()(const PhysicalFilter& n) { diff --git a/src/stewkk/sql/logic/executor/plan_serializer_test.cpp b/src/stewkk/sql/logic/executor/plan_serializer_test.cpp index b617101..190692e 100644 --- a/src/stewkk/sql/logic/executor/plan_serializer_test.cpp +++ b/src/stewkk/sql/logic/executor/plan_serializer_test.cpp @@ -20,6 +20,12 @@ TEST(PlanSerializerTest, SeqScan) { EXPECT_THAT(Serialize(plan), Eq("(SeqScan users)")); } +TEST(PlanSerializerTest, SeqScanAlias) { + PhysicalPlanNode plan = SeqScan{"customer", "c"}; + EXPECT_THAT(RoundTrip(plan), Eq(plan)); + EXPECT_THAT(Serialize(plan), Eq("(SeqScan customer c)")); +} + TEST(PlanSerializerTest, PhysicalFilter) { PhysicalPlanNode plan = PhysicalFilter{ std::make_shared(SeqScan{"users"}), @@ -90,6 +96,15 @@ TEST(PlanSerializerTest, ExprIntConst) { EXPECT_THAT(RoundTrip(plan), Eq(plan)); } +TEST(PlanSerializerTest, ExprStringConst) { + PhysicalPlanNode plan = PhysicalFilter{ + std::make_shared(SeqScan{"t"}), + StringConst{"Bob's Market \"North\""}, + }; + EXPECT_THAT(RoundTrip(plan), Eq(plan)); + EXPECT_THAT(Serialize(plan), Eq("(PhysicalFilter (str \"Bob's Market \\\"North\\\"\") (SeqScan t))")); +} + TEST(PlanSerializerTest, ExprLiterals) { for (auto lit : {Literal::kNull, Literal::kTrue, Literal::kFalse, Literal::kUnknown}) { PhysicalPlanNode plan = PhysicalFilter{ diff --git a/src/stewkk/sql/logic/executor/sequential_scan.cpp b/src/stewkk/sql/logic/executor/sequential_scan.cpp index 26dc178..453c1a5 100644 --- a/src/stewkk/sql/logic/executor/sequential_scan.cpp +++ b/src/stewkk/sql/logic/executor/sequential_scan.cpp @@ -1,8 +1,11 @@ #include #include +#include #include #include +#include +#include #include @@ -17,38 +20,80 @@ Type GetTypeFromString(const std::string& s) { if (s == "int") { return Type::kInt; } + if (s == "string") { + return Type::kString; + } std::unreachable(); } -Value BuildValueFromString(Type type, const std::string& table, const std::string attr_name, const std::string& value) { +Value BuildValueFromString(Type type, const std::string& value) { if (value == "NULL") { return Value{true}; } switch (type) { case Type::kInt: return Value{false, std::stoi(value)}; + case Type::kString: + return Value{false, {.string_id = InternString(value)}}; default: std::unreachable(); } } +std::vector ParseCsvLine(const std::string& line) { + auto input = std::string_view{line}; + if (!input.empty() && input.back() == '\r') { + input.remove_suffix(1); + } + std::vector fields; + std::string field; + bool in_quotes = false; + for (size_t i = 0; i < input.size(); ++i) { + char c = input[i]; + if (in_quotes) { + if (c == '"') { + if (i + 1 < input.size() && input[i + 1] == '"') { + field.push_back('"'); + ++i; + } else { + in_quotes = false; + } + } else { + field.push_back(c); + } + } else if (c == '"') { + in_quotes = true; + } else if (c == ',') { + fields.push_back(std::move(field)); + field.clear(); + } else { + field.push_back(c); + } + } + fields.push_back(std::move(field)); + return fields; +} + Tuple ParseTuple(const std::string& line, - const AttributesInfo& attributes, - const std::string& table_name) { - return line | std::views::split(',') | std::views::enumerate - | std::views::transform([&attributes, &table_name](const auto& attr) { - const auto& [index, value_range] = attr; - auto value = value_range | std::ranges::to(); + const AttributesInfo& attributes) { + auto fields = ParseCsvLine(line); + if (fields.size() != attributes.size()) { + throw std::runtime_error{std::format("CSV row has {} fields, expected {}", fields.size(), attributes.size())}; + } + return fields | std::views::enumerate + | std::views::transform([&attributes](const auto& attr) { + const auto& [index, value] = attr; const auto& [_, attr_name, type] = attributes[index]; - return BuildValueFromString(type, table_name, attr_name, value); + return BuildValueFromString(type, value); }) - | std::ranges::to(); + | std::ranges::to(); } } // namespace boost::asio::awaitable> CsvDirSequentialScanner::operator()( - const std::string& table_name, AttributesInfoChannel& attrs_chan, + const std::string& table_name, const std::string& output_table_name, + AttributesInfoChannel& attrs_chan, TuplesChannel& tuples_chan) const { #ifdef DEBUG std::clog << "Executing sequential scan\n"; @@ -57,11 +102,11 @@ boost::asio::awaitable> CsvDirSequentialScanner::operator()( std::ifstream input{std::move(path)}; std::string line; std::getline(input, line); - auto attributes = line | std::views::split(',') | std::views::transform([&table_name](const auto& attr) { + auto attributes = line | std::views::split(',') | std::views::transform([&output_table_name](const auto& attr) { auto mid = std::find(attr.begin(), attr.end(), ':'); auto attr_name = std::string{attr.begin(), mid}; auto attr_type = GetTypeFromString(std::string{mid + 1, attr.end()}); - return AttributeInfo{table_name, std::move(attr_name), attr_type}; + return AttributeInfo{output_table_name, std::move(attr_name), attr_type}; }) | std::ranges::to(); @@ -72,7 +117,7 @@ boost::asio::awaitable> CsvDirSequentialScanner::operator()( Tuples buf; buf.reserve(kBufSize); while (std::getline(input, line)) { - auto tuple = ParseTuple(line, attributes, table_name); + auto tuple = ParseTuple(line, attributes); buf.emplace_back(std::move(tuple)); #ifdef DEBUG std::clog << std::format("buf size is {}\n", buf.size()); diff --git a/src/stewkk/sql/logic/implementation_rules/implement_table.cpp b/src/stewkk/sql/logic/implementation_rules/implement_table.cpp index b164175..623b26e 100644 --- a/src/stewkk/sql/logic/implementation_rules/implement_table.cpp +++ b/src/stewkk/sql/logic/implementation_rules/implement_table.cpp @@ -8,7 +8,7 @@ bool ImplementTable::IsApplicable(utils::NotNull expr) { utils::NotNull ImplementTable::Apply(utils::NotNull expr, Memo&) { auto& table = std::get(expr->root_operator); - return expr->group->AddPhysicalExpr(physical::SeqScan{table.name}); + return expr->group->AddPhysicalExpr(physical::SeqScan{table.name, table.alias}); } } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/memo.cpp b/src/stewkk/sql/logic/optimizer/memo.cpp index 51653c7..8741918 100644 --- a/src/stewkk/sql/logic/optimizer/memo.cpp +++ b/src/stewkk/sql/logic/optimizer/memo.cpp @@ -8,7 +8,7 @@ namespace { std::string ToKey(const LogicalOperator& op) { return std::visit(utils::Overloaded{ [](const logical::Table& t) { - return "Table(" + t.name + ")"; + return "Table(" + t.name + "," + std::string{VisibleName(t)} + ")"; }, [](const logical::Filter& f) { return "Filter(" + ToString(f.predicate) + "," + std::to_string(f.source->GetId()) + ")"; @@ -74,7 +74,7 @@ utils::NotNull Memo::AddLogicalExprToGroup(utils::NotNull utils::NotNull Memo::Populate(const Operator& op) { return std::visit(utils::Overloaded{ [this](const Table& t) { - return AddGroup(logical::Table{t.name}); + return AddGroup(logical::Table{t.name, t.alias}); }, [this](const Filter& f) { auto source = Populate(*f.source); diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index ad7ac30..be5ffa3 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -400,7 +400,7 @@ PhysicalPlanNode Optimizer::BuildOptimalPlan(G return std::visit( utils::Overloaded{ [](const physical::SeqScan& op) -> PhysicalPlanNode { - return SeqScan{.table = op.table}; + return SeqScan{.table = op.table, .alias = op.alias}; }, [this, best_expr_nn, required](const physical::Projection& op) -> PhysicalPlanNode { return PhysicalProjection{ diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index 01c40fa..45190f3 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -102,6 +102,23 @@ TEST(OptimizerTest, OrderBy) { ASSERT_THAT(Serialize(got), Eq("(Sort (keys users.id Asc) (SeqScan users))")); } +TEST(OptimizerTest, AliasedJoinOptimizesWithAliasQualifiedAttrs) { + std::stringstream s{"SELECT c.id FROM customers AS c JOIN orders AS o ON c.id = o.customer_id WHERE c.region_id = 1;"}; + Operator op = GetAST(s).value().op; + Optimizer optimizer(op, MakeMainRules(), CardinalityEstimates({ + {"customers", 500}, + {"orders", 5000}, + })); + + auto got = optimizer.Optimize(); + auto serialized = Serialize(got); + + ASSERT_THAT(serialized, HasSubstr("(SeqScan customers c)")); + ASSERT_THAT(serialized, HasSubstr("(SeqScan orders o)")); + ASSERT_THAT(serialized, HasSubstr("(attr c id)")); + ASSERT_THAT(serialized, HasSubstr("(attr o customer_id)")); +} + TEST(ReachabilityTest, SeqScanReachable) { std::stringstream s{"SELECT * FROM users;"}; auto result = IsPlanReachable(s, SeqScan{"users"}); diff --git a/src/stewkk/sql/logic/optimizer/reachability.cpp b/src/stewkk/sql/logic/optimizer/reachability.cpp index 8f8101a..b4ef7d5 100644 --- a/src/stewkk/sql/logic/optimizer/reachability.cpp +++ b/src/stewkk/sql/logic/optimizer/reachability.cpp @@ -28,6 +28,10 @@ InternalMatch TryMatchExpr(utils::NotNull pe, if (op.table != t->table) return {false, depth, std::format("SeqScan table '{}' != '{}'", op.table, t->table)}; + if (op.alias != t->alias) + return {false, depth, + std::format("SeqScan alias '{}' != '{}'", + op.alias.value_or(""), t->alias.value_or(""))}; return {true, depth + 1, {}}; }, [&](const physical::Filter& op) -> InternalMatch { diff --git a/src/stewkk/sql/logic/optimizer/schema_catalog.cpp b/src/stewkk/sql/logic/optimizer/schema_catalog.cpp index d1d04f1..1502c64 100644 --- a/src/stewkk/sql/logic/optimizer/schema_catalog.cpp +++ b/src/stewkk/sql/logic/optimizer/schema_catalog.cpp @@ -23,7 +23,11 @@ std::optional SchemaCatalog::Derive(const LogicalOperator& op) { [this](const logical::Table& t) -> std::optional { auto it = tables_.find(t.name); if (it == tables_.end()) return std::nullopt; - return it->second; + auto schema = it->second; + for (auto& attr : schema) { + attr.table = std::string{VisibleName(t)}; + } + return schema; }, [this](const logical::Filter& f) -> std::optional { return GetSchema(f.source); diff --git a/src/stewkk/sql/logic/parser/parser.cpp b/src/stewkk/sql/logic/parser/parser.cpp index e84a997..b65f902 100644 --- a/src/stewkk/sql/logic/parser/parser.cpp +++ b/src/stewkk/sql/logic/parser/parser.cpp @@ -67,6 +67,9 @@ std::string GetDotRepresentation(const Expression& expr) { std::string operator()(const IntConst& expr) { return std::to_string(expr); } + std::string operator()(const StringConst& expr) { + return ToString(Expression{expr}); + } std::string operator()(const Literal& expr) { return ToString(expr); } @@ -91,7 +94,8 @@ std::string GetDotRepresentation(const Operator& op) { return {node, std::format("\"{}\"\n\"{}\" -> \"{}\"\n{}", node, source_node, node, rest)}; } std::pair operator()(const Table& op) { - auto node = std::format("{}", op.name); + auto node = op.alias ? std::format("{} AS {}", op.name, *op.alias) + : std::format("{}", op.name); return {node, std::format("\"{}\"", node)}; } std::pair operator()(const CrossJoin& op) { diff --git a/src/stewkk/sql/logic/parser/parser_test.cpp b/src/stewkk/sql/logic/parser/parser_test.cpp index cc6afd3..4c73ad7 100644 --- a/src/stewkk/sql/logic/parser/parser_test.cpp +++ b/src/stewkk/sql/logic/parser/parser_test.cpp @@ -72,6 +72,53 @@ TEST(ParserTest, SelectWithWhereClause) { std::make_shared(Table{"users"})})})); } +TEST(ParserTest, SelectWithTableAlias) { + std::stringstream s{"SELECT u.id FROM users u WHERE u.age > 18;"}; + + Operator got = GetAST(s).value().op; + + ASSERT_THAT(got, VariantWith(Projection{ + std::vector{Attribute{"u", "id"}}, + std::make_shared( + Filter{Expression{BinaryExpression{ + std::make_shared(Attribute{"u", "age"}), + BinaryOp::kGt, std::make_shared(IntConst{18})}}, + std::make_shared(Table{"users", "u"})})})); +} + +TEST(ParserTest, SelectWithTableAsAlias) { + std::stringstream s{"SELECT u.id FROM users AS u;"}; + + Operator got = GetAST(s).value().op; + + ASSERT_THAT(got, VariantWith(Projection{ + std::vector{Attribute{"u", "id"}}, + std::make_shared(Table{"users", "u"})})); +} + +TEST(ParserTest, AliasColumnListRejected) { + std::stringstream s{"SELECT u.id FROM users AS u(id);"}; + + auto got = GetAST(s).error(); + + ASSERT_THAT(got.Wraps(ErrorType::kQueryNotSupported), IsTrue()); +} + +TEST(ParserTest, SelectWithStringLiteral) { + std::stringstream s{"SELECT users.id FROM users WHERE users.name = 'Bob''s Market';"}; + + Operator got = GetAST(s).value().op; + + ASSERT_THAT(got, VariantWith(Projection{ + std::vector{Attribute{"users", "id"}}, + std::make_shared( + Filter{Expression{BinaryExpression{ + std::make_shared(Attribute{"users", "name"}), + BinaryOp::kEq, + std::make_shared(StringConst{"Bob's Market"})}}, + std::make_shared(Table{"users"})})})); +} + TEST(ParserTest, GetDotRepresentation) { std::stringstream s{"SELECT users.id FROM users WHERE users.age > 18;"}; Operator op = GetAST(s).value().op; diff --git a/src/stewkk/sql/logic/parser/visitor.cpp b/src/stewkk/sql/logic/parser/visitor.cpp index 8a2c392..37477cc 100644 --- a/src/stewkk/sql/logic/parser/visitor.cpp +++ b/src/stewkk/sql/logic/parser/visitor.cpp @@ -10,6 +10,24 @@ namespace stewkk::sql { namespace { +std::string UnquoteStandardString(std::string_view text) { + if (text.size() < 2 || text.front() != '\'' || text.back() != '\'') { + throw Error{ErrorType::kQueryNotSupported, "only standard single-quoted strings are supported"}; + } + + std::string result; + result.reserve(text.size() - 2); + for (size_t i = 1; i + 1 < text.size(); ++i) { + if (text[i] == '\'' && i + 1 < text.size() - 1 && text[i + 1] == '\'') { + result.push_back('\''); + ++i; + continue; + } + result.push_back(text[i]); + } + return result; +} + Operator GetOperatorWithChild(Operator&& op, Operator&& child) { struct ChildSetter { Operator operator() (Table&& parent) { @@ -209,9 +227,6 @@ std::pair ExtractAtom(Visitor* v, TableRefCtx* ctx) { if (ctx->LATERAL_P()) { throw Error{ErrorType::kQueryNotSupported, "LATERAL clause is not supported"}; } - if (ctx->alias_clause()) { - throw Error{ErrorType::kQueryNotSupported, "alias_clause is not supported"}; - } if (ctx->tablesample_clause()) { throw Error{ErrorType::kQueryNotSupported, "tablesample_clause is not supported"}; } @@ -220,12 +235,28 @@ std::pair ExtractAtom(Visitor* v, TableRefCtx* ctx) { auto it = children.cbegin(); Operator res; if (ctx->relation_expr()) { - res = Table{std::any_cast(v->visit(ctx->relation_expr()))}; + auto table = Table{std::any_cast(v->visit(ctx->relation_expr()))}; + if (auto* alias = ctx->alias_clause()) { + if (alias->name_list()) { + throw Error{ErrorType::kQueryNotSupported, "alias column lists are not supported"}; + } + table.alias = alias->colid()->getText(); + } + res = std::move(table); ++it; + if (ctx->alias_clause()) { + ++it; + } } else if (ctx->select_with_parens()) { + if (ctx->alias_clause()) { + throw Error{ErrorType::kQueryNotSupported, "aliases on subqueries are not supported"}; + } res = std::any_cast(v->visit(ctx->select_with_parens())); ++it; } else if (ctx->OPEN_PAREN()) { + if (ctx->alias_clause()) { + throw Error{ErrorType::kQueryNotSupported, "aliases on parenthesized table refs are not supported"}; + } res = BuildTableRef(v, ctx->table_ref(0)); it += 3; } @@ -704,7 +735,15 @@ std::any Visitor::visitAexprconst(codegen::PostgreSQLParser::AexprconstContext * throw Error{ErrorType::kQueryNotSupported, "intervals are not supported"}; } if (ctx->sconst()) { - throw Error{ErrorType::kQueryNotSupported, "strings are not supported"}; + auto* sconst = ctx->sconst(); + if (sconst->uescape_()) { + throw Error{ErrorType::kQueryNotSupported, "UESCAPE strings are not supported"}; + } + auto* any = sconst->anysconst(); + if (!any->StringConstant()) { + throw Error{ErrorType::kQueryNotSupported, "only standard single-quoted strings are supported"}; + } + return Expression{StringConst{UnquoteStandardString(any->StringConstant()->getText())}}; } if (ctx->xconst()) { throw Error{ErrorType::kQueryNotSupported, "hex literals are not supported"}; diff --git a/src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.cpp b/src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.cpp index 0a0a5d9..2ab27b2 100644 --- a/src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.cpp +++ b/src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.cpp @@ -20,6 +20,7 @@ void CollectAttributes(const Expression& e, std::vector& out) { }, [&](const UnaryExpression& u) { CollectAttributes(*u.child, out); }, [&](const IntConst&) {}, + [&](const StringConst&) {}, [&](const Literal&) {}, }, e); } diff --git a/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp b/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp index 4c73ee6..c8128e8 100644 --- a/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp +++ b/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp @@ -43,6 +43,7 @@ void CollectAttrTables(const Expression& e, std::unordered_set& out }, [&](const UnaryExpression& u) { CollectAttrTables(*u.child, out); }, [&](const IntConst&) {}, + [&](const StringConst&) {}, [&](const Literal&) {}, }, e); } @@ -63,7 +64,7 @@ void CollectGroupTables(utils::NotNull g, std::unordered_setGetLogicalExprs(); if (exprs.empty()) return; std::visit(utils::Overloaded{ - [&](const logical::Table& t) { out.insert(t.name); }, + [&](const logical::Table& t) { out.insert(std::string{VisibleName(t)}); }, [&](const logical::Filter& f) { CollectGroupTables(f.source, out, seen); }, [&](const logical::Projection& p) { CollectGroupTables(p.source, out, seen); }, [&](const logical::CrossJoin& j) { diff --git a/src/stewkk/sql/main.cpp b/src/stewkk/sql/main.cpp index 6358eb9..041f940 100644 --- a/src/stewkk/sql/main.cpp +++ b/src/stewkk/sql/main.cpp @@ -120,7 +120,8 @@ std::string SerializeAst(const Operator& op) { return std::visit([](auto&& node) -> std::string { using T = std::decay_t; if constexpr (std::is_same_v) { - return "(Table " + node.name + ")"; + return node.alias ? "(Table " + node.name + " AS " + *node.alias + ")" + : "(Table " + node.name + ")"; } else if constexpr (std::is_same_v) { return "(Filter " + ToString(node.expr) + " " + SerializeAst(*node.source) + ")"; } else if constexpr (std::is_same_v) { @@ -144,6 +145,8 @@ std::string ValueToString(const Value& v, const AttributeInfo& attr) { return std::to_string(v.value.int_value); case Type::kBool: return v.value.bool_value ? "1" : "0"; + case Type::kString: + return GetInternedString(v.value.string_id); } return "?"; } @@ -154,6 +157,8 @@ std::string TypeName(Type t) { return "int"; case Type::kBool: return "bool"; + case Type::kString: + return "string"; } return "?"; } diff --git a/src/stewkk/sql/models/executor/tuple.cpp b/src/stewkk/sql/models/executor/tuple.cpp index 463cbb3..5601a26 100644 --- a/src/stewkk/sql/models/executor/tuple.cpp +++ b/src/stewkk/sql/models/executor/tuple.cpp @@ -1,16 +1,42 @@ #include +#include +#include +#include #include #include +#include +#include namespace stewkk::sql { +namespace { + +std::mutex& StringPoolMutex() { + static std::mutex mutex; + return mutex; +} + +std::deque& StringPoolValues() { + static std::deque values; + return values; +} + +std::unordered_map& StringPoolIds() { + static std::unordered_map ids; + return ids; +} + +} // namespace + std::string ToString(Type type) { switch (type) { case Type::kInt: return "int"; case Type::kBool: return "bool"; + case Type::kString: + return "string"; } std::unreachable(); } @@ -29,6 +55,9 @@ std::string ToString(Value v, const AttributeInfo& attr) { if (attr.type == Type::kInt) { return std::format("{:<8}", v.value.int_value); } + if (attr.type == Type::kString) { + return std::format("{:<8}", GetInternedString(v.value.string_id)); + } return ToString(v.value.bool_value)+' '; } @@ -48,4 +77,26 @@ bool Value::operator==(const Value& other) const { return (is_null && other.is_null) || (!is_null && !other.is_null && value.int_value == other.value.int_value); } +int64_t InternString(std::string value) { + std::lock_guard lock{StringPoolMutex()}; + auto& ids = StringPoolIds(); + if (auto it = ids.find(value); it != ids.end()) { + return it->second; + } + auto& values = StringPoolValues(); + auto id = static_cast(values.size()); + values.push_back(std::move(value)); + ids.emplace(values.back(), id); + return id; +} + +const std::string& GetInternedString(int64_t id) { + std::lock_guard lock{StringPoolMutex()}; + auto& values = StringPoolValues(); + if (id < 0 || static_cast(id) >= values.size()) { + throw std::out_of_range{"string id is not interned"}; + } + return values[static_cast(id)]; +} + } // namespace stewkk::sql diff --git a/src/stewkk/sql/models/parser/expression.cpp b/src/stewkk/sql/models/parser/expression.cpp index cb37c9b..ec31786 100644 --- a/src/stewkk/sql/models/parser/expression.cpp +++ b/src/stewkk/sql/models/parser/expression.cpp @@ -84,6 +84,17 @@ std::string ToString(const Expression& expr) { std::string operator()(const IntConst& expr) { return std::to_string(expr); } + std::string operator()(const StringConst& expr) { + std::string escaped; + escaped.reserve(expr.size() + 2); + escaped.push_back('\''); + for (char c : expr) { + if (c == '\'') escaped += "''"; + else escaped.push_back(c); + } + escaped.push_back('\''); + return escaped; + } std::string operator()(const UnaryExpression& expr) { return std::format("{} {}", ToString(expr.op), ToString(*expr.child)); } diff --git a/src/stewkk/sql/models/parser/relational_algebra_ast.cpp b/src/stewkk/sql/models/parser/relational_algebra_ast.cpp index 9fadfcf..2774e59 100644 --- a/src/stewkk/sql/models/parser/relational_algebra_ast.cpp +++ b/src/stewkk/sql/models/parser/relational_algebra_ast.cpp @@ -2,6 +2,10 @@ namespace stewkk::sql { +std::string_view VisibleName(const Table& table) { + return table.alias ? std::string_view{*table.alias} : std::string_view{table.name}; +} + bool Projection::operator==(const Projection& other) const { return expressions == other.expressions && *source == *other.source; } diff --git a/test/static/executor/test_data/markets.csv b/test/static/executor/test_data/markets.csv new file mode 100644 index 0000000..59c1ab1 --- /dev/null +++ b/test/static/executor/test_data/markets.csv @@ -0,0 +1,4 @@ +id:int,region:string,note:string +1,AMERICA,"North, South" +2,EUROPE,Old World +3,AMERICA,Fast lane From 950a8d5d914a50c2115a108a6ab3dbc1a2fe736e Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Fri, 29 May 2026 22:57:59 +0300 Subject: [PATCH 057/120] Impl IN and BETWEEN support --- .../stewkk/sql/models/parser/expression.hpp | 13 +- src/stewkk/sql/logic/executor/executor.cpp | 81 +++++++++++ .../sql/logic/executor/executor_test.cpp | 133 ++++++++++++++++++ src/stewkk/sql/logic/executor/llvm.cpp | 3 + .../sql/logic/executor/plan_serializer.cpp | 28 ++++ .../logic/executor/plan_serializer_test.cpp | 27 ++++ src/stewkk/sql/logic/parser/parser.cpp | 3 + src/stewkk/sql/logic/parser/parser_test.cpp | 100 +++++++++++++ src/stewkk/sql/logic/parser/visitor.cpp | 56 ++++++-- .../filter_pushdown_through_projection.cpp | 6 + .../transformation_rules/predicate_utils.cpp | 6 + src/stewkk/sql/models/parser/expression.cpp | 11 ++ 12 files changed, 458 insertions(+), 9 deletions(-) diff --git a/include/stewkk/sql/models/parser/expression.hpp b/include/stewkk/sql/models/parser/expression.hpp index 2a33c02..4c53676 100644 --- a/include/stewkk/sql/models/parser/expression.hpp +++ b/include/stewkk/sql/models/parser/expression.hpp @@ -4,6 +4,7 @@ #include #include #include +#include namespace stewkk::sql { @@ -57,8 +58,10 @@ std::string ToString(UnaryOp op); struct BinaryExpression; struct UnaryExpression; +struct InExpression; -using Expression = std::variant; +using Expression = std::variant; std::string ToString(const Expression& expr); @@ -77,4 +80,12 @@ struct UnaryExpression { bool operator==(const UnaryExpression& other) const; }; +struct InExpression { + std::shared_ptr lhs; + std::vector values; + bool negated = false; + + bool operator==(const InExpression& other) const; +}; + } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/executor/executor.cpp b/src/stewkk/sql/logic/executor/executor.cpp index 8917912..8d762cb 100644 --- a/src/stewkk/sql/logic/executor/executor.cpp +++ b/src/stewkk/sql/logic/executor/executor.cpp @@ -86,6 +86,20 @@ Type GetExpressionType(const Expression& expr, const AttributesInfo& available_a } return Type::kBool; } + Type operator()(const InExpression& in) const { + auto lhs_type = std::visit(*this, *in.lhs); + for (const auto& value : in.values) { + if (const auto* lit = std::get_if(&value); + lit && (*lit == Literal::kNull || *lit == Literal::kUnknown)) { + continue; + } + auto value_type = std::visit(*this, value); + if (value_type != lhs_type) { + throw std::logic_error{"types mismatch"}; + } + } + return Type::kBool; + } Type operator()(const IntConst& iconst) const { return Type::kInt; } @@ -138,6 +152,9 @@ Type GetExpressionTypeUnchecked(const Expression& expr, const AttributesInfo& av } return Type::kBool; } + Type operator()(const InExpression& in) const { + return Type::kBool; + } Type operator()(const IntConst& iconst) const { return Type::kInt; } @@ -189,6 +206,17 @@ AttributesInfo GetAttributesAfterProjection(const AttributesInfo& attrs, const P Value CalcExpression(const Tuple& source, const AttributesInfo& source_attrs, const Expression& expr) { struct ExpressionVisitor { + bool ValuesEqual(Value lhs, Value rhs, Type type) { + switch (type) { + case Type::kInt: + return lhs.value.int_value == rhs.value.int_value; + case Type::kBool: + return lhs.value.bool_value == rhs.value.bool_value; + case Type::kString: + return lhs.value.string_id == rhs.value.string_id; + } + } + Value operator()(const BinaryExpression& expr) { auto lhs = std::visit(*this, *expr.lhs); auto rhs = std::visit(*this, *expr.rhs); @@ -272,6 +300,30 @@ Value CalcExpression(const Tuple& source, const AttributesInfo& source_attrs, co return Value{false, child.is_null}; } } + Value operator()(const InExpression& expr) { + auto lhs = std::visit(*this, *expr.lhs); + if (lhs.is_null) { + return Value{true}; + } + + auto lhs_type = GetExpressionTypeUnchecked(*expr.lhs, source_attrs); + bool saw_null = false; + for (const auto& value_expr : expr.values) { + auto value = std::visit(*this, value_expr); + if (value.is_null) { + saw_null = true; + continue; + } + if (ValuesEqual(lhs, value, lhs_type)) { + return Value{false, !expr.negated}; + } + } + + if (saw_null) { + return Value{true}; + } + return Value{false, expr.negated}; + } Value operator()(const Attribute& expr) { auto it = std::find_if(source_attrs.begin(), source_attrs.end(), [&expr](const AttributeInfo& attr_info) { @@ -373,6 +425,12 @@ bool ContainsStringExpression(const Expression& expr, const AttributesInfo& attr bool operator()(const UnaryExpression& expr) const { return std::visit(*this, *expr.child); } + bool operator()(const InExpression& expr) const { + return std::visit(*this, *expr.lhs) + || std::ranges::any_of(expr.values, [&](const Expression& value) { + return std::visit(*this, value); + }); + } bool operator()(const Attribute& attr) const { auto it = std::find_if(attrs.begin(), attrs.end(), [&](const AttributeInfo& attr_info) { return attr_info.name == attr.name && attr_info.table == attr.table; @@ -388,6 +446,23 @@ bool ContainsStringExpression(const Expression& expr, const AttributesInfo& attr return std::visit(Visitor{attrs}, expr); } +bool ContainsInExpression(const Expression& expr) { + struct Visitor { + bool operator()(const BinaryExpression& expr) const { + return std::visit(*this, *expr.lhs) || std::visit(*this, *expr.rhs); + } + bool operator()(const UnaryExpression& expr) const { + return std::visit(*this, *expr.child); + } + bool operator()(const InExpression&) const { return true; } + bool operator()(const Attribute&) const { return false; } + bool operator()(const StringConst&) const { return false; } + bool operator()(const IntConst&) const { return false; } + bool operator()(const Literal&) const { return false; } + }; + return std::visit(Visitor{}, expr); +} + boost::asio::awaitable InterpretedExpressionExecutor::GetExpressionExecutor(const Expression& expr, const AttributesInfo& attrs) { struct Executor { Value operator()(const Tuple& source, const AttributesInfo& source_attrs) { @@ -400,6 +475,9 @@ boost::asio::awaitable InterpretedExpressionExecutor::GetExpress } boost::asio::awaitable JitCompiledExpressionExecutor::GetExpressionExecutor(const Expression& expr, const AttributesInfo& attrs) { + if (ContainsInExpression(expr)) { + throw std::logic_error{"IN expressions are not supported by JIT"}; + } if (ContainsStringExpression(expr, attrs)) { throw std::logic_error{"string expressions are not supported by JIT"}; } @@ -422,6 +500,9 @@ JitCompiledExpressionExecutor::JitCompiledExpressionExecutor(boost::asio::any_io InterpretedExpressionExecutor::InterpretedExpressionExecutor(boost::asio::any_io_executor executor) {} boost::asio::awaitable CachedJitCompiledExpressionExecutor::GetExpressionExecutor(const Expression& expr, const AttributesInfo& attrs) { + if (ContainsInExpression(expr)) { + throw std::logic_error{"IN expressions are not supported by JIT"}; + } if (ContainsStringExpression(expr, attrs)) { throw std::logic_error{"string expressions are not supported by JIT"}; } diff --git a/src/stewkk/sql/logic/executor/executor_test.cpp b/src/stewkk/sql/logic/executor/executor_test.cpp index e191b26..162f2ae 100644 --- a/src/stewkk/sql/logic/executor/executor_test.cpp +++ b/src/stewkk/sql/logic/executor/executor_test.cpp @@ -155,6 +155,112 @@ TEST(ExecutorTest, StringFilterProjectionAndCsvQuotes) { ctx.run(); } +TEST(ExecutorTest, StringInFilter) { + boost::asio::io_context ctx; + boost::asio::co_spawn( + ctx, + []() -> boost::asio::awaitable> { + std::stringstream s{"SELECT m.id FROM markets AS m WHERE m.region IN ('AMERICA', 'ASIA');"}; + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value().op); + CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; + Executor executor( + std::move(seq_scan), co_await boost::asio::this_coro::executor); + + auto got = co_await executor.Execute(op); + + co_return got; + }(), + [](std::exception_ptr p, Result got) { + if (p) std::rethrow_exception(p); + + ASSERT_THAT(got.value().attributes, Eq(AttributesInfo{{"m", "id", Type::kInt}})); + ASSERT_THAT(got.value().tuples, Eq(Tuples{{Value{false, 1}}, {Value{false, 3}}})); + }); + + ctx.run(); +} + +TEST(ExecutorTest, StringNotInFilter) { + boost::asio::io_context ctx; + boost::asio::co_spawn( + ctx, + []() -> boost::asio::awaitable> { + std::stringstream s{"SELECT m.id FROM markets AS m WHERE m.region NOT IN ('AMERICA', 'ASIA');"}; + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value().op); + CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; + Executor executor( + std::move(seq_scan), co_await boost::asio::this_coro::executor); + + auto got = co_await executor.Execute(op); + + co_return got; + }(), + [](std::exception_ptr p, Result got) { + if (p) std::rethrow_exception(p); + + ASSERT_THAT(got.value().attributes, Eq(AttributesInfo{{"m", "id", Type::kInt}})); + ASSERT_THAT(got.value().tuples, Eq(Tuples{{Value{false, 2}}})); + }); + + ctx.run(); +} + +TEST(ExecutorTest, IntegerBetweenFilter) { + boost::asio::io_context ctx; + boost::asio::co_spawn( + ctx, + []() -> boost::asio::awaitable> { + std::stringstream s{"SELECT users.id FROM users WHERE users.age BETWEEN 5 AND 11;"}; + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value().op); + CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; + Executor executor( + std::move(seq_scan), co_await boost::asio::this_coro::executor); + + auto got = co_await executor.Execute(op); + + co_return got; + }(), + [](std::exception_ptr p, Result got) { + if (p) std::rethrow_exception(p); + + ASSERT_THAT(got.value().attributes, Eq(AttributesInfo{{"users", "id", Type::kInt}})); + ASSERT_THAT(got.value().tuples, Eq(Tuples{{Value{false, 4}}, {Value{false, 6}}})); + }); + + ctx.run(); +} + +TEST(ExecutorTest, InNullSemantics) { + AttributesInfo attrs{{"t", "x", Type::kInt}}; + Tuple tuple{Value{false, 2}}; + + Expression positive = InExpression{ + std::make_shared(Attribute{"t", "x"}), + {IntConst{1}, Literal::kNull}, + false, + }; + Expression negative = InExpression{ + std::make_shared(Attribute{"t", "x"}), + {IntConst{1}, Literal::kNull}, + true, + }; + Expression matched = InExpression{ + std::make_shared(Attribute{"t", "x"}), + {IntConst{2}, Literal::kNull}, + false, + }; + Expression null_lhs = InExpression{ + std::make_shared(Literal::kNull), + {IntConst{1}, IntConst{2}}, + false, + }; + + ASSERT_THAT(CalcExpression(tuple, attrs, positive).is_null, Eq(true)); + ASSERT_THAT(CalcExpression(tuple, attrs, negative).is_null, Eq(true)); + ASSERT_THAT(CalcExpression(tuple, attrs, matched), Eq(Value{false, true})); + ASSERT_THAT(CalcExpression(tuple, attrs, null_lhs).is_null, Eq(true)); +} + TEST(ExecutorTest, JitRejectsStringExpressions) { boost::asio::io_context ctx; bool rejected = false; @@ -182,6 +288,33 @@ TEST(ExecutorTest, JitRejectsStringExpressions) { ASSERT_THAT(rejected, Eq(true)); } +TEST(ExecutorTest, JitRejectsInExpressions) { + boost::asio::io_context ctx; + bool rejected = false; + boost::asio::co_spawn( + ctx, + [&rejected]() -> boost::asio::awaitable { + std::stringstream s{"SELECT users.id FROM users WHERE users.age IN (1, 2);"}; + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value().op); + CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; + Executor executor( + std::move(seq_scan), co_await boost::asio::this_coro::executor); + + try { + (void) co_await executor.Execute(op); + } catch (const std::logic_error& e) { + rejected = std::string_view{e.what()}.find("IN expressions are not supported by JIT") + != std::string_view::npos; + } + }(), + [](std::exception_ptr p) { + if (p) std::rethrow_exception(p); + }); + + ctx.run(); + ASSERT_THAT(rejected, Eq(true)); +} + TYPED_TEST_P(ExecutorTest, Projection) { boost::asio::thread_pool pool{4}; boost::asio::co_spawn( diff --git a/src/stewkk/sql/logic/executor/llvm.cpp b/src/stewkk/sql/logic/executor/llvm.cpp index 12bb750..470add5 100644 --- a/src/stewkk/sql/logic/executor/llvm.cpp +++ b/src/stewkk/sql/logic/executor/llvm.cpp @@ -310,6 +310,9 @@ llvm::Function* JITCompiler::GenerateIR( llvm::Value* operator()(const StringConst&) { throw std::logic_error{"string expressions are not supported by JIT"}; } + llvm::Value* operator()(const InExpression&) { + throw std::logic_error{"IN expressions are not supported by JIT"}; + } llvm::Value* operator()(const Literal& expr) { switch (expr) { case Literal::kNull: diff --git a/src/stewkk/sql/logic/executor/plan_serializer.cpp b/src/stewkk/sql/logic/executor/plan_serializer.cpp index 0480822..9729557 100644 --- a/src/stewkk/sql/logic/executor/plan_serializer.cpp +++ b/src/stewkk/sql/logic/executor/plan_serializer.cpp @@ -112,6 +112,15 @@ std::string SerializeExpr(const Expression& expr) { std::string operator()(const UnaryExpression& e) const { return std::format("({} {})", SerializeUnaryOp(e.op), SerializeExpr(*e.child)); } + std::string operator()(const InExpression& e) const { + std::string values; + for (const auto& value : e.values) { + if (!values.empty()) values += ' '; + values += SerializeExpr(value); + } + return std::format("({} {} (values {}))", + e.negated ? "notin" : "in", SerializeExpr(*e.lhs), values); + } std::string operator()(Literal l) const { switch (l) { case Literal::kNull: return "NULL"; @@ -317,6 +326,25 @@ Expression ParseExpr(ParseState& s) { s.ExpectRParen(); return StringConst{std::move(value)}; } + if (head == "in" || head == "notin") { + auto lhs = ParseExpr(s); + s.ExpectLParen(); + auto values_head = s.ExpectAtom(); + if (values_head != "values") { + throw std::runtime_error(std::format("expected values but got '{}'", values_head)); + } + std::vector values; + while (s.Peek().kind != TokenKind::RParen) { + values.push_back(ParseExpr(s)); + } + s.ExpectRParen(); + s.ExpectRParen(); + return InExpression{ + std::make_shared(std::move(lhs)), + std::move(values), + head == "notin", + }; + } if (auto it = kBinaryOps.find(head); it != kBinaryOps.end()) { auto lhs = ParseExpr(s); auto rhs = ParseExpr(s); diff --git a/src/stewkk/sql/logic/executor/plan_serializer_test.cpp b/src/stewkk/sql/logic/executor/plan_serializer_test.cpp index 190692e..7a8229b 100644 --- a/src/stewkk/sql/logic/executor/plan_serializer_test.cpp +++ b/src/stewkk/sql/logic/executor/plan_serializer_test.cpp @@ -105,6 +105,33 @@ TEST(PlanSerializerTest, ExprStringConst) { EXPECT_THAT(Serialize(plan), Eq("(PhysicalFilter (str \"Bob's Market \\\"North\\\"\") (SeqScan t))")); } +TEST(PlanSerializerTest, ExprInList) { + PhysicalPlanNode plan = PhysicalFilter{ + std::make_shared(SeqScan{"t"}), + InExpression{ + std::make_shared(Attribute{"t", "region"}), + {StringConst{"AMERICA"}, StringConst{"ASIA"}, Literal::kNull}, + false, + }, + }; + EXPECT_THAT(RoundTrip(plan), Eq(plan)); + EXPECT_THAT(Serialize(plan), + Eq("(PhysicalFilter (in (attr t region) (values (str \"AMERICA\") (str \"ASIA\") NULL)) (SeqScan t))")); +} + +TEST(PlanSerializerTest, ExprNotInList) { + PhysicalPlanNode plan = PhysicalFilter{ + std::make_shared(SeqScan{"t"}), + InExpression{ + std::make_shared(Attribute{"t", "id"}), + {IntConst{1}, IntConst{2}}, + true, + }, + }; + EXPECT_THAT(RoundTrip(plan), Eq(plan)); + EXPECT_THAT(Serialize(plan), Eq("(PhysicalFilter (notin (attr t id) (values 1 2)) (SeqScan t))")); +} + TEST(PlanSerializerTest, ExprLiterals) { for (auto lit : {Literal::kNull, Literal::kTrue, Literal::kFalse, Literal::kUnknown}) { PhysicalPlanNode plan = PhysicalFilter{ diff --git a/src/stewkk/sql/logic/parser/parser.cpp b/src/stewkk/sql/logic/parser/parser.cpp index b65f902..9319e77 100644 --- a/src/stewkk/sql/logic/parser/parser.cpp +++ b/src/stewkk/sql/logic/parser/parser.cpp @@ -61,6 +61,9 @@ std::string GetDotRepresentation(const Expression& expr) { std::string operator()(const UnaryExpression& expr) { return std::format("({} {})",ToString(expr.op), std::visit(DotFormatter{}, *expr.child)); } + std::string operator()(const InExpression& expr) { + return ToString(Expression{expr}); + } std::string operator()(const Attribute& expr) { return ToString(expr); } diff --git a/src/stewkk/sql/logic/parser/parser_test.cpp b/src/stewkk/sql/logic/parser/parser_test.cpp index 4c73ad7..a0e23e1 100644 --- a/src/stewkk/sql/logic/parser/parser_test.cpp +++ b/src/stewkk/sql/logic/parser/parser_test.cpp @@ -119,6 +119,106 @@ TEST(ParserTest, SelectWithStringLiteral) { std::make_shared(Table{"users"})})})); } +TEST(ParserTest, SelectWithBetween) { + std::stringstream s{"SELECT users.id FROM users WHERE users.age BETWEEN 18 AND 30;"}; + + Operator got = GetAST(s).value().op; + + auto ge = BinaryExpression{ + std::make_shared(Attribute{"users", "age"}), + BinaryOp::kGe, + std::make_shared(IntConst{18}), + }; + auto le = BinaryExpression{ + std::make_shared(Attribute{"users", "age"}), + BinaryOp::kLe, + std::make_shared(IntConst{30}), + }; + ASSERT_THAT(got, VariantWith(Projection{ + std::vector{Attribute{"users", "id"}}, + std::make_shared( + Filter{Expression{BinaryExpression{ + std::make_shared(std::move(ge)), + BinaryOp::kAnd, + std::make_shared(std::move(le))}}, + std::make_shared(Table{"users"})})})); +} + +TEST(ParserTest, SelectWithNotBetween) { + std::stringstream s{"SELECT users.id FROM users WHERE users.age NOT BETWEEN 18 AND 30;"}; + + Operator got = GetAST(s).value().op; + + auto ge = BinaryExpression{ + std::make_shared(Attribute{"users", "age"}), + BinaryOp::kGe, + std::make_shared(IntConst{18}), + }; + auto le = BinaryExpression{ + std::make_shared(Attribute{"users", "age"}), + BinaryOp::kLe, + std::make_shared(IntConst{30}), + }; + auto between = BinaryExpression{ + std::make_shared(std::move(ge)), + BinaryOp::kAnd, + std::make_shared(std::move(le)), + }; + ASSERT_THAT(got, VariantWith(Projection{ + std::vector{Attribute{"users", "id"}}, + std::make_shared( + Filter{Expression{UnaryExpression{ + UnaryOp::kNot, + std::make_shared(std::move(between))}}, + std::make_shared(Table{"users"})})})); +} + +TEST(ParserTest, SelectWithInList) { + std::stringstream s{"SELECT m.id FROM markets AS m WHERE m.region IN ('AMERICA', 'ASIA');"}; + + Operator got = GetAST(s).value().op; + + ASSERT_THAT(got, VariantWith(Projection{ + std::vector{Attribute{"m", "id"}}, + std::make_shared( + Filter{Expression{InExpression{ + std::make_shared(Attribute{"m", "region"}), + {StringConst{"AMERICA"}, StringConst{"ASIA"}}, + false}}, + std::make_shared(Table{"markets", "m"})})})); +} + +TEST(ParserTest, SelectWithNotInList) { + std::stringstream s{"SELECT m.id FROM markets AS m WHERE m.region NOT IN ('AMERICA', 'ASIA');"}; + + Operator got = GetAST(s).value().op; + + ASSERT_THAT(got, VariantWith(Projection{ + std::vector{Attribute{"m", "id"}}, + std::make_shared( + Filter{Expression{InExpression{ + std::make_shared(Attribute{"m", "region"}), + {StringConst{"AMERICA"}, StringConst{"ASIA"}}, + true}}, + std::make_shared(Table{"markets", "m"})})})); +} + +TEST(ParserTest, SelectWithBetweenSymmetricRejected) { + std::stringstream s{"SELECT users.id FROM users WHERE users.age BETWEEN SYMMETRIC 18 AND 30;"}; + + auto got = GetAST(s).error(); + + ASSERT_THAT(got.Wraps(ErrorType::kQueryNotSupported), IsTrue()); +} + +TEST(ParserTest, SelectWithInSubqueryRejected) { + std::stringstream s{"SELECT users.id FROM users WHERE users.age IN (SELECT users.age FROM users);"}; + + auto got = GetAST(s).error(); + + ASSERT_THAT(got.Wraps(ErrorType::kQueryNotSupported), IsTrue()); +} + TEST(ParserTest, GetDotRepresentation) { std::stringstream s{"SELECT users.id FROM users WHERE users.age > 18;"}; Operator op = GetAST(s).value().op; diff --git a/src/stewkk/sql/logic/parser/visitor.cpp b/src/stewkk/sql/logic/parser/visitor.cpp index 37477cc..c51e0f0 100644 --- a/src/stewkk/sql/logic/parser/visitor.cpp +++ b/src/stewkk/sql/logic/parser/visitor.cpp @@ -213,6 +213,18 @@ using ChildIt = std::vector::const_iterator; Operator BuildTableRef(Visitor* v, TableRefCtx* ctx); +Expression MakeBinary(Expression lhs, BinaryOp op, Expression rhs) { + return BinaryExpression{ + std::make_shared(std::move(lhs)), + op, + std::make_shared(std::move(rhs)), + }; +} + +Expression MakeUnary(UnaryOp op, Expression child) { + return UnaryExpression{op, std::make_shared(std::move(child))}; +} + // Returns the table_ref's atom (no joins) and an iterator pointing to its first // post-atom child. The grammar's `(... join ...)*` tail is greedy on the rhs // table_ref, so the caller is responsible for flattening that chain instead of @@ -497,19 +509,47 @@ std::any Visitor::visitA_expr_and(codegen::PostgreSQLParser::A_expr_andContext * } std::any Visitor::visitA_expr_between(codegen::PostgreSQLParser::A_expr_betweenContext *ctx) { - if (ctx->BETWEEN()) { - // NOTE: may want to support - throw Error{ErrorType::kQueryNotSupported, "BETWEEN clause is not supported"}; + if (!ctx->BETWEEN()) { + return visit(ctx->a_expr_in(0)); + } + if (ctx->SYMMETRIC()) { + throw Error{ErrorType::kQueryNotSupported, "BETWEEN SYMMETRIC clause is not supported"}; + } + + auto exprs = ctx->a_expr_in(); + auto value = std::any_cast(visit(exprs[0])); + auto lower = std::any_cast(visit(exprs[1])); + auto upper = std::any_cast(visit(exprs[2])); + + auto ge = MakeBinary(value, BinaryOp::kGe, std::move(lower)); + auto le = MakeBinary(std::move(value), BinaryOp::kLe, std::move(upper)); + auto result = MakeBinary(std::move(ge), BinaryOp::kAnd, std::move(le)); + if (ctx->NOT()) { + result = MakeUnary(UnaryOp::kNot, std::move(result)); } - return visit(ctx->a_expr_in(0)); + return result; } std::any Visitor::visitA_expr_in(codegen::PostgreSQLParser::A_expr_inContext *ctx) { - if (ctx->IN_P()) { - // NOTE: may want to support - throw Error{ErrorType::kQueryNotSupported, "IN clause is not supported"}; + auto lhs = std::any_cast(visit(ctx->a_expr_unary_not())); + if (!ctx->IN_P()) { + return lhs; + } + + auto* in_list = dynamic_cast(ctx->in_expr()); + if (!in_list) { + throw Error{ErrorType::kQueryNotSupported, "IN subqueries are not supported"}; + } + + std::vector values; + for (auto* expr : in_list->expr_list()->a_expr()) { + values.push_back(std::any_cast(visit(expr))); } - return visit(ctx->a_expr_unary_not()); + return Expression{InExpression{ + std::make_shared(std::move(lhs)), + std::move(values), + ctx->NOT() != nullptr, + }}; } std::any Visitor::visitA_expr_unary_not(codegen::PostgreSQLParser::A_expr_unary_notContext *ctx) { diff --git a/src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.cpp b/src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.cpp index 2ab27b2..178f27f 100644 --- a/src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.cpp +++ b/src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.cpp @@ -19,6 +19,12 @@ void CollectAttributes(const Expression& e, std::vector& out) { CollectAttributes(*b.rhs, out); }, [&](const UnaryExpression& u) { CollectAttributes(*u.child, out); }, + [&](const InExpression& i) { + CollectAttributes(*i.lhs, out); + for (const auto& value : i.values) { + CollectAttributes(value, out); + } + }, [&](const IntConst&) {}, [&](const StringConst&) {}, [&](const Literal&) {}, diff --git a/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp b/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp index c8128e8..0ad685c 100644 --- a/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp +++ b/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp @@ -42,6 +42,12 @@ void CollectAttrTables(const Expression& e, std::unordered_set& out CollectAttrTables(*b.rhs, out); }, [&](const UnaryExpression& u) { CollectAttrTables(*u.child, out); }, + [&](const InExpression& i) { + CollectAttrTables(*i.lhs, out); + for (const auto& value : i.values) { + CollectAttrTables(value, out); + } + }, [&](const IntConst&) {}, [&](const StringConst&) {}, [&](const Literal&) {}, diff --git a/src/stewkk/sql/models/parser/expression.cpp b/src/stewkk/sql/models/parser/expression.cpp index ec31786..4d5c9a9 100644 --- a/src/stewkk/sql/models/parser/expression.cpp +++ b/src/stewkk/sql/models/parser/expression.cpp @@ -1,6 +1,7 @@ #include #include +#include namespace stewkk::sql { @@ -12,6 +13,10 @@ bool UnaryExpression::operator==(const UnaryExpression& other) const { return op == other.op && *child == *other.child; } +bool InExpression::operator==(const InExpression& other) const { + return *lhs == *other.lhs && values == other.values && negated == other.negated; +} + std::string ToString(const Attribute& attr) { return std::format("{}.{}", attr.table, attr.name); } @@ -98,6 +103,12 @@ std::string ToString(const Expression& expr) { std::string operator()(const UnaryExpression& expr) { return std::format("{} {}", ToString(expr.op), ToString(*expr.child)); } + std::string operator()(const InExpression& expr) { + auto values = expr.values | std::views::transform([](const Expression& v) { + return ToString(v); + }) | std::views::join_with(',') | std::ranges::to(); + return std::format("{} {}in ({})", ToString(*expr.lhs), expr.negated ? "not " : "", values); + } std::string operator()(const Literal& expr) { return ToString(expr); } From fd68c6844d9e8b90f0ea431ea68b8926887309c7 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sat, 30 May 2026 01:50:35 +0300 Subject: [PATCH 058/120] Impl group by --- benchmarks/main.cpp | 3 + .../stewkk/sql/logic/executor/executor.hpp | 3 + include/stewkk/sql/logic/executor/plan.hpp | 11 +- .../implement_aggregation.hpp | 13 + .../sql/logic/optimizer/logical_expr.hpp | 12 +- .../sql/logic/optimizer/physical_expr.hpp | 10 +- include/stewkk/sql/logic/optimizer/rules.hpp | 3 +- .../stewkk/sql/models/parser/expression.hpp | 18 +- .../models/parser/relational_algebra_ast.hpp | 12 +- src/stewkk/sql/CMakeLists.txt | 1 + src/stewkk/sql/logic/executor/executor.cpp | 160 ++++++++++ .../sql/logic/executor/executor_test.cpp | 76 +++++ src/stewkk/sql/logic/executor/llvm.cpp | 3 + src/stewkk/sql/logic/executor/plan.cpp | 4 + .../sql/logic/executor/plan_serializer.cpp | 62 ++++ .../implement_aggregation.cpp | 15 + .../sql/logic/optimizer/cardinality.cpp | 7 + src/stewkk/sql/logic/optimizer/memo.cpp | 24 +- src/stewkk/sql/logic/optimizer/optimizer.cpp | 24 +- .../sql/logic/optimizer/reachability.cpp | 11 + src/stewkk/sql/logic/optimizer/rules.cpp | 3 +- .../sql/logic/optimizer/rules_applier.cpp | 3 +- .../sql/logic/optimizer/schema_catalog.cpp | 16 + src/stewkk/sql/logic/parser/parser.cpp | 14 + src/stewkk/sql/logic/parser/parser_test.cpp | 123 +++++++- src/stewkk/sql/logic/parser/visitor.cpp | 291 +++++++++++++++++- src/stewkk/sql/logic/parser/visitor.hpp | 6 + .../filter_pushdown_through_projection.cpp | 7 +- .../transformation_rules/predicate_utils.cpp | 6 + src/stewkk/sql/main.cpp | 14 +- src/stewkk/sql/models/parser/expression.cpp | 25 ++ .../models/parser/relational_algebra_ast.cpp | 14 +- 32 files changed, 967 insertions(+), 27 deletions(-) create mode 100644 include/stewkk/sql/logic/implementation_rules/implement_aggregation.hpp create mode 100644 src/stewkk/sql/logic/implementation_rules/implement_aggregation.cpp diff --git a/benchmarks/main.cpp b/benchmarks/main.cpp index dfd9de0..06275b6 100644 --- a/benchmarks/main.cpp +++ b/benchmarks/main.cpp @@ -40,6 +40,9 @@ PhysicalPlanNode ToPhysicalPlan(const Operator& op) { .predicate = f.expr, }; }, + [](const Aggregation&) -> PhysicalPlanNode { + throw std::runtime_error{"Aggregation execution is not implemented"}; + }, [](const CrossJoin& j) -> PhysicalPlanNode { return NestedLoopCrossJoin{ .lhs = std::make_shared(ToPhysicalPlan(*j.lhs)), diff --git a/include/stewkk/sql/logic/executor/executor.hpp b/include/stewkk/sql/logic/executor/executor.hpp index 7ca29ef..a5cb53b 100644 --- a/include/stewkk/sql/logic/executor/executor.hpp +++ b/include/stewkk/sql/logic/executor/executor.hpp @@ -66,6 +66,9 @@ class Executor { TuplesChannel& tuples_chan); boost::asio::awaitable ExecuteHashJoin(const HashJoin& join, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan); + boost::asio::awaitable ExecuteHashAggregate(const PhysicalAggregation& agg, + AttributesInfoChannel& attr_chan, + TuplesChannel& tuples_chan); boost::asio::experimental::promise SpawnExecutor( boost::asio::any_io_executor exec, const PhysicalPlanNode& op, AttributesInfoChannel& attr_chan, TuplesChannel& tuple_chan); diff --git a/include/stewkk/sql/logic/executor/plan.hpp b/include/stewkk/sql/logic/executor/plan.hpp index 67fe7fb..3e6c7f9 100644 --- a/include/stewkk/sql/logic/executor/plan.hpp +++ b/include/stewkk/sql/logic/executor/plan.hpp @@ -22,8 +22,9 @@ struct HashJoin; struct MergeJoin; struct IndexSeek; struct PhysicalSort; +struct PhysicalAggregation; -using PhysicalPlanNode = std::variant; +using PhysicalPlanNode = std::variant; struct SeqScan { std::string table; @@ -96,4 +97,12 @@ struct PhysicalSort { bool operator==(const PhysicalSort&) const; }; +struct PhysicalAggregation { + std::shared_ptr source; + std::vector group_by; + std::vector aggregates; + + bool operator==(const PhysicalAggregation&) const; +}; + } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/implementation_rules/implement_aggregation.hpp b/include/stewkk/sql/logic/implementation_rules/implement_aggregation.hpp new file mode 100644 index 0000000..b92318f --- /dev/null +++ b/include/stewkk/sql/logic/implementation_rules/implement_aggregation.hpp @@ -0,0 +1,13 @@ +#pragma once + +#include + +namespace stewkk::sql { + +class ImplementAggregation : public ImplementationRule { + public: + bool IsApplicable(utils::NotNull expr) override; + utils::NotNull Apply(utils::NotNull expr, Memo& memo) override; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/logical_expr.hpp b/include/stewkk/sql/logic/optimizer/logical_expr.hpp index 6a74cf2..8e2af14 100644 --- a/include/stewkk/sql/logic/optimizer/logical_expr.hpp +++ b/include/stewkk/sql/logic/optimizer/logical_expr.hpp @@ -21,10 +21,19 @@ struct Filter { struct Projection { utils::NotNull source; std::vector expressions; + std::vector> aliases; bool operator==(const Projection&) const = default; }; +struct Aggregation { + utils::NotNull source; + std::vector group_by; + std::vector aggregates; + + bool operator==(const Aggregation&) const = default; +}; + struct CrossJoin { utils::NotNull lhs; utils::NotNull rhs; @@ -45,7 +54,8 @@ struct Join { } // namespace logical struct LogicalExpr { - std::variant root_operator; + std::variant root_operator; utils::NotNull group; }; diff --git a/include/stewkk/sql/logic/optimizer/physical_expr.hpp b/include/stewkk/sql/logic/optimizer/physical_expr.hpp index 6342731..452885f 100644 --- a/include/stewkk/sql/logic/optimizer/physical_expr.hpp +++ b/include/stewkk/sql/logic/optimizer/physical_expr.hpp @@ -68,12 +68,20 @@ struct Sort { bool operator==(const Sort&) const = default; }; +struct Aggregation { + utils::NotNull source; + std::vector group_by; + std::vector aggregates; + + bool operator==(const Aggregation&) const = default; +}; + } // namespace physical struct PhysicalExpr { std::variant root_operator; + physical::HashJoin, physical::Sort, physical::Aggregation> root_operator; utils::NotNull group; bool is_enforcer = false; }; diff --git a/include/stewkk/sql/logic/optimizer/rules.hpp b/include/stewkk/sql/logic/optimizer/rules.hpp index d655cc7..7568b91 100644 --- a/include/stewkk/sql/logic/optimizer/rules.hpp +++ b/include/stewkk/sql/logic/optimizer/rules.hpp @@ -16,6 +16,7 @@ #include #include #include +#include namespace stewkk::sql { @@ -25,6 +26,6 @@ struct Rules { std::array, NImplementation> implementation_rules; }; -Rules<6, 6> MakeMainRules(); +Rules<6, 7> MakeMainRules(); } // namespace stewkk::sql diff --git a/include/stewkk/sql/models/parser/expression.hpp b/include/stewkk/sql/models/parser/expression.hpp index 4c53676..54d8a18 100644 --- a/include/stewkk/sql/models/parser/expression.hpp +++ b/include/stewkk/sql/models/parser/expression.hpp @@ -59,9 +59,17 @@ std::string ToString(UnaryOp op); struct BinaryExpression; struct UnaryExpression; struct InExpression; +struct AggregateExpression; + +enum class AggregateFunction { + kSum, + kCount, +}; + +std::string ToString(AggregateFunction function); using Expression = std::variant; + InExpression, AggregateExpression, Literal>; std::string ToString(const Expression& expr); @@ -88,4 +96,12 @@ struct InExpression { bool operator==(const InExpression& other) const; }; +struct AggregateExpression { + AggregateFunction function; + std::shared_ptr argument; + bool is_star = false; + + bool operator==(const AggregateExpression& other) const; +}; + } // namespace stewkk::sql diff --git a/include/stewkk/sql/models/parser/relational_algebra_ast.hpp b/include/stewkk/sql/models/parser/relational_algebra_ast.hpp index 70f9e3f..2f3fcc4 100644 --- a/include/stewkk/sql/models/parser/relational_algebra_ast.hpp +++ b/include/stewkk/sql/models/parser/relational_algebra_ast.hpp @@ -17,10 +17,11 @@ constexpr static std::string kEmptyTableName = "_EMPTY_TABLE_"; struct Table; struct Projection; struct Filter; +struct Aggregation; struct CrossJoin; struct Join; -using Operator = std::variant; +using Operator = std::variant; struct Table { std::string name; @@ -34,6 +35,7 @@ std::string_view VisibleName(const Table& table); struct Projection { std::vector expressions; std::shared_ptr source; + std::vector> aliases; bool operator==(const Projection& other) const; }; @@ -45,6 +47,14 @@ struct Filter { bool operator==(const Filter& other) const; }; +struct Aggregation { + std::vector group_by; + std::vector aggregates; + std::shared_ptr source; + + bool operator==(const Aggregation& other) const; +}; + struct CrossJoin { std::shared_ptr lhs; std::shared_ptr rhs; diff --git a/src/stewkk/sql/CMakeLists.txt b/src/stewkk/sql/CMakeLists.txt index fea3108..dd9d927 100644 --- a/src/stewkk/sql/CMakeLists.txt +++ b/src/stewkk/sql/CMakeLists.txt @@ -51,6 +51,7 @@ add_library(libsql logic/implementation_rules/implement_join.cpp logic/implementation_rules/implement_cross_join.cpp logic/implementation_rules/implement_hash_join.cpp + logic/implementation_rules/implement_aggregation.cpp logic/optimizer/properties/sort_order.cpp logic/optimizer/properties/sort_property.cpp logic/optimizer/properties/property_set.cpp diff --git a/src/stewkk/sql/logic/executor/executor.cpp b/src/stewkk/sql/logic/executor/executor.cpp index 8d762cb..6a5deb2 100644 --- a/src/stewkk/sql/logic/executor/executor.cpp +++ b/src/stewkk/sql/logic/executor/executor.cpp @@ -1,6 +1,7 @@ #include #include +#include #include #include #include @@ -9,6 +10,7 @@ #include #include #include +#include #include #include @@ -100,6 +102,15 @@ Type GetExpressionType(const Expression& expr, const AttributesInfo& available_a } return Type::kBool; } + Type operator()(const AggregateExpression& aggregate) const { + if (aggregate.function == AggregateFunction::kCount) { + return Type::kInt; + } + if (aggregate.is_star) { + throw std::logic_error{"star aggregate has no scalar type"}; + } + return std::visit(*this, *aggregate.argument); + } Type operator()(const IntConst& iconst) const { return Type::kInt; } @@ -155,6 +166,9 @@ Type GetExpressionTypeUnchecked(const Expression& expr, const AttributesInfo& av Type operator()(const InExpression& in) const { return Type::kBool; } + Type operator()(const AggregateExpression& aggregate) const { + return Type::kInt; + } Type operator()(const IntConst& iconst) const { return Type::kInt; } @@ -324,6 +338,9 @@ Value CalcExpression(const Tuple& source, const AttributesInfo& source_attrs, co } return Value{false, expr.negated}; } + Value operator()(const AggregateExpression&) { + throw std::logic_error{"aggregate expressions cannot be evaluated as scalar expressions"}; + } Value operator()(const Attribute& expr) { auto it = std::find_if(source_attrs.begin(), source_attrs.end(), [&expr](const AttributeInfo& attr_info) { @@ -431,6 +448,9 @@ bool ContainsStringExpression(const Expression& expr, const AttributesInfo& attr return std::visit(*this, value); }); } + bool operator()(const AggregateExpression& expr) const { + return !expr.is_star && expr.argument && std::visit(*this, *expr.argument); + } bool operator()(const Attribute& attr) const { auto it = std::find_if(attrs.begin(), attrs.end(), [&](const AttributeInfo& attr_info) { return attr_info.name == attr.name && attr_info.table == attr.table; @@ -455,6 +475,9 @@ bool ContainsInExpression(const Expression& expr) { return std::visit(*this, *expr.child); } bool operator()(const InExpression&) const { return true; } + bool operator()(const AggregateExpression& expr) const { + return !expr.is_star && expr.argument && std::visit(*this, *expr.argument); + } bool operator()(const Attribute&) const { return false; } bool operator()(const StringConst&) const { return false; } bool operator()(const IntConst&) const { return false; } @@ -601,6 +624,10 @@ boost::asio::awaitable Executor::Execute(const Physica co_await executor.ExecuteHashJoin(join, attr_chan, tuples_chan); co_return; } + boost::asio::awaitable operator()(const PhysicalAggregation& agg) { + co_await executor.ExecuteHashAggregate(agg, attr_chan, tuples_chan); + co_return; + } boost::asio::awaitable operator()(const MergeJoin&) { throw std::runtime_error("MergeJoin execution not implemented"); co_return; @@ -1102,6 +1129,139 @@ boost::asio::awaitable Executor::ExecuteHashJoin( tuples_chan.close(); } +template +boost::asio::awaitable Executor::ExecuteHashAggregate( + const PhysicalAggregation& agg, AttributesInfoChannel& out_attr_chan, + TuplesChannel& out_tuples_chan) { + Log("Executing hash aggregate"); + auto close_on_fail = boost::scope::make_scope_fail( + [&] { out_attr_chan.close(); out_tuples_chan.close(); }); + auto exec = co_await boost::asio::this_coro::executor; + auto [in_attrs_chan, in_tuples_chan] = co_await GetChannels(); + auto task = SpawnExecutor(exec, *agg.source, in_attrs_chan, in_tuples_chan); + + auto in_attrs = co_await in_attrs_chan.async_receive(boost::asio::use_awaitable); + + // Build output AttributesInfo: group_by cols then one slot per aggregate. + AttributesInfo out_attrs; + for (const auto& expr : agg.group_by) { + if (const auto* attr = std::get_if(&expr)) { + auto it = std::find_if(in_attrs.begin(), in_attrs.end(), [&](const AttributeInfo& ai) { + return ai.table == attr->table && ai.name == attr->name; + }); + if (it != in_attrs.end()) { + out_attrs.push_back(*it); + } + } + } + for (size_t i = 0; i < agg.aggregates.size(); ++i) { + const auto& agg_expr = std::get(agg.aggregates[i]); + Type t = (agg_expr.function == AggregateFunction::kCount) + ? Type::kInt + : (agg_expr.is_star ? Type::kInt : GetExpressionTypeUnchecked(*agg_expr.argument, in_attrs)); + out_attrs.push_back(AttributeInfo{"", std::format("__agg{}", i), t}); + } + co_await out_attr_chan.async_send(boost::system::error_code{}, out_attrs, + boost::asio::use_awaitable); + out_attr_chan.close(); + + // Per-group state: vector of int64_t accumulators, one per aggregate. + // For SUM: running total (null if all inputs null). + // For COUNT: running count of non-null inputs (or all rows for COUNT(*)). + struct GroupState { + std::vector accumulators; + std::vector any_non_null; // for SUM null tracking + }; + struct TupleKeyHash { + size_t operator()(const std::vector& key) const { + size_t seed = 0; + for (const auto& v : key) { + boost::hash_combine(seed, v.is_null); + if (!v.is_null) boost::hash_combine(seed, v.value.int_value); + } + return seed; + } + }; + std::unordered_map, GroupState, TupleKeyHash> groups; + + // Scalar evaluator for group-by keys and aggregate arguments. + auto do_scalar = [&](const Expression& expr, const Tuple& tuple) -> Value { + return CalcExpression(tuple, in_attrs, expr); + }; + + const bool scalar_agg = agg.group_by.empty(); + + auto init_state = [&]() -> GroupState { + GroupState s; + s.accumulators.resize(agg.aggregates.size(), 0); + s.any_non_null.resize(agg.aggregates.size(), false); + return s; + }; + + for (;;) { + auto buf = co_await ReceiveTuples(in_tuples_chan); + if (buf.empty()) break; + for (const auto& tuple : buf) { + std::vector key; + for (const auto& expr : agg.group_by) { + key.push_back(do_scalar(expr, tuple)); + } + auto [it, inserted] = groups.emplace(key, GroupState{}); + if (inserted) it->second = init_state(); + auto& state = it->second; + for (size_t i = 0; i < agg.aggregates.size(); ++i) { + const auto& agg_expr = std::get(agg.aggregates[i]); + if (agg_expr.function == AggregateFunction::kCount) { + if (agg_expr.is_star) { + state.accumulators[i]++; + } else { + auto v = do_scalar(*agg_expr.argument, tuple); + if (!v.is_null) state.accumulators[i]++; + } + } else { // SUM + auto v = do_scalar(*agg_expr.argument, tuple); + if (!v.is_null) { + state.accumulators[i] += v.value.int_value; + state.any_non_null[i] = true; + } + } + } + } + } + + // For scalar aggregate over empty input, emit one row with COUNT=0 / SUM=NULL. + if (scalar_agg && groups.empty()) { + groups.emplace(std::vector{}, init_state()); + } + + Tuples out_buf; + out_buf.reserve(kBufSize); + for (auto& [key, state] : groups) { + Tuple tuple = key; + for (size_t i = 0; i < agg.aggregates.size(); ++i) { + const auto& agg_expr = std::get(agg.aggregates[i]); + if (agg_expr.function == AggregateFunction::kSum && !state.any_non_null[i]) { + tuple.push_back(Value{true}); // NULL + } else { + tuple.push_back(Value{false, {.int_value = state.accumulators[i]}}); + } + } + out_buf.push_back(std::move(tuple)); + if (out_buf.size() == kBufSize) { + co_await out_tuples_chan.async_send(boost::system::error_code{}, std::move(out_buf), + boost::asio::use_awaitable); + out_buf.clear(); + out_buf.reserve(kBufSize); + } + } + if (!out_buf.empty()) { + co_await out_tuples_chan.async_send(boost::system::error_code{}, std::move(out_buf), + boost::asio::use_awaitable); + } + co_await task(boost::asio::use_awaitable); + out_tuples_chan.close(); +} + template boost::asio::experimental::promise Executor::SpawnExecutor(boost::asio::any_io_executor exec, diff --git a/src/stewkk/sql/logic/executor/executor_test.cpp b/src/stewkk/sql/logic/executor/executor_test.cpp index 162f2ae..fabfdfd 100644 --- a/src/stewkk/sql/logic/executor/executor_test.cpp +++ b/src/stewkk/sql/logic/executor/executor_test.cpp @@ -37,6 +37,13 @@ PhysicalPlanNode ToPhysicalPlan(const Operator& op) { .predicate = f.expr, }; }, + [](const Aggregation& a) -> PhysicalPlanNode { + return PhysicalAggregation{ + .source = std::make_shared(ToPhysicalPlan(*a.source)), + .group_by = a.group_by, + .aggregates = a.aggregates, + }; + }, [](const CrossJoin& j) -> PhysicalPlanNode { return NestedLoopCrossJoin{ .lhs = std::make_shared(ToPhysicalPlan(*j.lhs)), @@ -572,4 +579,73 @@ REGISTER_TYPED_TEST_SUITE_P(ExecutorTest, Projection, Filter, FilterMany, CrossJ using ExecutorTypes = ::testing::Types; INSTANTIATE_TYPED_TEST_SUITE_P(TypedExecutorTest, ExecutorTest, ExecutorTypes); +TEST(ExecutorTest, ScalarCountStar) { + boost::asio::io_context ctx; + boost::asio::co_spawn( + ctx, + []() -> boost::asio::awaitable> { + std::stringstream s{"SELECT COUNT(*) FROM users;"}; + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value().op); + CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; + Executor executor( + std::move(seq_scan), co_await boost::asio::this_coro::executor); + auto got = co_await executor.Execute(op); + co_return got; + }(), + [](std::exception_ptr p, Result got) { + if (p) std::rethrow_exception(p); + ASSERT_THAT(got.value().tuples.size(), Eq(1u)); + ASSERT_THAT(got.value().tuples[0][0], Eq(Value{false, {.int_value = 17}})); + }); + ctx.run(); +} + +TEST(ExecutorTest, ScalarSum) { + boost::asio::io_context ctx; + boost::asio::co_spawn( + ctx, + []() -> boost::asio::awaitable> { + std::stringstream s{"SELECT SUM(users.age) FROM users;"}; + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value().op); + CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; + Executor executor( + std::move(seq_scan), co_await boost::asio::this_coro::executor); + auto got = co_await executor.Execute(op); + co_return got; + }(), + [](std::exception_ptr p, Result got) { + if (p) std::rethrow_exception(p); + ASSERT_THAT(got.value().tuples.size(), Eq(1u)); + ASSERT_THAT(got.value().tuples[0][0], Eq(Value{false, {.int_value = 1182}})); + }); + ctx.run(); +} + +TEST(ExecutorTest, GroupByCountStar) { + boost::asio::io_context ctx; + boost::asio::co_spawn( + ctx, + []() -> boost::asio::awaitable> { + std::stringstream s{"SELECT users.age, COUNT(*) FROM users GROUP BY users.age;"}; + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value().op); + CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; + Executor executor( + std::move(seq_scan), co_await boost::asio::this_coro::executor); + auto got = co_await executor.Execute(op); + co_return got; + }(), + [](std::exception_ptr p, Result got) { + if (p) std::rethrow_exception(p); + // 11 distinct age values in users.csv + ASSERT_THAT(got.value().tuples.size(), Eq(11u)); + // Sum of counts must equal total rows (17) + int64_t total = 0; + for (const auto& t : got.value().tuples) { + total += t[1].value.int_value; + } + ASSERT_THAT(total, Eq(17)); + }); + ctx.run(); +} + } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/executor/llvm.cpp b/src/stewkk/sql/logic/executor/llvm.cpp index 470add5..a83e292 100644 --- a/src/stewkk/sql/logic/executor/llvm.cpp +++ b/src/stewkk/sql/logic/executor/llvm.cpp @@ -313,6 +313,9 @@ llvm::Function* JITCompiler::GenerateIR( llvm::Value* operator()(const InExpression&) { throw std::logic_error{"IN expressions are not supported by JIT"}; } + llvm::Value* operator()(const AggregateExpression&) { + throw std::logic_error{"aggregate expressions are not supported by JIT"}; + } llvm::Value* operator()(const Literal& expr) { switch (expr) { case Literal::kNull: diff --git a/src/stewkk/sql/logic/executor/plan.cpp b/src/stewkk/sql/logic/executor/plan.cpp index cbba2f1..212769b 100644 --- a/src/stewkk/sql/logic/executor/plan.cpp +++ b/src/stewkk/sql/logic/executor/plan.cpp @@ -38,4 +38,8 @@ bool PhysicalSort::operator==(const PhysicalSort& other) const { return *source == *other.source && keys == other.keys; } +bool PhysicalAggregation::operator==(const PhysicalAggregation& other) const { + return *source == *other.source && group_by == other.group_by && aggregates == other.aggregates; +} + } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/executor/plan_serializer.cpp b/src/stewkk/sql/logic/executor/plan_serializer.cpp index 9729557..0121417 100644 --- a/src/stewkk/sql/logic/executor/plan_serializer.cpp +++ b/src/stewkk/sql/logic/executor/plan_serializer.cpp @@ -121,6 +121,12 @@ std::string SerializeExpr(const Expression& expr) { return std::format("({} {} (values {}))", e.negated ? "notin" : "in", SerializeExpr(*e.lhs), values); } + std::string operator()(const AggregateExpression& e) const { + if (e.is_star) { + return std::format("({} *)", ToString(e.function)); + } + return std::format("({} {})", ToString(e.function), SerializeExpr(*e.argument)); + } std::string operator()(Literal l) const { switch (l) { case Literal::kNull: return "NULL"; @@ -187,6 +193,20 @@ std::string SerializeNode(const PhysicalPlanNode& node) { } return std::format("(Sort (keys {}) {})", keys, SerializeNode(*n.source)); } + std::string operator()(const PhysicalAggregation& n) const { + std::string group_by_str; + for (const auto& e : n.group_by) { + if (!group_by_str.empty()) group_by_str += ' '; + group_by_str += SerializeExpr(e); + } + std::string aggs_str; + for (const auto& e : n.aggregates) { + if (!aggs_str.empty()) aggs_str += ' '; + aggs_str += SerializeExpr(e); + } + return std::format("(HashAggregate (group_by {}) (aggs {}) {})", + group_by_str, aggs_str, SerializeNode(*n.source)); + } }; return std::visit(Visitor{}, node); } @@ -465,6 +485,32 @@ PhysicalPlanNode ParseNode(ParseState& s) { }; } + if (head == "HashAggregate") { + s.ExpectLParen(); + auto kw1 = s.ExpectAtom(); + if (kw1 != "group_by") + throw std::runtime_error(std::format("expected 'group_by' but got '{}'", kw1)); + std::vector group_by; + while (s.Peek().kind != TokenKind::RParen) + group_by.push_back(ParseExpr(s)); + s.ExpectRParen(); + s.ExpectLParen(); + auto kw2 = s.ExpectAtom(); + if (kw2 != "aggs") + throw std::runtime_error(std::format("expected 'aggs' but got '{}'", kw2)); + std::vector aggregates; + while (s.Peek().kind != TokenKind::RParen) + aggregates.push_back(ParseExpr(s)); + s.ExpectRParen(); + auto source = ParseNode(s); + s.ExpectRParen(); + return PhysicalAggregation{ + std::make_shared(std::move(source)), + std::move(group_by), + std::move(aggregates), + }; + } + throw std::runtime_error(std::format("unknown plan node: '{}'", head)); } @@ -563,6 +609,22 @@ struct DotBuilder { EmitEdge(src, id); return id; } + int operator()(const PhysicalAggregation& n) { + int src = std::visit(*this, *n.source); + std::string aggs; + for (const auto& e : n.aggregates) { + if (!aggs.empty()) aggs += ", "; + aggs += ToString(e); + } + std::string group_by; + for (const auto& e : n.group_by) { + if (!group_by.empty()) group_by += ", "; + group_by += ToString(e); + } + int id = Emit(std::format("HashAgg\\nGROUP BY {}\\n{}", group_by, aggs)); + EmitEdge(src, id); + return id; + } }; } // namespace diff --git a/src/stewkk/sql/logic/implementation_rules/implement_aggregation.cpp b/src/stewkk/sql/logic/implementation_rules/implement_aggregation.cpp new file mode 100644 index 0000000..3e57792 --- /dev/null +++ b/src/stewkk/sql/logic/implementation_rules/implement_aggregation.cpp @@ -0,0 +1,15 @@ +#include + +namespace stewkk::sql { + +bool ImplementAggregation::IsApplicable(utils::NotNull expr) { + return std::holds_alternative(expr->root_operator); +} + +utils::NotNull ImplementAggregation::Apply(utils::NotNull expr, Memo&) { + auto& agg = std::get(expr->root_operator); + return expr->group->AddPhysicalExpr( + physical::Aggregation{agg.source, agg.group_by, agg.aggregates}); +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/cardinality.cpp b/src/stewkk/sql/logic/optimizer/cardinality.cpp index 28c5a83..28ff243 100644 --- a/src/stewkk/sql/logic/optimizer/cardinality.cpp +++ b/src/stewkk/sql/logic/optimizer/cardinality.cpp @@ -54,6 +54,13 @@ int64_t CardinalityEstimates::GetCardinality(const LogicalOperator& op) { [this](const logical::Projection& p) -> int64_t { return GetCardinality(p.source); }, + [this](const logical::Aggregation& a) -> int64_t { + auto source_cardinality = GetCardinality(a.source); + if (a.group_by.empty()) { + return 1; + } + return source_cardinality; + }, [this](const logical::CrossJoin& j) -> int64_t { return GetCardinality(j.lhs) * GetCardinality(j.rhs); }, diff --git a/src/stewkk/sql/logic/optimizer/memo.cpp b/src/stewkk/sql/logic/optimizer/memo.cpp index 8741918..2515a79 100644 --- a/src/stewkk/sql/logic/optimizer/memo.cpp +++ b/src/stewkk/sql/logic/optimizer/memo.cpp @@ -18,7 +18,23 @@ std::string ToKey(const LogicalOperator& op) { for (const auto& e : p.expressions) { exprs += ToString(e) + ","; } - return "Projection(" + exprs + std::to_string(p.source->GetId()) + ")"; + std::string aliases; + for (const auto& alias : p.aliases) { + aliases += alias.value_or("") + ","; + } + return "Projection(" + exprs + aliases + std::to_string(p.source->GetId()) + ")"; + }, + [](const logical::Aggregation& a) { + std::string group_by; + for (const auto& e : a.group_by) { + group_by += ToString(e) + ","; + } + std::string aggregates; + for (const auto& e : a.aggregates) { + aggregates += ToString(e) + ","; + } + return "Aggregation(" + group_by + ";" + aggregates + ";" + + std::to_string(a.source->GetId()) + ")"; }, [](const logical::CrossJoin& j) { return "CrossJoin(" + std::to_string(j.lhs->GetId()) + "," + std::to_string(j.rhs->GetId()) + ")"; @@ -82,7 +98,11 @@ utils::NotNull Memo::Populate(const Operator& op) { }, [this](const Projection& p) { auto source = Populate(*p.source); - return AddGroup(logical::Projection{source->group, p.expressions}); + return AddGroup(logical::Projection{source->group, p.expressions, p.aliases}); + }, + [this](const Aggregation& a) { + auto source = Populate(*a.source); + return AddGroup(logical::Aggregation{source->group, a.group_by, a.aggregates}); }, [this](const CrossJoin& j) { auto lhs = Populate(*j.lhs); diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index be5ffa3..976fc16 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -44,6 +44,7 @@ PropertySet RequiredInputProps(utils::NotNull expr, return PropertySet::Any(); }, [&](const physical::Sort&) { return PropertySet::Any(); }, + [&](const physical::Aggregation&) { return PropertySet::Any(); }, }, expr->root_operator); } @@ -60,6 +61,7 @@ PropertySet DeriveOutputProps(utils::NotNull expr, [&](const physical::NestedLoopCrossJoin&) { return child_delivered[0]; }, [&](const physical::HashJoin&) { return PropertySet::Any(); }, [&](const physical::Sort& s) { return PropertySet{SortProperty{s.keys}}; }, + [&](const physical::Aggregation&) { return PropertySet::Any(); }, }, expr->root_operator); } @@ -78,6 +80,9 @@ int64_t LowerBoundLocalCost(utils::NotNull expr, CardinalityEstima [&](const logical::Projection&) -> int64_t { return cardinality.GetCardinality(expr->group); }, + [&](const logical::Aggregation&) -> int64_t { + return cardinality.GetCardinality(expr->group); + }, [&](const logical::CrossJoin& j) -> int64_t { auto p_l = (cardinality.GetCardinality(j.lhs) + kBufSize - 1) / kBufSize; auto p_r = (cardinality.GetCardinality(j.rhs) + kBufSize - 1) / kBufSize; @@ -124,6 +129,9 @@ int64_t CalcCost(utils::NotNull expr, CardinalityEstimates& cardi auto n = cardinality.GetCardinality(s.input); return n > 1 ? n * static_cast(std::bit_width(static_cast(n))) : n; }, + [&](const physical::Aggregation& a) -> int64_t { + return cardinality.GetCardinality(a.source); + }, }, expr->root_operator); } @@ -140,6 +148,9 @@ std::vector> GetChildren(utils::NotNull exp [](const logical::Projection& p) -> std::vector> { return {p.source}; }, + [](const logical::Aggregation& a) -> std::vector> { + return {a.source}; + }, [](const logical::CrossJoin& j) -> std::vector> { return {j.lhs, j.rhs}; }, @@ -172,6 +183,9 @@ std::vector> GetChildren(utils::NotNull ex [](const physical::Sort& s) -> std::vector> { return {s.input}; }, + [](const physical::Aggregation& a) -> std::vector> { + return {a.source}; + }, }, expr->root_operator); } @@ -451,6 +465,14 @@ PhysicalPlanNode Optimizer::BuildOptimalPlan(G .keys = op.keys, }; }, + [this, best_expr_nn, required](const physical::Aggregation& op) -> PhysicalPlanNode { + return PhysicalAggregation{ + .source = std::make_shared( + BuildOptimalPlan(op.source.get(), RequiredInputProps(best_expr_nn, required, 0))), + .group_by = op.group_by, + .aggregates = op.aggregates, + }; + }, }, best_expr->root_operator); } @@ -483,6 +505,6 @@ utils::NotNull Optimizer::GetRootGroup return root_->group; } -template class Optimizer<6, 6>; +template class Optimizer<6, 7>; } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/reachability.cpp b/src/stewkk/sql/logic/optimizer/reachability.cpp index b4ef7d5..ddebf83 100644 --- a/src/stewkk/sql/logic/optimizer/reachability.cpp +++ b/src/stewkk/sql/logic/optimizer/reachability.cpp @@ -105,6 +105,17 @@ InternalMatch TryMatchExpr(utils::NotNull pe, if (!child.ok) child.reason = "Sort.input: " + child.reason; return child; }, + [&](const physical::Aggregation& op) -> InternalMatch { + const auto* t = std::get_if(&target); + if (!t) return {false, depth, "type mismatch: expected HashAggregate"}; + if (op.group_by != t->group_by) + return {false, depth + 1, "Aggregation group_by mismatch"}; + if (op.aggregates != t->aggregates) + return {false, depth + 1, "Aggregation aggregates mismatch"}; + auto child = MatchGroup(op.source.get(), *t->source, depth + 1); + if (!child.ok) child.reason = "Aggregation.source: " + child.reason; + return child; + }, }, pe->root_operator); } diff --git a/src/stewkk/sql/logic/optimizer/rules.cpp b/src/stewkk/sql/logic/optimizer/rules.cpp index 3b4eed0..3a052d0 100644 --- a/src/stewkk/sql/logic/optimizer/rules.cpp +++ b/src/stewkk/sql/logic/optimizer/rules.cpp @@ -2,7 +2,7 @@ namespace stewkk::sql { -Rules<6, 6> MakeMainRules() { +Rules<6, 7> MakeMainRules() { return { .transformation_rules = { std::make_unique(), @@ -19,6 +19,7 @@ Rules<6, 6> MakeMainRules() { std::make_unique(), std::make_unique(), std::make_unique(), + std::make_unique(), }, }; } diff --git a/src/stewkk/sql/logic/optimizer/rules_applier.cpp b/src/stewkk/sql/logic/optimizer/rules_applier.cpp index 1ade608..4588aea 100644 --- a/src/stewkk/sql/logic/optimizer/rules_applier.cpp +++ b/src/stewkk/sql/logic/optimizer/rules_applier.cpp @@ -30,7 +30,6 @@ utils::NotNull RulesApplier::Ap return rules_.implementation_rules[rule.value]->Apply(expr, memo); } -template class RulesApplier<2, 0>; -template class RulesApplier<6, 6>; +template class RulesApplier<6, 7>; } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/schema_catalog.cpp b/src/stewkk/sql/logic/optimizer/schema_catalog.cpp index 1502c64..fe74d20 100644 --- a/src/stewkk/sql/logic/optimizer/schema_catalog.cpp +++ b/src/stewkk/sql/logic/optimizer/schema_catalog.cpp @@ -1,5 +1,7 @@ #include +#include + #include namespace stewkk::sql { @@ -45,6 +47,20 @@ std::optional SchemaCatalog::Derive(const LogicalOperator& op) { } return out; }, + [this](const logical::Aggregation& a) -> std::optional { + auto input = GetSchema(a.source); + if (!input) return std::nullopt; + Schema out; + for (const auto& expr : a.group_by) { + if (const auto* attr = std::get_if(&expr)) { + out.push_back(*attr); + } + } + for (size_t i = 0; i < a.aggregates.size(); ++i) { + out.push_back(Attribute{"", std::format("__agg{}", i)}); + } + return out; + }, [this](const logical::CrossJoin& j) -> std::optional { auto l = GetSchema(j.lhs); auto r = GetSchema(j.rhs); diff --git a/src/stewkk/sql/logic/parser/parser.cpp b/src/stewkk/sql/logic/parser/parser.cpp index 9319e77..f7deaeb 100644 --- a/src/stewkk/sql/logic/parser/parser.cpp +++ b/src/stewkk/sql/logic/parser/parser.cpp @@ -64,6 +64,9 @@ std::string GetDotRepresentation(const Expression& expr) { std::string operator()(const InExpression& expr) { return ToString(Expression{expr}); } + std::string operator()(const AggregateExpression& expr) { + return ToString(Expression{expr}); + } std::string operator()(const Attribute& expr) { return ToString(expr); } @@ -96,6 +99,17 @@ std::string GetDotRepresentation(const Operator& op) { auto [source_node, rest] = std::visit(DotFormatter{}, *op.source); return {node, std::format("\"{}\"\n\"{}\" -> \"{}\"\n{}", node, source_node, node, rest)}; } + std::pair operator()(const Aggregation& op) { + auto groups = op.group_by + | std::views::transform([](const Expression& expr) { return ToString(expr); }) + | std::views::join_with(',') | std::ranges::to(); + auto aggregates = op.aggregates + | std::views::transform([](const Expression& expr) { return ToString(expr); }) + | std::views::join_with(',') | std::ranges::to(); + auto node = std::format("γ group_by=[{}] aggregates=[{}]", groups, aggregates); + auto [source_node, rest] = std::visit(DotFormatter{}, *op.source); + return {node, std::format("\"{}\"\n\"{}\" -> \"{}\"\n{}", node, source_node, node, rest)}; + } std::pair operator()(const Table& op) { auto node = op.alias ? std::format("{} AS {}", op.name, *op.alias) : std::format("{}", op.name); diff --git a/src/stewkk/sql/logic/parser/parser_test.cpp b/src/stewkk/sql/logic/parser/parser_test.cpp index a0e23e1..7f080b7 100644 --- a/src/stewkk/sql/logic/parser/parser_test.cpp +++ b/src/stewkk/sql/logic/parser/parser_test.cpp @@ -403,8 +403,125 @@ TEST(ParserTest, OrderByNullsFirstRejected) { ASSERT_THAT(got.Wraps(ErrorType::kQueryNotSupported), IsTrue()); } -/* -** aggregations: SELECT kind, sum(len) AS total FROM films GROUP BY kind; - */ +TEST(ParserTest, SelectWithSumAggregateAlias) { + std::stringstream s{ + "SELECT SUM(lineorder.lo_extendedprice * lineorder.lo_discount) AS revenue " + "FROM lineorder;"}; + + Operator got = GetAST(s).value().op; + + Expression revenue_arg = BinaryExpression{ + std::make_shared(Attribute{"lineorder", "lo_extendedprice"}), + BinaryOp::kMul, + std::make_shared(Attribute{"lineorder", "lo_discount"}), + }; + Expression revenue = AggregateExpression{ + AggregateFunction::kSum, + std::make_shared(revenue_arg), + false, + }; + + ASSERT_THAT(got, VariantWith(Projection{ + std::vector{Attribute{"", "__agg0"}}, + std::make_shared(Aggregation{ + {}, + std::vector{revenue}, + std::make_shared(Table{"lineorder"}), + }), + std::vector>{"revenue"}, + })); +} + +TEST(ParserTest, SelectWithGroupByAndAggregate) { + std::stringstream s{ + "SELECT date.d_year, SUM(lineorder.lo_revenue) AS revenue " + "FROM lineorder JOIN date ON lineorder.lo_orderdate = date.d_datekey " + "GROUP BY date.d_year;"}; + + Operator got = GetAST(s).value().op; + + Expression year = Attribute{"date", "d_year"}; + Expression revenue = AggregateExpression{ + AggregateFunction::kSum, + std::make_shared(Attribute{"lineorder", "lo_revenue"}), + false, + }; + Expression join_qual = BinaryExpression{ + std::make_shared(Attribute{"lineorder", "lo_orderdate"}), + BinaryOp::kEq, + std::make_shared(Attribute{"date", "d_datekey"}), + }; + + ASSERT_THAT(got, VariantWith(Projection{ + std::vector{year, Attribute{"", "__agg0"}}, + std::make_shared(Aggregation{ + std::vector{year}, + std::vector{revenue}, + std::make_shared(Join{ + JoinType::kInner, + join_qual, + std::make_shared(Table{"lineorder"}), + std::make_shared(Table{"date"}), + }), + }), + std::vector>{std::nullopt, "revenue"}, + })); +} + +TEST(ParserTest, SelectWithCountStar) { + std::stringstream s{"SELECT COUNT(*) FROM lineorder;"}; + + Operator got = GetAST(s).value().op; + + Expression count_star = AggregateExpression{ + AggregateFunction::kCount, + nullptr, + true, + }; + ASSERT_THAT(got, VariantWith(Projection{ + std::vector{Attribute{"", "__agg0"}}, + std::make_shared(Aggregation{ + {}, + std::vector{count_star}, + std::make_shared(Table{"lineorder"}), + }), + })); +} + +TEST(ParserTest, UnsupportedAggregateModifiersRejected) { + for (const char* sql : { + "SELECT SUM(DISTINCT lineorder.lo_revenue) FROM lineorder;", + "SELECT SUM(ALL lineorder.lo_revenue) FROM lineorder;", + "SELECT SUM(lineorder.lo_revenue ORDER BY lineorder.lo_revenue) FROM lineorder;", + "SELECT SUM(lineorder.lo_revenue) FILTER (WHERE lineorder.lo_discount > 0) FROM lineorder;", + "SELECT SUM(lineorder.lo_revenue) OVER () FROM lineorder;", + }) { + std::stringstream s{sql}; + + auto got = GetAST(s).error(); + + ASSERT_THAT(got.Wraps(ErrorType::kQueryNotSupported), IsTrue()); + } +} + +TEST(ParserTest, UnsupportedGroupByFormsRejected) { + std::stringstream s{ + "SELECT date.d_year, SUM(lineorder.lo_revenue) FROM lineorder " + "GROUP BY ROLLUP(date.d_year);"}; + + auto got = GetAST(s).error(); + + ASSERT_THAT(got.Wraps(ErrorType::kQueryNotSupported), IsTrue()); +} + +TEST(ParserTest, HavingRejected) { + std::stringstream s{ + "SELECT SUM(lineorder.lo_revenue) FROM lineorder " + "HAVING SUM(lineorder.lo_revenue) > 0;"}; + + auto got = GetAST(s).error(); + + ASSERT_THAT(got.Wraps(ErrorType::kQueryNotSupported), IsTrue()); +} } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/parser/visitor.cpp b/src/stewkk/sql/logic/parser/visitor.cpp index c51e0f0..72ea9dc 100644 --- a/src/stewkk/sql/logic/parser/visitor.cpp +++ b/src/stewkk/sql/logic/parser/visitor.cpp @@ -1,5 +1,8 @@ #include +#include +#include +#include #include #include @@ -10,6 +13,11 @@ namespace stewkk::sql { namespace { +struct ParsedTarget { + Expression expression; + std::optional alias; +}; + std::string UnquoteStandardString(std::string_view text) { if (text.size() < 2 || text.front() != '\'' || text.back() != '\'') { throw Error{ErrorType::kQueryNotSupported, "only standard single-quoted strings are supported"}; @@ -41,6 +49,10 @@ Operator GetOperatorWithChild(Operator&& op, Operator&& child) { filter.source = std::make_shared(std::move(child)); return filter; } + Operator operator() (Aggregation&& aggregation) { + aggregation.source = std::make_shared(std::move(child)); + return aggregation; + } Operator operator() (CrossJoin&& cross_join) { std::unreachable(); } @@ -53,6 +65,121 @@ Operator GetOperatorWithChild(Operator&& op, Operator&& child) { return std::visit(ChildSetter{std::move(child)}, std::move(op)); } +std::string ToLower(std::string s) { + std::ranges::transform(s, s.begin(), [](unsigned char c) { + return static_cast(std::tolower(c)); + }); + return s; +} + +bool ContainsAggregate(const Expression& expr) { + struct Visitor { + bool operator()(const BinaryExpression& expr) const { + return ContainsAggregate(*expr.lhs) || ContainsAggregate(*expr.rhs); + } + bool operator()(const UnaryExpression& expr) const { + return ContainsAggregate(*expr.child); + } + bool operator()(const InExpression& expr) const { + return ContainsAggregate(*expr.lhs) + || std::ranges::any_of(expr.values, [](const Expression& value) { + return ContainsAggregate(value); + }); + } + bool operator()(const AggregateExpression&) const { + return true; + } + bool operator()(const Attribute&) const { return false; } + bool operator()(const IntConst&) const { return false; } + bool operator()(const StringConst&) const { return false; } + bool operator()(const Literal&) const { return false; } + }; + return std::visit(Visitor{}, expr); +} + +void CollectAggregates(const Expression& expr, std::vector& out) { + struct Visitor { + void operator()(const BinaryExpression& expr) const { + CollectAggregates(*expr.lhs, out); + CollectAggregates(*expr.rhs, out); + } + void operator()(const UnaryExpression& expr) const { + CollectAggregates(*expr.child, out); + } + void operator()(const InExpression& expr) const { + CollectAggregates(*expr.lhs, out); + for (const auto& value : expr.values) { + CollectAggregates(value, out); + } + } + void operator()(const AggregateExpression& expr) const { + out.push_back(Expression{expr}); + } + void operator()(const Attribute&) const {} + void operator()(const IntConst&) const {} + void operator()(const StringConst&) const {} + void operator()(const Literal&) const {} + + std::vector& out; + }; + std::visit(Visitor{out}, expr); +} + +std::vector CollectAggregates(const std::vector& expressions) { + std::vector result; + for (const auto& expr : expressions) { + CollectAggregates(expr, result); + } + return result; +} + +Expression ReplaceAggregatesWithAttributes(const Expression& expr, size_t& counter); + +Expression ReplaceAggregatesWithAttributes(const Expression& expr, size_t& counter) { + struct Visitor { + Expression operator()(const BinaryExpression& e) const { + return BinaryExpression{ + std::make_shared(ReplaceAggregatesWithAttributes(*e.lhs, counter)), + e.binop, + std::make_shared(ReplaceAggregatesWithAttributes(*e.rhs, counter)), + }; + } + Expression operator()(const UnaryExpression& e) const { + return UnaryExpression{ + e.op, + std::make_shared(ReplaceAggregatesWithAttributes(*e.child, counter)), + }; + } + Expression operator()(const InExpression& e) const { + auto new_lhs = std::make_shared(ReplaceAggregatesWithAttributes(*e.lhs, counter)); + std::vector new_values; + for (const auto& v : e.values) { + new_values.push_back(ReplaceAggregatesWithAttributes(v, counter)); + } + return InExpression{std::move(new_lhs), std::move(new_values), e.negated}; + } + Expression operator()(const AggregateExpression&) const { + return Attribute{"", std::format("__agg{}", counter++)}; + } + Expression operator()(const Attribute& a) const { return a; } + Expression operator()(const IntConst& c) const { return c; } + Expression operator()(const StringConst& s) const { return s; } + Expression operator()(const Literal& l) const { return l; } + + size_t& counter; + }; + return std::visit(Visitor{counter}, expr); +} + +std::vector ReplaceAggregatesWithAttributes(const std::vector& exprs) { + std::vector result; + size_t counter = 0; + for (const auto& expr : exprs) { + result.push_back(ReplaceAggregatesWithAttributes(expr, counter)); + } + return result; +} + } // namespace std::any Visitor::visitStmt(codegen::PostgreSQLParser::StmtContext *ctx) { @@ -164,8 +291,15 @@ std::any Visitor::visitSimple_select_intersect(codegen::PostgreSQLParser::Simple } std::any Visitor::visitTarget_label(codegen::PostgreSQLParser::Target_labelContext *ctx) { - // TODO: AS (rename operation) - return visit(ctx->a_expr()); + ParsedTarget target{ + .expression = std::any_cast(visit(ctx->a_expr())), + }; + if (ctx->colLabel()) { + target.alias = ctx->colLabel()->getText(); + } else if (ctx->bareColLabel()) { + target.alias = ctx->bareColLabel()->getText(); + } + return target; } std::any Visitor::visitTarget_star(codegen::PostgreSQLParser::Target_starContext* ctx) { @@ -174,21 +308,49 @@ std::any Visitor::visitTarget_star(codegen::PostgreSQLParser::Target_starContext std::any Visitor::visitTarget_list(codegen::PostgreSQLParser::Target_listContext* ctx) { const auto& targets = ctx->target_el(); - auto target_expressions + auto parsed_targets = targets | std::views::transform([this](const auto& target) { return visit(target); }) | std::views::filter( [](const std::any& expr) { return expr.has_value(); }) | std::views::transform( - [](std::any expr) { return std::any_cast(expr); }) + [](std::any expr) { return std::any_cast(expr); }) | std::ranges::to(); - if (!target_expressions.empty()) { - return Operator{Projection{std::move(target_expressions), nullptr}}; + if (!parsed_targets.empty()) { + std::vector target_expressions; + std::vector> aliases; + target_expressions.reserve(parsed_targets.size()); + aliases.reserve(parsed_targets.size()); + for (auto& target : parsed_targets) { + aliases.push_back(std::move(target.alias)); + target_expressions.push_back(std::move(target.expression)); + } + return Operator{Projection{std::move(target_expressions), nullptr, std::move(aliases)}}; } return {}; } +std::any Visitor::visitGroup_clause(codegen::PostgreSQLParser::Group_clauseContext *ctx) { + return visit(ctx->group_by_list()); +} + +std::any Visitor::visitGroup_by_list(codegen::PostgreSQLParser::Group_by_listContext *ctx) { + std::vector result; + for (auto* item : ctx->group_by_item()) { + result.push_back(std::any_cast(visit(item))); + } + return result; +} + +std::any Visitor::visitGroup_by_item(codegen::PostgreSQLParser::Group_by_itemContext *ctx) { + if (!ctx->a_expr()) { + throw Error{ErrorType::kQueryNotSupported, + "ROLLUP, CUBE, GROUPING SETS and empty GROUP BY items are not supported"}; + } + return visit(ctx->a_expr()); +} + std::any Visitor::visitFrom_clause(codegen::PostgreSQLParser::From_clauseContext *ctx) { return visit(ctx->from_list()); } @@ -380,9 +542,15 @@ std::any Visitor::visitSimple_select_pramary( } // NOTE: all_clause_ is ignored - // TODO: support group_clause, having_clause + if (ctx->having_clause()) { + throw Error{ErrorType::kQueryNotSupported, "HAVING clause is not supported"}; + } + if (ctx->window_clause()) { + throw Error{ErrorType::kQueryNotSupported, "WINDOW clause is not supported"}; + } Operator result = Table{kEmptyTableName}; + std::optional projection; if (ctx->from_clause()) { result = std::any_cast(visit(ctx->from_clause())); @@ -396,11 +564,40 @@ std::any Visitor::visitSimple_select_pramary( if (ctx->target_list_()) { auto target_list_opt = visit(ctx->target_list_()); if (target_list_opt.has_value()) { - auto projection = std::any_cast(target_list_opt); - result = GetOperatorWithChild(std::move(projection), std::move(result)); + auto projection_op = std::any_cast(target_list_opt); + projection = std::get(std::move(projection_op)); } } + std::vector group_by; + if (ctx->group_clause()) { + group_by = std::any_cast>(visit(ctx->group_clause())); + } + + std::vector aggregates; + if (projection) { + aggregates = CollectAggregates(projection->expressions); + } + + if (!group_by.empty() || !aggregates.empty()) { + if (projection) { + projection->expressions = ReplaceAggregatesWithAttributes(projection->expressions); + } + result = Aggregation{ + std::move(group_by), + std::move(aggregates), + std::make_shared(std::move(result)), + }; + } + + if (projection) { + result = Projection{ + std::move(projection->expressions), + std::make_shared(std::move(result)), + std::move(projection->aliases), + }; + } + return result; } @@ -761,10 +958,86 @@ std::any Visitor::visitC_expr_expr(codegen::PostgreSQLParser::C_expr_exprContext if (ctx->a_expr_in_parens != nullptr) { return visit(ctx->a_expr_in_parens); } + if (ctx->func_expr()) { + return visit(ctx->func_expr()); + } // NOTE: may want to support throw Error{ErrorType::kQueryNotSupported, "c_expr is not fully supported"}; } +std::any Visitor::visitFunc_expr(codegen::PostgreSQLParser::Func_exprContext *ctx) { + if (ctx->within_group_clause()) { + throw Error{ErrorType::kQueryNotSupported, "WITHIN GROUP clause is not supported"}; + } + if (ctx->filter_clause()) { + throw Error{ErrorType::kQueryNotSupported, "aggregate FILTER clause is not supported"}; + } + if (ctx->over_clause()) { + throw Error{ErrorType::kQueryNotSupported, "window functions are not supported"}; + } + if (!ctx->func_application()) { + throw Error{ErrorType::kQueryNotSupported, "function expression is not supported"}; + } + return visit(ctx->func_application()); +} + +std::any Visitor::visitFunc_application(codegen::PostgreSQLParser::Func_applicationContext *ctx) { + if (ctx->VARIADIC()) { + throw Error{ErrorType::kQueryNotSupported, "VARIADIC function arguments are not supported"}; + } + if (ctx->ALL()) { + throw Error{ErrorType::kQueryNotSupported, "ALL in aggregate calls is not supported"}; + } + if (ctx->DISTINCT()) { + throw Error{ErrorType::kQueryNotSupported, "DISTINCT in aggregate calls is not supported"}; + } + if (ctx->sort_clause_()) { + throw Error{ErrorType::kQueryNotSupported, "ORDER BY in aggregate calls is not supported"}; + } + + auto name = ToLower(ctx->func_name()->getText()); + AggregateFunction function; + if (name == "sum") { + function = AggregateFunction::kSum; + } else if (name == "count") { + function = AggregateFunction::kCount; + } else { + throw Error{ErrorType::kQueryNotSupported, + std::format("function {} is not supported", ctx->func_name()->getText())}; + } + + if (ctx->STAR()) { + if (function != AggregateFunction::kCount) { + throw Error{ErrorType::kQueryNotSupported, "only COUNT(*) supports star arguments"}; + } + return Expression{AggregateExpression{function, nullptr, true}}; + } + + auto* args = ctx->func_arg_list(); + if (!args || args->func_arg_expr().size() != 1) { + throw Error{ErrorType::kQueryNotSupported, + std::format("{} requires exactly one argument", ToString(function))}; + } + + auto argument = std::any_cast(visit(args->func_arg_expr(0))); + if (ContainsAggregate(argument)) { + throw Error{ErrorType::kQueryNotSupported, "nested aggregate calls are not supported"}; + } + + return Expression{AggregateExpression{ + function, + std::make_shared(std::move(argument)), + false, + }}; +} + +std::any Visitor::visitFunc_arg_expr(codegen::PostgreSQLParser::Func_arg_exprContext *ctx) { + if (ctx->param_name()) { + throw Error{ErrorType::kQueryNotSupported, "named function arguments are not supported"}; + } + return visit(ctx->a_expr()); +} + std::any Visitor::visitC_expr_case(codegen::PostgreSQLParser::C_expr_caseContext *ctx) { // NOTE: may want to support throw Error{ErrorType::kQueryNotSupported, "CASE clause is not supported"}; diff --git a/src/stewkk/sql/logic/parser/visitor.hpp b/src/stewkk/sql/logic/parser/visitor.hpp index d7ab26e..cf24d9f 100644 --- a/src/stewkk/sql/logic/parser/visitor.hpp +++ b/src/stewkk/sql/logic/parser/visitor.hpp @@ -25,6 +25,12 @@ class Visitor : public codegen::PostgreSQLParserBaseVisitor { virtual std::any visitTarget_list(codegen::PostgreSQLParser::Target_listContext *ctx) override; virtual std::any visitTarget_label(codegen::PostgreSQLParser::Target_labelContext *ctx) override; virtual std::any visitTarget_star(codegen::PostgreSQLParser::Target_starContext *ctx) override; + virtual std::any visitGroup_clause(codegen::PostgreSQLParser::Group_clauseContext *ctx) override; + virtual std::any visitGroup_by_list(codegen::PostgreSQLParser::Group_by_listContext *ctx) override; + virtual std::any visitGroup_by_item(codegen::PostgreSQLParser::Group_by_itemContext *ctx) override; + virtual std::any visitFunc_expr(codegen::PostgreSQLParser::Func_exprContext *ctx) override; + virtual std::any visitFunc_application(codegen::PostgreSQLParser::Func_applicationContext *ctx) override; + virtual std::any visitFunc_arg_expr(codegen::PostgreSQLParser::Func_arg_exprContext *ctx) override; virtual std::any visitFrom_clause(codegen::PostgreSQLParser::From_clauseContext *ctx) override; virtual std::any visitWhere_clause(codegen::PostgreSQLParser::Where_clauseContext *ctx) override; virtual std::any visitSelect_with_parens(codegen::PostgreSQLParser::Select_with_parensContext *ctx) override; diff --git a/src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.cpp b/src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.cpp index 178f27f..1765de7 100644 --- a/src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.cpp +++ b/src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.cpp @@ -25,6 +25,11 @@ void CollectAttributes(const Expression& e, std::vector& out) { CollectAttributes(value, out); } }, + [&](const AggregateExpression& a) { + if (!a.is_star && a.argument) { + CollectAttributes(*a.argument, out); + } + }, [&](const IntConst&) {}, [&](const StringConst&) {}, [&](const Literal&) {}, @@ -72,7 +77,7 @@ LogicalOperator FilterPushdownThroughProjection::ApplyImpl(utils::NotNullsource, f.predicate})->group; - return logical::Projection{new_filter_group, proj->expressions}; + return logical::Projection{new_filter_group, proj->expressions, proj->aliases}; } } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp b/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp index 0ad685c..7554cec 100644 --- a/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp +++ b/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp @@ -48,6 +48,11 @@ void CollectAttrTables(const Expression& e, std::unordered_set& out CollectAttrTables(value, out); } }, + [&](const AggregateExpression& a) { + if (!a.is_star && a.argument) { + CollectAttrTables(*a.argument, out); + } + }, [&](const IntConst&) {}, [&](const StringConst&) {}, [&](const Literal&) {}, @@ -73,6 +78,7 @@ void CollectGroupTables(utils::NotNull g, std::unordered_set) { std::string exprs; - for (const auto& e : node.expressions) exprs += " " + ToString(e); + for (size_t i = 0; i < node.expressions.size(); ++i) { + exprs += " " + ToString(node.expressions[i]); + if (i < node.aliases.size() && node.aliases[i]) { + exprs += " AS " + *node.aliases[i]; + } + } return "(Projection" + exprs + " " + SerializeAst(*node.source) + ")"; + } else if constexpr (std::is_same_v) { + std::string group_by; + for (const auto& e : node.group_by) group_by += " " + ToString(e); + std::string aggregates; + for (const auto& e : node.aggregates) aggregates += " " + ToString(e); + return "(Aggregation (GroupBy" + group_by + ") (Aggregates" + aggregates + ") " + + SerializeAst(*node.source) + ")"; } else if constexpr (std::is_same_v) { return "(CrossJoin " + SerializeAst(*node.lhs) + " " + SerializeAst(*node.rhs) + ")"; } else if constexpr (std::is_same_v) { diff --git a/src/stewkk/sql/models/parser/expression.cpp b/src/stewkk/sql/models/parser/expression.cpp index 4d5c9a9..e539e9a 100644 --- a/src/stewkk/sql/models/parser/expression.cpp +++ b/src/stewkk/sql/models/parser/expression.cpp @@ -17,6 +17,25 @@ bool InExpression::operator==(const InExpression& other) const { return *lhs == *other.lhs && values == other.values && negated == other.negated; } +bool AggregateExpression::operator==(const AggregateExpression& other) const { + if (function != other.function || is_star != other.is_star) { + return false; + } + if (is_star) { + return true; + } + return argument && other.argument && *argument == *other.argument; +} + +std::string ToString(AggregateFunction function) { + switch (function) { + case AggregateFunction::kSum: + return "SUM"; + case AggregateFunction::kCount: + return "COUNT"; + } +} + std::string ToString(const Attribute& attr) { return std::format("{}.{}", attr.table, attr.name); } @@ -109,6 +128,12 @@ std::string ToString(const Expression& expr) { }) | std::views::join_with(',') | std::ranges::to(); return std::format("{} {}in ({})", ToString(*expr.lhs), expr.negated ? "not " : "", values); } + std::string operator()(const AggregateExpression& expr) { + if (expr.is_star) { + return std::format("{}(*)", ToString(expr.function)); + } + return std::format("{}({})", ToString(expr.function), ToString(*expr.argument)); + } std::string operator()(const Literal& expr) { return ToString(expr); } diff --git a/src/stewkk/sql/models/parser/relational_algebra_ast.cpp b/src/stewkk/sql/models/parser/relational_algebra_ast.cpp index 2774e59..24ce926 100644 --- a/src/stewkk/sql/models/parser/relational_algebra_ast.cpp +++ b/src/stewkk/sql/models/parser/relational_algebra_ast.cpp @@ -7,13 +7,25 @@ std::string_view VisibleName(const Table& table) { } bool Projection::operator==(const Projection& other) const { - return expressions == other.expressions && *source == *other.source; + auto normalize = [](const std::vector>& aliases) { + auto result = aliases; + while (!result.empty() && !result.back()) { + result.pop_back(); + } + return result; + }; + return expressions == other.expressions && *source == *other.source + && normalize(aliases) == normalize(other.aliases); } bool Filter::operator==(const Filter& other) const { return expr == other.expr && *source == *other.source; } +bool Aggregation::operator==(const Aggregation& other) const { + return group_by == other.group_by && aggregates == other.aggregates && *source == *other.source; +} + bool CrossJoin::operator==(const CrossJoin& other) const { return *lhs == *other.lhs && *rhs == *other.rhs; } From 03859f3a43066b680e3c2f7022b17ab36205b97f Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sat, 30 May 2026 02:30:25 +0300 Subject: [PATCH 059/120] Add SBB benchmark --- benchmarks/datasets/ssb/README.md | 26 +++- benchmarks/datasets/ssb/queries/q1.1.sql | 6 + benchmarks/datasets/ssb/queries/q1.2.sql | 6 + benchmarks/datasets/ssb/queries/q1.3.sql | 7 + benchmarks/datasets/ssb/queries/q2.1.sql | 9 ++ benchmarks/datasets/ssb/queries/q2.2.sql | 9 ++ benchmarks/datasets/ssb/queries/q2.3.sql | 9 ++ benchmarks/datasets/ssb/queries/q3.1.sql | 10 ++ benchmarks/datasets/ssb/queries/q3.2.sql | 10 ++ benchmarks/datasets/ssb/queries/q3.3.sql | 10 ++ benchmarks/datasets/ssb/queries/q3.4.sql | 10 ++ benchmarks/datasets/ssb/queries/q4.1.sql | 11 ++ benchmarks/datasets/ssb/queries/q4.2.sql | 12 ++ benchmarks/datasets/ssb/queries/q4.3.sql | 11 ++ benchmarks/datasets/ssb/test_queries.py | 34 +++++ benchmarks/main.cpp | 138 +++++++++++++++++- include/stewkk/sql/logic/executor/plan.hpp | 1 + .../sql/logic/optimizer/physical_expr.hpp | 1 + src/stewkk/sql/logic/executor/executor.cpp | 134 +++++++++++------ .../sql/logic/executor/executor_test.cpp | 47 ++++++ src/stewkk/sql/logic/executor/plan.cpp | 2 +- .../sql/logic/executor/plan_serializer.cpp | 56 ++++++- .../implement_projection.cpp | 3 +- src/stewkk/sql/logic/optimizer/optimizer.cpp | 1 + .../sql/logic/optimizer/schema_catalog.cpp | 12 +- .../sql/logic/optimizer/sort_enforcer.cpp | 5 +- src/stewkk/sql/logic/parser/parser_test.cpp | 6 +- src/stewkk/sql/logic/parser/visitor.cpp | 12 +- 28 files changed, 525 insertions(+), 73 deletions(-) create mode 100644 benchmarks/datasets/ssb/queries/q1.1.sql create mode 100644 benchmarks/datasets/ssb/queries/q1.2.sql create mode 100644 benchmarks/datasets/ssb/queries/q1.3.sql create mode 100644 benchmarks/datasets/ssb/queries/q2.1.sql create mode 100644 benchmarks/datasets/ssb/queries/q2.2.sql create mode 100644 benchmarks/datasets/ssb/queries/q2.3.sql create mode 100644 benchmarks/datasets/ssb/queries/q3.1.sql create mode 100644 benchmarks/datasets/ssb/queries/q3.2.sql create mode 100644 benchmarks/datasets/ssb/queries/q3.3.sql create mode 100644 benchmarks/datasets/ssb/queries/q3.4.sql create mode 100644 benchmarks/datasets/ssb/queries/q4.1.sql create mode 100644 benchmarks/datasets/ssb/queries/q4.2.sql create mode 100644 benchmarks/datasets/ssb/queries/q4.3.sql create mode 100644 benchmarks/datasets/ssb/test_queries.py diff --git a/benchmarks/datasets/ssb/README.md b/benchmarks/datasets/ssb/README.md index 192b4f1..3ec27c8 100644 --- a/benchmarks/datasets/ssb/README.md +++ b/benchmarks/datasets/ssb/README.md @@ -29,12 +29,28 @@ preserved as `string` values in the generated CSV. Generated full-scale data should stay under `benchmarks/datasets/ssb/generated/` or another ignored location. -## Current engine limitation +## Queries -This first slice only adds data layout and conversion. The current C++ scanner -supports `int` and `NULL` values, so SSB CSV files containing `string` columns -are not executable until string type support is added to the parser, scanner, -executor, and output formatting. +The standard 13 SSB query templates are in `queries/`. They are written in the +subset supported by this engine: explicit joins, table aliases, string +predicates, `BETWEEN`, list `IN`, `SUM`, `COUNT`, `GROUP BY`, and `ORDER BY`. + +Run one query through the CLI: + +```sh +./build/bin/sql --data-dir benchmarks/datasets/ssb/generated/sf1 \ + < benchmarks/datasets/ssb/queries/q1.1.sql +``` + +Run SSB benchmarks after generating data: + +```sh +SSB_DATA_DIR=benchmarks/datasets/ssb/generated/sf1 \ + ./build/bin/benchmarks --benchmark_filter='SSB/' +``` + +The JIT expression executor still rejects string and `IN` expressions, so the +SSB benchmark registrations currently use the interpreted expression executor. ## Test the converter diff --git a/benchmarks/datasets/ssb/queries/q1.1.sql b/benchmarks/datasets/ssb/queries/q1.1.sql new file mode 100644 index 0000000..5e812f9 --- /dev/null +++ b/benchmarks/datasets/ssb/queries/q1.1.sql @@ -0,0 +1,6 @@ +SELECT SUM(lo.lo_extendedprice * lo.lo_discount) AS revenue +FROM lineorder AS lo +JOIN date AS d ON lo.lo_orderdate = d.d_datekey +WHERE d.d_year = 1993 + AND lo.lo_discount BETWEEN 1 AND 3 + AND lo.lo_quantity < 25; diff --git a/benchmarks/datasets/ssb/queries/q1.2.sql b/benchmarks/datasets/ssb/queries/q1.2.sql new file mode 100644 index 0000000..9a07697 --- /dev/null +++ b/benchmarks/datasets/ssb/queries/q1.2.sql @@ -0,0 +1,6 @@ +SELECT SUM(lo.lo_extendedprice * lo.lo_discount) AS revenue +FROM lineorder AS lo +JOIN date AS d ON lo.lo_orderdate = d.d_datekey +WHERE d.d_yearmonthnum = 199401 + AND lo.lo_discount BETWEEN 4 AND 6 + AND lo.lo_quantity BETWEEN 26 AND 35; diff --git a/benchmarks/datasets/ssb/queries/q1.3.sql b/benchmarks/datasets/ssb/queries/q1.3.sql new file mode 100644 index 0000000..d001733 --- /dev/null +++ b/benchmarks/datasets/ssb/queries/q1.3.sql @@ -0,0 +1,7 @@ +SELECT SUM(lo.lo_extendedprice * lo.lo_discount) AS revenue +FROM lineorder AS lo +JOIN date AS d ON lo.lo_orderdate = d.d_datekey +WHERE d.d_weeknuminyear = 6 + AND d.d_year = 1994 + AND lo.lo_discount BETWEEN 5 AND 7 + AND lo.lo_quantity BETWEEN 26 AND 35; diff --git a/benchmarks/datasets/ssb/queries/q2.1.sql b/benchmarks/datasets/ssb/queries/q2.1.sql new file mode 100644 index 0000000..37a59f2 --- /dev/null +++ b/benchmarks/datasets/ssb/queries/q2.1.sql @@ -0,0 +1,9 @@ +SELECT SUM(lo.lo_revenue) AS lo_revenue, d.d_year, p.p_brand +FROM lineorder AS lo +JOIN date AS d ON lo.lo_orderdate = d.d_datekey +JOIN part AS p ON lo.lo_partkey = p.p_partkey +JOIN supplier AS s ON lo.lo_suppkey = s.s_suppkey +WHERE p.p_category = 'MFGR#12' + AND s.s_region = 'AMERICA' +GROUP BY d.d_year, p.p_brand +ORDER BY d.d_year, p.p_brand; diff --git a/benchmarks/datasets/ssb/queries/q2.2.sql b/benchmarks/datasets/ssb/queries/q2.2.sql new file mode 100644 index 0000000..9488eaa --- /dev/null +++ b/benchmarks/datasets/ssb/queries/q2.2.sql @@ -0,0 +1,9 @@ +SELECT SUM(lo.lo_revenue) AS lo_revenue, d.d_year, p.p_brand +FROM lineorder AS lo +JOIN date AS d ON lo.lo_orderdate = d.d_datekey +JOIN part AS p ON lo.lo_partkey = p.p_partkey +JOIN supplier AS s ON lo.lo_suppkey = s.s_suppkey +WHERE p.p_brand BETWEEN 'MFGR#2221' AND 'MFGR#2228' + AND s.s_region = 'ASIA' +GROUP BY d.d_year, p.p_brand +ORDER BY d.d_year, p.p_brand; diff --git a/benchmarks/datasets/ssb/queries/q2.3.sql b/benchmarks/datasets/ssb/queries/q2.3.sql new file mode 100644 index 0000000..88d4b48 --- /dev/null +++ b/benchmarks/datasets/ssb/queries/q2.3.sql @@ -0,0 +1,9 @@ +SELECT SUM(lo.lo_revenue) AS lo_revenue, d.d_year, p.p_brand +FROM lineorder AS lo +JOIN date AS d ON lo.lo_orderdate = d.d_datekey +JOIN part AS p ON lo.lo_partkey = p.p_partkey +JOIN supplier AS s ON lo.lo_suppkey = s.s_suppkey +WHERE p.p_brand = 'MFGR#2221' + AND s.s_region = 'EUROPE' +GROUP BY d.d_year, p.p_brand +ORDER BY d.d_year, p.p_brand; diff --git a/benchmarks/datasets/ssb/queries/q3.1.sql b/benchmarks/datasets/ssb/queries/q3.1.sql new file mode 100644 index 0000000..f615732 --- /dev/null +++ b/benchmarks/datasets/ssb/queries/q3.1.sql @@ -0,0 +1,10 @@ +SELECT c.c_nation, s.s_nation, d.d_year, SUM(lo.lo_revenue) AS lo_revenue +FROM lineorder AS lo +JOIN customer AS c ON lo.lo_custkey = c.c_custkey +JOIN supplier AS s ON lo.lo_suppkey = s.s_suppkey +JOIN date AS d ON lo.lo_orderdate = d.d_datekey +WHERE c.c_region = 'ASIA' + AND s.s_region = 'ASIA' + AND d.d_year BETWEEN 1992 AND 1997 +GROUP BY c.c_nation, s.s_nation, d.d_year +ORDER BY d.d_year, lo_revenue DESC; diff --git a/benchmarks/datasets/ssb/queries/q3.2.sql b/benchmarks/datasets/ssb/queries/q3.2.sql new file mode 100644 index 0000000..484de9b --- /dev/null +++ b/benchmarks/datasets/ssb/queries/q3.2.sql @@ -0,0 +1,10 @@ +SELECT c.c_city, s.s_city, d.d_year, SUM(lo.lo_revenue) AS lo_revenue +FROM lineorder AS lo +JOIN customer AS c ON lo.lo_custkey = c.c_custkey +JOIN supplier AS s ON lo.lo_suppkey = s.s_suppkey +JOIN date AS d ON lo.lo_orderdate = d.d_datekey +WHERE c.c_nation = 'UNITED STATES' + AND s.s_nation = 'UNITED STATES' + AND d.d_year BETWEEN 1992 AND 1997 +GROUP BY c.c_city, s.s_city, d.d_year +ORDER BY d.d_year, lo_revenue DESC; diff --git a/benchmarks/datasets/ssb/queries/q3.3.sql b/benchmarks/datasets/ssb/queries/q3.3.sql new file mode 100644 index 0000000..6900fba --- /dev/null +++ b/benchmarks/datasets/ssb/queries/q3.3.sql @@ -0,0 +1,10 @@ +SELECT c.c_city, s.s_city, d.d_year, SUM(lo.lo_revenue) AS lo_revenue +FROM lineorder AS lo +JOIN customer AS c ON lo.lo_custkey = c.c_custkey +JOIN supplier AS s ON lo.lo_suppkey = s.s_suppkey +JOIN date AS d ON lo.lo_orderdate = d.d_datekey +WHERE c.c_city IN ('UNITED KI1', 'UNITED KI5') + AND s.s_city IN ('UNITED KI1', 'UNITED KI5') + AND d.d_year BETWEEN 1992 AND 1997 +GROUP BY c.c_city, s.s_city, d.d_year +ORDER BY d.d_year, lo_revenue DESC; diff --git a/benchmarks/datasets/ssb/queries/q3.4.sql b/benchmarks/datasets/ssb/queries/q3.4.sql new file mode 100644 index 0000000..c220c29 --- /dev/null +++ b/benchmarks/datasets/ssb/queries/q3.4.sql @@ -0,0 +1,10 @@ +SELECT c.c_city, s.s_city, d.d_year, SUM(lo.lo_revenue) AS lo_revenue +FROM lineorder AS lo +JOIN customer AS c ON lo.lo_custkey = c.c_custkey +JOIN supplier AS s ON lo.lo_suppkey = s.s_suppkey +JOIN date AS d ON lo.lo_orderdate = d.d_datekey +WHERE c.c_city IN ('UNITED KI1', 'UNITED KI5') + AND s.s_city IN ('UNITED KI1', 'UNITED KI5') + AND d.d_yearmonth = 'Dec1997' +GROUP BY c.c_city, s.s_city, d.d_year +ORDER BY d.d_year, lo_revenue DESC; diff --git a/benchmarks/datasets/ssb/queries/q4.1.sql b/benchmarks/datasets/ssb/queries/q4.1.sql new file mode 100644 index 0000000..6efbe87 --- /dev/null +++ b/benchmarks/datasets/ssb/queries/q4.1.sql @@ -0,0 +1,11 @@ +SELECT d.d_year, c.c_nation, SUM(lo.lo_revenue - lo.lo_supplycost) AS profit +FROM lineorder AS lo +JOIN date AS d ON lo.lo_orderdate = d.d_datekey +JOIN customer AS c ON lo.lo_custkey = c.c_custkey +JOIN supplier AS s ON lo.lo_suppkey = s.s_suppkey +JOIN part AS p ON lo.lo_partkey = p.p_partkey +WHERE c.c_region = 'AMERICA' + AND s.s_region = 'AMERICA' + AND p.p_mfgr IN ('MFGR#1', 'MFGR#2') +GROUP BY d.d_year, c.c_nation +ORDER BY d.d_year, c.c_nation; diff --git a/benchmarks/datasets/ssb/queries/q4.2.sql b/benchmarks/datasets/ssb/queries/q4.2.sql new file mode 100644 index 0000000..09b04b9 --- /dev/null +++ b/benchmarks/datasets/ssb/queries/q4.2.sql @@ -0,0 +1,12 @@ +SELECT d.d_year, s.s_nation, p.p_category, SUM(lo.lo_revenue - lo.lo_supplycost) AS profit +FROM lineorder AS lo +JOIN date AS d ON lo.lo_orderdate = d.d_datekey +JOIN customer AS c ON lo.lo_custkey = c.c_custkey +JOIN supplier AS s ON lo.lo_suppkey = s.s_suppkey +JOIN part AS p ON lo.lo_partkey = p.p_partkey +WHERE c.c_region = 'AMERICA' + AND s.s_region = 'AMERICA' + AND d.d_year IN (1997, 1998) + AND p.p_mfgr IN ('MFGR#1', 'MFGR#2') +GROUP BY d.d_year, s.s_nation, p.p_category +ORDER BY d.d_year, s.s_nation, p.p_category; diff --git a/benchmarks/datasets/ssb/queries/q4.3.sql b/benchmarks/datasets/ssb/queries/q4.3.sql new file mode 100644 index 0000000..a83e97d --- /dev/null +++ b/benchmarks/datasets/ssb/queries/q4.3.sql @@ -0,0 +1,11 @@ +SELECT d.d_year, s.s_city, p.p_brand, SUM(lo.lo_revenue - lo.lo_supplycost) AS profit +FROM lineorder AS lo +JOIN date AS d ON lo.lo_orderdate = d.d_datekey +JOIN customer AS c ON lo.lo_custkey = c.c_custkey +JOIN supplier AS s ON lo.lo_suppkey = s.s_suppkey +JOIN part AS p ON lo.lo_partkey = p.p_partkey +WHERE s.s_nation = 'UNITED STATES' + AND d.d_year IN (1997, 1998) + AND p.p_category = 'MFGR#14' +GROUP BY d.d_year, s.s_city, p.p_brand +ORDER BY d.d_year, s.s_city, p.p_brand; diff --git a/benchmarks/datasets/ssb/test_queries.py b/benchmarks/datasets/ssb/test_queries.py new file mode 100644 index 0000000..34982fe --- /dev/null +++ b/benchmarks/datasets/ssb/test_queries.py @@ -0,0 +1,34 @@ +from __future__ import annotations + +import subprocess +from pathlib import Path + +import pytest + + +SSB_DIR = Path(__file__).resolve().parent +PROJECT_DIR = SSB_DIR.parents[2] +SQL_CLI = PROJECT_DIR / "build" / "bin" / "sql" +QUERY_DIR = SSB_DIR / "queries" +EXPECTED_CSV = SSB_DIR / "fixtures" / "expected_csv" + +SSB_QUERIES = tuple(sorted(QUERY_DIR.glob("*.sql"))) + + +@pytest.mark.parametrize("query_path", SSB_QUERIES, ids=lambda p: p.stem) +def test_ssb_query_executes_against_expected_csv(query_path: Path) -> None: + assert SQL_CLI.exists(), f"missing SQL CLI: {SQL_CLI}; build the sql target first" + + result = subprocess.run( + [str(SQL_CLI), "--data-dir", str(EXPECTED_CSV)], + input=query_path.read_text(encoding="utf-8"), + text=True, + capture_output=True, + check=False, + ) + + assert result.returncode == 0, ( + f"{query_path.name} failed with exit code {result.returncode}\n" + f"stderr:\n{result.stderr}\n" + f"stdout:\n{result.stdout}" + ) diff --git a/benchmarks/main.cpp b/benchmarks/main.cpp index 06275b6..1177b72 100644 --- a/benchmarks/main.cpp +++ b/benchmarks/main.cpp @@ -1,19 +1,28 @@ #include +#include #include +#include #include #include #include +#include +#include +#include +#include #include #include #include +#include #include #include #include #include #include +#include +#include #include #include #include @@ -22,7 +31,6 @@ namespace stewkk::sql { namespace { -// FIXME: ORDER BY support missing PhysicalPlanNode ToPhysicalPlan(const Operator& op) { return std::visit(utils::Overloaded{ [](const Table& t) -> PhysicalPlanNode { @@ -32,6 +40,7 @@ PhysicalPlanNode ToPhysicalPlan(const Operator& op) { return PhysicalProjection{ .source = std::make_shared(ToPhysicalPlan(*p.source)), .expressions = p.expressions, + .aliases = p.aliases, }; }, [](const Filter& f) -> PhysicalPlanNode { @@ -40,8 +49,12 @@ PhysicalPlanNode ToPhysicalPlan(const Operator& op) { .predicate = f.expr, }; }, - [](const Aggregation&) -> PhysicalPlanNode { - throw std::runtime_error{"Aggregation execution is not implemented"}; + [](const Aggregation& a) -> PhysicalPlanNode { + return PhysicalAggregation{ + .source = std::make_shared(ToPhysicalPlan(*a.source)), + .group_by = a.group_by, + .aggregates = a.aggregates, + }; }, [](const CrossJoin& j) -> PhysicalPlanNode { return NestedLoopCrossJoin{ @@ -79,6 +92,22 @@ CardinalityEstimates MakeBenchCardinality() { }); } +CardinalityEstimates LoadCardinalityFromCsvDir(const std::filesystem::path& dir) { + std::unordered_map counts; + if (!std::filesystem::is_directory(dir)) { + return CardinalityEstimates{}; + } + for (const auto& entry : std::filesystem::directory_iterator{dir}) { + if (entry.path().extension() != ".csv") continue; + std::ifstream in{entry.path()}; + std::string line; + int64_t rows = -1; // header + while (std::getline(in, line)) ++rows; + counts.emplace(entry.path().stem().string(), std::max(0, rows)); + } + return CardinalityEstimates{std::move(counts)}; +} + enum class PlannerMode { kNaive, kOptimized }; template @@ -91,6 +120,20 @@ PhysicalPlanNode MakePlan(const Operator& op) { } } +template +PhysicalPlanNode MakePlan(const ParsedQuery& query, CardinalityEstimates cardinality) { + if constexpr (Mode == PlannerMode::kNaive) { + return ToPhysicalPlan(query.op); + } else { + PropertySet required = query.required_order + ? PropertySet{SortProperty{*query.required_order}} + : PropertySet::Any(); + Optimizer optimizer(query.op, MakeMainRules(), std::move(cardinality), {}, + std::move(required)); + return optimizer.Optimize(); + } +} + } // namespace const static std::string kProjectDir = std::getenv("PWD"); @@ -212,6 +255,95 @@ REGISTER_BM_SQL_MT(kComplex4000) REGISTER_BM_SQL_MT(kComplex8000) REGISTER_BM_SQL_MT(kComplex16000) +namespace { + +std::string ReadTextFile(const std::filesystem::path& path) { + std::ifstream in{path}; + std::ostringstream out; + out << in.rdbuf(); + return out.str(); +} + +std::filesystem::path SsbDataDir() { + if (const char* env = std::getenv("SSB_DATA_DIR")) { + return env; + } + return std::filesystem::path{kProjectDir} / "benchmarks/datasets/ssb/generated/sf1"; +} + +template +void BM_SSB(benchmark::State& state, std::string query, std::filesystem::path data_dir) { + std::ofstream nullstream("/dev/null"); + std::clog.rdbuf(nullstream.rdbuf()); + + if (!std::filesystem::is_directory(data_dir)) { + state.SkipWithError(("SSB data directory not found: " + data_dir.string() + + " (set SSB_DATA_DIR)").c_str()); + return; + } + + std::stringstream sql{query}; + auto parsed = GetAST(sql); + if (!parsed.has_value()) { + state.SkipWithError(("SSB query parse failed: " + What(parsed.error())).c_str()); + return; + } + + PhysicalPlanNode plan; + try { + plan = MakePlan(parsed.value(), LoadCardinalityFromCsvDir(data_dir)); + } catch (const std::exception& e) { + state.SkipWithError(e.what()); + return; + } + + boost::asio::io_context ctx; + CsvDirSequentialScanner seq_scan{data_dir.string()}; + Executor executor(std::move(seq_scan), ctx.get_executor()); + + auto run_once = [&]() { + auto fut = boost::asio::co_spawn(ctx, executor.Execute(plan), boost::asio::use_future); + ctx.restart(); + ctx.run(); + return fut.get(); + }; + + try { + benchmark::DoNotOptimize(run_once()); + for (auto _ : state) { + benchmark::DoNotOptimize(run_once()); + } + } catch (const std::exception& e) { + state.SkipWithError(e.what()); + } +} + +struct SsbRegistration { + SsbRegistration() { + const auto query_dir = std::filesystem::path{kProjectDir} / "benchmarks/datasets/ssb/queries"; + const auto data_dir = SsbDataDir(); + if (!std::filesystem::is_directory(query_dir)) return; + + for (const auto& entry : std::filesystem::directory_iterator{query_dir}) { + if (entry.path().extension() != ".sql") continue; + auto query = ReadTextFile(entry.path()); + auto name = "SSB/" + entry.path().stem().string(); + benchmark::RegisterBenchmark( + (name + "/Interpreted/Naive").c_str(), + &BM_SSB, query, data_dir) + ->UseRealTime(); + benchmark::RegisterBenchmark( + (name + "/Interpreted/Optimized").c_str(), + &BM_SSB, query, data_dir) + ->UseRealTime(); + } + } +}; + +const SsbRegistration kRegisterSsb; + +} // namespace + } // namespace stewkk::sql BENCHMARK_MAIN(); diff --git a/include/stewkk/sql/logic/executor/plan.hpp b/include/stewkk/sql/logic/executor/plan.hpp index 3e6c7f9..b6f295a 100644 --- a/include/stewkk/sql/logic/executor/plan.hpp +++ b/include/stewkk/sql/logic/executor/plan.hpp @@ -38,6 +38,7 @@ std::string_view OutputTable(const SeqScan& scan); struct PhysicalProjection { std::shared_ptr source; std::vector expressions; + std::vector> aliases; bool operator==(const PhysicalProjection&) const; }; diff --git a/include/stewkk/sql/logic/optimizer/physical_expr.hpp b/include/stewkk/sql/logic/optimizer/physical_expr.hpp index 452885f..193257c 100644 --- a/include/stewkk/sql/logic/optimizer/physical_expr.hpp +++ b/include/stewkk/sql/logic/optimizer/physical_expr.hpp @@ -25,6 +25,7 @@ struct SeqScan { struct Projection { utils::NotNull source; std::vector expressions; + std::vector> aliases; bool operator==(const Projection&) const = default; }; diff --git a/src/stewkk/sql/logic/executor/executor.cpp b/src/stewkk/sql/logic/executor/executor.cpp index 6a5deb2..b55423d 100644 --- a/src/stewkk/sql/logic/executor/executor.cpp +++ b/src/stewkk/sql/logic/executor/executor.cpp @@ -35,7 +35,7 @@ Value ApplyBooleanOperator(Value lhs, Value rhs) { if (lhs.is_null || rhs.is_null) { return Value{true}; } - return Value{false, Op{}(lhs.value.bool_value, rhs.value.bool_value)}; + return Value{false, {.bool_value = Op{}(lhs.value.bool_value, rhs.value.bool_value)}}; } struct IntPow { @@ -67,8 +67,11 @@ Type GetExpressionType(const Expression& expr, const AttributesInfo& available_a return Type::kBool; } if (lhs_type == Type::kString - && !std::ranges::contains(std::vector{BinaryOp::kEq, BinaryOp::kNotEq}, binop.binop)) { - throw std::logic_error{"strings support only = and != operators"}; + && !std::ranges::contains(std::vector{BinaryOp::kEq, BinaryOp::kNotEq, + BinaryOp::kLt, BinaryOp::kLe, + BinaryOp::kGt, BinaryOp::kGe}, + binop.binop)) { + throw std::logic_error{"strings support only comparison operators"}; } return Type::kBool; } @@ -150,8 +153,8 @@ Type GetExpressionType(const Expression& expr, const AttributesInfo& available_a Type GetExpressionTypeUnchecked(const Expression& expr, const AttributesInfo& available_attrs) { struct ExpressionTypeVisitor { Type operator()(const BinaryExpression& binop) const { - if (std::ranges::contains(std::vector{BinaryOp::kPlus, BinaryOp::kMinus, BinaryOp::kDiv, - BinaryOp::kMod, BinaryOp::kPow}, + if (std::ranges::contains(std::vector{BinaryOp::kPlus, BinaryOp::kMinus, BinaryOp::kMul, + BinaryOp::kDiv, BinaryOp::kMod, BinaryOp::kPow}, binop.binop)) { return Type::kInt; } @@ -206,10 +209,13 @@ Type GetExpressionTypeUnchecked(const Expression& expr, const AttributesInfo& av AttributesInfo GetAttributesAfterProjection(const AttributesInfo& attrs, const PhysicalProjection& proj) { AttributesInfo result_attributes; result_attributes.reserve(proj.expressions.size()); - for (const auto& target : proj.expressions) { + for (const auto& [index, target] : proj.expressions | std::views::enumerate) { AttributeInfo projection_result; projection_result.type = GetExpressionType(target, attrs); - if (const Attribute* attr = std::get_if(&target)) { + if (index < static_cast(proj.aliases.size()) && proj.aliases[index]) { + projection_result.name = *proj.aliases[index]; + projection_result.table = ""; + } else if (const Attribute* attr = std::get_if(&target)) { projection_result.name = std::move(attr->name); projection_result.table = std::move(attr->table); } @@ -231,53 +237,70 @@ Value CalcExpression(const Tuple& source, const AttributesInfo& source_attrs, co } } + Value CompareValues(Value lhs, Value rhs, BinaryOp op, Type type) { + if (lhs.is_null || rhs.is_null) { + return Value{true}; + } + int cmp = 0; + switch (type) { + case Type::kInt: + cmp = (lhs.value.int_value > rhs.value.int_value) + - (lhs.value.int_value < rhs.value.int_value); + break; + case Type::kBool: + cmp = (lhs.value.bool_value > rhs.value.bool_value) + - (lhs.value.bool_value < rhs.value.bool_value); + break; + case Type::kString: { + const auto& l = GetInternedString(lhs.value.string_id); + const auto& r = GetInternedString(rhs.value.string_id); + cmp = (l > r) - (l < r); + break; + } + } + switch (op) { + case BinaryOp::kGt: + return Value{false, {.bool_value = cmp > 0}}; + case BinaryOp::kLt: + return Value{false, {.bool_value = cmp < 0}}; + case BinaryOp::kLe: + return Value{false, {.bool_value = cmp <= 0}}; + case BinaryOp::kGe: + return Value{false, {.bool_value = cmp >= 0}}; + case BinaryOp::kNotEq: + return Value{false, {.bool_value = cmp != 0}}; + case BinaryOp::kEq: + return Value{false, {.bool_value = cmp == 0}}; + default: + std::unreachable(); + } + } + Value operator()(const BinaryExpression& expr) { auto lhs = std::visit(*this, *expr.lhs); auto rhs = std::visit(*this, *expr.rhs); switch (expr.binop) { case BinaryOp::kGt: - if (GetExpressionTypeUnchecked(*expr.lhs, source_attrs) == Type::kBool) { - return ApplyBooleanOperator>(std::move(lhs), std::move(rhs)); - } - return ApplyIntegersOperator, bool>(std::move(lhs), std::move(rhs)); case BinaryOp::kLt: - if (GetExpressionTypeUnchecked(*expr.lhs, source_attrs) == Type::kBool) { - return ApplyBooleanOperator>(std::move(lhs), std::move(rhs)); - } - return ApplyIntegersOperator, bool>(std::move(lhs), std::move(rhs)); case BinaryOp::kLe: - if (GetExpressionTypeUnchecked(*expr.lhs, source_attrs) == Type::kBool) { - return ApplyBooleanOperator>(std::move(lhs), std::move(rhs)); - } - return ApplyIntegersOperator, bool>(std::move(lhs), std::move(rhs)); case BinaryOp::kGe: - if (GetExpressionTypeUnchecked(*expr.lhs, source_attrs) == Type::kBool) { - return ApplyBooleanOperator>(std::move(lhs), std::move(rhs)); - } - return ApplyIntegersOperator, bool>(std::move(lhs), std::move(rhs)); case BinaryOp::kNotEq: - if (GetExpressionTypeUnchecked(*expr.lhs, source_attrs) == Type::kBool) { - return ApplyBooleanOperator>(std::move(lhs), std::move(rhs)); - } - return ApplyIntegersOperator, bool>(std::move(lhs), std::move(rhs)); case BinaryOp::kEq: - if (GetExpressionTypeUnchecked(*expr.lhs, source_attrs) == Type::kBool) { - return ApplyBooleanOperator>(std::move(lhs), std::move(rhs)); - } - return ApplyIntegersOperator, bool>(std::move(lhs), std::move(rhs)); + return CompareValues(std::move(lhs), std::move(rhs), expr.binop, + GetExpressionTypeUnchecked(*expr.lhs, source_attrs)); case BinaryOp::kOr: { bool lhs_true = !lhs.is_null && lhs.value.bool_value; bool rhs_true = !rhs.is_null && rhs.value.bool_value; - if (lhs_true || rhs_true) return Value{false, true}; + if (lhs_true || rhs_true) return Value{false, {.bool_value = true}}; if (lhs.is_null || rhs.is_null) return Value{true}; - return Value{false, false}; + return Value{false, {.bool_value = false}}; } case BinaryOp::kAnd: { bool lhs_false = !lhs.is_null && !lhs.value.bool_value; bool rhs_false = !rhs.is_null && !rhs.value.bool_value; - if (lhs_false || rhs_false) return Value{false, false}; + if (lhs_false || rhs_false) return Value{false, {.bool_value = false}}; if (lhs.is_null || rhs.is_null) return Value{true}; - return Value{false, true}; + return Value{false, {.bool_value = true}}; } case BinaryOp::kPlus: return ApplyIntegersOperator, int64_t>(std::move(lhs), std::move(rhs)); @@ -302,16 +325,16 @@ Value CalcExpression(const Tuple& source, const AttributesInfo& source_attrs, co return Value{true}; } if (child.value.bool_value) { - return Value{false, false}; + return Value{false, {.bool_value = false}}; } - return Value{false, true}; + return Value{false, {.bool_value = true}}; case UnaryOp::kMinus: if (child.is_null) { return Value{true}; } return Value{false, -child.value.int_value}; case UnaryOp::kIsNull: - return Value{false, child.is_null}; + return Value{false, {.bool_value = child.is_null}}; } } Value operator()(const InExpression& expr) { @@ -329,14 +352,14 @@ Value CalcExpression(const Tuple& source, const AttributesInfo& source_attrs, co continue; } if (ValuesEqual(lhs, value, lhs_type)) { - return Value{false, !expr.negated}; + return Value{false, {.bool_value = !expr.negated}}; } } if (saw_null) { return Value{true}; } - return Value{false, expr.negated}; + return Value{false, {.bool_value = expr.negated}}; } Value operator()(const AggregateExpression&) { throw std::logic_error{"aggregate expressions cannot be evaluated as scalar expressions"}; @@ -361,10 +384,10 @@ Value CalcExpression(const Tuple& source, const AttributesInfo& source_attrs, co case Literal::kNull: return Value{true}; case Literal::kTrue: { - return Value{false, true}; + return Value{false, {.bool_value = true}}; } case Literal::kFalse: { - return Value{false, false}; + return Value{false, {.bool_value = false}}; } case Literal::kUnknown: { return Value{true}; @@ -659,7 +682,7 @@ boost::asio::awaitable Executor::Execute(const Physica for (const auto& key : sort.keys.keys) { auto it = std::find_if(attrs.begin(), attrs.end(), [&](const AttributeInfo& a) { - return a.table == key.table && a.name == key.column; + return a.name == key.column && (key.table.empty() || a.table == key.table); }); if (it == attrs.end()) throw std::runtime_error{"sort key column not found: " + key.table + "." + key.column}; @@ -669,15 +692,32 @@ boost::asio::awaitable Executor::Execute(const Physica std::sort(all_tuples.begin(), all_tuples.end(), [&](const Tuple& a, const Tuple& b) { for (const auto& [idx, dir] : key_indices) { + const auto& attr = attrs[idx]; const auto& va = a[idx]; const auto& vb = b[idx]; if (va.is_null && vb.is_null) continue; if (va.is_null) return false; if (vb.is_null) return true; - if (va.value.int_value != vb.value.int_value) - return dir == Direction::kAsc - ? va.value.int_value < vb.value.int_value - : va.value.int_value > vb.value.int_value; + bool less = false; + bool greater = false; + switch (attr.type) { + case Type::kInt: + less = va.value.int_value < vb.value.int_value; + greater = va.value.int_value > vb.value.int_value; + break; + case Type::kBool: + less = va.value.bool_value < vb.value.bool_value; + greater = va.value.bool_value > vb.value.bool_value; + break; + case Type::kString: { + const auto& sa = GetInternedString(va.value.string_id); + const auto& sb = GetInternedString(vb.value.string_id); + less = sa < sb; + greater = sa > sb; + break; + } + } + if (less || greater) return dir == Direction::kAsc ? less : greater; } return false; }); diff --git a/src/stewkk/sql/logic/executor/executor_test.cpp b/src/stewkk/sql/logic/executor/executor_test.cpp index fabfdfd..b42284b 100644 --- a/src/stewkk/sql/logic/executor/executor_test.cpp +++ b/src/stewkk/sql/logic/executor/executor_test.cpp @@ -29,6 +29,7 @@ PhysicalPlanNode ToPhysicalPlan(const Operator& op) { return PhysicalProjection{ .source = std::make_shared(ToPhysicalPlan(*p.source)), .expressions = p.expressions, + .aliases = p.aliases, }; }, [](const Filter& f) -> PhysicalPlanNode { @@ -187,6 +188,31 @@ TEST(ExecutorTest, StringInFilter) { ctx.run(); } +TEST(ExecutorTest, StringOrderedFilter) { + boost::asio::io_context ctx; + boost::asio::co_spawn( + ctx, + []() -> boost::asio::awaitable> { + std::stringstream s{"SELECT m.id FROM markets AS m WHERE m.region >= 'AMERICA';"}; + PhysicalPlanNode op = ToPhysicalPlan(GetAST(s).value().op); + CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; + Executor executor( + std::move(seq_scan), co_await boost::asio::this_coro::executor); + + auto got = co_await executor.Execute(op); + + co_return got; + }(), + [](std::exception_ptr p, Result got) { + if (p) std::rethrow_exception(p); + + ASSERT_THAT(got.value().attributes, Eq(AttributesInfo{{"m", "id", Type::kInt}})); + ASSERT_THAT(got.value().tuples, Eq(Tuples{{Value{false, 1}}, {Value{false, 2}}, {Value{false, 3}}})); + }); + + ctx.run(); +} + TEST(ExecutorTest, StringNotInFilter) { boost::asio::io_context ctx; boost::asio::co_spawn( @@ -268,6 +294,27 @@ TEST(ExecutorTest, InNullSemantics) { ASSERT_THAT(CalcExpression(tuple, attrs, null_lhs).is_null, Eq(true)); } +TEST(ExecutorTest, StringOrderedComparison) { + AttributesInfo attrs{{"t", "x", Type::kString}}; + Tuple tuple{Value{false, {.string_id = InternString("MFGR#2223")}}}; + + Expression lower = BinaryExpression{ + std::make_shared(Attribute{"t", "x"}), + BinaryOp::kGe, + std::make_shared(StringConst{"MFGR#2221"}), + }; + Expression upper = BinaryExpression{ + std::make_shared(Attribute{"t", "x"}), + BinaryOp::kLe, + std::make_shared(StringConst{"MFGR#2228"}), + }; + + ASSERT_THAT(CalcExpression(tuple, attrs, lower), + Eq(Value{false, {.bool_value = true}})); + ASSERT_THAT(CalcExpression(tuple, attrs, upper), + Eq(Value{false, {.bool_value = true}})); +} + TEST(ExecutorTest, JitRejectsStringExpressions) { boost::asio::io_context ctx; bool rejected = false; diff --git a/src/stewkk/sql/logic/executor/plan.cpp b/src/stewkk/sql/logic/executor/plan.cpp index 212769b..8d47a38 100644 --- a/src/stewkk/sql/logic/executor/plan.cpp +++ b/src/stewkk/sql/logic/executor/plan.cpp @@ -7,7 +7,7 @@ std::string_view OutputTable(const SeqScan& scan) { } bool PhysicalProjection::operator==(const PhysicalProjection& other) const { - return *source == *other.source && expressions == other.expressions; + return *source == *other.source && expressions == other.expressions && aliases == other.aliases; } bool PhysicalFilter::operator==(const PhysicalFilter& other) const { diff --git a/src/stewkk/sql/logic/executor/plan_serializer.cpp b/src/stewkk/sql/logic/executor/plan_serializer.cpp index 0121417..9f32e1b 100644 --- a/src/stewkk/sql/logic/executor/plan_serializer.cpp +++ b/src/stewkk/sql/logic/executor/plan_serializer.cpp @@ -157,8 +157,21 @@ std::string SerializeNode(const PhysicalPlanNode& node) { if (!exprs.empty()) exprs += ' '; exprs += SerializeExpr(e); } - return std::format("(PhysicalProjection (exprs {}) {})", - exprs, SerializeNode(*n.source)); + if (!n.aliases.empty()) { + std::string aliases; + for (size_t i = 0; i < n.expressions.size(); ++i) { + if (!aliases.empty()) aliases += ' '; + if (i < n.aliases.size() && n.aliases[i]) { + aliases += QuoteString(*n.aliases[i]); + } else { + aliases += "-"; + } + } + return std::format("(PhysicalProjection (exprs {}) (aliases {}) {})", + exprs, aliases, SerializeNode(*n.source)); + } + return std::format("(PhysicalProjection (exprs {}) {})", exprs, + SerializeNode(*n.source)); } std::string operator()(const NestedLoopCrossJoin& n) const { return std::format("(NestedLoopCrossJoin {} {})", @@ -314,6 +327,11 @@ const std::unordered_map kLiterals = { {"FALSE", Literal::kFalse}, {"UNKNOWN", Literal::kUnknown}, }; +const std::unordered_map kAggregateFunctions = { + {"SUM", AggregateFunction::kSum}, + {"COUNT", AggregateFunction::kCount}, +}; + Expression ParseExpr(ParseState& s); PhysicalPlanNode ParseNode(ParseState& s); @@ -365,6 +383,20 @@ Expression ParseExpr(ParseState& s) { head == "notin", }; } + if (auto it = kAggregateFunctions.find(head); it != kAggregateFunctions.end()) { + if (s.Peek().kind == TokenKind::Atom && s.Peek().text == "*") { + s.ExpectAtom(); + s.ExpectRParen(); + return AggregateExpression{it->second, nullptr, true}; + } + auto arg = ParseExpr(s); + s.ExpectRParen(); + return AggregateExpression{ + it->second, + std::make_shared(std::move(arg)), + false, + }; + } if (auto it = kBinaryOps.find(head); it != kBinaryOps.end()) { auto lhs = ParseExpr(s); auto rhs = ParseExpr(s); @@ -417,11 +449,31 @@ PhysicalPlanNode ParseNode(ParseState& s) { while (s.Peek().kind != TokenKind::RParen) exprs.push_back(ParseExpr(s)); s.ExpectRParen(); + std::vector> aliases; + if (s.Peek().kind == TokenKind::LParen + && s.pos + 1 < s.tokens.size() + && s.tokens[s.pos + 1].kind == TokenKind::Atom + && s.tokens[s.pos + 1].text == "aliases") { + s.ExpectLParen(); + auto aliases_kw = s.ExpectAtom(); + if (aliases_kw != "aliases") + throw std::runtime_error(std::format("expected 'aliases' but got '{}'", aliases_kw)); + while (s.Peek().kind != TokenKind::RParen) { + auto alias = s.ExpectAtom(); + if (alias == "-") { + aliases.push_back(std::nullopt); + } else { + aliases.push_back(UnquoteString(alias)); + } + } + s.ExpectRParen(); + } auto source = ParseNode(s); s.ExpectRParen(); return PhysicalProjection{ std::make_shared(std::move(source)), std::move(exprs), + std::move(aliases), }; } if (head == "NestedLoopCrossJoin") { diff --git a/src/stewkk/sql/logic/implementation_rules/implement_projection.cpp b/src/stewkk/sql/logic/implementation_rules/implement_projection.cpp index b447330..51889c6 100644 --- a/src/stewkk/sql/logic/implementation_rules/implement_projection.cpp +++ b/src/stewkk/sql/logic/implementation_rules/implement_projection.cpp @@ -8,7 +8,8 @@ bool ImplementProjection::IsApplicable(utils::NotNull expr) { utils::NotNull ImplementProjection::Apply(utils::NotNull expr, Memo&) { auto& proj = std::get(expr->root_operator); - return expr->group->AddPhysicalExpr(physical::Projection{proj.source, proj.expressions}); + return expr->group->AddPhysicalExpr( + physical::Projection{proj.source, proj.expressions, proj.aliases}); } } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index 976fc16..448737a 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -421,6 +421,7 @@ PhysicalPlanNode Optimizer::BuildOptimalPlan(G .source = std::make_shared( BuildOptimalPlan(op.source.get(), RequiredInputProps(best_expr_nn, required, 0))), .expressions = op.expressions, + .aliases = op.aliases, }; }, [this, best_expr_nn, required](const physical::Filter& op) -> PhysicalPlanNode { diff --git a/src/stewkk/sql/logic/optimizer/schema_catalog.cpp b/src/stewkk/sql/logic/optimizer/schema_catalog.cpp index fe74d20..9c67fe8 100644 --- a/src/stewkk/sql/logic/optimizer/schema_catalog.cpp +++ b/src/stewkk/sql/logic/optimizer/schema_catalog.cpp @@ -35,13 +35,13 @@ std::optional SchemaCatalog::Derive(const LogicalOperator& op) { return GetSchema(f.source); }, [](const logical::Projection& p) -> std::optional { - // Schema after projection is exactly the Attributes named in its - // expression list. Computed (non-Attribute) expressions produce - // columns that can't be referenced by (table, name), so they are - // dropped from the schema — sorting on them is impossible anyway. + // Aliased projection outputs can be referenced by unqualified ORDER + // BY names. Unaliased computed expressions are not addressable. Schema out; - for (const auto& expr : p.expressions) { - if (const auto* a = std::get_if(&expr)) { + for (size_t i = 0; i < p.expressions.size(); ++i) { + if (i < p.aliases.size() && p.aliases[i]) { + out.push_back(Attribute{"", *p.aliases[i]}); + } else if (const auto* a = std::get_if(&p.expressions[i])) { out.push_back(*a); } } diff --git a/src/stewkk/sql/logic/optimizer/sort_enforcer.cpp b/src/stewkk/sql/logic/optimizer/sort_enforcer.cpp index 0a183de..694111a 100644 --- a/src/stewkk/sql/logic/optimizer/sort_enforcer.cpp +++ b/src/stewkk/sql/logic/optimizer/sort_enforcer.cpp @@ -15,7 +15,10 @@ std::optional SortEnforcer::TryBuild( for (const auto& sk : req->order.keys) { bool found = false; for (const auto& a : *sch) { - if (a.table == sk.table && a.name == sk.column) { found = true; break; } + if (a.name == sk.column && (sk.table.empty() || a.table == sk.table)) { + found = true; + break; + } } if (!found) return std::nullopt; } diff --git a/src/stewkk/sql/logic/parser/parser_test.cpp b/src/stewkk/sql/logic/parser/parser_test.cpp index 7f080b7..8e2d7ce 100644 --- a/src/stewkk/sql/logic/parser/parser_test.cpp +++ b/src/stewkk/sql/logic/parser/parser_test.cpp @@ -387,12 +387,12 @@ TEST(ParserTest, OrderByIntOrdinalRejected) { ASSERT_THAT(got.Wraps(ErrorType::kQueryNotSupported), IsTrue()); } -TEST(ParserTest, OrderByUnqualifiedColumnRejected) { +TEST(ParserTest, OrderByUnqualifiedColumnAccepted) { std::stringstream s{"SELECT * FROM users ORDER BY id;"}; - auto got = GetAST(s).error(); + auto parsed = GetAST(s).value(); - ASSERT_THAT(got.Wraps(ErrorType::kQueryNotSupported), IsTrue()); + ASSERT_THAT(parsed.required_order, Optional(SortOrder{{SortKey{"", "id", Direction::kAsc}}})); } TEST(ParserTest, OrderByNullsFirstRejected) { diff --git a/src/stewkk/sql/logic/parser/visitor.cpp b/src/stewkk/sql/logic/parser/visitor.cpp index 72ea9dc..eff49cd 100644 --- a/src/stewkk/sql/logic/parser/visitor.cpp +++ b/src/stewkk/sql/logic/parser/visitor.cpp @@ -325,6 +325,9 @@ std::any Visitor::visitTarget_list(codegen::PostgreSQLParser::Target_listContext aliases.push_back(std::move(target.alias)); target_expressions.push_back(std::move(target.expression)); } + if (std::ranges::none_of(aliases, [](const auto& alias) { return alias.has_value(); })) { + aliases.clear(); + } return Operator{Projection{std::move(target_expressions), nullptr, std::move(aliases)}}; } @@ -591,6 +594,10 @@ std::any Visitor::visitSimple_select_pramary( } if (projection) { + if (std::ranges::none_of(projection->aliases, + [](const auto& alias) { return alias.has_value(); })) { + projection->aliases.clear(); + } result = Projection{ std::move(projection->expressions), std::make_shared(std::move(result)), @@ -648,10 +655,7 @@ std::any Visitor::visitSortby(codegen::PostgreSQLParser::SortbyContext *ctx) { auto expr = std::any_cast(visit(ctx->a_expr())); auto* attr = std::get_if(&expr); if (!attr) { - throw Error{ErrorType::kQueryNotSupported, "ORDER BY expression must be a qualified column reference"}; - } - if (attr->table.empty()) { - throw Error{ErrorType::kQueryNotSupported, "ORDER BY requires a qualified column reference (table.column)"}; + throw Error{ErrorType::kQueryNotSupported, "ORDER BY expression must be a column reference"}; } Direction dir = Direction::kAsc; From 809b8c80afa041dff7294f9f7779f4e40c4d39ab Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sat, 30 May 2026 05:47:09 +0300 Subject: [PATCH 060/120] Support GROUP BY in fuzzers --- .gitmodules | 3 + flake.nix | 1 + .../sql/logic/optimizer/reachability.hpp | 9 +- include/stewkk/sql/logic/optimizer/rules.hpp | 3 +- .../transformation_rules/in_to_or_chain.hpp | 22 ++ research/converter.py | 135 +++++++- research/fuzz/diff_fuzz.py | 55 +++- research/fuzz/mssql_runner.py | 17 +- research/fuzz/reach_fuzz.py | 156 ++++++++- research/ms-server-plans/fuzz/group_by.xml | 1 + research/ms-server-plans/fuzz/group_order.xml | 1 + research/ms-server-plans/fuzz/in_filter.xml | 1 + research/ms-server-plans/fuzz/order_by.xml | 1 + .../ms-server-plans/fuzz/string_filter.xml | 1 + research/query_generator.py | 311 ++++++++++++++---- research/ssb-dbgen | 1 + research/test_converter.py | 156 +++++++++ src/stewkk/sql/CMakeLists.txt | 1 + .../sql/logic/executor/plan_serializer.cpp | 6 +- src/stewkk/sql/logic/optimizer/optimizer.cpp | 2 +- .../sql/logic/optimizer/optimizer_test.cpp | 68 ++++ .../sql/logic/optimizer/reachability.cpp | 14 +- src/stewkk/sql/logic/optimizer/rules.cpp | 3 +- .../sql/logic/optimizer/rules_applier.cpp | 2 +- .../transformation_rules/in_to_or_chain.cpp | 80 +++++ src/stewkk/sql/main.cpp | 6 +- 26 files changed, 963 insertions(+), 93 deletions(-) create mode 100644 include/stewkk/sql/logic/transformation_rules/in_to_or_chain.hpp create mode 100644 research/ms-server-plans/fuzz/group_by.xml create mode 100644 research/ms-server-plans/fuzz/group_order.xml create mode 100644 research/ms-server-plans/fuzz/in_filter.xml create mode 100644 research/ms-server-plans/fuzz/order_by.xml create mode 100644 research/ms-server-plans/fuzz/string_filter.xml create mode 160000 research/ssb-dbgen create mode 100644 src/stewkk/sql/logic/transformation_rules/in_to_or_chain.cpp diff --git a/.gitmodules b/.gitmodules index 68eabaa..d550f5e 100644 --- a/.gitmodules +++ b/.gitmodules @@ -4,3 +4,6 @@ [submodule "research/imdb-sqlserver"] path = research/imdb-sqlserver url = git@github.com:sqlsunday/imdb-to-sqlserver.git +[submodule "research/ssb-dbgen"] + path = research/ssb-dbgen + url = https://github.com/electrum/ssb-dbgen.git diff --git a/flake.nix b/flake.nix index e8162f2..f99f3e8 100644 --- a/flake.nix +++ b/flake.nix @@ -62,6 +62,7 @@ nodejs_24 graphviz codex + claude-code ]; nativeBuildInputs = [ diff --git a/include/stewkk/sql/logic/optimizer/reachability.hpp b/include/stewkk/sql/logic/optimizer/reachability.hpp index 6bf5edc..8e74e67 100644 --- a/include/stewkk/sql/logic/optimizer/reachability.hpp +++ b/include/stewkk/sql/logic/optimizer/reachability.hpp @@ -5,6 +5,7 @@ #include #include +#include #include #include @@ -18,7 +19,13 @@ struct MatchResult { MatchResult IsReachable(utils::NotNull root, const PhysicalPlanNode& target); +// Runs an exhaustive search over `sql` and reports whether `target` is among +// the physical alternatives the optimizer enumerates. The query's ORDER BY (if +// any) is propagated as a required sort property so the search also generates +// the Sort enforcers needed to reach an ordered plan; `schema` lets those +// enforcers validate that the sort keys exist on the group they sit above. MatchResult IsPlanReachable(std::istream& sql, const PhysicalPlanNode& target, - CardinalityEstimates cardinality = {}); + CardinalityEstimates cardinality = {}, + SchemaCatalog schema = {}); } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/rules.hpp b/include/stewkk/sql/logic/optimizer/rules.hpp index 7568b91..57c675b 100644 --- a/include/stewkk/sql/logic/optimizer/rules.hpp +++ b/include/stewkk/sql/logic/optimizer/rules.hpp @@ -10,6 +10,7 @@ #include #include #include +#include #include #include #include @@ -26,6 +27,6 @@ struct Rules { std::array, NImplementation> implementation_rules; }; -Rules<6, 7> MakeMainRules(); +Rules<7, 7> MakeMainRules(); } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/transformation_rules/in_to_or_chain.hpp b/include/stewkk/sql/logic/transformation_rules/in_to_or_chain.hpp new file mode 100644 index 0000000..923824d --- /dev/null +++ b/include/stewkk/sql/logic/transformation_rules/in_to_or_chain.hpp @@ -0,0 +1,22 @@ +#pragma once + +#include +#include + +namespace stewkk::sql { + +// Filter(... x IN (v0, v1, ...) ..., X) +// → Filter(... (x = v0 OR x = v1 OR ...) ..., X) +// and the NOT IN case to (x != v0 AND x != v1 AND ...). +// +// IN and the OR-chain are equal under three-valued logic, so this just adds the +// expanded form as a sibling in the same group. It lets the exhaustive search +// reach plans that materialize the OR-chain (as MS SQL Server does), and the +// expanded predicate is JIT-able whereas the InExpression node is not. +class InToOrChain : public TransformationRule { + public: + bool IsApplicable(utils::NotNull expr) override; + LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo) override; +}; + +} // namespace stewkk::sql diff --git a/research/converter.py b/research/converter.py index aa46dce..fd24d01 100644 --- a/research/converter.py +++ b/research/converter.py @@ -9,14 +9,18 @@ (PhysicalProjection (exprs EXPR...) SOURCE) (NestedLoopCrossJoin LHS RHS) (NestedLoopJoin JoinType EXPR LHS RHS) JoinType: Inner | Full | Left | Right + (Sort (keys table.col Asc|Desc ...) SOURCE) + (HashAggregate (group_by EXPR...) (aggs EXPR...) SOURCE) Expressions: 42 NULL TRUE FALSE UNKNOWN + (str "value") (attr table column) (OP LHS RHS) OP: = != > < >= <= and or + - * / % ^ (not EXPR) (uminus EXPR) (isnull EXPR) + (SUM EXPR) (COUNT EXPR) (COUNT *) """ import xml.etree.ElementTree as ET @@ -70,9 +74,17 @@ def _convert_relop(relop: ET.Element) -> str: if phys_op in ("Clustered Index Scan", "Index Scan", "Table Scan", "Clustered Index Seek", "Index Seek"): return _convert_scan(relop) + if phys_op == "Compute Scalar": + return _convert_compute_scalar(relop) + if phys_op == "Sort": + return _convert_sort(relop) + if phys_op == "Stream Aggregate": + return _convert_aggregate(relop, "StreamAggregate", "GroupBy", drop_enforcer_sort=True) if phys_op == "Nested Loops": return _convert_nested_loops(relop, logical_op) if phys_op == "Hash Match": + if logical_op == "Aggregate": + return _convert_aggregate(relop, "Hash", "HashKeysBuild", drop_enforcer_sort=False) return _convert_hash_match(relop, logical_op) if phys_op == "Merge Join": return _convert_merge_join(relop, logical_op) @@ -80,6 +92,102 @@ def _convert_relop(relop: ET.Element) -> str: raise NotImplementedError(f"unhandled PhysicalOp: {phys_op!r} (LogicalOp={logical_op!r})") +def _convert_compute_scalar(relop: ET.Element) -> str: + # MS SQL wraps aggregate outputs, casts and computed projections in a + # Compute Scalar node. The serialized projection is re-added by + # reach_fuzz._wrap_projection, so pass straight through to the child. + child = relop.find(f"{NS}ComputeScalar/{NS}RelOp") + if child is None: + raise ValueError("Compute Scalar has no child RelOp") + return _convert_relop(child) + + +def _convert_sort(relop: ET.Element) -> str: + sort = relop.find(f"{NS}Sort") + if sort is None: + raise ValueError("Sort element missing") + child = sort.find(f"{NS}RelOp") + if child is None: + raise ValueError("Sort has no child RelOp") + + keys = [] + for ob in sort.findall(f"{NS}OrderBy/{NS}OrderByColumn"): + ascending = (ob.get("Ascending", "true").lower() != "false") + cr = ob.find(f"{NS}ColumnReference") + if cr is None: + continue + table = _strip_sql_name(cr.get("Alias") or cr.get("Table")) + col = cr.get("Column", "").strip("[]") + keys.append(f"{table}.{col} {'Asc' if ascending else 'Desc'}") + if not keys: + raise ValueError("Sort node has no OrderByColumn entries") + return f"(Sort (keys {' '.join(keys)}) {_convert_relop(child)})" + + +_AGG_TYPES = { + "SUM": "SUM", + "COUNT": "COUNT", + "COUNT_BIG": "COUNT", + "CNT": "COUNT", + "CNT_BIG": "COUNT", +} + + +def _convert_aggregate( + relop: ET.Element, container_tag: str, groupby_tag: str, drop_enforcer_sort: bool +) -> str: + container = relop.find(f"{NS}{container_tag}") + if container is None: + raise ValueError(f"{container_tag} element missing from aggregate node") + + child = container.find(f"{NS}RelOp") + if child is None: + raise ValueError("aggregate node has no child RelOp") + # A Sort directly under a Stream Aggregate is MS's enforcer for ordered + # stream aggregation; our model uses hash aggregation over unordered input, + # so drop it. A user ORDER BY sort lives above the aggregate/projection. + if drop_enforcer_sort and child.get("PhysicalOp", "") == "Sort": + inner_sort = child.find(f"{NS}Sort/{NS}RelOp") + if inner_sort is None: + raise ValueError("enforcer Sort has no child RelOp") + child_str = _convert_relop(inner_sort) + else: + child_str = _convert_relop(child) + + group_by = [ + _col_ref_to_attr(cr) + for cr in container.findall(f"{NS}{groupby_tag}/{NS}ColumnReference") + ] + + aggs = [] + for dv in container.findall(f"{NS}DefinedValues/{NS}DefinedValue"): + agg = dv.find(f".//{NS}Aggregate") + if agg is None: + continue + aggs.append(_convert_agg_func(agg)) + + return ( + f"(HashAggregate (group_by {' '.join(group_by)})" + f" (aggs {' '.join(aggs)}) {child_str})" + ) + + +def _convert_agg_func(agg: ET.Element) -> str: + agg_type = agg.get("AggType", "") + if agg_type == "countstar": + return "(COUNT *)" + + func = _AGG_TYPES.get(agg_type.upper()) + if func is None: + raise NotImplementedError(f"unhandled AggType: {agg_type!r}") + + inner = agg.find(f"{NS}ScalarOperator") + if inner is None: + # COUNT with no argument behaves like COUNT(*). + return f"({func} *)" if func == "COUNT" else f"({func})" + return f"({func} {_convert_scalar(inner)})" + + _SEEK_SCAN_TYPES = {"GT": ">", "GE": ">=", "LT": "<", "LE": "<=", "EQ": "="} @@ -323,5 +431,28 @@ def _convert_identifier(identifier: ET.Element) -> str: def _convert_const(const: ET.Element) -> str: - value = const.get("ConstValue", "").strip("()") - return value + raw = const.get("ConstValue", "").strip() + # String literal: 'text' or N'text', with '' as an escaped single quote. + s = raw[1:] if raw.startswith("N'") else raw + if len(s) >= 2 and s.startswith("'") and s.endswith("'"): + inner = s[1:-1].replace("''", "'") + return f"(str {_quote_cpp_string(inner)})" + return raw.strip("()") + + +def _quote_cpp_string(value: str) -> str: + """Re-quote a string into the C++ double-quoted form (plan_serializer.cpp).""" + out = ['"'] + for c in value: + if c == "\\": + out.append("\\\\") + elif c == '"': + out.append('\\"') + elif c == "\n": + out.append("\\n") + elif c == "\t": + out.append("\\t") + else: + out.append(c) + out.append('"') + return "".join(out) diff --git a/research/fuzz/diff_fuzz.py b/research/fuzz/diff_fuzz.py index 622e2ed..f631f0b 100644 --- a/research/fuzz/diff_fuzz.py +++ b/research/fuzz/diff_fuzz.py @@ -24,6 +24,7 @@ from research.fuzz.mssql_runner import MsSqlRunner, RunResult from research.query_generator import ( DIALECTS, + Attr, QueryGenerator, SelectQuery, load_schema, @@ -66,6 +67,55 @@ def _run_ours(cli: str, data_dir: str, query: str, jit: bool) -> subprocess.Comp ) +def _typed(cell: str): + if cell == "NULL": + return None + try: + return int(cell) + except ValueError: + return cell + + +def _order_by_indices(query: SelectQuery) -> list[tuple[int, str]]: + """Map each ORDER BY key to its column index in the projection order.""" + out: list[tuple[int, str]] = [] + for key_attr, direction in query.order_by: + for i, t in enumerate(query.targets): + e = t.expr + if isinstance(e, Attr) and e.table == key_attr.table and e.column == key_attr.column: + out.append((i, direction)) + break + return out + + +def _check_order(query: SelectQuery, our_rows: list[str]) -> str | None: + """ + Verify the CLI's (order-preserved) output is monotonic in the ORDER BY keys. + NULL key values are skipped because the two engines disagree on NULL + ordering; this still catches gross ordering bugs without tie-break flakiness. + """ + keys = _order_by_indices(query) + if not keys: + return None + + prev: list | None = None + for row in our_rows: + cells = row.split("\t") + cur = [(_typed(cells[i]), direction) for i, direction in keys if i < len(cells)] + if prev is not None: + for (va, da), (vb, _) in zip(prev, cur): + if va is None or vb is None or va == vb: + continue + less = va < vb + if da == "desc": + less = not less + if not less: + return "ORDER BY ordering violated in our output" + break + prev = cur + return None + + def _compare( query: SelectQuery, ours_proc: subprocess.CompletedProcess, @@ -84,11 +134,12 @@ def _compare( if our_cols != their_cols: return f"column count: ours={our_cols} theirs={their_cols}" - # Generator does not emit ORDER BY, so always compare as multisets. + # Row correctness: compare as multisets regardless of ordering. if sorted(our_rows) != sorted(their_rows): return "row contents differ" - return None + # If the query asked for an order, additionally check our output respects it. + return _check_order(query, our_rows) def main() -> None: diff --git a/research/fuzz/mssql_runner.py b/research/fuzz/mssql_runner.py index f4f5b7d..db4d056 100644 --- a/research/fuzz/mssql_runner.py +++ b/research/fuzz/mssql_runner.py @@ -90,19 +90,30 @@ def setup_schema(self, data_dir: str | Path) -> None: continue self._create_and_load(cur, path) + _SQL_TYPE = {"int": "INT NULL", "string": "VARCHAR(255) NULL"} + def _create_and_load(self, cur, path: Path) -> None: with path.open() as f: reader = csv.reader(f) header = next(reader) cols = [] + types = [] for h in header: name, type_ = (s.strip() for s in h.split(":")) - if type_ != "int": + if type_ not in self._SQL_TYPE: raise ValueError(f"{path}: unsupported column type {type_!r}") cols.append(name) - rows = [tuple(None if v.strip() == "NULL" else int(v) for v in r) for r in reader] + types.append(type_) + + def conv(v: str, type_: str): + v = v.strip() + if v == "NULL": + return None + return int(v) if type_ == "int" else v + + rows = [tuple(conv(v, t) for v, t in zip(r, types)) for r in reader] - col_defs = ", ".join(f"[{c}] INT NULL" for c in cols) + col_defs = ", ".join(f"[{c}] {self._SQL_TYPE[t]}" for c, t in zip(cols, types)) cur.execute(f"CREATE TABLE [dbo].[{path.stem}] ({col_defs})") if rows: diff --git a/research/fuzz/reach_fuzz.py b/research/fuzz/reach_fuzz.py index c2530d6..1c632e0 100644 --- a/research/fuzz/reach_fuzz.py +++ b/research/fuzz/reach_fuzz.py @@ -10,6 +10,12 @@ (unhandled physical operators or scalar shapes) are logged and skipped, since they are converter limitations rather than optimizer bugs. +ORDER BY is part of the comparison: MS SQL's plan keeps the ORDER BY Sort on +top, and we keep it too. The CLI propagates the query's ORDER BY as a required +sort property, so the exhaustive search generates the matching Sort enforcer on +the root group and the ordered plan is genuinely reachable (or not — which is +exactly the divergence we want to surface). + Usage: python -m research.fuzz.reach_fuzz \\ --cli build/bin/sql \\ @@ -29,6 +35,7 @@ from research.converter import convert as convert_plan from research.fuzz.mssql_runner import MsSqlRunner from research.query_generator import ( + Aggregate, DIALECTS, QueryGenerator, SelectQuery, @@ -37,22 +44,153 @@ ) +def _quote_alias(alias: str) -> str: + return '"' + alias.replace("\\", "\\\\").replace('"', '\\"') + '"' + + +def _render_attr(a) -> str: + return f"(attr {a.table} {a.column})" + + +def _render_agg(agg: Aggregate) -> str: + if agg.func == "COUNT" and agg.arg is None: + return "(COUNT *)" + return f"({agg.func} {_render_attr(agg.arg)})" + + +def _split_sexpr_args(inner: str) -> list[str]: + """Split the body of an s-expr into top-level atoms/groups, respecting + nested parens and double-quoted strings.""" + out: list[str] = [] + i, n = 0, len(inner) + while i < n: + while i < n and inner[i].isspace(): + i += 1 + if i >= n: + break + if inner[i] == "(": + depth, j = 0, i + while j < n: + c = inner[j] + if c == '"': + j += 1 + while j < n and inner[j] != '"': + j += 2 if inner[j] == "\\" else 1 + j += 1 + continue + if c == "(": + depth += 1 + elif c == ")": + depth -= 1 + if depth == 0: + j += 1 + break + j += 1 + out.append(inner[i:j]) + i = j + else: + j = i + while j < n and not inner[j].isspace(): + j += 1 + out.append(inner[i:j]) + i = j + return out + + +def _top_node(sexpr: str) -> tuple[str, list[str]]: + s = sexpr.strip() + args = _split_sexpr_args(s[1:-1]) + return args[0], args[1:] + + +def _make_projection(plan_sexpr: str, query: SelectQuery) -> str: + """Wrap `plan_sexpr` in the PhysicalProjection our visitor emits. + + For GROUP BY queries this sits above the HashAggregate the converter + produced: the C++ visitor replaces each aggregate target with a synthetic + attribute ``__aggN`` (empty table, counter in target order) and keeps the + HashAggregate's aggregate expressions in its ``aggs`` list.""" + exprs = [] + agg_counter = 0 + for t in query.targets: + if isinstance(t.expr, Aggregate): + # Synthetic aggregate-output attribute: empty table, serialized "-". + exprs.append(f"(attr - __agg{agg_counter})") + agg_counter += 1 + else: # plain column target + a = t.expr + exprs.append(f"(attr {a.table} {a.column})") + exprs_str = " ".join(exprs) + + # The visitor clears the alias list when no target is aliased; mirror that + # so the serialized shape matches (with vs without the (aliases ...) form). + if any(t.alias for t in query.targets): + aliases = " ".join(_quote_alias(t.alias) if t.alias else "-" for t in query.targets) + return f"(PhysicalProjection (exprs {exprs_str}) (aliases {aliases}) {plan_sexpr})" + return f"(PhysicalProjection (exprs {exprs_str}) {plan_sexpr})" + + +def _rebuild_aggregate(plan_sexpr: str, query: SelectQuery) -> str: + """Replace a converted HashAggregate's group_by/aggs with query-derived + ones, keeping only its converted source subtree. + + MS SQL augments aggregation with hidden helper aggregates (e.g. a COUNT_BIG + alongside every SUM, for the empty-set NULL semantics) and orders them by + its own internal column ids, so its agg list never matches ours. The + aggregates are pure query semantics both engines share, so we emit them from + the query in target order — the same order the C++ visitor's + CollectAggregates uses — and reuse only the real input subtree from MS.""" + head, args = _top_node(plan_sexpr) + if head != "HashAggregate" or len(args) != 3: + return plan_sexpr # unexpected shape; leave as-is (best effort) + child = args[2] + + group_by = " ".join(_render_attr(a) for a in query.group_by) + aggs = " ".join( + _render_agg(t.expr) for t in query.targets if isinstance(t.expr, Aggregate) + ) + return f"(HashAggregate (group_by {group_by}) (aggs {aggs}) {child})" + + def _wrap_projection(plan_sexpr: str, query: SelectQuery) -> str: - """Our parser always emits a Projection at the top of a SELECT with - targets. MS SQL Server's plans express projection implicitly via the - scan/join OutputList rather than as a node, so we add the wrapper here so - the shape matches what the optimizer's exhaustive search produces.""" - exprs = " ".join(f"(attr {a.table} {a.column})" for a in query.targets) - return f"(PhysicalProjection (exprs {exprs}) {plan_sexpr})" + """Insert the projection node the optimizer's search produces, keeping any + top-of-plan ORDER BY Sort. + + MS SQL places the ORDER BY Sort above the projection. Our optimizer reaches + that exact shape: with the query's ORDER BY propagated as a required sort + property (see IsPlanReachable), the search puts a Sort *enforcer* on the + root projection group. So we lift the converter's Sort off the top, nest the + synthesized projection beneath it, and put the Sort back — yielding + ``(Sort (keys ...) (PhysicalProjection ... ))``. The Sort keys pass + through verbatim from the converter; they match the enforcer's keys, which + the CLI derives from the same ORDER BY clause. + + For non-aggregate column targets the converter emits no projection of its + own (MS SQL just selects the columns), so the projection is always + synthesized here regardless of the Sort.""" + head, args = _top_node(plan_sexpr) + sort_keys = None + if head == "Sort" and len(args) == 2: + sort_keys, plan_sexpr = args[0], args[1] + if query.group_by: + plan_sexpr = _rebuild_aggregate(plan_sexpr, query) + projected = _make_projection(plan_sexpr, query) + if sort_keys is not None: + return f"(Sort {sort_keys} {projected})" + return projected -def _run_reach_check(cli: str, sql: str, plan_sexpr: str) -> subprocess.CompletedProcess: +def _run_reach_check( + cli: str, data_dir: str, sql: str, plan_sexpr: str +) -> subprocess.CompletedProcess: with tempfile.NamedTemporaryFile("w", suffix=".plan", delete=False) as f: f.write(plan_sexpr) plan_path = f.name try: + # --data-dir lets the Sort enforcer validate ORDER BY keys against the + # group schema, matching how the real optimizer places enforcers. return subprocess.run( - [cli, "--check-reachable", plan_path], + [cli, "--check-reachable", plan_path, "--data-dir", data_dir], input=sql, capture_output=True, text=True, @@ -123,7 +261,7 @@ def main() -> None: target_plan = _wrap_projection(converted, query) try: - proc = _run_reach_check(args.cli, ours_sql, target_plan) + proc = _run_reach_check(args.cli, args.data_dir, ours_sql, target_plan) except subprocess.TimeoutExpired: print(f"DIVERGENCE seed={seed}: reachability check timed out") print(f"\n--- query (ours):\n{ours_sql}") diff --git a/research/ms-server-plans/fuzz/group_by.xml b/research/ms-server-plans/fuzz/group_by.xml new file mode 100644 index 0000000..d180a60 --- /dev/null +++ b/research/ms-server-plans/fuzz/group_by.xml @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/research/ms-server-plans/fuzz/group_order.xml b/research/ms-server-plans/fuzz/group_order.xml new file mode 100644 index 0000000..2bbcbea --- /dev/null +++ b/research/ms-server-plans/fuzz/group_order.xml @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/research/ms-server-plans/fuzz/in_filter.xml b/research/ms-server-plans/fuzz/in_filter.xml new file mode 100644 index 0000000..228c3c6 --- /dev/null +++ b/research/ms-server-plans/fuzz/in_filter.xml @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/research/ms-server-plans/fuzz/order_by.xml b/research/ms-server-plans/fuzz/order_by.xml new file mode 100644 index 0000000..183c84c --- /dev/null +++ b/research/ms-server-plans/fuzz/order_by.xml @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/research/ms-server-plans/fuzz/string_filter.xml b/research/ms-server-plans/fuzz/string_filter.xml new file mode 100644 index 0000000..13e48d2 --- /dev/null +++ b/research/ms-server-plans/fuzz/string_filter.xml @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/research/query_generator.py b/research/query_generator.py index 9af297f..0ae442e 100644 --- a/research/query_generator.py +++ b/research/query_generator.py @@ -2,8 +2,10 @@ """ Random SQL query generator. -Builds queries in a strict SELECT-Project-Filter-Join (SPJ) subset that matches -what the C++ parser accepts, then renders them in pluggable SQL dialects. +Builds queries in the subset that the C++ parser accepts — SELECT-Project- +Filter-Join plus table/column aliases, GROUP BY with SUM/COUNT aggregates, +IN/NOT IN, BETWEEN, string columns/literals and ORDER BY — then renders them in +pluggable SQL dialects. Currently supported dialects: - PostgresSubsetDialect (the subset the C++ ANTLR visitor accepts) @@ -20,7 +22,7 @@ import csv import re import random -from dataclasses import dataclass +from dataclasses import dataclass, field from pathlib import Path from typing import Protocol @@ -32,14 +34,14 @@ @dataclass class Column: name: str - type: str # "int" only in current C++ implementation + type: str # "int" | "string" @dataclass class TableSchema: name: str columns: list[Column] - sample_values: dict[str, list[int]] # col_name -> sampled actual values + sample_values: dict[str, list] # col_name -> sampled actual values (typed) @dataclass @@ -65,17 +67,22 @@ def load_schema(csv_dir: Path, max_sample: int = 200) -> Schema: col_name, col_type = h.strip().split(":") columns.append(Column(col_name.strip(), col_type.strip())) - sample_values: dict[str, list[int]] = {c.name: [] for c in columns} + by_name = {c.name: c for c in columns} + sample_values: dict[str, list] = {c.name: [] for c in columns} for i, row in enumerate(reader): if i >= max_sample: break for col, val in zip(columns, row): v = val.strip() - if v != "NULL": + if v == "NULL": + continue + if by_name[col.name].type == "int": try: sample_values[col.name].append(int(v)) except ValueError: pass + else: + sample_values[col.name].append(v) tables[path.stem] = TableSchema(path.stem, columns, sample_values) return Schema(tables) @@ -87,7 +94,7 @@ def load_schema(csv_dir: Path, max_sample: int = 200) -> Schema: @dataclass class Attr: - table: str + table: str # visible name (alias if the scan is aliased, else table name) column: str @@ -96,6 +103,11 @@ class IntLit: value: int +@dataclass +class StrLit: + value: str + + @dataclass class NullLit: pass @@ -128,12 +140,37 @@ class IsNullExpr: negated: bool # False -> IS NULL, True -> IS NOT NULL -Expr = Attr | IntLit | NullLit | BoolLit | BinaryExpr | UnaryExpr | IsNullExpr +@dataclass +class InExpr: + attr: Attr + values: list["Expr"] # IntLit / StrLit, emitted sorted ascending + negated: bool # False -> IN, True -> NOT IN + + +@dataclass +class BetweenExpr: + attr: Attr # int columns only + lo: "Expr" + hi: "Expr" + negated: bool # False -> BETWEEN, True -> NOT BETWEEN + + +@dataclass +class Aggregate: + func: str # "SUM" | "COUNT" + arg: "Expr | None" # None -> COUNT(*) + + +Expr = ( + Attr | IntLit | StrLit | NullLit | BoolLit | BinaryExpr | UnaryExpr + | IsNullExpr | InExpr | BetweenExpr | Aggregate +) @dataclass class TableScan: - name: str + name: str # real table name + alias: str | None = None @dataclass @@ -153,39 +190,40 @@ class CrossJoin: TableRef = TableScan | ExplicitJoin | CrossJoin +# A projected output: an expression with an optional `AS alias`. +@dataclass +class Target: + expr: Expr + alias: str | None = None + + @dataclass class SelectQuery: - targets: list[Attr] + targets: list[Target] from_: TableRef - where: Expr | None + where: Expr | None = None + group_by: list[Attr] = field(default_factory=list) + order_by: list[tuple[Attr, str]] = field(default_factory=list) # (key, "asc"|"desc") # --------------------------------------------------------------------------- # Helpers # --------------------------------------------------------------------------- -def _tables_in_ref(ref: TableRef) -> list[str]: +def _scan_bindings(ref: TableRef) -> list[tuple[str, str]]: + """List of (visible_name, real_table) for every scan in the ref tree.""" if isinstance(ref, TableScan): - return [ref.name] - return _tables_in_ref(ref.lhs) + _tables_in_ref(ref.rhs) - - -def _available_attrs(schema: Schema, ref: TableRef) -> list[Attr]: - return [ - Attr(t, col.name) - for t in _tables_in_ref(ref) - if t in schema.tables - for col in schema.tables[t].columns - ] + return [(ref.alias or ref.name, ref.name)] + return _scan_bindings(ref.lhs) + _scan_bindings(ref.rhs) # --------------------------------------------------------------------------- # Random query builder # --------------------------------------------------------------------------- -_COMPARISON_OPS = ["=", "!=", "<", ">", "<=", ">="] +_INT_COMPARISON_OPS = ["=", "!=", "<", ">", "<=", ">="] +_STR_COMPARISON_OPS = ["=", "!="] _LOGICAL_OPS = ["and", "or"] -_ARITHMETIC_OPS = ["+", "-", "*"] _JOIN_TYPES = ["INNER", "LEFT", "RIGHT", "FULL"] @@ -193,52 +231,133 @@ class QueryGenerator: def __init__(self, schema: Schema, rng: random.Random): self.schema = schema self.rng = rng + # Per-query map: visible table name -> real table name. + self._bindings: dict[str, str] = {} + + # -- attribute / type helpers ------------------------------------------ + + def _real_table(self, visible: str) -> str: + return self._bindings[visible] + + def _column_type(self, attr: Attr) -> str: + real = self._real_table(attr.table) + for c in self.schema.tables[real].columns: + if c.name == attr.column: + return c.type + return "int" + + def _samples(self, attr: Attr) -> list: + real = self._real_table(attr.table) + return self.schema.tables[real].sample_values.get(attr.column, []) + + def _available_attrs(self) -> list[Attr]: + attrs = [] + for visible, real in self._bindings.items(): + for col in self.schema.tables[real].columns: + attrs.append(Attr(visible, col.name)) + return attrs + + # -- top-level --------------------------------------------------------- def generate(self) -> SelectQuery: from_ = self._gen_from() - attrs = _available_attrs(self.schema, from_) - - n_targets = self.rng.randint(1, min(3, len(attrs))) - targets = self.rng.sample(attrs, n_targets) + self._bindings = dict(_scan_bindings(from_)) + attrs = self._available_attrs() where = self._gen_predicate(attrs) if self.rng.random() < 0.6 else None - return SelectQuery(targets, from_, where) + + if self.rng.random() < 0.3 and attrs: + return self._gen_aggregated(from_, attrs, where) + + n_targets = self.rng.randint(1, min(3, len(attrs))) + chosen = self.rng.sample(attrs, n_targets) + targets = [Target(a, self._maybe_alias()) for a in chosen] + order_by = self._gen_order_by(chosen) + return SelectQuery(targets, from_, where, [], order_by) + + def _gen_aggregated( + self, from_: TableRef, attrs: list[Attr], where: Expr | None + ) -> SelectQuery: + n_group = self.rng.randint(1, min(2, len(attrs))) + group_by = self.rng.sample(attrs, n_group) + + # Every non-aggregate target must be a group-by column (MS SQL rule). + targets = [Target(a, self._maybe_alias()) for a in group_by] + + int_attrs = [a for a in attrs if self._column_type(a) == "int"] + for _ in range(self.rng.randint(0, 2)): + agg = self._gen_aggregate(int_attrs) + targets.append(Target(agg, self._maybe_alias())) + + order_by = self._gen_order_by(group_by) + return SelectQuery(targets, from_, where, group_by, order_by) + + def _gen_aggregate(self, int_attrs: list[Attr]) -> Aggregate: + choice = self.rng.random() + if choice < 0.34 and int_attrs: + return Aggregate("SUM", self.rng.choice(int_attrs)) + if choice < 0.67: + return Aggregate("COUNT", None) # COUNT(*) + return Aggregate("COUNT", self.rng.choice(int_attrs) if int_attrs else None) + + def _maybe_alias(self) -> str | None: + if self.rng.random() < 0.3: + return f"c{self.rng.randint(0, 9999)}" + return None + + def _gen_order_by(self, output_attrs: list[Attr]) -> list[tuple[Attr, str]]: + # ORDER BY keys restricted to projected output columns (C++ allows only + # column refs there). + if not output_attrs or self.rng.random() >= 0.3: + return [] + n = self.rng.randint(1, len(output_attrs)) + keys = self.rng.sample(output_attrs, n) + return [(k, self.rng.choice(["asc", "desc"])) for k in keys] + + # -- FROM -------------------------------------------------------------- def _gen_from(self) -> TableRef: names = list(self.schema.tables.keys()) n = self.rng.randint(1, min(3, len(names))) chosen = self.rng.sample(names, n) - - ref: TableRef = TableScan(chosen[0]) - for table_name in chosen[1:]: - rhs: TableRef = TableScan(table_name) - - # Try equi-join on a shared column name first. - lhs_col_names = { - c.name - for t in _tables_in_ref(ref) - for c in self.schema.tables[t].columns - } - rhs_col_names = {c.name for c in self.schema.tables[table_name].columns} - shared = sorted(lhs_col_names & rhs_col_names) - - if shared and self.rng.random() < 0.7: - col = self.rng.choice(shared) - # Pick a LHS table that actually has this column. - lhs_candidate = self.rng.choice([ - t for t in _tables_in_ref(ref) - if any(c.name == col for c in self.schema.tables[t].columns) - ]) - on: Expr = BinaryExpr("=", Attr(lhs_candidate, col), Attr(table_name, col)) + use_alias = self.rng.random() < 0.4 + + def make_scan(table: str, idx: int) -> TableScan: + return TableScan(table, f"t{idx}" if use_alias else None) + + # Track visible name -> real table while building so joins can pick + # a valid lhs binding. + scans: list[TableScan] = [make_scan(chosen[0], 0)] + ref: TableRef = scans[0] + + for i, table_name in enumerate(chosen[1:], start=1): + rhs_scan = make_scan(table_name, i) + scans.append(rhs_scan) + + rhs_cols = {c.name for c in self.schema.tables[table_name].columns} + # Candidate lhs scans that share a column name with rhs. + candidates = [ + (s, c.name) + for s in scans[:-1] + for c in self.schema.tables[s.name].columns + if c.name in rhs_cols + ] + + if candidates and self.rng.random() < 0.7: + lhs_scan, col = self.rng.choice(candidates) + lhs_vis = lhs_scan.alias or lhs_scan.name + rhs_vis = rhs_scan.alias or rhs_scan.name + on: Expr = BinaryExpr("=", Attr(lhs_vis, col), Attr(rhs_vis, col)) jtype = self.rng.choice(_JOIN_TYPES) - ref = ExplicitJoin(jtype, ref, rhs, on) + ref = ExplicitJoin(jtype, ref, rhs_scan, on) else: - ref = CrossJoin(ref, rhs) + ref = CrossJoin(ref, rhs_scan) return ref + # -- WHERE ------------------------------------------------------------- + def _gen_predicate(self, attrs: list[Attr], depth: int = 0) -> Expr: - # Deeper recursion becomes a leaf comparison to avoid runaway trees. if depth >= 2 or self.rng.random() < 0.45: return self._gen_comparison(attrs) @@ -249,16 +368,40 @@ def _gen_predicate(self, attrs: list[Attr], depth: int = 0) -> Expr: def _gen_comparison(self, attrs: list[Attr]) -> Expr: attr = self.rng.choice(attrs) + is_string = self._column_type(attr) == "string" - if self.rng.random() < 0.12: + roll = self.rng.random() + if roll < 0.1: return IsNullExpr(attr, negated=self.rng.random() < 0.5) - - op = self.rng.choice(_COMPARISON_OPS) - rhs = self._gen_int_expr(attr) - return BinaryExpr(op, attr, rhs) - - def _gen_int_expr(self, hint_attr: Attr) -> Expr: - sample = self.schema.tables[hint_attr.table].sample_values.get(hint_attr.column, []) + if roll < 0.3: + return self._gen_in(attr) + if roll < 0.4 and not is_string: + return self._gen_between(attr) + + ops = _STR_COMPARISON_OPS if is_string else _INT_COMPARISON_OPS + op = self.rng.choice(ops) + return BinaryExpr(op, attr, self._gen_literal(attr)) + + def _gen_in(self, attr: Attr) -> InExpr: + n = self.rng.randint(1, 4) + values = [self._gen_literal(attr) for _ in range(n)] + # Emit sorted ascending so the OR-chain MS SQL expands matches ours. + values.sort(key=lambda v: v.value) + return InExpr(attr, values, negated=self.rng.random() < 0.3) + + def _gen_between(self, attr: Attr) -> BetweenExpr: + lo = self._gen_literal(attr) + hi = self._gen_literal(attr) + if lo.value > hi.value: + lo, hi = hi, lo + return BetweenExpr(attr, lo, hi, negated=self.rng.random() < 0.3) + + def _gen_literal(self, attr: Attr) -> Expr: + sample = self._samples(attr) + if self._column_type(attr) == "string": + if sample and self.rng.random() < 0.8: + return StrLit(self.rng.choice(sample)) + return StrLit(self.rng.choice(["AMERICA", "EUROPE", "ASIA", "unknown"])) if sample and self.rng.random() < 0.75: return IntLit(self.rng.choice(sample)) return IntLit(self.rng.randint(1, 100)) @@ -326,16 +469,33 @@ def fmt_join_keyword(self, join_type: str) -> str: # Renderer # --------------------------------------------------------------------------- +def _fmt_str_literal(value: str) -> str: + return "'" + value.replace("'", "''") + "'" + + def render_expr(expr: Expr, d: Dialect) -> str: match expr: case Attr(table, col): return d.fmt_col_ref(table, col) case IntLit(value): return str(value) + case StrLit(value): + return _fmt_str_literal(value) case NullLit(): return "NULL" case BoolLit(value): return "TRUE" if value else "FALSE" + case Aggregate("COUNT", None): + return "COUNT(*)" + case Aggregate(func, arg): + return f"{func}({render_expr(arg, d)})" + case InExpr(attr, values, negated): + kw = "NOT IN" if negated else "IN" + vals = ", ".join(render_expr(v, d) for v in values) + return f"{render_expr(attr, d)} {kw} ({vals})" + case BetweenExpr(attr, lo, hi, negated): + kw = "NOT BETWEEN" if negated else "BETWEEN" + return f"{render_expr(attr, d)} {kw} {render_expr(lo, d)} AND {render_expr(hi, d)}" case BinaryExpr(op, lhs, rhs): l = render_expr(lhs, d) r = render_expr(rhs, d) @@ -357,8 +517,9 @@ def render_expr(expr: Expr, d: Dialect) -> str: def render_table_ref(ref: TableRef, d: Dialect) -> str: match ref: - case TableScan(name): - return d.fmt_table(name) + case TableScan(name, alias): + base = d.fmt_table(name) + return f"{base} AS {alias}" if alias else base case CrossJoin(lhs, rhs): return f"{render_table_ref(lhs, d)} CROSS JOIN {render_table_ref(rhs, d)}" case ExplicitJoin(jtype, lhs, rhs, on): @@ -372,11 +533,27 @@ def render_table_ref(ref: TableRef, d: Dialect) -> str: def render_query(query: SelectQuery, d: Dialect) -> str: - targets = ", ".join(d.fmt_col_ref(a.table, a.column) for a in query.targets) + target_parts = [] + for t in query.targets: + rendered = render_expr(t.expr, d) + if t.alias: + rendered += f" AS {t.alias}" + target_parts.append(rendered) + targets = ", ".join(target_parts) + from_clause = render_table_ref(query.from_, d) sql = f"SELECT {targets}\nFROM {from_clause}" if query.where is not None: sql += f"\nWHERE {render_expr(query.where, d)}" + if query.group_by: + cols = ", ".join(d.fmt_col_ref(a.table, a.column) for a in query.group_by) + sql += f"\nGROUP BY {cols}" + if query.order_by: + keys = ", ".join( + f"{d.fmt_col_ref(a.table, a.column)} {direction.upper()}" + for a, direction in query.order_by + ) + sql += f"\nORDER BY {keys}" return sql diff --git a/research/ssb-dbgen b/research/ssb-dbgen new file mode 160000 index 0000000..219403a --- /dev/null +++ b/research/ssb-dbgen @@ -0,0 +1 @@ +Subproject commit 219403ad7d1dd32ae1f97b5553abf92129fccd7f diff --git a/research/test_converter.py b/research/test_converter.py index 15ee9d0..941f171 100644 --- a/research/test_converter.py +++ b/research/test_converter.py @@ -193,3 +193,159 @@ def test_join_compound_predicate(extractor): ) result = convert(plan) assert result == "(HashJoin Inner (= (attr TitlePrincipals principalId) (attr Principals principalId)) (PhysicalFilter (= (attr TitlePrincipals ordinal) 1) (SeqScan TitlePrincipals)) (SeqScan Principals))" + + +# ─── XML-driven tests for the new operators ────────────────────────────────── +# The XML shapes below are inferred from the ShowPlanXML schema; they MUST be +# re-validated against live MS SQL output and captured as ms-server-plans/*.xml +# fixtures (see the plan's Verification step). + +def test_string_const_filter_from_xml(): + plan = showplan(""" + + + + + + + + + + + + + + + + + + + + """) + + result = convert(plan) + + assert result == '(PhysicalFilter (= (attr m region) (str "AMERICA")) (SeqScan markets m))' + + +def test_sort_from_xml(): + plan = showplan(""" + + + + + + + + + + + + + + + + """) + + result = convert(plan) + + assert result == "(Sort (keys users.id Asc users.age Desc) (SeqScan users))" + + +def test_stream_aggregate_drops_enforcer_sort_from_xml(): + plan = showplan(""" + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + """) + + result = convert(plan) + + assert result == ( + "(HashAggregate (group_by (attr users id))" + " (aggs (SUM (attr users age)) (COUNT *)) (SeqScan users))" + ) + + +def test_hash_aggregate_from_xml(): + plan = showplan(""" + + + + + + + + + + + + + + + + + + + + + + + """) + + result = convert(plan) + + assert result == ( + "(HashAggregate (group_by (attr users id))" + " (aggs (COUNT (attr users age))) (SeqScan users))" + ) + + +def test_compute_scalar_passthrough_from_xml(): + plan = showplan(""" + + + + + + + + """) + + result = convert(plan) + + assert result == "(SeqScan users)" diff --git a/src/stewkk/sql/CMakeLists.txt b/src/stewkk/sql/CMakeLists.txt index dd9d927..d6d743f 100644 --- a/src/stewkk/sql/CMakeLists.txt +++ b/src/stewkk/sql/CMakeLists.txt @@ -45,6 +45,7 @@ add_library(libsql logic/transformation_rules/filter_merge.cpp logic/transformation_rules/filter_pushdown_through_projection.cpp logic/transformation_rules/filter_pushdown_through_join.cpp + logic/transformation_rules/in_to_or_chain.cpp logic/implementation_rules/implement_table.cpp logic/implementation_rules/implement_filter.cpp logic/implementation_rules/implement_projection.cpp diff --git a/src/stewkk/sql/logic/executor/plan_serializer.cpp b/src/stewkk/sql/logic/executor/plan_serializer.cpp index 9f32e1b..c9e3618 100644 --- a/src/stewkk/sql/logic/executor/plan_serializer.cpp +++ b/src/stewkk/sql/logic/executor/plan_serializer.cpp @@ -101,7 +101,9 @@ std::string SerializeExpr(const Expression& expr) { SerializeExpr(*e.lhs), SerializeExpr(*e.rhs)); } std::string operator()(const Attribute& a) const { - return std::format("(attr {} {})", a.table, a.name); + // Synthetic aggregate-output attributes have an empty table; emit + // "-" so the field stays a parseable atom on the deserialize side. + return std::format("(attr {} {})", a.table.empty() ? "-" : a.table, a.name); } std::string operator()(IntConst n) const { return std::to_string(n); @@ -357,7 +359,7 @@ Expression ParseExpr(ParseState& s) { auto table = s.ExpectAtom(); auto name = s.ExpectAtom(); s.ExpectRParen(); - return Attribute{std::move(table), std::move(name)}; + return Attribute{table == "-" ? "" : std::move(table), std::move(name)}; } if (head == "str") { auto value = UnquoteString(s.ExpectAtom()); diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index 448737a..e999e7a 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -506,6 +506,6 @@ utils::NotNull Optimizer::GetRootGroup return root_->group; } -template class Optimizer<6, 7>; +template class Optimizer<7, 7>; } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index 45190f3..b32cb15 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -195,4 +195,72 @@ TEST(ReachabilityTest, WrongJoinQual) { ASSERT_THAT(result.mismatch, HasSubstr("qual")); } +TEST(ReachabilityTest, InExpandsToOrChain) { + std::stringstream s{"SELECT * FROM users WHERE users.id IN (1, 2, 3);"}; + Expression eq1 = BinaryExpression{ + std::make_shared(Attribute{"users", "id"}), + BinaryOp::kEq, + std::make_shared(IntConst{1})}; + Expression eq2 = BinaryExpression{ + std::make_shared(Attribute{"users", "id"}), + BinaryOp::kEq, + std::make_shared(IntConst{2})}; + Expression eq3 = BinaryExpression{ + std::make_shared(Attribute{"users", "id"}), + BinaryOp::kEq, + std::make_shared(IntConst{3})}; + Expression or_chain = BinaryExpression{ + std::make_shared(BinaryExpression{ + std::make_shared(std::move(eq1)), + BinaryOp::kOr, + std::make_shared(std::move(eq2))}), + BinaryOp::kOr, + std::make_shared(std::move(eq3))}; + auto result = IsPlanReachable(s, PhysicalFilter{ + std::make_shared(SeqScan{"users"}), or_chain}); + ASSERT_THAT(result.reachable, IsTrue()); +} + +// An ORDER BY query becomes a required sort property, so the search generates a +// Sort enforcer on the root projection group and the ordered plan is reachable. +TEST(ReachabilityTest, OrderByReachableViaSortEnforcer) { + std::stringstream s{"SELECT users.id FROM users ORDER BY users.id;"}; + PhysicalSort target{ + std::make_shared(PhysicalProjection{ + std::make_shared(SeqScan{"users"}), + {Attribute{"users", "id"}}, + {}}), + SortOrder{{SortKey{"users", "id", Direction::kAsc}}}}; + auto result = IsPlanReachable(s, target); + ASSERT_THAT(result.reachable, IsTrue()); +} + +// The enforcer is generated for the ORDER BY direction the query asked for, so +// a plan whose Sort runs the other way is not reachable. +TEST(ReachabilityTest, OrderByWrongDirectionNotReachable) { + std::stringstream s{"SELECT users.id FROM users ORDER BY users.id ASC;"}; + PhysicalSort target{ + std::make_shared(PhysicalProjection{ + std::make_shared(SeqScan{"users"}), + {Attribute{"users", "id"}}, + {}}), + SortOrder{{SortKey{"users", "id", Direction::kDesc}}}}; + auto result = IsPlanReachable(s, target); + ASSERT_THAT(result.reachable, IsFalse()); +} + +// Without an ORDER BY no required sort is propagated, so no Sort enforcer is +// ever built and a plan that carries a Sort cannot be reached. +TEST(ReachabilityTest, SortNotReachableWithoutOrderBy) { + std::stringstream s{"SELECT users.id FROM users;"}; + PhysicalSort target{ + std::make_shared(PhysicalProjection{ + std::make_shared(SeqScan{"users"}), + {Attribute{"users", "id"}}, + {}}), + SortOrder{{SortKey{"users", "id", Direction::kAsc}}}}; + auto result = IsPlanReachable(s, target); + ASSERT_THAT(result.reachable, IsFalse()); +} + } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/reachability.cpp b/src/stewkk/sql/logic/optimizer/reachability.cpp index ddebf83..f9fd6bd 100644 --- a/src/stewkk/sql/logic/optimizer/reachability.cpp +++ b/src/stewkk/sql/logic/optimizer/reachability.cpp @@ -5,6 +5,8 @@ #include #include #include +#include +#include #include namespace stewkk::sql { @@ -139,9 +141,17 @@ MatchResult IsReachable(utils::NotNull root, const PhysicalPlanNode& tar } MatchResult IsPlanReachable(std::istream& sql, const PhysicalPlanNode& target, - CardinalityEstimates cardinality) { + CardinalityEstimates cardinality, SchemaCatalog schema) { auto parsed = GetAST(sql).value(); - Optimizer optimizer(parsed.op, MakeMainRules(), std::move(cardinality)); + // Mirror main.cpp: a query ORDER BY becomes a required sort property so the + // exhaustive search enumerates Sort enforcers and the ordered plan MS SQL + // produced is actually reachable. Without it the root group never gets a + // Sort and every ORDER BY plan would spuriously read as unreachable. + PropertySet required = parsed.required_order + ? PropertySet{SortProperty{*parsed.required_order}} + : PropertySet::Any(); + Optimizer optimizer(parsed.op, MakeMainRules(), std::move(cardinality), + std::move(schema), std::move(required)); optimizer.OptimizeExhaustive(); return IsReachable(optimizer.GetRootGroup(), target); } diff --git a/src/stewkk/sql/logic/optimizer/rules.cpp b/src/stewkk/sql/logic/optimizer/rules.cpp index 3a052d0..b230f4f 100644 --- a/src/stewkk/sql/logic/optimizer/rules.cpp +++ b/src/stewkk/sql/logic/optimizer/rules.cpp @@ -2,7 +2,7 @@ namespace stewkk::sql { -Rules<6, 7> MakeMainRules() { +Rules<7, 7> MakeMainRules() { return { .transformation_rules = { std::make_unique(), @@ -11,6 +11,7 @@ Rules<6, 7> MakeMainRules() { std::make_unique(), std::make_unique(), std::make_unique(), + std::make_unique(), }, .implementation_rules = { std::make_unique(), diff --git a/src/stewkk/sql/logic/optimizer/rules_applier.cpp b/src/stewkk/sql/logic/optimizer/rules_applier.cpp index 4588aea..cbf5808 100644 --- a/src/stewkk/sql/logic/optimizer/rules_applier.cpp +++ b/src/stewkk/sql/logic/optimizer/rules_applier.cpp @@ -30,6 +30,6 @@ utils::NotNull RulesApplier::Ap return rules_.implementation_rules[rule.value]->Apply(expr, memo); } -template class RulesApplier<6, 7>; +template class RulesApplier<7, 7>; } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/transformation_rules/in_to_or_chain.cpp b/src/stewkk/sql/logic/transformation_rules/in_to_or_chain.cpp new file mode 100644 index 0000000..7488f6b --- /dev/null +++ b/src/stewkk/sql/logic/transformation_rules/in_to_or_chain.cpp @@ -0,0 +1,80 @@ +#include + +#include +#include + +#include + +namespace stewkk::sql { + +namespace { + +bool ContainsIn(const Expression& e) { + return std::visit(utils::Overloaded{ + [](const BinaryExpression& b) { return ContainsIn(*b.lhs) || ContainsIn(*b.rhs); }, + [](const UnaryExpression& u) { return ContainsIn(*u.child); }, + [](const InExpression&) { return true; }, + [](const AggregateExpression& a) { + return !a.is_star && a.argument && ContainsIn(*a.argument); + }, + [](const Attribute&) { return false; }, + [](const IntConst&) { return false; }, + [](const StringConst&) { return false; }, + [](const Literal&) { return false; }, + }, e); +} + +std::shared_ptr Share(Expression e) { + return std::make_shared(std::move(e)); +} + +// Expand a single InExpression into a left-associative chain. IN folds with OR +// over `lhs = value_i`; NOT IN folds with AND over `lhs != value_i`. The +// associativity and operand order match converter.py's OR/AND fold so the +// reachability target plan compares equal. +Expression ExpandIn(const InExpression& in); + +Expression Expand(const Expression& e) { + return std::visit(utils::Overloaded{ + [](const BinaryExpression& b) -> Expression { + return BinaryExpression{Share(Expand(*b.lhs)), b.binop, Share(Expand(*b.rhs))}; + }, + [](const UnaryExpression& u) -> Expression { + return UnaryExpression{u.op, Share(Expand(*u.child))}; + }, + [](const InExpression& in) -> Expression { return ExpandIn(in); }, + [](const AggregateExpression& a) -> Expression { + if (a.is_star || !a.argument) return a; + return AggregateExpression{a.function, Share(Expand(*a.argument)), a.is_star}; + }, + [](const auto& leaf) -> Expression { return leaf; }, + }, e); +} + +Expression ExpandIn(const InExpression& in) { + Expression lhs = Expand(*in.lhs); + BinaryOp leaf_op = in.negated ? BinaryOp::kNotEq : BinaryOp::kEq; + BinaryOp join_op = in.negated ? BinaryOp::kAnd : BinaryOp::kOr; + + Expression acc = BinaryExpression{Share(lhs), leaf_op, Share(Expand(in.values.front()))}; + for (size_t i = 1; i < in.values.size(); ++i) { + Expression cmp = BinaryExpression{Share(lhs), leaf_op, Share(Expand(in.values[i]))}; + acc = BinaryExpression{Share(std::move(acc)), join_op, Share(std::move(cmp))}; + } + return acc; +} + +} // namespace + +bool InToOrChain::IsApplicable(utils::NotNull expr) { + if (!std::holds_alternative(expr->root_operator)) return false; + const auto& f = std::get(expr->root_operator); + return ContainsIn(f.predicate); +} + +LogicalOperator InToOrChain::ApplyImpl(utils::NotNull expr, Memo& memo) { + const auto& f = std::get(expr->root_operator); + return logical::Filter{f.source, Expand(f.predicate)}; +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/main.cpp b/src/stewkk/sql/main.cpp index 372daa4..ea45158 100644 --- a/src/stewkk/sql/main.cpp +++ b/src/stewkk/sql/main.cpp @@ -256,7 +256,11 @@ int main(int argc, char** argv) { } MatchResult mr; try { - mr = IsPlanReachable(sql_stream, target); + // The schema lets the Sort enforcer confirm the ORDER BY keys exist on + // the group it sits above; without --data-dir it stays permissive. + SchemaCatalog schema = args.data_dir.empty() ? SchemaCatalog{} + : LoadSchema(args.data_dir); + mr = IsPlanReachable(sql_stream, target, {}, std::move(schema)); } catch (const std::exception& e) { std::cerr << "reachability error: " << e.what() << "\n"; return kOptimizerError; From 0963a8db11890c376ce7604df932ea28f7f2a7cc Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sat, 30 May 2026 06:02:37 +0300 Subject: [PATCH 061/120] Fix --- research/converter.py | 2 +- src/stewkk/sql/logic/optimizer/reachability.cpp | 13 ++++++++----- 2 files changed, 9 insertions(+), 6 deletions(-) diff --git a/research/converter.py b/research/converter.py index fd24d01..439c346 100644 --- a/research/converter.py +++ b/research/converter.py @@ -112,7 +112,7 @@ def _convert_sort(relop: ET.Element) -> str: keys = [] for ob in sort.findall(f"{NS}OrderBy/{NS}OrderByColumn"): - ascending = (ob.get("Ascending", "true").lower() != "false") + ascending = ob.get("Ascending", "true").lower() not in ("false", "0") cr = ob.find(f"{NS}ColumnReference") if cr is None: continue diff --git a/src/stewkk/sql/logic/optimizer/reachability.cpp b/src/stewkk/sql/logic/optimizer/reachability.cpp index f9fd6bd..22d1445 100644 --- a/src/stewkk/sql/logic/optimizer/reachability.cpp +++ b/src/stewkk/sql/logic/optimizer/reachability.cpp @@ -28,19 +28,22 @@ InternalMatch TryMatchExpr(utils::NotNull pe, const auto* t = std::get_if(&target); if (!t) return {false, depth, "type mismatch: expected SeqScan"}; if (op.table != t->table) - return {false, depth, + return {false, depth + 1, std::format("SeqScan table '{}' != '{}'", op.table, t->table)}; if (op.alias != t->alias) - return {false, depth, + return {false, depth + 1, std::format("SeqScan alias '{}' != '{}'", op.alias.value_or(""), t->alias.value_or(""))}; return {true, depth + 1, {}}; }, [&](const physical::Filter& op) -> InternalMatch { const auto* t = std::get_if(&target); + // depth+1 once the operator type matches: a same-type partial + // mismatch is more informative than a foreign-type rejection, so + // it should outrank type-mismatch reports for the same group. if (!t) return {false, depth, "type mismatch: expected Filter"}; if (op.predicate != t->predicate) - return {false, depth, + return {false, depth + 1, std::format("Filter predicate '{}' != '{}'", ToString(op.predicate), ToString(t->predicate))}; auto child = MatchGroup(op.source.get(), *t->source, depth + 1); @@ -51,7 +54,7 @@ InternalMatch TryMatchExpr(utils::NotNull pe, const auto* t = std::get_if(&target); if (!t) return {false, depth, "type mismatch: expected Projection"}; if (op.expressions != t->expressions) - return {false, depth, "Projection expressions mismatch"}; + return {false, depth + 1, "Projection expressions mismatch"}; auto child = MatchGroup(op.source.get(), *t->source, depth + 1); if (!child.ok) child.reason = "Projection.source: " + child.reason; return child; @@ -102,7 +105,7 @@ InternalMatch TryMatchExpr(utils::NotNull pe, const auto* t = std::get_if(&target); if (!t) return {false, depth, "type mismatch: expected Sort"}; if (op.keys != t->keys) - return {false, depth, "Sort keys mismatch"}; + return {false, depth + 1, "Sort keys mismatch"}; auto child = MatchGroup(op.input.get(), *t->source, depth + 1); if (!child.ok) child.reason = "Sort.input: " + child.reason; return child; From 1f2d8a8d0bbb3487d72b422563784ac3f6f0cec6 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sat, 30 May 2026 13:00:29 +0300 Subject: [PATCH 062/120] Add dot visualization of plans --- include/stewkk/sql/logic/optimizer/optimizer.hpp | 7 ++----- src/stewkk/sql/logic/optimizer/optimizer.cpp | 9 +++++---- src/stewkk/sql/logic/optimizer/reachability.cpp | 10 ---------- src/stewkk/sql/main.cpp | 11 +++++++++-- 4 files changed, 16 insertions(+), 21 deletions(-) diff --git a/include/stewkk/sql/logic/optimizer/optimizer.hpp b/include/stewkk/sql/logic/optimizer/optimizer.hpp index 531d4f9..779b62f 100644 --- a/include/stewkk/sql/logic/optimizer/optimizer.hpp +++ b/include/stewkk/sql/logic/optimizer/optimizer.hpp @@ -30,10 +30,7 @@ class Optimizer { PropertySet required = PropertySet::Any()); PhysicalPlanNode Optimize(); - - // Runs exhaustive search, populating all physical_exprs_ in every reachable - // group. - void OptimizeExhaustive(); + PhysicalPlanNode OptimizeExhaustive(); utils::NotNull GetRootGroup() const; @@ -75,7 +72,7 @@ class Optimizer { utils::NotNull root_; CardinalityEstimates cardinality_; SchemaCatalog schema_; - PropertySet required_; + PropertySet global_required_; std::unordered_map local_cost_; std::unordered_map winner_; std::unordered_set enforcers_added_; diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index e999e7a..0a31f1d 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -195,7 +195,7 @@ Optimizer::Optimizer( CardinalityEstimates cardinality, SchemaCatalog schema, PropertySet required) : memo_(), rules_applier_(std::move(rules)), root_(memo_.Populate(expr)), cardinality_(std::move(cardinality)), schema_(std::move(schema)), - required_(std::move(required)) { + global_required_(std::move(required)) { } template @@ -481,7 +481,7 @@ PhysicalPlanNode Optimizer::BuildOptimalPlan(G template void Optimizer::RunSearch(Limit limit) { Log("Starting optimization"); - tasks_.emplace([this, limit]() { OptimizeGroup(root_->group, required_, limit); }); + tasks_.emplace([this, limit]() { OptimizeGroup(root_->group, global_required_, limit); }); while (!tasks_.empty()) { auto next_task = std::move(tasks_.top()); tasks_.pop(); @@ -493,12 +493,13 @@ template PhysicalPlanNode Optimizer::Optimize() { RunSearch(std::numeric_limits::max()); Log("Optimization complete, building plan"); - return BuildOptimalPlan(root_->group.get(), required_); + return BuildOptimalPlan(root_->group.get(), global_required_); } template -void Optimizer::OptimizeExhaustive() { +PhysicalPlanNode Optimizer::OptimizeExhaustive() { RunSearch(std::nullopt); + return BuildOptimalPlan(root_->group.get(), global_required_); } template diff --git a/src/stewkk/sql/logic/optimizer/reachability.cpp b/src/stewkk/sql/logic/optimizer/reachability.cpp index 22d1445..c7dd2aa 100644 --- a/src/stewkk/sql/logic/optimizer/reachability.cpp +++ b/src/stewkk/sql/logic/optimizer/reachability.cpp @@ -38,9 +38,6 @@ InternalMatch TryMatchExpr(utils::NotNull pe, }, [&](const physical::Filter& op) -> InternalMatch { const auto* t = std::get_if(&target); - // depth+1 once the operator type matches: a same-type partial - // mismatch is more informative than a foreign-type rejection, so - // it should outrank type-mismatch reports for the same group. if (!t) return {false, depth, "type mismatch: expected Filter"}; if (op.predicate != t->predicate) return {false, depth + 1, @@ -61,9 +58,6 @@ InternalMatch TryMatchExpr(utils::NotNull pe, }, [&](const physical::NestedLoopJoin& op) -> InternalMatch { const auto* t = std::get_if(&target); - // depth+1 once the operator type matches: a same-type partial - // mismatch is more informative than a foreign-type rejection, so - // it should outrank type-mismatch reports for the same group. if (!t) return {false, depth, "type mismatch: expected NestedLoopJoin"}; if (op.type != t->type) return {false, depth + 1, "NestedLoopJoin join type mismatch"}; @@ -146,10 +140,6 @@ MatchResult IsReachable(utils::NotNull root, const PhysicalPlanNode& tar MatchResult IsPlanReachable(std::istream& sql, const PhysicalPlanNode& target, CardinalityEstimates cardinality, SchemaCatalog schema) { auto parsed = GetAST(sql).value(); - // Mirror main.cpp: a query ORDER BY becomes a required sort property so the - // exhaustive search enumerates Sort enforcers and the ordered plan MS SQL - // produced is actually reachable. Without it the root group never gets a - // Sort and every ORDER BY plan would spuriously read as unreachable. PropertySet required = parsed.required_order ? PropertySet{SortProperty{*parsed.required_order}} : PropertySet::Any(); diff --git a/src/stewkk/sql/main.cpp b/src/stewkk/sql/main.cpp index ea45158..56802e2 100644 --- a/src/stewkk/sql/main.cpp +++ b/src/stewkk/sql/main.cpp @@ -1,4 +1,5 @@ #include +#include #include #include #include @@ -256,8 +257,6 @@ int main(int argc, char** argv) { } MatchResult mr; try { - // The schema lets the Sort enforcer confirm the ORDER BY keys exist on - // the group it sits above; without --data-dir it stays permissive. SchemaCatalog schema = args.data_dir.empty() ? SchemaCatalog{} : LoadSchema(args.data_dir); mr = IsPlanReachable(sql_stream, target, {}, std::move(schema)); @@ -297,6 +296,14 @@ int main(int argc, char** argv) { return kOptimizerError; } + { + std::filesystem::create_directories(".plans"); + auto ts = std::chrono::duration_cast( + std::chrono::system_clock::now().time_since_epoch()).count(); + std::ofstream dot_file{std::format(".plans/{}.dot", ts)}; + dot_file << SerializeDot(plan); + } + if (args.print_plan) { std::cerr << Serialize(plan) << "\n"; } From c5c9a327e56ff63b587c41e786a5ebd7794bfcd5 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sat, 30 May 2026 13:20:18 +0300 Subject: [PATCH 063/120] Dump plans to disk --- .gitignore | 1 + include/stewkk/sql/utils/output_dot_plans.hpp | 9 +++++++ src/stewkk/sql/CMakeLists.txt | 1 + .../sql/logic/optimizer/reachability.cpp | 4 ++- src/stewkk/sql/main.cpp | 10 ++----- src/stewkk/sql/utils/non_null.cpp | 1 - src/stewkk/sql/utils/output_dot_plans.cpp | 26 +++++++++++++++++++ 7 files changed, 42 insertions(+), 10 deletions(-) create mode 100644 include/stewkk/sql/utils/output_dot_plans.hpp delete mode 100644 src/stewkk/sql/utils/non_null.cpp create mode 100644 src/stewkk/sql/utils/output_dot_plans.cpp diff --git a/.gitignore b/.gitignore index 7f94a71..66e2d9b 100644 --- a/.gitignore +++ b/.gitignore @@ -15,3 +15,4 @@ FlameGraph/ /build-tsan/ /benchmarks/datasets/ssb/generated/ /benchmarks/datasets/ssb/raw/ +/.plans/ diff --git a/include/stewkk/sql/utils/output_dot_plans.hpp b/include/stewkk/sql/utils/output_dot_plans.hpp new file mode 100644 index 0000000..3cd5556 --- /dev/null +++ b/include/stewkk/sql/utils/output_dot_plans.hpp @@ -0,0 +1,9 @@ +#pragma once + +#include + +namespace stewkk::sql { + +void OutputDot(const PhysicalPlanNode& optimizer_plan, const std::optional other_plan); + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/CMakeLists.txt b/src/stewkk/sql/CMakeLists.txt index d6d743f..312cef8 100644 --- a/src/stewkk/sql/CMakeLists.txt +++ b/src/stewkk/sql/CMakeLists.txt @@ -56,6 +56,7 @@ add_library(libsql logic/optimizer/properties/sort_order.cpp logic/optimizer/properties/sort_property.cpp logic/optimizer/properties/property_set.cpp + utils/output_dot_plans.cpp ) add_library(stewkk::libsql ALIAS libsql) target_compile_features(libsql PUBLIC cxx_std_23) diff --git a/src/stewkk/sql/logic/optimizer/reachability.cpp b/src/stewkk/sql/logic/optimizer/reachability.cpp index c7dd2aa..c4509c6 100644 --- a/src/stewkk/sql/logic/optimizer/reachability.cpp +++ b/src/stewkk/sql/logic/optimizer/reachability.cpp @@ -8,6 +8,7 @@ #include #include #include +#include namespace stewkk::sql { @@ -145,7 +146,8 @@ MatchResult IsPlanReachable(std::istream& sql, const PhysicalPlanNode& target, : PropertySet::Any(); Optimizer optimizer(parsed.op, MakeMainRules(), std::move(cardinality), std::move(schema), std::move(required)); - optimizer.OptimizeExhaustive(); + auto plan = optimizer.OptimizeExhaustive(); + OutputDot(plan, target); return IsReachable(optimizer.GetRootGroup(), target); } diff --git a/src/stewkk/sql/main.cpp b/src/stewkk/sql/main.cpp index 56802e2..1afb522 100644 --- a/src/stewkk/sql/main.cpp +++ b/src/stewkk/sql/main.cpp @@ -1,5 +1,4 @@ #include -#include #include #include #include @@ -33,6 +32,7 @@ #include #include #include +#include namespace stewkk::sql { @@ -296,13 +296,7 @@ int main(int argc, char** argv) { return kOptimizerError; } - { - std::filesystem::create_directories(".plans"); - auto ts = std::chrono::duration_cast( - std::chrono::system_clock::now().time_since_epoch()).count(); - std::ofstream dot_file{std::format(".plans/{}.dot", ts)}; - dot_file << SerializeDot(plan); - } + OutputDot(plan, std::nullopt); if (args.print_plan) { std::cerr << Serialize(plan) << "\n"; diff --git a/src/stewkk/sql/utils/non_null.cpp b/src/stewkk/sql/utils/non_null.cpp deleted file mode 100644 index 9b62def..0000000 --- a/src/stewkk/sql/utils/non_null.cpp +++ /dev/null @@ -1 +0,0 @@ -#include diff --git a/src/stewkk/sql/utils/output_dot_plans.cpp b/src/stewkk/sql/utils/output_dot_plans.cpp new file mode 100644 index 0000000..e78bebd --- /dev/null +++ b/src/stewkk/sql/utils/output_dot_plans.cpp @@ -0,0 +1,26 @@ +#include + +#include +#include +#include + +#include + +namespace stewkk::sql { + +void OutputDot(const PhysicalPlanNode& optimizer_plan, const std::optional other_plan) { + std::filesystem::create_directories(".plans"); + auto ts = std::chrono::duration_cast( + std::chrono::system_clock::now().time_since_epoch()) + .count(); + { + std::ofstream dot_file{std::format(".plans/{}.dot", ts)}; + dot_file << SerializeDot(optimizer_plan); + } + { + std::ofstream dot_file{std::format(".plans/{}.ref.dot", ts)}; + dot_file << SerializeDot(optimizer_plan); + } +} + +} // namespace stewkk::sql From db3660732018457202c638f86e117a2aaee7c4bd Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sat, 30 May 2026 13:52:10 +0300 Subject: [PATCH 064/120] Output only on diffs --- src/stewkk/sql/logic/optimizer/reachability.cpp | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/src/stewkk/sql/logic/optimizer/reachability.cpp b/src/stewkk/sql/logic/optimizer/reachability.cpp index c4509c6..8aa6544 100644 --- a/src/stewkk/sql/logic/optimizer/reachability.cpp +++ b/src/stewkk/sql/logic/optimizer/reachability.cpp @@ -147,8 +147,11 @@ MatchResult IsPlanReachable(std::istream& sql, const PhysicalPlanNode& target, Optimizer optimizer(parsed.op, MakeMainRules(), std::move(cardinality), std::move(schema), std::move(required)); auto plan = optimizer.OptimizeExhaustive(); - OutputDot(plan, target); - return IsReachable(optimizer.GetRootGroup(), target); + auto result = IsReachable(optimizer.GetRootGroup(), target); + if (!result.reachable) { + OutputDot(plan, target); + } + return result; } } // namespace stewkk::sql From 7fc9bb3598e43b55a8bdb08fff6fb554e9a494b7 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sat, 30 May 2026 14:39:30 +0300 Subject: [PATCH 065/120] Fix --- Makefile | 3 +++ src/stewkk/sql/utils/output_dot_plans.cpp | 4 ++-- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/Makefile b/Makefile index f0e0c80..846caa7 100644 --- a/Makefile +++ b/Makefile @@ -14,4 +14,7 @@ codegen: test-ssb-converter: pytest benchmarks/datasets/ssb +dot: + dot -T png -O .plans/*.dot + .PHONY: codegen build sanitize test-ssb-converter diff --git a/src/stewkk/sql/utils/output_dot_plans.cpp b/src/stewkk/sql/utils/output_dot_plans.cpp index e78bebd..d32cb31 100644 --- a/src/stewkk/sql/utils/output_dot_plans.cpp +++ b/src/stewkk/sql/utils/output_dot_plans.cpp @@ -17,9 +17,9 @@ void OutputDot(const PhysicalPlanNode& optimizer_plan, const std::optional Date: Sat, 30 May 2026 14:41:36 +0300 Subject: [PATCH 066/120] wip --- .../stewkk/sql/logic/executor/executor.hpp | 5 + include/stewkk/sql/logic/executor/plan.hpp | 1 + .../sql/logic/executor/sequential_scan.hpp | 11 + src/stewkk/sql/logic/executor/executor.cpp | 16 +- .../sql/logic/executor/executor_test.cpp | 55 +++ src/stewkk/sql/logic/executor/plan.cpp | 2 +- .../sql/logic/executor/plan_serializer.cpp | 9 +- .../logic/executor/plan_serializer_test.cpp | 1 + .../sql/logic/executor/sequential_scan.cpp | 314 +++++++++++++++++- src/stewkk/sql/main.cpp | 3 +- 10 files changed, 404 insertions(+), 13 deletions(-) diff --git a/include/stewkk/sql/logic/executor/executor.hpp b/include/stewkk/sql/logic/executor/executor.hpp index a5cb53b..38ae688 100644 --- a/include/stewkk/sql/logic/executor/executor.hpp +++ b/include/stewkk/sql/logic/executor/executor.hpp @@ -49,7 +49,11 @@ class Executor { using SequentialScan = std::function>( const std::string& table_name, const std::string& output_table_name, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan)>; + using IndexScan = std::function>( + const std::string& table_name, const std::string& output_table_name, + const Expression& predicate, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan)>; Executor(SequentialScan seq_scan, boost::asio::any_io_executor executor); + Executor(SequentialScan seq_scan, IndexScan index_scan, boost::asio::any_io_executor executor); boost::asio::awaitable> Execute(const PhysicalPlanNode& op); private: @@ -75,6 +79,7 @@ class Executor { private: SequentialScan sequential_scan_; + IndexScan index_scan_; ExpressionExecutor expression_executor_; }; diff --git a/include/stewkk/sql/logic/executor/plan.hpp b/include/stewkk/sql/logic/executor/plan.hpp index b6f295a..af919b0 100644 --- a/include/stewkk/sql/logic/executor/plan.hpp +++ b/include/stewkk/sql/logic/executor/plan.hpp @@ -86,6 +86,7 @@ struct MergeJoin { struct IndexSeek { std::string table; + std::optional alias; Expression predicate; bool operator==(const IndexSeek&) const; diff --git a/include/stewkk/sql/logic/executor/sequential_scan.hpp b/include/stewkk/sql/logic/executor/sequential_scan.hpp index 0ee7435..543ae6b 100644 --- a/include/stewkk/sql/logic/executor/sequential_scan.hpp +++ b/include/stewkk/sql/logic/executor/sequential_scan.hpp @@ -3,6 +3,7 @@ #include #include +#include #include namespace stewkk::sql { @@ -16,4 +17,14 @@ struct CsvDirSequentialScanner { TuplesChannel& tuples_chan) const; }; +struct CsvDirIndexedScanner { + std::string dir; + + boost::asio::awaitable> operator()(const std::string& table_name, + const std::string& output_table_name, + const Expression& predicate, + AttributesInfoChannel& attrs_chan, + TuplesChannel& tuples_chan) const; +}; + } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/executor/executor.cpp b/src/stewkk/sql/logic/executor/executor.cpp index b55423d..e348324 100644 --- a/src/stewkk/sql/logic/executor/executor.cpp +++ b/src/stewkk/sql/logic/executor/executor.cpp @@ -579,6 +579,10 @@ template Executor::Executor(SequentialScan seq_scan, boost::asio::any_io_executor executor) : sequential_scan_(std::move(seq_scan)), expression_executor_(executor) {} +template +Executor::Executor(SequentialScan seq_scan, IndexScan index_scan, boost::asio::any_io_executor executor) + : sequential_scan_(std::move(seq_scan)), index_scan_(std::move(index_scan)), expression_executor_(executor) {} + template boost::asio::awaitable> Executor::Execute(const PhysicalPlanNode& op) { auto exec = co_await boost::asio::this_coro::executor; @@ -619,6 +623,10 @@ boost::asio::awaitable> Executor::Execute(c template boost::asio::awaitable Executor::Execute(const PhysicalPlanNode& op, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan) { + auto close_on_fail = boost::scope::make_scope_fail([&] { + attr_chan.close(); + tuples_chan.close(); + }); struct ExecuteVisitor{ boost::asio::awaitable operator()(const SeqScan& seq_scan) { co_await executor.sequential_scan_(seq_scan.table, std::string{OutputTable(seq_scan)}, @@ -655,8 +663,12 @@ boost::asio::awaitable Executor::Execute(const Physica throw std::runtime_error("MergeJoin execution not implemented"); co_return; } - boost::asio::awaitable operator()(const IndexSeek&) { - throw std::runtime_error("IndexSeek execution not implemented"); + boost::asio::awaitable operator()(const IndexSeek& seek) { + if (!executor.index_scan_) { + throw std::runtime_error("IndexSeek execution requested, but no index scanner is configured"); + } + co_await executor.index_scan_(seek.table, seek.alias ? *seek.alias : seek.table, seek.predicate, + attr_chan, tuples_chan); co_return; } // FIXME: that's in-memory sort diff --git a/src/stewkk/sql/logic/executor/executor_test.cpp b/src/stewkk/sql/logic/executor/executor_test.cpp index b42284b..c1fb3b9 100644 --- a/src/stewkk/sql/logic/executor/executor_test.cpp +++ b/src/stewkk/sql/logic/executor/executor_test.cpp @@ -75,6 +75,24 @@ std::string ReadFromFile(std::filesystem::path path) { return stream.str(); } +boost::asio::awaitable> RunIndexSeekTestQuery(std::string dir) { + PhysicalPlanNode op = IndexSeek{ + "users", + std::nullopt, + BinaryExpression{ + std::make_shared(Attribute{"users", "id"}), + BinaryOp::kGe, + std::make_shared(IntConst{8}), + }, + }; + CsvDirSequentialScanner seq_scan{dir}; + CsvDirIndexedScanner index_scan{dir}; + Executor executor(std::move(seq_scan), std::move(index_scan), + co_await boost::asio::this_coro::executor); + + co_return co_await executor.Execute(op); +} + } // namespace template @@ -134,6 +152,43 @@ TEST(ExecutorTest, SimpleSelectWithParallelism) { pool.join(); } +TEST(ExecutorTest, IndexSeekBuildsAndUsesSortedIntIndex) { + auto dir = std::filesystem::temp_directory_path() / "iu9_sql_index_seek_test"; + std::filesystem::remove_all(dir); + std::filesystem::create_directories(dir); + { + std::ofstream csv{dir / "users.csv"}; + csv << "id:int,age:int\n" + << "10,22\n" + << "1,33\n" + << "8,64\n" + << "8,70\n"; + std::ofstream meta{dir / "indexes.meta"}; + meta << "users id sorted users.id.sorted.idx\n"; + } + + boost::asio::io_context ctx; + boost::asio::co_spawn( + ctx, + RunIndexSeekTestQuery(dir.string()), + [dir](std::exception_ptr p, Result got) { + if (p) std::rethrow_exception(p); + + ASSERT_THAT(got.value().attributes, + Eq(AttributesInfo{{"users", "id", Type::kInt}, {"users", "age", Type::kInt}})); + ASSERT_THAT(got.value().tuples, + Eq(Tuples{ + Tuple{Value{false, 8}, Value{false, 64}}, + Tuple{Value{false, 8}, Value{false, 70}}, + Tuple{Value{false, 10}, Value{false, 22}}, + })); + ASSERT_TRUE(std::filesystem::exists(dir / "users.id.sorted.idx")); + std::filesystem::remove_all(dir); + }); + + ctx.run(); +} + TEST(ExecutorTest, StringFilterProjectionAndCsvQuotes) { boost::asio::io_context ctx; boost::asio::co_spawn( diff --git a/src/stewkk/sql/logic/executor/plan.cpp b/src/stewkk/sql/logic/executor/plan.cpp index 8d47a38..7bbbed7 100644 --- a/src/stewkk/sql/logic/executor/plan.cpp +++ b/src/stewkk/sql/logic/executor/plan.cpp @@ -31,7 +31,7 @@ bool MergeJoin::operator==(const MergeJoin& other) const { } bool IndexSeek::operator==(const IndexSeek& other) const { - return table == other.table && predicate == other.predicate; + return table == other.table && alias == other.alias && predicate == other.predicate; } bool PhysicalSort::operator==(const PhysicalSort& other) const { diff --git a/src/stewkk/sql/logic/executor/plan_serializer.cpp b/src/stewkk/sql/logic/executor/plan_serializer.cpp index c9e3618..4cb8af9 100644 --- a/src/stewkk/sql/logic/executor/plan_serializer.cpp +++ b/src/stewkk/sql/logic/executor/plan_serializer.cpp @@ -185,6 +185,9 @@ std::string SerializeNode(const PhysicalPlanNode& node) { SerializeNode(*n.lhs), SerializeNode(*n.rhs)); } std::string operator()(const IndexSeek& n) const { + if (n.alias) { + return std::format("(IndexSeek {} {} {})", SerializeExpr(n.predicate), n.table, *n.alias); + } return std::format("(IndexSeek {} {})", SerializeExpr(n.predicate), n.table); } std::string operator()(const HashJoin& n) const { @@ -507,8 +510,12 @@ PhysicalPlanNode ParseNode(ParseState& s) { if (head == "IndexSeek") { auto pred = ParseExpr(s); auto table = s.ExpectAtom(); + std::optional alias; + if (s.Peek().kind != TokenKind::RParen) { + alias = s.ExpectAtom(); + } s.ExpectRParen(); - return IndexSeek{std::move(table), std::move(pred)}; + return IndexSeek{std::move(table), std::move(alias), std::move(pred)}; } if (head == "NestedLoopJoin") return ParseJoinNode.template operator()(); if (head == "HashJoin") return ParseJoinNode.template operator()(); diff --git a/src/stewkk/sql/logic/executor/plan_serializer_test.cpp b/src/stewkk/sql/logic/executor/plan_serializer_test.cpp index 7a8229b..0c33461 100644 --- a/src/stewkk/sql/logic/executor/plan_serializer_test.cpp +++ b/src/stewkk/sql/logic/executor/plan_serializer_test.cpp @@ -202,6 +202,7 @@ TEST(PlanSerializerTest, DeepNestedPlan) { TEST(PlanSerializerTest, IndexSeek) { PhysicalPlanNode plan = IndexSeek{ "users", + std::nullopt, BinaryExpression{ std::make_shared(Attribute{"users", "id"}), BinaryOp::kGt, diff --git a/src/stewkk/sql/logic/executor/sequential_scan.cpp b/src/stewkk/sql/logic/executor/sequential_scan.cpp index 453c1a5..51663ce 100644 --- a/src/stewkk/sql/logic/executor/sequential_scan.cpp +++ b/src/stewkk/sql/logic/executor/sequential_scan.cpp @@ -1,14 +1,23 @@ #include +#include +#include +#include +#include #include #include #include #include +#include +#include #include #include +#include #include +#include +#include #include #include @@ -16,6 +25,20 @@ namespace stewkk::sql { namespace { +constexpr std::array kIntSortedIndexMagic{'I', '9', 'I', 'D', 'X', '0', '0', '1'}; + +struct IndexMeta { + std::string table; + std::string column; + std::string type; + std::string file; +}; + +struct IntIndexEntry { + int64_t key; + uint64_t row_offset; +}; + Type GetTypeFromString(const std::string& s) { if (s == "int") { return Type::kInt; @@ -89,6 +112,219 @@ Tuple ParseTuple(const std::string& line, | std::ranges::to(); } +AttributesInfo ReadAttributes(std::istream& input, const std::string& output_table_name) { + std::string line; + if (!std::getline(input, line)) { + throw std::runtime_error{"CSV file is empty"}; + } + return line | std::views::split(',') | std::views::transform([&output_table_name](const auto& attr) { + auto mid = std::find(attr.begin(), attr.end(), ':'); + auto attr_name = std::string{attr.begin(), mid}; + auto attr_type = GetTypeFromString(std::string{mid + 1, attr.end()}); + return AttributeInfo{output_table_name, std::move(attr_name), attr_type}; + }) + | std::ranges::to(); +} + +std::vector ReadIndexMeta(const std::filesystem::path& path) { + std::ifstream input{path}; + if (!input) { + throw std::runtime_error{std::format("index metadata file not found: {}", path.string())}; + } + + std::vector result; + std::string line; + while (std::getline(input, line)) { + auto first = line.find_first_not_of(" \t\r\n"); + if (first == std::string::npos || line[first] == '#') { + continue; + } + + std::istringstream fields{line}; + IndexMeta meta; + if (!(fields >> meta.table >> meta.column >> meta.type >> meta.file)) { + throw std::runtime_error{std::format("malformed index metadata line: {}", line)}; + } + result.push_back(std::move(meta)); + } + return result; +} + +std::optional FindIndexMeta(const std::filesystem::path& dir, + const std::string& table, + const std::string& column) { + for (auto meta : ReadIndexMeta(dir / "indexes.meta")) { + if (meta.table == table && meta.column == column && meta.type == "sorted") { + return meta; + } + } + return std::nullopt; +} + +BinaryOp InvertComparison(BinaryOp op) { + switch (op) { + case BinaryOp::kGt: + return BinaryOp::kLt; + case BinaryOp::kLt: + return BinaryOp::kGt; + case BinaryOp::kLe: + return BinaryOp::kGe; + case BinaryOp::kGe: + return BinaryOp::kLe; + case BinaryOp::kEq: + return BinaryOp::kEq; + default: + throw std::runtime_error{"unsupported index seek operator"}; + } +} + +struct SeekCondition { + std::string column; + BinaryOp op; + int64_t key; +}; + +std::optional ExtractSeekCondition(const Expression& predicate, + const std::string& table_name, + const std::string& output_table_name) { + const auto* binary = std::get_if(&predicate); + if (!binary) { + return std::nullopt; + } + if (binary->binop == BinaryOp::kAnd) { + if (auto lhs = ExtractSeekCondition(*binary->lhs, table_name, output_table_name)) { + return lhs; + } + return ExtractSeekCondition(*binary->rhs, table_name, output_table_name); + } + if (!std::ranges::contains(std::vector{BinaryOp::kEq, BinaryOp::kLt, BinaryOp::kLe, + BinaryOp::kGt, BinaryOp::kGe}, binary->binop)) { + return std::nullopt; + } + + auto attr_matches = [&](const Attribute& attr) { + return attr.table.empty() || attr.table == table_name || attr.table == output_table_name; + }; + if (const auto* attr = std::get_if(binary->lhs.get()); + attr && attr_matches(*attr)) { + if (const auto* key = std::get_if(binary->rhs.get())) { + return SeekCondition{attr->name, binary->binop, *key}; + } + } + if (const auto* attr = std::get_if(binary->rhs.get()); + attr && attr_matches(*attr)) { + if (const auto* key = std::get_if(binary->lhs.get())) { + return SeekCondition{attr->name, InvertComparison(binary->binop), *key}; + } + } + return std::nullopt; +} + +std::vector ReadIndexEntries(const std::filesystem::path& path) { + std::ifstream input{path, std::ios::binary}; + if (!input) { + throw std::runtime_error{std::format("index file not found: {}", path.string())}; + } + + std::array magic{}; + uint64_t count = 0; + input.read(magic.data(), magic.size()); + input.read(reinterpret_cast(&count), sizeof(count)); + if (!input || magic != kIntSortedIndexMagic) { + throw std::runtime_error{std::format("invalid int sorted index file: {}", path.string())}; + } + + std::vector entries(count); + input.read(reinterpret_cast(entries.data()), + static_cast(entries.size() * sizeof(IntIndexEntry))); + if (!input) { + throw std::runtime_error{std::format("truncated index file: {}", path.string())}; + } + return entries; +} + +void WriteIndexEntries(const std::filesystem::path& path, const std::vector& entries) { + std::ofstream output{path, std::ios::binary | std::ios::trunc}; + if (!output) { + throw std::runtime_error{std::format("cannot write index file: {}", path.string())}; + } + + uint64_t count = entries.size(); + output.write(kIntSortedIndexMagic.data(), kIntSortedIndexMagic.size()); + output.write(reinterpret_cast(&count), sizeof(count)); + output.write(reinterpret_cast(entries.data()), + static_cast(entries.size() * sizeof(IntIndexEntry))); +} + +void BuildIntSortedIndex(const std::filesystem::path& csv_path, + const std::filesystem::path& index_path, + const std::string& output_table_name, + const std::string& column) { + std::ifstream input{csv_path}; + if (!input) { + throw std::runtime_error{std::format("CSV file not found: {}", csv_path.string())}; + } + + auto attrs = ReadAttributes(input, output_table_name); + auto it = std::find_if(attrs.begin(), attrs.end(), [&](const AttributeInfo& attr) { + return attr.name == column; + }); + if (it == attrs.end()) { + throw std::runtime_error{std::format("indexed column not found: {}", column)}; + } + if (it->type != Type::kInt) { + throw std::runtime_error{std::format("only int indexes are supported: {}", column)}; + } + size_t column_index = static_cast(it - attrs.begin()); + + std::vector entries; + std::string line; + while (input) { + auto row_offset = input.tellg(); + if (!std::getline(input, line)) { + break; + } + auto tuple = ParseTuple(line, attrs); + const auto& key = tuple[column_index]; + if (!key.is_null) { + entries.push_back(IntIndexEntry{ + .key = key.value.int_value, + .row_offset = static_cast(row_offset), + }); + } + } + + std::ranges::sort(entries, [](const IntIndexEntry& lhs, const IntIndexEntry& rhs) { + return std::tie(lhs.key, lhs.row_offset) < std::tie(rhs.key, rhs.row_offset); + }); + WriteIndexEntries(index_path, entries); +} + +std::pair::const_iterator, std::vector::const_iterator> +FindIndexRange(const std::vector& entries, const SeekCondition& condition) { + auto lower = [&](int64_t key) { + return std::ranges::lower_bound(entries, key, {}, &IntIndexEntry::key); + }; + auto upper = [&](int64_t key) { + return std::ranges::upper_bound(entries, key, {}, &IntIndexEntry::key); + }; + + switch (condition.op) { + case BinaryOp::kEq: + return {lower(condition.key), upper(condition.key)}; + case BinaryOp::kLt: + return {entries.begin(), lower(condition.key)}; + case BinaryOp::kLe: + return {entries.begin(), upper(condition.key)}; + case BinaryOp::kGt: + return {upper(condition.key), entries.end()}; + case BinaryOp::kGe: + return {lower(condition.key), entries.end()}; + default: + throw std::runtime_error{"unsupported index seek operator"}; + } +} + } // namespace boost::asio::awaitable> CsvDirSequentialScanner::operator()( @@ -101,14 +337,7 @@ boost::asio::awaitable> CsvDirSequentialScanner::operator()( auto path = std::format("{}/{}.csv", dir, table_name); std::ifstream input{std::move(path)}; std::string line; - std::getline(input, line); - auto attributes = line | std::views::split(',') | std::views::transform([&output_table_name](const auto& attr) { - auto mid = std::find(attr.begin(), attr.end(), ':'); - auto attr_name = std::string{attr.begin(), mid}; - auto attr_type = GetTypeFromString(std::string{mid + 1, attr.end()}); - return AttributeInfo{output_table_name, std::move(attr_name), attr_type}; - }) - | std::ranges::to(); + auto attributes = ReadAttributes(input, output_table_name); co_await attrs_chan.async_send(boost::system::error_code{}, attributes, boost::asio::use_awaitable); @@ -149,4 +378,73 @@ boost::asio::awaitable> CsvDirSequentialScanner::operator()( co_return Ok(); } +boost::asio::awaitable> CsvDirIndexedScanner::operator()( + const std::string& table_name, const std::string& output_table_name, + const Expression& predicate, + AttributesInfoChannel& attrs_chan, + TuplesChannel& tuples_chan) const { +#ifdef DEBUG + std::clog << "Executing index seek\n"; +#endif + auto condition = ExtractSeekCondition(predicate, table_name, output_table_name); + if (!condition) { + throw std::runtime_error{"IndexSeek predicate must be a comparison between one indexed int column and an int constant"}; + } + + std::filesystem::path data_dir{dir}; + auto meta = FindIndexMeta(data_dir, table_name, condition->column); + if (!meta) { + throw std::runtime_error{std::format("no sorted int index declared for {}.{}", table_name, condition->column)}; + } + + auto csv_path = data_dir / std::format("{}.csv", table_name); + auto index_path = data_dir / meta->file; + if (!std::filesystem::exists(index_path)) { + BuildIntSortedIndex(csv_path, index_path, output_table_name, condition->column); + } + auto entries = ReadIndexEntries(index_path); + + std::ifstream input{csv_path}; + if (!input) { + throw std::runtime_error{std::format("CSV file not found: {}", csv_path.string())}; + } + auto attributes = ReadAttributes(input, output_table_name); + auto filter = InterpretedExpressionExecutor{co_await boost::asio::this_coro::executor}; + auto residual = co_await filter.GetExpressionExecutor(predicate, attributes); + + co_await attrs_chan.async_send(boost::system::error_code{}, attributes, + boost::asio::use_awaitable); + attrs_chan.close(); + + auto [begin, end] = FindIndexRange(entries, *condition); + Tuples buf; + buf.reserve(kBufSize); + std::string line; + for (auto it = begin; it != end; ++it) { + input.clear(); + input.seekg(static_cast(it->row_offset)); + if (!std::getline(input, line)) { + throw std::runtime_error{"failed to read indexed CSV row"}; + } + auto tuple = ParseTuple(line, attributes); + if (!ApplyFilter(tuple, attributes, residual)) { + continue; + } + buf.emplace_back(std::move(tuple)); + if (buf.size() == kBufSize) { + co_await tuples_chan.async_send(boost::system::error_code{}, std::move(buf), + boost::asio::use_awaitable); + buf.clear(); + } + } + + if (!buf.empty()) { + co_await tuples_chan.async_send(boost::system::error_code{}, std::move(buf), + boost::asio::use_awaitable); + } + tuples_chan.close(); + + co_return Ok(); +} + } // namespace stewkk::sql diff --git a/src/stewkk/sql/main.cpp b/src/stewkk/sql/main.cpp index 1afb522..ae31689 100644 --- a/src/stewkk/sql/main.cpp +++ b/src/stewkk/sql/main.cpp @@ -215,7 +215,8 @@ void PrintRelation(const Relation& rel, bool preserve_order) { boost::asio::awaitable> RunQuery(const std::string& data_dir, const PhysicalPlanNode& plan) { CsvDirSequentialScanner seq_scan{data_dir}; - Executor executor(std::move(seq_scan), co_await boost::asio::this_coro::executor); + CsvDirIndexedScanner index_scan{data_dir}; + Executor executor(std::move(seq_scan), std::move(index_scan), co_await boost::asio::this_coro::executor); co_return co_await executor.Execute(plan); } From 5ab09a358b182ee618aa7c47f539b38a27acd9f9 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sat, 30 May 2026 17:42:15 +0300 Subject: [PATCH 067/120] Update ssb-dbgen --- .gitmodules | 2 +- research/ssb-dbgen | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.gitmodules b/.gitmodules index d550f5e..4c644f5 100644 --- a/.gitmodules +++ b/.gitmodules @@ -6,4 +6,4 @@ url = git@github.com:sqlsunday/imdb-to-sqlserver.git [submodule "research/ssb-dbgen"] path = research/ssb-dbgen - url = https://github.com/electrum/ssb-dbgen.git + url = git@github.com:stewkk/ssb-dbgen.git diff --git a/research/ssb-dbgen b/research/ssb-dbgen index 219403a..90141f6 160000 --- a/research/ssb-dbgen +++ b/research/ssb-dbgen @@ -1 +1 @@ -Subproject commit 219403ad7d1dd32ae1f97b5553abf92129fccd7f +Subproject commit 90141f692abfed592627462996a12eb242d2f053 From 031fe24a36b13fda37f7bc7ad80f635630aefa0c Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sat, 30 May 2026 19:36:24 +0300 Subject: [PATCH 068/120] Add physical operator cost proof benchmarks --- benchmarks/CMakeLists.txt | 2 +- benchmarks/operator_cost.cpp | 376 +++++++++++++++++++ research/research.ipynb | 156 ++++---- src/stewkk/sql/logic/optimizer/optimizer.cpp | 2 - 4 files changed, 460 insertions(+), 76 deletions(-) create mode 100644 benchmarks/operator_cost.cpp diff --git a/benchmarks/CMakeLists.txt b/benchmarks/CMakeLists.txt index 8cfaea0..a58c2a2 100644 --- a/benchmarks/CMakeLists.txt +++ b/benchmarks/CMakeLists.txt @@ -1,6 +1,6 @@ include(FetchGBenchmark) -add_executable(benchmarks main.cpp) +add_executable(benchmarks main.cpp operator_cost.cpp) target_compile_features(benchmarks PRIVATE cxx_std_23) set_target_properties(benchmarks PROPERTIES CXX_STANDART 23 diff --git a/benchmarks/operator_cost.cpp b/benchmarks/operator_cost.cpp new file mode 100644 index 0000000..3e94ff5 --- /dev/null +++ b/benchmarks/operator_cost.cpp @@ -0,0 +1,376 @@ +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include + +#include +#include +#include +#include + +namespace stewkk::sql { +namespace { + +struct BenchTable { + AttributesInfo attrs; + Tuples tuples; +}; + +using BenchTables = std::unordered_map; + +Value IntValue(int64_t value) { + return Value{false, {.int_value = value}}; +} + +Attribute Attr(std::string table, std::string name) { + return Attribute{std::move(table), std::move(name)}; +} + +Expression AttrExpr(std::string table, std::string name) { + return Expression{Attr(std::move(table), std::move(name))}; +} + +Expression IntExpr(int64_t value) { + return Expression{IntConst{value}}; +} + +Expression Binary(Expression lhs, BinaryOp op, Expression rhs) { + return Expression{BinaryExpression{ + .lhs = std::make_shared(std::move(lhs)), + .binop = op, + .rhs = std::make_shared(std::move(rhs)), + }}; +} + +Expression CountStar() { + return Expression{AggregateExpression{ + .function = AggregateFunction::kCount, + .argument = nullptr, + .is_star = true, + }}; +} + +PhysicalPlanNode Scan(std::string table) { + return PhysicalPlanNode{SeqScan{.table = std::move(table), .alias = std::nullopt}}; +} + +std::shared_ptr PlanPtr(PhysicalPlanNode plan) { + return std::make_shared(std::move(plan)); +} + +BenchTable MakeTable(std::string table, int64_t rows) { + BenchTable result; + result.attrs = { + AttributeInfo{table, "id", Type::kInt}, + AttributeInfo{table, "k", Type::kInt}, + AttributeInfo{table, "v", Type::kInt}, + }; + result.tuples.reserve(static_cast(rows)); + for (int64_t i = 0; i < rows; ++i) { + result.tuples.push_back(Tuple{ + IntValue(i), + IntValue(i), + IntValue(i % 1024), + }); + } + return result; +} + +std::shared_ptr MakeTables(int64_t rows_t, int64_t rows_u = 0) { + auto tables = std::make_shared(); + tables->emplace("t", MakeTable("t", rows_t)); + tables->emplace("u", MakeTable("u", rows_u)); + return tables; +} + +class InMemorySequentialScanner { +public: + explicit InMemorySequentialScanner(std::shared_ptr tables) + : tables_(std::move(tables)) {} + + boost::asio::awaitable> operator()(const std::string& table_name, + const std::string& output_table_name, + AttributesInfoChannel& attrs_chan, + TuplesChannel& tuples_chan) const { + auto it = tables_->find(table_name); + if (it == tables_->end()) { + throw std::runtime_error{"unknown benchmark table: " + table_name}; + } + + auto attrs = it->second.attrs; + for (auto& attr : attrs) { + attr.table = output_table_name; + } + co_await attrs_chan.async_send(boost::system::error_code{}, std::move(attrs), + boost::asio::use_awaitable); + attrs_chan.close(); + + Tuples buf; + buf.reserve(kBufSize); + for (const auto& tuple : it->second.tuples) { + buf.push_back(tuple); + if (buf.size() == kBufSize) { + co_await tuples_chan.async_send(boost::system::error_code{}, std::move(buf), + boost::asio::use_awaitable); + buf.clear(); + buf.reserve(kBufSize); + } + } + if (!buf.empty()) { + co_await tuples_chan.async_send(boost::system::error_code{}, std::move(buf), + boost::asio::use_awaitable); + } + tuples_chan.close(); + co_return Ok(); + } + +private: + std::shared_ptr tables_; +}; + +Relation RunPlan(const PhysicalPlanNode& plan, std::shared_ptr tables) { + boost::asio::io_context ctx; + Executor executor(InMemorySequentialScanner{std::move(tables)}, + ctx.get_executor()); + auto fut = boost::asio::co_spawn(ctx, executor.Execute(plan), boost::asio::use_future); + ctx.run(); + auto result = fut.get(); + if (!result.has_value()) { + throw std::runtime_error{What(result.error())}; + } + return std::move(result).value(); +} + +int64_t CeilPages(int64_t rows) { + return (rows + static_cast(kBufSize) - 1) / static_cast(kBufSize); +} + +int64_t SortModelCost(int64_t rows) { + return rows > 1 ? rows * static_cast(std::bit_width(static_cast(rows))) : rows; +} + +void SetUnaryCounters(benchmark::State& state, int64_t rows, int64_t model_cost, + int64_t output_rows) { + state.counters["rows"] = static_cast(rows); + state.counters["model_cost"] = static_cast(model_cost); + state.counters["output_rows"] = static_cast(output_rows); +} + +void SetBinaryCounters(benchmark::State& state, int64_t lhs_rows, int64_t rhs_rows, + int64_t model_cost, int64_t output_rows) { + state.counters["lhs_rows"] = static_cast(lhs_rows); + state.counters["rhs_rows"] = static_cast(rhs_rows); + state.counters["model_cost"] = static_cast(model_cost); + state.counters["output_rows"] = static_cast(output_rows); +} + +void SuppressClog() { + static std::ofstream nullstream("/dev/null"); + std::clog.rdbuf(nullstream.rdbuf()); +} + +void BM_OperatorSeqScan(benchmark::State& state) { + const int64_t rows = state.range(0); + auto tables = MakeTables(rows); + auto plan = Scan("t"); + + SuppressClog(); + + benchmark::DoNotOptimize(RunPlan(plan, tables)); + for (auto _ : state) { + benchmark::DoNotOptimize(RunPlan(plan, tables)); + } + SetUnaryCounters(state, rows, rows, rows); +} + +void BM_OperatorFilter(benchmark::State& state) { + const int64_t rows = state.range(0); + constexpr int64_t kThreshold = 512; + auto tables = MakeTables(rows); + auto plan = PhysicalPlanNode{PhysicalFilter{ + .source = PlanPtr(Scan("t")), + .predicate = Binary(AttrExpr("t", "v"), BinaryOp::kLt, IntExpr(kThreshold)), + }}; + + SuppressClog(); + + benchmark::DoNotOptimize(RunPlan(plan, tables)); + for (auto _ : state) { + benchmark::DoNotOptimize(RunPlan(plan, tables)); + } + const int64_t full_cycles = rows / 1024; + const int64_t tail = rows % 1024; + const int64_t output_rows = full_cycles * kThreshold + std::min(tail, kThreshold); + SetUnaryCounters(state, rows, rows, output_rows); +} + +void BM_OperatorProjection(benchmark::State& state) { + const int64_t rows = state.range(0); + auto tables = MakeTables(rows); + auto plan = PhysicalPlanNode{PhysicalProjection{ + .source = PlanPtr(Scan("t")), + .expressions = { + AttrExpr("t", "id"), + Binary(AttrExpr("t", "v"), BinaryOp::kPlus, IntExpr(1)), + }, + .aliases = {std::nullopt, std::nullopt}, + }}; + + SuppressClog(); + + benchmark::DoNotOptimize(RunPlan(plan, tables)); + for (auto _ : state) { + benchmark::DoNotOptimize(RunPlan(plan, tables)); + } + SetUnaryCounters(state, rows, rows, rows); +} + +void BM_OperatorSort(benchmark::State& state) { + const int64_t rows = state.range(0); + auto tables = MakeTables(rows); + auto plan = PhysicalPlanNode{PhysicalSort{ + .source = PlanPtr(Scan("t")), + .keys = SortOrder{{SortKey{.table = "t", .column = "v", .dir = Direction::kAsc}}}, + }}; + + SuppressClog(); + + benchmark::DoNotOptimize(RunPlan(plan, tables)); + for (auto _ : state) { + benchmark::DoNotOptimize(RunPlan(plan, tables)); + } + SetUnaryCounters(state, rows, SortModelCost(rows), rows); +} + +void BM_OperatorAggregation(benchmark::State& state) { + const int64_t rows = state.range(0); + auto tables = MakeTables(rows); + auto plan = PhysicalPlanNode{PhysicalAggregation{ + .source = PlanPtr(Scan("t")), + .group_by = {AttrExpr("t", "k")}, + .aggregates = {CountStar()}, + }}; + + SuppressClog(); + + benchmark::DoNotOptimize(RunPlan(plan, tables)); + for (auto _ : state) { + benchmark::DoNotOptimize(RunPlan(plan, tables)); + } + SetUnaryCounters(state, rows, rows, rows); +} + +void BM_OperatorNestedLoopJoin(benchmark::State& state) { + const int64_t lhs_rows = state.range(0); + const int64_t rhs_rows = state.range(1); + auto tables = MakeTables(lhs_rows, rhs_rows); + auto plan = PhysicalPlanNode{NestedLoopJoin{ + .lhs = PlanPtr(Scan("t")), + .rhs = PlanPtr(Scan("u")), + .type = JoinType::kInner, + .qual = Binary(AttrExpr("t", "k"), BinaryOp::kEq, AttrExpr("u", "k")), + }}; + + SuppressClog(); + + benchmark::DoNotOptimize(RunPlan(plan, tables)); + for (auto _ : state) { + benchmark::DoNotOptimize(RunPlan(plan, tables)); + } + SetBinaryCounters(state, lhs_rows, rhs_rows, lhs_rows * rhs_rows, + std::min(lhs_rows, rhs_rows)); +} + +void BM_OperatorNestedLoopCrossJoin(benchmark::State& state) { + const int64_t lhs_rows = state.range(0); + const int64_t rhs_rows = state.range(1); + auto tables = MakeTables(lhs_rows, rhs_rows); + auto plan = PhysicalPlanNode{NestedLoopCrossJoin{ + .lhs = PlanPtr(Scan("t")), + .rhs = PlanPtr(Scan("u")), + }}; + + SuppressClog(); + + benchmark::DoNotOptimize(RunPlan(plan, tables)); + for (auto _ : state) { + benchmark::DoNotOptimize(RunPlan(plan, tables)); + } + SetBinaryCounters(state, lhs_rows, rhs_rows, CeilPages(lhs_rows) * (1 + CeilPages(rhs_rows)), + lhs_rows * rhs_rows); +} + +void BM_OperatorHashJoin(benchmark::State& state) { + const int64_t lhs_rows = state.range(0); + const int64_t rhs_rows = state.range(1); + auto tables = MakeTables(lhs_rows, rhs_rows); + auto plan = PhysicalPlanNode{HashJoin{ + .lhs = PlanPtr(Scan("t")), + .rhs = PlanPtr(Scan("u")), + .type = JoinType::kInner, + .qual = Binary(AttrExpr("t", "k"), BinaryOp::kEq, AttrExpr("u", "k")), + }}; + + SuppressClog(); + + benchmark::DoNotOptimize(RunPlan(plan, tables)); + for (auto _ : state) { + benchmark::DoNotOptimize(RunPlan(plan, tables)); + } + SetBinaryCounters(state, lhs_rows, rhs_rows, lhs_rows + rhs_rows, + std::min(lhs_rows, rhs_rows)); +} + +void RegisterUnary(void (*benchmark_fn)(benchmark::State&), const char* name) { + for (int64_t rows : {1024, 4096, 16384, 65536, 262144}) { + benchmark::RegisterBenchmark(name, benchmark_fn)->Arg(rows)->UseRealTime(); + } +} + +struct OperatorCostRegistration { + OperatorCostRegistration() { + RegisterUnary(BM_OperatorSeqScan, "OperatorCost/SeqScan"); + RegisterUnary(BM_OperatorFilter, "OperatorCost/Filter"); + RegisterUnary(BM_OperatorProjection, "OperatorCost/Projection"); + RegisterUnary(BM_OperatorSort, "OperatorCost/Sort"); + RegisterUnary(BM_OperatorAggregation, "OperatorCost/Aggregation"); + + for (auto size : {1024, 4096, 16384, 65536}) { + benchmark::RegisterBenchmark("OperatorCost/HashJoin", BM_OperatorHashJoin) + ->Args({size, size}) + ->UseRealTime(); + } + for (auto size : {128, 256, 512, 1024}) { + benchmark::RegisterBenchmark("OperatorCost/NestedLoopJoin", BM_OperatorNestedLoopJoin) + ->Args({size, size}) + ->UseRealTime(); + } + for (auto size : {64, 128, 256, 512}) { + benchmark::RegisterBenchmark("OperatorCost/NestedLoopCrossJoin", + BM_OperatorNestedLoopCrossJoin) + ->Args({size, size}) + ->UseRealTime(); + } + } +}; + +const OperatorCostRegistration kRegisterOperatorCost; + +} // namespace +} // namespace stewkk::sql diff --git a/research/research.ipynb b/research/research.ipynb index 3af90cd..78f91ac 100644 --- a/research/research.ipynb +++ b/research/research.ipynb @@ -3,90 +3,100 @@ { "cell_type": "code", "execution_count": null, - "id": "6b61780a", + "id": "f336c923", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Recreating database...\n", - "Creating schema and tables...\n" - ] - }, - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", - "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", - "\u001b[1;31mClick here for more info. \n", - "\u001b[1;31mView Jupyter log for further details." - ] - }, - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mCanceled future for execute_request message before replies were done" - ] - }, { "ename": "", "evalue": "", "output_type": "error", "traceback": [ - "\u001b[1;31mCanceled future for execute_request message before replies were done. \n", + "\u001b[1;31mFailed to start the Kernel. \n", + "\u001b[1;31mFailed to find the URL of the launched Jupyter notebook server\n", + "\u001b[1;31m[W 2026-05-30 19:32:44.464 ServerApp] jupyterlab | error adding extension (enabled: True): Unknown scheme for proxy URL URL('socks://localhost:1081')\n", + "\u001b[1;31m Traceback (most recent call last):\n", + "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/manager.py\", line 365, in add_extension\n", + "\u001b[1;31m extpkg = ExtensionPackage(name=extension_name, enabled=enabled)\n", + "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/manager.py\", line 219, in __init__\n", + "\u001b[1;31m self._load_metadata()\n", + "\u001b[1;31m ~~~~~~~~~~~~~~~~~~~^^\n", + "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/manager.py\", line 228, in _load_metadata\n", + "\u001b[1;31m self.module, self.metadata = get_metadata(name, logger=self.log)\n", + "\u001b[1;31m ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^\n", + "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/utils.py\", line 76, in get_metadata\n", + "\u001b[1;31m return module, module._jupyter_server_extension_points()\n", + "\u001b[1;31m ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^\n", + "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/__init__.py\", line 20, in _jupyter_server_extension_points\n", + "\u001b[1;31m from .labapp import LabApp\n", + "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/labapp.py\", line 46, in \n", + "\u001b[1;31m from .extensions import MANAGERS as EXT_MANAGERS\n", + "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/extensions/__init__.py\", line 12, in \n", + "\u001b[1;31m from .pypi import PyPIExtensionManager\n", + "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/extensions/pypi.py\", line 76, in \n", + "\u001b[1;31m \"http://\": httpx.AsyncHTTPTransport(proxy=http_proxy_url),\n", + "\u001b[1;31m ~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^\n", + "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/httpx/_transports/default.py\", line 296, in __init__\n", + "\u001b[1;31m proxy = Proxy(url=proxy) if isinstance(proxy, (str, URL)) else proxy\n", + "\u001b[1;31m ~~~~~^^^^^^^^^^^\n", + "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/httpx/_config.py\", line 214, in __init__\n", + "\u001b[1;31m raise ValueError(f\"Unknown scheme for proxy URL {url!r}\")\n", + "\u001b[1;31m ValueError: Unknown scheme for proxy URL URL('socks://localhost:1081')\n", + "\u001b[1;31m[I 2026-05-30 19:32:44.469 ServerApp] jupyter_lsp | extension was successfully linked.\n", + "\u001b[1;31m[I 2026-05-30 19:32:44.478 ServerApp] jupyter_server_terminals | extension was successfully linked.\n", + "\u001b[1;31m[W 2026-05-30 19:32:44.482 JupyterNotebookApp] 'iopub_data_rate_limit' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.\n", + "\u001b[1;31m[W 2026-05-30 19:32:44.487 ServerApp] ServerApp.iopub_data_rate_limit config is deprecated in 2.0. Use ZMQChannelsWebsocketConnection.iopub_data_rate_limit.\n", + "\u001b[1;31m[I 2026-05-30 19:32:44.487 ServerApp] notebook | extension was successfully linked.\n", + "\u001b[1;31m[W 2026-05-30 19:32:45.266 ServerApp] jupyterlab | error adding extension (enabled: True): Unknown scheme for proxy URL URL('socks://localhost:1081')\n", + "\u001b[1;31m Traceback (most recent call last):\n", + "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/manager.py\", line 365, in add_extension\n", + "\u001b[1;31m extpkg = ExtensionPackage(name=extension_name, enabled=enabled)\n", + "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/manager.py\", line 219, in __init__\n", + "\u001b[1;31m self._load_metadata()\n", + "\u001b[1;31m ~~~~~~~~~~~~~~~~~~~^^\n", + "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/manager.py\", line 228, in _load_metadata\n", + "\u001b[1;31m self.module, self.metadata = get_metadata(name, logger=self.log)\n", + "\u001b[1;31m ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^\n", + "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/utils.py\", line 76, in get_metadata\n", + "\u001b[1;31m return module, module._jupyter_server_extension_points()\n", + "\u001b[1;31m ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^\n", + "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/__init__.py\", line 20, in _jupyter_server_extension_points\n", + "\u001b[1;31m from .labapp import LabApp\n", + "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/labapp.py\", line 46, in \n", + "\u001b[1;31m from .extensions import MANAGERS as EXT_MANAGERS\n", + "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/extensions/__init__.py\", line 12, in \n", + "\u001b[1;31m from .pypi import PyPIExtensionManager\n", + "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/extensions/pypi.py\", line 76, in \n", + "\u001b[1;31m \"http://\": httpx.AsyncHTTPTransport(proxy=http_proxy_url),\n", + "\u001b[1;31m ~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^\n", + "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/httpx/_transports/default.py\", line 296, in __init__\n", + "\u001b[1;31m proxy = Proxy(url=proxy) if isinstance(proxy, (str, URL)) else proxy\n", + "\u001b[1;31m ~~~~~^^^^^^^^^^^\n", + "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/httpx/_config.py\", line 214, in __init__\n", + "\u001b[1;31m raise ValueError(f\"Unknown scheme for proxy URL {url!r}\")\n", + "\u001b[1;31m ValueError: Unknown scheme for proxy URL URL('socks://localhost:1081')\n", + "\u001b[1;31m[I 2026-05-30 19:32:45.269 ServerApp] notebook_shim | extension was successfully linked.\n", + "\u001b[1;31m[I 2026-05-30 19:32:45.309 ServerApp] notebook_shim | extension was successfully loaded.\n", + "\u001b[1;31m[I 2026-05-30 19:32:45.327 ServerApp] jupyter_lsp | extension was successfully loaded.\n", + "\u001b[1;31m[I 2026-05-30 19:32:45.328 ServerApp] jupyter_server_terminals | extension was successfully loaded.\n", + "\u001b[1;31m[I 2026-05-30 19:32:45.352 ServerApp] notebook | extension was successfully loaded.\n", + "\u001b[1;31m[I 2026-05-30 19:32:45.356 ServerApp] Serving notebooks from local directory: /home/st/c/iu9-sql-compiler/research\n", + "\u001b[1;31m[I 2026-05-30 19:32:45.357 ServerApp] Jupyter Server 2.17.0 is running at:\n", + "\u001b[1;31m[I 2026-05-30 19:32:45.357 ServerApp] http://localhost:8888/tree?token=e21488556739a4094b439f3d08d3db57aeefebd1d85ae34a\n", + "\u001b[1;31m[I 2026-05-30 19:32:45.357 ServerApp] http://127.0.0.1:8888/tree?token=e21488556739a4094b439f3d08d3db57aeefebd1d85ae34a\n", + "\u001b[1;31m[I 2026-05-30 19:32:45.357 ServerApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).\n", + "\u001b[1;31m[C 2026-05-30 19:32:45.363 ServerApp] \n", + "\u001b[1;31m \n", + "\u001b[1;31m To access the server, open this file in a browser:\n", + "\u001b[1;31m file:///home/st/.local/share/jupyter/runtime/jpserver-116536-open.html\n", + "\u001b[1;31m Or copy and paste one of these URLs:\n", + "\u001b[1;31m http://localhost:8888/tree?token=e21488556739a4094b439f3d08d3db57aeefebd1d85ae34a\n", + "\u001b[1;31m http://127.0.0.1:8888/tree?token=e21488556739a4094b439f3d08d3db57aeefebd1d85ae34a\n", + "\u001b[1;31m[I 2026-05-30 19:32:46.626 ServerApp] Skipped non-installed server(s): basedpyright, bash-language-server, dockerfile-language-server-nodejs, javascript-typescript-langserver, jedi-language-server, julia-language-server, pyrefly, pyright, python-language-server, python-lsp-server, r-languageserver, sql-language-server, texlab, typescript-language-server, unified-language-server, vscode-css-languageserver-bin, vscode-html-languageserver-bin, vscode-json-languageserver-bin, yaml-language-server. \n", "\u001b[1;31mView Jupyter log for further details." ] } ], - "source": [ - "import extractor, ms_sql_server_extractor\n", - "\n", - "from ms_sql_server_extractor import MsSqlServerExtractor\n", - "\n", - "extractor_obj = MsSqlServerExtractor(\n", - " dataset=\"/home/st/c/iu9-sql-compiler/research/datasets/mysql-data/imdb\",\n", - " host=\"localhost\",\n", - " port=1433,\n", - " user=\"sa\",\n", - " password=\"Password123!\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "70ba9fa9", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'extractor_obj' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 9\u001b[39m\n\u001b[32m 1\u001b[39m query = \u001b[33m\"\"\"\u001b[39m\n\u001b[32m 2\u001b[39m \u001b[33mSELECT p.primaryName, t.averageRating\u001b[39m\n\u001b[32m 3\u001b[39m \u001b[33mFROM dbo.TitlePrincipals tp\u001b[39m\n\u001b[32m (...)\u001b[39m\u001b[32m 6\u001b[39m \u001b[33mWHERE t.averageRating IS NOT NULL\u001b[39m\n\u001b[32m 7\u001b[39m \u001b[33m\"\"\"\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m9\u001b[39m plan = \u001b[43mextractor_obj\u001b[49m.extract(query)\n\u001b[32m 10\u001b[39m \u001b[38;5;28mprint\u001b[39m(plan)\n", - "\u001b[31mNameError\u001b[39m: name 'extractor_obj' is not defined" - ] - } - ], - "source": [ - "query = \"\"\"\n", - "SELECT p.primaryName, t.averageRating\n", - "FROM dbo.TitlePrincipals tp\n", - "JOIN dbo.Principals p ON p.principalId = tp.principalId\n", - "JOIN dbo.Titles t ON t.titleId = tp.titleId\n", - "WHERE t.averageRating IS NOT NULL\n", - "\"\"\"\n", - "\n", - "plan = extractor_obj.extract(query)\n", - "print(plan)" - ] + "source": [] } ], "metadata": { diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index 0a31f1d..c32f335 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -111,7 +111,6 @@ int64_t CalcCost(utils::NotNull expr, CardinalityEstimates& cardi return cardinality.GetCardinality(expr->group); }, [&](const physical::NestedLoopJoin& j) -> int64_t { - // Per-tuple work: each rhs tuple compared against every lhs tuple. auto n_l = cardinality.GetCardinality(j.lhs); auto n_r = cardinality.GetCardinality(j.rhs); return n_l * n_r; @@ -122,7 +121,6 @@ int64_t CalcCost(utils::NotNull expr, CardinalityEstimates& cardi return p_l * (1 + p_r); }, [&](const physical::HashJoin& j) -> int64_t { - // Build hash on lhs (n_l inserts), probe with rhs (n_r O(1) lookups). return cardinality.GetCardinality(j.lhs) + cardinality.GetCardinality(j.rhs); }, [&](const physical::Sort& s) -> int64_t { From 07e6a0345212dfd15822687c2178e100b9c8e2f9 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sat, 30 May 2026 20:03:21 +0300 Subject: [PATCH 069/120] Plot benchmarks --- flake.nix | 2 + research/research.ipynb | 1203 ++++++++++++++++++++++++++++++++++++--- 2 files changed, 1116 insertions(+), 89 deletions(-) diff --git a/flake.nix b/flake.nix index f99f3e8..0557b90 100644 --- a/flake.nix +++ b/flake.nix @@ -27,6 +27,8 @@ ps.notebook ps.pymssql ps.pytest + ps.pandas + ps.matplotlib ]); tex = (pkgs.texlive.combine { inherit (pkgs.texlive) scheme-full diff --git a/research/research.ipynb b/research/research.ipynb index 78f91ac..57a595b 100644 --- a/research/research.ipynb +++ b/research/research.ipynb @@ -2,101 +2,1126 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "f336c923", "metadata": {}, "outputs": [ { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mFailed to start the Kernel. \n", - "\u001b[1;31mFailed to find the URL of the launched Jupyter notebook server\n", - "\u001b[1;31m[W 2026-05-30 19:32:44.464 ServerApp] jupyterlab | error adding extension (enabled: True): Unknown scheme for proxy URL URL('socks://localhost:1081')\n", - "\u001b[1;31m Traceback (most recent call last):\n", - "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/manager.py\", line 365, in add_extension\n", - "\u001b[1;31m extpkg = ExtensionPackage(name=extension_name, enabled=enabled)\n", - "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/manager.py\", line 219, in __init__\n", - "\u001b[1;31m self._load_metadata()\n", - "\u001b[1;31m ~~~~~~~~~~~~~~~~~~~^^\n", - "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/manager.py\", line 228, in _load_metadata\n", - "\u001b[1;31m self.module, self.metadata = get_metadata(name, logger=self.log)\n", - "\u001b[1;31m ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^\n", - "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/utils.py\", line 76, in get_metadata\n", - "\u001b[1;31m return module, module._jupyter_server_extension_points()\n", - "\u001b[1;31m ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^\n", - "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/__init__.py\", line 20, in _jupyter_server_extension_points\n", - "\u001b[1;31m from .labapp import LabApp\n", - "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/labapp.py\", line 46, in \n", - "\u001b[1;31m from .extensions import MANAGERS as EXT_MANAGERS\n", - "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/extensions/__init__.py\", line 12, in \n", - "\u001b[1;31m from .pypi import PyPIExtensionManager\n", - "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/extensions/pypi.py\", line 76, in \n", - "\u001b[1;31m \"http://\": httpx.AsyncHTTPTransport(proxy=http_proxy_url),\n", - "\u001b[1;31m ~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^\n", - "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/httpx/_transports/default.py\", line 296, in __init__\n", - "\u001b[1;31m proxy = Proxy(url=proxy) if isinstance(proxy, (str, URL)) else proxy\n", - "\u001b[1;31m ~~~~~^^^^^^^^^^^\n", - "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/httpx/_config.py\", line 214, in __init__\n", - "\u001b[1;31m raise ValueError(f\"Unknown scheme for proxy URL {url!r}\")\n", - "\u001b[1;31m ValueError: Unknown scheme for proxy URL URL('socks://localhost:1081')\n", - "\u001b[1;31m[I 2026-05-30 19:32:44.469 ServerApp] jupyter_lsp | extension was successfully linked.\n", - "\u001b[1;31m[I 2026-05-30 19:32:44.478 ServerApp] jupyter_server_terminals | extension was successfully linked.\n", - "\u001b[1;31m[W 2026-05-30 19:32:44.482 JupyterNotebookApp] 'iopub_data_rate_limit' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.\n", - "\u001b[1;31m[W 2026-05-30 19:32:44.487 ServerApp] ServerApp.iopub_data_rate_limit config is deprecated in 2.0. Use ZMQChannelsWebsocketConnection.iopub_data_rate_limit.\n", - "\u001b[1;31m[I 2026-05-30 19:32:44.487 ServerApp] notebook | extension was successfully linked.\n", - "\u001b[1;31m[W 2026-05-30 19:32:45.266 ServerApp] jupyterlab | error adding extension (enabled: True): Unknown scheme for proxy URL URL('socks://localhost:1081')\n", - "\u001b[1;31m Traceback (most recent call last):\n", - "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/manager.py\", line 365, in add_extension\n", - "\u001b[1;31m extpkg = ExtensionPackage(name=extension_name, enabled=enabled)\n", - "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/manager.py\", line 219, in __init__\n", - "\u001b[1;31m self._load_metadata()\n", - "\u001b[1;31m ~~~~~~~~~~~~~~~~~~~^^\n", - "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/manager.py\", line 228, in _load_metadata\n", - "\u001b[1;31m self.module, self.metadata = get_metadata(name, logger=self.log)\n", - "\u001b[1;31m ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^\n", - "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/utils.py\", line 76, in get_metadata\n", - "\u001b[1;31m return module, module._jupyter_server_extension_points()\n", - "\u001b[1;31m ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^\n", - "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/__init__.py\", line 20, in _jupyter_server_extension_points\n", - "\u001b[1;31m from .labapp import LabApp\n", - "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/labapp.py\", line 46, in \n", - "\u001b[1;31m from .extensions import MANAGERS as EXT_MANAGERS\n", - "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/extensions/__init__.py\", line 12, in \n", - "\u001b[1;31m from .pypi import PyPIExtensionManager\n", - "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/extensions/pypi.py\", line 76, in \n", - "\u001b[1;31m \"http://\": httpx.AsyncHTTPTransport(proxy=http_proxy_url),\n", - "\u001b[1;31m ~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^\n", - "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/httpx/_transports/default.py\", line 296, in __init__\n", - "\u001b[1;31m proxy = Proxy(url=proxy) if isinstance(proxy, (str, URL)) else proxy\n", - "\u001b[1;31m ~~~~~^^^^^^^^^^^\n", - "\u001b[1;31m File \"/nix/store/r0rcvfxvfrf05xr5ya7gxkdwdjsnrvng-python3-3.13.13-env/lib/python3.13/site-packages/httpx/_config.py\", line 214, in __init__\n", - "\u001b[1;31m raise ValueError(f\"Unknown scheme for proxy URL {url!r}\")\n", - "\u001b[1;31m ValueError: Unknown scheme for proxy URL URL('socks://localhost:1081')\n", - "\u001b[1;31m[I 2026-05-30 19:32:45.269 ServerApp] notebook_shim | extension was successfully linked.\n", - "\u001b[1;31m[I 2026-05-30 19:32:45.309 ServerApp] notebook_shim | extension was successfully loaded.\n", - "\u001b[1;31m[I 2026-05-30 19:32:45.327 ServerApp] jupyter_lsp | extension was successfully loaded.\n", - "\u001b[1;31m[I 2026-05-30 19:32:45.328 ServerApp] jupyter_server_terminals | extension was successfully loaded.\n", - "\u001b[1;31m[I 2026-05-30 19:32:45.352 ServerApp] notebook | extension was successfully loaded.\n", - "\u001b[1;31m[I 2026-05-30 19:32:45.356 ServerApp] Serving notebooks from local directory: /home/st/c/iu9-sql-compiler/research\n", - "\u001b[1;31m[I 2026-05-30 19:32:45.357 ServerApp] Jupyter Server 2.17.0 is running at:\n", - "\u001b[1;31m[I 2026-05-30 19:32:45.357 ServerApp] http://localhost:8888/tree?token=e21488556739a4094b439f3d08d3db57aeefebd1d85ae34a\n", - "\u001b[1;31m[I 2026-05-30 19:32:45.357 ServerApp] http://127.0.0.1:8888/tree?token=e21488556739a4094b439f3d08d3db57aeefebd1d85ae34a\n", - "\u001b[1;31m[I 2026-05-30 19:32:45.357 ServerApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).\n", - "\u001b[1;31m[C 2026-05-30 19:32:45.363 ServerApp] \n", - "\u001b[1;31m \n", - "\u001b[1;31m To access the server, open this file in a browser:\n", - "\u001b[1;31m file:///home/st/.local/share/jupyter/runtime/jpserver-116536-open.html\n", - "\u001b[1;31m Or copy and paste one of these URLs:\n", - "\u001b[1;31m http://localhost:8888/tree?token=e21488556739a4094b439f3d08d3db57aeefebd1d85ae34a\n", - "\u001b[1;31m http://127.0.0.1:8888/tree?token=e21488556739a4094b439f3d08d3db57aeefebd1d85ae34a\n", - "\u001b[1;31m[I 2026-05-30 19:32:46.626 ServerApp] Skipped non-installed server(s): basedpyright, bash-language-server, dockerfile-language-server-nodejs, javascript-typescript-langserver, jedi-language-server, julia-language-server, pyrefly, pyright, python-language-server, python-lsp-server, r-languageserver, sql-language-server, texlab, typescript-language-server, unified-language-server, vscode-css-languageserver-bin, vscode-html-languageserver-bin, vscode-json-languageserver-bin, yaml-language-server. \n", - "\u001b[1;31mView Jupyter log for further details." + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-05-30T19:44:06+03:00\n", + "Running ./build-release/bin/benchmarks\n", + "Run on (8 X 4200 MHz CPU s)\n", + "CPU Caches:\n", + " L1 Data 48 KiB (x4)\n", + " L1 Instruction 32 KiB (x4)\n", + " L2 Unified 1280 KiB (x4)\n", + " L3 Unified 8192 KiB (x1)\n", + "Load Average: 0.78, 1.77, 2.40\n", + "***WARNING*** CPU scaling is enabled, the benchmark real time measurements may be noisy and will incur extra overhead.\n", + "-------------------------------------------------------------------------------------------------------------\n", + "Benchmark Time CPU Iterations UserCounters...\n", + "-------------------------------------------------------------------------------------------------------------\n", + "\u001b[0;32mOperatorCost/SeqScan/1024/real_time \u001b[m\u001b[0;33m 49602 ns 49352 ns \u001b[m\u001b[0;36m 12617\u001b[m model_cost=1.024k\u001b[m output_rows=1.024k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/4096/real_time \u001b[m\u001b[0;33m 217833 ns 215541 ns \u001b[m\u001b[0;36m 3460\u001b[m model_cost=4.096k\u001b[m output_rows=4.096k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/16384/real_time \u001b[m\u001b[0;33m 1000898 ns 992381 ns \u001b[m\u001b[0;36m 643\u001b[m model_cost=16.384k\u001b[m output_rows=16.384k\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/65536/real_time \u001b[m\u001b[0;33m 5791037 ns 5735118 ns \u001b[m\u001b[0;36m 94\u001b[m model_cost=65.536k\u001b[m output_rows=65.536k\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/262144/real_time \u001b[m\u001b[0;33m 26528860 ns 26162456 ns \u001b[m\u001b[0;36m 29\u001b[m model_cost=262.144k\u001b[m output_rows=262.144k\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/1024/real_time \u001b[m\u001b[0;33m 93398 ns 92982 ns \u001b[m\u001b[0;36m 7344\u001b[m model_cost=1.024k\u001b[m output_rows=512\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/4096/real_time \u001b[m\u001b[0;33m 320979 ns 319410 ns \u001b[m\u001b[0;36m 2181\u001b[m model_cost=4.096k\u001b[m output_rows=2.048k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/16384/real_time \u001b[m\u001b[0;33m 1286237 ns 1278126 ns \u001b[m\u001b[0;36m 515\u001b[m model_cost=16.384k\u001b[m output_rows=8.192k\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/65536/real_time \u001b[m\u001b[0;33m 5559294 ns 5526669 ns \u001b[m\u001b[0;36m 118\u001b[m model_cost=65.536k\u001b[m output_rows=32.768k\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/262144/real_time \u001b[m\u001b[0;33m 24117997 ns 23984381 ns \u001b[m\u001b[0;36m 29\u001b[m model_cost=262.144k\u001b[m output_rows=131.072k\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/1024/real_time \u001b[m\u001b[0;33m 122635 ns 122065 ns \u001b[m\u001b[0;36m 5688\u001b[m model_cost=1.024k\u001b[m output_rows=1.024k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/4096/real_time \u001b[m\u001b[0;33m 446143 ns 444091 ns \u001b[m\u001b[0;36m 1570\u001b[m model_cost=4.096k\u001b[m output_rows=4.096k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/16384/real_time \u001b[m\u001b[0;33m 1848225 ns 1837810 ns \u001b[m\u001b[0;36m 296\u001b[m model_cost=16.384k\u001b[m output_rows=16.384k\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/65536/real_time \u001b[m\u001b[0;33m 8299086 ns 8240248 ns \u001b[m\u001b[0;36m 79\u001b[m model_cost=65.536k\u001b[m output_rows=65.536k\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/262144/real_time \u001b[m\u001b[0;33m 39251824 ns 38972836 ns \u001b[m\u001b[0;36m 17\u001b[m model_cost=262.144k\u001b[m output_rows=262.144k\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/1024/real_time \u001b[m\u001b[0;33m 112101 ns 111640 ns \u001b[m\u001b[0;36m 5396\u001b[m model_cost=11.264k\u001b[m output_rows=1.024k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/4096/real_time \u001b[m\u001b[0;33m 654773 ns 650822 ns \u001b[m\u001b[0;36m 883\u001b[m model_cost=53.248k\u001b[m output_rows=4.096k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/16384/real_time \u001b[m\u001b[0;33m 2819925 ns 2803767 ns \u001b[m\u001b[0;36m 242\u001b[m model_cost=245.76k\u001b[m output_rows=16.384k\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/65536/real_time \u001b[m\u001b[0;33m 15388325 ns 15277274 ns \u001b[m\u001b[0;36m 44\u001b[m model_cost=1.11411M\u001b[m output_rows=65.536k\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/262144/real_time \u001b[m\u001b[0;33m 101966347 ns 101224833 ns \u001b[m\u001b[0;36m 7\u001b[m model_cost=4.98074M\u001b[m output_rows=262.144k\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/1024/real_time \u001b[m\u001b[0;33m 360181 ns 358344 ns \u001b[m\u001b[0;36m 2003\u001b[m model_cost=1.024k\u001b[m output_rows=1.024k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/4096/real_time \u001b[m\u001b[0;33m 1595273 ns 1584971 ns \u001b[m\u001b[0;36m 494\u001b[m model_cost=4.096k\u001b[m output_rows=4.096k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/16384/real_time \u001b[m\u001b[0;33m 7530658 ns 7483873 ns \u001b[m\u001b[0;36m 86\u001b[m model_cost=16.384k\u001b[m output_rows=16.384k\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/65536/real_time \u001b[m\u001b[0;33m 63945720 ns 63470390 ns \u001b[m\u001b[0;36m 11\u001b[m model_cost=65.536k\u001b[m output_rows=65.536k\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/262144/real_time \u001b[m\u001b[0;33m 375139164 ns 372296986 ns \u001b[m\u001b[0;36m 2\u001b[m model_cost=262.144k\u001b[m output_rows=262.144k\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/1024/1024/real_time \u001b[m\u001b[0;33m 205582 ns 204648 ns \u001b[m\u001b[0;36m 3372\u001b[m lhs_rows=1.024k\u001b[m model_cost=2.048k\u001b[m output_rows=1.024k\u001b[m rhs_rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/4096/4096/real_time \u001b[m\u001b[0;33m 835751 ns 830190 ns \u001b[m\u001b[0;36m 875\u001b[m lhs_rows=4.096k\u001b[m model_cost=8.192k\u001b[m output_rows=4.096k\u001b[m rhs_rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/16384/16384/real_time \u001b[m\u001b[0;33m 3621875 ns 3595669 ns \u001b[m\u001b[0;36m 213\u001b[m lhs_rows=16.384k\u001b[m model_cost=32.768k\u001b[m output_rows=16.384k\u001b[m rhs_rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/65536/65536/real_time \u001b[m\u001b[0;33m 17024603 ns 16899541 ns \u001b[m\u001b[0;36m 39\u001b[m lhs_rows=65.536k\u001b[m model_cost=131.072k\u001b[m output_rows=65.536k\u001b[m rhs_rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/128/128/real_time \u001b[m\u001b[0;33m 1354150 ns 1347082 ns \u001b[m\u001b[0;36m 513\u001b[m lhs_rows=128\u001b[m model_cost=16.384k\u001b[m output_rows=128\u001b[m rhs_rows=128\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/256/256/real_time \u001b[m\u001b[0;33m 4866823 ns 4842638 ns \u001b[m\u001b[0;36m 144\u001b[m lhs_rows=256\u001b[m model_cost=65.536k\u001b[m output_rows=256\u001b[m rhs_rows=256\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/512/512/real_time \u001b[m\u001b[0;33m 18213617 ns 18143285 ns \u001b[m\u001b[0;36m 38\u001b[m lhs_rows=512\u001b[m model_cost=262.144k\u001b[m output_rows=512\u001b[m rhs_rows=512\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/1024/1024/real_time \u001b[m\u001b[0;33m 71634074 ns 71360157 ns \u001b[m\u001b[0;36m 10\u001b[m lhs_rows=1.024k\u001b[m model_cost=1.04858M\u001b[m output_rows=1.024k\u001b[m rhs_rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/64/64/real_time \u001b[m\u001b[0;33m 365470 ns 358505 ns \u001b[m\u001b[0;36m 1931\u001b[m lhs_rows=64\u001b[m model_cost=2\u001b[m output_rows=4.096k\u001b[m rhs_rows=64\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/128/128/real_time \u001b[m\u001b[0;33m 1144605 ns 1133688 ns \u001b[m\u001b[0;36m 590\u001b[m lhs_rows=128\u001b[m model_cost=2\u001b[m output_rows=16.384k\u001b[m rhs_rows=128\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/256/256/real_time \u001b[m\u001b[0;33m 5925916 ns 5857764 ns \u001b[m\u001b[0;36m 106\u001b[m lhs_rows=256\u001b[m model_cost=2\u001b[m output_rows=65.536k\u001b[m rhs_rows=256\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/512/512/real_time \u001b[m\u001b[0;33m 26708787 ns 26467263 ns \u001b[m\u001b[0;36m 26\u001b[m lhs_rows=512\u001b[m model_cost=2\u001b[m output_rows=262.144k\u001b[m rhs_rows=512\u001b[m\n", + "\u001b[m" ] } ], - "source": [] + "source": [ + "!cd ~/c/iu9-sql-compiler/ && ./build-release/bin/benchmarks --benchmark_filter='OperatorCost*' --benchmark_out=/tmp/operator-cost.json --benchmark_out_format=json" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "fb1ddd67", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-rw-r--r-- 1 st users 22146 May 30 19:44 operator-cost.json\n", + "-rw-r--r-- 1 st users 2010 May 30 19:02 operator-cost-smoke.json\n" + ] + } + ], + "source": [ + "!ls -l /tmp | grep operator-cost" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2d033649", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'context': {'date': '2026-05-30T19:44:06+03:00',\n", + " 'host_name': 'nixos',\n", + " 'executable': './build-release/bin/benchmarks',\n", + " 'num_cpus': 8,\n", + " 'mhz_per_cpu': 4200,\n", + " 'cpu_scaling_enabled': True,\n", + " 'caches': [{'type': 'Data', 'level': 1, 'size': 49152, 'num_sharing': 2},\n", + " {'type': 'Instruction', 'level': 1, 'size': 32768, 'num_sharing': 2},\n", + " {'type': 'Unified', 'level': 2, 'size': 1310720, 'num_sharing': 2},\n", + " {'type': 'Unified', 'level': 3, 'size': 8388608, 'num_sharing': 8}],\n", + " 'load_avg': [0.782715, 1.77148, 2.39893],\n", + " 'library_version': 'v1.9.0',\n", + " 'library_build_type': 'release',\n", + " 'json_schema_version': 1},\n", + " 'benchmarks': [{'name': 'OperatorCost/SeqScan/1024/real_time',\n", + " 'family_index': 0,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCost/SeqScan/1024/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 12617,\n", + " 'real_time': 49602.278037666736,\n", + " 'cpu_time': 49352.30649124199,\n", + " 'time_unit': 'ns',\n", + " 'model_cost': 1024.0,\n", + " 'output_rows': 1024.0,\n", + " 'rows': 1024.0},\n", + " {'name': 'OperatorCost/SeqScan/4096/real_time',\n", + " 'family_index': 1,\n", + " 'per_family_instance_index': 0,\n", + " 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'OperatorCost/Aggregation/16384/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 86,\n", + " 'real_time': 7530658.232585559,\n", + " 'cpu_time': 7483872.790697673,\n", + " 'time_unit': 'ns',\n", + " 'model_cost': 16384.0,\n", + " 'output_rows': 16384.0,\n", + " 'rows': 16384.0},\n", + " {'name': 'OperatorCost/Aggregation/65536/real_time',\n", + " 'family_index': 23,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCost/Aggregation/65536/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 11,\n", + " 'real_time': 63945720.36383165,\n", + " 'cpu_time': 63470390.27272736,\n", + " 'time_unit': 'ns',\n", + " 'model_cost': 65536.0,\n", + " 'output_rows': 65536.0,\n", + " 'rows': 65536.0},\n", + " {'name': 'OperatorCost/Aggregation/262144/real_time',\n", + " 'family_index': 24,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCost/Aggregation/262144/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 2,\n", + " 'real_time': 375139163.5014443,\n", + " 'cpu_time': 372296986.0000003,\n", + " 'time_unit': 'ns',\n", + " 'model_cost': 262144.0,\n", + " 'output_rows': 262144.0,\n", + " 'rows': 262144.0},\n", + " {'name': 'OperatorCost/HashJoin/1024/1024/real_time',\n", + " 'family_index': 25,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCost/HashJoin/1024/1024/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 3372,\n", + " 'real_time': 205581.95996281473,\n", + " 'cpu_time': 204647.6450177935,\n", + " 'time_unit': 'ns',\n", + " 'lhs_rows': 1024.0,\n", + " 'model_cost': 2048.0,\n", + " 'output_rows': 1024.0,\n", + " 'rhs_rows': 1024.0},\n", + " {'name': 'OperatorCost/HashJoin/4096/4096/real_time',\n", + " 'family_index': 26,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCost/HashJoin/4096/4096/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 875,\n", + " 'real_time': 835751.0091404298,\n", + " 'cpu_time': 830190.1679999974,\n", + " 'time_unit': 'ns',\n", + " 'lhs_rows': 4096.0,\n", + " 'model_cost': 8192.0,\n", + " 'output_rows': 4096.0,\n", + " 'rhs_rows': 4096.0},\n", + " {'name': 'OperatorCost/HashJoin/16384/16384/real_time',\n", + " 'family_index': 27,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCost/HashJoin/16384/16384/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 213,\n", + " 'real_time': 3621875.4178325166,\n", + " 'cpu_time': 3595668.521126762,\n", + " 'time_unit': 'ns',\n", + " 'lhs_rows': 16384.0,\n", + " 'model_cost': 32768.0,\n", + " 'output_rows': 16384.0,\n", + " 'rhs_rows': 16384.0},\n", + " {'name': 'OperatorCost/HashJoin/65536/65536/real_time',\n", + " 'family_index': 28,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCost/HashJoin/65536/65536/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 39,\n", + " 'real_time': 17024603.33334061,\n", + " 'cpu_time': 16899541.128205113,\n", + " 'time_unit': 'ns',\n", + " 'lhs_rows': 65536.0,\n", + " 'model_cost': 131072.0,\n", + " 'output_rows': 65536.0,\n", + " 'rhs_rows': 65536.0},\n", + " {'name': 'OperatorCost/NestedLoopJoin/128/128/real_time',\n", + " 'family_index': 29,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCost/NestedLoopJoin/128/128/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 513,\n", + " 'real_time': 1354149.7193008196,\n", + " 'cpu_time': 1347082.3060428796,\n", + " 'time_unit': 'ns',\n", + " 'lhs_rows': 128.0,\n", + " 'model_cost': 16384.0,\n", + " 'output_rows': 128.0,\n", + " 'rhs_rows': 128.0},\n", + " {'name': 'OperatorCost/NestedLoopJoin/256/256/real_time',\n", + " 'family_index': 30,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCost/NestedLoopJoin/256/256/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 144,\n", + " 'real_time': 4866823.090297759,\n", + " 'cpu_time': 4842637.791666653,\n", + " 'time_unit': 'ns',\n", + " 'lhs_rows': 256.0,\n", + " 'model_cost': 65536.0,\n", + " 'output_rows': 256.0,\n", + " 'rhs_rows': 256.0},\n", + " {'name': 'OperatorCost/NestedLoopJoin/512/512/real_time',\n", + " 'family_index': 31,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCost/NestedLoopJoin/512/512/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 38,\n", + " 'real_time': 18213616.658038903,\n", + " 'cpu_time': 18143284.894736815,\n", + " 'time_unit': 'ns',\n", + " 'lhs_rows': 512.0,\n", + " 'model_cost': 262144.0,\n", + " 'output_rows': 512.0,\n", + " 'rhs_rows': 512.0},\n", + " {'name': 'OperatorCost/NestedLoopJoin/1024/1024/real_time',\n", + " 'family_index': 32,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCost/NestedLoopJoin/1024/1024/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 10,\n", + " 'real_time': 71634074.20011935,\n", + " 'cpu_time': 71360157.29999982,\n", + " 'time_unit': 'ns',\n", + " 'lhs_rows': 1024.0,\n", + " 'model_cost': 1048576.0,\n", + " 'output_rows': 1024.0,\n", + " 'rhs_rows': 1024.0},\n", + " {'name': 'OperatorCost/NestedLoopCrossJoin/64/64/real_time',\n", + " 'family_index': 33,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCost/NestedLoopCrossJoin/64/64/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 1931,\n", + " 'real_time': 365470.0756105372,\n", + " 'cpu_time': 358505.36147074035,\n", + " 'time_unit': 'ns',\n", + " 'lhs_rows': 64.0,\n", + " 'model_cost': 2.0,\n", + " 'output_rows': 4096.0,\n", + " 'rhs_rows': 64.0},\n", + " {'name': 'OperatorCost/NestedLoopCrossJoin/128/128/real_time',\n", + " 'family_index': 34,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCost/NestedLoopCrossJoin/128/128/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 590,\n", + " 'real_time': 1144605.2237307273,\n", + " 'cpu_time': 1133687.6745762767,\n", + " 'time_unit': 'ns',\n", + " 'lhs_rows': 128.0,\n", + " 'model_cost': 2.0,\n", + " 'output_rows': 16384.0,\n", + " 'rhs_rows': 128.0},\n", + " {'name': 'OperatorCost/NestedLoopCrossJoin/256/256/real_time',\n", + " 'family_index': 35,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCost/NestedLoopCrossJoin/256/256/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 106,\n", + " 'real_time': 5925916.377339679,\n", + " 'cpu_time': 5857763.575471662,\n", + " 'time_unit': 'ns',\n", + " 'lhs_rows': 256.0,\n", + " 'model_cost': 2.0,\n", + " 'output_rows': 65536.0,\n", + " 'rhs_rows': 256.0},\n", + " {'name': 'OperatorCost/NestedLoopCrossJoin/512/512/real_time',\n", + " 'family_index': 36,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCost/NestedLoopCrossJoin/512/512/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 26,\n", + " 'real_time': 26708787.384660143,\n", + " 'cpu_time': 26467262.923076782,\n", + " 'time_unit': 'ns',\n", + " 'lhs_rows': 512.0,\n", + " 'model_cost': 2.0,\n", + " 'output_rows': 262144.0,\n", + " 'rhs_rows': 512.0}]}" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import json\n", + "\n", + "results = None\n", + "with open('/tmp/operator-cost.json') as f:\n", + " results = json.load(f)\n", + "\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1e9f5e6f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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operatorleft_rowsright_rowsreal_time_msmodel_cost
20Aggregation1024NaN0.3601811024.0
21Aggregation4096NaN1.5952734096.0
22Aggregation16384NaN7.53065816384.0
23Aggregation65536NaN63.94572065536.0
24Aggregation262144NaN375.139164262144.0
5Filter1024NaN0.0933981024.0
6Filter4096NaN0.3209794096.0
7Filter16384NaN1.28623716384.0
8Filter65536NaN5.55929465536.0
9Filter262144NaN24.117997262144.0
25HashJoin10241024.00.2055822048.0
26HashJoin40964096.00.8357518192.0
27HashJoin1638416384.03.62187532768.0
28HashJoin6553665536.017.024603131072.0
33NestedLoopCrossJoin6464.00.3654702.0
34NestedLoopCrossJoin128128.01.1446052.0
35NestedLoopCrossJoin256256.05.9259162.0
36NestedLoopCrossJoin512512.026.7087872.0
29NestedLoopJoin128128.01.35415016384.0
30NestedLoopJoin256256.04.86682365536.0
31NestedLoopJoin512512.018.213617262144.0
32NestedLoopJoin10241024.071.6340741048576.0
10Projection1024NaN0.1226351024.0
11Projection4096NaN0.4461434096.0
12Projection16384NaN1.84822516384.0
13Projection65536NaN8.29908665536.0
14Projection262144NaN39.251824262144.0
0SeqScan1024NaN0.0496021024.0
1SeqScan4096NaN0.2178334096.0
2SeqScan16384NaN1.00089816384.0
3SeqScan65536NaN5.79103765536.0
4SeqScan262144NaN26.528860262144.0
15Sort1024NaN0.11210111264.0
16Sort4096NaN0.65477353248.0
17Sort16384NaN2.819925245760.0
18Sort65536NaN15.3883251114112.0
19Sort262144NaN101.9663474980736.0
\n", + "" + ], + "text/plain": [ + " operator left_rows right_rows real_time_ms model_cost\n", + "20 Aggregation 1024 NaN 0.360181 1024.0\n", + "21 Aggregation 4096 NaN 1.595273 4096.0\n", + "22 Aggregation 16384 NaN 7.530658 16384.0\n", + "23 Aggregation 65536 NaN 63.945720 65536.0\n", + "24 Aggregation 262144 NaN 375.139164 262144.0\n", + "5 Filter 1024 NaN 0.093398 1024.0\n", + "6 Filter 4096 NaN 0.320979 4096.0\n", + "7 Filter 16384 NaN 1.286237 16384.0\n", + "8 Filter 65536 NaN 5.559294 65536.0\n", + "9 Filter 262144 NaN 24.117997 262144.0\n", + "25 HashJoin 1024 1024.0 0.205582 2048.0\n", + "26 HashJoin 4096 4096.0 0.835751 8192.0\n", + "27 HashJoin 16384 16384.0 3.621875 32768.0\n", + "28 HashJoin 65536 65536.0 17.024603 131072.0\n", + "33 NestedLoopCrossJoin 64 64.0 0.365470 2.0\n", + "34 NestedLoopCrossJoin 128 128.0 1.144605 2.0\n", + "35 NestedLoopCrossJoin 256 256.0 5.925916 2.0\n", + "36 NestedLoopCrossJoin 512 512.0 26.708787 2.0\n", + "29 NestedLoopJoin 128 128.0 1.354150 16384.0\n", + "30 NestedLoopJoin 256 256.0 4.866823 65536.0\n", + "31 NestedLoopJoin 512 512.0 18.213617 262144.0\n", + "32 NestedLoopJoin 1024 1024.0 71.634074 1048576.0\n", + "10 Projection 1024 NaN 0.122635 1024.0\n", + "11 Projection 4096 NaN 0.446143 4096.0\n", + "12 Projection 16384 NaN 1.848225 16384.0\n", + "13 Projection 65536 NaN 8.299086 65536.0\n", + "14 Projection 262144 NaN 39.251824 262144.0\n", + "0 SeqScan 1024 NaN 0.049602 1024.0\n", + "1 SeqScan 4096 NaN 0.217833 4096.0\n", + "2 SeqScan 16384 NaN 1.000898 16384.0\n", + "3 SeqScan 65536 NaN 5.791037 65536.0\n", + "4 SeqScan 262144 NaN 26.528860 262144.0\n", + "15 Sort 1024 NaN 0.112101 11264.0\n", + "16 Sort 4096 NaN 0.654773 53248.0\n", + "17 Sort 16384 NaN 2.819925 245760.0\n", + "18 Sort 65536 NaN 15.388325 1114112.0\n", + "19 Sort 262144 NaN 101.966347 4980736.0" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "df = pd.DataFrame(results['benchmarks'])\n", + "df = df[df['run_type'] == 'iteration'].copy()\n", + "\n", + "parts = df['name'].str.split('/')\n", + "df['operator'] = parts.str[1]\n", + "df['left_rows'] = parts.str[2].astype(int)\n", + "df['right_rows'] = parts.apply(lambda p: int(p[3]) if len(p) == 5 else None)\n", + "\n", + "df['real_time_ms'] = df['real_time'] / 1e6\n", + "\n", + "df[['operator', 'left_rows', 'right_rows', 'real_time_ms', 'model_cost']].sort_values(['operator', 'left_rows'])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d49c7121", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 5))\n", + "for op, grp in df.groupby('operator'):\n", + " ax.plot(grp['model_cost'], grp['real_time_ms'], marker='o', label=op)\n", + "\n", + "ax.set_xlabel('model_cost')\n", + "ax.set_ylabel('real_time (ms)')\n", + "ax.set_title('Operator cost model vs measured time')\n", + "ax.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] } ], "metadata": { @@ -115,7 +1140,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.7" + "version": "3.13.13" } }, "nbformat": 4, From cd2e6a8bd70a07a218a5984c0762e9acef5317c2 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sun, 31 May 2026 20:44:15 +0300 Subject: [PATCH 070/120] Update coefficients --- benchmarks/operator_cost.cpp | 100 +- research/research.ipynb | 5015 ++++++++++++++++-- src/stewkk/sql/logic/optimizer/optimizer.cpp | 36 +- 3 files changed, 4586 insertions(+), 565 deletions(-) diff --git a/benchmarks/operator_cost.cpp b/benchmarks/operator_cost.cpp index 3e94ff5..c6426f3 100644 --- a/benchmarks/operator_cost.cpp +++ b/benchmarks/operator_cost.cpp @@ -8,6 +8,7 @@ #include #include #include +#include #include #include #include @@ -99,6 +100,15 @@ std::shared_ptr MakeTables(int64_t rows_t, int64_t rows_u = 0 return tables; } +std::shared_ptr MakeSortTables(int64_t rows) { + auto tables = std::make_shared(); + auto table = MakeTable("t", rows); + std::mt19937 generator{0}; + std::shuffle(table.tuples.begin(), table.tuples.end(), generator); + tables->emplace("t", std::move(table)); + return tables; +} + class InMemorySequentialScanner { public: explicit InMemorySequentialScanner(std::shared_ptr tables) @@ -157,26 +167,44 @@ Relation RunPlan(const PhysicalPlanNode& plan, std::shared_ptr(kBufSize) - 1) / static_cast(kBufSize); +int64_t SortModelCost(int64_t rows) { + return 11 * (rows > 1 ? rows * static_cast(std::bit_width(static_cast(rows))) : rows); +} + +int64_t FindSortRowsForTargetCost(int64_t target_cost) { + int64_t best_rows = 1; + for (int64_t rows = 2; SortModelCost(rows) <= 2 * target_cost; ++rows) { + if (std::abs(SortModelCost(rows) - target_cost) + < std::abs(SortModelCost(best_rows) - target_cost)) { + best_rows = rows; + } + } + return best_rows; } -int64_t SortModelCost(int64_t rows) { - return rows > 1 ? rows * static_cast(std::bit_width(static_cast(rows))) : rows; +int64_t DivideRounded(int64_t value, int64_t divisor) { + return std::max(1, (value + divisor / 2) / divisor); +} + +int64_t SqrtRounded(int64_t value) { + const auto floor = static_cast(std::sqrt(value)); + return value - floor * floor < (floor + 1) * (floor + 1) - value ? floor : floor + 1; } void SetUnaryCounters(benchmark::State& state, int64_t rows, int64_t model_cost, - int64_t output_rows) { + int64_t plan_cost, int64_t output_rows) { state.counters["rows"] = static_cast(rows); state.counters["model_cost"] = static_cast(model_cost); + state.counters["plan_cost"] = static_cast(plan_cost); state.counters["output_rows"] = static_cast(output_rows); } void SetBinaryCounters(benchmark::State& state, int64_t lhs_rows, int64_t rhs_rows, - int64_t model_cost, int64_t output_rows) { + int64_t model_cost, int64_t plan_cost, int64_t output_rows) { state.counters["lhs_rows"] = static_cast(lhs_rows); state.counters["rhs_rows"] = static_cast(rhs_rows); state.counters["model_cost"] = static_cast(model_cost); + state.counters["plan_cost"] = static_cast(plan_cost); state.counters["output_rows"] = static_cast(output_rows); } @@ -196,7 +224,7 @@ void BM_OperatorSeqScan(benchmark::State& state) { for (auto _ : state) { benchmark::DoNotOptimize(RunPlan(plan, tables)); } - SetUnaryCounters(state, rows, rows, rows); + SetUnaryCounters(state, rows, 100 * rows, 100 * rows, rows); } void BM_OperatorFilter(benchmark::State& state) { @@ -217,7 +245,7 @@ void BM_OperatorFilter(benchmark::State& state) { const int64_t full_cycles = rows / 1024; const int64_t tail = rows % 1024; const int64_t output_rows = full_cycles * kThreshold + std::min(tail, kThreshold); - SetUnaryCounters(state, rows, rows, output_rows); + SetUnaryCounters(state, rows, 100 * rows, 200 * rows, output_rows); } void BM_OperatorProjection(benchmark::State& state) { @@ -238,15 +266,15 @@ void BM_OperatorProjection(benchmark::State& state) { for (auto _ : state) { benchmark::DoNotOptimize(RunPlan(plan, tables)); } - SetUnaryCounters(state, rows, rows, rows); + SetUnaryCounters(state, rows, 22 * rows, 122 * rows, rows); } void BM_OperatorSort(benchmark::State& state) { const int64_t rows = state.range(0); - auto tables = MakeTables(rows); + auto tables = MakeSortTables(rows); auto plan = PhysicalPlanNode{PhysicalSort{ .source = PlanPtr(Scan("t")), - .keys = SortOrder{{SortKey{.table = "t", .column = "v", .dir = Direction::kAsc}}}, + .keys = SortOrder{{SortKey{.table = "t", .column = "id", .dir = Direction::kAsc}}}, }}; SuppressClog(); @@ -255,7 +283,7 @@ void BM_OperatorSort(benchmark::State& state) { for (auto _ : state) { benchmark::DoNotOptimize(RunPlan(plan, tables)); } - SetUnaryCounters(state, rows, SortModelCost(rows), rows); + SetUnaryCounters(state, rows, SortModelCost(rows), SortModelCost(rows) + 100 * rows, rows); } void BM_OperatorAggregation(benchmark::State& state) { @@ -273,7 +301,7 @@ void BM_OperatorAggregation(benchmark::State& state) { for (auto _ : state) { benchmark::DoNotOptimize(RunPlan(plan, tables)); } - SetUnaryCounters(state, rows, rows, rows); + SetUnaryCounters(state, rows, 510 * rows, 610 * rows, rows); } void BM_OperatorNestedLoopJoin(benchmark::State& state) { @@ -293,7 +321,8 @@ void BM_OperatorNestedLoopJoin(benchmark::State& state) { for (auto _ : state) { benchmark::DoNotOptimize(RunPlan(plan, tables)); } - SetBinaryCounters(state, lhs_rows, rhs_rows, lhs_rows * rhs_rows, + SetBinaryCounters(state, lhs_rows, rhs_rows, 70 * lhs_rows * rhs_rows, + 70 * lhs_rows * rhs_rows + 100 * (lhs_rows + rhs_rows), std::min(lhs_rows, rhs_rows)); } @@ -312,7 +341,8 @@ void BM_OperatorNestedLoopCrossJoin(benchmark::State& state) { for (auto _ : state) { benchmark::DoNotOptimize(RunPlan(plan, tables)); } - SetBinaryCounters(state, lhs_rows, rhs_rows, CeilPages(lhs_rows) * (1 + CeilPages(rhs_rows)), + SetBinaryCounters(state, lhs_rows, rhs_rows, 104 * lhs_rows * rhs_rows, + 104 * lhs_rows * rhs_rows + 100 * (lhs_rows + rhs_rows), lhs_rows * rhs_rows); } @@ -333,16 +363,42 @@ void BM_OperatorHashJoin(benchmark::State& state) { for (auto _ : state) { benchmark::DoNotOptimize(RunPlan(plan, tables)); } - SetBinaryCounters(state, lhs_rows, rhs_rows, lhs_rows + rhs_rows, + SetBinaryCounters(state, lhs_rows, rhs_rows, 69 * (lhs_rows + rhs_rows), + 169 * (lhs_rows + rhs_rows), std::min(lhs_rows, rhs_rows)); } void RegisterUnary(void (*benchmark_fn)(benchmark::State&), const char* name) { - for (int64_t rows : {1024, 4096, 16384, 65536, 262144}) { + for (int64_t rows : {1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072, 262144}) { benchmark::RegisterBenchmark(name, benchmark_fn)->Arg(rows)->UseRealTime(); } } +void RegisterCostMatched(int64_t target_cost) { + const auto prefix = "OperatorCostMatched/target_cost:" + std::to_string(target_cost) + "/"; + const auto register_unary = [&](const char* name, void (*benchmark_fn)(benchmark::State&), + int64_t rows) { + benchmark::RegisterBenchmark((prefix + name).c_str(), benchmark_fn)->Arg(rows)->UseRealTime(); + }; + const auto register_binary = [&](const char* name, void (*benchmark_fn)(benchmark::State&), + int64_t rows) { + benchmark::RegisterBenchmark((prefix + name).c_str(), benchmark_fn) + ->Args({rows, rows}) + ->UseRealTime(); + }; + + register_unary("SeqScan", BM_OperatorSeqScan, DivideRounded(target_cost, 100)); + register_unary("Filter", BM_OperatorFilter, DivideRounded(target_cost, 100)); + register_unary("Projection", BM_OperatorProjection, DivideRounded(target_cost, 22)); + register_unary("Sort", BM_OperatorSort, FindSortRowsForTargetCost(target_cost)); + register_unary("Aggregation", BM_OperatorAggregation, DivideRounded(target_cost, 510)); + register_binary("HashJoin", BM_OperatorHashJoin, DivideRounded(target_cost, 2 * 69)); + register_binary("NestedLoopJoin", BM_OperatorNestedLoopJoin, + SqrtRounded(DivideRounded(target_cost, 70))); + register_binary("NestedLoopCrossJoin", BM_OperatorNestedLoopCrossJoin, + SqrtRounded(DivideRounded(target_cost, 104))); +} + struct OperatorCostRegistration { OperatorCostRegistration() { RegisterUnary(BM_OperatorSeqScan, "OperatorCost/SeqScan"); @@ -351,22 +407,26 @@ struct OperatorCostRegistration { RegisterUnary(BM_OperatorSort, "OperatorCost/Sort"); RegisterUnary(BM_OperatorAggregation, "OperatorCost/Aggregation"); - for (auto size : {1024, 4096, 16384, 65536}) { + for (auto size : {1024, 2048, 4096, 8192, 16384, 32768, 65536}) { benchmark::RegisterBenchmark("OperatorCost/HashJoin", BM_OperatorHashJoin) ->Args({size, size}) ->UseRealTime(); } - for (auto size : {128, 256, 512, 1024}) { + for (auto size : {64, 128, 256, 512, 1024, 2048}) { benchmark::RegisterBenchmark("OperatorCost/NestedLoopJoin", BM_OperatorNestedLoopJoin) ->Args({size, size}) ->UseRealTime(); } - for (auto size : {64, 128, 256, 512}) { + for (auto size : {32, 64, 128, 256, 512, 1024}) { benchmark::RegisterBenchmark("OperatorCost/NestedLoopCrossJoin", BM_OperatorNestedLoopCrossJoin) ->Args({size, size}) ->UseRealTime(); } + + for (auto target_cost : {640000, 1000000, 3240000, 6760000, 12250000, 26010000}) { + RegisterCostMatched(target_cost); + } } }; diff --git a/research/research.ipynb b/research/research.ipynb index 57a595b..b205b4c 100644 --- a/research/research.ipynb +++ b/research/research.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "id": "f336c923", "metadata": {}, "outputs": [ @@ -10,7 +10,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2026-05-30T19:44:06+03:00\n", + "2026-05-31T16:14:53+03:00\n", "Running ./build-release/bin/benchmarks\n", "Run on (8 X 4200 MHz CPU s)\n", "CPU Caches:\n", @@ -18,59 +18,92 @@ " L1 Instruction 32 KiB (x4)\n", " L2 Unified 1280 KiB (x4)\n", " L3 Unified 8192 KiB (x1)\n", - "Load Average: 0.78, 1.77, 2.40\n", - "***WARNING*** CPU scaling is enabled, the benchmark real time measurements may be noisy and will incur extra overhead.\n", - "-------------------------------------------------------------------------------------------------------------\n", - "Benchmark Time CPU Iterations UserCounters...\n", - "-------------------------------------------------------------------------------------------------------------\n", - "\u001b[0;32mOperatorCost/SeqScan/1024/real_time \u001b[m\u001b[0;33m 49602 ns 49352 ns \u001b[m\u001b[0;36m 12617\u001b[m model_cost=1.024k\u001b[m output_rows=1.024k\u001b[m rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/4096/real_time \u001b[m\u001b[0;33m 217833 ns 215541 ns \u001b[m\u001b[0;36m 3460\u001b[m model_cost=4.096k\u001b[m output_rows=4.096k\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/16384/real_time \u001b[m\u001b[0;33m 1000898 ns 992381 ns \u001b[m\u001b[0;36m 643\u001b[m model_cost=16.384k\u001b[m output_rows=16.384k\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/65536/real_time \u001b[m\u001b[0;33m 5791037 ns 5735118 ns \u001b[m\u001b[0;36m 94\u001b[m model_cost=65.536k\u001b[m output_rows=65.536k\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/262144/real_time \u001b[m\u001b[0;33m 26528860 ns 26162456 ns \u001b[m\u001b[0;36m 29\u001b[m model_cost=262.144k\u001b[m output_rows=262.144k\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/1024/real_time \u001b[m\u001b[0;33m 93398 ns 92982 ns \u001b[m\u001b[0;36m 7344\u001b[m model_cost=1.024k\u001b[m output_rows=512\u001b[m rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/4096/real_time \u001b[m\u001b[0;33m 320979 ns 319410 ns \u001b[m\u001b[0;36m 2181\u001b[m model_cost=4.096k\u001b[m output_rows=2.048k\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/16384/real_time \u001b[m\u001b[0;33m 1286237 ns 1278126 ns \u001b[m\u001b[0;36m 515\u001b[m model_cost=16.384k\u001b[m output_rows=8.192k\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/65536/real_time \u001b[m\u001b[0;33m 5559294 ns 5526669 ns \u001b[m\u001b[0;36m 118\u001b[m model_cost=65.536k\u001b[m output_rows=32.768k\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/262144/real_time \u001b[m\u001b[0;33m 24117997 ns 23984381 ns \u001b[m\u001b[0;36m 29\u001b[m model_cost=262.144k\u001b[m output_rows=131.072k\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/1024/real_time \u001b[m\u001b[0;33m 122635 ns 122065 ns \u001b[m\u001b[0;36m 5688\u001b[m model_cost=1.024k\u001b[m output_rows=1.024k\u001b[m rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/4096/real_time \u001b[m\u001b[0;33m 446143 ns 444091 ns \u001b[m\u001b[0;36m 1570\u001b[m model_cost=4.096k\u001b[m output_rows=4.096k\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/16384/real_time \u001b[m\u001b[0;33m 1848225 ns 1837810 ns \u001b[m\u001b[0;36m 296\u001b[m model_cost=16.384k\u001b[m output_rows=16.384k\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/65536/real_time \u001b[m\u001b[0;33m 8299086 ns 8240248 ns \u001b[m\u001b[0;36m 79\u001b[m model_cost=65.536k\u001b[m output_rows=65.536k\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/262144/real_time \u001b[m\u001b[0;33m 39251824 ns 38972836 ns \u001b[m\u001b[0;36m 17\u001b[m model_cost=262.144k\u001b[m output_rows=262.144k\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/1024/real_time \u001b[m\u001b[0;33m 112101 ns 111640 ns \u001b[m\u001b[0;36m 5396\u001b[m model_cost=11.264k\u001b[m output_rows=1.024k\u001b[m rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/4096/real_time \u001b[m\u001b[0;33m 654773 ns 650822 ns \u001b[m\u001b[0;36m 883\u001b[m model_cost=53.248k\u001b[m output_rows=4.096k\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/16384/real_time \u001b[m\u001b[0;33m 2819925 ns 2803767 ns \u001b[m\u001b[0;36m 242\u001b[m model_cost=245.76k\u001b[m output_rows=16.384k\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/65536/real_time \u001b[m\u001b[0;33m 15388325 ns 15277274 ns \u001b[m\u001b[0;36m 44\u001b[m model_cost=1.11411M\u001b[m output_rows=65.536k\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/262144/real_time \u001b[m\u001b[0;33m 101966347 ns 101224833 ns \u001b[m\u001b[0;36m 7\u001b[m model_cost=4.98074M\u001b[m output_rows=262.144k\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/1024/real_time \u001b[m\u001b[0;33m 360181 ns 358344 ns \u001b[m\u001b[0;36m 2003\u001b[m model_cost=1.024k\u001b[m output_rows=1.024k\u001b[m rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/4096/real_time \u001b[m\u001b[0;33m 1595273 ns 1584971 ns \u001b[m\u001b[0;36m 494\u001b[m model_cost=4.096k\u001b[m output_rows=4.096k\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/16384/real_time \u001b[m\u001b[0;33m 7530658 ns 7483873 ns \u001b[m\u001b[0;36m 86\u001b[m model_cost=16.384k\u001b[m output_rows=16.384k\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/65536/real_time \u001b[m\u001b[0;33m 63945720 ns 63470390 ns \u001b[m\u001b[0;36m 11\u001b[m model_cost=65.536k\u001b[m output_rows=65.536k\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/262144/real_time \u001b[m\u001b[0;33m 375139164 ns 372296986 ns \u001b[m\u001b[0;36m 2\u001b[m model_cost=262.144k\u001b[m output_rows=262.144k\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/1024/1024/real_time \u001b[m\u001b[0;33m 205582 ns 204648 ns \u001b[m\u001b[0;36m 3372\u001b[m lhs_rows=1.024k\u001b[m model_cost=2.048k\u001b[m output_rows=1.024k\u001b[m rhs_rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/4096/4096/real_time \u001b[m\u001b[0;33m 835751 ns 830190 ns \u001b[m\u001b[0;36m 875\u001b[m lhs_rows=4.096k\u001b[m model_cost=8.192k\u001b[m output_rows=4.096k\u001b[m rhs_rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/16384/16384/real_time \u001b[m\u001b[0;33m 3621875 ns 3595669 ns \u001b[m\u001b[0;36m 213\u001b[m lhs_rows=16.384k\u001b[m model_cost=32.768k\u001b[m output_rows=16.384k\u001b[m rhs_rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/65536/65536/real_time \u001b[m\u001b[0;33m 17024603 ns 16899541 ns \u001b[m\u001b[0;36m 39\u001b[m lhs_rows=65.536k\u001b[m model_cost=131.072k\u001b[m output_rows=65.536k\u001b[m rhs_rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/128/128/real_time \u001b[m\u001b[0;33m 1354150 ns 1347082 ns \u001b[m\u001b[0;36m 513\u001b[m lhs_rows=128\u001b[m model_cost=16.384k\u001b[m output_rows=128\u001b[m rhs_rows=128\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/256/256/real_time \u001b[m\u001b[0;33m 4866823 ns 4842638 ns \u001b[m\u001b[0;36m 144\u001b[m lhs_rows=256\u001b[m model_cost=65.536k\u001b[m output_rows=256\u001b[m rhs_rows=256\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/512/512/real_time \u001b[m\u001b[0;33m 18213617 ns 18143285 ns \u001b[m\u001b[0;36m 38\u001b[m lhs_rows=512\u001b[m model_cost=262.144k\u001b[m output_rows=512\u001b[m rhs_rows=512\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/1024/1024/real_time \u001b[m\u001b[0;33m 71634074 ns 71360157 ns \u001b[m\u001b[0;36m 10\u001b[m lhs_rows=1.024k\u001b[m model_cost=1.04858M\u001b[m output_rows=1.024k\u001b[m rhs_rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/64/64/real_time \u001b[m\u001b[0;33m 365470 ns 358505 ns \u001b[m\u001b[0;36m 1931\u001b[m lhs_rows=64\u001b[m model_cost=2\u001b[m output_rows=4.096k\u001b[m rhs_rows=64\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/128/128/real_time \u001b[m\u001b[0;33m 1144605 ns 1133688 ns \u001b[m\u001b[0;36m 590\u001b[m lhs_rows=128\u001b[m model_cost=2\u001b[m output_rows=16.384k\u001b[m rhs_rows=128\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/256/256/real_time \u001b[m\u001b[0;33m 5925916 ns 5857764 ns \u001b[m\u001b[0;36m 106\u001b[m lhs_rows=256\u001b[m model_cost=2\u001b[m output_rows=65.536k\u001b[m rhs_rows=256\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/512/512/real_time \u001b[m\u001b[0;33m 26708787 ns 26467263 ns \u001b[m\u001b[0;36m 26\u001b[m lhs_rows=512\u001b[m model_cost=2\u001b[m output_rows=262.144k\u001b[m rhs_rows=512\u001b[m\n", + "Load Average: 4.44, 2.58, 1.61\n", + "***WARNING*** CPU scaling is enabled, the benchmark real time measurements may be noisy and will incur extra overhead.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------------------------------------------------------------------------\n", + "Benchmark Time CPU Iterations UserCounters...\n", + "---------------------------------------------------------------------------------------------------------------\n", + "\u001b[0;32mOperatorCost/SeqScan/1024/real_time \u001b[m\u001b[0;33m 79997 ns 78049 ns \u001b[m\u001b[0;36m 8741\u001b[m model_cost=102.4k\u001b[m output_rows=1.024k\u001b[m plan_cost=102.4k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/2048/real_time \u001b[m\u001b[0;33m 144761 ns 142224 ns \u001b[m\u001b[0;36m 5232\u001b[m model_cost=204.8k\u001b[m output_rows=2.048k\u001b[m plan_cost=204.8k\u001b[m rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/4096/real_time \u001b[m\u001b[0;33m 338605 ns 327024 ns \u001b[m\u001b[0;36m 2056\u001b[m model_cost=409.6k\u001b[m output_rows=4.096k\u001b[m plan_cost=409.6k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/8192/real_time \u001b[m\u001b[0;33m 813515 ns 787366 ns \u001b[m\u001b[0;36m 726\u001b[m model_cost=819.2k\u001b[m output_rows=8.192k\u001b[m plan_cost=819.2k\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/16384/real_time \u001b[m\u001b[0;33m 2055770 ns 1963689 ns \u001b[m\u001b[0;36m 575\u001b[m model_cost=1.6384M\u001b[m output_rows=16.384k\u001b[m plan_cost=1.6384M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/32768/real_time \u001b[m\u001b[0;33m 3635850 ns 3580957 ns \u001b[m\u001b[0;36m 159\u001b[m model_cost=3.2768M\u001b[m output_rows=32.768k\u001b[m plan_cost=3.2768M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/65536/real_time \u001b[m\u001b[0;33m 7848736 ns 7709842 ns \u001b[m\u001b[0;36m 101\u001b[m model_cost=6.5536M\u001b[m output_rows=65.536k\u001b[m plan_cost=6.5536M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/131072/real_time \u001b[m\u001b[0;33m 14549342 ns 14285211 ns \u001b[m\u001b[0;36m 48\u001b[m model_cost=13.1072M\u001b[m output_rows=131.072k\u001b[m plan_cost=13.1072M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/262144/real_time \u001b[m\u001b[0;33m 46805723 ns 44962657 ns \u001b[m\u001b[0;36m 24\u001b[m model_cost=26.2144M\u001b[m output_rows=262.144k\u001b[m plan_cost=26.2144M\u001b[m rows=262.144k\u001b[m\n", + 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ns 26782809 ns \u001b[m\u001b[0;36m 24\u001b[m model_cost=26.2144M\u001b[m output_rows=131.072k\u001b[m plan_cost=52.4288M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/1024/real_time \u001b[m\u001b[0;33m 131940 ns 130699 ns \u001b[m\u001b[0;36m 5179\u001b[m model_cost=22.528k\u001b[m output_rows=1.024k\u001b[m plan_cost=124.928k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/2048/real_time \u001b[m\u001b[0;33m 226190 ns 225141 ns \u001b[m\u001b[0;36m 2989\u001b[m model_cost=45.056k\u001b[m output_rows=2.048k\u001b[m plan_cost=249.856k\u001b[m rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/4096/real_time \u001b[m\u001b[0;33m 514393 ns 509783 ns \u001b[m\u001b[0;36m 1536\u001b[m model_cost=90.112k\u001b[m output_rows=4.096k\u001b[m plan_cost=499.712k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/8192/real_time \u001b[m\u001b[0;33m 945676 ns 940030 ns 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model_cost=585.728k\u001b[m output_rows=4.096k\u001b[m plan_cost=995.328k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/8192/real_time \u001b[m\u001b[0;33m 1840524 ns 1830438 ns \u001b[m\u001b[0;36m 379\u001b[m model_cost=1.26157M\u001b[m output_rows=8.192k\u001b[m plan_cost=2.08077M\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/16384/real_time \u001b[m\u001b[0;33m 4673407 ns 4626060 ns \u001b[m\u001b[0;36m 164\u001b[m model_cost=2.70336M\u001b[m output_rows=16.384k\u001b[m plan_cost=4.34176M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/32768/real_time \u001b[m\u001b[0;33m 10560526 ns 10441042 ns \u001b[m\u001b[0;36m 60\u001b[m model_cost=5.76717M\u001b[m output_rows=32.768k\u001b[m plan_cost=9.04397M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/65536/real_time \u001b[m\u001b[0;33m 27512323 ns 27313637 ns \u001b[m\u001b[0;36m 26\u001b[m model_cost=12.2552M\u001b[m output_rows=65.536k\u001b[m plan_cost=18.8088M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/131072/real_time \u001b[m\u001b[0;33m 76878972 ns 76256341 ns \u001b[m\u001b[0;36m 9\u001b[m model_cost=25.9523M\u001b[m output_rows=131.072k\u001b[m plan_cost=39.0595M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/262144/real_time \u001b[m\u001b[0;33m 210729772 ns 208907082 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=54.7881M\u001b[m output_rows=262.144k\u001b[m plan_cost=81.0025M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/1024/real_time \u001b[m\u001b[0;33m 367198 ns 365161 ns \u001b[m\u001b[0;36m 2007\u001b[m model_cost=522.24k\u001b[m output_rows=1.024k\u001b[m plan_cost=624.64k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/2048/real_time \u001b[m\u001b[0;33m 714949 ns 711449 ns \u001b[m\u001b[0;36m 1030\u001b[m model_cost=1.04448M\u001b[m output_rows=2.048k\u001b[m plan_cost=1.24928M\u001b[m rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/4096/real_time \u001b[m\u001b[0;33m 1378878 ns 1372416 ns \u001b[m\u001b[0;36m 506\u001b[m model_cost=2.08896M\u001b[m output_rows=4.096k\u001b[m plan_cost=2.49856M\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/8192/real_time \u001b[m\u001b[0;33m 2970154 ns 2953446 ns \u001b[m\u001b[0;36m 233\u001b[m model_cost=4.17792M\u001b[m output_rows=8.192k\u001b[m plan_cost=4.99712M\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/16384/real_time \u001b[m\u001b[0;33m 7566779 ns 7487774 ns \u001b[m\u001b[0;36m 94\u001b[m model_cost=8.35584M\u001b[m output_rows=16.384k\u001b[m plan_cost=9.99424M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/32768/real_time \u001b[m\u001b[0;33m 21294147 ns 21136412 ns \u001b[m\u001b[0;36m 28\u001b[m model_cost=16.7117M\u001b[m output_rows=32.768k\u001b[m plan_cost=19.9885M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/65536/real_time \u001b[m\u001b[0;33m 58811825 ns 58334161 ns \u001b[m\u001b[0;36m 11\u001b[m model_cost=33.4234M\u001b[m output_rows=65.536k\u001b[m plan_cost=39.977M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/131072/real_time \u001b[m\u001b[0;33m 145317421 ns 144445410 ns \u001b[m\u001b[0;36m 5\u001b[m model_cost=66.8467M\u001b[m output_rows=131.072k\u001b[m plan_cost=79.9539M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/262144/real_time \u001b[m\u001b[0;33m 341980080 ns 339244361 ns \u001b[m\u001b[0;36m 2\u001b[m model_cost=133.693M\u001b[m output_rows=262.144k\u001b[m plan_cost=159.908M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/1024/1024/real_time \u001b[m\u001b[0;33m 203598 ns 202661 ns \u001b[m\u001b[0;36m 3434\u001b[m lhs_rows=1.024k\u001b[m model_cost=141.312k\u001b[m output_rows=1.024k\u001b[m plan_cost=346.112k\u001b[m rhs_rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/2048/2048/real_time \u001b[m\u001b[0;33m 383152 ns 381209 ns \u001b[m\u001b[0;36m 1850\u001b[m lhs_rows=2.048k\u001b[m model_cost=282.624k\u001b[m output_rows=2.048k\u001b[m plan_cost=692.224k\u001b[m rhs_rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/4096/4096/real_time \u001b[m\u001b[0;33m 762752 ns 757843 ns \u001b[m\u001b[0;36m 903\u001b[m lhs_rows=4.096k\u001b[m model_cost=565.248k\u001b[m output_rows=4.096k\u001b[m plan_cost=1.38445M\u001b[m rhs_rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/8192/8192/real_time \u001b[m\u001b[0;33m 1662913 ns 1651698 ns \u001b[m\u001b[0;36m 461\u001b[m lhs_rows=8.192k\u001b[m model_cost=1.1305M\u001b[m output_rows=8.192k\u001b[m plan_cost=2.7689M\u001b[m rhs_rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/16384/16384/real_time 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plan_cost=299.52k\u001b[m rhs_rows=64\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/128/128/real_time \u001b[m\u001b[0;33m 1326260 ns 1316630 ns \u001b[m\u001b[0;36m 462\u001b[m lhs_rows=128\u001b[m model_cost=1.14688M\u001b[m output_rows=128\u001b[m plan_cost=1.17248M\u001b[m rhs_rows=128\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/256/256/real_time \u001b[m\u001b[0;33m 4755416 ns 4716712 ns \u001b[m\u001b[0;36m 148\u001b[m lhs_rows=256\u001b[m model_cost=4.58752M\u001b[m output_rows=256\u001b[m plan_cost=4.63872M\u001b[m rhs_rows=256\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/512/512/real_time \u001b[m\u001b[0;33m 18824481 ns 18740105 ns \u001b[m\u001b[0;36m 39\u001b[m lhs_rows=512\u001b[m model_cost=18.3501M\u001b[m output_rows=512\u001b[m plan_cost=18.4525M\u001b[m rhs_rows=512\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/1024/1024/real_time \u001b[m\u001b[0;33m 69821057 ns 69534977 ns \u001b[m\u001b[0;36m 10\u001b[m lhs_rows=1.024k\u001b[m model_cost=73.4003M\u001b[m output_rows=1.024k\u001b[m plan_cost=73.6051M\u001b[m rhs_rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/2048/2048/real_time \u001b[m\u001b[0;33m 275065905 ns 274101250 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=2.048k\u001b[m model_cost=293.601M\u001b[m output_rows=2.048k\u001b[m plan_cost=294.011M\u001b[m rhs_rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/32/32/real_time \u001b[m\u001b[0;33m 161658 ns 160319 ns \u001b[m\u001b[0;36m 4263\u001b[m lhs_rows=32\u001b[m model_cost=106.496k\u001b[m output_rows=1.024k\u001b[m plan_cost=112.896k\u001b[m rhs_rows=32\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/64/64/real_time \u001b[m\u001b[0;33m 391432 ns 386106 ns \u001b[m\u001b[0;36m 1896\u001b[m lhs_rows=64\u001b[m model_cost=425.984k\u001b[m output_rows=4.096k\u001b[m plan_cost=438.784k\u001b[m rhs_rows=64\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/128/128/real_time \u001b[m\u001b[0;33m 1644326 ns 1610732 ns \u001b[m\u001b[0;36m 331\u001b[m lhs_rows=128\u001b[m model_cost=1.70394M\u001b[m output_rows=16.384k\u001b[m plan_cost=1.72954M\u001b[m rhs_rows=128\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/256/256/real_time \u001b[m\u001b[0;33m 6994495 ns 6864530 ns \u001b[m\u001b[0;36m 87\u001b[m lhs_rows=256\u001b[m model_cost=6.81574M\u001b[m output_rows=65.536k\u001b[m plan_cost=6.86694M\u001b[m rhs_rows=256\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/512/512/real_time \u001b[m\u001b[0;33m 27484957 ns 27127956 ns \u001b[m\u001b[0;36m 25\u001b[m lhs_rows=512\u001b[m model_cost=27.263M\u001b[m output_rows=262.144k\u001b[m plan_cost=27.3654M\u001b[m rhs_rows=512\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/1024/1024/real_time \u001b[m\u001b[0;33m 130720901 ns 129132758 ns \u001b[m\u001b[0;36m 5\u001b[m lhs_rows=1.024k\u001b[m model_cost=109.052M\u001b[m output_rows=1.04858M\u001b[m plan_cost=109.257M\u001b[m rhs_rows=1.024k\u001b[m\n", "\u001b[m" ] } ], "source": [ - "!cd ~/c/iu9-sql-compiler/ && ./build-release/bin/benchmarks --benchmark_filter='OperatorCost*' --benchmark_out=/tmp/operator-cost.json --benchmark_out_format=json" + "!cd ~/c/iu9-sql-compiler/ && ./build-release/bin/benchmarks --benchmark_filter='^OperatorCost/' --benchmark_out=/tmp/operator-cost.json --benchmark_out_format=json" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "id": "fb1ddd67", "metadata": {}, "outputs": [ @@ -78,7 +111,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "-rw-r--r-- 1 st users 22146 May 30 19:44 operator-cost.json\n", + "-rw-r--r-- 1 st users 40311 May 31 16:16 operator-cost.json\n", + "-rw-r--r-- 1 st users 33223 May 31 16:03 operator-cost-matched-calibrated-smoke.json\n", + "-rw-r--r-- 1 st users 33307 May 31 16:13 operator-cost-matched.json\n", + "-rw-r--r-- 1 st users 6149 May 31 15:08 operator-cost-matched-plan-cost-smoke.json\n", "-rw-r--r-- 1 st users 2010 May 30 19:02 operator-cost-smoke.json\n" ] } @@ -89,14 +125,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "2d033649", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'context': {'date': '2026-05-30T19:44:06+03:00',\n", + "{'context': {'date': '2026-05-31T16:14:53+03:00',\n", " 'host_name': 'nixos',\n", " 'executable': './build-release/bin/benchmarks',\n", " 'num_cpus': 8,\n", @@ -106,7 +142,7 @@ " {'type': 'Instruction', 'level': 1, 'size': 32768, 'num_sharing': 2},\n", " {'type': 'Unified', 'level': 2, 'size': 1310720, 'num_sharing': 2},\n", " {'type': 'Unified', 'level': 3, 'size': 8388608, 'num_sharing': 8}],\n", - " 'load_avg': [0.782715, 1.77148, 2.39893],\n", + " 'load_avg': [4.44287, 2.58252, 1.60791],\n", " 'library_version': 'v1.9.0',\n", " 'library_build_type': 'release',\n", " 'json_schema_version': 1},\n", @@ -118,240 +154,464 @@ " 'repetitions': 1,\n", " 'repetition_index': 0,\n", " 'threads': 1,\n", - " 'iterations': 12617,\n", - " 'real_time': 49602.278037666736,\n", - " 'cpu_time': 49352.30649124199,\n", + " 'iterations': 8741,\n", + " 'real_time': 79996.77325230725,\n", + " 'cpu_time': 78049.49124814094,\n", " 'time_unit': 'ns',\n", - " 'model_cost': 1024.0,\n", + " 'model_cost': 102400.0,\n", " 'output_rows': 1024.0,\n", + " 'plan_cost': 102400.0,\n", " 'rows': 1024.0},\n", - " {'name': 'OperatorCost/SeqScan/4096/real_time',\n", + " {'name': 'OperatorCost/SeqScan/2048/real_time',\n", " 'family_index': 1,\n", " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCost/SeqScan/2048/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 5232,\n", + " 'real_time': 144761.2392951388,\n", + " 'cpu_time': 142223.85569571867,\n", + " 'time_unit': 'ns',\n", + " 'model_cost': 204800.0,\n", + " 'output_rows': 2048.0,\n", + " 'plan_cost': 204800.0,\n", + " 'rows': 2048.0},\n", + " {'name': 'OperatorCost/SeqScan/4096/real_time',\n", + " 'family_index': 2,\n", + " 'per_family_instance_index': 0,\n", " 'run_name': 'OperatorCost/SeqScan/4096/real_time',\n", " 'run_type': 'iteration',\n", " 'repetitions': 1,\n", " 'repetition_index': 0,\n", " 'threads': 1,\n", - " 'iterations': 3460,\n", - " 'real_time': 217833.0014451714,\n", - " 'cpu_time': 215540.5517341041,\n", + " 'iterations': 2056,\n", + " 'real_time': 338605.32199002034,\n", + " 'cpu_time': 327024.20865758776,\n", " 'time_unit': 'ns',\n", - " 'model_cost': 4096.0,\n", + " 'model_cost': 409600.0,\n", " 'output_rows': 4096.0,\n", + " 'plan_cost': 409600.0,\n", " 'rows': 4096.0},\n", + " {'name': 'OperatorCost/SeqScan/8192/real_time',\n", + " 'family_index': 3,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCost/SeqScan/8192/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 726,\n", + " 'real_time': 813515.0275632519,\n", + " 'cpu_time': 787365.6776859501,\n", + " 'time_unit': 'ns',\n", + " 'model_cost': 819200.0,\n", + " 'output_rows': 8192.0,\n", + " 'plan_cost': 819200.0,\n", + " 'rows': 8192.0},\n", " {'name': 'OperatorCost/SeqScan/16384/real_time',\n", - " 'family_index': 2,\n", + " 'family_index': 4,\n", " 'per_family_instance_index': 0,\n", " 'run_name': 'OperatorCost/SeqScan/16384/real_time',\n", " 'run_type': 'iteration',\n", " 'repetitions': 1,\n", " 'repetition_index': 0,\n", " 'threads': 1,\n", - " 'iterations': 643,\n", - " 'real_time': 1000897.6454201117,\n", - " 'cpu_time': 992380.8351477447,\n", + " 'iterations': 575,\n", + " 'real_time': 2055769.5026155156,\n", + " 'cpu_time': 1963688.9999999995,\n", " 'time_unit': 'ns',\n", - " 'model_cost': 16384.0,\n", + " 'model_cost': 1638400.0,\n", " 'output_rows': 16384.0,\n", + " 'plan_cost': 1638400.0,\n", " 'rows': 16384.0},\n", + " {'name': 'OperatorCost/SeqScan/32768/real_time',\n", + " 'family_index': 5,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCost/SeqScan/32768/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 159,\n", + " 'real_time': 3635850.289299112,\n", + " 'cpu_time': 3580956.962264152,\n", + " 'time_unit': 'ns',\n", + " 'model_cost': 3276800.0,\n", + " 'output_rows': 32768.0,\n", + " 'plan_cost': 3276800.0,\n", + " 'rows': 32768.0},\n", " {'name': 'OperatorCost/SeqScan/65536/real_time',\n", - " 'family_index': 3,\n", + " 'family_index': 6,\n", " 'per_family_instance_index': 0,\n", " 'run_name': 'OperatorCost/SeqScan/65536/real_time',\n", " 'run_type': 'iteration',\n", " 'repetitions': 1,\n", " 'repetition_index': 0,\n", " 'threads': 1,\n", - " 'iterations': 94,\n", - " 'real_time': 5791036.819186473,\n", - " 'cpu_time': 5735117.925531914,\n", + " 'iterations': 101,\n", + " 'real_time': 7848735.722813451,\n", + " 'cpu_time': 7709841.534653468,\n", " 'time_unit': 'ns',\n", - " 'model_cost': 65536.0,\n", + " 'model_cost': 6553600.0,\n", " 'output_rows': 65536.0,\n", + " 'plan_cost': 6553600.0,\n", " 'rows': 65536.0},\n", + " {'name': 'OperatorCost/SeqScan/131072/real_time',\n", + " 'family_index': 7,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCost/SeqScan/131072/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 48,\n", + " 'real_time': 14549342.416406339,\n", + " 'cpu_time': 14285210.916666662,\n", + " 'time_unit': 'ns',\n", + " 'model_cost': 13107200.0,\n", + " 'output_rows': 131072.0,\n", + " 'plan_cost': 13107200.0,\n", + " 'rows': 131072.0},\n", " {'name': 'OperatorCost/SeqScan/262144/real_time',\n", - " 'family_index': 4,\n", + " 'family_index': 8,\n", " 'per_family_instance_index': 0,\n", " 'run_name': 'OperatorCost/SeqScan/262144/real_time',\n", " 'run_type': 'iteration',\n", " 'repetitions': 1,\n", " 'repetition_index': 0,\n", " 'threads': 1,\n", - " 'iterations': 29,\n", - " 'real_time': 26528860.482734483,\n", - " 'cpu_time': 26162455.5862069,\n", + " 'iterations': 24,\n", + " 'real_time': 46805722.70869258,\n", + " 'cpu_time': 44962657.16666666,\n", " 'time_unit': 'ns',\n", - " 'model_cost': 262144.0,\n", + " 'model_cost': 26214400.0,\n", " 'output_rows': 262144.0,\n", + " 'plan_cost': 26214400.0,\n", " 'rows': 262144.0},\n", " {'name': 'OperatorCost/Filter/1024/real_time',\n", - " 'family_index': 5,\n", + " 'family_index': 9,\n", " 'per_family_instance_index': 0,\n", " 'run_name': 'OperatorCost/Filter/1024/real_time',\n", " 'run_type': 'iteration',\n", " 'repetitions': 1,\n", " 'repetition_index': 0,\n", " 'threads': 1,\n", - " 'iterations': 7344,\n", - " 'real_time': 93397.88044690267,\n", - " 'cpu_time': 92981.83809912855,\n", + " 'iterations': 6051,\n", + " 'real_time': 110847.93670647572,\n", + " 'cpu_time': 109579.46868286227,\n", " 'time_unit': 'ns',\n", - " 'model_cost': 1024.0,\n", + " 'model_cost': 102400.0,\n", " 'output_rows': 512.0,\n", + " 'plan_cost': 204800.0,\n", " 'rows': 1024.0},\n", + " {'name': 'OperatorCost/Filter/2048/real_time',\n", + " 'family_index': 10,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCost/Filter/2048/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 4178,\n", + " 'real_time': 175850.847057794,\n", + " 'cpu_time': 174124.4133556728,\n", + " 'time_unit': 'ns',\n", + " 'model_cost': 204800.0,\n", + " 'output_rows': 1024.0,\n", + " 'plan_cost': 409600.0,\n", + " 'rows': 2048.0},\n", " {'name': 'OperatorCost/Filter/4096/real_time',\n", - " 'family_index': 6,\n", + " 'family_index': 11,\n", " 'per_family_instance_index': 0,\n", " 'run_name': 'OperatorCost/Filter/4096/real_time',\n", " 'run_type': 'iteration',\n", " 'repetitions': 1,\n", " 'repetition_index': 0,\n", " 'threads': 1,\n", - " 'iterations': 2181,\n", - " 'real_time': 320979.35579735413,\n", - " 'cpu_time': 319409.67125171947,\n", + " 'iterations': 2166,\n", + " 'real_time': 315093.1071093506,\n", + " 'cpu_time': 312199.11911357404,\n", " 'time_unit': 'ns',\n", - " 'model_cost': 4096.0,\n", + " 'model_cost': 409600.0,\n", " 'output_rows': 2048.0,\n", + " 'plan_cost': 819200.0,\n", " 'rows': 4096.0},\n", + " {'name': 'OperatorCost/Filter/8192/real_time',\n", + " 'family_index': 12,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCost/Filter/8192/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 1078,\n", + " 'real_time': 673538.2050130877,\n", + " 'cpu_time': 667478.5055658632,\n", + " 'time_unit': 'ns',\n", + " 'model_cost': 819200.0,\n", + " 'output_rows': 4096.0,\n", + " 'plan_cost': 1638400.0,\n", + " 'rows': 8192.0},\n", " {'name': 'OperatorCost/Filter/16384/real_time',\n", - " 'family_index': 7,\n", + " 'family_index': 13,\n", " 'per_family_instance_index': 0,\n", " 'run_name': 'OperatorCost/Filter/16384/real_time',\n", " 'run_type': 'iteration',\n", " 'repetitions': 1,\n", " 'repetition_index': 0,\n", " 'threads': 1,\n", - " 'iterations': 515,\n", - " 'real_time': 1286236.7009731615,\n", - " 'cpu_time': 1278125.551456312,\n", + " 'iterations': 456,\n", + " 'real_time': 1303801.1556841012,\n", + " 'cpu_time': 1294735.6688596508,\n", " 'time_unit': 'ns',\n", - " 'model_cost': 16384.0,\n", + " 'model_cost': 1638400.0,\n", " 'output_rows': 8192.0,\n", + " 'plan_cost': 3276800.0,\n", " 'rows': 16384.0},\n", + " {'name': 'OperatorCost/Filter/32768/real_time',\n", + " 'family_index': 14,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCost/Filter/32768/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 256,\n", + " 'real_time': 2952031.402287503,\n", + " 'cpu_time': 2928449.1679687495,\n", + " 'time_unit': 'ns',\n", + " 'model_cost': 3276800.0,\n", + " 'output_rows': 16384.0,\n", + " 'plan_cost': 6553600.0,\n", + " 'rows': 32768.0},\n", " {'name': 'OperatorCost/Filter/65536/real_time',\n", - " 'family_index': 8,\n", + " 'family_index': 15,\n", " 'per_family_instance_index': 0,\n", " 'run_name': 'OperatorCost/Filter/65536/real_time',\n", " 'run_type': 'iteration',\n", " 'repetitions': 1,\n", " 'repetition_index': 0,\n", " 'threads': 1,\n", - " 'iterations': 118,\n", - " 'real_time': 5559294.127108888,\n", - " 'cpu_time': 5526669.3644067785,\n", + " 'iterations': 102,\n", + " 'real_time': 7472368.745177792,\n", + " 'cpu_time': 7365271.343137254,\n", " 'time_unit': 'ns',\n", - " 'model_cost': 65536.0,\n", + " 'model_cost': 6553600.0,\n", " 'output_rows': 32768.0,\n", + " 'plan_cost': 13107200.0,\n", " 'rows': 65536.0},\n", + " {'name': 'OperatorCost/Filter/131072/real_time',\n", + " 'family_index': 16,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCost/Filter/131072/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 58,\n", + " 'real_time': 14015431.344762027,\n", + " 'cpu_time': 13847255.206896517,\n", + " 'time_unit': 'ns',\n", + " 'model_cost': 13107200.0,\n", + " 'output_rows': 65536.0,\n", + " 'plan_cost': 26214400.0,\n", + " 'rows': 131072.0},\n", " {'name': 'OperatorCost/Filter/262144/real_time',\n", - " 'family_index': 9,\n", + " 'family_index': 17,\n", " 'per_family_instance_index': 0,\n", " 'run_name': 'OperatorCost/Filter/262144/real_time',\n", " 'run_type': 'iteration',\n", " 'repetitions': 1,\n", " 'repetition_index': 0,\n", " 'threads': 1,\n", - " 'iterations': 29,\n", - " 'real_time': 24117996.82760892,\n", - " 'cpu_time': 23984380.5172414,\n", + " 'iterations': 24,\n", + " 'real_time': 27073700.12528069,\n", + " 'cpu_time': 26782809.49999993,\n", " 'time_unit': 'ns',\n", - " 'model_cost': 262144.0,\n", + " 'model_cost': 26214400.0,\n", " 'output_rows': 131072.0,\n", + " 'plan_cost': 52428800.0,\n", " 'rows': 262144.0},\n", " {'name': 'OperatorCost/Projection/1024/real_time',\n", - " 'family_index': 10,\n", + " 'family_index': 18,\n", " 'per_family_instance_index': 0,\n", " 'run_name': 'OperatorCost/Projection/1024/real_time',\n", " 'run_type': 'iteration',\n", " 'repetitions': 1,\n", " 'repetition_index': 0,\n", " 'threads': 1,\n", - " 'iterations': 5688,\n", - " 'real_time': 122634.84564069705,\n", - " 'cpu_time': 122065.49367088616,\n", + " 'iterations': 5179,\n", + " 'real_time': 131939.9635067602,\n", + " 'cpu_time': 130698.92662676195,\n", " 'time_unit': 'ns',\n", - " 'model_cost': 1024.0,\n", + " 'model_cost': 22528.0,\n", " 'output_rows': 1024.0,\n", + " 'plan_cost': 124928.0,\n", " 'rows': 1024.0},\n", + " {'name': 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0.112101\n", - " 11264.0\n", + " 4.673407\n", + " 2703360.0\n", " \n", " \n", - " 16\n", + " 32\n", " Sort\n", - " 4096\n", + " 32768\n", " NaN\n", - " 0.654773\n", - " 53248.0\n", + " 10.560526\n", + " 5767168.0\n", " \n", " \n", - " 17\n", + " 33\n", " Sort\n", - " 16384\n", + " 65536\n", " NaN\n", - " 2.819925\n", - " 245760.0\n", + " 27.512323\n", + " 12255232.0\n", " \n", " \n", - " 18\n", + " 34\n", " Sort\n", - " 65536\n", + " 131072\n", " NaN\n", - " 15.388325\n", - " 1114112.0\n", + " 76.878972\n", + " 25952256.0\n", " \n", " \n", - " 19\n", + " 35\n", " Sort\n", " 262144\n", " NaN\n", - " 101.966347\n", - " 4980736.0\n", + " 210.729772\n", + " 54788096.0\n", " \n", " \n", "\n", + "

64 rows × 5 columns

\n", "" ], "text/plain": [ - " operator left_rows right_rows real_time_ms model_cost\n", - "20 Aggregation 1024 NaN 0.360181 1024.0\n", - "21 Aggregation 4096 NaN 1.595273 4096.0\n", - "22 Aggregation 16384 NaN 7.530658 16384.0\n", - "23 Aggregation 65536 NaN 63.945720 65536.0\n", - "24 Aggregation 262144 NaN 375.139164 262144.0\n", - "5 Filter 1024 NaN 0.093398 1024.0\n", - "6 Filter 4096 NaN 0.320979 4096.0\n", - "7 Filter 16384 NaN 1.286237 16384.0\n", - "8 Filter 65536 NaN 5.559294 65536.0\n", - "9 Filter 262144 NaN 24.117997 262144.0\n", - "25 HashJoin 1024 1024.0 0.205582 2048.0\n", - "26 HashJoin 4096 4096.0 0.835751 8192.0\n", - "27 HashJoin 16384 16384.0 3.621875 32768.0\n", - "28 HashJoin 65536 65536.0 17.024603 131072.0\n", - "33 NestedLoopCrossJoin 64 64.0 0.365470 2.0\n", - "34 NestedLoopCrossJoin 128 128.0 1.144605 2.0\n", - "35 NestedLoopCrossJoin 256 256.0 5.925916 2.0\n", - "36 NestedLoopCrossJoin 512 512.0 26.708787 2.0\n", - "29 NestedLoopJoin 128 128.0 1.354150 16384.0\n", - "30 NestedLoopJoin 256 256.0 4.866823 65536.0\n", - "31 NestedLoopJoin 512 512.0 18.213617 262144.0\n", - "32 NestedLoopJoin 1024 1024.0 71.634074 1048576.0\n", - "10 Projection 1024 NaN 0.122635 1024.0\n", - "11 Projection 4096 NaN 0.446143 4096.0\n", - "12 Projection 16384 NaN 1.848225 16384.0\n", - "13 Projection 65536 NaN 8.299086 65536.0\n", - "14 Projection 262144 NaN 39.251824 262144.0\n", - "0 SeqScan 1024 NaN 0.049602 1024.0\n", - "1 SeqScan 4096 NaN 0.217833 4096.0\n", - "2 SeqScan 16384 NaN 1.000898 16384.0\n", - "3 SeqScan 65536 NaN 5.791037 65536.0\n", - "4 SeqScan 262144 NaN 26.528860 262144.0\n", - "15 Sort 1024 NaN 0.112101 11264.0\n", - "16 Sort 4096 NaN 0.654773 53248.0\n", - "17 Sort 16384 NaN 2.819925 245760.0\n", - "18 Sort 65536 NaN 15.388325 1114112.0\n", - "19 Sort 262144 NaN 101.966347 4980736.0" + " operator left_rows right_rows real_time_ms model_cost\n", + "36 Aggregation 1024 NaN 0.367198 522240.0\n", + "37 Aggregation 2048 NaN 0.714949 1044480.0\n", + "38 Aggregation 4096 NaN 1.378878 2088960.0\n", + "39 Aggregation 8192 NaN 2.970154 4177920.0\n", + "40 Aggregation 16384 NaN 7.566779 8355840.0\n", + ".. ... ... ... ... ...\n", + "31 Sort 16384 NaN 4.673407 2703360.0\n", + "32 Sort 32768 NaN 10.560526 5767168.0\n", + "33 Sort 65536 NaN 27.512323 12255232.0\n", + "34 Sort 131072 NaN 76.878972 25952256.0\n", + "35 Sort 262144 NaN 210.729772 54788096.0\n", + "\n", + "[64 rows x 5 columns]" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -1078,6 +1359,8 @@ "source": [ "import pandas as pd\n", "\n", + "assert results\n", + "\n", "df = pd.DataFrame(results['benchmarks'])\n", "df = df[df['run_type'] == 'iteration'].copy()\n", "\n", @@ -1093,13 +1376,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "d49c7121", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1122,6 +1405,3686 @@ "plt.tight_layout()\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "dfeea54d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "pivot = df.set_index(['operator', 'left_rows'])[['cpu_time', 'model_cost']]\n", + "\n", + "all_counts = sorted(df['left_rows'].unique())\n", + "valid_counts = [rc for rc in all_counts\n", + " if pivot.index.get_level_values('left_rows').isin([rc]).sum() >= 2]\n", + "\n", + "ncols = 2\n", + "nrows = (len(valid_counts) + ncols - 1) // ncols\n", + "fig, axes = plt.subplots(nrows, ncols, figsize=(5 * ncols, 4.5 * nrows), squeeze=False)\n", + "\n", + "for idx, rc in enumerate(valid_counts):\n", + " ax = axes[idx // ncols][idx % ncols]\n", + " sub = pivot.xs(rc, level='left_rows')\n", + " ops = sub.index.tolist()\n", + " m = len(ops)\n", + "\n", + " mat = np.zeros((m, m))\n", + " for i, a in enumerate(ops):\n", + " for j, b in enumerate(ops):\n", + " if i != j:\n", + " mat[i, j] = (np.log(sub.loc[a, 'cpu_time']) - np.log(sub.loc[b, 'cpu_time']) -\n", + " (np.log(sub.loc[a, 'model_cost']) - np.log(sub.loc[b, 'model_cost'])))\n", + "\n", + " vmax = np.abs(mat).max() or 1.0\n", + " im = ax.imshow(mat, cmap='coolwarm', vmin=-vmax, vmax=vmax)\n", + " ax.set_xticks(range(m)); ax.set_xticklabels(ops, rotation=45, ha='right', fontsize=8)\n", + " ax.set_yticks(range(m)); ax.set_yticklabels(ops, fontsize=8)\n", + " ax.set_title(f'rows = {rc:,}', fontsize=10)\n", + " plt.colorbar(im, ax=ax, shrink=0.8)\n", + " for i in range(m):\n", + " for j in range(m):\n", + " ax.text(j, i, f'{mat[i,j]:.2f}', ha='center', va='center', fontsize=7,\n", + " color='black' if abs(mat[i,j]) < vmax * 0.6 else 'white')\n", + "\n", + "for idx in range(len(valid_counts), nrows * ncols):\n", + " axes[idx // ncols][idx % ncols].set_visible(False)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c13aca01", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NestedLoopCrossJoinNaNNaNNaNNaN-0.527NaNNaNNaNNaNNaNNaNNaNNaN
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SeqScanNaNNaNNaNNaN-0.086-0.019-0.195-0.307-0.2910.1460.3940.6840.392
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FilterAggregationNaNNaNNaNNaN0.4260.2220.1490.142-0.126-0.347-0.440-0.716-0.910
HashJoinNaNNaNNaNNaN-0.293-0.461-0.565-0.584-0.799-0.695-0.503NaNNaN
NestedLoopCrossJoinNaNNaNNaNNaN-0.101NaNNaNNaNNaNNaNNaNNaNNaN
NestedLoopJoinNaNNaNNaNNaN0.122-0.094NaNNaNNaNNaNNaNNaNNaN
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SortNaNNaNNaNNaN-0.194-0.406-0.574-0.577-0.773-0.706-0.685-1.023-1.317
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FilterNaNNaNNaNNaN0.2930.4610.5650.5840.7990.6950.503NaNNaN
NestedLoopCrossJoinNaNNaNNaNNaN0.192NaNNaNNaNNaNNaNNaNNaNNaN
NestedLoopJoinNaNNaNNaNNaN0.4150.368NaNNaNNaNNaNNaNNaNNaN
ProjectionNaNNaNNaNNaN-1.398-1.310-1.440-1.273-1.095-1.042-1.019NaNNaN
SeqScanNaNNaNNaNNaN0.6320.6640.5180.4190.3820.4940.458NaNNaN
SortNaNNaNNaNNaN0.0990.056-0.0090.0070.026-0.011-0.181NaNNaN
NestedLoopCrossJoinAggregationNaNNaNNaNNaN0.527NaNNaNNaNNaNNaNNaNNaNNaN
FilterNaNNaNNaNNaN0.101NaNNaNNaNNaNNaNNaNNaNNaN
HashJoinNaNNaNNaNNaN-0.192NaNNaNNaNNaNNaNNaNNaNNaN
NestedLoopJoin-0.559-0.194-0.021-0.0260.223NaNNaNNaNNaNNaNNaNNaNNaN
ProjectionNaNNaNNaNNaN-1.589NaNNaNNaNNaNNaNNaNNaNNaN
SeqScanNaNNaNNaNNaN0.441NaNNaNNaNNaNNaNNaNNaNNaN
SortNaNNaNNaNNaN-0.092NaNNaNNaNNaNNaNNaNNaNNaN
NestedLoopJoinAggregationNaNNaNNaNNaN0.3040.315NaNNaNNaNNaNNaNNaNNaN
FilterNaNNaNNaNNaN-0.1220.094NaNNaNNaNNaNNaNNaNNaN
HashJoinNaNNaNNaNNaN-0.415-0.368NaNNaNNaNNaNNaNNaNNaN
NestedLoopCrossJoin0.5590.1940.0210.026-0.223NaNNaNNaNNaNNaNNaNNaNNaN
ProjectionNaNNaNNaNNaN-1.812-1.678NaNNaNNaNNaNNaNNaNNaN
SeqScanNaNNaNNaNNaN0.2170.296NaNNaNNaNNaNNaNNaNNaN
SortNaNNaNNaNNaN-0.315-0.312NaNNaNNaNNaNNaNNaNNaN
ProjectionAggregationNaNNaNNaNNaN2.1161.9932.1531.9991.7681.3891.0830.8981.023
FilterNaNNaNNaNNaN1.6901.7712.0041.8571.8941.7361.5231.6141.933
HashJoinNaNNaNNaNNaN1.3981.3101.4401.2731.0951.0421.019NaNNaN
NestedLoopCrossJoinNaNNaNNaNNaN1.589NaNNaNNaNNaNNaNNaNNaNNaN
NestedLoopJoinNaNNaNNaNNaN1.8121.678NaNNaNNaNNaNNaNNaNNaN
SeqScanNaNNaNNaNNaN2.0301.9731.9581.6911.4771.5351.4771.5821.415
SortNaNNaNNaNNaN1.4971.3651.4311.2801.1211.0300.8380.5910.616
SeqScanAggregationNaNNaNNaNNaN0.0860.0190.1950.3070.291-0.146-0.394-0.684-0.392
FilterNaNNaNNaNNaN-0.339-0.2020.0460.1650.4170.2010.0460.0310.518
HashJoinNaNNaNNaNNaN-0.632-0.664-0.518-0.419-0.382-0.494-0.458NaNNaN
NestedLoopCrossJoinNaNNaNNaNNaN-0.441NaNNaNNaNNaNNaNNaNNaNNaN
NestedLoopJoinNaNNaNNaNNaN-0.217-0.296NaNNaNNaNNaNNaNNaNNaN
ProjectionNaNNaNNaNNaN-2.030-1.973-1.958-1.691-1.477-1.535-1.477-1.582-1.415
SortNaNNaNNaNNaN-0.533-0.608-0.528-0.412-0.356-0.505-0.639-0.992-0.799
SortAggregationNaNNaNNaNNaN0.6190.6270.7220.7190.6470.3590.2450.3070.407
FilterNaNNaNNaNNaN0.1940.4060.5740.5770.7730.7060.6851.0231.317
HashJoinNaNNaNNaNNaN-0.099-0.0560.009-0.007-0.0260.0110.181NaNNaN
NestedLoopCrossJoinNaNNaNNaNNaN0.092NaNNaNNaNNaNNaNNaNNaNNaN
NestedLoopJoinNaNNaNNaNNaN0.3150.312NaNNaNNaNNaNNaNNaNNaN
ProjectionNaNNaNNaNNaN-1.497-1.365-1.431-1.280-1.121-1.030-0.838-0.591-0.616
SeqScanNaNNaNNaNNaN0.5330.6080.5280.4120.3560.5050.6390.9920.799
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" + ], + "text/plain": [ + "row_count 64 128 256 512 \\\n", + "a b \n", + "Aggregation Filter NaN NaN NaN NaN \n", + " HashJoin NaN NaN NaN NaN \n", + " NestedLoopCrossJoin NaN NaN NaN NaN \n", + " NestedLoopJoin NaN NaN NaN NaN \n", + " Projection NaN NaN NaN NaN \n", + " SeqScan NaN NaN NaN NaN \n", + " Sort NaN NaN NaN NaN \n", + "Filter Aggregation NaN NaN NaN NaN \n", + " HashJoin NaN NaN NaN NaN \n", + " NestedLoopCrossJoin NaN NaN NaN NaN \n", + " NestedLoopJoin NaN NaN NaN NaN \n", + " Projection NaN NaN NaN NaN \n", + " SeqScan NaN NaN NaN NaN \n", + " Sort NaN NaN NaN NaN \n", + "HashJoin Aggregation NaN NaN NaN NaN \n", + " Filter NaN NaN NaN NaN \n", + " NestedLoopCrossJoin NaN NaN NaN NaN \n", + " NestedLoopJoin NaN NaN NaN NaN \n", + " Projection NaN NaN NaN NaN \n", + " SeqScan NaN NaN NaN NaN \n", + " Sort NaN NaN NaN NaN \n", + "NestedLoopCrossJoin Aggregation NaN NaN NaN NaN \n", + " Filter NaN NaN NaN NaN \n", + " HashJoin NaN NaN NaN NaN \n", + " NestedLoopJoin -0.559 -0.194 -0.021 -0.026 \n", + " Projection NaN NaN NaN NaN \n", + " SeqScan NaN NaN NaN NaN \n", + " Sort NaN NaN NaN NaN \n", + "NestedLoopJoin Aggregation NaN NaN NaN NaN \n", + " Filter NaN NaN NaN NaN \n", + " HashJoin NaN NaN NaN NaN \n", + " NestedLoopCrossJoin 0.559 0.194 0.021 0.026 \n", + " Projection NaN NaN NaN NaN \n", + " SeqScan NaN NaN NaN NaN \n", + " Sort NaN NaN NaN NaN \n", + "Projection Aggregation NaN NaN NaN NaN \n", + " Filter NaN NaN NaN NaN \n", + " HashJoin NaN NaN NaN NaN \n", + " NestedLoopCrossJoin NaN NaN NaN NaN \n", + " NestedLoopJoin NaN NaN NaN NaN \n", + " SeqScan NaN NaN NaN NaN \n", + " Sort NaN NaN NaN NaN \n", + "SeqScan Aggregation NaN NaN NaN NaN \n", + " Filter NaN NaN NaN NaN \n", + " HashJoin NaN NaN NaN NaN \n", + " NestedLoopCrossJoin NaN NaN NaN NaN \n", + " NestedLoopJoin NaN NaN NaN NaN \n", + " Projection NaN NaN NaN NaN \n", + " Sort NaN NaN NaN NaN \n", + "Sort Aggregation NaN NaN NaN NaN \n", + " Filter NaN NaN NaN NaN \n", + " HashJoin NaN NaN NaN NaN \n", + " NestedLoopCrossJoin NaN NaN NaN NaN \n", + " NestedLoopJoin NaN NaN NaN NaN \n", + " Projection NaN NaN NaN NaN \n", + " SeqScan NaN NaN NaN NaN \n", + "\n", + "row_count 1024 2048 4096 8192 \\\n", + "a b \n", + "Aggregation Filter -0.426 -0.222 -0.149 -0.142 \n", + " HashJoin -0.718 -0.683 -0.713 -0.726 \n", + " NestedLoopCrossJoin -0.527 NaN NaN NaN \n", + " NestedLoopJoin -0.304 -0.315 NaN NaN \n", + " Projection -2.116 -1.993 -2.153 -1.999 \n", + " SeqScan -0.086 -0.019 -0.195 -0.307 \n", + " Sort -0.619 -0.627 -0.722 -0.719 \n", + "Filter Aggregation 0.426 0.222 0.149 0.142 \n", + " HashJoin -0.293 -0.461 -0.565 -0.584 \n", + " NestedLoopCrossJoin -0.101 NaN NaN NaN \n", + " NestedLoopJoin 0.122 -0.094 NaN NaN \n", + " Projection -1.690 -1.771 -2.004 -1.857 \n", + " SeqScan 0.339 0.202 -0.046 -0.165 \n", + " Sort -0.194 -0.406 -0.574 -0.577 \n", + "HashJoin Aggregation 0.718 0.683 0.713 0.726 \n", + " Filter 0.293 0.461 0.565 0.584 \n", + " NestedLoopCrossJoin 0.192 NaN NaN NaN \n", + " NestedLoopJoin 0.415 0.368 NaN NaN \n", + " Projection -1.398 -1.310 -1.440 -1.273 \n", + " SeqScan 0.632 0.664 0.518 0.419 \n", + " Sort 0.099 0.056 -0.009 0.007 \n", + "NestedLoopCrossJoin Aggregation 0.527 NaN NaN NaN \n", + " Filter 0.101 NaN NaN NaN \n", + " HashJoin -0.192 NaN NaN NaN \n", + " NestedLoopJoin 0.223 NaN NaN NaN \n", + " Projection -1.589 NaN NaN NaN \n", + " SeqScan 0.441 NaN NaN NaN \n", + " Sort -0.092 NaN NaN NaN \n", + "NestedLoopJoin Aggregation 0.304 0.315 NaN NaN \n", + " Filter -0.122 0.094 NaN NaN \n", + " HashJoin -0.415 -0.368 NaN NaN \n", + " NestedLoopCrossJoin -0.223 NaN NaN NaN \n", + " Projection -1.812 -1.678 NaN NaN \n", + " SeqScan 0.217 0.296 NaN NaN \n", + " Sort -0.315 -0.312 NaN NaN \n", + "Projection Aggregation 2.116 1.993 2.153 1.999 \n", + " Filter 1.690 1.771 2.004 1.857 \n", + " HashJoin 1.398 1.310 1.440 1.273 \n", + " NestedLoopCrossJoin 1.589 NaN NaN NaN \n", + " NestedLoopJoin 1.812 1.678 NaN NaN \n", + " SeqScan 2.030 1.973 1.958 1.691 \n", + " Sort 1.497 1.365 1.431 1.280 \n", + "SeqScan Aggregation 0.086 0.019 0.195 0.307 \n", + " Filter -0.339 -0.202 0.046 0.165 \n", + " HashJoin -0.632 -0.664 -0.518 -0.419 \n", + " NestedLoopCrossJoin -0.441 NaN NaN NaN \n", + " NestedLoopJoin -0.217 -0.296 NaN NaN \n", + " Projection -2.030 -1.973 -1.958 -1.691 \n", + " Sort -0.533 -0.608 -0.528 -0.412 \n", + "Sort Aggregation 0.619 0.627 0.722 0.719 \n", + " Filter 0.194 0.406 0.574 0.577 \n", + " HashJoin -0.099 -0.056 0.009 -0.007 \n", + " NestedLoopCrossJoin 0.092 NaN NaN NaN \n", + " NestedLoopJoin 0.315 0.312 NaN NaN \n", + " Projection -1.497 -1.365 -1.431 -1.280 \n", + " SeqScan 0.533 0.608 0.528 0.412 \n", + "\n", + "row_count 16384 32768 65536 131072 \\\n", + "a b \n", + "Aggregation Filter 0.126 0.347 0.440 0.716 \n", + " HashJoin -0.673 -0.347 -0.063 NaN \n", + " NestedLoopCrossJoin NaN NaN NaN NaN \n", + " NestedLoopJoin NaN NaN NaN NaN \n", + " Projection -1.768 -1.389 -1.083 -0.898 \n", + " SeqScan -0.291 0.146 0.394 0.684 \n", + " Sort -0.647 -0.359 -0.245 -0.307 \n", + "Filter Aggregation -0.126 -0.347 -0.440 -0.716 \n", + " HashJoin -0.799 -0.695 -0.503 NaN \n", + " NestedLoopCrossJoin NaN NaN NaN NaN \n", + " NestedLoopJoin NaN NaN NaN NaN \n", + " Projection -1.894 -1.736 -1.523 -1.614 \n", + " SeqScan -0.417 -0.201 -0.046 -0.031 \n", + " Sort -0.773 -0.706 -0.685 -1.023 \n", + "HashJoin Aggregation 0.673 0.347 0.063 NaN \n", + " Filter 0.799 0.695 0.503 NaN \n", + " NestedLoopCrossJoin NaN NaN NaN NaN \n", + " NestedLoopJoin NaN NaN NaN NaN \n", + " Projection -1.095 -1.042 -1.019 NaN \n", + " SeqScan 0.382 0.494 0.458 NaN \n", + " Sort 0.026 -0.011 -0.181 NaN \n", + "NestedLoopCrossJoin Aggregation NaN NaN NaN NaN \n", + " Filter NaN NaN NaN NaN \n", + " HashJoin NaN NaN NaN NaN \n", + " NestedLoopJoin NaN NaN NaN NaN \n", + " Projection NaN NaN NaN NaN \n", + " SeqScan NaN NaN NaN NaN \n", + " Sort NaN NaN NaN NaN \n", + "NestedLoopJoin Aggregation NaN NaN NaN NaN \n", + " Filter NaN NaN NaN NaN \n", + " HashJoin NaN NaN NaN NaN \n", + " NestedLoopCrossJoin NaN NaN NaN NaN \n", + " Projection NaN NaN NaN NaN \n", + " SeqScan NaN NaN NaN NaN \n", + " Sort NaN NaN NaN NaN \n", + "Projection Aggregation 1.768 1.389 1.083 0.898 \n", + " Filter 1.894 1.736 1.523 1.614 \n", + " HashJoin 1.095 1.042 1.019 NaN \n", + " NestedLoopCrossJoin NaN NaN NaN NaN \n", + " NestedLoopJoin NaN NaN NaN NaN \n", + " SeqScan 1.477 1.535 1.477 1.582 \n", + " Sort 1.121 1.030 0.838 0.591 \n", + "SeqScan Aggregation 0.291 -0.146 -0.394 -0.684 \n", + " Filter 0.417 0.201 0.046 0.031 \n", + " HashJoin -0.382 -0.494 -0.458 NaN \n", + " NestedLoopCrossJoin NaN NaN NaN NaN \n", + " NestedLoopJoin NaN NaN NaN NaN \n", + " Projection -1.477 -1.535 -1.477 -1.582 \n", + " Sort -0.356 -0.505 -0.639 -0.992 \n", + "Sort Aggregation 0.647 0.359 0.245 0.307 \n", + " Filter 0.773 0.706 0.685 1.023 \n", + " HashJoin -0.026 0.011 0.181 NaN \n", + " NestedLoopCrossJoin NaN NaN NaN NaN \n", + " NestedLoopJoin NaN NaN NaN NaN \n", + " Projection -1.121 -1.030 -0.838 -0.591 \n", + " SeqScan 0.356 0.505 0.639 0.992 \n", + "\n", + "row_count 262144 \n", + "a b \n", + "Aggregation Filter 0.910 \n", + " HashJoin NaN \n", + " NestedLoopCrossJoin NaN \n", + " NestedLoopJoin NaN \n", + " Projection -1.023 \n", + " SeqScan 0.392 \n", + " Sort -0.407 \n", + "Filter Aggregation -0.910 \n", + " HashJoin NaN \n", + " NestedLoopCrossJoin NaN \n", + " NestedLoopJoin NaN \n", + " Projection -1.933 \n", + " SeqScan -0.518 \n", + " Sort -1.317 \n", + "HashJoin Aggregation NaN \n", + " Filter NaN \n", + " NestedLoopCrossJoin NaN \n", + " NestedLoopJoin NaN \n", + " Projection NaN \n", + " SeqScan NaN \n", + " Sort NaN \n", + "NestedLoopCrossJoin Aggregation NaN \n", + " Filter NaN \n", + " HashJoin NaN \n", + " NestedLoopJoin NaN \n", + " Projection NaN \n", + " SeqScan NaN \n", + " Sort NaN \n", + "NestedLoopJoin Aggregation NaN \n", + " Filter NaN \n", + " HashJoin NaN \n", + " NestedLoopCrossJoin NaN \n", + " Projection NaN \n", + " SeqScan NaN \n", + " Sort NaN \n", + "Projection Aggregation 1.023 \n", + " Filter 1.933 \n", + " HashJoin NaN \n", + " NestedLoopCrossJoin NaN \n", + " NestedLoopJoin NaN \n", + " SeqScan 1.415 \n", + " Sort 0.616 \n", + "SeqScan Aggregation -0.392 \n", + " Filter 0.518 \n", + " HashJoin NaN \n", + " NestedLoopCrossJoin NaN \n", + " NestedLoopJoin NaN \n", + " Projection -1.415 \n", + " Sort -0.799 \n", + "Sort Aggregation 0.407 \n", + " Filter 1.317 \n", + " HashJoin NaN \n", + " NestedLoopCrossJoin NaN \n", + " NestedLoopJoin NaN \n", + " Projection -0.616 \n", + " SeqScan 0.799 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rows = []\n", + "for rc in valid_counts:\n", + " sub = pivot.xs(rc, level='left_rows')\n", + " ops = sub.index.tolist()\n", + " for a in ops:\n", + " for b in ops:\n", + " if a != b:\n", + " delta = (np.log(sub.loc[a, 'cpu_time']) - np.log(sub.loc[b, 'cpu_time']) -\n", + " (np.log(sub.loc[a, 'model_cost']) - np.log(sub.loc[b, 'model_cost'])))\n", + " rows.append({'row_count': rc, 'a': a, 'b': b, 'delta': delta})\n", + "\n", + "comparison = pd.DataFrame(rows)\n", + "comparison.pivot_table(index=['a', 'b'], columns='row_count', values='delta').round(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "68ae0220", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-05-31T16:16:05+03:00\n", + "Running ./build-release/bin/benchmarks\n", + "Run on (8 X 4200 MHz CPU s)\n", + "CPU Caches:\n", + " L1 Data 48 KiB (x4)\n", + " L1 Instruction 32 KiB (x4)\n", + " L2 Unified 1280 KiB (x4)\n", + " L3 Unified 8192 KiB (x1)\n", + "Load Average: 3.38, 2.81, 1.77\n", + "***WARNING*** CPU scaling is enabled, the benchmark real time measurements may be noisy and will incur extra overhead.\n", + "-----------------------------------------------------------------------------------------------------------------------------------------\n", + "Benchmark Time CPU Iterations UserCounters...\n", + "-----------------------------------------------------------------------------------------------------------------------------------------\n", + "\u001b[0;32mOperatorCostMatched/target_cost:640000/SeqScan/6400/real_time \u001b[m\u001b[0;33m 532418 ns 521056 ns \u001b[m\u001b[0;36m 1116\u001b[m model_cost=640k\u001b[m output_rows=6.4k\u001b[m plan_cost=640k\u001b[m rows=6.4k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Filter/6400/real_time \u001b[m\u001b[0;33m 508270 ns 501445 ns \u001b[m\u001b[0;36m 1089\u001b[m model_cost=640k\u001b[m output_rows=3.328k\u001b[m plan_cost=1.28M\u001b[m rows=6.4k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Projection/29091/real_time \u001b[m\u001b[0;33m 4068856 ns 4010747 ns \u001b[m\u001b[0;36m 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"\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopJoin/96/96/real_time \u001b[m\u001b[0;33m 875389 ns 866351 ns \u001b[m\u001b[0;36m 804\u001b[m lhs_rows=96\u001b[m model_cost=645.12k\u001b[m output_rows=96\u001b[m plan_cost=664.32k\u001b[m rhs_rows=96\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopCrossJoin/78/78/real_time \u001b[m\u001b[0;33m 527338 ns 522288 ns \u001b[m\u001b[0;36m 1107\u001b[m lhs_rows=78\u001b[m model_cost=632.736k\u001b[m output_rows=6.084k\u001b[m plan_cost=648.336k\u001b[m rhs_rows=78\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/SeqScan/10000/real_time \u001b[m\u001b[0;33m 507202 ns 502389 ns \u001b[m\u001b[0;36m 1000\u001b[m model_cost=1M\u001b[m output_rows=10k\u001b[m plan_cost=1M\u001b[m rows=10k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Filter/10000/real_time \u001b[m\u001b[0;33m 861315 ns 854085 ns \u001b[m\u001b[0;36m 818\u001b[m model_cost=1M\u001b[m output_rows=5.12k\u001b[m plan_cost=2M\u001b[m rows=10k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Projection/45455/real_time \u001b[m\u001b[0;33m 6698145 ns 6632289 ns \u001b[m\u001b[0;36m 94\u001b[m model_cost=1.00001M\u001b[m output_rows=45.455k\u001b[m plan_cost=5.54551M\u001b[m rows=45.455k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Sort/6993/real_time \u001b[m\u001b[0;33m 1503435 ns 1491454 ns \u001b[m\u001b[0;36m 442\u001b[m model_cost=0.999999M\u001b[m output_rows=6.993k\u001b[m plan_cost=1.6993M\u001b[m rows=6.993k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Aggregation/1961/real_time \u001b[m\u001b[0;33m 702024 ns 697631 ns \u001b[m\u001b[0;36m 968\u001b[m model_cost=1.00011M\u001b[m output_rows=1.961k\u001b[m plan_cost=1.19621M\u001b[m rows=1.961k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/HashJoin/7246/7246/real_time 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output_rows=32.4k\u001b[m plan_cost=3.24M\u001b[m rows=32.4k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Filter/32400/real_time \u001b[m\u001b[0;33m 2615187 ns 2600696 ns \u001b[m\u001b[0;36m 253\u001b[m model_cost=3.24M\u001b[m output_rows=16.384k\u001b[m plan_cost=6.48M\u001b[m rows=32.4k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Projection/147273/real_time \u001b[m\u001b[0;33m 24615070 ns 24428312 ns \u001b[m\u001b[0;36m 28\u001b[m model_cost=3.24001M\u001b[m output_rows=147.273k\u001b[m plan_cost=17.9673M\u001b[m rows=147.273k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Sort/19636/real_time \u001b[m\u001b[0;33m 5716537 ns 5678245 ns \u001b[m\u001b[0;36m 122\u001b[m model_cost=3.23994M\u001b[m output_rows=19.636k\u001b[m plan_cost=5.20354M\u001b[m rows=19.636k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Aggregation/6353/real_time \u001b[m\u001b[0;33m 2528047 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output_rows=31.329k\u001b[m plan_cost=3.29362M\u001b[m rhs_rows=177\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/SeqScan/67600/real_time \u001b[m\u001b[0;33m 4220053 ns 4185107 ns \u001b[m\u001b[0;36m 169\u001b[m model_cost=6.76M\u001b[m output_rows=67.6k\u001b[m plan_cost=6.76M\u001b[m rows=67.6k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Filter/67600/real_time \u001b[m\u001b[0;33m 5592034 ns 5560841 ns \u001b[m\u001b[0;36m 126\u001b[m model_cost=6.76M\u001b[m output_rows=33.808k\u001b[m plan_cost=13.52M\u001b[m rows=67.6k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Projection/307273/real_time \u001b[m\u001b[0;33m 51498743 ns 51127846 ns \u001b[m\u001b[0;36m 14\u001b[m model_cost=6.76001M\u001b[m output_rows=307.273k\u001b[m plan_cost=37.4873M\u001b[m rows=307.273k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Sort/38409/real_time \u001b[m\u001b[0;33m 12802465 ns 12732765 ns \u001b[m\u001b[0;36m 50\u001b[m model_cost=6.75998M\u001b[m output_rows=38.409k\u001b[m plan_cost=10.6009M\u001b[m rows=38.409k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Aggregation/13255/real_time \u001b[m\u001b[0;33m 5687200 ns 5655535 ns \u001b[m\u001b[0;36m 118\u001b[m model_cost=6.76005M\u001b[m output_rows=13.255k\u001b[m plan_cost=8.08555M\u001b[m rows=13.255k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/HashJoin/48986/48986/real_time \u001b[m\u001b[0;33m 13564179 ns 13440945 ns \u001b[m\u001b[0;36m 51\u001b[m lhs_rows=48.986k\u001b[m model_cost=6.76007M\u001b[m output_rows=48.986k\u001b[m plan_cost=16.5573M\u001b[m rhs_rows=48.986k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopJoin/311/311/real_time \u001b[m\u001b[0;33m 7198310 ns 7163077 ns \u001b[m\u001b[0;36m 93\u001b[m lhs_rows=311\u001b[m model_cost=6.77047M\u001b[m output_rows=311\u001b[m plan_cost=6.83267M\u001b[m rhs_rows=311\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopCrossJoin/255/255/real_time \u001b[m\u001b[0;33m 6012240 ns 5936779 ns \u001b[m\u001b[0;36m 87\u001b[m lhs_rows=255\u001b[m model_cost=6.7626M\u001b[m output_rows=65.025k\u001b[m plan_cost=6.8136M\u001b[m rhs_rows=255\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/SeqScan/122500/real_time \u001b[m\u001b[0;33m 7610143 ns 7525813 ns \u001b[m\u001b[0;36m 83\u001b[m model_cost=12.25M\u001b[m output_rows=122.5k\u001b[m plan_cost=12.25M\u001b[m rows=122.5k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Filter/122500/real_time \u001b[m\u001b[0;33m 10313976 ns 10227032 ns \u001b[m\u001b[0;36m 63\u001b[m model_cost=12.25M\u001b[m output_rows=61.44k\u001b[m plan_cost=24.5M\u001b[m rows=122.5k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Projection/556818/real_time \u001b[m\u001b[0;33m 90203371 ns 89533850 ns \u001b[m\u001b[0;36m 8\u001b[m model_cost=12.25M\u001b[m output_rows=556.818k\u001b[m plan_cost=67.9318M\u001b[m rows=556.818k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Sort/65536/real_time \u001b[m\u001b[0;33m 26495662 ns 26260681 ns \u001b[m\u001b[0;36m 22\u001b[m model_cost=12.2552M\u001b[m output_rows=65.536k\u001b[m plan_cost=18.8088M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Aggregation/24020/real_time \u001b[m\u001b[0;33m 15047581 ns 14860662 ns \u001b[m\u001b[0;36m 50\u001b[m model_cost=12.2502M\u001b[m output_rows=24.02k\u001b[m plan_cost=14.6522M\u001b[m rows=24.02k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/HashJoin/88768/88768/real_time \u001b[m\u001b[0;33m 25706484 ns 25401595 ns \u001b[m\u001b[0;36m 28\u001b[m lhs_rows=88.768k\u001b[m model_cost=12.25M\u001b[m output_rows=88.768k\u001b[m plan_cost=30.0036M\u001b[m rhs_rows=88.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopJoin/418/418/real_time \u001b[m\u001b[0;33m 12686905 ns 12628552 ns \u001b[m\u001b[0;36m 47\u001b[m lhs_rows=418\u001b[m model_cost=12.2307M\u001b[m output_rows=418\u001b[m plan_cost=12.3143M\u001b[m rhs_rows=418\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopCrossJoin/343/343/real_time \u001b[m\u001b[0;33m 12634892 ns 12474012 ns \u001b[m\u001b[0;36m 52\u001b[m lhs_rows=343\u001b[m model_cost=12.2355M\u001b[m output_rows=117.649k\u001b[m plan_cost=12.3041M\u001b[m rhs_rows=343\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/SeqScan/260100/real_time \u001b[m\u001b[0;33m 18734465 ns 18479865 ns \u001b[m\u001b[0;36m 37\u001b[m model_cost=26.01M\u001b[m output_rows=260.1k\u001b[m plan_cost=26.01M\u001b[m rows=260.1k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Filter/260100/real_time \u001b[m\u001b[0;33m 26934394 ns 26755899 ns \u001b[m\u001b[0;36m 28\u001b[m model_cost=26.01M\u001b[m output_rows=130.052k\u001b[m plan_cost=52.02M\u001b[m rows=260.1k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Projection/1182273/real_time \u001b[m\u001b[0;33m 202987386 ns 201290217 ns \u001b[m\u001b[0;36m 4\u001b[m model_cost=26.01M\u001b[m output_rows=1.18227M\u001b[m plan_cost=144.237M\u001b[m rows=1.18227M\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Sort/131364/real_time \u001b[m\u001b[0;33m 74202993 ns 73103763 ns \u001b[m\u001b[0;36m 9\u001b[m model_cost=26.0101M\u001b[m output_rows=131.364k\u001b[m plan_cost=39.1465M\u001b[m rows=131.364k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Aggregation/51000/real_time \u001b[m\u001b[0;33m 48595095 ns 48139275 ns \u001b[m\u001b[0;36m 16\u001b[m model_cost=26.01M\u001b[m output_rows=51k\u001b[m plan_cost=31.11M\u001b[m rows=51k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/HashJoin/188478/188478/real_time \u001b[m\u001b[0;33m 52584297 ns 52158890 ns \u001b[m\u001b[0;36m 14\u001b[m lhs_rows=188.478k\u001b[m model_cost=26.01M\u001b[m output_rows=188.478k\u001b[m plan_cost=63.7056M\u001b[m rhs_rows=188.478k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopJoin/610/610/real_time \u001b[m\u001b[0;33m 25448562 ns 25343677 ns \u001b[m\u001b[0;36m 26\u001b[m lhs_rows=610\u001b[m model_cost=26.047M\u001b[m output_rows=610\u001b[m plan_cost=26.169M\u001b[m rhs_rows=610\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopCrossJoin/500/500/real_time \u001b[m\u001b[0;33m 30993358 ns 30600644 ns \u001b[m\u001b[0;36m 27\u001b[m lhs_rows=500\u001b[m model_cost=26M\u001b[m output_rows=250k\u001b[m plan_cost=26.1M\u001b[m rhs_rows=500\u001b[m\n", + "\u001b[m" + ] + } + ], + "source": [ + "!cd ~/c/iu9-sql-compiler/ && ./build-release/bin/benchmarks --benchmark_filter='OperatorCostMatched*' --benchmark_out=/tmp/operator-cost-matched.json --benchmark_out_format=json" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1c313f56", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'context': {'date': '2026-05-31T16:16:05+03:00',\n", + " 'host_name': 'nixos',\n", + " 'executable': './build-release/bin/benchmarks',\n", + " 'num_cpus': 8,\n", + " 'mhz_per_cpu': 4200,\n", + " 'cpu_scaling_enabled': True,\n", + " 'caches': [{'type': 'Data', 'level': 1, 'size': 49152, 'num_sharing': 2},\n", + " {'type': 'Instruction', 'level': 1, 'size': 32768, 'num_sharing': 2},\n", + " {'type': 'Unified', 'level': 2, 'size': 1310720, 'num_sharing': 2},\n", + " {'type': 'Unified', 'level': 3, 'size': 8388608, 'num_sharing': 8}],\n", + " 'load_avg': [3.37842, 2.81006, 1.77441],\n", + " 'library_version': 'v1.9.0',\n", + " 'library_build_type': 'release',\n", + " 'json_schema_version': 1},\n", + " 'benchmarks': [{'name': 'OperatorCostMatched/target_cost:640000/SeqScan/6400/real_time',\n", + " 'family_index': 0,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCostMatched/target_cost:640000/SeqScan/6400/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 1116,\n", + " 'real_time': 532417.982082007,\n", + " 'cpu_time': 521056.23745519714,\n", + " 'time_unit': 'ns',\n", + " 'model_cost': 640000.0,\n", + " 'output_rows': 6400.0,\n", + " 'plan_cost': 640000.0,\n", + " 'rows': 6400.0},\n", + " {'name': 'OperatorCostMatched/target_cost:640000/Filter/6400/real_time',\n", + " 'family_index': 1,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCostMatched/target_cost:640000/Filter/6400/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 1089,\n", + " 'real_time': 508270.4343416542,\n", + " 'cpu_time': 501445.3149678604,\n", + " 'time_unit': 'ns',\n", + " 'model_cost': 640000.0,\n", + " 'output_rows': 3328.0,\n", + " 'plan_cost': 1280000.0,\n", + " 'rows': 6400.0},\n", + " {'name': 'OperatorCostMatched/target_cost:640000/Projection/29091/real_time',\n", + " 'family_index': 2,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCostMatched/target_cost:640000/Projection/29091/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 156,\n", + " 'real_time': 4068856.4935967755,\n", + " 'cpu_time': 4010746.6025641034,\n", + " 'time_unit': 'ns',\n", + " 'model_cost': 640002.0,\n", + " 'output_rows': 29091.0,\n", + " 'plan_cost': 3549102.0,\n", + " 'rows': 29091.0},\n", + " {'name': 'OperatorCostMatched/target_cost:640000/Sort/4476/real_time',\n", + " 'family_index': 3,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCostMatched/target_cost:640000/Sort/4476/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 786,\n", + " 'real_time': 997565.7201017806,\n", + " 'cpu_time': 988471.3625954194,\n", + " 'time_unit': 'ns',\n", + " 'model_cost': 640068.0,\n", + " 'output_rows': 4476.0,\n", + " 'plan_cost': 1087668.0,\n", + " 'rows': 4476.0},\n", + " {'name': 'OperatorCostMatched/target_cost:640000/Aggregation/1255/real_time',\n", + " 'family_index': 4,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCostMatched/target_cost:640000/Aggregation/1255/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 1000,\n", + " 'real_time': 529187.3500100337,\n", + " 'cpu_time': 520614.8160000001,\n", + " 'time_unit': 'ns',\n", + " 'model_cost': 640050.0,\n", + " 'output_rows': 1255.0,\n", + " 'plan_cost': 765550.0,\n", + " 'rows': 1255.0},\n", + " {'name': 'OperatorCostMatched/target_cost:640000/HashJoin/4638/4638/real_time',\n", + " 'family_index': 5,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCostMatched/target_cost:640000/HashJoin/4638/4638/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 714,\n", + " 'real_time': 1026884.7717186474,\n", + " 'cpu_time': 1016820.3893557423,\n", + " 'time_unit': 'ns',\n", + " 'lhs_rows': 4638.0,\n", + " 'model_cost': 640044.0,\n", + " 'output_rows': 4638.0,\n", + " 'plan_cost': 1567644.0,\n", + " 'rhs_rows': 4638.0},\n", + " {'name': 'OperatorCostMatched/target_cost:640000/NestedLoopJoin/96/96/real_time',\n", + " 'family_index': 6,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCostMatched/target_cost:640000/NestedLoopJoin/96/96/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 804,\n", + " 'real_time': 875389.360694883,\n", + " 'cpu_time': 866351.0472636818,\n", + " 'time_unit': 'ns',\n", + " 'lhs_rows': 96.0,\n", + " 'model_cost': 645120.0,\n", + " 'output_rows': 96.0,\n", + " 'plan_cost': 664320.0,\n", + " 'rhs_rows': 96.0},\n", + " {'name': 'OperatorCostMatched/target_cost:640000/NestedLoopCrossJoin/78/78/real_time',\n", + " 'family_index': 7,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCostMatched/target_cost:640000/NestedLoopCrossJoin/78/78/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 1107,\n", + " 'real_time': 527338.0876234216,\n", + " 'cpu_time': 522288.25112917816,\n", + " 'time_unit': 'ns',\n", + " 'lhs_rows': 78.0,\n", + " 'model_cost': 632736.0,\n", + " 'output_rows': 6084.0,\n", + " 'plan_cost': 648336.0,\n", + " 'rhs_rows': 78.0},\n", + " {'name': 'OperatorCostMatched/target_cost:1000000/SeqScan/10000/real_time',\n", + " 'family_index': 8,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCostMatched/target_cost:1000000/SeqScan/10000/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 1000,\n", + " 'real_time': 507201.76300092135,\n", + " 'cpu_time': 502388.66499999975,\n", + " 'time_unit': 'ns',\n", + " 'model_cost': 1000000.0,\n", + " 'output_rows': 10000.0,\n", + " 'plan_cost': 1000000.0,\n", + " 'rows': 10000.0},\n", + " {'name': 'OperatorCostMatched/target_cost:1000000/Filter/10000/real_time',\n", + " 'family_index': 9,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCostMatched/target_cost:1000000/Filter/10000/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 818,\n", + " 'real_time': 861314.8814182236,\n", + " 'cpu_time': 854085.0378973101,\n", + " 'time_unit': 'ns',\n", + " 'model_cost': 1000000.0,\n", + " 'output_rows': 5120.0,\n", + " 'plan_cost': 2000000.0,\n", + " 'rows': 10000.0},\n", + " {'name': 'OperatorCostMatched/target_cost:1000000/Projection/45455/real_time',\n", + " 'family_index': 10,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCostMatched/target_cost:1000000/Projection/45455/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 94,\n", + " 'real_time': 6698144.617133793,\n", + " 'cpu_time': 6632289.042553187,\n", + " 'time_unit': 'ns',\n", + " 'model_cost': 1000010.0,\n", + " 'output_rows': 45455.0,\n", + " 'plan_cost': 5545510.0,\n", + " 'rows': 45455.0},\n", + " {'name': 'OperatorCostMatched/target_cost:1000000/Sort/6993/real_time',\n", + " 'family_index': 11,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 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1182273.0,\n", + " 'plan_cost': 144237306.0,\n", + " 'rows': 1182273.0},\n", + " {'name': 'OperatorCostMatched/target_cost:26010000/Sort/131364/real_time',\n", + " 'family_index': 43,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCostMatched/target_cost:26010000/Sort/131364/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 9,\n", + " 'real_time': 74202993.22176208,\n", + " 'cpu_time': 73103763.44444415,\n", + " 'time_unit': 'ns',\n", + " 'model_cost': 26010072.0,\n", + " 'output_rows': 131364.0,\n", + " 'plan_cost': 39146472.0,\n", + " 'rows': 131364.0},\n", + " {'name': 'OperatorCostMatched/target_cost:26010000/Aggregation/51000/real_time',\n", + " 'family_index': 44,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCostMatched/target_cost:26010000/Aggregation/51000/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 16,\n", + " 'real_time': 48595095.37436679,\n", + " 'cpu_time': 48139274.8124998,\n", + " 'time_unit': 'ns',\n", + " 'model_cost': 26010000.0,\n", + " 'output_rows': 51000.0,\n", + " 'plan_cost': 31110000.0,\n", + " 'rows': 51000.0},\n", + " {'name': 'OperatorCostMatched/target_cost:26010000/HashJoin/188478/188478/real_time',\n", + " 'family_index': 45,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCostMatched/target_cost:26010000/HashJoin/188478/188478/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 14,\n", + " 'real_time': 52584296.71396568,\n", + " 'cpu_time': 52158890.000000164,\n", + " 'time_unit': 'ns',\n", + " 'lhs_rows': 188478.0,\n", + " 'model_cost': 26009964.0,\n", + " 'output_rows': 188478.0,\n", + " 'plan_cost': 63705564.0,\n", + " 'rhs_rows': 188478.0},\n", + " {'name': 'OperatorCostMatched/target_cost:26010000/NestedLoopJoin/610/610/real_time',\n", + " 'family_index': 46,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCostMatched/target_cost:26010000/NestedLoopJoin/610/610/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 26,\n", + " 'real_time': 25448562.268978164,\n", + " 'cpu_time': 25343677.269230917,\n", + " 'time_unit': 'ns',\n", + " 'lhs_rows': 610.0,\n", + " 'model_cost': 26047000.0,\n", + " 'output_rows': 610.0,\n", + " 'plan_cost': 26169000.0,\n", + " 'rhs_rows': 610.0},\n", + " {'name': 'OperatorCostMatched/target_cost:26010000/NestedLoopCrossJoin/500/500/real_time',\n", + " 'family_index': 47,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCostMatched/target_cost:26010000/NestedLoopCrossJoin/500/500/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 27,\n", + " 'real_time': 30993357.51849813,\n", + " 'cpu_time': 30600644.111111116,\n", + " 'time_unit': 'ns',\n", + " 'lhs_rows': 500.0,\n", + " 'model_cost': 26000000.0,\n", + " 'output_rows': 250000.0,\n", + " 'plan_cost': 26100000.0,\n", + " 'rhs_rows': 500.0}]}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import json\n", + "results = None\n", + "with open('/tmp/operator-cost-matched.json') as f:\n", + " results = json.load(f)\n", + "\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "operator-cost-matched-dataframe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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target_costoperatorleft_rowsright_rowsreal_time_msmodel_costplan_costmatching_error
4640000Aggregation1255NaN0.529187640050.0765550.07.812500e-05
1640000Filter6400NaN0.508270640000.01280000.00.000000e+00
5640000HashJoin46384638.01.026885640044.01567644.06.875000e-05
7640000NestedLoopCrossJoin7878.00.527338632736.0648336.01.135000e-02
6640000NestedLoopJoin9696.00.875389645120.0664320.08.000000e-03
2640000Projection29091NaN4.068856640002.03549102.03.125000e-06
0640000SeqScan6400NaN0.532418640000.0640000.00.000000e+00
3640000Sort4476NaN0.997566640068.01087668.01.062500e-04
121000000Aggregation1961NaN0.7020241000110.01196210.01.100000e-04
91000000Filter10000NaN0.8613151000000.02000000.00.000000e+00
131000000HashJoin72467246.01.406982999948.02449148.05.200000e-05
151000000NestedLoopCrossJoin9898.00.735577998816.01018416.01.184000e-03
141000000NestedLoopJoin120120.01.2049521008000.01032000.08.000000e-03
101000000Projection45455NaN6.6981451000010.05545510.01.000000e-05
81000000SeqScan10000NaN0.5072021000000.01000000.00.000000e+00
111000000Sort6993NaN1.503435999999.01699299.01.000000e-06
203240000Aggregation6353NaN2.5280473240030.03875330.09.259259e-06
173240000Filter32400NaN2.6151873240000.06480000.00.000000e+00
213240000HashJoin2347823478.05.3005513239964.07935564.01.111111e-05
233240000NestedLoopCrossJoin177177.02.2945543258216.03293616.05.622222e-03
223240000NestedLoopJoin215215.03.4407693235750.03278750.01.311728e-03
183240000Projection147273NaN24.6150703240006.017967306.01.851852e-06
163240000SeqScan32400NaN1.5526183240000.03240000.00.000000e+00
193240000Sort19636NaN5.7165373239940.05203540.01.851852e-05
286760000Aggregation13255NaN5.6872006760050.08085550.07.396450e-06
256760000Filter67600NaN5.5920346760000.013520000.00.000000e+00
296760000HashJoin4898648986.013.5641796760068.016557268.01.005917e-05
316760000NestedLoopCrossJoin255255.06.0122406762600.06813600.03.846154e-04
306760000NestedLoopJoin311311.07.1983106770470.06832670.01.548817e-03
266760000Projection307273NaN51.4987436760006.037487306.08.875740e-07
246760000SeqScan67600NaN4.2200536760000.06760000.00.000000e+00
276760000Sort38409NaN12.8024656759984.010600884.02.366864e-06
3612250000Aggregation24020NaN15.04758112250200.014652200.01.632653e-05
3312250000Filter122500NaN10.31397612250000.024500000.00.000000e+00
3712250000HashJoin8876888768.025.70648412249984.030003584.01.306122e-06
3912250000NestedLoopCrossJoin343343.012.63489212235496.012304096.01.184000e-03
3812250000NestedLoopJoin418418.012.68690512230680.012314280.01.577143e-03
3412250000Projection556818NaN90.20337112249996.067931796.03.265306e-07
3212250000SeqScan122500NaN7.61014312250000.012250000.00.000000e+00
3512250000Sort65536NaN26.49566212255232.018808832.04.271020e-04
4426010000Aggregation51000NaN48.59509526010000.031110000.00.000000e+00
4126010000Filter260100NaN26.93439426010000.052020000.00.000000e+00
4526010000HashJoin188478188478.052.58429726009964.063705564.01.384083e-06
4726010000NestedLoopCrossJoin500500.030.99335826000000.026100000.03.844675e-04
4626010000NestedLoopJoin610610.025.44856226047000.026169000.01.422530e-03
4226010000Projection1182273NaN202.98738626010006.0144237306.02.306805e-07
4026010000SeqScan260100NaN18.73446526010000.026010000.00.000000e+00
4326010000Sort131364NaN74.20299326010072.039146472.02.768166e-06
\n", + "
" + ], + "text/plain": [ + " target_cost operator left_rows right_rows real_time_ms \\\n", + "4 640000 Aggregation 1255 NaN 0.529187 \n", + "1 640000 Filter 6400 NaN 0.508270 \n", + "5 640000 HashJoin 4638 4638.0 1.026885 \n", + "7 640000 NestedLoopCrossJoin 78 78.0 0.527338 \n", + "6 640000 NestedLoopJoin 96 96.0 0.875389 \n", + "2 640000 Projection 29091 NaN 4.068856 \n", + "0 640000 SeqScan 6400 NaN 0.532418 \n", + "3 640000 Sort 4476 NaN 0.997566 \n", + "12 1000000 Aggregation 1961 NaN 0.702024 \n", + "9 1000000 Filter 10000 NaN 0.861315 \n", + "13 1000000 HashJoin 7246 7246.0 1.406982 \n", + "15 1000000 NestedLoopCrossJoin 98 98.0 0.735577 \n", + "14 1000000 NestedLoopJoin 120 120.0 1.204952 \n", + "10 1000000 Projection 45455 NaN 6.698145 \n", + "8 1000000 SeqScan 10000 NaN 0.507202 \n", + "11 1000000 Sort 6993 NaN 1.503435 \n", + "20 3240000 Aggregation 6353 NaN 2.528047 \n", + "17 3240000 Filter 32400 NaN 2.615187 \n", + "21 3240000 HashJoin 23478 23478.0 5.300551 \n", + "23 3240000 NestedLoopCrossJoin 177 177.0 2.294554 \n", + "22 3240000 NestedLoopJoin 215 215.0 3.440769 \n", + "18 3240000 Projection 147273 NaN 24.615070 \n", + "16 3240000 SeqScan 32400 NaN 1.552618 \n", + "19 3240000 Sort 19636 NaN 5.716537 \n", + "28 6760000 Aggregation 13255 NaN 5.687200 \n", + "25 6760000 Filter 67600 NaN 5.592034 \n", + "29 6760000 HashJoin 48986 48986.0 13.564179 \n", + "31 6760000 NestedLoopCrossJoin 255 255.0 6.012240 \n", + "30 6760000 NestedLoopJoin 311 311.0 7.198310 \n", + "26 6760000 Projection 307273 NaN 51.498743 \n", + "24 6760000 SeqScan 67600 NaN 4.220053 \n", + "27 6760000 Sort 38409 NaN 12.802465 \n", + "36 12250000 Aggregation 24020 NaN 15.047581 \n", + "33 12250000 Filter 122500 NaN 10.313976 \n", + "37 12250000 HashJoin 88768 88768.0 25.706484 \n", + "39 12250000 NestedLoopCrossJoin 343 343.0 12.634892 \n", + "38 12250000 NestedLoopJoin 418 418.0 12.686905 \n", + "34 12250000 Projection 556818 NaN 90.203371 \n", + "32 12250000 SeqScan 122500 NaN 7.610143 \n", + "35 12250000 Sort 65536 NaN 26.495662 \n", + "44 26010000 Aggregation 51000 NaN 48.595095 \n", + "41 26010000 Filter 260100 NaN 26.934394 \n", + "45 26010000 HashJoin 188478 188478.0 52.584297 \n", + "47 26010000 NestedLoopCrossJoin 500 500.0 30.993358 \n", + "46 26010000 NestedLoopJoin 610 610.0 25.448562 \n", + "42 26010000 Projection 1182273 NaN 202.987386 \n", + "40 26010000 SeqScan 260100 NaN 18.734465 \n", + "43 26010000 Sort 131364 NaN 74.202993 \n", + "\n", + " model_cost plan_cost matching_error \n", + "4 640050.0 765550.0 7.812500e-05 \n", + "1 640000.0 1280000.0 0.000000e+00 \n", + "5 640044.0 1567644.0 6.875000e-05 \n", + "7 632736.0 648336.0 1.135000e-02 \n", + "6 645120.0 664320.0 8.000000e-03 \n", + "2 640002.0 3549102.0 3.125000e-06 \n", + "0 640000.0 640000.0 0.000000e+00 \n", + "3 640068.0 1087668.0 1.062500e-04 \n", + "12 1000110.0 1196210.0 1.100000e-04 \n", + "9 1000000.0 2000000.0 0.000000e+00 \n", + "13 999948.0 2449148.0 5.200000e-05 \n", + "15 998816.0 1018416.0 1.184000e-03 \n", + "14 1008000.0 1032000.0 8.000000e-03 \n", + "10 1000010.0 5545510.0 1.000000e-05 \n", + "8 1000000.0 1000000.0 0.000000e+00 \n", + "11 999999.0 1699299.0 1.000000e-06 \n", + "20 3240030.0 3875330.0 9.259259e-06 \n", + "17 3240000.0 6480000.0 0.000000e+00 \n", + "21 3239964.0 7935564.0 1.111111e-05 \n", + "23 3258216.0 3293616.0 5.622222e-03 \n", + "22 3235750.0 3278750.0 1.311728e-03 \n", + "18 3240006.0 17967306.0 1.851852e-06 \n", + "16 3240000.0 3240000.0 0.000000e+00 \n", + "19 3239940.0 5203540.0 1.851852e-05 \n", + "28 6760050.0 8085550.0 7.396450e-06 \n", + "25 6760000.0 13520000.0 0.000000e+00 \n", + "29 6760068.0 16557268.0 1.005917e-05 \n", + "31 6762600.0 6813600.0 3.846154e-04 \n", + "30 6770470.0 6832670.0 1.548817e-03 \n", + "26 6760006.0 37487306.0 8.875740e-07 \n", + "24 6760000.0 6760000.0 0.000000e+00 \n", + "27 6759984.0 10600884.0 2.366864e-06 \n", + "36 12250200.0 14652200.0 1.632653e-05 \n", + "33 12250000.0 24500000.0 0.000000e+00 \n", + "37 12249984.0 30003584.0 1.306122e-06 \n", + "39 12235496.0 12304096.0 1.184000e-03 \n", + "38 12230680.0 12314280.0 1.577143e-03 \n", + "34 12249996.0 67931796.0 3.265306e-07 \n", + "32 12250000.0 12250000.0 0.000000e+00 \n", + "35 12255232.0 18808832.0 4.271020e-04 \n", + "44 26010000.0 31110000.0 0.000000e+00 \n", + "41 26010000.0 52020000.0 0.000000e+00 \n", + "45 26009964.0 63705564.0 1.384083e-06 \n", + "47 26000000.0 26100000.0 3.844675e-04 \n", + "46 26047000.0 26169000.0 1.422530e-03 \n", + "42 26010006.0 144237306.0 2.306805e-07 \n", + "40 26010000.0 26010000.0 0.000000e+00 \n", + "43 26010072.0 39146472.0 2.768166e-06 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "matched_df = pd.DataFrame(results['benchmarks'])\n", + "matched_df = matched_df[matched_df['run_type'] == 'iteration'].copy()\n", + "\n", + "parts = matched_df['name'].str.split('/')\n", + "matched_df['target_cost'] = parts.str[1].str.removeprefix('target_cost:').astype(int)\n", + "matched_df['operator'] = parts.str[2]\n", + "matched_df['left_rows'] = parts.str[3].astype(int)\n", + "matched_df['right_rows'] = parts.apply(lambda p: int(p[4]) if len(p) == 6 else None)\n", + "matched_df['real_time_ms'] = matched_df['real_time'] / 1e6\n", + "\n", + "matched_df['matching_error'] = (matched_df['model_cost'] - matched_df['target_cost']).abs() / matched_df['target_cost']\n", + "assert (matched_df['matching_error'] < 0.02).all()\n", + "\n", + "matched_df[['target_cost', 'operator', 'left_rows', 'right_rows', 'real_time_ms', 'model_cost', 'plan_cost', 'matching_error']].sort_values(['target_cost', 'operator'])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "operator-cost-matched-plot", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(8, 5))\n", + "for op, grp in matched_df.groupby('operator'):\n", + " grp = grp.sort_values('plan_cost')\n", + " ax.plot(grp['plan_cost'], grp['real_time_ms'], marker='o', label=op)\n", + "\n", + "ax.set_xlabel('plan_cost (local operator cost + child scan costs)')\n", + "ax.set_ylabel('real_time (ms)')\n", + "ax.set_title('Measured time vs plan_cost for local-cost-matched operators')\n", + "ax.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "operator-cost-matched-heatmaps", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "matched_pivot = matched_df.set_index(['target_cost', 'operator'])[['cpu_time', 'model_cost', 'plan_cost']]\n", + "matched_costs = sorted(matched_df['target_cost'].unique())\n", + "\n", + "ncols = 2\n", + "nrows = (len(matched_costs) + ncols - 1) // ncols\n", + "fig, axes = plt.subplots(nrows, ncols, figsize=(5 * ncols, 4.5 * nrows), squeeze=False)\n", + "\n", + "for idx, target_cost in enumerate(matched_costs):\n", + " ax = axes[idx // ncols][idx % ncols]\n", + " sub = matched_pivot.xs(target_cost, level='target_cost')\n", + " ops = sub.index.tolist()\n", + " m = len(ops)\n", + "\n", + " mat = np.zeros((m, m))\n", + " for i, a in enumerate(ops):\n", + " for j, b in enumerate(ops):\n", + " if i != j:\n", + " mat[i, j] = (np.log(sub.loc[a, 'cpu_time']) - np.log(sub.loc[b, 'cpu_time']) -\n", + " (np.log(sub.loc[a, 'plan_cost']) - np.log(sub.loc[b, 'plan_cost'])))\n", + "\n", + " vmax = np.abs(mat).max() or 1.0\n", + " im = ax.imshow(mat, cmap='coolwarm', vmin=-vmax, vmax=vmax)\n", + " ax.set_xticks(range(m)); ax.set_xticklabels(ops, rotation=45, ha='right', fontsize=8)\n", + " ax.set_yticks(range(m)); ax.set_yticklabels(ops, fontsize=8)\n", + " ax.set_title(f'target_cost = {target_cost:,}', fontsize=10)\n", + " plt.colorbar(im, ax=ax, shrink=0.8)\n", + " for i in range(m):\n", + " for j in range(m):\n", + " ax.text(j, i, f'{mat[i,j]:.2f}', ha='center', va='center', fontsize=7,\n", + " color='black' if abs(mat[i,j]) < vmax * 0.6 else 'white')\n", + "\n", + "for idx in range(len(matched_costs), nrows * ncols):\n", + " axes[idx // ncols][idx % ncols].set_visible(False)\n", + "\n", + "fig.suptitle('Local-cost-matched operators: log(cpu(a)/cpu(b)) - log(plan_cost(a)/plan_cost(b)) [0 = perfect, +/-ln2 about 0.69 = 2x off]', fontsize=12)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "operator-cost-matched-comparison-table", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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target_cost6400001000000324000067600001225000026010000
ab
AggregationFilter0.5520.3120.4780.5310.8881.101
HashJoin0.0470.021-0.025-0.1490.1810.637
NestedLoopCrossJoin-0.169-0.206-0.065-0.2200.0000.277
NestedLoopJoin-0.651-0.688-0.479-0.405-0.0110.469
Projection-0.508-0.718-0.743-0.668-0.2620.103
SeqScan-0.1800.1490.3070.1220.5010.778
Sort-0.290-0.409-0.523-0.541-0.320-0.188
FilterAggregation-0.552-0.312-0.478-0.531-0.888-1.101
HashJoin-0.504-0.290-0.502-0.680-0.707-0.465
NestedLoopCrossJoin-0.721-0.517-0.543-0.751-0.887-0.824
NestedLoopJoin-1.203-0.999-0.956-0.936-0.899-0.633
Projection-1.059-1.030-1.220-1.199-1.150-0.998
SeqScan-0.732-0.162-0.171-0.409-0.386-0.323
Sort-0.841-0.720-1.000-1.072-1.207-1.289
HashJoinAggregation-0.047-0.0210.0250.149-0.181-0.637
Filter0.5040.2900.5020.6800.7070.465
NestedLoopCrossJoin-0.217-0.227-0.041-0.071-0.180-0.359
NestedLoopJoin-0.698-0.709-0.454-0.256-0.192-0.168
Projection-0.555-0.740-0.718-0.519-0.443-0.533
SeqScan-0.2270.1280.3310.2710.3210.142
Sort-0.337-0.430-0.498-0.392-0.500-0.825
NestedLoopCrossJoinAggregation0.1690.2060.0650.220-0.000-0.277
Filter0.7210.5170.5430.7510.8870.824
HashJoin0.2170.2270.0410.0710.1800.359
NestedLoopJoin-0.482-0.482-0.413-0.185-0.0110.191
Projection-0.338-0.512-0.677-0.448-0.262-0.174
SeqScan-0.0110.3550.3720.3420.5010.501
Sort-0.121-0.203-0.457-0.321-0.320-0.465
NestedLoopJoinAggregation0.6510.6880.4790.4050.011-0.469
Filter1.2030.9990.9560.9360.8990.633
HashJoin0.6980.7090.4540.2560.1920.168
NestedLoopCrossJoin0.4820.4820.4130.1850.011-0.191
Projection0.143-0.030-0.264-0.263-0.251-0.365
SeqScan0.4710.8370.7860.5270.5120.310
Sort0.3610.279-0.044-0.136-0.309-0.657
ProjectionAggregation0.5080.7180.7430.6680.262-0.103
Filter1.0591.0301.2201.1991.1500.998
HashJoin0.5550.7400.7180.5190.4430.533
NestedLoopCrossJoin0.3380.5120.6770.4480.2620.174
NestedLoopJoin-0.1430.0300.2640.2630.2510.365
SeqScan0.3280.8671.0490.7900.7630.675
Sort0.2180.3090.2200.127-0.058-0.291
SeqScanAggregation0.180-0.149-0.307-0.122-0.501-0.778
Filter0.7320.1620.1710.4090.3860.323
HashJoin0.227-0.128-0.331-0.271-0.321-0.142
NestedLoopCrossJoin0.011-0.355-0.372-0.342-0.501-0.501
NestedLoopJoin-0.471-0.837-0.786-0.527-0.512-0.310
Projection-0.328-0.867-1.049-0.790-0.763-0.675
Sort-0.110-0.558-0.830-0.663-0.821-0.966
SortAggregation0.2900.4090.5230.5410.3200.188
Filter0.8410.7201.0001.0721.2071.289
HashJoin0.3370.4300.4980.3920.5000.825
NestedLoopCrossJoin0.1210.2030.4570.3210.3200.465
NestedLoopJoin-0.361-0.2790.0440.1360.3090.657
Projection-0.218-0.309-0.220-0.1270.0580.291
SeqScan0.1100.5580.8300.6630.8210.966
\n", + "
" + ], + "text/plain": [ + "target_cost 640000 1000000 3240000 \\\n", + "a b \n", + "Aggregation Filter 0.552 0.312 0.478 \n", + " HashJoin 0.047 0.021 -0.025 \n", + " NestedLoopCrossJoin -0.169 -0.206 -0.065 \n", + " NestedLoopJoin -0.651 -0.688 -0.479 \n", + " Projection -0.508 -0.718 -0.743 \n", + " SeqScan -0.180 0.149 0.307 \n", + " Sort -0.290 -0.409 -0.523 \n", + "Filter Aggregation -0.552 -0.312 -0.478 \n", + " HashJoin -0.504 -0.290 -0.502 \n", + " NestedLoopCrossJoin -0.721 -0.517 -0.543 \n", + " NestedLoopJoin -1.203 -0.999 -0.956 \n", + " Projection -1.059 -1.030 -1.220 \n", + " SeqScan -0.732 -0.162 -0.171 \n", + " Sort -0.841 -0.720 -1.000 \n", + "HashJoin Aggregation -0.047 -0.021 0.025 \n", + " Filter 0.504 0.290 0.502 \n", + " NestedLoopCrossJoin -0.217 -0.227 -0.041 \n", + " NestedLoopJoin -0.698 -0.709 -0.454 \n", + " Projection -0.555 -0.740 -0.718 \n", + " SeqScan -0.227 0.128 0.331 \n", + " Sort -0.337 -0.430 -0.498 \n", + "NestedLoopCrossJoin Aggregation 0.169 0.206 0.065 \n", + " Filter 0.721 0.517 0.543 \n", + " HashJoin 0.217 0.227 0.041 \n", + " NestedLoopJoin -0.482 -0.482 -0.413 \n", + " Projection -0.338 -0.512 -0.677 \n", + " SeqScan -0.011 0.355 0.372 \n", + " Sort -0.121 -0.203 -0.457 \n", + "NestedLoopJoin Aggregation 0.651 0.688 0.479 \n", + " Filter 1.203 0.999 0.956 \n", + " HashJoin 0.698 0.709 0.454 \n", + " NestedLoopCrossJoin 0.482 0.482 0.413 \n", + " Projection 0.143 -0.030 -0.264 \n", + " SeqScan 0.471 0.837 0.786 \n", + " Sort 0.361 0.279 -0.044 \n", + "Projection Aggregation 0.508 0.718 0.743 \n", + " Filter 1.059 1.030 1.220 \n", + " HashJoin 0.555 0.740 0.718 \n", + " NestedLoopCrossJoin 0.338 0.512 0.677 \n", + " NestedLoopJoin -0.143 0.030 0.264 \n", + " SeqScan 0.328 0.867 1.049 \n", + " Sort 0.218 0.309 0.220 \n", + "SeqScan Aggregation 0.180 -0.149 -0.307 \n", + " Filter 0.732 0.162 0.171 \n", + " HashJoin 0.227 -0.128 -0.331 \n", + " NestedLoopCrossJoin 0.011 -0.355 -0.372 \n", + " NestedLoopJoin -0.471 -0.837 -0.786 \n", + " Projection -0.328 -0.867 -1.049 \n", + " Sort -0.110 -0.558 -0.830 \n", + "Sort Aggregation 0.290 0.409 0.523 \n", + " Filter 0.841 0.720 1.000 \n", + " HashJoin 0.337 0.430 0.498 \n", + " NestedLoopCrossJoin 0.121 0.203 0.457 \n", + " NestedLoopJoin -0.361 -0.279 0.044 \n", + " Projection -0.218 -0.309 -0.220 \n", + " SeqScan 0.110 0.558 0.830 \n", + "\n", + "target_cost 6760000 12250000 26010000 \n", + "a b \n", + "Aggregation Filter 0.531 0.888 1.101 \n", + " HashJoin -0.149 0.181 0.637 \n", + " NestedLoopCrossJoin -0.220 0.000 0.277 \n", + " NestedLoopJoin -0.405 -0.011 0.469 \n", + " Projection -0.668 -0.262 0.103 \n", + " SeqScan 0.122 0.501 0.778 \n", + " Sort -0.541 -0.320 -0.188 \n", + "Filter Aggregation -0.531 -0.888 -1.101 \n", + " HashJoin -0.680 -0.707 -0.465 \n", + " NestedLoopCrossJoin -0.751 -0.887 -0.824 \n", + " NestedLoopJoin -0.936 -0.899 -0.633 \n", + " Projection -1.199 -1.150 -0.998 \n", + " SeqScan -0.409 -0.386 -0.323 \n", + " Sort -1.072 -1.207 -1.289 \n", + "HashJoin Aggregation 0.149 -0.181 -0.637 \n", + " Filter 0.680 0.707 0.465 \n", + " NestedLoopCrossJoin -0.071 -0.180 -0.359 \n", + " NestedLoopJoin -0.256 -0.192 -0.168 \n", + " Projection -0.519 -0.443 -0.533 \n", + " SeqScan 0.271 0.321 0.142 \n", + " Sort -0.392 -0.500 -0.825 \n", + "NestedLoopCrossJoin Aggregation 0.220 -0.000 -0.277 \n", + " Filter 0.751 0.887 0.824 \n", + " HashJoin 0.071 0.180 0.359 \n", + " NestedLoopJoin -0.185 -0.011 0.191 \n", + " Projection -0.448 -0.262 -0.174 \n", + " SeqScan 0.342 0.501 0.501 \n", + " Sort -0.321 -0.320 -0.465 \n", + "NestedLoopJoin Aggregation 0.405 0.011 -0.469 \n", + " Filter 0.936 0.899 0.633 \n", + " HashJoin 0.256 0.192 0.168 \n", + " NestedLoopCrossJoin 0.185 0.011 -0.191 \n", + " Projection -0.263 -0.251 -0.365 \n", + " SeqScan 0.527 0.512 0.310 \n", + " Sort -0.136 -0.309 -0.657 \n", + "Projection Aggregation 0.668 0.262 -0.103 \n", + " Filter 1.199 1.150 0.998 \n", + " HashJoin 0.519 0.443 0.533 \n", + " NestedLoopCrossJoin 0.448 0.262 0.174 \n", + " NestedLoopJoin 0.263 0.251 0.365 \n", + " SeqScan 0.790 0.763 0.675 \n", + " Sort 0.127 -0.058 -0.291 \n", + "SeqScan Aggregation -0.122 -0.501 -0.778 \n", + " Filter 0.409 0.386 0.323 \n", + " HashJoin -0.271 -0.321 -0.142 \n", + " NestedLoopCrossJoin -0.342 -0.501 -0.501 \n", + " NestedLoopJoin -0.527 -0.512 -0.310 \n", + " Projection -0.790 -0.763 -0.675 \n", + " Sort -0.663 -0.821 -0.966 \n", + "Sort Aggregation 0.541 0.320 0.188 \n", + " Filter 1.072 1.207 1.289 \n", + " HashJoin 0.392 0.500 0.825 \n", + " NestedLoopCrossJoin 0.321 0.320 0.465 \n", + " NestedLoopJoin 0.136 0.309 0.657 \n", + " Projection -0.127 0.058 0.291 \n", + " SeqScan 0.663 0.821 0.966 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "matched_rows = []\n", + "for target_cost in matched_costs:\n", + " sub = matched_pivot.xs(target_cost, level='target_cost')\n", + " ops = sub.index.tolist()\n", + " for a in ops:\n", + " for b in ops:\n", + " if a != b:\n", + " delta = (np.log(sub.loc[a, 'cpu_time']) - np.log(sub.loc[b, 'cpu_time']) -\n", + " (np.log(sub.loc[a, 'plan_cost']) - np.log(sub.loc[b, 'plan_cost'])))\n", + " matched_rows.append({'target_cost': target_cost, 'a': a, 'b': b, 'delta': delta})\n", + "\n", + "matched_comparison = pd.DataFrame(matched_rows)\n", + "matched_comparison.pivot_table(index=['a', 'b'], columns='target_cost', values='delta').round(3)" + ] } ], "metadata": { diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index c32f335..e726a66 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -72,21 +72,19 @@ PropertySet DeriveOutputProps(utils::NotNull expr, int64_t LowerBoundLocalCost(utils::NotNull expr, CardinalityEstimates& cardinality) { return std::visit(utils::Overloaded{ [&](const logical::Table&) -> int64_t { - return cardinality.GetCardinality(expr->group); + return 100 * cardinality.GetCardinality(expr->group); }, [&](const logical::Filter&) -> int64_t { - return cardinality.GetCardinality(expr->group); + return 100 * cardinality.GetCardinality(expr->group); }, [&](const logical::Projection&) -> int64_t { - return cardinality.GetCardinality(expr->group); + return 22 * cardinality.GetCardinality(expr->group); }, - [&](const logical::Aggregation&) -> int64_t { - return cardinality.GetCardinality(expr->group); + [&](const logical::Aggregation& a) -> int64_t { + return 510 * cardinality.GetCardinality(a.source); }, [&](const logical::CrossJoin& j) -> int64_t { - auto p_l = (cardinality.GetCardinality(j.lhs) + kBufSize - 1) / kBufSize; - auto p_r = (cardinality.GetCardinality(j.rhs) + kBufSize - 1) / kBufSize; - return p_l * (1 + p_r); + return 104 * cardinality.GetCardinality(j.lhs) * cardinality.GetCardinality(j.rhs); }, [&](const logical::Join& j) -> int64_t { // Must stay ≤ every physical impl. NLJ ~ n_l*n_r, HJ ~ n_l+n_r @@ -94,7 +92,7 @@ int64_t LowerBoundLocalCost(utils::NotNull expr, CardinalityEstima // take the min so the bound is safe for both alternatives. auto n_l = cardinality.GetCardinality(j.lhs); auto n_r = cardinality.GetCardinality(j.rhs); - return std::min(n_l + n_r, n_l * n_r); + return std::min(69 * (n_l + n_r), 70 * n_l * n_r); }, }, expr->root_operator); } @@ -102,33 +100,33 @@ int64_t LowerBoundLocalCost(utils::NotNull expr, CardinalityEstima int64_t CalcCost(utils::NotNull expr, CardinalityEstimates& cardinality) { return std::visit(utils::Overloaded{ [&](const physical::SeqScan&) -> int64_t { - return cardinality.GetCardinality(expr->group); + return 100 * cardinality.GetCardinality(expr->group); }, [&](const physical::Filter&) -> int64_t { - return cardinality.GetCardinality(expr->group); + return 100 * cardinality.GetCardinality(expr->group); }, [&](const physical::Projection&) -> int64_t { - return cardinality.GetCardinality(expr->group); + return 22 * cardinality.GetCardinality(expr->group); }, [&](const physical::NestedLoopJoin& j) -> int64_t { auto n_l = cardinality.GetCardinality(j.lhs); auto n_r = cardinality.GetCardinality(j.rhs); - return n_l * n_r; + return 70 * n_l * n_r; }, [&](const physical::NestedLoopCrossJoin& j) -> int64_t { - auto p_l = (cardinality.GetCardinality(j.lhs) + kBufSize - 1) / kBufSize; - auto p_r = (cardinality.GetCardinality(j.rhs) + kBufSize - 1) / kBufSize; - return p_l * (1 + p_r); + auto n_l = cardinality.GetCardinality(j.lhs); + auto n_r = cardinality.GetCardinality(j.rhs); + return 104 * n_l * n_r; }, [&](const physical::HashJoin& j) -> int64_t { - return cardinality.GetCardinality(j.lhs) + cardinality.GetCardinality(j.rhs); + return 69 * (cardinality.GetCardinality(j.lhs) + cardinality.GetCardinality(j.rhs)); }, [&](const physical::Sort& s) -> int64_t { auto n = cardinality.GetCardinality(s.input); - return n > 1 ? n * static_cast(std::bit_width(static_cast(n))) : n; + return 11 * (n > 1 ? n * static_cast(std::bit_width(static_cast(n))) : n); }, [&](const physical::Aggregation& a) -> int64_t { - return cardinality.GetCardinality(a.source); + return 510 * cardinality.GetCardinality(a.source); }, }, expr->root_operator); } From 3ea373e1dc9575a1ea0cf8fffd165e61c692d266 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 1 Jun 2026 14:22:36 +0300 Subject: [PATCH 071/120] Fix --- .gitignore | 1 + benchmarks/main.cpp | 6 +-- .../stewkk/sql/logic/executor/buffer_size.hpp | 3 +- .../stewkk/sql/logic/optimizer/optimizer.hpp | 4 -- .../sql/logic/optimizer/reachability.hpp | 5 -- .../sql/logic/optimizer/schema_catalog.hpp | 2 +- .../transformation_rules/filter_merge.hpp | 3 -- .../filter_pushdown_through_join.hpp | 5 -- .../filter_pushdown_through_projection.hpp | 5 -- .../transformation_rules/filter_split.hpp | 3 -- .../transformation_rules/in_to_or_chain.hpp | 8 ---- include/stewkk/sql/utils/not_null.hpp | 4 +- src/stewkk/sql/logic/executor/executor.cpp | 46 +++++++++---------- .../sql/logic/executor/executor_test.cpp | 4 +- .../sql/logic/executor/plan_serializer.cpp | 2 - .../implement_hash_join.cpp | 4 -- .../sql/logic/optimizer/cardinality.cpp | 4 +- src/stewkk/sql/logic/optimizer/optimizer.cpp | 35 +++++--------- .../sql/logic/optimizer/optimizer_test.cpp | 12 ++--- .../sql/logic/optimizer/schema_catalog.cpp | 2 - src/stewkk/sql/logic/parser/visitor.cpp | 7 --- .../transformation_rules/in_to_or_chain.cpp | 4 -- .../join_associativity.cpp | 4 -- .../transformation_rules/predicate_utils.cpp | 2 - src/stewkk/sql/main.cpp | 14 ------ 25 files changed, 50 insertions(+), 139 deletions(-) diff --git a/.gitignore b/.gitignore index 66e2d9b..b1ab3b0 100644 --- a/.gitignore +++ b/.gitignore @@ -16,3 +16,4 @@ FlameGraph/ /benchmarks/datasets/ssb/generated/ /benchmarks/datasets/ssb/raw/ /.plans/ +/report/build-practice/ diff --git a/benchmarks/main.cpp b/benchmarks/main.cpp index 1177b72..14d29e7 100644 --- a/benchmarks/main.cpp +++ b/benchmarks/main.cpp @@ -101,7 +101,7 @@ CardinalityEstimates LoadCardinalityFromCsvDir(const std::filesystem::path& dir) if (entry.path().extension() != ".csv") continue; std::ifstream in{entry.path()}; std::string line; - int64_t rows = -1; // header + int64_t rows = -1; while (std::getline(in, line)) ++rows; counts.emplace(entry.path().stem().string(), std::max(0, rows)); } @@ -148,10 +148,6 @@ static constexpr char kComplex4000[]{"SELECT departments_4000.id*2, employees_20 static constexpr char kComplex8000[]{"SELECT departments_8000.id*2, employees_200.id+1 FROM employees_200 RIGHT JOIN departments_8000 ON employees_200.department_id = departments_8000.id AND departments_8000.id > 3 AND departments_8000.id*2*2/2/2*2 < 30;"}; static constexpr char kComplex16000[]{"SELECT departments_16000.id*2, employees_200.id+1 FROM employees_200 RIGHT JOIN departments_16000 ON employees_200.department_id = departments_16000.id AND departments_16000.id > 3 AND departments_16000.id*2*2/2/2*2 < 30;"}; -// 3-way joins on skewed tables (regions=10, customers=500, orders=5000). -// Naive textual order (orders ⋈ customers) ⋈ regions builds a ~2.5M-tuple -// intermediate. JoinAssociativity + JoinCommutativity let the optimizer pick a -// shape with much smaller intermediates. static constexpr char kMultiwayOCR[]{ "SELECT orders.id, customers.id, regions.id FROM orders " "JOIN customers ON orders.customer_id = customers.id " diff --git a/include/stewkk/sql/logic/executor/buffer_size.hpp b/include/stewkk/sql/logic/executor/buffer_size.hpp index cf4bb29..176aeef 100644 --- a/include/stewkk/sql/logic/executor/buffer_size.hpp +++ b/include/stewkk/sql/logic/executor/buffer_size.hpp @@ -6,5 +6,4 @@ namespace stewkk::sql { constexpr static std::size_t kBufSize = 2048; -} // stewkk::sql - +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/optimizer.hpp b/include/stewkk/sql/logic/optimizer/optimizer.hpp index 779b62f..b4975da 100644 --- a/include/stewkk/sql/logic/optimizer/optimizer.hpp +++ b/include/stewkk/sql/logic/optimizer/optimizer.hpp @@ -43,10 +43,6 @@ class Optimizer { void OptimizeInputs(utils::NotNull expr, PropertySet required, std::vector child_delivered, int64_t accum, Limit limit, size_t child_index = 0); - // Property-independent admissible lower bound on the cheapest physical plan - // for this group: min over logical alternatives of (best-case local cost + - // sum of children's lower bounds). Cached; safe to memoize once Phase 1 - // exploration has populated the group's logical alternatives. std::int64_t LowerBoundCost(utils::NotNull group); void ApplyRule(TransformationRuleId rule, utils::NotNull expr, Limit limit); diff --git a/include/stewkk/sql/logic/optimizer/reachability.hpp b/include/stewkk/sql/logic/optimizer/reachability.hpp index 8e74e67..c62eda9 100644 --- a/include/stewkk/sql/logic/optimizer/reachability.hpp +++ b/include/stewkk/sql/logic/optimizer/reachability.hpp @@ -19,11 +19,6 @@ struct MatchResult { MatchResult IsReachable(utils::NotNull root, const PhysicalPlanNode& target); -// Runs an exhaustive search over `sql` and reports whether `target` is among -// the physical alternatives the optimizer enumerates. The query's ORDER BY (if -// any) is propagated as a required sort property so the search also generates -// the Sort enforcers needed to reach an ordered plan; `schema` lets those -// enforcers validate that the sort keys exist on the group they sit above. MatchResult IsPlanReachable(std::istream& sql, const PhysicalPlanNode& target, CardinalityEstimates cardinality = {}, SchemaCatalog schema = {}); diff --git a/include/stewkk/sql/logic/optimizer/schema_catalog.hpp b/include/stewkk/sql/logic/optimizer/schema_catalog.hpp index 93c0446..7a336e9 100644 --- a/include/stewkk/sql/logic/optimizer/schema_catalog.hpp +++ b/include/stewkk/sql/logic/optimizer/schema_catalog.hpp @@ -17,7 +17,7 @@ class SchemaCatalog { public: SchemaCatalog(std::unordered_map tables = {}); - // Returns nullopt when any table in the subtree is unknown to the catalog. + std::optional GetSchema(utils::NotNull group); private: diff --git a/include/stewkk/sql/logic/transformation_rules/filter_merge.hpp b/include/stewkk/sql/logic/transformation_rules/filter_merge.hpp index b45801c..dbbf035 100644 --- a/include/stewkk/sql/logic/transformation_rules/filter_merge.hpp +++ b/include/stewkk/sql/logic/transformation_rules/filter_merge.hpp @@ -5,9 +5,6 @@ namespace stewkk::sql { -// Filter(p_outer, Filter(p_inner, X)) → Filter(p_outer AND p_inner, X) -// Collapses adjacent filters so the merged predicate can be pushed down or -// matched against join quals as a single conjunction. class FilterMerge : public TransformationRule { public: bool IsApplicable(utils::NotNull expr) override; diff --git a/include/stewkk/sql/logic/transformation_rules/filter_pushdown_through_join.hpp b/include/stewkk/sql/logic/transformation_rules/filter_pushdown_through_join.hpp index b659e71..9db2b4f 100644 --- a/include/stewkk/sql/logic/transformation_rules/filter_pushdown_through_join.hpp +++ b/include/stewkk/sql/logic/transformation_rules/filter_pushdown_through_join.hpp @@ -5,11 +5,6 @@ namespace stewkk::sql { -// Filter(p, Join(L, R, T, q)) → [Filter(rest,)] Join(maybeFilter(p_L,L), maybeFilter(p_R,R), T, q) -// Conjuncts of p whose attributes lie entirely within one side are pushed -// into that side; conjuncts spanning both sides remain above the join. -// Outer-join safety: a conjunct may only be pushed into a *preserved* side — -// LEFT preserves lhs, RIGHT preserves rhs, INNER preserves both, FULL neither. class FilterPushdownThroughJoin : public TransformationRule { public: bool IsApplicable(utils::NotNull expr) override; diff --git a/include/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.hpp b/include/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.hpp index 8217370..b22944d 100644 --- a/include/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.hpp +++ b/include/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.hpp @@ -5,11 +5,6 @@ namespace stewkk::sql { -// Filter(p, Projection(exprs, X)) → Projection(exprs, Filter(p, X)) -// Safe only when every Attribute referenced by p appears as a bare Attribute -// in exprs (i.e. is passed through unchanged). Computed projections cannot be -// referenced by name in this AST, so the conservative bare-attribute check -// also matches the wider correctness condition. class FilterPushdownThroughProjection : public TransformationRule { public: bool IsApplicable(utils::NotNull expr) override; diff --git a/include/stewkk/sql/logic/transformation_rules/filter_split.hpp b/include/stewkk/sql/logic/transformation_rules/filter_split.hpp index 51bc362..5eba6d8 100644 --- a/include/stewkk/sql/logic/transformation_rules/filter_split.hpp +++ b/include/stewkk/sql/logic/transformation_rules/filter_split.hpp @@ -5,9 +5,6 @@ namespace stewkk::sql { -// Filter(p1 AND p2 AND ... AND pn, X) → Filter(p1, Filter(p2 AND ... AND pn, X)) -// Splits the first conjunct off; remaining conjuncts cascade-split when the -// rule fires on the inner Filter. class FilterSplit : public TransformationRule { public: bool IsApplicable(utils::NotNull expr) override; diff --git a/include/stewkk/sql/logic/transformation_rules/in_to_or_chain.hpp b/include/stewkk/sql/logic/transformation_rules/in_to_or_chain.hpp index 923824d..bbcd362 100644 --- a/include/stewkk/sql/logic/transformation_rules/in_to_or_chain.hpp +++ b/include/stewkk/sql/logic/transformation_rules/in_to_or_chain.hpp @@ -5,14 +5,6 @@ namespace stewkk::sql { -// Filter(... x IN (v0, v1, ...) ..., X) -// → Filter(... (x = v0 OR x = v1 OR ...) ..., X) -// and the NOT IN case to (x != v0 AND x != v1 AND ...). -// -// IN and the OR-chain are equal under three-valued logic, so this just adds the -// expanded form as a sibling in the same group. It lets the exhaustive search -// reach plans that materialize the OR-chain (as MS SQL Server does), and the -// expanded predicate is JIT-able whereas the InExpression node is not. class InToOrChain : public TransformationRule { public: bool IsApplicable(utils::NotNull expr) override; diff --git a/include/stewkk/sql/utils/not_null.hpp b/include/stewkk/sql/utils/not_null.hpp index d48d54f..d34f78a 100644 --- a/include/stewkk/sql/utils/not_null.hpp +++ b/include/stewkk/sql/utils/not_null.hpp @@ -9,14 +9,14 @@ template requires std::is_pointer_v class NotNull { public: - NotNull(T ptr) : ptr_(ptr) { assert(ptr_ != nullptr); } // NOLINT(google-explicit-constructor) + NotNull(T ptr) : ptr_(ptr) { assert(ptr_ != nullptr); } NotNull(std::nullptr_t) = delete; T get() const { return ptr_; } auto& operator*() const { return *ptr_; } T operator->() const { return ptr_; } - operator T() const { return ptr_; } // NOLINT(google-explicit-constructor) + operator T() const { return ptr_; } bool operator==(const NotNull& other) const = default; diff --git a/src/stewkk/sql/logic/executor/executor.cpp b/src/stewkk/sql/logic/executor/executor.cpp index e348324..9d174a1 100644 --- a/src/stewkk/sql/logic/executor/executor.cpp +++ b/src/stewkk/sql/logic/executor/executor.cpp @@ -122,7 +122,7 @@ Type GetExpressionType(const Expression& expr, const AttributesInfo& available_a } Type operator()(const Literal& literal) const { // NOTE: we are using switch because compiler will remind about adding - // type of new literal here + switch (literal) { case Literal::kNull: return Type::kBool; @@ -589,9 +589,9 @@ boost::asio::awaitable> Executor::Execute(c auto [attr_chan, tuples_chan] = co_await GetChannels(); auto task = SpawnExecutor(exec, op, attr_chan, tuples_chan); - // If the child throws, its scope_fail closes both channels, which wakes - // our async_receive with channel_closed. We then await the task to surface - // the child's real exception instead of the channel_closed wrapper. + + + std::exception_ptr eptr; AttributesInfo attrs; Tuples result; @@ -635,7 +635,7 @@ boost::asio::awaitable Executor::Execute(const Physica } boost::asio::awaitable operator()(const PhysicalProjection& projection) { // NOTE: We are using multiset relational algebra projection (i.e. not - // eleminating duplicate tuples) + co_await executor.ExecuteProjection(projection, attr_chan, tuples_chan); co_return; } @@ -1079,11 +1079,11 @@ size_t FindAttrIndex(const AttributesInfo& attrs, const Attribute& a) { } // namespace -// Inner equi-join: build a hash table on lhs.key, then probe with rhs. -// ImplementHashJoin gates applicability to `attr = attr` quals on Inner joins, -// so the only shapes that reach here are: lhs_attr == rhs_attr or rhs_attr == -// lhs_attr. We resolve which side owns each attribute via the lhs attribute -// stream; the remaining one must belong to rhs. + + + + + template boost::asio::awaitable Executor::ExecuteHashJoin( const HashJoin& join, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan) { @@ -1112,7 +1112,7 @@ boost::asio::awaitable Executor::ExecuteHashJoin( auto lhs_attrs = co_await lhs_attrs_chan.async_receive(boost::asio::use_awaitable); auto rhs_attrs = co_await rhs_attrs_chan.async_receive(boost::asio::use_awaitable); - // Pick the (lhs_key, rhs_key) pair regardless of which side the qual wrote first. + auto lhs_has = [&](const Attribute& attr) { return std::any_of(lhs_attrs.begin(), lhs_attrs.end(), [&](const AttributeInfo& ai) { return ai.table == attr.table && ai.name == attr.name; @@ -1135,8 +1135,8 @@ boost::asio::awaitable Executor::ExecuteHashJoin( boost::asio::use_awaitable); attr_chan.close(); - // Build phase: collect all lhs tuples into a hash multimap keyed by lhs.key. - // NULL keys never match (SQL = on NULL is unknown), so drop them. + + std::unordered_multimap build; for (;;) { auto buf = co_await ReceiveTuples(lhs_tuples_chan); @@ -1150,7 +1150,7 @@ boost::asio::awaitable Executor::ExecuteHashJoin( } Log("HashJoin build phase done; {} entries", build.size()); - // Probe phase: stream rhs, lookup matches, emit joined tuples in kBufSize chunks. + Tuples out_buf; out_buf.reserve(kBufSize); for (;;) { @@ -1194,7 +1194,7 @@ boost::asio::awaitable Executor::ExecuteHashAggregate( auto in_attrs = co_await in_attrs_chan.async_receive(boost::asio::use_awaitable); - // Build output AttributesInfo: group_by cols then one slot per aggregate. + AttributesInfo out_attrs; for (const auto& expr : agg.group_by) { if (const auto* attr = std::get_if(&expr)) { @@ -1217,12 +1217,12 @@ boost::asio::awaitable Executor::ExecuteHashAggregate( boost::asio::use_awaitable); out_attr_chan.close(); - // Per-group state: vector of int64_t accumulators, one per aggregate. - // For SUM: running total (null if all inputs null). - // For COUNT: running count of non-null inputs (or all rows for COUNT(*)). + + + struct GroupState { std::vector accumulators; - std::vector any_non_null; // for SUM null tracking + std::vector any_non_null; }; struct TupleKeyHash { size_t operator()(const std::vector& key) const { @@ -1236,7 +1236,7 @@ boost::asio::awaitable Executor::ExecuteHashAggregate( }; std::unordered_map, GroupState, TupleKeyHash> groups; - // Scalar evaluator for group-by keys and aggregate arguments. + auto do_scalar = [&](const Expression& expr, const Tuple& tuple) -> Value { return CalcExpression(tuple, in_attrs, expr); }; @@ -1270,7 +1270,7 @@ boost::asio::awaitable Executor::ExecuteHashAggregate( auto v = do_scalar(*agg_expr.argument, tuple); if (!v.is_null) state.accumulators[i]++; } - } else { // SUM + } else { auto v = do_scalar(*agg_expr.argument, tuple); if (!v.is_null) { state.accumulators[i] += v.value.int_value; @@ -1281,7 +1281,7 @@ boost::asio::awaitable Executor::ExecuteHashAggregate( } } - // For scalar aggregate over empty input, emit one row with COUNT=0 / SUM=NULL. + if (scalar_agg && groups.empty()) { groups.emplace(std::vector{}, init_state()); } @@ -1293,7 +1293,7 @@ boost::asio::awaitable Executor::ExecuteHashAggregate( for (size_t i = 0; i < agg.aggregates.size(); ++i) { const auto& agg_expr = std::get(agg.aggregates[i]); if (agg_expr.function == AggregateFunction::kSum && !state.any_non_null[i]) { - tuple.push_back(Value{true}); // NULL + tuple.push_back(Value{true}); } else { tuple.push_back(Value{false, {.int_value = state.accumulators[i]}}); } diff --git a/src/stewkk/sql/logic/executor/executor_test.cpp b/src/stewkk/sql/logic/executor/executor_test.cpp index c1fb3b9..cb50130 100644 --- a/src/stewkk/sql/logic/executor/executor_test.cpp +++ b/src/stewkk/sql/logic/executor/executor_test.cpp @@ -738,9 +738,9 @@ TEST(ExecutorTest, GroupByCountStar) { }(), [](std::exception_ptr p, Result got) { if (p) std::rethrow_exception(p); - // 11 distinct age values in users.csv + ASSERT_THAT(got.value().tuples.size(), Eq(11u)); - // Sum of counts must equal total rows (17) + int64_t total = 0; for (const auto& t : got.value().tuples) { total += t[1].value.int_value; diff --git a/src/stewkk/sql/logic/executor/plan_serializer.cpp b/src/stewkk/sql/logic/executor/plan_serializer.cpp index 4cb8af9..9ae53dc 100644 --- a/src/stewkk/sql/logic/executor/plan_serializer.cpp +++ b/src/stewkk/sql/logic/executor/plan_serializer.cpp @@ -101,8 +101,6 @@ std::string SerializeExpr(const Expression& expr) { SerializeExpr(*e.lhs), SerializeExpr(*e.rhs)); } std::string operator()(const Attribute& a) const { - // Synthetic aggregate-output attributes have an empty table; emit - // "-" so the field stays a parseable atom on the deserialize side. return std::format("(attr {} {})", a.table.empty() ? "-" : a.table, a.name); } std::string operator()(IntConst n) const { diff --git a/src/stewkk/sql/logic/implementation_rules/implement_hash_join.cpp b/src/stewkk/sql/logic/implementation_rules/implement_hash_join.cpp index a207506..4306956 100644 --- a/src/stewkk/sql/logic/implementation_rules/implement_hash_join.cpp +++ b/src/stewkk/sql/logic/implementation_rules/implement_hash_join.cpp @@ -4,10 +4,6 @@ namespace stewkk::sql { namespace { -// HashJoin currently handles only `attr_l = attr_r` quals on Inner joins. -// Outer-join semantics need a "right-side matched" bitmap during probe; not -// implemented yet. Composite/expression keys would require key extraction -// from arbitrary Expression nodes, also not done. bool IsSimpleEquiJoin(const Expression& qual) { const auto* bin = std::get_if(&qual); if (!bin || bin->binop != BinaryOp::kEq) return false; diff --git a/src/stewkk/sql/logic/optimizer/cardinality.cpp b/src/stewkk/sql/logic/optimizer/cardinality.cpp index 28ff243..2749864 100644 --- a/src/stewkk/sql/logic/optimizer/cardinality.cpp +++ b/src/stewkk/sql/logic/optimizer/cardinality.cpp @@ -8,8 +8,8 @@ namespace stewkk::sql { namespace { -// System R: each `attr = attr` conjunct contributes 1/max(lhs_card, rhs_card); -// AND multiplies; anything else (including TRUE) contributes 1.0. + + double JoinSelectivity(const Expression& qual, int64_t lhs_card, int64_t rhs_card) { const auto* b = std::get_if(&qual); if (!b) return 1.0; diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index e726a66..43c3d86 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -24,7 +24,7 @@ static const std::vector> kEnforcers = [] { return v; }(); -// Top-down: given `required` of this expr, what must child `child_index` deliver? + PropertySet RequiredInputProps(utils::NotNull expr, PropertySet required, size_t child_index) { return std::visit(utils::Overloaded{ @@ -38,9 +38,6 @@ PropertySet RequiredInputProps(utils::NotNull expr, return child_index == 0 ? required : PropertySet::Any(); }, [&](const physical::HashJoin&) -> PropertySet { - // HashJoin reorders output (hash-partitioned scan of build side, then - // probe-driven emit). It does not preserve any input sort order, so - // never ask children to satisfy `required`. return PropertySet::Any(); }, [&](const physical::Sort&) { return PropertySet::Any(); }, @@ -48,9 +45,6 @@ PropertySet RequiredInputProps(utils::NotNull expr, }, expr->root_operator); } -// Bottom-up: given what children delivered, what does this expr deliver? -// Sort property is schema-blind — operators that need column access (sort -// enforcer placement, merge join applicability) check the schema separately. PropertySet DeriveOutputProps(utils::NotNull expr, const std::vector& child_delivered) { return std::visit(utils::Overloaded{ @@ -65,10 +59,6 @@ PropertySet DeriveOutputProps(utils::NotNull expr, }, expr->root_operator); } -// Best-case local cost achievable by any physical impl of this logical -// operator, ignoring required physical properties. Used to compute group lower -// bounds for B&B pruning. Must remain ≤ every CalcCost over physical impls of -// the same logical alternative — update when adding cheaper physical impls. int64_t LowerBoundLocalCost(utils::NotNull expr, CardinalityEstimates& cardinality) { return std::visit(utils::Overloaded{ [&](const logical::Table&) -> int64_t { @@ -87,9 +77,6 @@ int64_t LowerBoundLocalCost(utils::NotNull expr, CardinalityEstima return 104 * cardinality.GetCardinality(j.lhs) * cardinality.GetCardinality(j.rhs); }, [&](const logical::Join& j) -> int64_t { - // Must stay ≤ every physical impl. NLJ ~ n_l*n_r, HJ ~ n_l+n_r - // (equi only). When either side has 1 tuple, NLJ can beat HJ, so - // take the min so the bound is safe for both alternatives. auto n_l = cardinality.GetCardinality(j.lhs); auto n_r = cardinality.GetCardinality(j.rhs); return std::min(69 * (n_l + n_r), 70 * n_l * n_r); @@ -199,7 +186,7 @@ int64_t Optimizer::LowerBoundCost(utils::NotNu if (auto it = lower_bounds_.find(group.get()); it != lower_bounds_.end()) { return it->second; } - // Memo groups form a DAG (no cycles), so recursion terminates. + int64_t best = std::numeric_limits::max(); for (auto expr : group->GetLogicalExprs()) { int64_t local = LowerBoundLocalCost(expr, cardinality_); @@ -232,8 +219,8 @@ template void Optimizer::OptimizeInputs( utils::NotNull expr, PropertySet required, std::vector child_delivered, int64_t accum, Limit limit, size_t child_index) { - // accum = local_cost(expr) + cost of children processed so far. - // child_delivered[i] = what child i actually delivered (filled as we go). + + WinnerKey self_key{expr->group.get(), required}; if (auto it = winner_.find(self_key); it != winner_.end()) { limit = limit ? Limit{std::min(*limit, it->second.cost)} : Limit{it->second.cost}; @@ -282,9 +269,9 @@ void Optimizer::ApplyRule( Log("Applying transformation rule {} to group {}", rule.value, expr->group->GetId()); auto new_expr = rules_applier_.Apply(rule, expr, memo_); tasks_.emplace([this, new_expr, limit]() { ExploreExpression(new_expr, limit); }); - // A new logical alternative in new_expr->group may unlock parent-side rules - // whose patterns inspect child operators. Re-try rules on every parent; - // already-applied (expr, rule) pairs are no-ops thanks to RulesApplier gating. + + + if (auto it = group_parents_.find(new_expr->group.get()); it != group_parents_.end()) { for (auto* parent : it->second) { tasks_.emplace([this, parent, limit]() { @@ -367,9 +354,9 @@ void Optimizer::OptimizeGroup( return; } - // Try every non-enforcer phys expr under `required`. Whether it actually - // serves `required` is decided at the bottom of OptimizeInputs once children - // have resolved and DeriveOutputProps can compute the true delivered. + + + for (auto phys_expr : group->GetPhysicalExprs()) { if (phys_expr->is_enforcer) continue; auto lc = local_cost_[phys_expr.get()]; @@ -384,7 +371,7 @@ void Optimizer::OptimizeGroup( for (const auto& enforcer : kEnforcers) { auto op = enforcer->TryBuild(group, required, schema_); if (!op) continue; - auto enf_expr = group->AddPhysicalExpr(*op, /*is_enforcer=*/true); + auto enf_expr = group->AddPhysicalExpr(*op, true); auto lc = CalcCost(enf_expr, cardinality_); Log("Enforcer local cost for group {}: {}", group->GetId(), lc); local_cost_[enf_expr.get()] = lc; diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index b32cb15..8bfeba0 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -221,8 +221,8 @@ TEST(ReachabilityTest, InExpandsToOrChain) { ASSERT_THAT(result.reachable, IsTrue()); } -// An ORDER BY query becomes a required sort property, so the search generates a -// Sort enforcer on the root projection group and the ordered plan is reachable. + + TEST(ReachabilityTest, OrderByReachableViaSortEnforcer) { std::stringstream s{"SELECT users.id FROM users ORDER BY users.id;"}; PhysicalSort target{ @@ -235,8 +235,8 @@ TEST(ReachabilityTest, OrderByReachableViaSortEnforcer) { ASSERT_THAT(result.reachable, IsTrue()); } -// The enforcer is generated for the ORDER BY direction the query asked for, so -// a plan whose Sort runs the other way is not reachable. + + TEST(ReachabilityTest, OrderByWrongDirectionNotReachable) { std::stringstream s{"SELECT users.id FROM users ORDER BY users.id ASC;"}; PhysicalSort target{ @@ -249,8 +249,8 @@ TEST(ReachabilityTest, OrderByWrongDirectionNotReachable) { ASSERT_THAT(result.reachable, IsFalse()); } -// Without an ORDER BY no required sort is propagated, so no Sort enforcer is -// ever built and a plan that carries a Sort cannot be reached. + + TEST(ReachabilityTest, SortNotReachableWithoutOrderBy) { std::stringstream s{"SELECT users.id FROM users;"}; PhysicalSort target{ diff --git a/src/stewkk/sql/logic/optimizer/schema_catalog.cpp b/src/stewkk/sql/logic/optimizer/schema_catalog.cpp index 9c67fe8..43c801f 100644 --- a/src/stewkk/sql/logic/optimizer/schema_catalog.cpp +++ b/src/stewkk/sql/logic/optimizer/schema_catalog.cpp @@ -35,8 +35,6 @@ std::optional SchemaCatalog::Derive(const LogicalOperator& op) { return GetSchema(f.source); }, [](const logical::Projection& p) -> std::optional { - // Aliased projection outputs can be referenced by unqualified ORDER - // BY names. Unaliased computed expressions are not addressable. Schema out; for (size_t i = 0; i < p.expressions.size(); ++i) { if (i < p.aliases.size() && p.aliases[i]) { diff --git a/src/stewkk/sql/logic/parser/visitor.cpp b/src/stewkk/sql/logic/parser/visitor.cpp index eff49cd..cf7f5e1 100644 --- a/src/stewkk/sql/logic/parser/visitor.cpp +++ b/src/stewkk/sql/logic/parser/visitor.cpp @@ -390,10 +390,6 @@ Expression MakeUnary(UnaryOp op, Expression child) { return UnaryExpression{op, std::make_shared(std::move(child))}; } -// Returns the table_ref's atom (no joins) and an iterator pointing to its first -// post-atom child. The grammar's `(... join ...)*` tail is greedy on the rhs -// table_ref, so the caller is responsible for flattening that chain instead of -// visiting it whole. std::pair ExtractAtom(Visitor* v, TableRefCtx* ctx) { if (ctx->xmltable()) { throw Error{ErrorType::kQueryNotSupported, "xmltable is not supported"}; @@ -440,8 +436,6 @@ std::pair ExtractAtom(Visitor* v, TableRefCtx* ctx) { return {std::move(res), it}; } -// Walks the join tail of `ctx` starting at `it`, descending into each rhs -// table_ref so that nested greedy joins become left-associative. Operator ContinueChain(Visitor* v, Operator lhs, TableRefCtx* ctx, ChildIt it) { const auto end = ctx->children.cend(); while (it != end) { @@ -817,7 +811,6 @@ std::any Visitor::visitA_expr_qual_op(codegen::PostgreSQLParser::A_expr_qual_opC if (exprs.size() == 1) { return visit(exprs.front()); } - // Postgres allows != as an alias for <>, which the lexer produces as Operator (qual_op). if (exprs.size() == 2 && ctx->qual_op(0)->getText() == "!=") { auto lhs = std::any_cast(visit(exprs[0])); auto rhs = std::any_cast(visit(exprs[1])); diff --git a/src/stewkk/sql/logic/transformation_rules/in_to_or_chain.cpp b/src/stewkk/sql/logic/transformation_rules/in_to_or_chain.cpp index 7488f6b..4fb62c8 100644 --- a/src/stewkk/sql/logic/transformation_rules/in_to_or_chain.cpp +++ b/src/stewkk/sql/logic/transformation_rules/in_to_or_chain.cpp @@ -28,10 +28,6 @@ std::shared_ptr Share(Expression e) { return std::make_shared(std::move(e)); } -// Expand a single InExpression into a left-associative chain. IN folds with OR -// over `lhs = value_i`; NOT IN folds with AND over `lhs != value_i`. The -// associativity and operand order match converter.py's OR/AND fold so the -// reachability target plan compares equal. Expression ExpandIn(const InExpression& in); Expression Expand(const Expression& e) { diff --git a/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp b/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp index ac5de43..0e85871 100644 --- a/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp +++ b/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp @@ -17,10 +17,6 @@ bool JoinAssociativity::IsApplicable(utils::NotNull expr) { return false; } -// (A ⋈_p1 B) ⋈_p2 C → A ⋈_pa (B ⋈_pbc C) -// where pbc collects every conjunct of (p1 ∧ p2) whose attributes lie inside -// B ∪ C, and pa keeps the rest. Avoids the degenerate ⋈_TRUE inner that drops -// otherwise-pushable predicates onto the outer join. LogicalOperator JoinAssociativity::ApplyImpl(utils::NotNull expr, Memo& memo) { const auto& outer = std::get(expr->root_operator); for (auto inner_expr : outer.lhs->GetLogicalExprs()) { diff --git a/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp b/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp index 7554cec..84b7dbd 100644 --- a/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp +++ b/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp @@ -67,8 +67,6 @@ std::unordered_set ExprTables(const Expression& e) { namespace { -// Equivalent logical exprs in a memo group all expose the same output tables, -// so one expression suffices. Recurses across child groups via their fronts. void CollectGroupTables(utils::NotNull g, std::unordered_set& out, std::unordered_set& seen) { if (!seen.insert(g.get()).second) return; diff --git a/src/stewkk/sql/main.cpp b/src/stewkk/sql/main.cpp index ae31689..d477e22 100644 --- a/src/stewkk/sql/main.cpp +++ b/src/stewkk/sql/main.cpp @@ -88,11 +88,6 @@ Args ParseArgs(int argc, char** argv) { return args; } -// Build a SchemaCatalog by reading the header of every .csv in the -// directory. Mirrors the conventions used by the query generator and -// CsvDirSequentialScanner: header is "col:type,col:type,...", and files whose -// stem ends in _ are benchmark-only siblings of a base table and share -// its schema, so they are skipped. SchemaCatalog LoadSchema(const std::string& dir) { std::unordered_map tables; static const std::regex kBench{R"(_\d+$)"}; @@ -176,11 +171,6 @@ std::string TypeName(Type t) { return "?"; } -// Canonical text format the fuzzer compares against MS SQL Server output. -// First line: tab-separated "table.col:type" header (or just ":type" when the -// attribute has no name, e.g. an expression in the projection). -// Following lines: one row each, tab-separated values, "NULL" for nulls. -// Rows are sorted lexicographically unless the query had ORDER BY. void PrintRelation(const Relation& rel, bool preserve_order) { std::ostringstream header; for (size_t i = 0; i < rel.attributes.size(); ++i) { @@ -208,10 +198,6 @@ void PrintRelation(const Relation& rel, bool preserve_order) { for (const auto& r : rows) std::cout << r << '\n'; } -// Coroutine declared as a free function (not a captureless lambda) so the -// awaitable holds its parameters by reference into main's stack, sidestepping -// the lifetime trap of an IIFE'd `[&]` lambda whose closure dies before the -// coroutine frame. boost::asio::awaitable> RunQuery(const std::string& data_dir, const PhysicalPlanNode& plan) { CsvDirSequentialScanner seq_scan{data_dir}; From 5d0e60172ec72e12ebe918d8ff98fe4d519bf0bc Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 1 Jun 2026 14:34:51 +0300 Subject: [PATCH 072/120] Add converted notebook --- report/practice.tex | 476 ++++++++++ research/research.ipynb | 1887 +-------------------------------------- research/research.pdf | Bin 0 -> 726468 bytes 3 files changed, 481 insertions(+), 1882 deletions(-) create mode 100644 report/practice.tex create mode 100644 research/research.pdf diff --git a/report/practice.tex b/report/practice.tex new file mode 100644 index 0000000..7dbc091 --- /dev/null +++ b/report/practice.tex @@ -0,0 +1,476 @@ +% !TeX TXS-program:bibliography = txs:///biber +% chktex-file 1 +\documentclass[fontsize=14pt, russian]{scrartcl} +\input{header.tex} +\addbibresource{biblio.bib} + +\begin{document} +\sloppy + +\def\figurename{Рисунок} + +\newcommand{\sqlkw}[1]{\texttt{#1}} +\newcommand{\SELECT}{\sqlkw{SELECT}} +\newcommand{\FROM}{\sqlkw{FROM}} +\newcommand{\WHERE}{\sqlkw{WHERE}} +\newcommand{\JOIN}{\sqlkw{JOIN}} +\newcommand{\GROUPBY}{\sqlkw{GROUP BY}} +\newcommand{\ORDERBY}{\sqlkw{ORDER BY}} + +\newcommand{\SeqScan}{\sqlkw{SeqScan}} +\newcommand{\HashJoin}{\sqlkw{HashJoin}} +\newcommand{\NestedLoopJoin}{\sqlkw{NestedLoopJoin}} +\newcommand{\Sort}{\sqlkw{Sort}} +\newcommand{\FilterOp}{\sqlkw{Filter}} +\newcommand{\ProjectionOp}{\sqlkw{Projection}} + +\newcommand{\screenplaceholder}[3]{% + \begin{figure}[H] + \centering + \fbox{% + \begin{minipage}[c][2.5cm][c]{0.91\textwidth} + \centering + \textbf{МЕСТО ДЛЯ СКРИНШОТА}\\[0.4cm] + #3 + \end{minipage}% + } + \caption{#1} + \label{#2} + \end{figure} +} + +\setcounter{page}{2} + +\newpage +\renewcommand\contentsname{\hfill{\normalfont{СОДЕРЖАНИЕ}}\hfill} +\tableofcontents +\newpage + +\anonsection{ВВЕДЕНИЕ} + +Одним из определяющих факторов конкурентоспособности между различными +реализациями СУБД является качество оптимизатора запросов. Оптимизатор +представляет собой компонент, ответственный за преобразование декларативного +SQL-запроса в эффективный физический план исполнения. Именно от качества работы +оптимизатора в значительной степени зависит производительность всей системы: +выбор неоптимального плана исполнения может привести к увеличению времени +выполнения запроса в десятки раз относительно оптимального случая. + +Также оптимизатор запросов считается наиболее сложной составной частью любой +СУБД. Задача нахождения оптимального плана является NP-полной в общем случае, +что обуславливает необходимость применения эвристических методов и алгоритмов +ограниченного перебора. Одним из первых практических подходов к стоимостной +оптимизации стал алгоритм System~R~\cite{Selinger1979}. В дальнейшем были +разработаны расширяемые архитектуры Volcano~\cite{GraefeMcKenna1993} и +Cascades~\cite{Graefe1995}. + +Преимущества архитектуры Cascades заключаются в расширяемости, модульности и +возможности управлять поиском с помощью набора правил. + +Целью практики является разработка и исследование программного прототипа +SQL-компилятора с оптимизатором запросов на основе архитектуры Cascades. + +Для достижения поставленной цели необходимо решить следующие задачи: +\begin{itemize} + \item изучить существующие архитектуры оптимизаторов запросов; + \item реализовать разбор подмножества SQL и внутреннее представление + операторов реляционной алгебры; + \item реализовать структуру данных Memo и алгоритм поиска оптимального плана + на основе архитектуры Cascades; + \item реализовать набор правил трансформации и реализации для основных + операторов реляционной алгебры; + \item реализовать метод ветвей и границ для эффективного отсечения + неоптимальных планов; + \item реализовать исполнение физических планов над таблицами в формате CSV; + \item добавить интерпретацию и JIT-компиляцию выражений; + \item подготовить тесты и вспомогательные средства для исследования планов. +\end{itemize} + +В рамках практики разработана модельная СУБД, включающая основные этапы +обработки SQL-запроса. Программа разбирает текст запроса, строит дерево +операторов реляционной алгебры, выполняет стоимостную оптимизацию, формирует +физический план и исполняет его над таблицами в формате CSV. Дополнительно +реализована JIT-компиляция скалярных выражений с использованием LLVM. + +\section{ПРОЕКТИРОВАНИЕ} + +\subsection{Назначение и область применения} + +Разработанная программа является исследовательским прототипом обработчика +SQL-запросов. Она не заменяет промышленную СУБД, поскольку не содержит +транзакционного менеджера, журналирования, многоверсионности и сетевого +протокола. Программа предназначена для изучения этапов компиляции запросов, +экспериментальной проверки правил оптимизации и измерения стоимости физических +операторов. + +Входными данными служат SQL-запрос и директория с CSV-файлами. Каждый файл +соответствует одной таблице. Первая строка файла задает схему отношения, а +остальные строки содержат кортежи. Результат запроса выводится в стандартный +поток вывода в текстовом виде. + +Поддерживаются запросы \SELECT{} с предложениями \FROM{} и \WHERE{}, +проекции столбцов и выражений, соединения и декартовы произведения таблиц. +Скалярные выражения включают сравнения, \sqlkw{BETWEEN}, \sqlkw{IN}, строковые +и целочисленные значения, а также \sqlkw{NULL}. Реализованы агрегатные функции +\sqlkw{SUM}, \sqlkw{COUNT}, группировка \GROUPBY{} и сортировка \ORDERBY{}. + +Некоторые возможности полного SQL намеренно не реализованы. К ним относятся +\sqlkw{DISTINCT}, \sqlkw{HAVING}, оконные функции, подзапросы и операции над +множествами. При встрече неподдерживаемой конструкции парсер возвращает +диагностическое сообщение. + +\subsection{Общая архитектура программы} + +Сначала программа читает SQL-запрос, выполняет лексический и синтаксический +анализ, затем строит логическое представление. Оптимизатор заполняет Memo, +перечисляет эквивалентные выражения и выбирает физический план с минимальной +стоимостью. Исполнитель обрабатывает план над CSV-файлами и выводит схему и +кортежи результирующего отношения. + +\section{РЕАЛИЗАЦИЯ} + +\subsection{Выбор инструментов разработки} + +Программа написана на языке C++ с использованием стандарта C++23. Этот язык +позволяет контролировать представление данных в памяти, использовать +стандартные алгебраические типы и корутины, а также интегрироваться с LLVM. +Для сборки применяется CMake. Воспроизводимое окружение разработки описано +в файле \texttt{flake.nix}. + +Для синтаксического анализа выбран генератор парсеров ANTLR4. Асинхронное +взаимодействие физических операторов построено на Boost.Asio. LLVM +используется для генерации машинного кода скалярных выражений. Модульные тесты +реализованы с помощью GoogleTest, а измерения производительности физических +операторов --- с помощью Google Benchmark. + +\subsection{Модуль синтаксического анализа} + +Для синтаксического анализа используется грамматика PostgreSQL и генератор +парсеров ANTLR4. Сгенерированные классы лексического и синтаксического +анализаторов находятся в каталоге +\texttt{src/stewkk/sql/logic/parser/codegen}. Функция \texttt{GetAST} +инициализирует анализаторы, строит дерево разбора и запускает обход дерева. + +Обход реализован классом \texttt{Visitor}. Для интересующих конструкций SQL +переопределены методы посещения узлов грамматики. Например, предложение +\WHERE{} преобразуется в логический оператор фильтрации, список выражений +после \SELECT{} преобразуется в проекцию, а список таблиц после \FROM{} --- +в дерево соединений. + +Результатом работы парсера является структура \texttt{ParsedQuery}. Она +содержит логический оператор верхнего уровня и необязательное требование к +порядку строк для \ORDERBY{}. Логические операторы и скалярные выражения +представлены алгебраическими типами на основе \texttt{std::variant}. В них +входят операторы таблицы, фильтрации, проекции, агрегации, соединения, ссылки +на атрибуты, константы и арифметические выражения. + +\subsection{Модуль оптимизации запросов} + +Оптимизатор получает логическое дерево, набор правил, оценки кардинальности, +каталог схем и требуемые свойства результата. Центральной структурой данных +является Memo. Она хранит группы логически эквивалентных выражений. + +Каждая группа содержит логические и физические выражения. Дочерние узлы +ссылаются на группы, поэтому одно выражение компактно представляет множество +возможных поддеревьев. Повторяющиеся выражения не добавляются в Memo повторно. + +Правила оптимизации разделены на две категории. Трансформационные правила +создают новые логические выражения, эквивалентные исходным. Реализационные +правила добавляют физические операторы, которые могут быть непосредственно +исполнены. + +В текущей версии реализованы следующие трансформации: +\begin{itemize} + \item коммутативность соединения; + \item ассоциативность соединения; + \item разбиение фильтра с конъюнкцией на последовательность фильтров; + \item объединение последовательных фильтров; + \item проталкивание фильтра через проекцию; + \item проталкивание фильтра во входы соединения; + \item преобразование списка \sqlkw{IN} в цепочку сравнений. +\end{itemize} + +Реализационные правила создают последовательное сканирование, фильтрацию, +проекцию, агрегацию, соединение вложенными циклами, декартово произведение +вложенными циклами и хеш-соединение. Хеш-соединение применяется только для +простого внутреннего соединения по равенству двух атрибутов. + +Поиск выполняется сверху вниз с использованием стека отложенных задач. Для +каждой пары из группы и требуемого набора физических свойств сохраняется +лучший найденный вариант. Такой вариант называется победителем группы. +Стоимость полного плана равна сумме локальных стоимостей его операторов. + +Для уменьшения пространства поиска используется метод ветвей и границ. +Оптимизатор вычисляет допустимую нижнюю оценку стоимости группы. Если даже +нижняя оценка не меньше стоимости уже известного решения, дальнейшее +исследование ветви прекращается. + +Физические свойства описывают дополнительные требования к результату. В +текущей версии реализовано свойство сортировки. Если запрос содержит +\ORDERBY{}, оптимизатор должен построить план, выдающий строки в требуемом +порядке. При необходимости в план добавляется обеспечивающий оператор +\Sort{}. + +\subsection{Физический план и его сериализация} + +После завершения поиска оптимизатор преобразует выбранные физические выражения +в дерево \texttt{PhysicalPlanNode}. Физический план отделен от внутренней +структуры Memo и может быть исполнен независимо от процесса оптимизации. + +К основным физическим операторам относятся последовательное чтение CSV-файла +\SeqScan{}, фильтрация \FilterOp{}, проекция \ProjectionOp{}, соединения +\NestedLoopJoin{} и \HashJoin{}, сортировка \Sort{} и хеш-агрегация +\sqlkw{HashAggregate}. + +Планы сериализуются в текстовый формат S-expression. Его удобно читать, +сравнивать в тестах и передавать вспомогательным утилитам. Дополнительно +реализован экспорт плана в формат Graphviz DOT. После запуска программы файл +сохраняется в каталоге \texttt{.plans}. + +\subsection{Модуль хранения и исполнения данных} + +Каждой таблице соответствует CSV-файл с совпадающим именем. Например, таблица +\texttt{users} хранится в файле \texttt{users.csv}. Первая строка содержит +описание столбцов и их типов: + +\begin{Verbatim}[fontsize=\small] +id:int,name:string,age:int +1,Alice,25 +2,Bob,31 +\end{Verbatim} + +Исполнитель физических планов реализован классом \texttt{Executor}. Он +параметризуется способом вычисления выражений и функциями доступа к таблицам. +Благодаря этому один исполнитель используется с интерпретатором, JIT-компилятором +и различными реализациями сканирования. + +Операторы взаимодействуют через каналы Boost.Asio. Отдельные каналы передают +описание атрибутов и порции размером до 2048 кортежей. Это уменьшает число +операций синхронизации по сравнению с передачей отдельных строк. + +Проекция и фильтрация являются потоковыми операторами. Они получают порцию +входных кортежей, обрабатывают ее и передают результат следующему оператору. +Для блокирующих операций может потребоваться накопление данных. Например, +сортировка сохраняет все входные кортежи в памяти. + +Соединение вложенными циклами материализует один вход во временный бинарный +файл, после чего многократно читает его при обработке второго входа. +Хеш-соединение строит хеш-таблицу по ключу левого входа и выполняет поиск +совпадений для кортежей правого входа. + +Для строковых значений используется интернирование. Строка помещается в общий +пул, а внутри кортежа хранится целочисленный идентификатор. Это сохраняет +компактное представление значения и упрощает передачу кортежей между +операторами. + +\subsection{JIT-компиляция выражений} + +Для часто вычисляемых выражений реализована JIT-компиляция с использованием +LLVM ORC JIT. Класс \texttt{JITCompiler} преобразует дерево выражения в LLVM IR, +запускает оптимизирующие проходы и возвращает указатель на машинную функцию. + +Скомпилированная функция получает указатель на результирующее значение, +указатель на входной кортеж и описание атрибутов. Структура значения содержит +флаг \texttt{is\_null} и объединение для непосредственного значения. Такое +представление учитывает трехзначную логику SQL. + +В проекте доступны интерпретируемая, JIT-компилируемая и кэшируемая +JIT-компилируемая стратегии вычисления выражений. Последняя сохраняет +полученные машинные функции и переиспользует их. + +Текущая реализация JIT предназначена для целочисленных и логических выражений. +Строковые операции, \sqlkw{IN}, агрегатные выражения и возведение в степень +через JIT не поддерживаются. Для них следует использовать интерпретируемый +режим. + +\subsection{Руководство пользователя} + +\subsubsection{Требования к окружению} + +Для сборки программы требуются CMake, компилятор C++ с поддержкой C++23, LLVM, +ANTLR4 и библиотеки Boost. В репозитории присутствует файл \texttt{flake.nix}, +поэтому в системе с пакетным менеджером Nix рекомендуется открыть окружение: + +\begin{Verbatim}[fontsize=\small] +nix develop +\end{Verbatim} + +Все команды далее выполняются из корневого каталога проекта. + +\subsubsection{Сборка программы} + +Для первичной конфигурации и сборки следует выполнить: + +\begin{Verbatim}[fontsize=\small] +cmake -S . -B build -DCMAKE_BUILD_TYPE=Release +cmake --build build -- -j 6 +\end{Verbatim} + +После успешной сборки исполняемый файл находится по пути +\texttt{build/bin/sql}. Для повторной сборки также можно использовать цель +\texttt{make build}. + +\subsubsection{Подготовка входных данных} + +Пользователь должен создать отдельную директорию и поместить в нее CSV-файлы. +Имя каждого файла без расширения считается именем таблицы. В заголовке каждого +файла для столбца указывается имя и тип через двоеточие. Поддерживаются типы +\texttt{int} и \texttt{string}. Пустое SQL-значение записывается как +\texttt{NULL}. + +Для первого запуска можно использовать готовые тестовые данные: + +\begin{Verbatim}[fontsize=\small] +test/static/executor/test_data +\end{Verbatim} + +\subsubsection{Запуск SQL-запроса} + +SQL-запрос передается программе через стандартный поток ввода. Простейший +пример запуска: + +\begin{Verbatim}[fontsize=\small] +echo 'SELECT users.id FROM users;' | \ + ./build/bin/sql --data-dir test/static/executor/test_data +\end{Verbatim} + +Для запуска запроса из файла следует использовать перенаправление ввода: + +\begin{Verbatim}[fontsize=\small] +./build/bin/sql --data-dir path/to/csv_dir < query.sql +\end{Verbatim} + +Программа выводит заголовок отношения, содержащий имена и типы столбцов, а +затем полученные строки. Если запрос не содержит \ORDERBY{}, строки вывода +сортируются лексикографически для воспроизводимости результата. + +\screenplaceholder{Выполнение SQL-запроса над тестовыми данными}{fig:practice-run}{% + Вставить скриншот запуска запроса через стандартный поток ввода\\ + и напечатанного результирующего отношения.} + +\subsubsection{Вывод логического и физического планов} + +Для диагностики предусмотрены параметры \texttt{--print-ast} и +\texttt{--print-plan}. Первый параметр выводит логическое дерево после +синтаксического анализа, второй --- выбранный физический план: + +\begin{Verbatim}[fontsize=\small] +echo 'SELECT users.id FROM users WHERE users.age > 18 \ +ORDER BY users.id;' | \ + ./build/bin/sql --data-dir test/static/executor/test_data \ + --print-ast --print-plan +\end{Verbatim} + +\screenplaceholder{Вывод логического дерева и физического плана}{fig:practice-plan}{% + Вставить скриншот запуска с параметрами\\ + \texttt{--print-ast --print-plan}.} + +\subsubsection{Запуск с JIT-компиляцией} + +Для выбора JIT-компиляции выражений используется параметр \texttt{--jit}: + +\begin{Verbatim}[fontsize=\small] +echo 'SELECT users.id FROM users WHERE users.age > 18;' | \ + ./build/bin/sql --data-dir test/static/executor/test_data --jit +\end{Verbatim} + +Параметр рекомендуется применять к запросам с целочисленными арифметическими и +логическими выражениями. Запросы со строковыми выражениями и \sqlkw{IN} следует +запускать без JIT. + +\screenplaceholder{Выполнение запроса с JIT-компиляцией выражений}{fig:practice-jit}{% + Вставить скриншот запуска программы с параметром \texttt{--jit}.} + +\subsubsection{Доступные параметры командной строки} + +\begin{longtable}{|p{4.2cm}|p{9.3cm}|} + \caption{Параметры командной строки программы.}\label{tbl:practice-cli}\\ + \hline + Параметр & Назначение \\ + \hline + \endfirsthead + \multicolumn{2}{l}{\small Продолжение таблицы~\ref{tbl:practice-cli}}\\ + \hline + Параметр & Назначение \\ + \hline + \endhead + \texttt{--data-dir DIR} & + Директория с CSV-файлами таблиц. Обязательна при обычном исполнении запроса. \\ + \hline + \texttt{--print-ast} & + Вывод логического дерева операторов после синтаксического анализа. \\ + \hline + \texttt{--print-plan} & + Вывод выбранного физического плана в формате S-expression. \\ + \hline + \texttt{--jit} & + Использование кэшируемой JIT-компиляции скалярных выражений. \\ + \hline + \texttt{--check-reachable FILE} & + Проверка достижимости физического плана, сериализованного в файле. \\ + \hline +\end{longtable} + +\section{ТЕСТИРОВАНИЕ} + +Модульные тесты написаны с использованием GoogleTest. Они проверяют парсер, +вычисление выражений, исполнение операторов, сериализацию планов, свойства +сортировки, Memo и оптимизатор. Отдельные тесты проверяют интерпретируемое и +JIT-исполнение. + +Для проверки корректности C++-части проекта следует выполнить: + +\begin{Verbatim}[fontsize=\small] +ctest --test-dir build --output-on-failure +\end{Verbatim} + +В результате запуска успешно выполняются 126 тестов. Для проверки конвертера +запросов Star Schema Benchmark используется команда: + +\begin{Verbatim}[fontsize=\small] +pytest -q benchmarks/datasets/ssb +\end{Verbatim} + +Набор содержит 17 тестов, которые также выполняются успешно. В каталоге +\texttt{benchmarks} находятся тесты производительности на основе Google +Benchmark. Они предназначены для измерения характеристик отдельных физических +операторов. + +Вспомогательные Python-скрипты из каталога \texttt{research} генерируют +запросы, сравнивают результаты с Microsoft SQL Server и проверяют достижимость +внешних физических планов. Для последней задачи программа поддерживает режим +\texttt{--check-reachable}, запускающий полный перебор пространства планов без +отсечения. + +\anonsection{ЗАКЛЮЧЕНИЕ} + +В ходе практики разработан программный прототип SQL-компилятора и модельной +системы исполнения запросов. Реализован сквозной процесс от разбора SQL-текста +до получения результирующего отношения. Программа работает с CSV-файлами, +поддерживает основные операторы реляционной алгебры и формирует физический план +на основе стоимостной модели. + +Ключевой частью проекта является расширяемый оптимизатор, использующий +структуру Memo и правила преобразования. Реализован поиск планов с учетом +физических свойств, нижних оценок стоимости и метода ветвей и границ. +Поддержка обеспечивающего оператора сортировки позволяет корректно обрабатывать +требования \ORDERBY{}. + +Исполнитель построен на корутинах C++ и каналах Boost.Asio. Потоковая передача +порций кортежей сочетается с материализацией для блокирующих операторов. +Для ускорения вычисления выражений добавлена JIT-компиляция с использованием +LLVM и кэширование скомпилированных функций. + +Корректность основных компонентов проверяется модульными тестами. Дополнительно +подготовлены бенчмарки физических операторов и инструменты дифференциального +сравнения с Microsoft SQL Server. Эти средства позволяют использовать проект +как основу для дальнейшего исследования правил оптимизации и уточнения +стоимостной модели. + +\renewcommand\refname{СПИСОК ИСПОЛЬЗОВАННЫХ ИСТОЧНИКОВ} +{\catcode`"\active\def"{\relax} + \addcontentsline{toc}{section}{\protect\numberline{}\refname}% + \printbibliography +} + +\end{document} diff --git a/research/research.ipynb b/research/research.ipynb index b205b4c..454f982 100644 --- a/research/research.ipynb +++ b/research/research.ipynb @@ -125,1077 +125,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "2d033649", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'context': {'date': '2026-05-31T16:14:53+03:00',\n", - " 'host_name': 'nixos',\n", - " 'executable': './build-release/bin/benchmarks',\n", - " 'num_cpus': 8,\n", - " 'mhz_per_cpu': 4200,\n", - " 'cpu_scaling_enabled': True,\n", - " 'caches': [{'type': 'Data', 'level': 1, 'size': 49152, 'num_sharing': 2},\n", - " {'type': 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ssb_interpreted = ssb[ssb.executor == \"Interpreted\"].copy()\n", + "\n", + "wide = ssb_interpreted.pivot(index=\"query\", columns=\"mode\", values=\"time_ms\").sort_index()\n", + "ax = wide[[\"Naive\", \"Optimized\"]].plot.bar(figsize=(14, 5))\n", + "ax.set_ylabel(\"Execution time, ms\")\n", + "ax.set_xlabel(\"Query\")\n", + "ax.set_title(\"Naive and optimized physical plans (SSB)\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c36bdd23", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "speedup = (wide[\"Naive\"] / wide[\"Optimized\"]).sort_index()\n", + "ax = speedup.plot.bar(figsize=(14, 4))\n", + "ax.axhline(1.0, color=\"black\", linewidth=1)\n", + "ax.set_ylabel(\"Speedup, Naive / Optimized\")\n", + "ax.set_xlabel(\"Query\")\n", + "ax.set_title(\"Optimizer speedup\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "4ee5fc7c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "jit = synthetic[synthetic[\"mode\"] == \"Optimized\"].copy()\n", + "jit[\"executor\"] = jit[\"executor\"].replace({\n", + " \"InterpretedExpressionExecutor\": \"Interpreted\",\n", + " \"CachedJitCompiledExpressionExecutor\": \"Cached JIT\",\n", + "})\n", + "ax = jit.pivot(index=\"query\", columns=\"executor\", values=\"time_ms\").plot.bar(figsize=(9, 4))\n", + "ax.set_ylabel(\"Execution time, ms\")\n", + "ax.set_xlabel(\"Query\")\n", + "ax.set_title(\"Expression evaluation for optimized plans\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "bbe140d5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(9, 5))\n", + "for name, group in operator.groupby(\"operator\"):\n", + " group = group.sort_values(\"input_rows\")\n", + " ax.plot(group.input_rows, group.time_ms, marker=\"o\", label=name)\n", + "ax.set_xscale(\"log\")\n", + "ax.set_yscale(\"log\")\n", + "ax.set_xlabel(\"Input rows\")\n", + "ax.set_ylabel(\"Execution time, ms\")\n", + "ax.set_title(\"Physical operator scaling\")\n", + "ax.legend()\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "cde81524", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(9, 5))\n", + "for name, group in matched.groupby(\"operator\"):\n", + " ax.scatter(group.model_cost, group.time_ms, label=name)\n", + "ax.set_xscale(\"log\")\n", + "ax.set_yscale(\"log\")\n", + "ax.set_xlabel(\"Model cost\")\n", + "ax.set_ylabel(\"Execution time, ms\")\n", + "ax.set_title(\"Cost-model calibration\")\n", + "ax.legend()\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "7b508249", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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z8s+d`DCl|wE3BdMr} z{SuizIll<$5{*8wqKN(ygMMp={~J9s%JV~RuBkzhJHJ$u$)4T~*E_v*f4-@{8@s&x zn_)`p@RQc7aZ~G?BrTYIPpdeD7UZ5pcJu%vuDAi>kr760|7?1gu|17kpF?_FP#p+d z9rVaPcy{;@2l&rjmF!S5jmUWi$f?164tO&|1iB%QR^+rDPn`%rD`Lw&`9|RSHPK~1 zZz~c~xR(-UXVejUz%(paUJFFCX8?WqgGCTqJX32+c{#$tK`biejO?&vWTNt7u>TL^ag_@I literal 0 HcmV?d00001 From 8bbd77203abfb47205348b65299e59e220f8cdef Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 1 Jun 2026 22:44:14 +0300 Subject: [PATCH 073/120] Add practice report --- report/biblio.bib | 71 ++ report/practice.tex | 1322 ++++++++++++++++++++++++++++-------- report/reach-fuzz.png | Bin 0 -> 111748 bytes report/run-example.png | Bin 0 -> 33521 bytes report/run-with-prints.png | Bin 0 -> 49763 bytes research/research.ipynb | 2 +- 6 files changed, 1113 insertions(+), 282 deletions(-) create mode 100644 report/reach-fuzz.png create mode 100644 report/run-example.png create mode 100644 report/run-with-prints.png diff --git a/report/biblio.bib b/report/biblio.bib index 9203646..cd24608 100644 --- a/report/biblio.bib +++ b/report/biblio.bib @@ -84,3 +84,74 @@ @article{leis2015good language = {english}, note = {(дата обращения: 01.05.2026)} } + + +@misc{DBReport, +title = {Исследование рынка СУБД}, +url = {https://www.mordorintelligence.com/industry-reports/database-market}, +language = {english}, +note = {(дата обращения: 17.05.2026)} +} + +@misc{PostgresDocs, +title = {Документация PostgreSQL}, +url = {https://www.postgresql.org/docs/current/sql-select.html}, +language = {english}, +note = {(дата обращения: 17.05.2026)} +} + +@misc{PostgresBisonGrammar, +title = {Грамматика PostgreSQL}, +url = {https://github.com/postgres/postgres/blob/master/src/backend/parser/gram.y}, +language = {english}, +note = {(дата обращения: 17.05.2026)} +} + +@misc{Antlr4PostgreSQL, +title = {Грамматика PostgreSQL для ANTLR4}, +url = {https://github.com/antlr/grammars-v4/blob/master/sql/postgresql/PostgreSQLParser.g4}, +language = {english}, +note = {(дата обращения: 17.05.2026)} +} + +@book{silberschatz2020database, + title={Database system concepts}, + author={Silberschatz, Abraham and Korth, Henry F and Sudarshan, Shashank and others}, + year={2020}, + publisher={Mcgraw-hill New York}, + language = {english}, + note = {(дата обращения: 17.05.2026)} +} + +@inproceedings{pantilimonov2019machine, + title={Machine code caching in postgresql query jit-compiler}, + author={Pantilimonov, Michael and Buchatskiy, Ruben and Zhuykov, Roman and Sharygin, Eugene and Melnik, Dmitry}, + booktitle={2019 Ivannikov Memorial Workshop (IVMEM)}, + pages={18--25}, + year={2019}, + organization={IEEE}, + language = {english}, + note = {(дата обращения: 17.05.2026)} +} + +@book{petrov2019database, + title={Database Internals: A deep dive into how distributed data systems work}, + author={Petrov, Alex}, + year={2019}, + publisher={O'Reilly Media}, + language = {english}, + note = {(дата обращения: 17.05.2026)} +} + +@article{graefe2002volcano, + title={Volcano - an extensible and parallel query evaluation system}, + author={Graefe, Goetz}, + journal={IEEE Transactions on Knowledge and Data Engineering}, + volume={6}, + number={1}, + pages={120--135}, + year={2002}, + publisher={IEEE}, + language = {english}, + note = {(дата обращения: 17.05.2026)} +} diff --git a/report/practice.tex b/report/practice.tex index 7dbc091..6b32298 100644 --- a/report/practice.tex +++ b/report/practice.tex @@ -48,6 +48,14 @@ \anonsection{ВВЕДЕНИЕ} +В эпоху стремительного роста объемов данных системы управления базами данных +(СУБД) являются важной частью информационных систем. По данным аналитических +исследований, объем мирового рынка СУБД ежегодно увеличивается, что обусловлено +как расширением круга решаемых задач, так и усложнением структуры обрабатываемых +данных и увеличением их объема. Реляционные базы данных, работающие с различными +диалектами языка SQL, продолжают занимать доминирующее положение в сегменте +обработки транзакций и аналитических запросов. + Одним из определяющих факторов конкурентоспособности между различными реализациями СУБД является качество оптимизатора запросов. Оптимизатор представляет собой компонент, ответственный за преобразование декларативного @@ -67,8 +75,9 @@ Преимущества архитектуры Cascades заключаются в расширяемости, модульности и возможности управлять поиском с помощью набора правил. -Целью практики является разработка и исследование программного прототипа -SQL-компилятора с оптимизатором запросов на основе архитектуры Cascades. +Целью данной работы является разработка и реализация оптимизатора подмножества +SQL-запросов на основе архитектуры Cascades, а также создание каркаса для его +итеративного улучшения путем сравнения с промышленными реализациями. Для достижения поставленной цели необходимо решить следующие задачи: \begin{itemize} @@ -82,235 +91,775 @@ \item реализовать метод ветвей и границ для эффективного отсечения неоптимальных планов; \item реализовать исполнение физических планов над таблицами в формате CSV; - \item добавить интерпретацию и JIT-компиляцию выражений; - \item подготовить тесты и вспомогательные средства для исследования планов. + \item реализовать метод дифференциального анализа физических планов для + автоматизированного поиска примеров неоптимальных планов. \end{itemize} -В рамках практики разработана модельная СУБД, включающая основные этапы -обработки SQL-запроса. Программа разбирает текст запроса, строит дерево -операторов реляционной алгебры, выполняет стоимостную оптимизацию, формирует -физический план и исполняет его над таблицами в формате CSV. Дополнительно -реализована JIT-компиляция скалярных выражений с использованием LLVM. - -\section{ПРОЕКТИРОВАНИЕ} - -\subsection{Назначение и область применения} - -Разработанная программа является исследовательским прототипом обработчика -SQL-запросов. Она не заменяет промышленную СУБД, поскольку не содержит -транзакционного менеджера, журналирования, многоверсионности и сетевого -протокола. Программа предназначена для изучения этапов компиляции запросов, -экспериментальной проверки правил оптимизации и измерения стоимости физических -операторов. - -Входными данными служат SQL-запрос и директория с CSV-файлами. Каждый файл -соответствует одной таблице. Первая строка файла задает схему отношения, а -остальные строки содержат кортежи. Результат запроса выводится в стандартный -поток вывода в текстовом виде. - -Поддерживаются запросы \SELECT{} с предложениями \FROM{} и \WHERE{}, -проекции столбцов и выражений, соединения и декартовы произведения таблиц. -Скалярные выражения включают сравнения, \sqlkw{BETWEEN}, \sqlkw{IN}, строковые -и целочисленные значения, а также \sqlkw{NULL}. Реализованы агрегатные функции -\sqlkw{SUM}, \sqlkw{COUNT}, группировка \GROUPBY{} и сортировка \ORDERBY{}. - -Некоторые возможности полного SQL намеренно не реализованы. К ним относятся -\sqlkw{DISTINCT}, \sqlkw{HAVING}, оконные функции, подзапросы и операции над -множествами. При встрече неподдерживаемой конструкции парсер возвращает -диагностическое сообщение. - -\subsection{Общая архитектура программы} +\section{Проектирование} -Сначала программа читает SQL-запрос, выполняет лексический и синтаксический -анализ, затем строит логическое представление. Оптимизатор заполняет Memo, -перечисляет эквивалентные выражения и выбирает физический план с минимальной -стоимостью. Исполнитель обрабатывает план над CSV-файлами и выводит схему и -кортежи результирующего отношения. +В рамках данной работы был разработан оптимизатор запросов для модельной +реляционной СУБД. -\section{РЕАЛИЗАЦИЯ} +Структурно реализация модельной СУБД состоит из следующих модулей: +\begin{itemize} + \item модуль синтаксического анализа SQL-запросов; + \item модуль построения логического представления запроса; + \item модуль стоимостной оптимизации на основе структуры Memo; + \item подсистема физического хранения на основе статических CSV-таблиц; + \item модуль исполнения физических операторов. +\end{itemize} -\subsection{Выбор инструментов разработки} +Основой для проектирования оптимизатора была выбрана архитектура Cascades. В ней +пространство допустимых планов формируется с помощью набора правил +преобразования и хранится в структуре Memo. Эта структура объединяет логически +эквивалентные выражения в группы, что позволяет избежать повторного рассмотрения +одинаковых поддеревьев. Поиск оптимального физического плана выполняется с учетом +стоимостной модели и требуемых свойств результата, например порядка сортировки. + +Данная архитектура была выбрана по причине ее модульности и расширяемости. +Добавление новых логических преобразований и физических операторов выполняется +путем реализации отдельных правил и не требует изменения основного алгоритма +поиска. Это также удобно для итеративного процесса разработки, предполагающего +постепенное улучшение качества итоговых планов путем уточнения и расширения +пространства поиска. + +Модуль синтаксического анализа SQL-запросов отвечает за лексический и +синтаксический разбор поступающих на вход SQL-запросов. Для реализации этого +модуля было решено использовать генератор синтаксических и лексических +анализаторов, ввиду того, что грамматика SQL является очень объемной, и +использование готового формального описания для генератора гарантирует +совместимость с одним из диалектов языка. + +Модуль построения логического представления запроса отвечает за преобразование +результата синтаксического анализа во внутреннее представление, пригодное для +оптимизации. В качестве такого представления решено использовать дерево +операторов реляционной алгебры. Этот подход является общепринятым и позволяет +отделить особенности синтаксиса SQL от последующих этапов обработки запроса, а +также выполнять оптимизацию над ограниченным набором формализованных операций: +фильтрацией, проекцией, соединением, агрегацией и сортировкой. + +Подсистема физического хранения отвечает за предоставление модулю исполнения +табличных данных. Для хранения выбраны статические таблицы в формате CSV. +Использование простого текстового формата позволяет сосредоточиться на +проектировании оптимизатора и не усложнять прототип реализацией транзакций, +индексов, буферного кэша и управления дисковыми страницами. Кроме того, +CSV-файлы удобно создавать, изменять и использовать при проведении тестов. + +Модуль исполнения физических операторов отвечает за выполнение выбранного +оптимизатором плана и формирование результата запроса. Физический план решено +представлять в виде дерева операторов с единым интерфейсом взаимодействия. +Каждый оператор получает данные от дочерних узлов, выполняет определенное +преобразование и передает результат вышестоящему оператору. Такой подход +обеспечивает модульность подсистемы исполнения и позволяет добавлять новые +физические операторы независимо от уже реализованных. + +% TODO: Описание выбранных правил реализации / трансформации +% TODO: Описание подмножества SQL. + +\section{Реализация} Программа написана на языке C++ с использованием стандарта C++23. Этот язык -позволяет контролировать представление данных в памяти, использовать -стандартные алгебраические типы и корутины, а также интегрироваться с LLVM. -Для сборки применяется CMake. Воспроизводимое окружение разработки описано -в файле \texttt{flake.nix}. - -Для синтаксического анализа выбран генератор парсеров ANTLR4. Асинхронное -взаимодействие физических операторов построено на Boost.Asio. LLVM -используется для генерации машинного кода скалярных выражений. Модульные тесты -реализованы с помощью GoogleTest, а измерения производительности физических -операторов --- с помощью Google Benchmark. +популярен в сфере разработки СУБД, являясь достаточно низкоуровневым и +производительным, но в то же время удобным для реализации больших систем. Для +сборки применяется CMake и его встроенные средства для зависимостей в виде +библиотек на C++. Окружение для разработки описывается декларативно и +воспроизводимо при помощи функционального доменно-специфичного языка \verb|nix| +для соответствующего пакетного менеджера. + +Для синтаксического анализа выбран генератор парсеров ANTLR4, ввиду простоты и +удобства в использовании. Асинхронное взаимодействие физических операторов +построено на Boost.Asio, по причине совместимости со встроенными в язык +корутинами и понятной модели асинхронности. Для тестирования корректности и +производительности используется Google Tests, Google Benchmarks и pytest. \subsection{Модуль синтаксического анализа} -Для синтаксического анализа используется грамматика PostgreSQL и генератор -парсеров ANTLR4. Сгенерированные классы лексического и синтаксического -анализаторов находятся в каталоге -\texttt{src/stewkk/sql/logic/parser/codegen}. Функция \texttt{GetAST} -инициализирует анализаторы, строит дерево разбора и запускает обход дерева. - -Обход реализован классом \texttt{Visitor}. Для интересующих конструкций SQL -переопределены методы посещения узлов грамматики. Например, предложение -\WHERE{} преобразуется в логический оператор фильтрации, список выражений -после \SELECT{} преобразуется в проекцию, а список таблиц после \FROM{} --- -в дерево соединений. - -Результатом работы парсера является структура \texttt{ParsedQuery}. Она -содержит логический оператор верхнего уровня и необязательное требование к -порядку строк для \ORDERBY{}. Логические операторы и скалярные выражения -представлены алгебраическими типами на основе \texttt{std::variant}. В них -входят операторы таблицы, фильтрации, проекции, агрегации, соединения, ссылки -на атрибуты, константы и арифметические выражения. +Для реализации парсера был выбран ANTLR4, ввиду его удобства и возможности +генерировать лексические и синтаксические анализаторы под практически любой язык +программирования. + +Грамматика PostgreSQL была взята из официального репозитория проекта +ANTLR4~\cite{Antlr4PostgreSQL}. Она была портирована автоматически из грамматики +для Bison~\cite{PostgresBisonGrammar} из официального репозитория Postgres, +поэтому является наиболее полной из существующих. + +ANTLR4 генерирует несколько файлов на C++, в том числе лексический и +синтаксический анализаторы, а также класс \verb|Visitor| для обхода дерева +разбора. Для интересующих конструкций SQL переопределены методы посещения узлов +грамматики. Например, предложение \WHERE{} преобразуется в логический оператор +фильтрации, список выражений после \SELECT{} преобразуется в проекцию, а список +таблиц после \FROM{} --- в дерево соединений. + +Результатом работы парсера является структура \texttt{ParsedQuery}. Она содержит +логический оператор верхнего уровня и необязательное требование к порядку строк +для \ORDERBY{}. Логические операторы и скалярные выражения представлены +алгебраическими типами на основе \texttt{std::variant}. В них входят операторы +таблицы, фильтрации, проекции, агрегации, соединения, ссылки на атрибуты, +константы и арифметические выражения~\refAlgo{lst:logical}. + +\begin{listing}[H] + \caption{Объявление типов для операторов реляционной алегебры и выражений.} + \label{lst:logical} + \begin{minted}[style=bw, breaklines, frame=single, fontsize = \footnotesize, linenos=false, xleftmargin = 1.5em]{C++} +using Operator = std::variant; + +using Expression = std::variant; + +struct Projection { + std::vector expressions; + std::shared_ptr source; + std::vector> aliases; + + bool operator==(const Projection& other) const; +}; + \end{minted} +\end{listing} + +Выражения хранятся в алгебраическом типе \verb|Expression|, реализованном +аналогично \verb|Operator|. + +Функция \verb|GetAST| внутри строит абстрактное синтаксическое дерево и обходит +его с помощью \verb|Visitor|, который и генерирует представление запроса в виде +дерева из \verb|Operator|. + +Дополнительно реализована визуализация дерева из операторов реляционной алгебры +в формате Graphviz с помощью функции \verb|GetDotRepresentation|. \subsection{Модуль оптимизации запросов} -Оптимизатор получает логическое дерево, набор правил, оценки кардинальности, -каталог схем и требуемые свойства результата. Центральной структурой данных -является Memo. Она хранит группы логически эквивалентных выражений. - -Каждая группа содержит логические и физические выражения. Дочерние узлы -ссылаются на группы, поэтому одно выражение компактно представляет множество -возможных поддеревьев. Повторяющиеся выражения не добавляются в Memo повторно. - -Правила оптимизации разделены на две категории. Трансформационные правила -создают новые логические выражения, эквивалентные исходным. Реализационные -правила добавляют физические операторы, которые могут быть непосредственно -исполнены. - -В текущей версии реализованы следующие трансформации: +Модуль оптимизации запросов отвечает за преобразование логического дерева +операторов в физический план исполнения с минимальной оцененной стоимостью. +Пространство исследуемых эквивалентных планов эффективно хранится в структуре +Memo и расширяется с помощью добавления новых правил. Поиск выполняется сверху +вниз с использованием стоимостной модели, физических свойств и метода ветвей и +границ. + +\subsubsection{Структура Memo} + +Структура Memo хранит группы логически эквивалентных выражений. Выражения из +одной группы возвращают одинаковый результат. Например, $A \Join B$ и +$B \Join A$ являются эквивалентными. + +Дочерние узлы выражения ссылаются не на конкретные операторы, а на группы. +Благодаря этому одно выражение компактно представляет множество деревьев. +Объявления классов \verb|Memo| и \verb|Group| приведены в +листинге~\ref{lst:memo}. + +\begin{listing}[H] + \caption{Основные элементы структуры Memo.} + \label{lst:memo} + \begin{minted}[style=bw, breaklines, frame=single, + fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{cpp} +class Memo { +public: + utils::NotNull AddGroup(LogicalOperator op); + utils::NotNull AddLogicalExprToGroup( + utils::NotNull group, LogicalOperator op); + utils::NotNull Populate(const Operator& op); + +private: + std::deque groups_; + std::unordered_map expr_index_; +}; + +class Group { +public: + utils::NotNull AddLogicalExpr(LogicalOperator op); + utils::NotNull AddPhysicalExpr( + PhysicalOperator op, bool is_enforcer = false); + + LogicalExprs GetLogicalExprs(); + PhysicalExprs GetPhysicalExprs(); +}; + \end{minted} +\end{listing} + +Метод \verb|Memo::Populate| рекурсивно обходит исходное логическое дерево и +создает первоначальные группы. Для предотвращения повторного добавления +выражений используется отображение \verb|expr_index_|. Ключ строится из типа +оператора, его параметров и идентификаторов дочерних групп. Таким образом, +одинаковые выражения добавляются в Memo только один раз. + +Каждая группа содержит логические и физические выражения. Логические выражения +описывают семантику запроса, а физические выражения определяют конкретный способ +исполнения. Например, логическому соединению могут соответствовать физические +операторы соединения вложенными циклами и хеш-соединения. + +\subsubsection{Правила оптимизации} + +Расширение пространства поиска выполняется с помощью правил. Реализовано два +типа правил: трансформационные и реализационные. Их интерфейсы приведены в +листинге~\ref{lst:optimizer-rules}. + +\begin{listing}[H] + \caption{Интерфейсы правил оптимизации.} + \label{lst:optimizer-rules} + \begin{minted}[style=bw, breaklines, frame=single, + fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{cpp} +class TransformationRule { +public: + virtual bool IsApplicable(utils::NotNull expr) = 0; + utils::NotNull Apply( + utils::NotNull expr, Memo& memo); + +private: + virtual LogicalOperator ApplyImpl( + utils::NotNull expr, Memo& memo) = 0; +}; + +class ImplementationRule { +public: + virtual bool IsApplicable(utils::NotNull expr) = 0; + virtual utils::NotNull Apply( + utils::NotNull expr, Memo& memo) = 0; +}; + \end{minted} +\end{listing} + +Правило трансформации создает новое логическое выражение и добавляет его в +ту же группу Memo, что и исходное выражение. Следовательно, правило должно +сохранять семантику запроса. Правило реализации создает физическое выражение, +которое может быть передано модулю исполнения. + +Реализованы следующие трансформационные правила: \begin{itemize} \item коммутативность соединения; \item ассоциативность соединения; - \item разбиение фильтра с конъюнкцией на последовательность фильтров; + \item разбиение конъюнкции на последовательность фильтров; \item объединение последовательных фильтров; \item проталкивание фильтра через проекцию; \item проталкивание фильтра во входы соединения; - \item преобразование списка \sqlkw{IN} в цепочку сравнений. + \item преобразование выражения \verb|IN| в цепочку сравнений. +\end{itemize} + +Например, правило коммутативности преобразует выражение $A \Join B$ в +$B \Join A$. Для левого и правого внешнего соединения одновременно изменяется +тип соединения. Реализация правила приведена в +листинге~\ref{lst:join-commutativity}. + +\begin{listing}[H] + \caption{Правило коммутативности соединения.} + \label{lst:join-commutativity} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize, + linenos=false, xleftmargin=1.5em]{cpp} +LogicalOperator JoinCommutativity::ApplyImpl( + utils::NotNull expr, Memo&) { + auto join = std::get(expr->root_operator); + std::swap(join.lhs, join.rhs); + + if (join.type == JoinType::kLeft) { + join.type = JoinType::kRight; + } else if (join.type == JoinType::kRight) { + join.type = JoinType::kLeft; + } + return join; +} + \end{minted} +\end{listing} + +Правило проталкивания фильтра через соединение разбивает предикат на конъюнкты. +Каждый конъюнкт анализируется отдельно. Если он ссылается только на атрибуты +одного входа соединения, фильтр переносится ближе к соответствующей таблице. Это +позволяет уменьшить число кортежей до выполнения соединения. Для внешних +соединений учитывается направление: фильтр нельзя переносить в такой вход, для +которого это изменит семантику результата. + +Реализационные правила создают: +\begin{itemize} + \item последовательное сканирование таблицы; + \item фильтрацию; + \item проекцию; + \item агрегацию; + \item декартово произведение вложенными циклами; + \item соединение вложенными циклами; + \item хеш-соединение. \end{itemize} -Реализационные правила создают последовательное сканирование, фильтрацию, -проекцию, агрегацию, соединение вложенными циклами, декартово произведение -вложенными циклами и хеш-соединение. Хеш-соединение применяется только для -простого внутреннего соединения по равенству двух атрибутов. - -Поиск выполняется сверху вниз с использованием стека отложенных задач. Для -каждой пары из группы и требуемого набора физических свойств сохраняется -лучший найденный вариант. Такой вариант называется победителем группы. -Стоимость полного плана равна сумме локальных стоимостей его операторов. - -Для уменьшения пространства поиска используется метод ветвей и границ. -Оптимизатор вычисляет допустимую нижнюю оценку стоимости группы. Если даже -нижняя оценка не меньше стоимости уже известного решения, дальнейшее -исследование ветви прекращается. - -Физические свойства описывают дополнительные требования к результату. В -текущей версии реализовано свойство сортировки. Если запрос содержит -\ORDERBY{}, оптимизатор должен построить план, выдающий строки в требуемом -порядке. При необходимости в план добавляется обеспечивающий оператор -\Sort{}. - -\subsection{Физический план и его сериализация} - -После завершения поиска оптимизатор преобразует выбранные физические выражения -в дерево \texttt{PhysicalPlanNode}. Физический план отделен от внутренней -структуры Memo и может быть исполнен независимо от процесса оптимизации. - -К основным физическим операторам относятся последовательное чтение CSV-файла -\SeqScan{}, фильтрация \FilterOp{}, проекция \ProjectionOp{}, соединения -\NestedLoopJoin{} и \HashJoin{}, сортировка \Sort{} и хеш-агрегация -\sqlkw{HashAggregate}. - -Планы сериализуются в текстовый формат S-expression. Его удобно читать, -сравнивать в тестах и передавать вспомогательным утилитам. Дополнительно -реализован экспорт плана в формат Graphviz DOT. После запуска программы файл -сохраняется в каталоге \texttt{.plans}. - -\subsection{Модуль хранения и исполнения данных} - -Каждой таблице соответствует CSV-файл с совпадающим именем. Например, таблица -\texttt{users} хранится в файле \texttt{users.csv}. Первая строка содержит -описание столбцов и их типов: - -\begin{Verbatim}[fontsize=\small] -id:int,name:string,age:int -1,Alice,25 -2,Bob,31 -\end{Verbatim} - -Исполнитель физических планов реализован классом \texttt{Executor}. Он -параметризуется способом вычисления выражений и функциями доступа к таблицам. -Благодаря этому один исполнитель используется с интерпретатором, JIT-компилятором -и различными реализациями сканирования. - -Операторы взаимодействуют через каналы Boost.Asio. Отдельные каналы передают -описание атрибутов и порции размером до 2048 кортежей. Это уменьшает число -операций синхронизации по сравнению с передачей отдельных строк. - -Проекция и фильтрация являются потоковыми операторами. Они получают порцию -входных кортежей, обрабатывают ее и передают результат следующему оператору. -Для блокирующих операций может потребоваться накопление данных. Например, -сортировка сохраняет все входные кортежи в памяти. - -Соединение вложенными циклами материализует один вход во временный бинарный -файл, после чего многократно читает его при обработке второго входа. -Хеш-соединение строит хеш-таблицу по ключу левого входа и выполняет поиск -совпадений для кортежей правого входа. - -Для строковых значений используется интернирование. Строка помещается в общий -пул, а внутри кортежа хранится целочисленный идентификатор. Это сохраняет -компактное представление значения и упрощает передачу кортежей между -операторами. - -\subsection{JIT-компиляция выражений} - -Для часто вычисляемых выражений реализована JIT-компиляция с использованием -LLVM ORC JIT. Класс \texttt{JITCompiler} преобразует дерево выражения в LLVM IR, -запускает оптимизирующие проходы и возвращает указатель на машинную функцию. - -Скомпилированная функция получает указатель на результирующее значение, -указатель на входной кортеж и описание атрибутов. Структура значения содержит -флаг \texttt{is\_null} и объединение для непосредственного значения. Такое -представление учитывает трехзначную логику SQL. - -В проекте доступны интерпретируемая, JIT-компилируемая и кэшируемая -JIT-компилируемая стратегии вычисления выражений. Последняя сохраняет -полученные машинные функции и переиспользует их. - -Текущая реализация JIT предназначена для целочисленных и логических выражений. -Строковые операции, \sqlkw{IN}, агрегатные выражения и возведение в степень -через JIT не поддерживаются. Для них следует использовать интерпретируемый -режим. +Хеш-соединение применяется только для внутреннего эквисоединения двух атрибутов. +Для остальных условий доступно соединение вложенными циклами. Проверка +применимости правила приведена в листинге~\ref{lst:implement-hash-join}. + +\begin{listing}[H] + \caption{Проверка применимости хеш-соединения.} + \label{lst:implement-hash-join} + \begin{minted}[style=bw, breaklines, frame=single, + fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{cpp} +bool ImplementHashJoin::IsApplicable( + utils::NotNull expr) { + if (!std::holds_alternative( + expr->root_operator)) { + return false; + } + + const auto& join = + std::get(expr->root_operator); + + if (join.type != JoinType::kInner) return false; + + const auto* bin = + std::get_if(&join.qual); + + return bin && bin->binop == BinaryOp::kEq + && std::holds_alternative(*bin->lhs) + && std::holds_alternative(*bin->rhs); +} + \end{minted} +\end{listing} -\subsection{Руководство пользователя} +Для каждого выражения хранится информация о ранее примененных правилах. Это +предотвращает повторное выполнение одного правила над одним выражением и +ограничивает образование циклов при заполнении Memo. -\subsubsection{Требования к окружению} +\subsubsection{Оценка кардинальности} -Для сборки программы требуются CMake, компилятор C++ с поддержкой C++23, LLVM, -ANTLR4 и библиотеки Boost. В репозитории присутствует файл \texttt{flake.nix}, -поэтому в системе с пакетным менеджером Nix рекомендуется открыть окружение: +Стоимость физического плана зависит от количества обрабатываемых кортежей. +Для получения этой величины используется класс +\verb|CardinalityEstimates| из файла +\verb|src/stewkk/sql/logic/optimizer/cardinality.cpp|. -\begin{Verbatim}[fontsize=\small] -nix develop -\end{Verbatim} +Оценка кардинальности вычисляется для группы Memo и кэшируется. В текущей +версии используются следующие эвристики: +\begin{itemize} + \item для таблицы используется известное количество строк или значение по + умолчанию; + \item фильтрация и проекция сохраняют оценку кардинальности входа; + \item скалярная агрегация возвращает одну строку; + \item декартово произведение имеет кардинальность $N_l N_r$; + \item для соединения используется произведение кардинальностей входов и + коэффициента селективности. +\end{itemize} + +Для эквисоединения двух атрибутов коэффициент селективности оценивается как +\[ + S = \frac{1}{\max(1, N_l, N_r)}. +\] +Для конъюнкции условий коэффициенты селективности перемножаются. + +\subsubsection{Стоимостная модель} + +Стоимость плана вычисляется как сумма локальных стоимостей физических +операторов. Коэффициенты были подобраны с помощью бенчмарков отдельных +операторов, описанных в разделе тестирования. + +Для входных кардинальностей $N$, $N_l$ и $N_r$ используются следующие формулы: +\[ +\begin{aligned} + C_{\text{scan}} &= 100N, \\ + C_{\text{filter}} &= 100N, \\ + C_{\text{projection}} &= 22N, \\ + C_{\text{aggregation}} &= 510N, \\ + C_{\text{nested-loop join}} &= 70N_lN_r, \\ + C_{\text{cross join}} &= 104N_lN_r, \\ + C_{\text{hash join}} &= 69(N_l + N_r), \\ + C_{\text{sort}} &\approx 11N\log_2N. +\end{aligned} +\] + +Фрагмент реализации стоимостной модели приведен в +листинге~\ref{lst:optimizer-cost}. + +\begin{listing}[H] + \caption{Вычисление локальной стоимости физических операторов.} + \label{lst:optimizer-cost} + \begin{minted}[style=bw, breaklines, frame=single, + fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{cpp} +int64_t CalcCost(PhysicalExpr* expr, + CardinalityEstimates& cardinality) { + return std::visit(utils::Overloaded{ + [&](const physical::SeqScan&) { + return 100 * cardinality.GetCardinality(expr->group); + }, + [&](const physical::NestedLoopJoin& j) { + return 70 * cardinality.GetCardinality(j.lhs) + * cardinality.GetCardinality(j.rhs); + }, + [&](const physical::HashJoin& j) { + return 69 * (cardinality.GetCardinality(j.lhs) + + cardinality.GetCardinality(j.rhs)); + }, + [&](const physical::Sort& s) { + auto n = cardinality.GetCardinality(s.input); + return 11 * n * std::bit_width( + static_cast(n)); + }, + ... + }, expr->root_operator); +} + \end{minted} +\end{listing} + +\subsubsection{Алгоритм поиска} + +Класс \verb|Optimizer| реализует алгоритм поиска. При создании объекта исходное +логическое дерево помещается в Memo. Поиск запускается для корневой группы и +требуемых физических свойств результата. + +Алгоритм использует стек отложенных задач. Это позволяет разделить процесс на +небольшие операции: исследование группы, применение правила, оптимизацию +физического выражения и обработку его дочерних узлов. + +Основной метод оптимизации группы приведен в +листинге~\ref{lst:optimize-group}. + +\begin{listing}[H] + \caption{Оптимизация группы Memo.} + \label{lst:optimize-group} + \begin{minted}[style=bw, breaklines, frame=single, + fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{cpp} +void Optimizer::OptimizeGroup(Group* group, + PropertySet required, + Limit limit) { + WinnerKey key{group, required}; + if (winner_.contains(key)) return; + + if (IsExplored(group) + && limit + && LowerBoundCost(group) >= *limit) { + return; + } + + if (!IsExplored(group)) { + tasks_.emplace([=] { + OptimizeGroup(group, required, limit); + }); + tasks_.emplace([=] { + ExploreGroup(group, limit); + }); + return; + } + + for (auto expr : group->GetPhysicalExprs()) { + if (expr->is_enforcer) continue; + auto cost = local_cost_[expr.get()]; + if (!limit || cost < *limit) { + tasks_.emplace([=] { + OptimizeInputs(expr, required, {}, cost, limit); + }); + } + } + + AddEnforcers(group, required, limit); +} + \end{minted} +\end{listing} + +Если группа еще не исследована, в стек добавляются две задачи. Первая расширяет +группу с помощью правил, вторая повторно запускает ее оптимизацию после +расширения. Если группа уже исследована, для каждого физического выражения +вычисляется полная стоимость с учетом дочерних групп. + +Для каждой пары из группы и требуемого набора свойств хранится лучший найденный +вариант. Ключ такого варианта представлен структурой \verb|WinnerKey|: + +\begin{minted}[style=bw, breaklines, frame=single, + fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{cpp} +struct WinnerKey { + Group* group; + PropertySet required; +}; +\end{minted} + +Хранение победителя отдельно для каждого набора свойств необходимо, поскольку +самый дешевый неотсортированный план может отличаться от самого дешевого плана, +удовлетворяющего требованию \verb|ORDER BY|. + +Дочерние группы физического выражения оптимизируются последовательно. После +получения победителя дочерней группы его стоимость прибавляется к накопленной +стоимости. Если все дочерние группы обработаны, проверяются выходные свойства +плана. Затем найденный вариант при необходимости становится новым победителем. + +\subsubsection{Метод ветвей и границ} + +Полный перебор пространства планов быстро становится непрактичным при +увеличении количества соединяемых таблиц. Для сокращения перебора используется +метод ветвей и границ. + +Для каждой группы вычисляется нижняя граница стоимости. Она равна минимальной +сумме нижней оценки локального оператора и нижних оценок дочерних групп. +Например, для логического соединения выбирается минимум между стоимостью +хеш-соединения и стоимостью соединения вложенными циклами: +\[ + LB_{\text{join}} = + \min(69(N_l + N_r), 70N_lN_r). +\] + +Если нижняя граница не меньше стоимости уже известного решения, исследование +ветви прекращается. Фрагмент реализации приведен в +листинге~\ref{lst:lower-bound}. + +\begin{listing}[H] + \caption{Вычисление нижней границы стоимости группы.} + \label{lst:lower-bound} + \begin{minted}[style=bw, breaklines, frame=single, + fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{cpp} +int64_t Optimizer::LowerBoundCost(Group* group) { + if (auto it = lower_bounds_.find(group); + it != lower_bounds_.end()) { + return it->second; + } + + int64_t best = std::numeric_limits::max(); + + for (auto expr : group->GetLogicalExprs()) { + int64_t cost = LowerBoundLocalCost(expr, cardinality_); + + for (auto child : GetChildren(expr)) { + cost += LowerBoundCost(child); + } + best = std::min(best, cost); + } + + lower_bounds_[group] = best; + return best; +} + \end{minted} +\end{listing} + +Метод \verb|OptimizeInputs| также уменьшает границу для очередного дочернего +узла. Из общей допустимой стоимости вычитаются уже накопленная стоимость и +нижние оценки еще не обработанных дочерних групп. Благодаря этому заведомо +неоптимальные ветви отбрасываются до построения полного плана. + +Для исследовательских задач предусмотрен метод \verb|OptimizeExhaustive|. Он +запускает тот же алгоритм без ограничения стоимости и используется при +проверке достижимости внешних физических планов. + +\subsubsection{Физические свойства} + +Некоторые требования к результату нельзя выразить только стоимостью. Например, +конструкция \verb|ORDER BY| требует от результата определенного порядка +кортежей. Для представления таких требований используется класс +\verb|PropertySet|. + +В текущей версии реализовано свойство сортировки \verb|SortProperty|. Порядок +считается удовлетворяющим требованию, если требуемые ключи являются префиксом +фактически полученного порядка. Например, сортировка по $(a, b)$ удовлетворяет +требованию сортировки по $a$. + +Некоторые операторы сохраняют порядок дочернего узла. Фильтрация и проекция +сохраняют свойства входа. Соединение вложенными циклами сохраняет свойства +левого входа. Хеш-соединение и агрегация не гарантируют сохранение порядка. + +Если подходящий план не найден, оптимизатор пытается добавить обеспечивающий +оператор. Для сортировки используется класс \verb|SortEnforcer|. Он проверяет +наличие требуемых атрибутов в схеме группы и создает физический оператор +сортировки. + +\begin{listing}[H] + \caption{Добавление оператора сортировки для обеспечения свойства.} + \label{lst:sort-enforcer} + \begin{minted}[style=bw, breaklines, frame=single, + fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{cpp} +std::optional SortEnforcer::TryBuild( + utils::NotNull group, + const PropertySet& required, + SchemaCatalog& schema) const { + const auto* sort = required.Get(); + if (!sort) return std::nullopt; + + if (!ContainsRequiredKeys(schema.GetSchema(group), + sort->order)) { + return std::nullopt; + } + + return physical::Sort{group, sort->order}; +} + \end{minted} +\end{listing} + +\subsubsection{Формирование физического плана} + +После завершения поиска метод \verb|BuildOptimalPlan| рекурсивно восстанавливает +итоговое дерево физических операторов. Для каждой группы выбирается сохраненный +победитель с учетом требуемых свойств. Затем аналогичным образом +восстанавливаются планы дочерних групп. + +Результатом работы оптимизатора является объект \verb|PhysicalPlanNode|, который +не зависит от структуры Memo. Он может быть сериализован для анализа или передан +модулю исполнения запросов. + +Таким образом, модуль оптимизации разделяет представление пространства планов, +правила его расширения, стоимостную модель и алгоритм поиска. Такая организация +позволяет добавлять новые правила и физические операторы без изменения основной +логики оптимизатора. + +\subsection{Модуль физического хранения данных} + +Данные хранятся в одной директории, где каждому отношению соответствует +файл с CSV-таблицей. Название файла совпадает с названием отношения. +Первая строка CSV-файла содержит описание атрибутов и их типов, +последующие строки содержат кортежи отношения. + +Для чтения таблиц реализован класс +\verb|CsvDirSequentialScanner|~\refAlgo{lst:csv-dir-scanners}. Он +выполняет последовательное сканирование CSV-файла и передает прочитанные +кортежи модулю исполнения физических операторов. + +\begin{listing}[H] + \caption{Объявления классов сканирования CSV-таблиц.} + \label{lst:csv-dir-scanners} + \begin{minted}[style=bw, breaklines, frame=single, fontsize = \footnotesize, linenos=false, xleftmargin = 1.5em]{C++} +struct CsvDirSequentialScanner { + std::string dir; + + boost::asio::awaitable> operator()( + const std::string& table_name, + const std::string& output_table_name, + AttributesInfoChannel& attrs_chan, + TuplesChannel& tuples_chan) const; +}; + +struct CsvDirIndexedScanner { + std::string dir; + + boost::asio::awaitable> operator()( + const std::string& table_name, + const std::string& output_table_name, + const Expression& predicate, + AttributesInfoChannel& attrs_chan, + TuplesChannel& tuples_chan) const; +}; + \end{minted} +\end{listing} + +Дополнительно реализована поддержка индексов. Индекс представляет собой +отсортированный бинарный файл, содержащий целочисленные ключи и смещения +соответствующих строк в CSV-файле. Использование смещений позволяет +извлекать подходящие кортежи без полного сканирования отношения. + +Список доступных индексов задается в файле \verb|indexes.meta|, +расположенном в директории с данными. Каждая строка файла содержит +название отношения, название индексируемого атрибута, тип индекса и имя +бинарного файла~\refAlgo{lst:meta}. + +\begin{listing}[H] + \caption{Пример файла с метаданными о доступных индексах.} + \label{lst:meta} +\begin{minted}[style=bw, breaklines, frame=single, fontsize = \footnotesize, linenos=false, xleftmargin = 1.5em]{text} +users id sorted users.id.sorted.idx +\end{minted} +\end{listing} + +Для чтения данных с использованием индекса реализован класс +\verb|CsvDirIndexedScanner|~\refAlgo{lst:csv-dir-scanners}. Если указанный в +метаданных бинарный файл отсутствует, индекс строится при первом обращении к +таблице. Для поиска диапазона подходящих ключей используется упорядоченность +записей индекса. + +Поддерживаются индексы только для целочисленных атрибутов. Индексное +сканирование применяется для предикатов сравнения атрибута с +целочисленной константой. Поддерживаются операции равенства, строгого и +нестрогого сравнения. После чтения найденных строк дополнительно +вычисляется исходный предикат, что обеспечивает корректную обработку +составных условий. + +\subsection{Модуль исполнения физических планов} + +За исполнение запросов отвечает класс \verb|Executor|~\refAlgo{lst:executor}. +Он получает физический план, запускает исполнение его операторов и формирует +результирующее отношение. Физический план представлен деревом объектов типа +\verb|PhysicalPlanNode|. Для каждого типа узла предусмотрена отдельная функция +исполнения. + +Поддерживается исполнение последовательного и индексного сканирования, +фильтрации, проекции, сортировки, агрегации, декартова произведения, соединения +вложенными циклами и хеш-соединения. Операторы могут обрабатывать дочерние узлы +плана независимо и передавать результаты вышестоящим операторам по мере их +готовности. + +\begin{listing}[H] + \caption{Основные элементы объявления класса Executor.} + \label{lst:executor} + \begin{minted}[style=bw, breaklines, frame=single, fontsize = \footnotesize, linenos=false, xleftmargin = 1.5em]{C++} +template +class Executor { +public: + using SequentialScan = std::function>( + const std::string& table_name, + const std::string& output_table_name, + AttributesInfoChannel& attr_chan, + TuplesChannel& tuples_chan)>; + + using IndexScan = std::function>( + const std::string& table_name, + const std::string& output_table_name, + const Expression& predicate, + AttributesInfoChannel& attr_chan, + TuplesChannel& tuples_chan)>; + + Executor(SequentialScan seq_scan, + IndexScan index_scan, + boost::asio::any_io_executor executor); + + boost::asio::awaitable> + Execute(const PhysicalPlanNode& op); + +private: + SequentialScan sequential_scan_; + IndexScan index_scan_; + ExpressionExecutor expression_executor_; +}; + \end{minted} +\end{listing} + +Класс параметризуется интерфейсами \verb|SequentialScan|, \verb|IndexScan| и +\verb|ExpressionExecutor|. Первые два интерфейса отделяют исполнение физического +плана от конкретного способа хранения данных. \verb|SequentialScan| выполняет +полное чтение таблицы, а \verb|IndexScan| получает дополнительный предикат и +извлекает строки с использованием индекса. Благодаря этому исполнитель не +зависит от формата CSV-файлов и устройства индексных структур. + +Параметр \verb|ExpressionExecutor| определяет способ вычисления скалярных +выражений. Реализованы стратегии для интерпретации выражений и для +JIT-компиляции. По умолчанию используется интерпретируемый вариант. + +Значения атрибутов представлены структурой \verb|Value|. Она содержит признак +\verb|NULL| и значение одного из поддерживаемых типов: целого числа, логического +значения или идентификатора строки. Исходное значение для строк сохраняется в +пул, а в кортеже хранится числовой идентификатор строки. Кортеж представлен +вектором значений, а отношение состоит из описания атрибутов и списка кортежей. + +Коммуникация между физическими операторами осуществляется с помощью асинхронных +каналов. Используются два типа каналов: \verb|AttributesInfoChannel| для +однократной передачи схемы отношения и \verb|TuplesChannel| для передачи данных. +Кортежи передаются векторами. Это уменьшает число операций синхронизации и +позволяет потоковым операторам начинать обработку до полного завершения дочерних +узлов плана. + +Для реализации каналов используется +\verb|boost::asio::experimental::concurrent_channel|. Данный класс обеспечивает +потокобезопасность, буферизацию, асинхронную передачу данных и совместимость с +корутинами. Такой подход позволяет единообразно организовать взаимодействие +между операторами независимо от конкретных алгоритмов для их реализации. + +\subsection{Руководство пользователя} + +Для сборки программы требуются CMake, компилятор C++ с поддержкой C++23, LLVM, +ANTLR4 и библиотеки Boost. Дополнительно требуется Python, pytest и Docker. В +репозитории присутствует файл \texttt{flake.nix}, описывающий необходимые +зависимости, поэтому в системе с пакетным менеджером Nix рекомендуется открыть +окружение при помощи команды \verb|nix develop|. Все команды далее выполняются из корневого каталога проекта. \subsubsection{Сборка программы} -Для первичной конфигурации и сборки следует выполнить: +Для выполнения конфигурации и сборки следует запустить команды на листинге~\ref{lst:build}. -\begin{Verbatim}[fontsize=\small] +\begin{listing}[H] + \caption{Конфигурация и сборка проекта.} + \label{lst:build} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{bash} cmake -S . -B build -DCMAKE_BUILD_TYPE=Release cmake --build build -- -j 6 -\end{Verbatim} + \end{minted} +\end{listing} После успешной сборки исполняемый файл находится по пути \texttt{build/bin/sql}. Для повторной сборки также можно использовать цель \texttt{make build}. -\subsubsection{Подготовка входных данных} +\subsubsection{Запуск программы} Пользователь должен создать отдельную директорию и поместить в нее CSV-файлы. Имя каждого файла без расширения считается именем таблицы. В заголовке каждого @@ -318,70 +867,201 @@ \subsubsection{Подготовка входных данных} \texttt{int} и \texttt{string}. Пустое SQL-значение записывается как \texttt{NULL}. -Для первого запуска можно использовать готовые тестовые данные: - -\begin{Verbatim}[fontsize=\small] -test/static/executor/test_data -\end{Verbatim} - -\subsubsection{Запуск SQL-запроса} +Для первого запуска можно использовать готовые тестовые данные в \verb|test/static/executor/test_data|. SQL-запрос передается программе через стандартный поток ввода. Простейший -пример запуска: +пример запуска представлен на листинге~\ref{lst:run-example}. -\begin{Verbatim}[fontsize=\small] +\begin{listing}[H] + \caption{Простейший пример запуска программы.} + \label{lst:run-example} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{bash} echo 'SELECT users.id FROM users;' | \ ./build/bin/sql --data-dir test/static/executor/test_data -\end{Verbatim} - -Для запуска запроса из файла следует использовать перенаправление ввода: - -\begin{Verbatim}[fontsize=\small] -./build/bin/sql --data-dir path/to/csv_dir < query.sql -\end{Verbatim} + \end{minted} +\end{listing} Программа выводит заголовок отношения, содержащий имена и типы столбцов, а затем полученные строки. Если запрос не содержит \ORDERBY{}, строки вывода сортируются лексикографически для воспроизводимости результата. -\screenplaceholder{Выполнение SQL-запроса над тестовыми данными}{fig:practice-run}{% - Вставить скриншот запуска запроса через стандартный поток ввода\\ - и напечатанного результирующего отношения.} +\begin{figure}[H] +\centering +\includegraphics[width=0.8\textwidth]{run-example.png} +\caption{Выполнение SQL-запроса над тестовыми данными}% +\label{fig:practice-run} +\end{figure} \subsubsection{Вывод логического и физического планов} Для диагностики предусмотрены параметры \texttt{--print-ast} и \texttt{--print-plan}. Первый параметр выводит логическое дерево после -синтаксического анализа, второй --- выбранный физический план: +синтаксического анализа, второй --- выбранный физический план. +Пример использования представлен на листинге~\ref{lst:run-plan}. -\begin{Verbatim}[fontsize=\small] +\begin{listing}[H] + \caption{Запуск программы с выводом логического дерева и физического плана.} + \label{lst:run-plan} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{bash} echo 'SELECT users.id FROM users WHERE users.age > 18 \ ORDER BY users.id;' | \ ./build/bin/sql --data-dir test/static/executor/test_data \ --print-ast --print-plan -\end{Verbatim} - -\screenplaceholder{Вывод логического дерева и физического плана}{fig:practice-plan}{% - Вставить скриншот запуска с параметрами\\ - \texttt{--print-ast --print-plan}.} - -\subsubsection{Запуск с JIT-компиляцией} - -Для выбора JIT-компиляции выражений используется параметр \texttt{--jit}: - -\begin{Verbatim}[fontsize=\small] + \end{minted} +\end{listing} + +\begin{figure}[H] +\centering +\includegraphics[width=0.8\textwidth]{run-with-prints.png} +\caption{Вывод логического дерева и физического плана}% +\label{fig:practice-plan} +\end{figure} + +Для проверки полноты набора правил оптимизации предусмотрен режим +\verb|--check-reachable|. В этом режиме программа получает SQL-запрос и +сериализованный физический план, после чего выполняет полный перебор +пространства поиска без отсечения по стоимости. Если заданный план может быть +получен с помощью реализованных правил, программа выводит сообщение +\verb|REACHABLE|. + +План передается в отдельном файле в формате S-expression. Например, содержимое +файла \verb|target.plan| может иметь вид, представленный на листинге~\ref{lst:target-plan}. + +\begin{listing}[H] + \caption{Пример содержимого файла с целевым физическим планом.} + \label{lst:target-plan} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} +(PhysicalProjection (exprs (attr users id)) + (PhysicalFilter (> (attr users age) 18) + (SeqScan users))) + \end{minted} +\end{listing} + +Проверка выполняется командой на листинге~\ref{lst:check-reachable}. + +\begin{listing}[H] + \caption{Запуск программы в режиме проверки достижимости плана.} + \label{lst:check-reachable} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} echo 'SELECT users.id FROM users WHERE users.age > 18;' | \ - ./build/bin/sql --data-dir test/static/executor/test_data --jit -\end{Verbatim} - -Параметр рекомендуется применять к запросам с целочисленными арифметическими и -логическими выражениями. Запросы со строковыми выражениями и \sqlkw{IN} следует -запускать без JIT. - -\screenplaceholder{Выполнение запроса с JIT-компиляцией выражений}{fig:practice-jit}{% - Вставить скриншот запуска программы с параметром \texttt{--jit}.} - -\subsubsection{Доступные параметры командной строки} + ./build/bin/sql \ + --data-dir test/static/executor/test_data \ + --check-reachable target.plan + \end{minted} +\end{listing} + +Если план недостижим, программа выводит сообщение \verb|NOT REACHABLE| и +описание первого обнаруженного расхождения. Параметр \verb|--data-dir| +необходим для загрузки схем таблиц. В частности, схема используется при +проверке достижимости планов с сортировкой. + +Для автоматизированной проверки достижимости планов используется Microsoft SQL +Server. Контейнер с СУБД запускается с помощью Docker Compose. Перед запуском +скриптов в директории research, следует выполнить команды на листинге~\ref{lst:docker-up}. + +\begin{listing}[H] + \caption{Запуск контейнера с Microsoft SQL Server.} + \label{lst:docker-up} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} +cd research +docker compose up -d +cd .. + \end{minted} +\end{listing} + +Для запуска модульных тестов C++ следует выполнить команду на листинге~\ref{lst:ctest}. + +\begin{listing}[H] + \caption{Запуск модульных тестов C++.} + \label{lst:ctest} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} +ctest --test-dir build --output-on-failure + \end{minted} +\end{listing} + +Модульные тесты проверяют синтаксический анализ запросов, вычисление выражений, +исполнение физических операторов, индексное сканирование, сериализацию планов, +структуру Memo, правила оптимизации и физические свойства. + +Для запуска Python-тестов преобразователя планов Microsoft SQL Server +необходимо предварительно запустить контейнер, после чего выполнить команды на листинге~\ref{lst:pytest-converter}. + +\begin{listing}[H] + \caption{Запуск тестов преобразователя планов Microsoft SQL Server.} + \label{lst:pytest-converter} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} +cd research +pytest -q test_converter.py +cd .. + \end{minted} +\end{listing} + +Тесты инструментов Star Schema Benchmark запускаются командой на листинге~\ref{lst:pytest-ssb}. + +\begin{listing}[H] + \caption{Запуск тестов инструментов Star Schema Benchmark.} + \label{lst:pytest-ssb} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} +pytest -q benchmarks/datasets/ssb + \end{minted} +\end{listing} + +Для поиска ошибок исполнения реализован дифференциальный фаззер. Он генерирует +случайные SQL-запросы, выполняет их в модельной СУБД и Microsoft SQL Server, +после чего сравнивает полученные результаты. Перед запуском необходимо поднять +контейнер с Microsoft SQL Server. + +Фаззер запускается из корневого каталога проекта командой на листинге~\ref{lst:diff-fuzz}. + +\begin{listing}[H] + \caption{Запуск дифференциального фаззера.} + \label{lst:diff-fuzz} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} +python -m research.fuzz.diff_fuzz \ + --cli build/bin/sql \ + --data-dir test/static/executor/test_data + \end{minted} +\end{listing} + +Для проверки режима JIT-компиляции можно дополнительно передать параметр +\verb|--jit|. При обнаружении расхождения фаззер выводит начальное значение +генератора случайных чисел и текст запроса, что позволяет воспроизвести ошибку. + +Второй фаззер проверяет полноту набора правил оптимизатора. Он получает +физический план запроса из Microsoft SQL Server, преобразует его во внутренний +формат проекта и запускает программу в режиме \verb|--check-reachable|, как показано на листинге~\ref{lst:reach-fuzz}. + +\begin{listing}[H] + \caption{Запуск фаззера проверки полноты правил оптимизатора.} + \label{lst:reach-fuzz} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} +python -m research.fuzz.reach_fuzz \ + --cli build/bin/sql \ + --data-dir test/static/executor/test_data + \end{minted} +\end{listing} + +\begin{figure}[H] +\centering +\includegraphics[width=0.8\textwidth]{reach-fuzz.png} +\caption{Пример вывода фаззера проверки полноты правил оптимизатора}% +\label{fig:reach-fuzz} +\end{figure} + +Оба фаззера поддерживают параметр \verb|--start-seed|. Он задает начальное +значение генератора и используется для воспроизводимого повторного запуска, +как показано на листинге~\ref{lst:reach-fuzz-seed}. + +\begin{listing}[H] + \caption{Запуск фаззера с фиксированным начальным значением генератора.} + \label{lst:reach-fuzz-seed} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} +python -m research.fuzz.reach_fuzz \ + --cli build/bin/sql \ + --data-dir test/static/executor/test_data \ + --start-seed 1000 + \end{minted} +\end{listing} \begin{longtable}{|p{4.2cm}|p{9.3cm}|} \caption{Параметры командной строки программы.}\label{tbl:practice-cli}\\ @@ -411,61 +1091,141 @@ \subsubsection{Доступные параметры командной стро \hline \end{longtable} -\section{ТЕСТИРОВАНИЕ} +\section{Тестирование} -Модульные тесты написаны с использованием GoogleTest. Они проверяют парсер, -вычисление выражений, исполнение операторов, сериализацию планов, свойства -сортировки, Memo и оптимизатор. Отдельные тесты проверяют интерпретируемое и -JIT-исполнение. +Тестирование разработанной модельной СУБД выполнялось на нескольких уровнях. Для +проверки отдельных компонентов использовались модульные тесты. Корректность +выполнения запросов и полнота пространства поиска оптимизатора дополнительно +проверялись с помощью фаззинга. Для оценки производительности и калибровки +стоимостной модели были реализованы бенчмарки. Вспомогательные инструменты на +языке Python проверяются с помощью библиотеки \verb|pytest|. -Для проверки корректности C++-части проекта следует выполнить: +\subsection{Модульные тесты} -\begin{Verbatim}[fontsize=\small] -ctest --test-dir build --output-on-failure -\end{Verbatim} +Модульные тесты реализованы с использованием библиотеки GoogleTest. Они +проверяют синтаксический анализ SQL-запросов, исполнение физических операторов, +индексное сканирование, вычисление выражений, сериализацию физических планов, +структуру Memo, правила оптимизации и обработку свойств сортировки. -В результате запуска успешно выполняются 126 тестов. Для проверки конвертера -запросов Star Schema Benchmark используется команда: +Отдельные тесты проверяют выбор физического плана. Например, при различных +оценках кардинальности оптимизатор должен выбирать подходящий порядок соединения +таблиц. Также проверяется достижимость физических планов при полном переборе +пространства поиска. -\begin{Verbatim}[fontsize=\small] -pytest -q benchmarks/datasets/ssb -\end{Verbatim} +Пример теста синтаксического анализатора приведен в +листинге~\ref{lst:googletest}. + +\begin{listing}[H] + \caption{Пример модульного теста.} + \label{lst:googletest} + \begin{minted}[style=bw, breaklines, frame=single, fontsize = \footnotesize, linenos=false, xleftmargin = 1.5em]{cpp} +TEST(ParserTest, SelectSingleColumnFromSingleTable) { + std::stringstream s{"SELECT users.id FROM users;"}; -Набор содержит 17 тестов, которые также выполняются успешно. В каталоге -\texttt{benchmarks} находятся тесты производительности на основе Google -Benchmark. Они предназначены для измерения характеристик отдельных физических -операторов. + Operator got = GetAST(s).value().op; -Вспомогательные Python-скрипты из каталога \texttt{research} генерируют -запросы, сравнивают результаты с Microsoft SQL Server и проверяют достижимость -внешних физических планов. Для последней задачи программа поддерживает режим -\texttt{--check-reachable}, запускающий полный перебор пространства планов без -отсечения. + ASSERT_THAT(got, VariantWith( + Projection{ + std::vector{{Attribute{"users", "id"}}}, + std::make_shared(Table{"users"}) + })); +} + \end{minted} +\end{listing} + +\subsection{Python-тесты} + +Для проверки вспомогательных инструментов используются тесты на языке Python, +запускаемые с помощью библиотеки \verb|pytest|. Они разделены на две группы. + +Первая группа проверяет преобразование физических планов Microsoft SQL Server из +формата ShowPlanXML во внутренний текстовый формат проекта. Рассматриваются +последовательное и индексное сканирование, фильтрация, сортировка, агрегация и +соединения. Часть тестов использует заранее подготовленные XML-фрагменты, часть +может быть запущена с подключением к экземпляру Microsoft SQL Server. + +Вторая группа относится к Star Schema Benchmark. Она проверяет преобразование +исходных таблиц SSB в CSV-формат модельной СУБД, обработку некорректных входных +данных и успешное выполнение стандартных аналитических запросов. + +\subsection{Фаззинг} + +Для проверки системы на большом количестве входных данных реализован генератор +случайных SQL-запросов. Он строит запросы из поддерживаемого подмножества языка: +проекции, фильтры, соединения, группировки, агрегатные функции, сортировки, +строковые выражения, \verb|IN| и \verb|BETWEEN|. Использование фиксированного +начального значения генератора позволяет воспроизводить найденные ошибки. + +% TODO: заменить на макросы \sqkkw + +Дифференциальный фаззер выполняет каждый сгенерированный запрос в модельной СУБД +и в Microsoft SQL Server. Результаты сравниваются как мультимножества строк. +Для запросов с \verb|ORDER BY| дополнительно проверяется корректность порядка +результатов. При обнаружении расхождения выводятся начальное значение +генератора, текст запроса и результаты обеих систем. + +Отдельный фаззер используется для исследования полноты набора правил +оптимизации. Для случайного запроса из Microsoft SQL Server извлекается +физический план, после чего план преобразуется во внутренний формат проекта. +Затем модельный оптимизатор выполняет полный перебор пространства поиска и +проверяет, достижим ли полученный план с помощью реализованных правил. Такой +подход позволяет находить отсутствующие преобразования и физические операторы. + +\subsection{Бенчмарки} + +Тесты производительности реализованы с помощью библиотеки Google Benchmark. +Для запросов предусмотрены два режима построения физического плана: +\verb|Naive| и \verb|Optimized|. В первом случае физический план строится +непосредственно по логическому дереву без применения оптимизатора. Во втором +случае используется стоимостная оптимизация. Сравнение этих режимов позволяет +оценить влияние выбранного физического плана на время выполнения запроса. + +Бенчмарки выполняются как для небольших синтетических тестовых запросов, так и +для набора аналитических запросов Star Schema Benchmark. Дополнительно +поддерживается сравнение интерпретируемого и JIT-компилируемого способов +вычисления выражений. + +\subsection{Калибровка стоимостной модели} + +Для калибровки коэффициентов в стоимостных формулах реализованы отдельные +бенчмарки физических операторов. Измеряется время выполнения последовательного +сканирования, фильтрации, проекции, сортировки, агрегации, хеш-соединения, +соединения вложенными циклами и декартова произведения. + +При выполнении этих бенчмарков данные хранятся в оперативной памяти. Это +позволяет уменьшить влияние файловой системы и измерять преимущественно +стоимость самого физического оператора. Для каждого оператора выполняются замеры +на таблицах различного размера. + +Дополнительно формируются группы запусков с приблизительно одинаковой расчетной +стоимостью. Для этого размер входных отношений выбирается на основе стоимостной +формулы оператора. Сопоставление расчетной стоимости с фактическим временем +выполнения позволяет сравнить операторы между собой и скорректировать +коэффициенты модели. Благодаря этому оптимизатор принимает решения на основе +оценок, соответствующих характеристикам реализованного исполнителя. \anonsection{ЗАКЛЮЧЕНИЕ} -В ходе практики разработан программный прототип SQL-компилятора и модельной -системы исполнения запросов. Реализован сквозной процесс от разбора SQL-текста -до получения результирующего отношения. Программа работает с CSV-файлами, -поддерживает основные операторы реляционной алгебры и формирует физический план -на основе стоимостной модели. - -Ключевой частью проекта является расширяемый оптимизатор, использующий -структуру Memo и правила преобразования. Реализован поиск планов с учетом -физических свойств, нижних оценок стоимости и метода ветвей и границ. -Поддержка обеспечивающего оператора сортировки позволяет корректно обрабатывать -требования \ORDERBY{}. - -Исполнитель построен на корутинах C++ и каналах Boost.Asio. Потоковая передача -порций кортежей сочетается с материализацией для блокирующих операторов. -Для ускорения вычисления выражений добавлена JIT-компиляция с использованием -LLVM и кэширование скомпилированных функций. - -Корректность основных компонентов проверяется модульными тестами. Дополнительно -подготовлены бенчмарки физических операторов и инструменты дифференциального -сравнения с Microsoft SQL Server. Эти средства позволяют использовать проект -как основу для дальнейшего исследования правил оптимизации и уточнения -стоимостной модели. +В ходе практики разработана модельная СУБД и оптимизатор для нее. Программа +работает с CSV-файлами, поддерживает основные операторы реляционной алгебры и +формирует оптимальный физический план на основе стоимостной модели. + +Ключевой частью проекта является расширяемый оптимизатор, использующий структуру +Memo и правила преобразования. Реализован поиск планов с учетом физических +свойств, нижних оценок стоимости и метода ветвей и границ. + +Корректность основных компонентов проверена модульными тестами. Дополнительно +подготовлены бенчмарки физических операторов, оценен прирост производительности +относительно наивного плана и реализованы инструменты дифференциального +сравнения с промышленной СУБД Microsoft SQL Server. + +Цель курсовой работы была достигнута. Получена стабильная и расширяемая система, +демонстрирующая возможности оптимизатора. + +В заключение, несмотря на достигнутые положительные результаты, существует +потенциал для дальнейшего улучшения реализации оптимихатора. 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zad#D-nRk4&k`!oGNwDLY+3x!{^HRTy!X)Z@`taHg>SYO(1KEq3 zbF*~b@I;9h=)nQhg&Lclr&ykY&DZn_^cGPH%Qnx;Ci>Q{1$BRA3fgVE)Gt>-LA0bm zt3*`g=}MGWRk>byt=CQ$nRXFUG_G*#3!2ANIz~}BrmKg*4ur#LC;)6g`Ykip_;CSS z^SH#)f8zdd8yM^qA=ops&iz>tR<_i9iZqfjcDB z%DnCJMB0i%Uppl13{XcY(BFuNiPS@{NTHmfKPXbH?<``QdLCyW(*x z8{^p^Jm*qu)9K)+P{~q$(Dz63xo1y0Rxl&a^A?5CGhy&?G3_-ch=v}Xh#GIR#V?kY zTd%t+kHoUU*FEkyoj&2NXjR|EQ;z(g#h8qeCOpWfslhRt#xOHA(2yn_zAT{%J|wRM zwutj!YIFzwsrskn2EswNWO?Us$s vmb)W+|9l#FTXp~c=C}V}(|!N%5fk~=yH|*F2I-JLXH%UV5T|J;FW>tw-PSSM literal 0 HcmV?d00001 diff --git a/research/research.ipynb b/research/research.ipynb index 454f982..b36f0be 100644 --- a/research/research.ipynb +++ b/research/research.ipynb @@ -3226,7 +3226,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.7" + "version": "3.13.13" } }, "nbformat": 4, From 012be6fd01fe78aa483a2b9a7e8837deaffb56c0 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 1 Jun 2026 23:04:16 +0300 Subject: [PATCH 074/120] Fix segfault --- benchmarks/main.cpp | 19 +++++-- benchmarks/operator_cost.cpp | 29 ++++++---- .../sql/logic/executor/materialization.hpp | 2 +- src/stewkk/sql/logic/executor/executor.cpp | 44 +++++++++------ .../sql/logic/executor/materialization.cpp | 56 ++++++++++++++++--- 5 files changed, 105 insertions(+), 45 deletions(-) diff --git a/benchmarks/main.cpp b/benchmarks/main.cpp index 14d29e7..a5cfc3c 100644 --- a/benchmarks/main.cpp +++ b/benchmarks/main.cpp @@ -108,6 +108,16 @@ CardinalityEstimates LoadCardinalityFromCsvDir(const std::filesystem::path& dir) return CardinalityEstimates{std::move(counts)}; } +class ScopedClogSuppression { +public: + ScopedClogSuppression() : nullstream_("/dev/null"), previous_(std::clog.rdbuf(nullstream_.rdbuf())) {} + ~ScopedClogSuppression() { std::clog.rdbuf(previous_); } + +private: + std::ofstream nullstream_; + std::streambuf* previous_; +}; + enum class PlannerMode { kNaive, kOptimized }; template @@ -159,8 +169,7 @@ static constexpr char kMultiwayROC[]{ template void BM_SQL(benchmark::State& state) { - std::ofstream nullstream("/dev/null"); - std::clog.rdbuf(nullstream.rdbuf()); + ScopedClogSuppression suppress_clog; boost::asio::io_context ctx; boost::asio::co_spawn( ctx, @@ -205,8 +214,7 @@ REGISTER_BM_SQL(kMultiwayROC) template void BM_SQL_Multithreaded(benchmark::State& state) { - std::ofstream nullstream("/dev/null"); - std::clog.rdbuf(nullstream.rdbuf()); + ScopedClogSuppression suppress_clog; boost::asio::io_context ctx; boost::asio::co_spawn( ctx, @@ -269,8 +277,7 @@ std::filesystem::path SsbDataDir() { template void BM_SSB(benchmark::State& state, std::string query, std::filesystem::path data_dir) { - std::ofstream nullstream("/dev/null"); - std::clog.rdbuf(nullstream.rdbuf()); + ScopedClogSuppression suppress_clog; if (!std::filesystem::is_directory(data_dir)) { state.SkipWithError(("SSB data directory not found: " + data_dir.string() diff --git a/benchmarks/operator_cost.cpp b/benchmarks/operator_cost.cpp index c6426f3..9f4d8e9 100644 --- a/benchmarks/operator_cost.cpp +++ b/benchmarks/operator_cost.cpp @@ -208,17 +208,22 @@ void SetBinaryCounters(benchmark::State& state, int64_t lhs_rows, int64_t rhs_ro state.counters["output_rows"] = static_cast(output_rows); } -void SuppressClog() { - static std::ofstream nullstream("/dev/null"); - std::clog.rdbuf(nullstream.rdbuf()); -} +class ScopedClogSuppression { +public: + ScopedClogSuppression() : nullstream_("/dev/null"), previous_(std::clog.rdbuf(nullstream_.rdbuf())) {} + ~ScopedClogSuppression() { std::clog.rdbuf(previous_); } + +private: + std::ofstream nullstream_; + std::streambuf* previous_; +}; void BM_OperatorSeqScan(benchmark::State& state) { const int64_t rows = state.range(0); auto tables = MakeTables(rows); auto plan = Scan("t"); - SuppressClog(); + ScopedClogSuppression suppress_clog; benchmark::DoNotOptimize(RunPlan(plan, tables)); for (auto _ : state) { @@ -236,7 +241,7 @@ void BM_OperatorFilter(benchmark::State& state) { .predicate = Binary(AttrExpr("t", "v"), BinaryOp::kLt, IntExpr(kThreshold)), }}; - SuppressClog(); + ScopedClogSuppression suppress_clog; benchmark::DoNotOptimize(RunPlan(plan, tables)); for (auto _ : state) { @@ -260,7 +265,7 @@ void BM_OperatorProjection(benchmark::State& state) { .aliases = {std::nullopt, std::nullopt}, }}; - SuppressClog(); + ScopedClogSuppression suppress_clog; benchmark::DoNotOptimize(RunPlan(plan, tables)); for (auto _ : state) { @@ -277,7 +282,7 @@ void BM_OperatorSort(benchmark::State& state) { .keys = SortOrder{{SortKey{.table = "t", .column = "id", .dir = Direction::kAsc}}}, }}; - SuppressClog(); + ScopedClogSuppression suppress_clog; benchmark::DoNotOptimize(RunPlan(plan, tables)); for (auto _ : state) { @@ -295,7 +300,7 @@ void BM_OperatorAggregation(benchmark::State& state) { .aggregates = {CountStar()}, }}; - SuppressClog(); + ScopedClogSuppression suppress_clog; benchmark::DoNotOptimize(RunPlan(plan, tables)); for (auto _ : state) { @@ -315,7 +320,7 @@ void BM_OperatorNestedLoopJoin(benchmark::State& state) { .qual = Binary(AttrExpr("t", "k"), BinaryOp::kEq, AttrExpr("u", "k")), }}; - SuppressClog(); + ScopedClogSuppression suppress_clog; benchmark::DoNotOptimize(RunPlan(plan, tables)); for (auto _ : state) { @@ -335,7 +340,7 @@ void BM_OperatorNestedLoopCrossJoin(benchmark::State& state) { .rhs = PlanPtr(Scan("u")), }}; - SuppressClog(); + ScopedClogSuppression suppress_clog; benchmark::DoNotOptimize(RunPlan(plan, tables)); for (auto _ : state) { @@ -357,7 +362,7 @@ void BM_OperatorHashJoin(benchmark::State& state) { .qual = Binary(AttrExpr("t", "k"), BinaryOp::kEq, AttrExpr("u", "k")), }}; - SuppressClog(); + ScopedClogSuppression suppress_clog; benchmark::DoNotOptimize(RunPlan(plan, tables)); for (auto _ : state) { diff --git a/include/stewkk/sql/logic/executor/materialization.hpp b/include/stewkk/sql/logic/executor/materialization.hpp index 75ed8ce..6ed9d7d 100644 --- a/include/stewkk/sql/logic/executor/materialization.hpp +++ b/include/stewkk/sql/logic/executor/materialization.hpp @@ -32,7 +32,7 @@ class DiskFileWriter { private: fs::path path_; std::ofstream f_; - std::size_t tuple_size_; + std::size_t tuple_size_ = 0; }; } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/executor/executor.cpp b/src/stewkk/sql/logic/executor/executor.cpp index 9d174a1..0669992 100644 --- a/src/stewkk/sql/logic/executor/executor.cpp +++ b/src/stewkk/sql/logic/executor/executor.cpp @@ -18,6 +18,14 @@ namespace stewkk::sql { +namespace { + +Value MakeBooleanValue(bool value) { + return Value{false, static_cast(value)}; +} + +} // namespace + template requires std::invocable && std::same_as, Ret> @@ -35,7 +43,7 @@ Value ApplyBooleanOperator(Value lhs, Value rhs) { if (lhs.is_null || rhs.is_null) { return Value{true}; } - return Value{false, {.bool_value = Op{}(lhs.value.bool_value, rhs.value.bool_value)}}; + return MakeBooleanValue(Op{}(lhs.value.bool_value, rhs.value.bool_value)); } struct IntPow { @@ -260,17 +268,17 @@ Value CalcExpression(const Tuple& source, const AttributesInfo& source_attrs, co } switch (op) { case BinaryOp::kGt: - return Value{false, {.bool_value = cmp > 0}}; + return MakeBooleanValue(cmp > 0); case BinaryOp::kLt: - return Value{false, {.bool_value = cmp < 0}}; + return MakeBooleanValue(cmp < 0); case BinaryOp::kLe: - return Value{false, {.bool_value = cmp <= 0}}; + return MakeBooleanValue(cmp <= 0); case BinaryOp::kGe: - return Value{false, {.bool_value = cmp >= 0}}; + return MakeBooleanValue(cmp >= 0); case BinaryOp::kNotEq: - return Value{false, {.bool_value = cmp != 0}}; + return MakeBooleanValue(cmp != 0); case BinaryOp::kEq: - return Value{false, {.bool_value = cmp == 0}}; + return MakeBooleanValue(cmp == 0); default: std::unreachable(); } @@ -291,16 +299,16 @@ Value CalcExpression(const Tuple& source, const AttributesInfo& source_attrs, co case BinaryOp::kOr: { bool lhs_true = !lhs.is_null && lhs.value.bool_value; bool rhs_true = !rhs.is_null && rhs.value.bool_value; - if (lhs_true || rhs_true) return Value{false, {.bool_value = true}}; + if (lhs_true || rhs_true) return MakeBooleanValue(true); if (lhs.is_null || rhs.is_null) return Value{true}; - return Value{false, {.bool_value = false}}; + return MakeBooleanValue(false); } case BinaryOp::kAnd: { bool lhs_false = !lhs.is_null && !lhs.value.bool_value; bool rhs_false = !rhs.is_null && !rhs.value.bool_value; - if (lhs_false || rhs_false) return Value{false, {.bool_value = false}}; + if (lhs_false || rhs_false) return MakeBooleanValue(false); if (lhs.is_null || rhs.is_null) return Value{true}; - return Value{false, {.bool_value = true}}; + return MakeBooleanValue(true); } case BinaryOp::kPlus: return ApplyIntegersOperator, int64_t>(std::move(lhs), std::move(rhs)); @@ -325,16 +333,16 @@ Value CalcExpression(const Tuple& source, const AttributesInfo& source_attrs, co return Value{true}; } if (child.value.bool_value) { - return Value{false, {.bool_value = false}}; + return MakeBooleanValue(false); } - return Value{false, {.bool_value = true}}; + return MakeBooleanValue(true); case UnaryOp::kMinus: if (child.is_null) { return Value{true}; } return Value{false, -child.value.int_value}; case UnaryOp::kIsNull: - return Value{false, {.bool_value = child.is_null}}; + return MakeBooleanValue(child.is_null); } } Value operator()(const InExpression& expr) { @@ -352,14 +360,14 @@ Value CalcExpression(const Tuple& source, const AttributesInfo& source_attrs, co continue; } if (ValuesEqual(lhs, value, lhs_type)) { - return Value{false, {.bool_value = !expr.negated}}; + return MakeBooleanValue(!expr.negated); } } if (saw_null) { return Value{true}; } - return Value{false, {.bool_value = expr.negated}}; + return MakeBooleanValue(expr.negated); } Value operator()(const AggregateExpression&) { throw std::logic_error{"aggregate expressions cannot be evaluated as scalar expressions"}; @@ -384,10 +392,10 @@ Value CalcExpression(const Tuple& source, const AttributesInfo& source_attrs, co case Literal::kNull: return Value{true}; case Literal::kTrue: { - return Value{false, {.bool_value = true}}; + return MakeBooleanValue(true); } case Literal::kFalse: { - return Value{false, {.bool_value = false}}; + return MakeBooleanValue(false); } case Literal::kUnknown: { return Value{true}; diff --git a/src/stewkk/sql/logic/executor/materialization.cpp b/src/stewkk/sql/logic/executor/materialization.cpp index e1018e2..78f4890 100644 --- a/src/stewkk/sql/logic/executor/materialization.cpp +++ b/src/stewkk/sql/logic/executor/materialization.cpp @@ -1,23 +1,56 @@ #include +#include +#include +#include +#include + #include // TODO: implement async file io with asio (and benchmark it) namespace stewkk::sql { +namespace { + +constexpr std::size_t kDiskValueSize = sizeof(uint8_t) + sizeof(int64_t); + +void AppendValue(std::vector& buf, const Value& value) { + const int64_t payload = value.is_null ? 0 : value.value.int_value; + buf.push_back(static_cast(value.is_null)); + const auto* payload_bytes = reinterpret_cast(&payload); + buf.insert(buf.end(), payload_bytes, payload_bytes + sizeof(payload)); +} + +Value ReadValue(const char* buf) { + int64_t payload; + std::memcpy(&payload, buf + sizeof(uint8_t), sizeof(payload)); + return Value{static_cast(buf[0]), {.int_value = payload}}; +} + +} // namespace + DiskFileWriter::DiskFileWriter() : path_(fs::temp_directory_path() / fs::unique_path("%%%%.tmp")), f_(path_, std::ios::binary) {} void DiskFileWriter::Write(const Tuples& tuples) { - tuple_size_ = tuples.front().size(); + if (tuples.empty()) { + return; + } + if (tuple_size_ == 0) { + tuple_size_ = tuples.front().size(); + } + std::vector buf; + buf.reserve(tuples.size() * tuple_size_ * kDiskValueSize); for (const auto& tuple : tuples) { - std::size_t tuple_size = tuple.size(); - // NOTE: better to store tuples in continious buffer and than flush to - // disk - f_.write(reinterpret_cast(tuple.data()), - tuple.size() * sizeof(Tuple::value_type)); + if (tuple.size() != tuple_size_) { + throw std::runtime_error{"cannot materialize tuples with different sizes"}; + } + for (const auto& value : tuple) { + AppendValue(buf, value); + } } + f_.write(buf.data(), static_cast(buf.size())); } DiskFileReader DiskFileWriter::GetDiskFileReader() && { @@ -42,13 +75,20 @@ void DiskFileReader::Rewind() { Tuples DiskFileReader::Read() { Tuples buf; buf.reserve(kBufSize); + std::vector tuple_buf(tuple_size_ * kDiskValueSize); while (buf.size() < kBufSize) { Tuple tuple; - tuple.resize(tuple_size_); - f_.read(reinterpret_cast(tuple.data()), tuple_size_*sizeof(Tuple::value_type)); + tuple.reserve(tuple_size_); + f_.read(tuple_buf.data(), static_cast(tuple_buf.size())); if (f_.gcount() == 0) { break; } + if (f_.gcount() != static_cast(tuple_buf.size())) { + throw std::runtime_error{"truncated materialized tuple"}; + } + for (std::size_t offset = 0; offset < tuple_buf.size(); offset += kDiskValueSize) { + tuple.push_back(ReadValue(tuple_buf.data() + offset)); + } buf.push_back(std::move(tuple)); } return buf; From 081074b961b7c35c4e45aad4e12b5235b6540baa Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Tue, 2 Jun 2026 01:49:42 +0300 Subject: [PATCH 075/120] Add more benchmarks --- benchmarks/main.cpp | 47 +- benchmarks/operator_cost.cpp | 18 +- .../stewkk/sql/logic/optimizer/optimizer.hpp | 1 + .../sql/logic/optimizer/schema_catalog.hpp | 6 +- report/Makefile | 2 +- report/calibration.png | Bin 0 -> 36668 bytes report/naive-vs-optimizer.png | Bin 0 -> 25109 bytes report/optimizer-speedup.png | Bin 0 -> 28446 bytes report/practice.tex | 100 + report/scaling.png | Bin 0 -> 80847 bytes .../operator-cost-matched.json | 6408 +++++++++++++ research/benchmark-results/operator-cost.json | 8495 +++++++++++++++++ research/benchmark-results/query.json | 1266 +++ research/benchmark-results/ssb-sf001.json | 3240 +++++++ research/benchmarks.ipynb | 1248 +++ research/benchmarks.pdf | Bin 0 -> 283622 bytes research/research.ipynb | 2 +- .../sql/logic/optimizer/cardinality.cpp | 50 +- src/stewkk/sql/logic/optimizer/optimizer.cpp | 57 +- .../sql/logic/optimizer/optimizer_test.cpp | 69 +- .../sql/logic/optimizer/schema_catalog.cpp | 37 + src/stewkk/sql/main.cpp | 32 +- 22 files changed, 21024 insertions(+), 54 deletions(-) create mode 100644 report/calibration.png create mode 100644 report/naive-vs-optimizer.png create mode 100644 report/optimizer-speedup.png create mode 100644 report/scaling.png create mode 100644 research/benchmark-results/operator-cost-matched.json create mode 100644 research/benchmark-results/operator-cost.json create mode 100644 research/benchmark-results/query.json create mode 100644 research/benchmark-results/ssb-sf001.json create mode 100644 research/benchmarks.ipynb create mode 100644 research/benchmarks.pdf diff --git a/benchmarks/main.cpp b/benchmarks/main.cpp index a5cfc3c..eb158b4 100644 --- a/benchmarks/main.cpp +++ b/benchmarks/main.cpp @@ -1,10 +1,12 @@ #include +#include #include #include #include #include #include +#include #include #include #include @@ -24,6 +26,7 @@ #include #include #include +#include #include #include @@ -120,6 +123,11 @@ class ScopedClogSuppression { enum class PlannerMode { kNaive, kOptimized }; +struct PlanStats { + std::chrono::nanoseconds optimizer_runtime{}; + std::optional cost; +}; + template PhysicalPlanNode MakePlan(const Operator& op) { if constexpr (Mode == PlannerMode::kNaive) { @@ -131,16 +139,30 @@ PhysicalPlanNode MakePlan(const Operator& op) { } template -PhysicalPlanNode MakePlan(const ParsedQuery& query, CardinalityEstimates cardinality) { +PhysicalPlanNode MakePlan(const ParsedQuery& query, CardinalityEstimates cardinality, + SchemaCatalog schema, PlanStats* stats = nullptr) { if constexpr (Mode == PlannerMode::kNaive) { - return ToPhysicalPlan(query.op); + auto plan = ToPhysicalPlan(query.op); + if (query.required_order) { + return PhysicalSort{ + .source = std::make_shared(std::move(plan)), + .keys = *query.required_order, + }; + } + return plan; } else { PropertySet required = query.required_order ? PropertySet{SortProperty{*query.required_order}} : PropertySet::Any(); - Optimizer optimizer(query.op, MakeMainRules(), std::move(cardinality), {}, + const auto started = std::chrono::steady_clock::now(); + Optimizer optimizer(query.op, MakeMainRules(), std::move(cardinality), std::move(schema), std::move(required)); - return optimizer.Optimize(); + auto plan = optimizer.Optimize(); + if (stats) { + stats->optimizer_runtime = std::chrono::steady_clock::now() - started; + stats->cost = optimizer.GetBestCost(); + } + return plan; } } @@ -293,8 +315,10 @@ void BM_SSB(benchmark::State& state, std::string query, std::filesystem::path da } PhysicalPlanNode plan; + PlanStats plan_stats; try { - plan = MakePlan(parsed.value(), LoadCardinalityFromCsvDir(data_dir)); + plan = MakePlan(parsed.value(), LoadCardinalityFromCsvDir(data_dir), + LoadSchemaFromCsvDir(data_dir), &plan_stats); } catch (const std::exception& e) { state.SkipWithError(e.what()); return; @@ -311,13 +335,26 @@ void BM_SSB(benchmark::State& state, std::string query, std::filesystem::path da return fut.get(); }; + std::chrono::nanoseconds execution_runtime{}; try { benchmark::DoNotOptimize(run_once()); for (auto _ : state) { + const auto started = std::chrono::steady_clock::now(); benchmark::DoNotOptimize(run_once()); + execution_runtime += std::chrono::steady_clock::now() - started; } } catch (const std::exception& e) { state.SkipWithError(e.what()); + return; + } + + using Microseconds = std::chrono::duration; + state.counters["execution_us"] = benchmark::Counter{ + Microseconds{execution_runtime}.count(), benchmark::Counter::kAvgIterations}; + state.counters["includes_order_by"] = parsed.value().required_order.has_value() ? 1.0 : 0.0; + if (plan_stats.cost) { + state.counters["optimizer_us"] = Microseconds{plan_stats.optimizer_runtime}.count(); + state.counters["plan_cost"] = static_cast(*plan_stats.cost); } } diff --git a/benchmarks/operator_cost.cpp b/benchmarks/operator_cost.cpp index 9f4d8e9..fc56ac1 100644 --- a/benchmarks/operator_cost.cpp +++ b/benchmarks/operator_cost.cpp @@ -171,6 +171,17 @@ int64_t SortModelCost(int64_t rows) { return 11 * (rows > 1 ? rows * static_cast(std::bit_width(static_cast(rows))) : rows); } +int64_t HashJoinModelCost(int64_t lhs_rows, int64_t rhs_rows) { + constexpr int64_t kInputWidth = 3; + constexpr int64_t kOutputWidth = 2 * kInputWidth; + constexpr int64_t kHashBuild = 100; + constexpr int64_t kHashProbe = 35; + constexpr int64_t kTupleCopy = 10; + return kHashBuild * lhs_rows * kInputWidth + + kHashProbe * rhs_rows + + kTupleCopy * std::min(lhs_rows, rhs_rows) * kOutputWidth; +} + int64_t FindSortRowsForTargetCost(int64_t target_cost) { int64_t best_rows = 1; for (int64_t rows = 2; SortModelCost(rows) <= 2 * target_cost; ++rows) { @@ -368,8 +379,8 @@ void BM_OperatorHashJoin(benchmark::State& state) { for (auto _ : state) { benchmark::DoNotOptimize(RunPlan(plan, tables)); } - SetBinaryCounters(state, lhs_rows, rhs_rows, 69 * (lhs_rows + rhs_rows), - 169 * (lhs_rows + rhs_rows), + SetBinaryCounters(state, lhs_rows, rhs_rows, HashJoinModelCost(lhs_rows, rhs_rows), + HashJoinModelCost(lhs_rows, rhs_rows) + 100 * (lhs_rows + rhs_rows), std::min(lhs_rows, rhs_rows)); } @@ -397,7 +408,8 @@ void RegisterCostMatched(int64_t target_cost) { register_unary("Projection", BM_OperatorProjection, DivideRounded(target_cost, 22)); register_unary("Sort", BM_OperatorSort, FindSortRowsForTargetCost(target_cost)); register_unary("Aggregation", BM_OperatorAggregation, DivideRounded(target_cost, 510)); - register_binary("HashJoin", BM_OperatorHashJoin, DivideRounded(target_cost, 2 * 69)); + register_binary("HashJoin", BM_OperatorHashJoin, + DivideRounded(target_cost, HashJoinModelCost(1, 1))); register_binary("NestedLoopJoin", BM_OperatorNestedLoopJoin, SqrtRounded(DivideRounded(target_cost, 70))); register_binary("NestedLoopCrossJoin", BM_OperatorNestedLoopCrossJoin, diff --git a/include/stewkk/sql/logic/optimizer/optimizer.hpp b/include/stewkk/sql/logic/optimizer/optimizer.hpp index b4975da..c0dbbc8 100644 --- a/include/stewkk/sql/logic/optimizer/optimizer.hpp +++ b/include/stewkk/sql/logic/optimizer/optimizer.hpp @@ -32,6 +32,7 @@ class Optimizer { PhysicalPlanNode Optimize(); PhysicalPlanNode OptimizeExhaustive(); + std::int64_t GetBestCost() const; utils::NotNull GetRootGroup() const; private: diff --git a/include/stewkk/sql/logic/optimizer/schema_catalog.hpp b/include/stewkk/sql/logic/optimizer/schema_catalog.hpp index 7a336e9..b7abe72 100644 --- a/include/stewkk/sql/logic/optimizer/schema_catalog.hpp +++ b/include/stewkk/sql/logic/optimizer/schema_catalog.hpp @@ -1,5 +1,7 @@ #pragma once +#include +#include #include #include #include @@ -17,8 +19,8 @@ class SchemaCatalog { public: SchemaCatalog(std::unordered_map tables = {}); - std::optional GetSchema(utils::NotNull group); + std::int64_t GetWidth(utils::NotNull group); private: std::optional Derive(const LogicalOperator& op); @@ -27,4 +29,6 @@ class SchemaCatalog { std::unordered_map> cache_; }; +SchemaCatalog LoadSchemaFromCsvDir(const std::filesystem::path& dir); + } // namespace stewkk::sql diff --git a/report/Makefile b/report/Makefile index 4694b53..c99d8c0 100644 --- a/report/Makefile +++ b/report/Makefile @@ -3,7 +3,7 @@ all: build build: - @latexmk -f -pdf -output-directory=build -shell-escape ./thesis.tex + @latexmk -f -pdf -output-directory=build-practice -shell-escape ./practice.tex bib: @cp ./biblio.bib ./build diff --git a/report/calibration.png b/report/calibration.png new file mode 100644 index 0000000000000000000000000000000000000000..865144ebe7ee7641d6c02f4bb467ccff645a2d22 GIT binary patch literal 36668 zcma&O2Rzqrzd!yFN+gsBg^)C5WMw5I5)Gn~k;+Q;o)Hle?U0a+29=!=3aJ#5Lb7Gc z-hR(Z-*e7=&bfd8-{0dNrO$Ye>w3MO>-7#ktfj_C&q+_AP#Dz@C?BOzsNEIEK@gn@6jH8Oa<1w4_jxHwl=O~9v9BnVyI9{+g!|Qy`-oe7=qS!8x-MgfB@R~b1 z+B(Px3tRu^D|Xq~n+Xf5)}Fwpth7CF!hu3zG9mw^N>)g+pir(Qt1Iu*agFZ(>1LqQ 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\subsection{Бенчмарки} поддерживается сравнение интерпретируемого и JIT-компилируемого способов вычисления выражений. +\begin{figure}[H] +\centering +\includegraphics[width=0.8\textwidth]{naive-vs-optimizer.png} +\caption{Сравнение времени выполнения запросов при наивном и оптимизированном + построении физического плана.} +\label{fig:benchmark-naive-optimized} +\end{figure} + +\begin{figure}[H] +\centering +\includegraphics[width=0.8\textwidth]{optimizer-speedup.png} +\caption{Относительное ускорение выполнения запросов после применения оптимизатора.} +\label{fig:benchmark-speedup} +\end{figure} + +В таблице~\ref{tbl:ssb-benchmark} приведено среднее время выполнения трех +повторений запросов SSB для набора данных масштаба \texttt{sf001}. Ускорение +рассчитывается как отношение времени выполнения наивного плана ко времени +выполнения оптимизированного плана. + +\begin{table}[H] + \centering + \caption{Результаты выполнения запросов Star Schema Benchmark.} + \label{tbl:ssb-benchmark} + \begin{tabular}{|l|r|r|r|} + \hline + Запрос & \texttt{Naive}, мс & \texttt{Optimized}, мс & Ускорение \\ + \hline + \texttt{q1.1} & 8.11 & 6.51 & 1.25 \\ + \texttt{q1.2} & 8.20 & 6.59 & 1.24 \\ + \texttt{q1.3} & 8.00 & 6.58 & 1.22 \\ + \texttt{q2.1} & 8.80 & 9.33 & 0.94 \\ + \texttt{q2.2} & 8.84 & 6.18 & 1.43 \\ + \texttt{q2.3} & 8.68 & 5.80 & 1.50 \\ + \texttt{q3.1} & 35.67 & 5.90 & 6.04 \\ + \texttt{q3.2} & 35.88 & 5.58 & 6.43 \\ + \texttt{q3.3} & 38.05 & 5.86 & 6.49 \\ + \texttt{q3.4} & 37.12 & 5.77 & 6.43 \\ + \texttt{q4.1} & 9.06 & 12.21 & 0.74 \\ + \texttt{q4.2} & 9.05 & 11.12 & 0.81 \\ + \texttt{q4.3} & 9.28 & 6.31 & 1.47 \\ + \hline + \end{tabular} +\end{table} + \subsection{Калибровка стоимостной модели} Для калибровки коэффициентов в стоимостных формулах реализованы отдельные @@ -1204,6 +1264,46 @@ \subsection{Калибровка стоимостной модели} коэффициенты модели. Благодаря этому оптимизатор принимает решения на основе оценок, соответствующих характеристикам реализованного исполнителя. +\begin{figure}[H] +\centering +\includegraphics[width=0.8\textwidth]{scaling.png} +\caption{Зависимость времени выполнения физических операторов от размера входных + данных.} +\label{fig:operator-benchmark} +\end{figure} + +\begin{figure}[H] +\centering +\includegraphics[width=0.8\textwidth]{calibration.png} +\caption{Сопоставление расчетной стоимости физических операторов с фактическим + временем выполнения.} +\label{fig:cost-model-calibration} +\end{figure} + +Стоимостные формулы физических операторов, полученные по результатам +калибровки, приведены в таблице~\ref{tbl:cost-model-coefficients}. + +\begin{table}[H] + \centering + \caption{Стоимостные формулы физических операторов.} + \label{tbl:cost-model-coefficients} + \small + \begin{tabular}{|p{0.40\textwidth}|l|} + \hline + Физический оператор & Формула стоимости \\ + \hline + Последовательное сканирование & $100\,n$ \\ + Фильтрация & $100\,n_{out}$ \\ + Проекция & $22\,n_{out}$ \\ + Сортировка & $11\,n\bigl(\lfloor\log_2 n\rfloor + 1\bigr)$ \\ + Агрегация & $510\,n$ \\ + Соединение вложенными циклами & $70\,n_l n_r$ \\ + Декартово произведение & $104\,n_l n_r$ \\ + Хеш-соединение & $100\,n_b w_b + 35\,n_p + 10\,n_o w_o$ \\ + \hline + \end{tabular} +\end{table} + \anonsection{ЗАКЛЮЧЕНИЕ} В ходе практики разработана модельная СУБД и оптимизатор для нее. 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2.7045925938180931e-02, + "plan_cost": 0.0000000000000000e+00 + } + ] +} diff --git a/research/benchmarks.ipynb b/research/benchmarks.ipynb new file mode 100644 index 0000000..f475c98 --- /dev/null +++ b/research/benchmarks.ipynb @@ -0,0 +1,1248 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "b5291871", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-06-02T01:16:13+03:00\n", + "Running ./build-release/bin/benchmarks\n", + "Run on (8 X 4200 MHz CPU s)\n", + "CPU Caches:\n", + " L1 Data 48 KiB (x4)\n", + " L1 Instruction 32 KiB (x4)\n", + " L2 Unified 1280 KiB (x4)\n", + " L3 Unified 8192 KiB (x1)\n", + "Load Average: 0.38, 0.38, 0.46\n", + "***WARNING*** CPU scaling is enabled, the benchmark real time measurements may be noisy and will incur extra overhead.\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------\n", + "Benchmark Time CPU Iterations\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 22492 ns 22349 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 22447 ns 22296 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 1558 ns 1526 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 6.93 % 6.83 % \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 20696 ns 20601 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 20835 ns 20742 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 834 ns 810 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 4.03 % 3.93 % \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 20039 ns 19954 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 19980 ns 19905 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 172 ns 177 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 0.86 % 0.89 % \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 19665 ns 19591 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 19751 ns 19677 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 207 ns 204 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 1.05 % 1.04 % \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 71494 ns 70618 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 72186 ns 71298 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 3465 ns 3354 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 4.85 % 4.75 % \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 71193 ns 70379 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 71837 ns 70986 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 1447 ns 1398 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 2.03 % 1.99 % \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 74523 ns 73518 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 74232 ns 73228 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 3582 ns 3406 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 4.81 % 4.63 % \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 70604 ns 69783 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 68387 ns 67663 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 6690 ns 6389 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 9.48 % 9.16 % \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 88746 ns 87780 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 87787 ns 86834 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 4903 ns 4717 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 5.52 % 5.37 % \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 93122 ns 92033 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 94811 ns 93696 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 3892 ns 3747 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 4.18 % 4.07 % \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 84456 ns 83477 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 85501 ns 84546 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 3598 ns 3410 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 4.26 % 4.08 % \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 80405 ns 79621 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 80566 ns 79795 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 536 ns 528 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 0.67 % 0.66 % \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m" + ] + } + ], + "source": [ + "!mkdir -p benchmark-results\n", + "!cd .. && timeout 120s ./build-release/bin/benchmarks --benchmark_filter='^BM_SQL<(InterpretedExpressionExecutor|CachedJitCompiledExpressionExecutor), (kSimpleSelectSmall|kJoinSmall|kComplex5), PlannerMode::(kNaive|kOptimized)>/real_time$' --benchmark_repetitions=3 --benchmark_display_aggregates_only=true --benchmark_out=research/benchmark-results/query.json --benchmark_out_format=json" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "94b2cd75", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-06-02T01:16:41+03:00\n", + "Running ./build-release/bin/benchmarks\n", + "Run on (8 X 4200 MHz CPU s)\n", + "CPU Caches:\n", + " L1 Data 48 KiB (x4)\n", + " L1 Instruction 32 KiB (x4)\n", + " L2 Unified 1280 KiB (x4)\n", + " L3 Unified 8192 KiB (x1)\n", + "Load Average: 0.80, 0.49, 0.49\n", + "***WARNING*** CPU scaling is enabled, the benchmark real time measurements may be noisy and will incur extra overhead.\n", + "----------------------------------------------------------------------------------------------------------\n", + "Benchmark Time CPU Iterations UserCounters...\n", + "----------------------------------------------------------------------------------------------------------\n", + "\u001b[0;32mSSB/q2.1/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 9535917 ns 9457917 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.53583k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 9539993 ns 9459233 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.53993k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 26903 ns 26949 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=26.9284\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 0.28 % 0.28 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.28%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 10018523 ns 9946935 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=10.0184k\u001b[m includes_order_by=1\u001b[m optimizer_us=818.029\u001b[m plan_cost=836.603k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 10025189 ns 9956589 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=10.0251k\u001b[m includes_order_by=1\u001b[m optimizer_us=797.011\u001b[m plan_cost=836.603k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 23507 ns 26853 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=23.4951\u001b[m includes_order_by=0\u001b[m optimizer_us=47.3596\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 0.23 % 0.27 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.23%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=5.79%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 8894810 ns 8830456 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.89474k\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 8703923 ns 8643932 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.70386k\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 370836 ns 362794 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=370.843\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 4.17 % 4.11 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=4.17%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 7216482 ns 7178427 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.21641k\u001b[m includes_order_by=0\u001b[m optimizer_us=419.653\u001b[m plan_cost=654.297k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 7171734 ns 7135746 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.17166k\u001b[m includes_order_by=0\u001b[m optimizer_us=404.975\u001b[m plan_cost=654.297k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 378775 ns 369597 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=378.779\u001b[m includes_order_by=0\u001b[m optimizer_us=40.6977\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 5.25 % 5.15 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=5.25%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=9.70%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 8755501 ns 8695939 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.75534k\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 8634674 ns 8580077 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.63449k\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 313421 ns 306308 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=313.48\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 3.58 % 3.52 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=3.58%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 6842163 ns 6813610 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.8421k\u001b[m includes_order_by=0\u001b[m optimizer_us=225.618\u001b[m plan_cost=654.297k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 6857864 ns 6828042 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.8578k\u001b[m includes_order_by=0\u001b[m optimizer_us=225.797\u001b[m plan_cost=654.297k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 47976 ns 47617 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=47.9819\u001b[m includes_order_by=0\u001b[m optimizer_us=2.58966\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 0.70 % 0.70 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.70%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=1.15%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 38770551 ns 38545894 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=38.7704k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 38937311 ns 38712070 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=38.9372k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 403708 ns 387320 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=403.68\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 1.04 % 1.00 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=1.04%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 6165384 ns 6131574 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.16533k\u001b[m includes_order_by=1\u001b[m optimizer_us=1.97412k\u001b[m plan_cost=817.083k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 6220261 ns 6184963 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.2202k\u001b[m includes_order_by=1\u001b[m optimizer_us=1.92956k\u001b[m plan_cost=817.083k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 109818 ns 106871 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=109.811\u001b[m includes_order_by=0\u001b[m optimizer_us=127.329\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 1.78 % 1.74 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=1.78%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=6.45%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 38014861 ns 37785646 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=38.0147k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 37373498 ns 37160567 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=37.3733k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 1152550 ns 1124559 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=1.15257k\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 3.03 % 2.98 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=3.03%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 6548677 ns 6491466 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.54861k\u001b[m includes_order_by=1\u001b[m optimizer_us=1.73735k\u001b[m plan_cost=817.083k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 6752747 ns 6702527 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.75267k\u001b[m includes_order_by=1\u001b[m optimizer_us=1.76762k\u001b[m plan_cost=817.083k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 547461 ns 516050 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=547.457\u001b[m includes_order_by=0\u001b[m optimizer_us=55.8846\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 8.36 % 7.95 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.36%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=3.22%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 9839082 ns 9773621 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.83901k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 9830951 ns 9757826 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.83088k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 29124 ns 30559 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=29.1302\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 0.30 % 0.31 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.30%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 6726511 ns 6685741 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.72644k\u001b[m includes_order_by=1\u001b[m optimizer_us=3.74304k\u001b[m plan_cost=841.733k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 6733679 ns 6696410 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.73358k\u001b[m includes_order_by=1\u001b[m optimizer_us=3.75501k\u001b[m plan_cost=841.733k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 20124 ns 21507 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=20.1209\u001b[m includes_order_by=0\u001b[m optimizer_us=100.716\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 0.30 % 0.32 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.30%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=2.69%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 8337287 ns 8286855 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.33722k\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 8332199 ns 8279335 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.33213k\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 30591 ns 33255 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=30.5828\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 0.37 % 0.40 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.37%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 7009090 ns 6972349 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.00901k\u001b[m includes_order_by=0\u001b[m optimizer_us=203.094\u001b[m plan_cost=705.057k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 7011279 ns 6975684 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.01123k\u001b[m includes_order_by=0\u001b[m optimizer_us=203.772\u001b[m plan_cost=705.057k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 7876 ns 7272 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.88332\u001b[m includes_order_by=0\u001b[m optimizer_us=12.9813\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 0.11 % 0.10 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.11%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=6.39%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 41168218 ns 40861121 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=41.168k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 39951647 ns 39624417 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=39.9514k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 2772999 ns 2719430 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=2.77305k\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 6.74 % 6.66 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.74%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 7331045 ns 7263440 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.33098k\u001b[m includes_order_by=1\u001b[m optimizer_us=1.77516k\u001b[m plan_cost=816.743k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 7250476 ns 7179529 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.25042k\u001b[m includes_order_by=1\u001b[m optimizer_us=1.45913k\u001b[m plan_cost=816.743k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 560067 ns 542530 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=560.073\u001b[m includes_order_by=0\u001b[m optimizer_us=631.618\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 7.64 % 7.47 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.64%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=35.58%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 10611863 ns 10523713 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=10.6118k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 10571693 ns 10485617 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=10.5716k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 700693 ns 680849 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=700.727\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 6.60 % 6.47 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.60%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 7990721 ns 7922960 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.99066k\u001b[m includes_order_by=1\u001b[m optimizer_us=1.30379k\u001b[m plan_cost=841.703k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 7968509 ns 7901081 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.96844k\u001b[m includes_order_by=1\u001b[m optimizer_us=1.07317k\u001b[m plan_cost=841.703k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 412217 ns 399538 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=412.219\u001b[m includes_order_by=0\u001b[m optimizer_us=435.934\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 5.16 % 5.04 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=5.16%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=33.44%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 41047048 ns 40776811 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=41.0468k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 40607061 ns 40351481 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=40.6066k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 1022743 ns 1007568 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=1.02279k\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 2.49 % 2.47 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=2.49%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 7329905 ns 7261047 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.32985k\u001b[m includes_order_by=1\u001b[m optimizer_us=1.56312k\u001b[m plan_cost=816.743k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 7444323 ns 7376360 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.44426k\u001b[m includes_order_by=1\u001b[m optimizer_us=1.60169k\u001b[m plan_cost=816.743k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 377349 ns 367470 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=377.353\u001b[m includes_order_by=0\u001b[m optimizer_us=243.913\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 5.15 % 5.06 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=5.15%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=15.60%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 11383627 ns 11273121 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=11.3835k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 11182684 ns 11075830 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=11.1826k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 462093 ns 441146 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=462.11\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 4.06 % 3.91 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=4.06%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 13753622 ns 13641036 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=13.7534k\u001b[m includes_order_by=1\u001b[m optimizer_us=6.6919k\u001b[m plan_cost=843.203k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 13773635 ns 13660823 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=13.7735k\u001b[m includes_order_by=1\u001b[m optimizer_us=6.40666k\u001b[m plan_cost=843.203k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 1461768 ns 1423624 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=1.46187k\u001b[m includes_order_by=0\u001b[m optimizer_us=859.324\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 10.63 % 10.44 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=10.63%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=12.84%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 9885573 ns 9821993 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.88549k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 9935466 ns 9868635 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.93537k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 126113 ns 121567 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=126.119\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 1.28 % 1.24 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=1.28%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 13621071 ns 13528927 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=13.621k\u001b[m includes_order_by=1\u001b[m optimizer_us=3.96999k\u001b[m plan_cost=843.173k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 13572908 ns 13484295 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=13.5728k\u001b[m includes_order_by=1\u001b[m optimizer_us=3.91961k\u001b[m plan_cost=843.173k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 319283 ns 314861 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=319.283\u001b[m includes_order_by=0\u001b[m optimizer_us=115.745\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 2.34 % 2.33 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=2.34%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=2.92%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 9506406 ns 9448201 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.50634k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 9494186 ns 9433948 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.49412k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 72200 ns 71487 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=72.194\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 0.76 % 0.76 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.76%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 6827825 ns 6787123 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.82777k\u001b[m includes_order_by=1\u001b[m optimizer_us=852.138\u001b[m plan_cost=836.603k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 6823835 ns 6784459 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.82378k\u001b[m includes_order_by=1\u001b[m optimizer_us=847.591\u001b[m plan_cost=836.603k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 195671 ns 191222 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=195.664\u001b[m includes_order_by=0\u001b[m optimizer_us=23.0469\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 2.87 % 2.82 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=2.87%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=2.70%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m" + ] + } + ], + "source": [ + "!cd .. && SSB_DATA_DIR=benchmarks/datasets/ssb/generated/sf001 timeout 180s ./build-release/bin/benchmarks --benchmark_filter='^SSB/' --benchmark_repetitions=3 --benchmark_display_aggregates_only=true --benchmark_out=research/benchmark-results/ssb-sf001.json --benchmark_out_format=json" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6f8e3873", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-06-02T01:17:41+03:00\n", + "Running ./build-release/bin/benchmarks\n", + "Run on (8 X 4200 MHz CPU s)\n", + "CPU Caches:\n", + " L1 Data 48 KiB (x4)\n", + " L1 Instruction 32 KiB (x4)\n", + " L2 Unified 1280 KiB (x4)\n", + " L3 Unified 8192 KiB (x1)\n", + "Load Average: 1.43, 0.74, 0.58\n", + "***WARNING*** CPU scaling is enabled, the benchmark real time measurements may be noisy and will incur extra overhead.\n", + "----------------------------------------------------------------------------------------------------------------------\n", + "Benchmark Time CPU Iterations UserCounters...\n", + "----------------------------------------------------------------------------------------------------------------------\n", + "\u001b[0;32mOperatorCost/SeqScan/1024/real_time_mean \u001b[m\u001b[0;33m 38473 ns 38210 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=102.4k\u001b[m output_rows=1.024k\u001b[m plan_cost=102.4k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/1024/real_time_median \u001b[m\u001b[0;33m 38428 ns 38240 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=102.4k\u001b[m output_rows=1.024k\u001b[m plan_cost=102.4k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/1024/real_time_stddev \u001b[m\u001b[0;33m 753 ns 851 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/1024/real_time_cv \u001b[m\u001b[0;33m 1.96 % 2.23 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/2048/real_time_mean \u001b[m\u001b[0;33m 69707 ns 69380 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=204.8k\u001b[m output_rows=2.048k\u001b[m plan_cost=204.8k\u001b[m rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/2048/real_time_median \u001b[m\u001b[0;33m 69583 ns 69238 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=204.8k\u001b[m output_rows=2.048k\u001b[m plan_cost=204.8k\u001b[m rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/2048/real_time_stddev \u001b[m\u001b[0;33m 909 ns 889 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/2048/real_time_cv \u001b[m\u001b[0;33m 1.30 % 1.28 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/4096/real_time_mean \u001b[m\u001b[0;33m 154163 ns 152385 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=409.6k\u001b[m output_rows=4.096k\u001b[m plan_cost=409.6k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/4096/real_time_median \u001b[m\u001b[0;33m 153859 ns 152316 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=409.6k\u001b[m output_rows=4.096k\u001b[m plan_cost=409.6k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/4096/real_time_stddev \u001b[m\u001b[0;33m 901 ns 563 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/4096/real_time_cv \u001b[m\u001b[0;33m 0.58 % 0.37 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/8192/real_time_mean \u001b[m\u001b[0;33m 390148 ns 385602 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=819.2k\u001b[m output_rows=8.192k\u001b[m plan_cost=819.2k\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/8192/real_time_median \u001b[m\u001b[0;33m 399998 ns 395298 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=819.2k\u001b[m output_rows=8.192k\u001b[m plan_cost=819.2k\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/8192/real_time_stddev \u001b[m\u001b[0;33m 30561 ns 29527 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/8192/real_time_cv \u001b[m\u001b[0;33m 7.83 % 7.66 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/16384/real_time_mean \u001b[m\u001b[0;33m 924368 ns 914961 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.6384M\u001b[m output_rows=16.384k\u001b[m plan_cost=1.6384M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/16384/real_time_median \u001b[m\u001b[0;33m 931261 ns 921625 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.6384M\u001b[m output_rows=16.384k\u001b[m plan_cost=1.6384M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/16384/real_time_stddev \u001b[m\u001b[0;33m 17627 ns 17412 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/16384/real_time_cv \u001b[m\u001b[0;33m 1.91 % 1.90 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/32768/real_time_mean \u001b[m\u001b[0;33m 1901611 ns 1885331 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.2768M\u001b[m output_rows=32.768k\u001b[m plan_cost=3.2768M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/32768/real_time_median \u001b[m\u001b[0;33m 1933730 ns 1916091 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.2768M\u001b[m output_rows=32.768k\u001b[m plan_cost=3.2768M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/32768/real_time_stddev \u001b[m\u001b[0;33m 90232 ns 87205 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/32768/real_time_cv \u001b[m\u001b[0;33m 4.75 % 4.63 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/65536/real_time_mean \u001b[m\u001b[0;33m 4965998 ns 4909459 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.5536M\u001b[m output_rows=65.536k\u001b[m plan_cost=6.5536M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/65536/real_time_median \u001b[m\u001b[0;33m 4998141 ns 4946201 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.5536M\u001b[m output_rows=65.536k\u001b[m plan_cost=6.5536M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/65536/real_time_stddev \u001b[m\u001b[0;33m 155421 ns 163668 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/65536/real_time_cv \u001b[m\u001b[0;33m 3.13 % 3.33 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/131072/real_time_mean \u001b[m\u001b[0;33m 12138600 ns 11998804 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=13.1072M\u001b[m output_rows=131.072k\u001b[m plan_cost=13.1072M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/131072/real_time_median \u001b[m\u001b[0;33m 11162739 ns 11038913 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=13.1072M\u001b[m output_rows=131.072k\u001b[m plan_cost=13.1072M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/131072/real_time_stddev \u001b[m\u001b[0;33m 1830952 ns 1805064 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/131072/real_time_cv \u001b[m\u001b[0;33m 15.08 % 15.04 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/262144/real_time_mean \u001b[m\u001b[0;33m 25644707 ns 25374424 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.2144M\u001b[m output_rows=262.144k\u001b[m plan_cost=26.2144M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/262144/real_time_median \u001b[m\u001b[0;33m 23244999 ns 23024292 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.2144M\u001b[m output_rows=262.144k\u001b[m plan_cost=26.2144M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/262144/real_time_stddev \u001b[m\u001b[0;33m 4388803 ns 4299232 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/262144/real_time_cv \u001b[m\u001b[0;33m 17.11 % 16.94 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/1024/real_time_mean \u001b[m\u001b[0;33m 70425 ns 70146 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=102.4k\u001b[m output_rows=512\u001b[m plan_cost=204.8k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/1024/real_time_median \u001b[m\u001b[0;33m 70365 ns 70086 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=102.4k\u001b[m output_rows=512\u001b[m plan_cost=204.8k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/1024/real_time_stddev \u001b[m\u001b[0;33m 535 ns 551 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/1024/real_time_cv \u001b[m\u001b[0;33m 0.76 % 0.79 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/2048/real_time_mean \u001b[m\u001b[0;33m 125482 ns 125054 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=204.8k\u001b[m output_rows=1.024k\u001b[m plan_cost=409.6k\u001b[m rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/2048/real_time_median \u001b[m\u001b[0;33m 125359 ns 124936 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=204.8k\u001b[m output_rows=1.024k\u001b[m plan_cost=409.6k\u001b[m rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/2048/real_time_stddev \u001b[m\u001b[0;33m 329 ns 322 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/2048/real_time_cv \u001b[m\u001b[0;33m 0.26 % 0.26 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/4096/real_time_mean \u001b[m\u001b[0;33m 244461 ns 243272 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=409.6k\u001b[m output_rows=2.048k\u001b[m plan_cost=819.2k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/4096/real_time_median \u001b[m\u001b[0;33m 244237 ns 242959 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=409.6k\u001b[m output_rows=2.048k\u001b[m plan_cost=819.2k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/4096/real_time_stddev \u001b[m\u001b[0;33m 1017 ns 1018 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/4096/real_time_cv \u001b[m\u001b[0;33m 0.42 % 0.42 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/8192/real_time_mean \u001b[m\u001b[0;33m 479696 ns 477349 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=819.2k\u001b[m output_rows=4.096k\u001b[m plan_cost=1.6384M\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/8192/real_time_median \u001b[m\u001b[0;33m 477488 ns 475398 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=819.2k\u001b[m output_rows=4.096k\u001b[m plan_cost=1.6384M\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/8192/real_time_stddev \u001b[m\u001b[0;33m 6388 ns 6050 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/8192/real_time_cv \u001b[m\u001b[0;33m 1.33 % 1.27 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/16384/real_time_mean \u001b[m\u001b[0;33m 993383 ns 988008 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.6384M\u001b[m output_rows=8.192k\u001b[m plan_cost=3.2768M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/16384/real_time_median \u001b[m\u001b[0;33m 990927 ns 985217 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.6384M\u001b[m output_rows=8.192k\u001b[m plan_cost=3.2768M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/16384/real_time_stddev \u001b[m\u001b[0;33m 5275 ns 5696 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/16384/real_time_cv \u001b[m\u001b[0;33m 0.53 % 0.58 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/32768/real_time_mean \u001b[m\u001b[0;33m 2178959 ns 2166464 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.2768M\u001b[m output_rows=16.384k\u001b[m plan_cost=6.5536M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/32768/real_time_median \u001b[m\u001b[0;33m 2129820 ns 2118195 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.2768M\u001b[m output_rows=16.384k\u001b[m plan_cost=6.5536M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/32768/real_time_stddev \u001b[m\u001b[0;33m 101295 ns 99833 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/32768/real_time_cv \u001b[m\u001b[0;33m 4.65 % 4.61 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/65536/real_time_mean \u001b[m\u001b[0;33m 4436384 ns 4413484 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.5536M\u001b[m output_rows=32.768k\u001b[m plan_cost=13.1072M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/65536/real_time_median \u001b[m\u001b[0;33m 4473698 ns 4450082 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.5536M\u001b[m output_rows=32.768k\u001b[m plan_cost=13.1072M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/65536/real_time_stddev \u001b[m\u001b[0;33m 137556 ns 135091 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/65536/real_time_cv \u001b[m\u001b[0;33m 3.10 % 3.06 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/131072/real_time_mean \u001b[m\u001b[0;33m 9775161 ns 9717069 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=13.1072M\u001b[m output_rows=65.536k\u001b[m plan_cost=26.2144M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/131072/real_time_median \u001b[m\u001b[0;33m 9723418 ns 9663916 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=13.1072M\u001b[m output_rows=65.536k\u001b[m plan_cost=26.2144M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/131072/real_time_stddev \u001b[m\u001b[0;33m 113812 ns 109969 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/131072/real_time_cv \u001b[m\u001b[0;33m 1.16 % 1.13 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/262144/real_time_mean \u001b[m\u001b[0;33m 20011648 ns 19895345 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.2144M\u001b[m output_rows=131.072k\u001b[m plan_cost=52.4288M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/262144/real_time_median \u001b[m\u001b[0;33m 20028071 ns 19914759 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.2144M\u001b[m output_rows=131.072k\u001b[m plan_cost=52.4288M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/262144/real_time_stddev \u001b[m\u001b[0;33m 218024 ns 212289 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/262144/real_time_cv \u001b[m\u001b[0;33m 1.09 % 1.07 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/1024/real_time_mean \u001b[m\u001b[0;33m 94356 ns 94028 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=22.528k\u001b[m output_rows=1.024k\u001b[m plan_cost=124.928k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/1024/real_time_median \u001b[m\u001b[0;33m 94592 ns 94279 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=22.528k\u001b[m output_rows=1.024k\u001b[m plan_cost=124.928k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/1024/real_time_stddev \u001b[m\u001b[0;33m 520 ns 549 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/1024/real_time_cv \u001b[m\u001b[0;33m 0.55 % 0.58 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/2048/real_time_mean \u001b[m\u001b[0;33m 176410 ns 175840 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=45.056k\u001b[m output_rows=2.048k\u001b[m plan_cost=249.856k\u001b[m rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/2048/real_time_median 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3\u001b[m model_cost=90.112k\u001b[m output_rows=4.096k\u001b[m plan_cost=499.712k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/4096/real_time_stddev \u001b[m\u001b[0;33m 6628 ns 6489 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/4096/real_time_cv \u001b[m\u001b[0;33m 1.89 % 1.86 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/8192/real_time_mean \u001b[m\u001b[0;33m 688010 ns 684898 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=180.224k\u001b[m output_rows=8.192k\u001b[m plan_cost=0.999424M\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/8192/real_time_median \u001b[m\u001b[0;33m 687349 ns 684331 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=180.224k\u001b[m output_rows=8.192k\u001b[m plan_cost=0.999424M\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/8192/real_time_stddev \u001b[m\u001b[0;33m 6078 ns 6140 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/8192/real_time_cv \u001b[m\u001b[0;33m 0.88 % 0.90 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/16384/real_time_mean \u001b[m\u001b[0;33m 1408705 ns 1401811 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=360.448k\u001b[m output_rows=16.384k\u001b[m plan_cost=1.99885M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/16384/real_time_median \u001b[m\u001b[0;33m 1405017 ns 1397537 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=360.448k\u001b[m output_rows=16.384k\u001b[m plan_cost=1.99885M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/16384/real_time_stddev \u001b[m\u001b[0;33m 8952 ns 8860 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/16384/real_time_cv \u001b[m\u001b[0;33m 0.64 % 0.63 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/32768/real_time_mean \u001b[m\u001b[0;33m 3063583 ns 3046674 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=720.896k\u001b[m output_rows=32.768k\u001b[m plan_cost=3.9977M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/32768/real_time_median \u001b[m\u001b[0;33m 3021017 ns 3006734 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=720.896k\u001b[m output_rows=32.768k\u001b[m plan_cost=3.9977M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/32768/real_time_stddev \u001b[m\u001b[0;33m 134698 ns 132627 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/32768/real_time_cv \u001b[m\u001b[0;33m 4.40 % 4.35 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/65536/real_time_mean \u001b[m\u001b[0;33m 6409382 ns 6374643 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.44179M\u001b[m output_rows=65.536k\u001b[m plan_cost=7.99539M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/65536/real_time_median \u001b[m\u001b[0;33m 6414271 ns 6383091 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.44179M\u001b[m output_rows=65.536k\u001b[m plan_cost=7.99539M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/65536/real_time_stddev \u001b[m\u001b[0;33m 25434 ns 28186 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/65536/real_time_cv \u001b[m\u001b[0;33m 0.40 % 0.44 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/131072/real_time_mean \u001b[m\u001b[0;33m 13727593 ns 13637236 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.88358M\u001b[m output_rows=131.072k\u001b[m plan_cost=15.9908M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/131072/real_time_median \u001b[m\u001b[0;33m 13847824 ns 13759305 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.88358M\u001b[m output_rows=131.072k\u001b[m plan_cost=15.9908M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/131072/real_time_stddev \u001b[m\u001b[0;33m 291770 ns 293141 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/131072/real_time_cv \u001b[m\u001b[0;33m 2.13 % 2.15 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/262144/real_time_mean \u001b[m\u001b[0;33m 33247794 ns 33037538 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=5.76717M\u001b[m output_rows=262.144k\u001b[m plan_cost=31.9816M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/262144/real_time_median \u001b[m\u001b[0;33m 33275241 ns 33069358 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=5.76717M\u001b[m output_rows=262.144k\u001b[m plan_cost=31.9816M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/262144/real_time_stddev \u001b[m\u001b[0;33m 180580 ns 170487 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/262144/real_time_cv \u001b[m\u001b[0;33m 0.54 % 0.52 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/1024/real_time_mean \u001b[m\u001b[0;33m 114160 ns 113790 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=123.904k\u001b[m output_rows=1.024k\u001b[m plan_cost=226.304k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/1024/real_time_median \u001b[m\u001b[0;33m 114326 ns 113955 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=123.904k\u001b[m output_rows=1.024k\u001b[m plan_cost=226.304k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/1024/real_time_stddev 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output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/2048/real_time_cv \u001b[m\u001b[0;33m 0.28 % 0.26 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/4096/real_time_mean \u001b[m\u001b[0;33m 600007 ns 597438 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=585.728k\u001b[m output_rows=4.096k\u001b[m plan_cost=995.328k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/4096/real_time_median \u001b[m\u001b[0;33m 600843 ns 598069 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=585.728k\u001b[m output_rows=4.096k\u001b[m plan_cost=995.328k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/4096/real_time_stddev \u001b[m\u001b[0;33m 3889 ns 3608 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/4096/real_time_cv \u001b[m\u001b[0;33m 0.65 % 0.60 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/8192/real_time_mean \u001b[m\u001b[0;33m 1353285 ns 1347286 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.26157M\u001b[m output_rows=8.192k\u001b[m plan_cost=2.08077M\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/8192/real_time_median \u001b[m\u001b[0;33m 1352224 ns 1346166 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.26157M\u001b[m output_rows=8.192k\u001b[m plan_cost=2.08077M\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/8192/real_time_stddev \u001b[m\u001b[0;33m 3159 ns 3530 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/8192/real_time_cv \u001b[m\u001b[0;33m 0.23 % 0.26 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/16384/real_time_mean \u001b[m\u001b[0;33m 3330166 ns 3313456 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.70336M\u001b[m output_rows=16.384k\u001b[m plan_cost=4.34176M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/16384/real_time_median \u001b[m\u001b[0;33m 3280666 ns 3263886 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.70336M\u001b[m output_rows=16.384k\u001b[m plan_cost=4.34176M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/16384/real_time_stddev \u001b[m\u001b[0;33m 102432 ns 100860 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/16384/real_time_cv \u001b[m\u001b[0;33m 3.08 % 3.04 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/32768/real_time_mean \u001b[m\u001b[0;33m 8325146 ns 8276866 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=5.76717M\u001b[m output_rows=32.768k\u001b[m plan_cost=9.04397M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/32768/real_time_median \u001b[m\u001b[0;33m 8328091 ns 8280027 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=5.76717M\u001b[m output_rows=32.768k\u001b[m plan_cost=9.04397M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/32768/real_time_stddev \u001b[m\u001b[0;33m 8924 ns 5969 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/32768/real_time_cv \u001b[m\u001b[0;33m 0.11 % 0.07 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/65536/real_time_mean \u001b[m\u001b[0;33m 22543761 ns 22399862 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.2552M\u001b[m output_rows=65.536k\u001b[m plan_cost=18.8088M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/65536/real_time_median \u001b[m\u001b[0;33m 21748033 ns 21608460 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.2552M\u001b[m output_rows=65.536k\u001b[m plan_cost=18.8088M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/65536/real_time_stddev \u001b[m\u001b[0;33m 1392405 ns 1376023 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/65536/real_time_cv \u001b[m\u001b[0;33m 6.18 % 6.14 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/131072/real_time_mean \u001b[m\u001b[0;33m 63230431 ns 62728449 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=25.9523M\u001b[m output_rows=131.072k\u001b[m plan_cost=39.0595M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/131072/real_time_median \u001b[m\u001b[0;33m 62867420 ns 62387321 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=25.9523M\u001b[m output_rows=131.072k\u001b[m plan_cost=39.0595M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/131072/real_time_stddev \u001b[m\u001b[0;33m 1185317 ns 1168169 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/131072/real_time_cv \u001b[m\u001b[0;33m 1.87 % 1.86 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/262144/real_time_mean \u001b[m\u001b[0;33m 176649519 ns 175484283 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=54.7881M\u001b[m output_rows=262.144k\u001b[m plan_cost=81.0025M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/262144/real_time_median \u001b[m\u001b[0;33m 175323726 ns 174144845 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=54.7881M\u001b[m output_rows=262.144k\u001b[m plan_cost=81.0025M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/262144/real_time_stddev \u001b[m\u001b[0;33m 6750024 ns 6601525 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/262144/real_time_cv \u001b[m\u001b[0;33m 3.82 % 3.76 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/1024/real_time_mean \u001b[m\u001b[0;33m 274703 ns 273781 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=522.24k\u001b[m output_rows=1.024k\u001b[m plan_cost=624.64k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/1024/real_time_median \u001b[m\u001b[0;33m 275378 ns 274420 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=522.24k\u001b[m output_rows=1.024k\u001b[m plan_cost=624.64k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/1024/real_time_stddev \u001b[m\u001b[0;33m 1834 ns 1818 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/1024/real_time_cv \u001b[m\u001b[0;33m 0.67 % 0.66 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/2048/real_time_mean \u001b[m\u001b[0;33m 547245 ns 544909 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.04448M\u001b[m output_rows=2.048k\u001b[m plan_cost=1.24928M\u001b[m rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/2048/real_time_median \u001b[m\u001b[0;33m 542243 ns 539784 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.04448M\u001b[m output_rows=2.048k\u001b[m plan_cost=1.24928M\u001b[m rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/2048/real_time_stddev \u001b[m\u001b[0;33m 15295 ns 15170 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/2048/real_time_cv \u001b[m\u001b[0;33m 2.79 % 2.78 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/4096/real_time_mean \u001b[m\u001b[0;33m 1141244 ns 1135478 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.08896M\u001b[m output_rows=4.096k\u001b[m plan_cost=2.49856M\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/4096/real_time_median \u001b[m\u001b[0;33m 1143881 ns 1138252 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.08896M\u001b[m output_rows=4.096k\u001b[m plan_cost=2.49856M\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/4096/real_time_stddev \u001b[m\u001b[0;33m 15733 ns 15158 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/4096/real_time_cv \u001b[m\u001b[0;33m 1.38 % 1.33 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/8192/real_time_mean \u001b[m\u001b[0;33m 2426264 ns 2414370 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=4.17792M\u001b[m output_rows=8.192k\u001b[m plan_cost=4.99712M\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/8192/real_time_median \u001b[m\u001b[0;33m 2422686 ns 2411175 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=4.17792M\u001b[m output_rows=8.192k\u001b[m plan_cost=4.99712M\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/8192/real_time_stddev \u001b[m\u001b[0;33m 43691 ns 43834 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/8192/real_time_cv \u001b[m\u001b[0;33m 1.80 % 1.82 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/16384/real_time_mean \u001b[m\u001b[0;33m 5812245 ns 5776502 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=8.35584M\u001b[m output_rows=16.384k\u001b[m plan_cost=9.99424M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/16384/real_time_median \u001b[m\u001b[0;33m 5826207 ns 5793681 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=8.35584M\u001b[m output_rows=16.384k\u001b[m plan_cost=9.99424M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/16384/real_time_stddev \u001b[m\u001b[0;33m 108021 ns 105107 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/16384/real_time_cv \u001b[m\u001b[0;33m 1.86 % 1.82 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/32768/real_time_mean \u001b[m\u001b[0;33m 16768921 ns 16655176 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=16.7117M\u001b[m output_rows=32.768k\u001b[m plan_cost=19.9885M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/32768/real_time_median \u001b[m\u001b[0;33m 16630918 ns 16514790 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=16.7117M\u001b[m output_rows=32.768k\u001b[m plan_cost=19.9885M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/32768/real_time_stddev \u001b[m\u001b[0;33m 241465 ns 245756 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/32768/real_time_cv \u001b[m\u001b[0;33m 1.44 % 1.48 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/65536/real_time_mean \u001b[m\u001b[0;33m 52625470 ns 52271134 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=33.4234M\u001b[m output_rows=65.536k\u001b[m plan_cost=39.977M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/65536/real_time_median \u001b[m\u001b[0;33m 52315635 ns 51975856 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=33.4234M\u001b[m output_rows=65.536k\u001b[m plan_cost=39.977M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/65536/real_time_stddev \u001b[m\u001b[0;33m 2766613 ns 2683321 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/65536/real_time_cv \u001b[m\u001b[0;33m 5.26 % 5.13 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/131072/real_time_mean \u001b[m\u001b[0;33m 129696984 ns 128861029 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=66.8467M\u001b[m output_rows=131.072k\u001b[m plan_cost=79.9539M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/131072/real_time_median \u001b[m\u001b[0;33m 128465716 ns 127653219 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=66.8467M\u001b[m output_rows=131.072k\u001b[m plan_cost=79.9539M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/131072/real_time_stddev \u001b[m\u001b[0;33m 3404042 ns 3352711 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/131072/real_time_cv \u001b[m\u001b[0;33m 2.62 % 2.60 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/262144/real_time_mean \u001b[m\u001b[0;33m 300294351 ns 298535917 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=133.693M\u001b[m output_rows=262.144k\u001b[m plan_cost=159.908M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/262144/real_time_median \u001b[m\u001b[0;33m 298677499 ns 297021558 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=133.693M\u001b[m output_rows=262.144k\u001b[m plan_cost=159.908M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/262144/real_time_stddev \u001b[m\u001b[0;33m 11210695 ns 10988337 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/262144/real_time_cv \u001b[m\u001b[0;33m 3.73 % 3.68 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/1024/1024/real_time_mean \u001b[m\u001b[0;33m 155098 ns 154565 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=404.48k\u001b[m output_rows=1.024k\u001b[m plan_cost=609.28k\u001b[m rhs_rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/1024/1024/real_time_median \u001b[m\u001b[0;33m 155342 ns 154745 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=404.48k\u001b[m output_rows=1.024k\u001b[m plan_cost=609.28k\u001b[m rhs_rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/1024/1024/real_time_stddev \u001b[m\u001b[0;33m 1119 ns 1070 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/1024/1024/real_time_cv \u001b[m\u001b[0;33m 0.72 % 0.69 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/2048/2048/real_time_mean \u001b[m\u001b[0;33m 301359 ns 300041 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=2.048k\u001b[m model_cost=808.96k\u001b[m output_rows=2.048k\u001b[m plan_cost=1.21856M\u001b[m rhs_rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/2048/2048/real_time_median \u001b[m\u001b[0;33m 302359 ns 301088 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=2.048k\u001b[m model_cost=808.96k\u001b[m output_rows=2.048k\u001b[m plan_cost=1.21856M\u001b[m rhs_rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/2048/2048/real_time_stddev \u001b[m\u001b[0;33m 2615 ns 2492 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/2048/2048/real_time_cv \u001b[m\u001b[0;33m 0.87 % 0.83 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/4096/4096/real_time_mean \u001b[m\u001b[0;33m 599260 ns 596096 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=4.096k\u001b[m model_cost=1.61792M\u001b[m output_rows=4.096k\u001b[m plan_cost=2.43712M\u001b[m rhs_rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/4096/4096/real_time_median \u001b[m\u001b[0;33m 599183 ns 596316 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=4.096k\u001b[m model_cost=1.61792M\u001b[m output_rows=4.096k\u001b[m plan_cost=2.43712M\u001b[m rhs_rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/4096/4096/real_time_stddev \u001b[m\u001b[0;33m 2586 ns 2808 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/4096/4096/real_time_cv \u001b[m\u001b[0;33m 0.43 % 0.47 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/8192/8192/real_time_mean \u001b[m\u001b[0;33m 1274549 ns 1266452 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=8.192k\u001b[m model_cost=3.23584M\u001b[m output_rows=8.192k\u001b[m plan_cost=4.87424M\u001b[m rhs_rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/8192/8192/real_time_median \u001b[m\u001b[0;33m 1234395 ns 1226034 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=8.192k\u001b[m model_cost=3.23584M\u001b[m output_rows=8.192k\u001b[m plan_cost=4.87424M\u001b[m rhs_rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/8192/8192/real_time_stddev \u001b[m\u001b[0;33m 78831 ns 77036 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/8192/8192/real_time_cv \u001b[m\u001b[0;33m 6.18 % 6.08 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/16384/16384/real_time_mean \u001b[m\u001b[0;33m 2941091 ns 2919158 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=16.384k\u001b[m model_cost=6.47168M\u001b[m output_rows=16.384k\u001b[m plan_cost=9.74848M\u001b[m rhs_rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/16384/16384/real_time_median \u001b[m\u001b[0;33m 2911038 ns 2889304 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=16.384k\u001b[m model_cost=6.47168M\u001b[m output_rows=16.384k\u001b[m plan_cost=9.74848M\u001b[m rhs_rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/16384/16384/real_time_stddev \u001b[m\u001b[0;33m 67545 ns 67104 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/16384/16384/real_time_cv \u001b[m\u001b[0;33m 2.30 % 2.30 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/32768/32768/real_time_mean \u001b[m\u001b[0;33m 6940540 ns 6889217 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=32.768k\u001b[m model_cost=12.9434M\u001b[m output_rows=32.768k\u001b[m plan_cost=19.497M\u001b[m rhs_rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/32768/32768/real_time_median \u001b[m\u001b[0;33m 6908295 ns 6859205 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=32.768k\u001b[m model_cost=12.9434M\u001b[m output_rows=32.768k\u001b[m plan_cost=19.497M\u001b[m rhs_rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/32768/32768/real_time_stddev \u001b[m\u001b[0;33m 70111 ns 72151 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/32768/32768/real_time_cv \u001b[m\u001b[0;33m 1.01 % 1.05 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/65536/65536/real_time_mean \u001b[m\u001b[0;33m 15832571 ns 15709972 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=65.536k\u001b[m model_cost=25.8867M\u001b[m output_rows=65.536k\u001b[m plan_cost=38.9939M\u001b[m rhs_rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/65536/65536/real_time_median \u001b[m\u001b[0;33m 15705191 ns 15604051 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=65.536k\u001b[m model_cost=25.8867M\u001b[m output_rows=65.536k\u001b[m plan_cost=38.9939M\u001b[m rhs_rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/65536/65536/real_time_stddev \u001b[m\u001b[0;33m 240004 ns 235698 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/65536/65536/real_time_cv \u001b[m\u001b[0;33m 1.52 % 1.50 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/64/64/real_time_mean \u001b[m\u001b[0;33m 341299 ns 339396 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=64\u001b[m model_cost=286.72k\u001b[m output_rows=64\u001b[m plan_cost=299.52k\u001b[m rhs_rows=64\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/64/64/real_time_median \u001b[m\u001b[0;33m 340857 ns 338876 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=64\u001b[m model_cost=286.72k\u001b[m output_rows=64\u001b[m plan_cost=299.52k\u001b[m rhs_rows=64\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/64/64/real_time_stddev \u001b[m\u001b[0;33m 7369 ns 7193 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/64/64/real_time_cv \u001b[m\u001b[0;33m 2.16 % 2.12 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/128/128/real_time_mean \u001b[m\u001b[0;33m 1072887 ns 1067776 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=128\u001b[m model_cost=1.14688M\u001b[m output_rows=128\u001b[m plan_cost=1.17248M\u001b[m rhs_rows=128\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/128/128/real_time_median \u001b[m\u001b[0;33m 1069796 ns 1064744 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=128\u001b[m model_cost=1.14688M\u001b[m output_rows=128\u001b[m plan_cost=1.17248M\u001b[m rhs_rows=128\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/128/128/real_time_stddev \u001b[m\u001b[0;33m 8259 ns 8226 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/128/128/real_time_cv \u001b[m\u001b[0;33m 0.77 % 0.77 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/256/256/real_time_mean \u001b[m\u001b[0;33m 3943250 ns 3924191 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=256\u001b[m model_cost=4.58752M\u001b[m output_rows=256\u001b[m plan_cost=4.63872M\u001b[m rhs_rows=256\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/256/256/real_time_median \u001b[m\u001b[0;33m 3886867 ns 3870416 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=256\u001b[m model_cost=4.58752M\u001b[m output_rows=256\u001b[m plan_cost=4.63872M\u001b[m rhs_rows=256\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/256/256/real_time_stddev \u001b[m\u001b[0;33m 115332 ns 111745 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/256/256/real_time_cv \u001b[m\u001b[0;33m 2.92 % 2.85 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/512/512/real_time_mean \u001b[m\u001b[0;33m 14965494 ns 14909916 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=512\u001b[m model_cost=18.3501M\u001b[m output_rows=512\u001b[m plan_cost=18.4525M\u001b[m rhs_rows=512\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/512/512/real_time_median \u001b[m\u001b[0;33m 15020643 ns 14962434 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=512\u001b[m model_cost=18.3501M\u001b[m output_rows=512\u001b[m plan_cost=18.4525M\u001b[m rhs_rows=512\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/512/512/real_time_stddev \u001b[m\u001b[0;33m 136569 ns 133694 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/512/512/real_time_cv \u001b[m\u001b[0;33m 0.91 % 0.90 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/1024/1024/real_time_mean \u001b[m\u001b[0;33m 57691018 ns 57436935 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=73.4003M\u001b[m output_rows=1.024k\u001b[m plan_cost=73.6051M\u001b[m rhs_rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/1024/1024/real_time_median \u001b[m\u001b[0;33m 57607890 ns 57430034 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=73.4003M\u001b[m output_rows=1.024k\u001b[m plan_cost=73.6051M\u001b[m rhs_rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/1024/1024/real_time_stddev \u001b[m\u001b[0;33m 451356 ns 317271 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/1024/1024/real_time_cv \u001b[m\u001b[0;33m 0.78 % 0.55 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/2048/2048/real_time_mean \u001b[m\u001b[0;33m 229988242 ns 229099036 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=2.048k\u001b[m model_cost=293.601M\u001b[m output_rows=2.048k\u001b[m plan_cost=294.011M\u001b[m rhs_rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/2048/2048/real_time_median \u001b[m\u001b[0;33m 229828521 ns 229065432 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=2.048k\u001b[m model_cost=293.601M\u001b[m output_rows=2.048k\u001b[m plan_cost=294.011M\u001b[m rhs_rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/2048/2048/real_time_stddev \u001b[m\u001b[0;33m 2792600 ns 2747616 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/2048/2048/real_time_cv \u001b[m\u001b[0;33m 1.21 % 1.20 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/32/32/real_time_mean \u001b[m\u001b[0;33m 130942 ns 129796 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=32\u001b[m model_cost=106.496k\u001b[m output_rows=1.024k\u001b[m plan_cost=112.896k\u001b[m rhs_rows=32\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/32/32/real_time_median \u001b[m\u001b[0;33m 131308 ns 130105 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=32\u001b[m model_cost=106.496k\u001b[m output_rows=1.024k\u001b[m plan_cost=112.896k\u001b[m rhs_rows=32\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/32/32/real_time_stddev \u001b[m\u001b[0;33m 1935 ns 1924 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/32/32/real_time_cv \u001b[m\u001b[0;33m 1.48 % 1.48 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/64/64/real_time_mean \u001b[m\u001b[0;33m 288639 ns 286761 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=64\u001b[m model_cost=425.984k\u001b[m output_rows=4.096k\u001b[m plan_cost=438.784k\u001b[m rhs_rows=64\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/64/64/real_time_median \u001b[m\u001b[0;33m 288545 ns 286708 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=64\u001b[m model_cost=425.984k\u001b[m output_rows=4.096k\u001b[m plan_cost=438.784k\u001b[m rhs_rows=64\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/64/64/real_time_stddev \u001b[m\u001b[0;33m 3202 ns 3171 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/64/64/real_time_cv \u001b[m\u001b[0;33m 1.11 % 1.11 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/128/128/real_time_mean \u001b[m\u001b[0;33m 899354 ns 893066 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=128\u001b[m model_cost=1.70394M\u001b[m output_rows=16.384k\u001b[m plan_cost=1.72954M\u001b[m rhs_rows=128\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/128/128/real_time_median \u001b[m\u001b[0;33m 881125 ns 874885 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=128\u001b[m model_cost=1.70394M\u001b[m output_rows=16.384k\u001b[m plan_cost=1.72954M\u001b[m rhs_rows=128\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/128/128/real_time_stddev \u001b[m\u001b[0;33m 52481 ns 51442 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/128/128/real_time_cv \u001b[m\u001b[0;33m 5.84 % 5.76 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/256/256/real_time_mean \u001b[m\u001b[0;33m 5036885 ns 4983636 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=256\u001b[m model_cost=6.81574M\u001b[m output_rows=65.536k\u001b[m plan_cost=6.86694M\u001b[m rhs_rows=256\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/256/256/real_time_median \u001b[m\u001b[0;33m 5043142 ns 4984503 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=256\u001b[m model_cost=6.81574M\u001b[m output_rows=65.536k\u001b[m plan_cost=6.86694M\u001b[m rhs_rows=256\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/256/256/real_time_stddev \u001b[m\u001b[0;33m 47815 ns 44463 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/256/256/real_time_cv \u001b[m\u001b[0;33m 0.95 % 0.89 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/512/512/real_time_mean \u001b[m\u001b[0;33m 25155229 ns 24914236 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=512\u001b[m model_cost=27.263M\u001b[m output_rows=262.144k\u001b[m plan_cost=27.3654M\u001b[m rhs_rows=512\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/512/512/real_time_median \u001b[m\u001b[0;33m 25126405 ns 24885776 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=512\u001b[m model_cost=27.263M\u001b[m output_rows=262.144k\u001b[m plan_cost=27.3654M\u001b[m rhs_rows=512\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/512/512/real_time_stddev \u001b[m\u001b[0;33m 71506 ns 52169 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/512/512/real_time_cv \u001b[m\u001b[0;33m 0.28 % 0.21 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/1024/1024/real_time_mean \u001b[m\u001b[0;33m 125736836 ns 124681210 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=109.052M\u001b[m output_rows=1.04858M\u001b[m plan_cost=109.257M\u001b[m rhs_rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/1024/1024/real_time_median \u001b[m\u001b[0;33m 124916767 ns 123921643 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=109.052M\u001b[m output_rows=1.04858M\u001b[m plan_cost=109.257M\u001b[m rhs_rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/1024/1024/real_time_stddev \u001b[m\u001b[0;33m 2950666 ns 2904895 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/1024/1024/real_time_cv \u001b[m\u001b[0;33m 2.35 % 2.33 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m" + ] + } + ], + "source": [ + "!cd .. && timeout 300s ./build-release/bin/benchmarks --benchmark_filter='^OperatorCost/' --benchmark_repetitions=3 --benchmark_display_aggregates_only=true --benchmark_out=research/benchmark-results/operator-cost.json --benchmark_out_format=json" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "bcfd11fc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-06-02T01:20:14+03:00\n", + "Running ./build-release/bin/benchmarks\n", + "Run on (8 X 4200 MHz CPU s)\n", + "CPU Caches:\n", + " L1 Data 48 KiB (x4)\n", + " L1 Instruction 32 KiB (x4)\n", + " L2 Unified 1280 KiB (x4)\n", + " L3 Unified 8192 KiB (x1)\n", + "Load Average: 1.60, 1.07, 0.73\n", + "***WARNING*** CPU scaling is enabled, the benchmark real time measurements may be noisy and will incur extra overhead.\n", + "------------------------------------------------------------------------------------------------------------------------------------------------\n", + "Benchmark Time CPU Iterations UserCounters...\n", + "------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\u001b[0;32mOperatorCostMatched/target_cost:640000/SeqScan/6400/real_time_mean \u001b[m\u001b[0;33m 320159 ns 317346 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640k\u001b[m output_rows=6.4k\u001b[m plan_cost=640k\u001b[m rows=6.4k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/SeqScan/6400/real_time_median \u001b[m\u001b[0;33m 319599 ns 316785 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640k\u001b[m output_rows=6.4k\u001b[m plan_cost=640k\u001b[m rows=6.4k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/SeqScan/6400/real_time_stddev \u001b[m\u001b[0;33m 1160 ns 1115 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/SeqScan/6400/real_time_cv \u001b[m\u001b[0;33m 0.36 % 0.35 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Filter/6400/real_time_mean \u001b[m\u001b[0;33m 409248 ns 407255 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640k\u001b[m output_rows=3.328k\u001b[m plan_cost=1.28M\u001b[m rows=6.4k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Filter/6400/real_time_median \u001b[m\u001b[0;33m 399719 ns 397882 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640k\u001b[m output_rows=3.328k\u001b[m plan_cost=1.28M\u001b[m rows=6.4k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Filter/6400/real_time_stddev \u001b[m\u001b[0;33m 18699 ns 18531 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Filter/6400/real_time_cv \u001b[m\u001b[0;33m 4.57 % 4.55 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Projection/29091/real_time_mean \u001b[m\u001b[0;33m 3097292 ns 3075629 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640.002k\u001b[m output_rows=29.091k\u001b[m plan_cost=3.5491M\u001b[m rows=29.091k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Projection/29091/real_time_median \u001b[m\u001b[0;33m 3113549 ns 3091459 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640.002k\u001b[m output_rows=29.091k\u001b[m plan_cost=3.5491M\u001b[m rows=29.091k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Projection/29091/real_time_stddev \u001b[m\u001b[0;33m 46014 ns 45803 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Projection/29091/real_time_cv \u001b[m\u001b[0;33m 1.49 % 1.49 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Sort/4476/real_time_mean \u001b[m\u001b[0;33m 714273 ns 707808 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640.068k\u001b[m output_rows=4.476k\u001b[m plan_cost=1.08767M\u001b[m rows=4.476k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Sort/4476/real_time_median \u001b[m\u001b[0;33m 704145 ns 697639 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640.068k\u001b[m output_rows=4.476k\u001b[m plan_cost=1.08767M\u001b[m rows=4.476k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Sort/4476/real_time_stddev \u001b[m\u001b[0;33m 21201 ns 20423 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Sort/4476/real_time_cv \u001b[m\u001b[0;33m 2.97 % 2.89 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Aggregation/1255/real_time_mean \u001b[m\u001b[0;33m 353749 ns 351405 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640.05k\u001b[m output_rows=1.255k\u001b[m plan_cost=765.55k\u001b[m rows=1.255k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Aggregation/1255/real_time_median \u001b[m\u001b[0;33m 352784 ns 350809 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640.05k\u001b[m output_rows=1.255k\u001b[m plan_cost=765.55k\u001b[m rows=1.255k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Aggregation/1255/real_time_stddev \u001b[m\u001b[0;33m 1705 ns 2169 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Aggregation/1255/real_time_cv \u001b[m\u001b[0;33m 0.48 % 0.62 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/HashJoin/1620/1620/real_time_mean \u001b[m\u001b[0;33m 251418 ns 250042 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.62k\u001b[m model_cost=639.9k\u001b[m output_rows=1.62k\u001b[m plan_cost=963.9k\u001b[m rhs_rows=1.62k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/HashJoin/1620/1620/real_time_median \u001b[m\u001b[0;33m 250657 ns 249485 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.62k\u001b[m model_cost=639.9k\u001b[m output_rows=1.62k\u001b[m plan_cost=963.9k\u001b[m rhs_rows=1.62k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/HashJoin/1620/1620/real_time_stddev \u001b[m\u001b[0;33m 1990 ns 1973 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/HashJoin/1620/1620/real_time_cv \u001b[m\u001b[0;33m 0.79 % 0.79 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopJoin/96/96/real_time_mean \u001b[m\u001b[0;33m 676351 ns 670795 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=96\u001b[m model_cost=645.12k\u001b[m output_rows=96\u001b[m plan_cost=664.32k\u001b[m rhs_rows=96\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopJoin/96/96/real_time_median \u001b[m\u001b[0;33m 675430 ns 670332 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=96\u001b[m model_cost=645.12k\u001b[m output_rows=96\u001b[m plan_cost=664.32k\u001b[m rhs_rows=96\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopJoin/96/96/real_time_stddev \u001b[m\u001b[0;33m 2046 ns 2832 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopJoin/96/96/real_time_cv \u001b[m\u001b[0;33m 0.30 % 0.42 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopCrossJoin/78/78/real_time_mean \u001b[m\u001b[0;33m 398744 ns 394565 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=78\u001b[m model_cost=632.736k\u001b[m output_rows=6.084k\u001b[m plan_cost=648.336k\u001b[m rhs_rows=78\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopCrossJoin/78/78/real_time_median 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rows=10k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/SeqScan/10000/real_time_median \u001b[m\u001b[0;33m 349456 ns 347781 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1M\u001b[m output_rows=10k\u001b[m plan_cost=1M\u001b[m rows=10k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/SeqScan/10000/real_time_stddev \u001b[m\u001b[0;33m 6767 ns 6945 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/SeqScan/10000/real_time_cv \u001b[m\u001b[0;33m 1.92 % 1.98 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Filter/10000/real_time_mean \u001b[m\u001b[0;33m 664349 ns 660903 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1M\u001b[m output_rows=5.12k\u001b[m 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output_rows=45.455k\u001b[m plan_cost=5.54551M\u001b[m rows=45.455k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Projection/45455/real_time_median \u001b[m\u001b[0;33m 5563214 ns 5519348 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.00001M\u001b[m output_rows=45.455k\u001b[m plan_cost=5.54551M\u001b[m rows=45.455k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Projection/45455/real_time_stddev \u001b[m\u001b[0;33m 17569 ns 19382 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Projection/45455/real_time_cv \u001b[m\u001b[0;33m 0.32 % 0.35 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Sort/6993/real_time_mean \u001b[m\u001b[0;33m 1181814 ns 1176185 ns 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plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/NestedLoopJoin/120/120/real_time_cv \u001b[m\u001b[0;33m 0.78 % 0.15 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/NestedLoopCrossJoin/98/98/real_time_mean \u001b[m\u001b[0;33m 600207 ns 596002 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=98\u001b[m model_cost=998.816k\u001b[m output_rows=9.604k\u001b[m plan_cost=1.01842M\u001b[m rhs_rows=98\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/NestedLoopCrossJoin/98/98/real_time_median \u001b[m\u001b[0;33m 600347 ns 596164 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=98\u001b[m model_cost=998.816k\u001b[m output_rows=9.604k\u001b[m plan_cost=1.01842M\u001b[m rhs_rows=98\u001b[m\n", + 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3\u001b[m model_cost=3.24M\u001b[m output_rows=32.4k\u001b[m plan_cost=3.24M\u001b[m rows=32.4k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/SeqScan/32400/real_time_stddev \u001b[m\u001b[0;33m 75695 ns 74938 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/SeqScan/32400/real_time_cv \u001b[m\u001b[0;33m 5.89 % 5.87 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Filter/32400/real_time_mean \u001b[m\u001b[0;33m 2176167 ns 2165969 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.24M\u001b[m output_rows=16.384k\u001b[m plan_cost=6.48M\u001b[m rows=32.4k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Filter/32400/real_time_median \u001b[m\u001b[0;33m 2159127 ns 2150028 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.24M\u001b[m output_rows=16.384k\u001b[m plan_cost=6.48M\u001b[m rows=32.4k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Filter/32400/real_time_stddev \u001b[m\u001b[0;33m 45990 ns 44955 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Filter/32400/real_time_cv \u001b[m\u001b[0;33m 2.11 % 2.08 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Projection/147273/real_time_mean \u001b[m\u001b[0;33m 20058836 ns 19892307 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.24001M\u001b[m output_rows=147.273k\u001b[m plan_cost=17.9673M\u001b[m rows=147.273k\u001b[m\n", + 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output_rows=19.636k\u001b[m plan_cost=5.20354M\u001b[m rows=19.636k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Sort/19636/real_time_median \u001b[m\u001b[0;33m 4182936 ns 4160248 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.23994M\u001b[m output_rows=19.636k\u001b[m plan_cost=5.20354M\u001b[m rows=19.636k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Sort/19636/real_time_stddev \u001b[m\u001b[0;33m 21162 ns 17617 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Sort/19636/real_time_cv \u001b[m\u001b[0;33m 0.51 % 0.42 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Aggregation/6353/real_time_mean \u001b[m\u001b[0;33m 1881536 ns 1872941 ns 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0.10 % 0.10 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopJoin/215/215/real_time_mean \u001b[m\u001b[0;33m 2883331 ns 2862880 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=215\u001b[m model_cost=3.23575M\u001b[m output_rows=215\u001b[m plan_cost=3.27875M\u001b[m rhs_rows=215\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopJoin/215/215/real_time_median \u001b[m\u001b[0;33m 2883138 ns 2870337 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=215\u001b[m model_cost=3.23575M\u001b[m output_rows=215\u001b[m plan_cost=3.27875M\u001b[m rhs_rows=215\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopJoin/215/215/real_time_stddev \u001b[m\u001b[0;33m 25044 ns 36841 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopJoin/215/215/real_time_cv \u001b[m\u001b[0;33m 0.87 % 1.29 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopCrossJoin/177/177/real_time_mean \u001b[m\u001b[0;33m 1967699 ns 1948450 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=177\u001b[m model_cost=3.25822M\u001b[m output_rows=31.329k\u001b[m plan_cost=3.29362M\u001b[m rhs_rows=177\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopCrossJoin/177/177/real_time_median \u001b[m\u001b[0;33m 1961065 ns 1941733 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=177\u001b[m model_cost=3.25822M\u001b[m output_rows=31.329k\u001b[m plan_cost=3.29362M\u001b[m rhs_rows=177\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopCrossJoin/177/177/real_time_stddev \u001b[m\u001b[0;33m 26641 ns 26322 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopCrossJoin/177/177/real_time_cv \u001b[m\u001b[0;33m 1.35 % 1.35 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/SeqScan/67600/real_time_mean \u001b[m\u001b[0;33m 3804148 ns 3769127 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76M\u001b[m output_rows=67.6k\u001b[m plan_cost=6.76M\u001b[m rows=67.6k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/SeqScan/67600/real_time_median \u001b[m\u001b[0;33m 3743306 ns 3710163 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76M\u001b[m output_rows=67.6k\u001b[m plan_cost=6.76M\u001b[m rows=67.6k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/SeqScan/67600/real_time_stddev \u001b[m\u001b[0;33m 161694 ns 156528 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/SeqScan/67600/real_time_cv \u001b[m\u001b[0;33m 4.25 % 4.15 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Filter/67600/real_time_mean \u001b[m\u001b[0;33m 4921571 ns 4893957 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76M\u001b[m output_rows=33.808k\u001b[m plan_cost=13.52M\u001b[m rows=67.6k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Filter/67600/real_time_median \u001b[m\u001b[0;33m 4900259 ns 4872979 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76M\u001b[m output_rows=33.808k\u001b[m plan_cost=13.52M\u001b[m rows=67.6k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Filter/67600/real_time_stddev \u001b[m\u001b[0;33m 133893 ns 133878 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Filter/67600/real_time_cv \u001b[m\u001b[0;33m 2.72 % 2.74 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Projection/307273/real_time_mean \u001b[m\u001b[0;33m 41998731 ns 41673692 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76001M\u001b[m output_rows=307.273k\u001b[m plan_cost=37.4873M\u001b[m rows=307.273k\u001b[m\n", + 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output_rows=38.409k\u001b[m plan_cost=10.6009M\u001b[m rows=38.409k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Sort/38409/real_time_median \u001b[m\u001b[0;33m 10293266 ns 10238813 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.75998M\u001b[m output_rows=38.409k\u001b[m plan_cost=10.6009M\u001b[m rows=38.409k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Sort/38409/real_time_stddev \u001b[m\u001b[0;33m 86829 ns 86031 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Sort/38409/real_time_cv \u001b[m\u001b[0;33m 0.85 % 0.84 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Aggregation/13255/real_time_mean \u001b[m\u001b[0;33m 4438157 ns 4412963 ns 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model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopJoin/311/311/real_time_cv \u001b[m\u001b[0;33m 1.13 % 1.13 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopCrossJoin/255/255/real_time_mean \u001b[m\u001b[0;33m 5684609 ns 5595092 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=255\u001b[m model_cost=6.7626M\u001b[m output_rows=65.025k\u001b[m plan_cost=6.8136M\u001b[m rhs_rows=255\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopCrossJoin/255/255/real_time_median \u001b[m\u001b[0;33m 5585757 ns 5512270 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=255\u001b[m model_cost=6.7626M\u001b[m output_rows=65.025k\u001b[m plan_cost=6.8136M\u001b[m rhs_rows=255\u001b[m\n", + 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output_rows=65.536k\u001b[m plan_cost=18.8088M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Sort/65536/real_time_median \u001b[m\u001b[0;33m 21592685 ns 21449075 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.2552M\u001b[m output_rows=65.536k\u001b[m plan_cost=18.8088M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Sort/65536/real_time_stddev \u001b[m\u001b[0;33m 209379 ns 211435 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Sort/65536/real_time_cv \u001b[m\u001b[0;33m 0.97 % 0.98 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Aggregation/24020/real_time_mean \u001b[m\u001b[0;33m 10929372 ns 10855646 ns 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model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopJoin/418/418/real_time_cv \u001b[m\u001b[0;33m 0.25 % 0.28 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopCrossJoin/343/343/real_time_mean \u001b[m\u001b[0;33m 11246723 ns 11120161 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=343\u001b[m model_cost=12.2355M\u001b[m output_rows=117.649k\u001b[m plan_cost=12.3041M\u001b[m rhs_rows=343\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopCrossJoin/343/343/real_time_median \u001b[m\u001b[0;33m 11156642 ns 11021816 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=343\u001b[m model_cost=12.2355M\u001b[m output_rows=117.649k\u001b[m plan_cost=12.3041M\u001b[m rhs_rows=343\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopCrossJoin/343/343/real_time_stddev \u001b[m\u001b[0;33m 358953 ns 352800 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopCrossJoin/343/343/real_time_cv \u001b[m\u001b[0;33m 3.19 % 3.17 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/SeqScan/260100/real_time_mean \u001b[m\u001b[0;33m 17194941 ns 17060021 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=260.1k\u001b[m plan_cost=26.01M\u001b[m rows=260.1k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/SeqScan/260100/real_time_median \u001b[m\u001b[0;33m 17199810 ns 17065589 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=260.1k\u001b[m plan_cost=26.01M\u001b[m rows=260.1k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/SeqScan/260100/real_time_stddev \u001b[m\u001b[0;33m 69765 ns 71670 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/SeqScan/260100/real_time_cv \u001b[m\u001b[0;33m 0.41 % 0.42 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Filter/260100/real_time_mean \u001b[m\u001b[0;33m 20268578 ns 20143106 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=130.052k\u001b[m plan_cost=52.02M\u001b[m rows=260.1k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Filter/260100/real_time_median \u001b[m\u001b[0;33m 20226671 ns 20105167 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=130.052k\u001b[m plan_cost=52.02M\u001b[m rows=260.1k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Filter/260100/real_time_stddev \u001b[m\u001b[0;33m 948813 ns 923956 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Filter/260100/real_time_cv \u001b[m\u001b[0;33m 4.68 % 4.59 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Projection/1182273/real_time_mean \u001b[m\u001b[0;33m 158375198 ns 157223598 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=1.18227M\u001b[m plan_cost=144.237M\u001b[m rows=1.18227M\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Projection/1182273/real_time_median \u001b[m\u001b[0;33m 158155639 ns 157165675 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=1.18227M\u001b[m plan_cost=144.237M\u001b[m rows=1.18227M\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Projection/1182273/real_time_stddev \u001b[m\u001b[0;33m 1530329 ns 1447113 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Projection/1182273/real_time_cv \u001b[m\u001b[0;33m 0.97 % 0.92 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Sort/131364/real_time_mean \u001b[m\u001b[0;33m 66118518 ns 65605278 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.0101M\u001b[m output_rows=131.364k\u001b[m plan_cost=39.1465M\u001b[m rows=131.364k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Sort/131364/real_time_median \u001b[m\u001b[0;33m 65766383 ns 65262983 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.0101M\u001b[m output_rows=131.364k\u001b[m plan_cost=39.1465M\u001b[m rows=131.364k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Sort/131364/real_time_stddev \u001b[m\u001b[0;33m 1233820 ns 1224222 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Sort/131364/real_time_cv \u001b[m\u001b[0;33m 1.87 % 1.87 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Aggregation/51000/real_time_mean \u001b[m\u001b[0;33m 36824897 ns 36546612 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=51k\u001b[m plan_cost=31.11M\u001b[m rows=51k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Aggregation/51000/real_time_median \u001b[m\u001b[0;33m 37531672 ns 37260669 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=51k\u001b[m plan_cost=31.11M\u001b[m rows=51k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Aggregation/51000/real_time_stddev \u001b[m\u001b[0;33m 1680229 ns 1661849 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Aggregation/51000/real_time_cv \u001b[m\u001b[0;33m 4.56 % 4.55 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/HashJoin/65848/65848/real_time_mean \u001b[m\u001b[0;33m 15544874 ns 15433459 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=65.848k\u001b[m model_cost=26.01M\u001b[m output_rows=65.848k\u001b[m plan_cost=39.1796M\u001b[m rhs_rows=65.848k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/HashJoin/65848/65848/real_time_median \u001b[m\u001b[0;33m 15391909 ns 15276462 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=65.848k\u001b[m model_cost=26.01M\u001b[m output_rows=65.848k\u001b[m plan_cost=39.1796M\u001b[m rhs_rows=65.848k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/HashJoin/65848/65848/real_time_stddev \u001b[m\u001b[0;33m 395978 ns 386476 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/HashJoin/65848/65848/real_time_cv \u001b[m\u001b[0;33m 2.55 % 2.50 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopJoin/610/610/real_time_mean \u001b[m\u001b[0;33m 21482184 ns 21403674 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=610\u001b[m model_cost=26.047M\u001b[m output_rows=610\u001b[m plan_cost=26.169M\u001b[m rhs_rows=610\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopJoin/610/610/real_time_median \u001b[m\u001b[0;33m 21404013 ns 21323764 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=610\u001b[m model_cost=26.047M\u001b[m output_rows=610\u001b[m plan_cost=26.169M\u001b[m rhs_rows=610\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopJoin/610/610/real_time_stddev \u001b[m\u001b[0;33m 178260 ns 184469 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopJoin/610/610/real_time_cv \u001b[m\u001b[0;33m 0.83 % 0.86 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopCrossJoin/500/500/real_time_mean \u001b[m\u001b[0;33m 24928068 ns 24677118 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=500\u001b[m model_cost=26M\u001b[m output_rows=250k\u001b[m plan_cost=26.1M\u001b[m rhs_rows=500\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopCrossJoin/500/500/real_time_median \u001b[m\u001b[0;33m 24799747 ns 24566264 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=500\u001b[m model_cost=26M\u001b[m output_rows=250k\u001b[m plan_cost=26.1M\u001b[m rhs_rows=500\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopCrossJoin/500/500/real_time_stddev \u001b[m\u001b[0;33m 300738 ns 283592 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopCrossJoin/500/500/real_time_cv \u001b[m\u001b[0;33m 1.21 % 1.15 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m" + ] + } + ], + "source": [ + "!cd .. && timeout 300s ./build-release/bin/benchmarks --benchmark_filter='^OperatorCostMatched/' --benchmark_repetitions=3 --benchmark_display_aggregates_only=true --benchmark_out=research/benchmark-results/operator-cost-matched.json --benchmark_out_format=json" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b16b9fb4", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import re\n", + "from pathlib import Path\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "RESULTS = Path(\"benchmark-results\")\n", + "UNIT_TO_MS = {\"ns\": 1e-6, \"us\": 1e-3, \"ms\": 1.0, \"s\": 1e3}\n", + "\n", + "def mean_rows(filename):\n", + " data = json.loads((RESULTS / filename).read_text())\n", + " return [row for row in data[\"benchmarks\"] if row.get(\"aggregate_name\") == \"mean\"]\n", + "\n", + "def time_ms(row):\n", + " return row[\"real_time\"] * UNIT_TO_MS[row[\"time_unit\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d3172304", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "( executor query mode \\\n", + " 0 InterpretedExpressionExecutor kSimpleSelectSmall Naive \n", + " 1 InterpretedExpressionExecutor kSimpleSelectSmall Optimized \n", + " 2 CachedJitCompiledExpressionExecutor kSimpleSelectSmall Naive \n", + " 3 CachedJitCompiledExpressionExecutor kSimpleSelectSmall Optimized \n", + " 4 InterpretedExpressionExecutor kJoinSmall Naive \n", + " 5 InterpretedExpressionExecutor kJoinSmall Optimized \n", + " 6 CachedJitCompiledExpressionExecutor kJoinSmall Naive \n", + " 7 CachedJitCompiledExpressionExecutor kJoinSmall Optimized \n", + " 8 InterpretedExpressionExecutor kComplex5 Naive \n", + " 9 InterpretedExpressionExecutor kComplex5 Optimized \n", + " 10 CachedJitCompiledExpressionExecutor kComplex5 Naive \n", + " 11 CachedJitCompiledExpressionExecutor kComplex5 Optimized \n", + " \n", + " time_ms \n", + " 0 0.022492 \n", + " 1 0.020696 \n", + " 2 0.020039 \n", + " 3 0.019665 \n", + " 4 0.071494 \n", + " 5 0.071193 \n", + " 6 0.074523 \n", + " 7 0.070604 \n", + " 8 0.088746 \n", + " 9 0.093122 \n", + " 10 0.084456 \n", + " 11 0.080405 ,\n", + " query executor mode time_ms\n", + " 0 q2.1 Interpreted Naive 9.535917\n", + " 1 q2.1 Interpreted Optimized 10.018523\n", + " 2 q1.3 Interpreted Naive 8.894810\n", + " 3 q1.3 Interpreted Optimized 7.216482\n", + " 4 q1.2 Interpreted Naive 8.755501\n", + " 5 q1.2 Interpreted Optimized 6.842163\n", + " 6 q3.3 Interpreted Naive 38.770551\n", + " 7 q3.3 Interpreted Optimized 6.165384\n", + " 8 q3.4 Interpreted Naive 38.014861\n", + " 9 q3.4 Interpreted Optimized 6.548677\n", + " 10 q4.3 Interpreted Naive 9.839082\n", + " 11 q4.3 Interpreted Optimized 6.726511\n", + " 12 q1.1 Interpreted Naive 8.337287\n", + " 13 q1.1 Interpreted Optimized 7.009090\n", + " 14 q3.2 Interpreted Naive 41.168218\n", + " 15 q3.2 Interpreted Optimized 7.331045\n", + " 16 q2.2 Interpreted Naive 10.611863\n", + " 17 q2.2 Interpreted Optimized 7.990721\n", + " 18 q3.1 Interpreted Naive 41.047048\n", + " 19 q3.1 Interpreted Optimized 7.329905\n", + " 20 q4.2 Interpreted Naive 11.383627\n", + " 21 q4.2 Interpreted Optimized 13.753622\n", + " 22 q4.1 Interpreted Naive 9.885573\n", + " 23 q4.1 Interpreted Optimized 13.621071\n", + " 24 q2.3 Interpreted Naive 9.506406\n", + " 25 q2.3 Interpreted Optimized 6.827825)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "synthetic_pattern = re.compile(\n", + " r\"BM_SQL<(?P[^,]+), (?P[^,]+), PlannerMode::k(?P[^>]+)>/real_time_mean\"\n", + ")\n", + "synthetic = []\n", + "for row in mean_rows(\"query.json\"):\n", + " match = synthetic_pattern.fullmatch(row[\"name\"])\n", + " synthetic.append({**match.groupdict(), \"time_ms\": time_ms(row)})\n", + "synthetic = pd.DataFrame(synthetic)\n", + "\n", + "ssb = []\n", + "for row in mean_rows(\"ssb-sf001.json\"):\n", + " _, query, executor, mode, _ = row[\"name\"].split(\"/\")\n", + " ssb.append({\"query\": query, \"executor\": executor, \"mode\": mode, \"time_ms\": time_ms(row)})\n", + "ssb = pd.DataFrame(ssb)\n", + "\n", + "synthetic, ssb" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "f4e060f2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "( operator input_rows model_cost time_ms\n", + " 0 SeqScan 1024 102400.0 0.038473\n", + " 1 SeqScan 2048 204800.0 0.069707\n", + " 2 SeqScan 4096 409600.0 0.154163\n", + " 3 SeqScan 8192 819200.0 0.390148\n", + " 4 SeqScan 16384 1638400.0 0.924368\n", + " .. ... ... ... ...\n", + " 59 NestedLoopCrossJoin 64 425984.0 0.288639\n", + " 60 NestedLoopCrossJoin 128 1703936.0 0.899354\n", + " 61 NestedLoopCrossJoin 256 6815744.0 5.036885\n", + " 62 NestedLoopCrossJoin 512 27262976.0 25.155229\n", + " 63 NestedLoopCrossJoin 1024 109051904.0 125.736836\n", + " \n", + " [64 rows x 4 columns],\n", + " target_cost operator input_rows model_cost time_ms\n", + " 0 640000 SeqScan 6400 640000.0 0.320159\n", + " 1 640000 Filter 6400 640000.0 0.409248\n", + " 2 640000 Projection 29091 640002.0 3.097292\n", + " 3 640000 Sort 4476 640068.0 0.714273\n", + " 4 640000 Aggregation 1255 640050.0 0.353749\n", + " 5 640000 HashJoin 1620 639900.0 0.251418\n", + " 6 640000 NestedLoopJoin 96 645120.0 0.676351\n", + " 7 640000 NestedLoopCrossJoin 78 632736.0 0.398744\n", + " 8 1000000 SeqScan 10000 1000000.0 0.352346\n", + " 9 1000000 Filter 10000 1000000.0 0.664349\n", + " 10 1000000 Projection 45455 1000010.0 5.560017\n", + " 11 1000000 Sort 6993 999999.0 1.181814\n", + " 12 1000000 Aggregation 1961 1000110.0 0.540896\n", + " 13 1000000 HashJoin 2532 1000140.0 0.408221\n", + " 14 1000000 NestedLoopJoin 120 1008000.0 1.006221\n", + " 15 1000000 NestedLoopCrossJoin 98 998816.0 0.600207\n", + " 16 3240000 SeqScan 32400 3240000.0 1.284652\n", + " 17 3240000 Filter 32400 3240000.0 2.176167\n", + " 18 3240000 Projection 147273 3240006.0 20.058836\n", + " 19 3240000 Sort 19636 3239940.0 4.184701\n", + " 20 3240000 Aggregation 6353 3240030.0 1.881536\n", + " 21 3240000 HashJoin 8203 3240185.0 1.306558\n", + " 22 3240000 NestedLoopJoin 215 3235750.0 2.883331\n", + " 23 3240000 NestedLoopCrossJoin 177 3258216.0 1.967699\n", + " 24 6760000 SeqScan 67600 6760000.0 3.804148\n", + " 25 6760000 Filter 67600 6760000.0 4.921571\n", + " 26 6760000 Projection 307273 6760006.0 41.998731\n", + " 27 6760000 Sort 38409 6759984.0 10.271929\n", + " 28 6760000 Aggregation 13255 6760050.0 4.438157\n", + " 29 6760000 HashJoin 17114 6760030.0 3.043218\n", + " 30 6760000 NestedLoopJoin 311 6770470.0 5.812015\n", + " 31 6760000 NestedLoopCrossJoin 255 6762600.0 5.684609\n", + " 32 12250000 SeqScan 122500 12250000.0 7.200013\n", + " 33 12250000 Filter 122500 12250000.0 9.183200\n", + " 34 12250000 Projection 556818 12249996.0 76.154538\n", + " 35 12250000 Sort 65536 12255232.0 21.631748\n", + " 36 12250000 Aggregation 24020 12250200.0 10.929372\n", + " 37 12250000 HashJoin 31013 12250135.0 6.579551\n", + " 38 12250000 NestedLoopJoin 418 12230680.0 10.203401\n", + " 39 12250000 NestedLoopCrossJoin 343 12235496.0 11.246723\n", + " 40 26010000 SeqScan 260100 26010000.0 17.194941\n", + " 41 26010000 Filter 260100 26010000.0 20.268578\n", + " 42 26010000 Projection 1182273 26010006.0 158.375198\n", + " 43 26010000 Sort 131364 26010072.0 66.118518\n", + " 44 26010000 Aggregation 51000 26010000.0 36.824897\n", + " 45 26010000 HashJoin 65848 26009960.0 15.544874\n", + " 46 26010000 NestedLoopJoin 610 26047000.0 21.482184\n", + " 47 26010000 NestedLoopCrossJoin 500 26000000.0 24.928068)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "operator = []\n", + "for row in mean_rows(\"operator-cost.json\"):\n", + " parts = row[\"name\"].split(\"/\")\n", + " operator.append({\n", + " \"operator\": parts[1],\n", + " \"input_rows\": max(map(int, parts[2:-1])),\n", + " \"model_cost\": row[\"model_cost\"],\n", + " \"time_ms\": time_ms(row),\n", + " })\n", + "operator = pd.DataFrame(operator)\n", + "\n", + "matched = []\n", + "for row in mean_rows(\"operator-cost-matched.json\"):\n", + " parts = row[\"name\"].split(\"/\")\n", + " matched.append({\n", + " \"target_cost\": int(parts[1].split(\":\")[1]),\n", + " \"operator\": parts[2],\n", + " \"input_rows\": max(map(int, parts[3:-1])),\n", + " \"model_cost\": row[\"model_cost\"],\n", + " \"time_ms\": time_ms(row),\n", + " })\n", + "matched = pd.DataFrame(matched)\n", + "\n", + "operator, matched" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "2075a20f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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operatorcoefficientvalue
0SeqScanrow100
1Filteroutput row100
2Projectionoutput row22
3Sortn * (floor(log2(n)) + 1)11
4Aggregationinput row510
5NestedLoopJoininput row pair70
6NestedLoopCrossJoininput row pair104
7HashJoinbuild row * width100
8HashJoinprobe row35
9HashJoinoutput row * width10
\n", + "" + ], + "text/plain": [ + " operator coefficient value\n", + "0 SeqScan row 100\n", + "1 Filter output row 100\n", + "2 Projection output row 22\n", + "3 Sort n * (floor(log2(n)) + 1) 11\n", + "4 Aggregation input row 510\n", + "5 NestedLoopJoin input row pair 70\n", + "6 NestedLoopCrossJoin input row pair 104\n", + "7 HashJoin build row * width 100\n", + "8 HashJoin probe row 35\n", + "9 HashJoin output row * width 10" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coefficients = pd.DataFrame([\n", + " (\"SeqScan\", \"row\", 100),\n", + " (\"Filter\", \"output row\", 100),\n", + " (\"Projection\", \"output row\", 22),\n", + " (\"Sort\", \"n * (floor(log2(n)) + 1)\", 11),\n", + " (\"Aggregation\", \"input row\", 510),\n", + " (\"NestedLoopJoin\", \"input row pair\", 70),\n", + " (\"NestedLoopCrossJoin\", \"input row pair\", 104),\n", + " (\"HashJoin\", \"build row * width\", 100),\n", + " (\"HashJoin\", \"probe row\", 35),\n", + " (\"HashJoin\", \"output row * width\", 10),\n", + "], columns=[\"operator\", \"coefficient\", \"value\"])\n", + "coefficients" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/research/benchmarks.pdf b/research/benchmarks.pdf new file mode 100644 index 0000000000000000000000000000000000000000..cb0f791cc2100b5357c8fded6fe61fcc35442da9 GIT binary patch literal 283622 zcma&NQ*>r+_vKx&Rk5v#ZQH7fo!l`h#vR+XZQHEawryKqy*>IrzJ9vjgJ6Fmnk`Tq6AGb|H35hIbUp#>~2FN3(bl_S8OLEOr~5g-aMvNZ-U z$O3Fk9nFZCxY$_u`C%O#?EwbXu&%2YI8rvG9M@TD*I>K9^?Z7uLCGKw4}ER@xy))a 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diff --git a/src/stewkk/sql/logic/optimizer/cardinality.cpp b/src/stewkk/sql/logic/optimizer/cardinality.cpp index 2749864..9097843 100644 --- a/src/stewkk/sql/logic/optimizer/cardinality.cpp +++ b/src/stewkk/sql/logic/optimizer/cardinality.cpp @@ -8,7 +8,52 @@ namespace stewkk::sql { namespace { +constexpr double kEqualitySelectivity = 0.10; +constexpr double kRangeSelectivity = 0.50; +constexpr double kUnknownSelectivity = 0.50; +bool IsConstant(const Expression& expr) { + return std::holds_alternative(expr) + || std::holds_alternative(expr) + || std::holds_alternative(expr); +} + +double EstimateFilterSelectivity(const Expression& predicate) { + return std::visit(utils::Overloaded{ + [](const BinaryExpression& b) { + const auto lhs = EstimateFilterSelectivity(*b.lhs); + const auto rhs = EstimateFilterSelectivity(*b.rhs); + switch (b.binop) { + case BinaryOp::kAnd: + return lhs * rhs; + case BinaryOp::kOr: + return lhs + rhs - lhs * rhs; + case BinaryOp::kEq: + return (std::holds_alternative(*b.lhs) && IsConstant(*b.rhs)) + || (IsConstant(*b.lhs) && std::holds_alternative(*b.rhs)) + ? kEqualitySelectivity + : kUnknownSelectivity; + case BinaryOp::kGt: + case BinaryOp::kLt: + case BinaryOp::kLe: + case BinaryOp::kGe: + return kRangeSelectivity; + default: + return kUnknownSelectivity; + } + }, + [](const UnaryExpression& u) { + return u.op == UnaryOp::kNot + ? 1.0 - EstimateFilterSelectivity(*u.child) + : kUnknownSelectivity; + }, + [](const InExpression& in) { + const auto selectivity = std::min(0.50, in.values.size() * kEqualitySelectivity); + return in.negated ? 1.0 - selectivity : selectivity; + }, + [](const auto&) { return kUnknownSelectivity; }, + }, predicate); +} double JoinSelectivity(const Expression& qual, int64_t lhs_card, int64_t rhs_card) { const auto* b = std::get_if(&qual); @@ -49,7 +94,10 @@ int64_t CardinalityEstimates::GetCardinality(const LogicalOperator& op) { return 10; }, [this](const logical::Filter& f) -> int64_t { - return GetCardinality(f.source); + auto source_cardinality = GetCardinality(f.source); + auto filtered = static_cast( + source_cardinality * EstimateFilterSelectivity(f.predicate)); + return std::max(1, filtered); }, [this](const logical::Projection& p) -> int64_t { return GetCardinality(p.source); diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index 43c3d86..c76cf2f 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -1,6 +1,7 @@ #include #include +#include #include #include @@ -59,7 +60,19 @@ PropertySet DeriveOutputProps(utils::NotNull expr, }, expr->root_operator); } -int64_t LowerBoundLocalCost(utils::NotNull expr, CardinalityEstimates& cardinality) { +int64_t HashJoinCost(utils::NotNull build, utils::NotNull probe, + utils::NotNull output, CardinalityEstimates& cardinality, + SchemaCatalog& schema) { + constexpr int64_t kHashBuild = 100; + constexpr int64_t kHashProbe = 35; + constexpr int64_t kTupleCopy = 10; + return kHashBuild * cardinality.GetCardinality(build) * schema.GetWidth(build) + + kHashProbe * cardinality.GetCardinality(probe) + + kTupleCopy * cardinality.GetCardinality(output) * schema.GetWidth(output); +} + +int64_t LowerBoundLocalCost(utils::NotNull expr, CardinalityEstimates& cardinality, + SchemaCatalog& schema) { return std::visit(utils::Overloaded{ [&](const logical::Table&) -> int64_t { return 100 * cardinality.GetCardinality(expr->group); @@ -79,12 +92,17 @@ int64_t LowerBoundLocalCost(utils::NotNull expr, CardinalityEstima [&](const logical::Join& j) -> int64_t { auto n_l = cardinality.GetCardinality(j.lhs); auto n_r = cardinality.GetCardinality(j.rhs); - return std::min(69 * (n_l + n_r), 70 * n_l * n_r); + return std::min({ + HashJoinCost(j.lhs, j.rhs, expr->group, cardinality, schema), + HashJoinCost(j.rhs, j.lhs, expr->group, cardinality, schema), + 70 * n_l * n_r, + }); }, }, expr->root_operator); } -int64_t CalcCost(utils::NotNull expr, CardinalityEstimates& cardinality) { +int64_t CalcCost(utils::NotNull expr, CardinalityEstimates& cardinality, + SchemaCatalog& schema) { return std::visit(utils::Overloaded{ [&](const physical::SeqScan&) -> int64_t { return 100 * cardinality.GetCardinality(expr->group); @@ -106,7 +124,7 @@ int64_t CalcCost(utils::NotNull expr, CardinalityEstimates& cardi return 104 * n_l * n_r; }, [&](const physical::HashJoin& j) -> int64_t { - return 69 * (cardinality.GetCardinality(j.lhs) + cardinality.GetCardinality(j.rhs)); + return HashJoinCost(j.lhs, j.rhs, expr->group, cardinality, schema); }, [&](const physical::Sort& s) -> int64_t { auto n = cardinality.GetCardinality(s.input); @@ -189,7 +207,7 @@ int64_t Optimizer::LowerBoundCost(utils::NotNu int64_t best = std::numeric_limits::max(); for (auto expr : group->GetLogicalExprs()) { - int64_t local = LowerBoundLocalCost(expr, cardinality_); + int64_t local = LowerBoundLocalCost(expr, cardinality_, schema_); int64_t children = 0; bool overflow = false; for (auto child : GetChildren(expr)) { @@ -300,7 +318,7 @@ void Optimizer::TryRules( tasks_.emplace([this, expr, rule]() { Log("Applying implementation rule {} to group {}", rule, expr->group->GetId()); auto new_expr = rules_applier_.Apply(ImplementationRuleId{rule}, expr, memo_); - auto lc = CalcCost(new_expr, cardinality_); + auto lc = CalcCost(new_expr, cardinality_, schema_); Log("Local cost for group {} expression: {}", new_expr->group->GetId(), lc); local_cost_[new_expr.get()] = lc; }); @@ -372,7 +390,7 @@ void Optimizer::OptimizeGroup( auto op = enforcer->TryBuild(group, required, schema_); if (!op) continue; auto enf_expr = group->AddPhysicalExpr(*op, true); - auto lc = CalcCost(enf_expr, cardinality_); + auto lc = CalcCost(enf_expr, cardinality_, schema_); Log("Enforcer local cost for group {}: {}", group->GetId(), lc); local_cost_[enf_expr.get()] = lc; if (!limit || lc < *limit) { @@ -474,15 +492,36 @@ void Optimizer::RunSearch(Limit limit) { template PhysicalPlanNode Optimizer::Optimize() { + const auto started = std::chrono::steady_clock::now(); RunSearch(std::numeric_limits::max()); Log("Optimization complete, building plan"); - return BuildOptimalPlan(root_->group.get(), global_required_); + auto plan = BuildOptimalPlan(root_->group.get(), global_required_); + const auto runtime = std::chrono::duration_cast( + std::chrono::steady_clock::now() - started); + Log("Optimization finished in {} us, chosen plan cost {}", runtime.count(), GetBestCost()); + return plan; } template PhysicalPlanNode Optimizer::OptimizeExhaustive() { + const auto started = std::chrono::steady_clock::now(); RunSearch(std::nullopt); - return BuildOptimalPlan(root_->group.get(), global_required_); + auto plan = BuildOptimalPlan(root_->group.get(), global_required_); + const auto runtime = std::chrono::duration_cast( + std::chrono::steady_clock::now() - started); + Log("Exhaustive optimization finished in {} us, chosen plan cost {}", runtime.count(), + GetBestCost()); + return plan; +} + +template +std::int64_t Optimizer::GetBestCost() const { + WinnerKey key{root_->group.get(), global_required_}; + auto it = winner_.find(key); + if (it == winner_.end()) { + throw std::runtime_error{"no optimal plan cost"}; + } + return it->second.cost; } template diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index 8bfeba0..7d44fc9 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -1,5 +1,8 @@ #include +#include +#include + #include #include #include @@ -20,6 +23,41 @@ namespace stewkk::sql { // FIXME: сделать API в виде DoStep(), которое возвращает какой-то внутренний стейт оптимизатора // FIXME: применение правила (по крайней мере трансформации), должно создавать несколько выражений +int64_t EstimateCardinality( + std::string_view sql, std::unordered_map table_sizes) { + std::stringstream s{std::string{sql}}; + Memo memo; + auto root = memo.Populate(GetAST(s).value().op); + CardinalityEstimates cardinality{std::move(table_sizes)}; + return cardinality.GetCardinality(root->group); +} + +TEST(CardinalityEstimatesTest, AppliesFilterHeuristics) { + ASSERT_THAT(EstimateCardinality("SELECT * FROM users WHERE users.id = 1;", {{"users", 1000}}), + Eq(100)); + ASSERT_THAT( + EstimateCardinality("SELECT * FROM users WHERE users.age BETWEEN 18 AND 30;", + {{"users", 1000}}), + Eq(250)); + ASSERT_THAT( + EstimateCardinality("SELECT * FROM users WHERE users.id IN (1, 2, 3);", + {{"users", 1000}}), + Eq(300)); +} + +TEST(SchemaCatalogTest, DerivesJoinWidth) { + std::stringstream s{"SELECT * FROM users JOIN orders ON users.id = orders.user_id;"}; + Memo memo; + auto root = memo.Populate(GetAST(s).value().op); + SchemaCatalog schema({ + {"users", {Attribute{"users", "id"}, Attribute{"users", "name"}}}, + {"orders", {Attribute{"orders", "id"}, Attribute{"orders", "user_id"}, + Attribute{"orders", "total"}}}, + }); + + ASSERT_THAT(schema.GetWidth(root->group), Eq(5)); +} + TEST(OptimizerTest, Simple) { std::stringstream s{"SELECT * FROM users;"}; Operator op = GetAST(s).value().op; @@ -28,6 +66,7 @@ TEST(OptimizerTest, Simple) { auto got = optimizer.Optimize(); ASSERT_THAT(SerializeDot(got), Eq("digraph G { rankdir=BT;\n n0 [label=\"SeqScan\\\\nusers\"]\n}\n")); + ASSERT_THAT(optimizer.GetBestCost(), Eq(1000)); } TEST(OptimizerTest, JoinCommutativity) { @@ -62,8 +101,8 @@ TEST(OptimizerTest, MultiwayJoinOCR) { Serialize(got), Eq("(PhysicalProjection (exprs (attr orders id) (attr customers id) (attr regions id))" " (HashJoin Inner (= (attr orders customer_id) (attr customers id))" - " (SeqScan orders) (HashJoin Inner (= (attr customers region_id) (attr regions id))" - " (SeqScan regions) (SeqScan customers))))")); + " (HashJoin Inner (= (attr customers region_id) (attr regions id))" + " (SeqScan regions) (SeqScan customers)) (SeqScan orders)))")); } TEST(OptimizerTest, MultiwayJoinROC) { @@ -85,7 +124,7 @@ TEST(OptimizerTest, MultiwayJoinROC) { Eq("(PhysicalProjection (exprs (attr orders id) (attr customers id) (attr regions id))" " (HashJoin Inner (= (attr orders customer_id) (attr customers id))" " (HashJoin Inner (= (attr customers region_id) (attr regions id))" - " (SeqScan customers) (SeqScan regions)) (SeqScan orders)))")); + " (SeqScan regions) (SeqScan customers)) (SeqScan orders)))")); } TEST(OptimizerTest, OrderBy) { @@ -119,6 +158,30 @@ TEST(OptimizerTest, AliasedJoinOptimizesWithAliasQualifiedAttrs) { ASSERT_THAT(serialized, HasSubstr("(attr o customer_id)")); } +TEST(OptimizerTest, PushesSelectiveFilterIntoHashBuildSide) { + std::stringstream s{ + "SELECT * FROM lineorder AS lo " + "JOIN supplier AS s ON lo.suppkey = s.id " + "WHERE s.region = 'AMERICA';"}; + Operator op = GetAST(s).value().op; + SchemaCatalog schema({ + {"lineorder", {Attribute{"lineorder", "suppkey"}, Attribute{"lineorder", "value"}}}, + {"supplier", {Attribute{"supplier", "id"}, Attribute{"supplier", "region"}}}, + }); + Optimizer optimizer(op, MakeMainRules(), CardinalityEstimates({ + {"lineorder", 6000}, + {"supplier", 100}, + }), std::move(schema)); + + auto got = optimizer.Optimize(); + + ASSERT_THAT( + Serialize(got), + Eq("(HashJoin Inner (= (attr lo suppkey) (attr s id))" + " (PhysicalFilter (= (attr s region) (str \"AMERICA\")) (SeqScan supplier s))" + " (SeqScan lineorder lo))")); +} + TEST(ReachabilityTest, SeqScanReachable) { std::stringstream s{"SELECT * FROM users;"}; auto result = IsPlanReachable(s, SeqScan{"users"}); diff --git a/src/stewkk/sql/logic/optimizer/schema_catalog.cpp b/src/stewkk/sql/logic/optimizer/schema_catalog.cpp index 43c801f..1a10aba 100644 --- a/src/stewkk/sql/logic/optimizer/schema_catalog.cpp +++ b/src/stewkk/sql/logic/optimizer/schema_catalog.cpp @@ -1,6 +1,10 @@ #include +#include +#include #include +#include +#include #include @@ -18,6 +22,11 @@ std::optional SchemaCatalog::GetSchema(utils::NotNull group) { return schema; } +std::int64_t SchemaCatalog::GetWidth(utils::NotNull group) { + auto schema = GetSchema(group); + return schema ? std::max(1, schema->size()) : 1; +} + // TODO: refactor to remove duplicate logic: both executor and optimizer derive // attributes std::optional SchemaCatalog::Derive(const LogicalOperator& op) { @@ -76,4 +85,32 @@ std::optional SchemaCatalog::Derive(const LogicalOperator& op) { }, op); } +SchemaCatalog LoadSchemaFromCsvDir(const std::filesystem::path& dir) { + std::unordered_map tables; + if (!std::filesystem::is_directory(dir)) { + return SchemaCatalog{}; + } + + static const std::regex kBench{R"(_\d+$)"}; + for (const auto& entry : std::filesystem::directory_iterator{dir}) { + if (entry.path().extension() != ".csv") continue; + auto stem = entry.path().stem().string(); + if (std::regex_search(stem, kBench)) continue; + + std::ifstream in{entry.path()}; + std::string header; + if (!std::getline(in, header)) continue; + + Schema schema; + for (const auto& part : header | std::views::split(',')) { + std::string token{part.begin(), part.end()}; + auto colon = token.find(':'); + if (colon == std::string::npos) continue; + schema.push_back(Attribute{stem, token.substr(0, colon)}); + } + tables.emplace(std::move(stem), std::move(schema)); + } + return SchemaCatalog{std::move(tables)}; +} + } // namespace stewkk::sql diff --git a/src/stewkk/sql/main.cpp b/src/stewkk/sql/main.cpp index d477e22..f1d0e25 100644 --- a/src/stewkk/sql/main.cpp +++ b/src/stewkk/sql/main.cpp @@ -1,14 +1,10 @@ #include #include #include -#include #include #include -#include -#include #include #include -#include #include #include @@ -88,30 +84,6 @@ Args ParseArgs(int argc, char** argv) { return args; } -SchemaCatalog LoadSchema(const std::string& dir) { - std::unordered_map tables; - static const std::regex kBench{R"(_\d+$)"}; - for (const auto& entry : std::filesystem::directory_iterator{dir}) { - if (entry.path().extension() != ".csv") continue; - auto stem = entry.path().stem().string(); - if (std::regex_search(stem, kBench)) continue; - - std::ifstream in{entry.path()}; - std::string header; - if (!std::getline(in, header)) continue; - - Schema schema; - for (const auto& part : header | std::views::split(',')) { - std::string token{part.begin(), part.end()}; - auto colon = token.find(':'); - if (colon == std::string::npos) continue; - schema.push_back(Attribute{stem, token.substr(0, colon)}); - } - tables.emplace(std::move(stem), std::move(schema)); - } - return SchemaCatalog{std::move(tables)}; -} - std::string SerializeAst(const Operator& op) { return std::visit([](auto&& node) -> std::string { using T = std::decay_t; @@ -245,7 +217,7 @@ int main(int argc, char** argv) { MatchResult mr; try { SchemaCatalog schema = args.data_dir.empty() ? SchemaCatalog{} - : LoadSchema(args.data_dir); + : LoadSchemaFromCsvDir(args.data_dir); mr = IsPlanReachable(sql_stream, target, {}, std::move(schema)); } catch (const std::exception& e) { std::cerr << "reachability error: " << e.what() << "\n"; @@ -275,7 +247,7 @@ int main(int argc, char** argv) { PropertySet required = parsed.required_order ? PropertySet{SortProperty{*parsed.required_order}} : PropertySet::Any(); - Optimizer optimizer(parsed.op, MakeMainRules(), {}, LoadSchema(args.data_dir), + Optimizer optimizer(parsed.op, MakeMainRules(), {}, LoadSchemaFromCsvDir(args.data_dir), std::move(required)); plan = optimizer.Optimize(); } catch (const std::exception& e) { From 53b7421139fba95321586dcc4e1ab1c5f5caa938 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Tue, 2 Jun 2026 02:18:11 +0300 Subject: [PATCH 076/120] Add cost to runtime comparison on random queries --- research/cost-on-random.ipynb | 311 ++++++++ research/cost-stats.csv | 1305 +++++++++++++++++++++++++++++++++ research/fuzz/cost_stats.py | 128 ++++ src/stewkk/sql/main.cpp | 18 +- 4 files changed, 1761 insertions(+), 1 deletion(-) create mode 100644 research/cost-on-random.ipynb create mode 100644 research/cost-stats.csv create mode 100644 research/fuzz/cost_stats.py diff --git a/research/cost-on-random.ipynb b/research/cost-on-random.ipynb new file mode 100644 index 0000000..bba8ee6 --- /dev/null +++ b/research/cost-on-random.ipynb @@ -0,0 +1,311 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "aed19ec2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-01T23:13:22.984825Z", + "iopub.status.busy": "2026-06-01T23:13:22.984699Z", + "iopub.status.idle": "2026-06-01T23:13:22.988427Z", + "shell.execute_reply": "2026-06-01T23:13:22.987712Z" + } + }, + "outputs": [], + "source": [ + "# !cd ~/c/iu9-sql-compiler/ && python -m research.fuzz.cost_stats \\\n", + "# --cli build-release/bin/sql \\\n", + "# --data-dir test/static/executor/test_data \\\n", + "# --count 400 --repeats 5 \\\n", + "# --out research/cost-stats.csv" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c50c4c78", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-01T23:13:22.990006Z", + "iopub.status.busy": "2026-06-01T23:13:22.989865Z", + "iopub.status.idle": "2026-06-01T23:13:23.532199Z", + "shell.execute_reply": "2026-06-01T23:13:23.531501Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "400 samples\n" + ] + }, + { + "data": { + "text/html": [ + "

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seedplan_costexec_usrows
count400.000000400.0000004.000000e+02400.000000
mean199.50000027901.1225003.037728e+043445.482500
std115.61430187392.0421812.148684e+0524278.190578
min0.0000001122.0000001.170000e+020.000000
25%99.7500002085.0000001.705000e+022.000000
50%199.5000009220.0000003.205000e+025.000000
75%299.25000017220.0000001.303000e+0329.250000
max399.000000687256.0000003.085577e+06384231.000000
\n", + "
" + ], + "text/plain": [ + " seed plan_cost exec_us rows\n", + "count 400.000000 400.000000 4.000000e+02 400.000000\n", + "mean 199.500000 27901.122500 3.037728e+04 3445.482500\n", + "std 115.614301 87392.042181 2.148684e+05 24278.190578\n", + "min 0.000000 1122.000000 1.170000e+02 0.000000\n", + "25% 99.750000 2085.000000 1.705000e+02 2.000000\n", + "50% 199.500000 9220.000000 3.205000e+02 5.000000\n", + "75% 299.250000 17220.000000 1.303000e+03 29.250000\n", + "max 399.000000 687256.000000 3.085577e+06 384231.000000" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "df = pd.read_csv('cost-stats.csv')\n", + "df['exec_ms'] = df['exec_us'] / 1e3\n", + "print(f'{len(df)} samples')\n", + "df[['seed', 'plan_cost', 'exec_us', 'rows']].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "49d0ecdc", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-01T23:13:23.533678Z", + "iopub.status.busy": "2026-06-01T23:13:23.533532Z", + "iopub.status.idle": "2026-06-01T23:13:23.674869Z", + "shell.execute_reply": "2026-06-01T23:13:23.674232Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cost = df['plan_cost'].to_numpy(dtype=float)\n", + "time_ms = df['exec_ms'].to_numpy(dtype=float)\n", + "\n", + "# Proportional fit through the origin: time = slope * cost.\n", + "slope = float(np.sum(cost * time_ms) / np.sum(cost * cost))\n", + "r = float(np.corrcoef(cost, time_ms)[0, 1])\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "ax.scatter(cost, time_ms, s=14, alpha=0.5, edgecolor='none')\n", + "xs = np.array([0.0, cost.max()])\n", + "ax.plot(xs, slope * xs, color='crimson', lw=1.5,\n", + " label=f'time = {slope:.3g} ms x cost')\n", + "ax.set_xlabel('plan cost (optimizer units)')\n", + "ax.set_ylabel('execution time (ms)')\n", + "ax.set_title(f'Plan cost vs. real time (Pearson r = {r:.3f})')\n", + "ax.legend()\n", + "ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "226372f9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-01T23:13:23.676337Z", + "iopub.status.busy": "2026-06-01T23:13:23.676195Z", + "iopub.status.idle": "2026-06-01T23:13:24.113644Z", + "shell.execute_reply": "2026-06-01T23:13:24.112874Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mask = (cost > 0) & (time_ms > 0)\n", + "lc = np.log10(cost[mask])\n", + "lt = np.log10(time_ms[mask])\n", + "\n", + "a, b = np.polyfit(lc, lt, 1)\n", + "r_log = float(np.corrcoef(lc, lt)[0, 1])\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "ax.scatter(cost[mask], time_ms[mask], s=14, alpha=0.5, edgecolor='none')\n", + "xs = np.array([cost[mask].min(), cost[mask].max()])\n", + "ax.plot(xs, 10**b * xs**a, color='crimson', lw=1.5,\n", + " label=f'log-log fit, slope = {a:.2f}')\n", + "ax.set_xscale('log')\n", + "ax.set_yscale('log')\n", + "ax.set_xlabel('plan cost (optimizer units)')\n", + "ax.set_ylabel('execution time (ms)')\n", + "ax.set_title(f'Plan cost vs. real time, log-log (r = {r_log:.3f})')\n", + "ax.legend()\n", + "ax.grid(True, which='both', alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5dd0a12c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-01T23:13:24.115407Z", + "iopub.status.busy": "2026-06-01T23:13:24.115256Z", + "iopub.status.idle": "2026-06-01T23:13:24.118679Z", + "shell.execute_reply": "2026-06-01T23:13:24.118166Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "samples : 400\n", + "cost range : 1122 .. 687256\n", + "time range (ms) : 0.117 .. 3085.6\n", + "Pearson r (linear) : 0.086\n", + "log-log slope : 0.794 (1.0 == perfectly proportional)\n", + "log-log correlation : 0.581\n" + ] + } + ], + "source": [ + "print(f'samples : {len(df)}')\n", + "print(f'cost range : {int(cost.min())} .. {int(cost.max())}')\n", + "print(f'time range (ms) : {time_ms.min():.3f} .. {time_ms.max():.1f}')\n", + "print(f'Pearson r (linear) : {r:.3f}')\n", + "print(f'log-log slope : {a:.3f} (1.0 == perfectly proportional)')\n", + "print(f'log-log correlation : {r_log:.3f}')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/research/cost-stats.csv b/research/cost-stats.csv new file mode 100644 index 0000000..0680edf --- /dev/null +++ b/research/cost-stats.csv @@ -0,0 +1,1305 @@ +seed,plan_cost,exec_us,rows,query +0,9220,262,3,"SELECT t1.price AS c2289 +FROM regions AS t0 RIGHT JOIN books AS t1 ON t0.id = t1.id;" +1,1122,249,55,"SELECT t0.region_id AS c3748 +FROM customers AS t0 +WHERE t0.region_id = 7;" +2,1660,248,500,"SELECT t0.region_id, t0.id +FROM customers AS t0 +ORDER BY t0.id DESC, t0.region_id DESC;" +3,1220,129,5,"SELECT t0.id +FROM departments AS t0;" +4,4325,164,0,"SELECT t0.id, SUM(t0.id) +FROM markets AS t0 +WHERE t0.id IS NULL +GROUP BY t0.id +ORDER BY t0.id DESC;" +5,649400,20979,3,"SELECT markets.region, markets.id AS c7685 +FROM markets CROSS JOIN orders CROSS JOIN departments +GROUP BY markets.region, markets.id;" +6,17610,722,4,"SELECT t1.id, t0.id AS c6716, t0.region_id +FROM customers AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id RIGHT JOIN departments AS t2 ON t0.id = t2.id +WHERE t1.department_id <= 11;" +7,3343,282,1,"SELECT departments.id +FROM departments RIGHT JOIN employees ON departments.id = employees.id +WHERE (employees.id != 33 AND employees.department_id IN (34, 74)) +GROUP BY departments.id +ORDER BY departments.id DESC;" +8,1133,1113,1,"SELECT orders.customer_id, orders.id +FROM orders +WHERE (orders.id = 117 AND orders.id > 60) +ORDER BY orders.customer_id DESC, orders.id DESC;" +9,15694,7696,2,"SELECT t0.customer_id AS c3674 +FROM orders AS t0 CROSS JOIN departments AS t1 +WHERE (t1.id >= 4 AND (t0.id IN (130) OR t1.id IS NULL));" +10,12035,296,3,"SELECT regions.id +FROM books RIGHT JOIN employees ON books.id = employees.id JOIN regions ON books.id = regions.id +WHERE books.price >= 6;" +11,9660,335,22,"SELECT employees.department_id +FROM users FULL JOIN employees ON users.id = employees.id +ORDER BY employees.department_id DESC;" +12,5070,1115,3,"SELECT orders.id AS c2380, markets.region, markets.note +FROM markets JOIN orders ON markets.id = orders.id;" +13,14600,2984,15000,"SELECT markets.region, orders.customer_id AS c8705, markets.note +FROM markets CROSS JOIN orders;" +14,1854,132,10,"SELECT t0.department_id, t0.id AS c1827 +FROM employees AS t0 +WHERE (t0.department_id BETWEEN 5 AND 34 OR t0.id NOT IN (2, 68, 69));" +15,1488,140,0,"SELECT books.price +FROM books +WHERE (books.id < 2 AND (books.price NOT BETWEEN 41 AND 55 OR books.price IN (57, 63, 77, 77)));" +16,9220,308,22,"SELECT t0.age AS c4948 +FROM users AS t0 FULL JOIN employees AS t1 ON t0.id = t1.id;" +17,52970,336,4,"SELECT users.age, COUNT(departments.id), COUNT(*) +FROM regions JOIN users ON regions.id = users.id CROSS JOIN departments +WHERE (users.age >= 52 OR regions.id IN (4, 9)) +GROUP BY users.age;" +18,1775,283,436,"SELECT customers.region_id +FROM customers +WHERE customers.region_id != 8 +ORDER BY customers.region_id ASC;" +19,8275,321,3,"SELECT t0.id +FROM books AS t0 RIGHT JOIN regions AS t1 ON t0.id = t1.id JOIN markets AS t2 ON t1.id = t2.id +WHERE t1.id < 3 +ORDER BY t0.id ASC;" +20,129600,28568,85,"SELECT orders.id AS c5497, orders.customer_id, departments.id AS c8484 +FROM departments CROSS JOIN users CROSS JOIN orders +WHERE orders.id = 82;" +21,1220,126,10,"SELECT regions.id +FROM regions;" +22,6760,146,11,"SELECT t0.department_id +FROM employees AS t0 +GROUP BY t0.department_id +ORDER BY t0.department_id ASC;" +23,14760,484,10,"SELECT customers.region_id +FROM customers LEFT JOIN books ON customers.id = books.id +GROUP BY customers.region_id +ORDER BY customers.region_id ASC;" +24,88720,418,4,"SELECT t2.id, COUNT(t0.id) AS c2763, COUNT(t2.id) +FROM regions AS t0 CROSS JOIN users AS t1 LEFT JOIN markets AS t2 ON t0.id = t2.id +GROUP BY t2.id;" +25,5410,250,3,"SELECT customers.id +FROM books JOIN customers ON books.id = customers.id +ORDER BY customers.id DESC;" +26,17660,144888,5489,"SELECT customers.region_id, employees.department_id +FROM employees FULL JOIN orders ON employees.id = orders.id FULL JOIN customers ON employees.id = customers.id +ORDER BY customers.region_id ASC;" +27,33040,11022,2,"SELECT orders.customer_id, users.id +FROM users RIGHT JOIN orders ON users.id = orders.id CROSS JOIN departments +WHERE orders.customer_id IN (34, 321) +GROUP BY orders.customer_id, users.id;" +28,6760,158,5,"SELECT departments.id AS c2364, SUM(departments.id) +FROM departments +GROUP BY departments.id +ORDER BY departments.id ASC;" +29,11870,375,5,"SELECT customers.id, markets.id +FROM customers FULL JOIN departments ON customers.id = departments.id JOIN markets ON customers.id = markets.id;" +30,178400,391,51,"SELECT markets.id AS c2619 +FROM markets CROSS JOIN users CROSS JOIN books +WHERE markets.region != 'AMERICA';" +31,2896,155,8,"SELECT t0.id, SUM(t0.id) +FROM users AS t0 +WHERE ((t0.id NOT IN (5) AND t0.age != 12) AND t0.age NOT BETWEEN 33 AND 64) +GROUP BY t0.id;" +32,6760,163,11,"SELECT t0.id AS c8338, COUNT(t0.department_id) +FROM employees AS t0 +GROUP BY t0.id +ORDER BY t0.id DESC;" +33,24825,209075,1000,"SELECT orders.customer_id, customers.region_id +FROM departments RIGHT JOIN orders ON departments.id = orders.id CROSS JOIN customers +WHERE departments.id BETWEEN 4 AND 96 +ORDER BY customers.region_id DESC;" +34,687256,33154,3,"SELECT markets.note AS c1833, markets.region, SUM(books.id) AS c1917, COUNT(*) +FROM orders CROSS JOIN markets CROSS JOIN books +WHERE ((orders.id != 89 OR markets.note IS NOT NULL) OR (markets.id IN (22) AND books.id NOT BETWEEN 1 AND 3)) +GROUP BY markets.note, markets.region +ORDER BY markets.region DESC;" +35,20060,18309,50000,"SELECT regions.id, orders.id +FROM orders CROSS JOIN regions JOIN customers ON regions.id = customers.id +ORDER BY orders.id DESC, regions.id DESC;" +36,14600,405,51,"SELECT books.price AS c7084 +FROM books CROSS JOIN users;" +37,84010,424140,1591,"SELECT orders.customer_id +FROM customers CROSS JOIN markets FULL JOIN orders ON markets.id = orders.id +WHERE orders.id < 95;" +38,18962,5240,2,"SELECT t1.department_id, COUNT(*) +FROM regions AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id RIGHT JOIN orders AS t2 ON t0.id = t2.id +WHERE ((t1.id = 3 OR t2.id = 185) OR (t1.id < 3 AND t2.id IS NULL)) +GROUP BY t1.department_id +ORDER BY t1.department_id DESC;" +39,1775,139,0,"SELECT t0.id, t0.note, t0.region +FROM markets AS t0 +WHERE t0.note IS NULL +ORDER BY t0.note DESC, t0.id DESC;" +40,65600,615,500,"SELECT t1.id, t1.region_id AS c2127 +FROM books AS t0 CROSS JOIN customers AS t1 +GROUP BY t1.id, t1.region_id;" +41,61064,491931,50000,"SELECT customers.region_id, orders.id AS c1616 +FROM orders CROSS JOIN customers +WHERE ((orders.id >= 3 OR customers.region_id != 41) OR orders.id IN (73)) +GROUP BY customers.region_id, orders.id;" +42,7635,1383,3,"SELECT t1.price, t0.region_id, t2.customer_id AS c2615 +FROM customers AS t0 JOIN books AS t1 ON t0.id = t1.id JOIN orders AS t2 ON t0.id = t2.id +WHERE t2.id IS NOT NULL;" +43,1122,162,0,"SELECT markets.region, markets.id AS c9574 +FROM markets +WHERE ((markets.note IS NOT NULL OR markets.region != 'AMERICA') AND (markets.region IN ('EUROPE', 'EUROPE') AND markets.note IN ('North, South', 'North, South')));" +44,4325,271,6,"SELECT t1.id AS c8372, t1.age AS c1550 +FROM customers AS t0 JOIN users AS t1 ON t0.id = t1.id +WHERE t1.id > 11 +ORDER BY t1.id ASC, t1.age DESC;" +45,6160,184,0,"SELECT t1.department_id AS c7018, t0.id AS c6686 +FROM regions AS t0 JOIN employees AS t1 ON t0.id = t1.id +WHERE t0.id >= 10 +GROUP BY t1.department_id, t0.id;" +46,1122,130,1,"SELECT t0.id +FROM regions AS t0 +WHERE ((t0.id IS NOT NULL AND t0.id != 5) AND (t0.id > 8 AND t0.id > 9));" +47,9133,1124,1,"SELECT employees.id +FROM customers FULL JOIN employees ON customers.id = employees.id +WHERE employees.department_id = 32 +ORDER BY employees.id ASC;" +48,22320,125403,11,"SELECT customers.region_id AS c1201, SUM(customers.region_id) +FROM orders FULL JOIN customers ON orders.id = customers.id FULL JOIN markets ON orders.id = markets.id +GROUP BY customers.region_id;" +49,1133,1222,0,"SELECT orders.customer_id, orders.id +FROM orders +WHERE ((orders.id = 130 OR orders.id <= 96) AND orders.id = 97) +ORDER BY orders.customer_id DESC, orders.id DESC;" +50,9610,263,3,"SELECT markets.id +FROM markets RIGHT JOIN departments ON markets.id = departments.id +WHERE markets.id < 45;" +51,1220,127,5,"SELECT t0.id +FROM departments AS t0;" +52,50320,5259,5000,"SELECT orders.id, COUNT(*) +FROM books CROSS JOIN orders +WHERE books.price NOT IN (45, 55, 55, 66) +GROUP BY orders.id;" +53,26360,331,3,"SELECT users.id, COUNT(employees.id) AS c5902 +FROM employees CROSS JOIN users JOIN markets ON users.id = markets.id +GROUP BY users.id +ORDER BY users.id ASC;" +54,4990,179,15,"SELECT users.age, users.id AS c207, SUM(users.id) +FROM users +WHERE (users.age > 27 OR (users.age <= 123 AND users.age > 1)) +GROUP BY users.age, users.id +ORDER BY users.id ASC;" +55,1220,121,11,"SELECT t0.department_id AS c7732 +FROM employees AS t0;" +56,433400,17179,3,"SELECT books.id, SUM(orders.customer_id) AS c2549, COUNT(*) AS c1539 +FROM books CROSS JOIN employees CROSS JOIN orders +WHERE employees.id < 68 +GROUP BY books.id;" +57,1732,1401,3001,"SELECT orders.id AS c9114 +FROM orders +WHERE (orders.customer_id > 214 OR orders.customer_id BETWEEN 75 AND 84);" +58,86560,1627742,110,"SELECT employees.department_id, customers.region_id, COUNT(*), SUM(orders.customer_id) +FROM employees CROSS JOIN customers FULL JOIN orders ON customers.id = orders.id +WHERE customers.id <= 149 +GROUP BY employees.department_id, customers.region_id;" +59,6320,383,500,"SELECT customers.id, customers.region_id, COUNT(customers.region_id) +FROM customers +GROUP BY customers.id, customers.region_id;" +60,9660,265,17,"SELECT t0.id +FROM markets AS t0 FULL JOIN users AS t1 ON t0.id = t1.id +ORDER BY t0.id ASC;" +61,13485,287,5,"SELECT t1.region, t1.id, t1.note +FROM departments AS t0 CROSS JOIN markets AS t1 +WHERE ((t0.id < 43 OR t0.id >= 2) AND t1.note = 'Old World') +ORDER BY t1.note DESC, t1.id ASC, t1.region ASC;" +62,11870,487,3,"SELECT departments.id AS c7718 +FROM departments LEFT JOIN books ON departments.id = books.id JOIN customers ON departments.id = customers.id;" +63,14760,300,11,"SELECT users.age AS c5029, COUNT(*) AS c2096, SUM(users.age) +FROM users FULL JOIN departments ON users.id = departments.id +GROUP BY users.age +ORDER BY users.age ASC;" +64,9660,116327,500,"SELECT customers.region_id AS c6827, orders.id, customers.id +FROM customers LEFT JOIN orders ON customers.id = orders.id +ORDER BY customers.region_id ASC, customers.id ASC, orders.id DESC;" +65,9632,280,0,"SELECT markets.id AS c8656, markets.region +FROM markets FULL JOIN departments ON markets.id = departments.id +WHERE departments.id = 60 +GROUP BY markets.id, markets.region;" +66,1632,162,1,"SELECT markets.note AS c5953, SUM(markets.id), SUM(markets.id) +FROM markets +WHERE markets.region = 'EUROPE' +GROUP BY markets.note;" +67,1220,236,500,"SELECT customers.region_id, customers.id AS c651 +FROM customers;" +68,17660,9937,5001,"SELECT users.id, regions.id AS c5610, orders.id AS c9918 +FROM users FULL JOIN orders ON users.id = orders.id FULL JOIN regions ON orders.id = regions.id +ORDER BY users.id DESC;" +69,32064,2232,2380,"SELECT t0.id AS c499 +FROM books AS t0 FULL JOIN users AS t1 ON t0.id = t1.id CROSS JOIN customers AS t2 +WHERE ((t0.price > 66 OR t0.price NOT IN (55, 77, 93)) OR t2.id < 93) +ORDER BY t0.id ASC;" +70,2308,161,1,"SELECT markets.region, markets.note +FROM markets +WHERE markets.id IN (2, 31) +GROUP BY markets.region, markets.note +ORDER BY markets.note ASC, markets.region ASC;" +71,65600,379,5,"SELECT departments.id AS c3175, SUM(departments.id), COUNT(books.price) +FROM books CROSS JOIN departments +GROUP BY departments.id;" +72,1775,166,4,"SELECT t0.id +FROM departments AS t0 +WHERE t0.id >= 2 +ORDER BY t0.id ASC;" +73,2462,320,0,"SELECT customers.id, customers.region_id +FROM customers JOIN markets ON customers.id = markets.id +WHERE ((markets.id = 3 AND markets.region != 'AMERICA') AND (customers.id < 17 AND customers.region_id IS NULL));" +74,10610,621,3,"SELECT books.id AS c9785 +FROM customers RIGHT JOIN departments ON customers.id = departments.id RIGHT JOIN books ON departments.id = books.id +WHERE books.price >= 55;" +75,14600,296,110,"SELECT t1.department_id AS c5193, t1.id +FROM regions AS t0 CROSS JOIN employees AS t1;" +76,10098,282,17,"SELECT t0.id AS c3431 +FROM users AS t0 LEFT JOIN employees AS t1 ON t0.id = t1.id +WHERE ((t1.id != 67 AND t0.id IS NOT NULL) OR (t0.age NOT BETWEEN 33 AND 123 OR t0.age >= 22));" +77,15560,209476,0,"SELECT t1.region_id AS c1177, t0.id AS c3278, COUNT(*), COUNT(t0.id) AS c3474 +FROM orders AS t0 CROSS JOIN customers AS t1 +WHERE (t0.id = 64 AND t0.customer_id > 397) +GROUP BY t1.region_id, t0.id;" +78,1220,243,500,"SELECT t0.id, t0.region_id +FROM customers AS t0;" +79,1366,146,2,"SELECT t0.id AS c7963, t0.age AS c5437 +FROM users AS t0 +WHERE t0.age IN (5, 11, 63);" +80,18500,267,30,"SELECT regions.id AS c2137 +FROM regions CROSS JOIN markets +WHERE markets.note != 'AMERICA';" +81,14110,329,5,"SELECT markets.region AS c3832, users.age, departments.id +FROM users LEFT JOIN departments ON users.id = departments.id RIGHT JOIN markets ON users.id = markets.id +WHERE departments.id <= 5;" +82,1133,138,0,"SELECT users.age AS c4898, users.id AS c4014 +FROM users +WHERE ((users.id = 4 OR users.id BETWEEN 11 AND 16) AND (users.age = 22 AND users.id <= 57)) +ORDER BY users.age DESC;" +83,6110,265,6,"SELECT t0.age AS c499, t1.price AS c7644, t0.id +FROM users AS t0 LEFT JOIN books AS t1 ON t0.id = t1.id +WHERE t0.id < 7;" +84,18098,330,3,"SELECT books.price, regions.id, markets.id AS c8988 +FROM markets LEFT JOIN regions ON markets.id = regions.id LEFT JOIN books ON regions.id = books.id +WHERE ((books.price BETWEEN 6 AND 77 OR markets.id NOT IN (1, 2, 2, 3)) OR books.price NOT IN (55));" +85,6320,334,500,"SELECT t0.id, t0.region_id +FROM customers AS t0 +GROUP BY t0.id, t0.region_id;" +86,1220,1184,5000,"SELECT orders.customer_id, orders.id +FROM orders;" +87,6320,141,11,"SELECT employees.department_id, employees.id AS c6314 +FROM employees +GROUP BY employees.department_id, employees.id;" +88,14760,297,5,"SELECT departments.id AS c2839, COUNT(*) +FROM employees RIGHT JOIN departments ON employees.id = departments.id +GROUP BY departments.id +ORDER BY departments.id DESC;" +89,6320,148,3,"SELECT t0.region AS c5477, t0.id AS c2794, COUNT(*) AS c9570 +FROM markets AS t0 +GROUP BY t0.region, t0.id;" +90,4160,436,500,"SELECT customers.region_id, customers.id AS c6247, COUNT(*) AS c8943 +FROM customers +WHERE customers.region_id <= 40 +GROUP BY customers.region_id, customers.id;" +91,1220,142,5,"SELECT departments.id +FROM departments;" +92,73300,291,3,"SELECT markets.region, markets.id +FROM markets CROSS JOIN regions +GROUP BY markets.region, markets.id +ORDER BY markets.region ASC;" +93,4970,933,3,"SELECT t0.id, t1.id AS c1156, t0.customer_id +FROM orders AS t0 JOIN books AS t1 ON t0.id = t1.id;" +94,27722,329,0,"SELECT t1.price +FROM departments AS t0 CROSS JOIN books AS t1 RIGHT JOIN users AS t2 ON t1.id = t2.id +WHERE (t2.id BETWEEN 45 AND 86 AND t1.id IN (2));" +95,20644,350,0,"SELECT t2.id +FROM departments AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id CROSS JOIN books AS t2 +WHERE ((t1.department_id IN (1, 5, 12, 34) AND t1.id IN (69)) AND t1.department_id <= 1);" +96,10070,948,16,"SELECT t1.id AS c5555, t0.id +FROM orders AS t0 JOIN users AS t1 ON t0.id = t1.id +GROUP BY t1.id, t0.id;" +97,1643,163,0,"SELECT regions.id, SUM(regions.id), COUNT(regions.id) AS c9550 +FROM regions +WHERE ((regions.id < 2 AND regions.id = 32) AND (regions.id <= 2 AND regions.id != 3)) +GROUP BY regions.id +ORDER BY regions.id ASC;" +98,12888,254,0,"SELECT books.id +FROM books CROSS JOIN departments +WHERE ((books.price IS NULL AND departments.id = 1) AND (books.price IS NULL OR books.price NOT IN (55, 77)));" +99,3212,258,0,"SELECT customers.id, SUM(customers.id), COUNT(customers.id) AS c8662 +FROM regions JOIN customers ON regions.id = customers.id +WHERE customers.region_id = 63 +GROUP BY customers.id;" +100,1660,132,17,"SELECT users.age, users.id AS c7470 +FROM users +ORDER BY users.id ASC;" +101,26500,369,50,"SELECT markets.id, departments.id AS c1258, markets.region +FROM employees LEFT JOIN markets ON employees.id = markets.id CROSS JOIN departments +WHERE ((markets.id IN (3) AND markets.region = 'EUROPE') OR employees.id < 69);" +102,1220,1173,5000,"SELECT t0.customer_id, t0.id AS c8685 +FROM orders AS t0;" +103,17410,1080,11,"SELECT users.age AS c405 +FROM users JOIN orders ON users.id = orders.id RIGHT JOIN regions ON orders.id = regions.id +GROUP BY users.age +ORDER BY users.age DESC;" +104,1122,135,0,"SELECT employees.id +FROM employees +WHERE employees.id = 14;" +105,22320,2174,3,"SELECT markets.id, orders.customer_id, COUNT(*) +FROM orders RIGHT JOIN markets ON orders.id = markets.id LEFT JOIN books ON markets.id = books.id +GROUP BY markets.id, orders.customer_id;" +106,21032,333,25,"SELECT books.price AS c2329, markets.note +FROM users CROSS JOIN books JOIN markets ON books.id = markets.id +WHERE (markets.region = 'EUROPE' OR (users.age NOT IN (5, 22, 64, 123) AND users.age < 22)) +ORDER BY markets.note ASC;" +107,1643,160,1,"SELECT regions.id +FROM regions +WHERE regions.id IN (5) +GROUP BY regions.id +ORDER BY regions.id ASC;" +108,1775,242,51,"SELECT customers.id AS c9166, customers.region_id +FROM customers +WHERE customers.id < 52 +ORDER BY customers.region_id ASC, customers.id DESC;" +109,9220,297,22,"SELECT employees.id, users.age, employees.department_id +FROM employees FULL JOIN users ON employees.id = users.id;" +110,9632,285,0,"SELECT employees.department_id, employees.id, COUNT(users.id) AS c6401, COUNT(*) +FROM employees FULL JOIN users ON employees.id = users.id +WHERE ((employees.department_id = 31 AND employees.department_id NOT IN (6, 59, 72)) AND (employees.department_id NOT IN (1, 5, 6, 32) AND users.id = 5)) +GROUP BY employees.department_id, employees.id;" +111,1122,2084,28,"SELECT orders.customer_id, orders.id +FROM orders +WHERE ((orders.id IN (97, 99, 184, 198) OR orders.id BETWEEN 31 AND 58) AND (orders.customer_id IS NOT NULL AND orders.id BETWEEN 3 AND 65));" +112,11660,2819,2,"SELECT markets.id, markets.note, COUNT(orders.id), SUM(orders.customer_id) +FROM markets RIGHT JOIN orders ON markets.id = orders.id +WHERE ((orders.id < 188 AND orders.customer_id >= 193) OR (orders.customer_id BETWEEN 239 AND 436 OR markets.id = 1)) +GROUP BY markets.id, markets.note +ORDER BY markets.id DESC, markets.note DESC;" +113,1610,157,1,"SELECT t0.region, t0.note AS c6295 +FROM markets AS t0 +WHERE t0.id < 2;" +114,1220,218,500,"SELECT t0.id +FROM customers AS t0;" +115,9220,276,10,"SELECT t1.id AS c2831, t0.id, t0.department_id +FROM employees AS t0 RIGHT JOIN regions AS t1 ON t0.id = t1.id;" +116,8825,1378,3,"SELECT t2.customer_id +FROM markets AS t0 LEFT JOIN books AS t1 ON t0.id = t1.id JOIN orders AS t2 ON t1.id = t2.id +WHERE t2.customer_id > 244 +ORDER BY t2.customer_id DESC;" +117,1610,156,1,"SELECT t0.id AS c6139 +FROM departments AS t0 +WHERE t0.id >= 5;" +118,12132,344,0,"SELECT books.id AS c118 +FROM departments RIGHT JOIN users ON departments.id = users.id FULL JOIN books ON departments.id = books.id +WHERE ((users.id > 3 AND books.price = 77) AND users.age > 443) +GROUP BY books.id;" +119,6160,979,5,"SELECT orders.id, COUNT(departments.id) +FROM departments JOIN orders ON departments.id = orders.id +WHERE departments.id < 36 +GROUP BY orders.id;" +120,12970,4674,11,"SELECT orders.customer_id, employees.department_id +FROM employees JOIN customers ON employees.id = customers.id LEFT JOIN orders ON employees.id = orders.id;" +121,1122,136,0,"SELECT employees.id +FROM employees +WHERE ((employees.id > 68 OR employees.department_id = 5) AND (employees.id BETWEEN 2 AND 4 AND employees.department_id IN (3, 5, 5)));" +122,4402,297,0,"SELECT markets.note, employees.id +FROM employees LEFT JOIN markets ON employees.id = markets.id JOIN departments ON employees.id = departments.id +WHERE ((departments.id != 36 OR markets.region IS NULL) AND departments.id = 4);" +123,1854,157,1,"SELECT t0.id, t0.note, t0.region +FROM markets AS t0 +WHERE t0.id NOT BETWEEN 2 AND 21;" +124,2833,342,1,"SELECT books.price AS c5178 +FROM books RIGHT JOIN customers ON books.id = customers.id +WHERE customers.id = 158 +ORDER BY books.price DESC;" +125,1122,153,0,"SELECT employees.id AS c9929, employees.department_id +FROM employees +WHERE (employees.department_id = 35 AND employees.department_id < 32);" +126,17632,351,0,"SELECT books.price, books.id +FROM books FULL JOIN markets ON books.id = markets.id FULL JOIN regions ON books.id = regions.id +WHERE (markets.region != 'EUROPE' AND (regions.id IS NULL AND books.price > 66)) +GROUP BY books.price, books.id;" +127,1432,254,3,"SELECT t0.region_id +FROM customers AS t0 +WHERE t0.id IN (16, 32, 137) +ORDER BY t0.region_id DESC;" +128,1288,144,2,"SELECT regions.id +FROM regions +WHERE regions.id BETWEEN 9 AND 10 +ORDER BY regions.id DESC;" +129,22760,3072,3,"SELECT orders.customer_id, COUNT(orders.id) AS c1702 +FROM markets LEFT JOIN books ON markets.id = books.id LEFT JOIN orders ON markets.id = orders.id +GROUP BY orders.customer_id +ORDER BY orders.customer_id DESC;" +130,11770,2770,5,"SELECT regions.id, orders.id AS c452, orders.customer_id +FROM regions JOIN departments ON regions.id = departments.id LEFT JOIN orders ON departments.id = orders.id;" +131,14320,7939,11,"SELECT regions.id AS c9060, COUNT(orders.customer_id), COUNT(orders.id) +FROM orders FULL JOIN regions ON orders.id = regions.id +GROUP BY regions.id;" +132,3666,248,0,"SELECT t0.id AS c4692, t1.department_id AS c2149, t1.id +FROM departments AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id +WHERE (t1.id IN (32, 66, 68, 80) AND (t1.id <= 26 OR t1.department_id <= 5));" +133,4970,923,11,"SELECT t1.id AS c8251, t0.customer_id, t0.id AS c2451 +FROM orders AS t0 JOIN employees AS t1 ON t0.id = t1.id;" +134,22300,15759,55000,"SELECT orders.customer_id AS c6356 +FROM employees CROSS JOIN orders +ORDER BY orders.customer_id ASC;" +135,17660,12773,11,"SELECT users.id, regions.id AS c3154 +FROM orders FULL JOIN users ON orders.id = users.id RIGHT JOIN regions ON orders.id = regions.id +ORDER BY users.id DESC, regions.id DESC;" +136,89160,385,4,"SELECT books.price +FROM regions CROSS JOIN markets LEFT JOIN books ON regions.id = books.id +GROUP BY books.price +ORDER BY books.price DESC;" +137,6320,148,5,"SELECT departments.id, SUM(departments.id), COUNT(*) AS c6998 +FROM departments +GROUP BY departments.id;" +138,6320,136,10,"SELECT regions.id AS c7258 +FROM regions +GROUP BY regions.id;" +139,4160,154,2,"SELECT markets.id +FROM markets +WHERE ((markets.note = 'Old World' OR markets.id >= 2) OR (markets.region = 'AMERICA' AND markets.region = 'EUROPE')) +GROUP BY markets.id;" +140,1610,151,3,"SELECT t0.price, t0.id AS c3105 +FROM books AS t0 +WHERE t0.id IS NOT NULL;" +141,20160,323,0,"SELECT t1.id AS c8895, t1.note AS c8854, SUM(t1.id) +FROM books AS t0 RIGHT JOIN markets AS t1 ON t0.id = t1.id RIGHT JOIN employees AS t2 ON t0.id = t2.id +WHERE t1.id > 24 +GROUP BY t1.id, t1.note;" +142,12970,374,17,"SELECT t1.region_id AS c1630 +FROM users AS t0 JOIN customers AS t1 ON t0.id = t1.id LEFT JOIN employees AS t2 ON t0.id = t2.id;" +143,1610,159,1,"SELECT t0.id, t0.price +FROM books AS t0 +WHERE t0.price <= 55;" +144,44000,291,10,"SELECT t1.department_id AS c3966, SUM(t1.id) AS c1392, COUNT(*) AS c3892 +FROM books AS t0 CROSS JOIN employees AS t1 +WHERE t1.department_id != 12 +GROUP BY t1.department_id;" +145,6122,270,2,"SELECT t1.department_id AS c6376, t1.id AS c3596 +FROM books AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id +WHERE ((t0.id < 1 OR t1.id IS NOT NULL) AND (t0.id != 3 AND t1.department_id <= 35));" +146,6760,157,5,"SELECT departments.id +FROM departments +GROUP BY departments.id +ORDER BY departments.id DESC;" +147,22600,351,110,"SELECT regions.id +FROM departments FULL JOIN employees ON departments.id = employees.id CROSS JOIN regions;" +148,18500,255,20,"SELECT books.id, books.price AS c5525, regions.id +FROM books CROSS JOIN regions +WHERE books.price < 69;" +149,1775,283,422,"SELECT t0.region_id AS c6318 +FROM customers AS t0 +WHERE t0.id > 78 +ORDER BY t0.region_id DESC;" +150,31360,1154,1040,"SELECT t0.id, t1.id AS c921 +FROM regions AS t0 CROSS JOIN customers AS t1 +WHERE t1.region_id IN (5, 9, 51) +GROUP BY t0.id, t1.id;" +151,11688,6072,0,"SELECT regions.id, users.age, orders.id +FROM regions RIGHT JOIN users ON regions.id = users.id FULL JOIN orders ON users.id = orders.id +WHERE ((orders.customer_id BETWEEN 283 AND 359 AND users.id IN (10, 17, 17, 39)) OR (users.id IN (14, 16, 64, 75) AND users.id >= 81)) +ORDER BY orders.id ASC, regions.id DESC, users.age ASC;" +152,22300,262,55,"SELECT departments.id, employees.department_id, employees.id +FROM departments CROSS JOIN employees +ORDER BY employees.id ASC;" +153,14600,1238,1500,"SELECT t1.id, t0.region_id, t0.id +FROM customers AS t0 CROSS JOIN books AS t1;" +154,5655,182,10,"SELECT regions.id, SUM(regions.id) AS c8443 +FROM regions +WHERE (regions.id >= 1 OR (regions.id >= 8 OR regions.id = 5)) +GROUP BY regions.id +ORDER BY regions.id ASC;" +155,18170,1267,2,"SELECT markets.region +FROM markets LEFT JOIN orders ON markets.id = orders.id JOIN employees ON markets.id = employees.id +GROUP BY markets.region;" +156,9220,3877,10,"SELECT t1.customer_id, t1.id AS c2544, t0.id AS c7170 +FROM regions AS t0 LEFT JOIN orders AS t1 ON t0.id = t1.id;" +157,26500,1949,4840,"SELECT t1.region_id, t2.id +FROM users AS t0 RIGHT JOIN customers AS t1 ON t0.id = t1.id CROSS JOIN regions AS t2 +WHERE t0.id IS NULL;" +158,12260,290,0,"SELECT t1.id, t0.id AS c3888, t2.id AS c5161 +FROM employees AS t0 FULL JOIN regions AS t1 ON t0.id = t1.id JOIN books AS t2 ON t0.id = t2.id +WHERE t0.id IS NULL;" +159,14056,2677,3,"SELECT orders.customer_id AS c3770, departments.id AS c4567, COUNT(*) AS c2771 +FROM departments LEFT JOIN orders ON departments.id = orders.id +WHERE ((orders.customer_id NOT BETWEEN 115 AND 380 OR departments.id IS NULL) OR orders.id = 174) +GROUP BY orders.customer_id, departments.id;" +160,1220,1139,5000,"SELECT t0.id, t0.customer_id AS c1926 +FROM orders AS t0;" +161,19820,395,33,"SELECT t1.region AS c961, t2.id +FROM employees AS t0 CROSS JOIN markets AS t1 JOIN customers AS t2 ON t1.id = t2.id;" +162,1366,155,3,"SELECT employees.id, employees.department_id +FROM employees +WHERE ((employees.id NOT IN (5, 67, 68) AND employees.department_id = 28) OR (employees.id > 4 AND employees.department_id > 32));" +163,56060,1836,2,"SELECT markets.region, COUNT(*), COUNT(*) +FROM users CROSS JOIN markets LEFT JOIN customers ON markets.id = customers.id +WHERE users.age >= 1 +GROUP BY markets.region;" +164,1775,171,3,"SELECT markets.id, markets.note AS c529, markets.region +FROM markets +WHERE (markets.region = 'EUROPE' OR markets.id <= 91) +ORDER BY markets.region ASC, markets.id ASC, markets.note ASC;" +165,6320,155,3,"SELECT books.id, COUNT(books.price), SUM(books.id) +FROM books +GROUP BY books.id;" +166,2328,151,9,"SELECT regions.id +FROM regions +WHERE regions.id NOT IN (2, 95) +ORDER BY regions.id ASC;" +167,1122,265,0,"SELECT t0.id +FROM customers AS t0 +WHERE ((t0.region_id > 32 AND t0.id = 78) AND (t0.id >= 20 OR t0.id IS NULL));" +168,31392,1276,1,"SELECT t1.region AS c6909, t1.note, SUM(t2.id) +FROM customers AS t0 LEFT JOIN markets AS t1 ON t0.id = t1.id CROSS JOIN departments AS t2 +WHERE ((t0.id = 89 OR t1.id BETWEEN 49 AND 71) AND t0.id IS NOT NULL) +GROUP BY t1.region, t1.note +ORDER BY t1.region ASC;" +169,12160,272,2,"SELECT departments.id +FROM departments RIGHT JOIN markets ON departments.id = markets.id +WHERE departments.id != 1 +GROUP BY departments.id;" +170,14760,2948,505,"SELECT t1.id AS c5737, t0.customer_id +FROM orders AS t0 LEFT JOIN departments AS t1 ON t0.id = t1.id +GROUP BY t1.id, t0.customer_id +ORDER BY t0.customer_id DESC, t1.id ASC;" +171,83620,525,110,"SELECT employees.id AS c3715 +FROM regions CROSS JOIN employees FULL JOIN books ON regions.id = books.id;" +172,9775,683,0,"SELECT t0.id, t0.department_id AS c3676, t1.region_id +FROM employees AS t0 FULL JOIN customers AS t1 ON t0.id = t1.id +WHERE t0.department_id >= 57 +ORDER BY t0.id DESC, t1.region_id ASC;" +173,22300,20218,85000,"SELECT orders.customer_id +FROM users CROSS JOIN orders +ORDER BY orders.customer_id DESC;" +174,1432,152,3,"SELECT users.id, users.age +FROM users +WHERE users.id IN (3, 11, 12) +ORDER BY users.age DESC, users.id DESC;" +175,2822,250,1,"SELECT books.id, employees.id, employees.department_id +FROM employees RIGHT JOIN books ON employees.id = books.id +WHERE books.price = 77;" +176,4160,169,2,"SELECT books.price, SUM(books.price) AS c5357 +FROM books +WHERE books.price >= 66 +GROUP BY books.price;" +177,2085,150,14,"SELECT users.id, users.age AS c4847 +FROM users +WHERE (users.age < 16 OR users.age < 71) +ORDER BY users.id ASC, users.age DESC;" +178,1660,133,17,"SELECT users.age AS c7380, users.id +FROM users +ORDER BY users.id DESC;" +179,6688,415,500,"SELECT customers.id, customers.region_id +FROM customers +WHERE ((customers.id != 141 OR customers.region_id != 10) OR (customers.id NOT IN (197) OR customers.region_id IN (8))) +GROUP BY customers.id, customers.region_id;" +180,6760,402,500,"SELECT customers.id AS c95, customers.region_id +FROM customers +GROUP BY customers.id, customers.region_id +ORDER BY customers.region_id DESC;" +181,649400,2991,500,"SELECT customers.region_id AS c397, customers.id AS c5003, SUM(books.id) +FROM customers CROSS JOIN books CROSS JOIN departments +GROUP BY customers.region_id, customers.id;" +182,10070,1156,500,"SELECT t1.id AS c6022, t0.id +FROM orders AS t0 JOIN customers AS t1 ON t0.id = t1.id +GROUP BY t1.id, t0.id;" +183,1632,229,1,"SELECT customers.id, COUNT(customers.id), COUNT(customers.id) +FROM customers +WHERE customers.id = 24 +GROUP BY customers.id;" +184,18500,494,500,"SELECT books.price AS c5308, customers.region_id AS c1694 +FROM books CROSS JOIN customers +WHERE books.price <= 55;" +185,1220,134,5,"SELECT t0.id AS c9527 +FROM departments AS t0;" +186,9220,284,18,"SELECT users.id +FROM regions FULL JOIN users ON regions.id = users.id;" +187,9930,306,8,"SELECT users.id, regions.id, users.age AS c4755 +FROM users LEFT JOIN regions ON users.id = regions.id +WHERE ((regions.id != 31 AND users.age <= 64) OR users.id >= 66) +ORDER BY users.age DESC;" +188,27984,334,0,"SELECT t2.price AS c5573, COUNT(*) +FROM employees AS t0 RIGHT JOIN departments AS t1 ON t0.id = t1.id CROSS JOIN books AS t2 +WHERE (t2.price > 55 AND t0.id BETWEEN 66 AND 69) +GROUP BY t2.price;" +189,2292,319,0,"SELECT markets.id AS c7660 +FROM markets JOIN customers ON markets.id = customers.id +WHERE (markets.note IS NULL AND (customers.id = 198 AND markets.region IN ('AMERICA', 'EUROPE')));" +190,6760,164,5,"SELECT departments.id +FROM departments +GROUP BY departments.id +ORDER BY departments.id DESC;" +191,49850,319,55,"SELECT departments.id, employees.id +FROM departments JOIN regions ON departments.id = regions.id CROSS JOIN employees +WHERE employees.id IS NOT NULL +GROUP BY departments.id, employees.id +ORDER BY departments.id DESC, employees.id ASC;" +192,13620,267,3,"SELECT books.id, markets.region +FROM markets CROSS JOIN books +WHERE markets.region = 'EUROPE';" +193,11870,661,10,"SELECT customers.region_id, regions.id +FROM users JOIN regions ON users.id = regions.id LEFT JOIN customers ON regions.id = customers.id;" +194,225614,8292,25000,"SELECT t2.id +FROM customers AS t0 CROSS JOIN markets AS t1 CROSS JOIN users AS t2 +WHERE ((t1.id IN (3) OR t2.age != 1) OR (t1.id != 2 OR t2.id > 61));" +195,17220,7112,10,"SELECT t0.id +FROM regions AS t0 FULL JOIN books AS t1 ON t0.id = t1.id LEFT JOIN orders AS t2 ON t1.id = t2.id;" +196,68250,595,30,"SELECT customers.region_id, books.id, SUM(regions.id), SUM(customers.region_id) +FROM books JOIN regions ON books.id = regions.id CROSS JOIN customers +GROUP BY customers.region_id, books.id;" +197,7720,252,3,"SELECT t0.price AS c8028, t2.id +FROM books AS t0 JOIN customers AS t1 ON t0.id = t1.id JOIN regions AS t2 ON t1.id = t2.id;" +198,1220,134,17,"SELECT t0.age AS c2991 +FROM users AS t0;" +199,18134,295,30,"SELECT t1.id, t0.id +FROM regions AS t0 CROSS JOIN users AS t1 +WHERE (t1.age BETWEEN 33 AND 33 OR t1.id IN (12, 49, 95));" +200,2085,145,6,"SELECT employees.department_id +FROM employees +WHERE ((employees.id > 66 OR employees.id IN (33)) OR employees.department_id IN (6, 12, 12, 50)) +ORDER BY employees.department_id DESC;" +201,1220,1137,5000,"SELECT orders.customer_id, orders.id +FROM orders;" +202,4666,206,6,"SELECT employees.id AS c3304, users.id AS c2088 +FROM users JOIN employees ON users.id = employees.id +WHERE ((users.age IN (42, 64) OR users.id < 16) AND users.id != 99) +GROUP BY employees.id, users.id;" +203,6320,150,3,"SELECT books.id, books.price +FROM books +GROUP BY books.id, books.price;" +204,10170,272,3,"SELECT t0.id AS c8592, t1.region +FROM customers AS t0 JOIN markets AS t1 ON t0.id = t1.id +GROUP BY t0.id, t1.region;" +205,13664,12314,1,"SELECT t1.id AS c8725, SUM(t1.id), COUNT(*) +FROM users AS t0 CROSS JOIN orders AS t1 +WHERE (t0.age IN (33, 64) AND t1.id = 156) +GROUP BY t1.id;" +206,1220,250,500,"SELECT t0.id AS c4257 +FROM customers AS t0;" +207,20110,4160,0,"SELECT t1.customer_id, t0.department_id +FROM employees AS t0 CROSS JOIN orders AS t1 JOIN customers AS t2 ON t1.id = t2.id +WHERE t0.department_id IS NULL;" +208,9220,264,3,"SELECT books.price AS c3720, employees.department_id, books.id +FROM employees RIGHT JOIN books ON employees.id = books.id;" +209,11210,449,2,"SELECT t2.region, t1.id, COUNT(t2.id) +FROM books AS t0 JOIN customers AS t1 ON t0.id = t1.id FULL JOIN markets AS t2 ON t0.id = t2.id +WHERE t0.id < 1 +GROUP BY t2.region, t1.id;" +210,14600,3063,15000,"SELECT orders.id, books.id AS c7933, orders.customer_id +FROM books CROSS JOIN orders;" +211,84010,3085577,0,"SELECT t2.region_id, t2.id, t0.customer_id +FROM orders AS t0 CROSS JOIN users AS t1 LEFT JOIN customers AS t2 ON t1.id = t2.id +WHERE t2.region_id >= 23;" +212,14320,2182,3,"SELECT orders.id +FROM books LEFT JOIN orders ON books.id = orders.id +GROUP BY orders.id;" +213,1620,141,2,"SELECT t0.id +FROM regions AS t0 +WHERE t0.id IN (4, 4, 9, 88) +ORDER BY t0.id ASC;" +214,178400,91814,247665,"SELECT orders.id, orders.customer_id, employees.id +FROM orders CROSS JOIN employees CROSS JOIN departments +WHERE orders.customer_id >= 52;" +215,1610,966,29,"SELECT t0.customer_id +FROM orders AS t0 +WHERE t0.id < 30;" +216,1488,142,3,"SELECT departments.id +FROM departments +WHERE departments.id IN (1, 3, 4, 60);" +217,12160,683,3,"SELECT t0.id, t1.region_id +FROM employees AS t0 LEFT JOIN customers AS t1 ON t0.id = t1.id +WHERE t1.region_id >= 8 +GROUP BY t0.id, t1.region_id;" +218,9220,241,5,"SELECT books.price AS c5412, books.id, departments.id AS c9618 +FROM departments LEFT JOIN books ON departments.id = books.id;" +219,178400,23808,45000,"SELECT t1.note, t0.customer_id AS c4395 +FROM orders AS t0 CROSS JOIN markets AS t1 CROSS JOIN books AS t2 +WHERE t1.region IS NOT NULL;" +220,83832,1508595,45001,"SELECT customers.region_id, orders.id, employees.id +FROM employees CROSS JOIN orders RIGHT JOIN customers ON employees.id = customers.id +WHERE (customers.id IN (181) OR employees.id BETWEEN 3 AND 92) +ORDER BY customers.region_id ASC;" +221,1133,144,0,"SELECT t0.id AS c9777 +FROM regions AS t0 +WHERE ((t0.id <= 5 OR t0.id IN (4, 12)) AND (t0.id = 9 AND t0.id NOT IN (2, 4, 5, 89))) +ORDER BY t0.id ASC;" +222,6056,174,11,"SELECT t0.id, t0.department_id AS c2878, COUNT(t0.id), COUNT(*) AS c2633 +FROM employees AS t0 +WHERE ((t0.department_id IS NOT NULL OR t0.department_id IS NOT NULL) OR (t0.id > 26 OR t0.id = 52)) +GROUP BY t0.id, t0.department_id;" +223,14600,13517,55000,"SELECT t0.id +FROM orders AS t0 CROSS JOIN employees AS t1;" +224,1220,136,11,"SELECT employees.department_id +FROM employees;" +225,12970,4690,11,"SELECT orders.customer_id, customers.id AS c4775, employees.department_id AS c6986 +FROM employees LEFT JOIN customers ON employees.id = customers.id JOIN orders ON employees.id = orders.id;" +226,9854,279,3,"SELECT books.id, regions.id, books.price AS c1520 +FROM books LEFT JOIN regions ON books.id = regions.id +WHERE ((regions.id < 10 OR books.price = 77) OR books.price > 60);" +227,9122,5911,9,"SELECT orders.id, books.price, orders.customer_id AS c7568 +FROM orders FULL JOIN books ON orders.id = books.id +WHERE orders.customer_id = 63;" +228,25402,1409,1000,"SELECT t1.id AS c1443, t2.region_id AS c2237 +FROM employees AS t0 LEFT JOIN markets AS t1 ON t0.id = t1.id CROSS JOIN customers AS t2 +WHERE ((t1.id < 3 OR t1.region = 'AMERICA') AND (t2.region_id IS NULL OR t1.note != 'North, South'));" +229,26940,545,0,"SELECT departments.id +FROM departments LEFT JOIN customers ON departments.id = customers.id CROSS JOIN markets +WHERE markets.region IS NULL +ORDER BY departments.id ASC;" +230,11870,10874,5000,"SELECT departments.id, users.age AS c5139, orders.id +FROM departments JOIN users ON departments.id = users.id FULL JOIN orders ON users.id = orders.id;" +231,1220,123,5,"SELECT departments.id +FROM departments;" +232,14760,765,16,"SELECT customers.id, COUNT(*) AS c3789 +FROM customers RIGHT JOIN users ON customers.id = users.id +GROUP BY customers.id +ORDER BY customers.id DESC;" +233,22470,746,1353,"SELECT departments.id +FROM departments JOIN markets ON departments.id = markets.id CROSS JOIN customers +WHERE (customers.region_id NOT IN (10, 10) AND (customers.id <= 80 OR departments.id >= 1));" +234,9220,256,10,"SELECT t0.note +FROM markets AS t0 FULL JOIN regions AS t1 ON t0.id = t1.id;" +235,5604,255,1,"SELECT t1.region_id AS c5511 +FROM books AS t0 JOIN customers AS t1 ON t0.id = t1.id +WHERE (t0.price NOT BETWEEN 66 AND 94 OR (t0.id > 3 AND t1.region_id = 10));" +236,17660,4310,17,"SELECT t1.department_id AS c2338, t0.customer_id +FROM orders AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id RIGHT JOIN users AS t2 ON t1.id = t2.id +ORDER BY t1.department_id ASC;" +237,22600,2092,5010,"SELECT regions.id +FROM users FULL JOIN customers ON users.id = customers.id CROSS JOIN regions;" +238,6122,652,0,"SELECT customers.region_id +FROM customers LEFT JOIN employees ON customers.id = employees.id +WHERE (employees.id IN (38) AND customers.region_id != 5);" +239,4325,176,6,"SELECT employees.id +FROM employees +WHERE employees.id > 5 +GROUP BY employees.id +ORDER BY employees.id DESC;" +240,649400,379,3,"SELECT t0.price AS c6143, SUM(t2.id) +FROM books AS t0 CROSS JOIN users AS t1 CROSS JOIN departments AS t2 +GROUP BY t0.price;" +241,22600,3857,25000,"SELECT customers.id, customers.region_id, departments.id +FROM customers RIGHT JOIN departments ON customers.id = departments.id CROSS JOIN orders;" +242,13864,236,0,"SELECT books.id +FROM books CROSS JOIN employees +WHERE (employees.id != 33 AND (employees.id < 1 AND employees.department_id < 12));" +243,7730,1760,5,"SELECT employees.department_id, books.price AS c4475, books.id AS c4655 +FROM orders JOIN employees ON orders.id = employees.id RIGHT JOIN books ON employees.id = books.id +WHERE ((orders.id >= 13 AND orders.customer_id != 233) OR (orders.id > 39 AND orders.customer_id <= 359)) +ORDER BY books.id DESC;" +244,10922,333,0,"SELECT regions.id +FROM regions RIGHT JOIN employees ON regions.id = employees.id LEFT JOIN markets ON employees.id = markets.id +WHERE (regions.id NOT BETWEEN 2 AND 8 AND markets.note = 'Fast lane');" +245,1775,142,1,"SELECT markets.note, markets.region, markets.id +FROM markets +WHERE ((markets.region != 'AMERICA' AND markets.region = 'AMERICA') OR markets.id < 2) +ORDER BY markets.note DESC, markets.id ASC, markets.region DESC;" +246,1244,154,9,"SELECT regions.id AS c713 +FROM regions +WHERE regions.id BETWEEN 2 AND 45;" +247,14408,2095,3,"SELECT t0.id AS c7208 +FROM orders AS t0 RIGHT JOIN books AS t1 ON t0.id = t1.id +WHERE t0.customer_id NOT IN (244, 365) +GROUP BY t0.id +ORDER BY t0.id ASC;" +248,13424,7694,5000,"SELECT t1.id, COUNT(*), SUM(t0.id) AS c7387 +FROM users AS t0 RIGHT JOIN orders AS t1 ON t0.id = t1.id +WHERE ((t1.customer_id = 494 AND t1.customer_id != 145) OR (t0.age > 53 OR t1.customer_id IS NOT NULL)) +GROUP BY t1.id;" +249,9610,256,3,"SELECT markets.region, users.age +FROM markets LEFT JOIN users ON markets.id = users.id +WHERE users.id < 17;" +250,9220,591,10,"SELECT customers.id, customers.region_id +FROM regions LEFT JOIN customers ON regions.id = customers.id;" +251,9775,4003,42,"SELECT orders.customer_id, regions.id, orders.id AS c3346 +FROM regions FULL JOIN orders ON regions.id = orders.id +WHERE orders.id <= 42 +ORDER BY regions.id ASC;" +252,83533,19602,39,"SELECT orders.id AS c8852, orders.customer_id, books.price AS c8813 +FROM books CROSS JOIN orders FULL JOIN departments ON orders.id = departments.id +WHERE ((orders.customer_id = 163 OR orders.id IN (69, 96, 152)) AND (orders.id IS NOT NULL AND books.id IS NOT NULL)) +ORDER BY orders.id DESC;" +253,18894,439,40,"SELECT customers.id +FROM regions CROSS JOIN employees JOIN customers ON employees.id = customers.id +WHERE (employees.id IS NOT NULL AND customers.id >= 65);" +254,9220,246,11,"SELECT markets.id AS c3977, employees.department_id +FROM markets FULL JOIN employees ON markets.id = employees.id;" +255,6320,2350,5000,"SELECT orders.id, orders.customer_id AS c7286 +FROM orders +GROUP BY orders.id, orders.customer_id;" +256,31360,277,9,"SELECT t0.id, t1.id AS c9218 +FROM markets AS t0 CROSS JOIN employees AS t1 +WHERE t1.department_id IN (5, 12, 33) +GROUP BY t0.id, t1.id;" +257,8970,178,3,"SELECT t1.price, t0.id AS c7828 +FROM regions AS t0 JOIN books AS t1 ON t0.id = t1.id +GROUP BY t1.price, t0.id;" +258,18510,378,3,"SELECT customers.region_id +FROM customers JOIN users ON customers.id = users.id RIGHT JOIN markets ON customers.id = markets.id +GROUP BY customers.region_id +ORDER BY customers.region_id DESC;" +259,1660,255,500,"SELECT customers.id +FROM customers +ORDER BY customers.id ASC;" +260,1366,237,118,"SELECT customers.region_id, customers.id +FROM customers +WHERE ((customers.region_id IS NULL OR customers.id >= 198) AND customers.region_id <= 4);" +261,14760,449,3,"SELECT customers.region_id AS c4278, books.id +FROM books LEFT JOIN customers ON books.id = customers.id +GROUP BY customers.region_id, books.id +ORDER BY books.id ASC;" +262,14600,277,110,"SELECT t1.department_id AS c5186, t0.id, t1.id +FROM regions AS t0 CROSS JOIN employees AS t1;" +263,8650,330,5,"SELECT markets.id, departments.id, regions.id +FROM regions LEFT JOIN departments ON regions.id = departments.id JOIN markets ON regions.id = markets.id +WHERE markets.note != 'Old World' +ORDER BY markets.id ASC, regions.id DESC;" +264,17220,885,500,"SELECT users.id AS c6920, markets.region AS c5238, users.age AS c1104 +FROM markets RIGHT JOIN users ON markets.id = users.id RIGHT JOIN customers ON markets.id = customers.id;" +265,9220,277,17,"SELECT t1.id, t0.id AS c2693 +FROM users AS t0 LEFT JOIN regions AS t1 ON t0.id = t1.id;" +266,1133,239,1,"SELECT customers.id AS c604, customers.region_id +FROM customers +WHERE customers.id = 135 +ORDER BY customers.id DESC, customers.region_id DESC;" +267,1610,140,0,"SELECT books.id +FROM books +WHERE books.price IS NULL;" +268,649400,33743,11,"SELECT employees.department_id, SUM(orders.customer_id) +FROM orders CROSS JOIN books CROSS JOIN employees +GROUP BY employees.department_id;" +269,1220,121,3,"SELECT books.price AS c8205 +FROM books;" +270,1220,1113,5000,"SELECT orders.id AS c7922 +FROM orders;" +271,2972,1247,0,"SELECT orders.id AS c8902, departments.id AS c4368, orders.customer_id +FROM orders JOIN departments ON orders.id = departments.id +WHERE ((orders.customer_id = 483 AND departments.id > 4) AND orders.id != 24);" +272,8660,1918,2,"SELECT orders.customer_id, orders.id, COUNT(*), COUNT(*) +FROM orders RIGHT JOIN books ON orders.id = books.id +WHERE books.id != 1 +GROUP BY orders.customer_id, orders.id;" +273,6320,156,11,"SELECT t0.id, SUM(t0.department_id), COUNT(t0.department_id) +FROM employees AS t0 +GROUP BY t0.id;" +274,1244,152,2,"SELECT t0.id AS c7269, t0.age +FROM users AS t0 +WHERE t0.age IN (33, 33);" +275,1122,1452,0,"SELECT orders.id AS c6877 +FROM orders +WHERE ((orders.customer_id <= 56 OR orders.customer_id != 343) AND (orders.customer_id IN (52, 86) AND orders.customer_id >= 224));" +276,73300,1476,11,"SELECT users.age AS c7960, SUM(users.id) +FROM customers CROSS JOIN users +GROUP BY users.age +ORDER BY users.age ASC;" +277,1643,169,1,"SELECT t0.id +FROM departments AS t0 +WHERE ((t0.id IN (2, 10, 59) OR t0.id IS NULL) AND (t0.id != 5 AND t0.id IS NOT NULL)) +GROUP BY t0.id +ORDER BY t0.id DESC;" +278,9976,278,5,"SELECT t1.id AS c8751 +FROM users AS t0 RIGHT JOIN departments AS t1 ON t0.id = t1.id +WHERE (t0.age IS NULL OR (t1.id != 5 OR t1.id < 14));" +279,3432,1033,0,"SELECT regions.id, orders.id, COUNT(*), SUM(orders.customer_id) +FROM orders RIGHT JOIN regions ON orders.id = regions.id +WHERE ((regions.id IN (8, 9, 22) AND regions.id <= 6) AND orders.id IN (39, 49, 104, 106)) +GROUP BY regions.id, orders.id;" +280,9122,6240,0,"SELECT orders.customer_id +FROM users FULL JOIN orders ON users.id = orders.id +WHERE ((users.age > 33 AND orders.customer_id < 84) AND (users.id = 15 OR orders.id IN (68, 128, 128, 198)));" +281,1133,155,0,"SELECT t0.id, t0.note, t0.region +FROM markets AS t0 +WHERE ((t0.note = 'unknown' AND t0.id != 1) AND t0.id > 67) +ORDER BY t0.id ASC, t0.note DESC;" +282,84010,11429,9,"SELECT employees.id, orders.customer_id AS c5471, books.price +FROM books CROSS JOIN orders LEFT JOIN employees ON orders.id = employees.id +WHERE employees.id > 66;" +283,83533,2140,0,"SELECT books.id, markets.id, books.price AS c2723 +FROM books CROSS JOIN customers FULL JOIN markets ON customers.id = markets.id +WHERE ((markets.id > 55 AND customers.region_id NOT IN (8, 9, 10)) AND markets.region IN ('AMERICA', 'EUROPE')) +ORDER BY books.id DESC, markets.id ASC;" +284,6760,178,2,"SELECT t0.region AS c1153, SUM(t0.id) AS c3569 +FROM markets AS t0 +GROUP BY t0.region +ORDER BY t0.region ASC;" +285,20522,379,0,"SELECT employees.id AS c5654, users.id +FROM markets FULL JOIN employees ON markets.id = employees.id CROSS JOIN users +WHERE ((markets.note = 'Fast lane' AND employees.department_id NOT BETWEEN 11 AND 37) AND (users.id != 4 AND users.age <= 443));" +286,10308,882,3,"SELECT customers.id, COUNT(customers.id) AS c434, COUNT(*) +FROM customers FULL JOIN departments ON customers.id = departments.id +WHERE departments.id BETWEEN 2 AND 4 +GROUP BY customers.id +ORDER BY customers.id DESC;" +287,8435,270,0,"SELECT t2.id, t1.id, t0.id +FROM books AS t0 JOIN departments AS t1 ON t0.id = t1.id RIGHT JOIN regions AS t2 ON t1.id = t2.id +WHERE t0.id IS NULL;" +288,4122,173,1,"SELECT regions.id AS c1673 +FROM users JOIN regions ON users.id = regions.id +WHERE (users.age IN (33, 47, 98) OR users.id > 54);" +289,14060,267,3,"SELECT departments.id, books.price AS c551 +FROM departments CROSS JOIN books +WHERE departments.id = 5 +ORDER BY departments.id DESC;" +290,8660,1278,6,"SELECT t2.region, t1.department_id, t0.customer_id AS c8493 +FROM orders AS t0 JOIN employees AS t1 ON t0.id = t1.id RIGHT JOIN markets AS t2 ON t1.id = t2.id +WHERE t0.customer_id < 239;" +291,23720,4263,10,"SELECT orders.id, COUNT(orders.id), SUM(regions.id) +FROM users CROSS JOIN orders JOIN regions ON orders.id = regions.id +GROUP BY orders.id;" +292,6760,174,3,"SELECT t0.region, t0.id, SUM(t0.id), COUNT(*) +FROM markets AS t0 +GROUP BY t0.region, t0.id +ORDER BY t0.region ASC, t0.id DESC;" +293,9122,273,0,"SELECT t0.age, t1.id +FROM users AS t0 FULL JOIN books AS t1 ON t0.id = t1.id +WHERE (t0.age = 18 AND (t0.age != 123 AND t1.id <= 89));" +294,6688,160,3,"SELECT books.price AS c6589, books.id, COUNT(*) +FROM books +WHERE ((books.price > 77 OR books.id < 3) OR (books.price IN (49, 55, 55, 77) OR books.price IN (26, 66, 66, 68))) +GROUP BY books.price, books.id;" +295,8970,187,5,"SELECT users.id, SUM(users.age), COUNT(users.id) +FROM users JOIN departments ON users.id = departments.id +GROUP BY users.id;" +296,1660,154,3,"SELECT t0.region, t0.id AS c2775, t0.note +FROM markets AS t0 +ORDER BY t0.id DESC;" +297,13848,1207,22,"SELECT t2.department_id +FROM orders AS t0 JOIN users AS t1 ON t0.id = t1.id FULL JOIN employees AS t2 ON t0.id = t2.id +WHERE ((t2.id NOT BETWEEN 4 AND 6 OR t1.age != 21) OR t0.id <= 61);" +298,2098,152,4,"SELECT departments.id +FROM departments +WHERE ((departments.id NOT IN (4, 4, 5, 7) OR departments.id NOT IN (2, 5)) OR (departments.id NOT BETWEEN 3 AND 27 OR departments.id IS NULL));" +299,8375,263,0,"SELECT books.price, employees.id AS c7128 +FROM employees RIGHT JOIN books ON employees.id = books.id +WHERE ((books.id > 68 OR books.id >= 52) AND (employees.department_id < 6 OR employees.department_id != 1)) +ORDER BY books.price DESC;" +300,10775,4138,4965,"SELECT orders.id AS c9121 +FROM orders LEFT JOIN departments ON orders.id = departments.id RIGHT JOIN books ON departments.id = books.id +WHERE orders.customer_id <= 497 +ORDER BY orders.id ASC;" +301,10170,180,3,"SELECT users.id, COUNT(markets.id), SUM(markets.id) +FROM users JOIN markets ON users.id = markets.id +GROUP BY users.id;" +302,12310,647,500,"SELECT regions.id, customers.region_id, employees.id +FROM regions FULL JOIN employees ON regions.id = employees.id JOIN customers ON regions.id = customers.id +ORDER BY customers.region_id ASC, employees.id ASC, regions.id ASC;" +303,1610,151,8,"SELECT employees.id, employees.department_id +FROM employees +WHERE ((employees.id <= 33 OR employees.id = 3) OR (employees.department_id NOT IN (5) AND employees.id = 67));" +304,1220,128,11,"SELECT employees.department_id AS c3770 +FROM employees;" +305,2896,164,2,"SELECT regions.id AS c2143, COUNT(*), COUNT(*) +FROM regions +WHERE regions.id IN (3, 10, 89) +GROUP BY regions.id;" +306,8074,195,5,"SELECT employees.id, employees.department_id, SUM(employees.id), SUM(employees.department_id) AS c8963 +FROM employees JOIN departments ON employees.id = departments.id +WHERE (departments.id <= 3 OR (employees.id = 68 OR employees.id <= 6)) +GROUP BY employees.id, employees.department_id;" +307,12510,1269,3,"SELECT t0.note AS c4138 +FROM markets AS t0 JOIN regions AS t1 ON t0.id = t1.id RIGHT JOIN orders AS t2 ON t0.id = t2.id +WHERE ((t2.customer_id = 298 OR t0.region = 'AMERICA') OR t0.region != 'AMERICA') +GROUP BY t0.note;" +308,4325,166,1,"SELECT departments.id, COUNT(*) AS c9125 +FROM departments +WHERE (departments.id > 56 OR departments.id = 4) +GROUP BY departments.id +ORDER BY departments.id DESC;" +309,1632,153,1,"SELECT t0.id AS c7541, COUNT(t0.id) +FROM markets AS t0 +WHERE (t0.note != 'Fast lane' AND (t0.id = 3 OR t0.note = 'Old World')) +GROUP BY t0.id;" +310,20325,11529,7,"SELECT t1.id, t2.id, SUM(t2.id) +FROM customers AS t0 RIGHT JOIN users AS t1 ON t0.id = t1.id LEFT JOIN orders AS t2 ON t0.id = t2.id +WHERE (t2.customer_id < 243 OR (t2.id >= 38 AND t2.customer_id = 302)) +GROUP BY t1.id, t2.id +ORDER BY t2.id ASC;" +311,9432,2641,2,"SELECT t0.region, t1.customer_id AS c7183 +FROM markets AS t0 RIGHT JOIN orders AS t1 ON t0.id = t1.id +WHERE ((t0.id <= 3 OR t0.note = 'Fast lane') AND (t0.note != 'North, South' OR t0.note = 'Fast lane')) +ORDER BY t0.region ASC;" +312,11870,1089,5,"SELECT t1.id, t0.id +FROM departments AS t0 LEFT JOIN orders AS t1 ON t0.id = t1.id JOIN employees AS t2 ON t1.id = t2.id;" +313,12504,1024,2,"SELECT employees.id, orders.id AS c4699, orders.customer_id +FROM departments JOIN orders ON departments.id = orders.id LEFT JOIN employees ON departments.id = employees.id +WHERE employees.department_id NOT BETWEEN 5 AND 33;" +314,2264,168,13,"SELECT t0.age, t0.id +FROM users AS t0 +WHERE (t0.age > 5 AND t0.id != 2) +GROUP BY t0.age, t0.id;" +315,6110,185,5,"SELECT t0.id +FROM regions AS t0 JOIN departments AS t1 ON t0.id = t1.id +WHERE t1.id IS NOT NULL +GROUP BY t0.id;" +316,6320,133,3,"SELECT books.id +FROM books +GROUP BY books.id;" +317,9220,2702,5000,"SELECT orders.id AS c7996, books.price +FROM books FULL JOIN orders ON books.id = orders.id;" +318,22320,329,4,"SELECT books.price, books.id +FROM books FULL JOIN employees ON books.id = employees.id RIGHT JOIN regions ON employees.id = regions.id +GROUP BY books.price, books.id;" +319,2896,146,3,"SELECT books.id AS c9150, books.price, COUNT(books.id) +FROM books +WHERE ((books.price IN (76, 77) AND books.id BETWEEN 1 AND 96) OR books.price IN (55, 66, 77)) +GROUP BY books.id, books.price;" +320,4970,237,11,"SELECT customers.region_id, employees.id +FROM customers JOIN employees ON customers.id = employees.id;" +321,16824,934,10,"SELECT t0.id AS c6071, COUNT(*), COUNT(*) AS c7241 +FROM regions AS t0 CROSS JOIN customers AS t1 +WHERE ((t1.id >= 59 OR t0.id IS NULL) AND t1.id = 126) +GROUP BY t0.id;" +322,17220,823,500,"SELECT t1.region_id, t1.id, t2.price +FROM regions AS t0 RIGHT JOIN customers AS t1 ON t0.id = t1.id LEFT JOIN books AS t2 ON t0.id = t2.id;" +323,14275,522,3,"SELECT t0.region_id +FROM customers AS t0 RIGHT JOIN users AS t1 ON t0.id = t1.id RIGHT JOIN markets AS t2 ON t1.id = t2.id +WHERE t1.id < 14 +ORDER BY t0.region_id ASC;" +324,12160,2620,3,"SELECT orders.id AS c4187, regions.id +FROM departments JOIN orders ON departments.id = orders.id LEFT JOIN regions ON departments.id = regions.id +WHERE regions.id < 4;" +325,15822,329,4,"SELECT markets.note AS c7216, books.price +FROM departments CROSS JOIN markets LEFT JOIN books ON markets.id = books.id +WHERE ((departments.id != 2 AND markets.note = 'Old World') AND (departments.id BETWEEN 1 AND 5 OR departments.id >= 41));" +326,1220,129,17,"SELECT t0.age +FROM users AS t0;" +327,6760,156,11,"SELECT t0.age +FROM users AS t0 +GROUP BY t0.age +ORDER BY t0.age ASC;" +328,84498,84707,54310,"SELECT employees.department_id +FROM orders CROSS JOIN employees LEFT JOIN regions ON employees.id = regions.id +WHERE ((employees.id <= 25 OR orders.id NOT BETWEEN 47 AND 184) OR orders.id IS NULL);" +329,5680,195,3,"SELECT t0.id +FROM markets AS t0 JOIN users AS t1 ON t0.id = t1.id +WHERE (t0.id != 2 OR t0.id != 1) +ORDER BY t0.id ASC;" +330,11143,1166,0,"SELECT books.price, employees.id, COUNT(*) +FROM customers LEFT JOIN books ON customers.id = books.id FULL JOIN employees ON customers.id = employees.id +WHERE ((customers.id > 96 AND employees.department_id != 32) AND (employees.id > 5 OR books.id IS NOT NULL)) +GROUP BY books.price, employees.id +ORDER BY books.price DESC, employees.id DESC;" +331,83620,1674673,50490,"SELECT t2.region_id, t2.id +FROM orders AS t0 CROSS JOIN regions AS t1 FULL JOIN customers AS t2 ON t1.id = t2.id;" +332,1610,145,10,"SELECT regions.id AS c7728 +FROM regions +WHERE regions.id IS NOT NULL;" +333,22320,304862,6,"SELECT departments.id, COUNT(*) +FROM orders FULL JOIN departments ON orders.id = departments.id LEFT JOIN customers ON departments.id = customers.id +GROUP BY departments.id;" +334,1775,165,10,"SELECT regions.id AS c1480 +FROM regions +WHERE regions.id IS NOT NULL +ORDER BY regions.id DESC;" +335,1133,144,0,"SELECT books.price, books.id +FROM books +WHERE (books.price < 44 AND (books.price IN (55, 55, 66, 66) AND books.price != 77)) +ORDER BY books.price DESC, books.id DESC;" +336,1122,139,1,"SELECT t0.department_id +FROM employees AS t0 +WHERE ((t0.department_id = 12 AND t0.department_id < 35) AND (t0.id IN (4) OR t0.department_id != 31));" +337,1488,156,10,"SELECT users.id, users.age +FROM users +WHERE users.age IN (1, 21, 33, 64);" +338,1732,149,9,"SELECT t0.id +FROM regions AS t0 +WHERE ((t0.id NOT BETWEEN 1 AND 15 OR t0.id < 10) AND (t0.id <= 40 OR t0.id != 3));" +339,1610,151,1,"SELECT departments.id +FROM departments +WHERE ((departments.id <= 98 OR departments.id > 1) AND (departments.id IS NULL OR departments.id IN (3, 3, 30, 91)));" +340,12160,557,13,"SELECT customers.region_id AS c4276, markets.region AS c7002, COUNT(*), COUNT(*) AS c3968 +FROM customers LEFT JOIN markets ON customers.id = markets.id +WHERE (markets.note = 'Fast lane' OR customers.id IS NOT NULL) +GROUP BY customers.region_id, markets.region;" +341,18070,391,3,"SELECT customers.id, SUM(customers.region_id) +FROM markets JOIN users ON markets.id = users.id RIGHT JOIN customers ON users.id = customers.id +GROUP BY customers.id;" +342,1122,140,0,"SELECT employees.department_id, employees.id +FROM employees +WHERE ((employees.id >= 66 OR employees.id != 2) AND (employees.id > 69 AND employees.department_id = 33));" +343,1660,145,17,"SELECT users.age +FROM users +ORDER BY users.age ASC;" +344,6110,278,3,"SELECT t1.price AS c605, t0.id AS c1651, t1.id +FROM regions AS t0 RIGHT JOIN books AS t1 ON t0.id = t1.id +WHERE (t1.id != 3 OR t1.id = 3);" +345,9660,441,3,"SELECT books.id, customers.id AS c5626 +FROM books LEFT JOIN customers ON books.id = customers.id +ORDER BY customers.id DESC;" +346,84032,439,0,"SELECT t1.region, t2.id, COUNT(t1.id) +FROM employees AS t0 CROSS JOIN markets AS t1 FULL JOIN regions AS t2 ON t1.id = t2.id +WHERE t0.department_id = 36 +GROUP BY t1.region, t2.id;" +347,9610,5218,0,"SELECT users.age AS c6973 +FROM users RIGHT JOIN orders ON users.id = orders.id +WHERE users.id > 94;" +348,19356,1399,1187,"SELECT t1.id, t0.id +FROM departments AS t0 CROSS JOIN customers AS t1 +WHERE ((t1.id BETWEEN 28 AND 146 AND t1.id IN (46, 50, 104, 146)) OR (t0.id < 4 AND t1.region_id NOT IN (2, 2, 10, 12))) +ORDER BY t1.id DESC;" +349,8825,280,5,"SELECT regions.id, departments.id AS c6050, COUNT(*), COUNT(*) +FROM regions RIGHT JOIN departments ON regions.id = departments.id +WHERE departments.id IS NOT NULL +GROUP BY regions.id, departments.id +ORDER BY departments.id ASC;" +350,14320,278,11,"SELECT employees.department_id, departments.id AS c3106, COUNT(*) AS c7893 +FROM departments RIGHT JOIN employees ON departments.id = employees.id +GROUP BY employees.department_id, departments.id;" +351,83766,55158,261,"SELECT t0.customer_id, t2.id AS c6006, t2.age +FROM orders AS t0 CROSS JOIN departments AS t1 FULL JOIN users AS t2 ON t1.id = t2.id +WHERE (t1.id >= 3 AND (t0.id < 55 OR t0.id < 88));" +352,21794,3404,80,"SELECT t1.customer_id, t1.id, t0.id +FROM departments AS t0 CROSS JOIN orders AS t1 +WHERE (t1.customer_id IN (426) OR (t1.customer_id IS NULL OR t0.id >= 57));" +353,1220,237,500,"SELECT t0.id, t0.region_id +FROM customers AS t0;" +354,2962,167,1,"SELECT t0.department_id, t0.id +FROM employees AS t0 +WHERE (t0.department_id >= 31 AND (t0.department_id > 34 OR t0.id < 33)) +GROUP BY t0.department_id, t0.id +ORDER BY t0.department_id DESC;" +355,1244,243,2,"SELECT customers.id, customers.region_id +FROM customers +WHERE customers.id IN (153, 183);" +356,17220,320,5,"SELECT employees.department_id, departments.id +FROM books LEFT JOIN employees ON books.id = employees.id FULL JOIN departments ON employees.id = departments.id;" +357,5424,3425,4914,"SELECT orders.id AS c1456, orders.customer_id, COUNT(*), COUNT(orders.customer_id) +FROM orders +WHERE (orders.id > 86 OR orders.customer_id IS NULL) +GROUP BY orders.id, orders.customer_id;" +358,4160,153,0,"SELECT t0.department_id, t0.id AS c8666, COUNT(*) AS c1170, COUNT(*) +FROM employees AS t0 +WHERE t0.id IS NULL +GROUP BY t0.department_id, t0.id;" +359,6320,149,10,"SELECT regions.id, COUNT(regions.id) AS c1004, COUNT(regions.id) AS c9498 +FROM regions +GROUP BY regions.id;" +360,1220,137,3,"SELECT t0.id, t0.note AS c1949, t0.region +FROM markets AS t0;" +361,649400,49930,11,"SELECT t2.department_id, COUNT(t2.id), SUM(t0.id) +FROM departments AS t0 CROSS JOIN orders AS t1 CROSS JOIN employees AS t2 +GROUP BY t2.department_id;" +362,88720,780221,3,"SELECT markets.id AS c6682, markets.note AS c3493 +FROM orders CROSS JOIN customers RIGHT JOIN markets ON customers.id = markets.id +GROUP BY markets.id, markets.note;" +363,1775,156,3,"SELECT books.price +FROM books +WHERE books.price IS NOT NULL +ORDER BY books.price DESC;" +364,1288,261,44,"SELECT customers.region_id AS c2372 +FROM customers +WHERE customers.region_id IN (4, 89) +ORDER BY customers.region_id DESC;" +365,13135,2618,3,"SELECT t0.id AS c4468, t1.customer_id, t0.region +FROM markets AS t0 FULL JOIN orders AS t1 ON t0.id = t1.id JOIN customers AS t2 ON t0.id = t2.id +WHERE t0.id <= 17;" +366,1366,153,1,"SELECT users.id, users.age +FROM users +WHERE (users.id = 15 OR (users.id != 3 AND users.id IS NULL));" +367,1220,141,17,"SELECT t0.age +FROM users AS t0;" +368,21794,262,49,"SELECT t0.id, t1.age +FROM books AS t0 CROSS JOIN users AS t1 +WHERE (t1.id = 96 OR (t1.age != 18 OR t0.price <= 61));" +369,3332,1069,0,"SELECT t0.id AS c4185, SUM(t0.id) +FROM markets AS t0 LEFT JOIN orders AS t1 ON t0.id = t1.id +WHERE ((t0.id BETWEEN 1 AND 7 OR t0.note != 'North, South') AND (t0.note != 'Old World' AND t0.note IN ('ASIA', 'Old World', 'unknown'))) +GROUP BY t0.id;" +370,1610,145,2,"SELECT markets.note AS c2495, markets.id, markets.region +FROM markets +WHERE ((markets.note = 'North, South' AND markets.note != 'North, South') OR markets.note != 'Fast lane');" +371,14600,283,110,"SELECT regions.id +FROM regions CROSS JOIN employees;" +372,1930,1577,3214,"SELECT t0.id +FROM orders AS t0 +WHERE (t0.id <= 95 OR t0.customer_id BETWEEN 55 AND 369) +ORDER BY t0.id DESC;" +373,6320,361,500,"SELECT customers.id, COUNT(customers.region_id) +FROM customers +GROUP BY customers.id;" +374,36830,5349,0,"SELECT orders.id AS c9907, COUNT(*) AS c8627 +FROM departments CROSS JOIN orders +WHERE ((orders.id >= 79 OR orders.customer_id IN (63, 185, 226, 480)) AND departments.id IS NULL) +GROUP BY orders.id +ORDER BY orders.id ASC;" +375,1854,1405,4999,"SELECT t0.customer_id AS c3627, t0.id AS c1197 +FROM orders AS t0 +WHERE (t0.customer_id <= 56 OR t0.id > 1);" +376,18098,331,9,"SELECT t1.id, t0.id, t1.department_id +FROM regions AS t0 FULL JOIN employees AS t1 ON t0.id = t1.id FULL JOIN departments AS t2 ON t0.id = t2.id +WHERE t0.id NOT IN (3);" +377,1643,469,341,"SELECT customers.id, customers.region_id, SUM(customers.region_id), SUM(customers.id) +FROM customers +WHERE ((customers.region_id != 9 OR customers.id = 20) AND (customers.region_id >= 3 AND customers.id > 5)) +GROUP BY customers.id, customers.region_id +ORDER BY customers.id DESC, customers.region_id ASC;" +378,6320,130,3,"SELECT markets.region, markets.id AS c5516 +FROM markets +GROUP BY markets.region, markets.id;" +379,1220,117,11,"SELECT employees.department_id, employees.id +FROM employees;" +380,2494,2285,4329,"SELECT orders.customer_id +FROM orders +WHERE (orders.id < 86 OR (orders.customer_id IN (21, 71) OR orders.customer_id NOT BETWEEN 371 AND 436)) +ORDER BY orders.customer_id ASC;" +381,18500,308,34,"SELECT t0.age AS c6869, t0.id +FROM users AS t0 CROSS JOIN markets AS t1 +WHERE t1.note != 'Old World';" +382,6056,163,3,"SELECT t0.id AS c3548, t0.note, SUM(t0.id) AS c5991, COUNT(*) +FROM markets AS t0 +WHERE ((t0.note IN ('Fast lane', 'Fast lane', 'Old World') OR t0.region = 'AMERICA') OR t0.id NOT IN (1, 2, 3)) +GROUP BY t0.id, t0.note;" +383,178400,10350,8500,"SELECT customers.id, customers.region_id +FROM regions CROSS JOIN customers CROSS JOIN users +WHERE regions.id < 2;" +384,433400,370,9,"SELECT markets.id, books.id, COUNT(markets.id), COUNT(markets.id) +FROM regions CROSS JOIN books CROSS JOIN markets +WHERE regions.id != 5 +GROUP BY markets.id, books.id;" +385,1366,133,0,"SELECT t0.id AS c9611, t0.age AS c4657 +FROM users AS t0 +WHERE ((t0.id >= 3 OR t0.id IS NULL) AND t0.age IS NULL);" +386,17220,2250,3,"SELECT markets.note, markets.region AS c394 +FROM markets LEFT JOIN orders ON markets.id = orders.id LEFT JOIN employees ON orders.id = employees.id;" +387,2922,304,0,"SELECT employees.id AS c1393 +FROM employees LEFT JOIN books ON employees.id = books.id +WHERE ((employees.id IS NULL AND books.id IS NOT NULL) AND (books.price = 55 AND employees.department_id = 12));" +388,14600,10525,25000,"SELECT orders.customer_id AS c3285 +FROM orders CROSS JOIN departments;" +389,13135,2093,2,"SELECT employees.id AS c6990 +FROM orders LEFT JOIN markets ON orders.id = markets.id JOIN employees ON orders.id = employees.id +WHERE markets.id < 3;" +390,22600,1888,5000,"SELECT markets.note AS c1438, customers.region_id, markets.id AS c7869 +FROM customers LEFT JOIN markets ON customers.id = markets.id CROSS JOIN regions;" +391,14760,2533,5,"SELECT orders.customer_id AS c6961, departments.id +FROM departments LEFT JOIN orders ON departments.id = orders.id +GROUP BY orders.customer_id, departments.id +ORDER BY departments.id DESC;" +392,88055,535,11,"SELECT employees.department_id AS c8803, SUM(users.id), SUM(employees.id) +FROM users CROSS JOIN employees LEFT JOIN markets ON employees.id = markets.id +WHERE ((markets.id >= 18 OR users.id = 4) OR employees.id >= 2) +GROUP BY employees.department_id +ORDER BY employees.department_id ASC;" +393,13620,498913,384231,"SELECT orders.customer_id, customers.id +FROM orders CROSS JOIN customers +WHERE (orders.id IS NOT NULL AND (orders.customer_id <= 190 AND customers.region_id IN (1, 5, 8, 9)));" +394,1732,136,8,"SELECT t0.id +FROM employees AS t0 +WHERE t0.department_id NOT IN (6, 11, 32, 94);" +395,14600,10322,15000,"SELECT orders.id, orders.customer_id, books.price AS c2061 +FROM orders CROSS JOIN books;" +396,1610,148,0,"SELECT markets.region, markets.id, markets.note +FROM markets +WHERE markets.region IS NULL;" +397,6320,392,500,"SELECT t0.id, t0.region_id, SUM(t0.region_id) +FROM customers AS t0 +GROUP BY t0.id, t0.region_id;" +398,4626,240,10,"SELECT regions.id, customers.id, customers.region_id +FROM regions JOIN customers ON regions.id = customers.id +WHERE (customers.id NOT BETWEEN 18 AND 150 OR regions.id IN (1, 9, 10, 63));" +399,4325,156,1,"SELECT departments.id AS c2818, COUNT(departments.id), COUNT(departments.id) +FROM departments +WHERE departments.id <= 1 +GROUP BY departments.id +ORDER BY departments.id DESC;" diff --git a/research/fuzz/cost_stats.py b/research/fuzz/cost_stats.py new file mode 100644 index 0000000..190a4d7 --- /dev/null +++ b/research/fuzz/cost_stats.py @@ -0,0 +1,128 @@ +""" +Collect (plan cost, actual execution time) pairs over random queries. + +Generates random queries with the shared query generator, runs each through our +compiler with --stats, and records the optimizer's chosen-plan cost alongside +the measured execution wall time (microseconds, taken inside the process so it +excludes interpreter/process startup). To damp timing noise each query is run +several times and the median execution time is kept. + +The resulting CSV (seed, plan_cost, exec_us, rows, query) feeds +research/cost-on-random.ipynb, which checks whether cost tracks real time. + +Usage: + python -m research.fuzz.cost_stats \\ + --cli build-release/bin/sql \\ + --data-dir test/static/executor/test_data \\ + --count 500 \\ + --out research/cost-stats.csv +""" +from __future__ import annotations + +import argparse +import csv +import random +import re +import statistics +import subprocess +import sys +from itertools import count +from pathlib import Path + +from research.query_generator import DIALECTS, QueryGenerator, load_schema, render_query + +_STATS_RE = re.compile(r"STATS plan_cost=(-?\d+) exec_us=(-?\d+) rows=(\d+)") + + +def _run_ours(cli: str, data_dir: str, query: str, jit: bool) -> subprocess.CompletedProcess: + cmd = [cli, "--data-dir", data_dir, "--stats"] + if jit: + cmd.append("--jit") + return subprocess.run( + cmd, + input=query, + capture_output=True, + text=True, + timeout=60, + ) + + +def _parse_stats(stderr: str) -> tuple[int, int, int] | None: + """Return (plan_cost, exec_us, rows) from the CLI's STATS line, or None.""" + m = _STATS_RE.search(stderr) + if m is None: + return None + return int(m.group(1)), int(m.group(2)), int(m.group(3)) + + +def main() -> None: + ap = argparse.ArgumentParser(description=__doc__) + ap.add_argument("--cli", required=True, help="Path to our sql binary") + ap.add_argument("--data-dir", required=True, help="CSV table directory") + ap.add_argument("--out", required=True, help="Output CSV path") + ap.add_argument("--start-seed", type=int, default=0) + ap.add_argument("--count", type=int, default=500, help="How many valid samples to collect") + ap.add_argument("--repeats", type=int, default=5, + help="Runs per query; the median exec time is kept") + ap.add_argument("--jit", action="store_true", help="Run our CLI with the JIT executor") + args = ap.parse_args() + + schema = load_schema(Path(args.data_dir)) + pg = DIALECTS["pg"] + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + + collected = 0 + attempted = 0 + skipped = 0 + with out_path.open("w", newline="") as f: + writer = csv.writer(f) + writer.writerow(["seed", "plan_cost", "exec_us", "rows", "query"]) + + for seed in count(args.start_seed): + if collected >= args.count: + break + attempted += 1 + rng = random.Random(seed) + query = QueryGenerator(schema, rng).generate() + sql = render_query(query, pg) + ";" + + cost: int | None = None + rows: int | None = None + samples: list[int] = [] + failed = False + for _ in range(args.repeats): + try: + proc = _run_ours(args.cli, args.data_dir, sql, args.jit) + except subprocess.TimeoutExpired: + failed = True + break + if proc.returncode != 0: + failed = True + break + parsed = _parse_stats(proc.stderr) + if parsed is None: + failed = True + break + cost, exec_us, rows = parsed + samples.append(exec_us) + + if failed or not samples: + skipped += 1 + continue + + exec_us = int(statistics.median(samples)) + writer.writerow([seed, cost, exec_us, rows, sql]) + f.flush() + collected += 1 + if collected % 25 == 0: + print(f"collected={collected} attempted={attempted} skipped={skipped}", + flush=True) + + print(f"done: collected={collected} attempted={attempted} skipped={skipped} -> {out_path}", + file=sys.stderr) + + +if __name__ == "__main__": + main() diff --git a/src/stewkk/sql/main.cpp b/src/stewkk/sql/main.cpp index f1d0e25..42e445b 100644 --- a/src/stewkk/sql/main.cpp +++ b/src/stewkk/sql/main.cpp @@ -1,4 +1,6 @@ #include +#include +#include #include #include #include @@ -46,6 +48,7 @@ struct Args { bool print_plan = false; bool print_ast = false; bool jit = false; + bool stats = false; std::string check_reachable_path; }; @@ -65,6 +68,8 @@ Args ParseArgs(int argc, char** argv) { args.print_ast = true; } else if (a == "--jit") { args.jit = true; + } else if (a == "--stats") { + args.stats = true; } else if (a == "--check-reachable") { if (i + 1 >= argc) { std::cerr << "--check-reachable requires a plan file path\n"; @@ -77,7 +82,7 @@ Args ParseArgs(int argc, char** argv) { } } if (args.data_dir.empty() && args.check_reachable_path.empty()) { - std::cerr << "usage: sql --data-dir [--print-plan] [--jit] < query.sql\n" + std::cerr << "usage: sql --data-dir [--print-plan] [--jit] [--stats] < query.sql\n" << " sql --check-reachable < query.sql\n"; std::exit(kUsage); } @@ -243,6 +248,7 @@ int main(int argc, char** argv) { } PhysicalPlanNode plan; + std::int64_t plan_cost = 0; try { PropertySet required = parsed.required_order ? PropertySet{SortProperty{*parsed.required_order}} @@ -250,6 +256,7 @@ int main(int argc, char** argv) { Optimizer optimizer(parsed.op, MakeMainRules(), {}, LoadSchemaFromCsvDir(args.data_dir), std::move(required)); plan = optimizer.Optimize(); + plan_cost = optimizer.GetBestCost(); } catch (const std::exception& e) { std::cerr << "optimizer error: " << e.what() << "\n"; return kOptimizerError; @@ -262,7 +269,9 @@ int main(int argc, char** argv) { } Result result; + std::int64_t exec_us = 0; try { + const auto exec_started = std::chrono::steady_clock::now(); boost::asio::io_context ctx; boost::asio::any_io_executor exec = ctx.get_executor(); auto fut = args.jit @@ -272,6 +281,8 @@ int main(int argc, char** argv) { boost::asio::use_future); ctx.run(); result = fut.get(); + exec_us = std::chrono::duration_cast( + std::chrono::steady_clock::now() - exec_started).count(); } catch (const std::exception& e) { std::cerr << "runtime error: " << e.what() << "\n"; return kRuntimeError; @@ -283,5 +294,10 @@ int main(int argc, char** argv) { } PrintRelation(result.value(), parsed.required_order.has_value()); + + if (args.stats) { + std::cerr << "STATS plan_cost=" << plan_cost << " exec_us=" << exec_us + << " rows=" << result.value().tuples.size() << "\n"; + } return kOk; } From aa7cec6576c683b53bf7fbf2bb4359b934521fa1 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Tue, 2 Jun 2026 02:35:21 +0300 Subject: [PATCH 077/120] Fix --- .../sql/logic/optimizer/schema_catalog.hpp | 3 + research/cost-on-random.ipynb | 116 ++- research/cost-on-random.pdf | Bin 0 -> 126649 bytes research/cost-stats.csv | 800 +++++++++--------- .../sql/logic/optimizer/schema_catalog.cpp | 26 + src/stewkk/sql/main.cpp | 5 +- 6 files changed, 502 insertions(+), 448 deletions(-) create mode 100644 research/cost-on-random.pdf diff --git a/include/stewkk/sql/logic/optimizer/schema_catalog.hpp b/include/stewkk/sql/logic/optimizer/schema_catalog.hpp index b7abe72..f3d1082 100644 --- a/include/stewkk/sql/logic/optimizer/schema_catalog.hpp +++ b/include/stewkk/sql/logic/optimizer/schema_catalog.hpp @@ -31,4 +31,7 @@ class SchemaCatalog { SchemaCatalog LoadSchemaFromCsvDir(const std::filesystem::path& dir); +std::unordered_map LoadTableSizesFromCsvDir( + const std::filesystem::path& dir); + } // namespace stewkk::sql diff --git a/research/cost-on-random.ipynb b/research/cost-on-random.ipynb index bba8ee6..65f1656 100644 --- a/research/cost-on-random.ipynb +++ b/research/cost-on-random.ipynb @@ -12,13 +12,37 @@ "shell.execute_reply": "2026-06-01T23:13:22.987712Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "collected=25 attempted=25 skipped=0\n", + "collected=50 attempted=50 skipped=0\n", + "collected=75 attempted=75 skipped=0\n", + "collected=100 attempted=100 skipped=0\n", + "collected=125 attempted=125 skipped=0\n", + "collected=150 attempted=150 skipped=0\n", + "collected=175 attempted=175 skipped=0\n", + "collected=200 attempted=200 skipped=0\n", + "collected=225 attempted=225 skipped=0\n", + "collected=250 attempted=250 skipped=0\n", + "collected=275 attempted=275 skipped=0\n", + "collected=300 attempted=300 skipped=0\n", + "collected=325 attempted=325 skipped=0\n", + "collected=350 attempted=350 skipped=0\n", + "collected=375 attempted=375 skipped=0\n", + "collected=400 attempted=400 skipped=0\n", + "done: collected=400 attempted=400 skipped=0 -> research/cost-stats.csv\n" + ] + } + ], "source": [ - "# !cd ~/c/iu9-sql-compiler/ && python -m research.fuzz.cost_stats \\\n", - "# --cli build-release/bin/sql \\\n", - "# --data-dir test/static/executor/test_data \\\n", - "# --count 400 --repeats 5 \\\n", - "# --out research/cost-stats.csv" + "!cd ~/c/iu9-sql-compiler/ && python -m research.fuzz.cost_stats \\\n", + " --cli build-release/bin/sql \\\n", + " --data-dir test/static/executor/test_data \\\n", + " --count 400 --repeats 5 \\\n", + " --out research/cost-stats.csv" ] }, { @@ -72,73 +96,73 @@ " \n", " count\n", " 400.000000\n", - " 400.000000\n", " 4.000000e+02\n", - " 400.000000\n", + " 4.000000e+02\n", + " 400.00000\n", " \n", " \n", " mean\n", " 199.500000\n", - " 27901.122500\n", - " 3.037728e+04\n", - " 3445.482500\n", + " 3.293322e+07\n", + " 3.063712e+04\n", + " 3446.78750\n", " \n", " \n", " std\n", " 115.614301\n", - " 87392.042181\n", - " 2.148684e+05\n", - " 24278.190578\n", + " 2.369086e+08\n", + " 2.124881e+05\n", + " 24278.01832\n", " \n", " \n", " min\n", " 0.000000\n", - " 1122.000000\n", - " 1.170000e+02\n", - " 0.000000\n", + " 3.660000e+02\n", + " 1.220000e+02\n", + " 0.00000\n", " \n", " \n", " 25%\n", " 99.750000\n", - " 2085.000000\n", - " 1.705000e+02\n", - " 2.000000\n", + " 2.987250e+03\n", + " 1.787500e+02\n", + " 2.00000\n", " \n", " \n", " 50%\n", " 199.500000\n", - " 9220.000000\n", - " 3.205000e+02\n", - " 5.000000\n", + " 4.735800e+04\n", + " 3.300000e+02\n", + " 5.00000\n", " \n", " \n", " 75%\n", " 299.250000\n", - " 17220.000000\n", - " 1.303000e+03\n", - " 29.250000\n", + " 6.809730e+05\n", + " 1.214250e+03\n", + " 30.00000\n", " \n", " \n", " max\n", " 399.000000\n", - " 687256.000000\n", - " 3.085577e+06\n", - " 384231.000000\n", + " 2.984422e+09\n", + " 2.956573e+06\n", + " 384231.00000\n", " \n", " \n", "\n", "" ], "text/plain": [ - " seed plan_cost exec_us rows\n", - "count 400.000000 400.000000 4.000000e+02 400.000000\n", - "mean 199.500000 27901.122500 3.037728e+04 3445.482500\n", - "std 115.614301 87392.042181 2.148684e+05 24278.190578\n", - "min 0.000000 1122.000000 1.170000e+02 0.000000\n", - "25% 99.750000 2085.000000 1.705000e+02 2.000000\n", - "50% 199.500000 9220.000000 3.205000e+02 5.000000\n", - "75% 299.250000 17220.000000 1.303000e+03 29.250000\n", - "max 399.000000 687256.000000 3.085577e+06 384231.000000" + " seed plan_cost exec_us rows\n", + "count 400.000000 4.000000e+02 4.000000e+02 400.00000\n", + "mean 199.500000 3.293322e+07 3.063712e+04 3446.78750\n", + "std 115.614301 2.369086e+08 2.124881e+05 24278.01832\n", + "min 0.000000 3.660000e+02 1.220000e+02 0.00000\n", + "25% 99.750000 2.987250e+03 1.787500e+02 2.00000\n", + "50% 199.500000 4.735800e+04 3.300000e+02 5.00000\n", + "75% 299.250000 6.809730e+05 1.214250e+03 30.00000\n", + "max 399.000000 2.984422e+09 2.956573e+06 384231.00000" ] }, "execution_count": 2, @@ -172,7 +196,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -205,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "226372f9", "metadata": { "execution": { @@ -218,7 +242,7 @@ "outputs": [ { "data": { - "image/png": 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gyZIl8Hq9UKvV8Hg8KCoqwoQJE/y2si0qKsLAgQPx97//HX/9619D/j4kSeLCbUoYnFgQJYARI0a0eE65PV6vF5MnT0ZNTQ3uu+8+9OvXD8nJyTh+/DjmzZvXYntRtVotd8ohueOOO3DxxRfjo48+wpdffokHH3wQTz31FL766quQn2++8sor8fLLL2Pp0qWYOXMm3n//ffTr18/v09b+/ftj9+7d+Oyzz7Bs2TIsXrwYL7zwAh566CE8+uijIbV7+pum5j6/5ppr2nzG/uyzzwYAvPXWW5g3bx5mzpyJe+65Bzk5OVCr1Xjqqad8k7lIa+t3oq3rzW9qm7/PN998s9UJ4al3O5TkH//4h9/PuqCgwLdpQDzIzMxs94OAQN/Ee71eVFZWBlQ2IyOjzTsSQNMOTF6vFxUVFcjJyfFdd7lcqK6uRufOnQNq53TdunWDy+WCzWZDSkoKvvnmG+zYsQPPPPOMX7nevXujf//+WLduXUjtNKurq0NWVlZYMYjihTL/hiaimNq+fTv27NmDhQsX4rrrrvNdX758uazt9OzZEwCwY8eONj+FLSgoAADs3r27xafYu3fv9r1+asw//OEP+MMf/oC9e/diyJAhePrpp/HWW28BQNCfHI4bNw6dOnXCe++9hzFjxuCrr77yLfw+VXJyMq644gpcccUVcLlcmDVrFp544gk88MADsmwTm52dDbPZDK/Xe8ZPhBctWoQePXpgyZIlft/vww8/HFLb0fy0tfl3IicnR7ZPvltTUFCA3bt3t7i+a9cu3+vN/925c2eLT5337dvnV++6667ze6yqvTfiBQUF+PHHHyGKot9di9baXrVqFex2u99di9PblkO/fv3a3SUuUEePHg347saqVavaPZG8eZOBTZs24cILL/Rd37RpE0RRDGoTglMdOHAAer0eJpMJAHyHhba2i53b7W5zN6lAHD9+HC6Xy3c3iqij4xoLImqh+VPlUx+NkCQJzz33nKztTJkyBWazGU899RQcDoffa81tDx8+HDk5OXjppZfgdDp9ry9duhQ///wzpk+fDgCw2+0tYvTs2RNms9mvXnJyMurq6gLOsXkv+k8//RRvvvkmPB6P32NQQNPz6afS6XQYMGAAJEnyPYZkt9uxa9eukJ+1VqvVmD17NhYvXowdO3a0eP3UT4lb+/l9//33WL9+fUhtN59ZEEy/hWrq1KlISUnBk08+6fcIV7NAPw0/kwsvvBAbNmzw6xObzYZXXnkFhYWFvvUtU6dOxfHjx/HJJ5/4yjkcDrz66qt+8Xr06IGioiLfn/Yeg7vwwgtRVlbmt3bH4/Hg+eefh8lkwvjx431tu91uv7ZEUURJSUl433wrzj//fOzYscNvrIRCzjUWkyZNQkZGBl588UW/6y+++CKMRqNv7ANNB9ft2rULdrvdd62135Vt27bhk08+wZQpU3yTuj59+gAA3n33Xb+ymzdvxu7du8PazemHH34AcOZd1Ig6Ct6xIKIW+vXrh549e+Luu+/G8ePHkZKSgsWLF8u2KLpZSkoKnn32Wfz2t7/Fueee69snf9u2bbDb7Vi4cCG0Wi3++te/Yv78+Rg/fjyuuuoq33azhYWFuPPOOwEAe/bswQUXXIDLL78cAwYMgEajwYcffojy8nLftrBA0/aPL774Iv7yl7+gV69eyMnJafN5/mZXXHEFnn/+eTz88MM466yzWnz6OGXKFOTl5WH06NHIzc3Fzz//jH/+85+YPn06zGYzAGDDhg2YOHEiHn74YTzyyCMh9df//d//YdWqVRg5ciRuuOEGDBgwADU1Ndi8eTNWrFjh2+bzoosuwpIlS3DppZdi+vTpOHjwIF566SUMGDDAbx1AoHr27Im0tDS89NJLMJvNSE5OxsiRIyOyXiMlJQUvvvgirr32Wpxzzjm48sorkZ2djSNHjuDzzz/H6NGj8c9//hNA05kZ3bt3x9y5c4M+0+H+++/Hf//7X0ybNg233XYbMjIysHDhQhw8eBCLFy/2ven83e9+h3/+85+46qqrcPvtt6NTp054++23fXehQrmbc+ONN+Lll1/GvHnz8MMPP6CwsBCLFi3CunXrsGDBAt/vzMyZMzFixAj84Q9/wL59+9CvXz988sknvp+znHeSZsyYgccffxxff/01pkyZEnIcuddYPP744yguLsZll12GqVOnYs2aNXjrrbfwxBNPICMjw1f2n//8Jx599FG/uyBXXHEFDAYDRo0ahZycHOzcuROvvPIKjEYj/u///s9Xd9iwYZg8eTIWLlyIhoYGTJkyBaWlpXj++edhMBh82zif2lZdXR1OnDgBAPj0009x7NgxAMCtt97qt2nB8uXLkZ+fz61mKXHEZjMqIoqG5u1mN27c2G651rab3blzp1RUVCSZTCYpKytLuuGGG6Rt27a12F5x7ty5UnJycouYzdtUBuKTTz6RRo0aJRkMBiklJUUaMWKE9N///tevzHvvvScNHTpUSkpKkjIyMqRf//rXvu1yJUmSqqqqpOLiYqlfv35ScnKylJqaKo0cOVJ6//33/eKUlZVJ06dPl8xmswQgoK1nRVGUunXrJgGQ/vKXv7R4/eWXX5bGjRsnZWZmSklJSVLPnj2le+65R6qvr/eVae7jM23tKklNW8AWFxe3+lp5eblUXFwsdevWTdJqtVJeXp50wQUXSK+88opfvk8++aRUUFAgJSUlSUOHDpU+++yzFlvDNrcVSE4ff/yxNGDAAEmj0fj9DrS13ezp2/42f/8ffPCB3/W2fkdXrVolTZ06VUpNTZX0er3Us2dPad68edKmTZt8ZbZv3y4BkO6///4z5n/6drOSJEn79++X5syZI6WlpUl6vV4aMWKE9Nlnn7Woe+DAAWn69OmSwWCQsrOzpT/84Q++rWC/++67M7Z9+nazktT0c5w/f76UlZUl6XQ66ayzzmp129LKykrp6quvlsxms5SamirNmzdPWrdunQRAevfdd8/YdjDOPvts6frrr/e71jyOKysrZW0rGK+88orUt29fSafTST179pSeffZZ33bUzZrzPPXvsOeee04aMWKElJGRIWk0GqlTp07SNddcI+3du7dFG3a7XXrsscekAQMGSAaDQUpNTZUuuugiacuWLS3KFhQUtLmF7sGDB33lvF6v1KlTJ+nPf/6zXF1BpHiCJLWzDQQREZFCvfDCC7j33nuxf//+Ng86jJQFCxbgzjvvxLFjx9rdtjkSPvroI1x66aVYu3ZtWDuPne7NN99EcXExjhw54jt0kkL30Ucf4eqrr8b+/fvRqVOnWKdDFBWcWBARUVw69TToSGpsbPRbjO1wODB06FB4vV7s2bMnqm17vV5MmTIFmzZtQllZWUhbrrZFFEWcffbZuOqqq1rdoICC03yK99/+9rdYp0IUNVxjQUREcemDDz6ISjuzZs1Cfn4+hgwZgvr6erz11lvYtWsX3n777Yi3feutt6KxsRHnn38+nE4nlixZgm+//RZPPvmkrJMKoGmjgtY2BqDQhLpZAlE84x0LIiKidixYsAD/+te/cOjQIXi9XgwYMAD33ntvi93BIuGdd97B008/jX379sHhcKBXr164+eabccstt0S8bSKiYHFiQUREREREYeM5FkREREREFDZOLIiIiIiIKGxcvN0OURRx4sQJmM1mWQ8iIiIiIiKKB5IkwWKxoHPnzr7DQ9vCiUU7Tpw4gW7dusU6DSIiIiKimDp69Ci6du3abhlOLNphNpsBAIcPH0ZKSkq7ZUVRRFVVFbKystqdzQVaLtEovV9ikV+k2pQzbjixQq0bTL1IlU0U8dAnHJvyxwmlfrB1ODbDEw99wrEpf5xYjc2GhgYUFBT43he3hxOLVpSUlKCkpARerxcA4HQ64XA42q0jiiK8Xi8cDscZJxaBlEs0Su+XWOQXqTbljBtOrFDrBlMvUmUTRTz0Ccem/HFCqR9sHY7N8MRDn3Bsyh8nVmPT6XQCQEDLAjixaEVxcTGKi4vR0NCA1NRUZGdnB3THQhAEZGdnn3FiEUi5RKP0folFfpFqU8644cQKtW4w9SJVNlHEQ59wbMofJ5T6wdbh2AxPPPQJx6b8cWI1NvV6fcA5cmIRAJVKFdAPQxCEgMoGWi7RKL1fYpFfpNqUM244sUKtG0y9SJVNFPHQJxyb8scJpX6wdTg2wxMPfcKxKX+cWIzNYNpS7m8jERERERHFDd6xICIiorjl9XrhdrtjnUZUiaIIt9ut+DUW0c4xUm3KFTfcOKHUD6SOVquFWq0OOp/WcGJBREREcUeSJJSVlaGuri7WqUSdJEkQRREWi0Wx52zFIsdItSlX3HDjhFI/0DppaWnIy8sLOqfTcWJBREREcad5UpGTkwOj0ajYN9iRIEkSPB4PNBqNYr/vWOQYqTblihtunFDqn6mOJEmw2+2oqKgAAOTm5gad16k4sSAiIqK44vV6fZOKzMzMWKcTdZxYRLfNjjyxAACDwQAAqKioQFZWVtB5nYoTiwCIoghRFM9Ypvl2kxzlEo3S+yUW+UWqTTnjhhMr1LrB1ItU2UQRD33CsSl/nFDqB1sn3LHpdDohSRIMBgMkSQo4z46k+ftW8vcfixwj1aZcccONE0r9QOo0jyWXy9VivAXzdwEnFq04/YC8ysrKgA7Iq6+vhyRJZzzHIpByiUbp/RKL/CLVppxxw4kVat1g6kWqbKKIhz7h2JQ/Tij1g60T7th0u92+g7w8Hk9AOXYkkiT53qMo+Y5FtHOMVJtyxQ03Tij1A63j9XohiiKqq6ths9n8xpvFYgk4R04sWsED8qJP6f3Cg37kj8UD8pQvHvqEY1P+OPFwQJ7D4YDFYoFGo4FGk7hvZbRabaxTOKNY5BipNuWKG26cUOqfqY5Go4FKpUJGRga0Wi0PyIskHpAXHUrvFx70I38sHpCnfPHQJxyb8sdR+gF5KpUKgiD4/sSTCRMmYMiQIViwYEHIMSRJ8n3fbX3/8+bNQ11dHT766KOQ2wlEWVkZrr32Wnz77bfQarWoq6uDIAhYsmQJLrroonZzDEdr/RhIv4RCrrjhxgmlfqB1msdS89jiAXlEREREFFXPPvssSktLsXXrVuzZswcAUFpaimnTpgEADh06BEEQsHXr1hhmqWwffPAB+vXrB71ej7POOgtffPFFu+Xnz5/vN7Fu/jNw4EBfGYvFgjvuuAOFhYVISUnB6NGjsXHjxkh/K5xYEBEREVFo9u/fj2HDhqF3797IyckBAOTl5SEpKSnGmcWHb7/9FldddRWuv/56bNmyBTNnzsTMmTOxY8eONussWLAApaWlvj9Hjx5FRkYGLrvsMl+Z3/72t1i+fDn+85//YPPmzZg8eTKKiopw/PjxiH4/nFgQERERxUhtbS2uu+46pKenw2g0Ytq0adi7d69fmVdffRXdunWD0WjEpZdeimeeeQbZ2dlBteN0OnHbbbchJycHer0eY8aMafEJ9ieffILevXtDr9dj4sSJWLhwIQRBaPMQwsLCQixevBj/+c9/IAgC5s2bB6DpsZrmR7B69OgBABg6dCgEQcCECRMCzvmFF17w5ZObm4s5c+a0Wba2thZz585FTk4OkpOTW/TjG2+8gbS0NHz00Ue+mFOnTsXRo0f94nz88cc455xzoNfr0aNHDzz66KMR3SDgueeew69+9Svcc8896N+/Px5//HGcc845+Oc//9lmndTUVOTl5fn+bNq0CbW1tZg/fz4AoLGxEYsXL8bf/vY3jBs3Dr169cIjjzyCXr164cUXX4zY9wJwYkFEREQdgCRJEG2NMfkTzhak8+bNw6ZNm/DJJ59g/fr1kCQJF154IdxuNwBg3bp1uOmmm3D77bdj69atmDx5Mp588smg27n33nuxePFiLFy4EJs3b0avXr0wdepU1NTUAAAOHjyIOXPmYObMmdi2bRt+97vf4U9/+lO7MTdu3Ihf/epXuPzyy1FaWornnnuuRZnvv/8eALBixQqUlpZiyZIlAeW7adMm3HbbbXjsscewe/duLFu2DOPGjWuzfHM/LlmyBN9++22LfgQAu92OJ554Av/5z3+wbt061NXV4corr/S9vmbNGlx33XW4/fbbsXPnTrz88st444038MQTT7TZ7ttvvw2TydTunzVr1rRZf/369SgqKvK7NnXqVKxfvz6QbgIAvPbaaygqKkJBQQEAwOPxwOv1tlh0bTAYsHbt2oDjhoKLt4mIiCjuSXYHDhZOiUnb3Q/9D0KyIeh6e/fuxSeffIJ169Zh1KhRAJreqHbr1g0fffQRLrvsMjz//POYNm0a7r77bgBAnz598O233+Kzzz4LuB2bzYYXX3wRb7zxhm/tw6uvvorly5fjtddewz333IOXX34Zffv2xd///ncAQN++fbFjx45231RnZ2cjKSkJBoMBeXl5bZYBgMzMzDbLtObIkSNITk7GRRddBLPZjIKCAgwdOrTVss39uHbtWowYMQIajaZFPwJN2xT/85//xMiRIwEACxcuRP/+/bFhwwaMGDECjz76KO6//37MnTsXQNPdlscffxz33ntvm5OsSy65xBevLV26dGnztbKyshanXefm5qKsrKzdmM1OnDiBpUuX4p133vFdM5vNOP/88/H444+jX79+yMzMxLvvvov169ejV69eAcUNFe9YEBEREcXAzz//DI1G4/fGNDMzE3379sXPP/8MANi9ezdGjBjhV+/cc8/1/f+RI0f8Ph1v7W7G/v374Xa7MXr0aN81rVaLESNG+LVzalwALdqNpsmTJ6OgoAA9evTAtddei7fffht2u73VsoH0I9C0peqp32O/fv2QlpbmK7Nt2zY89thjfv15ww03oLS0tM22zWYzevXq1e6f5pOtI2HhwoVIS0vDzJkz/a6/+eabkCQJXbt2hclkwvPPP4+rrroq4rvn8Y4FERERycrp8eLnUgvsLg/65JqRZYr8Ql7BqEf3Q/+LeDtttR0rnTt39ttxKSMjI2a5yMlsNmPz5s1YvXo1/ve//+Ghhx7CI488go0bNyItLS0ibVqtVjz66KOYNWuW33VJkto8y+Htt9/G7373u3bjLl26FGPHjm31tby8PJSXl/tdKy8vD+jujiRJeP3113HttddCp9P5vdazZ098/fXXsFqtqKmpQbdu3XDllVf61rxECicWREREJBuPV8T7G4+iyuoCAGw6VItLh3ZBtwxjRNsVBCGkx5FiqX///vB4PPj+++99j0JVV1dj9+7dGDBgAICmR5JOX2S9adMm3/9rNJozPt7Ss2dP6HQ6rFu3zvccvtvtxsaNG3HHHXf42jl9m1M5tidtfsPbfPpzMDQaDYqKilBUVISHH34YaWlp+Oqrr1q88T+1H5vvspzej0DT2oNNmzb5yuzevRt1dXXo378/AOCcc87B7t27W/SnJEltLuAO91Go888/HytXrvT9HABg+fLlOP/889uNCQBff/019u3bh+uvv77NMsnJyUhKSkJtbS2+/PJL/O1vfztj3HBwYkFERESyOVhl800qAMArSth8pDbiE4t41Lt3b8yYMQM33HADXn75ZZjNZtx///3o0qULZsyYAQC49dZbMW7cODzzzDO4+OKL8dVXX2Hp0qVBHbCWnJyMm2++Gffccw8yMjKQn5+Pv/3tb7Db7b43pb/73e/wzDPP4L777sP111+PrVu34o033gAQ3qFwOTk5MBgMWLZsGbp27Qq9Xo/U1NQz1vvss89w4MABjBs3Dunp6fjiiy8giiL69u3bomxzP954440oKSlBWloaHnjgAb9+BJoe/7r11lvx//7f/4NGo8Ett9yC8847zzfReOihh3DRRRchPz8fc+bMgUqlwrZt27B9+3Y88sgjreZpNpthNptD6xwAt99+O8aPH4+nn34a06dPx7vvvotNmzbhlVde8ZV54IEHcPz4cSxcuNCv7muvvYaRI0di0KBBLeJ++eWXkCQJffr0we7du/HAAw+gX79+vp2jIoUTiwCIoghRFM9YRpIk2colGqX3Syzyi1SbcsYNJ1aodYOpF6myiSIe+oRjU/44odQ/tY5H9AKn7ZJ0+r+j4Y7N5mvNf+LNqXm//vrruOOOO3DRRRfB5XJh3Lhx+Pzzz6HRaCBJEkaNGoUXX3wRjz32GP785z9j6tSpuOOOO1BSUuKLdaa2AOCpp56C1+vFtddeC4vFguHDh2PZsmVIS0uDJEkoLCzEBx98gLvvvhvPPfcczj//fPzxj3/E73//e+h0uoDbOf2aVqvFc889h8cffxwPPfQQxo4di1WrVmH16tWYNGkSDhw4gMLCwhZ1U1NTsWTJEjzyyCNwOBzo3bs33nnnHQwYMMDXVmv9OHPmzFb7UZIkGI1G3Hvvvbj66qtx/PhxjB07Fv/61798MaZMmYJPP/0Ujz/+OP76179Cq9WiX79+uP766/3aDMfpcc4//3y8/fbbePDBB/HHP/4RvXv3xocffoiBAwf6ypSWluLIkSN+9erq6rB48WIsWLCg1Zzq6urwxz/+EceOHUNGRgZmzZqFJ554wtcfreXVPM5aG2+BEqR4HJERVlJSgpKSEni9XuzZswd79uw542xUFEXU19cjNTW13YUxgZZLNErvl1jkF6k25YwbTqxQ6wZTL1JlE0U89AnHpvxxQql/ah2vBKzaVYFG1y+PvgjAiO4Z6JRqaLV8KGPT7Xajvr4eBQUFbT773pHddNNN2LVrF1atWhXW3YQzeeqpp/Dqq6/iwIEDQdeVJAlerxdqtbrVHBcuXIi//vWv2LZtG7RarRzpttvmf/7zH/zhD39AZWWlrHGjGSeU+oHWcTgcOHz4MFJSUmCz2fzGm8ViQZ8+fVBfX4+UlJR22+Mdi1YUFxejuLgYDQ0NSE1NRXZ29hk7UhRFCIKA7OzsM04sAimXaJTeL7HIL1Jtyhk3nFih1g2mXqTKJop46BOOTfnjhFL/9DozM7Lw47F62F1e9MszoWu6sd3ywebjcDhgsVig0Wig0XT8tzL/+Mc/MHnyZCQnJ2Pp0qV488038f/+3/+T7Q15sxdeeAHnnnsuMjMzsW7dOjzzzDMoLi4Oq4/byvHLL7/EE088EZEdk1prs/l3JxLfS7TjhFL/THU0Gg1UKhUyMjKg1Wr9xlswk/eOPxploFKpAvrLVRCEgMoGWi7RKL1fYpFfpNqUM244sUKtG0y9SJVNFPHQJxyb8scJpf6pdVIMOozp3f7J0OGMTZVK1bRY+5c/Hd3GjRvx97//HRaLBT169MBzzz3nWxsh5/e/b98+PPHEE6ipqUF+fj7+8Ic/4IEHHgj50/Xmeq3V/+CDD8LON5g228slnLjRjBNK/UDrNI+l5rF1+ngLFCcWRERERAr2/vvv+33d3i5F4Xj22Wfx7LPPyh5XCebNm4d58+bFOo0OT7kfQRERERERUdzgxIKIiIiIiMLGiQURERHFJSVvhUwUT5rHUrhrdrjGgoiIiOKKTqeDSqXCiRMnkJ2dDZ1OlxCLuJs1r7HQaDSK/b5jkWOk2pQrbrhxQql/pjqSJMHlcqGyshIqlSrsHas4sSAiIqK4olKp0L17d5SWluLEiROxTifqmg8wa97BR4likWOk2pQrbrhxQqkfaB2j0Yj8/Pywd6TjxIKIiIjijk6nQ35+PjweD7xeb6zTiSpRFFFdXY3MzEzFbgUdixwj1aZcccONE0r9QOqo1WrfHY1wHy/kxIKIiIjikiAI0Gq1sh8Up3SiKEKr1UKv1yt6YhHtHCPVplxxw40TSv1o/xyU+dtIRERERERxhRMLIiIiIiIKGycWREREREQUNk4siIiIiIgobJxYEBERERFR2DixICIiIiKisHG72QCIonjGfX1FUfQdQiJHuUSj9H6JRX6RalPOuOHECrVuMPUiVTZRxEOfcGzKHyeU+sHW4dgMTzz0Ccem/HFiNTaDaY8Ti1aUlJSgpKTEd+BOZWUlHA5Hu3VEUUR9fT0kSWp3n+BAyyUapfdLLPKLVJtyxg0nVqh1g6kXqbKJIh76hGNT/jih1A+2DsdmeOKhTzg25Y8Tq7FpsVgCzpETi1YUFxejuLgYDQ0NSE1NRXZ2NlJSUtqtI4oiBEFAdnb2GScWgZRLNErvl1jkF6k25YwbTqxQ6wZTL1JlE0U89AnHpvxxQqkfbB2OzfDEQ59wbMofJ1ZjU6/XB5wjJxYBUKlUAf0wBEEIqGyg5RKN0vslFvlFqk0544YTK9S6wdSLVNlEEQ99wrEpf5xQ6gdbh2MzPPHQJxyb8seJxdgMpi3l/jYSEREREVHc4MSCiIiIiIjCxokFERERycrjFXG0xo76RnesUyGiKOIaCyIiIpJNpcWJj7Ych9XpgSAA5xZmYHSvrFinRURRwIkFERERyWbdvipYnR4AgCQBX2wvxQ+Ha6ASBPTNS8HEvtlQCTFOkogigo9CERERkWxq7S7f/zvcXuyrsKLK6oLbK2HH8XpsPFQbw+yIKJI4sSAiIiLZdM9K9v1/Q6MbapUAs/7kAxJHa+2xSIuIooATCyIiIpLN6F5Z6JmdjIoGByAAhZnJ0JyyD362KSmG2RFRJHFiQURERLJpaHTjSI0dOSl65Jj18IgiXB4RAJCXqseI7hkxzpCIIoWLt4mIiEg2O040wO2VfF93SjVgaH4azu6ahoxkHQBAFMVYpUdEEcQ7FkRERCSb1jZ80mvVvkkFEXVcnFgQERGRbAZ1SYVOc/LthV6rxoDOKTHMiIiihY9CERERkWwyknX49ch87DzRAEEQMLBLClL02linRURRwIkFERERySrNqMMonrZNlHD4KBQREREREYWNEwsiIiIiIgobJxZERERERBQ2TiyIiIiIiChsnFgQEREREVHYOLEgIiIiIqKwcWJBRERERERh48SCiIiIiIjC1uEnFnV1dRg+fDiGDBmCQYMG4dVXX411SkREREREHU6HP3nbbDbjm2++gdFohM1mw6BBgzBr1ixkZmbGOjUiIiIiog6jw9+xUKvVMBqNAACn0wlJkiBJUoyzIiIiIiLqWBQ/sfjmm29w8cUXo3PnzhAEAR999FGLMiUlJSgsLIRer8fIkSOxYcMGv9fr6uowePBgdO3aFffccw+ysrKilD0RERERUWJQ/MTCZrNh8ODBKCkpafX19957D3fddRcefvhhbN68GYMHD8bUqVNRUVHhK5OWloZt27bh4MGDeOedd1BeXh6t9ImIiIiIEoLi11hMmzYN06ZNa/P1Z555BjfccAPmz58PAHjppZfw+eef4/XXX8f999/vVzY3NxeDBw/GmjVrMGfOnBaxnE4nnE6n7+uGhgYAgCiKEEWx3TxFUYQkSbKVSzRK75dY5BepNuWMG06sUOsGUy9SZRNFPPQJx6b8cUKpH2wdjs3wxEOfcGzKHydWYzOY9hQ/sWiPy+XCDz/8gAceeMB3TaVSoaioCOvXrwcAlJeXw2g0wmw2o76+Ht988w1uvvnmVuM99dRTePTRR1tcr6yshMPhaDcXURRRX18PSZKgUrV9IyjQcolG6f0Si/wi1aacccOJFWrdYOpFqmyiiIc+4diUP04o9YOtw7EZnnjoE45N+ePEamxaLJaAc4zriUVVVRW8Xi9yc3P9rufm5mLXrl0AgMOHD+PGG2/0Ldq+9dZbcdZZZ7Ua74EHHsBdd93l+7qhoQHdunVDdnY2UlJS2s1FFEUIgoDs7OwzTiwCKZdolN4vscgvUm3KGTecWKHWDaZepMominjoE45N+eOEUj/YOhyb4YmHPuHYlD9OrMamXq8POMe4nlgEYsSIEdi6dWtAZZOSkpCUlNTiukqlCuiHIQhCQGUDLZdolN4vscgvUm3KGTecWKHWDaZepMominjoE45N+eOEUj/YOhyb4YmHPuHYlD9OLMZmMG0p97cxAFlZWVCr1S0WY5eXlyMvLy9GWRERERERJZ64vmOh0+kwbNgwrFy5EjNnzgTQdAtn5cqVuOWWW2Rrh4u3I0/p/cJFaPLH4uJt5YuHPuHYlD8OF28rXzz0Ccem/HG4eFsGVqsV+/bt83198OBBbN26FRkZGcjPz8ddd92FuXPnYvjw4RgxYgQWLFgAm83m2yUqFCUlJSgpKYHX6wXAxdvRoPR+4SI0+WNx8bbyxUOfcGzKH4eLt5UvHvqEY1P+OFy8LYNNmzZh4sSJvq+bF1fPnTsXb7zxBq644gpUVlbioYceQllZGYYMGYJly5a1WNAdjOLiYhQXF6OhoQGpqalcvB0FSu8XLkKTPxYXbytfPPQJx6b8cbh4W/nioU84NuWPw8XbMpgwYQIkSWq3zC233CLro0+n4+Lt6FB6v3ARmvyxuHhb+eKhTzg25Y/DxdvKFw99wrEpfxwu3iYiIiICIEkSqq1OOD3eWKdCRBGg+DsWSsDF25Gn9H7hIjT5Y3HxtvLFQ59wbMofJ1ILRKutTny6rRT1jW7o1AKG5aqQlZUVkXw6unjoE45N+eNw8Xac4uLt6FN6v3ARmvyxuHhb+eKhTzg25Y8TqQWi6/ZVwW11wggAXgl7jjiRl6KHXtf+W5F4+D2MtnjoE45N+eNw8Xac4uLt6FN6v3ARmvyxuHhb+eKhTzg25Y8TqQWiFT9b4VBrm76QJMALqE2pyElLlj2fji4e+oRjU/44XLzdQXDxdnQovV+4CE3+WFy8rXzx0Cccm/LHicQC0W6ZRuwtt/q+1mrVyDEbODZDFA99wrEpfxylL97mxIKIiIgibmLfHHhFCQerbMhI1mFoth5atXLfFBNR8DixICIioohLTtJgxpAuAJoet6ioqIhxRkQkN04sAsBdoSJP6f3C3S3kj8VdoZQvHvqEY1P+OLHaeUbOfDq6eOgTjk3543BXqDjFXaGiT+n9wt0t5I/FXaGULx76hGNT/jix2nlGznw6unjoE45N+eNwV6g4xV2hok/p/cLdLeSPxV2hlC8e+oRjU/44sdp5Rs58Orp46BOOTfnjcFeoDoK7QkWH0vuFu1vIH4u7QilfPPQJx6b8cWKx84zc+XR08dAnHJvyx1H6rlDK/W0kIiIiIqK4wYkFERERERGFjRMLIiIiIiIKG9dYBIDbzUae0vuF2+bJH4vbzSpfPPQJx6b8cbjdrPLFQ59wbMofh9vNxiluNxt9Su8XbpsnfyxuN6t88dAnHJvyx+F2s8oXD33CsSl/HG43G6e43Wz0Kb1fuG2e/LG43azyxUOfcGzKH4fbzSpfPPQJx6b8cbjdbAfB7WajQ+n9wm3z5I/F7WaVLx76hGNT/jjcblb54qFPODblj8PtZomIiIiIqMPjxIKIiIiIiMLGiQURUQxJkoQKiwMNDnesUyEiIgoL11gQEcWIzenBh1uOo9LihCAAg7umYWK/nFinRR2AyyNi0+Fa2F1e9Mk1o3OaIdYpEVEC4MQiADzHIvKU3i/cj1v+WDzHAvj+QDUqG5q2spYkYOuRWvTKMaJLmjFqObRH6eMS4NhsjcvtwZo9Fahw6wFBwNYjtbh4cCd0zzJFLA+eYxFd8dAnHJvyx+E5FnGK51hEn9L7hftxyx+L51gAtdVVMHqdfteOnyiD1hXYG8BIU/q4BDg2W3Oizg53oxVGlQeAAADYtucIksWsiOXBcyyiKx76hGNT/jg8xyJO8RyL6FN6v3A/bvlj8RwLoFujFkf2Vvm+VgkC+hR0RVqyLirtn4nSxyXAsdmaBjTAKdTArjIBQtPEQtIbkZMT2GN2PMdC+eKhTzg25Y/Dcyw6CJ5jER1K7xfuxy1/rEQ/x2JYQQbsLi9+LrXAoFVhVK8sZJgD/ws8GpQ+LgGOzdMVZppgMmhhdwuAIEAQgCEF6RHd9z6UOkoem/EgHvqEY1P+OEo/x4ITCyKiGFGrBEzom4MJfblgm+SjUaswtnc2ytxJXLxNRFHFiQUREVEHo9OoMLxzcHcpiIjCxYkFERERyWpPuQXr9lXB7vKifyczxvfJgVolxDotIoowTiyIiIhINnV2F5ZuL4MoSQCAbUfrYdRpcF6PzBhnRkSRxnukREREJJujNY2+SUWzI9X2GGVDRNHEiQURERHJJtPUcrvk1q4RUcfDR6ECwJO3I0/p/cITROWPxZO3lS8e+oRjU/444Z7um5eShCHdUrHtaB0kCcgyJ+HcwvQWJ/lybIYuHvqEY1P+ODx5O07x5O3oU3q/8ARR+WPx5G3li4c+4diUP44cp/v2TwMKk01weUSkGrSw19fA3k75SH4/HVE89AnHpvxxePJ2nOLJ29Gn9H7hCaLyx+LJ28oXD33CsSl/HJ68rXzx0Cccm/LH4cnbHQRP3o4OpfcLTxCVP1ain7wdD+KhTzg25Y/Dk7eVLx76hGNT/jhKP3lbub+NREREREQUNzixICIiIiKisHFiQUREREREYePEgoiIiIiIwsaJBRERERERhY0TCyIiIiIiChsnFkREFDab04PS+kZ4RSnWqRARUYzwHAsiIgrLD4drsW5fFbyihOQkNWYO6YKclMAPVCIioo6BdyyIiChkVqcHa/dW+e5U2JxerN5TGeOsiIgoFnjHIgCiKEIUxTOWkSRJtnKJRun9Eov8ItWmnHHDiRVq3WDqRapsogikT2ptjhav11qdUetHjk3544RSP9g6HJvhiYc+4diUP06sxmYw7XFi0YqSkhKUlJTA6/UCACorK+FwONqtI4oi6uvrIUlSu0efB1ou0Si9X2KRX6TalDNuOLFCrRtMvUiVTRSB9IkgikhT2eFyn/yHp6vBiIqKCsXkGC9tyhU33Dih1A+2DsdmeOKhTzg25Y8Tq7FpsVgCzpETi1YUFxejuLgYDQ0NSE1NRXZ2NlJSUtqtI4oiBEFAdnb2GScWgZRLNErvl1jkF6k25YwbTqxQ6wZTL1JlE0WgfTLVkIpv9lShxu5C96xkjO+TDb1Wragc46FNueKGGyeU+sHW4dgMTzz0Ccem/HFiNTb1+sDXzHFiEQCVShXQD0MQhIDKBlou0Si9X2KRX6TalDNuOLFCrRtMvUiVTRSB9Enn9GRcOTI5iln549iUP04o9YOtw7EZnnjoE45N+ePEYmwG05ZyfxuJiIiIiChucGJBRERERERh48SCiIiIiIjCxokFERERERGFjRMLIiIiIiIKGycWREREREQUNk4siIiIiIgobJxYEBERERFR2DixICIiIiKisHFiQUREREREYdPEOgEiIgqO2ytiT7kFDrcXvbLNSDVqY51SwNxeEQerbACA7lnJ0Kr5+RYRUUfBiQURURzxihLe33QUFQ1OAMD6/dWYPawrOqUaYpzZmTW6vHh34xHU2d0AgDSjFleemw+DTh3jzIiISA78qIiIKI4crLL6JhUA4PZK+OFwbQwzCtz24/W+SQUA1Nnd2HGiPoYZERGRnDixICKKIy6P1Mo1MQaZBM/u8rRyzRuDTIiIKBL4KFQARFGEKLb/D7coipAkSbZyiUbp/RKL/CLVppxxw4kVat1g6kWqbCwVZhpg1Kr83pAP6myOSN5y90mfnGRsPVIL6Ze5kSAAfbKTw4rPsSl/nFDqB1unI47NaIqHPuHYlD9OrMZmMO1xYtGKkpISlJSUwOtt+oe7srISDoej3TqiKKK+vh6SJEGlavtGUKDlEo3S+yUW+UWqTTnjhhMr1LrB1ItU2Vi7oHsSDlTa4PSI6JpuQAoaUVHRKHs7cveJCsDEgiQcrPxl8XZ2MgRnAyoqGhSTYyzblCtuuHFCqR9snY46NqMlHvqEY1P+OLEamxaLJeAcObFoRXFxMYqLi9HQ0IDU1FRkZ2cjJSWl3TqiKEIQBGRnZ59xYhFIuUSj9H6JRX6RalPOuOHECrVuMPUiVTbWcgD06Bb5diLRJzk5wFm9ZAkFgGMzEnFCqR9snY46NqMlHvqEY1P+OLEam3q9PuAcObEIgEqlCuiHIQhCQGUDLZdolN4vscgvUm3KGTecWKHWDaZepMominjoE45N+eOEUj/YOhyb4YmHPuHYlD9OLMZmMG0p97eRiIiIiIjiBicWREREREQUNk4siIiIiIgobJxYEBERERFR2DixICIiIiKisHFiQUREREREYePEgoiIiIiIwsaJBRERERERhY0TCyKiBCGKEiRJinUaRETUQfHkbSKiDs4rSvhqVwV+Lm2ATqPCeT0yMaRbWqzTIiKiDoZ3LIiIOrjNR2qx43g9vKKERpcXq3ZVoLzBEeu0iIiog+HEgoiogzte29ji2rFWrhEREYWDEwsiog4ux5wU0DUiIqJwcGJBRNTBDStMR0GmEQCgVgkYXpiObhnGGGdFREQdDRdvExF1cEkaNWad0xUWhxtatQp6rTrWKRERUQfEiQURUYIw67URiev2ivjpRANq7S50z0xGYVZyRNohIiJl48SCiIjC8vHWEzhaYwcAbD1Sh0n9cjCY29kSESUcrrEgIqKQVVgcvklFs81HamOUDRERxRInFkSUkBpdXpyoa4TbK8Y6lQ6Hh3sTESUmPgpFRAlnx/F6rNpVAY8owaBT4+LBndEphduvhiLHrEe3DKPfXYtzCtJjmBEREcUKJxZElFAcbi9W726aVABNdy6+2lWBX4/oFuPM4teMIZ2x43g96uxuFGYlozsXbxMRJSROLIgooVgcHri9/s/q1NpcMcqmY9CqVRiaz7sURESJrsOvsTh69CgmTJiAAQMG4Oyzz8YHH3wQ65SIKIYyk3Uw6/0/U2k+PI6IiIhC1+HvWGg0GixYsABDhgxBWVkZhg0bhgsvvBDJybxVT5SIVCoBlwzpjNW7K1FldSI/w4hJ/XJinRYREVHc6/ATi06dOqFTp04AgLy8PGRlZaGmpoYTC6IElmPW4/Lh/msqRJG7QxEREYVD8Y9CffPNN7j44ovRuXNnCIKAjz76qEWZkpISFBYWQq/XY+TIkdiwYUOrsX744Qd4vV5068ZFmkREREREclL8xMJms2Hw4MEoKSlp9fX33nsPd911Fx5++GFs3rwZgwcPxtSpU1FRUeFXrqamBtdddx1eeeWVaKRNRERERJRQFP8o1LRp0zBt2rQ2X3/mmWdwww03YP78+QCAl156CZ9//jlef/113H///QAAp9OJmTNn4v7778eoUaPajOV0OuF0On1fNzQ0AGh6ROJMj0mIoghJkmQrl2iU3i+xyC9SbcoZN5xYodYNpl6kyiaKeOgTjk3544RSP9g6HJvhiYc+4diUP06sxmYw7Sl+YtEel8uFH374AQ888IDvmkqlQlFREdavXw8AkCQJ8+bNw6RJk3Dttde2G++pp57Co48+2uJ6ZWUlHA5Hu3VFUUR9fT0kSYJK1faNoEDLJRql90ss8otUm3LGDSdWqHWDqRepsokiHvqEY1P+OKHUD7YOx2Z44qFPODbljxOrsWmxWALOMa4nFlVVVfB6vcjNzfW7npubi127dgEA1q1bh/feew9nn322b33Gm2++ibPOOqtFvAceeAB33XWX7+uGhgZ069YN2dnZSElJaTcXURQhCAKys7PPOLEIpFyiUXq/xCK/SLUpZ9xwYoVaN5h6kSqbKOKhTzg25Y8TSv1g63Bs+vu5tAGHqu1IM2oxtFsa9Fp1u+XjoU84NuWPE6uxqdfrA84xricWgRgzZkzAt3CSkpKQlJTU4rpKpQrohyEIQkBlAy2XaJTeL7HIL1Jtyhk3nFih1g2mXqTKJop46BOOTfnjhFI/2Docm002HKzBun1Vvq8PVzfiqhHdIAhCu/XioU84NuWPE4uxGUxbyv1tDEBWVhbUajXKy8v9rpeXlyMvLy9GWREREREFZvvxer+vyxscqLA42yhNpGxxfcdCp9Nh2LBhWLlyJWbOnAmg6RbOypUrccstt8jWDhdvR57S+4WL0OSPxcXbyhcPfcKxKX8cOReIOj1eaFQqqFVCQOXlyieeaAQAkuR3TYX2v9946BOOTfnjcPG2DKxWK/bt2+f7+uDBg9i6dSsyMjKQn5+Pu+66C3PnzsXw4cMxYsQILFiwADabzbdLVChKSkpQUlICr9cLgIu3o0Hp/cJFaPLH4uJt5YuHPuHYlD+OHAtEXR4RPxyuQUWDEzqtCgM6paAgM7nN8pH8fpSuf7qELRYr8MvcIi/NAK+9HhX2tuvEQ59wbMofJyEWbzudzlbXJchl06ZNmDhxou/r5sXVc+fOxRtvvIErrrgClZWVeOihh1BWVoYhQ4Zg2bJlLRZ0B6O4uBjFxcVoaGhAamoqF29HgdL7hYvQ5I/FxdvKFw99wrEpfxw5Foh+tasCh2xaQK2FXQTWn/CgT0E6Uo3aoNuIh9/DcOTkAPmd83Cw2o50oxa9sk1QqdpfXxEPfcKxKX+cDrl4e+nSpXj33XexZs0aHD16FKIoIjk5GUOHDsWUKVMwf/58dO7cOdiwbZowYQKk024Rnu6WW26R9dGn03HxdnQovV+4CE3+WFy8rXzx0Cccm/LHCXeBaGmDEzhl8bEEoMLqQropqdXykcgnnuSlGZGXZgyqTjz0Ccem/HE6zOLtDz/8EH369MFvfvMbaDQa3HfffViyZAm+/PJL/Otf/8L48eOxYsUK9OjRAzfddBMqKysDToKIiIg6jk4p/p9wCgKQmxK5pxuISBkCvmPxt7/9Dc8++yymTZvW6szl8ssvBwAcP34czz//PN566y3ceeed8mUaQ1y8HXlK7xcuQpM/FhdvK1889AnHpvxx5Fggen6PDNTZXThaY0eSVoVRPbOQotf4XufYDE889AnHpvxxOtTi7eaTrM+kS5cu+L//+7+AE1AiLt6OPqX3CxehyR+Li7eVLx76hGNT/jhyLRAd01ULZ64ZGjWgVjlRUVERUhvx8HsYbfHQJxyb8sdJiMXbAOD1erF9+3YUFBQgPT1djpAxxcXb0af0fuEiNPljcfG28sVDn3Bsyh8nVgtE5cyno4uHPuHYlD9Oh1y8DQB33HEHzjrrLFx//fXwer0YP348vv32WxiNRnz22WeYMGFCKGEVi4u3o0Pp/cJFaPLH4uJt5YuHPuHYlD8OT95WvnjoE45N+eN0mMXbp1q0aBEGDx4MAPj0009x8OBB7Nq1C3feeSf+9Kc/hRKSiIiIiIjiWEgTi6qqKuTl5QEAvvjiC1x22WW+HaO2b98ua4JERERERKR8IT0KlZubi507d6JTp05YtmwZXnzxRQCA3W6HWq2WNUEl4K5Qkaf0fuHuFvLH4q5QyhcPfcKxKX+cWO08I2c+HV089AnHpvxxOtSuUKeaP38+Lr/8cnTq1AmCIKCoqAgA8P3336Nfv36hhFQU7goVfUrvF+5uIX8s7gqlfPHQJxyb8seJ1c4zcubT0cVDn3Bsyh+nw+4K9cgjj2DQoEE4evQoLrvsMiQlNR16o1arcf/994cSUlG4K1T0Kb1fuLuF/LG4K5TyxUOfcGzKH4e7QilfPPQJx6b8cTrsrlAAMGfOnBbX5s6dG2o4ReOuUNGh9H7h7hbyx+KuUMoXD33CsSl/HO4KpXzx0Cccm/LHUfquUCFPLDZu3IhVq1ahoqKixbNXzzzzTKhhiYiIiIgoDoU0sXjyySfx5z//GX379kVubi4EQfC9dur/ExERERFRYghpYvHcc8/h9ddfx7x582ROh4iIiIiI4lFIEwuVSoXRo0fLnYticbvZyFN6v3DbPPljcbtZ5YuHPuHYlD8Ot5tVvnjoE45N+eN02O1m77zzTpSUlGDBggWhVFc8bjcbfUrvF26bJ38sbjerfPHQJxyb8sfhdrPKFw99wrEpf5wOu93s3XffjenTp6Nnz54YMGAAtFqt3+tLliwJJaxicLvZ6FN6v3DbPPljcbtZ5YuHPuHYlD9OuFtaCoKADYdq8dPxeug0KpzbPR19c1PaLM+xGbx46BOOTfnjdNjtZm+77TasWrUKEydORGZmZodfsM3tZqND6f3CbfPkj8XtZpUvHvqEY1P+OOFsabmz1ILvDtQ0XXR68eVPFcg06ZFj1rdanmMzNPHQJxyb8sfpkNvNLly4EIsXL8b06dNDqU5EREQd1IEqm9/XkgQcrra3mFgQUccT0nQpIyMDPXv2lDsXIiIiinMZRl2La+mtXCOijiekicUjjzyChx9+GHa7Xe58iIiIKI4NK0hHtjnJ93WfXDN6ZCXHMCMi5XN7Rfx0oh4/HK5BfaM71umELKRHof7f//t/2L9/P3Jzc1FYWNhi8fbmzZtlSY6IiIjii0Gnxq9H5qO8wQmdRoWMZN6tIGqPxyvivY1HUWlxAgDW76/GZcO7ITcl/h4fDGliMXPmTJnTUDaeYxF5Su8X7sctfyyeY6F88dAnHJvyx5Frr/wcs873WjhtxMPvYbTFQ59wbAYeZ3+lBZUNJ481cHskbDpUg2mD8sLOIy7OsXj44YdDqRY3eI5F9Cm9X7gft/yxeI6F8sVDn3Bsyh+H51goXzz0Ccdm4HHqqm0weq1+Zd0WDyoqVAHVlzP3qJ1jIUlSh99WthnPsYg+pfcL9+OWPxbPsVC+eOgTjk3548Rqr3w58+no4qFPODYDj5OS7sWWSgmNLq/v2sBenZCTbQo7D8WeYzFw4EA89NBDmDVrFnS6tp+X3Lt3L5555hkUFBTg/vvvDzgRJeM5FtGh9H7hftzyx+I5FsoXD33CsSl/nFjslS93Ph1dPPQJx2ZgcYxJKlw5Ih+bj9Si0SWifyczepw2qQgnD0WeY/H888/jvvvuw+9//3tMnjwZw4cPR+fOnaHX61FbW4udO3di7dq1+Omnn3DLLbfg5ptvDjgJIiIiIqJElWbUYVK/3FinEbaAJxYXXHABNm3ahLVr1+K9997D22+/jcOHD6OxsRFZWVkYOnQorrvuOvz6179Genp6JHMmIiIiIiKFCXrx9pgxYzBmzJhI5EJERERERHFKuQ/mERERERFR3ODEgoiIiIiIwsaJBRERERERhS2kA/ISDU/ejjyl9wtPEJU/Fk/eVr546BOOTfnjxOp0Xznz6ejioU84NuWP02FP3u7oePJ29Cm9X3iCqPyxePK28sVDn3Bsyh+HJ28rXzz0Ccem/HE61Mnbp9u/fz/+/e9/Y//+/XjuueeQk5ODpUuXIj8/HwMHDgw1rCLw5O3oU3q/8ARR+WPx5G3li4c+4diUPw5P3la+eOgTjk3543Sok7dP9fXXX2PatGkYPXo0vvnmGzzxxBPIycnBtm3b8Nprr2HRokWhhFUsnrwdHUrvF54gKn8snrytfPHQJxyb8sfhydvKFw99wrEpfxyln7wd0nd1//334y9/+QuWL18OnU7nuz5p0iR89913oYQkIiIiIqI4FtLEYvv27bj00ktbXM/JyUFVVVXYSRERERERUXwJ6VGotLQ0lJaWonv37n7Xt2zZgi5dusiSGBERERFRIvjpRD3W76+Gw+1F/04pmNA3B2qVAACotDih16qQrFPHOMszC2liceWVV+K+++7DBx98AEEQIIoi1q1bh7vvvhvXXXed3DkSEREREXVI1VYnlu8shyQ1ff3jsXqY9VoM6pKCD7ccR0WDE4IAnN0lFQPSY5vrmYT0KNSTTz6Jfv36oVu3brBarRgwYADGjRuHUaNG4c9//rPcORIRERERdUhHaxt9k4pmR2rs+P5gDSoanAAASQK2Ha1DpcUZgwwDF9IdC51Oh1dffRUPPvggduzYAavViqFDh6J3795y50dERERE1GFlmXStXquyulpctzrc0UgpZGEdkJefn4/8/Hy5ciEiIiIiSihd0404pyAdW4/UQZQkdErVY2T3TOw4UY+jNXZfOUEAssxJMcz0zEKaWEiShEWLFmHVqlWoqKhocdT3kiVLZEmOiIiIiKijG98nG8ML0uHyiEhPbrqDcU5+OqxOD34ubYBBq8Z5PdJhFhwxzrR9IU0s7rjjDrz88suYOHEicnNzIQiC3HkRERERESWM5CQNkk+5IaFWCZjYNwcT++YAaDoVu6KiA04s3nzzTSxZsgQXXnih3PkokiiKLe7KtFZGkiTZyiUapfdLLPKLVJtyxg0nVqh1g6kXqbKJIh76hGNT/jih1A+2jtLG5pEaGw5VNyIzWYu+uWZo1Mo9zRrg2Ix2m4k+NoNpL6SJRWpqKnr06BFK1bhQUlKCkpISeL1eAEBlZSUcjvZniKIoor6+HpIktXv0eaDlEo3S+yUW+UWqTTnjhhMr1LrB1ItU2UQRD33CsSl/nFDqB1tHSWNzf6UVO47V+74+cESPkT0yZW9HThyb0W0z0cemxWIJOMeQJhaPPPIIHn30Ubz++uswGAyhhFC04uJiFBcXo6GhAampqcjOzkZKSkq7dURRhCAIyM7OPuPEIpByiUbp/RKL/CLVppxxw4kVat1g6kWqbKKIhz7h2JQ/Tij1g62jpLH52R4b7GqT7+sDVmBMcprvOXcl4tiMbpuJPjb1en3AOYY0sbj88svx3//+Fzk5OSgsLIRWq/V7ffPmzaGEVSyVShXQD0MQhIDKBlou0Si9X2KRX6TalDNuOLFCrRtMvUiVTRTx0Cccm/LHCaV+sHWUMjYlCE3b7fg3qOjfeYBjM9ptJvLYDKatkCYWc+fOxQ8//IBrrrmGi7eJiIgobg3JT8PavVW+r/MzjMg0KXtLTyKlCmli8fnnn+PLL7/EmDFj5M6HiIiIKGrOLcxAmkGLw9V2ZJh0OKtLaqxTIopbIU0sunXrdsY1B0RERETxoHeuGb1zzbFOgyjuhfSA19NPP417770Xhw4dkjkdIiIiIiKKRyHdsbjmmmtgt9vRs2dPGI3GFou3a2pqZEmOiIiIqKOrs7twuNqOdKMO3TIMXLtKcSukicWCBQtkToOIiIgo8eyrsOLzH0shShIAYEDnFEwdmBfjrIhCE/KuUEREREQUnvUHqn2TCgDYeaIBIwozFH2OBlFbAp5YNDQ0+BZsNzQ0tFuWC7uJiIiIzszp9ra45vC0vEaJR3K5YV+9AdZFyyEkG5D19D2xTumMAp5YpKeno7S0FDk5OUhLS2v1+T9JkiAIArxeDggiIiKiM+nfKQUbDp5cm5qRrENeSuAnHVPHIokiHN9vh3XJclg/XgWxtunDfMGoR8ZjxTHO7swCnlh89dVXyMjIAACsWrUqYgkRERERJYrze2TCoFPjYKUN6clajOieycXbCcj50z5YF6+A9cMV8Bwr911X52TAdOkFMM2eDMFoAGzWGGZ5ZgFPLMaPH+/7/+7du6Nbt24tfvElScLRo0fly46IiIioA1OpBJyTn45z8tNjnQpFmftIKaxLVsC6ZAVcPx/wXZeSDUi5eAJMc6bAMGYoBLUaACCKYqxSDVhIi7e7d+/ueyzqVDU1NejevTsfhSIiSjAujwiHx4sUvfbMhYmIEpRU24CGL76FbfEKODZs910XNRqUDz0bx0aNRPnQszF5aD5yOsffmuWQJhbNaylOZ7VaodfzuUAiokSy5Ugtvt1fDZdHRKdUPS4a3BmmpJD+eSEi6nBEWyNsy9bCsmg5GldvgL15cb4gQD96KOonjcFXXXvBbUr21dld3oABHX1icddddwEABEHAgw8+CKPR6HvN6/Xi+++/x5AhQ2RNkIiIlKvO7sLXeyrRvFtmab0Da/dW4VeDuA9/ovKKEn44XIsjNXZkmnQY2T0DRh0nmpRYJLcH9tUbYV28HLalayDZHb7XdGf3gXn2ZJguvQCaTtlorLDCve2EX32DNj7HTFBZb9myBUDTHYvt27dDpzu5x7JOp8PgwYNx9913y5uhAoiieMbn2kRRhCRJspVLNErvl1jkF6k25YwbTqxQ6wZTL1JlE0UgfVLe0AhJlPyuVTY0Rq0fOTbljxNK/VPrfLO3GtuO1gEAjlbbUFrbiCtHdAu5DY7NluKhTxJxbEqiCOemn5omE5+shlhT73tNU9gFyZdOgnP8UOSOHAqVSuWLXZhhQNc0PY7VNgIADDo1huentmgv3LEZavlg2gtqYtG8G9T8+fPx3HPPddjzKkpKSlBSUuJbK1JZWQmHw9FuHVEUUV9fD0mSfL8s4ZRLNErvl1jkF6k25YwbTqxQ6wZTL1JlE0UgfaJzeZAs2iCdcsBXjjYZFRUViskxXtqUK264cUKpf2qdg0fLYfSefCNiqbVi32EVUgzaVstzbAYvHvokkcamuPcIPF+sheeLtZBOVJ58ISMVmmmjoLlwLFRn9YJLktBQXw+hoqJFnNFdtKhMFeHyiMhN0cNjr0eFPfzvL9g6rZW3WCwBtQWEuMbi3//+dyjV4kZxcTGKi4vR0NCA1NRUZGdnn3ESJYoiBEFAdnb2GScWgZRLNErvl1jkF6k25YwbTqxQ6wZTL1JlE4EkSfjxWD2O1NQgTaXG8IIMGHTqVsuO1ZqwZk8V7C4vumclY+yAXOi1rZeVG8em/HFCqX9qHcHggL3R7XtNEIC8vFy/dTccm+GJhz7p6GPTc6wc1g9XwrZkBVw79/vKCskGJE8fh+TZk5t2dNIE/nt/pgdIwx2bgU4sTi8fzPrp+HyAK8pUKlVAPwxBEAIqG2i5RKP0folFfpFqU8644cQKtW4w9SJVtqNbt68KGw5Uw+h14oCtDsfrnLh6ZH6rZft3SkO/vFR4RQkadfT7jmNT/jih1G+uM6p3FpbtKPOtuxmcn4YUg67N8hyboYmHPuloY1Oqs8Dy+TewLloOx3fbTr6o1cBYdB7MsybDOHU0VIakiOUXztgMtM7p5YNpixMLIiJqYcfxer+vyxscqLA4kGNu/ZMrQRCgUfNQLwL65aUgx6zH0V8Wb3dNN565EpFCiXYHrEvXwPHfz3Fk3Vbg1B2dRg2BeXYRki+aAHV6x1weECxOLIiIqAWdRgW70/9akjo6jzdR/MtI1iEjueVdCqJYcHlErNtXhcPVNmSYkjC2VxbS2/n9lDwe2FdvgnXJctg+XwPJ3uh7TTeoN8xzJsM0cxI0XXKjkX5c4cSCiIhaOK9HJr7cUer7un+nFKQaefgdEcWfVbsrsPNEAwCg1u5GlcWJeaMKoVKdvMsqSRKcm36CZdFyWD/5CmJVne81TX4nCFPPR861M6Dv3yPa6ceVkCcWe/fuxapVq1BRUdFiG6qHHnoo7MSIiCh2+ndKQYZRgz2HjqNzp1z0yDbHOiUiopDsr7T6fV3f6Eal1YncFD1cuw/CsngFrEuWw3P45Icpqqw0mGZMgnn2ZGjP6Y/KykrocnKinXrcCWli8eqrr+Lmm29GVlYW8vLy/E7hFgSBEwsiog4g26yHlGtGTpbJ7+/5tkiSFFA5IqJoMuu1cLpPPttprKmF8PoGHP34K7h27PVdF4wGJE8fC/PsKTCMGwbhl0PqlHxeiNKENLH4y1/+gieeeAL33Xef3PkQEVGc2Vtuwdd7KmFzetErx4SiATlI0nA9BhEpw/je2Vj27R5krduILt9+j8xde2Bp3rZMo4bxgvNgml2E5KljoDIGvrUqtRTSxKK2thaXXXaZ3LkQEVGcsTo9WLqjDN5fTt/eU26BUafGxH58ZICIYktsdML+5TpolyzHBSu+A9we32v68wbDNGcyTBdPgDojNYZZdiwhTSwuu+wy/O9//8NNN90kdz5ERBRHTtQ1+iYVzY7V2tsoTUQUWZLHC/uqDbAtWQnb519Dsp2yo9PAnjDNngzTpUXQduWOTpEQ0sSiV69eePDBB/Hdd9/hrLPOglbrv1PIbbfdJktyRESkbNmmJAgCfIehAUC2ue3DoYiI5CZJEpybd8Lywf9g/3Al7DUnz+HRdMuDaVYRTLMnI4k7OkVcSBOLV155BSaTCV9//TW+/vprv9cEQeDEgogoQaQn6zCmVxbW76+GR5SQbU7CqF5ZsU6LiBKAa+9hWBcvh2XxCngOHfddV2WkwjRjEkyzi6AfcRY3lYiikCYWBw8elDsPIiKKU8MLMzCoSyocbi/SjDwUjYgix1NaCeuHK2FZvByuH/f4rgtGPYy/GgPPBeei04wiqJP4d1EshH1AnvTL/W/OBomI5Fdnd0GvVUOvVfYuS/GQIxHFJ2+dBbZPV8OyZAUc67acfPZSo4Zx4giYZk9G8q/GAIYkVFRU+LaJpegLuef/85//4O9//zv27m3a/7dPnz645557cO2118qWHBFRorI5Pfhk2wmU1TugVgk4tzAD5/fMjHVaRERRITY6YV/+LayLl8O24jvA5fa9ph95Nkyzi2C6ZCLUmWkn6/C8iZgLaWLxzDPP4MEHH8Qtt9yC0aNHAwDWrl2Lm266CVVVVbjzzjtlTZKIKNF8d6AaZfUOAIBXlPDdgWr0zElGjpl7rBNRxyR5vWhcs7lpMvH5NxAtNt9ruv49ftnR6QJo8zvFMEtqT0gTi+effx4vvvgirrvuOt+1Sy65BAMHDsQjjzzCiQURUZgqLc5Wr4U6sThR14idJxqQpFVhcLc0pOi1Z65ERBRhkiTBuXUXrIuWw/rRSngranyvabrmwnRpEUxzJiNpQM8YZkmBCmliUVpailGjRrW4PmrUKJSWloadFBFRouuabkTpL3csAEAQgK5pxpBiHa2xY/HmY77Hkn8ubcB15xdyTQQRxYxr/xFYF6+AdfFyuA8c811XpafANGMiTLMmQz/yLAgqVQyzpGCFfI7F+++/jz/+8Y9+19977z307t1blsSIiBLBjuP1WL+/Gg63F/07pWBivxyoVQJG9siA1enGnnIrjDo1RvfKQqoxtLsMPx6r9ztnwub0Ym+5FWd15WmzRBQ9nrIqWD9aCeviFXBu3eW7LhiSkPyrMTDNmQzjhBEQdLyjGq9Cmlg8+uijuOKKK/DNN9/41lisW7cOK1euxPvvvy9rgkREHVWV1YkVP5f73vRvP16PFIMWI7pnQKtW4VeDOmHqQCnsXfdUrVTnh4BEFA1igxXWz9fAumQ5GtdsPrmjk1oN44RzYZpdhORpY6EyhXZHlpQlpInF7Nmz8f333+PZZ5/FRx99BADo378/NmzYgKFDh8qZHxFRh3W0xu53J6H52ojuGb6v5djKe2h+OvZVWOERmxpLM2rRK8cUcH2PV4RKAjRqzkaI6MxEhxO25evheOczHFmzBZLT5XtNf+6gpkXYMyZCnZUewywpEkLebnbYsGF466235MyFiCihZJuTAroWrrxUPX59XgF2l1mQpFVhQKcUJGnOvL5CFCVsO1qH3TsaoFIJGJqfjtE8VZsCUN/oxpYjtWh0edGvUwq6ZyXHOiWKMMnrReO6LbAuXgHbZ19DbLD6XtP2LYR59mSYZhVBW9A5hllSpAU8sWhoaEBKSorv/9vTXI6IiNrWNd2IYQXp2HKkDqIkoUuawe9uhZwyknVBn4Oxs7QBh6ps8KpN8ErAhoM1yEvVo2d24Hc7lMLlEaFVCzzMNQqcHi/e33gUVqcHALCrzIKLzu6E3rnmGGdGcpMkCa4f98Cy6H+wfrgS3vJq32vqztlQTT0f2dfOgH5Qb469BBHwxCI9PR2lpaXIyclBWlpaq78gktT0LLDX65U1SSIipXN5ROytsEAUgd65poB3XBrXJxvDC9Ph8ohIM+oinGVwTpyyK1Wz0jpHXE0sGhxuLNtRhuO1jTDrNbigfy4/PY+wA5U236Si2fbj9ZxYdCCu/Udh/XAFrItXwL3viO+6Ks0M0yUTYZpVBN3Is1BZVYWknBxOKhJIwBOLr776ChkZTZ+krVq1KmIJRcKll16K1atX44ILLsCiRYtinQ4RdTAOtxf/3XAEdfamk2HXH6jClSPyAz4rwqjTQGFzCgBAbooeh1tck/9RrUhatasCx2sbAQAWhwdfbC/Fb8d2D+hRMAqNVt3yTSTX58Q/T3k1rB99Bevi5XBu+dl3XdDrmnZ0mj0ZxokjICQ1/WXGU7ATU8ATi/Hjx/v+v3v37ujWrVuLGagkSTh69Kh82cnk9ttvx29+8xssXLgw1qkQUQf004kG36QCaNrOddvROoztnR1yTLdXxFe7KrDjWD3MBi3G98lG37zofuI7qHMKTpQasLcBUKkEnNUlNahF30pwos7/rovLI6LK6kKXNEOMMur4umeZkG1O8h3yqFEJGFbARbrt8XhFlFucSDVoYUoKefmr7ESLDdbPvoZ1yQo0fvMD0DxZUKlgGD8c5jmTkXzhOO7oRD4h/fZ2797d91jUqWpqatC9e3fFPQo1YcIErF69OtZpEFEH5fK0/GTO6Q7v07o1eyuxaNMxNDjcUAkC9lVY8afp/ZFqiN7+7mqVgGEFGZiYngmVShWXB+rlpiThcLXd97VWLSAzWYG3hzoQtUrA5cO7YU+5BY1uL3plm5DOPm9TeYMDH205DrvLC5UgYHSvTAwvjMxaq0BIThfsK7+DZdFy2P/3rd+OTknDB8I8qwjJMyZBkxO7HEm5Qro32byW4nRWqxV6vT7spE71zTff4OKLL0bnzp0hCIJve9tTlZSUoLCwEHq9HiNHjsSGDRtkzYGIqD1988zQnHJYhCAAAzqHt4nFN3sq0eBougsiShIOV9uw43hdyPHsLg92HK/HnnILPN7gJj16rTouJxUAMLFvDrJ+2WnLoFNj8oC8uP1e4olOo8KgLqk4tzCDk4oz+HpPJeyupg9kRUnCun3VLdaoRJrk9aJx7WZU3PlXHBo4A2Vz/wTbp6shOV3Q9i5Axv2/Rf6Gd9F16UtIvWEOJxXUpqDuWNx1110AmvZVf/DBB2E0nrz15fV68f3332PIkCGyJmiz2TB48GD85je/waxZs1q8/t577+Guu+7CSy+9hJEjR2LBggWYOnUqdu/e3eKOChFRJGQk6zBneFdsPVIHryTh7C5p6BzmozatPZ58+pkXgaq2OvH+pmNwuJvevOSm6HH58K4J8dx7erIO155XAIvDDYNWnRDfM8WXWpvL72tRklBnd0X8kSjfjk5LVjTt6FRa6XtNnZcF06wimGdPhu4s7uhEgQvqt3bLli0Amn4Zt2/fDp3u5KcQOp0OgwcPxt133y1rgtOmTcO0adPafP2ZZ57BDTfcgPnz5wMAXnrpJXz++ed4/fXXcf/99wfVltPphNPp9H3dvK2uKIpnXIQkiiIkSZKtXKJRer/EIr9ItSln3HBihVo3mHqRKtuaXHMSpg7M9YsXjlG9MnG8zgab0wtBADqnGTAgzxxS3E2HauBwnfwEtLy+EfsqLOhzhl16lD4ugcBzTNapfeWj1Was4oYbJ5T6wdaJ5thUuoJMI34+cXIbf6NOjRyTrt3vN5w+cR86DuuSFbAtWQH33lN2dEo1wXjReJhmT4b+vLMhqJvGjCRJkEL4VIP/bsofJ1ZjM5j2gppYNO8GNX/+fDz33HMxP6/C5XLhhx9+wAMPPOC7plKpUFRUhPXr1wcd76mnnsKjjz7a4nplZSUcjpbbLp5KFEXU19dDkiSoVG1/IhZouUSj9H6JRX6RalPOuOHECrVuMPUiVTYaBqZLcPcz41CVDQadBmd1SYHLVocKW/CxHJZaGL2NftdqqipRITS2UaOJ0vqkNRyb8scJpX6wdeJ5bMptYLoE0S6iosEBU5IGAzunoqa6qt06wfaJVFUHz5fr4fliDcQf9558QaeFevwwaKaPhXrsUEg6LSwALNXVbcYKFMem/HFiNTYtFkvAOYZ0n+3f//53KNVkV1VVBa/Xi9zcXL/rubm52LVrl+/roqIibNu2DTabDV27dsUHH3yA888/v0W8Bx54wPe4F9B0x6Jbt27Izs4+4yRKFEUIgoDs7OwzTiwCKZdolN4vscgvUm3KGTecWKHWDaZepMpGS5dOefB4RahV4R3sNkAw4MC2Mt/XOo0Kg3rln/FRCyX2yek4NuWPE0r9YOvE+9iUW5dOeUGVD6RPRKsdti++gW3JyqYdnZo31lGpoB97DkyzJyP5wrFQmSNzrgvHpvxxYjU2g1k/HdLEYtKkSe2+/tVXX4USNmJWrFgRULmkpCQkJbXco12lUgX0wxAEIaCygZZLNErvl1jkF6k25YwbTqxQ6wZTL1Jlo0UnQy69c1Nx0WAVfjrRgCSNCsMK05FiCGxBrRL75HQcm/LHCaV+sHXifWzGWmt9IrncsH/1fdOOTl+uheQ4ZUenc/rDNGsyTDMnQZObGbMc47XNRB6bwbQV0sRi8ODBfl+73W5s3boVO3bswNy5c0MJGZKsrCyo1WqUl5f7XS8vL0deXnCzfyKijqx3rpknHxN1QJIowrF+GyyLl8P26WqIdScfW9H27AbTnMkwXVoEXc9usUuSEkZIE4tnn3221euPPPIIrFZrWAkFQ6fTYdiwYVi5ciVmzpwJoOkWzsqVK3HLLbfI1g4Xb0ee0vuFi9Dkj9XRFm93RPHQJxyb8sfh4m3l83q98P58ENUvLoLto6/gPXHKjk65mUieOQmm2ZOhO7uP7zHKaPcfx6b8cTrc4u0zueaaazBixAj84x//kC2m1WrFvn37fF8fPHgQW7duRUZGBvLz83HXXXdh7ty5GD58OEaMGIEFCxbAZrP5dokKRUlJCUpKSnwH/XHxduQpvV+4CE3+WFy8rXzx0Cccm/LH4eJt5RKPlcPzxVp4Pl8L6cAx+N6ZmI3QFI2EZvpYqIYPgFetRj0AVDZNOKqtTjg9IrLNSdBGactljk3543TYxdttWb9+vewH5G3atAkTJ070fd28uHru3Ll44403cMUVV6CyshIPPfQQysrKMGTIECxbtqzFgu5gFBcXo7i4GA0NDUhNTeXi7ShQer9wEZr8sbh4W/nioU84NuWPw8XbyuKtqoPtk1WwLl4O56afTr6g08I4+XyYZk+G4YKRUOlbrhGVJAmfbS/DgYqmKYhB58Xsc7og09SyrNw4NuWP02EXb59+UJ0kSSgtLcWmTZvw4IMPhhKyTRMmTDjj/sm33HKLrI8+nY6Lt6ND6f3CRWjyx+Li7dY53F7sr7QiSaNG96xkqFWxO5xKKX3SHo5N+eNw8XZsiVY7bEvXwLp4BeyrN57c0UkQYBh7DpJnFcF+bn/k9urebp8crbHjQKUN+OVxqEa3iI2H63DhWZ2i8W1wbEYgTodcvJ2amur3tUqlQt++ffHYY49hypQpoYQkIkpIkiT5bSV7+inZndP0mDOsW6uTi+YPXXgqLlH8k1xu2FdvgHXRctiWrYXUePLA3qQh/WCaXQTTzAugycuCKIporKg4Y0yLw9PimrWVa0RyietzLKKFi7cjT+n9wkVo8sdK9MXbx+vsWLWrEjU2F7qlG1E0IAdmvRYbTzsl+0RtI/ZXNKBXjv+OTt8frMHmw7WQJAlndU3DmF6Zsk8wlD4uAY7NSMTh4u3okUQRzg3bm07C/mQ1xNoG32ua7l2azpq49ALoeuX7rje/JwmkT/Iz9NCpBbg8J8v1ykmOSl9ybMofp8Mu3t64cSNEUcTIkSP9rn///fdQq9UYPnx4KGEVg4u3o0/p/cJFaPLHSuTF215RxIqd5XC5RRgAVFVasXprA0b2yISzoQZGr//fN9WVlUjByVOyy+ob8eOBGt9f4LsOWGDwWpGfYZQ1T6WPS4BjMxJxuHg78sTdh+H5Yg08S9dBKj15yraQlQb1r0ZDM30MVAN7wi0IqAOA0+5OBNMnkwqSsKfcAodbRJd0AzrpnKgI4G5HuDg25Y/TYRdvFxcX4957720xsTh+/Dj++te/4vvvvw8lrGJw8Xb0Kb1fuAhN/liJvHi7osGBOtECqE9eO9oo4OKcHPSVDDiw3f+U7IGnnZK9u74KdrXr1JCoFQ0YnpMja55KH5cAx2Yk4nDxdmS4j5bBtmQFrEtWwL3roO+6YDIi+aJxMM2aDP2YoRDU6naiNAmmT3IA9O0ebvbB49iUP06HXby9c+dOnHPOOS2uDx06FDt37gwlpKJx8XZ0KL1fuAhN/liJung73ZQEnVbt93hCTooBKpUK/TqlAoKAnScakKRR49xWTsnOTdX7FmOevGZokbcoSthXaUWNzYXuWcnITQl+1z6lj0uAYzMScbh4Wx7e6jpYP1kF66LlcGzYfvIFnRbJk8+HaVYRjJNHQWUIfpemeOgTjk3543TIxdtJSUkoLy9Hjx49/K6XlpZCo5F1B1siog4nSaPGBf1z8NWuCjjdIlINWkzol+17vV9eCvrltX2XtE+OGUc627GztOl57F45Jgzq3LL859tLsa+i6dDS7w5UY+rAPPTv1P7dVyIKj2hrhG3ZWlgXL4d91QbAc3JHJ/3ooTDPnozki8ZDnWZuPxBRHAppFjBlyhQ88MAD+Pjjj307RNXV1eGPf/wjJk+eLGuCREQdUb+8FPTIMsHq9CDdqA1q4bVKJWDKwDyM6Z0FUYLfY1LNqq1O36QCACQJ2HiohhMLogiQ3B7YV2+EdfFy2JaugWQ/uU5Kd3YfmGdPhunSC6DplN1OFKL4F9LE4h//+AfGjRuHgoICDB06FACwdetW5Obm4s0335Q1QSXgrlCRp/R+4e4W8sdK9F2hAECjAtIMGkiSdMbzelqj1zTdnm4tZ7fH2zSbOIXL7Y3obiKxwLEpfxzuChUYSRTh3PRT02Tik9UQa+p9r2kKu8A064KmHZ36FPquy/U9KLVPTsWxKX+cDrsrVJcuXfDjjz/i7bffxrZt22AwGDB//nxcddVV0Gq1oYRUFO4KFX1K7xfubiF/rETeFSoaJElC5yQX6uwnF3n3SU0JajeYeOgTjk3543BXqPaJe4/A88VaeL5YC+lE5ckXMlKhmTYKmgvHQnVWrzZ3dJIlB4X1SWs4NuWP02F3hQKA5ORk3HjjjaFWVzTuChV9Su8X7m4hf6xE3hUqWi7OyMKWo3WotbnRPcsY9GNQ8dAnHJvyx+GuUC15jpXD+uFK2JasgGvnft91IdmA5OnjkDx7MgxjhkKI0jpTJfTJmXBsyh+nw+4KBQBvvvkmXn75ZRw4cADr169HQUEBnn32WfTo0QMzZswINawicVeo6FB6v3B3C/ljRWNXqKO1jdhcWYGM5CQMzU+DXtv2Vo5K/x0MljFJhdG9wnumOx76hGNT/jjcFQrw1tTD+unqph2dvtt28gWtBsai82CeNRnGqaND2tFJDhyb0W0zkcdmxHeFevHFF/HQQw/hjjvuwF/+8hffI0Pp6elYsGBBh5tYEFF82nioFlsP18KuNgGCFYeqbbhqRP6ZKwaptL4RJ+oc6JymR6dUg+zxiSg6RLsDti/Xwrp4BexffQ+4PU0vCAL0o4bAPLsIyRdNgDqdmyAQtSakicXzzz+PV199FTNnzsT//d//+a4PHz4cd999t2zJERGFY/uxOoiQYHG4kaTToKzegYoGB3JCOM+hLRsO1mDdvpMn547ulYUR3TNki09EkSV5PLCv3gTrkuWwfb4Gkv3kKfe6Qb1hnjMZppmToOmSG8MsieJDSBOLgwcP+naDOlVSUhJsNlvYSRERycHu8uJopQ1HG10ABORnGqFRy3d73OMVsfFQjd+1jYdqcE5+mqztEJG8JEmCc9NPsCxaDusnX0GsqvO9pinoBNOsyTDPLoIuFkdWE8WxkCYW3bt3x9atW1FQUOB3fdmyZejfv78siSkJt5uNPKX3C7fNkz9WVLab9YrwiiIESJAA2BweaFWtb50XSj4uj/eXbV1PXnN7vHB5vFAFfiyFYil9XAIcm5GI05G3m3XtPgTrkhWwLVkBz5FS33VVZhpMMyYieVYRkoYP9J0ro9TffY7N6LaZ6GMz4tvN3nXXXSguLobD4YAkSdiwYQP++9//4qmnnsK//vWvUEIqCrebjT6l9wu3zZM/VjS2m01VNaJvuhqZbkCnVsGsl3DkeCkyTS0XW4aaTw+TF6V1Jx+d6JxuQENtNRoCjqBcSh+XAMdmJOJ0tO1mxbJqeJauhfeLtRB3HTr5giEJ6gtGQDN9LNQjz4JXq2kat5WVbURSDo7N6LaZ6GMz4tvN/va3v4XBYMCf//xn2O12XH311ejcuTOee+45XHnllaGEVBRuNxt9Su8Xbpsnf6xobDebmyvi6DEvjHoTIAgQdGr0LujS6mNKoeYzJSMLm4/Uoay+EXmpBgwrSIO2gzwGpfRxCXBsRiJOR9hu1lvbANunq2H7cCUc67edPCxSo4Zx0kgkzypq2tHJKN96q2ji2Ixum4k+NqOy3eyvf/1r/PrXv4bdbofVakVOTk6ooRSP281Gh9L7hdvmyR8r0tvNTuibjVWN9TjaqEFGchLG982GTtv2X3uh5KPXqTCqV1bA5eON0sclIF+OTo8X3x+owYm6RuSm6HFej0wYdK1vTxypfpGkk7HDEY9bWoZbVmx0wv7lOliWLId9xXcnd3QCoD9vMExzJsN08QSoM1IDyk/pEmlsKqFNbjcbmJAmFo888ggeeughqFQqGI1GGI1GAEB9fT1uuukm/Pe//w0lLBGRrPRaNYYVpGNaTo7fX4xHqu3YWVoPnUaFod3SkZ6si2GWsbfzRAO2HK0FAAztlo4BnRNzK83lO8uxt9wKACitd6Da5sKcYV2j0rbbK2L5T2U4dqIMkt6G0b2zMahLx3gDHEmSx4PGb36AZfEK2D7/GpLtlB2dBvaEadZkmGYVQduVOzoRRUNIE4vXXnsN//vf//DWW2+hR48eAIDVq1fjuuuuQ15enqwJEhHJ6XC1DR9uOe57MmJ3mRXXnV8Ag1a5n/pF0tEaO778qcz39Zc/lcGs16BbhjGGWUWf2ytiX4XV79rRGjtsTg+SkyJ/mvKGgzXYXWaBUZJgd3mx4udydE4zICPBJ72tkSQJzs07YV20HNaPv4K3stb3mqZbHkyzimCaPRlJ/XvEMEuixBTS35Y//vgjfve732HIkCF4+umnsWfPHjz33HO455578Oijj8qdIxGRbHYcb/BNKgDA4fZib4UVZ3dJzE/pT38zDQD7Kq0JN7FQCwL0WjUaXV7fNa1aiNp6meO1jX5fS1LTNU4sTnLtPQz7hythWbwCnkPHfddVGakwzZgE0+wi6Eec5dvRiYiiL6SJRXp6Ot5//3388Y9/xO9+9ztoNBosXboUF1xwgdz5ERHJSqNu+aZD28q1RJFi0La4ltrKtY5OpRIwumcWVu4q9008z+uRCZ0mOhOL7JQkHK+1+13LTWm5e1mi8ZRWwrJkBRrfWwrbzwd91wWjHsnTxsI0ezKME86F0M7aKSKKnpBH4vPPP4/nnnsOV111FX744QfcdttteOeddzB48GA581MEnmMReUrvF+7HLX+sqJxj0UrZoV1Tsa/cApen6VpGsg49s5IV/zsYKQM6mbC7tAHlDU1bauel6tE/z+T7e0/pfSJnjgM7m9ElNQnH6xuRa9Yjy5wk25knZzKiIB2V9Y2orbJAIwDDumcgy6SL6tgKp76c51h46yywf/Y1rEtWwPHtVr8dnQwTRsA06wIYfzUGqmQDgKZjZCQF/45GQqKNzVi3yXMsInyOxa9+9Sts2rQJCxcuxJw5c9DY2Ii77roL5513Hh599FHce++9oYRVDJ5jEX1K7xfuxy1/rGicY9FW2Wm9DDhR1wiNSoUu6Umoq6mKys/4u/3V2H6iDmpBhXML03FW17SItBOsiQU6VNua/j8zWYf6mmoAyh+XQGRyzNY07TBU0dj665HqlzFdtagw6JGelowkrQcVFRUhxYnHvfIlhxPebzbD8/kaeNds8dvRSTW0H1wTz4HpkolQZabBDsBuswC2wPfW72gSdWzGqk2eYxHhcyy8Xi9+/PFHdO7cGQBgMBjw4osv4qKLLsJvf/vbuJ9Y8ByL6FN6v3A/bvljReMci/bKFnYNvKwcVu2uwLs76gEIACTsqKnFnzvnoV+eMtZ2tLZnjtLHJcCxGYk40dorH6IE889HYP9oJeyfr4Fksfle1/brDtPsyUieOQnqrrmorKxU9O9htHFsRrfNRBubUT/HYvny5a1enz59OrZv3x5KSEXjORbRofR+4X7c8seK9DkWkSwbrA0HayHh5FoOrwSsP1CDAZ3TZG9LTkoflwDHZiTiRGqvfEmS4Ny6C5YP/gfHhyvQWFXne03TJadpe9g5k5E0oKfvevMbHaX/HkZbPPQJx6b8cTrkORYAsGbNGrz88svYv38/Fi1ahC5duuDNN99E9+7dMWbMmFDDEhF1SK3t7pNliu7iXIvDDa1aBb229UPfiCLFtf8IrItXwLp4OdwHjvmuq9JTYJoxEaZZk6EfeRYEBb9JJqIzC2lisXjxYlx77bX49a9/jS1btsDpdAJoOiDvySefxBdffCFrkkRE8W7WOV2w7Wgdqm0uAEB+phFTBkTn3B+nx4vPtpXiSI0dapWAoflpGNs7O+Ltur0ith2tQ4XFia7pBgzqnAqVKnF34Eo0nrIqWD9aCeviFXBu3eW7LhiSYJw6Gp6iEeg0owhqPXe/IuooQppY/OUvf8FLL72E6667Du+++67v+ujRo/GXv/xFtuSIiJSg2upErd2FLmlGGHShfdqfbdbjmSsGY+OhWiRpVDgnPx2aKJ2RsOlQLY7UNG1l6hUlbDpUi8LM5DOeVVFjdWJnaQMO27UY1CU16IPivtheigOVTc/N7y6zoNrmwsS+OaF9ExQXxAYrrJ+vgXXJcjSu2XxyRye1GsYJ58I0uwjJ08YCRj0qKiog6BJva2OijiykicXu3bsxbty4FtdTU1NRV1cXbk5ERIqxbl8VNhysAdB03sWMIV1CPjxOr9VE5U7B6SotzhbXKizOdr+PsnoHPth4FDq3BfZKCduP1+Oa8woCfozK4nD7JhXNfjpejwl9snmAWQcjOpywLV8Pxzuf4ciaLZCcLt9r+nMHwTR7MkwzJkKdlX6yjoK3SSWi0IU0scjLy8O+fftQWFjod33t2rXo0aOHHHkREUWU0+PFun1VOFrTiGxzEs7vkdGiTIPDjY2Hanxfu70S1uytwtUj86OZati6pBtwsMr/TX7XdEO7dbYerYNHlNC8MsTi8GBPuQVnB7hFrlolQCUIEE855pyPQXUckteLxnVbYF28ArbPvobYcPIEd23fQphnT4ZpVhG0BZ1jmCURRVtIE4sbbrgBt99+O15//XUIgoATJ05g/fr1uPvuu/Hggw/KnSMRUVgaHG4cqWlEil6LgkwjBEHAip0V2FPetDd3jc2FaosDk3v4v9m2OT045X0xgKZP4uPNOfnpqLO78XNpA3QaFc7rkYnclDNtHyi1vNLykh+vKKHa5kSqQQujToNBXVLw47F63+vnFmbwbkUckyQJrh/3wLLof7B+uBLe8mrfa+rO2VBNPR/Z186AflBv/pyJElRIE4v7778foijiggsugN1ux7hx45CUlIS7774bt956q9w5xhxP3o48pfcLTxCVP1a0Tt6uaHDg6+2H4PnlTXHPHBMuOrsT9pVb/N4pV1mcaGhUI+uUuNnJOqToNWhoPDmZ6JWTrNjf07YIAC7ol42JfbIgCE1bCZ7pezirSwr2lDY09ZEkITlJjV7ZbX/vpfWN+PzHUticXug0Kkzqn40JfbKQn2Foeuwq3YCu6UZF/z7Huk2lnu7rPnAM1g9XwLZkJdz7jvjKqdLMSL54ApJnFUE3YhCqqquhzc6GJEmQzjALDXYcK/nfh1iIhz7h2JQ/Toc9eVsQBPzpT3/CPffcg3379sFqtWLAgAEwmUyhhFMcnrwdfUrvF54gKn+saJ28vfdoGXQeDXS/nCFRWmrFnhQvMtSNsDtPnu6rFgCnTURFRYVf3PHdtPi5zAGLw40csx59U8WQT0SOJ2oAU7rrcbDUgqRkAd2z9LDUVaOt81e/2V0Bye6GEQC8wLfbrTANzIVZrYLZBMBtRUWFtY3aoePYlD+OKIqoP3AE7rc+h/eLtRB37D/5YpIW6onnQnPhGKhHD4Go08ICQKyqCvt030h9Px1RPPQJx6b8cTrsydvNdDodBgwYEE4IReLJ29Gn9H7hCaLyx4rWyduu3RWwq/TAKY9mJJnTcd7ANCzbXgaPKEEQgNG9MpGld7eImwOgR3wtqZBNVpaI1GRdQH1dvr0ektp/21C9OR2ZET6rg2NTvjiixQbb59/AungFdGt/gFv85a6DSgXD+OFI/mVHJ5Wp5aJ/OU73lfv76cjioU84NuWP02FP3k40PHk7OpTeLzxBVP5Y0Th5u2tGMsrKRd/EwqzXoFtGMjRqFbqkG3GirhFZpiSk6DW+uxVK/R2MhUD7Oj8zGYer7b6vzXoNMk36qCzY5tgMPY7kdMG+8jtYFi2H/X/f+u3olDRsAMyzJyN5xiRoclpubhBu7sGUV/q/D7EQD33CsSl/nA578jYRUTzolWOCOV2P/ZU2pBi0GFGY4Ts/wqjToFeOGQC3vwzX5AG5WPFzuW+XrUn9crgLlEJJXi8c67fBsng5bJ+uhlh/yo5OvQuQPKsIzvFDkDfsbEW/aSUi5eHEgoh87C4PvtlTiWO1jchJ0WN872ykGuP7ACtBEHB211QMyU8/c2EKSqXFiZ9O1EOtEnBWl1RcOrRrrFOiNvh2dFqyomlHp9JK32vqvCyYZhXBPHsydGf1hiRJCbGOiIjkx4kFEfl8+VMZDlU1Pc5icVhR3+jGtecVxDgrUqIKiwPvbTgKzy/P4TcfoJeij++JaEfjPngcliXLYV28Au69h33XVakmJF88AeY5U6A/72wI6pMHH55pRyciorZwYkFEAJrOIDj1GXmgaQvW+kY3Ug18s0j+dhyv900qAMDpFrGr1IIR3c/8LD5FlreiBu63l+LE8u/g/GGn77qQpINxyiiY50yG8YLzICTp2olCRBQ8TiyICACgEgBTkgYWx8ktWHUaFYw6dTu1KFG1dgAal1TEjmi1w/b5N7AsXo7Gb34AftkuHSoVDOOGwTR7MkzTx0FlTo5tokTUoXFiQUQAmt4oTuibg2U7SuH2SlAJAsb1zoZWzcWb1NLZXVKx80QDXJ6mRe9GnRr9OqU0HawkAWrOMiJOcrlh/+r7ph2dvlwLyXFyRyfVWb2Qdvk0mC+9AJrczBhmSUSJhBMLIvLplWPCb8f2QFm9A1nmJJiS+FdErIiihG/3V2NXWQOSkzQY3TML+Zktzw+IlUxTEn49Mh87SxugFgQM7JKKsnoHVu+ugNXpQWFmMqYOzIOBd7xkJYmi/45OdScPrtL27AbTnMlInnkBak06pObkcFcnIooqvmsgIj96rRqFWXxcItY2Ha7FF9tLUW1zQpKAfRVW3Dm5j6Ime2lGHUb1zAIANLq8WLq91Lfu4mCVDV/vqcSvBuXFMsUOQZIkuH7aD+vi/8GyZCW8J07u2KTOzYTp0gtgnjMFurP7QBCEpq2TuasTUVw5WmPHhoM1aHR7MaBzCs6J050MlfMvFBER+azZW4m95RZU2ZxweUT8XNqA3JQk3DC2R6vrG2KtrMHht5gbAI7XNcYom47BffgErItXwLJkOdy7D/muq1JMSL5oPExzJsMwaojfjk5EFH8aHG58tOW47+/Qr3dXQqtS4ayuqTHOLHicWARAFMUzHp4limLTs8UylUs0Su+XWOQXqTbljBtOrFDrBlMvUmVPZ3d5YHd6kWnSyfamv67RBavTDbfHCwFNn1r/fKIeB6usKMyMzh2lYPokw6iBCoB4ylal2SZdxMdMRxubYnU96j9dA9uSlXBu+sn3mpCkg2Hy+TDNKoLhgpFQ6ZMAABKaHo+SM79Q6gdbJ1pjs6OKhz7paGNTjrhtxTlYaYXH639tb3kDBnY2h52HHGMzmPY4sWhFSUkJSkpK4P1lV43Kyko4HI5264iiiPr6ekiS1O4zrYGWSzRK75dY5BepNuWMG06sUOsGUy9SZU+1u6wBu8sssDg90KoEFPXPRYYpKeD6bTkrA6goc6NR0/T3kF6rRjeDG8dPlMHoNZ+htjyC7ZORndT46UQDPF4RKUYtBqYnR/ygtY4wNiV7I7wrN8L9xRp41/+IxuY3GIIA1YhB0EwfC80FIyCkJMMGwNZQDzRELr9Q6gdbJxpjsyOLhz7pCGNT7rhtxml0wOi1+pU1eFseVBmrsWmxWM5Q6yROLFpRXFyM4uJiNDQ0IDU1FdnZ2UhJSWm3jiiKEAQB2dnZZ5xYBFIu0Si9X2KRX6TalDNuOLFCrRtMveayqemZKLM4kZKkafNNfyj51Nlc+KG8DrsqRDQ0Nk0A9lur8cC0fshJ0Qf8PbVmTJIZWytF/HyiASqVAL1eB8GQgt4FXcKOHahg+yQnBxjSR0Sj2xu1s0/idWxKLjcaV2+EdfFyNH65DlKjEwAgANAN7gvTrCIkz5wETV5W1PMLpX6wdUIZx0r99yEW4qFP4nVsRjJuW3GysyUcsmlwoNIGoGnr9/MGdEGqURdQfTlzb628Xh/4vzmcWARApVIF9MMQBCGgsoGWSzRK75dY5BepNuWMG06sUOsGU6++0Y2Pdx9Bo1sEIKFbhhGjemahU6q+xWNLweZT0+hGvcOD+kYPmt4SAvUODzYersPFgzsH9T0BTYfObTlSCwAYmp+O+37VHx9uOY6jtXbkpugxqmcW8tKiuzNUsH2i16mg10X3n5Z4GZuSKMLx/XZYlyyH9eNVEGtP3nbQdu+K5NlFcI4bgryRQ8P+XsLtk1DqB1snmPJK//chFuKhT+JlbEYzbltxZgztihN1jXC4veiWYWxzq/dYjM1g2uLEgog6tJ9LG9Do0sIrAbvKGvDdgRrsLrOgZ7YJs87pCp0m9H8kOqcZIPmvV4ZBq8a6fVU4VtuIzml6TOqXA7P+5Kf3h6ttWL+/GjaXF/3yzDi/RyZUKgGHqmxYvrPcV275znLMGdYV80Z3Dzk/UgbnT/tgXbwC1g9XwHPs5M9YnZMB06UXwDR7MpKG9IMktXz0gYgSR+c0Q6xTCBsnFkTUodldXgBaVFgcvlPFnR4RpfUObD9ej2EFoW/pZ9Rp8Ovz8vGP/+2G1eFBRnISrE4P0oxaONxeHKi0wekpw+XDuwEArE4PPtl6wrfzx4aDNdBpVDi3MAMHqqwt4u+vtCLbnIR9FVboNCr0yEqGhgcWxgX30TJYFy+HdckKuH4+4LsumIwwXTQepjlTYBgz1G9HJ+n0WSoRUZzhxIKIOrROqQZUVElw/nJCtEYlIOWXOwgNje6w4/fNS8FTs87GpkM1qLa6sONEPXLMJ59HPV7bCJdHhE6jwuFqW4stWQ9UWnFuYQZSDbrTQ0OlEvDGt4fQ6Gpav5GTkoTLh3fjaegK5a2ug/WTVbAuWg7Hhu0nX9BpkfzLjk7GyaOgMoS/uJ+ISIk4sSCiDq1fnhlakwYQBDS6mp5dVaua1kP0yJZn29YsUxJ+NagTPF4RDWs8cLi9vtfMeg206qb20owtJw/NE4pBXVKwt9yC0vqmHei6pBnQ6PT6JhUAUNHgxN5yKwZ0bn8zCYoe0dYI27K1sC5eDvuqDYDnl5+XIEA/eijMsycj+aLxUKdFZycvIqJY4sSCiGTl9HihFgTFPLKjUgkY3SsTY/tkNy2OPloHSBKG5qejQObzIDRqFSb1y8HynWVweyXoNE1fNy8S75JmwMDOKfjpRNOi3RSDFuf3yAQAJGnUuOLcbihrcECAgLxUPT778USLNhpPmbRQbEhuDzzfbEbFyo2wL1sLyX5yO3Ld2X1gnj0ZpksvgKZTdgyzJCKKPk4siEgWbq+I5TvLsafcAq26ad3AiO4ZsU7Lz6AuqRjUJbInmfbNM6Mg04hqmwtZJh2SNP6nIk8ZmIdhBemwu7zonGbw3T0Bmnbi6JR6cvFe/04p2Ft+cu2FTqNC71xTRPOn1kmiCMfGHU3rJj5eBbGmHs5fXtMUdoF5dhFMs4qg61MYyzSJiGKKEwvqkLyihO8PVONAlQ1pRi1G9cxCRnLLx1BIPpsP12J3WdMhOi6PiHX7qtAl3YAuHWCXi2Dptep2v+9MUxIyA4jTM9uEiwd3wo7jDdBpVBhWkO5bHxIot1eE3RW9cyU6GufPB3yLsD1Hy06+kJGKlFlFMM+ZjKRzBsh24joRUTzjxII6pLX7qrD5cNN5AJUWJ8rqHZg/urvfp8Mkr+a1AX7X6hoTcmIhp145ZvTKCe35/J0nGrB6TwWcbhGZJh0uPrsz0hNggu0VJWw6VIPD1XakJ+swskdGUBMy97FyWJesgHXJcrh+2u+7LiQbkDx9PJJnF8HSpysyO3dS9BkCRETRxokFdUh7y/2Pn7c4PDhR14huGdE9XCyR5KXqcbDK1uIaBe/n0gbsPNEAvVaN4YXpyA3hpO1Glxcrfy737UJVbXVh9Z4KXDq0q9zpwuURseNEPWptLhRmJaNndmwf11qztxJbjtQBAI7XNeJEXSOuO7+g3bsK3pp6WD9d3bSj03fbTr6g1cBYdB7MsybDOHU0VIYkiKIIK8+bICJqgRML6pBMSRrfmQUAIAhNu/NQ5AwrSEeV1Yl9FVZoVAKGFWSgazoncsHaXWbBsh0nH7k5VG3D3FGFMCUF9/tbbXO22Nq2vMHZRunwfLz1OI7VNgIAfjxWjwl9szE0P/TzQcLV/EhesxqbCxUWZ4sJmmh3wPblWlgXr4D9q+8B98m/M/SjhsA8ZzKSL5oAdTp34QqGw+3F13sqcbTGjkyTDuN6ZyPTxC12iRIB32kplChK2HqsDkdr7MhI1mF4QQYMOvWZKxIAYHSvLHy89Tjc3qY3VoO7pbW61SfJR6tW4aKzO8Ph9kKtEnjWQhsOVdnw7f5q2F0e9MtLwaieTSdvN9tV1oBGtxcHq2ywOT0w6zX48WgdRvXKCqqdbHMSdBoVXL+c3wEgIo+lVVgcOFbbCI9XxOEaO+ob3ThaY0dhZnLMHrsy6tS/HIzYRBCa1r0AgOTxwL56E6xLlsP2+RpI9kZfOd2g3jDNLoL50gug6ZIb9bw7iq92VfgmdxaHBzW2E5g/qtDv95yIOiZOLBRq7b4q/PDLGoEDlTYcq23EVSPyY5xV/OiWYcT1Y3rgaK0daQYtckJ4lIRC0/wGjlqyONz4dNvJk7c3Hmo6efvU3bP0WjX2lFt851fU2d344XBt0BOLJI0aF57VCV/tqkBDoxv5GUZM7Jcj3zfzCwFNbxYPVNlQY3MBaLpb8sm2E2d8/ChSRvXKwuc/lsL7Sz8P7pKKpB27UbloOayffAWxqs5XVlPQCaZZk2GeXQRd3+5Rz7UjOlDpf4p8Q6MbVTan38GRRNQxcWKhUM373Dcrq3egyupEFm8nB8ygU6NPLg+lIuU4XG1v8XjSwSqr38SiX54Z7lPuMpj1Gri9ou/07mB0z0rGb0YXwitKETtXJNuchPwMI74/UO271inVgBqbCzU2V0wegemZbcLcUYU4tnEXTCvWAF+sxvHDpb7XVVlpMM2YBPPsyUgaPpA7Osks1ahDleXkY3calQBzEnclI0oEnFgoVJJG5Xd6ryAg6DcVRIlIkiTFvlFs7dGg0x/R65puxPk9M3GirhEatQppBi1SjTrf6d3BEgQBmhDrBuqSIZ2xu6wB5RYn0o06pBq00KoFJAexLsTjFfHD4VqcqG9EjlmP4YXpLc4ACSjOiQpYlqyAdfEK6HfsRfOqCcFoQPL0sTDPngLDuGEQtPznL1Im9MnGJ9tOwOURoRIEjO6dxUd5iRIE/2YNgCiKEEXxjGUkSZKt3Mju6Vi+sxzSLx9uDuycCpNOfcZ68SrQfomVWOQXqTbljBtOrFDrtlWvosGBFT9XoNLiRJc0AyYPyIFZrwm4jWj8jDulJDWdvH28HgCQatBiREG6X5sCgCkDcrB8ZwVcHhFJWhUm9c2CJEmQJKmNyJERaJ+oBeCqEd3w8dYTqLG54HJ7Mb5PLnRqIeD+XL6zHLtKm+7UHqq0oaKhETOGdAkoR7HOgvql62H/6Cs41m+D7y9OjRrGSSORPKuoaUcnY9OjOBKaDrwLldLHZrhxQql/ap0uaXr8ZnQBKhqcSDNqYdZrW8QKpg2l//sQC/HQJ/x3U/444Y7NUMsH0x4nFq0oKSlBSUkJvN6mOwaVlZVwOFru0X8qURRRX18PSZLa3dc80HKZamBaLyMqLU6Y9RrkmCVUdODtDQPtl1iJRX6RalPOuOHECrVua/UkScKKnytgd3pgBFBbbcWKLfUY3TMz4Dai9TM+OxPokWyEwyMiw6iD01qLCv9H0pEC4JK+ybA43DDrtdB6baiosLUaL5KC6ZP6+kYYPFakCR4kSWrY62tQkeQKsB0JR4+XwnjKxKmi3Iojx1VtrtmRHE54V/8A9xdr4F2zBY2ek3d4Vef0h2b6WGimnAchzQw7ALu1AbA2tBorWEoZm26vCI1KaHGHLtz8QqnfWp0kAI0NTX/CaUPp/z7EQjz0Cf/dlD+OXGMz2PIWi+UMtU7ixKIVxcXFKC4uRkNDA1JTU5GdnY2UlPa3GxRFEYIgIDs7+4wTi0DKAUAOgN6hfANxKJh+iYVY5BepNuWMG06sUOu2Vq/e7kaVpwE45T2ovRHIzMoOuI1o/ozlX0IdGcH0ydJ9h2BXmyAYABeALRUizumbGdBiflGU4E2y+T3+qVEJyMnJ8asveTxoXLMZtiUrYP/8G0i2ph2dBADaAT1gmjUZpksvgKZrZHd0ivXYrG90438/leFEnQNmvQaT+uegMDNZtvxCqR9snWDKK/3fh1iIhz7hv5vyx4nV2NTrA994gROLAKhUqoB+GIIgBFQ20HKJRun9Eov8ItWmnHHDiRVIXavTA68oIdWgbbOe2aCFIUnj20kJADJNOmg16qDyU/rvYCwE2icNDm/TYrBfeCTA4ZFgTDpzX6pUwHk9M7F6d6Xv2rDCDBiTtJAkCc7NO2FdtBzWj7+Ct7LWV0bTLQ/Jl14A14RzkDd6eMKMza/3VOFEvRMQBFicXizdUY4bxvbwW4cXbn6h1A+2DsdmeOKhT/jvpvxxYjE2g2mLEwsiUiRRlLD853L8XNoASWra4Wj62Z3Q2jpkjVqFyQNy8b+fyuFwe2FK0qCoP88h+P/t/Xd8HNd96H9/ZrY3LBa9A+y9F4mqpESqWFYjFTvNlu3Evq8nku08SrPu8zi+Sa6v0+w4yeXv8b25SVxuimNTUmRJlkRShaRESiLFJvYGEkRdlO19Z54/FlgQRCHKAliQ3/frxZe0szNnzh7sAeY7c873TKU5ZQ7OtfeP6Sp2mvHYR58JaFWdh6pCG82+KGUuC2WdXrr//CWCO3aRamzO7qcWuXE+fh/ObZuxrl+Grt/cw0SH0uKPDnidSGl0heNUunO/TokQQoyFBBZCiLx03hvi5DVply91hjl21ceq2sIh959T6uTLd9sJxFIU2kyo6ugnDouJ27yoHKvRQFNPhBKnhXvmlY45O1dxOIjlld0Ed+yk6djZ7HbFbsXx8N04t23BvnHdgIxOUz2hPR+Uu6xc6Y5kX5uNKh5ZAFQIkQcksBBC5CXvNXnwR9p2LaNBpWiaVnu+1VlNBjYvHvtTorQvSPgX7xB8YRex9w4PzOi0aT3ObVtwPHQXqkPuxve5b2EZrxxvpTMYx242cN/CMlmYUgiRFySwEELkpVqPnQ8vdQ/YVuOxT1NtRC5p0TiRne8T2rGT8K4DkEhm37Pethznts04H9uEobhw+iqZxzwOM5+7vZ5QPIXNZMCg5ue6LUKIW48EFmLK6bpOeyBz57nCPfpMA+LWUlds5575JXzU2ENa01le42ZJVcEtOfTlZqCn00T3fpwJJl7dgxbsT59rXjQb57ZMRidTXeU01nJmcY5hAUIhhJgK8ltJTKl4Ks1Lh5tp8WXWBan22HhyVfWQE3KFWFNfxJr6ogHbJLCYOXRdJ37kdCaj00u7SXf0P4EyVpdl0sM+tQXL4jnTWEshhBC5IoGFmFKfNAeyQQVAc0+Uky0BllWPvE6IEGLmSFy4QmjHLkI7dpK8eDW7XfUU4Hx8E86tW7Detgwlj9NkCiGEGDsJLMSU8kcHr8TbExnd6rxCiPyVausk9NJuQjt2ET9yOrtdsVlwPHQXzqe2YN+4HsU8+hS0Ij8EY0naA3HKCiwUWOXnJ4QYngQWeeyCN8SVrghFDjNLqgowGmb+3b1ZJU6ONvkHbJtT6pym2gghJiIdCBH+xbuEXthJdO/H/RmdDAbsG9fh3LYZx8N3ozpl0v1MdbIlwM6T7Wi6jqoo3L+ojKXV7umulhAiT0lgkacONnaz91xn9nVjV5jHV1ZPY40mJppIoyiZRc7uX1TG4Ss+FAVW13moLbLLegNCzBBaLE5k1wFCP99JZNd+9Hj/E0fruqWZSdiPb8JQ4rlhWcFYkkAsRUWBVTIb5SFN09l7zovWGzBqus6ec14WVrhuihtdQojck8AiTx2+4hvw+qI3TE84gSdPc/R/0uznwMUu4imNxZUF3Du/FFVVSGs6O0+2cbotiILCspoCNi0oY3lN4XRXWeS5SCLFRW8Ym9nArGIH6hguPIOxJP5oklJnfvaXfPVJs5+Djd2kNJ0VtYWsqSsEMhmdIvs+JrRjF+FX3kUL9K+wbVrQgGvbFpxbN2Oqrxr1uQ5c7OKDi91ouo7TYuSJVdWUuiy5/khiApKaRiSRHrAtntRIpDUJLIQQQ5LAQkxYRzDGrlPt2VEQR5p8FNiMrKkv4pNmP6dagwDo6Bxt8lPjsTO/3DWNNRb5riMY42cHr5JIZZ5k1XhsbFtdM6rg4qPGbt4/34Wm69hMKvfWmimb7ArfBJp9UXaebM++3nfWi+vCJWyv7qTpzQOk27uy7xmqynBtvR/ntgcwL5kz5hW2/ZEkBy52ZX9nhOIp9p7zsnV1TU4+i8gNi9FAXZF9wCrf1YU27Ga5dBBCDE1+O+SpVXWFA4ZCzS515O3TiqbuKNdnAL3SHWFNfRFtgdig/dsDMQksxIgONvZkgwqAqz1RGrvCzL7BfJxQPJUNKiAzBO9Ei58Fs+SC9UYud2bWlXC0tlO9/wOq3/sAU2sbqd731UIXzsc24dy6GeuGFRPK6OSLJgb9zugOSxKHfPTwsgr2neukxRelwm3j7nkl010lIUQek8AiT61tKMLjMHOlK0Kx08ziyvxNx1rqHDx8oaR3W3WhjZMtgQHvVRXapqReYuaKXjf8AiCaHLzter5IIhtU9AnFU8PsLfqk2rsofvGX3P3znXguXspu1y1mjJvWUvxrn8Z5/20oltzc3Kh027CYVOLJ/uBxdqkjJ2WL3LKbjTywpGK6qyGEmCEksMhjc0qdMyJjUl2xnZV1hRxr8qPpOtWFNtY1ZBY1W1xZgDcY55NmP4oCq+o8M+Iziem1sNI1YPiFxaQyu2Tk740/kuRgYzefNPuxmgzUF9sxqQrlBdO3unsyrXGiJYAvkmB2iZO64vzIjtQRjHHpkhf3ex/i2r2P2N6PMWoaHkBXFLzLlhDachcbvvxpQskojrKynK45YTaqPLGymr3nvPgiSWaXOrlrbumEy40l07x/oZMWX4yKAit3zC2WYTtCCDGF5DeuyIlNC8pY31BEIqUNGLKlqgqbFpZx97wSFEWZcZlfukJxDlzsJhhNUGNLUFo68YsfcWNLqtwoKJxuC2AzGVjbUITNbBjxmJePtdAZjDOrxEFjV5im7gifXl7JYs/0rdT94uFmmnuiQCYhw5bF5WNK1emPJDnvDeGwGJhb6pzwhFk9nuDCC+/Q+i+vUf7xUQzJFH2DFS1rl+Daupn45rsoKC6krMCKpmmEOqITOudwqgptfHZdXU7LfONEGxe9mSFd3mCcnkiCX1lbm9NzCCGEGJ4EFnkslkyz56yXK90RSl0W7p5XSlGezrMAcFiMOIZJ6jITM4gkUho/P3Q1kxVF1/F3hzC7fKyfVTzdVbslLK4qYHHV6IYA+qNJOoNxIPM9XFLlRlHgvoVldHV6J7Oaw2rzx7JBRZ/DV3qGDSz80STtgRjlBVbcNhPNvigvHLpKSssERlWFVn5lTe2YsmMB6JpG7P0jBHfsJPyLd1D8IfpyNwWrKrl6522s/C9PULNizlg/Yl5JpDQu9c4T6XO1J0okkZKnFkIIMUXkt20e23WqnXPtmbSOwViK7nCCL9zRkM3AEkumOdkSQCcz5OhGd3TF2DT1RAalWjzTFpTAIg/ZTAbMRnXAhG+X1TStT8h0Bj8pGe7ZyfGrfnafzmRWUxTYvKicC95QNqgAaPHFuNwdYVbJjeci6LpO4vg5gjt2EnpxN+nW/uAqXuShacM6rt55O4H6OlAUltdUjvnz5RujqmA1GQbMzzEbVcwz8KaGEELMVBJY5LG+R/p9fJEkXeEEJU4LoViSb792isbOzDj0uiIb/59HFlFgy98nGjONY4i7nE6LBG/5yGxUuXd+KW+d7iCt6ZgMChsXjG3YWncoTmNnCM3iosoz9MW7pukcutLDhY4QBTYTt88uHvYpYkWBlapCKy2+/sxoq2oHLxqX1nT2nvdmsyTpOuw910mRwzRo32sDp6EkLzUTfGEnoR27SJ67nN2uup04Ht2I66kHOFNdx8lz/aljixxmajwzP6GCqircNbeEV462EE6kcFlNbFpYNiOflgohxEwlgUUeK7Sb6Ar1p2A0qgpOS+ZH9vonbRxt8tETTqAD/miCV4+38mvr66eptjefCreV+eUuzrZn1uEwGlRum1U0zbUSw1la7aahxEFXKE55gRWryZBd0d0fSWCzmLCahg4MP2n2s+tkG7ZUiP0taW6bXcwdcwen1fzgUjcHLmYuylt7hzp98c6GIS9eFUXhyVU1HG/2449mJm83DPG0IZnWBmRHgszTyAXlxQOCEofFMOTTilRHN+H/fIvgC7uIHzzRf36LGfsDd+B6agv2+2/PZnRaCdht5mxwtKqucMzDq/JVLJkGBTQ983RIbgQIIcTUksAij909t4R/2HuRrnACh8XIZ9bWZi+MPmkOcLk7Qjqduc0ZjKU4ftXPr62fzhrffB5ZXsmKHjeBaAJHOkKFe+bf2dV1nQveEO9cjuOwmrhtVhFl05g5KZecFmM2+IZMqtl3z3hpifszgeHsYtZfFxzqus77FzoHrKtw8HIPq+s9gwKRM20DUyeH4imafVHqi4d+wmE2qqypH/yU4lpWk4HaIjtN12TBqivKZFpzWAycbgvisBhYU1eE2ZgJYLRQhPCrewju2El0zyFI9w7/UVVs96zBuW0LzkfuQXUNXa/55a6bbi2ZWDLN/gtdWIwGLM7Mz+3ds53MLbu5PqcQQuSzmz6weOWVV/i93/s9NE3jj/7oj/jt3/7t6a7SqDV2R/DYzZgMKjazgctdYW6fnRnf74smSF4zLEJL6/giyemq6k2txmNHc1vp6Bi82N9MdPSqn0+u+okYnBCI09QT4Ut3zhr2bv5Mtv9CF75IAgxmUprOe+c7mV3qyK6zApmhR7HrnhikNZ14ShvUJnaLkZ7r+lnfxOBmX5TDV3pIazrLawpHNReiz6eWVfDe+S7a/JlFyO7qfVoyr9zFvN4AQE8kCb++j+DPdxJ5Yx96rP9ppmX1Ipxbt+B84j6M5YPnAHmDcTqCMaoLbRTab87hkrFEesCcFIBQLIWu62NeGVwIIcT43NSBRSqV4rnnnuPtt9/G7XazZs0annzySYqLZ8bk25MtAYwGNXsh0OKL0RNO4HGYcVmN2M1GYqk06GA1qRTYbuofZ95LpjX2nPVywRuiwGri7vmlVOfhYoDneod29YknNRq7wiysyN9FGMerMxQftM0bjA8ILFRVYX65i1Mt/uy2ao8Nt23wHIc75hTz0uFmkr1PChdXFVDqstAVirPj0FXSvRe2lzrDPLWmhhrP6NatsJuNbFlcPmi7rmnE9h/NZnTSfP0/O9OcWpxPbcH55GbMc4ZPqfrBxS7ev5AZvqUo8MDiilFn25pJCh1mSl0WvMH+n/m8cqcEFUIIMYVu6ivRDz/8kCVLllBdXQ3Aww8/zJtvvsmv/dqvTXPNRsdyXZYbRQFT71CIe+aV8t75TmLJzB1Vi0nl7nmyxsJ02ne+k2NXMxen4Xia//joChsXlFFWYM2rAMNmNuK7bttQE9VvBtUeO2d6+icqq4pC9RATle9fVIbLaqC1NcH80kJunz14fgVknl596a5ZXO6K4LaZsqvIn2kPZoMKyDwFOd0aHHVgcS1d10mcuEBox5sEX9hNuqUj+56hvBjnk/fjeuoBzMvn3/CiOZZM8+Gl7gH1eu98500ZWAA8trKK98510hmKU+Oxc8fcmXETSQghbhZ5fTWxZ88e/uqv/opDhw7R2trKiy++yBNPPDFgn+3bt/NXf/VXtLW1sWLFCv7+7/+e9eszEw1aWlqyQQVAdXU1zc3NU/kRJuT22cXsOtWeHfu9oqYwO378znklbG2v5XizD13PTFy9d0HZNNZWNF6TQ98XSXC2PUhPJInHbmZptXvIO9LT4bZZHl71dmRzn84uddwUWYGGcvusIsI9nZwPKjisRu6eV0qBdfCTCJNBZcPsYjqcacrKSlFHWGXabjayqHLghflQw8isJgPnO0LsOeslGEsxt8zJ5sVlWIxDDzlLXm4htGMXwRd2kjzTmN2uuhw4Ht2Ic9tmbHeuQjGMfshaPKUNGh4USaTRNP2mmbB9rQKriYeXzfzUuUIIMVPldWARDodZsWIFX/rSl9i6deug93/605/y3HPP8YMf/IDbbruN73//+zz44IOcOXOGsrKZf5G9tNpNWYGFpu4opU4LdcX9dz/tZiNfvmdWdp2LuWXOm3KM/EzisZuz81yaeqJoOtdMtveztt4zYFXy6VLqsrJ5UTkRgwOHxURt0djvqs8UZqPKmoYiHiotxTCGC/KxWlxZwPGrfrrDmXkPTouReWUO/uNg/wJ3Z9uD2Mwq9y3sDzDTnT2E/vNtgjt2Ev/ok+x2xWLGvmUDzm1bsG++HdU6zMqTN+C2mah0W2n1988PmlfuvCmDCiGEENMvrwOLhx9+mIcffnjY97/3ve/x5S9/mS9+8YsA/OAHP+DVV1/ln/7pn/jGN75BVVXVgCcUzc3N2acZM0WZy0qZa+iMPRajYdhVfCdDNJHmUmcYm9lAQ7Fdxi5f5865JXiDcULxFKm0RnWhDds1wV44kcqLwAIyF9w1Za4R78yPVkcgxhsXLuOLpphd6mDL4vK8C3In+7tqNRn49dvquOANkdZ05pQ6udoTGfS0oKk7msno9Mu9hHbsIvLOR/0ZnRQF292rcW7bguORezC4c5PN6LGVVey/0EVHME51oS2bAEIIIYTItbwOLEaSSCQ4dOgQzz//fHabqqps3ryZ/fv3A7B+/Xo++eQTmpubcbvd/PKXv+Sb3/zmsGXG43Hi8f6Jf4FAJrWkpmnZfPjD0TQNXddztl++6QzG+fnHV7P59muL7Dyxsipndz7zvV1GU79ih4kv3FFPezDG4koXJ1sC9I1jc9tMVLgsY/p8k9UmuSw3Ek/y0aVuAthBUTjfHsRkUHhgFMO+xluPsRw3ln0veAM0XvVRlbawoLxgzN9tgwLzy5zZ18V2Ewp6ZjXtVIqyYydY9PEhGg8cQo/2/54xr1yA88nNOJ64D2NF/9yOXP3crUaVTdctFjjasvO9X8L01DHf++ZEyxnP8WM9ZrL68a1iJrSJ9M3clzNdfXMs55uxgUVnZyfpdJry8oEXMOXl5Zw+fRoAo9HId7/7XTZt2oSmafzhH/7hiBmhvvOd7/Anf/Ing7Z7vV5isZFTjWqaht/vR9f1Ee8Cj3a/fHOwsRtDLErfoJkub4gTF9OU52j9g3xvl7HUzwgsKwJ7WqU9EMNuMbKg3Exnp3fSzjkauq5zqjXImbYAbkOC5bNjlLgmNreiPRDFkIpgVzUgcyHe0R6lo+TGF+Xj/XxjOW60+55uDXCmLYBFi3HRG+JysYPVdSOvP3Ejuqaxof088Vf2UnrgMOZwZp0KHVDqKjA+cjfGh+9EnVVNHIijQUfHiGWO1uWuMBe9IXQYdmG+0cj3fgnTU8fJOmeuyp1oOeM5fqzHTEY/vpXMhDaRvpn7cqarbwaDwRsc1W/GBhaj9dhjj/HYY4+Nat/nn3+e5557Lvs6EAhQW1tLaWkpBQUjZ1HRNA1FUSgtHXni52j3yzexq0ki141RNzjclJXlZihWvrfLeOpXUTGxydq5bpPdp9v50aEu0ppGpSXBga4uvv3kMhyWwZOZR8vijPPBhW4iqiOTtgyoKHKMao7TeD/fWI4bzb66rrPjZJCE6gQdIqqTs36Fu91FOCxj/xWZOHGB0Au7CL20m5Kr7dnthrIiHE/cj3Pr/ZhXLpy04VlN3RHeb/YDmaC/vSVFRYWDuqKxBxf53i9heuo4WefMVbkTLWc8x4/1mFz341vNTGgT6Zu5L2e6+qbVOvqbyDM2sCgpKcFgMNDe3j5ge3t7OxUVFeMq02KxYLEMniSpquqofhiKooxq39Hul08WVhbQ1BPNvraYVOaUFuT0M+R7u0xH/XJ5zrdPe0lqoKAACh3BBAcv+9m0cPyJDtx2C0tq3Bzu0Ehq4LGbuGd+2ajrO97PN5bjbrSvruuZoKj3n6ZDIJKkxR9nfrlpVAFAsqmN0I6dhF7YReLUxf5zO+04P30vzqcewHbX2DI6jdfFrkg2yNN1nZ5Igtc/aWfr6ppxrbCe7/0SZn7fnIxyJ1rOeI4f6zG57Me3opnQJtI3c1/OdPTNsZxrxgYWZrOZNWvWsHv37mwKWk3T2L17N88++2xOzyVzLGBxpQtd1zjTFsJmNrC2vhCLUcnZ58j3drkZxooqCijoKOhwzX8nUr6macwucbBibhGxpIbHYUZRRve9yKc5FsuqCzh0qZtUWuN4ew8Oi4nXjrVw1GPjiZVVGA2Df6mmu3yEf/EOoR27iH94vP8Nswn75ttxbt2MbfMGVFvmZoVOZnjUZHNbjdm5Pec7gnSHE8RTaf71g8s8sKR8TAsh5nu/hJujb+a63Jk6jjuX9bnZzYQ2kb6Z+3JkjsUEhUIhzp8/n3196dIljhw5QlFREXV1dTz33HM8/fTTrF27lvXr1/P973+fcDiczRI1Xtu3b2f79u2ke7O1yByLjFIjlNZkhs3o0QAd0UDOys73drkZxopurDWTCCZJaxrFphQeu5k5zjQdExjXf30dvZHxHzsZx41233kuHWutkZOX4izyGHDbQEmH6OkMcfRcKpuSV4/ESL/9EanX9pF+/yik+jM6qesWZ+ZN3H8bittJGAgH/TD6oak5UWrUqLUlaPFFsaYizHYaqLWnUFMhjpyNUaSOfohevvdLuDn6Zq7LnanjuHNZn5vdTGgT6Zu5L0fmWEzQwYMH2bRpU/Z13/yHp59+mh/+8Id89rOfxev18sd//Me0tbWxcuVKXn/99UETusfqmWee4ZlnniEQCOB2u2WOxRTI93a5GcaK3ldaSkFRCR9c6KLUGGXTyrmUuSe2hsVE6pgvcyz6lJZqtPnj9AQMRK8Z/qSbHDiPXSC0YyfRX+5Dj/bfZDAvn49za29Gp8rSoYqdFlsrKzje7Md3uBmL0UBHMo3TYsSgmsa0xk++90u4OfpmrsudqeO4c1mfm91MaBPpm7kvR+ZYTNDGjRsz459H8Oyzz+Z86NP1ZI7F1Mj3drkZxoreNruEdQ1FdHR0UOa256TcidQxH+ZYXKvGY+dCMAG6TtG5C9S89wElhw7R3tP/dM7YUI1r22acWzdjnt8wpnpPpeU1hbx6vJXDTT50MquL/8ZtdZPa1tPlZuibuS53Jo7jznV9bnYzoU2kb+a+HJljIYQQM0SF18vDu/ejvfIOlo5OIDM3wlDqwfnE/Ti3bcayevGMWBwynEhjNhioKrQRTaYptJsIxpLTXS0hhBA3MQkshBC3tOTV9kx62B07SZy8QF/yXcVhw/HIvbie2oLt7tUoxpn169IXSWBQlez8EICeiAQWQgghJs/M+ks5TSQr1OTL93aR7Ba5L2s6s0Klu/2EX3mX0I6dxA8c63/DaMB2/+04t23G/sCdU57R6XqaprP3fCcnWwKYDSrrZxexrHp0a8eUOc3YTSqRRDq7rb7YPqnZRKaD9M3clyNZofLfTGgT6Zu5L0eyQs1QkhVq6uV7u0h2i9yXNdVZoZR4kvQ7B0m9tpf0viP9GZ0Ade1iDJ+6i/D6Rdhqq4ioKpFpyOh0vQveEGev+jECGnDgEx+WZCmFdvOojr+n1syJFj+heIqyAitLPPqYsoCNpa2buiNc8IYAmF3qpK5oYokBRkv6Zu7LkaxQ+W8mtIn0zdyXI1mhZijJCjX18r1dJLtF7suaiqxQ6UQSbd8R1LcOEvnlPvRI/yKP5qVzcWzdjPOJ+zFWl6FpGl6vN6++gx+0pogY0gO2BRQH88s8ozq+DFgwqwaAnnCCZl+UEruZCrdtVMePtq2v9kTYd9UPZJ7wtF5NUl7mHDAMa7JI38x9OZIVKv/NhDaRvpn7ciQr1E1CskJNjXxvF8lukfuyJiMrlK7rxA+eIPjznYT+8y20Lh/x3veM9ZU4t27BtW0z5gWzcvpZJkOpy8rFzsigbWOt38mWAG+ebOtbN4/1s4q4c27JqI4dTZtc7Oxf7fvabfUlzjHVc7ykb+a+HMkKlf9mQptI38x9OZIVSgghpkDizCWCO3YRemEnqcut/W8UFVDwxP24nnoAy9olMyKjU5/V9R6u+qI090RRFFha5aaheOxPAd6/0JkNKgAOXe5hdZ0Hm9mQk3oW2ExDbJM/L0IIcauR3/xCiBkr1dJB8IVdhHbsIvHJuex2xW7D8cjdOLZuJrigluLqqkF3XCKJFG+f9tLUE6HUaeHeecU5qZMvkuDtMx20B+JUFdrYtKAUl3XwhfdoWE0GPrO2lp5wApNRxWkZ36/saGLgcKq0phNPpXMWWCypKuBMW5A2f2YuWlmBhaWjnGQuhBDi5iGBxShIVqjJl+/tItktcl/WeI9NdvlI/mwnLTs/yGR06rsVbzRgv+82HFs3Y3/wTlS7FU3TCHq9Q55j54k2LnrDAFyJp/hFJM6Dc2zjbpd4Ks35jhCvHmvFoCgYDSoX2oPEEym2rakZV5l93L13/8dbt/nlTk629C/yV+m2UmA15uz3lUlV+Myaalr8UXQdqgttKIoyJf1F+mbuy5GsUPlvJrSJ9M3clyNZoWYoyQo19fK9XSS7xdCSqTQnL7fSeaoDp9XIosqCIYfFTLQeeixO+p1DpF7bR3rvxwMzOq1ehPGRuzE+cDtKoYsIEAkFIBQY8Rxt7e3YrxkelAjptHoz8xnG2i7xpMaec1780QQd3jAGVaG8wEI8pXEppNJUYcRimr7v9dIiHXNSoSsUp8BmYkG5cVTZocb6Xen7yXt7s0NNBembuS9HskLlv5nQJtI3c1+OZIWaoSQr1NTL93aR7BZDe+dMB41+iBgsdKQUOq4kePqOSoyGiWeF0VMpons/JvzCLiKv7kEP92d0UubX4/6VB3Ft3Yyxpnxc57AVROkKJbKvzQaF0mIHZWVlY26Xj6/46ExZ0QwWOpIpArEkn3THcdtMmI0wtyPNE6sqxlRmrlWO4/T53i9B+uZklCNZofLfTGgT6Zu5L0eyQt0kJCvU1Mj3dpHsFoNd9IYz2YB6/4USadqDiVGnGb2+HrquE//4JKHejE5pb092X2NtBc6tm3E8eT++YieeUQYAw33WTQvL+cWxFuJJDaOqcM+CEszG+IB9L3hDnGkLYjcbWFXnwT3M05hkWgdFQTUo1Jc4ef9CFyZD5rwNJU4ud0fxhhKUF4z+l3O+yPd+CdI3x1NOJJGiO5z5TpqGuBEgWaHy30xoE+mbuS9HskIJIW5aBVYTPeH+14oCLuvYf60kzl0mtGMnwR27SDU2Z7erRW6cj9+Hc9tmrOuX9Y/bH8Mib8OpLbLz23fNpiMYo9hhwWJUBgwPOtMW5LXj/dmlzrWHePqOBszGwb9gF1a4OHS5m2Rap8RpodZjo8JtpcxlwdD7Czml6YOOE2I6HG3y8e5ZL2lNx2oy8OiKSmo8U7OgoRDi5iaBhRBi3O6YV8wbvq7s69V1nlGvCp1q9ZL8v7+g+c0DJI6dzW5X7FYcD9+Nc9sW7BvXoZgm/muqJ5ygsTtCgdXEnFInqppJOWs2qtkLqusnpx1v9g94HYqnuNQZZkGFa1D5HoeZX1lby+ErPjRdZ1Gli1Ot/WNSS1wWqtwz72mFuPnEkmn29AYVfa9f/6SNhmIH3ZEE9UV2VtcVTm8lhRAzlgQWoyBZoSZfvreLZLcYWoXLwuZFZSTMDjw2C0VOy4jlpv1BIq/sIbRjJ7H3jwzI6GTbuB7n1vuxP3QXqiOzMrQO6NeVN9ZsMm3+KO8e89O3d0OJncdXVg+577XlmlT669fLqA5/3lKnmQcWl2VfNxTbuegN47aZWFlbiK7r6PrMemqR7/0SpG+OtRx/JE4q3b9d13X2X+gkGE0C0NwdIRxPsqhQskLls5nQJtI3c1+OZIWaoSQr1NTL93aR7BbDlxUJBXC7FVKRGB2RwfvosTjpPR9nMjrt+RiSqf7jl8/F8ui9mB68A8VTkMnoFA5CePgMFGPNJnPhajvWtBHIPKXoaA9x+pJGkcMyYrlzXGm8HeHsnd1CuxlbKkzHUB9yCIUKrC5TgTRBXxejz6mRP/K9X4L0zbGWo2k6xYZodm2TWCpNtSWBPd2fyevy1QiVWCUrVB6bCW0ifTP35UhWqBlKskJNvXxvF8luMbay9HSa2L6PCb2wi8ire9GD/RMxTAtnZYY5Pb6JHqthzPUYazaZxFkvEdWSmQDSy+4uoqzIMWK5ZWVQVV7O+c4wdrPKvDLXkJNcrxeOp3j/QhcdwThVhVY2zC7GasrNQnRTLd/7JUjfHE85DzkK2XOuk85QnBqnhWRnmIja3z9sdhOFhTbJCpXHZkKbSN/MfTmSFeomIVmhpka+t4tktxi5LEVRiB85ncno9NJu0h3d2X2M1WU4t27B+dQWLIvnAL2/vDo6xlWPsdS/rthBW2s6G1gU2k3UFvXPsxip3CKXlfWusc2N+OUn7TT7MqlxO0MJQvH0kEOvZoqp/t5f7Ylw4GI30USKBRUFrGvwoCiDf1bTWcfJPOdUZJ4pc9t4am1t9vXbpzs40uQDQFUUNswtRtGjk5J5RtN0FIXs7wzJCjV+M6FNpG/mvhzJCiWEuKlpjS30/OhVwi/sInnxana76inA+fgmnFu3YL1tGco0/fGbU+qksMjKhc4IBVYja+o9GIYIKnIhFE9lg4o+lzrDJNPaqJ523OrC8RQvHW7OpO8FOs93YjQorK7zTHPNbm6bFpYxv8JFTziTKtplMdDREb3xgSPQdZ2ucAKnxYjVZEDTdN4+08HJlgBGg8q6Bg81ks9AiJuOBBZCiDFLtXUSemk3wR07SRw5Q98liGKz4HjoLpxPbcG+cT2KeXSrcE+2RZUFLKkunPTzWIwqZqNKItU/0c1mMmC4wR33awVjSd4+46XFF6W8wMKmBWWjzrQ10zV2hbNBRZ/z7SEJLKZAdaGN6sJM0oSJTlD1RRL855EWusMJjKrCHXOLURSFY1czmdZSWpq9Z708MNtKWdkNChNCzCgSWAghRiUdCBH+xbuEXthJdO/H/RmTDCq2jetwbduC4+G7UZ23bj58k0HljjnFvHvWi65nhpXcPa90yGFXw3n9kzau9mRCtcbOCL+It/K52+snq8p5pcA6OBAtsMmfqZlm77lOusOZVe1Tms7ec51UDJFuuat3HyHEzUN+YwshhqXF4kR2HSD0851Edu1Hj/dfCFjXLcWxdTPRO5ZSvnBeXo/znUqr6jzMKnHQHohTWWgd8mJ5OMm0lg0q+nQG4wRiyTGVM1rNviitvijlBdZRr5Y+mWo8NuaXuzjbnslA4rAYWD+reJprJcaqMxQf8FrXwTLEUMDhVrIXQsxcElgIIQbQ02mi7x0mtGMX4VfeRQv0p6E0LWjAtW0Lzq2bMdVXoWkasRysgn0zuOANcehyD5qms6K2kEWVI2eSG4pRVSiwmQj0rikAmbHqR6/4sJkNLK4qwG7Oza/tjxq72XeuM/v6tllF3DG3JCdlj5eiKDyyvJK1AQ/heIraIrvMTZmBaj12fJH+BSZNBoX7F5Xz1ukOLnWGURWF5bVuylz5uwaDEGJ8JLAYBVkgb7BALInZoOYsjWa+t8vNvtCPruskjp0ltGMn4ZfeIt3ev5q2oaoU55P349i2BfPiOdkMPX39Yrx1HO+xk7Ww1kQ+S0cgxi+ONGdHh7X6oliNCvXFjpEPHMKmBSX88ngbiZRGLJkmmkxzsDGTYevIlR5+/ba6Cfe7tKbz4cWuAQsAHmrsZnVdIWZj/4X8dPXLUqeZUqc5W4eR3Ox9czrKmegiXHfMKSKeSnOxI0SBzcTd80twWgw8tqKSYCyJUVWxGBW8Xq8skDdOM6FNpG/mvhxZIG+GkgXyhpdIaXzU2E1nMI6qKswpdbK4aux3Zq+X7+1ysy70Q1M7qdf2kXptH3pjS/9OBQ6MD2zA+Km7UNcsIqWq+AG83pzVcbzHTtbCWhP5LKdaA9hSoQHbTl+6ii099knHduCxBQ780SQXOsK0+ROQzgxBS4fh2DmdhhLnmMu9VkrTMCWCGK9dCVyD9vYOLKaBgUU+90u4efvmTF+Ea02Zypqy3r8N6TAdHeEB+/tlgbwJmQltIn0z9+XIAnkzlCyQN7x95zu5EjGBITM29minzvxZLqrctgmVm+/tcjMt9JNs6yT1yvuk3zxA4sjp7HbFasb+4F04tm7GvmkdiuXGmYgmUsfxHjtZC2uNZd9oIk0olqTYaUFVFdqTFiLegdmMCoqKKCsb//yAaqAp2kYkZCCSSGFQFSxGA5aCYsrK3OMut09tF5xu7f9jMbvMSW11xYB98r1fws3VN2/1RbhyWZ+b3UxoE+mbuS9HFsi7ScgCef28wcSAFYz7ttV4xj7k43r53i4zeaEfLRgm9Mq7hF7YRXTPIeh7rKmq2O5di+upLTg+dc+4MjpNpI7jPTYXC2u1+qN0BhPUFtmy6VxHU+6hyz28d76TtKZTYDPx+MoqFlW6OdkapM2febLpsZtYVeeZ8M9tQUUBL3zcQiCWmXNR47GxoNyVk+/glsUVlListPiilLosNBQ7SKT1QcOs8r1fwszum5NVbj4swhWOp4gm0xQ7zEMucigL5E3MTGgT6Zu5L0cWyBM3lRqPjSvdkQHbqj0Te1ohbuxIk4+jTT5UVWFNnWdUw8/0eILI7gMEf76TyJvvD8jopC6fR+FnH8b1xP0Yy4oms+p5ae85Lwcbe4BMnPzQ0grml914eFEwlmTfuU603iFEgWiSPWe9bF1dw2fX1nK1J0pa16krsudkEb5Wf4y6Ynt26GGpy0JbIMbs0okNhQJ6FykroiMQ4+WjLXxwsRuTQeHe+WUsq5n4ExFxa9t3rjOTzEDXKXFZeGJlFa5JyGwmhMgvEliIMVlT78EfTXK6LYjFqLJhTjFlLlk+dTJd8IZ4+3R/5qU3TrThtpuyi1ldS9c0Yu8fIbhjJ+FfvIPmvyaj07x6XNu2YH/yPnrsJtxlZXl9p2uyhOMpPr7sy77WdXjvfNeoAouecDIbVPTpy9evqgp1xblN2doZiuO0GHFa+n9Vd4UTzC7N3TneOeMlGEsBkEzrvHOmg3nlzpwlZhC3no5AjI96Ew5AJmXy/gtdPLAkM9ROv64PCSFuHhJYiDExGlQeWFLB5kXlKApDPt4WuXXRGx607ZI3nA0sdF0ncfwcwR07Cb24m3Rr/wRrQ0UJzq2bcW3bgnnZPBRFyWR3mMIUsWlN50iTj2ZflHKXhVV1ngGZh6ZaLJkeFBxEE6lRHVvutgxaWXsy13+oL3YM+PkrCtTn+Hze69YcSGk6vkiSCrcEFmJ8OkODF77rCicIx1O8ebKNy10RPDYTq8tUWXlbiJuMBBZiXMaykrCYmL7x/9dy20wkLzUTfGEnoR27SJ67nH1PdTtxPLoR17YtWDesQDFM7wXi26c7ON7sJ63pfHy5h8auMJ9dVzdt9Sl2WigvsNIe6M/0trBidJnNLEYDj62o4t2zXvzRJLNKHNw7P4ePD65T4jRjMii0+KLUFTm4a14JZQW5fUJYX2znXHv/ky272UCJ88YT94UYTk2RDVVRBgTw9UV23jrdQWNnZihtdzjBR40R5jdUY7kFn5wKcbOSwEKIPLesuoAL3jDtgRgWv5/Fx45S+N2PuHLoZHYfxWLG/sAduJ7agv3+20eV0WmyBGNJTL1rnKQ1nZOtAQLRJGfag6Q1ndOtAWo8djbMnr65HY+vrOKDS129k7ftrGsYfVrY2iI7v3l7/STWLuOCN8Qvjrag61DqsmIxqSyscOWkbF3X0XQwqAr3LSxD1+FyV5gih4WNC0oxqAqXOsO0+6O49LjcVRZjUmA18cjyCvad6yScSLOg3MX6WUX8w95LA/ZLpjS8oTg1HrkUgczT3VzMzRJiOklvFiLPmeNxPn3pBF3/8Sba+x9DWiMBmYxO96zBuW0LzkfuQXVNPDPXRMRTaV491sqFjhBGg8qaeg93zS3BaFBo7AqT1nrvXirwwcUuVk7jBGGHxch9C8sHbMu3haaOXPFdu34dvkiSxq4wc8smFlwcv+rn/QudxJIaCypc3L+ojEdXVA3Y5+3THRxp8oGuY0+HiJlcrKm/9Sb5i/GbW+Ya9F0tdVlo6o4QiCZJaxp2C3hs8nQsHE/xxonMEDG3zcR9C8toKJne3+dCjJcEFqMgK29Pvnxvl6mun55IEt59gNi/vcqVdw+hx/rHLFtWLcKxdTOOJzZhvGadhImsqjle15Z14EI3O0+20RNOoCrQ1BOmodjGunoP+855UchcJVcWWFGAYDR+06y83ScUT6HpOgUjZL9JazoKNx5OqKAPWBkbmHD9ukJxdp1sy74+cKGTq11h7llQwpxSJ4qiEEmkONYbVPT9e+tkG92hOEaDyopqN4WO/LkYlNV9x15OIqVxvNlPZyhBXZGNhRWuAfPlJmt133vmFvHnr3fQ4othVKGo3EA8kcJmHnm4Zr7/fZiot063c7kzM5fKH0nw6rEWvnRXAxbj8O0yE9pE+ubYygnEkhy76ieW1FhY4aTGM3g+nay8PUPJyttTL9/bZSrqp2sa2qFTpF7dS2rnAQhcM2m3oRLjp+7OrIRdX0kciJMe1yTsXH6Wa8s6ebEFWyqEzdL7ZiTBsXNXWNtQxKPzHVzyhrGaVBxmHbsxRjoSIBC4OVbe1jSdw00+rvZEQIcKt5U1DR6M15SjaTrHm/1c6Y6gKgpzyxwsGGFuxxxXGq83RG88hstmxJEO09ERGfaYG7nkDWFPZ+ZTdEcSBIJxLoUMpCM+LhQ7WFVXSCSRwprqWzhPh2SE9p4QF4kACpeaWti0oBSbOfd/PnyRBJ2hBIV2EyVOy40PQFb3HU8571/oxBvITNq/chU6OlwsqiwY9fHjrXurL8qCAo1ZDhNGRcFGjA9ON7KqbuShiPn+92Giurwd2NPXXLil4cKVlhH7wExok3zrm+2BGB2BGC6ridoiG4ZJ+rsznnISKY23T3cQS2auOy81we2ziym/bk6drLw9Q8nK21Mv39tlsuqn6zqJkxcI92V0arkmo1N5McoDt1PyG49iXbkwZxm4cvlZri0rZgjQFI0TTaRRFLCZDZichZSVlfG4p5i95zpp6o5Q5DBz97wSPHZTZm2GGbjy9vXOtAc46w+AmklZezEE1Qkrq+sKs/scu+rnZE8AlMwQh487dOprHNQVDT3koawMykrLONsRwmExsLiyYMIpYDWLiwOtmT9cJ7q7SWlmKq1WIgYHZ/xwl7uIMouRsk49M8lW1/HHYqQsdiKG3mEtOnRpNlaXjX5eymgcbvKx57y/91Wa9bPsbJhz45XLZXXfsZXjCye4HPaDof+p2mmfyr0rykZ1/ETq3prwEzEkwAApXUdNQ9zgpOwGk3jy/e/DRLmLktlJ7QBGVWFWTdWIT3JmQpvkU9883ORjT6MfUIAU7UmNR1dUTLjcXNXvZKufbs0G1/zIG8NGls0tG9Xxuay7rLw9BWTl7amR7+2Sy/olL7cQ2rGL4As7SZ5pzG5XXQ4cj27EuW0zlg0r8HZ1YZuE9SZy+Vn6ylpcVcgbJzsIxVIoCpiMBhZVulFVFbtF5cGllQOO6/vlNV0rb09032t1hVKDVqTvCicGlNPqjw3ap8Ufp6Fk+DkTlR47lUM8Dh8PTdMxGAysrCvkk+YAGgoum4mqQjsomYFqeu/nf2R5NR9f6aEjEKPbEKU7bQMls86FyaBgGKaNUmkt+8RhLEGQpul8eKlnQPt8fMXHmoaiUZUjq/uOvhyD0TDoe2gwKIP2m4zVfWeXOtl3rpNUdr6Vwpxy5y2/8vbGBeX8It5CVyiBxaSycX4ZjlEsJjgT2iRf+ubhK74B3/uLnRH80RSeMQzrnMy+aTQM1S+HPpesvC3EDJNIaZOyzkK6s4fQf75NcMdO4h99kt2uWMzYt2zAuW0L9s23o1ozj7/zeezsUDRdZ06pI7tKdI3HTiienu5qTYkaj42PGvtfh+MpFAZ+l8oKrJxuG/g4+frH3JOlKxTnpSMtBKJJDKrCmnoP88tdnGwNZPeZXerIzg0xG1Vun12Mpmmcv6zx4qkQJ5szY3/dNhOPr6wedI5mX5RfHG0hmkhjMijct7B8VCvEQ2a017Vrg0BmPY3shH+RM26biYUVrgHfxbUNUzMx320z8cSqaj5q7CaWSNPgMLCkSlZ59zjMfH5DA/5IErvFgMmQv4HCTDXUmoz59NtlTqmTIoc5u+CqUVVYfYMhgvlKAgsherX4ouw82U53OEGpy8LDSysoHuU47+FooQjhX+4ltGMXkXc+gt55OygKtrtX49y2Bccj92Bw5yaN6HRK6zpzy1zMLnWikLnjkUjnb3CUSmuEE2kKrMYJDzNrKMmsMXGwsYczbQEUBU60+LnUGWbr6hpKXRZW1Lhp88c41xFEVRSW1biZPUWZX/ad7yQQTQKZyeMfNXbzhTsaqPbYaOqOUFZgYXlN4ZDHuqxG3DYTpU4ripLJ7PPOGS+zShwD2u2tU+1EE5nvdzKt8/aZDuaWOUcVpBtUhQUVLk5dE+g0lNhxWORPlK7rtAfiqAo5W8PkwSUVzCp10BVKUFdkn9RFHq9X23s+TdPomMKFOmcCt/3GTynE+KysK2Tfuc7s6/piO0V5lITCZFD57LpaTrUGshn78ql+YyG/tYUgMxTjteOtBGOZFZi9wThvnGjn128b+0JueiJJ5J0PCf18J+HX96FH+1c2tqxciHPbZpxP3I+xoiRn9c8HiyoK2HminY5gDIOqMLvUybwy55SdX9d1LnaG8UUSNBQ7RgwKz3eE2NV7Ieyxm3hkeRXFjuH/qKfSGvsvdnGhI4TbbuLOOYMXqlvXUEStx86/fdj/lCaSSHPgYhePrqjCaFB5ZHklkUQpqqIMO8SnO5ygOxynutB+w2w5qbRGU08Ui1Glqncl9qF0XbMSclrTuOAN8/975zwPLa3k4WWVwx4HmSAhmkxT7bERTaa56A1zoiVAhdvK/YvKsplrmnoiRBMaBVYjRkNmdfJgLDnq4HxtQyFXuyN0huOsrC1k08IydF3nF0db+KTFj9tmZnWdZ1TzLm4WsWSaFw830+bPJA+pK7Lz+MrMd2kiVFUZ9aKQQtwM1jUU4bGbuNyVmee3tDr/npRZTYYbJjKYCSSwEDclTdOJpdLYR5m5JhhPZYOKPu2BGMm0NqrH0rqmEfvgOKEXdhL6z7fRevrvvJpm1eB8agvOrZsxz52+Facnmy+axG03kUxrGFQFi1ElFE9N6l3njmAMs0Gl0G7ml5+0caZ3eMdepZNHllUyp3TwE4FkWuPNk23Ek5mnKT2RJLtPtfOZtTXDnuf9C10cutyT3b890Mxv3TVr0HcjEEsOOvb6bSN9J/df6OLAxS4ATAaFTy+vGjafvT+S5GeHmrLf29mlDh5dXjUoja2m6VS4Lfh7n1i8e9ZLTyRJZzDO0SY/v7mhnk8vrxpUPmTWJmnxRQjGkhgNKqfbgsRTGhajypm2AKqi8NDSzEJol7si+CLJzEVruYuaIjueIVaNH+6z/PSjqyRSGhajgQveMKvrkvz7R1d473ymPWwmA+F4ihKnmXnlM/8J32gcu+rPBhUAV7ojnG4L5uVFkRD5bqi1VUTuSWAhZqSL3hDvne8kFE+zoMLJPfNKs3fxLnWG2X2qnWAsRanLwiPLKm84QctlMeK0GAnF+4OLEqf5hkFF/MR5Qjt2EXpxF6mr7dnthrIinE/ej3PbFiw5zOiUz672RPDYzQMuJq/2RCZlHkE8qfHvHzbRHsw8DarxWLnaEyWT8SMznvbApe4hAwtfJJkNKvp0BOOD9rvW+Y7QgNfRRJoWX5T64oHl1xXZMRvVAfMF5paO7qlNOJ7iw0vd2dfJtM7ec95hA4uPGrsHBMMXvWEudYWZc835zrUHeftMB4Fokp5IEk3TCMZSFDvMqGpmwvYvj7cNGVgkUhr/8dFVYgE/qmLlREuASCJNgc3I3DInoHDBG8IfTXLwcjezS51c8oYIxFIEYkkeXVF5w7U6+pxo8Q9os0RK440TbZzv6E+5HE2mafPHaOqJ3HSBha7rvHe+ixPNPpxamFWalcVVhfgiiUH7+iKDg1chhMgXEliIGScYS/LKsdbsxM6jTX6sRgN3zC0hmdb45Set2QtHbzDOrlPt/Mra2hHLVFWFB5dU8MaJNkLxFG6biQeWDJ2KLtnURmjHTkIv7CJx6mJ2u+K04/z0vTifegDbXatQDBNLDTrTlDgtXPSGB22bDBe8IdoDejaLxqnWIClNHxDUxJP9Q5LC8RRNPTEKbEYqCqzYzQYiif73ywus2fzhQymwmbJ3/Pu4hsjaYjUZ2Lq6mv0XugjFU8wrc7FulBNjw72L610rcN1TtGsF44MvMK8NNGLJNG+caCOZ1jGoKiVOCwZ14ITxZFqjJ5ygMxQf9LM63xGiO5zATmal8hW1bi51RphfngkqAAqsmWBc18FsULPrcjgsBspcYwgoh4g/ook0juuGgsVTGqXOqZnwPpWOXvXzUWN3JiJOp3jzRDulLhuzSx2caAkM2HfWEMGyEELkCwksxIxztSc6KFvM5e4IdwA94cSgu9HXDiUYSV2xnd+6axbhRAqzQeWDS93sPtVBeYGFdW6V5M/eoOXND4h/eLz/ILMJx5YNOLduxr7lDlTb5FxIzwRr6j20+KJc7YmiKgrLa92D7ujnSjCe4tqE306LcUCgAGSHi3SF4vz8RCN9X4uFFS4+tayS3afa6YkkSaY0rnZH+N97LjLLkeKBomJs5oFPqu6aW8KLh5uzwcfqes+wE+sq3Ta2rh5+WNVwSl0WCu2mAXek++aodIXimIzqgBW955W5Bue+v+bphjcYJ5ke2E9MBpWqQistvhiheApfJMnCCic/2X+Ze+aXsqa+f3xv6rqsZCaDgQXlLlAy178mg8I980upKLDishoHBDVzxzi3ZkmVmyNNvmzfNRtVNi0sI3SsBX80SVdvppTV9YWjzjQ1k1zuGhiQ6zpc6Q6zpr6IjQtSHG3yofZm86oeYS6NEEJMNwksxIwz1AVd351qj8OMxaQOCC4q3KO/w6mqCi6riVePtXLhcicVhw5jff8D2o+dQL0mo5P1zlW4tm3B8el7MRROz7CMtKZz6HIPl7vCFDvN3DareFqz6FhNBn5lbS2+SAKTQZ3UupS7zFwM9gcSqqrwmxvquNIVpSeSYFaJg2XVbnRd51RrgKYeje5IEotRJZZMs6bewxfunEVzT4T/OHgVyAxHafPHOHTZx13zSgecr8Jt5Ut3NdDcE8VtMw07ITmt6XzS7McbjFNTZGNBuWvUw+AUReHJVdXsO99JdziTrafCbeV3//1jmntieJxm7l9YxmfW1qIoCkur3STSGidbAlhNBm6bVYTb1h94lLosmAzKgOCiqtDGr62v45/2XWLXqXYWVrhYWp25UD9wsYvlNe7s8L95ZS7eP98J0b76wRMrqym0m+gOJ6gqtGUnoG9dXcN75zvp6Z04f8cYJ1i7bSZ+47Z6TrT4Qc8EGm67icdWVPPx5R5C8RRLqgq4bfbNOXE78/trYHBR5Mh8x1bVeW6KCZ1CiFuDBBajoGnaDdcU0DQNXddztt+tZiztUuo0s7qukMNXetD1TKBx2ywPmqZhUOChJeW8daqDYCxFWYGF+xeUjrq99WSK8Dsf4fp/XuTBQ4cxxq8Z47ywAc9nH8b55P0YK/svPCfrZ3mjNnn3rJejV3wAXO2O0NIT5dfW197wQjaX38GhyiqwGrPvTUY9NE2jrshOymzkZGsAs0Fl/ewi6jx26q5ZTK6v7Ks9Ea50pdBRCAKBaAJ/NE6J05yZW9E3/EjXQddp80WHrJNJVWgoto/42X55vI1z7UESKY1Xj0WpLrTz8LIKFlUOfZdd13W8wTgWkwG3zUSB1cinlmaG4F3sDPGXr5/mZO9QGG8wRjCSYGG5i2U1macxK2vcrKzpn8h7bb3MBoUHl5Tx9mkv4XiaSreVe+aVcK49iKbpFFiNhONJmnoi1BbaSSTTxJMpDErm52cxKnxmdRUfnUmStmQCkBpP5m759T/jQpuRR5YNHDo41p+ry2Lg9ln9w8Y0TWN2iZ3ZJfYB2643Hb9Tb3ROXzjBvgtd+CIJaovs3DGneMT5WmvqMhmx2v1R0HUWVrqo81jH/Zkm2ibXHh9NpNl3vpM2f4yyAit3zR36BsZYzzmW/eXv5mAzoU3ysW9Od7m57JuTdcxQ+4/lfBJYDGH79u1s376ddO8daq/XSyw28nAaTdPw+/3ouj7iCoWj3W8mO3ylh8bOMKUuC+sbijCPYuXcsbbLAjfULXAQT2UW7IoHe+joXe/JDnxqrp1kWsdiUkmEfXSEhy9L1zS0o2dJvbqX1Bv7wRekb/mvSFkJbRtW471jNctum4XNU0hC1WGY/OvJtEarP4quZ4bETGShvRu1SWNTK/Zr1okI+0Kcu6JSaBt5onouv4MTKWu8x/YdN9vtZu6CviE3MTo6+vtoWtOIpzSsRhWbHsOjJImndVRFwWUwcP5yCwdOJAhGk0SjCYodZlKaTioeQYv4aG0zYhjj54km0zS3tmHTddq6Iihpjbb2IPv0ED1dhYOGB0WTaQ5c6CQQTYEC9UUOVtYVZt8/dr4TNRag1poEJTPUqcAAJy81UW4eebJ5Hxfw6XmObF+I+rs5dq4Nl64xv0AjFE+hRBJYnWnKC6yEfN1cO01d0zTqnOB2g5oM0XHdJPZ8MB2/U0c6p6bp7D7dQaQ3EcRZfzexQA+rrvnZDuW+Bgu+METDKcqLM393JqN+Yz3+wKVuvIHM9+1KAN7wdQ56ojeec45l/1vh7+ZYzYQ2ybe+mQ/l5rJvjvb4XPTNYDB4g6P6SWAxhGeeeYZnnnmGQCCA2+2mtLSUgoKRx/VqmoaiKJSWlt4wsBjNfjPVP++7xM5TPb2vEhzvVvjWo4tveNx0tEvi1EVCL+wi/OJuUk1t2e1qiYfElrvYv2gZPbNngaJwx9xiSmzJEesXS6b56UdN+CKZCwpHZ4TPrKsdMC5+LG7UJqotQuSa8fiKApXl5UNOKh5LuWMRjiU53uyn0xenstDO7bOLhl2fIVf1uNFxp9sCvHPGSzypUeIwEcXKaX8KfzSF0aBS6bbw0pkQgWgmO1RPOI5OjFgyxbpyAz6TSropyVNrajCMMqsRZCZfRw0hfJEkl3uz+RhUhWqDk4shA3csLRuw/9tnOmhLWLNTRU75YPEcJzUeO580+9nZGOXD1jTRhIaqKpgMUFdspKaygrKy8a2BkkxrhJQgmkHH5bET9EUJxFM01FZz7/ySQT+7mfD76to6pvVMitbOUJwaj43FlQWTkpFtpHbpCMToTAWunQJEY9jAg2Vl3EiJpuH1eifc3hP9ufUdX+Ap4vInQTD0/06JRMFZWDQobfJYzzmW/WfC93CqzYQ2mY46TtY5c1VurvrmWI7PRd+0Wkc/pFwCi1FQVXVUPwxFUUa172j3m4n2nO9CvybFy+m2IM3+GLWeG6/sOhXtkrzaTuiFXYRe2EnixIX+cztsOB65F9dTW7DdvRrFaKQikqTZF6WswEKR3URHR8eI9TvV5scXTWUzFYUTGp80B7lr3vgXwhuuTa50RdCAyz0Ryl1WrCYDK2oLcdtHN3k8V2395skOrrYFuRKNYW0L4YsmxzRxebz1uPa4nnCCcx0h7GYDtR47u095SWmZjFGtgTinrvpp8aXQFTCqOpe7omh6Zk5IZzhBKJ5G18FmVOiJJDlzrpNmf5zFVQWsqB392HaXzczCygLeP99FVzhJMq1RVWglpYHZaBj0GXsi/d8VXddRFIWeSIpQPMTu017a/AkSmk5SV1A10ICFlW5un1My7p+bRVWZX+HidFsQo8FAXbGTOaWOERfJmwm/r/rq+Oqx1mxmstNtIXoiKe6ZP/juei7PeX27OG1mVFUdkOHLZTONuv1y1d4TLUdRFCxGI/brEiNYTCpWk3HIcsd6zrHsPxO+h1NtJrTJdNRxss6ZT31zrMdPtG+O5VwSWOQpXdc50RLgcleEYqeZlbWFo74TPJ2Gujk4hpu+kyLd7Sf0i3cI/XwnsQNH+98wGbFvvh3X1i3YH7xzUEYnt92E2565Uzea8YXx1OB0pUNtm6hLnWH+80gzug7lLiuxVJpfWVs75kw8ExVNpDl0pZuezgDngwZMRgOdoTiPLK/MrsY82Zq6w/zw/csk05khcRaTSjKtZe9SN3aFCcaTmAwK8TRoOmi6Rjiewh9L4o8mSaX1zPspnVhKI5JI0eqPsfNkx5gCC4AHFldwqjWIy2okpemkNbjYGWbrmsHBVkOxncudYRq7wnSGEpgMCm6bkUNXfPSEE/REEjgtRsxGDavJQJHdzOZF5aNasHEkWxaXU+y0cLY9yJXuMGfbQ/zrB1fYsricUtfYs5oFYkkMioJBVTIrmd9gzZjJEoglB6U7Pt7sn7TAYjhOi5F1szx8cDGzJonJoHDPEEOHZgJVVbh7Xik7T7aj6ZmhhNeuGSSEENeTwCJP7b/QxQd9i2W1Z1Zc/cwN1mKAzErEu0910BGIU1Vo5YHFFdmL46mwaUEZrx1vzb5eWu2muvDGTytyTYvECL+xj9COXUTe+gCS/akwrXesxPXUFhyf3ojBM7HUlbFkmrPtQTQ9M0b+UGNP5m45mSBr4TCTdifieLM/O9/YajJgNRmIJnIfwNyIyaBwojmAW0kTTUIkqaF1RegOJ6h0T35KzFRa4/9550L2YtJiVFlYUUBK07LDwTIXvZlMUGldIZXWURWVnkiSeDJFIq0DCk6LiVgi1RsEK5iNmTvO/mhyQKalG4n0pqO9a24J3ZFM6uMih5m6osF9YHWdh48v99ATSWAxqRRYjbxxoh23zUha00lrOpqeWXG62GGhyGEedrG8sTAaVNbPKuJcRxCbKfMnoD0Q47XjrXx+Q/2ohw4l0xqvHc88IWj1R4klNRqK7ZQVWHlsZdW4hwCOl6ooKEr/XPy+bdPhjjklLKwooDucoMZjmxE3hYazuKqA2iIb7YHM5O2p/rkKIWYWCSzy1LFm/4DXzT1RukLxQWkuU2kNnUx+el3XefVYazYP/tWeKK+faOWz6+qGPMfVngiHLveQSussr3GPejXb7CqxLX5MBpXbZxdnc8s/fUcD9cV2jl31U19s5+Glww+xyDU9lSLyzkFCL+wk/Ope9Eg0+5556Tyc2zbjevJ+jNXlw5ZxbXveSDSR5l8/vEKgd+E0u9nAg0vLudARJq3rLK8unJSc86YhHgEZDbm7gNI0naSmYVRV9p7zcqo1iN1s4I45xQO+IylNz8xB0PvOrWBUFRLJ3GXjiCXTXPCGMKoqs0sdA34u570hukL9WbviKY2OYIxHV1TR2BUhEE1S7bHTEsn0JZ1MsJdKa9hMKoqqYgRUMsOQXDYTDrNGjd3G8ho3drMBq2lsd2YtRjW78nZxb7pQh8WAUVXwRRIcuNhNIJpkTpmDVbUePA4za+ozmZAae9cycFiMRBKZgCQYS1JoNzOn1MHiKveAVbUnIp5K09QV4VJnmEA8hdNiZFaJg3AijXOUaYKPXfVz0RsmlkxzuSuznkaB1YiiKLx/vpOHprDvQ+ZJweLKggELyq1rmL40rUUO87Brncw0LqvphnO3hBACJLDIaxe9mTHrdrOB+iL7gMfPuq6z51wnx5p86MCyajer6goHLK4F0OKLkUprgx5dd4cTvPBxc3ahuSvdEZ5cpY7qjujRq352nmyjIxjHoCq0BWKUuMzZlXY3Lihj44IbT1TMBV3XiR88QfDnOwm9/BZapy/7nrG+EufWLbi2bca8YNYNy3rvfCeHr/SQ1jJ36e5fWIY6wjiuk63+bFABEEmk6QgkRhyvDhCKpzjY2I0/mmROqTO7kNtora73cMEbyq5PUOw052wY1Jm2IO+e7SAcT5PszThlMmTu+L92vI2nXRYKe9cMURWF+RUuIv4ERboRk8GAx2GmqjA3KyP7o0l++tEVwvHMU4Bip5nPrqvNBlbBWJryAsuAFbFVJTN0476FCilNxxeO8/s/bsdkUEBTMBoUFHQ0XcmmTAVYWuXisZU1XLzSTNpSgKIqbJhTMuYhXSaDyp1zS3jnTAe63l8fTYefH7qaXUSu2RclkdIpdVm42pMJgB29k2E9djPVhXZ8kQQrajNP/JxWIwsrCsY0mXwkZoNKsy+Kr7ftAtEkV7oi2MdwZ90bzGQKCif6nwaGE2mKr3lvqm1ZXE5DiQNvME6tx05d8dQ/LRVCiFuZBBb5SieTY5/MXVunxYjrmjuJZ9tD7D7VTqs/s4LugQtd3DariGgyje2aiwOP3TTkeNhz7cFBq1efbguOKrA42NjN6bb+1GPd4QSn24KUuazous6Z9iBN3VFKnGaWVbsnZTxu4swlgjsyk7BTl/uHXqklhTgfvw/Xti1Y1i4Z9bCOS51hPuwbegZ80uyn1GVhZW3hsMfEU4PvzCfSIw9J0jSdnx1sygaAF71h4ql09q71aJQXWPnc7Q2c7QhiMaosqHANuJOf6L1zX+QwD8rcMpJIIsUbJ9qy34tz7SHMRoW5ZZmnFJqu09QdzQYWZqPKI8uqOHAiStpsxGIy8ujKSkw5ml9xpMmXDSoAukIJTrcGWda7oNucEjslTiuqotAZimMyqPzmhvpsil+TQaHYaaG8wIKhI9X7xEJBIfNU4NrJtbfNKeFTyyq47NFImF2UF9iyn3OsVtYWMqvYQXswRoU7M3TkSldkwMrUAGfaAnx2XR3tgRgtvhjlBRZKnBZUNTOc5/Y5xTy0pGJy+k9ao8RpoSeSIJnWMRoUygssRJKjf2JR47FxqjWAy2rKDkHqC9ZqRpGsYTIoisL8chfzR/n0VQghRG5JYJGn0rrO3FIHp9qCpNI6HcE4R5t8rKrPPNo/0eLnaJOP9kCUcCKN3WTEbjFQXWjHqCq9Kx8b2Lw4M+wnmkjz8ZUeusOZVYltQ9yZtJtHd0HoiyYGvE5revbO/f6LXdlJi5B5EvL4ympyIdXSQfCFXYR27CLxybnsdsVuw/HI3bi2PYDtnjUoprF/rVt90SG3jRRYLKwo4OPLPdknB6qisLhy5KcPzb7ooKdKJ1oCYwosIDOxfF3D4GOauiO8cqyVWDKNQVXYuKCU5TX9n+F8R5DTrUHMyQDrXB48jv6hde2B+IBg02Y24IsM/FkXOQdebN85t5gCwoRVJ9UeG/XFE58D0CeWHBykXbutyGnh8ZVVfNTYTTKts6zanV047loFtsyaFElNR0HHYTGyvMZNZyhBWtOZVeLgU71PmWxmI/Vlrgln/bh24n+m3KH6mxGb2cBn19XhiyToDifoCMaxm1Uaih0U3GA9kokwG1SqPTZcViOxZBqLyYDTYhzy98JwllRl5hAcb/azpMpNStMpsmeent0x9+ZcIVsIIcTIJLDIU26biSNXEpgMKiZDZqLkvvOdLKvJPAFo7olyqTNMMqWR1nXiyQT+aJIlVSZW1RWytNqNx27GoCrous4Lh6/S0bvI0fmOEOsaPJS6LNkhCy6r8YYLOPWZU+LiWJM/e8e+1GXJrkh8pMk3YN+L3vCYJ8BeK90TIPzKOwR/vpPY/qP9MzONBuz3345z22YcD96Fap/Y8Jty9+Djh9p2rSKHmc+sreVwkw9d11lWU0jFDY4ZasE8cw7vSL9z1pu9+E5rOnvOeplf7sJqMnCqNcDrn7SBrmNPh7kcvsrTd87K1qnMZcGgKtngorrQlr0gVhRYUTP0nJHyAitlZUU5T+23uLKAU62B/h+5qgy6E91Q4rjhU7aUBiUuC9GkhkFRKLCZWFzpJq1n7tTfNquYSrdtUleHLXVZWFTp4lRrMPtZNszpv/i+0h1h96n+RRcXVcZ5aGnFoHJyRVEUNi8q57VPWlGUzGT1+xeVjWmolaIo3DO/lLvnlWSfDPalzRVCCHFrksAiT907v5Rf9mZXUoC6IjspTacnkuwNCGIYDQrJNNA7ljvaO9bZbTNRcs0k745gPBtU9DndFuSLd87iUmc4e9d2NKtEv3++kzPtAVKajtmoMr/MSUOJgwUVmeEp12dhUZShU9CORIvGibzxHsEXdhLZdWBgRqfbV+B8agvORzdiKBrb3ISRzCl1srrew9EmH5qus7DCxYpr7vQPp6zAyoNLRn8BWF5gZXapI5vJSFUU1s8a29OKkfSEBz5hSKZ1grEUVpOB49clBAjFUzR2hbMX6w6Lkc2Lynm3NzhpKLHz/1o6m1gqk+50qrPB1BbZeWxFFceb/RhVldX1hXgc5jEHAG6riRKnQt/DGLPRwINLyplV6kSBEefR5NJDSytZWu3GF0nSUOIYMOTo2mF4kFnk7465xZPa5g0lDn77rtl0hxN4HKZxpwi+NpCQoEIIIW5tEljkqdoiOw8vq+R8RxCryYDFaMBiUinsHV5R6bbjspgwG1QiiTQK4LSaKHFZshma+gyV4chkUDGoypgm/bb6o3xwqRuL0cDK2kK6wgnqih386vq67DnW1HvYd64ze8z8cteoLo70VIrUe0fwvnWQ8Kt70MPXZHRaMgfn1i04t27GVDN8RqeJund+KRtmF6Pp+qSmh3x0eRUXOzNPcmaXOHKa97+hxMGFjlD2dYHNRHFv+UM9Gbn+u7G4qoD55U4SaS07PyP3CXNHb3apk9kTzIS0tqGQ5niE7nASi0llYYWLWaXOnE2EHosaj52aIRIV9Q2n66PrkLpu22QwG9UbPmUTQgghRksCizz20NIKXjuu0xVKZOZLXLM41qdXVPD6iVY6ghp2k4ECu4nf2TiXO+eWDLpgKnKYmV/u4mx7ZhiGojCuu+TXZnoxGVQqCqzYzIYBF6frGooocVpo6o5Q4rSwsGL4SZS6rhP/+CShn+8k+NJbaJ099J3BWFuBc+tmnNu2YFk0e8x1Ha/RPLWZKHWMAd1YbFlUjlFVaOqOUOy0sHFBafaO/Jp6D03dkewFa6XbSv0Q6ysYDepNtQDW3DIXnysq5YI3TIHVxLpZRdMSVIxkWbWbjxr7n1rUeGw3TapSIYQQtw4JLPJYidPC5zc0EIqnsJkMAy6GUhqsqi3kvDeETmYoT1WhbdgLpoeXVrCgwkl3OJldxGqsqgttgxagqvEMHnM/q8TBrBHGvSfOXSa0YyfBHbtINTb3v1HowvXk/bi2bcG6fpkMqxgHm9mQnYh8vdoiO5/bUM+59iB61MTKedVTNgxoOimKwrJq95hX0Z5Kd84txm0zcaU7QrHTPOr5TkIIIUQ+kcBiBhgq/ePlrjBuu3lANqHLXeFh74Rn7pJPLAVjsdPC5kXlvH+hk1hSY0GFa8jMRENJtXoJvbib4I6dJI6dzW5X7FYcD9+NY+tmgovqKKmuyvkk4FtRqz9Ke+/q633riwAU2s2sqffQ0ZG8qZ5KzHSKorCsZuisVkIIIcRMIYHFDFXssAzaNhVDJ5ZWu1la7R5V9pe0P0j4F+8S3LGT2HuHB2Z02rQe57YtOB66C9WRycgT6ugYsTwxOgcudrH/Qlf29cYFpayqy9+79UIIIYS4OUhgMUMtqnRxsTOUzS7UUGIf8wrOEzFcUKFF40R2vk/ohV2Ed+6HRP+aDdb1yzIZnR7bhKG4cIpqemuJp9J8dF2GoQMXu1lRU3hLDHsSQgghxPSRwGIUNE27YYpLTdPQdT1n+92IqsCjyyvxhRPokM0sNJm5+Iejp9PE9n2cCSZe3YseDGffMy2clXky8cR9mOr6x/5fX89ctctkmY76jeeciWSaVFobYlsaI+q4yj3THuD9893EkmkWVrq4Z15pdi7PRNplPMd6AzHeOt1ByNdFSWmCTYvKR8w6NpZz5Pt3cDrMhDaZKX1zKsudaDnjOX6sx0jfnJiZ0CbSN3NfznT1zbGcTwKLIWzfvp3t27eTTmcWGvN6vcRisRGP0TQNv9+PrusjzhEY7X5j1RG+8T65pOs62okLpF7dS/r199E7fdn3lIpijJ+6C+Mjd6POrycBJABGGOo0We2SK9NRv/Ges8GRHLBuSY3HRndXfwrgsZQbiiXZc7oD9Mwvi/ONAYzxQHbdkom0y1iP1TSd3ac7iMSTWLQYHR0dvBUNcMfckmGPiSdTBPyBUZ0j37+D02EmtMlM6ptTVe5EyxnP8WPvz6PffyZ8D6faTGgT6Zu5L2e6+mYwGBx1HSWwGMIzzzzDM888QyAQwO12U1paSkHByNn8NU1DURRKS0tvGFiMZr98lbzQROiFXYRe2EXq4tXsdtVTgOOxjTi3bsayfhnKOC4y87ldpqN+4z3nQ0XFHGz00RGIUVloY0194YCUwGMpt73ZT0SNDNyWtHJ3WRkAyVSaxs4why/HsVtNbJxfSsEoV1kf6+frCSfoTAVAtYAOEdXJlajC46Wlg4bmaZrOrtMdnG4JYU9Hma162LRg8H4Tqc+tYCa0yUzqm1NV7kTLGc/xYz1mLPvPhO/hVJsJbSJ9M/flTFfftFpHn0lUAotRUFV1VD8MRVFGte9o98sXqbZOQi/tJrRjF/Ejp7PbFZsFx0N34XxqC/aN61HME1slON/bZTrqN55z2swqd88vHfb9WDJNJJEeVbkeu5nG7gjd4QQWo0pdkZ1SlzV73Ntn2zlwqoPTPgUdhXfOeHluywLqigevjzHRz1dgN2MxG4gn0tkl3YudFgyGwYsZnmjxc6o1CCjowPGrAWqLnCwYYV0VgGAsRUdrkPICmywc1yvf+yXMnL45leVOtJzxHD/WY8ay/0z4Hk61mdAm0jdzX8509M2xnEsCCzGkdCBE+BfvEnphJ9G9H/dndDIYsG9ch3PbZhwP343qHN0FpMitWDLNBW8Iq8nArGLHqCdmv3+hk4OXurEkg7jb0jy2qjq7wvZQLndHUIBkSiOR0rjaE2V5b0rUWDLN4cs9BKJJIDPHpy0QY995L79eXD+ovu+c8dLUHaHIYebeBaUU2ccWiJoMKvcvLGfXyTZIg8Ni4P5FQ6/E3haI0eaP0h6IUmFKoDiMtAViIwYWnzT72X+mI/OERlG4fXYxG+YUj6mOQgghxK1MAguRpcXiRHYdIPTznUR27UePJ7LvWdctxbltC87HN2EoGTl1qa7rBOMpHGZj3q1wPBP1hBMca8nME1ha5cZoUPmPg01EE5k5QFWFVp5aU3vDtm4PxPjgYnc2SGz1xzhwsYv7Fg59cQ5w0RumvthBpdtGStOwm434okmKnRYSKY0LnSFS4QSdMZ0CmxmL0UAknh5Uzhsn2th5sh1/NIndZOBqT4RnNs4Zc1ssqHBRX2SjsbmFObXVmIyDn1YApDWNxq4ICjoJVaPFG2ak5tF1PZOi95rFHw82drOqrhCraehzCCGEEGIgCSxucXo6TfS9w4R27CL8yrtogVD2PdOCBlzbtuDcuhlTfVV2e3sgxqnWAFaTgWXVbhzXLODnDcZ57Xgr3eEEdrOBzYvLmVM69KJ946VpOk09ERQUajy2SU+jmkxrA5cbn0LheIrXTzbhj6XwBmO8dryN5dXubFAB0OKLccEbYn75yMN8vMH4qLZdq9BuojucwGxUMfdmlSrsnUOx55wXh9mAn8wTiWQ6waq6QhZXDZ6PtPtUR/ZciZTGocs9eIMxxvOTMxtVCm3mEQMpg6JQXmClMxhDQaHSbR3xR6jpEEtqXDv4KaXpJNKaBBZCCCHEKElgcQvSdZ3EsbMEf/4moRd3k27vX0zNUFWGa+v9OLc9gHnJnEGTXZu6I7zwcTNa71XaJ81+PrehHkvvneM3T7Tx3vlOvME4bpuRaCLFM/fNGzB5eCJiyTQ/O3SVzt6L1PICK9vWVGfPnyvJtEaLL8rRq34uecMYVVji0SnrnbQ8VZq6I/giST5pCaD1Xhhf6QpTV2SnPRjDZDDQUGwfEGgMp7rQhqIMjJFqPCMPZbtjTgkdgTiheApFgdV1HoqdmcUZL3rD1Bc5iJsTRA06qqrw6RVV3D578PChSCKV+d6lNYyqSjKtEU8PvJDPJY/DzKwSB/UeG3YtRMzowGMffgFJg6owv8LJlav9mS9qPLYRU9kKIYQQYiAJLPKUpukcverjSneEYoeFtQ2eCd85TVxoIvTiLkI7dpE8fyW7XS104XxsE86tm7FuWDFiRqe957y8f8FLZyiB22ZkYYWb8x0hllRlxt2/9kkrjZ2Z3LcdQegKJ/iN2xsodQ1eKXw8jjf7s0FFStM43uzDbjbw6eWVGHMUvLT5Y7x0pJlL3jCXu8LUFtmpcls50xZiVm2E+pLcPoEZiUFV8Ibi2aACMnfS957vxNh7x77FF+XLd8++YVkeh5kHFlfw3jkvxBQWVhawflbRiMeUuix88c4GWv0xXFYjhddcnNtMKodafNjTcZxWO3NLXdw1t2TIJ0g1hTb+83IPyVQao8HAspoCSp1Wgr7JyZO8rMbNBW+Ilp4oqq5QV2RnUeXIT3TuW1jG+6kQ3pSVsgLrkAGSEEIIIYYngUWeeu9CJwcbe4DMneGrPRF+dX3dmMtJtXcReuktQjt2Ej98KrtdsZozGZ22bcG+aT2KpW+BPR10fdCTCl3XefdMB9vfPk9XOIFBgY6ASk8kyadX9C981zfcRe8twx9NEYgliSXTqKpClds6YsrPGwnGMit5x5NpPmnxk0zrRBNpgrEkn11Xh9k48eBiz1lvtkydzFODEqcZO9Dsi01pYFFTZMdi7L/4NqgKsaSOvTfIVBWwmQwcafJRWWi7YXmLqwpYWOGkvb2d8vLyUWV6MBpUaosGP9lQUAjGUthNEImniKc1zAYVTdN7kzb1Bz4fXOomnkqTTGkYDAYC0RRpbfKGl1mMBj67ro7Wngj+nk7mN1Tf8LOaDCqLKgu4t6wsr7OsCCGEEPlKAos89UlzgLSmcaU7SiSR4nJXmLvmlQw5dCWWTHO+I4SiwNwyJ6ZojNAr7xJ6YRfRPYegd8VEXVXwrViK+Yn7WfK5hzC4HNkyNE3n3XNePrnqx2hQWT/Lw5r6/rvZb5/p4M9+cZL2QIyUBgpgMep0hxPZJwgAxXYz3aEE8VQak0GlrMDE68dbSaQzF5HVhTaeXF097qFR88pcHG3y0xqIkewts8hhpjOU4Gx7kKXV7nGVe63ucIJWf5TOUBx/NInLaiKeTIMJKty5efIyWjaTgWc2zeF/vn2R9kAMj83ElWQKVVWIJzUUBRJpjUR6bKt4TiS46xNOpFlVW4gaD1JucmIyGvhf717geLMfi0nlsRVVbFpYzq5T7bQFYsSTGrqeWXSvxRflSk+YkkmevlDutqLEhx8CJYQQQojckcAiTyXSGu+c8dITSaAoCh67iZ0n2/ninbMG7BeMJfn+znNcafMz58wpbj95lNpjxyGezO5jWbOEDxYv4/Wa+QQcTqwmA792OcDcMp2Djd0kNR27SeVKdxSAlJZmz9lOylzW7J3qf//wCj3hOKne61cdSKZ1LEad/Re7eHhZJSaDSk2RnSs9keyTA4OiEEtpqL0Xss2+KKdaAyyvKRz2s7cHorx2rI1YKs3CygLumluSDURqi+w8vKyC/7v/MiGrEbc1M7k4FE/hjyaHLXMs4qk0l7syi8KldeiJJCiwmphbYqKh2HGDo3PPaTYSjKVoD8TpCMR7n1pk5lToOkQTaeaXjTzMZzKUuMx0+DXsZgMRg4ELnWHa/FFSmo6Cwj/svUS1x05XKEE0mc4kXFIy351ALIXZIBnDhBBCiJuJBBZ5Kq1p+HovlHVdJ5HS6ArF6Q4nKHJk7sDqmsb7//oW8/7jTZ48cRxbLJo93jSvPpvRqdlVyM9fOI6ma2i6TiyZ5oWPm1lcVZCdyHuuPUix00yRo/+O/NWeaDawuNQZxh8bOEFY08FmNuKwGHnjkzbag3FafFFmlWQuvj12M5qeGap0beaoQDQ17OfuCMT4//7nCYKxzD5n2oKk0zqbF/enRF1YUcCX75nNP+67xJm2zGRbVYETzX5un1084RS3FqNKqctCdzhBfZGNGo+dJ1dXY0mFbnzwJHj5aBvd4QTu3mxM3mCM8gILoGAyqMwvdxJN3Xjydq7dv7CcXxxpRotkMjWl0mnOe8NE4ikUMk+SPmrspsJtxaSqpBQNTc/8rJxmA13hJAVTH6cJIYQQYpJIYJGnzAaVao8Nrz9GPJ1ZXj2e0jAbFOLHzhLcsZPQi7uZ2+rNHuN3FXBs+UriD93L2ofWYimyYyqwEm31E06k8EUSpDUds1ElnkqzSO9PC2q3GHuDlv7Aoqyg//9D8RTXj4g3qeC0GAnHUuw730l5gRWLyUA4nqK+2E6l20Y4nhow6VxRYE7Z8FeTr3/SSqsvRjSZxqAqpNIax676BgQWAPXFDircVjoCMVRVyZwrkeZyV5jZE0xv2/dEIK3pJNI6ug4FViPx6Ykr8IZiA16bjQbsZuOAoWplOZocPxYVbitfuLOBi00t1FVX8Kv/+4PexfIyOoJx2v1RvnjnbH70fiOXOkOoeiYIKXJYWFHjJtDTPeX1FkIIIcTkkMAiT9V47Ki6TnsojqLrONo7qD/8Ht3fPkHy3OXsfjGbjf1zFnF85Sra58/DH0+zoNBF5FwnABsXlDK3zEkqrWUnyyZS2oDsPgAVBVY6gjEOX+nBaFB4YHEFs0v6A4ChJtoaDApGg8rJ1gBr6jOL5tUX2fmosZvjzX6SaY3Pb2jAZFA50uRDVRTWNniodA8/yfi8N9z7pCZzvngqPex8gFKnhQUVA9dMyMV0YB0I9T4xSaQ0/LEkVrOBkVd8mDxrG4o4eNmXfe22GllVl2lvg6qwqq7whmljJ4tBVXDbTFiMBsKJzJMKTc/MwVFUaOqO4nGYeWp1NT892IQvksRmNrK23oNBJkgLIYQQNxUJLPLUwgoXLRdbeejUCTZdOMGijhYAkoBiMaPdu56fls/l47o5tERTJFIaCwwqJS7jgIvMAxe7KXVZWNdQxKnWAOFEmjKXhRU1hVR7bFztyQyf0nSNzlCccCKNQiYr1eZFZZS4MisNOCxGCCYG1NFmMjK7xIHTaiQUT+Gymmj1R4kn0xgMKuFEmo8au/nKPXNGPalaVRVcVmPvUKhMmLC6rnDIfVfVFdLsi2aHcxU5zDmZA6HpsKK2EF8kgc1swG0z0RmKM10rGtwzr4SuUJJ3z3WgKgoPLinnU8uqCMSSmA1q3izgVmw3c1mNoPRGFgYlk+YVIJbSeHhpJfGUhsmgYlAVesKJG5QohBBCiJlEAos89aP9jTx86AC/cvQAAGlF4XT9bJZ+5XEafvUB/tehVn554DK+nhjo4LAamVvuotBmGnCHP5nW8NjNlLosFNpLsmlg60vsPLmqhhZflFRa54XDVwnH09m1EToCcfac87J1dS0AlW4bTd3R7JMLRYEih4l55ZlhR2lN55NmP8eu+jAYFKrdNvyRJO+f72LrqupsgHIjSyoL8EdTtAcyqzLPK3eysHLwSs4Ac8tcPLXGwNn2IE6LiWXV7gnPr4DM05toIk1F75MVk0Gh2GEhkJiesVCKovDU2hq2ranOvgbybvG2e+aXcKzZT0LPTN62m43cMTezFkR5gZWrPdFsEGQ2qhTazfhiI5UohBBCiJlEAos89f75LoyzF7O05TJvzV7MO7MXUVBTysufuxvVauJCR5i2QCy7cFoslcYXTrCytjCb0QhgQYULh8XI2oYifrL/MoFYkvoiO795Wz0AVb1rH6TSgwcRxVP921bUuDl+1UdKy8w50DSNzlCCPWe9rK734LQYmV/u5Ex7EF3XiaU0nAaVWCpNRzDO2fYQqqqwpKoA1wgXxBvmFNMRTGSfPMwtc1JeMHxQUuOx53wY0KYFZYQTLXQE4tjMBu5bWIbVZCCQ07OMXS5SxE4mRVGpLrTRGYqjKgqVhbbsd+j+ReW8eqyFzlACh8XAfQvLc7LmiBBCCCHyhwQWeSqWTNNZXMZXH3saHTAocHetG7033as3FMtc4Pde+ytkVrl+ZHklBxt76AjGqC60s7qukLSmc+yqjzmlDpKajtmgcriphweWVGTPd/e8Eo5d9RHvzSfrtPbfbQa4Z34Zp9uCXOmOEE+m6QnHcVmNpDU42NjNmvoiXFYTlQVWWvxRYsk0TouRigIrb55szw5XOtLk4zduqxs2uKjx2PnCnQ00dkYosBmzQ2mmkttu4jduqycUT2EzGTCoCpo2tnUibkXd4QS+aGZRQU3X6QrHCfemPS5ymPnchgZpUyGEEOImJoFFniq0m+iOJLKRg9mosrzGg9ueuSA3qpk1Fvr0/b/FaODOuSUDymrxRQnHM5Og+9YOuNgZHrDP+llFxJKz2XPOi8Wo8sDicuqL+ucrrGvwEIjV8c6ZDg42dmMx2Sl2mFEUhWgyzZXuCEuq3CyuKkBRMnMy5pU7KXNZs6ljIbPmwqnWIOtnFTEcl9XEspqJL3Q3UU6LdI+x6IkkMSgKmqJkVt5GoTsycG0RaVMhhBDi5iV/5fPU/AoXoViKaCqNpkOV28rDS/ufMMSSg+/2JoZZy6DAZkJVFDS9PxLx2Ac+MVAUhXsXlHLvgtIhyzAaVB5cUsGDSyr47htn+LCxP02o1ajSUJx5slBoN/PAkgo2zCmmxmPn4ys9HLniG1CWrucid5PIN4V2E1aTAWNv8GozGXCZ5VeMEEIIcauQv/p5av2sYlwWE22BKIU2M7VFdgod/Sli/UMsMheMDR1YOC1GNswp5v0Lneg6WE0G7pk/dAAxGo+tquKTlgCRRKYOVR4b/+/NC4gk08RTaarcNtTeSdTLqt2caPaT7H2kYjGpLKoaejK2mNnWNXg43xHEH02iKgrlBVYWDTPxXgghhBA3Hwks8tTG+aUEoklKXRYUBe6aW4L9mru/BdbBKUYdluEnw66fVcTCShf+SDKzErJh/BNn55W5+M7Wpew734nDbOSueSU4rEYc1sFfpxKnhV9dX8eJlgAGRWFZtTvvshmJ3LhrXimt/hhN3REMqsKymkLmlE1ssUIhhBBCzBwSWOSp2iI7v333LFp9MTwOM27bwIvxIocZhf4F4RSgyDny6ssFVlPOLuor3DaeWlM7qn1LnBbuncATEjEzOC1Gnt7QQLMvisWkUjbKFMNCCCGEuDlIYJHHLEYDDSVDL/hWaDdjNxtI9WbWMaoqhTZ5EiCml6oq1E5DJi8hhBBCTD9JJD9DrW0oxuMwYzcbsZuNFNpNrBsh05IQQgghhBCT6ZYILJ588kk8Hg9PPfXUdFclZ5bVuPnU0krW1HtYU+/hU8sqWVnrme5qCSGEEEKIW9QtEVh8/etf58c//vF0VyOn5pQ62by4nLUNRaypL+K+heXML3dNd7WEEEIIIcQt6paYY7Fx40beeeed6a5Gzq2fVTTiQnNCCCGEEEJMlWl/YrFnzx4effRRqqqqUBSFl156adA+27dvp6GhAavVym233caHH3449RUVQgghhBBCDGvaA4twOMyKFSvYvn37kO//9Kc/5bnnnuNb3/oWH3/8MStWrODBBx+ko6Mju8/KlStZunTpoH8tLS1T9TGEEEIIIYS4pU37UKiHH36Yhx9+eNj3v/e97/HlL3+ZL37xiwD84Ac/4NVXX+Wf/umf+MY3vgHAkSNHclKXeDxOPB7Pvg4EAgBomobWm9Z1OJqmoet6zva71eR7u0xH/SbrnLksdyJljffYsRw3WfveKmZCm0jfzH054zl+rMdI35yYmdAm0jdzX8509c2xnG/aA4uRJBIJDh06xPPPP5/dpqoqmzdvZv/+/Tk/33e+8x3+5E/+ZNB2r9dLLBYb8VhN0/D7/ei6jqoO/yBotPvdavK9XaajfpN1zlyWO5GyxnvsWI6brH1vFTOhTaRv5r6c8Rw/1mOkb07MTGgT6Zu5L2e6+mYwGBx1HfM6sOjs7CSdTlNeXj5ge3l5OadPnx51OZs3b+bo0aOEw2Fqamr42c9+xoYNGwbt9/zzz/Pcc89lXwcCAWprayktLaWgoGDEc2iahqIolJaW3jCwGM1+t5p8b5fpqN9knTOX5U6krPEeO5bjJmvfW8VMaBPpm7kvZzzHj/UY6ZsTMxPaRPpm7suZrr5ptVpHXce8DixyZdeuXaPaz2KxYLFYBm1XVXVUPwxFUUa172j3u9Xke7tMR/0m65y5LHciZY332LEcN1n73ipmQptI38x9OeM5fqzHSN+cmJnQJtI3c1/OdPTNsZwrf7+NQElJCQaDgfb29gHb29vbqaiomKZaCSGEEEIIIa6X108szKUigjcAABQTSURBVGYza9asYffu3TzxxBNA5hHN7t27efbZZ6esHjJ5e/Lle7vIJLTclyWTt/PfTGgT6Zu5L0cmb+e/mdAm0jdzX45M3h6FUCjE+fPns68vXbrEkSNHKCoqoq6ujueee46nn36atWvXsn79er7//e8TDoezWaImw/bt29m+fTvpdBqQydtTId/bRSah5b4smbyd/2ZCm0jfzH05Mnk7/82ENpG+mftyZPL2KBw8eJBNmzZlX/dNnn766af54Q9/yGc/+1m8Xi9//Md/TFtbGytXruT1118fNKE7l5555hmeeeYZAoEAbrdbJm9PgXxvF5mElvuyZPJ2/psJbSJ9M/flyOTt/DcT2kT6Zu7Lkcnbo7Bx40Z0XR9xn2effXZKhz5dTyZvT418bxeZhJb7smTydv6bCW0ifTP35cjk7fw3E9pE+mbuy5HJ20IIIYQQQoib3rQ/sZgJZPL25Mv3dpFJaLkvSyZv57+Z0CbSN3Nfjkzezn8zoU2kb+a+HJm8PUPJ5O2pl+/tIpPQcl+WTN7OfzOhTaRv5r4cmbyd/2ZCm0jfzH05Mnl7hpLJ21Mv39tFJqHlviyZvJ3/ZkKbSN/MfTkyeTv/zYQ2kb6Z+3Jk8vZNQiZvT418bxeZhJb7smTydv6bCW0ifTP35cjk7fw3E9pE+mbuy5HJ20IIIYQQQoibngQWQgghhBBCiAmToVAj6FtfIxAI3HBfTdMIBoNYrdYbzrEYzX63mnxvl+mo32SdM5flTqSs8R47luMma99bxUxoE+mbuS9nPMeP9RjpmxMzE9pE+mbuy5muvtl3HXyjdedAAosR9c2Cr62tneaaCCGEEEIIMX2CwSBut3vEfRR9NOHHLUrTNFpaWnC5XCiKcsP9161bx0cffTTiPoFAgNraWpqamm6YaepWM5r2m07TUb/JOmcuy51IWeM9dizHjXZf6ZtDy/d+CdI3J6Oc8Rw/1mOkb06M9M2pPeet3Dd1XScYDFJVVXXDpx7yxGIEqqpSU1Mz6v0NBsOof+kVFBTIL8jrjKX9psN01G+yzpnLcidS1niPHctxYz2H9M2B8r1fgvTNyShnPMeP9RjpmxMjfXNqz3mr980bPanok58D82aoZ555ZrqrMKPle/tNR/0m65y5LHciZY332LEcl+/fq3w3E9pP+mbuyxnP8WM9ZiZ8t/LZTGg/6Zu5Lyff+6YMhZpifYvu+f3+vL/TIMStRPqmEPlJ+qYQM4c8sZhiFouFb33rW1gslumuihDiGtI3hchP0jeFmDnkiYUQQgghhBBiwuSJhRBCCCGEEGLCJLAQQgghhBBCTJgEFkIIIYQQQogJk8BCCCGEEEIIMWESWOQJn8/H2rVrWblyJUuXLuUf/uEfprtKQohrRCIR6uvr+f3f//3prooQoldDQwPLly9n5cqVbNq0abqrI8QtT1bezhMul4s9e/Zgt9sJh8MsXbqUrVu3UlxcPN1VE0IA3/72t7n99tunuxpCiOu8//77OJ3O6a6GEAJ5YpE3DAYDdrsdgHg8jq7rSCZgIfLDuXPnOH36NA8//PB0V0UIIYTIWxJY5MiePXt49NFHqaqqQlEUXnrppUH7bN++nYaGBqxWK7fddhsffvjhgPd9Ph8rVqygpqaGP/iDP6CkpGSKai/EzSsXffP3f//3+c53vjNFNRbi1pCLvqkoCvfeey/r1q3jX/7lX6ao5kKI4UhgkSPhcJgVK1awffv2Id//6U9/ynPPPce3vvUtPv74Y1asWMGDDz5IR0dHdp/CwkKOHj3KpUuX+Nd//Vfa29unqvpC3LQm2jf/8z//k/nz5zN//vyprLYQN71c/N3ct28fhw4d4uWXX+Z//I//wbFjx6aq+kKIIcjK25NAURRefPFFnnjiiey22267jXXr1vE//+f/BEDTNGpra/nqV7/KN77xjUFl/M7v/A733XcfTz311FRVW4ib3nj65vPPP8///b//F4PBQCgUIplM8nu/93v88R//8TR9CiFuPrn4u/kHf/AHLFmyhC984QtTVGshxPXkicUUSCQSHDp0iM2bN2e3qarK5s2b2b9/PwDt7e0Eg0EA/H4/e/bsYcGCBdNSXyFuFaPpm9/5zndoamqisbGRv/7rv+bLX/6yBBVCTLLR9M1wOJz9uxkKhXjrrbdYsmTJtNRXCJEhWaGmQGdnJ+l0mvLy8gHby8vLOX36NACXL1/mK1/5SnbS9le/+lWWLVs2HdUV4pYxmr4phJh6o+mb7e3tPPnkkwCk02m+/OUvs27duimvqxCinwQWeWL9+vUcOXJkuqshhBiBDLEQIn/Mnj2bo0ePTnc1hBDXkKFQU6CkpASDwTBoMnZ7ezsVFRXTVCshhPRNIfKT9E0hZiYJLKaA2WxmzZo17N69O7tN0zR2797Nhg0bprFmQtzapG8KkZ+kbwoxM8lQqBwJhUKcP38++/rSpUscOXKEoqIi6urqeO6553j66adZu3Yt69ev5/vf/z7hcJgvfvGL01hrIW5+0jeFyE/SN4W4CekiJ95++20dGPTv6aefzu7z93//93pdXZ1uNpv19evX6wcOHJi+Cgtxi5C+KUR+kr4pxM1H1rEQQgghhBBCTJjMsRBCCCGEEEJMmAQWQgghhBBCiAmTwEIIIYQQQggxYRJYCCGEEEIIISZMAgshhBBCCCHEhElgIYQQQgghhJgwCSyEEEIIIYQQEyaBhRBCCCGEEGLCJLAQQgghhBBCTJgEFkIIMYkaGhr4/ve/P93VmDL/+I//yAMPPDAl59q4cSO/+7u/O6EyfvjDH1JYWJiT+kwHRVF46aWXxn38D37wAx599NHcVUgIcUuTwEIIIcSIRnvxGovF+OY3v8m3vvWtnJ7/nXfeQVEUfD7fgO0vvPACf/Znfzahsj/72c9y9uzZCZUxnVpbW3n44YcBaGxsRFEUjhw5Murjv/SlL/Hxxx+zd+/eSaqhEOJWIoGFEEKInPj5z39OQUEBd95555Scr6ioCJfLNaEybDYbZWVlOarRYIlEYtLKBqioqMBisYz7eLPZzK//+q/zd3/3dzmslRDiViWBhRBCjNPGjRt59tlnefbZZ3G73ZSUlPDNb34TXdeHPeZ73/sey5Ytw+FwUFtby+/8zu8QCoWy7/cNzXnjjTdYtGgRTqeThx56iNbW1hHrcuLECT796U9TUFCAy+Xi7rvv5sKFCwBomsaf/umfUlNTg8ViYeXKlbz++uvZYxOJBM8++yyVlZVYrVbq6+v5zne+A2SGcgE8+eSTKIqSfT2Uf//3fx80rOZG5+67y/7v//7v3HHHHVitVpYuXcq7776bfX/Tpk0AeDweFEXhC1/4Qrb9rx0K1dDQwH//7/+dz3/+8zidTurr63n55Zfxer08/vjjOJ1Oli9fzsGDBwe197VlKIoy6F+fpqYmPvOZz1BYWEhRURGPP/44jY2N2fe/8IUv8MQTT/Dtb3+bqqoqFixYMGRb9e13rd/93d9l48aN2dcbN27ka1/7Gn/4h39IUVERFRUV/Lf/9t8GHHPt06RZs2YBsGrVKhRFyZb1zjvvsH79ehwOB4WFhdx5551cvnw5W8ajjz7Kyy+/TDQaHbKuQggxWhJYCCHEBPzoRz/CaDTy4Ycf8rd/+7d873vf4//8n/8z7P6qqvJ3f/d3nDhxgh/96Ee89dZb/OEf/uGAfSKRCH/913/NT37yE/bs2cOVK1f4/d///WHLbG5u5p577sFisfDWW29x6NAhvvSlL5FKpQD427/9W7773e/y13/91xw7dowHH3yQxx57jHPnzgHwd3/3d7z88sv8x3/8B2fOnOFf/uVfsgHERx99BMA///M/09ramn09lH379rF27doB22507j5/8Ad/wO/93u9x+PBhNmzYwKOPPkpXVxe1tbXs2LEDgDNnztDa2srf/u3fDluHv/mbv+HOO+/k8OHDPPLII3zuc5/j85//PL/5m7/Jxx9/zJw5c/j85z8/bPD30Ucf0draSmtrK1evXuX222/n7rvvBiCZTPLggw/icrnYu3cv7733Xjbwu/bJxO7duzlz5gw7d+7klVdeGbauo/GjH/0Ih8PBBx98wF/+5V/yp3/6p+zcuXPIfT/88EMAdu3aRWtrKy+88AKpVIonnniCe++9l2PHjrF//36+8pWvDAiW1q5dSyqV4oMPPphQXYUQAl0IIcS43HvvvfqiRYt0TdOy2/7oj/5IX7RoUfZ1fX29/jd/8zfDlvGzn/1MLy4uzr7+53/+Zx3Qz58/n922fft2vby8fNgynn/+eX3WrFl6IpEY8v2qqir929/+9oBt69at03/nd35H13Vd/+pXv6rfd999Az7HtQD9xRdfHPb8uq7rPT09OqDv2bNnTOe+dOmSDuh//ud/nn0/mUzqNTU1+l/8xV/ouq7rb7/9tg7oPT09A8q599579a9//evZ1/X19fpv/uZvZl+3trbqgP7Nb34zu23//v06oLe2tuq6nmlvt9s95Gf62te+ptfX1+sdHR26ruv6T37yE33BggUD2ikej+s2m01/4403dF3X9aefflovLy/X4/H4sG3Vt9/jjz8+YNvXv/51/d577x3w+e66664B+6xbt07/oz/6o+zra382fW15+PDh7PtdXV06oL/zzjsj1sfj8eg//OEPR9xHCCFuRJ5YCCHEBNx+++0D7v5u2LCBc+fOkU6nh9x/165d3H///VRXV+Nyufjc5z5HV1cXkUgku4/dbmfOnDnZ15WVlXR0dAxbhyNHjnD33XdjMpkGvRcIBGhpaRk07+HOO+/k1KlTQGZYzpEjR1iwYAFf+9rXePPNN0f34a/RN4zGarWO6dx9NmzYkP1/o9HI2rVrB+0zGsuXL8/+f3l5OQDLli0btG2k9gT43//7f/OP//iPvPzyy5SWlgJw9OhRzp8/j8vlwul04nQ6KSoqIhaLZYed9Z3PbDaPue43+jxw4+/C9YqKivjCF77Agw8+yKOPPsrf/u3fDjmszmazDfgOCiHEeEhgIYQQU6SxsZFPf/rTLF++nB07dnDo0CG2b98ODJzke32AoCjKiPM2bDbbhOq1evVqLl26xJ/92Z8RjUb5zGc+w1NPPTWmMoqLi1EUhZ6engnVZaKubbu+gG+obZqmDVvG22+/zVe/+lV+/OMfD7iwD4VCrFmzhiNHjgz4d/bsWX791389u5/D4bhhPVVVHfQzTSaTI36evvqPVPeh/PM//zP79+/njjvu4Kc//Snz58/nwIEDA/bp7u7OBlBCCDFeElgIIcQEXD8u/cCBA8ybNw+DwTBo30OHDqFpGt/97ne5/fbbmT9/Pi0tLROuw/Lly9m7d++QF6YFBQVUVVXx3nvvDdj+3nvvsXjx4gH7ffazn+Uf/uEf+OlPf8qOHTvo7u4GMhe3wz2B6WM2m1m8eDEnT54c87mBARe6qVSKQ4cOsWjRomzZwA3rkAvnz5/nqaee4r/+1//K1q1bB7y3evVqzp07R1lZGXPnzh3wz+12j+k8paWlg54cjCVN7FBGaqdVq1bx/PPP8/7777N06VL+9V//NfvehQsXiMVirFq1akLnF0IICSyEEGICrly5wnPPPceZM2f4t3/7N/7+7/+er3/960PuO3fuXJLJJH//93/PxYsX+clPfsIPfvCDCdfh2WefJRAI8Ku/+qscPHiQc+fO8ZOf/IQzZ84AmYnRf/EXf8FPf/pTzpw5wze+8Q2OHDmSref3vvc9/u3f/o3Tp09z9uxZfvazn1FRUZHNltTQ0MDu3btpa2sb8YnEgw8+yL59+wZsu9G5+2zfvp0XX3yR06dP88wzz9DT08OXvvQlAOrr61EUhVdeeQWv1zsgi1YuRaNRHn30UVatWsVXvvIV2trasv8AfuM3foOSkhIef/xx9u7dy6VLl3jnnXf42te+xtWrV8d0rvvuu4+DBw/y4x//mHPnzvGtb32LTz75ZEL1Lysrw2az8frrr9Pe3o7f7+fSpUs8//zz7N+/n8uXL/Pmm29y7ty5bNAGsHfvXmbPnj1g+J0QQoyHBBZCCDEBn//854lGo6xfv55nnnmGr3/963zlK18Zct8VK1bwve99j7/4i79g6dKl/Mu//Es2retEFBcX89ZbbxEKhbj33ntZs2YN//AP/5AdRvO1r32N5557jt/7vd9j2bJlvP7667z88svMmzcPAJfLxV/+5V+ydu1a1q1bR2NjI6+99hqqmvkT8d3vfpedO3dSW1s74l3t3/qt3+K1117D7/dnt93o3H3+/M//nD//8z9nxYoV7Nu3j5dffpmSkhIAqqur+ZM/+RO+8Y1vUF5ezrPPPjvhNhtKe3s7p0+fZvfu3VRVVVFZWZn9B5m5L3v27KGuro6tW7eyaNEifuu3fotYLEZBQcGYzvXggw/yzW9+kz/8wz9k3bp1BINBPv/5z0+o/kajkb/7u7/jf/2v/0VVVRWPP/44drud06dPs23bNubPn89XvvIVnnnmGf7Lf/kv2eP+7d/+jS9/+csTOrcQQgAo+kgDd4UQQgxr48aNrFy5ku9///vTXZW88Su/8iusXr2a559/flT7NzY2MmvWLA4fPszKlSsnt3JikBMnTnDfffdx9uzZMQ/nEkKI68kTCyGEEDnzV3/1Vzidzumuhhil1tZWfvzjH0tQIYTICeN0V0AIIcTNo6Ghga9+9avTXQ0xSps3b57uKgghbiIyFEoIIYQQQggxYTIUSgghhBBCCDFhElgIIYQQQgghJkwCCyGEEEIIIcSESWAhhBBCCCGEmDAJLIQQQgghhBATJoGFEEIIIYQQYsIksBBCCCGEEEJMmAQWQgghhBBCiAmTwEIIIYQQQggxYf9/BBk8Gkpi0DQAAAAASUVORK5CYII=", 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" ] @@ -269,11 +293,11 @@ "output_type": "stream", "text": [ "samples : 400\n", - "cost range : 1122 .. 687256\n", - "time range (ms) : 0.117 .. 3085.6\n", - "Pearson r (linear) : 0.086\n", - "log-log slope : 0.794 (1.0 == perfectly proportional)\n", - "log-log correlation : 0.581\n" + "cost range : 366 .. 2984422200\n", + "time range (ms) : 0.122 .. 2956.6\n", + "Pearson r (linear) : 0.967\n", + "log-log slope : 0.487 (1.0 == perfectly proportional)\n", + "log-log correlation : 0.899\n" ] } ], diff --git a/research/cost-on-random.pdf b/research/cost-on-random.pdf new file mode 100644 index 0000000000000000000000000000000000000000..ef134a0e9f5d5ad0ae17441f0bbe2dd9515f7cb4 GIT binary patch literal 126649 zcmd41WmH|=vMmZxT*86`cMlKU|!QI^*LI@Dt-QC@S-$B0p?X&N_ z`4eQJ z?Tu{dge~>#jRcJhYz&R)B#o?1>`e(6n3>;mbHmx$+ZyRv!8y;&Yf6Nz3L~`~mJZ>U z$sfK%c@_-S_;hVzy5%`r-Ryj2K#H!(MVXhE8uElhBu$K=!K<@6t8yWXU1|(9qHW9; zBzuA*ftUSD_?X(Q-^a4YZz51G z_`hWNQoNv(_(HI(uKB~{Io&l@$+X+;Y_Ci!&jZ8ls!KA4dzyWfJVWktV*9bLNaW&E 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zob*Tr%7V$N73P!yrPKkODpb~3lJ#g3Yc(D0%Bz(5QP%=5UN7$lbE0YMhJZYo_aOQ^ z;k3^Sx*o08Y~gLs;Nbd!y|RmhFZ|VWwl@-XHxhqCe{-Iu6)+PrDLUAX{CG${cULat zB9K^*{)>qgdj9?@rwRh<5tF-AP;3!jeT+LQjC*{BMY+1+RrEFTjMy!V&kkVChKqU$?2Us9F11ka4rKXz8(%v6HbG z{145Kjs0KDubi{|O8qJrLwl|wu#l2KiCfsaySX1QrkmlZ4!vQ6xWKGdh`8{~U)-xe zomQAC1v-5Qp!T}XZeGfdm%E8PbG9}JK*30mz_Nv5&8TD>nWu#;$*7<=-9-JD$~ze@ z1anBv(XsI73W2l{iSw-;`UVN|P03U~%btscOnpj2y(gj0raVtAp}2`Kg>j3lWa~C6 zb~b6DbbIk0kkHiR$yWl&)&(}06P*j KwWP8X(*FVczSHdh literal 0 HcmV?d00001 diff --git a/research/cost-stats.csv b/research/cost-stats.csv index 0680edf..0496566 100644 --- a/research/cost-stats.csv +++ b/research/cost-stats.csv @@ -1,1304 +1,1304 @@ seed,plan_cost,exec_us,rows,query -0,9220,262,3,"SELECT t1.price AS c2289 +0,3466,255,3,"SELECT t1.price AS c2289 FROM regions AS t0 RIGHT JOIN books AS t1 ON t0.id = t1.id;" -1,1122,249,55,"SELECT t0.region_id AS c3748 +1,56100,240,55,"SELECT t0.region_id AS c3748 FROM customers AS t0 WHERE t0.region_id = 7;" -2,1660,248,500,"SELECT t0.region_id, t0.id +2,110500,250,500,"SELECT t0.region_id, t0.id FROM customers AS t0 ORDER BY t0.id DESC, t0.region_id DESC;" -3,1220,129,5,"SELECT t0.id +3,610,122,5,"SELECT t0.id FROM departments AS t0;" -4,4325,164,0,"SELECT t0.id, SUM(t0.id) +4,943,172,0,"SELECT t0.id, SUM(t0.id) FROM markets AS t0 WHERE t0.id IS NULL GROUP BY t0.id ORDER BY t0.id DESC;" -5,649400,20979,3,"SELECT markets.region, markets.id AS c7685 +5,49760800,22867,3,"SELECT markets.region, markets.id AS c7685 FROM markets CROSS JOIN orders CROSS JOIN departments GROUP BY markets.region, markets.id;" -6,17610,722,4,"SELECT t1.id, t0.id AS c6716, t0.region_id +6,230694,628,4,"SELECT t1.id, t0.id AS c6716, t0.region_id FROM customers AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id RIGHT JOIN departments AS t2 ON t0.id = t2.id WHERE t1.department_id <= 11;" -7,3343,282,1,"SELECT departments.id +7,2593,293,1,"SELECT departments.id FROM departments RIGHT JOIN employees ON departments.id = employees.id WHERE (employees.id != 33 AND employees.department_id IN (34, 74)) GROUP BY departments.id ORDER BY departments.id DESC;" -8,1133,1113,1,"SELECT orders.customer_id, orders.id +8,552500,1154,1,"SELECT orders.customer_id, orders.id FROM orders WHERE (orders.id = 117 AND orders.id > 60) ORDER BY orders.customer_id DESC, orders.id DESC;" -9,15694,7696,2,"SELECT t0.customer_id AS c3674 +9,3939128,8455,2,"SELECT t0.customer_id AS c3674 FROM orders AS t0 CROSS JOIN departments AS t1 WHERE (t1.id >= 4 AND (t0.id IN (130) OR t1.id IS NULL));" -10,12035,296,3,"SELECT regions.id +10,5532,325,3,"SELECT regions.id FROM books RIGHT JOIN employees ON books.id = employees.id JOIN regions ON books.id = regions.id WHERE books.price >= 6;" -11,9660,335,22,"SELECT employees.department_id +11,16616,301,22,"SELECT employees.department_id FROM users FULL JOIN employees ON users.id = employees.id ORDER BY employees.department_id DESC;" -12,5070,1115,3,"SELECT orders.id AS c2380, markets.region, markets.note +12,676416,975,3,"SELECT orders.id AS c2380, markets.region, markets.note FROM markets JOIN orders ON markets.id = orders.id;" -13,14600,2984,15000,"SELECT markets.region, orders.customer_id AS c8705, markets.note +13,2390300,2814,15000,"SELECT markets.region, orders.customer_id AS c8705, markets.note FROM markets CROSS JOIN orders;" -14,1854,132,10,"SELECT t0.department_id, t0.id AS c1827 +14,2076,143,10,"SELECT t0.department_id, t0.id AS c1827 FROM employees AS t0 WHERE (t0.department_id BETWEEN 5 AND 34 OR t0.id NOT IN (2, 68, 69));" -15,1488,140,0,"SELECT books.price +15,422,142,0,"SELECT books.price FROM books WHERE (books.id < 2 AND (books.price NOT BETWEEN 41 AND 55 OR books.price IN (57, 63, 77, 77)));" -16,9220,308,22,"SELECT t0.age AS c4948 +16,16132,334,22,"SELECT t0.age AS c4948 FROM users AS t0 FULL JOIN employees AS t1 ON t0.id = t1.id;" -17,52970,336,4,"SELECT users.age, COUNT(departments.id), COUNT(*) +17,29255,307,4,"SELECT users.age, COUNT(departments.id), COUNT(*) FROM regions JOIN users ON regions.id = users.id CROSS JOIN departments WHERE (users.age >= 52 OR regions.id IN (4, 9)) GROUP BY users.age;" -18,1775,283,436,"SELECT customers.region_id +18,102500,273,436,"SELECT customers.region_id FROM customers WHERE customers.region_id != 8 ORDER BY customers.region_id ASC;" -19,8275,321,3,"SELECT t0.id +19,3393,359,2,"SELECT t0.id FROM books AS t0 RIGHT JOIN regions AS t1 ON t0.id = t1.id JOIN markets AS t2 ON t1.id = t2.id WHERE t1.id < 3 ORDER BY t0.id ASC;" -20,129600,28568,85,"SELECT orders.id AS c5497, orders.customer_id, departments.id AS c8484 +20,49896040,29612,85,"SELECT orders.id AS c5497, orders.customer_id, departments.id AS c8484 FROM departments CROSS JOIN users CROSS JOIN orders WHERE orders.id = 82;" -21,1220,126,10,"SELECT regions.id +21,1220,146,10,"SELECT regions.id FROM regions;" -22,6760,146,11,"SELECT t0.department_id +22,7436,167,11,"SELECT t0.department_id FROM employees AS t0 GROUP BY t0.department_id ORDER BY t0.department_id ASC;" -23,14760,484,10,"SELECT customers.region_id +23,156962,489,10,"SELECT customers.region_id FROM customers LEFT JOIN books ON customers.id = books.id GROUP BY customers.region_id ORDER BY customers.region_id ASC;" -24,88720,418,4,"SELECT t2.id, COUNT(t0.id) AS c2763, COUNT(t2.id) +24,57976,440,4,"SELECT t2.id, COUNT(t0.id) AS c2763, COUNT(t2.id) FROM regions AS t0 CROSS JOIN users AS t1 LEFT JOIN markets AS t2 ON t0.id = t2.id GROUP BY t2.id;" -25,5410,250,3,"SELECT customers.id +25,68652,240,3,"SELECT customers.id FROM books JOIN customers ON books.id = customers.id ORDER BY customers.id DESC;" -26,17660,144888,5489,"SELECT customers.region_id, employees.department_id +26,4786826,149982,5489,"SELECT customers.region_id, employees.department_id FROM employees FULL JOIN orders ON employees.id = orders.id FULL JOIN customers ON employees.id = customers.id ORDER BY customers.region_id ASC;" -27,33040,11022,2,"SELECT orders.customer_id, users.id +27,6471784,12004,2,"SELECT orders.customer_id, users.id FROM users RIGHT JOIN orders ON users.id = orders.id CROSS JOIN departments WHERE orders.customer_id IN (34, 321) GROUP BY orders.customer_id, users.id;" -28,6760,158,5,"SELECT departments.id AS c2364, SUM(departments.id) +28,3325,150,5,"SELECT departments.id AS c2364, SUM(departments.id) FROM departments GROUP BY departments.id ORDER BY departments.id ASC;" -29,11870,375,5,"SELECT customers.id, markets.id +29,70066,376,5,"SELECT customers.id, markets.id FROM customers FULL JOIN departments ON customers.id = departments.id JOIN markets ON customers.id = markets.id;" -30,178400,391,51,"SELECT markets.id AS c2619 +30,32788,427,51,"SELECT markets.id AS c2619 FROM markets CROSS JOIN users CROSS JOIN books WHERE markets.region != 'AMERICA';" -31,2896,155,8,"SELECT t0.id, SUM(t0.id) +31,4860,223,8,"SELECT t0.id, SUM(t0.id) FROM users AS t0 WHERE ((t0.id NOT IN (5) AND t0.age != 12) AND t0.age NOT BETWEEN 33 AND 64) GROUP BY t0.id;" -32,6760,163,11,"SELECT t0.id AS c8338, COUNT(t0.department_id) +32,7436,190,11,"SELECT t0.id AS c8338, COUNT(t0.department_id) FROM employees AS t0 GROUP BY t0.id ORDER BY t0.id DESC;" -33,24825,209075,1000,"SELECT orders.customer_id, customers.region_id +33,2705500,201810,1000,"SELECT orders.customer_id, customers.region_id FROM departments RIGHT JOIN orders ON departments.id = orders.id CROSS JOIN customers WHERE departments.id BETWEEN 4 AND 96 ORDER BY customers.region_id DESC;" -34,687256,33154,3,"SELECT markets.note AS c1833, markets.region, SUM(books.id) AS c1917, COUNT(*) +34,34691744,33226,3,"SELECT markets.note AS c1833, markets.region, SUM(books.id) AS c1917, COUNT(*) FROM orders CROSS JOIN markets CROSS JOIN books WHERE ((orders.id != 89 OR markets.note IS NOT NULL) OR (markets.id IN (22) AND books.id NOT BETWEEN 1 AND 3)) GROUP BY markets.note, markets.region ORDER BY markets.region DESC;" -35,20060,18309,50000,"SELECT regions.id, orders.id +35,7686500,17925,50000,"SELECT regions.id, orders.id FROM orders CROSS JOIN regions JOIN customers ON regions.id = customers.id ORDER BY orders.id DESC, regions.id DESC;" -36,14600,405,51,"SELECT books.price AS c7084 +36,8426,254,51,"SELECT books.price AS c7084 FROM books CROSS JOIN users;" -37,84010,424140,1591,"SELECT orders.customer_id +37,525797800,426043,1591,"SELECT orders.customer_id FROM customers CROSS JOIN markets FULL JOIN orders ON markets.id = orders.id WHERE orders.id < 95;" -38,18962,5240,2,"SELECT t1.department_id, COUNT(*) +38,4011762,5469,2,"SELECT t1.department_id, COUNT(*) FROM regions AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id RIGHT JOIN orders AS t2 ON t0.id = t2.id WHERE ((t1.id = 3 OR t2.id = 185) OR (t1.id < 3 AND t2.id IS NULL)) GROUP BY t1.department_id ORDER BY t1.department_id DESC;" -39,1775,139,0,"SELECT t0.id, t0.note, t0.region +39,433,153,0,"SELECT t0.id, t0.note, t0.region FROM markets AS t0 WHERE t0.note IS NULL ORDER BY t0.note DESC, t0.id DESC;" -40,65600,615,500,"SELECT t1.id, t1.region_id AS c2127 +40,1004300,621,500,"SELECT t1.id, t1.region_id AS c2127 FROM books AS t0 CROSS JOIN customers AS t1 GROUP BY t1.id, t1.region_id;" -41,61064,491931,50000,"SELECT customers.region_id, orders.id AS c1616 +41,1485049368,499192,50000,"SELECT customers.region_id, orders.id AS c1616 FROM orders CROSS JOIN customers WHERE ((orders.id >= 3 OR customers.region_id != 41) OR orders.id IN (73)) GROUP BY customers.region_id, orders.id;" -42,7635,1383,3,"SELECT t1.price, t0.region_id, t2.customer_id AS c2615 +42,745022,1089,3,"SELECT t1.price, t0.region_id, t2.customer_id AS c2615 FROM customers AS t0 JOIN books AS t1 ON t0.id = t1.id JOIN orders AS t2 ON t0.id = t2.id WHERE t2.id IS NOT NULL;" -43,1122,162,0,"SELECT markets.region, markets.id AS c9574 +43,422,148,0,"SELECT markets.region, markets.id AS c9574 FROM markets WHERE ((markets.note IS NOT NULL OR markets.region != 'AMERICA') AND (markets.region IN ('EUROPE', 'EUROPE') AND markets.note IN ('North, South', 'North, South')));" -44,4325,271,6,"SELECT t1.id AS c8372, t1.age AS c1550 +44,72448,263,6,"SELECT t1.id AS c8372, t1.age AS c1550 FROM customers AS t0 JOIN users AS t1 ON t0.id = t1.id WHERE t1.id > 11 ORDER BY t1.id ASC, t1.age DESC;" -45,6160,184,0,"SELECT t1.department_id AS c7018, t0.id AS c6686 +45,6295,178,0,"SELECT t1.department_id AS c7018, t0.id AS c6686 FROM regions AS t0 JOIN employees AS t1 ON t0.id = t1.id WHERE t0.id >= 10 GROUP BY t1.department_id, t0.id;" -46,1122,130,1,"SELECT t0.id +46,1122,156,1,"SELECT t0.id FROM regions AS t0 WHERE ((t0.id IS NOT NULL AND t0.id != 5) AND (t0.id > 8 AND t0.id > 9));" -47,9133,1124,1,"SELECT employees.id +47,436233,1138,1,"SELECT employees.id FROM customers FULL JOIN employees ON customers.id = employees.id WHERE employees.department_id = 32 ORDER BY employees.id ASC;" -48,22320,125403,11,"SELECT customers.region_id AS c1201, SUM(customers.region_id) +48,1706896,142344,11,"SELECT customers.region_id AS c1201, SUM(customers.region_id) FROM orders FULL JOIN customers ON orders.id = customers.id FULL JOIN markets ON orders.id = markets.id GROUP BY customers.region_id;" -49,1133,1222,0,"SELECT orders.customer_id, orders.id +49,560554,1150,0,"SELECT orders.customer_id, orders.id FROM orders WHERE ((orders.id = 130 OR orders.id <= 96) AND orders.id = 97) ORDER BY orders.customer_id DESC, orders.id DESC;" -50,9610,263,3,"SELECT markets.id +50,1972,259,3,"SELECT markets.id FROM markets RIGHT JOIN departments ON markets.id = departments.id WHERE markets.id < 45;" -51,1220,127,5,"SELECT t0.id +51,610,133,5,"SELECT t0.id FROM departments AS t0;" -52,50320,5259,5000,"SELECT orders.id, COUNT(*) +52,7748300,4738,5000,"SELECT orders.id, COUNT(*) FROM books CROSS JOIN orders WHERE books.price NOT IN (45, 55, 55, 66) GROUP BY orders.id;" -53,26360,331,3,"SELECT users.id, COUNT(employees.id) AS c5902 +53,31865,316,3,"SELECT users.id, COUNT(employees.id) AS c5902 FROM employees CROSS JOIN users JOIN markets ON users.id = markets.id GROUP BY users.id ORDER BY users.id ASC;" -54,4990,179,15,"SELECT users.age, users.id AS c207, SUM(users.id) +54,8460,165,15,"SELECT users.age, users.id AS c207, SUM(users.id) FROM users WHERE (users.age > 27 OR (users.age <= 123 AND users.age > 1)) GROUP BY users.age, users.id ORDER BY users.id ASC;" -55,1220,121,11,"SELECT t0.department_id AS c7732 +55,1342,142,11,"SELECT t0.department_id AS c7732 FROM employees AS t0;" -56,433400,17179,3,"SELECT books.id, SUM(orders.customer_id) AS c2549, COUNT(*) AS c1539 +56,69804832,17366,3,"SELECT books.id, SUM(orders.customer_id) AS c2549, COUNT(*) AS c1539 FROM books CROSS JOIN employees CROSS JOIN orders WHERE employees.id < 68 GROUP BY books.id;" -57,1732,1401,3001,"SELECT orders.id AS c9114 +57,881250,1435,3001,"SELECT orders.id AS c9114 FROM orders WHERE (orders.customer_id > 214 OR orders.customer_id BETWEEN 75 AND 84);" -58,86560,1627742,110,"SELECT employees.department_id, customers.region_id, COUNT(*), SUM(orders.customer_id) +58,1927703100,1595702,110,"SELECT employees.department_id, customers.region_id, COUNT(*), SUM(orders.customer_id) FROM employees CROSS JOIN customers FULL JOIN orders ON customers.id = orders.id WHERE customers.id <= 149 GROUP BY employees.department_id, customers.region_id;" -59,6320,383,500,"SELECT customers.id, customers.region_id, COUNT(customers.region_id) +59,316000,390,500,"SELECT customers.id, customers.region_id, COUNT(customers.region_id) FROM customers GROUP BY customers.id, customers.region_id;" -60,9660,265,17,"SELECT t0.id +60,5702,286,17,"SELECT t0.id FROM markets AS t0 FULL JOIN users AS t1 ON t0.id = t1.id ORDER BY t0.id ASC;" -61,13485,287,5,"SELECT t1.region, t1.id, t1.note +61,2493,292,5,"SELECT t1.region, t1.id, t1.note FROM departments AS t0 CROSS JOIN markets AS t1 WHERE ((t0.id < 43 OR t0.id >= 2) AND t1.note = 'Old World') ORDER BY t1.note DESC, t1.id ASC, t1.region ASC;" -62,11870,487,3,"SELECT departments.id AS c7718 +62,70066,363,3,"SELECT departments.id AS c7718 FROM departments LEFT JOIN books ON departments.id = books.id JOIN customers ON departments.id = customers.id;" -63,14760,300,11,"SELECT users.age AS c5029, COUNT(*) AS c2096, SUM(users.age) +63,10975,294,11,"SELECT users.age AS c5029, COUNT(*) AS c2096, SUM(users.age) FROM users FULL JOIN departments ON users.id = departments.id GROUP BY users.age ORDER BY users.age ASC;" -64,9660,116327,500,"SELECT customers.region_id AS c6827, orders.id, customers.id +64,175610500,116231,500,"SELECT customers.region_id AS c6827, orders.id, customers.id FROM customers LEFT JOIN orders ON customers.id = orders.id ORDER BY customers.region_id ASC, customers.id ASC, orders.id DESC;" -65,9632,280,0,"SELECT markets.id AS c8656, markets.region +65,2482,255,0,"SELECT markets.id AS c8656, markets.region FROM markets FULL JOIN departments ON markets.id = departments.id WHERE departments.id = 60 GROUP BY markets.id, markets.region;" -66,1632,162,1,"SELECT markets.note AS c5953, SUM(markets.id), SUM(markets.id) +66,932,166,1,"SELECT markets.note AS c5953, SUM(markets.id), SUM(markets.id) FROM markets WHERE markets.region = 'EUROPE' GROUP BY markets.note;" -67,1220,236,500,"SELECT customers.region_id, customers.id AS c651 +67,61000,234,500,"SELECT customers.region_id, customers.id AS c651 FROM customers;" -68,17660,9937,5001,"SELECT users.id, regions.id AS c5610, orders.id AS c9918 +68,4015260,20935,5001,"SELECT users.id, regions.id AS c5610, orders.id AS c9918 FROM users FULL JOIN orders ON users.id = orders.id FULL JOIN regions ON orders.id = regions.id ORDER BY users.id DESC;" -69,32064,2232,2380,"SELECT t0.id AS c499 +69,380850,2234,2380,"SELECT t0.id AS c499 FROM books AS t0 FULL JOIN users AS t1 ON t0.id = t1.id CROSS JOIN customers AS t2 WHERE ((t0.price > 66 OR t0.price NOT IN (55, 77, 93)) OR t2.id < 93) ORDER BY t0.id ASC;" -70,2308,161,1,"SELECT markets.region, markets.note +70,943,192,1,"SELECT markets.region, markets.note FROM markets WHERE markets.id IN (2, 31) GROUP BY markets.region, markets.note ORDER BY markets.note ASC, markets.region ASC;" -71,65600,379,5,"SELECT departments.id AS c3175, SUM(departments.id), COUNT(books.price) +71,10340,246,5,"SELECT departments.id AS c3175, SUM(departments.id), COUNT(books.price) FROM books CROSS JOIN departments GROUP BY departments.id;" -72,1775,166,4,"SELECT t0.id +72,788,169,4,"SELECT t0.id FROM departments AS t0 WHERE t0.id >= 2 ORDER BY t0.id ASC;" -73,2462,320,0,"SELECT customers.id, customers.region_id +73,67647,262,0,"SELECT customers.id, customers.region_id FROM customers JOIN markets ON customers.id = markets.id WHERE ((markets.id = 3 AND markets.region != 'AMERICA') AND (customers.id < 17 AND customers.region_id IS NULL));" -74,10610,621,3,"SELECT books.id AS c9785 +74,86272,601,3,"SELECT books.id AS c9785 FROM customers RIGHT JOIN departments ON customers.id = departments.id RIGHT JOIN books ON departments.id = books.id WHERE books.price >= 55;" -75,14600,296,110,"SELECT t1.department_id AS c5193, t1.id +75,15960,274,110,"SELECT t1.department_id AS c5193, t1.id FROM regions AS t0 CROSS JOIN employees AS t1;" -76,10098,282,17,"SELECT t0.id AS c3431 +76,16988,294,17,"SELECT t0.id AS c3431 FROM users AS t0 LEFT JOIN employees AS t1 ON t0.id = t1.id WHERE ((t1.id != 67 AND t0.id IS NOT NULL) OR (t0.age NOT BETWEEN 33 AND 123 OR t0.age >= 22));" -77,15560,209476,0,"SELECT t1.region_id AS c1177, t0.id AS c3278, COUNT(*), COUNT(t0.id) AS c3474 +77,339550000,200988,0,"SELECT t1.region_id AS c1177, t0.id AS c3278, COUNT(*), COUNT(t0.id) AS c3474 FROM orders AS t0 CROSS JOIN customers AS t1 WHERE (t0.id = 64 AND t0.customer_id > 397) GROUP BY t1.region_id, t0.id;" -78,1220,243,500,"SELECT t0.id, t0.region_id +78,61000,244,500,"SELECT t0.id, t0.region_id FROM customers AS t0;" -79,1366,146,2,"SELECT t0.id AS c7963, t0.age AS c5437 +79,2310,135,2,"SELECT t0.id AS c7963, t0.age AS c5437 FROM users AS t0 WHERE t0.age IN (5, 11, 63);" -80,18500,267,30,"SELECT regions.id AS c2137 +80,6250,265,30,"SELECT regions.id AS c2137 FROM regions CROSS JOIN markets WHERE markets.note != 'AMERICA';" -81,14110,329,5,"SELECT markets.region AS c3832, users.age, departments.id +81,7242,334,5,"SELECT markets.region AS c3832, users.age, departments.id FROM users LEFT JOIN departments ON users.id = departments.id RIGHT JOIN markets ON users.id = markets.id WHERE departments.id <= 5;" -82,1133,138,0,"SELECT users.age AS c4898, users.id AS c4014 +82,1833,174,0,"SELECT users.age AS c4898, users.id AS c4014 FROM users WHERE ((users.id = 4 OR users.id BETWEEN 11 AND 16) AND (users.age = 22 AND users.id <= 57)) ORDER BY users.age DESC;" -83,6110,265,6,"SELECT t0.age AS c499, t1.price AS c7644, t0.id +83,4502,282,6,"SELECT t0.age AS c499, t1.price AS c7644, t0.id FROM users AS t0 LEFT JOIN books AS t1 ON t0.id = t1.id WHERE t0.id < 7;" -84,18098,330,3,"SELECT books.price, regions.id, markets.id AS c8988 +84,4574,324,3,"SELECT books.price, regions.id, markets.id AS c8988 FROM markets LEFT JOIN regions ON markets.id = regions.id LEFT JOIN books ON regions.id = books.id WHERE ((books.price BETWEEN 6 AND 77 OR markets.id NOT IN (1, 2, 2, 3)) OR books.price NOT IN (55));" -85,6320,334,500,"SELECT t0.id, t0.region_id +85,316000,330,500,"SELECT t0.id, t0.region_id FROM customers AS t0 GROUP BY t0.id, t0.region_id;" -86,1220,1184,5000,"SELECT orders.customer_id, orders.id +86,610000,1168,5000,"SELECT orders.customer_id, orders.id FROM orders;" -87,6320,141,11,"SELECT employees.department_id, employees.id AS c6314 +87,6952,133,11,"SELECT employees.department_id, employees.id AS c6314 FROM employees GROUP BY employees.department_id, employees.id;" -88,14760,297,5,"SELECT departments.id AS c2839, COUNT(*) +88,8275,273,5,"SELECT departments.id AS c2839, COUNT(*) FROM employees RIGHT JOIN departments ON employees.id = departments.id GROUP BY departments.id ORDER BY departments.id DESC;" -89,6320,148,3,"SELECT t0.region AS c5477, t0.id AS c2794, COUNT(*) AS c9570 +89,1896,151,3,"SELECT t0.region AS c5477, t0.id AS c2794, COUNT(*) AS c9570 FROM markets AS t0 GROUP BY t0.region, t0.id;" -90,4160,436,500,"SELECT customers.region_id, customers.id AS c6247, COUNT(*) AS c8943 +90,208000,409,500,"SELECT customers.region_id, customers.id AS c6247, COUNT(*) AS c8943 FROM customers WHERE customers.region_id <= 40 GROUP BY customers.region_id, customers.id;" -91,1220,142,5,"SELECT departments.id +91,610,132,5,"SELECT departments.id FROM departments;" -92,73300,291,3,"SELECT markets.region, markets.id +92,22030,249,3,"SELECT markets.region, markets.id FROM markets CROSS JOIN regions GROUP BY markets.region, markets.id ORDER BY markets.region ASC;" -93,4970,933,3,"SELECT t0.id, t1.id AS c1156, t0.customer_id +93,676086,907,3,"SELECT t0.id, t1.id AS c1156, t0.customer_id FROM orders AS t0 JOIN books AS t1 ON t0.id = t1.id;" -94,27722,329,0,"SELECT t1.price +94,8782,324,0,"SELECT t1.price FROM departments AS t0 CROSS JOIN books AS t1 RIGHT JOIN users AS t2 ON t1.id = t2.id WHERE (t2.id BETWEEN 45 AND 86 AND t1.id IN (2));" -95,20644,350,0,"SELECT t2.id +95,7432,344,0,"SELECT t2.id FROM departments AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id CROSS JOIN books AS t2 WHERE ((t1.department_id IN (1, 5, 12, 34) AND t1.id IN (69)) AND t1.department_id <= 1);" -96,10070,948,16,"SELECT t1.id AS c5555, t0.id +96,689824,912,16,"SELECT t1.id AS c5555, t0.id FROM orders AS t0 JOIN users AS t1 ON t0.id = t1.id GROUP BY t1.id, t0.id;" -97,1643,163,0,"SELECT regions.id, SUM(regions.id), COUNT(regions.id) AS c9550 +97,1643,148,0,"SELECT regions.id, SUM(regions.id), COUNT(regions.id) AS c9550 FROM regions WHERE ((regions.id < 2 AND regions.id = 32) AND (regions.id <= 2 AND regions.id != 3)) GROUP BY regions.id ORDER BY regions.id ASC;" -98,12888,254,0,"SELECT books.id +98,2482,253,0,"SELECT books.id FROM books CROSS JOIN departments WHERE ((books.price IS NULL AND departments.id = 1) AND (books.price IS NULL OR books.price NOT IN (55, 77)));" -99,3212,258,0,"SELECT customers.id, SUM(customers.id), COUNT(customers.id) AS c8662 +99,59312,262,0,"SELECT customers.id, SUM(customers.id), COUNT(customers.id) AS c8662 FROM regions JOIN customers ON regions.id = customers.id WHERE customers.region_id = 63 GROUP BY customers.id;" -100,1660,132,17,"SELECT users.age, users.id AS c7470 +100,3009,165,17,"SELECT users.age, users.id AS c7470 FROM users ORDER BY users.id ASC;" -101,26500,369,50,"SELECT markets.id, departments.id AS c1258, markets.region +101,6624,358,50,"SELECT markets.id, departments.id AS c1258, markets.region FROM employees LEFT JOIN markets ON employees.id = markets.id CROSS JOIN departments WHERE ((markets.id IN (3) AND markets.region = 'EUROPE') OR employees.id < 69);" -102,1220,1173,5000,"SELECT t0.customer_id, t0.id AS c8685 +102,610000,1114,5000,"SELECT t0.customer_id, t0.id AS c8685 FROM orders AS t0;" -103,17410,1080,11,"SELECT users.age AS c405 +103,696660,1089,11,"SELECT users.age AS c405 FROM users JOIN orders ON users.id = orders.id RIGHT JOIN regions ON orders.id = regions.id GROUP BY users.age ORDER BY users.age DESC;" -104,1122,135,0,"SELECT employees.id +104,1222,134,0,"SELECT employees.id FROM employees WHERE employees.id = 14;" -105,22320,2174,3,"SELECT markets.id, orders.customer_id, COUNT(*) +105,1552826,2164,3,"SELECT markets.id, orders.customer_id, COUNT(*) FROM orders RIGHT JOIN markets ON orders.id = markets.id LEFT JOIN books ON markets.id = books.id GROUP BY markets.id, orders.customer_id;" -106,21032,333,25,"SELECT books.price AS c2329, markets.note +106,10632,340,25,"SELECT books.price AS c2329, markets.note FROM users CROSS JOIN books JOIN markets ON books.id = markets.id WHERE (markets.region = 'EUROPE' OR (users.age NOT IN (5, 22, 64, 123) AND users.age < 22)) ORDER BY markets.note ASC;" -107,1643,160,1,"SELECT regions.id +107,1643,176,1,"SELECT regions.id FROM regions WHERE regions.id IN (5) GROUP BY regions.id ORDER BY regions.id ASC;" -108,1775,242,51,"SELECT customers.id AS c9166, customers.region_id +108,102500,228,51,"SELECT customers.id AS c9166, customers.region_id FROM customers WHERE customers.id < 52 ORDER BY customers.region_id ASC, customers.id DESC;" -109,9220,297,22,"SELECT employees.id, users.age, employees.department_id +109,16132,273,22,"SELECT employees.id, users.age, employees.department_id FROM employees FULL JOIN users ON employees.id = users.id;" -110,9632,285,0,"SELECT employees.department_id, employees.id, COUNT(users.id) AS c6401, COUNT(*) +110,16522,270,0,"SELECT employees.department_id, employees.id, COUNT(users.id) AS c6401, COUNT(*) FROM employees FULL JOIN users ON employees.id = users.id WHERE ((employees.department_id = 31 AND employees.department_id NOT IN (6, 59, 72)) AND (employees.department_id NOT IN (1, 5, 6, 32) AND users.id = 5)) GROUP BY employees.department_id, employees.id;" -111,1122,2084,28,"SELECT orders.customer_id, orders.id +111,541846,1998,28,"SELECT orders.customer_id, orders.id FROM orders WHERE ((orders.id IN (97, 99, 184, 198) OR orders.id BETWEEN 31 AND 58) AND (orders.customer_id IS NOT NULL AND orders.id BETWEEN 3 AND 65));" -112,11660,2819,2,"SELECT markets.id, markets.note, COUNT(orders.id), SUM(orders.customer_id) +112,1550943,2924,2,"SELECT markets.id, markets.note, COUNT(orders.id), SUM(orders.customer_id) FROM markets RIGHT JOIN orders ON markets.id = orders.id WHERE ((orders.id < 188 AND orders.customer_id >= 193) OR (orders.customer_id BETWEEN 239 AND 436 OR markets.id = 1)) GROUP BY markets.id, markets.note ORDER BY markets.id DESC, markets.note DESC;" -113,1610,157,1,"SELECT t0.region, t0.note AS c6295 +113,422,154,1,"SELECT t0.region, t0.note AS c6295 FROM markets AS t0 WHERE t0.id < 2;" -114,1220,218,500,"SELECT t0.id +114,61000,219,500,"SELECT t0.id FROM customers AS t0;" -115,9220,276,10,"SELECT t1.id AS c2831, t0.id, t0.department_id +115,10020,278,10,"SELECT t1.id AS c2831, t0.id, t0.department_id FROM employees AS t0 RIGHT JOIN regions AS t1 ON t0.id = t1.id;" -116,8825,1378,3,"SELECT t2.customer_id +116,677083,1090,1,"SELECT t2.customer_id FROM markets AS t0 LEFT JOIN books AS t1 ON t0.id = t1.id JOIN orders AS t2 ON t1.id = t2.id WHERE t2.customer_id > 244 ORDER BY t2.customer_id DESC;" -117,1610,156,1,"SELECT t0.id AS c6139 +117,744,141,1,"SELECT t0.id AS c6139 FROM departments AS t0 WHERE t0.id >= 5;" -118,12132,344,0,"SELECT books.id AS c118 +118,7052,335,0,"SELECT books.id AS c118 FROM departments RIGHT JOIN users ON departments.id = users.id FULL JOIN books ON departments.id = books.id WHERE ((users.id > 3 AND books.price = 77) AND users.age > 443) GROUP BY books.id;" -119,6160,979,5,"SELECT orders.id, COUNT(departments.id) +119,677024,1010,5,"SELECT orders.id, COUNT(departments.id) FROM departments JOIN orders ON departments.id = orders.id WHERE departments.id < 36 GROUP BY orders.id;" -120,12970,4674,11,"SELECT orders.customer_id, employees.department_id +120,1113982,1453,500,"SELECT orders.customer_id, employees.department_id FROM employees JOIN customers ON employees.id = customers.id LEFT JOIN orders ON employees.id = orders.id;" -121,1122,136,0,"SELECT employees.id +121,1222,133,0,"SELECT employees.id FROM employees WHERE ((employees.id > 68 OR employees.department_id = 5) AND (employees.id BETWEEN 2 AND 4 AND employees.department_id IN (3, 5, 5)));" -122,4402,297,0,"SELECT markets.note, employees.id +122,2847,295,0,"SELECT markets.note, employees.id FROM employees LEFT JOIN markets ON employees.id = markets.id JOIN departments ON employees.id = departments.id WHERE ((departments.id != 36 OR markets.region IS NULL) AND departments.id = 4);" -123,1854,157,1,"SELECT t0.id, t0.note, t0.region +123,544,147,1,"SELECT t0.id, t0.note, t0.region FROM markets AS t0 WHERE t0.id NOT BETWEEN 2 AND 21;" -124,2833,342,1,"SELECT books.price AS c5178 +124,65833,353,1,"SELECT books.price AS c5178 FROM books RIGHT JOIN customers ON books.id = customers.id WHERE customers.id = 158 ORDER BY books.price DESC;" -125,1122,153,0,"SELECT employees.id AS c9929, employees.department_id +125,1222,145,0,"SELECT employees.id AS c9929, employees.department_id FROM employees WHERE (employees.department_id = 35 AND employees.department_id < 32);" -126,17632,351,0,"SELECT books.price, books.id +126,4962,342,0,"SELECT books.price, books.id FROM books FULL JOIN markets ON books.id = markets.id FULL JOIN regions ON books.id = regions.id WHERE (markets.region != 'EUROPE' AND (regions.id IS NULL AND books.price > 66)) GROUP BY books.price, books.id;" -127,1432,254,3,"SELECT t0.region_id +127,81500,262,3,"SELECT t0.region_id FROM customers AS t0 WHERE t0.id IN (16, 32, 137) ORDER BY t0.region_id DESC;" -128,1288,144,2,"SELECT regions.id +128,1288,159,2,"SELECT regions.id FROM regions WHERE regions.id BETWEEN 9 AND 10 ORDER BY regions.id DESC;" -129,22760,3072,3,"SELECT orders.customer_id, COUNT(orders.id) AS c1702 +129,1552892,3046,3,"SELECT orders.customer_id, COUNT(orders.id) AS c1702 FROM markets LEFT JOIN books ON markets.id = books.id LEFT JOIN orders ON markets.id = orders.id GROUP BY orders.customer_id ORDER BY orders.customer_id DESC;" -130,11770,2770,5,"SELECT regions.id, orders.id AS c452, orders.customer_id +130,680760,1036,5,"SELECT regions.id, orders.id AS c452, orders.customer_id FROM regions JOIN departments ON regions.id = departments.id LEFT JOIN orders ON departments.id = orders.id;" -131,14320,7939,11,"SELECT regions.id AS c9060, COUNT(orders.customer_id), COUNT(orders.id) +131,4006320,8008,11,"SELECT regions.id AS c9060, COUNT(orders.customer_id), COUNT(orders.id) FROM orders FULL JOIN regions ON orders.id = regions.id GROUP BY regions.id;" -132,3666,248,0,"SELECT t0.id AS c4692, t1.department_id AS c2149, t1.id +132,2522,247,0,"SELECT t0.id AS c4692, t1.department_id AS c2149, t1.id FROM departments AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id WHERE (t1.id IN (32, 66, 68, 80) AND (t1.id <= 26 OR t1.department_id <= 5));" -133,4970,923,11,"SELECT t1.id AS c8251, t0.customer_id, t0.id AS c2451 +133,678982,931,11,"SELECT t1.id AS c8251, t0.customer_id, t0.id AS c2451 FROM orders AS t0 JOIN employees AS t1 ON t0.id = t1.id;" -134,22300,15759,55000,"SELECT orders.customer_id AS c6356 +134,17111100,16205,55000,"SELECT orders.customer_id AS c6356 FROM employees CROSS JOIN orders ORDER BY orders.customer_id ASC;" -135,17660,12773,11,"SELECT users.id, regions.id AS c3154 +135,4015260,8754,5001,"SELECT users.id, regions.id AS c3154 FROM orders FULL JOIN users ON orders.id = users.id RIGHT JOIN regions ON orders.id = regions.id ORDER BY users.id DESC, regions.id DESC;" -136,89160,385,4,"SELECT books.price +136,12682,387,4,"SELECT books.price FROM regions CROSS JOIN markets LEFT JOIN books ON regions.id = books.id GROUP BY books.price ORDER BY books.price DESC;" -137,6320,148,5,"SELECT departments.id, SUM(departments.id), COUNT(*) AS c6998 +137,3160,137,5,"SELECT departments.id, SUM(departments.id), COUNT(*) AS c6998 FROM departments GROUP BY departments.id;" -138,6320,136,10,"SELECT regions.id AS c7258 +138,6320,137,10,"SELECT regions.id AS c7258 FROM regions GROUP BY regions.id;" -139,4160,154,2,"SELECT markets.id +139,932,178,2,"SELECT markets.id FROM markets WHERE ((markets.note = 'Old World' OR markets.id >= 2) OR (markets.region = 'AMERICA' AND markets.region = 'EUROPE')) GROUP BY markets.id;" -140,1610,151,3,"SELECT t0.price, t0.id AS c3105 +140,422,152,3,"SELECT t0.price, t0.id AS c3105 FROM books AS t0 WHERE t0.id IS NOT NULL;" -141,20160,323,0,"SELECT t1.id AS c8895, t1.note AS c8854, SUM(t1.id) +141,5272,358,0,"SELECT t1.id AS c8895, t1.note AS c8854, SUM(t1.id) FROM books AS t0 RIGHT JOIN markets AS t1 ON t0.id = t1.id RIGHT JOIN employees AS t2 ON t0.id = t2.id WHERE t1.id > 24 GROUP BY t1.id, t1.note;" -142,12970,374,17,"SELECT t1.region_id AS c1630 +142,87712,367,17,"SELECT t1.region_id AS c1630 FROM users AS t0 JOIN customers AS t1 ON t0.id = t1.id LEFT JOIN employees AS t2 ON t0.id = t2.id;" -143,1610,159,1,"SELECT t0.id, t0.price +143,422,158,1,"SELECT t0.id, t0.price FROM books AS t0 WHERE t0.price <= 55;" -144,44000,291,10,"SELECT t1.department_id AS c3966, SUM(t1.id) AS c1392, COUNT(*) AS c3892 +144,14944,273,10,"SELECT t1.department_id AS c3966, SUM(t1.id) AS c1392, COUNT(*) AS c3892 FROM books AS t0 CROSS JOIN employees AS t1 WHERE t1.department_id != 12 GROUP BY t1.department_id;" -145,6122,270,2,"SELECT t1.department_id AS c6376, t1.id AS c3596 +145,3072,283,2,"SELECT t1.department_id AS c6376, t1.id AS c3596 FROM books AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id WHERE ((t0.id < 1 OR t1.id IS NOT NULL) AND (t0.id != 3 AND t1.department_id <= 35));" -146,6760,157,5,"SELECT departments.id +146,3325,163,5,"SELECT departments.id FROM departments GROUP BY departments.id ORDER BY departments.id DESC;" -147,22600,351,110,"SELECT regions.id +147,12750,343,110,"SELECT regions.id FROM departments FULL JOIN employees ON departments.id = employees.id CROSS JOIN regions;" -148,18500,255,20,"SELECT books.id, books.price AS c5525, regions.id +148,6250,268,20,"SELECT books.id, books.price AS c5525, regions.id FROM books CROSS JOIN regions WHERE books.price < 69;" -149,1775,283,422,"SELECT t0.region_id AS c6318 +149,102500,278,422,"SELECT t0.region_id AS c6318 FROM customers AS t0 WHERE t0.id > 78 ORDER BY t0.region_id DESC;" -150,31360,1154,1040,"SELECT t0.id, t1.id AS c921 +150,1519000,1214,1040,"SELECT t0.id, t1.id AS c921 FROM regions AS t0 CROSS JOIN customers AS t1 WHERE t1.region_id IN (5, 9, 51) GROUP BY t0.id, t1.id;" -151,11688,6072,0,"SELECT regions.id, users.age, orders.id +151,4014888,11011,0,"SELECT regions.id, users.age, orders.id FROM regions RIGHT JOIN users ON regions.id = users.id FULL JOIN orders ON users.id = orders.id WHERE ((orders.customer_id BETWEEN 283 AND 359 AND users.id IN (10, 17, 17, 39)) OR (users.id IN (14, 16, 64, 75) AND users.id >= 81)) ORDER BY orders.id ASC, regions.id DESC, users.age ASC;" -152,22300,262,55,"SELECT departments.id, employees.department_id, employees.id +152,12160,277,55,"SELECT departments.id, employees.department_id, employees.id FROM departments CROSS JOIN employees ORDER BY employees.id ASC;" -153,14600,1238,1500,"SELECT t1.id, t0.region_id, t0.id +153,239300,1323,1500,"SELECT t1.id, t0.region_id, t0.id FROM customers AS t0 CROSS JOIN books AS t1;" -154,5655,182,10,"SELECT regions.id, SUM(regions.id) AS c8443 +154,5655,174,10,"SELECT regions.id, SUM(regions.id) AS c8443 FROM regions WHERE (regions.id >= 1 OR (regions.id >= 8 OR regions.id = 5)) GROUP BY regions.id ORDER BY regions.id ASC;" -155,18170,1267,2,"SELECT markets.region +155,681356,1097,2,"SELECT markets.region FROM markets LEFT JOIN orders ON markets.id = orders.id JOIN employees ON markets.id = employees.id GROUP BY markets.region;" -156,9220,3877,10,"SELECT t1.customer_id, t1.id AS c2544, t0.id AS c7170 +156,4001220,3942,10,"SELECT t1.customer_id, t1.id AS c2544, t0.id AS c7170 FROM regions AS t0 LEFT JOIN orders AS t1 ON t0.id = t1.id;" -157,26500,1949,4840,"SELECT t1.region_id, t2.id +157,675750,1912,4840,"SELECT t1.region_id, t2.id FROM users AS t0 RIGHT JOIN customers AS t1 ON t0.id = t1.id CROSS JOIN regions AS t2 WHERE t0.id IS NULL;" -158,12260,290,0,"SELECT t1.id, t0.id AS c3888, t2.id AS c5161 +158,5727,298,7,"SELECT t1.id, t0.id AS c3888, t2.id AS c5161 FROM employees AS t0 FULL JOIN regions AS t1 ON t0.id = t1.id JOIN books AS t2 ON t0.id = t2.id WHERE t0.id IS NULL;" -159,14056,2677,3,"SELECT orders.customer_id AS c3770, departments.id AS c4567, COUNT(*) AS c2771 +159,2253028,2641,3,"SELECT orders.customer_id AS c3770, departments.id AS c4567, COUNT(*) AS c2771 FROM departments LEFT JOIN orders ON departments.id = orders.id WHERE ((orders.customer_id NOT BETWEEN 115 AND 380 OR departments.id IS NULL) OR orders.id = 174) GROUP BY orders.customer_id, departments.id;" -160,1220,1139,5000,"SELECT t0.id, t0.customer_id AS c1926 +160,610000,1128,5000,"SELECT t0.id, t0.customer_id AS c1926 FROM orders AS t0;" -161,19820,395,33,"SELECT t1.region AS c961, t2.id +161,91868,354,33,"SELECT t1.region AS c961, t2.id FROM employees AS t0 CROSS JOIN markets AS t1 JOIN customers AS t2 ON t1.id = t2.id;" -162,1366,155,3,"SELECT employees.id, employees.department_id +162,1466,157,3,"SELECT employees.id, employees.department_id FROM employees WHERE ((employees.id NOT IN (5, 67, 68) AND employees.department_id = 28) OR (employees.id > 4 AND employees.department_id > 32));" -163,56060,1836,2,"SELECT markets.region, COUNT(*), COUNT(*) +163,948104,1813,2,"SELECT markets.region, COUNT(*), COUNT(*) FROM users CROSS JOIN markets LEFT JOIN customers ON markets.id = customers.id WHERE users.age >= 1 GROUP BY markets.region;" -164,1775,171,3,"SELECT markets.id, markets.note AS c529, markets.region +164,433,184,3,"SELECT markets.id, markets.note AS c529, markets.region FROM markets WHERE (markets.region = 'EUROPE' OR markets.id <= 91) ORDER BY markets.region ASC, markets.id ASC, markets.note ASC;" -165,6320,155,3,"SELECT books.id, COUNT(books.price), SUM(books.id) +165,1896,151,3,"SELECT books.id, COUNT(books.price), SUM(books.id) FROM books GROUP BY books.id;" -166,2328,151,9,"SELECT regions.id +166,2328,149,9,"SELECT regions.id FROM regions WHERE regions.id NOT IN (2, 95) ORDER BY regions.id ASC;" -167,1122,265,0,"SELECT t0.id +167,52196,252,0,"SELECT t0.id FROM customers AS t0 WHERE ((t0.region_id > 32 AND t0.id = 78) AND (t0.id >= 20 OR t0.id IS NULL));" -168,31392,1276,1,"SELECT t1.region AS c6909, t1.note, SUM(t2.id) +168,158668,1265,1,"SELECT t1.region AS c6909, t1.note, SUM(t2.id) FROM customers AS t0 LEFT JOIN markets AS t1 ON t0.id = t1.id CROSS JOIN departments AS t2 WHERE ((t0.id = 89 OR t1.id BETWEEN 49 AND 71) AND t0.id IS NOT NULL) GROUP BY t1.region, t1.note ORDER BY t1.region ASC;" -169,12160,272,2,"SELECT departments.id +169,2482,273,2,"SELECT departments.id FROM departments RIGHT JOIN markets ON departments.id = markets.id WHERE departments.id != 1 GROUP BY departments.id;" -170,14760,2948,505,"SELECT t1.id AS c5737, t0.customer_id +170,2253325,2928,505,"SELECT t1.id AS c5737, t0.customer_id FROM orders AS t0 LEFT JOIN departments AS t1 ON t0.id = t1.id GROUP BY t1.id, t0.customer_id ORDER BY t0.customer_id DESC, t1.id ASC;" -171,83620,525,110,"SELECT employees.id AS c3715 +171,37006,538,110,"SELECT employees.id AS c3715 FROM regions CROSS JOIN employees FULL JOIN books ON regions.id = books.id;" -172,9775,683,0,"SELECT t0.id, t0.department_id AS c3676, t1.region_id +172,436875,673,0,"SELECT t0.id, t0.department_id AS c3676, t1.region_id FROM employees AS t0 FULL JOIN customers AS t1 ON t0.id = t1.id WHERE t0.department_id >= 57 ORDER BY t0.id DESC, t1.region_id ASC;" -173,22300,20218,85000,"SELECT orders.customer_id +173,27106700,19617,85000,"SELECT orders.customer_id FROM users CROSS JOIN orders ORDER BY orders.customer_id DESC;" -174,1432,152,3,"SELECT users.id, users.age +174,2475,163,3,"SELECT users.id, users.age FROM users WHERE users.id IN (3, 11, 12) ORDER BY users.age DESC, users.id DESC;" -175,2822,250,1,"SELECT books.id, employees.id, employees.department_id +175,2292,433,1,"SELECT books.id, employees.id, employees.department_id FROM employees RIGHT JOIN books ON employees.id = books.id WHERE books.price = 77;" -176,4160,169,2,"SELECT books.price, SUM(books.price) AS c5357 +176,932,152,2,"SELECT books.price, SUM(books.price) AS c5357 FROM books WHERE books.price >= 66 GROUP BY books.price;" -177,2085,150,14,"SELECT users.id, users.age AS c4847 +177,3692,162,14,"SELECT users.id, users.age AS c4847 FROM users WHERE (users.age < 16 OR users.age < 71) ORDER BY users.id ASC, users.age DESC;" -178,1660,133,17,"SELECT users.age AS c7380, users.id +178,3009,140,17,"SELECT users.age AS c7380, users.id FROM users ORDER BY users.id DESC;" -179,6688,415,500,"SELECT customers.id, customers.region_id +179,358416,524,500,"SELECT customers.id, customers.region_id FROM customers WHERE ((customers.id != 141 OR customers.region_id != 10) OR (customers.id NOT IN (197) OR customers.region_id IN (8))) GROUP BY customers.id, customers.region_id;" -180,6760,402,500,"SELECT customers.id AS c95, customers.region_id +180,365500,614,500,"SELECT customers.id AS c95, customers.region_id FROM customers GROUP BY customers.id, customers.region_id ORDER BY customers.region_id DESC;" -181,649400,2991,500,"SELECT customers.region_id AS c397, customers.id AS c5003, SUM(books.id) +181,4976800,3138,500,"SELECT customers.region_id AS c397, customers.id AS c5003, SUM(books.id) FROM customers CROSS JOIN books CROSS JOIN departments GROUP BY customers.region_id, customers.id;" -182,10070,1156,500,"SELECT t1.id AS c6022, t0.id +182,1111000,1136,500,"SELECT t1.id AS c6022, t0.id FROM orders AS t0 JOIN customers AS t1 ON t0.id = t1.id GROUP BY t1.id, t0.id;" -183,1632,229,1,"SELECT customers.id, COUNT(customers.id), COUNT(customers.id) +183,81600,287,1,"SELECT customers.id, COUNT(customers.id), COUNT(customers.id) FROM customers WHERE customers.id = 24 GROUP BY customers.id;" -184,18500,494,500,"SELECT books.price AS c5308, customers.region_id AS c1694 +184,297800,441,500,"SELECT books.price AS c5308, customers.region_id AS c1694 FROM books CROSS JOIN customers WHERE books.price <= 55;" -185,1220,134,5,"SELECT t0.id AS c9527 +185,610,134,5,"SELECT t0.id AS c9527 FROM departments AS t0;" -186,9220,284,18,"SELECT users.id +186,14820,282,18,"SELECT users.id FROM regions FULL JOIN users ON regions.id = users.id;" -187,9930,306,8,"SELECT users.id, regions.id, users.age AS c4755 +187,15530,290,8,"SELECT users.id, regions.id, users.age AS c4755 FROM users LEFT JOIN regions ON users.id = regions.id WHERE ((regions.id != 31 AND users.age <= 64) OR users.id >= 66) ORDER BY users.age DESC;" -188,27984,334,0,"SELECT t2.price AS c5573, COUNT(*) +188,7942,327,0,"SELECT t2.price AS c5573, COUNT(*) FROM employees AS t0 RIGHT JOIN departments AS t1 ON t0.id = t1.id CROSS JOIN books AS t2 WHERE (t2.price > 55 AND t0.id BETWEEN 66 AND 69) GROUP BY t2.price;" -189,2292,319,0,"SELECT markets.id AS c7660 +189,57522,266,0,"SELECT markets.id AS c7660 FROM markets JOIN customers ON markets.id = customers.id WHERE (markets.note IS NULL AND (customers.id = 198 AND markets.region IN ('AMERICA', 'EUROPE')));" -190,6760,164,5,"SELECT departments.id +190,3325,146,5,"SELECT departments.id FROM departments GROUP BY departments.id ORDER BY departments.id DESC;" -191,49850,319,55,"SELECT departments.id, employees.id +191,27819,351,55,"SELECT departments.id, employees.id FROM departments JOIN regions ON departments.id = regions.id CROSS JOIN employees WHERE employees.id IS NOT NULL GROUP BY departments.id, employees.id ORDER BY departments.id DESC, employees.id ASC;" -192,13620,267,3,"SELECT books.id, markets.region +192,1658,507,3,"SELECT books.id, markets.region FROM markets CROSS JOIN books WHERE markets.region = 'EUROPE';" -193,11870,661,10,"SELECT customers.region_id, regions.id +193,83620,1178,11,"SELECT customers.region_id, regions.id FROM users JOIN regions ON users.id = regions.id LEFT JOIN customers ON regions.id = customers.id;" -194,225614,8292,25000,"SELECT t2.id +194,5620982,10404,25000,"SELECT t2.id FROM customers AS t0 CROSS JOIN markets AS t1 CROSS JOIN users AS t2 WHERE ((t1.id IN (3) OR t2.age != 1) OR (t1.id != 2 OR t2.id > 61));" -195,17220,7112,10,"SELECT t0.id +195,1553466,8004,10,"SELECT t0.id FROM regions AS t0 FULL JOIN books AS t1 ON t0.id = t1.id LEFT JOIN orders AS t2 ON t1.id = t2.id;" -196,68250,595,30,"SELECT customers.region_id, books.id, SUM(regions.id), SUM(customers.region_id) +196,1006340,585,30,"SELECT customers.region_id, books.id, SUM(regions.id), SUM(customers.region_id) FROM books JOIN regions ON books.id = regions.id CROSS JOIN customers GROUP BY customers.region_id, books.id;" -197,7720,252,3,"SELECT t0.price AS c8028, t2.id +197,70841,337,3,"SELECT t0.price AS c8028, t2.id FROM books AS t0 JOIN customers AS t1 ON t0.id = t1.id JOIN regions AS t2 ON t1.id = t2.id;" -198,1220,134,17,"SELECT t0.age AS c2991 +198,2074,129,17,"SELECT t0.age AS c2991 FROM users AS t0;" -199,18134,295,30,"SELECT t1.id, t0.id +199,30140,292,30,"SELECT t1.id, t0.id FROM regions AS t0 CROSS JOIN users AS t1 WHERE (t1.age BETWEEN 33 AND 33 OR t1.id IN (12, 49, 95));" -200,2085,145,6,"SELECT employees.department_id +200,2428,163,6,"SELECT employees.department_id FROM employees WHERE ((employees.id > 66 OR employees.id IN (33)) OR employees.department_id IN (6, 12, 12, 50)) ORDER BY employees.department_id DESC;" -201,1220,1137,5000,"SELECT orders.customer_id, orders.id +201,610000,1297,5000,"SELECT orders.customer_id, orders.id FROM orders;" -202,4666,206,6,"SELECT employees.id AS c3304, users.id AS c2088 +202,6401,205,6,"SELECT employees.id AS c3304, users.id AS c2088 FROM users JOIN employees ON users.id = employees.id WHERE ((users.age IN (42, 64) OR users.id < 16) AND users.id != 99) GROUP BY employees.id, users.id;" -203,6320,150,3,"SELECT books.id, books.price +203,1896,159,3,"SELECT books.id, books.price FROM books GROUP BY books.id, books.price;" -204,10170,272,3,"SELECT t0.id AS c8592, t1.region +204,70446,345,3,"SELECT t0.id AS c8592, t1.region FROM customers AS t0 JOIN markets AS t1 ON t0.id = t1.id GROUP BY t0.id, t1.region;" -205,13664,12314,1,"SELECT t1.id AS c8725, SUM(t1.id), COUNT(*) +205,10416100,12344,1,"SELECT t1.id AS c8725, SUM(t1.id), COUNT(*) FROM users AS t0 CROSS JOIN orders AS t1 WHERE (t0.age IN (33, 64) AND t1.id = 156) GROUP BY t1.id;" -206,1220,250,500,"SELECT t0.id AS c4257 +206,61000,229,500,"SELECT t0.id AS c4257 FROM customers AS t0;" -207,20110,4160,0,"SELECT t1.customer_id, t0.department_id +207,8356600,3939,0,"SELECT t1.customer_id, t0.department_id FROM employees AS t0 CROSS JOIN orders AS t1 JOIN customers AS t2 ON t1.id = t2.id WHERE t0.department_id IS NULL;" -208,9220,264,3,"SELECT books.price AS c3720, employees.department_id, books.id +208,3776,263,3,"SELECT books.price AS c3720, employees.department_id, books.id FROM employees RIGHT JOIN books ON employees.id = books.id;" -209,11210,449,2,"SELECT t2.region, t1.id, COUNT(t2.id) +209,69182,369,2,"SELECT t2.region, t1.id, COUNT(t2.id) FROM books AS t0 JOIN customers AS t1 ON t0.id = t1.id FULL JOIN markets AS t2 ON t0.id = t2.id WHERE t0.id < 1 GROUP BY t2.region, t1.id;" -210,14600,3063,15000,"SELECT orders.id, books.id AS c7933, orders.customer_id +210,2390300,2862,15000,"SELECT orders.id, books.id AS c7933, orders.customer_id FROM books CROSS JOIN orders;" -211,84010,3085577,0,"SELECT t2.region_id, t2.id, t0.customer_id +211,2984422200,2956573,0,"SELECT t2.region_id, t2.id, t0.customer_id FROM orders AS t0 CROSS JOIN users AS t1 LEFT JOIN customers AS t2 ON t1.id = t2.id WHERE t2.region_id >= 23;" -212,14320,2182,3,"SELECT orders.id +212,1551896,2851,3,"SELECT orders.id FROM books LEFT JOIN orders ON books.id = orders.id GROUP BY orders.id;" -213,1620,141,2,"SELECT t0.id +213,1620,151,2,"SELECT t0.id FROM regions AS t0 WHERE t0.id IN (4, 4, 9, 88) ORDER BY t0.id ASC;" -214,178400,91814,247665,"SELECT orders.id, orders.customer_id, employees.id +214,51596600,97240,247665,"SELECT orders.id, orders.customer_id, employees.id FROM orders CROSS JOIN employees CROSS JOIN departments WHERE orders.customer_id >= 52;" -215,1610,966,29,"SELECT t0.customer_id +215,805000,985,29,"SELECT t0.customer_id FROM orders AS t0 WHERE t0.id < 30;" -216,1488,142,3,"SELECT departments.id +216,744,129,3,"SELECT departments.id FROM departments WHERE departments.id IN (1, 3, 4, 60);" -217,12160,683,3,"SELECT t0.id, t1.region_id +217,439260,709,3,"SELECT t0.id, t1.region_id FROM employees AS t0 LEFT JOIN customers AS t1 ON t0.id = t1.id WHERE t1.region_id >= 8 GROUP BY t0.id, t1.region_id;" -218,9220,241,5,"SELECT books.price AS c5412, books.id, departments.id AS c9618 +218,1916,288,5,"SELECT books.price AS c5412, books.id, departments.id AS c9618 FROM departments LEFT JOIN books ON departments.id = books.id;" -219,178400,23808,45000,"SELECT t1.note, t0.customer_id AS c4395 +219,9485600,25795,45000,"SELECT t1.note, t0.customer_id AS c4395 FROM orders AS t0 CROSS JOIN markets AS t1 CROSS JOIN books AS t2 WHERE t1.region IS NOT NULL;" -220,83832,1508595,45001,"SELECT customers.region_id, orders.id, employees.id +220,1931305120,1531952,45001,"SELECT customers.region_id, orders.id, employees.id FROM employees CROSS JOIN orders RIGHT JOIN customers ON employees.id = customers.id WHERE (customers.id IN (181) OR employees.id BETWEEN 3 AND 92) ORDER BY customers.region_id ASC;" -221,1133,144,0,"SELECT t0.id AS c9777 +221,1133,157,0,"SELECT t0.id AS c9777 FROM regions AS t0 WHERE ((t0.id <= 5 OR t0.id IN (4, 12)) AND (t0.id = 9 AND t0.id NOT IN (2, 4, 5, 89))) ORDER BY t0.id ASC;" -222,6056,174,11,"SELECT t0.id, t0.department_id AS c2878, COUNT(t0.id), COUNT(*) AS c2633 +222,6788,169,11,"SELECT t0.id, t0.department_id AS c2878, COUNT(t0.id), COUNT(*) AS c2633 FROM employees AS t0 WHERE ((t0.department_id IS NOT NULL OR t0.department_id IS NOT NULL) OR (t0.id > 26 OR t0.id = 52)) GROUP BY t0.id, t0.department_id;" -223,14600,13517,55000,"SELECT t0.id +223,7431100,14977,55000,"SELECT t0.id FROM orders AS t0 CROSS JOIN employees AS t1;" -224,1220,136,11,"SELECT employees.department_id +224,1342,148,11,"SELECT employees.department_id FROM employees;" -225,12970,4690,11,"SELECT orders.customer_id, customers.id AS c4775, employees.department_id AS c6986 +225,1113982,1738,500,"SELECT orders.customer_id, customers.id AS c4775, employees.department_id AS c6986 FROM employees LEFT JOIN customers ON employees.id = customers.id JOIN orders ON employees.id = orders.id;" -226,9854,279,3,"SELECT books.id, regions.id, books.price AS c1520 +226,3644,270,3,"SELECT books.id, regions.id, books.price AS c1520 FROM books LEFT JOIN regions ON books.id = regions.id WHERE ((regions.id < 10 OR books.price = 77) OR books.price > 60);" -227,9122,5911,9,"SELECT orders.id, books.price, orders.customer_id AS c7568 +227,1550422,6413,9,"SELECT orders.id, books.price, orders.customer_id AS c7568 FROM orders FULL JOIN books ON orders.id = books.id WHERE orders.customer_id = 63;" -228,25402,1409,1000,"SELECT t1.id AS c1443, t2.region_id AS c2237 +228,285106,1546,1000,"SELECT t1.id AS c1443, t2.region_id AS c2237 FROM employees AS t0 LEFT JOIN markets AS t1 ON t0.id = t1.id CROSS JOIN customers AS t2 WHERE ((t1.id < 3 OR t1.region = 'AMERICA') AND (t2.region_id IS NULL OR t1.note != 'North, South'));" -229,26940,545,0,"SELECT departments.id +229,228379,592,0,"SELECT departments.id FROM departments LEFT JOIN customers ON departments.id = customers.id CROSS JOIN markets WHERE markets.region IS NULL ORDER BY departments.id ASC;" -230,11870,10874,5000,"SELECT departments.id, users.age AS c5139, orders.id +230,685010,1055,17,"SELECT departments.id, users.age AS c5139, orders.id FROM departments JOIN users ON departments.id = users.id FULL JOIN orders ON users.id = orders.id;" -231,1220,123,5,"SELECT departments.id +231,610,158,5,"SELECT departments.id FROM departments;" -232,14760,765,16,"SELECT customers.id, COUNT(*) AS c3789 +232,656679,819,16,"SELECT customers.id, COUNT(*) AS c3789 FROM customers RIGHT JOIN users ON customers.id = users.id GROUP BY customers.id ORDER BY customers.id DESC;" -233,22470,746,1353,"SELECT departments.id +233,317325,812,1353,"SELECT departments.id FROM departments JOIN markets ON departments.id = markets.id CROSS JOIN customers WHERE (customers.region_id NOT IN (10, 10) AND (customers.id <= 80 OR departments.id >= 1));" -234,9220,256,10,"SELECT t0.note +234,3466,261,10,"SELECT t0.note FROM markets AS t0 FULL JOIN regions AS t1 ON t0.id = t1.id;" -235,5604,255,1,"SELECT t1.region_id AS c5511 +235,68764,248,1,"SELECT t1.region_id AS c5511 FROM books AS t0 JOIN customers AS t1 ON t0.id = t1.id WHERE (t0.price NOT BETWEEN 66 AND 94 OR (t0.id > 3 AND t1.region_id = 10));" -236,17660,4310,17,"SELECT t1.department_id AS c2338, t0.customer_id +236,4366616,4378,17,"SELECT t1.department_id AS c2338, t0.customer_id FROM orders AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id RIGHT JOIN users AS t2 ON t1.id = t2.id ORDER BY t1.department_id ASC;" -237,22600,2092,5010,"SELECT regions.id +237,669120,2379,5010,"SELECT regions.id FROM users FULL JOIN customers ON users.id = customers.id CROSS JOIN regions;" -238,6122,652,0,"SELECT customers.region_id +238,268722,690,0,"SELECT customers.region_id FROM customers LEFT JOIN employees ON customers.id = employees.id WHERE (employees.id IN (38) AND customers.region_id != 5);" -239,4325,176,6,"SELECT employees.id +239,4425,166,6,"SELECT employees.id FROM employees WHERE employees.id > 5 GROUP BY employees.id ORDER BY employees.id DESC;" -240,649400,379,3,"SELECT t0.price AS c6143, SUM(t2.id) +240,169984,385,3,"SELECT t0.price AS c6143, SUM(t2.id) FROM books AS t0 CROSS JOIN users AS t1 CROSS JOIN departments AS t2 GROUP BY t0.price;" -241,22600,3857,25000,"SELECT customers.id, customers.region_id, departments.id +241,3875500,5272,25000,"SELECT customers.id, customers.region_id, departments.id FROM customers RIGHT JOIN departments ON customers.id = departments.id CROSS JOIN orders;" -242,13864,236,0,"SELECT books.id +242,5320,321,0,"SELECT books.id FROM books CROSS JOIN employees WHERE (employees.id != 33 AND (employees.id < 1 AND employees.department_id < 12));" -243,7730,1760,5,"SELECT employees.department_id, books.price AS c4475, books.id AS c4655 +243,680223,1097,5,"SELECT employees.department_id, books.price AS c4475, books.id AS c4655 FROM orders JOIN employees ON orders.id = employees.id RIGHT JOIN books ON employees.id = books.id WHERE ((orders.id >= 13 AND orders.customer_id != 233) OR (orders.id > 39 AND orders.customer_id <= 359)) ORDER BY books.id DESC;" -244,10922,333,0,"SELECT regions.id +244,5632,333,0,"SELECT regions.id FROM regions RIGHT JOIN employees ON regions.id = employees.id LEFT JOIN markets ON employees.id = markets.id WHERE (regions.id NOT BETWEEN 2 AND 8 AND markets.note = 'Fast lane');" -245,1775,142,1,"SELECT markets.note, markets.region, markets.id +245,433,172,1,"SELECT markets.note, markets.region, markets.id FROM markets WHERE ((markets.region != 'AMERICA' AND markets.region = 'AMERICA') OR markets.id < 2) ORDER BY markets.note DESC, markets.id ASC, markets.region DESC;" -246,1244,154,9,"SELECT regions.id AS c713 +246,1244,145,9,"SELECT regions.id AS c713 FROM regions WHERE regions.id BETWEEN 2 AND 45;" -247,14408,2095,3,"SELECT t0.id AS c7208 +247,1551608,2262,3,"SELECT t0.id AS c7208 FROM orders AS t0 RIGHT JOIN books AS t1 ON t0.id = t1.id WHERE t0.customer_id NOT IN (244, 365) GROUP BY t0.id ORDER BY t0.id ASC;" -248,13424,7694,5000,"SELECT t1.id, COUNT(*), SUM(t0.id) AS c7387 +248,6459284,8849,5000,"SELECT t1.id, COUNT(*), SUM(t0.id) AS c7387 FROM users AS t0 RIGHT JOIN orders AS t1 ON t0.id = t1.id WHERE ((t1.customer_id = 494 AND t1.customer_id != 145) OR (t0.age > 53 OR t1.customer_id IS NOT NULL)) GROUP BY t1.id;" -249,9610,256,3,"SELECT markets.region, users.age +249,5692,274,3,"SELECT markets.region, users.age FROM markets LEFT JOIN users ON markets.id = users.id WHERE users.id < 17;" -250,9220,591,10,"SELECT customers.id, customers.region_id +250,401220,748,10,"SELECT customers.id, customers.region_id FROM regions LEFT JOIN customers ON regions.id = customers.id;" -251,9775,4003,42,"SELECT orders.customer_id, regions.id, orders.id AS c3346 +251,4001775,4600,42,"SELECT orders.customer_id, regions.id, orders.id AS c3346 FROM regions FULL JOIN orders ON regions.id = orders.id WHERE orders.id <= 42 ORDER BY regions.id ASC;" -252,83533,19602,39,"SELECT orders.id AS c8852, orders.customer_id, books.price AS c8813 +252,7310933,21711,39,"SELECT orders.id AS c8852, orders.customer_id, books.price AS c8813 FROM books CROSS JOIN orders FULL JOIN departments ON orders.id = departments.id WHERE ((orders.customer_id = 163 OR orders.id IN (69, 96, 152)) AND (orders.id IS NOT NULL AND books.id IS NOT NULL)) ORDER BY orders.id DESC;" -253,18894,439,40,"SELECT customers.id +253,121234,453,40,"SELECT customers.id FROM regions CROSS JOIN employees JOIN customers ON employees.id = customers.id WHERE (employees.id IS NOT NULL AND customers.id >= 65);" -254,9220,246,11,"SELECT markets.id AS c3977, employees.department_id +254,3776,262,11,"SELECT markets.id AS c3977, employees.department_id FROM markets FULL JOIN employees ON markets.id = employees.id;" -255,6320,2350,5000,"SELECT orders.id, orders.customer_id AS c7286 +255,3160000,2415,5000,"SELECT orders.id, orders.customer_id AS c7286 FROM orders GROUP BY orders.id, orders.customer_id;" -256,31360,277,9,"SELECT t0.id, t1.id AS c9218 +256,10520,274,9,"SELECT t0.id, t1.id AS c9218 FROM markets AS t0 CROSS JOIN employees AS t1 WHERE t1.department_id IN (5, 12, 33) GROUP BY t0.id, t1.id;" -257,8970,178,3,"SELECT t1.price, t0.id AS c7828 +257,3936,183,3,"SELECT t1.price, t0.id AS c7828 FROM regions AS t0 JOIN books AS t1 ON t0.id = t1.id GROUP BY t1.price, t0.id;" -258,18510,378,3,"SELECT customers.region_id +258,75782,410,3,"SELECT customers.region_id FROM customers JOIN users ON customers.id = users.id RIGHT JOIN markets ON customers.id = markets.id GROUP BY customers.region_id ORDER BY customers.region_id DESC;" -259,1660,255,500,"SELECT customers.id +259,110500,255,500,"SELECT customers.id FROM customers ORDER BY customers.id ASC;" -260,1366,237,118,"SELECT customers.region_id, customers.id +260,72814,282,118,"SELECT customers.region_id, customers.id FROM customers WHERE ((customers.region_id IS NULL OR customers.id >= 198) AND customers.region_id <= 4);" -261,14760,449,3,"SELECT customers.region_id AS c4278, books.id +261,156962,566,3,"SELECT customers.region_id AS c4278, books.id FROM books LEFT JOIN customers ON books.id = customers.id GROUP BY customers.region_id, books.id ORDER BY books.id ASC;" -262,14600,277,110,"SELECT t1.department_id AS c5186, t0.id, t1.id +262,15960,322,110,"SELECT t1.department_id AS c5186, t0.id, t1.id FROM regions AS t0 CROSS JOIN employees AS t1;" -263,8650,330,5,"SELECT markets.id, departments.id, regions.id +263,2973,330,5,"SELECT markets.id, departments.id, regions.id FROM regions LEFT JOIN departments ON regions.id = departments.id JOIN markets ON regions.id = markets.id WHERE markets.note != 'Old World' ORDER BY markets.id ASC, regions.id DESC;" -264,17220,885,500,"SELECT users.id AS c6920, markets.region AS c5238, users.age AS c1104 +264,160636,939,500,"SELECT users.id AS c6920, markets.region AS c5238, users.age AS c1104 FROM markets RIGHT JOIN users ON markets.id = users.id RIGHT JOIN customers ON markets.id = customers.id;" -265,9220,277,17,"SELECT t1.id, t0.id AS c2693 +265,14820,312,17,"SELECT t1.id, t0.id AS c2693 FROM users AS t0 LEFT JOIN regions AS t1 ON t0.id = t1.id;" -266,1133,239,1,"SELECT customers.id AS c604, customers.region_id +266,59400,248,1,"SELECT customers.id AS c604, customers.region_id FROM customers WHERE customers.id = 135 ORDER BY customers.id DESC, customers.region_id DESC;" -267,1610,140,0,"SELECT books.id +267,422,139,0,"SELECT books.id FROM books WHERE books.price IS NULL;" -268,649400,33743,11,"SELECT employees.department_id, SUM(orders.customer_id) +268,107001400,33265,11,"SELECT employees.department_id, SUM(orders.customer_id) FROM orders CROSS JOIN books CROSS JOIN employees GROUP BY employees.department_id;" -269,1220,121,3,"SELECT books.price AS c8205 +269,366,135,3,"SELECT books.price AS c8205 FROM books;" -270,1220,1113,5000,"SELECT orders.id AS c7922 +270,610000,1139,5000,"SELECT orders.id AS c7922 FROM orders;" -271,2972,1247,0,"SELECT orders.id AS c8902, departments.id AS c4368, orders.customer_id +271,534702,1221,0,"SELECT orders.id AS c8902, departments.id AS c4368, orders.customer_id FROM orders JOIN departments ON orders.id = departments.id WHERE ((orders.customer_id = 483 AND departments.id > 4) AND orders.id != 24);" -272,8660,1918,2,"SELECT orders.customer_id, orders.id, COUNT(*), COUNT(*) +272,850932,2012,2,"SELECT orders.customer_id, orders.id, COUNT(*), COUNT(*) FROM orders RIGHT JOIN books ON orders.id = books.id WHERE books.id != 1 GROUP BY orders.customer_id, orders.id;" -273,6320,156,11,"SELECT t0.id, SUM(t0.department_id), COUNT(t0.department_id) +273,6952,177,11,"SELECT t0.id, SUM(t0.department_id), COUNT(t0.department_id) FROM employees AS t0 GROUP BY t0.id;" -274,1244,152,2,"SELECT t0.id AS c7269, t0.age +274,2066,148,2,"SELECT t0.id AS c7269, t0.age FROM users AS t0 WHERE t0.age IN (33, 33);" -275,1122,1452,0,"SELECT orders.id AS c6877 +275,545750,1512,0,"SELECT orders.id AS c6877 FROM orders WHERE ((orders.customer_id <= 56 OR orders.customer_id != 343) AND (orders.customer_id IN (52, 86) AND orders.customer_id >= 224));" -276,73300,1476,11,"SELECT users.age AS c7960, SUM(users.id) +276,6766700,1560,11,"SELECT users.age AS c7960, SUM(users.id) FROM customers CROSS JOIN users GROUP BY users.age ORDER BY users.age ASC;" -277,1643,169,1,"SELECT t0.id +277,1143,182,1,"SELECT t0.id FROM departments AS t0 WHERE ((t0.id IN (2, 10, 59) OR t0.id IS NULL) AND (t0.id != 5 AND t0.id IS NOT NULL)) GROUP BY t0.id ORDER BY t0.id DESC;" -278,9976,278,5,"SELECT t1.id AS c8751 +278,8638,307,5,"SELECT t1.id AS c8751 FROM users AS t0 RIGHT JOIN departments AS t1 ON t0.id = t1.id WHERE (t0.age IS NULL OR (t1.id != 5 OR t1.id < 14));" -279,3432,1033,0,"SELECT regions.id, orders.id, COUNT(*), SUM(orders.customer_id) +279,851732,1199,0,"SELECT regions.id, orders.id, COUNT(*), SUM(orders.customer_id) FROM orders RIGHT JOIN regions ON orders.id = regions.id WHERE ((regions.id IN (8, 9, 22) AND regions.id <= 6) AND orders.id IN (39, 49, 104, 106)) GROUP BY regions.id, orders.id;" -280,9122,6240,0,"SELECT orders.customer_id +280,6451822,7192,0,"SELECT orders.customer_id FROM users FULL JOIN orders ON users.id = orders.id WHERE ((users.age > 33 AND orders.customer_id < 84) AND (users.id = 15 OR orders.id IN (68, 128, 128, 198)));" -281,1133,155,0,"SELECT t0.id, t0.note, t0.region +281,433,170,0,"SELECT t0.id, t0.note, t0.region FROM markets AS t0 WHERE ((t0.note = 'unknown' AND t0.id != 1) AND t0.id > 67) ORDER BY t0.id ASC, t0.note DESC;" -282,84010,11429,9,"SELECT employees.id, orders.customer_id AS c5471, books.price +282,13612010,12919,9,"SELECT employees.id, orders.customer_id AS c5471, books.price FROM books CROSS JOIN orders LEFT JOIN employees ON orders.id = employees.id WHERE employees.id > 66;" -283,83533,2140,0,"SELECT books.id, markets.id, books.price AS c2723 +283,521733,2237,0,"SELECT books.id, markets.id, books.price AS c2723 FROM books CROSS JOIN customers FULL JOIN markets ON customers.id = markets.id WHERE ((markets.id > 55 AND customers.region_id NOT IN (8, 9, 10)) AND markets.region IN ('AMERICA', 'EUROPE')) ORDER BY books.id DESC, markets.id ASC;" -284,6760,178,2,"SELECT t0.region AS c1153, SUM(t0.id) AS c3569 +284,1962,176,2,"SELECT t0.region AS c1153, SUM(t0.id) AS c3569 FROM markets AS t0 GROUP BY t0.region ORDER BY t0.region ASC;" -285,20522,379,0,"SELECT employees.id AS c5654, users.id +285,10836,413,0,"SELECT employees.id AS c5654, users.id FROM markets FULL JOIN employees ON markets.id = employees.id CROSS JOIN users WHERE ((markets.note = 'Fast lane' AND employees.department_id NOT BETWEEN 11 AND 37) AND (users.id != 4 AND users.age <= 443));" -286,10308,882,3,"SELECT customers.id, COUNT(customers.id) AS c434, COUNT(*) +286,226143,997,3,"SELECT customers.id, COUNT(customers.id) AS c434, COUNT(*) FROM customers FULL JOIN departments ON customers.id = departments.id WHERE departments.id BETWEEN 2 AND 4 GROUP BY customers.id ORDER BY customers.id DESC;" -287,8435,270,0,"SELECT t2.id, t1.id, t0.id +287,2962,263,5,"SELECT t2.id, t1.id, t0.id FROM books AS t0 JOIN departments AS t1 ON t0.id = t1.id RIGHT JOIN regions AS t2 ON t1.id = t2.id WHERE t0.id IS NULL;" -288,4122,173,1,"SELECT regions.id AS c1673 +288,5327,193,1,"SELECT regions.id AS c1673 FROM users JOIN regions ON users.id = regions.id WHERE (users.age IN (33, 47, 98) OR users.id > 54);" -289,14060,267,3,"SELECT departments.id, books.price AS c551 +289,2493,286,3,"SELECT departments.id, books.price AS c551 FROM departments CROSS JOIN books WHERE departments.id = 5 ORDER BY departments.id DESC;" -290,8660,1278,6,"SELECT t2.region, t1.department_id, t0.customer_id AS c8493 +290,680542,1100,6,"SELECT t2.region, t1.department_id, t0.customer_id AS c8493 FROM orders AS t0 JOIN employees AS t1 ON t0.id = t1.id RIGHT JOIN markets AS t2 ON t1.id = t2.id WHERE t0.customer_id < 239;" -291,23720,4263,10,"SELECT orders.id, COUNT(orders.id), SUM(regions.id) +291,12324520,4720,10,"SELECT orders.id, COUNT(orders.id), SUM(regions.id) FROM users CROSS JOIN orders JOIN regions ON orders.id = regions.id GROUP BY orders.id;" -292,6760,174,3,"SELECT t0.region, t0.id, SUM(t0.id), COUNT(*) +292,1962,172,3,"SELECT t0.region, t0.id, SUM(t0.id), COUNT(*) FROM markets AS t0 GROUP BY t0.region, t0.id ORDER BY t0.region ASC, t0.id DESC;" -293,9122,273,0,"SELECT t0.age, t1.id +293,5692,311,0,"SELECT t0.age, t1.id FROM users AS t0 FULL JOIN books AS t1 ON t0.id = t1.id WHERE (t0.age = 18 AND (t0.age != 123 AND t1.id <= 89));" -294,6688,160,3,"SELECT books.price AS c6589, books.id, COUNT(*) +294,1564,165,3,"SELECT books.price AS c6589, books.id, COUNT(*) FROM books WHERE ((books.price > 77 OR books.id < 3) OR (books.price IN (49, 55, 55, 77) OR books.price IN (26, 66, 66, 68))) GROUP BY books.price, books.id;" -295,8970,187,5,"SELECT users.id, SUM(users.age), COUNT(users.id) +295,6105,182,5,"SELECT users.id, SUM(users.age), COUNT(users.id) FROM users JOIN departments ON users.id = departments.id GROUP BY users.id;" -296,1660,154,3,"SELECT t0.region, t0.id AS c2775, t0.note +296,432,162,3,"SELECT t0.region, t0.id AS c2775, t0.note FROM markets AS t0 ORDER BY t0.id DESC;" -297,13848,1207,22,"SELECT t2.department_id +297,694750,1166,21,"SELECT t2.department_id FROM orders AS t0 JOIN users AS t1 ON t0.id = t1.id FULL JOIN employees AS t2 ON t0.id = t2.id WHERE ((t2.id NOT BETWEEN 4 AND 6 OR t1.age != 21) OR t0.id <= 61);" -298,2098,152,4,"SELECT departments.id +298,988,157,4,"SELECT departments.id FROM departments WHERE ((departments.id NOT IN (4, 4, 5, 7) OR departments.id NOT IN (2, 5)) OR (departments.id NOT BETWEEN 3 AND 27 OR departments.id IS NULL));" -299,8375,263,0,"SELECT books.price, employees.id AS c7128 +299,3273,295,0,"SELECT books.price, employees.id AS c7128 FROM employees RIGHT JOIN books ON employees.id = books.id WHERE ((books.id > 68 OR books.id >= 52) AND (employees.department_id < 6 OR employees.department_id != 1)) ORDER BY books.price DESC;" -300,10775,4138,4965,"SELECT orders.id AS c9121 +300,1276883,3244,4965,"SELECT orders.id AS c9121 FROM orders LEFT JOIN departments ON orders.id = departments.id RIGHT JOIN books ON departments.id = books.id WHERE orders.customer_id <= 497 ORDER BY orders.id ASC;" -301,10170,180,3,"SELECT users.id, COUNT(markets.id), SUM(markets.id) +301,5241,175,3,"SELECT users.id, COUNT(markets.id), SUM(markets.id) FROM users JOIN markets ON users.id = markets.id GROUP BY users.id;" -302,12310,647,500,"SELECT regions.id, customers.region_id, employees.id +302,79260,419,15,"SELECT regions.id, customers.region_id, employees.id FROM regions FULL JOIN employees ON regions.id = employees.id JOIN customers ON regions.id = customers.id ORDER BY customers.region_id ASC, employees.id ASC, regions.id ASC;" -303,1610,151,8,"SELECT employees.id, employees.department_id +303,1832,150,8,"SELECT employees.id, employees.department_id FROM employees WHERE ((employees.id <= 33 OR employees.id = 3) OR (employees.department_id NOT IN (5) AND employees.id = 67));" -304,1220,128,11,"SELECT employees.department_id AS c3770 +304,1342,135,11,"SELECT employees.department_id AS c3770 FROM employees;" -305,2896,164,2,"SELECT regions.id AS c2143, COUNT(*), COUNT(*) +305,2896,173,2,"SELECT regions.id AS c2143, COUNT(*), COUNT(*) FROM regions WHERE regions.id IN (3, 10, 89) GROUP BY regions.id;" -306,8074,195,5,"SELECT employees.id, employees.department_id, SUM(employees.id), SUM(employees.department_id) AS c8963 +306,4531,199,5,"SELECT employees.id, employees.department_id, SUM(employees.id), SUM(employees.department_id) AS c8963 FROM employees JOIN departments ON employees.id = departments.id WHERE (departments.id <= 3 OR (employees.id = 68 OR employees.id <= 6)) GROUP BY employees.id, employees.department_id;" -307,12510,1269,3,"SELECT t0.note AS c4138 +307,678682,1225,3,"SELECT t0.note AS c4138 FROM markets AS t0 JOIN regions AS t1 ON t0.id = t1.id RIGHT JOIN orders AS t2 ON t0.id = t2.id WHERE ((t2.customer_id = 298 OR t0.region = 'AMERICA') OR t0.region != 'AMERICA') GROUP BY t0.note;" -308,4325,166,1,"SELECT departments.id, COUNT(*) AS c9125 +308,1808,156,1,"SELECT departments.id, COUNT(*) AS c9125 FROM departments WHERE (departments.id > 56 OR departments.id = 4) GROUP BY departments.id ORDER BY departments.id DESC;" -309,1632,153,1,"SELECT t0.id AS c7541, COUNT(t0.id) +309,932,163,1,"SELECT t0.id AS c7541, COUNT(t0.id) FROM markets AS t0 WHERE (t0.note != 'Fast lane' AND (t0.id = 3 OR t0.note = 'Old World')) GROUP BY t0.id;" -310,20325,11529,7,"SELECT t1.id, t2.id, SUM(t2.id) +310,7102108,12990,7,"SELECT t1.id, t2.id, SUM(t2.id) FROM customers AS t0 RIGHT JOIN users AS t1 ON t0.id = t1.id LEFT JOIN orders AS t2 ON t0.id = t2.id WHERE (t2.customer_id < 243 OR (t2.id >= 38 AND t2.customer_id = 302)) GROUP BY t1.id, t2.id ORDER BY t2.id ASC;" -311,9432,2641,2,"SELECT t0.region, t1.customer_id AS c7183 +311,1550433,2827,2,"SELECT t0.region, t1.customer_id AS c7183 FROM markets AS t0 RIGHT JOIN orders AS t1 ON t0.id = t1.id WHERE ((t0.id <= 3 OR t0.note = 'Fast lane') AND (t0.note != 'North, South' OR t0.note = 'Fast lane')) ORDER BY t0.region ASC;" -312,11870,1089,5,"SELECT t1.id, t0.id +312,681210,1215,5,"SELECT t1.id, t0.id FROM departments AS t0 LEFT JOIN orders AS t1 ON t0.id = t1.id JOIN employees AS t2 ON t1.id = t2.id;" -313,12504,1024,2,"SELECT employees.id, orders.id AS c4699, orders.customer_id +313,681466,1112,2,"SELECT employees.id, orders.id AS c4699, orders.customer_id FROM departments JOIN orders ON departments.id = orders.id LEFT JOIN employees ON departments.id = employees.id WHERE employees.department_id NOT BETWEEN 5 AND 33;" -314,2264,168,13,"SELECT t0.age, t0.id +314,4228,179,13,"SELECT t0.age, t0.id FROM users AS t0 WHERE (t0.age > 5 AND t0.id != 2) GROUP BY t0.age, t0.id;" -315,6110,185,5,"SELECT t0.id +315,3354,192,5,"SELECT t0.id FROM regions AS t0 JOIN departments AS t1 ON t0.id = t1.id WHERE t1.id IS NOT NULL GROUP BY t0.id;" -316,6320,133,3,"SELECT books.id +316,1896,147,3,"SELECT books.id FROM books GROUP BY books.id;" -317,9220,2702,5000,"SELECT orders.id AS c7996, books.price +317,1550366,2810,5000,"SELECT orders.id AS c7996, books.price FROM books FULL JOIN orders ON books.id = orders.id;" -318,22320,329,4,"SELECT books.price, books.id +318,8406,346,4,"SELECT books.price, books.id FROM books FULL JOIN employees ON books.id = employees.id RIGHT JOIN regions ON employees.id = regions.id GROUP BY books.price, books.id;" -319,2896,146,3,"SELECT books.id AS c9150, books.price, COUNT(books.id) +319,932,163,3,"SELECT books.id AS c9150, books.price, COUNT(books.id) FROM books WHERE ((books.price IN (76, 77) AND books.id BETWEEN 1 AND 96) OR books.price IN (55, 66, 77)) GROUP BY books.id, books.price;" -320,4970,237,11,"SELECT customers.region_id, employees.id +320,71482,247,11,"SELECT customers.region_id, employees.id FROM customers JOIN employees ON customers.id = employees.id;" -321,16824,934,10,"SELECT t0.id AS c6071, COUNT(*), COUNT(*) AS c7241 +321,808000,991,10,"SELECT t0.id AS c6071, COUNT(*), COUNT(*) AS c7241 FROM regions AS t0 CROSS JOIN customers AS t1 WHERE ((t1.id >= 59 OR t0.id IS NULL) AND t1.id = 126) GROUP BY t0.id;" -322,17220,823,500,"SELECT t1.region_id, t1.id, t2.price +322,158466,723,500,"SELECT t1.region_id, t1.id, t2.price FROM regions AS t0 RIGHT JOIN customers AS t1 ON t0.id = t1.id LEFT JOIN books AS t2 ON t0.id = t2.id;" -323,14275,522,3,"SELECT t0.region_id +323,90703,528,3,"SELECT t0.region_id FROM customers AS t0 RIGHT JOIN users AS t1 ON t0.id = t1.id RIGHT JOIN markets AS t2 ON t1.id = t2.id WHERE t1.id < 14 ORDER BY t0.region_id ASC;" -324,12160,2620,3,"SELECT orders.id AS c4187, regions.id +324,680894,1183,3,"SELECT orders.id AS c4187, regions.id FROM departments JOIN orders ON departments.id = orders.id LEFT JOIN regions ON departments.id = regions.id WHERE regions.id < 4;" -325,15822,329,4,"SELECT markets.note AS c7216, books.price +325,2992,402,4,"SELECT markets.note AS c7216, books.price FROM departments CROSS JOIN markets LEFT JOIN books ON markets.id = books.id WHERE ((departments.id != 2 AND markets.note = 'Old World') AND (departments.id BETWEEN 1 AND 5 OR departments.id >= 41));" -326,1220,129,17,"SELECT t0.age +326,2074,142,17,"SELECT t0.age FROM users AS t0;" -327,6760,156,11,"SELECT t0.age +327,11679,179,11,"SELECT t0.age FROM users AS t0 GROUP BY t0.age ORDER BY t0.age ASC;" -328,84498,84707,54310,"SELECT employees.department_id +328,44723198,91834,54310,"SELECT employees.department_id FROM orders CROSS JOIN employees LEFT JOIN regions ON employees.id = regions.id WHERE ((employees.id <= 25 OR orders.id NOT BETWEEN 47 AND 184) OR orders.id IS NULL);" -329,5680,195,3,"SELECT t0.id +329,3583,199,3,"SELECT t0.id FROM markets AS t0 JOIN users AS t1 ON t0.id = t1.id WHERE (t0.id != 2 OR t0.id != 1) ORDER BY t0.id ASC;" -330,11143,1166,0,"SELECT books.price, employees.id, COUNT(*) +330,131853,2259,0,"SELECT books.price, employees.id, COUNT(*) FROM customers LEFT JOIN books ON customers.id = books.id FULL JOIN employees ON customers.id = employees.id WHERE ((customers.id > 96 AND employees.department_id != 32) AND (employees.id > 5 OR books.id IS NOT NULL)) GROUP BY books.price, employees.id ORDER BY books.price DESC, employees.id DESC;" -331,83620,1674673,50490,"SELECT t2.region_id, t2.id +331,1755762000,1746374,50490,"SELECT t2.region_id, t2.id FROM orders AS t0 CROSS JOIN regions AS t1 FULL JOIN customers AS t2 ON t1.id = t2.id;" -332,1610,145,10,"SELECT regions.id AS c7728 +332,1610,133,10,"SELECT regions.id AS c7728 FROM regions WHERE regions.id IS NOT NULL;" -333,22320,304862,6,"SELECT departments.id, COUNT(*) +333,2478160,315148,6,"SELECT departments.id, COUNT(*) FROM orders FULL JOIN departments ON orders.id = departments.id LEFT JOIN customers ON departments.id = customers.id GROUP BY departments.id;" -334,1775,165,10,"SELECT regions.id AS c1480 +334,1775,159,10,"SELECT regions.id AS c1480 FROM regions WHERE regions.id IS NOT NULL ORDER BY regions.id DESC;" -335,1133,144,0,"SELECT books.price, books.id +335,433,148,0,"SELECT books.price, books.id FROM books WHERE (books.price < 44 AND (books.price IN (55, 55, 66, 66) AND books.price != 77)) ORDER BY books.price DESC, books.id DESC;" -336,1122,139,1,"SELECT t0.department_id +336,1222,143,1,"SELECT t0.department_id FROM employees AS t0 WHERE ((t0.department_id = 12 AND t0.department_id < 35) AND (t0.id IN (4) OR t0.department_id != 31));" -337,1488,156,10,"SELECT users.id, users.age +337,2432,152,10,"SELECT users.id, users.age FROM users WHERE users.age IN (1, 21, 33, 64);" -338,1732,149,9,"SELECT t0.id +338,1732,155,9,"SELECT t0.id FROM regions AS t0 WHERE ((t0.id NOT BETWEEN 1 AND 15 OR t0.id < 10) AND (t0.id <= 40 OR t0.id != 3));" -339,1610,151,1,"SELECT departments.id +339,744,145,1,"SELECT departments.id FROM departments WHERE ((departments.id <= 98 OR departments.id > 1) AND (departments.id IS NULL OR departments.id IN (3, 3, 30, 91)));" -340,12160,557,13,"SELECT customers.region_id AS c4276, markets.region AS c7002, COUNT(*), COUNT(*) AS c3968 +340,155932,606,13,"SELECT customers.region_id AS c4276, markets.region AS c7002, COUNT(*), COUNT(*) AS c3968 FROM customers LEFT JOIN markets ON customers.id = markets.id WHERE (markets.note = 'Fast lane' OR customers.id IS NOT NULL) GROUP BY customers.region_id, markets.region;" -341,18070,391,3,"SELECT customers.id, SUM(customers.region_id) +341,76376,408,16,"SELECT customers.id, SUM(customers.region_id) FROM markets JOIN users ON markets.id = users.id RIGHT JOIN customers ON users.id = customers.id GROUP BY customers.id;" -342,1122,140,0,"SELECT employees.department_id, employees.id +342,1222,161,0,"SELECT employees.department_id, employees.id FROM employees WHERE ((employees.id >= 66 OR employees.id != 2) AND (employees.id > 69 AND employees.department_id = 33));" -343,1660,145,17,"SELECT users.age +343,3009,154,17,"SELECT users.age FROM users ORDER BY users.age ASC;" -344,6110,278,3,"SELECT t1.price AS c605, t0.id AS c1651, t1.id +344,2122,341,3,"SELECT t1.price AS c605, t0.id AS c1651, t1.id FROM regions AS t0 RIGHT JOIN books AS t1 ON t0.id = t1.id WHERE (t1.id != 3 OR t1.id = 3);" -345,9660,441,3,"SELECT books.id, customers.id AS c5626 +345,155432,493,3,"SELECT books.id, customers.id AS c5626 FROM books LEFT JOIN customers ON books.id = customers.id ORDER BY customers.id DESC;" -346,84032,439,0,"SELECT t1.region, t2.id, COUNT(t1.id) +346,29564,444,0,"SELECT t1.region, t2.id, COUNT(t1.id) FROM employees AS t0 CROSS JOIN markets AS t1 FULL JOIN regions AS t2 ON t1.id = t2.id WHERE t0.department_id = 36 GROUP BY t1.region, t2.id;" -347,9610,5218,0,"SELECT users.age AS c6973 +347,6452676,5531,0,"SELECT users.age AS c6973 FROM users RIGHT JOIN orders ON users.id = orders.id WHERE users.id > 94;" -348,19356,1399,1187,"SELECT t1.id, t0.id +348,525100,1524,1187,"SELECT t1.id, t0.id FROM departments AS t0 CROSS JOIN customers AS t1 WHERE ((t1.id BETWEEN 28 AND 146 AND t1.id IN (46, 50, 104, 146)) OR (t0.id < 4 AND t1.region_id NOT IN (2, 2, 10, 12))) ORDER BY t1.id DESC;" -349,8825,280,5,"SELECT regions.id, departments.id AS c6050, COUNT(*), COUNT(*) +349,4208,350,5,"SELECT regions.id, departments.id AS c6050, COUNT(*), COUNT(*) FROM regions RIGHT JOIN departments ON regions.id = departments.id WHERE departments.id IS NOT NULL GROUP BY regions.id, departments.id ORDER BY departments.id ASC;" -350,14320,278,11,"SELECT employees.department_id, departments.id AS c3106, COUNT(*) AS c7893 +350,8110,276,11,"SELECT employees.department_id, departments.id AS c3106, COUNT(*) AS c7893 FROM departments RIGHT JOIN employees ON departments.id = employees.id GROUP BY employees.department_id, departments.id;" -351,83766,55158,261,"SELECT t0.customer_id, t2.id AS c6006, t2.age +351,32852932,60056,261,"SELECT t0.customer_id, t2.id AS c6006, t2.age FROM orders AS t0 CROSS JOIN departments AS t1 FULL JOIN users AS t2 ON t1.id = t2.id WHERE (t1.id >= 3 AND (t0.id < 55 OR t0.id < 88));" -352,21794,3404,80,"SELECT t1.customer_id, t1.id, t0.id +352,5464128,3748,80,"SELECT t1.customer_id, t1.id, t0.id FROM departments AS t0 CROSS JOIN orders AS t1 WHERE (t1.customer_id IN (426) OR (t1.customer_id IS NULL OR t0.id >= 57));" -353,1220,237,500,"SELECT t0.id, t0.region_id +353,61000,245,500,"SELECT t0.id, t0.region_id FROM customers AS t0;" -354,2962,167,1,"SELECT t0.department_id, t0.id +354,3760,176,1,"SELECT t0.department_id, t0.id FROM employees AS t0 WHERE (t0.department_id >= 31 AND (t0.department_id > 34 OR t0.id < 33)) GROUP BY t0.department_id, t0.id ORDER BY t0.department_id DESC;" -355,1244,243,2,"SELECT customers.id, customers.region_id +355,62200,362,2,"SELECT customers.id, customers.region_id FROM customers WHERE customers.id IN (153, 183);" -356,17220,320,5,"SELECT employees.department_id, departments.id +356,5326,320,5,"SELECT employees.department_id, departments.id FROM books LEFT JOIN employees ON books.id = employees.id FULL JOIN departments ON employees.id = departments.id;" -357,5424,3425,4914,"SELECT orders.id AS c1456, orders.customer_id, COUNT(*), COUNT(orders.customer_id) +357,2870000,3657,4914,"SELECT orders.id AS c1456, orders.customer_id, COUNT(*), COUNT(orders.customer_id) FROM orders WHERE (orders.id > 86 OR orders.customer_id IS NULL) GROUP BY orders.id, orders.customer_id;" -358,4160,153,0,"SELECT t0.department_id, t0.id AS c8666, COUNT(*) AS c1170, COUNT(*) +358,4260,153,0,"SELECT t0.department_id, t0.id AS c8666, COUNT(*) AS c1170, COUNT(*) FROM employees AS t0 WHERE t0.id IS NULL GROUP BY t0.department_id, t0.id;" -359,6320,149,10,"SELECT regions.id, COUNT(regions.id) AS c1004, COUNT(regions.id) AS c9498 +359,6320,159,10,"SELECT regions.id, COUNT(regions.id) AS c1004, COUNT(regions.id) AS c9498 FROM regions GROUP BY regions.id;" -360,1220,137,3,"SELECT t0.id, t0.note AS c1949, t0.region +360,366,145,3,"SELECT t0.id, t0.note AS c1949, t0.region FROM markets AS t0;" -361,649400,49930,11,"SELECT t2.department_id, COUNT(t2.id), SUM(t0.id) +361,178001600,53741,11,"SELECT t2.department_id, COUNT(t2.id), SUM(t0.id) FROM departments AS t0 CROSS JOIN orders AS t1 CROSS JOIN employees AS t2 GROUP BY t2.department_id;" -362,88720,780221,3,"SELECT markets.id AS c6682, markets.note AS c3493 +362,785551896,851162,3,"SELECT markets.id AS c6682, markets.note AS c3493 FROM orders CROSS JOIN customers RIGHT JOIN markets ON customers.id = markets.id GROUP BY markets.id, markets.note;" -363,1775,156,3,"SELECT books.price +363,433,159,3,"SELECT books.price FROM books WHERE books.price IS NOT NULL ORDER BY books.price DESC;" -364,1288,261,44,"SELECT customers.region_id AS c2372 +364,69900,242,44,"SELECT customers.region_id AS c2372 FROM customers WHERE customers.region_id IN (4, 89) ORDER BY customers.region_id DESC;" -365,13135,2618,3,"SELECT t0.id AS c4468, t1.customer_id, t0.region +365,831472,1381,3,"SELECT t0.id AS c4468, t1.customer_id, t0.region FROM markets AS t0 FULL JOIN orders AS t1 ON t0.id = t1.id JOIN customers AS t2 ON t0.id = t2.id WHERE t0.id <= 17;" -366,1366,153,1,"SELECT users.id, users.age +366,2310,178,1,"SELECT users.id, users.age FROM users WHERE (users.id = 15 OR (users.id != 3 AND users.id IS NULL));" -367,1220,141,17,"SELECT t0.age +367,2074,151,17,"SELECT t0.age FROM users AS t0;" -368,21794,262,49,"SELECT t0.id, t1.age +368,12062,286,49,"SELECT t0.id, t1.age FROM books AS t0 CROSS JOIN users AS t1 WHERE (t1.id = 96 OR (t1.age != 18 OR t0.price <= 61));" -369,3332,1069,0,"SELECT t0.id AS c4185, SUM(t0.id) +369,850932,1083,0,"SELECT t0.id AS c4185, SUM(t0.id) FROM markets AS t0 LEFT JOIN orders AS t1 ON t0.id = t1.id WHERE ((t0.id BETWEEN 1 AND 7 OR t0.note != 'North, South') AND (t0.note != 'Old World' AND t0.note IN ('ASIA', 'Old World', 'unknown'))) GROUP BY t0.id;" -370,1610,145,2,"SELECT markets.note AS c2495, markets.id, markets.region +370,422,154,2,"SELECT markets.note AS c2495, markets.id, markets.region FROM markets WHERE ((markets.note = 'North, South' AND markets.note != 'North, South') OR markets.note != 'Fast lane');" -371,14600,283,110,"SELECT regions.id +371,15960,288,110,"SELECT regions.id FROM regions CROSS JOIN employees;" -372,1930,1577,3214,"SELECT t0.id +372,1293750,1736,3214,"SELECT t0.id FROM orders AS t0 WHERE (t0.id <= 95 OR t0.customer_id BETWEEN 55 AND 369) ORDER BY t0.id DESC;" -373,6320,361,500,"SELECT customers.id, COUNT(customers.region_id) +373,316000,380,500,"SELECT customers.id, COUNT(customers.region_id) FROM customers GROUP BY customers.id;" -374,36830,5349,0,"SELECT orders.id AS c9907, COUNT(*) AS c8627 +374,9978000,5935,0,"SELECT orders.id AS c9907, COUNT(*) AS c8627 FROM departments CROSS JOIN orders WHERE ((orders.id >= 79 OR orders.customer_id IN (63, 185, 226, 480)) AND departments.id IS NULL) GROUP BY orders.id ORDER BY orders.id ASC;" -375,1854,1405,4999,"SELECT t0.customer_id AS c3627, t0.id AS c1197 +375,957500,1679,4999,"SELECT t0.customer_id AS c3627, t0.id AS c1197 FROM orders AS t0 WHERE (t0.customer_id <= 56 OR t0.id > 1);" -376,18098,331,9,"SELECT t1.id, t0.id, t1.department_id +376,10438,379,9,"SELECT t1.id, t0.id, t1.department_id FROM regions AS t0 FULL JOIN employees AS t1 ON t0.id = t1.id FULL JOIN departments AS t2 ON t0.id = t2.id WHERE t0.id NOT IN (3);" -377,1643,469,341,"SELECT customers.id, customers.region_id, SUM(customers.region_id), SUM(customers.id) +377,98212,508,341,"SELECT customers.id, customers.region_id, SUM(customers.region_id), SUM(customers.id) FROM customers WHERE ((customers.region_id != 9 OR customers.id = 20) AND (customers.region_id >= 3 AND customers.id > 5)) GROUP BY customers.id, customers.region_id ORDER BY customers.id DESC, customers.region_id ASC;" -378,6320,130,3,"SELECT markets.region, markets.id AS c5516 +378,1896,155,3,"SELECT markets.region, markets.id AS c5516 FROM markets GROUP BY markets.region, markets.id;" -379,1220,117,11,"SELECT employees.department_id, employees.id +379,1342,142,11,"SELECT employees.department_id, employees.id FROM employees;" -380,2494,2285,4329,"SELECT orders.customer_id +380,1692500,2838,4329,"SELECT orders.customer_id FROM orders WHERE (orders.id < 86 OR (orders.customer_id IN (21, 71) OR orders.customer_id NOT BETWEEN 371 AND 436)) ORDER BY orders.customer_id ASC;" -381,18500,308,34,"SELECT t0.age AS c6869, t0.id +381,10354,279,34,"SELECT t0.age AS c6869, t0.id FROM users AS t0 CROSS JOIN markets AS t1 WHERE t1.note != 'Old World';" -382,6056,163,3,"SELECT t0.id AS c3548, t0.note, SUM(t0.id) AS c5991, COUNT(*) +382,1564,211,3,"SELECT t0.id AS c3548, t0.note, SUM(t0.id) AS c5991, COUNT(*) FROM markets AS t0 WHERE ((t0.note IN ('Fast lane', 'Fast lane', 'Old World') OR t0.region = 'AMERICA') OR t0.id NOT IN (1, 2, 3)) GROUP BY t0.id, t0.note;" -383,178400,10350,8500,"SELECT customers.id, customers.region_id +383,14597700,11406,8500,"SELECT customers.id, customers.region_id FROM regions CROSS JOIN customers CROSS JOIN users WHERE regions.id < 2;" -384,433400,370,9,"SELECT markets.id, books.id, COUNT(markets.id), COUNT(markets.id) +384,42520,429,9,"SELECT markets.id, books.id, COUNT(markets.id), COUNT(markets.id) FROM regions CROSS JOIN books CROSS JOIN markets WHERE regions.id != 5 GROUP BY markets.id, books.id;" -385,1366,133,0,"SELECT t0.id AS c9611, t0.age AS c4657 +385,2432,189,0,"SELECT t0.id AS c9611, t0.age AS c4657 FROM users AS t0 WHERE ((t0.id >= 3 OR t0.id IS NULL) AND t0.age IS NULL);" -386,17220,2250,3,"SELECT markets.note, markets.region AS c394 +386,1553776,2370,3,"SELECT markets.note, markets.region AS c394 FROM markets LEFT JOIN orders ON markets.id = orders.id LEFT JOIN employees ON orders.id = employees.id;" -387,2922,304,0,"SELECT employees.id AS c1393 +387,1832,254,0,"SELECT employees.id AS c1393 FROM employees LEFT JOIN books ON employees.id = books.id WHERE ((employees.id IS NULL AND books.id IS NOT NULL) AND (books.price = 55 AND employees.department_id = 12));" -388,14600,10525,25000,"SELECT orders.customer_id AS c3285 +388,3650500,11472,25000,"SELECT orders.customer_id AS c3285 FROM orders CROSS JOIN departments;" -389,13135,2093,2,"SELECT employees.id AS c6990 +389,679882,1202,2,"SELECT employees.id AS c6990 FROM orders LEFT JOIN markets ON orders.id = markets.id JOIN employees ON orders.id = employees.id WHERE markets.id < 3;" -390,22600,1888,5000,"SELECT markets.note AS c1438, customers.region_id, markets.id AS c7869 +390,160080,1984,5000,"SELECT markets.note AS c1438, customers.region_id, markets.id AS c7869 FROM customers LEFT JOIN markets ON customers.id = markets.id CROSS JOIN regions;" -391,14760,2533,5,"SELECT orders.customer_id AS c6961, departments.id +391,2253325,2732,5,"SELECT orders.customer_id AS c6961, departments.id FROM departments LEFT JOIN orders ON departments.id = orders.id GROUP BY orders.customer_id, departments.id ORDER BY departments.id DESC;" -392,88055,535,11,"SELECT employees.department_id AS c8803, SUM(users.id), SUM(employees.id) +392,63126,581,11,"SELECT employees.department_id AS c8803, SUM(users.id), SUM(employees.id) FROM users CROSS JOIN employees LEFT JOIN markets ON employees.id = markets.id WHERE ((markets.id >= 18 OR users.id = 4) OR employees.id >= 2) GROUP BY employees.department_id ORDER BY employees.department_id ASC;" -393,13620,498913,384231,"SELECT orders.customer_id, customers.id +393,291050000,530380,384231,"SELECT orders.customer_id, customers.id FROM orders CROSS JOIN customers WHERE (orders.id IS NOT NULL AND (orders.customer_id <= 190 AND customers.region_id IN (1, 5, 8, 9)));" -394,1732,136,8,"SELECT t0.id +394,1832,168,8,"SELECT t0.id FROM employees AS t0 WHERE t0.department_id NOT IN (6, 11, 32, 94);" -395,14600,10322,15000,"SELECT orders.id, orders.customer_id, books.price AS c2061 +395,2390300,11567,15000,"SELECT orders.id, orders.customer_id, books.price AS c2061 FROM orders CROSS JOIN books;" -396,1610,148,0,"SELECT markets.region, markets.id, markets.note +396,422,160,0,"SELECT markets.region, markets.id, markets.note FROM markets WHERE markets.region IS NULL;" -397,6320,392,500,"SELECT t0.id, t0.region_id, SUM(t0.region_id) +397,316000,404,500,"SELECT t0.id, t0.region_id, SUM(t0.region_id) FROM customers AS t0 GROUP BY t0.id, t0.region_id;" -398,4626,240,10,"SELECT regions.id, customers.id, customers.region_id +398,70776,252,10,"SELECT regions.id, customers.id, customers.region_id FROM regions JOIN customers ON regions.id = customers.id WHERE (customers.id NOT BETWEEN 18 AND 150 OR regions.id IN (1, 9, 10, 63));" -399,4325,156,1,"SELECT departments.id AS c2818, COUNT(departments.id), COUNT(departments.id) +399,1808,162,1,"SELECT departments.id AS c2818, COUNT(departments.id), COUNT(departments.id) FROM departments WHERE departments.id <= 1 GROUP BY departments.id diff --git a/src/stewkk/sql/logic/optimizer/schema_catalog.cpp b/src/stewkk/sql/logic/optimizer/schema_catalog.cpp index 1a10aba..3c75ca6 100644 --- a/src/stewkk/sql/logic/optimizer/schema_catalog.cpp +++ b/src/stewkk/sql/logic/optimizer/schema_catalog.cpp @@ -113,4 +113,30 @@ SchemaCatalog LoadSchemaFromCsvDir(const std::filesystem::path& dir) { return SchemaCatalog{std::move(tables)}; } +std::unordered_map LoadTableSizesFromCsvDir( + const std::filesystem::path& dir) { + std::unordered_map sizes; + if (!std::filesystem::is_directory(dir)) { + return sizes; + } + + static const std::regex kBench{R"(_\d+$)"}; + for (const auto& entry : std::filesystem::directory_iterator{dir}) { + if (entry.path().extension() != ".csv") continue; + auto stem = entry.path().stem().string(); + if (std::regex_search(stem, kBench)) continue; + + std::ifstream in{entry.path()}; + std::string line; + if (!std::getline(in, line)) continue; // skip header + + std::int64_t rows = 0; + while (std::getline(in, line)) { + if (!line.empty()) ++rows; + } + sizes.emplace(std::move(stem), rows); + } + return sizes; +} + } // namespace stewkk::sql diff --git a/src/stewkk/sql/main.cpp b/src/stewkk/sql/main.cpp index 42e445b..f13e12d 100644 --- a/src/stewkk/sql/main.cpp +++ b/src/stewkk/sql/main.cpp @@ -253,8 +253,9 @@ int main(int argc, char** argv) { PropertySet required = parsed.required_order ? PropertySet{SortProperty{*parsed.required_order}} : PropertySet::Any(); - Optimizer optimizer(parsed.op, MakeMainRules(), {}, LoadSchemaFromCsvDir(args.data_dir), - std::move(required)); + Optimizer optimizer(parsed.op, MakeMainRules(), + CardinalityEstimates{LoadTableSizesFromCsvDir(args.data_dir)}, + LoadSchemaFromCsvDir(args.data_dir), std::move(required)); plan = optimizer.Optimize(); plan_cost = optimizer.GetBestCost(); } catch (const std::exception& e) { From 2b63f14682b1504b60598def3aebe41212a3240b Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Tue, 2 Jun 2026 12:18:25 +0300 Subject: [PATCH 078/120] Fix --- src/stewkk/sql/logic/optimizer/rule_test.cpp | 15 +++++++++++++++ .../transformation_rules/join_associativity.cpp | 5 ++++- 2 files changed, 19 insertions(+), 1 deletion(-) diff --git a/src/stewkk/sql/logic/optimizer/rule_test.cpp b/src/stewkk/sql/logic/optimizer/rule_test.cpp index da720d4..269f88e 100644 --- a/src/stewkk/sql/logic/optimizer/rule_test.cpp +++ b/src/stewkk/sql/logic/optimizer/rule_test.cpp @@ -81,4 +81,19 @@ TEST_F(JoinAssociativityTest, ReturnsCorrectExpression) { EXPECT_EQ(outer.qual, Expression{Literal::kTrue}); } +TEST_F(JoinAssociativityTest, DoesNotApplyWhenInnerJoinIsOuter) { + auto outer_ab = memo.AddGroup(logical::Join{a, b, JoinType::kFull, Literal::kTrue})->group; + auto expr = memo.AddGroup( + logical::Join{outer_ab, c, JoinType::kInner, Literal::kTrue}); + + EXPECT_FALSE(rule.IsApplicable(expr)); +} + +TEST_F(JoinAssociativityTest, DoesNotApplyWhenOuterJoinIsOuter) { + auto expr = memo.AddGroup( + logical::Join{ab, c, JoinType::kFull, Literal::kTrue}); + + EXPECT_FALSE(rule.IsApplicable(expr)); +} + } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp b/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp index 0e85871..c5a0e47 100644 --- a/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp +++ b/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp @@ -11,8 +11,10 @@ namespace stewkk::sql { bool JoinAssociativity::IsApplicable(utils::NotNull expr) { if (!std::holds_alternative(expr->root_operator)) return false; const auto& outer = std::get(expr->root_operator); + if (outer.type != JoinType::kInner) return false; for (auto inner_expr : outer.lhs->GetLogicalExprs()) { - if (std::holds_alternative(inner_expr->root_operator)) return true; + const auto* inner = std::get_if(&inner_expr->root_operator); + if (inner != nullptr && inner->type == JoinType::kInner) return true; } return false; } @@ -22,6 +24,7 @@ LogicalOperator JoinAssociativity::ApplyImpl(utils::NotNull expr, for (auto inner_expr : outer.lhs->GetLogicalExprs()) { if (!std::holds_alternative(inner_expr->root_operator)) continue; const auto& inner = std::get(inner_expr->root_operator); + if (outer.type != JoinType::kInner || inner.type != JoinType::kInner) continue; auto bc_tables = GroupTables(inner.rhs); auto c_tables = GroupTables(outer.rhs); From 299bcd155236ebc940c91ba67e3fd740485e3159 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Tue, 2 Jun 2026 12:23:21 +0300 Subject: [PATCH 079/120] Fix --- research/fuzz/diff_fuzz.py | 4 +++- research/fuzz/test_diff_fuzz.py | 24 ++++++++++++++++++++++++ 2 files changed, 27 insertions(+), 1 deletion(-) create mode 100644 research/fuzz/test_diff_fuzz.py diff --git a/research/fuzz/diff_fuzz.py b/research/fuzz/diff_fuzz.py index f631f0b..1429b59 100644 --- a/research/fuzz/diff_fuzz.py +++ b/research/fuzz/diff_fuzz.py @@ -104,8 +104,10 @@ def _check_order(query: SelectQuery, our_rows: list[str]) -> str | None: cur = [(_typed(cells[i]), direction) for i, direction in keys if i < len(cells)] if prev is not None: for (va, da), (vb, _) in zip(prev, cur): - if va is None or vb is None or va == vb: + if va == vb: continue + if va is None or vb is None: + break less = va < vb if da == "desc": less = not less diff --git a/research/fuzz/test_diff_fuzz.py b/research/fuzz/test_diff_fuzz.py new file mode 100644 index 0000000..ca3af52 --- /dev/null +++ b/research/fuzz/test_diff_fuzz.py @@ -0,0 +1,24 @@ +from research.fuzz.diff_fuzz import _check_order +from research.query_generator import Attr, SelectQuery, TableScan, Target + + +def _query() -> SelectQuery: + users_id = Attr("users", "id") + regions_id = Attr("regions", "id") + return SelectQuery( + targets=[Target(users_id), Target(regions_id)], + from_=TableScan("users"), + order_by=[(users_id, "desc"), (regions_id, "desc")], + ) + + +def test_check_order_does_not_use_later_key_after_null_difference() -> None: + assert _check_order(_query(), ["10\t10", "1\t1", "NULL\t9"]) is None + + +def test_check_order_uses_later_key_when_earlier_null_keys_are_equal() -> None: + assert _check_order(_query(), ["NULL\t1", "NULL\t9"]) is not None + + +def test_check_order_rejects_non_null_ordering_violation() -> None: + assert _check_order(_query(), ["10\t10", "1\t1", "5\t5"]) is not None From 3d4a815e5a9ae130add15b26586bddee49ecdc01 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Tue, 2 Jun 2026 12:30:28 +0300 Subject: [PATCH 080/120] Fix --- src/stewkk/sql/logic/optimizer/optimizer.cpp | 12 ++---- .../sql/logic/optimizer/optimizer_test.cpp | 38 +++++++++++++++++++ 2 files changed, 42 insertions(+), 8 deletions(-) diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index c76cf2f..e9fc4c1 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -32,12 +32,8 @@ PropertySet RequiredInputProps(utils::NotNull expr, [&](const physical::SeqScan&) { return PropertySet::Any(); }, [&](const physical::Filter&) { return required; }, [&](const physical::Projection&) { return required; }, - [&](const physical::NestedLoopJoin&) -> PropertySet { - return child_index == 0 ? required : PropertySet::Any(); - }, - [&](const physical::NestedLoopCrossJoin&) -> PropertySet { - return child_index == 0 ? required : PropertySet::Any(); - }, + [&](const physical::NestedLoopJoin&) { return PropertySet::Any(); }, + [&](const physical::NestedLoopCrossJoin&) { return PropertySet::Any(); }, [&](const physical::HashJoin&) -> PropertySet { return PropertySet::Any(); }, @@ -52,8 +48,8 @@ PropertySet DeriveOutputProps(utils::NotNull expr, [&](const physical::SeqScan&) { return PropertySet::Any(); }, [&](const physical::Filter&) { return child_delivered[0]; }, [&](const physical::Projection&) { return child_delivered[0]; }, - [&](const physical::NestedLoopJoin&) { return child_delivered[0]; }, - [&](const physical::NestedLoopCrossJoin&) { return child_delivered[0]; }, + [&](const physical::NestedLoopJoin&) { return PropertySet::Any(); }, + [&](const physical::NestedLoopCrossJoin&) { return PropertySet::Any(); }, [&](const physical::HashJoin&) { return PropertySet::Any(); }, [&](const physical::Sort& s) { return PropertySet{SortProperty{s.keys}}; }, [&](const physical::Aggregation&) { return PropertySet::Any(); }, diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index 7d44fc9..e87784b 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -141,6 +141,44 @@ TEST(OptimizerTest, OrderBy) { ASSERT_THAT(Serialize(got), Eq("(Sort (keys users.id Asc) (SeqScan users))")); } +TEST(OptimizerTest, OrderBySortsAfterCrossJoin) { + std::stringstream s{ + "SELECT * FROM departments CROSS JOIN orders ORDER BY departments.id;"}; + auto parsed = GetAST(s).value(); + SchemaCatalog schema({ + {"departments", {Attribute{"departments", "id"}}}, + {"orders", {Attribute{"orders", "id"}}}, + }); + PropertySet required{SortProperty{*parsed.required_order}}; + Optimizer optimizer(parsed.op, MakeMainRules(), {}, std::move(schema), std::move(required)); + + auto got = optimizer.Optimize(); + + ASSERT_THAT( + Serialize(got), + Eq("(Sort (keys departments.id Asc)" + " (NestedLoopCrossJoin (SeqScan departments) (SeqScan orders)))")); +} + +TEST(OptimizerTest, OrderBySortsAfterNestedLoopJoin) { + std::stringstream s{ + "SELECT * FROM departments JOIN orders ON departments.id < orders.id " + "ORDER BY departments.id;"}; + auto parsed = GetAST(s).value(); + SchemaCatalog schema({ + {"departments", {Attribute{"departments", "id"}}}, + {"orders", {Attribute{"orders", "id"}}}, + }); + PropertySet required{SortProperty{*parsed.required_order}}; + Optimizer optimizer(parsed.op, MakeMainRules(), {}, std::move(schema), std::move(required)); + + auto got = optimizer.Optimize(); + + ASSERT_THAT( + Serialize(got), + HasSubstr("(Sort (keys departments.id Asc) (NestedLoopJoin Inner")); +} + TEST(OptimizerTest, AliasedJoinOptimizesWithAliasQualifiedAttrs) { std::stringstream s{"SELECT c.id FROM customers AS c JOIN orders AS o ON c.id = o.customer_id WHERE c.region_id = 1;"}; Operator op = GetAST(s).value().op; From 7688ae2e5f192b56cbe1c8a4967fbd468a2aa4e7 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sat, 6 Jun 2026 00:23:14 +0300 Subject: [PATCH 081/120] Add report draft --- .gitignore | 1 + report/Makefile | 2 +- report/vkr.tex | 2658 +++++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 2660 insertions(+), 1 deletion(-) create mode 100644 report/vkr.tex diff --git a/.gitignore b/.gitignore index b1ab3b0..fa471f8 100644 --- a/.gitignore +++ b/.gitignore @@ -17,3 +17,4 @@ FlameGraph/ /benchmarks/datasets/ssb/raw/ /.plans/ /report/build-practice/ +/report/build-vkr/ diff --git a/report/Makefile b/report/Makefile index c99d8c0..7258f3b 100644 --- a/report/Makefile +++ b/report/Makefile @@ -3,7 +3,7 @@ all: build build: - @latexmk -f -pdf -output-directory=build-practice -shell-escape ./practice.tex + @latexmk -f -pdf -output-directory=build-vkr -shell-escape ./vkr.tex bib: @cp ./biblio.bib ./build diff --git a/report/vkr.tex b/report/vkr.tex new file mode 100644 index 0000000..c75cf16 --- /dev/null +++ b/report/vkr.tex @@ -0,0 +1,2658 @@ +% !TeX TXS-program:bibliography = txs:///biber +% chktex-file 1 +\documentclass[fontsize=14pt, russian]{scrartcl} +\input{header.tex} +\addbibresource{biblio.bib} + +\usepackage{svg} + +\begin{document} +\sloppy + +\def\figurename{Рисунок} + +\newcommand{\sqlkw}[1]{\texttt{#1}} +\newcommand{\SELECT}{\sqlkw{SELECT}} +\newcommand{\FROM}{\sqlkw{FROM}} +\newcommand{\WHERE}{\sqlkw{WHERE}} +\newcommand{\JOIN}{\sqlkw{JOIN}} +\newcommand{\GROUPBY}{\sqlkw{GROUP BY}} +\newcommand{\ORDERBY}{\sqlkw{ORDER BY}} + +\newcommand{\COUNT}{\sqlkw{COUNT}} +\newcommand{\SUM}{\sqlkw{SUM}} +\newcommand{\AVG}{\sqlkw{AVG}} +\newcommand{\MIN}{\sqlkw{MIN}} +\newcommand{\MAX}{\sqlkw{MAX}} + +\newcommand{\LogicalGet}{\sqlkw{LogicalGet}} +\newcommand{\SeqScan}{\sqlkw{SeqScan}} +\newcommand{\IndexScan}{\sqlkw{IndexScan}} +\newcommand{\NestedLoopJoin}{\sqlkw{NestedLoopJoin}} +\newcommand{\NestedLoopCrossJoin}{\sqlkw{NestedLoopCrossJoin}} +\newcommand{\HashJoin}{\sqlkw{HashJoin}} +\newcommand{\MergeJoin}{\sqlkw{MergeJoin}} +\newcommand{\Sort}{\sqlkw{Sort}} +\newcommand{\FilterOp}{\sqlkw{Filter}} +\newcommand{\ProjOp}{\sqlkw{Projection}} + +\newcommand{\SelOp}[1]{\sigma_{#1}} +\newcommand{\ProjectionOp}[1]{\pi_{#1}} + +\newcommand{\Sel}[2]{\sigma_{#1}(#2)} +\newcommand{\Projection}[2]{\pi_{#1}(#2)} +\newcommand{\Agg}[3]{\gamma_{#1;\,#2}(#3)} +\newcommand{\AggP}[3]{\gamma_{#1;\,#2}^{partial}(#3)} +\newcommand{\NJoin}[2]{#1 \bowtie #2} +\newcommand{\InnerJoin}[3]{#1 \bowtie_{#2} #3} +\newcommand{\CProd}[2]{#1 \times #2} +\newcommand{\LOJ}[3]{#1 \ltimes_{#2} #3} +\newcommand{\ROJ}[3]{#1 \rtimes_{#2} #3} +\newcommand{\FOJ}[3]{#1 \mathbin{\ltimes\!\!\!\rtimes}_{#2} #3} +\newcommand{\Tr}[1]{[\![ #1 ]\!]} +\newcommand{\Apply}[2]{#1 \mathbin{\mathcal{A}} #2} +\newcommand{\TransRule}[3]{% + \ensuremath{% + \begin{array}{c} + #1 \\[1pt] + \xRightarrow{\,#2\,} \\[1pt] + #3 + \end{array}% + }% +} + +\setlength{\tabcolsep}{3pt} +\setcounter{page}{2} +%---------------------------------------------------------------------------- +% ОТСЮДА --- СОБСТВЕННО ТЕКСТ +%---------------------------------------------------------------------------- +\section*{АННОТАЦИЯ} + +Работа посвящена разработке оптимизатора SQL-запросов для модельной реляционной +СУБД. Цель работы состоит в реализации оптимизатора на основе архитектуры +Cascades и создании основы для его дальнейшего улучшения за счет сопоставления +порождаемых физических планов с планами промышленных систем. + +В ходе исследования рассмотрены архитектуры оптимизаторов System~R, Volcano и +Cascades, формализовано поддерживаемое подмножество SQL и соответствующее ему +подмножество реляционной алгебры. При реализации использованы структура Memo, +правила логических преобразований и физических реализаций, стоимостная модель, +учет требуемых физических свойств, а также метод ветвей и границ для отсечения +неперспективных вариантов планов. Для оценки работы системы применены модульное +тестирование, генерация SQL-запросов, бенчмарки физических операторов и +дифференциальное сравнение планов с Microsoft SQL Server. + +В результате разработана модельная СУБД, работающая с CSV-файлами и +поддерживающая основные операторы реляционной алгебры, а также расширяемый +оптимизатор, способный строить физические планы с учетом стоимости и физических +свойств результата. Получены стоимостные формулы операторов, подтвержден прирост +производительности относительно наивного плана и подготовлены инструменты для +поиска случаев, в которых пространство поиска оптимизатора требует расширения. +Практическая значимость работы заключается в возможности использовать +реализованную систему как основу для экспериментального исследования методов +оптимизации запросов и итеративного развития набора правил оптимизатора. + +\newpage +\renewcommand\contentsname{\hfill{\normalfont{СОДЕРЖАНИЕ}}\hfill} +\tableofcontents +\newpage +\anonsection{ВВЕДЕНИЕ} + +В эпоху стремительного роста объемов данных системы управления базами данных +(СУБД) являются важной частью информационных систем. По данным аналитических +исследований, объем мирового рынка СУБД ежегодно увеличивается, что обусловлено +как расширением круга решаемых задач, так и усложнением структуры обрабатываемых +данных и увеличением их объема. Реляционные базы данных, работающие с различными +диалектами языка SQL, продолжают занимать доминирующее положение в сегменте +обработки транзакций и аналитических запросов. + +Одним из определяющих факторов конкурентоспособности между различными +реализациями СУБД является качество оптимизатора запросов. Оптимизатор +представляет собой компонент, ответственный за преобразование декларативного +SQL-запроса в эффективный физический план исполнения. Именно от качества работы +оптимизатора в значительной степени зависит производительность всей системы: +выбор неоптимального плана исполнения может привести к увеличению времени +выполнения запроса в десятки раз относительно оптимального случая. + +Также оптимизатор запросов считается наиболее сложной составной частью любой +СУБД. Задача нахождения оптимального плана является NP-полной в общем случае, +что обуславливает необходимость применения эвристических методов и алгоритмов +ограниченного перебора. Современные коммерческие и открытые системы используют +различные архитектуры оптимизаторов, среди них наиболее популярны устоявшиеся и +проверенные временем архитектуры Cascades и Volcano. + +Архитектура Cascades используется в таких промышленных системах, как Microsoft +SQL Server, Apache Orca, а также в ряде исследовательских проектов, включая +Columbia и CockroachDB\@. Преимущества этой архитектуры заключаются в +расширяемости, модульности, а также понятном и эффективном алгоритме поиска, +допускающим параллельность исполнения. + +Целью данной работы является разработка и реализация оптимизатора подмножества +SQL-запросов на основе архитектуры Cascades, а также создание каркаса для его +итеративного улучшения путем сравнения с промышленными реализациями. + +Для достижения поставленной цели необходимо решить следующие задачи: +\begin{itemize} + \item изучить существующие архитектуры оптимизаторов запросов, включая + System~R, Volcano и Cascades; + \item формализовать подмножество SQL и соответствующее подмножество + реляционной алгебры; + \item реализовать структуру данных Memo и алгоритм поиска оптимального плана + на основе архитектуры Cascades; + \item реализовать набор правил трансформации и реализации для основных + операторов реляционной алгебры; + \item реализовать метод ветвей и границ для эффективного отсечения + неоптимальных планов; + \item реализовать метод дифференциального анализа физических планов для + автоматизированного поиска примеров неоптимальных планов. +\end{itemize} + +\section{Архитектура СУБД} + +Система управления базами данных представляет собой программный комплекс, +обеспечивающий хранение, извлечение и модификацию структурированных +данных. Несмотря на разнообразие существующих реализаций, архитектура +большинства реляционных СУБД включает ряд типовых компонентов, +взаимодействие которых обеспечивает выполнение пользовательских запросов. + +К основным компонентам СУБД относятся~\refImage{fig::dbms_arch}: + +\begin{itemize} + \item транспортный уровень, принимающий клиентские подключения и запросы; + \item синтаксический анализатор (парсер), выполняющий разбор текста +SQL-запроса и построение синтаксического дерева; + \item оптимизатор запросов, преобразующий логическое представление запроса в +эффективный физический план исполнения; + \item исполнитель, непосредственно реализующий физический план; + \item менеджер хранения данных, управляющий размещением данных на физических +носителях; + \item менеджер транзакций и менеджер блокировок, обеспечивающие гарантии +транзакций; + \item менеджер страниц памяти, отвечающий за кэширование страниц данных в +оперативной памяти. +\end{itemize} + +\begin{figure}[!htb]\centering +\usetikzlibrary{fit} +\begin{tikzpicture}[ + box/.style={rectangle, draw, minimum width=4.1cm, minimum height=1cm, + align=center, font=\small}, + wbox/.style={rectangle, draw, minimum width=9.1cm, minimum height=1cm, + align=center, font=\small}, + grp/.style={draw, dashed, inner sep=0.3cm}, + lbl/.style={font=\small}, +] + \node[box] (clust) at (-2.5, 0) {Кластерные\\соединения}; + \node[box] (client) at ( 2.5, 0) {Клиентские\\соединения}; + \node[grp, fit=(clust)(client)] (TG) {}; + \node[lbl, anchor=south west] at (TG.north west) {Транспорт}; + + \node[wbox] (parser) at (0, -2.8) {Синтаксический анализатор}; + \node[wbox, fill=gray!20] (optim) at (0, -4.05) {\textbf{Оптимизатор запросов}}; + \node[grp, fit=(parser)(optim)] (QPG) {}; + \node[lbl, anchor=south west] at (QPG.north west) {Обработчик запросов}; + + \node[box] (remote) at (-2.5, -6.85) {Удалённое\\исполнение}; + \node[box] (local) at ( 2.5, -6.85) {Локальное\\исполнение}; + \node[grp, fit=(remote)(local)] (EEG) {}; + \node[lbl, anchor=south west] at (EEG.north west) {Исполнитель}; + + \node[box] (txmgr) at (-2.5, -9.65) {Менеджер\\транзакций}; + \node[box] (lockmgr) at ( 2.5, -9.65) {Менеджер\\блокировок}; + \node[wbox] (access) at ( 0, -10.9) {Методы доступа}; + \node[box] (bufmgr) at (-2.5, -12.15) {Менеджер\\страниц}; + \node[box] (recmgr) at ( 2.5, -12.15) {Менеджер\\восстановления}; + \node[grp, fit=(txmgr)(lockmgr)(access)(bufmgr)(recmgr)] (SEG) {}; + \node[lbl, anchor=south west] at (SEG.north west) {Подсистема хранения}; + + \draw[<->, thick, >=stealth] + ([xshift=0.4cm]TG.south east) -- ([xshift=0.4cm]QPG.north east); + \draw[<->, thick, >=stealth] + ([xshift=0.4cm]QPG.south east) -- ([xshift=0.4cm]EEG.north east); + \draw[<->, thick, >=stealth] + ([xshift=0.4cm]EEG.south east) -- ([xshift=0.4cm]SEG.north east); + + \draw[thick] ([xshift=-0.5cm]clust.west) -- ([xshift=-0.5cm]remote.west); + \draw[->, thick, >=stealth] ([xshift=-0.5cm]clust.west) -- (clust.west); + \draw[->, thick, >=stealth] ([xshift=-0.5cm]remote.west) -- (remote.west); +\end{tikzpicture} + \caption{Архитектура реляционной системы управления базами данных}% + \label{fig::dbms_arch} +\end{figure} + +\subsection{Процесс исполнения SQL-запроса} + +Процесс исполнения SQL-запроса в реляционной СУБД проходит несколько +последовательных этапов. + +\begin{enumerate} + \item \emph{Синтаксический анализ} --- запроса проходит лексический и + синтаксический разбор. Результатом данного этапа является синтаксическое + дерево, узлы которого соответствуют конструкциям языка SQL: операторам + \SELECT, \FROM, \WHERE, \JOIN, \GROUPBY, \ORDERBY{} и другим. На этом же + этапе выполняется семантический анализ: проверяется существование + указанных таблиц и столбцов, разрешаются имена объектов и определяются + типы выражений. + + \item \emph{Оптимизация} --- синтаксическое дерево преобразуется в логический + план запроса, выраженный в терминах реляционной алгебры. Оптимизатор + исследует пространство эквивалентных логических планов и для каждого из + них рассматривает возможные физические реализации операторов. С помощью + модели стоимости оптимизатор оценивает затраты на выполнение каждого + плана и выбирает план с наименьшей оценочной стоимостью. Результатом + этого этапа является физический план исполнения. Под этим понимается + дерево физических операторов с указанием конкретных алгоритмов + соединения, методов доступа к данным и порядка операций. + + \item \emph{Исполнение физического плана} --- движок выполнения реализует + физический план, порождая потоки кортежей. Данные извлекаются из + хранилища, обрабатываются операторами плана и формируют результирующий + набор, возвращаемый пользователю. +\end{enumerate} + + +Сформулируем задачу, которую решает оптимизатор: по запросу, представленному в +форме дерева операторов реляционной алгебры, построить эквивалентный и +оптимальный, с точки зрения необходимых для исполнения ресурсов, план +исполнения. Итоговый план является деревом, вершины которого помечены +конкретными указаниями на алгоритмы, реализующими один или несколько операторов +реляционной алгебры. Эквивалентность означает, что итоговый физический план и +исходный запрос при исполнении на любом наборе данных порождают одинаковые +отношения с точностью до требуемых свойств, таких как сортировка. + +Запрос в форме реляционной алгебры также называют логическим планом, а операторы +реляционной алгебры --- логическими операторами. + +Для каждого логического оператора существует несколько возможных физических +реализаций. Например, оператор соединения \(\bowtie\) может быть реализован как +соединение вложенными циклами, хеш-соединение или соединение слиянием. +Аналогично, оператор доступа к данным может быть реализован как последовательное +сканирование таблицы или сканирование индекса. Еще одним важным примером +является эквивалентность сканирования индекса с предикатом и двух логических +операторов, примененных последовательно: доступ к данным и фильтрация по тому же +условию. + +\section{Реляционная алгебра} + +Реляционная алгебра представляет собой формальный язык для описания операций над +отношениями реляционной базы данных. В отличие от декларативного SQL, +реляционная алгебра является процедурной, так как она определяет конкретную +последовательность операций, необходимых для получения результата. + +Отношение определяется как конечное множество кортежей, каждый из которых +представляет собой упорядоченный набор значений атрибутов: + +\[ + R \subseteq \text{dom}(A_1) \times \text{dom}(A_2) \times \ldots \times \text{dom}(A_n). +\] + +\subsection{Основные операторы реляционной алгебры} + +Среди основных операторов реляционной алгебры выделяют следующие. + +\begin{enumerate} + \item \emph{Фильтрация} \(\Sel{p}{R}\) --- возвращает подмножество кортежей + отношения \(R\), удовлетворяющих предикату \(p\). Например, + \(\Sel{\text{age} > 30}{\text{Employee}}\) вернет все кортежи из + отношения Employee, для которых значение атрибута age больше 30. + \item \emph{Проекция} \(\Projection{A_1, \ldots, A_k}{R}\) --- формирует новое + отношение, содержащее только перечисленные атрибуты. + \item \emph{Декартово произведение} \(\CProd{R}{S}\) --- формирует отношение, + каждый кортеж которого является конкатенацией кортежа из \(R\) и кортежа + из \(S\), при этом результат содержит \(|\CProd{R}{S}| = |R| \cdot |S|\) + элементов. + \item \emph{Соединение} \(\InnerJoin{R}{p}{S}\) --- комбинирует кортежи двух + отношений на основании предиката \(p\). Внутреннее соединение + определяется как + \(\InnerJoin{R}{R.a = S.b}{S} = \Sel{R.a = S.b}{\CProd{R}{S}}\). + \item \emph{Объединение} \(R \cup S\) --- возвращает все кортежи, + принадлежащие хотя бы одному из двух совместимых по схеме отношений. + \item \emph{Разность} \(R - S\) --- выбирает кортежи, принадлежащие \(R\), но + отсутствующие в \(S\). + \item \emph{Агрегация} \(\Agg{G}{f}{R}\) --- разбивает отношение \(R\) на + непересекающиеся множества по набору атрибутов \(G\) и вычисляет + агрегированные значения функции \(f\) по каждому такому множеству. + Примеры функций: \COUNT, \SUM, \AVG, \MIN, \MAX. + \item \emph{Apply} \(\Apply{R}{S(t)}\) --- для каждого кортежа \(t\) + отношения \(R\) вычисляет параметризованное им отношение \(S(t)\) и + конкатенирует \(t\) с каждым кортежем результата: + \[ + \Apply{R}{S(t)} = \bigcup_{t \in R} \{t\} \times S(t). + \] + В отличие от соединения, правая сторона зависит от текущего кортежа + левой и используется для представления коррелированных подзапросов. +\end{enumerate} + +\subsection{Преобразование синтаксического дерева к реляционной алгебре}\label{sec:translation} + +Полученное после лексического и синтаксического разбора абстрактное +синтаксическое дерево преобразуется в выражение реляционной алгебры. Этот +процесс определяется структурной индукцией по дереву запроса. Введём оператор +конверсии \(\Tr{\cdot}\), сопоставляющий узлу синтаксического дерева его +алгебраическое представление. Тогда правила трансляции записываются в виде +правил вывода. Если для всех поддеревьев известны алгебраические представления, +то алгебраическое представление составной конструкции выражается через них. + +\begin{enumerate} + \item \emph{Базовый случай.} Для ссылки на таблицу \(T\) выполняется + \(\Tr{T} = T\). + \item \emph{Блок \texttt{SELECT-FROM-WHERE}.} Пусть \(\bar{T} = T_1, \ldots, + T_n\) --- ссылки на таблицы или вложенные подзапросы, \(p\) --- + предикат фильтрации, \(\bar{a} = a_1, \ldots, a_k\) --- список + выражений проекции. Тогда + \[ + \frac{\Tr{T_1} = E_1 \quad \cdots \quad \Tr{T_n} = E_n} + {\Tr{\SELECT\ \bar{a}\ \FROM\ \bar{T}\ \WHERE\ p} + = \Projection{\bar{a}}{\Sel{p}{E_1 \times \cdots \times E_n}}}. + \] + \item \emph{Группировка.} Пусть \(\bar{g} = g_1, \ldots, g_m\) --- список + группирующих атрибутов, \(\bar{f} = f_1, \ldots, f_l\) --- список + агрегирующих функций. Тогда + \[ + \frac{\Tr{T_1} = E_1 \quad \cdots \quad \Tr{T_n} = E_n} + {\Tr{\SELECT\ \bar{g}, \bar{f}\ \FROM\ \bar{T}\ \WHERE\ p\ \GROUPBY\ \bar{g}} + = \Agg{\bar{g}}{\bar{f}}{\Sel{p}{E_1 \times \cdots \times E_n}}}. + \] + \item \emph{Вложенные подзапросы.} Пусть \(Q\) --- внешний блок, а \(Q'(t)\) + --- коррелированный подзапрос, зависящий от кортежа \(t\) из + \(\Tr{Q}\). Тогда + \[ + \frac{\Tr{Q} = E \quad \Tr{Q'(t)} = E'(t)} + {\Tr{Q\ \text{с подзапросом}\ Q'} = \Apply{E}{E'(t)}}, + \] + где \(\mathcal{A}\) --- оператор Apply, который впоследствии может быть + удалён процедурой декорреляции. + \item \emph{Сортировка.} Конструкция \ORDERBY{} не выражается в виде + операторов реляционной алгебры. Вместо этого она задает требуемое + физическое свойство упорядоченности результата, учитываемое на этапе + выбора физического плана. +\end{enumerate} + +Приведём пример применения этих правил к SQL-запросу из +листинга~\ref{lst:correlated_subquery}. Трансляция этого запроса в реляционную +алгебру выполняется по следующим шагам: + +\begin{listing}[H] + \caption{Запрос с коррелированным подзапросом.} + \label{lst:correlated_subquery} + \begin{minted}[style=bw, breaklines, frame=single, fontsize = \footnotesize, linenos=false, xleftmargin = 1.5em]{SQL} + SELECT e.name + FROM Employee e + WHERE e.salary > ( + SELECT AVG(salary) + FROM Employee + WHERE dept = e.dept + ) + \end{minted} +\end{listing} + +\begin{enumerate} + \item По базовому правилу оба вхождения таблицы остаются как есть + \(\Tr{\text{Employee}} = \text{Employee}\). + \item Внутренний подзапрос + \(Q'(e) = (\SELECT\ \AVG(\text{salary})\ \FROM\ \ldots \WHERE\ \ldots\)) + транслируется по правилу группировки, где \(\bar{g} = \varnothing\), + \(\bar{f} = \AVG(\text{salary})\): + \[ + \Tr{Q'(e)} = \Agg{\varnothing}{\AVG(\text{salary})}{\Sel{\text{dept} = e.\text{dept}}{\text{Employee}}}. + \] + Подзапрос коррелирован, так как зависит от атрибута \(e.\text{dept}\) + внешнего кортежа. + \item По правилу вложенных подзапросов результат \(\Tr{Q'(e)}\) присоединяется + к внешнему блоку оператором \(\mathcal{A}\), добавляя к каждому кортежу + столбец \(c\) с вычисленным значением средней зарплаты. + \item Оставшаяся часть внешнего запроса переводится по правилу + \texttt{SELECT-FROM-WHERE} с \(\bar{a} = (e.\text{name})\) и + \(p = (e.\text{salary} > c)\). +\end{enumerate} + +Итоговое алгебраическое выражение имеет вид +\[ + \Projection{e.\text{name}}{\Sel{e.\text{salary} > c}{\Apply{\text{Employee}\;e}{\Agg{\varnothing}{\text{AVG}(\text{salary}) \to c}{\Sel{\text{dept} = e.\text{dept}}{\text{Employee}}}}}}. +\] +Соответствующее дерево операторов изображено на +рисунке~\ref{fig:correlated_subquery_tree}. + +\begin{figure}[!htb]\centering +\begin{tikzpicture}[ + rel/.style={rectangle, draw, rounded corners=2pt, minimum width=2.2cm, + minimum height=0.8cm, align=center, font=\small}, + op/.style={rectangle, draw, fill=gray!12, minimum width=2.6cm, + minimum height=0.9cm, align=center, font=\small}, + level distance=1.15cm +] + \node[op] (proj) at (0, 6.5) {\(\Projection{e.\text{name}}{}\)}; + \node[op] (sel1) at (0, 5.2) {\(\Sel{e.\text{salary} > c}{}\)}; + \node[op] (apply) at (0, 3.9) {\(\mathcal{A}\)}; + \node[rel] (emp1) at (-2.8, 2.6) {\texttt{Employee} \(e\)}; + \node[op] (agg) at (2.8, 2.6) {\(\Agg{\varnothing}{\text{AVG}(\text{salary}) \to c}{}\)}; + \node[op] (sel2) at (2.8, 1.3) {\(\Sel{\text{dept} = e.\text{dept}}{}\)}; + \node[rel] (emp2) at (2.8, 0) {\texttt{Employee}}; + + \draw[->] (sel1) -- (proj); + \draw[->] (apply) -- (sel1); + \draw[->] (emp1) -- (apply); + \draw[->] (agg) -- (apply); + \draw[->] (sel2) -- (agg); + \draw[->] (emp2) -- (sel2); +\end{tikzpicture} +\caption{Дерево реляционной алгебры для запроса из +листинга~\ref{lst:correlated_subquery}.}% +\label{fig:correlated_subquery_tree} +\end{figure} + +Преобразование в форму реляционной алгебры необходимо по нескольким причинам. +Во-первых, это позволяет использовать формализованный набор правил +эквивалентности, который позволяют оптимизатору корректно преобразовывать план +запроса без изменения семантики. Во-вторых, алгебраическое представление +позволяет единообразно обрабатывать различные синтаксические формы SQL-запросов, +порождающие одинаковые логические планы. Наконец, разделение на логические и +физические операторы позволяет оптимизатору независимо исследовать порядок +операций и алгоритмы их реализации. + +\section{Поиск оптимального плана} + +Эквивалентность планов, исследуемых в процессе поиска оптимального, +обеспечивается применением алгебраических тождеств, например ассоциативности и +коммутативности соединений. Правила эквивалентности реляционной алгебры подробно +рассматриваются в разделе~\ref{sec:transformation_rules}. + +Оптимальность запроса принято оценивать с помощью функции стоимости +\(\mathrm{cost} \colon \mathcal{P}_L \to \mathbb{R}_{\ge 0}\)~\cite{Selinger1979}, +где \(\mathcal{P}_{L}\) --- множество эквивалентных физических планов. Это +отображение оценивает затраты на исполнение плана исходя из конкретных +реализаций алгоритмов, а также основываясь на эвристиках и статистике о данных в +отношениях. Другими словами, цель оптимизатора состоит в том, чтобы для входного +запроса найти план \(P^* \in \mathcal{P}_L\), минимизирующий \(\mathrm{cost}\): +\[ + P^* = \arg\min_{P \in \mathcal{P}_L} \mathrm{cost}(P). +\] + +Одной из подзадач в рамках построения оптимального плана является определение +порядка выполнения соединений. Благодаря правилам эквивалентости, таким как +ассоциативность и коммутативность соединения +(раздел~\ref{sec:transformation_rules}), одно и то же множество соединяемых +отношений допускает большое количество эквивалентных порядков вычисления. +Различные порядки имеют кардинально разные стоимости, ввиду особенностей +алгоритмов, реализующих соединение. Для \(n\) отношений количество таких +порядков выражается как +\(O(n! \cdot C_{n-1}) = O(\frac{(2n-2)!}{(n-1)!})\)~\cite{IntroToTheJoinOrderingProblem}, +где \(C_k\) --- \(k\)-е число Каталана. Таким образом, пространство поиска +растет экспоненциально с увеличением числа соединяемых таблиц. + +Поскольку полный перебор \(\mathcal{P}_L\) для достаточно больших \(n\) +невозможен, на практике речь идёт о поиске плана с приемлемо малой стоимостью в +определенном подмножестве пространства поиска. Именно эта задача и определяет +архитектуру современных оптимизаторов: расширяемое описание пространства поиска +через правила преобразования, эффективный алгоритм его исследования и +стоимостная модель, позволяющая отсекать заведомо неоптимальные ветви. + +Выбор плана исполнения запроса может оказывать огромное влияние на объем +требуемых вычислительных ресурсов. В качестве примера оценим количество чтений +страниц с диска для различных физических операторов. Пусть имеется три +отношения: таблица \texttt{customer} с \(|C| = 10^5\) строк, таблица +\texttt{orders} с \(|O| = 10^7\) строк и таблица \texttt{lineitem} с +\(|L| = 5 \cdot 10^7\) строк. Размер страницы --- \(8\) КБ. В одну страницу +помещается \(100\) строк \texttt{customer}, \(80\) строк \texttt{orders} и +\(50\) строк \texttt{lineitem}. Соответственно, общее число страниц составляет +\(P_C = 10^3\), \(P_O = 1{,}25 \cdot 10^5\), \(P_L = 10^6\). + +Для запроса из листинга~\ref{lst:join_query} +рассмотрим два альтернативных плана~\refImage{fig::join_plan_comparison}: +\begin{listing} + \caption{Соединение \texttt{customer}, \texttt{orders} и \texttt{lineitem}.} + \label{lst:join_query} + \begin{minted}[style=bw, breaklines, frame=single, fontsize = \footnotesize, linenos=false, xleftmargin = 1.5em]{SQL} + SELECT * + FROM customer c + JOIN orders o ON c.c_custkey = o.o_custkey + JOIN lineitem l ON o.o_orderkey = l.l_orderkey + \end{minted} +\end{listing} + +\begin{figure}[!htb]\centering +\begin{tikzpicture}[ + rel/.style={rectangle, draw, rounded corners=2pt, minimum width=2.45cm, + minimum height=0.8cm, align=center, font=\small}, + op/.style={rectangle, draw, fill=gray!12, minimum width=4.25cm, + minimum height=1.2cm, align=center, font=\small} +] + \node[op] (p1j2) at (-4.4, 2.9) {Hash Join\\\scriptsize \(10^6\) чтений}; + \node[op] (p1j1) at (-6.5, 1.2) {Hash Join\\\scriptsize \(1{,}26 \cdot 10^5\) чтений}; + \node[rel] (p1l) at (-2.3, 1.2) {\texttt{lineitem}}; + \node[rel] (p1c) at (-7.8, -0.3) {\texttt{customer}}; + \node[rel] (p1o) at (-4.4, -0.3) {\texttt{orders}}; + + \draw[->] (p1j1) -- (p1j2); + \draw[->] (p1l) -- (p1j2); + \draw[->] (p1c) -- (p1j1); + \draw[->] (p1o) -- (p1j1); + \node[font=\small\bfseries] at (-4.4, 4.0) {План \(P_1\)}; + + \node[op] (p2j2) at (4.4, 2.9) {Nested Loop Join\\\scriptsize \(1{,}25 \cdot 10^{14}\) чтений}; + \node[op] (p2j1) at (2.3, 1.2) {Nested Loop Join\\\scriptsize \(1{,}25 \cdot 10^8\) чтений}; + \node[rel] (p2l) at (6.5, 1.2) {\texttt{lineitem}}; + \node[rel] (p2c) at (1.0, -0.3) {\texttt{customer}}; + \node[rel] (p2o) at (4.4, -0.3) {\texttt{orders}}; + + \draw[->] (p2j1) -- (p2j2); + \draw[->] (p2l) -- (p2j2); + \draw[->] (p2c) -- (p2j1); + \draw[->] (p2o) -- (p2j1); + \node[font=\small\bfseries] at (4.4, 4.0) {План \(P_2\)}; +\end{tikzpicture} +\caption{Сравнение планов для запроса из листинга~\ref{lst:join_query}.}% +\label{fig::join_plan_comparison} +\end{figure} + +\begin{itemize} + \item План \(P_1\): + \(\NJoin{(\NJoin{\texttt{customer}}{\texttt{orders}})}{\texttt{lineitem}}\) + с реализацией обоих соединений через Hash Join, для чего сначала + полностью считываются \(P_C + P_O = 1{,}26 \cdot 10^5\) страниц для + построения хеш-таблицы, после чего промежуточный результат размером + \(10^7\) строк соединяется с \texttt{lineitem}, требуя считывания ещё % + \(P_L = 10^6\) страниц. В сумме --- порядка \(1{,}126 \cdot 10^6\) + операций чтения с диска. + \item План \(P_2\): тот же запрос, но реализованный как соединение с помощью + вложенных циклов без использования индексов: для каждой строки + \texttt{customer} полностью читается таблица \texttt{orders}, для каждой + результирующей пары --- \texttt{lineitem}. Совокупный объём ввода-вывода + оценивается величиной + \(P_C \cdot P_O \cdot P_L \approx 1{,}25 \cdot 10^{14}\) операций + чтения, что на восемь порядков превышает \(P_1\). +\end{itemize} + +На еще одном примере запроса из листинга~\ref{lst:logical_transform_query} +рассмотрим влияние логических преобразований. Отношение \texttt{Emp} содержит +\(10{,}000\) кортежей, что эквивалентно \(1{,}000\) страницам. \texttt{Dept} +включает \(500\) записей и \(50\) страниц соответственно. Атрибут +\texttt{Emp.did} является внешним ключом к \texttt{Dept.did}, поэтому каждому +кортежу из \texttt{Dept} соответствует \(10{,}000 / 500 = 20\) из \texttt{Emp}. +Пусть на \texttt{Emp.did} и \texttt{Dept.dname} построены индексы, а в +промежуточную страницу помещается \(5\) кортежей. + +\begin{listing} + \caption{Пример запроса с проекцией, фильтрацией и соединением.} + \label{lst:logical_transform_query} + \begin{minted}[style=bw, breaklines, frame=single, fontsize = \footnotesize, linenos=false, xleftmargin = 1.5em]{SQL} + SELECT DISTINCT ename + FROM Emp E JOIN Dept D ON E.did = D.did + WHERE D.dname = 'Toy' + \end{minted} +\end{listing} + +\begin{figure}[!htb]\centering +\begin{tikzpicture}[ + rel/.style={rectangle, draw, rounded corners=2pt, minimum width=2.0cm, + minimum height=0.8cm, align=center, font=\small}, + op/.style={rectangle, draw, fill=gray!12, minimum width=4.5cm, + minimum height=1.2cm, align=center, font=\small} +] + \node[font=\small\bfseries] at (-3.5, 8.8) {План \(P_1\)}; + \node[op] (p1proj) at (-3.5, 7.7) {\(\ProjectionOp{\texttt{ename}}\)}; + \node[op] (p1sig2) at (-3.5, 6.2) {\(\SelOp{\texttt{dname}=\texttt{'Toy'}}\)}; + \node[op] (p1sig1) at (-3.5, 4.7) {\(\SelOp{\texttt{Emp.did}=\texttt{Dept.did}}\)}; + \node[op] (p1cp) at (-3.5, 3.2) {\(\CProd{\texttt{Emp}}{\texttt{Dept}}\)\\\scriptsize \(50{,}050\) чтений}; + \node[rel] (p1emp) at (-5.2, 1.7) {\texttt{Emp}}; + \node[rel] (p1dept) at (-1.8, 1.7) {\texttt{Dept}}; + + \draw[->] (p1emp) -- (p1cp); + \draw[->] (p1dept) -- (p1cp); + \draw[->] (p1cp) -- (p1sig1); + \draw[->] (p1sig1) -- (p1sig2); + \draw[->] (p1sig2) -- (p1proj); + + \node[font=\small\bfseries] at (3.5, 8.8) {План \(P_2\)}; + \node[op] (p2proj) at (3.5, 7.7) {\(\ProjectionOp{\texttt{ename}}\)}; + \node[op] (p2join) at (3.5, 5.5) {Index NL Join\\\(\texttt{Emp.did}=\texttt{Dept.did}\)\\\scriptsize \(23\) чтения}; + \node[rel] (p2emp) at (1.6, 3.5) {\texttt{Emp}}; + \node[op] (p2sig) at (5.4, 3.5) {\(\SelOp{\texttt{dname}=\texttt{'Toy'}}\)\\\scriptsize \(3\) чтения}; + \node[rel] (p2dept) at (5.4, 1.8) {\texttt{Dept}}; + + \draw[->] (p2emp) -- (p2join); + \draw[->] (p2sig) -- (p2join); + \draw[->] (p2join) -- (p2proj); + \draw[->] (p2dept) -- (p2sig); +\end{tikzpicture} +\caption{Сравнение планов для запроса из + листинга~\ref{lst:logical_transform_query}.}% +\label{fig::logical_transform_plans} +\end{figure} + +Рассмотрим два плана~\refImage{fig::logical_transform_plans}: + +\begin{itemize} + \item План \(P_1\) соответствует прямому преобразованию запроса в реляционную + алгебру, выполненному по алгоритму из раздела~\ref{sec:translation}. + Если рассматривать стоимость исполнения такого плана в модели + итераторов, то каждый оператор запрашивает у дочернего следующий кортеж + через вызов \texttt{next()}, поэтому \(\sigma\) и~\(\pi\) работают в + режиме конвейера и не инициируют обращений к диску. Вся стоимость + ввода-вывода сосредоточена в операторе~\(\times\), реализованном через + вложенные циклы с \texttt{Dept} в роли внешней таблицы: \texttt{Dept} + читается один раз, а \texttt{Emp} перечитывается полностью для каждой + страницы~\texttt{Dept}: + \[ + 50 + 50 \times 1{,}000 = 50{,}050 \text{ чтений.} + \] + + \item План \(P_2\) является оптимизированным вариантом, где применено правило + проталкивания предиката и декартово произведение заменено на соединение + по предикату. Причем для доступа к таблице \texttt{Dept} применяется + сканирование по индексу, что дает всего \(3\) чтения с диска. Соединение + реализуется через вложенные циклы с использованием индекса, что в итоге дает: + \[ + 3 + (3 + 20) = 26 \text{ чтений.} + \] +\end{itemize} + +Оба приведенных примера наглядно демонстрируют, что оптимизатор отвечает за +принципиальную возможность исполнения сложных аналитических запросов за +приемлемое время. В первом случае разница между физическими планами составляет +восемь порядков: если \(P_1\) завершается за секунды, то наивный \(P_2\) +потребует десятков лет. Во втором случае даже единственное логическое +преобразование, в виде проталкивания предиката, снижает число чтений с диска в +\(1{,}900\) раз. Именно поэтому качественный оптимизатор является конкурентным +преимуществом и одной из самых важных частей СУБД. + +\section{Обзор архитектур оптимизаторов} + +Рассмотрим наиболее популярные архитектуры оптимизаторов. + +\subsection{System R} + +Оптимизатор System~R, разработанный в IBM Research в конце 1970-х +годов~\cite{Selinger1979}, стал первым стоимостным оптимизатором запросов и +заложил фундаментальные принципы, используемые во всех последующих системах. + +Архитектуру, аналогичную System~R, реализуют PostgreSQL и ранние версии IBM DB2. + +Оптимизатор, построенный по архитектуре System~R, разбивает запрос на блоки, +каждый из которых оптимизируется индивидуально. Блок запроса состоит из +конструкции \SELECT, конструкции \FROM, ссылающейся на одну или +несколько таблиц, и дерева предикатов \WHERE. Несколько блоков +появляется в случае запроса с вложенными подзапросами. + +Для каждого блока оптимизатор выполняет два основных действия. Во-первых, для +запросов к одному отношению выбирается наилучший метод доступа на основании +стоимостной модели. Во-вторых, для запросов с несколькими отношениями +определяется оптимальный порядок соединений методом восходящего динамического +программирования: сначала выбираются оптимальные планы доступа к отдельным +таблицам, затем перебираются двухтабличные соединения, трехтабличные и так +далее. + +Стоимость плана вычисляется по формуле +\(\text{COST} = \text{PageFetches} + w \cdot \text{RSICalls}\), учитывающей +ожидаемый объем операций ввода-вывода и потребление ресурсов процессора, где +\(w\) --- весовой коэффициент. + +Важным нововведением System~R стало понятие интересных порядков. Стоимость плана +зависит от непосредственных затрат на выполнение оператора и от +того, обеспечивает ли он упорядоченность результата, требуемую вышестоящими +операторами. Например, использование соединения слиянием вместо хеш-соединения +может быть выгодно, если результат должен быть отсортирован для последующей +операции \ORDERBY. + +К ограничениям архитектуры System~R относятся: + +\begin{itemize} + \item рассмотрение только левосторонних + деревьев соединений; + \item независимая оптимизация вложенных подзапросов; + \item невозможность гибкого расширения набора правил преобразования, что + необходимо при добавлении новых операторов языка SQL\@. +\end{itemize} + +Эти ограничения послужили мотивацией для создания расширяемых архитектур +оптимизаторов. + +\subsection{Volcano} + +Volcano --- расширяемый, основанный на правилах, стоимостной оптимизатор +запросов, разработанный Грефе в 1993 году~\cite{GraefeMcKenna1993}. Volcano ввел +ряд концепций, ставших основой для последующих расширяемых оптимизаторов: +физические свойства выражений, обеспечивающие правила, структуру данных Memo и +нисходящий алгоритм поиска на основе динамического программирования. + +Volcano разделяет поиск на две фазы: + +\begin{itemize} + \item \emph{фаза генерации} --- оптимизатор применяет правила трансформации + для порождения всех эквивалентных логических выражений. Применяется + рекурсивный нисходящий обход дерева: для каждого выражения сначала + исследуются дочерние выражения, затем применяются правила трансформации + к текущему выражению. Все порожденные выражения кэшируются в структуре + данных Memo для предотвращения дублирования. + + \item \emph{фаза стоимостного анализа} --- по завершении генерации всех + эквивалентных логических выражений оптимизатор ищет наилучший физический + план. Для каждого логического выражения применяются правила реализации, + порождающие физические операторы. Стоимость каждого физического плана + оценивается рекурсивно, и сохраняется наилучший найденный план. +\end{itemize} + +Поиск осуществляется нисходящим динамическим программированием с использованием +стратегии ветвей и границ: текущая верхняя граница стоимости передается при +рекурсивных вызовах и используется для отсечения заведомо неоптимальных планов. + +Основным недостаткам Volcano является полное раскрытие всех классов +эквивалентности на фазе генерации до начала стоимостного анализа, что приводит к +избыточным вычислениям. + +\section{Архитектура Cascades} + +Cascades --- это расширяемая архитектура оптимизатора запросов, являющаяся +логическим продолжением идеи Volcano~\cite{Graefe1995}. + +Основными объектами Cascades являются: + +\begin{itemize} + \item выражение --- дерево или фрагмент дерева, корнем которого является + логический либо физический оператор; + \item групповое выражение --- выражение, дочерние узлы которого являются + ссылками на группы Memo; + \item группа --- множество логически эквивалентных выражений, возвращающих + один и тот же результат; + \item правило трансформации --- правило, преобразующее логическое выражение в + логически эквивалентное выражение; + \item правило реализации --- правило, преобразующее логический оператор в + физический оператор; + \item физическое свойство --- требование относительно порядка, распределения, + разбиения или уникальности результата; + \item задача --- элементарная единица работы оптимизатора, например + исследование группы, применение правила или оптимизация выражения под + заданное свойство. +\end{itemize} + +Важным принципом данной архитектуры является то, что правила являются объектами +первого класса. Они определяются условиями применимости и собственно алгоритмом +применения. Предполагается, что система легко расширяется добавлением новых +правил, потому что все правила наследуются от общего интерфейса и имеют +одинаковую структуру. + +\subsection{Группы эквивалентности и выражения} + +Группа в Memo представляет собой множество выражений, которые эквивалентны с +точки зрения логического результата. Например, для отношений \(A\), \(B\) и +\(C\) выражения \( \NJoin{A}{(\NJoin{B}{C})} ; \qquad{} \NJoin{(\NJoin{A}{B})}{C} \) +при выполнении условий ассоциативности соединения принадлежат одной группе, +поскольку возвращают одинаковый результат. Причем физические реализации этих +выражений могут отличаться, а стоимость может зависеть от кардинальности +промежуточных результатов. + +Групповое выражение хранит оператор и ссылки на дочерние группы. Например, +выражение \(Join(G_1, G_2)\) не указывает конкретное дерево для левого и правого +входов, а ссылается на группы \(G_1\) и \(G_2\), каждая из которых может +содержать множество альтернатив. Поэтому одно групповое выражение кодирует +комбинацию многих конкретных деревьев, храня эту информацию компактно. + +\subsection{Структура Memo} + +Memo является основной структурой данных Cascades. Оно выполняет сразу +несколько функций: устраняет дублирующиеся выражения, хранит альтернативы, +фиксирует результаты оптимизации под разными физическими свойствами и обеспечивает +точку синхронизации для задач поиска. + +Группа в Memo включает следующую информацию: + +\begin{itemize} + \item уникальный идентификатор группы; + \item список логических выражений; + \item список физических выражений; + \item логические свойства группы; + \item статистику и оценку кардинальности; + \item состояние исследования группы. +\end{itemize} + +Выражение, в свою очередь, содержит сам оператор, массив идентификаторов +дочерних групп. Для физического выражения дополнительно могут храниться +предоставляемые физические свойства, например сортировка выходного потока, и +рассчитанная стоимость исполнения этого оператора. + +\begin{listing} + \caption{Пример запроса.} + \label{lst:query} + \begin{minted}[style=bw, breaklines, frame=single, fontsize = \footnotesize, linenos=false, xleftmargin = 1.5em]{SQL} + SELECT * FROM A JOIN B ON p_1 JOIN C ON p_2; + \end{minted} +\end{listing} + +На рисунке~\ref{fig:memo-structure} показан пример Memo для запроса из +листинга~\ref{lst:query}. Например, группа \(G_5\) представляет результат +соединения трех отношений, в ней находятся два логически эквивалентных выражения +с разной ассоциацией соединений и четыре физические реализации для них. Входными +данными для операторов являются группы, поэтому общие подвыражения не +дублируются. + +\begin{figure}[H] + \centering + \begin{tikzpicture}[ + scale=0.85, transform shape, + group/.style={draw, rounded corners, text width=4.8cm, inner sep=5pt}, + arrow/.style={-{Stealth[length=2.5mm]}, thick}, + altarrow/.style={-{Stealth[length=2.5mm]}, thick, dashed} + ] + \node[group] (g5) at (6,0) { + \textbf{Группа G5}\\[2pt] + \hrule\vspace{2pt} + \textit{Логические:}\\ + Join(G4,G3)\\ + Join(G1,G6)\\[2pt] + \hrule\vspace{2pt} + \textit{Физические:}\\ + HashJoin(G4,G3)\\ + NestedLoopJoin(G4,G3)\\ + HashJoin(G1,G6)\\ + NestedLoopJoin(G1,G6) + }; + \node[group] (g4) at (3,-6) { + \textbf{Группа G4}\\[2pt] + \hrule\vspace{2pt} + \textit{Логические:}\\ + Join(G1,G2)\\[2pt] + \hrule\vspace{2pt} + \textit{Физические:}\\ + HashJoin(G1,G2)\\ + NestedLoopJoin(G1,G2) + }; + \node[group] (g6) at (9,-6) { + \textbf{Группа G6}\\[2pt] + \hrule\vspace{2pt} + \textit{Логические:}\\ + Join(G2,G3)\\[2pt] + \hrule\vspace{2pt} + \textit{Физические:}\\ + HashJoin(G2,G3)\\ + NestedLoopJoin(G2,G3) + }; + \node[group] (g1) at (0,-12) { + \textbf{Группа G1}\\[2pt] + \hrule\vspace{2pt} + \textit{Логические:}\\ + Scan(A)\\[2pt] + \hrule\vspace{2pt} + \textit{Физические:}\\ + SeqScan(A) + }; + \node[group] (g2) at (6,-12) { + \textbf{Группа G2}\\[2pt] + \hrule\vspace{2pt} + \textit{Логические:}\\ + Scan(B)\\[2pt] + \hrule\vspace{2pt} + \textit{Физические:}\\ + SeqScan(B) + }; + \node[group] (g3) at (12,-12) { + \textbf{Группа G3}\\[2pt] + \hrule\vspace{2pt} + \textit{Логические:}\\ + Scan(C)\\[2pt] + \hrule\vspace{2pt} + \textit{Физические:}\\ + SeqScan(C) + }; + + \draw[arrow] (g5) -- (g4); + \draw[arrow, rounded corners=5pt] + (g5.east) -- (15.5,0) -- (15.5,-12) -- (g3.east); + \draw[altarrow] (g5) -- (g6); + \draw[altarrow, rounded corners=5pt] + (g5.west) -- (-3.5,0) -- (-3.5,-12) -- (g1.west); + \draw[arrow] (g4) -- (g1); + \draw[arrow] (g4) -- (g2); + \draw[arrow] (g6) -- (g2); + \draw[arrow] (g6) -- (g3); + \end{tikzpicture} + \caption{Пример структуры Memo.}% + \label{fig:memo-structure} +\end{figure} + +При добавлении нового выражения Memo выполняет проверку на дубликаты. Если +выражение уже существует в некоторой группе, оно не добавляется повторно. Если +алгоритм в процессе работы обнаруживает эквивалентность двух ранее различных +групп, то они могут быть объединены. При этом требуется обновление ссылок и +состояния правил. + +\subsection{Алгоритм поиска} + +Cascades организует оптимизацию как выполнение набора задач. Задача может +исследовать группу, исследовать выражение, применить правило, реализовать +логический оператор, оптимизировать дочернюю группу под заданное физическое +свойство или построить вспомогательный оператор для обеспечения свойства. Такой +подход позволяет гибко управлять порядком работы и откладывать дорогостоящие +действия до тех пор, пока они не станут необходимыми, а также разбивать поиск на +несколько этапов и распараллеливать исполнение независимых веток. При этом +однопоточные реализации хранят задачи в обычной очереди. + +На рисунке~\ref{fig:cascades-tasks} показана схема взаимодействия задач в +оптимизаторе, а на +листингах~\ref{alg:cascades-search}-\ref{alg:cascades-search-3} приведен +псевдокод алгоритма поиска в архитектуре Cascades~\cite{DingNarasayya2024}. + +Алгоритм реализуется в виде управляемого стеком поиска в пространстве +эквивалентных планов: сначала запускается оптимизация корневой группы, +\textsc{OptimizeGroup} в свою очередь исследует группу, а затем оптимизирует +каждое ее логическое выражение. Исследование выполняется через +\textsc{ExploreGroup} и \textsc{ExploreExpression}, которые сохраняют в Memo +новые логические выражения и группы с помощью правил преобразования. После этого +\textsc{OptimizeExpression} применяет правила реализации, превращая логические +выражения в физические. Для каждого физического варианта +\textsc{ApplyImplementation} проверяет локальную стоимость и передает план в +\textsc{OptimizeInputs}, где последовательно оптимизируются дочерние группы, +накапливается полная стоимость и обновляется информация о лучшем плане. Параметр +\texttt{limit} используется для отсечения заведомо дорогих вариантов. + +\begin{figure}[H] +\centering +\includegraphics[width=0.8\textwidth]{cascades.pdf} +\caption{Схема взаимодействия задач в оптимизаторе Cascades}% +\label{fig:cascades-tasks} +\end{figure} + +\section{Правила трансформации и реализации}\label{sec:transformation_rules} + +Правило в оптимизаторе Cascades состоит из образца, условия применимости и +алгоритма применения. Образец описывает форму логического выражения, к которому +может быть применено правило. Условие применимости проверяет дополнительные +требования: отсутствие внешних ссылок, тип соединения, сохранение семантики +\texttt{NULL}, совместимость свойств и другие ограничения. + +Формально правило записывается в виде +\[ + r : \TransRule{pattern(e)}{condition(e)}{substitute(e)}, +\] +где \(e\) --- исходное выражение, \(pattern\) --- структурный образец, +\(condition\) --- предикат применимости, \(substitute\) --- выражение-замена. +Для правил трансформации результатом применения является новое логическое +выражение в группе, а для правил реализации физическое выражение в той же +группе. + +\subsection{Правила трансформации} + +Правила трансформации задают эквивалентные преобразования логических выражений и +тем самым определяют пространство планов, исследуемое оптимизатором. Наиболее +важные из них представлены в таблице~\ref{tbl:transformation_rules}. + +\begin{Definition} +Null-отбрасывающим называется предикат, который при замене всех атрибутов +соответствующей стороны на \texttt{NULL} принимает значение, отличное от +\texttt{TRUE}. +\end{Definition} + +\begin{Definition} +Конъюнкты \(\theta_1 \land \theta_2\) называются перераспределяемыми, если их +можно разбить на две группы \(\theta'_1\) и \(\theta'_2\) такие, что атрибуты +\(\theta'_2\) принадлежат \(attrs(S) \cup attrs(T)\), а конъюнкты с атрибутами +из \(attrs(R)\) остаются в \(\theta'_1\). +\end{Definition} + +\begin{Definition} +Функция агрегации называется разделяемой, если она представима композицией +частичного агрегата и финального комбинатора. Например, \SUM{} раскладывается в +сумму частичных сумм, \AVG{} --- в пару \SUM{} и \COUNT{}. +\end{Definition} + +\subsection{Правила реализации} + +Правила реализации переводят логические операторы в физические. Используемые в +настоящей работе правила собраны в таблице~\ref{tbl:implementation_rules}. + +\subsection{Обеспечивающие операторы} + +Физические свойства в Cascades обрабатываются через требуемые и предоставляемые +свойства. Если родительский оператор требует вход, отсортированный по атрибуту +\(a\), а выбранный дочерний план такого порядка не предоставляет, оптимизатор +может добавить обеспечивающий оператор \Sort. + +Обеспечивающие операторы не изменяют логический результат, но меняют физические +свойства и увеличивают стоимость. Поэтому они рассматриваются наравне с другими +физическими альтернативами. Например, план с хеш-соединением и последующей +сортировкой может конкурировать с планом на основе соединения слиянием, которое +сохраняет порядок результата. + +\section{Метод ветвей и границ} + +Метод ветвей и границ используется для сокращения пространства поиска и, как +следствие, неасимптотического улучшения скорости работы алгоритма. В контексте +Cascades ветвями являются альтернативные выражения и комбинации физических +реализаций дочерних групп, а границей является текущая лучшая стоимость плана +для заданной группы и требуемых физических свойств. Если нижняя оценка стоимости +частично построенного плана уже превышает текущую лучшую, дальнейшее +исследование по этой ветке не имеет смысла. + +Пусть для группы \(G\) и требуемого свойства \(P\) уже найден план стоимости +\(C^*\). Рассматривается физическое выражение \(e\), имеющее локальную стоимость +\(C_{local}(e)\) и дочерние группы \(G_1, \ldots, G_k\). Если даже минимально +возможная оценка дочерних групп приводит к неравенству + +\[ + C_{local}(e) + \sum_{i=1}^{k} LB(G_i, P_i) \geq C^*, +\] + +то выражение \(e\) может быть отсечено. Здесь \(LB\) обозначает нижнюю границу +стоимости оптимизации дочерней группы под требуемым свойством \(P_i\). Чем +точнее нижняя граница, тем эффективнее отсечение. + +На практике в качестве \(LB(G_i, P_i)\) принимается лучшая стоимость, найденная +для пары \((G_i, P_i)\) на текущий момент работы алгоритма. Поскольку +оптимизация группы является рекурсивным процессом, к моменту отсечения для +\((G_i, P_i)\) уже могут быть найдены некоторые планы, и их минимальная +стоимость служит нижней границей. Если же группа \(G_i\) под свойством \(P_i\) +ещё не рассматривалась, используется тривиальная граница \(LB = 0\), или, при +условии наличия минимальной стоимости на кортеж, можно принимать +\(LB(G_i, P_i) = |G_i| \cdot \mathit{minCostPerRow}\). + +Требуемые свойства дочерних групп \(P_i\) определяются оператором выражения \(e\) +совместно с требованием родителя \(P\). Каждый физический оператор описывает, какие +свойства он ожидает от своих входов: например, оператор сортирующего слияния требует +упорядоченности обоих входов по соответствующим ключам, тогда как хеш-соединение не +предъявляет требований к порядку. Таким образом, при спуске по дереву поиска каждый +оператор транслирует глобальное требование \(P\) в конкретные требования \(P_1, +\ldots, P_k\) к своим дочерним группам. + +\subsection{Оценка кардинальности} + +Оценка кардинальности является процессом предсказания числа кортежей, +возвращаемых оператором. + +Для выборки используется селективность предиката \(sel(p)\), после чего +кардинальность результата оценивается как + +\[ + |\Sel{p}{R}| = |R| \cdot sel(p). +\] + +Для соединения на основе предиката равенства независимых атрибутов часто +применяется эвристика: + +\[ + |\InnerJoin{R}{R.a = S.b}{S}| \approx + \frac{|R| \cdot |S|}{\max(NDV(R.a), NDV(S.b))}, +\] + +где \(NDV\) --- число различных значений атрибута. Такая оценка удобна, но может +быть грубой, если данные имеют корреляции, сильный перекос или сложные +зависимости между атрибутами. Экспериментальные исследования показывают, что +ошибки кардинальности часто являются одной из основных причин выбора неудачных +планов~\cite{leis2015good}. + +Для повышения качества оценок используются гистограммы, статистики по +многоколоночным ключам, сведения об уникальности, функциональные зависимости, +ограничения на схему, а в современных исследованиях также модели машинного +обучения. + +\subsection{Оценка стоимости} + +Функция стоимости сопоставляет физическому плану численную оценку затрат на его +исполнение. Классическим считается представление стоимости в виде взвешенной +суммы операций чтения и записи страниц на диск, а также числа обработанных +кортежей и операций над ними~\cite{GraefeMcKenna1993,Graefe1995}: + +\[ + Cost(p) = w_{io} \cdot IO(p) + w_{cpu} \cdot CPU(p). +\] + +Коэффициенты \(w_{io}\), \(w_{cpu}\) зависят от конкретной аппаратной +конфигурации. + +Стоимость является аддитивной и вычисляется рекурсивно по дереву физического +плана: + +\[ + Cost(node) = LocalCost(node) + \sum_{child \in children(node)} Cost(child). +\] + +Локальная стоимость оператора определяется его типом и кардинальностью его +входов. Пусть \(|R|\) --- кардинальность отношения \(R\), \(b\) --- блочный +фактор (число кортежей на странице), тогда \(P(R) = \lceil|R|/b\rceil\) --- +число страниц, занимаемых \(R\); \(c_\theta\) --- стоимость вычисления +предиката \(\theta\) на одной паре кортежей; \(\sigma_\theta\) --- +селективность предиката \(\theta\) (доля кортежей входа, удовлетворяющих +\(\theta\)); \(M\) --- число страниц буфера, доступных оператору. Формулы +локальной стоимости физических операторов приведены в +таблице~\ref{tbl:local-cost}. + +\begin{longtable}{|l|c|c|} + \caption{Локальная стоимость физических операторов.}\label{tbl:local-cost}\\ + \hline + Оператор & \(IO(op)\) & \(CPU(op)\) \\ + \hline + \endfirsthead + \hline + \endfoot + + \(\SeqScan(T)\) & \(P(T)\) & \(|T|\) \\ \hline + \(\IndexScan(T, \theta)\) & \(\lceil \sigma_\theta \cdot P(T) \rceil\) & \(\sigma_\theta \cdot |T|\) \\ \hline + \(\FilterOp(R, \theta)\) & \(0\) & \(|R| \cdot c_\theta\) \\ \hline + \(\ProjOp(R)\) & \(0\) & \(|R|\) \\ \hline + \(\NestedLoopJoin(L, R, \theta)\) & \(P(L) \cdot (1 + P(R))\) & \(|L| \cdot |R| \cdot c_\theta\) \\ \hline + \(\NestedLoopCrossJoin(L, R)\) & \(P(L) \cdot (1 + P(R))\) & \(|L| \cdot |R|\) \\ \hline + \(\HashJoin(L, R, \theta)\) & \(P(L) + P(R)\) & \(|L| + |R| \cdot (1 + c_\theta)\) \\ \hline + \(\MergeJoin(L, R)\) & \(P(L) + P(R)\) & \(|L| + |R|\) \\ \hline + \(\Sort(R)\) & \(2 P(R) \cdot \lceil \log_M P(R) \rceil\) & \(|R| \cdot \log_2 |R|\) +\end{longtable} + +Для операторов \(\FilterOp\) и \(\ProjOp\) стоимость ввода-вывода равна \(0\), потому что это конвейерные операторы, которые не используют диск. + +\section{Дифференциальный анализ физических планов} + +Множество правил трансформации и реализации, используемых в +оптимизаторах запросов, изучено довольно хорошо и описано в +академической литературе. Вместе с тем условия активации правил +существенно различаются от системы к системе. Многие реализации +оптимизаторов упускают возможности для оптимизации из-за слишком +строгих условий активации правил или отсутствия определенных правил +в их наборе. + +Данная проблема особенно актуальна при разработке нового оптимизатора: после +реализации базового набора правил возникает вопрос о полноте и корректности +этого набора по сравнению с устоявшимися и проверенными временем промышленными +системами. Такие системы аккумулировали обратную связь от пользователей и +уточняли условия активации своих правил в течение многих лет. Не имея такого +преимущества сложно добиться отличного качества построенных планов. + +\subsection{Предлагаемый метод} + +Для поиска несоответствий между поведением реализуемой системы и внешней +эталонной системы предлагается применить дифференциальный анализ планов. + +Первым шагом нужно сформировать несколько наборов данных, состоящих из схем +таблиц и их наполнения. Разные данные позволяют всесторонне исследовать правила +активации, потому что они зависят от кардинальности отношений, наличия индексов +и т.п. + +Далее, по заданным схемам генерируются случайные SQL-запросы различной структуры +и сложности. Такой подход исключает тривиальные планы и приближает условия +работы оптимизатора к реальным нагрузкам при дальнейшем построении планов +запросов. + +Следующим шагом сгенерированные запросы транспилируются в диалекты SQL целевых +промышленных СУБД. В качестве эталонных систем рассматриваются PostgreSQL, +DuckDB, Umbra и Microsoft SQL Server. Эти системы являются зрелыми, имеют +документированный формат вывода планов и представляют разные архитектурные +подходы. Затем для получения физических планов исполняется аналог +\texttt{EXPLAIN ANALYZE}. + +Полученные физические планы промышленных СУБД конвертируются в формат +сериализованного плана разрабатываемого оптимизатора. Современные СУБД +предоставляют вывод планов в виде XML или JSON, что позволяет легко реализовать +их разбор. Для конвертации используется отображение между физическими +операторами разрабатываемой системы и операторами эталонной СУБД. Например, +\texttt{Hash Match} в SQL Server отображается в \HashJoin{} в разрабатываемой +системе, \texttt{Clustered Index Scan} --- в \IndexScan{} и так далее. +Восстановление предикатов, ключей соединения и выражений проекции выполняется +путем разбора соответствующих полей плана, причем не нужно поддерживать весь +синтаксис предикатов, так как генератор запросов и схемы данных ограничивают +множество допустимых выражений в планах. + +Корректность конвертации выполняется по построению, но дополнительно проверяется +путем исполнения исходного плана на внешней системе и сконвертированного плана +на разрабатываемой системе с последующим сравнением полученных результатов при +фиксированной схеме и различных вариантах сгенерированных для нее данных. + +Конвертированные планы сравниваются с планами, порождаемыми разрабатываемым +оптимизатором на исходных запросах, с целью определить, мог ли существующий +набор правил разрабатываемого оптимизатора породить план, эквивалентный плану +промышленной системы. Стоимостные модели и реализации операторов в разных СУБД +различаются, поэтому план, оптимальный для эталонной системы, не обязательно +будет оптимальным для разрабатываемой. По этоу причине сравнение направлено на +исследование полноты пространства поиска разрабатываемого оптимизатора, и не +предполагает сравнение эффективности планов. + +Для корректности структурного сравнения необходимо полное исследование +пространства эквивалентных планов. Определение требуемых изменений лежит на +разработчике системы, поэтому найденный запрос и примеры планов сохраняются для +последующего анализа. В дальнейшем, полученные после конвертации ``эталонные'' +планы можно использовать как тесты для оптимизатора, которые будут успешно +выполняться после уточнения соответвующих правил активации. + +\subsection{Поиск ближайшего плана} + +В случае невозможности построить ``эталонный'' план разрабатываемым +оптимизатором, предлагается искать ближайший план, т.е. план из пространства +поиска разрабатываемого оптимизатора, максимально близкий к плану промышленной +системы. При этом метрика расстояния между планами может быть определена как +расстояние редактирования деревьев. + +Анализ ближайшего плана может помочь упростить выявление трансформаций, которые +разрабатываемый план по какой-то причине не применил. + +\subsection{Область применения} + +Предлагаемый метод дифференциального анализа применим для сравнения как с +проприетарными системами, так и с СУБД с открытым исходным кодом, позволяя +уточнять условия применения правил. + +Систематическое применение этого подхода позволяет построить итеративный процесс +совершенствования оптимизатора: + +\begin{itemize} + \item генерация запросов; + \item запуск и сбор планов; + \item приведение к единому виду, сравнение и выявление расхождений; + \item добавление или корректировка правил; + \item повторное сравнение. +\end{itemize} + +Таким образом, дифференциальный анализ обеспечивает управляемое и измеримое +улучшение качества оптимизатора и позволяет новым или открытым оптимизаторам +быстрее достигать качества устоявшихся промышленных оптимизаторов. + +% {\color{red} TODO: описать как нормализовать и т.п.} + +\section{Реализация} + +Программа написана на языке C++ с использованием стандарта C++23. Этот язык +популярен в сфере разработки СУБД, являясь достаточно низкоуровневым и +производительным, но в то же время удобным для реализации больших систем. Для +сборки применяется CMake и его встроенные средства для зависимостей в виде +библиотек на C++. Окружение для разработки описывается декларативно и +воспроизводимо при помощи функционального доменно-специфичного языка \verb|nix| +для соответствующего пакетного менеджера. + +Для синтаксического анализа выбран генератор парсеров ANTLR4, ввиду простоты и +удобства в использовании. Асинхронное взаимодействие физических операторов +построено на Boost.Asio, по причине совместимости со встроенными в язык +корутинами и понятной модели асинхронности. Для тестирования корректности и +производительности используется Google Tests, Google Benchmarks и pytest. + +\subsection{Модуль синтаксического анализа} + +Для реализации парсера был выбран ANTLR4, ввиду его удобства и возможности +генерировать лексические и синтаксические анализаторы под практически любой язык +программирования. + +Грамматика PostgreSQL была взята из официального репозитория проекта +ANTLR4~\cite{Antlr4PostgreSQL}. Она была портирована автоматически из грамматики +для Bison~\cite{PostgresBisonGrammar} из официального репозитория Postgres, +поэтому является наиболее полной из существующих. + +ANTLR4 генерирует несколько файлов на C++, в том числе лексический и +синтаксический анализаторы, а также класс \verb|Visitor| для обхода дерева +разбора. Для интересующих конструкций SQL переопределены методы посещения узлов +грамматики. Например, предложение \WHERE{} преобразуется в логический оператор +фильтрации, список выражений после \SELECT{} преобразуется в проекцию, а список +таблиц после \FROM{} --- в дерево соединений. + +Результатом работы парсера является структура \texttt{ParsedQuery}. Она содержит +логический оператор верхнего уровня и необязательное требование к порядку строк +для \ORDERBY{}. Логические операторы и скалярные выражения представлены +алгебраическими типами на основе \texttt{std::variant}. В них входят операторы +таблицы, фильтрации, проекции, агрегации, соединения, ссылки на атрибуты, +константы и арифметические выражения~\refAlgo{lst:logical}. + +\begin{listing}[H] + \caption{Объявление типов для операторов реляционной алегебры и выражений.} + \label{lst:logical} + \begin{minted}[style=bw, breaklines, frame=single, fontsize = \footnotesize, linenos=false, xleftmargin = 1.5em]{C++} +using Operator = std::variant; + +using Expression = std::variant; + +struct Projection { + std::vector expressions; + std::shared_ptr source; + std::vector> aliases; + + bool operator==(const Projection& other) const; +}; + \end{minted} +\end{listing} + +Выражения хранятся в алгебраическом типе \verb|Expression|, реализованном +аналогично \verb|Operator|. + +Функция \verb|GetAST| внутри строит абстрактное синтаксическое дерево и обходит +его с помощью \verb|Visitor|, который и генерирует представление запроса в виде +дерева из \verb|Operator|. + +Дополнительно реализована визуализация дерева из операторов реляционной алгебры +в формате Graphviz с помощью функции \verb|GetDotRepresentation|. + +\subsection{Модуль оптимизации запросов} + +Модуль оптимизации запросов отвечает за преобразование логического дерева +операторов в физический план исполнения с минимальной оцененной стоимостью. +Пространство исследуемых эквивалентных планов эффективно хранится в структуре +Memo и расширяется с помощью добавления новых правил. Поиск выполняется сверху +вниз с использованием стоимостной модели, физических свойств и метода ветвей и +границ. + +\subsubsection{Структура Memo} + +Структура Memo хранит группы логически эквивалентных выражений. Выражения из +одной группы возвращают одинаковый результат. Например, $A \Join B$ и +$B \Join A$ являются эквивалентными. + +Дочерние узлы выражения ссылаются не на конкретные операторы, а на группы. +Благодаря этому одно выражение компактно представляет множество деревьев. +Объявления классов \verb|Memo| и \verb|Group| приведены в +листинге~\ref{lst:memo}. + +\begin{listing}[H] + \caption{Основные элементы структуры Memo.} + \label{lst:memo} + \begin{minted}[style=bw, breaklines, frame=single, + fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{cpp} +class Memo { +public: + utils::NotNull AddGroup(LogicalOperator op); + utils::NotNull AddLogicalExprToGroup( + utils::NotNull group, LogicalOperator op); + utils::NotNull Populate(const Operator& op); + +private: + std::deque groups_; + std::unordered_map expr_index_; +}; + +class Group { +public: + utils::NotNull AddLogicalExpr(LogicalOperator op); + utils::NotNull AddPhysicalExpr( + PhysicalOperator op, bool is_enforcer = false); + + LogicalExprs GetLogicalExprs(); + PhysicalExprs GetPhysicalExprs(); +}; + \end{minted} +\end{listing} + +Метод \verb|Memo::Populate| рекурсивно обходит исходное логическое дерево и +создает первоначальные группы. Для предотвращения повторного добавления +выражений используется отображение \verb|expr_index_|. Ключ строится из типа +оператора, его параметров и идентификаторов дочерних групп. Таким образом, +одинаковые выражения добавляются в Memo только один раз. + +Каждая группа содержит логические и физические выражения. Логические выражения +описывают семантику запроса, а физические выражения определяют конкретный способ +исполнения. Например, логическому соединению могут соответствовать физические +операторы соединения вложенными циклами и хеш-соединения. + +\subsubsection{Правила оптимизации} + +Расширение пространства поиска выполняется с помощью правил. Реализовано два +типа правил: трансформационные и реализационные. Их интерфейсы приведены в +листинге~\ref{lst:optimizer-rules}. + +\begin{listing}[H] + \caption{Интерфейсы правил оптимизации.} + \label{lst:optimizer-rules} + \begin{minted}[style=bw, breaklines, frame=single, + fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{cpp} +class TransformationRule { +public: + virtual bool IsApplicable(utils::NotNull expr) = 0; + utils::NotNull Apply( + utils::NotNull expr, Memo& memo); + +private: + virtual LogicalOperator ApplyImpl( + utils::NotNull expr, Memo& memo) = 0; +}; + +class ImplementationRule { +public: + virtual bool IsApplicable(utils::NotNull expr) = 0; + virtual utils::NotNull Apply( + utils::NotNull expr, Memo& memo) = 0; +}; + \end{minted} +\end{listing} + +Правило трансформации создает новое логическое выражение и добавляет его в +ту же группу Memo, что и исходное выражение. Следовательно, правило должно +сохранять семантику запроса. Правило реализации создает физическое выражение, +которое может быть передано модулю исполнения. + +Реализованы следующие трансформационные правила: +\begin{itemize} + \item коммутативность соединения; + \item ассоциативность соединения; + \item разбиение конъюнкции на последовательность фильтров; + \item объединение последовательных фильтров; + \item проталкивание фильтра через проекцию; + \item проталкивание фильтра во входы соединения; + \item преобразование выражения \verb|IN| в цепочку сравнений. +\end{itemize} + +Например, правило коммутативности преобразует выражение $A \Join B$ в +$B \Join A$. Для левого и правого внешнего соединения одновременно изменяется +тип соединения. Реализация правила приведена в +листинге~\ref{lst:join-commutativity}. + +\begin{listing}[H] + \caption{Правило коммутативности соединения.} + \label{lst:join-commutativity} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize, + linenos=false, xleftmargin=1.5em]{cpp} +LogicalOperator JoinCommutativity::ApplyImpl( + utils::NotNull expr, Memo&) { + auto join = std::get(expr->root_operator); + std::swap(join.lhs, join.rhs); + + if (join.type == JoinType::kLeft) { + join.type = JoinType::kRight; + } else if (join.type == JoinType::kRight) { + join.type = JoinType::kLeft; + } + return join; +} + \end{minted} +\end{listing} + +Правило проталкивания фильтра через соединение разбивает предикат на конъюнкты. +Каждый конъюнкт анализируется отдельно. Если он ссылается только на атрибуты +одного входа соединения, фильтр переносится ближе к соответствующей таблице. Это +позволяет уменьшить число кортежей до выполнения соединения. Для внешних +соединений учитывается направление: фильтр нельзя переносить в такой вход, для +которого это изменит семантику результата. + +Реализационные правила создают: +\begin{itemize} + \item последовательное сканирование таблицы; + \item фильтрацию; + \item проекцию; + \item агрегацию; + \item декартово произведение вложенными циклами; + \item соединение вложенными циклами; + \item хеш-соединение. +\end{itemize} + +Хеш-соединение применяется только для внутреннего эквисоединения двух атрибутов. +Для остальных условий доступно соединение вложенными циклами. Проверка +применимости правила приведена в листинге~\ref{lst:implement-hash-join}. + +\begin{listing}[H] + \caption{Проверка применимости хеш-соединения.} + \label{lst:implement-hash-join} + \begin{minted}[style=bw, breaklines, frame=single, + fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{cpp} +bool ImplementHashJoin::IsApplicable( + utils::NotNull expr) { + if (!std::holds_alternative( + expr->root_operator)) { + return false; + } + + const auto& join = + std::get(expr->root_operator); + + if (join.type != JoinType::kInner) return false; + + const auto* bin = + std::get_if(&join.qual); + + return bin && bin->binop == BinaryOp::kEq + && std::holds_alternative(*bin->lhs) + && std::holds_alternative(*bin->rhs); +} + \end{minted} +\end{listing} + +Для каждого выражения хранится информация о ранее примененных правилах. Это +предотвращает повторное выполнение одного правила над одним выражением и +ограничивает образование циклов при заполнении Memo. + +\subsubsection{Оценка кардинальности} + +Стоимость физического плана зависит от количества обрабатываемых кортежей. +Для получения этой величины используется класс +\verb|CardinalityEstimates| из файла +\verb|src/stewkk/sql/logic/optimizer/cardinality.cpp|. + +Оценка кардинальности вычисляется для группы Memo и кэшируется. В текущей +версии используются следующие эвристики: +\begin{itemize} + \item для таблицы используется известное количество строк или значение по + умолчанию; + \item фильтрация и проекция сохраняют оценку кардинальности входа; + \item скалярная агрегация возвращает одну строку; + \item декартово произведение имеет кардинальность $N_l N_r$; + \item для соединения используется произведение кардинальностей входов и + коэффициента селективности. +\end{itemize} + +Для эквисоединения двух атрибутов коэффициент селективности оценивается как +\[ + S = \frac{1}{\max(1, N_l, N_r)}. +\] +Для конъюнкции условий коэффициенты селективности перемножаются. + +\subsubsection{Стоимостная модель} + +Стоимость плана вычисляется как сумма локальных стоимостей физических +операторов. Коэффициенты были подобраны с помощью бенчмарков отдельных +операторов, описанных в разделе тестирования. + +Для входных кардинальностей $N$, $N_l$ и $N_r$ используются следующие формулы: +\[ +\begin{aligned} + C_{\text{scan}} &= 100N, \\ + C_{\text{filter}} &= 100N, \\ + C_{\text{projection}} &= 22N, \\ + C_{\text{aggregation}} &= 510N, \\ + C_{\text{nested-loop join}} &= 70N_lN_r, \\ + C_{\text{cross join}} &= 104N_lN_r, \\ + C_{\text{hash join}} &= 69(N_l + N_r), \\ + C_{\text{sort}} &\approx 11N\log_2N. +\end{aligned} +\] + +Фрагмент реализации стоимостной модели приведен в +листинге~\ref{lst:optimizer-cost}. + +\begin{listing}[H] + \caption{Вычисление локальной стоимости физических операторов.} + \label{lst:optimizer-cost} + \begin{minted}[style=bw, breaklines, frame=single, + fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{cpp} +int64_t CalcCost(PhysicalExpr* expr, + CardinalityEstimates& cardinality) { + return std::visit(utils::Overloaded{ + [&](const physical::SeqScan&) { + return 100 * cardinality.GetCardinality(expr->group); + }, + [&](const physical::NestedLoopJoin& j) { + return 70 * cardinality.GetCardinality(j.lhs) + * cardinality.GetCardinality(j.rhs); + }, + [&](const physical::HashJoin& j) { + return 69 * (cardinality.GetCardinality(j.lhs) + + cardinality.GetCardinality(j.rhs)); + }, + [&](const physical::Sort& s) { + auto n = cardinality.GetCardinality(s.input); + return 11 * n * std::bit_width( + static_cast(n)); + }, + ... + }, expr->root_operator); +} + \end{minted} +\end{listing} + +\subsubsection{Алгоритм поиска} + +Класс \verb|Optimizer| реализует алгоритм поиска. При создании объекта исходное +логическое дерево помещается в Memo. Поиск запускается для корневой группы и +требуемых физических свойств результата. + +Алгоритм использует стек отложенных задач. Это позволяет разделить процесс на +небольшие операции: исследование группы, применение правила, оптимизацию +физического выражения и обработку его дочерних узлов. + +Основной метод оптимизации группы приведен в +листинге~\ref{lst:optimize-group}. + +\begin{listing}[H] + \caption{Оптимизация группы Memo.} + \label{lst:optimize-group} + \begin{minted}[style=bw, breaklines, frame=single, + fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{cpp} +void Optimizer::OptimizeGroup(Group* group, + PropertySet required, + Limit limit) { + WinnerKey key{group, required}; + if (winner_.contains(key)) return; + + if (IsExplored(group) + && limit + && LowerBoundCost(group) >= *limit) { + return; + } + + if (!IsExplored(group)) { + tasks_.emplace([=] { + OptimizeGroup(group, required, limit); + }); + tasks_.emplace([=] { + ExploreGroup(group, limit); + }); + return; + } + + for (auto expr : group->GetPhysicalExprs()) { + if (expr->is_enforcer) continue; + auto cost = local_cost_[expr.get()]; + if (!limit || cost < *limit) { + tasks_.emplace([=] { + OptimizeInputs(expr, required, {}, cost, limit); + }); + } + } + + AddEnforcers(group, required, limit); +} + \end{minted} +\end{listing} + +Если группа еще не исследована, в стек добавляются две задачи. Первая расширяет +группу с помощью правил, вторая повторно запускает ее оптимизацию после +расширения. Если группа уже исследована, для каждого физического выражения +вычисляется полная стоимость с учетом дочерних групп. + +Для каждой пары из группы и требуемого набора свойств хранится лучший найденный +вариант. Ключ такого варианта представлен структурой \verb|WinnerKey|: + +\begin{minted}[style=bw, breaklines, frame=single, + fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{cpp} +struct WinnerKey { + Group* group; + PropertySet required; +}; +\end{minted} + +Хранение победителя отдельно для каждого набора свойств необходимо, поскольку +самый дешевый неотсортированный план может отличаться от самого дешевого плана, +удовлетворяющего требованию \verb|ORDER BY|. + +Дочерние группы физического выражения оптимизируются последовательно. После +получения победителя дочерней группы его стоимость прибавляется к накопленной +стоимости. Если все дочерние группы обработаны, проверяются выходные свойства +плана. Затем найденный вариант при необходимости становится новым победителем. + +\subsubsection{Метод ветвей и границ} + +Полный перебор пространства планов быстро становится непрактичным при +увеличении количества соединяемых таблиц. Для сокращения перебора используется +метод ветвей и границ. + +Для каждой группы вычисляется нижняя граница стоимости. Она равна минимальной +сумме нижней оценки локального оператора и нижних оценок дочерних групп. +Например, для логического соединения выбирается минимум между стоимостью +хеш-соединения и стоимостью соединения вложенными циклами: +\[ + LB_{\text{join}} = + \min(69(N_l + N_r), 70N_lN_r). +\] + +Если нижняя граница не меньше стоимости уже известного решения, исследование +ветви прекращается. Фрагмент реализации приведен в +листинге~\ref{lst:lower-bound}. + +\begin{listing}[H] + \caption{Вычисление нижней границы стоимости группы.} + \label{lst:lower-bound} + \begin{minted}[style=bw, breaklines, frame=single, + fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{cpp} +int64_t Optimizer::LowerBoundCost(Group* group) { + if (auto it = lower_bounds_.find(group); + it != lower_bounds_.end()) { + return it->second; + } + + int64_t best = std::numeric_limits::max(); + + for (auto expr : group->GetLogicalExprs()) { + int64_t cost = LowerBoundLocalCost(expr, cardinality_); + + for (auto child : GetChildren(expr)) { + cost += LowerBoundCost(child); + } + best = std::min(best, cost); + } + + lower_bounds_[group] = best; + return best; +} + \end{minted} +\end{listing} + +Метод \verb|OptimizeInputs| также уменьшает границу для очередного дочернего +узла. Из общей допустимой стоимости вычитаются уже накопленная стоимость и +нижние оценки еще не обработанных дочерних групп. Благодаря этому заведомо +неоптимальные ветви отбрасываются до построения полного плана. + +Для исследовательских задач предусмотрен метод \verb|OptimizeExhaustive|. Он +запускает тот же алгоритм без ограничения стоимости и используется при +проверке достижимости внешних физических планов. + +\subsubsection{Физические свойства} + +Некоторые требования к результату нельзя выразить только стоимостью. Например, +конструкция \verb|ORDER BY| требует от результата определенного порядка +кортежей. Для представления таких требований используется класс +\verb|PropertySet|. + +В текущей версии реализовано свойство сортировки \verb|SortProperty|. Порядок +считается удовлетворяющим требованию, если требуемые ключи являются префиксом +фактически полученного порядка. Например, сортировка по $(a, b)$ удовлетворяет +требованию сортировки по $a$. + +Некоторые операторы сохраняют порядок дочернего узла. Фильтрация и проекция +сохраняют свойства входа. Соединение вложенными циклами сохраняет свойства +левого входа. Хеш-соединение и агрегация не гарантируют сохранение порядка. + +Если подходящий план не найден, оптимизатор пытается добавить обеспечивающий +оператор. Для сортировки используется класс \verb|SortEnforcer|. Он проверяет +наличие требуемых атрибутов в схеме группы и создает физический оператор +сортировки. + +\begin{listing}[H] + \caption{Добавление оператора сортировки для обеспечения свойства.} + \label{lst:sort-enforcer} + \begin{minted}[style=bw, breaklines, frame=single, + fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{cpp} +std::optional SortEnforcer::TryBuild( + utils::NotNull group, + const PropertySet& required, + SchemaCatalog& schema) const { + const auto* sort = required.Get(); + if (!sort) return std::nullopt; + + if (!ContainsRequiredKeys(schema.GetSchema(group), + sort->order)) { + return std::nullopt; + } + + return physical::Sort{group, sort->order}; +} + \end{minted} +\end{listing} + +\subsubsection{Формирование физического плана} + +После завершения поиска метод \verb|BuildOptimalPlan| рекурсивно восстанавливает +итоговое дерево физических операторов. Для каждой группы выбирается сохраненный +победитель с учетом требуемых свойств. Затем аналогичным образом +восстанавливаются планы дочерних групп. + +Результатом работы оптимизатора является объект \verb|PhysicalPlanNode|, который +не зависит от структуры Memo. Он может быть сериализован для анализа или передан +модулю исполнения запросов. + +Таким образом, модуль оптимизации разделяет представление пространства планов, +правила его расширения, стоимостную модель и алгоритм поиска. Такая организация +позволяет добавлять новые правила и физические операторы без изменения основной +логики оптимизатора. + +\subsection{Модуль физического хранения данных} + +Данные хранятся в одной директории, где каждому отношению соответствует +файл с CSV-таблицей. Название файла совпадает с названием отношения. +Первая строка CSV-файла содержит описание атрибутов и их типов, +последующие строки содержат кортежи отношения. + +Для чтения таблиц реализован класс +\verb|CsvDirSequentialScanner|~\refAlgo{lst:csv-dir-scanners}. Он +выполняет последовательное сканирование CSV-файла и передает прочитанные +кортежи модулю исполнения физических операторов. + +\begin{listing}[H] + \caption{Объявления классов сканирования CSV-таблиц.} + \label{lst:csv-dir-scanners} + \begin{minted}[style=bw, breaklines, frame=single, fontsize = \footnotesize, linenos=false, xleftmargin = 1.5em]{C++} +struct CsvDirSequentialScanner { + std::string dir; + + boost::asio::awaitable> operator()( + const std::string& table_name, + const std::string& output_table_name, + AttributesInfoChannel& attrs_chan, + TuplesChannel& tuples_chan) const; +}; + +struct CsvDirIndexedScanner { + std::string dir; + + boost::asio::awaitable> operator()( + const std::string& table_name, + const std::string& output_table_name, + const Expression& predicate, + AttributesInfoChannel& attrs_chan, + TuplesChannel& tuples_chan) const; +}; + \end{minted} +\end{listing} + +Дополнительно реализована поддержка индексов. Индекс представляет собой +отсортированный бинарный файл, содержащий целочисленные ключи и смещения +соответствующих строк в CSV-файле. Использование смещений позволяет +извлекать подходящие кортежи без полного сканирования отношения. + +Список доступных индексов задается в файле \verb|indexes.meta|, +расположенном в директории с данными. Каждая строка файла содержит +название отношения, название индексируемого атрибута, тип индекса и имя +бинарного файла~\refAlgo{lst:meta}. + +\begin{listing}[H] + \caption{Пример файла с метаданными о доступных индексах.} + \label{lst:meta} +\begin{minted}[style=bw, breaklines, frame=single, fontsize = \footnotesize, linenos=false, xleftmargin = 1.5em]{text} +users id sorted users.id.sorted.idx +\end{minted} +\end{listing} + +Для чтения данных с использованием индекса реализован класс +\verb|CsvDirIndexedScanner|~\refAlgo{lst:csv-dir-scanners}. Если указанный в +метаданных бинарный файл отсутствует, индекс строится при первом обращении к +таблице. Для поиска диапазона подходящих ключей используется упорядоченность +записей индекса. + +Поддерживаются индексы только для целочисленных атрибутов. Индексное +сканирование применяется для предикатов сравнения атрибута с +целочисленной константой. Поддерживаются операции равенства, строгого и +нестрогого сравнения. После чтения найденных строк дополнительно +вычисляется исходный предикат, что обеспечивает корректную обработку +составных условий. + +\subsection{Модуль исполнения физических планов} + +За исполнение запросов отвечает класс \verb|Executor|~\refAlgo{lst:executor}. +Он получает физический план, запускает исполнение его операторов и формирует +результирующее отношение. Физический план представлен деревом объектов типа +\verb|PhysicalPlanNode|. Для каждого типа узла предусмотрена отдельная функция +исполнения. + +Поддерживается исполнение последовательного и индексного сканирования, +фильтрации, проекции, сортировки, агрегации, декартова произведения, соединения +вложенными циклами и хеш-соединения. Операторы могут обрабатывать дочерние узлы +плана независимо и передавать результаты вышестоящим операторам по мере их +готовности. + +\begin{listing}[H] + \caption{Основные элементы объявления класса Executor.} + \label{lst:executor} + \begin{minted}[style=bw, breaklines, frame=single, fontsize = \footnotesize, linenos=false, xleftmargin = 1.5em]{C++} +template +class Executor { +public: + using SequentialScan = std::function>( + const std::string& table_name, + const std::string& output_table_name, + AttributesInfoChannel& attr_chan, + TuplesChannel& tuples_chan)>; + + using IndexScan = std::function>( + const std::string& table_name, + const std::string& output_table_name, + const Expression& predicate, + AttributesInfoChannel& attr_chan, + TuplesChannel& tuples_chan)>; + + Executor(SequentialScan seq_scan, + IndexScan index_scan, + boost::asio::any_io_executor executor); + + boost::asio::awaitable> + Execute(const PhysicalPlanNode& op); + +private: + SequentialScan sequential_scan_; + IndexScan index_scan_; + ExpressionExecutor expression_executor_; +}; + \end{minted} +\end{listing} + +Класс параметризуется интерфейсами \verb|SequentialScan|, \verb|IndexScan| и +\verb|ExpressionExecutor|. Первые два интерфейса отделяют исполнение физического +плана от конкретного способа хранения данных. \verb|SequentialScan| выполняет +полное чтение таблицы, а \verb|IndexScan| получает дополнительный предикат и +извлекает строки с использованием индекса. Благодаря этому исполнитель не +зависит от формата CSV-файлов и устройства индексных структур. + +Параметр \verb|ExpressionExecutor| определяет способ вычисления скалярных +выражений. Реализованы стратегии для интерпретации выражений и для +JIT-компиляции. По умолчанию используется интерпретируемый вариант. + +Значения атрибутов представлены структурой \verb|Value|. Она содержит признак +\verb|NULL| и значение одного из поддерживаемых типов: целого числа, логического +значения или идентификатора строки. Исходное значение для строк сохраняется в +пул, а в кортеже хранится числовой идентификатор строки. Кортеж представлен +вектором значений, а отношение состоит из описания атрибутов и списка кортежей. + +Коммуникация между физическими операторами осуществляется с помощью асинхронных +каналов. Используются два типа каналов: \verb|AttributesInfoChannel| для +однократной передачи схемы отношения и \verb|TuplesChannel| для передачи данных. +Кортежи передаются векторами. Это уменьшает число операций синхронизации и +позволяет потоковым операторам начинать обработку до полного завершения дочерних +узлов плана. + +Для реализации каналов используется +\verb|boost::asio::experimental::concurrent_channel|. Данный класс обеспечивает +потокобезопасность, буферизацию, асинхронную передачу данных и совместимость с +корутинами. Такой подход позволяет единообразно организовать взаимодействие +между операторами независимо от конкретных алгоритмов для их реализации. + +\subsection{Руководство пользователя} + +Для сборки программы требуются CMake, компилятор C++ с поддержкой C++23, LLVM, +ANTLR4 и библиотеки Boost. Дополнительно требуется Python, pytest и Docker. В +репозитории присутствует файл \texttt{flake.nix}, описывающий необходимые +зависимости, поэтому в системе с пакетным менеджером Nix рекомендуется открыть +окружение при помощи команды \verb|nix develop|. + +Все команды далее выполняются из корневого каталога проекта. + +\subsubsection{Сборка программы} + +Для выполнения конфигурации и сборки следует запустить команды на листинге~\ref{lst:build}. + +\begin{listing}[H] + \caption{Конфигурация и сборка проекта.} + \label{lst:build} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{bash} +cmake -S . -B build -DCMAKE_BUILD_TYPE=Release +cmake --build build -- -j 6 + \end{minted} +\end{listing} + +После успешной сборки исполняемый файл находится по пути +\texttt{build/bin/sql}. Для повторной сборки также можно использовать цель +\texttt{make build}. + +\subsubsection{Запуск программы} + +Пользователь должен создать отдельную директорию и поместить в нее CSV-файлы. +Имя каждого файла без расширения считается именем таблицы. В заголовке каждого +файла для столбца указывается имя и тип через двоеточие. Поддерживаются типы +\texttt{int} и \texttt{string}. Пустое SQL-значение записывается как +\texttt{NULL}. + +Для первого запуска можно использовать готовые тестовые данные в \verb|test/static/executor/test_data|. + +SQL-запрос передается программе через стандартный поток ввода. Простейший +пример запуска представлен на листинге~\ref{lst:run-example}. + +\begin{listing}[H] + \caption{Простейший пример запуска программы.} + \label{lst:run-example} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{bash} +echo 'SELECT users.id FROM users;' | \ + ./build/bin/sql --data-dir test/static/executor/test_data + \end{minted} +\end{listing} + +Программа выводит заголовок отношения, содержащий имена и типы столбцов, а +затем полученные строки. Если запрос не содержит \ORDERBY{}, строки вывода +сортируются лексикографически для воспроизводимости результата. + +\begin{figure}[H] +\centering +\includegraphics[width=0.8\textwidth]{run-example.png} +\caption{Выполнение SQL-запроса над тестовыми данными}% +\label{fig:practice-run} +\end{figure} + +\subsubsection{Вывод логического и физического планов} + +Для диагностики предусмотрены параметры \texttt{--print-ast} и +\texttt{--print-plan}. Первый параметр выводит логическое дерево после +синтаксического анализа, второй --- выбранный физический план. +Пример использования представлен на листинге~\ref{lst:run-plan}. + +\begin{listing}[H] + \caption{Запуск программы с выводом логического дерева и физического плана.} + \label{lst:run-plan} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{bash} +echo 'SELECT users.id FROM users WHERE users.age > 18 \ +ORDER BY users.id;' | \ + ./build/bin/sql --data-dir test/static/executor/test_data \ + --print-ast --print-plan + \end{minted} +\end{listing} + +\begin{figure}[H] +\centering +\includegraphics[width=0.8\textwidth]{run-with-prints.png} +\caption{Вывод логического дерева и физического плана}% +\label{fig:practice-plan} +\end{figure} + +Для проверки полноты набора правил оптимизации предусмотрен режим +\verb|--check-reachable|. В этом режиме программа получает SQL-запрос и +сериализованный физический план, после чего выполняет полный перебор +пространства поиска без отсечения по стоимости. Если заданный план может быть +получен с помощью реализованных правил, программа выводит сообщение +\verb|REACHABLE|. + +План передается в отдельном файле в формате S-expression. Например, содержимое +файла \verb|target.plan| может иметь вид, представленный на листинге~\ref{lst:target-plan}. + +\begin{listing}[H] + \caption{Пример содержимого файла с целевым физическим планом.} + \label{lst:target-plan} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} +(PhysicalProjection (exprs (attr users id)) + (PhysicalFilter (> (attr users age) 18) + (SeqScan users))) + \end{minted} +\end{listing} + +Проверка выполняется командой на листинге~\ref{lst:check-reachable}. + +\begin{listing}[H] + \caption{Запуск программы в режиме проверки достижимости плана.} + \label{lst:check-reachable} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} +echo 'SELECT users.id FROM users WHERE users.age > 18;' | \ + ./build/bin/sql \ + --data-dir test/static/executor/test_data \ + --check-reachable target.plan + \end{minted} +\end{listing} + +Если план недостижим, программа выводит сообщение \verb|NOT REACHABLE| и +описание первого обнаруженного расхождения. Параметр \verb|--data-dir| +необходим для загрузки схем таблиц. В частности, схема используется при +проверке достижимости планов с сортировкой. + +Для автоматизированной проверки достижимости планов используется Microsoft SQL +Server. Контейнер с СУБД запускается с помощью Docker Compose. Перед запуском +скриптов в директории research, следует выполнить команды на листинге~\ref{lst:docker-up}. + +\begin{listing}[H] + \caption{Запуск контейнера с Microsoft SQL Server.} + \label{lst:docker-up} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} +cd research +docker compose up -d +cd .. + \end{minted} +\end{listing} + +Для запуска модульных тестов C++ следует выполнить команду на листинге~\ref{lst:ctest}. + +\begin{listing}[H] + \caption{Запуск модульных тестов C++.} + \label{lst:ctest} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} +ctest --test-dir build --output-on-failure + \end{minted} +\end{listing} + +Модульные тесты проверяют синтаксический анализ запросов, вычисление выражений, +исполнение физических операторов, индексное сканирование, сериализацию планов, +структуру Memo, правила оптимизации и физические свойства. + +Для запуска Python-тестов преобразователя планов Microsoft SQL Server +необходимо предварительно запустить контейнер, после чего выполнить команды на листинге~\ref{lst:pytest-converter}. + +\begin{listing}[H] + \caption{Запуск тестов преобразователя планов Microsoft SQL Server.} + \label{lst:pytest-converter} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} +cd research +pytest -q test_converter.py +cd .. + \end{minted} +\end{listing} + +Тесты инструментов Star Schema Benchmark запускаются командой на листинге~\ref{lst:pytest-ssb}. + +\begin{listing}[H] + \caption{Запуск тестов инструментов Star Schema Benchmark.} + \label{lst:pytest-ssb} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} +pytest -q benchmarks/datasets/ssb + \end{minted} +\end{listing} + +Для поиска ошибок исполнения реализован дифференциальный фаззер. Он генерирует +случайные SQL-запросы, выполняет их в модельной СУБД и Microsoft SQL Server, +после чего сравнивает полученные результаты. Перед запуском необходимо поднять +контейнер с Microsoft SQL Server. + +Фаззер запускается из корневого каталога проекта командой на листинге~\ref{lst:diff-fuzz}. + +\begin{listing}[H] + \caption{Запуск дифференциального фаззера.} + \label{lst:diff-fuzz} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} +python -m research.fuzz.diff_fuzz \ + --cli build/bin/sql \ + --data-dir test/static/executor/test_data + \end{minted} +\end{listing} + +Для проверки режима JIT-компиляции можно дополнительно передать параметр +\verb|--jit|. При обнаружении расхождения фаззер выводит начальное значение +генератора случайных чисел и текст запроса, что позволяет воспроизвести ошибку. + +Второй фаззер проверяет полноту набора правил оптимизатора. Он получает +физический план запроса из Microsoft SQL Server, преобразует его во внутренний +формат проекта и запускает программу в режиме \verb|--check-reachable|, как показано на листинге~\ref{lst:reach-fuzz}. + +\begin{listing}[H] + \caption{Запуск фаззера проверки полноты правил оптимизатора.} + \label{lst:reach-fuzz} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} +python -m research.fuzz.reach_fuzz \ + --cli build/bin/sql \ + --data-dir test/static/executor/test_data + \end{minted} +\end{listing} + +\begin{figure}[H] +\centering +\includegraphics[width=0.8\textwidth]{reach-fuzz.png} +\caption{Пример вывода фаззера проверки полноты правил оптимизатора}% +\label{fig:reach-fuzz} +\end{figure} + +Оба фаззера поддерживают параметр \verb|--start-seed|. Он задает начальное +значение генератора и используется для воспроизводимого повторного запуска, +как показано на листинге~\ref{lst:reach-fuzz-seed}. + +\begin{listing}[H] + \caption{Запуск фаззера с фиксированным начальным значением генератора.} + \label{lst:reach-fuzz-seed} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} +python -m research.fuzz.reach_fuzz \ + --cli build/bin/sql \ + --data-dir test/static/executor/test_data \ + --start-seed 1000 + \end{minted} +\end{listing} + +\begin{longtable}{|p{4.2cm}|p{9.3cm}|} + \caption{Параметры командной строки программы.}\label{tbl:practice-cli}\\ + \hline + Параметр & Назначение \\ + \hline + \endfirsthead + \multicolumn{2}{l}{\small Продолжение таблицы~\ref{tbl:practice-cli}}\\ + \hline + Параметр & Назначение \\ + \hline + \endhead + \texttt{--data-dir DIR} & + Директория с CSV-файлами таблиц. Обязательна при обычном исполнении запроса. \\ + \hline + \texttt{--print-ast} & + Вывод логического дерева операторов после синтаксического анализа. \\ + \hline + \texttt{--print-plan} & + Вывод выбранного физического плана в формате S-expression. \\ + \hline + \texttt{--jit} & + Использование кэшируемой JIT-компиляции скалярных выражений. \\ + \hline + \texttt{--check-reachable FILE} & + Проверка достижимости физического плана, сериализованного в файле. \\ + \hline +\end{longtable} + +\section{Тестирование} + +Тестирование разработанной модельной СУБД выполнялось на нескольких уровнях. Для +проверки отдельных компонентов использовались модульные тесты. Корректность +выполнения запросов и полнота пространства поиска оптимизатора дополнительно +проверялись с помощью фаззинга. Для оценки производительности и калибровки +стоимостной модели были реализованы бенчмарки. Вспомогательные инструменты на +языке Python проверяются с помощью библиотеки \verb|pytest|. + +\subsection{Модульные тесты} + +Модульные тесты реализованы с использованием библиотеки GoogleTest. Они +проверяют синтаксический анализ SQL-запросов, исполнение физических операторов, +индексное сканирование, вычисление выражений, сериализацию физических планов, +структуру Memo, правила оптимизации и обработку свойств сортировки. + +Отдельные тесты проверяют выбор физического плана. Например, при различных +оценках кардинальности оптимизатор должен выбирать подходящий порядок соединения +таблиц. Также проверяется достижимость физических планов при полном переборе +пространства поиска. + +Пример теста синтаксического анализатора приведен в +листинге~\ref{lst:googletest}. + +\begin{listing}[H] + \caption{Пример модульного теста.} + \label{lst:googletest} + \begin{minted}[style=bw, breaklines, frame=single, fontsize = \footnotesize, linenos=false, xleftmargin = 1.5em]{cpp} +TEST(ParserTest, SelectSingleColumnFromSingleTable) { + std::stringstream s{"SELECT users.id FROM users;"}; + + Operator got = GetAST(s).value().op; + + ASSERT_THAT(got, VariantWith( + Projection{ + std::vector{{Attribute{"users", "id"}}}, + std::make_shared(Table{"users"}) + })); +} + \end{minted} +\end{listing} + +\subsection{Python-тесты} + +Для проверки вспомогательных инструментов используются тесты на языке Python, +запускаемые с помощью библиотеки \verb|pytest|. Они разделены на две группы. + +Первая группа проверяет преобразование физических планов Microsoft SQL Server из +формата ShowPlanXML во внутренний текстовый формат проекта. Рассматриваются +последовательное и индексное сканирование, фильтрация, сортировка, агрегация и +соединения. Часть тестов использует заранее подготовленные XML-фрагменты, часть +может быть запущена с подключением к экземпляру Microsoft SQL Server. + +Вторая группа относится к Star Schema Benchmark. Она проверяет преобразование +исходных таблиц SSB в CSV-формат модельной СУБД, обработку некорректных входных +данных и успешное выполнение стандартных аналитических запросов. + +\subsection{Фаззинг} + +Для проверки системы на большом количестве входных данных реализован генератор +случайных SQL-запросов. Он строит запросы из поддерживаемого подмножества языка: +проекции, фильтры, соединения, группировки, агрегатные функции, сортировки, +строковые выражения, \verb|IN| и \verb|BETWEEN|. Использование фиксированного +начального значения генератора позволяет воспроизводить найденные ошибки. + +% TODO: заменить на макросы \sqkkw + +Дифференциальный фаззер выполняет каждый сгенерированный запрос в модельной СУБД +и в Microsoft SQL Server. Результаты сравниваются как мультимножества строк. +Для запросов с \verb|ORDER BY| дополнительно проверяется корректность порядка +результатов. При обнаружении расхождения выводятся начальное значение +генератора, текст запроса и результаты обеих систем. + +Отдельный фаззер используется для исследования полноты набора правил +оптимизации. Для случайного запроса из Microsoft SQL Server извлекается +физический план, после чего план преобразуется во внутренний формат проекта. +Затем модельный оптимизатор выполняет полный перебор пространства поиска и +проверяет, достижим ли полученный план с помощью реализованных правил. Такой +подход позволяет находить отсутствующие преобразования и физические операторы. + +\subsection{Бенчмарки} + +Тесты производительности реализованы с помощью библиотеки Google Benchmark. +Для запросов предусмотрены два режима построения физического плана: +\verb|Naive| и \verb|Optimized|. В первом случае физический план строится +непосредственно по логическому дереву без применения оптимизатора. Во втором +случае используется стоимостная оптимизация. Сравнение этих режимов позволяет +оценить влияние выбранного физического плана на время выполнения запроса. + +Бенчмарки выполняются как для небольших синтетических тестовых запросов, так и +для набора аналитических запросов Star Schema Benchmark. Дополнительно +поддерживается сравнение интерпретируемого и JIT-компилируемого способов +вычисления выражений. + +\begin{figure}[H] +\centering +\includegraphics[width=0.8\textwidth]{naive-vs-optimizer.png} +\caption{Сравнение времени выполнения запросов при наивном и оптимизированном + построении физического плана.} +\label{fig:benchmark-naive-optimized} +\end{figure} + +\begin{figure}[H] +\centering +\includegraphics[width=0.8\textwidth]{optimizer-speedup.png} +\caption{Относительное ускорение выполнения запросов после применения оптимизатора.} +\label{fig:benchmark-speedup} +\end{figure} + +В таблице~\ref{tbl:ssb-benchmark} приведено среднее время выполнения трех +повторений запросов SSB для набора данных масштаба \texttt{sf001}. Ускорение +рассчитывается как отношение времени выполнения наивного плана ко времени +выполнения оптимизированного плана. + +\begin{table}[H] + \centering + \caption{Результаты выполнения запросов Star Schema Benchmark.} + \label{tbl:ssb-benchmark} + \begin{tabular}{|l|r|r|r|} + \hline + Запрос & \texttt{Naive}, мс & \texttt{Optimized}, мс & Ускорение \\ + \hline + \texttt{q1.1} & 8.11 & 6.51 & 1.25 \\ + \texttt{q1.2} & 8.20 & 6.59 & 1.24 \\ + \texttt{q1.3} & 8.00 & 6.58 & 1.22 \\ + \texttt{q2.1} & 8.80 & 9.33 & 0.94 \\ + \texttt{q2.2} & 8.84 & 6.18 & 1.43 \\ + \texttt{q2.3} & 8.68 & 5.80 & 1.50 \\ + \texttt{q3.1} & 35.67 & 5.90 & 6.04 \\ + \texttt{q3.2} & 35.88 & 5.58 & 6.43 \\ + \texttt{q3.3} & 38.05 & 5.86 & 6.49 \\ + \texttt{q3.4} & 37.12 & 5.77 & 6.43 \\ + \texttt{q4.1} & 9.06 & 12.21 & 0.74 \\ + \texttt{q4.2} & 9.05 & 11.12 & 0.81 \\ + \texttt{q4.3} & 9.28 & 6.31 & 1.47 \\ + \hline + \end{tabular} +\end{table} + +\subsection{Калибровка стоимостной модели} + +Для калибровки коэффициентов в стоимостных формулах реализованы отдельные +бенчмарки физических операторов. Измеряется время выполнения последовательного +сканирования, фильтрации, проекции, сортировки, агрегации, хеш-соединения, +соединения вложенными циклами и декартова произведения. + +При выполнении этих бенчмарков данные хранятся в оперативной памяти. Это +позволяет уменьшить влияние файловой системы и измерять преимущественно +стоимость самого физического оператора. Для каждого оператора выполняются замеры +на таблицах различного размера. + +Дополнительно формируются группы запусков с приблизительно одинаковой расчетной +стоимостью. Для этого размер входных отношений выбирается на основе стоимостной +формулы оператора. Сопоставление расчетной стоимости с фактическим временем +выполнения позволяет сравнить операторы между собой и скорректировать +коэффициенты модели. Благодаря этому оптимизатор принимает решения на основе +оценок, соответствующих характеристикам реализованного исполнителя. + +\begin{figure}[H] +\centering +\includegraphics[width=0.8\textwidth]{scaling.png} +\caption{Зависимость времени выполнения физических операторов от размера входных + данных.} +\label{fig:operator-benchmark} +\end{figure} + +\begin{figure}[H] +\centering +\includegraphics[width=0.8\textwidth]{calibration.png} +\caption{Сопоставление расчетной стоимости физических операторов с фактическим + временем выполнения.} +\label{fig:cost-model-calibration} +\end{figure} + +Стоимостные формулы физических операторов, полученные по результатам +калибровки, приведены в таблице~\ref{tbl:cost-model-coefficients}. + +\begin{table}[H] + \centering + \caption{Стоимостные формулы физических операторов.} + \label{tbl:cost-model-coefficients} + \small + \begin{tabular}{|p{0.40\textwidth}|l|} + \hline + Физический оператор & Формула стоимости \\ + \hline + Последовательное сканирование & $100\,n$ \\ + Фильтрация & $100\,n_{out}$ \\ + Проекция & $22\,n_{out}$ \\ + Сортировка & $11\,n\bigl(\lfloor\log_2 n\rfloor + 1\bigr)$ \\ + Агрегация & $510\,n$ \\ + Соединение вложенными циклами & $70\,n_l n_r$ \\ + Декартово произведение & $104\,n_l n_r$ \\ + Хеш-соединение & $100\,n_b w_b + 35\,n_p + 10\,n_o w_o$ \\ + \hline + \end{tabular} +\end{table} + +\anonsection{ЗАКЛЮЧЕНИЕ} + +В ходе практики разработана модельная СУБД и оптимизатор для нее. Программа +работает с CSV-файлами, поддерживает основные операторы реляционной алгебры и +формирует оптимальный физический план на основе стоимостной модели. + +Ключевой частью проекта является расширяемый оптимизатор, использующий структуру +Memo и правила преобразования. Реализован поиск планов с учетом физических +свойств, нижних оценок стоимости и метода ветвей и границ. + +Корректность основных компонентов проверена модульными тестами. Дополнительно +подготовлены бенчмарки физических операторов, оценен прирост производительности +относительно наивного плана и реализованы инструменты дифференциального +сравнения с промышленной СУБД Microsoft SQL Server. + +Цель курсовой работы была достигнута. Получена стабильная и расширяемая система, +демонстрирующая возможности оптимизатора. + +В заключение, несмотря на достигнутые положительные результаты, существует +потенциал для дальнейшего улучшения реализации оптимихатора. Это включает в себя +реализацию дедупликации групп в процессе поиска, добавление новых правил +трансформации и реализации и поддержку подзапросов. + +\renewcommand\refname{СПИСОК ИСПОЛЬЗОВАННЫХ ИСТОЧНИКОВ} +\clearpage +{\catcode`"\active\def"{\relax} + \addcontentsline{toc}{section}{\protect\numberline{}\refname}% + \printbibliography +} +\newpage + +\settocdepth{section} +\anonsection{ПРИЛОЖЕНИЕ А} +\vspace{-30pt} + +\begin{algorithm}[H] + \caption{Алгоритм поиска (часть 1).}% + \label{alg:cascades-search} + \begin{algorithmic}[1] + + \Procedure{Optimize}{$root$} + \State Push task $\Call{OptimizeGroup}{root.group, \bot}$ onto task stack + \While{task stack is not empty} + \State Pop and execute next task + \EndWhile + \State \Return $best\_plan[root.group]$ + \EndProcedure + + \Procedure{OptimizeGroup}{$group, limit$} + \If{$best\_cost[group]$ exists \textbf{and} ($limit = \bot$ \textbf{or} $best\_cost[group] < limit$)} + \State \Return + \EndIf + \If{$group$ is not explored} + \State Push task $\Call{OptimizeGroup}{group, limit}$ onto task stack + \State Push task $\Call{ExploreGroup}{group, limit}$ onto task stack + \State \Return + \EndIf + \For{each logical expression $expr \in group$} + \State Push task $\Call{OptimizeExpression}{expr, limit}$ onto task stack + \EndFor + \EndProcedure + + \Procedure{ExploreGroup}{$group, limit$} + \State Mark $group$ as explored + \For{each logical expression $expr \in group$} + \State Push task $\Call{ExploreExpression}{expr, limit}$ onto task stack + \EndFor + \EndProcedure + \end{algorithmic} +\end{algorithm} + +\begin{algorithm}[H] + \caption{Алгоритм поиска (часть 2).}% + \label{alg:cascades-search-2} + \begin{algorithmic}[1] + \Procedure{ExploreExpression}{$expr, limit$} + \For{each applicable transformation rule $r$} + \State Push task $\Call{ApplyTransformation}{r, expr, limit}$ onto task stack + \EndFor + \For{each child group $child \in \Call{Children}{expr}$} + \If{$child$ is not explored} + \State Push task $\Call{ExploreGroup}{child, limit}$ onto task stack + \EndIf + \EndFor + \EndProcedure + + \Procedure{OptimizeExpression}{$expr, limit$} + \For{each applicable implementation rule $r$} + \State Push task $\Call{ApplyImplementation}{r, expr, limit}$ onto task stack + \EndFor + \For{each child group $child \in \Call{Children}{expr}$} + \If{$child$ is not explored} + \State Push task $\Call{ExploreGroup}{child, limit}$ onto task stack + \EndIf + \EndFor + \EndProcedure + + \Procedure{ApplyTransformation}{$rule, expr, limit$} + \State $new\_expr \leftarrow \Call{Apply}{rule, expr}$ + \State Push task $\Call{ExploreExpression}{new\_expr, limit}$ onto task stack + \EndProcedure + + \Procedure{ApplyImplementation}{$rule, expr, limit$} + \State $phys \leftarrow \Call{Apply}{rule, expr}$ + \State $lc \leftarrow \Call{CalcLocalCost}{phys}$ + \If{$limit \neq \bot$ \textbf{and} $lc \geq limit$} + \State \Return + \EndIf + \State $child\_limit \leftarrow (limit = \bot) \mathbin{?} \bot \mathbin{:} limit - lc$ + \State Push task $\Call{OptimizeInputs}{phys, child\_limit, 0}$ onto task stack + \EndProcedure + \end{algorithmic} +\end{algorithm} + +\begin{algorithm}[H] + \caption{Алгоритм поиска (часть 3).}% + \label{alg:cascades-search-3} + \begin{algorithmic}[1] + \Procedure{OptimizeInputs}{$expr, limit, i$} + \State $children \leftarrow \Call{Children}{expr}$ + \If{$i \geq |children|$} + \State $total \leftarrow local\_cost[expr] + accum\_child\_cost[expr]$ + \If{$best\_cost[expr.group]$ does not exist \textbf{or} $total < best\_cost[expr.group]$} + \State $best\_cost[expr.group] \leftarrow total$ + \State $best\_plan[expr.group] \leftarrow expr$ + \EndIf + \State \Return + \EndIf + \State $child \leftarrow children[i]$ + \State Push task: \textbf{if} $best\_cost[child]$ exists \textbf{then} accumulate child cost \textbf{and} push $\Call{OptimizeInputs}{expr, \ldots, i+1}$ + \State Push task $\Call{OptimizeGroup}{child, limit}$ onto task stack + \EndProcedure + \end{algorithmic} +\end{algorithm} + +\newpage{} + +\begin{scriptsize} +\begin{longtable}{|c|p{2.6cm}|c|p{4.2cm}|} + \caption{Правила трансформации.}\label{tbl:transformation_rules}\\ + \hline + № & Название & Правило & Комментарий \\ + \hline + \endfirsthead + \multicolumn{4}{l}{\small Продолжение таблицы~\ref{tbl:transformation_rules}}\\ + \hline + № & Название & Правило & Комментарий \\ + \hline + \endhead + \hline + \endfoot + \hline + \endlastfoot + + 1 & Коммутативность соединения & + \(\TransRule{\InnerJoin{R}{\theta}{S}}{\text{внутреннее или полное внешнее соединение}}{\InnerJoin{S}{\theta}{R}}\) & + Перестановка входов соединения. Позволяет рассмотреть оба порядка вычисления. \\ + \hline + + 2 & Ассоциативность соединения & + \(\TransRule{\InnerJoin{(\InnerJoin{R}{\theta_1}{S})}{\theta_2}{T}}{\substack{\text{оба соединения внутренние;}\\ \theta_1 \land \theta_2 \text{ перераспределяемы}}}{\InnerJoin{R}{\theta'_1}{(\InnerJoin{S}{\theta'_2}{T})}}\) & + Изменяет группировку цепочки соединений. Открывает альтернативные порядки и уменьшает размер промежуточных результатов. \\ + \hline + + 3 & Проталкивание фильтра в левый вход & + \(\TransRule{\Sel{p}{\InnerJoin{R}{\theta}{S}}}{\substack{attrs(p) \subseteq attrs(R); \\ \text{внутреннее соединение или сохраняющий вход}}}{\InnerJoin{\Sel{p}{R}}{\theta}{S}}\) & + Применяет селективный предикат до соединения, уменьшая кардинальность входа. \\ + \hline + + 4 & Проталкивание фильтра в правый вход & + \(\TransRule{\Sel{p}{\InnerJoin{R}{\theta}{S}}}{\substack{attrs(p) \subseteq attrs(S); \\ \text{внутреннее соединение или сохраняющий вход}}}{\InnerJoin{R}{\theta}{\Sel{p}{S}}}\) & + Симметричный случай для правого входа. \\ + \hline + + 5 & Перенос фильтра в предикат соединения & + \(\TransRule{\Sel{p}{\InnerJoin{R}{\theta}{S}}}{\substack{\text{соединение внутреннее;} \\ attrs(p) \subseteq attrs(R) \cup attrs(S)}}{\InnerJoin{R}{\theta \land p}{S}}\) & + Объединяет фильтр и предикат соединения. Может позволить применить хеш-соединение или соединение слиянием. \\ + \hline + + 6 & Превращение декартова произведения в соединение & + \(\TransRule{\Sel{p}{\CProd{R}{S}}}{\top}{\InnerJoin{R}{p}{S}}\) & + Частный случай правила~5. \\ + \hline + + 7 & Поднятие фильтра над соединением & + \(\TransRule{\InnerJoin{\Sel{p}{R}}{\theta}{S}}{\substack{attrs(p) \subseteq attrs(R); \\ \text{внутреннее соединение или сохраняющая сторона}}}{\Sel{p}{\InnerJoin{R}{\theta}{S}}}\) & + Переносит фильтр выше соединения для объединения с предикатами родительских операторов. \\ + \hline + + 8 & Разбиение фильтра на конъюнкты & + \(\TransRule{\Sel{p_1 \land \ldots \land p_n}{R}}{\top}{\Sel{p_1}{\ldots \Sel{p_n}{R} \ldots}}\) & + Позволяет обрабатывать каждый конъюнкт независимо. \\ + \hline + + 9 & Проталкивание проекции через соединение & + \(\TransRule{\Projection{L}{\InnerJoin{R}{\theta}{S}}}{\substack{L_R \cup J_R \subsetneq attrs(R) \\ \text{или}\ L_S \cup J_S \subsetneq attrs(S)}}{\Projection{L}{\InnerJoin{\Projection{L_R \cup J_R}{R}}{\theta}{\Projection{L_S \cup J_S}{S}}}}\) & + Удаляет неиспользуемые атрибуты до соединения, сокращая ширину кортежей. \\ + \hline + + 10 & Левое внешнее соединение во внутреннее & + \(\TransRule{\Sel{p}{\LOJ{R}{\theta}{S}}}{p\ \text{null-отбрасывающий по}\ attrs(S)}{\Sel{p}{\InnerJoin{R}{\theta}{S}}}\) & + Открывает для подплана правила, ограниченные внутренним соединением. \\ + \hline + + 11 & Правое внешнее соединение во внутреннее & + \(\TransRule{\Sel{p}{\ROJ{R}{\theta}{S}}}{p\ \text{null-отбрасывающий по}\ attrs(R)}{\Sel{p}{\InnerJoin{R}{\theta}{S}}}\) & + Симметричный случай. \\ + \hline + + 12 & Полное внешнее соединение во внутреннее & + \(\TransRule{\Sel{p}{\FOJ{R}{\theta}{S}}}{p\ \text{null-отбрасывающий по}\ attrs(R)\ \text{и}\ attrs(S)}{\Sel{p}{\InnerJoin{R}{\theta}{S}}}\) & + Требует null-отбрасывания с обеих сторон. \\ + \hline + + 13 & Проталкивание агрегации ниже соединения & + \(\TransRule{\Agg{G}{F}{\InnerJoin{R}{R.k = S.k}{S}}}{\substack{F\text{ разделяема;}\\ \text{внутреннее соединение по равенству}}}{\Agg{G}{F'}{\InnerJoin{\AggP{G_R \cup \{R.k\}}{F_R}{R}}{R.k=S.k}{S}}}\) & + Снижает кардинальность входа частичной группировкой. \(G_R = G \cap attrs(R)\); \(F_R\) --- частичные агрегаты по атрибутам \(R\); \(F'\) --- финальные комбинаторы. \\ + \hline + + 14 & Перестановка агрегации и соединения & + \(\TransRule{\Agg{G}{F_R}{\InnerJoin{R}{R.k=S.k}{S}}}{\substack{\text{внутреннее соединение по равенству;}\\ F_R\subseteq attrs(R);\ R.k \in G; \\ S.k\text{ уникален и полон}}}{\InnerJoin{\Agg{G}{F_R}{R}}{R.k=S.k}{S}}\) & + Полностью переносит агрегацию до соединения. \(S.k\) уникален: соединение не дублирует строки \(R\). \(S.k\) полон относительно \(R.k\): соединение не теряет строки \(R\). \\ + \hline +\end{longtable} + +\newpage{} + +\begin{longtable}{|c|p{2.8cm}|c|p{4.5cm}|} + \caption{Правила реализации.}\label{tbl:implementation_rules}\\ + \hline + № & Название & Правило & Комментарий \\ + \hline + \endfirsthead + \multicolumn{4}{l}{\small Продолжение таблицы~\ref{tbl:implementation_rules}}\\ + \hline + № & Название & Правило & Комментарий \\ + \hline + \endhead + \hline + \endfoot + \hline + \endlastfoot + + 1 & Последовательное сканирование & + \(\TransRule{\LogicalGet}{\top}{\SeqScan}\) & + Применимо к любому отношению. \\ + \hline + + 2 & Сканирование по индексу & + \(\TransRule{\LogicalGet}{\text{есть совместимый индекс}}{\IndexScan}\) & + Индекс должен быть совместим с предикатом или требуемым порядком. \\ + \hline + + 3 & Соединение вложенными циклами & + \(\TransRule{\InnerJoin{R}{\theta}{S}}{\top}{\NestedLoopJoin}\) & + Применимо при любом виде предиката. Стоимость высока без доступа по индексу к внутреннему отношению. \\ + \hline + + 4 & Хеш-соединение & + \(\TransRule{\InnerJoin{R}{\theta}{S}}{\theta\ \text{содержит равенство по ключам}}{\HashJoin}\) & + Хеш-таблица строится по ключам равенства. \\ + \hline + + 5 & Соединение слиянием & + \(\TransRule{\InnerJoin{R}{\theta}{S}}{\text{входы упорядочены по ключам соединения}}{\MergeJoin}\) & + Требуемый порядок может быть обеспечен явным оператором \Sort{}. \\ + \hline + + 6 & Хеш-агрегация & + \(\TransRule{\Agg{G}{F}{R}}{\top}{\sqlkw{HashAggregate}}\) & + Не требует упорядоченности входа. Потребляет память пропорционально числу групп. \\ + \hline + + 7 & Потоковая агрегация & + \(\TransRule{\Agg{G}{F}{R}}{\text{вход упорядочен по } G}{\sqlkw{StreamAggregate}}\) & + Эффективна при уже отсортированном входе. Может быть выгодна, если требуемый порядок нужен и родительскому оператору. \\ + \hline +\end{longtable} +\end{scriptsize} + +\end{document} From 1de9a98f296a40bff6b1981b7f269b81fd54eb1c Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sat, 6 Jun 2026 14:26:58 +0300 Subject: [PATCH 082/120] Intro --- report/vkr.tex | 68 +++++++++++++++++++++++++++++--------------------- 1 file changed, 39 insertions(+), 29 deletions(-) diff --git a/report/vkr.tex b/report/vkr.tex index c75cf16..e789632 100644 --- a/report/vkr.tex +++ b/report/vkr.tex @@ -112,40 +112,50 @@ \section*{АННОТАЦИЯ} SQL-запроса в эффективный физический план исполнения. Именно от качества работы оптимизатора в значительной степени зависит производительность всей системы: выбор неоптимального плана исполнения может привести к увеличению времени -выполнения запроса в десятки раз относительно оптимального случая. - -Также оптимизатор запросов считается наиболее сложной составной частью любой -СУБД. Задача нахождения оптимального плана является NP-полной в общем случае, -что обуславливает необходимость применения эвристических методов и алгоритмов -ограниченного перебора. Современные коммерческие и открытые системы используют -различные архитектуры оптимизаторов, среди них наиболее популярны устоявшиеся и -проверенные временем архитектуры Cascades и Volcano. - -Архитектура Cascades используется в таких промышленных системах, как Microsoft -SQL Server, Apache Orca, а также в ряде исследовательских проектов, включая -Columbia и CockroachDB\@. Преимущества этой архитектуры заключаются в -расширяемости, модульности, а также понятном и эффективном алгоритме поиска, -допускающим параллельность исполнения. +выполнения запроса в десятки раз относительно оптимального случая.Также +оптимизатор запросов считается наиболее сложной составной частью любой СУБД. +Задача нахождения оптимального плана является NP-полной в общем случае, что +обуславливает необходимость применения эвристических методов и алгоритмов +ограниченного перебора. + +Архитектура оптимизаторов Cascades используется в таких промышленных системах, +как Microsoft SQL Server, Apache Orca, а также в ряде исследовательских +проектов, включая Columbia и CockroachDB\@. Преимущества этой архитектуры +заключаются в расширяемости, модульности, а также понятном и эффективном +алгоритме поиска, допускающим параллельность исполнения. Целью данной работы является разработка и реализация оптимизатора подмножества SQL-запросов на основе архитектуры Cascades, а также создание каркаса для его итеративного улучшения путем сравнения с промышленными реализациями. -Для достижения поставленной цели необходимо решить следующие задачи: -\begin{itemize} - \item изучить существующие архитектуры оптимизаторов запросов, включая - System~R, Volcano и Cascades; - \item формализовать подмножество SQL и соответствующее подмножество - реляционной алгебры; - \item реализовать структуру данных Memo и алгоритм поиска оптимального плана - на основе архитектуры Cascades; - \item реализовать набор правил трансформации и реализации для основных - операторов реляционной алгебры; - \item реализовать метод ветвей и границ для эффективного отсечения - неоптимальных планов; - \item реализовать метод дифференциального анализа физических планов для - автоматизированного поиска примеров неоптимальных планов. -\end{itemize} +Для достижения поставленной цели прежде всего требуется изучить архитектуру +оптимиазторов Cascades и на ее основе проработать собственную. + +После выбора архитектурной основы необходимо формализовать поддерживаемое +подмножество SQL и соответствующее ему подмножество реляционной алгебры. Эта +задача определяет границы рассматриваемых запросов, их семантику и внутреннее +логическое представление, используемое на этапе оптимизации. + +Следующим шагом является реализация структуры данных Memo и алгоритма поиска +оптимального физического плана на основе архитектуры Cascades. В рамках этой +задачи требуется обеспечить хранение эквивалентных логических выражений, +повторное использование найденных вариантов планов и выбор плана с минимальной +оценочной стоимостью. + +Чтобы сформировать пространство поиска оптимизатора, требуется разработать набор +правил трансформации и реализации для основных операторов реляционной алгебры. +Правила трансформации должны порождать логически эквивалентные планы, а правила +реализации сопоставлять логическим операторам допустимые физические операторы. + +Для оптимизации алгоритма поиска нужно реазивовать метод ветвей и границ. Этот +метод должен использовать оценки стоимости для отсечения заведомо неоптимальных планов и +уменьшения объема пространства поиска, исследуемого оптимизатором. + +Для последующего развития системы также требуется реализовать метод +дифференциального анализа физических планов. Он должен обеспечивать +автоматизированный поиск примеров неоптимальных планов путем сравнения планов +разрабатываемого оптимизатора с планами промышленных СУБД, а найденные различия +должны использоваться для уточнения правил оптимизации. \section{Архитектура СУБД} From e2bea7e87d909d79fd4830e1ac747fbe9025f0a1 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sat, 6 Jun 2026 16:16:26 +0300 Subject: [PATCH 083/120] Refactor --- report/vkr.tex | 480 ++++++++++++++++++++++--------------------------- 1 file changed, 217 insertions(+), 263 deletions(-) diff --git a/report/vkr.tex b/report/vkr.tex index e789632..0471c91 100644 --- a/report/vkr.tex +++ b/report/vkr.tex @@ -148,8 +148,8 @@ \section*{АННОТАЦИЯ} реализации сопоставлять логическим операторам допустимые физические операторы. Для оптимизации алгоритма поиска нужно реазивовать метод ветвей и границ. Этот -метод должен использовать оценки стоимости для отсечения заведомо неоптимальных планов и -уменьшения объема пространства поиска, исследуемого оптимизатором. +метод должен использовать оценки стоимости для отсечения заведомо неоптимальных +планов и уменьшения объема пространства поиска, исследуемого оптимизатором. Для последующего развития системы также требуется реализовать метод дифференциального анализа физических планов. Он должен обеспечивать @@ -157,7 +157,7 @@ \section*{АННОТАЦИЯ} разрабатываемого оптимизатора с планами промышленных СУБД, а найденные различия должны использоваться для уточнения правил оптимизации. -\section{Архитектура СУБД} +\section{Исследовательская часть} Система управления базами данных представляет собой программный комплекс, обеспечивающий хранение, извлечение и модификацию структурированных @@ -282,7 +282,7 @@ \subsection{Процесс исполнения SQL-запроса} операторов, примененных последовательно: доступ к данным и фильтрация по тому же условию. -\section{Реляционная алгебра} +\subsection{Реляционная алгебра} Реляционная алгебра представляет собой формальный язык для описания операций над отношениями реляционной базы данных. В отличие от декларативного SQL, @@ -296,8 +296,6 @@ \section{Реляционная алгебра} R \subseteq \text{dom}(A_1) \times \text{dom}(A_2) \times \ldots \times \text{dom}(A_n). \] -\subsection{Основные операторы реляционной алгебры} - Среди основных операторов реляционной алгебры выделяют следующие. \begin{enumerate} @@ -460,7 +458,7 @@ \subsection{Преобразование синтаксического дере физические операторы позволяет оптимизатору независимо исследовать порядок операций и алгоритмы их реализации. -\section{Поиск оптимального плана} +\subsection{Поиск оптимального плана} Эквивалентность планов, исследуемых в процессе поиска оптимального, обеспечивается применением алгебраических тождеств, например ассоциативности и @@ -666,124 +664,47 @@ \section{Поиск оптимального плана} \(1{,}900\) раз. Именно поэтому качественный оптимизатор является конкурентным преимуществом и одной из самых важных частей СУБД. -\section{Обзор архитектур оптимизаторов} - -Рассмотрим наиболее популярные архитектуры оптимизаторов. - -\subsection{System R} - -Оптимизатор System~R, разработанный в IBM Research в конце 1970-х -годов~\cite{Selinger1979}, стал первым стоимостным оптимизатором запросов и -заложил фундаментальные принципы, используемые во всех последующих системах. - -Архитектуру, аналогичную System~R, реализуют PostgreSQL и ранние версии IBM DB2. - -Оптимизатор, построенный по архитектуре System~R, разбивает запрос на блоки, -каждый из которых оптимизируется индивидуально. Блок запроса состоит из -конструкции \SELECT, конструкции \FROM, ссылающейся на одну или -несколько таблиц, и дерева предикатов \WHERE. Несколько блоков -появляется в случае запроса с вложенными подзапросами. - -Для каждого блока оптимизатор выполняет два основных действия. Во-первых, для -запросов к одному отношению выбирается наилучший метод доступа на основании -стоимостной модели. Во-вторых, для запросов с несколькими отношениями -определяется оптимальный порядок соединений методом восходящего динамического -программирования: сначала выбираются оптимальные планы доступа к отдельным -таблицам, затем перебираются двухтабличные соединения, трехтабличные и так -далее. - -Стоимость плана вычисляется по формуле -\(\text{COST} = \text{PageFetches} + w \cdot \text{RSICalls}\), учитывающей -ожидаемый объем операций ввода-вывода и потребление ресурсов процессора, где -\(w\) --- весовой коэффициент. - -Важным нововведением System~R стало понятие интересных порядков. Стоимость плана -зависит от непосредственных затрат на выполнение оператора и от -того, обеспечивает ли он упорядоченность результата, требуемую вышестоящими -операторами. Например, использование соединения слиянием вместо хеш-соединения -может быть выгодно, если результат должен быть отсортирован для последующей -операции \ORDERBY. - -К ограничениям архитектуры System~R относятся: - -\begin{itemize} - \item рассмотрение только левосторонних - деревьев соединений; - \item независимая оптимизация вложенных подзапросов; - \item невозможность гибкого расширения набора правил преобразования, что - необходимо при добавлении новых операторов языка SQL\@. -\end{itemize} - -Эти ограничения послужили мотивацией для создания расширяемых архитектур -оптимизаторов. - -\subsection{Volcano} - -Volcano --- расширяемый, основанный на правилах, стоимостной оптимизатор -запросов, разработанный Грефе в 1993 году~\cite{GraefeMcKenna1993}. Volcano ввел -ряд концепций, ставших основой для последующих расширяемых оптимизаторов: -физические свойства выражений, обеспечивающие правила, структуру данных Memo и -нисходящий алгоритм поиска на основе динамического программирования. - -Volcano разделяет поиск на две фазы: - -\begin{itemize} - \item \emph{фаза генерации} --- оптимизатор применяет правила трансформации - для порождения всех эквивалентных логических выражений. Применяется - рекурсивный нисходящий обход дерева: для каждого выражения сначала - исследуются дочерние выражения, затем применяются правила трансформации - к текущему выражению. Все порожденные выражения кэшируются в структуре - данных Memo для предотвращения дублирования. - - \item \emph{фаза стоимостного анализа} --- по завершении генерации всех - эквивалентных логических выражений оптимизатор ищет наилучший физический - план. Для каждого логического выражения применяются правила реализации, - порождающие физические операторы. Стоимость каждого физического плана - оценивается рекурсивно, и сохраняется наилучший найденный план. -\end{itemize} - -Поиск осуществляется нисходящим динамическим программированием с использованием -стратегии ветвей и границ: текущая верхняя граница стоимости передается при -рекурсивных вызовах и используется для отсечения заведомо неоптимальных планов. - -Основным недостаткам Volcano является полное раскрытие всех классов -эквивалентности на фазе генерации до начала стоимостного анализа, что приводит к -избыточным вычислениям. - -\section{Архитектура Cascades} - -Cascades --- это расширяемая архитектура оптимизатора запросов, являющаяся -логическим продолжением идеи Volcano~\cite{Graefe1995}. - -Основными объектами Cascades являются: - -\begin{itemize} - \item выражение --- дерево или фрагмент дерева, корнем которого является - логический либо физический оператор; - \item групповое выражение --- выражение, дочерние узлы которого являются - ссылками на группы Memo; - \item группа --- множество логически эквивалентных выражений, возвращающих - один и тот же результат; - \item правило трансформации --- правило, преобразующее логическое выражение в - логически эквивалентное выражение; - \item правило реализации --- правило, преобразующее логический оператор в - физический оператор; - \item физическое свойство --- требование относительно порядка, распределения, - разбиения или уникальности результата; - \item задача --- элементарная единица работы оптимизатора, например - исследование группы, применение правила или оптимизация выражения под - заданное свойство. -\end{itemize} - -Важным принципом данной архитектуры является то, что правила являются объектами -первого класса. Они определяются условиями применимости и собственно алгоритмом -применения. Предполагается, что система легко расширяется добавлением новых -правил, потому что все правила наследуются от общего интерфейса и имеют -одинаковую структуру. - -\subsection{Группы эквивалентности и выражения} - -Группа в Memo представляет собой множество выражений, которые эквивалентны с +\subsection{Обзор архитектур оптимизаторов} + +System~R, разработанный в IBM Research в конце 1970-х годов~\cite{Selinger1979}, +стал первым стоимостным оптимизатором. Его основная идея состоит в восходящем +динамическом программировании. Оптимизатор сначала выбирает лучшие методы +доступа к отдельным отношениям, затем строит лучшие планы для соединений из +двух, трех и большего числа отношений. Преимущество System~R заключается в +простоте и предсказуемости поиска, а также в учете стоимости ввода-вывода, +процессорных затрат и физических свойств операторов. Недостатки связаны с +ограниченной расширяемостью, ориентацией на левосторонние деревья соединений и +независимой оптимизацией вложенных подзапросов. + +Volcano~\cite{GraefeMcKenna1993} развивает стоимостный подход System~R, но +делает оптимизатор расширяемым за счет правил и структуры Memo. В отличие от +System~R, пространство эквивалентных выражений задается не жестко встроенным +алгоритмом, а набором правил трансформации и реализации. Поиск выполняется +нисходящим динамическим программированием и может учитывать требуемые физические +свойства. Преимущество Volcano состоит в модульности и возможности добавлять +новые преобразования и операторы без переписывания всего оптимизатора. Основной +недостаток заключается в разделении поиска на фазу генерации и фазу стоимостного +анализа: предварительное раскрытие всех классов эквивалентности может приводить +к избыточной работе. + +Cascades~\cite{Graefe1995} является развитием Volcano и сохраняет его ключевые +идеи: правила, Memo, физические свойства и стоимостный поиск. Главное отличие +Cascades состоит в том, что генерация альтернатив и стоимостная оптимизация не +разделяются на две независимые фазы, а чередуются через систему задач. Благодаря +этому оптимизатор может порождать только те альтернативы, которые нужны для +текущего поиска, раньше применять отсечения по стоимости и удобнее +распараллеливать независимые ветви. Недостатком Cascades является более сложная +реализация, так как требуется управлять состоянием групп, задачами поиска, +правилами и физическими свойствами. + +\subsection{Архитектура Cascades} + +В следующих разделах подробно рассматривается основные элементы архитектуры +оптимизаторов Cascades. + +\subsubsection{Группы эквивалентности и выражения} + +Группа представляет собой множество выражений, которые эквивалентны с точки зрения логического результата. Например, для отношений \(A\), \(B\) и \(C\) выражения \( \NJoin{A}{(\NJoin{B}{C})} ; \qquad{} \NJoin{(\NJoin{A}{B})}{C} \) при выполнении условий ассоциативности соединения принадлежат одной группе, @@ -797,28 +718,10 @@ \subsection{Группы эквивалентности и выражения} содержать множество альтернатив. Поэтому одно групповое выражение кодирует комбинацию многих конкретных деревьев, храня эту информацию компактно. -\subsection{Структура Memo} - -Memo является основной структурой данных Cascades. Оно выполняет сразу -несколько функций: устраняет дублирующиеся выражения, хранит альтернативы, -фиксирует результаты оптимизации под разными физическими свойствами и обеспечивает -точку синхронизации для задач поиска. - -Группа в Memo включает следующую информацию: - -\begin{itemize} - \item уникальный идентификатор группы; - \item список логических выражений; - \item список физических выражений; - \item логические свойства группы; - \item статистику и оценку кардинальности; - \item состояние исследования группы. -\end{itemize} - -Выражение, в свою очередь, содержит сам оператор, массив идентификаторов -дочерних групп. Для физического выражения дополнительно могут храниться -предоставляемые физические свойства, например сортировка выходного потока, и -рассчитанная стоимость исполнения этого оператора. +Memo является основной структурой данных для хранения групп эквивалентных +выражений в Cascades. Она выполняет функции устранения дублирующихся выражений, +хранит альтернативы, фиксирует результаты оптимизации под разными физическими +свойствами и является точкой синхронизации для задач поиска. \begin{listing} \caption{Пример запроса.} @@ -835,97 +738,13 @@ \subsection{Структура Memo} данными для операторов являются группы, поэтому общие подвыражения не дублируются. -\begin{figure}[H] - \centering - \begin{tikzpicture}[ - scale=0.85, transform shape, - group/.style={draw, rounded corners, text width=4.8cm, inner sep=5pt}, - arrow/.style={-{Stealth[length=2.5mm]}, thick}, - altarrow/.style={-{Stealth[length=2.5mm]}, thick, dashed} - ] - \node[group] (g5) at (6,0) { - \textbf{Группа G5}\\[2pt] - \hrule\vspace{2pt} - \textit{Логические:}\\ - Join(G4,G3)\\ - Join(G1,G6)\\[2pt] - \hrule\vspace{2pt} - \textit{Физические:}\\ - HashJoin(G4,G3)\\ - NestedLoopJoin(G4,G3)\\ - HashJoin(G1,G6)\\ - NestedLoopJoin(G1,G6) - }; - \node[group] (g4) at (3,-6) { - \textbf{Группа G4}\\[2pt] - \hrule\vspace{2pt} - \textit{Логические:}\\ - Join(G1,G2)\\[2pt] - \hrule\vspace{2pt} - \textit{Физические:}\\ - HashJoin(G1,G2)\\ - NestedLoopJoin(G1,G2) - }; - \node[group] (g6) at (9,-6) { - \textbf{Группа G6}\\[2pt] - \hrule\vspace{2pt} - \textit{Логические:}\\ - Join(G2,G3)\\[2pt] - \hrule\vspace{2pt} - \textit{Физические:}\\ - HashJoin(G2,G3)\\ - NestedLoopJoin(G2,G3) - }; - \node[group] (g1) at (0,-12) { - \textbf{Группа G1}\\[2pt] - \hrule\vspace{2pt} - \textit{Логические:}\\ - Scan(A)\\[2pt] - \hrule\vspace{2pt} - \textit{Физические:}\\ - SeqScan(A) - }; - \node[group] (g2) at (6,-12) { - \textbf{Группа G2}\\[2pt] - \hrule\vspace{2pt} - \textit{Логические:}\\ - Scan(B)\\[2pt] - \hrule\vspace{2pt} - \textit{Физические:}\\ - SeqScan(B) - }; - \node[group] (g3) at (12,-12) { - \textbf{Группа G3}\\[2pt] - \hrule\vspace{2pt} - \textit{Логические:}\\ - Scan(C)\\[2pt] - \hrule\vspace{2pt} - \textit{Физические:}\\ - SeqScan(C) - }; - - \draw[arrow] (g5) -- (g4); - \draw[arrow, rounded corners=5pt] - (g5.east) -- (15.5,0) -- (15.5,-12) -- (g3.east); - \draw[altarrow] (g5) -- (g6); - \draw[altarrow, rounded corners=5pt] - (g5.west) -- (-3.5,0) -- (-3.5,-12) -- (g1.west); - \draw[arrow] (g4) -- (g1); - \draw[arrow] (g4) -- (g2); - \draw[arrow] (g6) -- (g2); - \draw[arrow] (g6) -- (g3); - \end{tikzpicture} - \caption{Пример структуры Memo.}% - \label{fig:memo-structure} -\end{figure} - При добавлении нового выражения Memo выполняет проверку на дубликаты. Если -выражение уже существует в некоторой группе, оно не добавляется повторно. Если -алгоритм в процессе работы обнаруживает эквивалентность двух ранее различных -групп, то они могут быть объединены. При этом требуется обновление ссылок и -состояния правил. +выражение уже существует в некоторой группе, оно не добавляется повторно. В +случае, когда алгоритм в процессе работы обнаруживает эквивалентность двух ранее +различных групп, они эти группы объединяются в одну. При этом требуется +обновление ссылок и состояния правил. -\subsection{Алгоритм поиска} +\subsubsection{Алгоритм поиска} Cascades организует оптимизацию как выполнение набора задач. Задача может исследовать группу, исследовать выражение, применить правило, реализовать @@ -954,14 +773,7 @@ \subsection{Алгоритм поиска} накапливается полная стоимость и обновляется информация о лучшем плане. Параметр \texttt{limit} используется для отсечения заведомо дорогих вариантов. -\begin{figure}[H] -\centering -\includegraphics[width=0.8\textwidth]{cascades.pdf} -\caption{Схема взаимодействия задач в оптимизаторе Cascades}% -\label{fig:cascades-tasks} -\end{figure} - -\section{Правила трансформации и реализации}\label{sec:transformation_rules} +\subsection{Правила трансформации и реализации}\label{sec:transformation_rules} Правило в оптимизаторе Cascades состоит из образца, условия применимости и алгоритма применения. Образец описывает форму логического выражения, к которому @@ -979,12 +791,13 @@ \section{Правила трансформации и реализации}\labe выражение в группе, а для правил реализации физическое выражение в той же группе. -\subsection{Правила трансформации} - Правила трансформации задают эквивалентные преобразования логических выражений и тем самым определяют пространство планов, исследуемое оптимизатором. Наиболее важные из них представлены в таблице~\ref{tbl:transformation_rules}. +% TODO: может это вообще убрать? Или актуализировать... Еще нигде не описано как +% свойства выводятся + \begin{Definition} Null-отбрасывающим называется предикат, который при замене всех атрибутов соответствующей стороны на \texttt{NULL} принимает значение, отличное от @@ -1004,13 +817,9 @@ \subsection{Правила трансформации} сумму частичных сумм, \AVG{} --- в пару \SUM{} и \COUNT{}. \end{Definition} -\subsection{Правила реализации} - Правила реализации переводят логические операторы в физические. Используемые в настоящей работе правила собраны в таблице~\ref{tbl:implementation_rules}. -\subsection{Обеспечивающие операторы} - Физические свойства в Cascades обрабатываются через требуемые и предоставляемые свойства. Если родительский оператор требует вход, отсортированный по атрибуту \(a\), а выбранный дочерний план такого порядка не предоставляет, оптимизатор @@ -1022,7 +831,9 @@ \subsection{Обеспечивающие операторы} сортировкой может конкурировать с планом на основе соединения слиянием, которое сохраняет порядок результата. -\section{Метод ветвей и границ} +\subsubsection{Метод ветвей и границ} + +% актуализировать: формулы, алгос и у меня другая селективность... Метод ветвей и границ используется для сокращения пространства поиска и, как следствие, неасимптотического улучшения скорости работы алгоритма. В контексте @@ -1062,7 +873,7 @@ \section{Метод ветвей и границ} оператор транслирует глобальное требование \(P\) в конкретные требования \(P_1, \ldots, P_k\) к своим дочерним группам. -\subsection{Оценка кардинальности} +\subsubsection{Оценка кардинальности} Оценка кардинальности является процессом предсказания числа кортежей, возвращаемых оператором. @@ -1093,7 +904,7 @@ \subsection{Оценка кардинальности} ограничения на схему, а в современных исследованиях также модели машинного обучения. -\subsection{Оценка стоимости} +\subsubsection{Оценка стоимости} Функция стоимости сопоставляет физическому плану численную оценку затрат на его исполнение. Классическим считается представление стоимости в виде взвешенной @@ -1146,7 +957,7 @@ \subsection{Оценка стоимости} Для операторов \(\FilterOp\) и \(\ProjOp\) стоимость ввода-вывода равна \(0\), потому что это конвейерные операторы, которые не используют диск. -\section{Дифференциальный анализ физических планов} +\subsection{Дифференциальный анализ физических планов} Множество правил трансформации и реализации, используемых в оптимизаторах запросов, изучено довольно хорошо и описано в @@ -1163,10 +974,8 @@ \section{Дифференциальный анализ физических пл уточняли условия активации своих правил в течение многих лет. Не имея такого преимущества сложно добиться отличного качества построенных планов. -\subsection{Предлагаемый метод} - -Для поиска несоответствий между поведением реализуемой системы и внешней -эталонной системы предлагается применить дифференциальный анализ планов. +Решением может быть поиск несоответствий между поведением реализуемой системы и +внешней эталонной системы путем применения дифференциального анализа планов. Первым шагом нужно сформировать несколько наборов данных, состоящих из схем таблиц и их наполнения. Разные данные позволяют всесторонне исследовать правила @@ -1218,8 +1027,6 @@ \subsection{Предлагаемый метод} планы можно использовать как тесты для оптимизатора, которые будут успешно выполняться после уточнения соответвующих правил активации. -\subsection{Поиск ближайшего плана} - В случае невозможности построить ``эталонный'' план разрабатываемым оптимизатором, предлагается искать ближайший план, т.е. план из пространства поиска разрабатываемого оптимизатора, максимально близкий к плану промышленной @@ -1229,8 +1036,6 @@ \subsection{Поиск ближайшего плана} Анализ ближайшего плана может помочь упростить выявление трансформаций, которые разрабатываемый план по какой-то причине не применил. -\subsection{Область применения} - Предлагаемый метод дифференциального анализа применим для сравнения как с проприетарными системами, так и с СУБД с открытым исходным кодом, позволяя уточнять условия применения правил. @@ -1252,7 +1057,65 @@ \subsection{Область применения} % {\color{red} TODO: описать как нормализовать и т.п.} -\section{Реализация} +\section{Конструкторская часть} + +В рамках данной работы был разработан оптимизатор запросов для модельной +реляционной СУБД. + +Структурно реализация модельной СУБД состоит из следующих модулей: +\begin{itemize} + \item модуль синтаксического анализа SQL-запросов; + \item модуль построения логического представления запроса; + \item модуль стоимостной оптимизации на основе структуры Memo; + \item подсистема физического хранения на основе статических CSV-таблиц; + \item модуль исполнения физических операторов. +\end{itemize} + +Основой для проектирования оптимизатора была выбрана архитектура Cascades. В ней +пространство допустимых планов формируется с помощью набора правил +преобразования и хранится в структуре Memo. Эта структура объединяет логически +эквивалентные выражения в группы, что позволяет избежать повторного рассмотрения +одинаковых поддеревьев. Поиск оптимального физического плана выполняется с учетом +стоимостной модели и требуемых свойств результата, например порядка сортировки. + +Данная архитектура была выбрана по причине ее модульности и расширяемости. +Добавление новых логических преобразований и физических операторов выполняется +путем реализации отдельных правил и не требует изменения основного алгоритма +поиска. Это также удобно для итеративного процесса разработки, предполагающего +постепенное улучшение качества итоговых планов путем уточнения и расширения +пространства поиска. + +Модуль синтаксического анализа SQL-запросов отвечает за лексический и +синтаксический разбор поступающих на вход SQL-запросов. Для реализации этого +модуля было решено использовать генератор синтаксических и лексических +анализаторов, ввиду того, что грамматика SQL является очень объемной, и +использование готового формального описания для генератора гарантирует +совместимость с одним из диалектов языка. + +Модуль построения логического представления запроса отвечает за преобразование +результата синтаксического анализа во внутреннее представление, пригодное для +оптимизации. В качестве такого представления решено использовать дерево +операторов реляционной алгебры. Этот подход является общепринятым и позволяет +отделить особенности синтаксиса SQL от последующих этапов обработки запроса, а +также выполнять оптимизацию над ограниченным набором формализованных операций: +фильтрацией, проекцией, соединением, агрегацией и сортировкой. + +Подсистема физического хранения отвечает за предоставление модулю исполнения +табличных данных. Для хранения выбраны статические таблицы в формате CSV. +Использование простого текстового формата позволяет сосредоточиться на +проектировании оптимизатора и не усложнять прототип реализацией транзакций, +индексов, буферного кэша и управления дисковыми страницами. Кроме того, +CSV-файлы удобно создавать, изменять и использовать при проведении тестов. + +Модуль исполнения физических операторов отвечает за выполнение выбранного +оптимизатором плана и формирование результата запроса. Физический план решено +представлять в виде дерева операторов с единым интерфейсом взаимодействия. +Каждый оператор получает данные от дочерних узлов, выполняет определенное +преобразование и передает результат вышестоящему оператору. Такой подход +обеспечивает модульность подсистемы исполнения и позволяет добавлять новые +физические операторы независимо от уже реализованных. + +\section{Технологическая часть} Программа написана на языке C++ с использованием стандарта C++23. Этот язык популярен в сфере разработки СУБД, являясь достаточно низкоуровневым и @@ -2420,6 +2283,97 @@ \subsection{Калибровка стоимостной модели} \anonsection{ПРИЛОЖЕНИЕ А} \vspace{-30pt} +\begin{figure}[H] +\centering +\includegraphics[width=0.8\textwidth]{cascades.pdf} +\caption{Схема взаимодействия задач в оптимизаторе Cascades}% +\label{fig:cascades-tasks} +\end{figure} + +\begin{figure}[H] + \centering + \begin{tikzpicture}[ + scale=0.85, transform shape, + group/.style={draw, rounded corners, text width=4.8cm, inner sep=5pt}, + arrow/.style={-{Stealth[length=2.5mm]}, thick}, + altarrow/.style={-{Stealth[length=2.5mm]}, thick, dashed} + ] + \node[group] (g5) at (6,0) { + \textbf{Группа G5}\\[2pt] + \hrule\vspace{2pt} + \textit{Логические:}\\ + Join(G4,G3)\\ + Join(G1,G6)\\[2pt] + \hrule\vspace{2pt} + \textit{Физические:}\\ + HashJoin(G4,G3)\\ + NestedLoopJoin(G4,G3)\\ + HashJoin(G1,G6)\\ + NestedLoopJoin(G1,G6) + }; + \node[group] (g4) at (3,-6) { + \textbf{Группа G4}\\[2pt] + \hrule\vspace{2pt} + \textit{Логические:}\\ + Join(G1,G2)\\[2pt] + \hrule\vspace{2pt} + \textit{Физические:}\\ + HashJoin(G1,G2)\\ + NestedLoopJoin(G1,G2) + }; + \node[group] (g6) at (9,-6) { + \textbf{Группа G6}\\[2pt] + \hrule\vspace{2pt} + \textit{Логические:}\\ + Join(G2,G3)\\[2pt] + \hrule\vspace{2pt} + \textit{Физические:}\\ + HashJoin(G2,G3)\\ + NestedLoopJoin(G2,G3) + }; + \node[group] (g1) at (0,-12) { + \textbf{Группа G1}\\[2pt] + \hrule\vspace{2pt} + \textit{Логические:}\\ + Scan(A)\\[2pt] + \hrule\vspace{2pt} + \textit{Физические:}\\ + SeqScan(A) + }; + \node[group] (g2) at (6,-12) { + \textbf{Группа G2}\\[2pt] + \hrule\vspace{2pt} + \textit{Логические:}\\ + Scan(B)\\[2pt] + \hrule\vspace{2pt} + \textit{Физические:}\\ + SeqScan(B) + }; + \node[group] (g3) at (12,-12) { + \textbf{Группа G3}\\[2pt] + \hrule\vspace{2pt} + \textit{Логические:}\\ + Scan(C)\\[2pt] + \hrule\vspace{2pt} + \textit{Физические:}\\ + SeqScan(C) + }; + + \draw[arrow] (g5) -- (g4); + \draw[arrow, rounded corners=5pt] + (g5.east) -- (15.5,0) -- (15.5,-12) -- (g3.east); + \draw[altarrow] (g5) -- (g6); + \draw[altarrow, rounded corners=5pt] + (g5.west) -- (-3.5,0) -- (-3.5,-12) -- (g1.west); + \draw[arrow] (g4) -- (g1); + \draw[arrow] (g4) -- (g2); + \draw[arrow] (g6) -- (g2); + \draw[arrow] (g6) -- (g3); + \end{tikzpicture} + \caption{Пример структуры Memo.}% + \label{fig:memo-structure} +\end{figure} + \begin{algorithm}[H] \caption{Алгоритм поиска (часть 1).}% \label{alg:cascades-search} From dc80227234dc498dc045c4e1004eb8a5a7860a4e Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sat, 6 Jun 2026 20:55:39 +0300 Subject: [PATCH 084/120] Refactor report --- report/vkr.tex | 58 +++++++++++++++++++++++++++++++++++++------------- 1 file changed, 43 insertions(+), 15 deletions(-) diff --git a/report/vkr.tex b/report/vkr.tex index 0471c91..1a462dc 100644 --- a/report/vkr.tex +++ b/report/vkr.tex @@ -1085,12 +1085,34 @@ \section{Конструкторская часть} постепенное улучшение качества итоговых планов путем уточнения и расширения пространства поиска. +\subsection{Модули синтаксического анализа и построения логического представления} + Модуль синтаксического анализа SQL-запросов отвечает за лексический и -синтаксический разбор поступающих на вход SQL-запросов. Для реализации этого -модуля было решено использовать генератор синтаксических и лексических -анализаторов, ввиду того, что грамматика SQL является очень объемной, и -использование готового формального описания для генератора гарантирует -совместимость с одним из диалектов языка. +синтаксический разбор поступающих на вход SQL-запросов. В качестве основы для +грамматики выбран диалект PostgreSQL, так как он широко используется на практике +и имеет открытый исходный код. Использование готовой грамматики из официального +репозитория~\cite{PostgresBisonGrammar} гарантирует совместимость. + +В рамках работы поддерживается подмножество диалекта PostgreSQL, достаточное для +исследования оптимизации запросов на чтение данных. В него входят операторы +\SELECT{} с предложениями \FROM, \WHERE, \GROUPBY{} и \ORDERBY{}, выбор всех +столбцов или списка выражений, псевдонимы, соединения \JOIN, \sqlkw{INNER JOIN}, +\sqlkw{LEFT JOIN}, \sqlkw{RIGHT JOIN}, \sqlkw{FULL JOIN} с условием \sqlkw{ON}, +а также \sqlkw{CROSS JOIN}. Для группировки поддерживаются обычные выражения в +\GROUPBY{} и агрегатные функции \SUM{}, \COUNT{} и \sqlkw{COUNT(*)}. Сортировка +результата задается через \ORDERBY{} по ссылкам на столбцы в прямом или обратном +порядке. + +Поддерживаемые скалярные выражения включают ссылки на столбцы, целочисленные +константы, строковые литералы в одинарных кавычках, значения \sqlkw{NULL}, +\sqlkw{TRUE}, \sqlkw{FALSE} и \sqlkw{UNKNOWN}, арифметические операции сложения, +вычитания, умножения, деления, остатка от деления и возведения в степень, а +также унарный минус. В предикатах поддерживаются операции сравнения, логические +связки \sqlkw{AND}, \sqlkw{OR} и \sqlkw{NOT}, проверки \sqlkw{IS NULL}, +\sqlkw{IS TRUE}, \sqlkw{IS FALSE}, \sqlkw{IS UNKNOWN}, условия \sqlkw{BETWEEN} и +\sqlkw{IN} со списком значений. Такое подмножество покрывает основные +конструкции, влияющие на построение логического плана: проекцию, фильтрацию, +соединение, агрегацию и требование сортировки результата. Модуль построения логического представления запроса отвечает за преобразование результата синтаксического анализа во внутреннее представление, пригодное для @@ -1100,6 +1122,8 @@ \section{Конструкторская часть} также выполнять оптимизацию над ограниченным набором формализованных операций: фильтрацией, проекцией, соединением, агрегацией и сортировкой. +\subsection{Модули физического хранения и исполнения} + Подсистема физического хранения отвечает за предоставление модулю исполнения табличных данных. Для хранения выбраны статические таблицы в формате CSV. Использование простого текстового формата позволяет сосредоточиться на @@ -1107,6 +1131,16 @@ \section{Конструкторская часть} индексов, буферного кэша и управления дисковыми страницами. Кроме того, CSV-файлы удобно создавать, изменять и использовать при проведении тестов. +Данные хранятся в одной директории, где каждому отношению соответствует +файл с CSV-таблицей. Название файла совпадает с названием отношения. +Первая строка CSV-файла содержит описание атрибутов и их типов, +последующие строки содержат кортежи отношения. + +Дополнительно предусмотрена поддержка индексов. Индекс представляет собой +отсортированный бинарный файл, содержащий целочисленные ключи и смещения +соответствующих строк в CSV-файле. Использование смещений позволяет +извлекать подходящие кортежи без полного сканирования отношения. + Модуль исполнения физических операторов отвечает за выполнение выбранного оптимизатором плана и формирование результата запроса. Физический план решено представлять в виде дерева операторов с единым интерфейсом взаимодействия. @@ -1115,6 +1149,10 @@ \section{Конструкторская часть} обеспечивает модульность подсистемы исполнения и позволяет добавлять новые физические операторы независимо от уже реализованных. +\subsection{Модуль стоимостной оптимизации планов} + + + \section{Технологическая часть} Программа написана на языке C++ с использованием стандарта C++23. Этот язык @@ -1644,11 +1682,6 @@ \subsubsection{Формирование физического плана} \subsection{Модуль физического хранения данных} -Данные хранятся в одной директории, где каждому отношению соответствует -файл с CSV-таблицей. Название файла совпадает с названием отношения. -Первая строка CSV-файла содержит описание атрибутов и их типов, -последующие строки содержат кортежи отношения. - Для чтения таблиц реализован класс \verb|CsvDirSequentialScanner|~\refAlgo{lst:csv-dir-scanners}. Он выполняет последовательное сканирование CSV-файла и передает прочитанные @@ -1681,11 +1714,6 @@ \subsection{Модуль физического хранения данных} \end{minted} \end{listing} -Дополнительно реализована поддержка индексов. Индекс представляет собой -отсортированный бинарный файл, содержащий целочисленные ключи и смещения -соответствующих строк в CSV-файле. Использование смещений позволяет -извлекать подходящие кортежи без полного сканирования отношения. - Список доступных индексов задается в файле \verb|indexes.meta|, расположенном в директории с данными. Каждая строка файла содержит название отношения, название индексируемого атрибута, тип индекса и имя From 061024efdff25f4da16edc1a95ddbcbefd4e0121 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sat, 6 Jun 2026 21:17:53 +0300 Subject: [PATCH 085/120] wip --- report/vkr.tex | 118 +++++++++++++++++++++++++++++++------------------ 1 file changed, 74 insertions(+), 44 deletions(-) diff --git a/report/vkr.tex b/report/vkr.tex index 1a462dc..b83149e 100644 --- a/report/vkr.tex +++ b/report/vkr.tex @@ -1151,7 +1151,69 @@ \subsection{Модули физического хранения и исполн \subsection{Модуль стоимостной оптимизации планов} +Модуль стоимостной оптимизации планов принимает на вход логическое представление +запроса и строит физический план исполнения с минимальной оцененной стоимостью. +В качестве основы выбран подход Cascades. Исходное дерево логических операторов +помещается в структуру Memo, после чего оптимизатор постепенно расширяет +пространство поиска с помощью правил преобразования и правил реализации. Такой +подход позволяет не хранить каждое дерево плана отдельно, а компактно +представлять множество эквивалентных вариантов через группы Memo. + +Каждая группа Memo соответствует множеству логически эквивалентных выражений, +то есть выражений, возвращающих один и тот же результат. Логические выражения +содержат ссылки на дочерние группы, поэтому один узел может представлять сразу +несколько вариантов построения поддерева. В процессе оптимизации в группы +добавляются как новые логические выражения, полученные правилами +трансформации, так и физические выражения, полученные правилами реализации. +Правило трансформации создает новое логическое выражение и добавляет его в +ту же группу Memo, что и исходное выражение. Следовательно, правило должно +сохранять семантику запроса. Правило реализации создает физическое выражение, +которое может быть передано модулю исполнения. + +Реализованы следующие трансформационные правила: +\begin{itemize} + \item коммутативность соединения; + \item ассоциативность соединения; + \item разбиение конъюнкции на последовательность фильтров; + \item объединение последовательных фильтров; + \item проталкивание фильтра через проекцию; + \item проталкивание фильтра во входы соединения; + \item преобразование выражения \verb|IN| в цепочку сравнений. +\end{itemize} + +И следующие правила реализации: +\begin{itemize} + \item последовательное сканирование таблицы; + \item фильтрацию; + \item проекцию; + \item агрегацию; + \item декартово произведение вложенными циклами; + \item соединение вложенными циклами; + \item хеш-соединение. +\end{itemize} + +% TODO: какой-то вывод о покрытии правилами или почему такие правила выбраны +% а у меня где-то описаны сами правила? + +Выбор лучшего плана выполняется на основе стоимостной модели. Стоимость +физического оператора рассчитывается с учетом оценок кардинальности входных и +выходных групп, а для хеш-соединения дополнительно учитывается ширина кортежей, +полученная из схемы отношений. Кардинальность базовых отношений берется из +описания входных таблиц, а для фильтров, соединений, агрегаций и проекций +оценивается эвристически. Чтобы сократить перебор, оптимизатор использует метод +ветвей и границ: если локальная стоимость или нижняя оценка стоимости поддерева +уже превышает текущий лучший результат, соответствующая ветвь пространства +поиска не рассматривается дальше. + +% Тут наверное стоит расписать выбранные формулы и обосновать + +Отдельно учитываются физические свойства результата. В данной работе таким +свойством является порядок строк, требуемый предложением \ORDERBY{}. Если +выбранный план не обеспечивает требуемую сортировку, оптимизатор может добавить +обеспечивающий физический оператор сортировки и сравнить его стоимость с +другими альтернативами. Результатом работы модуля является дерево физических +операторов, которое передается в модуль исполнения. \section{Технологическая часть} @@ -1313,26 +1375,8 @@ \subsubsection{Правила оптимизации} \end{minted} \end{listing} -Правило трансформации создает новое логическое выражение и добавляет его в -ту же группу Memo, что и исходное выражение. Следовательно, правило должно -сохранять семантику запроса. Правило реализации создает физическое выражение, -которое может быть передано модулю исполнения. - -Реализованы следующие трансформационные правила: -\begin{itemize} - \item коммутативность соединения; - \item ассоциативность соединения; - \item разбиение конъюнкции на последовательность фильтров; - \item объединение последовательных фильтров; - \item проталкивание фильтра через проекцию; - \item проталкивание фильтра во входы соединения; - \item преобразование выражения \verb|IN| в цепочку сравнений. -\end{itemize} - -Например, правило коммутативности преобразует выражение $A \Join B$ в -$B \Join A$. Для левого и правого внешнего соединения одновременно изменяется -тип соединения. Реализация правила приведена в -листинге~\ref{lst:join-commutativity}. +В качестве примера в листинге~\ref{lst:join-commutativity} приведено правило +коммутативности соединений. \begin{listing}[H] \caption{Правило коммутативности соединения.} @@ -1354,27 +1398,8 @@ \subsubsection{Правила оптимизации} \end{minted} \end{listing} -Правило проталкивания фильтра через соединение разбивает предикат на конъюнкты. -Каждый конъюнкт анализируется отдельно. Если он ссылается только на атрибуты -одного входа соединения, фильтр переносится ближе к соответствующей таблице. Это -позволяет уменьшить число кортежей до выполнения соединения. Для внешних -соединений учитывается направление: фильтр нельзя переносить в такой вход, для -которого это изменит семантику результата. - -Реализационные правила создают: -\begin{itemize} - \item последовательное сканирование таблицы; - \item фильтрацию; - \item проекцию; - \item агрегацию; - \item декартово произведение вложенными циклами; - \item соединение вложенными циклами; - \item хеш-соединение. -\end{itemize} - -Хеш-соединение применяется только для внутреннего эквисоединения двух атрибутов. -Для остальных условий доступно соединение вложенными циклами. Проверка -применимости правила приведена в листинге~\ref{lst:implement-hash-join}. +Пример метода для проверки применимости правила приведен в +листинге~\ref{lst:implement-hash-join}. \begin{listing}[H] \caption{Проверка применимости хеш-соединения.} @@ -1546,15 +1571,20 @@ \subsubsection{Алгоритм поиска} вычисляется полная стоимость с учетом дочерних групп. Для каждой пары из группы и требуемого набора свойств хранится лучший найденный -вариант. Ключ такого варианта представлен структурой \verb|WinnerKey|: +вариант. Ключ такого варианта представлен структурой +\verb|WinnerKey|~\refAlgo{winner-key}. -\begin{minted}[style=bw, breaklines, frame=single, - fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{cpp} +\begin{listing}[H] + \caption{Ключ для хранения лучшего плана по группе и свойствам.} + \label{lst:winner-key} + \begin{minted}[style=bw, breaklines, frame=single, + fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{cpp} struct WinnerKey { Group* group; PropertySet required; }; \end{minted} +\end{listing} Хранение победителя отдельно для каждого набора свойств необходимо, поскольку самый дешевый неотсортированный план может отличаться от самого дешевого плана, From 90f30ec79c49ed3372442558cb8dae2eedf14338 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sat, 6 Jun 2026 21:42:57 +0300 Subject: [PATCH 086/120] wip --- report/vkr.tex | 74 ++++++++++++++++++++++++-------------------------- 1 file changed, 35 insertions(+), 39 deletions(-) diff --git a/report/vkr.tex b/report/vkr.tex index b83149e..8863bb4 100644 --- a/report/vkr.tex +++ b/report/vkr.tex @@ -331,13 +331,13 @@ \subsection{Реляционная алгебра} левой и используется для представления коррелированных подзапросов. \end{enumerate} -\subsection{Преобразование синтаксического дерева к реляционной алгебре}\label{sec:translation} +\subsection{Преобразование синтаксического дерева в реляционную алгебру}\label{sec:translation} Полученное после лексического и синтаксического разбора абстрактное -синтаксическое дерево преобразуется в выражение реляционной алгебры. Этот -процесс определяется структурной индукцией по дереву запроса. Введём оператор -конверсии \(\Tr{\cdot}\), сопоставляющий узлу синтаксического дерева его -алгебраическое представление. Тогда правила трансляции записываются в виде +синтаксическое дерево преобразуется в выражение в форме реляционной алгебры. +Этот процесс определяется структурной индукцией по дереву запроса. Введём +оператор конверсии \(\Tr{\cdot}\), сопоставляющий узлу синтаксического дерева +его алгебраическое представление. Тогда правила трансляции записываются в виде правил вывода. Если для всех поддеревьев известны алгебраические представления, то алгебраическое представление составной конструкции выражается через них. @@ -1193,20 +1193,40 @@ \subsection{Модуль стоимостной оптимизации план \item хеш-соединение. \end{itemize} -% TODO: какой-то вывод о покрытии правилами или почему такие правила выбраны -% а у меня где-то описаны сами правила? +% TODO: актуализировать и сделать какой-то вывод о покрытии правилами или почему такие правила выбраны Выбор лучшего плана выполняется на основе стоимостной модели. Стоимость физического оператора рассчитывается с учетом оценок кардинальности входных и -выходных групп, а для хеш-соединения дополнительно учитывается ширина кортежей, -полученная из схемы отношений. Кардинальность базовых отношений берется из -описания входных таблиц, а для фильтров, соединений, агрегаций и проекций -оценивается эвристически. Чтобы сократить перебор, оптимизатор использует метод -ветвей и границ: если локальная стоимость или нижняя оценка стоимости поддерева -уже превышает текущий лучший результат, соответствующая ветвь пространства -поиска не рассматривается дальше. +выходных групп. Формулы для вычисления стоимости откалиброваны по результатам +бенчмарков и представлены в табилце~\ref{tbl:cost-model-coefficients}. +Кардинальность базовых отношений берется из описания входных таблиц, а для +фильтров, соединений, агрегаций и проекций оценивается эвристически. Чтобы +сократить перебор, оптимизатор использует метод ветвей и границ. Если локальная +стоимость или нижняя оценка стоимости поддерева уже превышает текущий лучший +результат, соответствующая ветвь пространства поиска не рассматривается дальше. -% Тут наверное стоит расписать выбранные формулы и обосновать +\begin{table}[H] + \centering + \caption{Стоимостные формулы физических операторов.} + \label{tbl:cost-model-coefficients} + \small + \begin{tabular}{|p{0.40\textwidth}|l|} + \hline + Физический оператор & Формула стоимости \\ + \hline + Последовательное сканирование & $100\,n$ \\ + Фильтрация & $100\,n_{out}$ \\ + Проекция & $22\,n_{out}$ \\ + Сортировка & $11\,n\bigl(\lfloor\log_2 n\rfloor + 1\bigr)$ \\ + Агрегация & $510\,n$ \\ + Соединение вложенными циклами & $70\,n_l n_r$ \\ + Декартово произведение & $104\,n_l n_r$ \\ + Хеш-соединение & $100\,n_b w_b + 35\,n_p + 10\,n_o w_o$ \\ + \hline + \end{tabular} +\end{table} + +% TODO: таблица с формулами для селективности и обоснование Отдельно учитываются физические свойства результата. В данной работе таким свойством является порядок строк, требуемый предложением \ORDERBY{}. Если @@ -2282,30 +2302,6 @@ \subsection{Калибровка стоимостной модели} \label{fig:cost-model-calibration} \end{figure} -Стоимостные формулы физических операторов, полученные по результатам -калибровки, приведены в таблице~\ref{tbl:cost-model-coefficients}. - -\begin{table}[H] - \centering - \caption{Стоимостные формулы физических операторов.} - \label{tbl:cost-model-coefficients} - \small - \begin{tabular}{|p{0.40\textwidth}|l|} - \hline - Физический оператор & Формула стоимости \\ - \hline - Последовательное сканирование & $100\,n$ \\ - Фильтрация & $100\,n_{out}$ \\ - Проекция & $22\,n_{out}$ \\ - Сортировка & $11\,n\bigl(\lfloor\log_2 n\rfloor + 1\bigr)$ \\ - Агрегация & $510\,n$ \\ - Соединение вложенными циклами & $70\,n_l n_r$ \\ - Декартово произведение & $104\,n_l n_r$ \\ - Хеш-соединение & $100\,n_b w_b + 35\,n_p + 10\,n_o w_o$ \\ - \hline - \end{tabular} -\end{table} - \anonsection{ЗАКЛЮЧЕНИЕ} В ходе практики разработана модельная СУБД и оптимизатор для нее. Программа From 162619b72ec12122eb92d4465aa521fe22fd9f21 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sat, 6 Jun 2026 21:57:27 +0300 Subject: [PATCH 087/120] wip --- report/vkr.tex | 394 +++++++++++++++++++++++++------------------------ 1 file changed, 199 insertions(+), 195 deletions(-) diff --git a/report/vkr.tex b/report/vkr.tex index 8863bb4..6d2c316 100644 --- a/report/vkr.tex +++ b/report/vkr.tex @@ -1205,28 +1205,73 @@ \subsection{Модуль стоимостной оптимизации план стоимость или нижняя оценка стоимости поддерева уже превышает текущий лучший результат, соответствующая ветвь пространства поиска не рассматривается дальше. +Для оценки кардинальности промежуточных результатов используется коэффициент +селективности \(sel(p)\), показывающий долю кортежей, удовлетворяющих предикату +\(p\). Используемые формулы приведены в таблице~\ref{tbl:selectivity-model}. + \begin{table}[H] \centering - \caption{Стоимостные формулы физических операторов.} - \label{tbl:cost-model-coefficients} + \caption{Формулы оценки селективности предикатов.} + \label{tbl:selectivity-model} \small - \begin{tabular}{|p{0.40\textwidth}|l|} + \begin{tabular}{|p{0.42\textwidth}|p{0.42\textwidth}|} \hline - Физический оператор & Формула стоимости \\ + Вид предиката & Формула селективности \\ \hline - Последовательное сканирование & $100\,n$ \\ - Фильтрация & $100\,n_{out}$ \\ - Проекция & $22\,n_{out}$ \\ - Сортировка & $11\,n\bigl(\lfloor\log_2 n\rfloor + 1\bigr)$ \\ - Агрегация & $510\,n$ \\ - Соединение вложенными циклами & $70\,n_l n_r$ \\ - Декартово произведение & $104\,n_l n_r$ \\ - Хеш-соединение & $100\,n_b w_b + 35\,n_p + 10\,n_o w_o$ \\ + \(a = c\), где \(a\) --- атрибут, \(c\) --- константа + & \(sel(a = c) = 0{,}10\) \\ + \hline + \(a < b\), \(a > b\), \(a \le b\), \(a \ge b\) + & \(sel(p) = 0{,}50\) \\ + \hline + \(p_1 \land p_2\) + & \(sel(p_1 \land p_2) = sel(p_1) \cdot sel(p_2)\) \\ + \hline + \(p_1 \lor p_2\) + & \(sel(p_1 \lor p_2) = + sel(p_1) + sel(p_2) - sel(p_1) \cdot sel(p_2)\) \\ + \hline + \(\lnot p\) + & \(sel(\lnot p) = 1 - sel(p)\) \\ + \hline + \(a \in \{c_1, \ldots, c_k\}\) + & \(sel(p) = \min(0{,}50,\; 0{,}10k)\) \\ + \hline + \(a \notin \{c_1, \ldots, c_k\}\) + & \(sel(p) = 1 - \min(0{,}50,\; 0{,}10k)\) \\ + \hline + \(a = b\) в условии соединения, где \(a\) и \(b\) --- атрибуты разных входов + & \(sel(a = b) = \frac{1}{\max(1,\; \max(n_l, n_r))}\) \\ + \hline + \(\theta_1 \land \theta_2\) в условии соединения + & \(sel(\theta_1 \land \theta_2) = + sel(\theta_1) \cdot sel(\theta_2)\) \\ + \hline + Иной или неизвестный предикат фильтрации + & \(sel(p) = 0{,}50\) \\ + \hline + Иной или неизвестный предикат соединения + & \(sel(\theta) = 1\) \\ \hline \end{tabular} \end{table} -% TODO: таблица с формулами для селективности и обоснование +Такие формулы выбраны как простая эвристическая модель, не требующая гистограмм, +сведений о количестве различных значений и статистик корреляции между +атрибутами. Равенство атрибута константе считается достаточно селективным, +поэтому ему сопоставляется коэффициент \(0{,}10\). Диапазонные и неизвестные +условия оцениваются нейтральным коэффициентом \(0{,}50\), так как без статистики +нельзя надежно определить, какую часть отношения они оставят. Для \sqlkw{IN} +используется сумма селективностей отдельных равенств с верхней границей +\(0{,}50\), чтобы длинный список значений не делал оценку слишком оптимистичной. + +Формулы для \sqlkw{AND}, \sqlkw{OR} и \sqlkw{NOT} основаны на предположении +независимости предикатов. Для эквисоединения двух атрибутов используется оценка +\(1 / \max(n_l, n_r)\), соответствующая типичному случаю соединения по ключу или +внешнему ключу, размер результата в таком случае оказывается сопоставим с +размером большего входа, а не с полным декартовым произведением. Для неизвестных +условий соединения применяется селективность \(1\), чтобы не занижать стоимость +плана при отсутствии надежной информации. Отдельно учитываются физические свойства результата. В данной работе таким свойством является порядок строк, требуемый предложением \ORDERBY{}. Если @@ -1480,24 +1525,7 @@ \subsubsection{Оценка кардинальности} \subsubsection{Стоимостная модель} Стоимость плана вычисляется как сумма локальных стоимостей физических -операторов. Коэффициенты были подобраны с помощью бенчмарков отдельных -операторов, описанных в разделе тестирования. - -Для входных кардинальностей $N$, $N_l$ и $N_r$ используются следующие формулы: -\[ -\begin{aligned} - C_{\text{scan}} &= 100N, \\ - C_{\text{filter}} &= 100N, \\ - C_{\text{projection}} &= 22N, \\ - C_{\text{aggregation}} &= 510N, \\ - C_{\text{nested-loop join}} &= 70N_lN_r, \\ - C_{\text{cross join}} &= 104N_lN_r, \\ - C_{\text{hash join}} &= 69(N_l + N_r), \\ - C_{\text{sort}} &\approx 11N\log_2N. -\end{aligned} -\] - -Фрагмент реализации стоимостной модели приведен в +операторов. Фрагмент реализации стоимостной модели приведен в листинге~\ref{lst:optimizer-cost}. \begin{listing}[H] @@ -1902,19 +1930,23 @@ \subsubsection{Запуск программы} Имя каждого файла без расширения считается именем таблицы. В заголовке каждого файла для столбца указывается имя и тип через двоеточие. Поддерживаются типы \texttt{int} и \texttt{string}. Пустое SQL-значение записывается как -\texttt{NULL}. - -Для первого запуска можно использовать готовые тестовые данные в \verb|test/static/executor/test_data|. - -SQL-запрос передается программе через стандартный поток ввода. Простейший -пример запуска представлен на листинге~\ref{lst:run-example}. +\texttt{NULL}. Для первого запуска можно использовать готовые тестовые данные в +\verb|test/static/executor/test_data|. SQL-запрос передается программе через +стандартный поток ввода. Простейший пример запуска представлен на +листинге~\ref{lst:run-example}. \begin{listing}[H] - \caption{Простейший пример запуска программы.} + \caption{Примеры команд для запуска программы.} \label{lst:run-example} \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{bash} echo 'SELECT users.id FROM users;' | \ ./build/bin/sql --data-dir test/static/executor/test_data + + +echo 'SELECT users.id FROM users WHERE users.age > 18 \ +ORDER BY users.id;' | \ + ./build/bin/sql --data-dir test/static/executor/test_data \ + --print-ast --print-plan \end{minted} \end{listing} @@ -1929,63 +1961,50 @@ \subsubsection{Запуск программы} \label{fig:practice-run} \end{figure} -\subsubsection{Вывод логического и физического планов} - Для диагностики предусмотрены параметры \texttt{--print-ast} и \texttt{--print-plan}. Первый параметр выводит логическое дерево после синтаксического анализа, второй --- выбранный физический план. -Пример использования представлен на листинге~\ref{lst:run-plan}. - -\begin{listing}[H] - \caption{Запуск программы с выводом логического дерева и физического плана.} - \label{lst:run-plan} - \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{bash} -echo 'SELECT users.id FROM users WHERE users.age > 18 \ -ORDER BY users.id;' | \ - ./build/bin/sql --data-dir test/static/executor/test_data \ - --print-ast --print-plan - \end{minted} -\end{listing} - -\begin{figure}[H] -\centering -\includegraphics[width=0.8\textwidth]{run-with-prints.png} -\caption{Вывод логического дерева и физического плана}% -\label{fig:practice-plan} -\end{figure} - -Для проверки полноты набора правил оптимизации предусмотрен режим -\verb|--check-reachable|. В этом режиме программа получает SQL-запрос и -сериализованный физический план, после чего выполняет полный перебор -пространства поиска без отсечения по стоимости. Если заданный план может быть -получен с помощью реализованных правил, программа выводит сообщение -\verb|REACHABLE|. - -План передается в отдельном файле в формате S-expression. Например, содержимое -файла \verb|target.plan| может иметь вид, представленный на листинге~\ref{lst:target-plan}. - -\begin{listing}[H] - \caption{Пример содержимого файла с целевым физическим планом.} - \label{lst:target-plan} - \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} -(PhysicalProjection (exprs (attr users id)) - (PhysicalFilter (> (attr users age) 18) - (SeqScan users))) - \end{minted} -\end{listing} - -Проверка выполняется командой на листинге~\ref{lst:check-reachable}. - -\begin{listing}[H] - \caption{Запуск программы в режиме проверки достижимости плана.} - \label{lst:check-reachable} - \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} -echo 'SELECT users.id FROM users WHERE users.age > 18;' | \ - ./build/bin/sql \ - --data-dir test/static/executor/test_data \ - --check-reachable target.plan - \end{minted} -\end{listing} +Пример использования представлен на листинге~\ref{lst:run-example}. + +% \begin{figure}[H] +% \centering +% \includegraphics[width=0.8\textwidth]{run-with-prints.png} +% \caption{Вывод логического дерева и физического плана}% +% \label{fig:practice-plan} +% \end{figure} + +% Для проверки полноты набора правил оптимизации предусмотрен режим +% \verb|--check-reachable|. В этом режиме программа получает SQL-запрос и +% сериализованный физический план, после чего выполняет полный перебор +% пространства поиска без отсечения по стоимости. Если заданный план может быть +% получен с помощью реализованных правил, программа выводит сообщение +% \verb|REACHABLE|. + +% План передается в отдельном файле в формате S-expression. Например, содержимое +% файла \verb|target.plan| может иметь вид, представленный на листинге~\ref{lst:target-plan}. + +% \begin{listing}[H] +% \caption{Пример содержимого файла с целевым физическим планом.} +% \label{lst:target-plan} +% \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} +% (PhysicalProjection (exprs (attr users id)) +% (PhysicalFilter (> (attr users age) 18) +% (SeqScan users))) +% \end{minted} +% \end{listing} + +% Проверка выполняется командой на листинге~\ref{lst:check-reachable}. + +% \begin{listing}[H] +% \caption{Запуск программы в режиме проверки достижимости плана.} +% \label{lst:check-reachable} +% \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} +% echo 'SELECT users.id FROM users WHERE users.age > 18;' | \ +% ./build/bin/sql \ +% --data-dir test/static/executor/test_data \ +% --check-reachable target.plan +% \end{minted} +% \end{listing} Если план недостижим, программа выводит сообщение \verb|NOT REACHABLE| и описание первого обнаруженного расхождения. Параметр \verb|--data-dir| @@ -2006,127 +2025,91 @@ \subsubsection{Вывод логического и физического пл \end{minted} \end{listing} -Для запуска модульных тестов C++ следует выполнить команду на листинге~\ref{lst:ctest}. - -\begin{listing}[H] - \caption{Запуск модульных тестов C++.} - \label{lst:ctest} - \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} -ctest --test-dir build --output-on-failure - \end{minted} -\end{listing} - -Модульные тесты проверяют синтаксический анализ запросов, вычисление выражений, -исполнение физических операторов, индексное сканирование, сериализацию планов, -структуру Memo, правила оптимизации и физические свойства. - -Для запуска Python-тестов преобразователя планов Microsoft SQL Server -необходимо предварительно запустить контейнер, после чего выполнить команды на листинге~\ref{lst:pytest-converter}. - -\begin{listing}[H] - \caption{Запуск тестов преобразователя планов Microsoft SQL Server.} - \label{lst:pytest-converter} - \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} -cd research -pytest -q test_converter.py -cd .. - \end{minted} -\end{listing} - -Тесты инструментов Star Schema Benchmark запускаются командой на листинге~\ref{lst:pytest-ssb}. - -\begin{listing}[H] - \caption{Запуск тестов инструментов Star Schema Benchmark.} - \label{lst:pytest-ssb} - \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} -pytest -q benchmarks/datasets/ssb - \end{minted} -\end{listing} +% Модульные тесты проверяют синтаксический анализ запросов, вычисление выражений, +% исполнение физических операторов, индексное сканирование, сериализацию планов, +% структуру Memo, правила оптимизации и физические свойства. + +% Для запуска Python-тестов преобразователя планов Microsoft SQL Server +% необходимо предварительно запустить контейнер, после чего выполнить команды на листинге~\ref{lst:pytest-converter}. + +% \begin{listing}[H] +% \caption{Запуск тестов преобразователя планов Microsoft SQL Server.} +% \label{lst:pytest-converter} +% \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} +% cd research +% pytest -q test_converter.py +% cd .. +% \end{minted} +% \end{listing} + +% Тесты инструментов Star Schema Benchmark запускаются командой на листинге~\ref{lst:pytest-ssb}. + +% \begin{listing}[H] +% \caption{Запуск тестов инструментов Star Schema Benchmark.} +% \label{lst:pytest-ssb} +% \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} +% pytest -q benchmarks/datasets/ssb +% \end{minted} +% \end{listing} Для поиска ошибок исполнения реализован дифференциальный фаззер. Он генерирует случайные SQL-запросы, выполняет их в модельной СУБД и Microsoft SQL Server, после чего сравнивает полученные результаты. Перед запуском необходимо поднять -контейнер с Microsoft SQL Server. - -Фаззер запускается из корневого каталога проекта командой на листинге~\ref{lst:diff-fuzz}. - -\begin{listing}[H] - \caption{Запуск дифференциального фаззера.} - \label{lst:diff-fuzz} - \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} -python -m research.fuzz.diff_fuzz \ - --cli build/bin/sql \ - --data-dir test/static/executor/test_data - \end{minted} -\end{listing} - -Для проверки режима JIT-компиляции можно дополнительно передать параметр -\verb|--jit|. При обнаружении расхождения фаззер выводит начальное значение -генератора случайных чисел и текст запроса, что позволяет воспроизвести ошибку. +контейнер с Microsoft SQL Server. Фаззер запускается из корневого каталога +проекта командой на листинге~\ref{lst:reach-fuzz}. Второй фаззер проверяет полноту набора правил оптимизатора. Он получает физический план запроса из Microsoft SQL Server, преобразует его во внутренний -формат проекта и запускает программу в режиме \verb|--check-reachable|, как показано на листинге~\ref{lst:reach-fuzz}. +формат проекта и запускает программу в режиме \verb|--check-reachable|, как +показано на листинге~\ref{lst:reach-fuzz}. \begin{listing}[H] - \caption{Запуск фаззера проверки полноты правил оптимизатора.} + \caption{Запуск фаззеров.} \label{lst:reach-fuzz} \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} python -m research.fuzz.reach_fuzz \ --cli build/bin/sql \ --data-dir test/static/executor/test_data - \end{minted} -\end{listing} - -\begin{figure}[H] -\centering -\includegraphics[width=0.8\textwidth]{reach-fuzz.png} -\caption{Пример вывода фаззера проверки полноты правил оптимизатора}% -\label{fig:reach-fuzz} -\end{figure} - -Оба фаззера поддерживают параметр \verb|--start-seed|. Он задает начальное -значение генератора и используется для воспроизводимого повторного запуска, -как показано на листинге~\ref{lst:reach-fuzz-seed}. - -\begin{listing}[H] - \caption{Запуск фаззера с фиксированным начальным значением генератора.} - \label{lst:reach-fuzz-seed} - \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} -python -m research.fuzz.reach_fuzz \ +python -m research.fuzz.diff_fuzz \ --cli build/bin/sql \ - --data-dir test/static/executor/test_data \ - --start-seed 1000 + --data-dir test/static/executor/test_data \end{minted} \end{listing} -\begin{longtable}{|p{4.2cm}|p{9.3cm}|} - \caption{Параметры командной строки программы.}\label{tbl:practice-cli}\\ - \hline - Параметр & Назначение \\ - \hline - \endfirsthead - \multicolumn{2}{l}{\small Продолжение таблицы~\ref{tbl:practice-cli}}\\ - \hline - Параметр & Назначение \\ - \hline - \endhead - \texttt{--data-dir DIR} & - Директория с CSV-файлами таблиц. Обязательна при обычном исполнении запроса. \\ - \hline - \texttt{--print-ast} & - Вывод логического дерева операторов после синтаксического анализа. \\ - \hline - \texttt{--print-plan} & - Вывод выбранного физического плана в формате S-expression. \\ - \hline - \texttt{--jit} & - Использование кэшируемой JIT-компиляции скалярных выражений. \\ - \hline - \texttt{--check-reachable FILE} & - Проверка достижимости физического плана, сериализованного в файле. \\ - \hline -\end{longtable} +% \begin{figure}[H] +% \centering +% \includegraphics[width=0.8\textwidth]{reach-fuzz.png} +% \caption{Пример вывода фаззера проверки полноты правил оптимизатора}% +% \label{fig:reach-fuzz} +% \end{figure} + +% \begin{longtable}{|p{4.2cm}|p{9.3cm}|} +% \caption{Параметры командной строки программы.}\label{tbl:practice-cli}\\ +% \hline +% Параметр & Назначение \\ +% \hline +% \endfirsthead +% \multicolumn{2}{l}{\small Продолжение таблицы~\ref{tbl:practice-cli}}\\ +% \hline +% Параметр & Назначение \\ +% \hline +% \endhead +% \texttt{--data-dir DIR} & +% Директория с CSV-файлами таблиц. Обязательна при обычном исполнении запроса. \\ +% \hline +% \texttt{--print-ast} & +% Вывод логического дерева операторов после синтаксического анализа. \\ +% \hline +% \texttt{--print-plan} & +% Вывод выбранного физического плана в формате S-expression. \\ +% \hline +% \texttt{--jit} & +% Использование кэшируемой JIT-компиляции скалярных выражений. \\ +% \hline +% \texttt{--check-reachable FILE} & +% Проверка достижимости физического плана, сериализованного в файле. \\ +% \hline +% \end{longtable} \section{Тестирование} @@ -2673,4 +2656,25 @@ \subsection{Калибровка стоимостной модели} \end{longtable} \end{scriptsize} +\begin{table}[H] + \centering + \caption{Стоимостные формулы физических операторов.} + \label{tbl:cost-model-coefficients} + \small + \begin{tabular}{|p{0.40\textwidth}|l|} + \hline + Физический оператор & Формула стоимости \\ + \hline + Последовательное сканирование & $100\,n$ \\ + Фильтрация & $100\,n_{out}$ \\ + Проекция & $22\,n_{out}$ \\ + Сортировка & $11\,n\bigl(\lfloor\log_2 n\rfloor + 1\bigr)$ \\ + Агрегация & $510\,n$ \\ + Соединение вложенными циклами & $70\,n_l n_r$ \\ + Декартово произведение & $104\,n_l n_r$ \\ + Хеш-соединение & $100\,n_b w_b + 35\,n_p + 10\,n_o w_o$ \\ + \hline + \end{tabular} +\end{table} + \end{document} From 6177f791f72a4721a9193a64369f42885875ff53 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sun, 7 Jun 2026 17:39:50 +0300 Subject: [PATCH 088/120] wip --- report/vkr.tex | 186 ++++++++++++++++--------------------------------- 1 file changed, 59 insertions(+), 127 deletions(-) diff --git a/report/vkr.tex b/report/vkr.tex index 6d2c316..e39006a 100644 --- a/report/vkr.tex +++ b/report/vkr.tex @@ -1358,11 +1358,10 @@ \subsection{Модуль оптимизации запросов} вниз с использованием стоимостной модели, физических свойств и метода ветвей и границ. -\subsubsection{Структура Memo} - -Структура Memo хранит группы логически эквивалентных выражений. Выражения из -одной группы возвращают одинаковый результат. Например, $A \Join B$ и -$B \Join A$ являются эквивалентными. +Центральное место в оптимизаторе занимает структура Memo, которая хранит группы +логически эквивалентных выражений. Выражения из одной группы возвращают +одинаковый результат. Например, $A \Join B$ и $B \Join A$ являются +эквивалентными. Дочерние узлы выражения ссылаются не на конкретные операторы, а на группы. Благодаря этому одно выражение компактно представляет множество деревьев. @@ -1409,7 +1408,7 @@ \subsubsection{Структура Memo} исполнения. Например, логическому соединению могут соответствовать физические операторы соединения вложенными циклами и хеш-соединения. -\subsubsection{Правила оптимизации} +\subsubsection{Правила трансформации и реализации} Расширение пространства поиска выполняется с помощью правил. Реализовано два типа правил: трансформационные и реализационные. Их интерфейсы приведены в @@ -1425,12 +1424,10 @@ \subsubsection{Правила оптимизации} virtual bool IsApplicable(utils::NotNull expr) = 0; utils::NotNull Apply( utils::NotNull expr, Memo& memo); - private: virtual LogicalOperator ApplyImpl( utils::NotNull expr, Memo& memo) = 0; }; - class ImplementationRule { public: virtual bool IsApplicable(utils::NotNull expr) = 0; @@ -1477,15 +1474,11 @@ \subsubsection{Правила оптимизации} expr->root_operator)) { return false; } - const auto& join = std::get(expr->root_operator); - if (join.type != JoinType::kInner) return false; - const auto* bin = std::get_if(&join.qual); - return bin && bin->binop == BinaryOp::kEq && std::holds_alternative(*bin->lhs) && std::holds_alternative(*bin->rhs); @@ -1497,33 +1490,12 @@ \subsubsection{Правила оптимизации} предотвращает повторное выполнение одного правила над одним выражением и ограничивает образование циклов при заполнении Memo. -\subsubsection{Оценка кардинальности} - -Стоимость физического плана зависит от количества обрабатываемых кортежей. -Для получения этой величины используется класс -\verb|CardinalityEstimates| из файла -\verb|src/stewkk/sql/logic/optimizer/cardinality.cpp|. - -Оценка кардинальности вычисляется для группы Memo и кэшируется. В текущей -версии используются следующие эвристики: -\begin{itemize} - \item для таблицы используется известное количество строк или значение по - умолчанию; - \item фильтрация и проекция сохраняют оценку кардинальности входа; - \item скалярная агрегация возвращает одну строку; - \item декартово произведение имеет кардинальность $N_l N_r$; - \item для соединения используется произведение кардинальностей входов и - коэффициента селективности. -\end{itemize} - -Для эквисоединения двух атрибутов коэффициент селективности оценивается как -\[ - S = \frac{1}{\max(1, N_l, N_r)}. -\] -Для конъюнкции условий коэффициенты селективности перемножаются. - \subsubsection{Стоимостная модель} +Стоимость физического плана зависит от количества обрабатываемых кортежей. Для +получения этой величины используется класс \verb|CardinalityEstimates|. Оценка +кардинальности вычисляется для группы Memo и кэшируется. + Стоимость плана вычисляется как сумма локальных стоимостей физических операторов. Фрагмент реализации стоимостной модели приведен в листинге~\ref{lst:optimizer-cost}. @@ -1566,10 +1538,8 @@ \subsubsection{Алгоритм поиска} Алгоритм использует стек отложенных задач. Это позволяет разделить процесс на небольшие операции: исследование группы, применение правила, оптимизацию -физического выражения и обработку его дочерних узлов. - -Основной метод оптимизации группы приведен в -листинге~\ref{lst:optimize-group}. +физического выражения и обработку его дочерних узлов. Основной метод оптимизации +группы приведен в листинге~\ref{lst:optimize-group}. \begin{listing}[H] \caption{Оптимизация группы Memo.} @@ -1581,13 +1551,11 @@ \subsubsection{Алгоритм поиска} Limit limit) { WinnerKey key{group, required}; if (winner_.contains(key)) return; - if (IsExplored(group) && limit && LowerBoundCost(group) >= *limit) { return; } - if (!IsExplored(group)) { tasks_.emplace([=] { OptimizeGroup(group, required, limit); @@ -1597,7 +1565,6 @@ \subsubsection{Алгоритм поиска} }); return; } - for (auto expr : group->GetPhysicalExprs()) { if (expr->is_enforcer) continue; auto cost = local_cost_[expr.get()]; @@ -1607,7 +1574,6 @@ \subsubsection{Алгоритм поиска} }); } } - AddEnforcers(group, required, limit); } \end{minted} @@ -1620,7 +1586,10 @@ \subsubsection{Алгоритм поиска} Для каждой пары из группы и требуемого набора свойств хранится лучший найденный вариант. Ключ такого варианта представлен структурой -\verb|WinnerKey|~\refAlgo{winner-key}. +\verb|WinnerKey|~\refAlgo{winner-key}. Хранение победителя отдельно для каждого +набора свойств необходимо, поскольку самый дешевый неотсортированный план может +отличаться от самого дешевого плана, удовлетворяющего требованию +\verb|ORDER BY|. \begin{listing}[H] \caption{Ключ для хранения лучшего плана по группе и свойствам.} @@ -1634,14 +1603,10 @@ \subsubsection{Алгоритм поиска} \end{minted} \end{listing} -Хранение победителя отдельно для каждого набора свойств необходимо, поскольку -самый дешевый неотсортированный план может отличаться от самого дешевого плана, -удовлетворяющего требованию \verb|ORDER BY|. - Дочерние группы физического выражения оптимизируются последовательно. После получения победителя дочерней группы его стоимость прибавляется к накопленной стоимости. Если все дочерние группы обработаны, проверяются выходные свойства -плана. Затем найденный вариант при необходимости становится новым победителем. +плана. Затем найденный вариант может стать новым лучшим планом. \subsubsection{Метод ветвей и границ} @@ -1672,18 +1637,14 @@ \subsubsection{Метод ветвей и границ} it != lower_bounds_.end()) { return it->second; } - int64_t best = std::numeric_limits::max(); - for (auto expr : group->GetLogicalExprs()) { int64_t cost = LowerBoundLocalCost(expr, cardinality_); - for (auto child : GetChildren(expr)) { cost += LowerBoundCost(child); } best = std::min(best, cost); } - lower_bounds_[group] = best; return best; } @@ -1695,9 +1656,9 @@ \subsubsection{Метод ветвей и границ} нижние оценки еще не обработанных дочерних групп. Благодаря этому заведомо неоптимальные ветви отбрасываются до построения полного плана. -Для исследовательских задач предусмотрен метод \verb|OptimizeExhaustive|. Он -запускает тот же алгоритм без ограничения стоимости и используется при -проверке достижимости внешних физических планов. +% Для исследовательских задач предусмотрен метод \verb|OptimizeExhaustive|. Он +% запускает тот же алгоритм без ограничения стоимости и используется при проверке +% достижимости внешних физических планов. \subsubsection{Физические свойства} @@ -1706,10 +1667,9 @@ \subsubsection{Физические свойства} кортежей. Для представления таких требований используется класс \verb|PropertySet|. -В текущей версии реализовано свойство сортировки \verb|SortProperty|. Порядок -считается удовлетворяющим требованию, если требуемые ключи являются префиксом -фактически полученного порядка. Например, сортировка по $(a, b)$ удовлетворяет -требованию сортировки по $a$. +Например, порядок сортировки считается удовлетворяющим требованию, если +требуемые ключи являются префиксом фактически полученного порядка. Таким образом +сортировка по $(a, b)$ удовлетворяет требованию сортировки по $a$. Некоторые операторы сохраняют порядок дочернего узла. Фильтрация и проекция сохраняют свойства входа. Соединение вложенными циклами сохраняет свойства @@ -1731,12 +1691,10 @@ \subsubsection{Физические свойства} SchemaCatalog& schema) const { const auto* sort = required.Get(); if (!sort) return std::nullopt; - if (!ContainsRequiredKeys(schema.GetSchema(group), sort->order)) { return std::nullopt; } - return physical::Sort{group, sort->order}; } \end{minted} @@ -1746,7 +1704,7 @@ \subsubsection{Формирование физического плана} После завершения поиска метод \verb|BuildOptimalPlan| рекурсивно восстанавливает итоговое дерево физических операторов. Для каждой группы выбирается сохраненный -победитель с учетом требуемых свойств. Затем аналогичным образом +лучший план с учетом требуемых свойств. Затем аналогичным образом восстанавливаются планы дочерних групп. Результатом работы оптимизатора является объект \verb|PhysicalPlanNode|, который @@ -1771,17 +1729,14 @@ \subsection{Модуль физического хранения данных} \begin{minted}[style=bw, breaklines, frame=single, fontsize = \footnotesize, linenos=false, xleftmargin = 1.5em]{C++} struct CsvDirSequentialScanner { std::string dir; - boost::asio::awaitable> operator()( const std::string& table_name, const std::string& output_table_name, AttributesInfoChannel& attrs_chan, TuplesChannel& tuples_chan) const; }; - struct CsvDirIndexedScanner { std::string dir; - boost::asio::awaitable> operator()( const std::string& table_name, const std::string& output_table_name, @@ -1812,25 +1767,18 @@ \subsection{Модуль физического хранения данных} записей индекса. Поддерживаются индексы только для целочисленных атрибутов. Индексное -сканирование применяется для предикатов сравнения атрибута с -целочисленной константой. Поддерживаются операции равенства, строгого и -нестрогого сравнения. После чтения найденных строк дополнительно -вычисляется исходный предикат, что обеспечивает корректную обработку -составных условий. +сканирование применяется для предикатов сравнения атрибута с целочисленной +константой. Из возможных операций доступно равенство, строгое и нестрогое +сравнение. После чтения найденных строк дополнительно вычисляется исходный +предикат, что обеспечивает корректную обработку составных условий. \subsection{Модуль исполнения физических планов} -За исполнение запросов отвечает класс \verb|Executor|~\refAlgo{lst:executor}. -Он получает физический план, запускает исполнение его операторов и формирует +За исполнение запросов отвечает класс \verb|Executor|~\refAlgo{lst:executor}. Он +получает физический план, запускает исполнение его операторов и формирует результирующее отношение. Физический план представлен деревом объектов типа \verb|PhysicalPlanNode|. Для каждого типа узла предусмотрена отдельная функция -исполнения. - -Поддерживается исполнение последовательного и индексного сканирования, -фильтрации, проекции, сортировки, агрегации, декартова произведения, соединения -вложенными циклами и хеш-соединения. Операторы могут обрабатывать дочерние узлы -плана независимо и передавать результаты вышестоящим операторам по мере их -готовности. +исполнения реализующая единый интерфейс и исполняющаяся независимо. \begin{listing}[H] \caption{Основные элементы объявления класса Executor.} @@ -1844,21 +1792,17 @@ \subsection{Модуль исполнения физических планов} const std::string& output_table_name, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan)>; - using IndexScan = std::function>( const std::string& table_name, const std::string& output_table_name, const Expression& predicate, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan)>; - Executor(SequentialScan seq_scan, IndexScan index_scan, boost::asio::any_io_executor executor); - boost::asio::awaitable> Execute(const PhysicalPlanNode& op); - private: SequentialScan sequential_scan_; IndexScan index_scan_; @@ -2111,7 +2055,7 @@ \subsubsection{Запуск программы} % \hline % \end{longtable} -\section{Тестирование} +\section{Аналитическая часть} Тестирование разработанной модельной СУБД выполнялось на нескольких уровнях. Для проверки отдельных компонентов использовались модульные тесты. Корректность @@ -2120,20 +2064,13 @@ \section{Тестирование} стоимостной модели были реализованы бенчмарки. Вспомогательные инструменты на языке Python проверяются с помощью библиотеки \verb|pytest|. -\subsection{Модульные тесты} - Модульные тесты реализованы с использованием библиотеки GoogleTest. Они проверяют синтаксический анализ SQL-запросов, исполнение физических операторов, индексное сканирование, вычисление выражений, сериализацию физических планов, -структуру Memo, правила оптимизации и обработку свойств сортировки. - -Отдельные тесты проверяют выбор физического плана. Например, при различных -оценках кардинальности оптимизатор должен выбирать подходящий порядок соединения -таблиц. Также проверяется достижимость физических планов при полном переборе -пространства поиска. - -Пример теста синтаксического анализатора приведен в -листинге~\ref{lst:googletest}. +структуру Memo, правила оптимизации и обработку свойств сортировки. Пример теста +синтаксического анализатора приведен в листинге~\ref{lst:googletest}. Также +проверяется выбор физического плана. Например, при различных оценках +кардинальности оптимизатор должен выбирать подходящий порядок соединения таблиц. \begin{listing}[H] \caption{Пример модульного теста.} @@ -2153,36 +2090,32 @@ \subsection{Модульные тесты} \end{minted} \end{listing} -\subsection{Python-тесты} - -Для проверки вспомогательных инструментов используются тесты на языке Python, -запускаемые с помощью библиотеки \verb|pytest|. Они разделены на две группы. - -Первая группа проверяет преобразование физических планов Microsoft SQL Server из -формата ShowPlanXML во внутренний текстовый формат проекта. Рассматриваются -последовательное и индексное сканирование, фильтрация, сортировка, агрегация и -соединения. Часть тестов использует заранее подготовленные XML-фрагменты, часть -может быть запущена с подключением к экземпляру Microsoft SQL Server. - -Вторая группа относится к Star Schema Benchmark. Она проверяет преобразование -исходных таблиц SSB в CSV-формат модельной СУБД, обработку некорректных входных -данных и успешное выполнение стандартных аналитических запросов. +Для проверки вспомогательных инструментов используются тесты на языке Python. +Они разделены на две группы. Первая группа проверяет преобразование физических +планов Microsoft SQL Server из формата ShowPlanXML во внутренний текстовый +формат проекта. Рассматриваются последовательное и индексное сканирование, +фильтрация, сортировка, агрегация и соединения. Часть тестов использует заранее +подготовленные XML-фрагменты, часть может быть запущена с подключением к +экземпляру Microsoft SQL Server. + +Вторая группа относится к стандартному Star Schema Benchmark. Она проверяет +преобразование исходных таблиц SSB в CSV-формат модельной СУБД, обработку +некорректных входных данных и успешное выполнение стандартных аналитических +запросов. \subsection{Фаззинг} Для проверки системы на большом количестве входных данных реализован генератор случайных SQL-запросов. Он строит запросы из поддерживаемого подмножества языка: проекции, фильтры, соединения, группировки, агрегатные функции, сортировки, -строковые выражения, \verb|IN| и \verb|BETWEEN|. Использование фиксированного -начального значения генератора позволяет воспроизводить найденные ошибки. +строковые выражения, \verb|IN| и \verb|BETWEEN|. % TODO: заменить на макросы \sqkkw Дифференциальный фаззер выполняет каждый сгенерированный запрос в модельной СУБД -и в Microsoft SQL Server. Результаты сравниваются как мультимножества строк. -Для запросов с \verb|ORDER BY| дополнительно проверяется корректность порядка -результатов. При обнаружении расхождения выводятся начальное значение -генератора, текст запроса и результаты обеих систем. +и в Microsoft SQL Server. Результаты сравниваются как мультимножества строк. Для +запросов с \verb|ORDER BY| дополнительно проверяется корректность порядка +результатов. Отдельный фаззер используется для исследования полноты набора правил оптимизации. Для случайного запроса из Microsoft SQL Server извлекается @@ -2193,12 +2126,12 @@ \subsection{Фаззинг} \subsection{Бенчмарки} -Тесты производительности реализованы с помощью библиотеки Google Benchmark. -Для запросов предусмотрены два режима построения физического плана: -\verb|Naive| и \verb|Optimized|. В первом случае физический план строится -непосредственно по логическому дереву без применения оптимизатора. Во втором -случае используется стоимостная оптимизация. Сравнение этих режимов позволяет -оценить влияние выбранного физического плана на время выполнения запроса. +Тесты производительности реализованы с помощью библиотеки Google Benchmark. Для +запросов предусмотрены два режима построения физического плана: \verb|Naive| и +\verb|Optimized|. В первом случае физический план строится непосредственно по +логическому дереву без применения оптимизатора. Во втором случае используется +стоимостная оптимизация. Сравнение этих режимов позволяет оценить влияние +выбранного физического плана на время выполнения запроса. Бенчмарки выполняются как для небольших синтетических тестовых запросов, так и для набора аналитических запросов Star Schema Benchmark. Дополнительно @@ -2221,9 +2154,8 @@ \subsection{Бенчмарки} \end{figure} В таблице~\ref{tbl:ssb-benchmark} приведено среднее время выполнения трех -повторений запросов SSB для набора данных масштаба \texttt{sf001}. Ускорение -рассчитывается как отношение времени выполнения наивного плана ко времени -выполнения оптимизированного плана. +повторений запросов SSB. Ускорение рассчитывается как отношение времени +выполнения наивного плана ко времени выполнения оптимизированного плана. \begin{table}[H] \centering From f7c9fed2fad426b55997245a0d378705e2f741e9 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sun, 7 Jun 2026 17:53:13 +0300 Subject: [PATCH 089/120] Done --- report/vkr.tex | 60 +++++++++++++++++++++++++++++++------------------- 1 file changed, 37 insertions(+), 23 deletions(-) diff --git a/report/vkr.tex b/report/vkr.tex index e39006a..339eb32 100644 --- a/report/vkr.tex +++ b/report/vkr.tex @@ -2103,7 +2103,7 @@ \section{Аналитическая часть} некорректных входных данных и успешное выполнение стандартных аналитических запросов. -\subsection{Фаззинг} +\subsection{Дифференциальный анализ и тестирование производительности} Для проверки системы на большом количестве входных данных реализован генератор случайных SQL-запросов. Он строит запросы из поддерживаемого подмножества языка: @@ -2124,8 +2124,6 @@ \subsection{Фаззинг} проверяет, достижим ли полученный план с помощью реализованных правил. Такой подход позволяет находить отсутствующие преобразования и физические операторы. -\subsection{Бенчмарки} - Тесты производительности реализованы с помощью библиотеки Google Benchmark. Для запросов предусмотрены два режима построения физического плана: \verb|Naive| и \verb|Optimized|. В первом случае физический план строится непосредственно по @@ -2219,26 +2217,42 @@ \subsection{Калибровка стоимостной модели} \anonsection{ЗАКЛЮЧЕНИЕ} -В ходе практики разработана модельная СУБД и оптимизатор для нее. Программа -работает с CSV-файлами, поддерживает основные операторы реляционной алгебры и -формирует оптимальный физический план на основе стоимостной модели. - -Ключевой частью проекта является расширяемый оптимизатор, использующий структуру -Memo и правила преобразования. Реализован поиск планов с учетом физических -свойств, нижних оценок стоимости и метода ветвей и границ. - -Корректность основных компонентов проверена модульными тестами. Дополнительно -подготовлены бенчмарки физических операторов, оценен прирост производительности -относительно наивного плана и реализованы инструменты дифференциального -сравнения с промышленной СУБД Microsoft SQL Server. - -Цель курсовой работы была достигнута. Получена стабильная и расширяемая система, -демонстрирующая возможности оптимизатора. - -В заключение, несмотря на достигнутые положительные результаты, существует -потенциал для дальнейшего улучшения реализации оптимихатора. Это включает в себя -реализацию дедупликации групп в процессе поиска, добавление новых правил -трансформации и реализации и поддержку подзапросов. +В ходе работы была изучена архитектура оптимизаторов Cascades и на ее основе +спроектирован собственный оптимизатор для модельной реляционной СУБД. В +результате были выделены основные компоненты системы, такие как структура Memo, +набор правил преобразования и реализации, стоимостная модель, механизм учета +физических свойств и алгоритм поиска физического плана. + +Было формализовано поддерживаемое подмножество SQL и соответствующее ему +представление в терминах реляционной алгебры. Реализованная система поддерживает +запросы на чтение данных с фильтрацией, проекцией, соединениями, агрегацией и +сортировкой. + +Был реализован алгоритм поиска оптимального физического плана и структура Memo +для эффективного хранения групп логически эквивалентных выражений. В результате +система выбирает физический план с минимальной оценочной стоимостью с учетом +требуемых свойств результата. + +Для формирования пространства поиска был разработан набор правил трансформации и +реализации для основных операторов реляционной алгебры. Реализованные правила +позволяют изменять порядок и группировку соединений, преобразовывать и +проталкивать фильтры, а также сопоставлять логическим операторам физические +реализации, включая последовательное сканирование, фильтрацию, проекцию, +агрегацию, соединение вложенными циклами и хеш-соединение. + +Для ускорения поиска был реализован метод ветвей и границ. Оптимизатор +использует текущую лучшую стоимость и нижние оценки стоимости поддеревьев для +отсечения заведомо неоптимальных вариантов, что уменьшает объем исследуемого +пространства планов ухудшения качества результирующих планов. + +Для последующего развития оптимизатора был реализован метод дифференциального +анализа физических планов. Подготовлены инструменты генерации запросов, +сравнения результатов исполнения и сопоставления планов с промышленной СУБД +Microsoft SQL Server. + +Тем самым цель работы была достигнута. Получена работоспособная и расширяемая +система, которую можно использовать как основу для дальнейшего уточнения правил +оптимизации и увеличения пространства исследуемых планов исполнения запросов. \renewcommand\refname{СПИСОК ИСПОЛЬЗОВАННЫХ ИСТОЧНИКОВ} \clearpage From 4ebbc71a52570e062a584dd5328fc014fd54a6b0 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sun, 7 Jun 2026 19:06:39 +0300 Subject: [PATCH 090/120] wip --- report/vkr.tex | 449 +++++++++------------ src/stewkk/sql/logic/executor/executor.cpp | 1 + 2 files changed, 185 insertions(+), 265 deletions(-) diff --git a/report/vkr.tex b/report/vkr.tex index 339eb32..890ac25 100644 --- a/report/vkr.tex +++ b/report/vkr.tex @@ -1209,53 +1209,6 @@ \subsection{Модуль стоимостной оптимизации план селективности \(sel(p)\), показывающий долю кортежей, удовлетворяющих предикату \(p\). Используемые формулы приведены в таблице~\ref{tbl:selectivity-model}. -\begin{table}[H] - \centering - \caption{Формулы оценки селективности предикатов.} - \label{tbl:selectivity-model} - \small - \begin{tabular}{|p{0.42\textwidth}|p{0.42\textwidth}|} - \hline - Вид предиката & Формула селективности \\ - \hline - \(a = c\), где \(a\) --- атрибут, \(c\) --- константа - & \(sel(a = c) = 0{,}10\) \\ - \hline - \(a < b\), \(a > b\), \(a \le b\), \(a \ge b\) - & \(sel(p) = 0{,}50\) \\ - \hline - \(p_1 \land p_2\) - & \(sel(p_1 \land p_2) = sel(p_1) \cdot sel(p_2)\) \\ - \hline - \(p_1 \lor p_2\) - & \(sel(p_1 \lor p_2) = - sel(p_1) + sel(p_2) - sel(p_1) \cdot sel(p_2)\) \\ - \hline - \(\lnot p\) - & \(sel(\lnot p) = 1 - sel(p)\) \\ - \hline - \(a \in \{c_1, \ldots, c_k\}\) - & \(sel(p) = \min(0{,}50,\; 0{,}10k)\) \\ - \hline - \(a \notin \{c_1, \ldots, c_k\}\) - & \(sel(p) = 1 - \min(0{,}50,\; 0{,}10k)\) \\ - \hline - \(a = b\) в условии соединения, где \(a\) и \(b\) --- атрибуты разных входов - & \(sel(a = b) = \frac{1}{\max(1,\; \max(n_l, n_r))}\) \\ - \hline - \(\theta_1 \land \theta_2\) в условии соединения - & \(sel(\theta_1 \land \theta_2) = - sel(\theta_1) \cdot sel(\theta_2)\) \\ - \hline - Иной или неизвестный предикат фильтрации - & \(sel(p) = 0{,}50\) \\ - \hline - Иной или неизвестный предикат соединения - & \(sel(\theta) = 1\) \\ - \hline - \end{tabular} -\end{table} - Такие формулы выбраны как простая эвристическая модель, не требующая гистограмм, сведений о количестве различных значений и статистик корреляции между атрибутами. Равенство атрибута константе считается достаточно селективным, @@ -1411,80 +1364,37 @@ \subsection{Модуль оптимизации запросов} \subsubsection{Правила трансформации и реализации} Расширение пространства поиска выполняется с помощью правил. Реализовано два -типа правил: трансформационные и реализационные. Их интерфейсы приведены в -листинге~\ref{lst:optimizer-rules}. - -\begin{listing}[H] - \caption{Интерфейсы правил оптимизации.} - \label{lst:optimizer-rules} - \begin{minted}[style=bw, breaklines, frame=single, - fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{cpp} -class TransformationRule { -public: - virtual bool IsApplicable(utils::NotNull expr) = 0; - utils::NotNull Apply( - utils::NotNull expr, Memo& memo); -private: - virtual LogicalOperator ApplyImpl( - utils::NotNull expr, Memo& memo) = 0; -}; -class ImplementationRule { -public: - virtual bool IsApplicable(utils::NotNull expr) = 0; - virtual utils::NotNull Apply( - utils::NotNull expr, Memo& memo) = 0; -}; - \end{minted} -\end{listing} - -В качестве примера в листинге~\ref{lst:join-commutativity} приведено правило -коммутативности соединений. - -\begin{listing}[H] - \caption{Правило коммутативности соединения.} - \label{lst:join-commutativity} - \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize, - linenos=false, xleftmargin=1.5em]{cpp} -LogicalOperator JoinCommutativity::ApplyImpl( - utils::NotNull expr, Memo&) { - auto join = std::get(expr->root_operator); - std::swap(join.lhs, join.rhs); - - if (join.type == JoinType::kLeft) { - join.type = JoinType::kRight; - } else if (join.type == JoinType::kRight) { - join.type = JoinType::kLeft; - } - return join; -} - \end{minted} -\end{listing} - -Пример метода для проверки применимости правила приведен в -листинге~\ref{lst:implement-hash-join}. +типа правил: трансформационные и реализационные. % Их интерфейсы приведены в +% листинге~\ref{lst:optimizer-rules}. +Правила реализуют единый интерфейс, состоящий из условия применимости и метода +для применения правила. Условие применимости выполняет быструю структурную +проверку по дереву выражений в Memo. Метод применения правила порождает +необходимые новые группы и результирующее логическое или физическое выражение. В +качестве примера в листинге~\ref{lst:join-commutativity} приведена реализация +правила коммутативности соединений. -\begin{listing}[H] - \caption{Проверка применимости хеш-соединения.} - \label{lst:implement-hash-join} - \begin{minted}[style=bw, breaklines, frame=single, - fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{cpp} -bool ImplementHashJoin::IsApplicable( - utils::NotNull expr) { - if (!std::holds_alternative( - expr->root_operator)) { - return false; - } - const auto& join = - std::get(expr->root_operator); - if (join.type != JoinType::kInner) return false; - const auto* bin = - std::get_if(&join.qual); - return bin && bin->binop == BinaryOp::kEq - && std::holds_alternative(*bin->lhs) - && std::holds_alternative(*bin->rhs); -} - \end{minted} -\end{listing} +% \begin{listing}[H] +% \caption{Интерфейсы правил оптимизации.} +% \label{lst:optimizer-rules} +% \begin{minted}[style=bw, breaklines, frame=single, +% fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{cpp} +% class TransformationRule { +% public: +% virtual bool IsApplicable(utils::NotNull expr) = 0; +% utils::NotNull Apply( +% utils::NotNull expr, Memo& memo); +% private: +% virtual LogicalOperator ApplyImpl( +% utils::NotNull expr, Memo& memo) = 0; +% }; +% class ImplementationRule { +% public: +% virtual bool IsApplicable(utils::NotNull expr) = 0; +% virtual utils::NotNull Apply( +% utils::NotNull expr, Memo& memo) = 0; +% }; +% \end{minted} +% \end{listing} Для каждого выражения хранится информация о ранее примененных правилах. Это предотвращает повторное выполнение одного правила над одним выражением и @@ -1500,36 +1410,6 @@ \subsubsection{Стоимостная модель} операторов. Фрагмент реализации стоимостной модели приведен в листинге~\ref{lst:optimizer-cost}. -\begin{listing}[H] - \caption{Вычисление локальной стоимости физических операторов.} - \label{lst:optimizer-cost} - \begin{minted}[style=bw, breaklines, frame=single, - fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{cpp} -int64_t CalcCost(PhysicalExpr* expr, - CardinalityEstimates& cardinality) { - return std::visit(utils::Overloaded{ - [&](const physical::SeqScan&) { - return 100 * cardinality.GetCardinality(expr->group); - }, - [&](const physical::NestedLoopJoin& j) { - return 70 * cardinality.GetCardinality(j.lhs) - * cardinality.GetCardinality(j.rhs); - }, - [&](const physical::HashJoin& j) { - return 69 * (cardinality.GetCardinality(j.lhs) - + cardinality.GetCardinality(j.rhs)); - }, - [&](const physical::Sort& s) { - auto n = cardinality.GetCardinality(s.input); - return 11 * n * std::bit_width( - static_cast(n)); - }, - ... - }, expr->root_operator); -} - \end{minted} -\end{listing} - \subsubsection{Алгоритм поиска} Класс \verb|Optimizer| реализует алгоритм поиска. При создании объекта исходное @@ -1541,44 +1421,6 @@ \subsubsection{Алгоритм поиска} физического выражения и обработку его дочерних узлов. Основной метод оптимизации группы приведен в листинге~\ref{lst:optimize-group}. -\begin{listing}[H] - \caption{Оптимизация группы Memo.} - \label{lst:optimize-group} - \begin{minted}[style=bw, breaklines, frame=single, - fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{cpp} -void Optimizer::OptimizeGroup(Group* group, - PropertySet required, - Limit limit) { - WinnerKey key{group, required}; - if (winner_.contains(key)) return; - if (IsExplored(group) - && limit - && LowerBoundCost(group) >= *limit) { - return; - } - if (!IsExplored(group)) { - tasks_.emplace([=] { - OptimizeGroup(group, required, limit); - }); - tasks_.emplace([=] { - ExploreGroup(group, limit); - }); - return; - } - for (auto expr : group->GetPhysicalExprs()) { - if (expr->is_enforcer) continue; - auto cost = local_cost_[expr.get()]; - if (!limit || cost < *limit) { - tasks_.emplace([=] { - OptimizeInputs(expr, required, {}, cost, limit); - }); - } - } - AddEnforcers(group, required, limit); -} - \end{minted} -\end{listing} - Если группа еще не исследована, в стек добавляются две задачи. Первая расширяет группу с помощью правил, вторая повторно запускает ее оптимизацию после расширения. Если группа уже исследована, для каждого физического выражения @@ -1586,7 +1428,7 @@ \subsubsection{Алгоритм поиска} Для каждой пары из группы и требуемого набора свойств хранится лучший найденный вариант. Ключ такого варианта представлен структурой -\verb|WinnerKey|~\refAlgo{winner-key}. Хранение победителя отдельно для каждого +\verb|WinnerKey|~\refAlgo{lst:winner-key}. Хранение победителя отдельно для каждого набора свойств необходимо, поскольку самый дешевый неотсортированный план может отличаться от самого дешевого плана, удовлетворяющего требованию \verb|ORDER BY|. @@ -1718,34 +1560,9 @@ \subsubsection{Формирование физического плана} \subsection{Модуль физического хранения данных} -Для чтения таблиц реализован класс -\verb|CsvDirSequentialScanner|~\refAlgo{lst:csv-dir-scanners}. Он -выполняет последовательное сканирование CSV-файла и передает прочитанные -кортежи модулю исполнения физических операторов. - -\begin{listing}[H] - \caption{Объявления классов сканирования CSV-таблиц.} - \label{lst:csv-dir-scanners} - \begin{minted}[style=bw, breaklines, frame=single, fontsize = \footnotesize, linenos=false, xleftmargin = 1.5em]{C++} -struct CsvDirSequentialScanner { - std::string dir; - boost::asio::awaitable> operator()( - const std::string& table_name, - const std::string& output_table_name, - AttributesInfoChannel& attrs_chan, - TuplesChannel& tuples_chan) const; -}; -struct CsvDirIndexedScanner { - std::string dir; - boost::asio::awaitable> operator()( - const std::string& table_name, - const std::string& output_table_name, - const Expression& predicate, - AttributesInfoChannel& attrs_chan, - TuplesChannel& tuples_chan) const; -}; - \end{minted} -\end{listing} +Для чтения таблиц реализован класс \verb|CsvDirSequentialScanner|. Он выполняет +последовательное сканирование CSV-файла и передает прочитанные кортежи модулю +исполнения физических операторов. Список доступных индексов задается в файле \verb|indexes.meta|, расположенном в директории с данными. Каждая строка файла содержит @@ -1761,10 +1578,9 @@ \subsection{Модуль физического хранения данных} \end{listing} Для чтения данных с использованием индекса реализован класс -\verb|CsvDirIndexedScanner|~\refAlgo{lst:csv-dir-scanners}. Если указанный в -метаданных бинарный файл отсутствует, индекс строится при первом обращении к -таблице. Для поиска диапазона подходящих ключей используется упорядоченность -записей индекса. +\verb|CsvDirIndexedScanner|. Если указанный в метаданных бинарный файл +отсутствует, индекс строится при первом обращении к таблице. Для поиска +диапазона подходящих ключей используется упорядоченность записей индекса. Поддерживаются индексы только для целочисленных атрибутов. Индексное сканирование применяется для предикатов сравнения атрибута с целочисленной @@ -1787,17 +1603,6 @@ \subsection{Модуль исполнения физических планов} template class Executor { public: - using SequentialScan = std::function>( - const std::string& table_name, - const std::string& output_table_name, - AttributesInfoChannel& attr_chan, - TuplesChannel& tuples_chan)>; - using IndexScan = std::function>( - const std::string& table_name, - const std::string& output_table_name, - const Expression& predicate, - AttributesInfoChannel& attr_chan, - TuplesChannel& tuples_chan)>; Executor(SequentialScan seq_scan, IndexScan index_scan, boost::asio::any_io_executor executor); @@ -1877,7 +1682,7 @@ \subsubsection{Запуск программы} \texttt{NULL}. Для первого запуска можно использовать готовые тестовые данные в \verb|test/static/executor/test_data|. SQL-запрос передается программе через стандартный поток ввода. Простейший пример запуска представлен на -листинге~\ref{lst:run-example}. +листинге~\ref{lst:run-example} и на рисунке~\ref{fig:practice-run}. \begin{listing}[H] \caption{Примеры команд для запуска программы.} @@ -1886,7 +1691,6 @@ \subsubsection{Запуск программы} echo 'SELECT users.id FROM users;' | \ ./build/bin/sql --data-dir test/static/executor/test_data - echo 'SELECT users.id FROM users WHERE users.age > 18 \ ORDER BY users.id;' | \ ./build/bin/sql --data-dir test/static/executor/test_data \ @@ -1898,13 +1702,6 @@ \subsubsection{Запуск программы} затем полученные строки. Если запрос не содержит \ORDERBY{}, строки вывода сортируются лексикографически для воспроизводимости результата. -\begin{figure}[H] -\centering -\includegraphics[width=0.8\textwidth]{run-example.png} -\caption{Выполнение SQL-запроса над тестовыми данными}% -\label{fig:practice-run} -\end{figure} - Для диагностики предусмотрены параметры \texttt{--print-ast} и \texttt{--print-plan}. Первый параметр выводит логическое дерево после синтаксического анализа, второй --- выбранный физический план. @@ -1955,20 +1752,6 @@ \subsubsection{Запуск программы} необходим для загрузки схем таблиц. В частности, схема используется при проверке достижимости планов с сортировкой. -Для автоматизированной проверки достижимости планов используется Microsoft SQL -Server. Контейнер с СУБД запускается с помощью Docker Compose. Перед запуском -скриптов в директории research, следует выполнить команды на листинге~\ref{lst:docker-up}. - -\begin{listing}[H] - \caption{Запуск контейнера с Microsoft SQL Server.} - \label{lst:docker-up} - \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} -cd research -docker compose up -d -cd .. - \end{minted} -\end{listing} - % Модульные тесты проверяют синтаксический анализ запросов, вычисление выражений, % исполнение физических операторов, индексное сканирование, сериализацию планов, % структуру Memo, правила оптимизации и физические свойства. @@ -2011,6 +1794,7 @@ \subsubsection{Запуск программы} \caption{Запуск фаззеров.} \label{lst:reach-fuzz} \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} +docker compose up -d python -m research.fuzz.reach_fuzz \ --cli build/bin/sql \ --data-dir test/static/executor/test_data @@ -2218,15 +2002,11 @@ \subsection{Калибровка стоимостной модели} \anonsection{ЗАКЛЮЧЕНИЕ} В ходе работы была изучена архитектура оптимизаторов Cascades и на ее основе -спроектирован собственный оптимизатор для модельной реляционной СУБД. В -результате были выделены основные компоненты системы, такие как структура Memo, -набор правил преобразования и реализации, стоимостная модель, механизм учета -физических свойств и алгоритм поиска физического плана. +спроектирован собственный оптимизатор для модельной реляционной СУБД. Было формализовано поддерживаемое подмножество SQL и соответствующее ему представление в терминах реляционной алгебры. Реализованная система поддерживает -запросы на чтение данных с фильтрацией, проекцией, соединениями, агрегацией и -сортировкой. +запросы на чтение данных с основными операторами диалекта PostgreSQL. Был реализован алгоритм поиска оптимального физического плана и структура Memo для эффективного хранения групп логически эквивалентных выражений. В результате @@ -2235,10 +2015,8 @@ \subsection{Калибровка стоимостной модели} Для формирования пространства поиска был разработан набор правил трансформации и реализации для основных операторов реляционной алгебры. Реализованные правила -позволяют изменять порядок и группировку соединений, преобразовывать и -проталкивать фильтры, а также сопоставлять логическим операторам физические -реализации, включая последовательное сканирование, фильтрацию, проекцию, -агрегацию, соединение вложенными циклами и хеш-соединение. +позволяют изменять порядок и группировку соединений, преобразовывать предикаты и +выражения, а также сопоставлять логическим операторам физические реализации. Для ускорения поиска был реализован метод ветвей и границ. Оптимизатор использует текущую лучшую стоимость и нижние оценки стоимости поддеревьев для @@ -2357,6 +2135,54 @@ \subsection{Калибровка стоимостной модели} \label{fig:memo-structure} \end{figure} +\begin{table}[H] + \centering + \caption{Формулы оценки селективности предикатов.} + \label{tbl:selectivity-model} + \small + \begin{tabular}{|p{0.42\textwidth}|p{0.42\textwidth}|} + \hline + Вид предиката & Формула селективности \\ + \hline + \(a = c\), где \(a\) --- атрибут, \(c\) --- константа + & \(sel(a = c) = 0{,}10\) \\ + \hline + \(a < b\), \(a > b\), \(a \le b\), \(a \ge b\) + & \(sel(p) = 0{,}50\) \\ + \hline + \(p_1 \land p_2\) + & \(sel(p_1 \land p_2) = sel(p_1) \cdot sel(p_2)\) \\ + \hline + \(p_1 \lor p_2\) + & \(sel(p_1 \lor p_2) = + sel(p_1) + sel(p_2) - sel(p_1) \cdot sel(p_2)\) \\ + \hline + \(\lnot p\) + & \(sel(\lnot p) = 1 - sel(p)\) \\ + \hline + \(a \in \{c_1, \ldots, c_k\}\) + & \(sel(p) = \min(0{,}50,\; 0{,}10k)\) \\ + \hline + \(a \notin \{c_1, \ldots, c_k\}\) + & \(sel(p) = 1 - \min(0{,}50,\; 0{,}10k)\) \\ + \hline + \(a = b\) в условии соединения, где \(a\) и \(b\) --- атрибуты разных входов + & \(sel(a = b) = \frac{1}{\max(1,\; \max(n_l, n_r))}\) \\ + \hline + \(\theta_1 \land \theta_2\) в условии соединения + & \(sel(\theta_1 \land \theta_2) = + sel(\theta_1) \cdot sel(\theta_2)\) \\ + \hline + Иной или неизвестный предикат фильтрации + & \(sel(p) = 0{,}50\) \\ + \hline + Иной или неизвестный предикат соединения + & \(sel(\theta) = 1\) \\ + \hline + \end{tabular} +\end{table} + + \begin{algorithm}[H] \caption{Алгоритм поиска (часть 1).}% \label{alg:cascades-search} @@ -2623,4 +2449,97 @@ \subsection{Калибровка стоимостной модели} \end{tabular} \end{table} +\begin{listing}[H] + \caption{Правило коммутативности соединения.} + \label{lst:join-commutativity} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize, + linenos=false, xleftmargin=1.5em]{cpp} +LogicalOperator JoinCommutativity::ApplyImpl( + utils::NotNull expr, Memo&) { + auto join = std::get(expr->root_operator); + std::swap(join.lhs, join.rhs); + + if (join.type == JoinType::kLeft) { + join.type = JoinType::kRight; + } else if (join.type == JoinType::kRight) { + join.type = JoinType::kLeft; + } + return join; +} +bool ImplementHashJoin::IsApplicable( + utils::NotNull expr) { + if (!std::holds_alternative( + expr->root_operator)) { + return false; + } + const auto& join = + std::get(expr->root_operator); + if (join.type != JoinType::kInner) return false; + const auto* bin = + std::get_if(&join.qual); + return bin && bin->binop == BinaryOp::kEq + && std::holds_alternative(*bin->lhs) + && std::holds_alternative(*bin->rhs); +} + \end{minted} +\end{listing} +\begin{listing}[H] + \caption{Вычисление локальной стоимости физических операторов.} + \label{lst:optimizer-cost} + \begin{minted}[style=bw, breaklines, frame=single, + fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{cpp} +int64_t CalcCost(PhysicalExpr* expr, + CardinalityEstimates& cardinality) { + return std::visit(utils::Overloaded{ + [&](const physical::NestedLoopJoin& j) { + return 70 * cardinality.GetCardinality(j.lhs) + * cardinality.GetCardinality(j.rhs); + }, + [&](const physical::HashJoin& j) { + return 69 * (cardinality.GetCardinality(j.lhs) + + cardinality.GetCardinality(j.rhs)); + }, + \end{minted} +\end{listing} + +\begin{figure}[H] +\centering +\includegraphics[width=0.8\textwidth]{run-example.png} +\caption{Выполнение SQL-запроса над тестовыми данными}% +\label{fig:practice-run} +\end{figure} + +\begin{listing}[H] + \caption{Оптимизация группы Memo.} + \label{lst:optimize-group} + \begin{minted}[style=bw, breaklines, frame=single, + fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{cpp} +void Optimizer::OptimizeGroup(Group* group, PropertySet required, + Limit limit) { + WinnerKey key{group, required}; + if (winner_.contains(key)) return; + if (IsExplored(group) && limit && LowerBoundCost(group) >= *limit) return; + if (!IsExplored(group)) { + tasks_.emplace([=] { + OptimizeGroup(group, required, limit); + }); + tasks_.emplace([=] { + ExploreGroup(group, limit); + }); + return; + } + for (auto expr : group->GetPhysicalExprs()) { + if (expr->is_enforcer) continue; + auto cost = local_cost_[expr.get()]; + if (!limit || cost < *limit) { + tasks_.emplace([=] { + OptimizeInputs(expr, required, {}, cost, limit); + }); + } + } + AddEnforcers(group, required, limit); +} + \end{minted} +\end{listing} + \end{document} diff --git a/src/stewkk/sql/logic/executor/executor.cpp b/src/stewkk/sql/logic/executor/executor.cpp index 0669992..6259936 100644 --- a/src/stewkk/sql/logic/executor/executor.cpp +++ b/src/stewkk/sql/logic/executor/executor.cpp @@ -672,6 +672,7 @@ boost::asio::awaitable Executor::Execute(const Physica co_return; } boost::asio::awaitable operator()(const IndexSeek& seek) { + // FIXME: make index_scan_ required! if (!executor.index_scan_) { throw std::runtime_error("IndexSeek execution requested, 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+LogicalOperator JoinCommutativity::ApplyImpl(utils::NotNull expr, Memo&) { auto join = std::get(expr->root_operator); std::swap(join.lhs, join.rhs); - if (join.type == JoinType::kLeft) { join.type = JoinType::kRight; } else if (join.type == JoinType::kRight) { @@ -2466,41 +2464,16 @@ \subsection{Калибровка стоимостной модели} } return join; } -bool ImplementHashJoin::IsApplicable( - utils::NotNull expr) { - if (!std::holds_alternative( - expr->root_operator)) { +bool ImplementHashJoin::IsApplicable(utils::NotNull expr) { + if (!std::holds_alternative(expr->root_operator)) return false; - } - const auto& join = - std::get(expr->root_operator); + const auto& join = std::get(expr->root_operator); if (join.type != JoinType::kInner) return false; - const auto* bin = - std::get_if(&join.qual); - return bin && bin->binop == BinaryOp::kEq - && std::holds_alternative(*bin->lhs) - && std::holds_alternative(*bin->rhs); + const auto* bin = std::get_if(&join.qual); + return bin && bin->binop == BinaryOp::kEq && std::holds_alternative(*bin->lhs) && std::holds_alternative(*bin->rhs); } \end{minted} \end{listing} -\begin{listing}[H] - \caption{Вычисление локальной стоимости физических операторов.} - \label{lst:optimizer-cost} - \begin{minted}[style=bw, breaklines, frame=single, - fontsize=\footnotesize, linenos=false, xleftmargin=1.5em]{cpp} -int64_t CalcCost(PhysicalExpr* expr, - CardinalityEstimates& cardinality) { - return std::visit(utils::Overloaded{ - [&](const physical::NestedLoopJoin& j) { - return 70 * cardinality.GetCardinality(j.lhs) - * cardinality.GetCardinality(j.rhs); - }, - [&](const physical::HashJoin& j) { - return 69 * (cardinality.GetCardinality(j.lhs) - + cardinality.GetCardinality(j.rhs)); - }, - \end{minted} -\end{listing} \begin{figure}[H] \centering From d029f0a6d121b824cc0130eed0c0babfd6738748 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sun, 7 Jun 2026 22:35:03 +0300 Subject: [PATCH 092/120] Add naive plan cost --- include/stewkk/sql/logic/optimizer/rules.hpp | 1 + src/stewkk/sql/logic/optimizer/optimizer.cpp | 1 + .../sql/logic/optimizer/optimizer_test.cpp | 31 +++++++++++++++++++ src/stewkk/sql/logic/optimizer/rules.cpp | 14 +++++++++ .../sql/logic/optimizer/rules_applier.cpp | 1 + src/stewkk/sql/main.cpp | 16 +++++++++- 6 files changed, 63 insertions(+), 1 deletion(-) diff --git a/include/stewkk/sql/logic/optimizer/rules.hpp b/include/stewkk/sql/logic/optimizer/rules.hpp index 57c675b..d3106f7 100644 --- a/include/stewkk/sql/logic/optimizer/rules.hpp +++ b/include/stewkk/sql/logic/optimizer/rules.hpp @@ -28,5 +28,6 @@ struct Rules { }; Rules<7, 7> MakeMainRules(); +Rules<0, 6> MakeNaiveRules(); } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index e9fc4c1..b3cb887 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -526,5 +526,6 @@ utils::NotNull Optimizer::GetRootGroup } template class Optimizer<7, 7>; +template class Optimizer<0, 6>; } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index e87784b..0993ddf 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -13,6 +13,7 @@ #include using ::testing::Eq; +using ::testing::Gt; using ::testing::IsTrue; using ::testing::IsFalse; using ::testing::HasSubstr; @@ -220,6 +221,36 @@ TEST(OptimizerTest, PushesSelectiveFilterIntoHashBuildSide) { " (SeqScan lineorder lo))")); } +TEST(OptimizerTest, NaiveRulesDisableLogicalTransformations) { + std::stringstream s{ + "SELECT * FROM lineorder AS lo " + "JOIN supplier AS s ON lo.suppkey = s.id " + "WHERE s.region = 'AMERICA';"}; + Operator op = GetAST(s).value().op; + SchemaCatalog schema({ + {"lineorder", {Attribute{"lineorder", "suppkey"}, Attribute{"lineorder", "value"}}}, + {"supplier", {Attribute{"supplier", "id"}, Attribute{"supplier", "region"}}}, + }); + CardinalityEstimates cardinality({ + {"lineorder", 6000}, + {"supplier", 100}, + }); + Optimizer optimizer(op, MakeMainRules(), cardinality, schema); + auto optimized = optimizer.Optimize(); + + Optimizer naive_optimizer(op, MakeNaiveRules(), std::move(cardinality), std::move(schema)); + auto naive = naive_optimizer.Optimize(); + + ASSERT_THAT(Serialize(optimized), + HasSubstr("(PhysicalFilter (= (attr s region) (str \"AMERICA\")) " + "(SeqScan supplier s))")); + ASSERT_THAT(Serialize(naive), + Eq("(PhysicalFilter (= (attr s region) (str \"AMERICA\"))" + " (NestedLoopJoin Inner (= (attr lo suppkey) (attr s id))" + " (SeqScan lineorder lo) (SeqScan supplier s)))")); + ASSERT_THAT(naive_optimizer.GetBestCost(), Gt(optimizer.GetBestCost())); +} + TEST(ReachabilityTest, SeqScanReachable) { std::stringstream s{"SELECT * FROM users;"}; auto result = IsPlanReachable(s, SeqScan{"users"}); diff --git a/src/stewkk/sql/logic/optimizer/rules.cpp b/src/stewkk/sql/logic/optimizer/rules.cpp index b230f4f..c4de335 100644 --- a/src/stewkk/sql/logic/optimizer/rules.cpp +++ b/src/stewkk/sql/logic/optimizer/rules.cpp @@ -25,4 +25,18 @@ Rules<7, 7> MakeMainRules() { }; } +Rules<0, 6> MakeNaiveRules() { + return { + .transformation_rules = {}, + .implementation_rules = { + std::make_unique(), + std::make_unique(), + std::make_unique(), + std::make_unique(), + std::make_unique(), + std::make_unique(), + }, + }; +} + } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/rules_applier.cpp b/src/stewkk/sql/logic/optimizer/rules_applier.cpp index cbf5808..c25860b 100644 --- a/src/stewkk/sql/logic/optimizer/rules_applier.cpp +++ b/src/stewkk/sql/logic/optimizer/rules_applier.cpp @@ -31,5 +31,6 @@ utils::NotNull RulesApplier::Ap } template class RulesApplier<7, 7>; +template class RulesApplier<0, 6>; } // namespace stewkk::sql diff --git a/src/stewkk/sql/main.cpp b/src/stewkk/sql/main.cpp index f13e12d..dfafb63 100644 --- a/src/stewkk/sql/main.cpp +++ b/src/stewkk/sql/main.cpp @@ -249,6 +249,7 @@ int main(int argc, char** argv) { PhysicalPlanNode plan; std::int64_t plan_cost = 0; + std::int64_t naive_plan_cost = 0; try { PropertySet required = parsed.required_order ? PropertySet{SortProperty{*parsed.required_order}} @@ -258,6 +259,17 @@ int main(int argc, char** argv) { LoadSchemaFromCsvDir(args.data_dir), std::move(required)); plan = optimizer.Optimize(); plan_cost = optimizer.GetBestCost(); + + if (args.stats) { + PropertySet naive_required = parsed.required_order + ? PropertySet{SortProperty{*parsed.required_order}} + : PropertySet::Any(); + Optimizer naive_optimizer(parsed.op, MakeNaiveRules(), + CardinalityEstimates{LoadTableSizesFromCsvDir(args.data_dir)}, + LoadSchemaFromCsvDir(args.data_dir), std::move(naive_required)); + naive_optimizer.Optimize(); + naive_plan_cost = naive_optimizer.GetBestCost(); + } } catch (const std::exception& e) { std::cerr << "optimizer error: " << e.what() << "\n"; return kOptimizerError; @@ -297,7 +309,9 @@ int main(int argc, char** argv) { PrintRelation(result.value(), parsed.required_order.has_value()); if (args.stats) { - std::cerr << "STATS plan_cost=" << plan_cost << " exec_us=" << exec_us + std::cerr << "STATS plan_cost=" << plan_cost + << " naive_plan_cost=" << naive_plan_cost + << " exec_us=" << exec_us << " rows=" << result.value().tuples.size() << "\n"; } return kOk; From b482cd61b6c1e900856b28ebff7cbeafc925f699 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sun, 7 Jun 2026 22:52:39 +0300 Subject: [PATCH 093/120] Add naive plan output --- src/stewkk/sql/main.cpp | 21 ++++++++++----------- 1 file changed, 10 insertions(+), 11 deletions(-) diff --git a/src/stewkk/sql/main.cpp b/src/stewkk/sql/main.cpp index dfafb63..d7fea11 100644 --- a/src/stewkk/sql/main.cpp +++ b/src/stewkk/sql/main.cpp @@ -248,6 +248,7 @@ int main(int argc, char** argv) { } PhysicalPlanNode plan; + PhysicalPlanNode naive_plan; std::int64_t plan_cost = 0; std::int64_t naive_plan_cost = 0; try { @@ -260,22 +261,20 @@ int main(int argc, char** argv) { plan = optimizer.Optimize(); plan_cost = optimizer.GetBestCost(); - if (args.stats) { - PropertySet naive_required = parsed.required_order - ? PropertySet{SortProperty{*parsed.required_order}} - : PropertySet::Any(); - Optimizer naive_optimizer(parsed.op, MakeNaiveRules(), - CardinalityEstimates{LoadTableSizesFromCsvDir(args.data_dir)}, - LoadSchemaFromCsvDir(args.data_dir), std::move(naive_required)); - naive_optimizer.Optimize(); - naive_plan_cost = naive_optimizer.GetBestCost(); - } + PropertySet naive_required = parsed.required_order + ? PropertySet{SortProperty{*parsed.required_order}} + : PropertySet::Any(); + Optimizer naive_optimizer(parsed.op, MakeNaiveRules(), + CardinalityEstimates{LoadTableSizesFromCsvDir(args.data_dir)}, + LoadSchemaFromCsvDir(args.data_dir), std::move(naive_required)); + naive_plan = naive_optimizer.Optimize(); + naive_plan_cost = naive_optimizer.GetBestCost(); } catch (const std::exception& e) { std::cerr << "optimizer error: " << e.what() << "\n"; return kOptimizerError; } - OutputDot(plan, std::nullopt); + OutputDot(plan, naive_plan); if (args.print_plan) { std::cerr << Serialize(plan) << "\n"; From 09eb82c9949f925753313c31a061034f608d7f20 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sun, 7 Jun 2026 23:16:32 +0300 Subject: [PATCH 094/120] Add cost and cardinality to dot graph --- include/stewkk/sql/logic/executor/plan.hpp | 30 +++++- src/stewkk/sql/logic/executor/executor.cpp | 2 +- src/stewkk/sql/logic/executor/plan.cpp | 4 + .../sql/logic/executor/plan_serializer.cpp | 101 +++++++++++------- .../logic/executor/plan_serializer_test.cpp | 9 ++ src/stewkk/sql/logic/optimizer/optimizer.cpp | 7 +- .../sql/logic/optimizer/optimizer_test.cpp | 5 +- .../sql/logic/optimizer/reachability.cpp | 16 +-- 8 files changed, 125 insertions(+), 49 deletions(-) diff --git a/include/stewkk/sql/logic/executor/plan.hpp b/include/stewkk/sql/logic/executor/plan.hpp index af919b0..74692d7 100644 --- a/include/stewkk/sql/logic/executor/plan.hpp +++ b/include/stewkk/sql/logic/executor/plan.hpp @@ -1,8 +1,12 @@ #pragma once #include +#include +#include #include #include +#include +#include #include #include @@ -23,8 +27,7 @@ struct MergeJoin; struct IndexSeek; struct PhysicalSort; struct PhysicalAggregation; - -using PhysicalPlanNode = std::variant; +struct PhysicalPlanNode; struct SeqScan { std::string table; @@ -107,4 +110,27 @@ struct PhysicalAggregation { bool operator==(const PhysicalAggregation&) const; }; +struct PlanNodeMetadata { + std::int64_t cardinality = 0; + std::int64_t local_cost = 0; + + bool operator==(const PlanNodeMetadata&) const = default; +}; + +using PhysicalPlanAlternative = std::variant; + +struct PhysicalPlanNode { + PhysicalPlanAlternative node; + std::optional metadata; + + PhysicalPlanNode() = default; + + template + requires (!std::same_as, PhysicalPlanNode> + && std::constructible_from) + PhysicalPlanNode(T&& value) : node(std::forward(value)) {} + + bool operator==(const PhysicalPlanNode& other) const; +}; + } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/executor/executor.cpp b/src/stewkk/sql/logic/executor/executor.cpp index 6259936..cbdd4ad 100644 --- a/src/stewkk/sql/logic/executor/executor.cpp +++ b/src/stewkk/sql/logic/executor/executor.cpp @@ -755,7 +755,7 @@ boost::asio::awaitable Executor::Execute(const Physica TuplesChannel& tuples_chan; Executor& executor; }; - co_await std::visit(ExecuteVisitor{attr_chan, tuples_chan, *this}, op); + co_await std::visit(ExecuteVisitor{attr_chan, tuples_chan, *this}, op.node); co_return; } diff --git a/src/stewkk/sql/logic/executor/plan.cpp b/src/stewkk/sql/logic/executor/plan.cpp index 7bbbed7..693aa5c 100644 --- a/src/stewkk/sql/logic/executor/plan.cpp +++ b/src/stewkk/sql/logic/executor/plan.cpp @@ -2,6 +2,10 @@ namespace stewkk::sql { +bool PhysicalPlanNode::operator==(const PhysicalPlanNode& other) const { + return node == other.node; +} + std::string_view OutputTable(const SeqScan& scan) { return scan.alias ? std::string_view{*scan.alias} : std::string_view{scan.table}; } diff --git a/src/stewkk/sql/logic/executor/plan_serializer.cpp b/src/stewkk/sql/logic/executor/plan_serializer.cpp index 9ae53dc..af8baa0 100644 --- a/src/stewkk/sql/logic/executor/plan_serializer.cpp +++ b/src/stewkk/sql/logic/executor/plan_serializer.cpp @@ -224,7 +224,7 @@ std::string SerializeNode(const PhysicalPlanNode& node) { group_by_str, aggs_str, SerializeNode(*n.source)); } }; - return std::visit(Visitor{}, node); + return std::visit(Visitor{}, node.node); } enum class TokenKind { LParen, RParen, Atom }; @@ -587,7 +587,11 @@ struct DotBuilder { std::ostringstream os; int next_id = 0; - int Emit(std::string_view label) { + int Emit(std::string label, const std::optional& metadata) { + if (metadata) { + label += std::format("\\ncard={}\\ncost={}", metadata->cardinality, + metadata->local_cost); + } int id = next_id++; os << std::format(" n{} [label=\"{}\"]\n", id, EscapeLabel(label)); return id; @@ -597,65 +601,87 @@ struct DotBuilder { os << std::format(" n{} -> n{}\n", from, to); } - int operator()(const SeqScan& n) { + int EmitNode(const PhysicalPlanNode& node) { + return std::visit([&](const auto& n) { return EmitAlternative(n, node.metadata); }, + node.node); + } + + int EmitAlternative(const SeqScan& n, const std::optional& metadata) { if (n.alias) { - return Emit(std::format("SeqScan\\n{} AS {}", n.table, *n.alias)); + return Emit(std::format("SeqScan\\n{} AS {}", n.table, *n.alias), metadata); } - return Emit(std::format("SeqScan\\n{}", n.table)); + return Emit(std::format("SeqScan\\n{}", n.table), metadata); } - int operator()(const PhysicalFilter& n) { - int src = std::visit(*this, *n.source); - int id = Emit(std::format("σ {}", ToString(n.predicate))); + + int EmitAlternative(const PhysicalFilter& n, + const std::optional& metadata) { + int src = EmitNode(*n.source); + int id = Emit(std::format("σ {}", ToString(n.predicate)), metadata); EmitEdge(src, id); return id; } - int operator()(const PhysicalProjection& n) { - int src = std::visit(*this, *n.source); + + int EmitAlternative(const PhysicalProjection& n, + const std::optional& metadata) { + int src = EmitNode(*n.source); auto exprs = n.expressions | std::views::transform([](const Expression& e) { return ToString(e); }) | std::views::join_with(std::string(", ")) | std::ranges::to(); - int id = Emit(std::format("π {}", exprs)); + int id = Emit(std::format("π {}", exprs), metadata); EmitEdge(src, id); return id; } - int operator()(const NestedLoopCrossJoin& n) { - int lhs = std::visit(*this, *n.lhs); - int rhs = std::visit(*this, *n.rhs); - int id = Emit("×"); + + int EmitAlternative(const NestedLoopCrossJoin& n, + const std::optional& metadata) { + int lhs = EmitNode(*n.lhs); + int rhs = EmitNode(*n.rhs); + int id = Emit("×", metadata); EmitEdge(lhs, id); EmitEdge(rhs, id); return id; } - int operator()(const NestedLoopJoin& n) { - int lhs = std::visit(*this, *n.lhs); - int rhs = std::visit(*this, *n.rhs); - int id = Emit(std::format("NL {}\\nON {}", ToString(n.type), ToString(n.qual))); + + int EmitAlternative(const NestedLoopJoin& n, + const std::optional& metadata) { + int lhs = EmitNode(*n.lhs); + int rhs = EmitNode(*n.rhs); + int id = Emit(std::format("NL {}\\nON {}", ToString(n.type), ToString(n.qual)), + metadata); EmitEdge(lhs, id); EmitEdge(rhs, id); return id; } - int operator()(const IndexSeek& n) { - return Emit(std::format("IndexSeek\\n{}\\n{}", n.table, ToString(n.predicate))); + + int EmitAlternative(const IndexSeek& n, const std::optional& metadata) { + return Emit(std::format("IndexSeek\\n{}\\n{}", n.table, ToString(n.predicate)), + metadata); } - int operator()(const HashJoin& n) { - int lhs = std::visit(*this, *n.lhs); - int rhs = std::visit(*this, *n.rhs); - int id = Emit(std::format("Hash {}\\nON {}", ToString(n.type), ToString(n.qual))); + + int EmitAlternative(const HashJoin& n, const std::optional& metadata) { + int lhs = EmitNode(*n.lhs); + int rhs = EmitNode(*n.rhs); + int id = Emit(std::format("Hash {}\\nON {}", ToString(n.type), ToString(n.qual)), + metadata); EmitEdge(lhs, id); EmitEdge(rhs, id); return id; } - int operator()(const MergeJoin& n) { - int lhs = std::visit(*this, *n.lhs); - int rhs = std::visit(*this, *n.rhs); - int id = Emit(std::format("Merge {}\\nON {}", ToString(n.type), ToString(n.qual))); + + int EmitAlternative(const MergeJoin& n, const std::optional& metadata) { + int lhs = EmitNode(*n.lhs); + int rhs = EmitNode(*n.rhs); + int id = Emit(std::format("Merge {}\\nON {}", ToString(n.type), ToString(n.qual)), + metadata); EmitEdge(lhs, id); EmitEdge(rhs, id); return id; } - int operator()(const PhysicalSort& n) { - int src = std::visit(*this, *n.source); + + int EmitAlternative(const PhysicalSort& n, + const std::optional& metadata) { + int src = EmitNode(*n.source); std::string keys; for (const auto& k : n.keys.keys) { if (!keys.empty()) keys += ", "; @@ -664,12 +690,14 @@ struct DotBuilder { keys += k.column; keys += k.dir == Direction::kAsc ? " asc" : " desc"; } - int id = Emit(std::format("Sort\\n{}", keys)); + int id = Emit(std::format("Sort\\n{}", keys), metadata); EmitEdge(src, id); return id; } - int operator()(const PhysicalAggregation& n) { - int src = std::visit(*this, *n.source); + + int EmitAlternative(const PhysicalAggregation& n, + const std::optional& metadata) { + int src = EmitNode(*n.source); std::string aggs; for (const auto& e : n.aggregates) { if (!aggs.empty()) aggs += ", "; @@ -680,7 +708,8 @@ struct DotBuilder { if (!group_by.empty()) group_by += ", "; group_by += ToString(e); } - int id = Emit(std::format("HashAgg\\nGROUP BY {}\\n{}", group_by, aggs)); + int id = Emit(std::format("HashAgg\\nGROUP BY {}\\n{}", group_by, aggs), + metadata); EmitEdge(src, id); return id; } @@ -700,7 +729,7 @@ PhysicalPlanNode Deserialize(std::string_view text) { std::string SerializeDot(const PhysicalPlanNode& node) { DotBuilder b; - std::visit(b, node); + b.EmitNode(node); return std::format("digraph G {{ rankdir=BT;\n{}}}\n", b.os.str()); } diff --git a/src/stewkk/sql/logic/executor/plan_serializer_test.cpp b/src/stewkk/sql/logic/executor/plan_serializer_test.cpp index 0c33461..34d6a69 100644 --- a/src/stewkk/sql/logic/executor/plan_serializer_test.cpp +++ b/src/stewkk/sql/logic/executor/plan_serializer_test.cpp @@ -261,4 +261,13 @@ TEST(PlanSerializerDotTest, SmokeTest) { EXPECT_THAT(dot, ::testing::HasSubstr("n1 -> n2")); } +TEST(PlanSerializerDotTest, RendersMetadataWhenPresent) { + PhysicalPlanNode plan = SeqScan{"users"}; + plan.metadata = PlanNodeMetadata{.cardinality = 42, .local_cost = 4200}; + + auto dot = SerializeDot(plan); + + EXPECT_THAT(dot, ::testing::HasSubstr("SeqScan\\\\nusers\\\\ncard=42\\\\ncost=4200")); +} + } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index b3cb887..2a8951b 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -408,7 +408,7 @@ PhysicalPlanNode Optimizer::BuildOptimalPlan(G } auto* best_expr = it->second.plan; utils::NotNull best_expr_nn{best_expr}; - return std::visit( + auto plan = std::visit( utils::Overloaded{ [](const physical::SeqScan& op) -> PhysicalPlanNode { return SeqScan{.table = op.table, .alias = op.alias}; @@ -473,6 +473,11 @@ PhysicalPlanNode Optimizer::BuildOptimalPlan(G }, }, best_expr->root_operator); + plan.metadata = PlanNodeMetadata{ + .cardinality = cardinality_.GetCardinality(best_expr->group), + .local_cost = local_cost_.at(best_expr), + }; + return plan; } template diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index 0993ddf..5e585df 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -66,7 +66,10 @@ TEST(OptimizerTest, Simple) { auto got = optimizer.Optimize(); - ASSERT_THAT(SerializeDot(got), Eq("digraph G { rankdir=BT;\n n0 [label=\"SeqScan\\\\nusers\"]\n}\n")); + ASSERT_THAT(SerializeDot(got), + Eq("digraph G { rankdir=BT;\n" + " n0 [label=\"SeqScan\\\\nusers\\\\ncard=10\\\\ncost=1000\"]\n" + "}\n")); ASSERT_THAT(optimizer.GetBestCost(), Eq(1000)); } diff --git a/src/stewkk/sql/logic/optimizer/reachability.cpp b/src/stewkk/sql/logic/optimizer/reachability.cpp index 8aa6544..90c9283 100644 --- a/src/stewkk/sql/logic/optimizer/reachability.cpp +++ b/src/stewkk/sql/logic/optimizer/reachability.cpp @@ -26,7 +26,7 @@ InternalMatch TryMatchExpr(utils::NotNull pe, const PhysicalPlanNode& target, int depth) { return std::visit(utils::Overloaded{ [&](const physical::SeqScan& op) -> InternalMatch { - const auto* t = std::get_if(&target); + const auto* t = std::get_if(&target.node); if (!t) return {false, depth, "type mismatch: expected SeqScan"}; if (op.table != t->table) return {false, depth + 1, @@ -38,7 +38,7 @@ InternalMatch TryMatchExpr(utils::NotNull pe, return {true, depth + 1, {}}; }, [&](const physical::Filter& op) -> InternalMatch { - const auto* t = std::get_if(&target); + const auto* t = std::get_if(&target.node); if (!t) return {false, depth, "type mismatch: expected Filter"}; if (op.predicate != t->predicate) return {false, depth + 1, @@ -49,7 +49,7 @@ InternalMatch TryMatchExpr(utils::NotNull pe, return child; }, [&](const physical::Projection& op) -> InternalMatch { - const auto* t = std::get_if(&target); + const auto* t = std::get_if(&target.node); if (!t) return {false, depth, "type mismatch: expected Projection"}; if (op.expressions != t->expressions) return {false, depth + 1, "Projection expressions mismatch"}; @@ -58,7 +58,7 @@ InternalMatch TryMatchExpr(utils::NotNull pe, return child; }, [&](const physical::NestedLoopJoin& op) -> InternalMatch { - const auto* t = std::get_if(&target); + const auto* t = std::get_if(&target.node); if (!t) return {false, depth, "type mismatch: expected NestedLoopJoin"}; if (op.type != t->type) return {false, depth + 1, "NestedLoopJoin join type mismatch"}; @@ -73,7 +73,7 @@ InternalMatch TryMatchExpr(utils::NotNull pe, return {true, std::max(lhs.depth, rhs.depth), {}}; }, [&](const physical::NestedLoopCrossJoin& op) -> InternalMatch { - const auto* t = std::get_if(&target); + const auto* t = std::get_if(&target.node); if (!t) return {false, depth, "type mismatch: expected NestedLoopCrossJoin"}; auto lhs = MatchGroup(op.lhs.get(), *t->lhs, depth + 1); if (!lhs.ok) { lhs.reason = "NestedLoopCrossJoin.lhs: " + lhs.reason; return lhs; } @@ -82,7 +82,7 @@ InternalMatch TryMatchExpr(utils::NotNull pe, return {true, std::max(lhs.depth, rhs.depth), {}}; }, [&](const physical::HashJoin& op) -> InternalMatch { - const auto* t = std::get_if(&target); + const auto* t = std::get_if(&target.node); if (!t) return {false, depth, "type mismatch: expected HashJoin"}; if (op.type != t->type) return {false, depth + 1, "HashJoin join type mismatch"}; @@ -97,7 +97,7 @@ InternalMatch TryMatchExpr(utils::NotNull pe, return {true, std::max(lhs.depth, rhs.depth), {}}; }, [&](const physical::Sort& op) -> InternalMatch { - const auto* t = std::get_if(&target); + const auto* t = std::get_if(&target.node); if (!t) return {false, depth, "type mismatch: expected Sort"}; if (op.keys != t->keys) return {false, depth + 1, "Sort keys mismatch"}; @@ -106,7 +106,7 @@ InternalMatch TryMatchExpr(utils::NotNull pe, return child; }, [&](const physical::Aggregation& op) -> InternalMatch { - const auto* t = std::get_if(&target); + const auto* t = std::get_if(&target.node); if (!t) return {false, depth, "type mismatch: expected HashAggregate"}; if (op.group_by != t->group_by) return {false, depth + 1, "Aggregation group_by mismatch"}; From c1d0eb75470ee8f39f1617421ff3a650d94f092c Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sun, 7 Jun 2026 23:21:34 +0300 Subject: [PATCH 095/120] Typo --- src/stewkk/sql/logic/executor/plan_serializer.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/stewkk/sql/logic/executor/plan_serializer.cpp b/src/stewkk/sql/logic/executor/plan_serializer.cpp index af8baa0..92265cb 100644 --- a/src/stewkk/sql/logic/executor/plan_serializer.cpp +++ b/src/stewkk/sql/logic/executor/plan_serializer.cpp @@ -589,7 +589,7 @@ struct DotBuilder { int Emit(std::string label, const std::optional& metadata) { if (metadata) { - label += std::format("\\ncard={}\\ncost={}", metadata->cardinality, + label += std::format("\ncard={}\ncost={}", metadata->cardinality, metadata->local_cost); } int id = next_id++; From a2ee42e9bb21a35096664db4adc6e6f701de4677 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sun, 7 Jun 2026 23:46:00 +0300 Subject: [PATCH 096/120] Index seek support in optimizer --- .../implement_index_seek.hpp | 21 +++++ .../sql/logic/optimizer/physical_expr.hpp | 11 ++- .../sql/logic/optimizer/reachability.hpp | 3 +- include/stewkk/sql/logic/optimizer/rules.hpp | 3 +- .../sql/logic/optimizer/schema_catalog.hpp | 19 ++++ research/converter.py | 16 ++-- research/fuzz/mssql_runner.py | 7 ++ research/fuzz/reach_fuzz.py | 30 ++++++- research/test_converter.py | 60 ++++++++++++- src/stewkk/sql/CMakeLists.txt | 1 + .../sql/logic/executor/executor_test.cpp | 45 ++++++++++ .../sql/logic/executor/plan_serializer.cpp | 4 + .../logic/executor/plan_serializer_test.cpp | 2 +- .../implement_index_seek.cpp | 86 +++++++++++++++++++ src/stewkk/sql/logic/optimizer/optimizer.cpp | 16 +++- .../sql/logic/optimizer/optimizer_test.cpp | 53 +++++++++++- .../sql/logic/optimizer/reachability.cpp | 22 ++++- src/stewkk/sql/logic/optimizer/rules.cpp | 5 +- .../sql/logic/optimizer/rules_applier.cpp | 2 +- .../sql/logic/optimizer/schema_catalog.cpp | 34 ++++++++ src/stewkk/sql/main.cpp | 9 +- 21 files changed, 427 insertions(+), 22 deletions(-) create mode 100644 include/stewkk/sql/logic/implementation_rules/implement_index_seek.hpp create mode 100644 src/stewkk/sql/logic/implementation_rules/implement_index_seek.cpp diff --git a/include/stewkk/sql/logic/implementation_rules/implement_index_seek.hpp b/include/stewkk/sql/logic/implementation_rules/implement_index_seek.hpp new file mode 100644 index 0000000..42682fa --- /dev/null +++ b/include/stewkk/sql/logic/implementation_rules/implement_index_seek.hpp @@ -0,0 +1,21 @@ +#pragma once + +#include +#include + +namespace stewkk::sql { + +class ImplementIndexSeek : public ImplementationRule { +public: + explicit ImplementIndexSeek(IndexCatalog indexes); + + bool IsApplicable(utils::NotNull expr) override; + utils::NotNull Apply(utils::NotNull expr, Memo& memo) override; + +private: + IndexCatalog indexes_; +}; + +bool HasCompatibleIndexSeek(const logical::Filter& filter, const IndexCatalog& indexes); + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/physical_expr.hpp b/include/stewkk/sql/logic/optimizer/physical_expr.hpp index 193257c..aedb4f5 100644 --- a/include/stewkk/sql/logic/optimizer/physical_expr.hpp +++ b/include/stewkk/sql/logic/optimizer/physical_expr.hpp @@ -22,6 +22,14 @@ struct SeqScan { bool operator==(const SeqScan&) const = default; }; +struct IndexSeek { + std::string table; + std::optional alias; + Expression predicate; + + bool operator==(const IndexSeek&) const = default; +}; + struct Projection { utils::NotNull source; std::vector expressions; @@ -82,7 +90,8 @@ struct Aggregation { struct PhysicalExpr { std::variant root_operator; + physical::HashJoin, physical::Sort, physical::Aggregation, + physical::IndexSeek> root_operator; utils::NotNull group; bool is_enforcer = false; }; diff --git a/include/stewkk/sql/logic/optimizer/reachability.hpp b/include/stewkk/sql/logic/optimizer/reachability.hpp index c62eda9..e5c7134 100644 --- a/include/stewkk/sql/logic/optimizer/reachability.hpp +++ b/include/stewkk/sql/logic/optimizer/reachability.hpp @@ -21,6 +21,7 @@ MatchResult IsReachable(utils::NotNull root, const PhysicalPlanNode& tar MatchResult IsPlanReachable(std::istream& sql, const PhysicalPlanNode& target, CardinalityEstimates cardinality = {}, - SchemaCatalog schema = {}); + SchemaCatalog schema = {}, + IndexCatalog indexes = {}); } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/rules.hpp b/include/stewkk/sql/logic/optimizer/rules.hpp index d3106f7..7db0a85 100644 --- a/include/stewkk/sql/logic/optimizer/rules.hpp +++ b/include/stewkk/sql/logic/optimizer/rules.hpp @@ -18,6 +18,7 @@ #include #include #include +#include namespace stewkk::sql { @@ -27,7 +28,7 @@ struct Rules { std::array, NImplementation> implementation_rules; }; -Rules<7, 7> MakeMainRules(); +Rules<7, 8> MakeMainRules(IndexCatalog indexes = {}); Rules<0, 6> MakeNaiveRules(); } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/schema_catalog.hpp b/include/stewkk/sql/logic/optimizer/schema_catalog.hpp index f3d1082..62383d7 100644 --- a/include/stewkk/sql/logic/optimizer/schema_catalog.hpp +++ b/include/stewkk/sql/logic/optimizer/schema_catalog.hpp @@ -15,6 +15,23 @@ namespace stewkk::sql { using Schema = std::vector; +struct IndexInfo { + std::string table; + std::string column; + std::string type; + std::string file; +}; + +class IndexCatalog { +public: + IndexCatalog(std::vector indexes = {}); + + bool HasSortedIndex(const std::string& table, const std::string& column) const; + +private: + std::vector indexes_; +}; + class SchemaCatalog { public: SchemaCatalog(std::unordered_map tables = {}); @@ -31,6 +48,8 @@ class SchemaCatalog { SchemaCatalog LoadSchemaFromCsvDir(const std::filesystem::path& dir); +IndexCatalog LoadIndexCatalogFromCsvDir(const std::filesystem::path& dir); + std::unordered_map LoadTableSizesFromCsvDir( const std::filesystem::path& dir); diff --git a/research/converter.py b/research/converter.py index 439c346..89d2ac9 100644 --- a/research/converter.py +++ b/research/converter.py @@ -210,19 +210,22 @@ def _convert_scan(relop: ET.Element) -> str: raise ValueError("cannot find Object element in scan") table, alias = _object_table_and_alias(obj) visible_table = alias or table + pred_elem = relop.find(f".//{NS}IndexScan/{NS}Predicate/{NS}ScalarOperator") + if pred_elem is None: + pred_elem = relop.find(f".//{NS}TableScan/{NS}Predicate/{NS}ScalarOperator") if "Seek" in phys_op: seek_pred = _convert_seek_predicates(relop, visible_table) if seek_pred is None: - raise ValueError(f"Index Seek node has no SeekPredicates: {phys_op!r}") - base = f"(IndexSeek {seek_pred} {table})" + if pred_elem is None: + raise ValueError(f"Index Seek node has no SeekPredicates: {phys_op!r}") + seek_pred = _convert_scalar(pred_elem) + pred_elem = None + base = f"(IndexSeek {seek_pred} {table} {alias})" if alias else f"(IndexSeek {seek_pred} {table})" else: base = f"(SeqScan {table} {alias})" if alias else f"(SeqScan {table})" # Residual predicate (pushed-down filter evaluated after the scan/seek) - pred_elem = relop.find(f".//{NS}IndexScan/{NS}Predicate/{NS}ScalarOperator") - if pred_elem is None: - pred_elem = relop.find(f".//{NS}TableScan/{NS}Predicate/{NS}ScalarOperator") if pred_elem is not None: return f"(PhysicalFilter {_convert_scalar(pred_elem)} {base})" @@ -235,7 +238,8 @@ def _convert_seek_predicates(relop: ET.Element, table: str) -> str | None: return None conditions = [] - for range_elem in (seek_preds.findall(f".//{NS}StartRange") + + for range_elem in (seek_preds.findall(f".//{NS}Prefix") + + seek_preds.findall(f".//{NS}StartRange") + seek_preds.findall(f".//{NS}EndRange")): op = _SEEK_SCAN_TYPES.get(range_elem.get("ScanType", "")) if not op: diff --git a/research/fuzz/mssql_runner.py b/research/fuzz/mssql_runner.py index db4d056..312ab21 100644 --- a/research/fuzz/mssql_runner.py +++ b/research/fuzz/mssql_runner.py @@ -123,6 +123,13 @@ def conv(v: str, type_: str): rows, ) + for col, type_ in zip(cols, types): + if type_ == "int": + index_name = f"ix_{path.stem}_{col}" + cur.execute( + f"CREATE INDEX [{index_name}] ON [dbo].[{path.stem}] ([{col}])" + ) + def run(self, query: str) -> RunResult: if self._conn is None: self._conn = self._connect(self._database) diff --git a/research/fuzz/reach_fuzz.py b/research/fuzz/reach_fuzz.py index 1c632e0..1eb77b5 100644 --- a/research/fuzz/reach_fuzz.py +++ b/research/fuzz/reach_fuzz.py @@ -25,7 +25,9 @@ from __future__ import annotations import argparse +import os import random +import shutil import subprocess import sys import tempfile @@ -44,6 +46,25 @@ ) +def _make_reachability_data_dir(source_dir: Path, schema: "Schema", tmp_dir: Path) -> Path: + for path in source_dir.glob("*.csv"): + target = tmp_dir / path.name + try: + os.symlink(path.resolve(), target) + except OSError: + shutil.copy2(path, target) + + with (tmp_dir / "indexes.meta").open("w") as meta: + meta.write("# Generated by reach_fuzz.py for optimizer reachability checks.\n") + for table in sorted(schema.tables.values(), key=lambda t: t.name): + for col in table.columns: + if col.type == "int": + meta.write( + f"{table.name} {col.name} sorted {table.name}.{col.name}.sorted.idx\n" + ) + return tmp_dir + + def _quote_alias(alias: str) -> str: return '"' + alias.replace("\\", "\\\\").replace('"', '\\"') + '"' @@ -212,7 +233,12 @@ def main() -> None: ap.add_argument("--mssql-database", default="fuzz") args = ap.parse_args() - schema = load_schema(Path(args.data_dir)) + source_data_dir = Path(args.data_dir) + schema = load_schema(source_data_dir) + reach_tmp = tempfile.TemporaryDirectory(prefix="iu9-sql-reach-") + reach_data_dir = _make_reachability_data_dir( + source_data_dir, schema, Path(reach_tmp.name) + ) mssql = MsSqlRunner( host=args.mssql_host, @@ -261,7 +287,7 @@ def main() -> None: target_plan = _wrap_projection(converted, query) try: - proc = _run_reach_check(args.cli, args.data_dir, ours_sql, target_plan) + proc = _run_reach_check(args.cli, str(reach_data_dir), ours_sql, target_plan) except subprocess.TimeoutExpired: print(f"DIVERGENCE seed={seed}: reachability check timed out") print(f"\n--- query (ours):\n{ours_sql}") diff --git a/research/test_converter.py b/research/test_converter.py index 941f171..a484424 100644 --- a/research/test_converter.py +++ b/research/test_converter.py @@ -129,7 +129,65 @@ def test_aliased_index_seek_from_xml(): result = convert(plan) - assert result == "(IndexSeek (> (attr t titleId) 5000) Titles)" + assert result == "(IndexSeek (> (attr t titleId) 5000) Titles t)" + + +def test_index_seek_without_seek_predicates_uses_predicate_from_xml(): + plan = showplan(""" + + + + + + + + + + + + + + + + + + + + """) + + result = convert(plan) + + assert result == "(IndexSeek (= (attr t0 region_id) 7) customers t0)" + + +def test_index_seek_prefix_eq_from_xml(): + plan = showplan(""" + + + + + + + + + + + + + + + + + + + + + + """) + + result = convert(plan) + + assert result == "(IndexSeek (= (attr t0 region_id) 7) customers t0)" def test_filter_lt(extractor): diff --git a/src/stewkk/sql/CMakeLists.txt b/src/stewkk/sql/CMakeLists.txt index 312cef8..8784d11 100644 --- a/src/stewkk/sql/CMakeLists.txt +++ b/src/stewkk/sql/CMakeLists.txt @@ -48,6 +48,7 @@ add_library(libsql logic/transformation_rules/in_to_or_chain.cpp logic/implementation_rules/implement_table.cpp logic/implementation_rules/implement_filter.cpp + logic/implementation_rules/implement_index_seek.cpp logic/implementation_rules/implement_projection.cpp logic/implementation_rules/implement_join.cpp logic/implementation_rules/implement_cross_join.cpp diff --git a/src/stewkk/sql/logic/executor/executor_test.cpp b/src/stewkk/sql/logic/executor/executor_test.cpp index cb50130..d199219 100644 --- a/src/stewkk/sql/logic/executor/executor_test.cpp +++ b/src/stewkk/sql/logic/executor/executor_test.cpp @@ -7,10 +7,15 @@ #include #include #include +#include #include #include #include +#include +#include +#include +#include #include #include @@ -189,6 +194,46 @@ TEST(ExecutorTest, IndexSeekBuildsAndUsesSortedIntIndex) { ctx.run(); } +TEST(ExecutorTest, OptimizedIndexSeekPlanExecutes) { + auto dir = std::filesystem::temp_directory_path() / "iu9_sql_optimized_index_seek_test"; + std::filesystem::remove_all(dir); + std::filesystem::create_directories(dir); + { + std::ofstream csv{dir / "users.csv"}; + csv << "id:int,age:int\n" + << "10,22\n" + << "1,33\n" + << "8,64\n" + << "8,70\n"; + std::ofstream meta{dir / "indexes.meta"}; + meta << "users id sorted users.id.sorted.idx\n"; + } + + std::stringstream sql{"SELECT * FROM users WHERE users.id >= 8 AND users.age < 70;"}; + auto parsed = GetAST(sql).value(); + Optimizer optimizer(parsed.op, MakeMainRules(LoadIndexCatalogFromCsvDir(dir)), + CardinalityEstimates({{"users", 4}}), + LoadSchemaFromCsvDir(dir)); + auto plan = optimizer.Optimize(); + + boost::asio::io_context ctx; + CsvDirSequentialScanner seq_scan{dir.string()}; + CsvDirIndexedScanner index_scan{dir.string()}; + Executor executor(std::move(seq_scan), std::move(index_scan), ctx.get_executor()); + auto fut = boost::asio::co_spawn(ctx, executor.Execute(plan), boost::asio::use_future); + ctx.run(); + auto got = fut.get(); + + ASSERT_THAT(got.value().attributes, + Eq(AttributesInfo{{"users", "id", Type::kInt}, {"users", "age", Type::kInt}})); + ASSERT_THAT(got.value().tuples, + Eq(Tuples{ + Tuple{Value{false, 8}, Value{false, 64}}, + Tuple{Value{false, 10}, Value{false, 22}}, + })); + std::filesystem::remove_all(dir); +} + TEST(ExecutorTest, StringFilterProjectionAndCsvQuotes) { boost::asio::io_context ctx; boost::asio::co_spawn( diff --git a/src/stewkk/sql/logic/executor/plan_serializer.cpp b/src/stewkk/sql/logic/executor/plan_serializer.cpp index 92265cb..14ad1c0 100644 --- a/src/stewkk/sql/logic/executor/plan_serializer.cpp +++ b/src/stewkk/sql/logic/executor/plan_serializer.cpp @@ -577,6 +577,10 @@ std::string EscapeLabel(std::string_view s) { std::string out; out.reserve(s.size()); for (char c : s) { + if (c == '\n') { + out += "\\n"; + continue; + } if (c == '"' || c == '\\') out += '\\'; out += c; } diff --git a/src/stewkk/sql/logic/executor/plan_serializer_test.cpp b/src/stewkk/sql/logic/executor/plan_serializer_test.cpp index 34d6a69..ed59687 100644 --- a/src/stewkk/sql/logic/executor/plan_serializer_test.cpp +++ b/src/stewkk/sql/logic/executor/plan_serializer_test.cpp @@ -267,7 +267,7 @@ TEST(PlanSerializerDotTest, RendersMetadataWhenPresent) { auto dot = SerializeDot(plan); - EXPECT_THAT(dot, ::testing::HasSubstr("SeqScan\\\\nusers\\\\ncard=42\\\\ncost=4200")); + EXPECT_THAT(dot, ::testing::HasSubstr("SeqScan\\\\nusers\\ncard=42\\ncost=4200")); } } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/implementation_rules/implement_index_seek.cpp b/src/stewkk/sql/logic/implementation_rules/implement_index_seek.cpp new file mode 100644 index 0000000..44204b0 --- /dev/null +++ b/src/stewkk/sql/logic/implementation_rules/implement_index_seek.cpp @@ -0,0 +1,86 @@ +#include + +#include +#include +#include +#include + +#include +#include + +namespace stewkk::sql { + +namespace { + +bool IsSupportedComparison(BinaryOp op) { + return std::ranges::contains(std::vector{BinaryOp::kEq, BinaryOp::kLt, BinaryOp::kLe, + BinaryOp::kGt, BinaryOp::kGe}, op); +} + +const logical::Table* SourceTable(utils::NotNull group) { + auto exprs = group->GetLogicalExprs(); + if (exprs.size() != 1) { + return nullptr; + } + return std::get_if(&exprs.front()->root_operator); +} + +bool AttrMatchesTable(const Attribute& attr, const logical::Table& table) { + auto visible = std::string_view{VisibleName(table)}; + return attr.table.empty() || attr.table == table.name || attr.table == visible; +} + +bool IsIndexedComparison(const Expression& expr, const logical::Table& table, + const IndexCatalog& indexes) { + const auto* binary = std::get_if(&expr); + if (!binary || !IsSupportedComparison(binary->binop)) { + return false; + } + + if (const auto* attr = std::get_if(binary->lhs.get()); + attr && AttrMatchesTable(*attr, table) && std::holds_alternative(*binary->rhs)) { + return indexes.HasSortedIndex(table.name, attr->name); + } + if (const auto* attr = std::get_if(binary->rhs.get()); + attr && AttrMatchesTable(*attr, table) && std::holds_alternative(*binary->lhs)) { + return indexes.HasSortedIndex(table.name, attr->name); + } + return false; +} + +} // namespace + +ImplementIndexSeek::ImplementIndexSeek(IndexCatalog indexes) + : indexes_(std::move(indexes)) {} + +bool HasCompatibleIndexSeek(const logical::Filter& filter, const IndexCatalog& indexes) { + const auto* table = SourceTable(filter.source); + if (!table) { + return false; + } + + std::vector conjuncts; + CollectConjuncts(filter.predicate, conjuncts); + return std::ranges::any_of(conjuncts, [&](const Expression& conjunct) { + return IsIndexedComparison(conjunct, *table, indexes); + }); +} + +bool ImplementIndexSeek::IsApplicable(utils::NotNull expr) { + if (!std::holds_alternative(expr->root_operator)) { + return false; + } + return HasCompatibleIndexSeek(std::get(expr->root_operator), indexes_); +} + +utils::NotNull ImplementIndexSeek::Apply(utils::NotNull expr, Memo&) { + auto& filter = std::get(expr->root_operator); + const auto* table = SourceTable(filter.source); + return expr->group->AddPhysicalExpr(physical::IndexSeek{ + .table = table->name, + .alias = table->alias, + .predicate = filter.predicate, + }); +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index 2a8951b..d1a1698 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -12,6 +12,7 @@ #include #include #include +#include #include #include @@ -30,6 +31,7 @@ PropertySet RequiredInputProps(utils::NotNull expr, PropertySet required, size_t child_index) { return std::visit(utils::Overloaded{ [&](const physical::SeqScan&) { return PropertySet::Any(); }, + [&](const physical::IndexSeek&) { return PropertySet::Any(); }, [&](const physical::Filter&) { return required; }, [&](const physical::Projection&) { return required; }, [&](const physical::NestedLoopJoin&) { return PropertySet::Any(); }, @@ -46,6 +48,7 @@ PropertySet DeriveOutputProps(utils::NotNull expr, const std::vector& child_delivered) { return std::visit(utils::Overloaded{ [&](const physical::SeqScan&) { return PropertySet::Any(); }, + [&](const physical::IndexSeek&) { return PropertySet::Any(); }, [&](const physical::Filter&) { return child_delivered[0]; }, [&](const physical::Projection&) { return child_delivered[0]; }, [&](const physical::NestedLoopJoin&) { return PropertySet::Any(); }, @@ -103,6 +106,11 @@ int64_t CalcCost(utils::NotNull expr, CardinalityEstimates& cardi [&](const physical::SeqScan&) -> int64_t { return 100 * cardinality.GetCardinality(expr->group); }, + [&](const physical::IndexSeek&) -> int64_t { + auto out = cardinality.GetCardinality(expr->group); + return 200 * out + 100 * static_cast( + std::bit_width(static_cast(std::max(1, out)))); + }, [&](const physical::Filter&) -> int64_t { return 100 * cardinality.GetCardinality(expr->group); }, @@ -162,6 +170,9 @@ std::vector> GetChildren(utils::NotNull ex [](const physical::SeqScan&) -> std::vector> { return {}; }, + [](const physical::IndexSeek&) -> std::vector> { + return {}; + }, [](const physical::Filter& f) -> std::vector> { return {f.source}; }, @@ -413,6 +424,9 @@ PhysicalPlanNode Optimizer::BuildOptimalPlan(G [](const physical::SeqScan& op) -> PhysicalPlanNode { return SeqScan{.table = op.table, .alias = op.alias}; }, + [](const physical::IndexSeek& op) -> PhysicalPlanNode { + return IndexSeek{.table = op.table, .alias = op.alias, .predicate = op.predicate}; + }, [this, best_expr_nn, required](const physical::Projection& op) -> PhysicalPlanNode { return PhysicalProjection{ .source = std::make_shared( @@ -530,7 +544,7 @@ utils::NotNull Optimizer::GetRootGroup return root_->group; } -template class Optimizer<7, 7>; +template class Optimizer<7, 8>; template class Optimizer<0, 6>; } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index 5e585df..edbf3e0 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -68,7 +68,7 @@ TEST(OptimizerTest, Simple) { ASSERT_THAT(SerializeDot(got), Eq("digraph G { rankdir=BT;\n" - " n0 [label=\"SeqScan\\\\nusers\\\\ncard=10\\\\ncost=1000\"]\n" + " n0 [label=\"SeqScan\\\\nusers\\ncard=10\\ncost=1000\"]\n" "}\n")); ASSERT_THAT(optimizer.GetBestCost(), Eq(1000)); } @@ -224,6 +224,42 @@ TEST(OptimizerTest, PushesSelectiveFilterIntoHashBuildSide) { " (SeqScan lineorder lo))")); } +TEST(OptimizerTest, UsesIndexSeekForIndexedIntegerPredicate) { + std::stringstream s{"SELECT * FROM users WHERE users.id = 8;"}; + Operator op = GetAST(s).value().op; + Optimizer optimizer(op, MakeMainRules(IndexCatalog({ + IndexInfo{.table = "users", .column = "id", .type = "sorted", .file = "users.id.sorted.idx"}, + })), CardinalityEstimates({{"users", 1000}})); + + auto got = optimizer.Optimize(); + + ASSERT_THAT(Serialize(got), Eq("(IndexSeek (= (attr users id) 8) users)")); +} + +TEST(OptimizerTest, KeepsSequentialFilterWhenIndexMetadataIsAbsent) { + std::stringstream s{"SELECT * FROM users WHERE users.id = 8;"}; + Operator op = GetAST(s).value().op; + Optimizer optimizer(op, MakeMainRules(), CardinalityEstimates({{"users", 1000}})); + + auto got = optimizer.Optimize(); + + ASSERT_THAT(Serialize(got), + Eq("(PhysicalFilter (= (attr users id) 8) (SeqScan users))")); +} + +TEST(OptimizerTest, IndexSeekSupportsAliasAndFullResidualPredicate) { + std::stringstream s{"SELECT * FROM users AS u WHERE u.id >= 8 AND u.age < 70;"}; + Operator op = GetAST(s).value().op; + Optimizer optimizer(op, MakeMainRules(IndexCatalog({ + IndexInfo{.table = "users", .column = "id", .type = "sorted", .file = "users.id.sorted.idx"}, + })), CardinalityEstimates({{"users", 1000}})); + + auto got = optimizer.Optimize(); + + ASSERT_THAT(Serialize(got), + Eq("(IndexSeek (and (>= (attr u id) 8) (< (attr u age) 70)) users u)")); +} + TEST(OptimizerTest, NaiveRulesDisableLogicalTransformations) { std::stringstream s{ "SELECT * FROM lineorder AS lo " @@ -267,6 +303,21 @@ TEST(ReachabilityTest, SeqScanWrongTable) { ASSERT_THAT(result.mismatch, HasSubstr("users")); } +TEST(ReachabilityTest, IndexSeekReachableWithIndexCatalog) { + std::stringstream s{"SELECT * FROM users WHERE users.id >= 8;"}; + Expression predicate = BinaryExpression{ + std::make_shared(Attribute{"users", "id"}), + BinaryOp::kGe, + std::make_shared(IntConst{8})}; + auto result = IsPlanReachable( + s, IndexSeek{"users", std::nullopt, predicate}, {}, {}, + IndexCatalog({ + IndexInfo{.table = "users", .column = "id", .type = "sorted", + .file = "users.id.sorted.idx"}, + })); + ASSERT_THAT(result.reachable, IsTrue()); +} + TEST(ReachabilityTest, WrongOperatorType) { std::stringstream s{"SELECT * FROM users;"}; Expression qual = BinaryExpression{ diff --git a/src/stewkk/sql/logic/optimizer/reachability.cpp b/src/stewkk/sql/logic/optimizer/reachability.cpp index 90c9283..cbf1497 100644 --- a/src/stewkk/sql/logic/optimizer/reachability.cpp +++ b/src/stewkk/sql/logic/optimizer/reachability.cpp @@ -1,6 +1,7 @@ #include #include +#include #include #include @@ -37,6 +38,22 @@ InternalMatch TryMatchExpr(utils::NotNull pe, op.alias.value_or(""), t->alias.value_or(""))}; return {true, depth + 1, {}}; }, + [&](const physical::IndexSeek& op) -> InternalMatch { + const auto* t = std::get_if(&target.node); + if (!t) return {false, depth, "type mismatch: expected IndexSeek"}; + if (op.table != t->table) + return {false, depth + 1, + std::format("IndexSeek table '{}' != '{}'", op.table, t->table)}; + if (op.alias != t->alias) + return {false, depth + 1, + std::format("IndexSeek alias '{}' != '{}'", + op.alias.value_or(""), t->alias.value_or(""))}; + if (op.predicate != t->predicate) + return {false, depth + 1, + std::format("IndexSeek predicate '{}' != '{}'", + ToString(op.predicate), ToString(t->predicate))}; + return {true, depth + 1, {}}; + }, [&](const physical::Filter& op) -> InternalMatch { const auto* t = std::get_if(&target.node); if (!t) return {false, depth, "type mismatch: expected Filter"}; @@ -139,12 +156,13 @@ MatchResult IsReachable(utils::NotNull root, const PhysicalPlanNode& tar } MatchResult IsPlanReachable(std::istream& sql, const PhysicalPlanNode& target, - CardinalityEstimates cardinality, SchemaCatalog schema) { + CardinalityEstimates cardinality, SchemaCatalog schema, + IndexCatalog indexes) { auto parsed = GetAST(sql).value(); PropertySet required = parsed.required_order ? PropertySet{SortProperty{*parsed.required_order}} : PropertySet::Any(); - Optimizer optimizer(parsed.op, MakeMainRules(), std::move(cardinality), + Optimizer optimizer(parsed.op, MakeMainRules(std::move(indexes)), std::move(cardinality), std::move(schema), std::move(required)); auto plan = optimizer.OptimizeExhaustive(); auto result = IsReachable(optimizer.GetRootGroup(), target); diff --git a/src/stewkk/sql/logic/optimizer/rules.cpp b/src/stewkk/sql/logic/optimizer/rules.cpp index c4de335..47a4839 100644 --- a/src/stewkk/sql/logic/optimizer/rules.cpp +++ b/src/stewkk/sql/logic/optimizer/rules.cpp @@ -1,8 +1,10 @@ #include +#include + namespace stewkk::sql { -Rules<7, 7> MakeMainRules() { +Rules<7, 8> MakeMainRules(IndexCatalog indexes) { return { .transformation_rules = { std::make_unique(), @@ -16,6 +18,7 @@ Rules<7, 7> MakeMainRules() { .implementation_rules = { std::make_unique(), std::make_unique(), + std::make_unique(std::move(indexes)), std::make_unique(), std::make_unique(), std::make_unique(), diff --git a/src/stewkk/sql/logic/optimizer/rules_applier.cpp b/src/stewkk/sql/logic/optimizer/rules_applier.cpp index c25860b..82abf06 100644 --- a/src/stewkk/sql/logic/optimizer/rules_applier.cpp +++ b/src/stewkk/sql/logic/optimizer/rules_applier.cpp @@ -30,7 +30,7 @@ utils::NotNull RulesApplier::Ap return rules_.implementation_rules[rule.value]->Apply(expr, memo); } -template class RulesApplier<7, 7>; +template class RulesApplier<7, 8>; template class RulesApplier<0, 6>; } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/schema_catalog.cpp b/src/stewkk/sql/logic/optimizer/schema_catalog.cpp index 3c75ca6..9591609 100644 --- a/src/stewkk/sql/logic/optimizer/schema_catalog.cpp +++ b/src/stewkk/sql/logic/optimizer/schema_catalog.cpp @@ -5,11 +5,22 @@ #include #include #include +#include +#include #include namespace stewkk::sql { +IndexCatalog::IndexCatalog(std::vector indexes) + : indexes_(std::move(indexes)) {} + +bool IndexCatalog::HasSortedIndex(const std::string& table, const std::string& column) const { + return std::ranges::any_of(indexes_, [&](const IndexInfo& index) { + return index.table == table && index.column == column && index.type == "sorted"; + }); +} + SchemaCatalog::SchemaCatalog(std::unordered_map tables) : tables_(std::move(tables)) {} @@ -113,6 +124,29 @@ SchemaCatalog LoadSchemaFromCsvDir(const std::filesystem::path& dir) { return SchemaCatalog{std::move(tables)}; } +IndexCatalog LoadIndexCatalogFromCsvDir(const std::filesystem::path& dir) { + std::vector indexes; + std::ifstream input{dir / "indexes.meta"}; + if (!input) { + return IndexCatalog{}; + } + + std::string line; + while (std::getline(input, line)) { + auto first = line.find_first_not_of(" \t\r\n"); + if (first == std::string::npos || line[first] == '#') { + continue; + } + + std::istringstream fields{line}; + IndexInfo index; + if (fields >> index.table >> index.column >> index.type >> index.file) { + indexes.push_back(std::move(index)); + } + } + return IndexCatalog{std::move(indexes)}; +} + std::unordered_map LoadTableSizesFromCsvDir( const std::filesystem::path& dir) { std::unordered_map sizes; diff --git a/src/stewkk/sql/main.cpp b/src/stewkk/sql/main.cpp index d7fea11..172431e 100644 --- a/src/stewkk/sql/main.cpp +++ b/src/stewkk/sql/main.cpp @@ -186,8 +186,9 @@ boost::asio::awaitable> RunQuery(const std::string& data_dir, boost::asio::awaitable> RunQueryJit(const std::string& data_dir, const PhysicalPlanNode& plan) { CsvDirSequentialScanner seq_scan{data_dir}; + CsvDirIndexedScanner index_scan{data_dir}; Executor executor( - std::move(seq_scan), co_await boost::asio::this_coro::executor); + std::move(seq_scan), std::move(index_scan), co_await boost::asio::this_coro::executor); co_return co_await executor.Execute(plan); } @@ -223,7 +224,9 @@ int main(int argc, char** argv) { try { SchemaCatalog schema = args.data_dir.empty() ? SchemaCatalog{} : LoadSchemaFromCsvDir(args.data_dir); - mr = IsPlanReachable(sql_stream, target, {}, std::move(schema)); + IndexCatalog indexes = args.data_dir.empty() ? IndexCatalog{} + : LoadIndexCatalogFromCsvDir(args.data_dir); + mr = IsPlanReachable(sql_stream, target, {}, std::move(schema), std::move(indexes)); } catch (const std::exception& e) { std::cerr << "reachability error: " << e.what() << "\n"; return kOptimizerError; @@ -255,7 +258,7 @@ int main(int argc, char** argv) { PropertySet required = parsed.required_order ? PropertySet{SortProperty{*parsed.required_order}} : PropertySet::Any(); - Optimizer optimizer(parsed.op, MakeMainRules(), + Optimizer optimizer(parsed.op, MakeMainRules(LoadIndexCatalogFromCsvDir(args.data_dir)), CardinalityEstimates{LoadTableSizesFromCsvDir(args.data_dir)}, LoadSchemaFromCsvDir(args.data_dir), std::move(required)); plan = optimizer.Optimize(); From 0841199947f459ff4c1436c84c2b0e0ca3863883 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 8 Jun 2026 00:42:08 +0300 Subject: [PATCH 097/120] Fix UB --- .../stewkk/sql/logic/executor/executor.hpp | 6 + src/stewkk/sql/logic/executor/executor.cpp | 212 +++++++++--------- 2 files changed, 115 insertions(+), 103 deletions(-) diff --git a/include/stewkk/sql/logic/executor/executor.hpp b/include/stewkk/sql/logic/executor/executor.hpp index 38ae688..63a1c31 100644 --- a/include/stewkk/sql/logic/executor/executor.hpp +++ b/include/stewkk/sql/logic/executor/executor.hpp @@ -59,6 +59,10 @@ class Executor { private: boost::asio::awaitable Execute(const PhysicalPlanNode& op, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan); + boost::asio::awaitable ExecuteSeqScan(const SeqScan& seq_scan, AttributesInfoChannel& attr_chan, + TuplesChannel& tuples_chan); + boost::asio::awaitable ExecuteIndexSeek(const IndexSeek& seek, AttributesInfoChannel& attr_chan, + TuplesChannel& tuples_chan); boost::asio::awaitable ExecuteProjection(const PhysicalProjection& proj, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan); boost::asio::awaitable ExecuteFilter(const PhysicalFilter& filter, AttributesInfoChannel& attr_chan, @@ -73,6 +77,8 @@ class Executor { boost::asio::awaitable ExecuteHashAggregate(const PhysicalAggregation& agg, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan); + boost::asio::awaitable ExecuteSort(const PhysicalSort& sort, AttributesInfoChannel& attr_chan, + TuplesChannel& tuples_chan); boost::asio::experimental::promise SpawnExecutor( boost::asio::any_io_executor exec, const PhysicalPlanNode& op, AttributesInfoChannel& attr_chan, TuplesChannel& tuple_chan); diff --git a/src/stewkk/sql/logic/executor/executor.cpp b/src/stewkk/sql/logic/executor/executor.cpp index cbdd4ad..96c8657 100644 --- a/src/stewkk/sql/logic/executor/executor.cpp +++ b/src/stewkk/sql/logic/executor/executor.cpp @@ -636,119 +636,36 @@ boost::asio::awaitable Executor::Execute(const Physica tuples_chan.close(); }); struct ExecuteVisitor{ - boost::asio::awaitable operator()(const SeqScan& seq_scan) { - co_await executor.sequential_scan_(seq_scan.table, std::string{OutputTable(seq_scan)}, - attr_chan, tuples_chan); - co_return; + boost::asio::awaitable operator()(const SeqScan& seq_scan) const { + return executor.ExecuteSeqScan(seq_scan, attr_chan, tuples_chan); } - boost::asio::awaitable operator()(const PhysicalProjection& projection) { + boost::asio::awaitable operator()(const PhysicalProjection& projection) const { // NOTE: We are using multiset relational algebra projection (i.e. not - - co_await executor.ExecuteProjection(projection, attr_chan, tuples_chan); - co_return; + return executor.ExecuteProjection(projection, attr_chan, tuples_chan); } - boost::asio::awaitable operator()(const PhysicalFilter& filter) { - co_await executor.ExecuteFilter(filter, attr_chan, tuples_chan); - co_return; + boost::asio::awaitable operator()(const PhysicalFilter& filter) const { + return executor.ExecuteFilter(filter, attr_chan, tuples_chan); } - boost::asio::awaitable operator()(const NestedLoopJoin& join) { - co_await executor.ExecuteJoin(join, attr_chan, tuples_chan); - co_return; + boost::asio::awaitable operator()(const NestedLoopJoin& join) const { + return executor.ExecuteJoin(join, attr_chan, tuples_chan); } - boost::asio::awaitable operator()(const NestedLoopCrossJoin& cross_join) { - co_await executor.ExecuteCrossJoin(cross_join, attr_chan, tuples_chan); - co_return; + boost::asio::awaitable operator()(const NestedLoopCrossJoin& cross_join) const { + return executor.ExecuteCrossJoin(cross_join, attr_chan, tuples_chan); } - boost::asio::awaitable operator()(const HashJoin& join) { - co_await executor.ExecuteHashJoin(join, attr_chan, tuples_chan); - co_return; + boost::asio::awaitable operator()(const HashJoin& join) const { + return executor.ExecuteHashJoin(join, attr_chan, tuples_chan); } - boost::asio::awaitable operator()(const PhysicalAggregation& agg) { - co_await executor.ExecuteHashAggregate(agg, attr_chan, tuples_chan); - co_return; + boost::asio::awaitable operator()(const PhysicalAggregation& agg) const { + return executor.ExecuteHashAggregate(agg, attr_chan, tuples_chan); } - boost::asio::awaitable operator()(const MergeJoin&) { + boost::asio::awaitable operator()(const MergeJoin&) const { throw std::runtime_error("MergeJoin execution not implemented"); - co_return; } - boost::asio::awaitable operator()(const IndexSeek& seek) { - // FIXME: make index_scan_ required! - if (!executor.index_scan_) { - throw std::runtime_error("IndexSeek execution requested, but no index scanner is configured"); - } - co_await executor.index_scan_(seek.table, seek.alias ? *seek.alias : seek.table, seek.predicate, - attr_chan, tuples_chan); - co_return; - } - // FIXME: that's in-memory sort - boost::asio::awaitable operator()(const PhysicalSort& sort) { - auto close_on_fail = boost::scope::make_scope_fail( - [this] { attr_chan.close(); tuples_chan.close(); }); - auto exec = co_await boost::asio::this_coro::executor; - auto [in_attrs_chan, in_tuples_chan] = co_await GetChannels(); - auto task = executor.SpawnExecutor(exec, *sort.source, in_attrs_chan, in_tuples_chan); - - auto attrs = co_await in_attrs_chan.async_receive(boost::asio::use_awaitable); - co_await attr_chan.async_send(boost::system::error_code{}, attrs, boost::asio::use_awaitable); - attr_chan.close(); - - Tuples all_tuples; - for (;;) { - auto buf = co_await ReceiveTuples(in_tuples_chan); - if (buf.empty()) break; - std::move(buf.begin(), buf.end(), std::back_inserter(all_tuples)); - } - - std::vector> key_indices; - for (const auto& key : sort.keys.keys) { - auto it = std::find_if(attrs.begin(), attrs.end(), - [&](const AttributeInfo& a) { - return a.name == key.column && (key.table.empty() || a.table == key.table); - }); - if (it == attrs.end()) - throw std::runtime_error{"sort key column not found: " + key.table + "." + key.column}; - key_indices.push_back({static_cast(it - attrs.begin()), key.dir}); - } - - std::sort(all_tuples.begin(), all_tuples.end(), - [&](const Tuple& a, const Tuple& b) { - for (const auto& [idx, dir] : key_indices) { - const auto& attr = attrs[idx]; - const auto& va = a[idx]; - const auto& vb = b[idx]; - if (va.is_null && vb.is_null) continue; - if (va.is_null) return false; - if (vb.is_null) return true; - bool less = false; - bool greater = false; - switch (attr.type) { - case Type::kInt: - less = va.value.int_value < vb.value.int_value; - greater = va.value.int_value > vb.value.int_value; - break; - case Type::kBool: - less = va.value.bool_value < vb.value.bool_value; - greater = va.value.bool_value > vb.value.bool_value; - break; - case Type::kString: { - const auto& sa = GetInternedString(va.value.string_id); - const auto& sb = GetInternedString(vb.value.string_id); - less = sa < sb; - greater = sa > sb; - break; - } - } - if (less || greater) return dir == Direction::kAsc ? less : greater; - } - return false; - }); - - if (!all_tuples.empty()) - co_await tuples_chan.async_send(boost::system::error_code{}, - std::move(all_tuples), boost::asio::use_awaitable); - co_await task(boost::asio::use_awaitable); - tuples_chan.close(); - co_return; + boost::asio::awaitable operator()(const IndexSeek& seek) const { + return executor.ExecuteIndexSeek(seek, attr_chan, tuples_chan); + } + boost::asio::awaitable operator()(const PhysicalSort& sort) const { + return executor.ExecuteSort(sort, attr_chan, tuples_chan); } AttributesInfoChannel& attr_chan; @@ -759,6 +676,95 @@ boost::asio::awaitable Executor::Execute(const Physica co_return; } +template +boost::asio::awaitable Executor::ExecuteSeqScan( + const SeqScan& seq_scan, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan) { + co_await sequential_scan_(seq_scan.table, std::string{OutputTable(seq_scan)}, attr_chan, tuples_chan); +} + +template +boost::asio::awaitable Executor::ExecuteIndexSeek( + const IndexSeek& seek, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan) { + // FIXME: make index_scan_ required! + if (!index_scan_) { + throw std::runtime_error("IndexSeek execution requested, but no index scanner is configured"); + } + co_await index_scan_(seek.table, seek.alias ? *seek.alias : seek.table, seek.predicate, + attr_chan, tuples_chan); +} + +template +boost::asio::awaitable Executor::ExecuteSort( + const PhysicalSort& sort, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan) { + // FIXME: that's in-memory sort + auto close_on_fail = boost::scope::make_scope_fail( + [&] { attr_chan.close(); tuples_chan.close(); }); + auto exec = co_await boost::asio::this_coro::executor; + auto [in_attrs_chan, in_tuples_chan] = co_await GetChannels(); + auto task = SpawnExecutor(exec, *sort.source, in_attrs_chan, in_tuples_chan); + + auto attrs = co_await in_attrs_chan.async_receive(boost::asio::use_awaitable); + co_await attr_chan.async_send(boost::system::error_code{}, attrs, boost::asio::use_awaitable); + attr_chan.close(); + + Tuples all_tuples; + for (;;) { + auto buf = co_await ReceiveTuples(in_tuples_chan); + if (buf.empty()) break; + std::move(buf.begin(), buf.end(), std::back_inserter(all_tuples)); + } + + std::vector> key_indices; + for (const auto& key : sort.keys.keys) { + auto it = std::find_if(attrs.begin(), attrs.end(), + [&](const AttributeInfo& a) { + return a.name == key.column && (key.table.empty() || a.table == key.table); + }); + if (it == attrs.end()) + throw std::runtime_error{"sort key column not found: " + key.table + "." + key.column}; + key_indices.push_back({static_cast(it - attrs.begin()), key.dir}); + } + + std::sort(all_tuples.begin(), all_tuples.end(), + [&](const Tuple& a, const Tuple& b) { + for (const auto& [idx, dir] : key_indices) { + const auto& attr = attrs[idx]; + const auto& va = a[idx]; + const auto& vb = b[idx]; + if (va.is_null && vb.is_null) continue; + if (va.is_null) return false; + if (vb.is_null) return true; + bool less = false; + bool greater = false; + switch (attr.type) { + case Type::kInt: + less = va.value.int_value < vb.value.int_value; + greater = va.value.int_value > vb.value.int_value; + break; + case Type::kBool: + less = va.value.bool_value < vb.value.bool_value; + greater = va.value.bool_value > vb.value.bool_value; + break; + case Type::kString: { + const auto& sa = GetInternedString(va.value.string_id); + const auto& sb = GetInternedString(vb.value.string_id); + less = sa < sb; + greater = sa > sb; + break; + } + } + if (less || greater) return dir == Direction::kAsc ? less : greater; + } + return false; + }); + + if (!all_tuples.empty()) + co_await tuples_chan.async_send(boost::system::error_code{}, + std::move(all_tuples), boost::asio::use_awaitable); + co_await task(boost::asio::use_awaitable); + tuples_chan.close(); +} + template boost::asio::awaitable Executor::ExecuteProjection(const PhysicalProjection& proj, AttributesInfoChannel& out_attr_chan, From 16a4f45e7458345199cd7779cfe7b04e40afee5b Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Tue, 9 Jun 2026 21:14:48 +0300 Subject: [PATCH 098/120] Add MergeJoin --- benchmarks/main.cpp | 22 +- .../stewkk/sql/logic/executor/executor.hpp | 2 + .../implement_merge_join.hpp | 13 + .../stewkk/sql/logic/optimizer/optimizer.hpp | 2 +- .../sql/logic/optimizer/physical_expr.hpp | 13 +- .../sql/logic/optimizer/reachability.hpp | 4 +- include/stewkk/sql/logic/optimizer/rules.hpp | 3 +- .../sql/logic/optimizer/schema_catalog.hpp | 7 +- query.sql | 11 + research/converter.py | 1 + src/stewkk/sql/CMakeLists.txt | 1 + src/stewkk/sql/logic/executor/executor.cpp | 243 +++++++++++++++++- .../sql/logic/executor/executor_test.cpp | 102 ++++++++ .../implement_merge_join.cpp | 28 ++ src/stewkk/sql/logic/optimizer/optimizer.cpp | 125 +++++++-- .../sql/logic/optimizer/optimizer_test.cpp | 108 ++++++-- .../sql/logic/optimizer/reachability.cpp | 15 ++ src/stewkk/sql/logic/optimizer/rules.cpp | 3 +- .../sql/logic/optimizer/rules_applier.cpp | 2 +- .../sql/logic/optimizer/schema_catalog.cpp | 33 ++- .../sql/logic/optimizer/sort_enforcer.cpp | 17 +- 21 files changed, 677 insertions(+), 78 deletions(-) create mode 100644 include/stewkk/sql/logic/implementation_rules/implement_merge_join.hpp create mode 100644 query.sql create mode 100644 src/stewkk/sql/logic/implementation_rules/implement_merge_join.cpp diff --git a/benchmarks/main.cpp b/benchmarks/main.cpp index eb158b4..ac93677 100644 --- a/benchmarks/main.cpp +++ b/benchmarks/main.cpp @@ -95,6 +95,26 @@ CardinalityEstimates MakeBenchCardinality() { }); } +SchemaCatalog MakeBenchSchema() { + return SchemaCatalog({ + {"users", {Attribute{"users", "id"}, Attribute{"users", "age"}}}, + {"employees", {Attribute{"employees", "id"}, Attribute{"employees", "department_id"}}}, + {"employees_200", {Attribute{"employees_200", "id"}, + Attribute{"employees_200", "department_id"}}}, + {"departments", {Attribute{"departments", "id"}}}, + {"departments_500", {Attribute{"departments_500", "id"}}}, + {"departments_1000", {Attribute{"departments_1000", "id"}}}, + {"departments_2000", {Attribute{"departments_2000", "id"}}}, + {"departments_4000", {Attribute{"departments_4000", "id"}}}, + {"departments_8000", {Attribute{"departments_8000", "id"}}}, + {"departments_16000", {Attribute{"departments_16000", "id"}}}, + {"books", {Attribute{"books", "id"}}}, + {"regions", {Attribute{"regions", "id"}}}, + {"customers", {Attribute{"customers", "id"}, Attribute{"customers", "region_id"}}}, + {"orders", {Attribute{"orders", "id"}, Attribute{"orders", "customer_id"}}}, + }); +} + CardinalityEstimates LoadCardinalityFromCsvDir(const std::filesystem::path& dir) { std::unordered_map counts; if (!std::filesystem::is_directory(dir)) { @@ -133,7 +153,7 @@ PhysicalPlanNode MakePlan(const Operator& op) { if constexpr (Mode == PlannerMode::kNaive) { return ToPhysicalPlan(op); } else { - Optimizer optimizer(op, MakeMainRules(), MakeBenchCardinality()); + Optimizer optimizer(op, MakeMainRules(), MakeBenchCardinality(), MakeBenchSchema()); return optimizer.Optimize(); } } diff --git a/include/stewkk/sql/logic/executor/executor.hpp b/include/stewkk/sql/logic/executor/executor.hpp index 63a1c31..24eefb4 100644 --- a/include/stewkk/sql/logic/executor/executor.hpp +++ b/include/stewkk/sql/logic/executor/executor.hpp @@ -74,6 +74,8 @@ class Executor { TuplesChannel& tuples_chan); boost::asio::awaitable ExecuteHashJoin(const HashJoin& join, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan); + boost::asio::awaitable ExecuteMergeJoin(const MergeJoin& join, AttributesInfoChannel& attr_chan, + TuplesChannel& tuples_chan); boost::asio::awaitable ExecuteHashAggregate(const PhysicalAggregation& agg, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan); diff --git a/include/stewkk/sql/logic/implementation_rules/implement_merge_join.hpp b/include/stewkk/sql/logic/implementation_rules/implement_merge_join.hpp new file mode 100644 index 0000000..1742d96 --- /dev/null +++ b/include/stewkk/sql/logic/implementation_rules/implement_merge_join.hpp @@ -0,0 +1,13 @@ +#pragma once + +#include + +namespace stewkk::sql { + +class ImplementMergeJoin : public ImplementationRule { + public: + bool IsApplicable(utils::NotNull expr) override; + utils::NotNull Apply(utils::NotNull expr, Memo& memo) override; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/optimizer.hpp b/include/stewkk/sql/logic/optimizer/optimizer.hpp index c0dbbc8..c5ecc04 100644 --- a/include/stewkk/sql/logic/optimizer/optimizer.hpp +++ b/include/stewkk/sql/logic/optimizer/optimizer.hpp @@ -26,7 +26,7 @@ template class Optimizer { public: Optimizer(const Operator& expr, Rules&& rules, - CardinalityEstimates cardinality = {}, SchemaCatalog schema = {}, + CardinalityEstimates cardinality, SchemaCatalog schema, PropertySet required = PropertySet::Any()); PhysicalPlanNode Optimize(); diff --git a/include/stewkk/sql/logic/optimizer/physical_expr.hpp b/include/stewkk/sql/logic/optimizer/physical_expr.hpp index aedb4f5..2081d8f 100644 --- a/include/stewkk/sql/logic/optimizer/physical_expr.hpp +++ b/include/stewkk/sql/logic/optimizer/physical_expr.hpp @@ -70,6 +70,15 @@ struct HashJoin { bool operator==(const HashJoin&) const = default; }; +struct MergeJoin { + utils::NotNull lhs; + utils::NotNull rhs; + JoinType type; + Expression qual; + + bool operator==(const MergeJoin&) const = default; +}; + struct Sort { utils::NotNull input; SortOrder keys; @@ -90,8 +99,8 @@ struct Aggregation { struct PhysicalExpr { std::variant root_operator; + physical::HashJoin, physical::MergeJoin, physical::Sort, + physical::Aggregation, physical::IndexSeek> root_operator; utils::NotNull group; bool is_enforcer = false; }; diff --git a/include/stewkk/sql/logic/optimizer/reachability.hpp b/include/stewkk/sql/logic/optimizer/reachability.hpp index e5c7134..ab16136 100644 --- a/include/stewkk/sql/logic/optimizer/reachability.hpp +++ b/include/stewkk/sql/logic/optimizer/reachability.hpp @@ -20,8 +20,8 @@ struct MatchResult { MatchResult IsReachable(utils::NotNull root, const PhysicalPlanNode& target); MatchResult IsPlanReachable(std::istream& sql, const PhysicalPlanNode& target, - CardinalityEstimates cardinality = {}, - SchemaCatalog schema = {}, + CardinalityEstimates cardinality, + SchemaCatalog schema, IndexCatalog indexes = {}); } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/rules.hpp b/include/stewkk/sql/logic/optimizer/rules.hpp index 7db0a85..8b9a31c 100644 --- a/include/stewkk/sql/logic/optimizer/rules.hpp +++ b/include/stewkk/sql/logic/optimizer/rules.hpp @@ -17,6 +17,7 @@ #include #include #include +#include #include #include @@ -28,7 +29,7 @@ struct Rules { std::array, NImplementation> implementation_rules; }; -Rules<7, 8> MakeMainRules(IndexCatalog indexes = {}); +Rules<7, 9> MakeMainRules(IndexCatalog indexes = {}); Rules<0, 6> MakeNaiveRules(); } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/schema_catalog.hpp b/include/stewkk/sql/logic/optimizer/schema_catalog.hpp index 62383d7..b7f5528 100644 --- a/include/stewkk/sql/logic/optimizer/schema_catalog.hpp +++ b/include/stewkk/sql/logic/optimizer/schema_catalog.hpp @@ -2,7 +2,6 @@ #include #include -#include #include #include #include @@ -36,14 +35,14 @@ class SchemaCatalog { public: SchemaCatalog(std::unordered_map tables = {}); - std::optional GetSchema(utils::NotNull group); + Schema GetSchema(utils::NotNull group); std::int64_t GetWidth(utils::NotNull group); private: - std::optional Derive(const LogicalOperator& op); + Schema Derive(const LogicalOperator& op); std::unordered_map tables_; - std::unordered_map> cache_; + std::unordered_map cache_; }; SchemaCatalog LoadSchemaFromCsvDir(const std::filesystem::path& dir); diff --git a/query.sql b/query.sql new file mode 100644 index 0000000..6efbe87 --- /dev/null +++ b/query.sql @@ -0,0 +1,11 @@ +SELECT d.d_year, c.c_nation, SUM(lo.lo_revenue - lo.lo_supplycost) AS profit +FROM lineorder AS lo +JOIN date AS d ON lo.lo_orderdate = d.d_datekey +JOIN customer AS c ON lo.lo_custkey = c.c_custkey +JOIN supplier AS s ON lo.lo_suppkey = s.s_suppkey +JOIN part AS p ON lo.lo_partkey = p.p_partkey +WHERE c.c_region = 'AMERICA' + AND s.s_region = 'AMERICA' + AND p.p_mfgr IN ('MFGR#1', 'MFGR#2') +GROUP BY d.d_year, c.c_nation +ORDER BY d.d_year, c.c_nation; diff --git a/research/converter.py b/research/converter.py index 89d2ac9..f6c9d4c 100644 --- a/research/converter.py +++ b/research/converter.py @@ -9,6 +9,7 @@ (PhysicalProjection (exprs EXPR...) SOURCE) (NestedLoopCrossJoin LHS RHS) (NestedLoopJoin JoinType EXPR LHS RHS) JoinType: Inner | Full | Left | Right + (MergeJoin JoinType EXPR LHS RHS) JoinType: Inner | Full | Left | Right (Sort (keys table.col Asc|Desc ...) SOURCE) (HashAggregate (group_by EXPR...) (aggs EXPR...) SOURCE) diff --git a/src/stewkk/sql/CMakeLists.txt b/src/stewkk/sql/CMakeLists.txt index 8784d11..15b2517 100644 --- a/src/stewkk/sql/CMakeLists.txt +++ b/src/stewkk/sql/CMakeLists.txt @@ -53,6 +53,7 @@ add_library(libsql logic/implementation_rules/implement_join.cpp logic/implementation_rules/implement_cross_join.cpp logic/implementation_rules/implement_hash_join.cpp + logic/implementation_rules/implement_merge_join.cpp logic/implementation_rules/implement_aggregation.cpp logic/optimizer/properties/sort_order.cpp logic/optimizer/properties/sort_property.cpp diff --git a/src/stewkk/sql/logic/executor/executor.cpp b/src/stewkk/sql/logic/executor/executor.cpp index 96c8657..c16f24f 100644 --- a/src/stewkk/sql/logic/executor/executor.cpp +++ b/src/stewkk/sql/logic/executor/executor.cpp @@ -658,8 +658,8 @@ boost::asio::awaitable Executor::Execute(const Physica boost::asio::awaitable operator()(const PhysicalAggregation& agg) const { return executor.ExecuteHashAggregate(agg, attr_chan, tuples_chan); } - boost::asio::awaitable operator()(const MergeJoin&) const { - throw std::runtime_error("MergeJoin execution not implemented"); + boost::asio::awaitable operator()(const MergeJoin& join) const { + return executor.ExecuteMergeJoin(join, attr_chan, tuples_chan); } boost::asio::awaitable operator()(const IndexSeek& seek) const { return executor.ExecuteIndexSeek(seek, attr_chan, tuples_chan); @@ -1092,6 +1092,84 @@ size_t FindAttrIndex(const AttributesInfo& attrs, const Attribute& a) { return static_cast(it - attrs.begin()); } +bool AttrMatches(const AttributeInfo& ai, const Attribute& a) { + return ai.name == a.name && (a.table.empty() || ai.table == a.table); +} + +bool HasAttr(const AttributesInfo& attrs, const Attribute& a) { + return std::ranges::any_of(attrs, [&](const AttributeInfo& ai) { + return AttrMatches(ai, a); + }); +} + +size_t FindAttrIndexFlexible(const AttributesInfo& attrs, const Attribute& a) { + auto it = std::find_if(attrs.begin(), attrs.end(), [&](const AttributeInfo& ai) { + return AttrMatches(ai, a); + }); + if (it == attrs.end()) { + throw std::runtime_error{"merge join key attribute not found: " + a.table + "." + a.name}; + } + return static_cast(it - attrs.begin()); +} + +struct ExecutorJoinKeys { + Attribute lhs; + Attribute rhs; +}; + +ExecutorJoinKeys ResolveExecutorJoinKeys(const Expression& qual, + const AttributesInfo& lhs_attrs, + const AttributesInfo& rhs_attrs) { + const auto* bin = std::get_if(&qual); + if (!bin || bin->binop != BinaryOp::kEq) { + throw std::logic_error{"MergeJoin qual must be an equality"}; + } + const auto* a = std::get_if(bin->lhs.get()); + const auto* b = std::get_if(bin->rhs.get()); + if (!a || !b) { + throw std::logic_error{"MergeJoin qual must be `attr = attr`"}; + } + + const bool a_lhs = HasAttr(lhs_attrs, *a); + const bool a_rhs = HasAttr(rhs_attrs, *a); + const bool b_lhs = HasAttr(lhs_attrs, *b); + const bool b_rhs = HasAttr(rhs_attrs, *b); + if (a_lhs && !a_rhs && b_rhs && !b_lhs) { + return {*a, *b}; + } + if (b_lhs && !b_rhs && a_rhs && !a_lhs) { + return {*b, *a}; + } + throw std::runtime_error{"MergeJoin qual cannot be mapped unambiguously to inputs"}; +} + +boost::asio::awaitable CollectAllTuples(TuplesChannel& tuples_chan) { + Tuples result; + for (;;) { + auto buf = co_await ReceiveTuples(tuples_chan); + if (buf.empty()) break; + std::move(buf.begin(), buf.end(), std::back_inserter(result)); + } + co_return result; +} + +int CompareNonNullValues(const Value& lhs, const Value& rhs, Type type) { + switch (type) { + case Type::kInt: + return (lhs.value.int_value > rhs.value.int_value) + - (lhs.value.int_value < rhs.value.int_value); + case Type::kBool: + return (lhs.value.bool_value > rhs.value.bool_value) + - (lhs.value.bool_value < rhs.value.bool_value); + case Type::kString: { + const auto& l = GetInternedString(lhs.value.string_id); + const auto& r = GetInternedString(rhs.value.string_id); + return (l > r) - (l < r); + } + } + std::unreachable(); +} + } // namespace @@ -1196,6 +1274,167 @@ boost::asio::awaitable Executor::ExecuteHashJoin( tuples_chan.close(); } +template +boost::asio::awaitable Executor::ExecuteMergeJoin( + const MergeJoin& join, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan) { + Log("Executing merge join"); + auto close_on_fail = boost::scope::make_scope_fail( + [&] { attr_chan.close(); tuples_chan.close(); }); + + auto exec = co_await boost::asio::this_coro::executor; + auto [lhs_attrs_chan, lhs_tuples_chan] = co_await GetChannels(); + auto lhs_task = SpawnExecutor(exec, *join.lhs, lhs_attrs_chan, lhs_tuples_chan); + auto [rhs_attrs_chan, rhs_tuples_chan] = co_await GetChannels(); + auto rhs_task = SpawnExecutor(exec, *join.rhs, rhs_attrs_chan, rhs_tuples_chan); + + auto lhs_attrs = co_await lhs_attrs_chan.async_receive(boost::asio::use_awaitable); + auto rhs_attrs = co_await rhs_attrs_chan.async_receive(boost::asio::use_awaitable); + auto keys = ResolveExecutorJoinKeys(join.qual, lhs_attrs, rhs_attrs); + const size_t lhs_key_idx = FindAttrIndexFlexible(lhs_attrs, keys.lhs); + const size_t rhs_key_idx = FindAttrIndexFlexible(rhs_attrs, keys.rhs); + if (lhs_attrs[lhs_key_idx].type != rhs_attrs[rhs_key_idx].type) { + throw std::logic_error{"MergeJoin key types mismatch"}; + } + const Type key_type = lhs_attrs[lhs_key_idx].type; + + AttributesInfo out_attrs = lhs_attrs; + std::ranges::copy(rhs_attrs, std::back_inserter(out_attrs)); + co_await attr_chan.async_send(boost::system::error_code{}, out_attrs, + boost::asio::use_awaitable); + attr_chan.close(); + + auto lhs = co_await CollectAllTuples(lhs_tuples_chan); + auto rhs = co_await CollectAllTuples(rhs_tuples_chan); + + Tuples out_buf; + out_buf.reserve(kBufSize); + auto emit = [&](Tuple tuple) -> boost::asio::awaitable { + out_buf.push_back(std::move(tuple)); + if (out_buf.size() == kBufSize) { + co_await tuples_chan.async_send(boost::system::error_code{}, std::move(out_buf), + boost::asio::use_awaitable); + out_buf.clear(); + out_buf.reserve(kBufSize); + } + }; + auto flush = [&]() -> boost::asio::awaitable { + if (!out_buf.empty()) { + co_await tuples_chan.async_send(boost::system::error_code{}, std::move(out_buf), + boost::asio::use_awaitable); + out_buf.clear(); + out_buf.reserve(kBufSize); + } + }; + + auto same_lhs_key = [&](size_t a, size_t b) { + const auto& av = lhs[a][lhs_key_idx]; + const auto& bv = lhs[b][lhs_key_idx]; + return !av.is_null && !bv.is_null && CompareNonNullValues(av, bv, key_type) == 0; + }; + auto same_rhs_key = [&](size_t a, size_t b) { + const auto& av = rhs[a][rhs_key_idx]; + const auto& bv = rhs[b][rhs_key_idx]; + return !av.is_null && !bv.is_null && CompareNonNullValues(av, bv, key_type) == 0; + }; + + if (join.type == JoinType::kRight) { + size_t i = 0; + size_t j = 0; + while (j < rhs.size()) { + const auto& rhs_key = rhs[j][rhs_key_idx]; + size_t rhs_end = j + 1; + while (rhs_end < rhs.size() && same_rhs_key(j, rhs_end)) ++rhs_end; + + if (rhs_key.is_null) { + for (size_t r = j; r < rhs_end; ++r) { + co_await emit(ConcatTuples(Tuple(lhs_attrs.size(), Value{true}), rhs[r])); + } + j = rhs_end; + continue; + } + + while (i < lhs.size()) { + const auto& lhs_key = lhs[i][lhs_key_idx]; + if (lhs_key.is_null || CompareNonNullValues(lhs_key, rhs_key, key_type) >= 0) break; + ++i; + } + + if (i < lhs.size() && !lhs[i][lhs_key_idx].is_null + && CompareNonNullValues(lhs[i][lhs_key_idx], rhs_key, key_type) == 0) { + size_t lhs_end = i + 1; + while (lhs_end < lhs.size() && same_lhs_key(i, lhs_end)) ++lhs_end; + for (size_t l = i; l < lhs_end; ++l) { + for (size_t r = j; r < rhs_end; ++r) { + co_await emit(ConcatTuples(lhs[l], rhs[r])); + } + } + i = lhs_end; + } else { + for (size_t r = j; r < rhs_end; ++r) { + co_await emit(ConcatTuples(Tuple(lhs_attrs.size(), Value{true}), rhs[r])); + } + } + j = rhs_end; + } + } else { + std::vector rhs_used(rhs.size(), false); + size_t i = 0; + size_t j = 0; + while (i < lhs.size()) { + const auto& lhs_key = lhs[i][lhs_key_idx]; + size_t lhs_end = i + 1; + while (lhs_end < lhs.size() && same_lhs_key(i, lhs_end)) ++lhs_end; + + if (lhs_key.is_null) { + if (join.type == JoinType::kLeft || join.type == JoinType::kFull) { + for (size_t l = i; l < lhs_end; ++l) { + co_await emit(ConcatTuples(lhs[l], Tuple(rhs_attrs.size(), Value{true}))); + } + } + i = lhs_end; + continue; + } + + while (j < rhs.size()) { + const auto& rhs_key = rhs[j][rhs_key_idx]; + if (rhs_key.is_null || CompareNonNullValues(rhs_key, lhs_key, key_type) >= 0) break; + ++j; + } + + if (j < rhs.size() && !rhs[j][rhs_key_idx].is_null + && CompareNonNullValues(rhs[j][rhs_key_idx], lhs_key, key_type) == 0) { + size_t rhs_end = j + 1; + while (rhs_end < rhs.size() && same_rhs_key(j, rhs_end)) ++rhs_end; + for (size_t l = i; l < lhs_end; ++l) { + for (size_t r = j; r < rhs_end; ++r) { + rhs_used[r] = true; + co_await emit(ConcatTuples(lhs[l], rhs[r])); + } + } + j = rhs_end; + } else if (join.type == JoinType::kLeft || join.type == JoinType::kFull) { + for (size_t l = i; l < lhs_end; ++l) { + co_await emit(ConcatTuples(lhs[l], Tuple(rhs_attrs.size(), Value{true}))); + } + } + i = lhs_end; + } + + if (join.type == JoinType::kFull) { + for (size_t r = 0; r < rhs.size(); ++r) { + if (!rhs_used[r]) { + co_await emit(ConcatTuples(Tuple(lhs_attrs.size(), Value{true}), rhs[r])); + } + } + } + } + + co_await flush(); + co_await lhs_task(boost::asio::use_awaitable); + co_await rhs_task(boost::asio::use_awaitable); + tuples_chan.close(); +} + template boost::asio::awaitable Executor::ExecuteHashAggregate( const PhysicalAggregation& agg, AttributesInfoChannel& out_attr_chan, diff --git a/src/stewkk/sql/logic/executor/executor_test.cpp b/src/stewkk/sql/logic/executor/executor_test.cpp index d199219..7b4a765 100644 --- a/src/stewkk/sql/logic/executor/executor_test.cpp +++ b/src/stewkk/sql/logic/executor/executor_test.cpp @@ -234,6 +234,108 @@ TEST(ExecutorTest, OptimizedIndexSeekPlanExecutes) { std::filesystem::remove_all(dir); } +TEST(ExecutorTest, MergeJoinFullOuterWithDuplicatesAndNulls) { + auto dir = std::filesystem::temp_directory_path() / "iu9_sql_merge_join_test"; + std::filesystem::remove_all(dir); + std::filesystem::create_directories(dir); + { + std::ofstream a{dir / "a.csv"}; + a << "id:int,v:int\n" + << "1,10\n" + << "1,11\n" + << "2,20\n" + << "NULL,99\n"; + std::ofstream b{dir / "b.csv"}; + b << "id:int,w:int\n" + << "1,100\n" + << "1,101\n" + << "3,300\n" + << "NULL,999\n"; + } + + Expression qual = BinaryExpression{ + std::make_shared(Attribute{"a", "id"}), + BinaryOp::kEq, + std::make_shared(Attribute{"b", "id"}), + }; + PhysicalPlanNode plan = MergeJoin{ + std::make_shared(PhysicalSort{ + std::make_shared(SeqScan{"a"}), + SortOrder{{SortKey{"a", "id", Direction::kAsc}}}}), + std::make_shared(PhysicalSort{ + std::make_shared(SeqScan{"b"}), + SortOrder{{SortKey{"b", "id", Direction::kAsc}}}}), + JoinType::kFull, + qual, + }; + + boost::asio::io_context ctx; + CsvDirSequentialScanner seq_scan{dir.string()}; + Executor executor(std::move(seq_scan), ctx.get_executor()); + auto fut = boost::asio::co_spawn(ctx, executor.Execute(plan), boost::asio::use_future); + ctx.run(); + auto got = fut.get(); + + ASSERT_THAT(got.value().tuples, + Eq(Tuples{ + Tuple{Value{false, 1}, Value{false, 10}, Value{false, 1}, Value{false, 100}}, + Tuple{Value{false, 1}, Value{false, 10}, Value{false, 1}, Value{false, 101}}, + Tuple{Value{false, 1}, Value{false, 11}, Value{false, 1}, Value{false, 100}}, + Tuple{Value{false, 1}, Value{false, 11}, Value{false, 1}, Value{false, 101}}, + Tuple{Value{false, 2}, Value{false, 20}, Value{true}, Value{true}}, + Tuple{Value{true}, Value{false, 99}, Value{true}, Value{true}}, + Tuple{Value{true}, Value{true}, Value{false, 3}, Value{false, 300}}, + Tuple{Value{true}, Value{true}, Value{true}, Value{false, 999}}, + })); + std::filesystem::remove_all(dir); +} + +TEST(ExecutorTest, MergeJoinStringKeys) { + auto dir = std::filesystem::temp_directory_path() / "iu9_sql_merge_join_string_test"; + std::filesystem::remove_all(dir); + std::filesystem::create_directories(dir); + { + std::ofstream lhs{dir / "lhs.csv"}; + lhs << "name:string,v:int\n" + << "alpha,1\n" + << "beta,2\n"; + std::ofstream rhs{dir / "rhs.csv"}; + rhs << "name:string,w:int\n" + << "alpha,10\n" + << "gamma,30\n"; + } + + Expression qual = BinaryExpression{ + std::make_shared(Attribute{"lhs", "name"}), + BinaryOp::kEq, + std::make_shared(Attribute{"rhs", "name"}), + }; + PhysicalPlanNode plan = MergeJoin{ + std::make_shared(PhysicalSort{ + std::make_shared(SeqScan{"lhs"}), + SortOrder{{SortKey{"lhs", "name", Direction::kAsc}}}}), + std::make_shared(PhysicalSort{ + std::make_shared(SeqScan{"rhs"}), + SortOrder{{SortKey{"rhs", "name", Direction::kAsc}}}}), + JoinType::kInner, + qual, + }; + + boost::asio::io_context ctx; + CsvDirSequentialScanner seq_scan{dir.string()}; + Executor executor(std::move(seq_scan), ctx.get_executor()); + auto fut = boost::asio::co_spawn(ctx, executor.Execute(plan), boost::asio::use_future); + ctx.run(); + auto got = fut.get(); + + ASSERT_THAT(got.value().tuples.size(), Eq(1u)); + ASSERT_THAT(GetInternedString(got.value().tuples[0][0].value.string_id), Eq("alpha")); + ASSERT_THAT(GetInternedString(got.value().tuples[0][2].value.string_id), Eq("alpha")); + ASSERT_THAT(got.value().tuples[0][1], Eq(Value{false, 1})); + ASSERT_THAT(got.value().tuples[0][3], Eq(Value{false, 10})); + std::filesystem::remove_all(dir); +} + TEST(ExecutorTest, StringFilterProjectionAndCsvQuotes) { boost::asio::io_context ctx; boost::asio::co_spawn( diff --git a/src/stewkk/sql/logic/implementation_rules/implement_merge_join.cpp b/src/stewkk/sql/logic/implementation_rules/implement_merge_join.cpp new file mode 100644 index 0000000..2d56b8f --- /dev/null +++ b/src/stewkk/sql/logic/implementation_rules/implement_merge_join.cpp @@ -0,0 +1,28 @@ +#include + +namespace stewkk::sql { + +namespace { + +bool IsSimpleEquiJoin(const Expression& qual) { + const auto* bin = std::get_if(&qual); + if (!bin || bin->binop != BinaryOp::kEq) return false; + return std::holds_alternative(*bin->lhs) + && std::holds_alternative(*bin->rhs); +} + +} // namespace + +bool ImplementMergeJoin::IsApplicable(utils::NotNull expr) { + if (!std::holds_alternative(expr->root_operator)) return false; + const auto& join = std::get(expr->root_operator); + return IsSimpleEquiJoin(join.qual); +} + +utils::NotNull ImplementMergeJoin::Apply(utils::NotNull expr, Memo&) { + auto& join = std::get(expr->root_operator); + return expr->group->AddPhysicalExpr( + physical::MergeJoin{join.lhs, join.rhs, join.type, join.qual}); +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index d1a1698..2ae8eee 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -4,6 +4,7 @@ #include #include #include +#include #include @@ -26,9 +27,56 @@ static const std::vector> kEnforcers = [] { return v; }(); +struct JoinKeys { + Attribute lhs; + Attribute rhs; +}; + +bool AttrMatchesSchema(const Attribute& attr, const Attribute& schema_attr) { + return attr.name == schema_attr.name + && (attr.table.empty() || attr.table == schema_attr.table); +} + +bool AttrInSchema(const Attribute& attr, const Schema& schema) { + return std::ranges::any_of(schema, [&](const Attribute& schema_attr) { + return AttrMatchesSchema(attr, schema_attr); + }); +} + +std::optional ResolveJoinKeys(const Expression& qual, + utils::NotNull lhs, + utils::NotNull rhs, + SchemaCatalog& schema) { + const auto* bin = std::get_if(&qual); + if (!bin || bin->binop != BinaryOp::kEq) return std::nullopt; + const auto* a = std::get_if(bin->lhs.get()); + const auto* b = std::get_if(bin->rhs.get()); + if (!a || !b) return std::nullopt; + + auto lhs_schema = schema.GetSchema(lhs); + auto rhs_schema = schema.GetSchema(rhs); + + const bool a_lhs = AttrInSchema(*a, lhs_schema); + const bool a_rhs = AttrInSchema(*a, rhs_schema); + const bool b_lhs = AttrInSchema(*b, lhs_schema); + const bool b_rhs = AttrInSchema(*b, rhs_schema); + + if (a_lhs && !a_rhs && b_rhs && !b_lhs) { + return JoinKeys{*a, *b}; + } + if (b_lhs && !b_rhs && a_rhs && !a_lhs) { + return JoinKeys{*b, *a}; + } + return std::nullopt; +} + +PropertySet SortOn(const Attribute& attr) { + return PropertySet{SortProperty{SortOrder{{SortKey{attr.table, attr.name, Direction::kAsc}}}}}; +} PropertySet RequiredInputProps(utils::NotNull expr, - PropertySet required, size_t child_index) { + PropertySet required, size_t child_index, + SchemaCatalog& schema) { return std::visit(utils::Overloaded{ [&](const physical::SeqScan&) { return PropertySet::Any(); }, [&](const physical::IndexSeek&) { return PropertySet::Any(); }, @@ -39,13 +87,19 @@ PropertySet RequiredInputProps(utils::NotNull expr, [&](const physical::HashJoin&) -> PropertySet { return PropertySet::Any(); }, + [&](const physical::MergeJoin& j) -> PropertySet { + auto keys = ResolveJoinKeys(j.qual, j.lhs, j.rhs, schema); + if (!keys) return PropertySet::Any(); + return SortOn(child_index == 0 ? keys->lhs : keys->rhs); + }, [&](const physical::Sort&) { return PropertySet::Any(); }, [&](const physical::Aggregation&) { return PropertySet::Any(); }, }, expr->root_operator); } PropertySet DeriveOutputProps(utils::NotNull expr, - const std::vector& child_delivered) { + const std::vector& child_delivered, + SchemaCatalog& schema) { return std::visit(utils::Overloaded{ [&](const physical::SeqScan&) { return PropertySet::Any(); }, [&](const physical::IndexSeek&) { return PropertySet::Any(); }, @@ -54,6 +108,11 @@ PropertySet DeriveOutputProps(utils::NotNull expr, [&](const physical::NestedLoopJoin&) { return PropertySet::Any(); }, [&](const physical::NestedLoopCrossJoin&) { return PropertySet::Any(); }, [&](const physical::HashJoin&) { return PropertySet::Any(); }, + [&](const physical::MergeJoin& j) -> PropertySet { + auto keys = ResolveJoinKeys(j.qual, j.lhs, j.rhs, schema); + if (!keys) return PropertySet::Any(); + return SortOn(j.type == JoinType::kRight ? keys->rhs : keys->lhs); + }, [&](const physical::Sort& s) { return PropertySet{SortProperty{s.keys}}; }, [&](const physical::Aggregation&) { return PropertySet::Any(); }, }, expr->root_operator); @@ -70,6 +129,15 @@ int64_t HashJoinCost(utils::NotNull build, utils::NotNull probe, + kTupleCopy * cardinality.GetCardinality(output) * schema.GetWidth(output); } +int64_t MergeJoinCost(utils::NotNull lhs, utils::NotNull rhs, + utils::NotNull output, CardinalityEstimates& cardinality, + SchemaCatalog& schema) { + constexpr int64_t kMergeRead = 18; + constexpr int64_t kTupleCopy = 10; + return kMergeRead * (cardinality.GetCardinality(lhs) + cardinality.GetCardinality(rhs)) + + kTupleCopy * cardinality.GetCardinality(output) * schema.GetWidth(output); +} + int64_t LowerBoundLocalCost(utils::NotNull expr, CardinalityEstimates& cardinality, SchemaCatalog& schema) { return std::visit(utils::Overloaded{ @@ -91,11 +159,15 @@ int64_t LowerBoundLocalCost(utils::NotNull expr, CardinalityEstima [&](const logical::Join& j) -> int64_t { auto n_l = cardinality.GetCardinality(j.lhs); auto n_r = cardinality.GetCardinality(j.rhs); - return std::min({ + std::vector alternatives{ HashJoinCost(j.lhs, j.rhs, expr->group, cardinality, schema), HashJoinCost(j.rhs, j.lhs, expr->group, cardinality, schema), 70 * n_l * n_r, - }); + }; + if (ResolveJoinKeys(j.qual, j.lhs, j.rhs, schema)) { + alternatives.push_back(MergeJoinCost(j.lhs, j.rhs, expr->group, cardinality, schema)); + } + return *std::ranges::min_element(alternatives); }, }, expr->root_operator); } @@ -130,6 +202,12 @@ int64_t CalcCost(utils::NotNull expr, CardinalityEstimates& cardi [&](const physical::HashJoin& j) -> int64_t { return HashJoinCost(j.lhs, j.rhs, expr->group, cardinality, schema); }, + [&](const physical::MergeJoin& j) -> int64_t { + if (!ResolveJoinKeys(j.qual, j.lhs, j.rhs, schema)) { + return std::numeric_limits::max() / 4; + } + return MergeJoinCost(j.lhs, j.rhs, expr->group, cardinality, schema); + }, [&](const physical::Sort& s) -> int64_t { auto n = cardinality.GetCardinality(s.input); return 11 * (n > 1 ? n * static_cast(std::bit_width(static_cast(n))) : n); @@ -188,6 +266,9 @@ std::vector> GetChildren(utils::NotNull ex [](const physical::HashJoin& j) -> std::vector> { return {j.lhs, j.rhs}; }, + [](const physical::MergeJoin& j) -> std::vector> { + return {j.lhs, j.rhs}; + }, [](const physical::Sort& s) -> std::vector> { return {s.input}; }, @@ -253,7 +334,7 @@ void Optimizer::OptimizeInputs( if (limit && accum >= *limit) return; auto children = GetChildren(expr); if (child_index >= children.size()) { - auto delivered = DeriveOutputProps(expr, child_delivered); + auto delivered = DeriveOutputProps(expr, child_delivered, schema_); if (!delivered.Satisfies(required)) return; WinnerKey key{expr->group.get(), required}; if (!winner_.contains(key) || accum < winner_.at(key).cost) { @@ -264,7 +345,7 @@ void Optimizer::OptimizeInputs( } auto child = children[child_index]; - auto child_required = RequiredInputProps(expr, required, child_index); + auto child_required = RequiredInputProps(expr, required, child_index, schema_); tasks_.emplace([this, expr, child, child_index, required, child_delivered, accum, limit, child_required]() mutable { WinnerKey child_key{child.get(), child_required}; @@ -430,7 +511,7 @@ PhysicalPlanNode Optimizer::BuildOptimalPlan(G [this, best_expr_nn, required](const physical::Projection& op) -> PhysicalPlanNode { return PhysicalProjection{ .source = std::make_shared( - BuildOptimalPlan(op.source.get(), RequiredInputProps(best_expr_nn, required, 0))), + BuildOptimalPlan(op.source.get(), RequiredInputProps(best_expr_nn, required, 0, schema_))), .expressions = op.expressions, .aliases = op.aliases, }; @@ -438,16 +519,16 @@ PhysicalPlanNode Optimizer::BuildOptimalPlan(G [this, best_expr_nn, required](const physical::Filter& op) -> PhysicalPlanNode { return PhysicalFilter{ .source = std::make_shared( - BuildOptimalPlan(op.source.get(), RequiredInputProps(best_expr_nn, required, 0))), + BuildOptimalPlan(op.source.get(), RequiredInputProps(best_expr_nn, required, 0, schema_))), .predicate = op.predicate, }; }, [this, best_expr_nn, required](const physical::NestedLoopJoin& op) -> PhysicalPlanNode { return NestedLoopJoin{ .lhs = std::make_shared( - BuildOptimalPlan(op.lhs.get(), RequiredInputProps(best_expr_nn, required, 0))), + BuildOptimalPlan(op.lhs.get(), RequiredInputProps(best_expr_nn, required, 0, schema_))), .rhs = std::make_shared( - BuildOptimalPlan(op.rhs.get(), RequiredInputProps(best_expr_nn, required, 1))), + BuildOptimalPlan(op.rhs.get(), RequiredInputProps(best_expr_nn, required, 1, schema_))), .type = op.type, .qual = op.qual, }; @@ -455,17 +536,27 @@ PhysicalPlanNode Optimizer::BuildOptimalPlan(G [this, best_expr_nn, required](const physical::NestedLoopCrossJoin& op) -> PhysicalPlanNode { return NestedLoopCrossJoin{ .lhs = std::make_shared( - BuildOptimalPlan(op.lhs.get(), RequiredInputProps(best_expr_nn, required, 0))), + BuildOptimalPlan(op.lhs.get(), RequiredInputProps(best_expr_nn, required, 0, schema_))), .rhs = std::make_shared( - BuildOptimalPlan(op.rhs.get(), RequiredInputProps(best_expr_nn, required, 1))), + BuildOptimalPlan(op.rhs.get(), RequiredInputProps(best_expr_nn, required, 1, schema_))), }; }, [this, best_expr_nn, required](const physical::HashJoin& op) -> PhysicalPlanNode { return HashJoin{ .lhs = std::make_shared( - BuildOptimalPlan(op.lhs.get(), RequiredInputProps(best_expr_nn, required, 0))), + BuildOptimalPlan(op.lhs.get(), RequiredInputProps(best_expr_nn, required, 0, schema_))), + .rhs = std::make_shared( + BuildOptimalPlan(op.rhs.get(), RequiredInputProps(best_expr_nn, required, 1, schema_))), + .type = op.type, + .qual = op.qual, + }; + }, + [this, best_expr_nn, required](const physical::MergeJoin& op) -> PhysicalPlanNode { + return MergeJoin{ + .lhs = std::make_shared( + BuildOptimalPlan(op.lhs.get(), RequiredInputProps(best_expr_nn, required, 0, schema_))), .rhs = std::make_shared( - BuildOptimalPlan(op.rhs.get(), RequiredInputProps(best_expr_nn, required, 1))), + BuildOptimalPlan(op.rhs.get(), RequiredInputProps(best_expr_nn, required, 1, schema_))), .type = op.type, .qual = op.qual, }; @@ -473,14 +564,14 @@ PhysicalPlanNode Optimizer::BuildOptimalPlan(G [this, best_expr_nn, required](const physical::Sort& op) -> PhysicalPlanNode { return PhysicalSort{ .source = std::make_shared( - BuildOptimalPlan(op.input.get(), RequiredInputProps(best_expr_nn, required, 0))), + BuildOptimalPlan(op.input.get(), RequiredInputProps(best_expr_nn, required, 0, schema_))), .keys = op.keys, }; }, [this, best_expr_nn, required](const physical::Aggregation& op) -> PhysicalPlanNode { return PhysicalAggregation{ .source = std::make_shared( - BuildOptimalPlan(op.source.get(), RequiredInputProps(best_expr_nn, required, 0))), + BuildOptimalPlan(op.source.get(), RequiredInputProps(best_expr_nn, required, 0, schema_))), .group_by = op.group_by, .aggregates = op.aggregates, }; @@ -544,7 +635,7 @@ utils::NotNull Optimizer::GetRootGroup return root_->group; } -template class Optimizer<7, 8>; +template class Optimizer<7, 9>; template class Optimizer<0, 6>; } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index edbf3e0..b2118b5 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -1,6 +1,7 @@ #include #include +#include #include #include @@ -33,6 +34,20 @@ int64_t EstimateCardinality( return cardinality.GetCardinality(root->group); } +SchemaCatalog MakeTestSchema() { + return SchemaCatalog({ + {"users", {Attribute{"users", "id"}, Attribute{"users", "age"}, + Attribute{"users", "name"}}}, + {"orders", {Attribute{"orders", "id"}, Attribute{"orders", "user_id"}, + Attribute{"orders", "customer_id"}, Attribute{"orders", "total"}}}, + {"customers", {Attribute{"customers", "id"}, Attribute{"customers", "region_id"}}}, + {"regions", {Attribute{"regions", "id"}}}, + {"departments", {Attribute{"departments", "id"}}}, + {"lineorder", {Attribute{"lineorder", "suppkey"}, Attribute{"lineorder", "value"}}}, + {"supplier", {Attribute{"supplier", "id"}, Attribute{"supplier", "region"}}}, + }); +} + TEST(CardinalityEstimatesTest, AppliesFilterHeuristics) { ASSERT_THAT(EstimateCardinality("SELECT * FROM users WHERE users.id = 1;", {{"users", 1000}}), Eq(100)); @@ -59,10 +74,19 @@ TEST(SchemaCatalogTest, DerivesJoinWidth) { ASSERT_THAT(schema.GetWidth(root->group), Eq(5)); } +TEST(SchemaCatalogTest, MissingTableSchemaThrows) { + std::stringstream s{"SELECT * FROM users;"}; + Memo memo; + auto root = memo.Populate(GetAST(s).value().op); + SchemaCatalog schema; + + ASSERT_THROW(schema.GetWidth(root->group), std::runtime_error); +} + TEST(OptimizerTest, Simple) { std::stringstream s{"SELECT * FROM users;"}; Operator op = GetAST(s).value().op; - Optimizer optimizer(op, MakeMainRules()); + Optimizer optimizer(op, MakeMainRules(), {}, MakeTestSchema()); auto got = optimizer.Optimize(); @@ -79,7 +103,7 @@ TEST(OptimizerTest, JoinCommutativity) { Optimizer optimizer(op, MakeMainRules(), CardinalityEstimates({ {"users", 10000}, {"orders", 100}, - })); + }), MakeTestSchema()); auto got = optimizer.Optimize(); @@ -97,7 +121,7 @@ TEST(OptimizerTest, MultiwayJoinOCR) { {"regions", 10}, {"customers", 500}, {"orders", 5000}, - })); + }), MakeTestSchema()); auto got = optimizer.Optimize(); @@ -119,7 +143,7 @@ TEST(OptimizerTest, MultiwayJoinROC) { {"regions", 10}, {"customers", 500}, {"orders", 5000}, - })); + }), MakeTestSchema()); auto got = optimizer.Optimize(); @@ -189,7 +213,7 @@ TEST(OptimizerTest, AliasedJoinOptimizesWithAliasQualifiedAttrs) { Optimizer optimizer(op, MakeMainRules(), CardinalityEstimates({ {"customers", 500}, {"orders", 5000}, - })); + }), MakeTestSchema()); auto got = optimizer.Optimize(); auto serialized = Serialize(got); @@ -229,7 +253,7 @@ TEST(OptimizerTest, UsesIndexSeekForIndexedIntegerPredicate) { Operator op = GetAST(s).value().op; Optimizer optimizer(op, MakeMainRules(IndexCatalog({ IndexInfo{.table = "users", .column = "id", .type = "sorted", .file = "users.id.sorted.idx"}, - })), CardinalityEstimates({{"users", 1000}})); + })), CardinalityEstimates({{"users", 1000}}), MakeTestSchema()); auto got = optimizer.Optimize(); @@ -239,7 +263,8 @@ TEST(OptimizerTest, UsesIndexSeekForIndexedIntegerPredicate) { TEST(OptimizerTest, KeepsSequentialFilterWhenIndexMetadataIsAbsent) { std::stringstream s{"SELECT * FROM users WHERE users.id = 8;"}; Operator op = GetAST(s).value().op; - Optimizer optimizer(op, MakeMainRules(), CardinalityEstimates({{"users", 1000}})); + Optimizer optimizer(op, MakeMainRules(), CardinalityEstimates({{"users", 1000}}), + MakeTestSchema()); auto got = optimizer.Optimize(); @@ -252,7 +277,7 @@ TEST(OptimizerTest, IndexSeekSupportsAliasAndFullResidualPredicate) { Operator op = GetAST(s).value().op; Optimizer optimizer(op, MakeMainRules(IndexCatalog({ IndexInfo{.table = "users", .column = "id", .type = "sorted", .file = "users.id.sorted.idx"}, - })), CardinalityEstimates({{"users", 1000}})); + })), CardinalityEstimates({{"users", 1000}}), MakeTestSchema()); auto got = optimizer.Optimize(); @@ -292,13 +317,13 @@ TEST(OptimizerTest, NaiveRulesDisableLogicalTransformations) { TEST(ReachabilityTest, SeqScanReachable) { std::stringstream s{"SELECT * FROM users;"}; - auto result = IsPlanReachable(s, SeqScan{"users"}); + auto result = IsPlanReachable(s, SeqScan{"users"}, {}, MakeTestSchema()); ASSERT_THAT(result.reachable, IsTrue()); } TEST(ReachabilityTest, SeqScanWrongTable) { std::stringstream s{"SELECT * FROM users;"}; - auto result = IsPlanReachable(s, SeqScan{"orders"}); + auto result = IsPlanReachable(s, SeqScan{"orders"}, {}, MakeTestSchema()); ASSERT_THAT(result.reachable, IsFalse()); ASSERT_THAT(result.mismatch, HasSubstr("users")); } @@ -310,7 +335,7 @@ TEST(ReachabilityTest, IndexSeekReachableWithIndexCatalog) { BinaryOp::kGe, std::make_shared(IntConst{8})}; auto result = IsPlanReachable( - s, IndexSeek{"users", std::nullopt, predicate}, {}, {}, + s, IndexSeek{"users", std::nullopt, predicate}, {}, MakeTestSchema(), IndexCatalog({ IndexInfo{.table = "users", .column = "id", .type = "sorted", .file = "users.id.sorted.idx"}, @@ -328,7 +353,7 @@ TEST(ReachabilityTest, WrongOperatorType) { std::make_shared(SeqScan{"users"}), std::make_shared(SeqScan{"orders"}), JoinType::kInner, - qual}); + qual}, {}, MakeTestSchema()); ASSERT_THAT(result.reachable, IsFalse()); } @@ -344,13 +369,13 @@ TEST(ReachabilityTest, BothJoinOrdersReachable) { auto optimal = IsPlanReachable(s1, NestedLoopJoin{ std::make_shared(SeqScan{"orders"}), std::make_shared(SeqScan{"users"}), - JoinType::kInner, qual}, cardinality); + JoinType::kInner, qual}, cardinality, MakeTestSchema()); ASSERT_THAT(optimal.reachable, IsTrue()); auto suboptimal = IsPlanReachable(s2, NestedLoopJoin{ std::make_shared(SeqScan{"users"}), std::make_shared(SeqScan{"orders"}), - JoinType::kInner, qual}, cardinality); + JoinType::kInner, qual}, cardinality, MakeTestSchema()); ASSERT_THAT(suboptimal.reachable, IsTrue()); } @@ -363,7 +388,50 @@ TEST(ReachabilityTest, HashJoinReachable) { auto result = IsPlanReachable(s, HashJoin{ std::make_shared(SeqScan{"orders"}), std::make_shared(SeqScan{"users"}), - JoinType::kInner, qual}); + JoinType::kInner, qual}, {}, MakeTestSchema()); + ASSERT_THAT(result.reachable, IsTrue()); +} + +TEST(ReachabilityTest, MergeJoinReachableWithSortedInputs) { + std::stringstream s{"SELECT * FROM users JOIN orders ON users.id = orders.user_id;"}; + Expression qual = BinaryExpression{ + std::make_shared(Attribute{"users", "id"}), + BinaryOp::kEq, + std::make_shared(Attribute{"orders", "user_id"})}; + SchemaCatalog schema({ + {"users", {Attribute{"users", "id"}}}, + {"orders", {Attribute{"orders", "user_id"}}}, + }); + auto result = IsPlanReachable(s, MergeJoin{ + std::make_shared(PhysicalSort{ + std::make_shared(SeqScan{"users"}), + SortOrder{{SortKey{"users", "id", Direction::kAsc}}}}), + std::make_shared(PhysicalSort{ + std::make_shared(SeqScan{"orders"}), + SortOrder{{SortKey{"orders", "user_id", Direction::kAsc}}}}), + JoinType::kInner, qual}, {}, std::move(schema)); + ASSERT_THAT(result.reachable, IsTrue()); +} + +TEST(ReachabilityTest, MergeJoinCanSatisfyOrderByJoinKey) { + std::stringstream s{ + "SELECT * FROM users JOIN orders ON users.id = orders.user_id ORDER BY users.id;"}; + Expression qual = BinaryExpression{ + std::make_shared(Attribute{"users", "id"}), + BinaryOp::kEq, + std::make_shared(Attribute{"orders", "user_id"})}; + SchemaCatalog schema({ + {"users", {Attribute{"users", "id"}}}, + {"orders", {Attribute{"orders", "user_id"}}}, + }); + auto result = IsPlanReachable(s, MergeJoin{ + std::make_shared(PhysicalSort{ + std::make_shared(SeqScan{"users"}), + SortOrder{{SortKey{"users", "id", Direction::kAsc}}}}), + std::make_shared(PhysicalSort{ + std::make_shared(SeqScan{"orders"}), + SortOrder{{SortKey{"orders", "user_id", Direction::kAsc}}}}), + JoinType::kInner, qual}, {}, std::move(schema)); ASSERT_THAT(result.reachable, IsTrue()); } @@ -376,7 +444,7 @@ TEST(ReachabilityTest, WrongJoinQual) { auto result = IsPlanReachable(s, NestedLoopJoin{ std::make_shared(SeqScan{"orders"}), std::make_shared(SeqScan{"users"}), - JoinType::kInner, wrong_qual}); + JoinType::kInner, wrong_qual}, {}, MakeTestSchema()); ASSERT_THAT(result.reachable, IsFalse()); ASSERT_THAT(result.mismatch, HasSubstr("qual")); } @@ -403,7 +471,7 @@ TEST(ReachabilityTest, InExpandsToOrChain) { BinaryOp::kOr, std::make_shared(std::move(eq3))}; auto result = IsPlanReachable(s, PhysicalFilter{ - std::make_shared(SeqScan{"users"}), or_chain}); + std::make_shared(SeqScan{"users"}), or_chain}, {}, MakeTestSchema()); ASSERT_THAT(result.reachable, IsTrue()); } @@ -417,7 +485,7 @@ TEST(ReachabilityTest, OrderByReachableViaSortEnforcer) { {Attribute{"users", "id"}}, {}}), SortOrder{{SortKey{"users", "id", Direction::kAsc}}}}; - auto result = IsPlanReachable(s, target); + auto result = IsPlanReachable(s, target, {}, MakeTestSchema()); ASSERT_THAT(result.reachable, IsTrue()); } @@ -431,7 +499,7 @@ TEST(ReachabilityTest, OrderByWrongDirectionNotReachable) { {Attribute{"users", "id"}}, {}}), SortOrder{{SortKey{"users", "id", Direction::kDesc}}}}; - auto result = IsPlanReachable(s, target); + auto result = IsPlanReachable(s, target, {}, MakeTestSchema()); ASSERT_THAT(result.reachable, IsFalse()); } @@ -445,7 +513,7 @@ TEST(ReachabilityTest, SortNotReachableWithoutOrderBy) { {Attribute{"users", "id"}}, {}}), SortOrder{{SortKey{"users", "id", Direction::kAsc}}}}; - auto result = IsPlanReachable(s, target); + auto result = IsPlanReachable(s, target, {}, MakeTestSchema()); ASSERT_THAT(result.reachable, IsFalse()); } diff --git a/src/stewkk/sql/logic/optimizer/reachability.cpp b/src/stewkk/sql/logic/optimizer/reachability.cpp index cbf1497..5a51448 100644 --- a/src/stewkk/sql/logic/optimizer/reachability.cpp +++ b/src/stewkk/sql/logic/optimizer/reachability.cpp @@ -113,6 +113,21 @@ InternalMatch TryMatchExpr(utils::NotNull pe, if (!rhs.ok) { rhs.reason = "HashJoin.rhs: " + rhs.reason; return rhs; } return {true, std::max(lhs.depth, rhs.depth), {}}; }, + [&](const physical::MergeJoin& op) -> InternalMatch { + const auto* t = std::get_if(&target.node); + if (!t) return {false, depth, "type mismatch: expected MergeJoin"}; + if (op.type != t->type) + return {false, depth + 1, "MergeJoin join type mismatch"}; + if (op.qual != t->qual) + return {false, depth + 1, + std::format("MergeJoin qual '{}' != '{}'", + ToString(op.qual), ToString(t->qual))}; + auto lhs = MatchGroup(op.lhs.get(), *t->lhs, depth + 1); + if (!lhs.ok) { lhs.reason = "MergeJoin.lhs: " + lhs.reason; return lhs; } + auto rhs = MatchGroup(op.rhs.get(), *t->rhs, depth + 1); + if (!rhs.ok) { rhs.reason = "MergeJoin.rhs: " + rhs.reason; return rhs; } + return {true, std::max(lhs.depth, rhs.depth), {}}; + }, [&](const physical::Sort& op) -> InternalMatch { const auto* t = std::get_if(&target.node); if (!t) return {false, depth, "type mismatch: expected Sort"}; diff --git a/src/stewkk/sql/logic/optimizer/rules.cpp b/src/stewkk/sql/logic/optimizer/rules.cpp index 47a4839..dadda82 100644 --- a/src/stewkk/sql/logic/optimizer/rules.cpp +++ b/src/stewkk/sql/logic/optimizer/rules.cpp @@ -4,7 +4,7 @@ namespace stewkk::sql { -Rules<7, 8> MakeMainRules(IndexCatalog indexes) { +Rules<7, 9> MakeMainRules(IndexCatalog indexes) { return { .transformation_rules = { std::make_unique(), @@ -23,6 +23,7 @@ Rules<7, 8> MakeMainRules(IndexCatalog indexes) { std::make_unique(), std::make_unique(), std::make_unique(), + std::make_unique(), std::make_unique(), }, }; diff --git a/src/stewkk/sql/logic/optimizer/rules_applier.cpp b/src/stewkk/sql/logic/optimizer/rules_applier.cpp index 82abf06..783aed1 100644 --- a/src/stewkk/sql/logic/optimizer/rules_applier.cpp +++ b/src/stewkk/sql/logic/optimizer/rules_applier.cpp @@ -30,7 +30,7 @@ utils::NotNull RulesApplier::Ap return rules_.implementation_rules[rule.value]->Apply(expr, memo); } -template class RulesApplier<7, 8>; +template class RulesApplier<7, 9>; template class RulesApplier<0, 6>; } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/schema_catalog.cpp b/src/stewkk/sql/logic/optimizer/schema_catalog.cpp index 9591609..e0a36fe 100644 --- a/src/stewkk/sql/logic/optimizer/schema_catalog.cpp +++ b/src/stewkk/sql/logic/optimizer/schema_catalog.cpp @@ -5,6 +5,7 @@ #include #include #include +#include #include #include @@ -24,7 +25,7 @@ bool IndexCatalog::HasSortedIndex(const std::string& table, const std::string& c SchemaCatalog::SchemaCatalog(std::unordered_map tables) : tables_(std::move(tables)) {} -std::optional SchemaCatalog::GetSchema(utils::NotNull group) { +Schema SchemaCatalog::GetSchema(utils::NotNull group) { if (auto it = cache_.find(group.get()); it != cache_.end()) { return it->second; } @@ -34,27 +35,28 @@ std::optional SchemaCatalog::GetSchema(utils::NotNull group) { } std::int64_t SchemaCatalog::GetWidth(utils::NotNull group) { - auto schema = GetSchema(group); - return schema ? std::max(1, schema->size()) : 1; + return std::max(1, GetSchema(group).size()); } // TODO: refactor to remove duplicate logic: both executor and optimizer derive // attributes -std::optional SchemaCatalog::Derive(const LogicalOperator& op) { +Schema SchemaCatalog::Derive(const LogicalOperator& op) { return std::visit(utils::Overloaded{ - [this](const logical::Table& t) -> std::optional { + [this](const logical::Table& t) -> Schema { auto it = tables_.find(t.name); - if (it == tables_.end()) return std::nullopt; + if (it == tables_.end()) { + throw std::runtime_error{std::format("missing schema for table '{}'", t.name)}; + } auto schema = it->second; for (auto& attr : schema) { attr.table = std::string{VisibleName(t)}; } return schema; }, - [this](const logical::Filter& f) -> std::optional { + [this](const logical::Filter& f) -> Schema { return GetSchema(f.source); }, - [](const logical::Projection& p) -> std::optional { + [](const logical::Projection& p) -> Schema { Schema out; for (size_t i = 0; i < p.expressions.size(); ++i) { if (i < p.aliases.size() && p.aliases[i]) { @@ -65,9 +67,8 @@ std::optional SchemaCatalog::Derive(const LogicalOperator& op) { } return out; }, - [this](const logical::Aggregation& a) -> std::optional { - auto input = GetSchema(a.source); - if (!input) return std::nullopt; + [this](const logical::Aggregation& a) -> Schema { + GetSchema(a.source); Schema out; for (const auto& expr : a.group_by) { if (const auto* attr = std::get_if(&expr)) { @@ -79,18 +80,16 @@ std::optional SchemaCatalog::Derive(const LogicalOperator& op) { } return out; }, - [this](const logical::CrossJoin& j) -> std::optional { + [this](const logical::CrossJoin& j) -> Schema { auto l = GetSchema(j.lhs); auto r = GetSchema(j.rhs); - if (!l || !r) return std::nullopt; - l->insert(l->end(), r->begin(), r->end()); + l.insert(l.end(), r.begin(), r.end()); return l; }, - [this](const logical::Join& j) -> std::optional { + [this](const logical::Join& j) -> Schema { auto l = GetSchema(j.lhs); auto r = GetSchema(j.rhs); - if (!l || !r) return std::nullopt; - l->insert(l->end(), r->begin(), r->end()); + l.insert(l.end(), r.begin(), r.end()); return l; }, }, op); diff --git a/src/stewkk/sql/logic/optimizer/sort_enforcer.cpp b/src/stewkk/sql/logic/optimizer/sort_enforcer.cpp index 694111a..d5c0060 100644 --- a/src/stewkk/sql/logic/optimizer/sort_enforcer.cpp +++ b/src/stewkk/sql/logic/optimizer/sort_enforcer.cpp @@ -11,17 +11,16 @@ std::optional SortEnforcer::TryBuild( SchemaCatalog& schema) const { const auto* req = required.Get(); if (!req) return std::nullopt; - if (auto sch = schema.GetSchema(group)) { - for (const auto& sk : req->order.keys) { - bool found = false; - for (const auto& a : *sch) { - if (a.name == sk.column && (sk.table.empty() || a.table == sk.table)) { - found = true; - break; - } + auto sch = schema.GetSchema(group); + for (const auto& sk : req->order.keys) { + bool found = false; + for (const auto& a : sch) { + if (a.name == sk.column && (sk.table.empty() || a.table == sk.table)) { + found = true; + break; } - if (!found) return std::nullopt; } + if (!found) return std::nullopt; } return PhysicalOperator{physical::Sort{group, req->order}}; } From 7cf088ecc6a696dc43f4f20373fb4c78d418a60e Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Tue, 9 Jun 2026 22:53:08 +0300 Subject: [PATCH 099/120] Add StreamAgg --- .../stewkk/sql/logic/executor/executor.hpp | 6 +- include/stewkk/sql/logic/executor/plan.hpp | 12 +- .../sql/logic/executor/sequential_scan.hpp | 3 + .../implement_aggregation.hpp | 2 +- .../implement_cross_join.hpp | 2 +- .../implementation_rules/implement_filter.hpp | 2 +- .../implement_hash_join.hpp | 2 +- .../implement_index_seek.hpp | 2 +- .../implementation_rules/implement_join.hpp | 2 +- .../implement_merge_join.hpp | 2 +- .../implement_projection.hpp | 2 +- .../implementation_rules/implement_table.hpp | 2 +- .../sql/logic/optimizer/physical_expr.hpp | 12 +- include/stewkk/sql/logic/optimizer/rule.hpp | 3 +- .../sql/logic/optimizer/rules_applier.hpp | 2 +- src/stewkk/sql/logic/executor/executor.cpp | 140 +++++++++++++++++- .../sql/logic/executor/executor_test.cpp | 36 +++++ src/stewkk/sql/logic/executor/plan.cpp | 7 +- .../sql/logic/executor/plan_serializer.cpp | 42 +++++- .../sql/logic/executor/sequential_scan.cpp | 14 ++ .../implement_aggregation.cpp | 17 ++- .../implement_cross_join.cpp | 4 +- .../implementation_rules/implement_filter.cpp | 4 +- .../implement_hash_join.cpp | 6 +- .../implement_index_seek.cpp | 56 ++++++- .../implementation_rules/implement_join.cpp | 6 +- .../implement_merge_join.cpp | 6 +- .../implement_projection.cpp | 6 +- .../implementation_rules/implement_table.cpp | 4 +- src/stewkk/sql/logic/optimizer/optimizer.cpp | 52 ++++++- .../sql/logic/optimizer/optimizer_test.cpp | 44 ++++++ .../sql/logic/optimizer/reachability.cpp | 11 ++ .../sql/logic/optimizer/rules_applier.cpp | 2 +- 33 files changed, 462 insertions(+), 51 deletions(-) diff --git a/include/stewkk/sql/logic/executor/executor.hpp b/include/stewkk/sql/logic/executor/executor.hpp index 24eefb4..0cfc999 100644 --- a/include/stewkk/sql/logic/executor/executor.hpp +++ b/include/stewkk/sql/logic/executor/executor.hpp @@ -51,7 +51,8 @@ class Executor { AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan)>; using IndexScan = std::function>( const std::string& table_name, const std::string& output_table_name, - const Expression& predicate, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan)>; + const Expression& predicate, const std::optional& index_column, + AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan)>; Executor(SequentialScan seq_scan, boost::asio::any_io_executor executor); Executor(SequentialScan seq_scan, IndexScan index_scan, boost::asio::any_io_executor executor); @@ -79,6 +80,9 @@ class Executor { boost::asio::awaitable ExecuteHashAggregate(const PhysicalAggregation& agg, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan); + boost::asio::awaitable ExecuteStreamAggregate(const PhysicalStreamAggregation& agg, + AttributesInfoChannel& attr_chan, + TuplesChannel& tuples_chan); boost::asio::awaitable ExecuteSort(const PhysicalSort& sort, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan); boost::asio::experimental::promise SpawnExecutor( diff --git a/include/stewkk/sql/logic/executor/plan.hpp b/include/stewkk/sql/logic/executor/plan.hpp index 74692d7..41b3a6f 100644 --- a/include/stewkk/sql/logic/executor/plan.hpp +++ b/include/stewkk/sql/logic/executor/plan.hpp @@ -27,6 +27,7 @@ struct MergeJoin; struct IndexSeek; struct PhysicalSort; struct PhysicalAggregation; +struct PhysicalStreamAggregation; struct PhysicalPlanNode; struct SeqScan { @@ -91,6 +92,7 @@ struct IndexSeek { std::string table; std::optional alias; Expression predicate; + std::optional index_column = std::nullopt; bool operator==(const IndexSeek&) const; }; @@ -110,6 +112,14 @@ struct PhysicalAggregation { bool operator==(const PhysicalAggregation&) const; }; +struct PhysicalStreamAggregation { + std::shared_ptr source; + std::vector group_by; + std::vector aggregates; + + bool operator==(const PhysicalStreamAggregation&) const; +}; + struct PlanNodeMetadata { std::int64_t cardinality = 0; std::int64_t local_cost = 0; @@ -117,7 +127,7 @@ struct PlanNodeMetadata { bool operator==(const PlanNodeMetadata&) const = default; }; -using PhysicalPlanAlternative = std::variant; +using PhysicalPlanAlternative = std::variant; struct PhysicalPlanNode { PhysicalPlanAlternative node; diff --git a/include/stewkk/sql/logic/executor/sequential_scan.hpp b/include/stewkk/sql/logic/executor/sequential_scan.hpp index 543ae6b..ecaf2a8 100644 --- a/include/stewkk/sql/logic/executor/sequential_scan.hpp +++ b/include/stewkk/sql/logic/executor/sequential_scan.hpp @@ -2,6 +2,8 @@ #include +#include + #include #include #include @@ -23,6 +25,7 @@ struct CsvDirIndexedScanner { boost::asio::awaitable> operator()(const std::string& table_name, const std::string& output_table_name, const Expression& predicate, + const std::optional& index_column, AttributesInfoChannel& attrs_chan, TuplesChannel& tuples_chan) const; }; diff --git a/include/stewkk/sql/logic/implementation_rules/implement_aggregation.hpp b/include/stewkk/sql/logic/implementation_rules/implement_aggregation.hpp index b92318f..c541068 100644 --- a/include/stewkk/sql/logic/implementation_rules/implement_aggregation.hpp +++ b/include/stewkk/sql/logic/implementation_rules/implement_aggregation.hpp @@ -7,7 +7,7 @@ namespace stewkk::sql { class ImplementAggregation : public ImplementationRule { public: bool IsApplicable(utils::NotNull expr) override; - utils::NotNull Apply(utils::NotNull expr, Memo& memo) override; + std::vector> Apply(utils::NotNull expr, Memo& memo) override; }; } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/implementation_rules/implement_cross_join.hpp b/include/stewkk/sql/logic/implementation_rules/implement_cross_join.hpp index fd0bd40..5db26a0 100644 --- a/include/stewkk/sql/logic/implementation_rules/implement_cross_join.hpp +++ b/include/stewkk/sql/logic/implementation_rules/implement_cross_join.hpp @@ -7,7 +7,7 @@ namespace stewkk::sql { class ImplementCrossJoin : public ImplementationRule { public: bool IsApplicable(utils::NotNull expr) override; - utils::NotNull Apply(utils::NotNull expr, Memo& memo) override; + std::vector> Apply(utils::NotNull expr, Memo& memo) override; }; } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/implementation_rules/implement_filter.hpp b/include/stewkk/sql/logic/implementation_rules/implement_filter.hpp index ff48d4a..884618c 100644 --- a/include/stewkk/sql/logic/implementation_rules/implement_filter.hpp +++ b/include/stewkk/sql/logic/implementation_rules/implement_filter.hpp @@ -7,7 +7,7 @@ namespace stewkk::sql { class ImplementFilter : public ImplementationRule { public: bool IsApplicable(utils::NotNull expr) override; - utils::NotNull Apply(utils::NotNull expr, Memo& memo) override; + std::vector> Apply(utils::NotNull expr, Memo& memo) override; }; } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/implementation_rules/implement_hash_join.hpp b/include/stewkk/sql/logic/implementation_rules/implement_hash_join.hpp index f2a11a3..1eecab2 100644 --- a/include/stewkk/sql/logic/implementation_rules/implement_hash_join.hpp +++ b/include/stewkk/sql/logic/implementation_rules/implement_hash_join.hpp @@ -7,7 +7,7 @@ namespace stewkk::sql { class ImplementHashJoin : public ImplementationRule { public: bool IsApplicable(utils::NotNull expr) override; - utils::NotNull Apply(utils::NotNull expr, Memo& memo) override; + std::vector> Apply(utils::NotNull expr, Memo& memo) override; }; } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/implementation_rules/implement_index_seek.hpp b/include/stewkk/sql/logic/implementation_rules/implement_index_seek.hpp index 42682fa..1842ba9 100644 --- a/include/stewkk/sql/logic/implementation_rules/implement_index_seek.hpp +++ b/include/stewkk/sql/logic/implementation_rules/implement_index_seek.hpp @@ -10,7 +10,7 @@ class ImplementIndexSeek : public ImplementationRule { explicit ImplementIndexSeek(IndexCatalog indexes); bool IsApplicable(utils::NotNull expr) override; - utils::NotNull Apply(utils::NotNull expr, Memo& memo) override; + std::vector> Apply(utils::NotNull expr, Memo& memo) override; private: IndexCatalog indexes_; diff --git a/include/stewkk/sql/logic/implementation_rules/implement_join.hpp b/include/stewkk/sql/logic/implementation_rules/implement_join.hpp index 7838bef..5ec710e 100644 --- a/include/stewkk/sql/logic/implementation_rules/implement_join.hpp +++ b/include/stewkk/sql/logic/implementation_rules/implement_join.hpp @@ -7,7 +7,7 @@ namespace stewkk::sql { class ImplementJoin : public ImplementationRule { public: bool IsApplicable(utils::NotNull expr) override; - utils::NotNull Apply(utils::NotNull expr, Memo& memo) override; + std::vector> Apply(utils::NotNull expr, Memo& memo) override; }; } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/implementation_rules/implement_merge_join.hpp b/include/stewkk/sql/logic/implementation_rules/implement_merge_join.hpp index 1742d96..5a9b564 100644 --- a/include/stewkk/sql/logic/implementation_rules/implement_merge_join.hpp +++ b/include/stewkk/sql/logic/implementation_rules/implement_merge_join.hpp @@ -7,7 +7,7 @@ namespace stewkk::sql { class ImplementMergeJoin : public ImplementationRule { public: bool IsApplicable(utils::NotNull expr) override; - utils::NotNull Apply(utils::NotNull expr, Memo& memo) override; + std::vector> Apply(utils::NotNull expr, Memo& memo) override; }; } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/implementation_rules/implement_projection.hpp b/include/stewkk/sql/logic/implementation_rules/implement_projection.hpp index f0c2716..2437041 100644 --- a/include/stewkk/sql/logic/implementation_rules/implement_projection.hpp +++ b/include/stewkk/sql/logic/implementation_rules/implement_projection.hpp @@ -7,7 +7,7 @@ namespace stewkk::sql { class ImplementProjection : public ImplementationRule { public: bool IsApplicable(utils::NotNull expr) override; - utils::NotNull Apply(utils::NotNull expr, Memo& memo) override; + std::vector> Apply(utils::NotNull expr, Memo& memo) override; }; } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/implementation_rules/implement_table.hpp b/include/stewkk/sql/logic/implementation_rules/implement_table.hpp index 1d554f9..b1e96ca 100644 --- a/include/stewkk/sql/logic/implementation_rules/implement_table.hpp +++ b/include/stewkk/sql/logic/implementation_rules/implement_table.hpp @@ -7,7 +7,7 @@ namespace stewkk::sql { class ImplementTable : public ImplementationRule { public: bool IsApplicable(utils::NotNull expr) override; - utils::NotNull Apply(utils::NotNull expr, Memo& memo) override; + std::vector> Apply(utils::NotNull expr, Memo& memo) override; }; } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/physical_expr.hpp b/include/stewkk/sql/logic/optimizer/physical_expr.hpp index 2081d8f..9dd2dba 100644 --- a/include/stewkk/sql/logic/optimizer/physical_expr.hpp +++ b/include/stewkk/sql/logic/optimizer/physical_expr.hpp @@ -26,6 +26,7 @@ struct IndexSeek { std::string table; std::optional alias; Expression predicate; + std::string index_column; bool operator==(const IndexSeek&) const = default; }; @@ -94,13 +95,22 @@ struct Aggregation { bool operator==(const Aggregation&) const = default; }; +struct StreamAggregation { + utils::NotNull source; + std::vector group_by; + std::vector aggregates; + + bool operator==(const StreamAggregation&) const = default; +}; + } // namespace physical struct PhysicalExpr { std::variant root_operator; + physical::Aggregation, physical::StreamAggregation, + physical::IndexSeek> root_operator; utils::NotNull group; bool is_enforcer = false; }; diff --git a/include/stewkk/sql/logic/optimizer/rule.hpp b/include/stewkk/sql/logic/optimizer/rule.hpp index 8690454..872c2f6 100644 --- a/include/stewkk/sql/logic/optimizer/rule.hpp +++ b/include/stewkk/sql/logic/optimizer/rule.hpp @@ -1,6 +1,7 @@ #pragma once #include +#include #include #include @@ -23,7 +24,7 @@ class TransformationRule { class ImplementationRule { public: virtual bool IsApplicable(utils::NotNull expr) = 0; - virtual utils::NotNull Apply(utils::NotNull, Memo& memo) = 0; + virtual std::vector> Apply(utils::NotNull, Memo& memo) = 0; virtual ~ImplementationRule() = default; }; diff --git a/include/stewkk/sql/logic/optimizer/rules_applier.hpp b/include/stewkk/sql/logic/optimizer/rules_applier.hpp index 59aca4f..4d0bbf7 100644 --- a/include/stewkk/sql/logic/optimizer/rules_applier.hpp +++ b/include/stewkk/sql/logic/optimizer/rules_applier.hpp @@ -26,7 +26,7 @@ class RulesApplier { utils::NotNull Apply(TransformationRuleId rule, utils::NotNull expr, Memo& memo); bool IsApplicable(ImplementationRuleId rule, utils::NotNull expr); - utils::NotNull Apply(ImplementationRuleId rule, utils::NotNull expr, Memo& memo); + std::vector> Apply(ImplementationRuleId rule, utils::NotNull expr, Memo& memo); private: Rules rules_; diff --git a/src/stewkk/sql/logic/executor/executor.cpp b/src/stewkk/sql/logic/executor/executor.cpp index c16f24f..22dbded 100644 --- a/src/stewkk/sql/logic/executor/executor.cpp +++ b/src/stewkk/sql/logic/executor/executor.cpp @@ -658,6 +658,9 @@ boost::asio::awaitable Executor::Execute(const Physica boost::asio::awaitable operator()(const PhysicalAggregation& agg) const { return executor.ExecuteHashAggregate(agg, attr_chan, tuples_chan); } + boost::asio::awaitable operator()(const PhysicalStreamAggregation& agg) const { + return executor.ExecuteStreamAggregate(agg, attr_chan, tuples_chan); + } boost::asio::awaitable operator()(const MergeJoin& join) const { return executor.ExecuteMergeJoin(join, attr_chan, tuples_chan); } @@ -690,7 +693,7 @@ boost::asio::awaitable Executor::ExecuteIndexSeek( throw std::runtime_error("IndexSeek execution requested, but no index scanner is configured"); } co_await index_scan_(seek.table, seek.alias ? *seek.alias : seek.table, seek.predicate, - attr_chan, tuples_chan); + seek.index_column, attr_chan, tuples_chan); } template @@ -1568,6 +1571,141 @@ boost::asio::awaitable Executor::ExecuteHashAggregate( out_tuples_chan.close(); } +template +boost::asio::awaitable Executor::ExecuteStreamAggregate( + const PhysicalStreamAggregation& agg, AttributesInfoChannel& out_attr_chan, + TuplesChannel& out_tuples_chan) { + Log("Executing stream aggregate"); + auto close_on_fail = boost::scope::make_scope_fail( + [&] { out_attr_chan.close(); out_tuples_chan.close(); }); + auto exec = co_await boost::asio::this_coro::executor; + auto [in_attrs_chan, in_tuples_chan] = co_await GetChannels(); + auto task = SpawnExecutor(exec, *agg.source, in_attrs_chan, in_tuples_chan); + + auto in_attrs = co_await in_attrs_chan.async_receive(boost::asio::use_awaitable); + + AttributesInfo out_attrs; + for (const auto& expr : agg.group_by) { + if (const auto* attr = std::get_if(&expr)) { + auto it = std::find_if(in_attrs.begin(), in_attrs.end(), [&](const AttributeInfo& ai) { + return ai.table == attr->table && ai.name == attr->name; + }); + if (it != in_attrs.end()) { + out_attrs.push_back(*it); + } + } + } + for (size_t i = 0; i < agg.aggregates.size(); ++i) { + const auto& agg_expr = std::get(agg.aggregates[i]); + Type t = (agg_expr.function == AggregateFunction::kCount) + ? Type::kInt + : (agg_expr.is_star ? Type::kInt : GetExpressionTypeUnchecked(*agg_expr.argument, in_attrs)); + out_attrs.push_back(AttributeInfo{"", std::format("__agg{}", i), t}); + } + co_await out_attr_chan.async_send(boost::system::error_code{}, out_attrs, + boost::asio::use_awaitable); + out_attr_chan.close(); + + struct GroupState { + std::vector accumulators; + std::vector any_non_null; + }; + + auto do_scalar = [&](const Expression& expr, const Tuple& tuple) -> Value { + return CalcExpression(tuple, in_attrs, expr); + }; + auto init_state = [&]() -> GroupState { + GroupState s; + s.accumulators.resize(agg.aggregates.size(), 0); + s.any_non_null.resize(agg.aggregates.size(), false); + return s; + }; + auto update_state = [&](GroupState& state, const Tuple& tuple) { + for (size_t i = 0; i < agg.aggregates.size(); ++i) { + const auto& agg_expr = std::get(agg.aggregates[i]); + if (agg_expr.function == AggregateFunction::kCount) { + if (agg_expr.is_star) { + state.accumulators[i]++; + } else { + auto v = do_scalar(*agg_expr.argument, tuple); + if (!v.is_null) state.accumulators[i]++; + } + } else { + auto v = do_scalar(*agg_expr.argument, tuple); + if (!v.is_null) { + state.accumulators[i] += v.value.int_value; + state.any_non_null[i] = true; + } + } + } + }; + + Tuples out_buf; + out_buf.reserve(kBufSize); + auto emit_group = [&](const std::vector& key, const GroupState& state) + -> boost::asio::awaitable { + Tuple tuple = key; + for (size_t i = 0; i < agg.aggregates.size(); ++i) { + const auto& agg_expr = std::get(agg.aggregates[i]); + if (agg_expr.function == AggregateFunction::kSum && !state.any_non_null[i]) { + tuple.push_back(Value{true}); + } else { + tuple.push_back(Value{false, {.int_value = state.accumulators[i]}}); + } + } + out_buf.push_back(std::move(tuple)); + if (out_buf.size() == kBufSize) { + co_await out_tuples_chan.async_send(boost::system::error_code{}, std::move(out_buf), + boost::asio::use_awaitable); + out_buf.clear(); + out_buf.reserve(kBufSize); + } + }; + auto flush_output = [&]() -> boost::asio::awaitable { + if (!out_buf.empty()) { + co_await out_tuples_chan.async_send(boost::system::error_code{}, std::move(out_buf), + boost::asio::use_awaitable); + out_buf.clear(); + out_buf.reserve(kBufSize); + } + }; + + std::vector current_key; + GroupState current_state = init_state(); + bool has_current = false; + + for (;;) { + auto buf = co_await ReceiveTuples(in_tuples_chan); + if (buf.empty()) break; + for (const auto& tuple : buf) { + std::vector key; + key.reserve(agg.group_by.size()); + for (const auto& expr : agg.group_by) { + key.push_back(do_scalar(expr, tuple)); + } + if (!has_current) { + current_key = std::move(key); + current_state = init_state(); + has_current = true; + } else if (key != current_key) { + co_await emit_group(current_key, current_state); + current_key = std::move(key); + current_state = init_state(); + } + update_state(current_state, tuple); + } + } + + if (has_current) { + co_await emit_group(current_key, current_state); + } else if (agg.group_by.empty()) { + co_await emit_group(std::vector{}, init_state()); + } + co_await flush_output(); + co_await task(boost::asio::use_awaitable); + out_tuples_chan.close(); +} + template boost::asio::experimental::promise Executor::SpawnExecutor(boost::asio::any_io_executor exec, diff --git a/src/stewkk/sql/logic/executor/executor_test.cpp b/src/stewkk/sql/logic/executor/executor_test.cpp index 7b4a765..2f9abac 100644 --- a/src/stewkk/sql/logic/executor/executor_test.cpp +++ b/src/stewkk/sql/logic/executor/executor_test.cpp @@ -897,4 +897,40 @@ TEST(ExecutorTest, GroupByCountStar) { ctx.run(); } +TEST(ExecutorTest, StreamGroupByCountStar) { + boost::asio::io_context ctx; + boost::asio::co_spawn( + ctx, + []() -> boost::asio::awaitable> { + std::stringstream s{"SELECT users.age, COUNT(*) FROM users GROUP BY users.age;"}; + auto ast = GetAST(s).value().op; + const auto& projection = std::get(ast); + const auto& agg = std::get(*projection.source); + PhysicalPlanNode op = PhysicalStreamAggregation{ + .source = std::make_shared(PhysicalSort{ + .source = std::make_shared(ToPhysicalPlan(*agg.source)), + .keys = SortOrder{{SortKey{"users", "age", Direction::kAsc}}}, + }), + .group_by = agg.group_by, + .aggregates = agg.aggregates, + }; + CsvDirSequentialScanner seq_scan{kProjectDir + "/test/static/executor/test_data"}; + Executor executor( + std::move(seq_scan), co_await boost::asio::this_coro::executor); + auto got = co_await executor.Execute(op); + co_return got; + }(), + [](std::exception_ptr p, Result got) { + if (p) std::rethrow_exception(p); + ASSERT_THAT(got.value().tuples.size(), Eq(11u)); + + int64_t total = 0; + for (const auto& t : got.value().tuples) { + total += t[1].value.int_value; + } + ASSERT_THAT(total, Eq(17)); + }); + ctx.run(); +} + } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/executor/plan.cpp b/src/stewkk/sql/logic/executor/plan.cpp index 693aa5c..5fddf24 100644 --- a/src/stewkk/sql/logic/executor/plan.cpp +++ b/src/stewkk/sql/logic/executor/plan.cpp @@ -35,7 +35,8 @@ bool MergeJoin::operator==(const MergeJoin& other) const { } bool IndexSeek::operator==(const IndexSeek& other) const { - return table == other.table && alias == other.alias && predicate == other.predicate; + return table == other.table && alias == other.alias && predicate == other.predicate + && index_column == other.index_column; } bool PhysicalSort::operator==(const PhysicalSort& other) const { @@ -46,4 +47,8 @@ bool PhysicalAggregation::operator==(const PhysicalAggregation& other) const { return *source == *other.source && group_by == other.group_by && aggregates == other.aggregates; } +bool PhysicalStreamAggregation::operator==(const PhysicalStreamAggregation& other) const { + return *source == *other.source && group_by == other.group_by && aggregates == other.aggregates; +} + } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/executor/plan_serializer.cpp b/src/stewkk/sql/logic/executor/plan_serializer.cpp index 14ad1c0..819fdea 100644 --- a/src/stewkk/sql/logic/executor/plan_serializer.cpp +++ b/src/stewkk/sql/logic/executor/plan_serializer.cpp @@ -223,6 +223,20 @@ std::string SerializeNode(const PhysicalPlanNode& node) { return std::format("(HashAggregate (group_by {}) (aggs {}) {})", group_by_str, aggs_str, SerializeNode(*n.source)); } + std::string operator()(const PhysicalStreamAggregation& n) const { + std::string group_by_str; + for (const auto& e : n.group_by) { + if (!group_by_str.empty()) group_by_str += ' '; + group_by_str += SerializeExpr(e); + } + std::string aggs_str; + for (const auto& e : n.aggregates) { + if (!aggs_str.empty()) aggs_str += ' '; + aggs_str += SerializeExpr(e); + } + return std::format("(StreamAggregate (group_by {}) (aggs {}) {})", + group_by_str, aggs_str, SerializeNode(*n.source)); + } }; return std::visit(Visitor{}, node.node); } @@ -544,7 +558,7 @@ PhysicalPlanNode ParseNode(ParseState& s) { }; } - if (head == "HashAggregate") { + if (head == "HashAggregate" || head == "StreamAggregate") { s.ExpectLParen(); auto kw1 = s.ExpectAtom(); if (kw1 != "group_by") @@ -563,6 +577,13 @@ PhysicalPlanNode ParseNode(ParseState& s) { s.ExpectRParen(); auto source = ParseNode(s); s.ExpectRParen(); + if (head == "StreamAggregate") { + return PhysicalStreamAggregation{ + std::make_shared(std::move(source)), + std::move(group_by), + std::move(aggregates), + }; + } return PhysicalAggregation{ std::make_shared(std::move(source)), std::move(group_by), @@ -717,6 +738,25 @@ struct DotBuilder { EmitEdge(src, id); return id; } + + int EmitAlternative(const PhysicalStreamAggregation& n, + const std::optional& metadata) { + int src = EmitNode(*n.source); + std::string aggs; + for (const auto& e : n.aggregates) { + if (!aggs.empty()) aggs += ", "; + aggs += ToString(e); + } + std::string group_by; + for (const auto& e : n.group_by) { + if (!group_by.empty()) group_by += ", "; + group_by += ToString(e); + } + int id = Emit(std::format("StreamAgg\\nGROUP BY {}\\n{}", group_by, aggs), + metadata); + EmitEdge(src, id); + return id; + } }; } // namespace diff --git a/src/stewkk/sql/logic/executor/sequential_scan.cpp b/src/stewkk/sql/logic/executor/sequential_scan.cpp index 51663ce..c78105d 100644 --- a/src/stewkk/sql/logic/executor/sequential_scan.cpp +++ b/src/stewkk/sql/logic/executor/sequential_scan.cpp @@ -20,6 +20,7 @@ #include #include #include +#include namespace stewkk::sql { @@ -381,12 +382,25 @@ boost::asio::awaitable> CsvDirSequentialScanner::operator()( boost::asio::awaitable> CsvDirIndexedScanner::operator()( const std::string& table_name, const std::string& output_table_name, const Expression& predicate, + const std::optional& index_column, AttributesInfoChannel& attrs_chan, TuplesChannel& tuples_chan) const { #ifdef DEBUG std::clog << "Executing index seek\n"; #endif auto condition = ExtractSeekCondition(predicate, table_name, output_table_name); + if (index_column && (!condition || condition->column != *index_column)) { + std::vector conjuncts; + CollectConjuncts(predicate, conjuncts); + condition = std::nullopt; + for (const auto& conjunct : conjuncts) { + auto candidate = ExtractSeekCondition(conjunct, table_name, output_table_name); + if (candidate && candidate->column == *index_column) { + condition = std::move(candidate); + break; + } + } + } if (!condition) { throw std::runtime_error{"IndexSeek predicate must be a comparison between one indexed int column and an int constant"}; } diff --git a/src/stewkk/sql/logic/implementation_rules/implement_aggregation.cpp b/src/stewkk/sql/logic/implementation_rules/implement_aggregation.cpp index 3e57792..8164a5c 100644 --- a/src/stewkk/sql/logic/implementation_rules/implement_aggregation.cpp +++ b/src/stewkk/sql/logic/implementation_rules/implement_aggregation.cpp @@ -1,15 +1,26 @@ #include +#include + namespace stewkk::sql { bool ImplementAggregation::IsApplicable(utils::NotNull expr) { return std::holds_alternative(expr->root_operator); } -utils::NotNull ImplementAggregation::Apply(utils::NotNull expr, Memo&) { +std::vector> ImplementAggregation::Apply(utils::NotNull expr, Memo&) { auto& agg = std::get(expr->root_operator); - return expr->group->AddPhysicalExpr( - physical::Aggregation{agg.source, agg.group_by, agg.aggregates}); + std::vector> result{ + expr->group->AddPhysicalExpr( + physical::Aggregation{agg.source, agg.group_by, agg.aggregates})}; + if (!agg.group_by.empty() + && std::ranges::all_of(agg.group_by, [](const Expression& group_expr) { + return std::holds_alternative(group_expr); + })) { + result.push_back(expr->group->AddPhysicalExpr( + physical::StreamAggregation{agg.source, agg.group_by, agg.aggregates})); + } + return result; } } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/implementation_rules/implement_cross_join.cpp b/src/stewkk/sql/logic/implementation_rules/implement_cross_join.cpp index c9a2677..d517ee3 100644 --- a/src/stewkk/sql/logic/implementation_rules/implement_cross_join.cpp +++ b/src/stewkk/sql/logic/implementation_rules/implement_cross_join.cpp @@ -6,9 +6,9 @@ bool ImplementCrossJoin::IsApplicable(utils::NotNull expr) { return std::holds_alternative(expr->root_operator); } -utils::NotNull ImplementCrossJoin::Apply(utils::NotNull expr, Memo&) { +std::vector> ImplementCrossJoin::Apply(utils::NotNull expr, Memo&) { auto& cj = std::get(expr->root_operator); - return expr->group->AddPhysicalExpr(physical::NestedLoopCrossJoin{cj.lhs, cj.rhs}); + return {expr->group->AddPhysicalExpr(physical::NestedLoopCrossJoin{cj.lhs, cj.rhs})}; } } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/implementation_rules/implement_filter.cpp b/src/stewkk/sql/logic/implementation_rules/implement_filter.cpp index 96b8b21..38baaaa 100644 --- a/src/stewkk/sql/logic/implementation_rules/implement_filter.cpp +++ b/src/stewkk/sql/logic/implementation_rules/implement_filter.cpp @@ -6,9 +6,9 @@ bool ImplementFilter::IsApplicable(utils::NotNull expr) { return std::holds_alternative(expr->root_operator); } -utils::NotNull ImplementFilter::Apply(utils::NotNull expr, Memo&) { +std::vector> ImplementFilter::Apply(utils::NotNull expr, Memo&) { auto& filter = std::get(expr->root_operator); - return expr->group->AddPhysicalExpr(physical::Filter{filter.source, filter.predicate}); + return {expr->group->AddPhysicalExpr(physical::Filter{filter.source, filter.predicate})}; } } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/implementation_rules/implement_hash_join.cpp b/src/stewkk/sql/logic/implementation_rules/implement_hash_join.cpp index 4306956..012a1cd 100644 --- a/src/stewkk/sql/logic/implementation_rules/implement_hash_join.cpp +++ b/src/stewkk/sql/logic/implementation_rules/implement_hash_join.cpp @@ -20,10 +20,10 @@ bool ImplementHashJoin::IsApplicable(utils::NotNull expr) { return IsSimpleEquiJoin(join.qual); } -utils::NotNull ImplementHashJoin::Apply(utils::NotNull expr, Memo&) { +std::vector> ImplementHashJoin::Apply(utils::NotNull expr, Memo&) { auto& join = std::get(expr->root_operator); - return expr->group->AddPhysicalExpr( - physical::HashJoin{join.lhs, join.rhs, join.type, join.qual}); + return {expr->group->AddPhysicalExpr( + physical::HashJoin{join.lhs, join.rhs, join.type, join.qual})}; } } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/implementation_rules/implement_index_seek.cpp b/src/stewkk/sql/logic/implementation_rules/implement_index_seek.cpp index 44204b0..6769399 100644 --- a/src/stewkk/sql/logic/implementation_rules/implement_index_seek.cpp +++ b/src/stewkk/sql/logic/implementation_rules/implement_index_seek.cpp @@ -48,6 +48,41 @@ bool IsIndexedComparison(const Expression& expr, const logical::Table& table, return false; } +void CollectIndexedColumns(const Expression& expr, const logical::Table& table, + const IndexCatalog& indexes, std::vector& columns) { + const auto* binary = std::get_if(&expr); + if (!binary) { + return; + } + if (binary->binop == BinaryOp::kAnd) { + CollectIndexedColumns(*binary->lhs, table, indexes, columns); + CollectIndexedColumns(*binary->rhs, table, indexes, columns); + return; + } + if (!IsSupportedComparison(binary->binop)) { + return; + } + + auto add_if_indexed = [&](const Attribute& attr, const Expression& other) { + if (!AttrMatchesTable(attr, table) || !std::holds_alternative(other)) { + return; + } + if (!indexes.HasSortedIndex(table.name, attr.name)) { + return; + } + if (!std::ranges::contains(columns, attr.name)) { + columns.push_back(attr.name); + } + }; + + if (const auto* attr = std::get_if(binary->lhs.get())) { + add_if_indexed(*attr, *binary->rhs); + } + if (const auto* attr = std::get_if(binary->rhs.get())) { + add_if_indexed(*attr, *binary->lhs); + } +} + } // namespace ImplementIndexSeek::ImplementIndexSeek(IndexCatalog indexes) @@ -73,14 +108,23 @@ bool ImplementIndexSeek::IsApplicable(utils::NotNull expr) { return HasCompatibleIndexSeek(std::get(expr->root_operator), indexes_); } -utils::NotNull ImplementIndexSeek::Apply(utils::NotNull expr, Memo&) { +std::vector> ImplementIndexSeek::Apply(utils::NotNull expr, Memo&) { auto& filter = std::get(expr->root_operator); const auto* table = SourceTable(filter.source); - return expr->group->AddPhysicalExpr(physical::IndexSeek{ - .table = table->name, - .alias = table->alias, - .predicate = filter.predicate, - }); + std::vector columns; + CollectIndexedColumns(filter.predicate, *table, indexes_, columns); + + std::vector> result; + result.reserve(columns.size()); + for (const auto& column : columns) { + result.push_back(expr->group->AddPhysicalExpr(physical::IndexSeek{ + .table = table->name, + .alias = table->alias, + .predicate = filter.predicate, + .index_column = column, + })); + } + return result; } } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/implementation_rules/implement_join.cpp b/src/stewkk/sql/logic/implementation_rules/implement_join.cpp index d7110f9..89ff20c 100644 --- a/src/stewkk/sql/logic/implementation_rules/implement_join.cpp +++ b/src/stewkk/sql/logic/implementation_rules/implement_join.cpp @@ -6,10 +6,10 @@ bool ImplementJoin::IsApplicable(utils::NotNull expr) { return std::holds_alternative(expr->root_operator); } -utils::NotNull ImplementJoin::Apply(utils::NotNull expr, Memo&) { +std::vector> ImplementJoin::Apply(utils::NotNull expr, Memo&) { auto& join = std::get(expr->root_operator); - return expr->group->AddPhysicalExpr( - physical::NestedLoopJoin{join.lhs, join.rhs, join.type, join.qual}); + return {expr->group->AddPhysicalExpr( + physical::NestedLoopJoin{join.lhs, join.rhs, join.type, join.qual})}; } } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/implementation_rules/implement_merge_join.cpp b/src/stewkk/sql/logic/implementation_rules/implement_merge_join.cpp index 2d56b8f..130e1bf 100644 --- a/src/stewkk/sql/logic/implementation_rules/implement_merge_join.cpp +++ b/src/stewkk/sql/logic/implementation_rules/implement_merge_join.cpp @@ -19,10 +19,10 @@ bool ImplementMergeJoin::IsApplicable(utils::NotNull expr) { return IsSimpleEquiJoin(join.qual); } -utils::NotNull ImplementMergeJoin::Apply(utils::NotNull expr, Memo&) { +std::vector> ImplementMergeJoin::Apply(utils::NotNull expr, Memo&) { auto& join = std::get(expr->root_operator); - return expr->group->AddPhysicalExpr( - physical::MergeJoin{join.lhs, join.rhs, join.type, join.qual}); + return {expr->group->AddPhysicalExpr( + physical::MergeJoin{join.lhs, join.rhs, join.type, join.qual})}; } } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/implementation_rules/implement_projection.cpp b/src/stewkk/sql/logic/implementation_rules/implement_projection.cpp index 51889c6..26d6891 100644 --- a/src/stewkk/sql/logic/implementation_rules/implement_projection.cpp +++ b/src/stewkk/sql/logic/implementation_rules/implement_projection.cpp @@ -6,10 +6,10 @@ bool ImplementProjection::IsApplicable(utils::NotNull expr) { return std::holds_alternative(expr->root_operator); } -utils::NotNull ImplementProjection::Apply(utils::NotNull expr, Memo&) { +std::vector> ImplementProjection::Apply(utils::NotNull expr, Memo&) { auto& proj = std::get(expr->root_operator); - return expr->group->AddPhysicalExpr( - physical::Projection{proj.source, proj.expressions, proj.aliases}); + return {expr->group->AddPhysicalExpr( + physical::Projection{proj.source, proj.expressions, proj.aliases})}; } } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/implementation_rules/implement_table.cpp b/src/stewkk/sql/logic/implementation_rules/implement_table.cpp index 623b26e..c12f35d 100644 --- a/src/stewkk/sql/logic/implementation_rules/implement_table.cpp +++ b/src/stewkk/sql/logic/implementation_rules/implement_table.cpp @@ -6,9 +6,9 @@ bool ImplementTable::IsApplicable(utils::NotNull expr) { return std::holds_alternative(expr->root_operator); } -utils::NotNull ImplementTable::Apply(utils::NotNull expr, Memo&) { +std::vector> ImplementTable::Apply(utils::NotNull expr, Memo&) { auto& table = std::get(expr->root_operator); - return expr->group->AddPhysicalExpr(physical::SeqScan{table.name, table.alias}); + return {expr->group->AddPhysicalExpr(physical::SeqScan{table.name, table.alias})}; } } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index 2ae8eee..02abba3 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -74,6 +74,21 @@ PropertySet SortOn(const Attribute& attr) { return PropertySet{SortProperty{SortOrder{{SortKey{attr.table, attr.name, Direction::kAsc}}}}}; } +PropertySet SortOn(const std::string& table, const std::string& column) { + return PropertySet{SortProperty{SortOrder{{SortKey{table, column, Direction::kAsc}}}}}; +} + +PropertySet SortOnGroupBy(const std::vector& group_by) { + SortOrder order; + for (const auto& expr : group_by) { + const auto* attr = std::get_if(&expr); + if (!attr) return PropertySet::Any(); + order.keys.push_back(SortKey{attr->table, attr->name, Direction::kAsc}); + } + if (order.keys.empty()) return PropertySet::Any(); + return PropertySet{SortProperty{std::move(order)}}; +} + PropertySet RequiredInputProps(utils::NotNull expr, PropertySet required, size_t child_index, SchemaCatalog& schema) { @@ -94,6 +109,7 @@ PropertySet RequiredInputProps(utils::NotNull expr, }, [&](const physical::Sort&) { return PropertySet::Any(); }, [&](const physical::Aggregation&) { return PropertySet::Any(); }, + [&](const physical::StreamAggregation& a) { return SortOnGroupBy(a.group_by); }, }, expr->root_operator); } @@ -102,7 +118,9 @@ PropertySet DeriveOutputProps(utils::NotNull expr, SchemaCatalog& schema) { return std::visit(utils::Overloaded{ [&](const physical::SeqScan&) { return PropertySet::Any(); }, - [&](const physical::IndexSeek&) { return PropertySet::Any(); }, + [&](const physical::IndexSeek& s) { + return SortOn(s.alias.value_or(s.table), s.index_column); + }, [&](const physical::Filter&) { return child_delivered[0]; }, [&](const physical::Projection&) { return child_delivered[0]; }, [&](const physical::NestedLoopJoin&) { return PropertySet::Any(); }, @@ -115,6 +133,7 @@ PropertySet DeriveOutputProps(utils::NotNull expr, }, [&](const physical::Sort& s) { return PropertySet{SortProperty{s.keys}}; }, [&](const physical::Aggregation&) { return PropertySet::Any(); }, + [&](const physical::StreamAggregation& a) { return SortOnGroupBy(a.group_by); }, }, expr->root_operator); } @@ -215,6 +234,9 @@ int64_t CalcCost(utils::NotNull expr, CardinalityEstimates& cardi [&](const physical::Aggregation& a) -> int64_t { return 510 * cardinality.GetCardinality(a.source); }, + [&](const physical::StreamAggregation& a) -> int64_t { + return 130 * cardinality.GetCardinality(a.source); + }, }, expr->root_operator); } @@ -275,6 +297,9 @@ std::vector> GetChildren(utils::NotNull ex [](const physical::Aggregation& a) -> std::vector> { return {a.source}; }, + [](const physical::StreamAggregation& a) -> std::vector> { + return {a.source}; + }, }, expr->root_operator); } @@ -405,10 +430,12 @@ void Optimizer::TryRules( } tasks_.emplace([this, expr, rule]() { Log("Applying implementation rule {} to group {}", rule, expr->group->GetId()); - auto new_expr = rules_applier_.Apply(ImplementationRuleId{rule}, expr, memo_); - auto lc = CalcCost(new_expr, cardinality_, schema_); - Log("Local cost for group {} expression: {}", new_expr->group->GetId(), lc); - local_cost_[new_expr.get()] = lc; + auto new_exprs = rules_applier_.Apply(ImplementationRuleId{rule}, expr, memo_); + for (auto new_expr : new_exprs) { + auto lc = CalcCost(new_expr, cardinality_, schema_); + Log("Local cost for group {} expression: {}", new_expr->group->GetId(), lc); + local_cost_[new_expr.get()] = lc; + } }); } } @@ -506,7 +533,12 @@ PhysicalPlanNode Optimizer::BuildOptimalPlan(G return SeqScan{.table = op.table, .alias = op.alias}; }, [](const physical::IndexSeek& op) -> PhysicalPlanNode { - return IndexSeek{.table = op.table, .alias = op.alias, .predicate = op.predicate}; + return IndexSeek{ + .table = op.table, + .alias = op.alias, + .predicate = op.predicate, + .index_column = op.index_column, + }; }, [this, best_expr_nn, required](const physical::Projection& op) -> PhysicalPlanNode { return PhysicalProjection{ @@ -576,6 +608,14 @@ PhysicalPlanNode Optimizer::BuildOptimalPlan(G .aggregates = op.aggregates, }; }, + [this, best_expr_nn, required](const physical::StreamAggregation& op) -> PhysicalPlanNode { + return PhysicalStreamAggregation{ + .source = std::make_shared( + BuildOptimalPlan(op.source.get(), RequiredInputProps(best_expr_nn, required, 0, schema_))), + .group_by = op.group_by, + .aggregates = op.aggregates, + }; + }, }, best_expr->root_operator); plan.metadata = PlanNodeMetadata{ diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index b2118b5..03a23c1 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -169,6 +169,21 @@ TEST(OptimizerTest, OrderBy) { ASSERT_THAT(Serialize(got), Eq("(Sort (keys users.id Asc) (SeqScan users))")); } +TEST(OptimizerTest, UsesStreamAggregateWhenOrderCanBeProvided) { + std::stringstream s{"SELECT users.id, COUNT(*) FROM users GROUP BY users.id ORDER BY users.id;"}; + auto parsed = GetAST(s).value(); + PropertySet required{SortProperty{*parsed.required_order}}; + Optimizer optimizer(parsed.op, MakeMainRules(), CardinalityEstimates({{"users", 1000}}), + MakeTestSchema(), std::move(required)); + + auto got = optimizer.Optimize(); + + ASSERT_THAT( + Serialize(got), + HasSubstr("(StreamAggregate (group_by (attr users id)) (aggs (COUNT *))" + " (Sort (keys users.id Asc) (SeqScan users)))")); +} + TEST(OptimizerTest, OrderBySortsAfterCrossJoin) { std::stringstream s{ "SELECT * FROM departments CROSS JOIN orders ORDER BY departments.id;"}; @@ -285,6 +300,35 @@ TEST(OptimizerTest, IndexSeekSupportsAliasAndFullResidualPredicate) { Eq("(IndexSeek (and (>= (attr u id) 8) (< (attr u age) 70)) users u)")); } +TEST(OptimizerTest, IndexSeekDeliversSortedIndexOrder) { + std::stringstream s{"SELECT * FROM users WHERE users.id >= 8 ORDER BY users.id;"}; + auto parsed = GetAST(s).value(); + Optimizer optimizer(parsed.op, MakeMainRules(IndexCatalog({ + IndexInfo{.table = "users", .column = "id", .type = "sorted", .file = "users.id.sorted.idx"}, + })), CardinalityEstimates({{"users", 1000}}), MakeTestSchema(), + PropertySet{SortProperty{*parsed.required_order}}); + + auto got = optimizer.Optimize(); + + ASSERT_THAT(Serialize(got), Eq("(IndexSeek (>= (attr users id) 8) users)")); +} + +TEST(OptimizerTest, IndexSeekCanChooseOrderCompatibleIndexedConjunct) { + std::stringstream s{ + "SELECT * FROM users AS u WHERE u.id >= 8 AND u.age < 70 ORDER BY u.age;"}; + auto parsed = GetAST(s).value(); + Optimizer optimizer(parsed.op, MakeMainRules(IndexCatalog({ + IndexInfo{.table = "users", .column = "id", .type = "sorted", .file = "users.id.sorted.idx"}, + IndexInfo{.table = "users", .column = "age", .type = "sorted", .file = "users.age.sorted.idx"}, + })), CardinalityEstimates({{"users", 1000}}), MakeTestSchema(), + PropertySet{SortProperty{*parsed.required_order}}); + + auto got = optimizer.Optimize(); + + ASSERT_THAT(Serialize(got), + Eq("(IndexSeek (and (>= (attr u id) 8) (< (attr u age) 70)) users u)")); +} + TEST(OptimizerTest, NaiveRulesDisableLogicalTransformations) { std::stringstream s{ "SELECT * FROM lineorder AS lo " diff --git a/src/stewkk/sql/logic/optimizer/reachability.cpp b/src/stewkk/sql/logic/optimizer/reachability.cpp index 5a51448..c36000c 100644 --- a/src/stewkk/sql/logic/optimizer/reachability.cpp +++ b/src/stewkk/sql/logic/optimizer/reachability.cpp @@ -148,6 +148,17 @@ InternalMatch TryMatchExpr(utils::NotNull pe, if (!child.ok) child.reason = "Aggregation.source: " + child.reason; return child; }, + [&](const physical::StreamAggregation& op) -> InternalMatch { + const auto* t = std::get_if(&target.node); + if (!t) return {false, depth, "type mismatch: expected StreamAggregate"}; + if (op.group_by != t->group_by) + return {false, depth + 1, "StreamAggregation group_by mismatch"}; + if (op.aggregates != t->aggregates) + return {false, depth + 1, "StreamAggregation aggregates mismatch"}; + auto child = MatchGroup(op.source.get(), *t->source, depth + 1); + if (!child.ok) child.reason = "StreamAggregation.source: " + child.reason; + return child; + }, }, pe->root_operator); } diff --git a/src/stewkk/sql/logic/optimizer/rules_applier.cpp b/src/stewkk/sql/logic/optimizer/rules_applier.cpp index 783aed1..41a9186 100644 --- a/src/stewkk/sql/logic/optimizer/rules_applier.cpp +++ b/src/stewkk/sql/logic/optimizer/rules_applier.cpp @@ -25,7 +25,7 @@ bool RulesApplier::IsApplicable(Implementation } template -utils::NotNull RulesApplier::Apply(ImplementationRuleId rule, utils::NotNull expr, Memo& memo) { +std::vector> RulesApplier::Apply(ImplementationRuleId rule, utils::NotNull expr, Memo& memo) { applied_implementation_rules_[expr.get()][rule.value] = 1; return rules_.implementation_rules[rule.value]->Apply(expr, memo); } From c06be56429ad9349f63737fc8e5ecabdd4091fff Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Wed, 10 Jun 2026 00:07:51 +0300 Subject: [PATCH 100/120] Update report and benchmarks --- report/vkr.tex | 21 +- .../operator-cost-matched.json | 1594 +++---- research/benchmark-results/operator-cost.json | 2102 ++++----- research/benchmark-results/query.json | 412 +- research/benchmark-results/ssb-sf001.json | 1510 +++--- research/benchmarks.ipynb | 1450 +++--- research/cost-on-random.ipynb | 86 +- research/cost-stats.csv | 800 ++-- research/fuzz/cost_stats.py | 7 +- research/research.ipynb | 4137 ++++++++++++----- 10 files changed, 7003 insertions(+), 5116 deletions(-) diff --git a/report/vkr.tex b/report/vkr.tex index 8bb96d1..732a605 100644 --- a/report/vkr.tex +++ b/report/vkr.tex @@ -795,8 +795,7 @@ \subsection{Правила трансформации и реализации}\l тем самым определяют пространство планов, исследуемое оптимизатором. Наиболее важные из них представлены в таблице~\ref{tbl:transformation_rules}. -% TODO: может это вообще убрать? Или актуализировать... Еще нигде не описано как -% свойства выводятся +% TODO: может это вообще убрать? + нигде не описываю как выводятся свойства \begin{Definition} Null-отбрасывающим называется предикат, который при замене всех атрибутов @@ -1185,15 +1184,21 @@ \subsection{Модуль стоимостной оптимизации план И следующие правила реализации: \begin{itemize} \item последовательное сканирование таблицы; - \item фильтрацию; - \item проекцию; - \item агрегацию; + \item фильтрация; + \item проекция; + \item хэш-агрегация; + \item потоковая агрегация; \item декартово произведение вложенными циклами; \item соединение вложенными циклами; - \item хеш-соединение. + \item хеш-соединение; + \item соединение слиянием. \end{itemize} -% TODO: актуализировать и сделать какой-то вывод о покрытии правилами или почему такие правила выбраны +Данный набор правил все основные алгоритмы, реализующие операторы реляционной +алгебры, и включает самые важные правила трансформации. Также более полное +покрытие правилами реализации хорошо подходит для демонтрации возможностей +дифференциального сравнения планов для последующего расширения покрытия +правилами трансформации. Выбор лучшего плана выполняется на основе стоимостной модели. Стоимость физического оператора рассчитывается с учетом оценок кардинальности входных и @@ -2365,7 +2370,9 @@ \subsection{Калибровка стоимостной модели} Проекция & $22\,n_{out}$ \\ Сортировка & $11\,n\bigl(\lfloor\log_2 n\rfloor + 1\bigr)$ \\ Агрегация & $510\,n$ \\ + Потоковая агрегация & $130\,n$ \\ Соединение вложенными циклами & $70\,n_l n_r$ \\ + Соединение слиянием & $18\,n_l + n_l\,+ 10\,n_o w_o$ \\ Декартово произведение & $104\,n_l n_r$ \\ Хеш-соединение & $100\,n_b w_b + 35\,n_p + 10\,n_o w_o$ \\ \hline diff --git a/research/benchmark-results/operator-cost-matched.json b/research/benchmark-results/operator-cost-matched.json index cd23275..e7e3666 100644 --- a/research/benchmark-results/operator-cost-matched.json +++ b/research/benchmark-results/operator-cost-matched.json @@ -1,6 +1,6 @@ { "context": { - "date": "2026-06-02T01:20:14+03:00", + "date": "2026-06-09T23:49:41+03:00", "host_name": "nixos", "executable": "./build-release/bin/benchmarks", "num_cpus": 8, @@ -32,7 +32,7 @@ "num_sharing": 8 } ], - "load_avg": [1.60498,1.06787,0.731934], + "load_avg": [2.20801,1.40576,1.14795], "library_version": "v1.9.0", "library_build_type": "release", "json_schema_version": 1 @@ -47,9 +47,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 2134, - "real_time": 3.1959937816721865e+05, - "cpu_time": 3.1678497282099345e+05, + "iterations": 2201, + "real_time": 3.1308477827712137e+05, + "cpu_time": 3.1023940345297597e+05, "time_unit": "ns", "model_cost": 6.4000000000000000e+05, "output_rows": 6.4000000000000000e+03, @@ -65,9 +65,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 2134, - "real_time": 3.2149249062863167e+05, - "cpu_time": 3.1863026569821930e+05, + "iterations": 2201, + "real_time": 3.1884279055965185e+05, + "cpu_time": 3.1639320490686060e+05, "time_unit": "ns", "model_cost": 6.4000000000000000e+05, "output_rows": 6.4000000000000000e+03, @@ -83,9 +83,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 2134, - "real_time": 3.1938551311514707e+05, - "cpu_time": 3.1662225585754460e+05, + "iterations": 2201, + "real_time": 3.1709757428868051e+05, + "cpu_time": 3.1426909904588806e+05, "time_unit": "ns", "model_cost": 6.4000000000000000e+05, "output_rows": 6.4000000000000000e+03, @@ -103,8 +103,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.2015912730366579e+05, - "cpu_time": 3.1734583145891904e+05, + "real_time": 3.1634171437515126e+05, + "cpu_time": 3.1363390246857487e+05, "time_unit": "ns", "model_cost": 6.4000000000000000e+05, "output_rows": 6.4000000000000000e+03, @@ -122,8 +122,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.1959937816721865e+05, - "cpu_time": 3.1678497282099345e+05, + "real_time": 3.1709757428868051e+05, + "cpu_time": 3.1426909904588811e+05, "time_unit": "ns", "model_cost": 6.4000000000000000e+05, "output_rows": 6.4000000000000000e+03, @@ -141,8 +141,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.1596671429184132e+03, - "cpu_time": 1.1153240286591688e+03, + "real_time": 2.9524853120924695e+03, + "cpu_time": 3.1256877806897505e+03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -160,8 +160,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 3.6221586205739732e-03, - "cpu_time": 3.5145381413448605e-03, + "real_time": 9.3332152477086927e-03, + "cpu_time": 9.9660392453999273e-03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -177,9 +177,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 1753, - "real_time": 4.3079205019653478e+05, - "cpu_time": 4.2860011123787781e+05, + "iterations": 1729, + "real_time": 4.2820256100733217e+05, + "cpu_time": 4.2593496992481180e+05, "time_unit": "ns", "model_cost": 6.4000000000000000e+05, "output_rows": 3.3280000000000000e+03, @@ -195,9 +195,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 1753, - "real_time": 3.9971894409109856e+05, - "cpu_time": 3.9788238676554454e+05, + "iterations": 1729, + "real_time": 4.0207458647550282e+05, + "cpu_time": 4.0061198843261990e+05, "time_unit": "ns", "model_cost": 6.4000000000000000e+05, "output_rows": 3.3280000000000000e+03, @@ -213,9 +213,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 1753, - "real_time": 3.9723416200586094e+05, - "cpu_time": 3.9528256189389637e+05, + "iterations": 1729, + "real_time": 4.0215632678001490e+05, + "cpu_time": 4.0059324117987277e+05, "time_unit": "ns", "model_cost": 6.4000000000000000e+05, "output_rows": 3.3280000000000000e+03, @@ -233,8 +233,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 4.0924838543116482e+05, - "cpu_time": 4.0725501996577292e+05, + "real_time": 4.1081115808761661e+05, + "cpu_time": 4.0904673317910143e+05, "time_unit": "ns", "model_cost": 6.4000000000000000e+05, "output_rows": 3.3280000000000000e+03, @@ -252,8 +252,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.9971894409109856e+05, - "cpu_time": 3.9788238676554454e+05, + "real_time": 4.0215632678001490e+05, + "cpu_time": 4.0061198843261978e+05, "time_unit": "ns", "model_cost": 6.4000000000000000e+05, "output_rows": 3.3280000000000000e+03, @@ -271,8 +271,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.8698680545555701e+04, - "cpu_time": 1.8531040537477937e+04, + "real_time": 1.5061452187829180e+04, + "cpu_time": 1.4625645050707279e+04, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -290,8 +290,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 4.5690297655922703e-02, - "cpu_time": 4.5502301086516615e-02, + "real_time": 3.6662714464578680e-02, + "cpu_time": 3.5755437861676870e-02, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -307,9 +307,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 221, - "real_time": 3.1135491538230260e+06, - "cpu_time": 3.0914587466063341e+06, + "iterations": 224, + "real_time": 3.0960619286426147e+06, + "cpu_time": 3.0747937633928573e+06, "time_unit": "ns", "model_cost": 6.4000200000000000e+05, "output_rows": 2.9091000000000000e+04, @@ -325,9 +325,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 221, - "real_time": 3.1329700317022041e+06, - "cpu_time": 3.1114170452488684e+06, + "iterations": 224, + "real_time": 3.1441242409917838e+06, + "cpu_time": 3.1229820491071432e+06, "time_unit": "ns", "model_cost": 6.4000200000000000e+05, "output_rows": 2.9091000000000000e+04, @@ -343,9 +343,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 221, - "real_time": 3.0453563801500718e+06, - "cpu_time": 3.0240103257918572e+06, + "iterations": 224, + "real_time": 3.1377971116463803e+06, + "cpu_time": 3.1165352053571432e+06, "time_unit": "ns", "model_cost": 6.4000200000000000e+05, "output_rows": 2.9091000000000000e+04, @@ -363,8 +363,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.0972918552251006e+06, - "cpu_time": 3.0756287058823537e+06, + "real_time": 3.1259944270935929e+06, + "cpu_time": 3.1047703392857146e+06, "time_unit": "ns", "model_cost": 6.4000200000000000e+05, "output_rows": 2.9091000000000000e+04, @@ -382,8 +382,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.1135491538230260e+06, - "cpu_time": 3.0914587466063336e+06, + "real_time": 3.1377971116463803e+06, + "cpu_time": 3.1165352053571437e+06, "time_unit": "ns", "model_cost": 6.4000200000000000e+05, "output_rows": 2.9091000000000000e+04, @@ -401,8 +401,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 4.6013724070689255e+04, - "cpu_time": 4.5803125368818633e+04, + "real_time": 2.6114631704802276e+04, + "cpu_time": 2.6159831331332960e+04, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -420,8 +420,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.4856115026120175e-02, - "cpu_time": 1.4892280489259635e-02, + "real_time": 8.3540237559164392e-03, + "cpu_time": 8.4256896557931282e-03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -437,9 +437,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 989, - "real_time": 7.3863877148847247e+05, - "cpu_time": 7.3131884327603586e+05, + "iterations": 996, + "real_time": 7.0582497386910755e+05, + "cpu_time": 7.0269638353413541e+05, "time_unit": "ns", "model_cost": 6.4006800000000000e+05, "output_rows": 4.4760000000000000e+03, @@ -455,9 +455,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 989, - "real_time": 7.0003620221743814e+05, - "cpu_time": 6.9446491102123272e+05, + "iterations": 996, + "real_time": 6.9997442669883172e+05, + "cpu_time": 6.9672412550200918e+05, "time_unit": "ns", "model_cost": 6.4006800000000000e+05, "output_rows": 4.4760000000000000e+03, @@ -473,9 +473,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 989, - "real_time": 7.0414523862990201e+05, - "cpu_time": 6.9763879676440801e+05, + "iterations": 996, + "real_time": 6.9949409538617241e+05, + "cpu_time": 6.9659677911646734e+05, "time_unit": "ns", "model_cost": 6.4006800000000000e+05, "output_rows": 4.4760000000000000e+03, @@ -493,8 +493,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 7.1427340411193762e+05, - "cpu_time": 7.0780751702055882e+05, + "real_time": 7.0176449865137052e+05, + "cpu_time": 6.9867242938420398e+05, "time_unit": "ns", "model_cost": 6.4006800000000000e+05, "output_rows": 4.4760000000000000e+03, @@ -512,8 +512,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 7.0414523862990213e+05, - "cpu_time": 6.9763879676440812e+05, + "real_time": 6.9997442669883172e+05, + "cpu_time": 6.9672412550200929e+05, "time_unit": "ns", "model_cost": 6.4006800000000000e+05, "output_rows": 4.4760000000000000e+03, @@ -531,8 +531,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.1200811083658697e+04, - "cpu_time": 2.0423154369398882e+04, + "real_time": 3.5246664789629790e+03, + "cpu_time": 3.4854281696968096e+03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -550,8 +550,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 2.9681647057848745e-02, - "cpu_time": 2.8854107759928853e-02, + "real_time": 5.0225773542785001e-03, + "cpu_time": 4.9886442102328220e-03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -567,9 +567,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 1951, - "real_time": 3.5571719989303959e+05, - "cpu_time": 3.5380934392619203e+05, + "iterations": 1997, + "real_time": 3.4492794541993242e+05, + "cpu_time": 3.4377248823234811e+05, "time_unit": "ns", "model_cost": 6.4005000000000000e+05, "output_rows": 1.2550000000000000e+03, @@ -585,9 +585,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 1951, - "real_time": 3.5278431522308628e+05, - "cpu_time": 3.5080882214249083e+05, + "iterations": 1997, + "real_time": 3.5217452829173906e+05, + "cpu_time": 3.5094974411617417e+05, "time_unit": "ns", "model_cost": 6.4005000000000000e+05, "output_rows": 1.2550000000000000e+03, @@ -603,9 +603,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 1951, - "real_time": 3.5274546797169390e+05, - "cpu_time": 3.4959611788826273e+05, + "iterations": 1997, + "real_time": 3.5108648071568750e+05, + "cpu_time": 3.4975750125187810e+05, "time_unit": "ns", "model_cost": 6.4005000000000000e+05, "output_rows": 1.2550000000000000e+03, @@ -623,8 +623,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.5374899436260661e+05, - "cpu_time": 3.5140476131898188e+05, + "real_time": 3.4939631814245303e+05, + "cpu_time": 3.4815991120013344e+05, "time_unit": "ns", "model_cost": 6.4005000000000000e+05, "output_rows": 1.2550000000000000e+03, @@ -642,8 +642,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.5278431522308622e+05, - "cpu_time": 3.5080882214249088e+05, + "real_time": 3.5108648071568744e+05, + "cpu_time": 3.4975750125187816e+05, "time_unit": "ns", "model_cost": 6.4005000000000000e+05, "output_rows": 1.2550000000000000e+03, @@ -661,8 +661,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.7046266555327054e+03, - "cpu_time": 2.1689112564212855e+03, + "real_time": 3.9077778816701375e+03, + "cpu_time": 3.8460981508032892e+03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -680,8 +680,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 4.8187462938351034e-03, - "cpu_time": 6.1721168725215204e-03, + "real_time": 1.1184370523552256e-02, + "cpu_time": 1.1046929951083395e-02, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -697,9 +697,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 2826, - "real_time": 2.5065704565115442e+05, - "cpu_time": 2.4948463481953283e+05, + "iterations": 2600, + "real_time": 2.5277129883761515e+05, + "cpu_time": 2.5169727038461503e+05, "time_unit": "ns", "lhs_rows": 1.6200000000000000e+03, "model_cost": 6.3990000000000000e+05, @@ -716,9 +716,9 @@ "repetitions": 3, 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@@ -1108,9 +1108,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 2017, - "real_time": 3.4750430193185079e+05, - "cpu_time": 3.4489420178482815e+05, + "iterations": 1998, + "real_time": 3.4563285885383381e+05, + "cpu_time": 3.4412943743743672e+05, "time_unit": "ns", "model_cost": 1.0000000000000000e+06, "output_rows": 1.0000000000000000e+04, @@ -1126,9 +1126,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 2017, - "real_time": 3.4945645017601806e+05, - "cpu_time": 3.4778087059990200e+05, + "iterations": 1998, + "real_time": 3.4439379078822228e+05, + "cpu_time": 3.4241372272272233e+05, "time_unit": "ns", "model_cost": 1.0000000000000000e+06, "output_rows": 1.0000000000000000e+04, @@ -1144,9 +1144,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 2017, - "real_time": 3.6007833664242481e+05, - "cpu_time": 3.5810382102131739e+05, + "iterations": 1998, + "real_time": 3.4484542842832737e+05, + "cpu_time": 3.4355080580580694e+05, "time_unit": "ns", "model_cost": 1.0000000000000000e+06, "output_rows": 1.0000000000000000e+04, @@ -1164,8 +1164,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.5234636291676457e+05, - "cpu_time": 3.5025963113534910e+05, + "real_time": 3.4495735935679451e+05, + "cpu_time": 3.4336465532198857e+05, "time_unit": "ns", "model_cost": 1.0000000000000000e+06, "output_rows": 1.0000000000000000e+04, @@ -1183,8 +1183,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.4945645017601800e+05, - "cpu_time": 3.4778087059990200e+05, + "real_time": 3.4484542842832737e+05, + "cpu_time": 3.4355080580580694e+05, "time_unit": "ns", "model_cost": 1.0000000000000000e+06, "output_rows": 1.0000000000000000e+04, @@ -1202,8 +1202,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 6.7668518501962935e+03, - "cpu_time": 6.9449046384655185e+03, + "real_time": 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0.0000000000000000e+00, @@ -1368,9 +1368,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 129, - "real_time": 5.5410689069680115e+06, - "cpu_time": 5.4980028914728574e+06, + "iterations": 139, + "real_time": 4.9726498345073937e+06, + "cpu_time": 4.9447566187050398e+06, "time_unit": "ns", "model_cost": 1.0000100000000000e+06, "output_rows": 4.5455000000000000e+04, @@ -1386,9 +1386,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 129, - "real_time": 5.5632144728244115e+06, - "cpu_time": 5.5193484728682078e+06, + "iterations": 139, + "real_time": 4.9559121439317176e+06, + "cpu_time": 4.9227157338129496e+06, "time_unit": "ns", "model_cost": 1.0000100000000000e+06, "output_rows": 4.5455000000000000e+04, @@ -1404,9 +1404,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 129, - "real_time": 5.5757678991956124e+06, - "cpu_time": 5.5366980000000084e+06, + "iterations": 139, + "real_time": 5.0025056403133646e+06, + "cpu_time": 4.9735199496402973e+06, "time_unit": "ns", "model_cost": 1.0000100000000000e+06, "output_rows": 4.5455000000000000e+04, @@ -1424,8 +1424,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 5.5600170929960115e+06, - "cpu_time": 5.5180164547803579e+06, + "real_time": 4.9770225395841580e+06, + "cpu_time": 4.9469974340527626e+06, "time_unit": "ns", "model_cost": 1.0000100000000000e+06, "output_rows": 4.5455000000000000e+04, @@ -1443,8 +1443,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 5.5632144728244124e+06, - "cpu_time": 5.5193484728682078e+06, + "real_time": 4.9726498345073927e+06, + "cpu_time": 4.9447566187050408e+06, "time_unit": "ns", "model_cost": 1.0000100000000000e+06, "output_rows": 4.5455000000000000e+04, @@ -1462,8 +1462,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.7569076348617778e+04, - "cpu_time": 1.9381913221220399e+04, + "real_time": 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@@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 557, - "real_time": 1.2573716068189740e+06, - "cpu_time": 1.2485316696588891e+06, + "iterations": 594, + "real_time": 1.1861069814454112e+06, + "cpu_time": 1.1779857457912401e+06, "time_unit": "ns", "model_cost": 3.2400000000000000e+06, "output_rows": 3.2400000000000000e+04, @@ -2187,9 +2187,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 557, - "real_time": 1.3702064416604533e+06, - "cpu_time": 1.3608439982046764e+06, + "iterations": 594, + "real_time": 1.3275793703588082e+06, + "cpu_time": 1.3171716414141336e+06, "time_unit": "ns", "model_cost": 3.2400000000000000e+06, "output_rows": 3.2400000000000000e+04, @@ -2205,9 +2205,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 557, - "real_time": 1.2263790502620919e+06, - "cpu_time": 1.2187431364452399e+06, + "iterations": 594, + "real_time": 1.1991726195162181e+06, + "cpu_time": 1.1916339781144718e+06, "time_unit": "ns", "model_cost": 3.2400000000000000e+06, "output_rows": 3.2400000000000000e+04, @@ -2225,8 +2225,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.2846523662471732e+06, - "cpu_time": 1.2760396014362683e+06, + "real_time": 1.2376196571068126e+06, + "cpu_time": 1.2289304551066153e+06, "time_unit": "ns", "model_cost": 3.2400000000000000e+06, "output_rows": 3.2400000000000000e+04, @@ -2244,8 +2244,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.2573716068189738e+06, - "cpu_time": 1.2485316696588891e+06, + "real_time": 1.1991726195162183e+06, + "cpu_time": 1.1916339781144720e+06, "time_unit": "ns", "model_cost": 3.2400000000000000e+06, "output_rows": 3.2400000000000000e+04, @@ -2263,8 +2263,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 7.5695174914580450e+04, - "cpu_time": 7.4937830641108099e+04, + "real_time": 7.8180817537522613e+04, + 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+2542,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.3565788364107668e-02, - "cpu_time": 1.3008802431689790e-02, + "real_time": 2.2690993386434214e-02, + "cpu_time": 2.1769183284949270e-02, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -2559,9 +2559,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 168, - "real_time": 4.2066907203012044e+06, - "cpu_time": 4.1801236726190434e+06, + "iterations": 176, + "real_time": 4.0440506760536330e+06, + "cpu_time": 4.0273345568181914e+06, "time_unit": "ns", "model_cost": 3.2399400000000000e+06, "output_rows": 1.9636000000000000e+04, @@ -2577,9 +2577,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 168, - "real_time": 4.1829359880856439e+06, - "cpu_time": 4.1602477023809501e+06, + "iterations": 176, + "real_time": 3.9938337897844822e+06, + "cpu_time": 3.9734451420454709e+06, "time_unit": "ns", "model_cost": 3.2399400000000000e+06, "output_rows": 1.9636000000000000e+04, @@ -2595,9 +2595,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 168, - "real_time": 4.1644775059726918e+06, - "cpu_time": 4.1449916666666875e+06, + "iterations": 176, + "real_time": 3.9726324203828964e+06, + "cpu_time": 3.9547886420454388e+06, "time_unit": "ns", "model_cost": 3.2399400000000000e+06, "output_rows": 1.9636000000000000e+04, @@ -2615,8 +2615,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 4.1847014047865137e+06, - "cpu_time": 4.1617876805555597e+06, + "real_time": 4.0035056287403367e+06, + "cpu_time": 3.9851894469696996e+06, "time_unit": "ns", "model_cost": 3.2399400000000000e+06, "output_rows": 1.9636000000000000e+04, @@ -2634,8 +2634,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 4.1829359880856429e+06, - "cpu_time": 4.1602477023809496e+06, + "real_time": 3.9938337897844817e+06, + "cpu_time": 3.9734451420454704e+06, "time_unit": "ns", "model_cost": 3.2399400000000000e+06, "output_rows": 1.9636000000000000e+04, @@ -2653,8 +2653,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.1161908895030607e+04, - "cpu_time": 1.7616557840267622e+04, + "real_time": 3.6678333691095635e+04, + "cpu_time": 3.7671925371151658e+04, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -2672,8 +2672,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 5.0569698642835914e-03, - "cpu_time": 4.2329304598056703e-03, + "real_time": 9.1615541708720185e-03, + "cpu_time": 9.4529823167621378e-03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -2689,9 +2689,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 308, - "real_time": 1.9028669123688270e+06, - "cpu_time": 1.8937144318181819e+06, + 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"model_cost": 3.2401850000000000e+06, @@ -2857,9 +2857,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 527, - "real_time": 1.3055427229554134e+06, - "cpu_time": 1.2979189677419360e+06, + "iterations": 530, + "real_time": 1.2904943339307003e+06, + "cpu_time": 1.2835080886792422e+06, "time_unit": "ns", "lhs_rows": 8.2030000000000000e+03, "model_cost": 3.2401850000000000e+06, @@ -2878,8 +2878,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.3065583535742073e+06, - "cpu_time": 1.2984317849462372e+06, + "real_time": 1.2967473534433718e+06, + "cpu_time": 1.2897041761006268e+06, "time_unit": "ns", "lhs_rows": 8.2030000000000000e+03, "model_cost": 3.2401850000000000e+06, @@ -2898,8 +2898,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.3061806280839662e+06, - "cpu_time": 1.2979189677419357e+06, + "real_time": 1.2904943339307001e+06, + "cpu_time": 1.2835080886792422e+06, "time_unit": "ns", "lhs_rows": 8.2030000000000000e+03, "model_cost": 3.2401850000000000e+06, @@ -2918,8 +2918,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.2481232605022940e+03, - "cpu_time": 1.2507617569397241e+03, + "real_time": 1.5252334209538914e+04, + "cpu_time": 1.5283817529826309e+04, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -2938,8 +2938,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 9.5527555817766674e-04, - "cpu_time": 9.6328645943576693e-04, + "real_time": 1.1761993706050754e-02, + "cpu_time": 1.1850638164200081e-02, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -2956,9 +2956,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 247, - "real_time": 2.8583840121206855e+06, - "cpu_time": 2.8228807085020225e+06, + "iterations": 239, + "real_time": 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-3131,9 +3131,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 361, - "real_time": 1.9970301218563691e+06, - "cpu_time": 1.9774793185595647e+06, + "iterations": 347, + "real_time": 1.9577290287935135e+06, + "cpu_time": 1.9377983141210261e+06, "time_unit": "ns", "lhs_rows": 1.7700000000000000e+02, "model_cost": 3.2582160000000000e+06, @@ -3152,8 +3152,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.9676991163316339e+06, - "cpu_time": 1.9484499427516193e+06, + "real_time": 1.9692692007499549e+06, + "cpu_time": 1.9501021373679123e+06, "time_unit": "ns", "lhs_rows": 1.7700000000000000e+02, "model_cost": 3.2582160000000000e+06, @@ -3172,8 +3172,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.9610651495925812e+06, - "cpu_time": 1.9417327396121898e+06, + "real_time": 1.9741251786079751e+06, + "cpu_time": 1.9561049567723265e+06, "time_unit": "ns", "lhs_rows": 1.7700000000000000e+02, "model_cost": 3.2582160000000000e+06, @@ -3192,8 +3192,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.6640879953787520e+04, - "cpu_time": 2.6321650090824391e+04, + "real_time": 1.0035799432207679e+04, + "cpu_time": 1.0656466504360225e+04, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -3212,8 +3212,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.3539102463721135e-02, - "cpu_time": 1.3509020433777585e-02, + "real_time": 5.0962049416025780e-03, + "cpu_time": 5.4645683937065200e-03, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -3231,8 +3231,8 @@ "repetition_index": 0, "threads": 1, "iterations": 184, - "real_time": 3.7433058206933425e+06, - "cpu_time": 3.7101630706521659e+06, + "real_time": 3.7718354294117056e+06, + "cpu_time": 3.7381105217391322e+06, "time_unit": "ns", "model_cost": 6.7600000000000000e+06, "output_rows": 6.7600000000000000e+04, @@ -3249,8 +3249,8 @@ "repetition_index": 1, "threads": 1, "iterations": 184, - "real_time": 3.6817014185109418e+06, - "cpu_time": 3.6506450652173855e+06, + "real_time": 3.7187801467750785e+06, + "cpu_time": 3.6843537336956598e+06, "time_unit": "ns", "model_cost": 6.7600000000000000e+06, "output_rows": 6.7600000000000000e+04, @@ -3267,8 +3267,8 @@ "repetition_index": 2, "threads": 1, "iterations": 184, - "real_time": 3.9874370923707434e+06, - "cpu_time": 3.9465742554347846e+06, + "real_time": 3.9800230490231034e+06, + "cpu_time": 3.9423033750000065e+06, "time_unit": "ns", "model_cost": 6.7600000000000000e+06, "output_rows": 6.7600000000000000e+04, @@ -3286,8 +3286,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.8041481105250097e+06, - "cpu_time": 3.7691274637681115e+06, + "real_time": 3.8235462084032954e+06, + "cpu_time": 3.7882558768115994e+06, "time_unit": "ns", "model_cost": 6.7600000000000000e+06, "output_rows": 6.7600000000000000e+04, @@ -3305,8 +3305,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.7433058206933425e+06, - "cpu_time": 3.7101630706521659e+06, + "real_time": 3.7718354294117051e+06, + "cpu_time": 3.7381105217391322e+06, "time_unit": "ns", "model_cost": 6.7600000000000000e+06, "output_rows": 6.7600000000000000e+04, @@ -3324,8 +3324,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.6169388900511208e+05, - "cpu_time": 1.5652833975779495e+05, + "real_time": 1.3808499915515727e+05, + "cpu_time": 1.3608975655343197e+05, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -3343,8 +3343,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 4.2504625032277400e-02, - "cpu_time": 4.1529065085347053e-02, + "real_time": 3.6114379591301253e-02, + "cpu_time": 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@@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 67, - "real_time": 1.0293265656740127e+07, - "cpu_time": 1.0238812626865799e+07, + "iterations": 65, + "real_time": 9.8258075384924617e+06, + "cpu_time": 9.7672615384615604e+06, "time_unit": "ns", "model_cost": 6.7599840000000000e+06, "output_rows": 3.8409000000000000e+04, @@ -3638,9 +3638,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 67, - "real_time": 1.0346101776135515e+07, - "cpu_time": 1.0287454641790923e+07, + "iterations": 65, + "real_time": 9.9867926923952140e+06, + "cpu_time": 9.9288823076921497e+06, "time_unit": "ns", "model_cost": 6.7599840000000000e+06, "output_rows": 3.8409000000000000e+04, @@ -3656,9 +3656,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 67, - "real_time": 1.0176420716547977e+07, - "cpu_time": 1.0120201791044865e+07, + "iterations": 65, + "real_time": 1.0194654030671630e+07, + "cpu_time": 1.0120535738461345e+07, "time_unit": 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1.7114000000000000e+04, "model_cost": 6.7600300000000000e+06, @@ -3939,8 +3939,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.0432181248134314e+06, - "cpu_time": 3.0220737797356672e+06, + "real_time": 3.1957424304330428e+06, + "cpu_time": 3.1688376382660479e+06, "time_unit": "ns", "lhs_rows": 1.7114000000000000e+04, "model_cost": 6.7600300000000000e+06, @@ -3959,8 +3959,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.0347369250991191e+06, - "cpu_time": 3.0130578149779700e+06, + "real_time": 3.1994071478732019e+06, + "cpu_time": 3.1710662376681413e+06, "time_unit": "ns", "lhs_rows": 1.7114000000000000e+04, "model_cost": 6.7600300000000000e+06, @@ -3979,8 +3979,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.5639191332011844e+04, - "cpu_time": 2.4888268240721183e+04, + "real_time": 9.2711024689398051e+04, + "cpu_time": 9.1091092311855245e+04, "time_unit": 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1.0988163307692388e+07, "time_unit": "ns", "model_cost": 1.2250200000000000e+07, "output_rows": 2.4020000000000000e+04, @@ -4847,9 +4847,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 63, - "real_time": 1.1005948904702174e+07, - "cpu_time": 1.0931545412698517e+07, + "iterations": 65, + "real_time": 1.0051195876887783e+07, + "cpu_time": 9.9845561846153103e+06, "time_unit": "ns", "model_cost": 1.2250200000000000e+07, "output_rows": 2.4020000000000000e+04, @@ -4867,8 +4867,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.0929371984001057e+07, - "cpu_time": 1.0855646243386285e+07, + "real_time": 1.0384281415295286e+07, + "cpu_time": 1.0310905415384643e+07, "time_unit": "ns", "model_cost": 1.2250200000000000e+07, "output_rows": 2.4020000000000000e+04, @@ -4886,8 +4886,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.1005948904702174e+07, - "cpu_time": 1.0931545412698517e+07, + 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4.1800000000000000e+02, "model_cost": 1.2230680000000000e+07, @@ -5116,9 +5116,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 73, - "real_time": 1.0207016904095238e+07, - "cpu_time": 1.0169490657534340e+07, + "iterations": 72, + "real_time": 1.0134249097366894e+07, + "cpu_time": 1.0097309069444431e+07, "time_unit": "ns", "lhs_rows": 4.1800000000000000e+02, "model_cost": 1.2230680000000000e+07, @@ -5137,8 +5137,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.0203401356234882e+07, - "cpu_time": 1.0165100333333405e+07, + "real_time": 1.0091080111090343e+07, + "cpu_time": 1.0051310555555565e+07, "time_unit": "ns", "lhs_rows": 4.1800000000000000e+02, "model_cost": 1.2230680000000000e+07, @@ -5157,8 +5157,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.0207016904095238e+07, - "cpu_time": 1.0169490657534340e+07, + "real_time": 1.0123201319301086e+07, + "cpu_time": 1.0083855902777826e+07, "time_unit": "ns", "lhs_rows": 4.1800000000000000e+02, "model_cost": 1.2230680000000000e+07, @@ -5177,8 +5177,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.5817311552813306e+04, - "cpu_time": 2.8217270647881960e+04, + "real_time": 6.5436789266549720e+04, + "cpu_time": 6.8352765211266247e+04, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -5197,8 +5197,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 2.5302652175921126e-03, - "cpu_time": 2.7758969142047583e-03, + "real_time": 6.4846169633152647e-03, + "cpu_time": 6.8003833762241344e-03, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -5215,9 +5215,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 62, - "real_time": 1.0941390048350101e+07, - "cpu_time": 1.0826967725806652e+07, + "iterations": 61, + "real_time": 1.0730578606570385e+07, + "cpu_time": 1.0601961098360822e+07, "time_unit": "ns", "lhs_rows": 3.4300000000000000e+02, "model_cost": 1.2235496000000000e+07, @@ -5234,9 +5234,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 62, - "real_time": 1.1156641854951534e+07, - "cpu_time": 1.1021816161290312e+07, + "iterations": 61, + "real_time": 1.0846012426047876e+07, + "cpu_time": 1.0703855475409841e+07, "time_unit": "ns", "lhs_rows": 3.4300000000000000e+02, "model_cost": 1.2235496000000000e+07, @@ -5253,9 +5253,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 62, - "real_time": 1.1642135887203978e+07, - "cpu_time": 1.1511697967741903e+07, + "iterations": 61, + "real_time": 1.0805561901909895e+07, + "cpu_time": 1.0678400049180320e+07, "time_unit": "ns", "lhs_rows": 3.4300000000000000e+02, "model_cost": 1.2235496000000000e+07, @@ -5274,8 +5274,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.1246722596835203e+07, - "cpu_time": 1.1120160618279621e+07, + "real_time": 1.0794050978176052e+07, + "cpu_time": 1.0661405540983660e+07, "time_unit": "ns", "lhs_rows": 3.4300000000000000e+02, "model_cost": 1.2235496000000000e+07, @@ -5294,8 +5294,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.1156641854951533e+07, - "cpu_time": 1.1021816161290312e+07, + "real_time": 1.0805561901909893e+07, + "cpu_time": 1.0678400049180320e+07, "time_unit": "ns", "lhs_rows": 3.4300000000000000e+02, "model_cost": 1.2235496000000000e+07, @@ -5314,8 +5314,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.5895276529303263e+05, - "cpu_time": 3.5279966017638613e+05, + "real_time": 5.8571475085485035e+04, + "cpu_time": 5.3030425231166446e+04, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -5334,8 +5334,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 3.1916210451748937e-02, - "cpu_time": 3.1726129890286343e-02, + "real_time": 5.4262737135397782e-03, + "cpu_time": 4.9740557215754945e-03, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -5353,8 +5353,8 @@ "repetition_index": 0, "threads": 1, "iterations": 40, - "real_time": 1.7262143825064413e+07, - "cpu_time": 1.7128744149999876e+07, + "real_time": 1.6767849800089607e+07, + "cpu_time": 1.6581281199999863e+07, "time_unit": "ns", "model_cost": 2.6010000000000000e+07, "output_rows": 2.6010000000000000e+05, @@ -5371,8 +5371,8 @@ "repetition_index": 1, "threads": 1, "iterations": 40, - "real_time": 1.7199810100282777e+07, - "cpu_time": 1.7065588749999706e+07, + "real_time": 1.8629027724819027e+07, + "cpu_time": 1.8440807474999942e+07, "time_unit": "ns", "model_cost": 2.6010000000000000e+07, "output_rows": 2.6010000000000000e+05, @@ -5389,8 +5389,8 @@ "repetition_index": 2, "threads": 1, 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1.3005200000000000e+05, @@ -5500,9 +5500,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 37, - "real_time": 2.0226670702843852e+07, - "cpu_time": 2.0105167108108174e+07, + "iterations": 36, + "real_time": 1.9265840666776057e+07, + "cpu_time": 1.9167215027777787e+07, "time_unit": "ns", "model_cost": 2.6010000000000000e+07, "output_rows": 1.3005200000000000e+05, @@ -5518,9 +5518,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 37, - "real_time": 2.1237649459303733e+07, - "cpu_time": 2.1085447324324492e+07, + "iterations": 36, + "real_time": 1.9092187249851927e+07, + "cpu_time": 1.8984599944444608e+07, "time_unit": "ns", "model_cost": 2.6010000000000000e+07, "output_rows": 1.3005200000000000e+05, @@ -5538,8 +5538,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.0268577513487395e+07, - "cpu_time": 2.0143105918918993e+07, + "real_time": 1.9234039787045699e+07, + "cpu_time": 1.9127108759259325e+07, "time_unit": "ns", "model_cost": 2.6010000000000000e+07, "output_rows": 1.3005200000000000e+05, @@ -5557,8 +5557,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.0226670702843852e+07, - "cpu_time": 2.0105167108108174e+07, + "real_time": 1.9265840666776057e+07, + "cpu_time": 1.9167215027777787e+07, "time_unit": "ns", "model_cost": 2.6010000000000000e+07, "output_rows": 1.3005200000000000e+05, @@ -5576,8 +5576,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 9.4881289115047723e+05, - "cpu_time": 9.2395636553275574e+05, + "real_time": 1.2892789762177157e+05, + "cpu_time": 1.2728620617041340e+05, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -5595,8 +5595,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 4.6812011870053780e-02, - "cpu_time": 4.5869607658913666e-02, + "real_time": 6.7031106854944589e-03, + "cpu_time": 6.6547541383532340e-03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -5613,8 +5613,8 @@ "repetition_index": 0, "threads": 1, "iterations": 5, - "real_time": 1.5696650660247543e+08, - "cpu_time": 1.5580631720000044e+08, + "real_time": 1.5620132120093331e+08, + "cpu_time": 1.5500595780000025e+08, "time_unit": "ns", "model_cost": 2.6010006000000000e+07, "output_rows": 1.1822730000000000e+06, @@ -5631,8 +5631,8 @@ "repetition_index": 1, "threads": 1, "iterations": 5, - "real_time": 1.6000344740168658e+08, - "cpu_time": 1.5869880280000073e+08, + "real_time": 1.5661140320007688e+08, + "cpu_time": 1.5556184579999977e+08, "time_unit": "ns", "model_cost": 2.6010006000000000e+07, "output_rows": 1.1822730000000000e+06, @@ -5649,8 +5649,8 @@ "repetition_index": 2, "threads": 1, "iterations": 5, - "real_time": 1.5815563900105190e+08, - "cpu_time": 1.5716567460000020e+08, + "real_time": 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5.1000000000000000e+04, @@ -5928,8 +5928,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.6824896851882197e+07, - "cpu_time": 3.6546611944444329e+07, + "real_time": 3.5574296233244240e+07, + "cpu_time": 3.5336538033333413e+07, "time_unit": "ns", "model_cost": 2.6010000000000000e+07, "output_rows": 5.1000000000000000e+04, @@ -5947,8 +5947,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.7531672499931626e+07, - "cpu_time": 3.7260668722221933e+07, + "real_time": 3.4202010498847812e+07, + "cpu_time": 3.3985413199999921e+07, "time_unit": "ns", "model_cost": 2.6010000000000000e+07, "output_rows": 5.1000000000000000e+04, @@ -5966,8 +5966,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.6802290246143334e+06, - "cpu_time": 1.6618493306008400e+06, + "real_time": 2.9620227596312016e+06, + "cpu_time": 2.9187021459107138e+06, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -5985,8 +5985,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 4.5627528336945056e-02, - "cpu_time": 4.5472049040471113e-02, + "real_time": 8.3263003720736625e-02, + "cpu_time": 8.2597286218515920e-02, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -6002,9 +6002,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 45, - "real_time": 1.5994518244406207e+07, - "cpu_time": 1.5873727133333344e+07, + "iterations": 43, + "real_time": 1.6169042418559277e+07, + "cpu_time": 1.6035586255813889e+07, "time_unit": "ns", "lhs_rows": 6.5848000000000000e+04, "model_cost": 2.6009960000000000e+07, @@ -6021,9 +6021,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 45, - "real_time": 1.5248194644421648e+07, - "cpu_time": 1.5150187600000069e+07, + "iterations": 43, + "real_time": 1.7688036069874976e+07, + "cpu_time": 1.7544754139534719e+07, "time_unit": "ns", "lhs_rows": 6.5848000000000000e+04, "model_cost": 2.6009960000000000e+07, @@ -6040,9 +6040,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 45, - "real_time": 1.5391908644556277e+07, - "cpu_time": 1.5276462155555731e+07, + "iterations": 43, + "real_time": 1.6172905348993966e+07, + "cpu_time": 1.6059888651162762e+07, "time_unit": "ns", "lhs_rows": 6.5848000000000000e+04, "model_cost": 2.6009960000000000e+07, @@ -6061,8 +6061,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.5544873844461376e+07, - "cpu_time": 1.5433458962963047e+07, + "real_time": 1.6676661279142739e+07, + "cpu_time": 1.6546743015503792e+07, "time_unit": "ns", "lhs_rows": 6.5848000000000000e+04, "model_cost": 2.6009960000000000e+07, @@ -6081,8 +6081,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.5391908644556275e+07, - "cpu_time": 1.5276462155555731e+07, + "real_time": 1.6172905348993966e+07, + "cpu_time": 1.6059888651162764e+07, "time_unit": "ns", "lhs_rows": 6.5848000000000000e+04, "model_cost": 2.6009960000000000e+07, @@ -6101,8 +6101,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.9597789491343574e+05, - "cpu_time": 3.8647556484990980e+05, + "real_time": 8.7587839113336650e+05, + "cpu_time": 8.6438839902667922e+05, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -6121,8 +6121,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 2.5473213798677584e-02, - "cpu_time": 2.5041409432413519e-02, + "real_time": 5.2521207720925230e-02, + "cpu_time": 5.2239186782363979e-02, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -6140,8 +6140,8 @@ "repetition_index": 0, "threads": 1, "iterations": 34, - "real_time": 2.1356363676365137e+07, - "cpu_time": 2.1272632235294130e+07, + "real_time": 2.2066501999849122e+07, + "cpu_time": 2.1965138823529091e+07, "time_unit": "ns", "lhs_rows": 6.1000000000000000e+02, "model_cost": 2.6047000000000000e+07, @@ -6159,8 +6159,8 @@ "repetition_index": 1, "threads": 1, "iterations": 34, - "real_time": 2.1404013382647570e+07, - "cpu_time": 2.1323763823529623e+07, + "real_time": 2.1873878794448342e+07, + "cpu_time": 2.1796083499999952e+07, "time_unit": "ns", "lhs_rows": 6.1000000000000000e+02, "model_cost": 2.6047000000000000e+07, @@ -6178,8 +6178,8 @@ "repetition_index": 2, "threads": 1, "iterations": 34, - "real_time": 2.1686173588129938e+07, - "cpu_time": 2.1614624823529694e+07, + "real_time": 2.1685511941220336e+07, + "cpu_time": 2.1595912823529605e+07, "time_unit": "ns", "lhs_rows": 6.1000000000000000e+02, "model_cost": 2.6047000000000000e+07, @@ -6198,8 +6198,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.1482183549047548e+07, - "cpu_time": 2.1403673627451148e+07, + "real_time": 2.1875297578505937e+07, + "cpu_time": 2.1785711715686217e+07, "time_unit": "ns", "lhs_rows": 6.1000000000000000e+02, "model_cost": 2.6047000000000000e+07, @@ -6218,8 +6218,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.1404013382647570e+07, - "cpu_time": 2.1323763823529623e+07, + "real_time": 2.1873878794448342e+07, + "cpu_time": 2.1796083499999952e+07, "time_unit": "ns", "lhs_rows": 6.1000000000000000e+02, "model_cost": 2.6047000000000000e+07, @@ -6238,8 +6238,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.7825985431668264e+05, - "cpu_time": 1.8446927975681992e+05, + "real_time": 1.9049899187319237e+05, + "cpu_time": 1.8483138316086572e+05, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -6258,8 +6258,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 8.2980323629432036e-03, - "cpu_time": 8.6185802945635444e-03, + "real_time": 8.7084068771878763e-03, + "cpu_time": 8.4840644902035873e-03, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -6276,9 +6276,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 28, - "real_time": 2.5271681428941000e+07, - "cpu_time": 2.4999393214286111e+07, + "iterations": 29, + "real_time": 2.4021322448569320e+07, + "cpu_time": 2.3765103241379101e+07, "time_unit": "ns", "lhs_rows": 5.0000000000000000e+02, "model_cost": 2.6000000000000000e+07, @@ -6295,9 +6295,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 28, - "real_time": 2.4712777035996884e+07, - "cpu_time": 2.4465697035714027e+07, + "iterations": 29, + "real_time": 2.3866731138570748e+07, + "cpu_time": 2.3617933034482863e+07, "time_unit": "ns", "lhs_rows": 5.0000000000000000e+02, "model_cost": 2.6000000000000000e+07, @@ -6314,9 +6314,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 28, - "real_time": 2.4799746999633498e+07, - "cpu_time": 2.4566264250000000e+07, + "iterations": 29, + "real_time": 2.3685086138169121e+07, + "cpu_time": 2.3443975241379227e+07, "time_unit": "ns", "lhs_rows": 5.0000000000000000e+02, "model_cost": 2.6000000000000000e+07, @@ -6335,8 +6335,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.4928068488190457e+07, - "cpu_time": 2.4677118166666713e+07, + "real_time": 2.3857713241769727e+07, + "cpu_time": 2.3609003839080393e+07, "time_unit": "ns", "lhs_rows": 5.0000000000000000e+02, "model_cost": 2.6000000000000000e+07, @@ -6355,8 +6355,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.4799746999633502e+07, - "cpu_time": 2.4566264250000000e+07, + "real_time": 2.3866731138570752e+07, + "cpu_time": 2.3617933034482863e+07, "time_unit": "ns", "lhs_rows": 5.0000000000000000e+02, "model_cost": 2.6000000000000000e+07, @@ -6375,8 +6375,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.0073798135960329e+05, - "cpu_time": 2.8359186498553347e+05, + "real_time": 1.6829945322241614e+05, + "cpu_time": 1.6075010418015547e+05, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -6395,8 +6395,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.2064231189916553e-02, - "cpu_time": 1.1492098188701909e-02, + "real_time": 7.0542994425702105e-03, + "cpu_time": 6.8088473904207268e-03, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, diff --git a/research/benchmark-results/operator-cost.json b/research/benchmark-results/operator-cost.json index 2fc790c..6cbf34b 100644 --- a/research/benchmark-results/operator-cost.json +++ b/research/benchmark-results/operator-cost.json @@ -1,6 +1,6 @@ { "context": { - "date": "2026-06-02T01:17:41+03:00", + "date": "2026-06-09T23:47:09+03:00", "host_name": "nixos", "executable": "./build-release/bin/benchmarks", "num_cpus": 8, @@ -32,7 +32,7 @@ "num_sharing": 8 } ], - "load_avg": [1.43018,0.73584,0.578125], + "load_avg": [1.61963,0.920898,0.974609], "library_version": "v1.9.0", "library_build_type": "release", "json_schema_version": 1 @@ -47,9 +47,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 18000, - "real_time": 3.9246624055926484e+04, - "cpu_time": 3.9044977722222226e+04, + "iterations": 18250, + "real_time": 3.8845030684980935e+04, + "cpu_time": 3.8709108164383571e+04, "time_unit": "ns", "model_cost": 1.0240000000000000e+05, "output_rows": 1.0240000000000000e+03, @@ -65,9 +65,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 18000, - "real_time": 3.7743562666744561e+04, - "cpu_time": 3.7344257222222237e+04, + "iterations": 18250, + "real_time": 3.9302844657843350e+04, + "cpu_time": 3.9168997315068496e+04, "time_unit": "ns", "model_cost": 1.0240000000000000e+05, "output_rows": 1.0240000000000000e+03, @@ -83,9 +83,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 18000, - "real_time": 3.8427845222435688e+04, - "cpu_time": 3.8240413166666687e+04, + "iterations": 18250, + "real_time": 3.9311777588222430e+04, + "cpu_time": 3.9168773041095883e+04, "time_unit": "ns", "model_cost": 1.0240000000000000e+05, "output_rows": 1.0240000000000000e+03, @@ -103,8 +103,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.8472677315035580e+04, - "cpu_time": 3.8209882703703712e+04, + "real_time": 3.9153217643682234e+04, + "cpu_time": 3.9015626173515986e+04, "time_unit": "ns", "model_cost": 1.0240000000000000e+05, "output_rows": 1.0240000000000000e+03, @@ -122,8 +122,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.8427845222435681e+04, - "cpu_time": 3.8240413166666687e+04, + "real_time": 3.9302844657843343e+04, + "cpu_time": 3.9168773041095883e+04, "time_unit": "ns", "model_cost": 1.0240000000000000e+05, "output_rows": 1.0240000000000000e+03, @@ -141,8 +141,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 7.5253293768943513e+02, - "cpu_time": 8.5077120112114517e+02, + "real_time": 2.6693510530977704e+02, + "cpu_time": 2.6545240631176927e+02, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -160,8 +160,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.9560191549116243e-02, - "cpu_time": 2.2265737053379631e-02, + "real_time": 6.8177054498827303e-03, + "cpu_time": 6.8037458922538020e-03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -177,9 +177,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 9874, - "real_time": 7.0670865505453388e+04, - "cpu_time": 7.0332341097832730e+04, + "iterations": 9649, + "real_time": 7.1402024148586672e+04, + "cpu_time": 7.1145830448751149e+04, "time_unit": "ns", "model_cost": 2.0480000000000000e+05, "output_rows": 2.0480000000000000e+03, @@ -195,9 +195,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 9874, - "real_time": 6.9583156370238663e+04, - "cpu_time": 6.9237687563297528e+04, + "iterations": 9649, + "real_time": 7.4413162504712483e+04, + "cpu_time": 7.4079988496217149e+04, "time_unit": "ns", "model_cost": 2.0480000000000000e+05, "output_rows": 2.0480000000000000e+03, @@ -213,9 +213,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 9874, - "real_time": 6.8866311321799498e+04, - "cpu_time": 6.8571118594288011e+04, + "iterations": 9649, + "real_time": 7.1228492588142806e+04, + "cpu_time": 7.1011361902787845e+04, "time_unit": "ns", "model_cost": 2.0480000000000000e+05, "output_rows": 2.0480000000000000e+03, @@ -233,8 +233,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 6.9706777732497183e+04, - "cpu_time": 6.9380382418472742e+04, + "real_time": 7.2347893080480644e+04, + "cpu_time": 7.2079060282585371e+04, "time_unit": "ns", "model_cost": 2.0480000000000000e+05, "output_rows": 2.0480000000000000e+03, @@ -252,8 +252,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 6.9583156370238663e+04, - "cpu_time": 6.9237687563297528e+04, + "real_time": 7.1402024148586672e+04, + "cpu_time": 7.1145830448751163e+04, "time_unit": "ns", "model_cost": 2.0480000000000000e+05, "output_rows": 2.0480000000000000e+03, @@ -271,8 +271,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 9.0860642268310050e+02, - "cpu_time": 8.8923986810085864e+02, + "real_time": 1.7906791020775183e+03, + "cpu_time": 1.7341585090601286e+03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -290,8 +290,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.3034692640218110e-02, - "cpu_time": 1.2816877582734336e-02, + "real_time": 2.4750950246547554e-02, + "cpu_time": 2.4059116507087832e-02, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -307,9 +307,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 4575, - "real_time": 1.5385901420873392e+05, - "cpu_time": 1.5231552612021856e+05, + "iterations": 4370, + "real_time": 1.6078171190391219e+05, + "cpu_time": 1.5910665194508014e+05, "time_unit": "ns", "model_cost": 4.0960000000000000e+05, "output_rows": 4.0960000000000000e+03, @@ -325,9 +325,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 4575, - "real_time": 1.5345261289733028e+05, - "cpu_time": 1.5185940131147558e+05, + "iterations": 4370, + "real_time": 1.5755818626769868e+05, + "cpu_time": 1.5598532494279178e+05, "time_unit": "ns", "model_cost": 4.0960000000000000e+05, "output_rows": 4.0960000000000000e+03, @@ -343,9 +343,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 4575, - "real_time": 1.5517629311506540e+05, - "cpu_time": 1.5297860153005453e+05, + "iterations": 4370, + "real_time": 1.5609967619557897e+05, + "cpu_time": 1.5458381922196803e+05, "time_unit": "ns", "model_cost": 4.0960000000000000e+05, "output_rows": 4.0960000000000000e+03, @@ -363,8 +363,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.5416264007370986e+05, - "cpu_time": 1.5238450965391620e+05, + "real_time": 1.5814652478906329e+05, + "cpu_time": 1.5655859870328000e+05, "time_unit": "ns", "model_cost": 4.0960000000000000e+05, "output_rows": 4.0960000000000000e+03, @@ -382,8 +382,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.5385901420873392e+05, - "cpu_time": 1.5231552612021856e+05, + "real_time": 1.5755818626769868e+05, + "cpu_time": 1.5598532494279181e+05, "time_unit": "ns", "model_cost": 4.0960000000000000e+05, "output_rows": 4.0960000000000000e+03, @@ -401,8 +401,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 9.0106041566070314e+02, - "cpu_time": 5.6277999987616192e+02, + "real_time": 2.3958237112342867e+03, + "cpu_time": 2.3152723519420706e+03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -420,8 +420,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 5.8448688685525793e-03, - "cpu_time": 3.6931575338878205e-03, + "real_time": 1.5149392087052497e-02, + "cpu_time": 1.4788535226545590e-02, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -437,9 +437,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 1956, - "real_time": 3.5587616564577649e+05, - "cpu_time": 3.5244620603271981e+05, + "iterations": 1960, + "real_time": 3.5702310612707929e+05, + "cpu_time": 3.5412047142857121e+05, "time_unit": "ns", "model_cost": 8.1920000000000000e+05, "output_rows": 8.1920000000000000e+03, @@ -455,9 +455,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 1956, - "real_time": 4.1456895245413884e+05, - "cpu_time": 4.0906104498977540e+05, + "iterations": 1960, + "real_time": 3.6373994132320932e+05, + "cpu_time": 3.6082918061224459e+05, "time_unit": "ns", "model_cost": 8.1920000000000000e+05, "output_rows": 8.1920000000000000e+03, @@ -473,9 +473,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 1956, - "real_time": 3.9999774540357903e+05, - "cpu_time": 3.9529778834355902e+05, + "iterations": 1960, + "real_time": 3.5611569438348245e+05, + "cpu_time": 3.5327081122448994e+05, "time_unit": "ns", "model_cost": 8.1920000000000000e+05, "output_rows": 8.1920000000000000e+03, @@ -493,8 +493,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.9014762116783141e+05, - "cpu_time": 3.8560167978868471e+05, + "real_time": 3.5895958061125694e+05, + "cpu_time": 3.5607348775510187e+05, "time_unit": "ns", "model_cost": 8.1920000000000000e+05, "output_rows": 8.1920000000000000e+03, @@ -512,8 +512,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.9999774540357915e+05, - "cpu_time": 3.9529778834355902e+05, + "real_time": 3.5702310612707929e+05, + "cpu_time": 3.5412047142857121e+05, "time_unit": "ns", "model_cost": 8.1920000000000000e+05, "output_rows": 8.1920000000000000e+03, @@ -531,8 +531,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.0561078456555570e+04, - "cpu_time": 2.9526613222045959e+04, + "real_time": 4.1647011201754240e+03, + "cpu_time": 4.1404035466580581e+03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -550,8 +550,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 7.8332089697425028e-02, - "cpu_time": 7.6572833495504908e-02, + "real_time": 1.1602145046758558e-02, + "cpu_time": 1.1627946727406227e-02, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -567,9 +567,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 822, - "real_time": 9.0433581508234388e+05, - "cpu_time": 8.9520096107055817e+05, + "iterations": 826, + "real_time": 7.9650909079100087e+05, + "cpu_time": 7.8999985230024299e+05, "time_unit": "ns", "model_cost": 1.6384000000000000e+06, "output_rows": 1.6384000000000000e+04, @@ -585,9 +585,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 822, - "real_time": 9.3126070681152702e+05, - "cpu_time": 9.2162533211678872e+05, + "iterations": 826, + "real_time": 7.9224021186698088e+05, + "cpu_time": 7.8613784987893619e+05, "time_unit": "ns", "model_cost": 1.6384000000000000e+06, "output_rows": 1.6384000000000000e+04, @@ -603,9 +603,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 822, - "real_time": 9.3750599270387401e+05, - "cpu_time": 9.2805541119221412e+05, + "iterations": 826, + "real_time": 8.0226254599427094e+05, + "cpu_time": 7.9617707748184004e+05, "time_unit": "ns", "model_cost": 1.6384000000000000e+06, "output_rows": 1.6384000000000000e+04, @@ -623,8 +623,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 9.2436750486591493e+05, - "cpu_time": 9.1496056812652026e+05, + "real_time": 7.9700394955075101e+05, + "cpu_time": 7.9077159322033974e+05, "time_unit": "ns", "model_cost": 1.6384000000000000e+06, "output_rows": 1.6384000000000000e+04, @@ -642,8 +642,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 9.3126070681152691e+05, - "cpu_time": 9.2162533211678872e+05, + "real_time": 7.9650909079100098e+05, + "cpu_time": 7.8999985230024299e+05, "time_unit": "ns", "model_cost": 1.6384000000000000e+06, "output_rows": 1.6384000000000000e+04, @@ -661,8 +661,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.7626750853705111e+04, - "cpu_time": 1.7411721123530708e+04, + "real_time": 5.0294591392965904e+03, + "cpu_time": 5.0639125931953331e+03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -680,8 +680,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.9068985831843993e-02, - "cpu_time": 1.9030023511486482e-02, + "real_time": 6.3104569834711074e-03, + "cpu_time": 6.4037613852225596e-03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -697,9 +697,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 320, - "real_time": 1.7997137062138792e+06, - "cpu_time": 1.7869147593749966e+06, + "iterations": 336, + "real_time": 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1.9016109770745970e+06, - "cpu_time": 1.8853310885416658e+06, + "real_time": 1.9908873779520886e+06, + "cpu_time": 1.9718231388888909e+06, "time_unit": "ns", "model_cost": 3.2768000000000000e+06, "output_rows": 3.2768000000000000e+04, @@ -772,8 +772,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.9337302750045639e+06, - "cpu_time": 1.9160913781250010e+06, + "real_time": 1.9899760088827906e+06, + "cpu_time": 1.9721588750000026e+06, "time_unit": "ns", "model_cost": 3.2768000000000000e+06, "output_rows": 3.2768000000000000e+04, @@ -791,8 +791,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 9.0232113503731802e+04, - "cpu_time": 8.7204670041214515e+04, + "real_time": 2.6044221085922400e+04, + "cpu_time": 2.4709055986958661e+04, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -810,8 +810,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 4.7450353721949598e-02, - "cpu_time": 4.6254300144527263e-02, + "real_time": 1.3081714904794159e-02, + "cpu_time": 1.2531071118722164e-02, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -827,9 +827,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 100, - "real_time": 5.1028332998976111e+06, - "cpu_time": 5.0516337499999860e+06, + "iterations": 136, + "real_time": 4.8432163528238609e+06, + "cpu_time": 4.7967932647058927e+06, "time_unit": "ns", "model_cost": 6.5536000000000000e+06, "output_rows": 6.5536000000000000e+04, @@ -845,9 +845,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 100, - "real_time": 4.9981412800843827e+06, - "cpu_time": 4.9462012100000056e+06, + "iterations": 136, + "real_time": 5.2586463675919846e+06, + "cpu_time": 5.2008186985294158e+06, "time_unit": "ns", "model_cost": 6.5536000000000000e+06, "output_rows": 6.5536000000000000e+04, @@ -863,9 +863,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 100, - "real_time": 4.7970186099701095e+06, - "cpu_time": 4.7305430800000010e+06, + "iterations": 136, + "real_time": 4.7411703380930703e+06, + "cpu_time": 4.6977030514705861e+06, "time_unit": "ns", "model_cost": 6.5536000000000000e+06, "output_rows": 6.5536000000000000e+04, @@ -883,8 +883,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 4.9659977299840339e+06, - "cpu_time": 4.9094593466666630e+06, + "real_time": 4.9476776861696383e+06, + "cpu_time": 4.8984383382352991e+06, "time_unit": "ns", "model_cost": 6.5536000000000000e+06, "output_rows": 6.5536000000000000e+04, @@ -902,8 +902,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 4.9981412800843827e+06, - "cpu_time": 4.9462012100000046e+06, + "real_time": 4.8432163528238619e+06, + "cpu_time": 4.7967932647058936e+06, "time_unit": "ns", "model_cost": 6.5536000000000000e+06, "output_rows": 6.5536000000000000e+04, @@ -921,8 +921,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.5542059710238207e+05, - "cpu_time": 1.6366819477578657e+05, + "real_time": 2.7409758827792853e+05, + "cpu_time": 2.6651478253270546e+05, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -940,8 +940,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 3.1296952909175370e-02, - "cpu_time": 3.3337315418837526e-02, + "real_time": 5.5399240949773276e-02, + "cpu_time": 5.4408112163502206e-02, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -957,9 +957,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 64, - "real_time": 1.1002289312500579e+07, - "cpu_time": 1.0876498062499989e+07, + "iterations": 63, + "real_time": 1.0852161285814844e+07, + "cpu_time": 1.0741227460317455e+07, "time_unit": "ns", "model_cost": 1.3107200000000000e+07, "output_rows": 1.3107200000000000e+05, @@ -975,9 +975,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 64, - "real_time": 1.4250770406306401e+07, - "cpu_time": 1.4081002734374959e+07, + "iterations": 63, + "real_time": 1.0996642793687653e+07, + "cpu_time": 1.0886871222222256e+07, "time_unit": "ns", "model_cost": 1.3107200000000000e+07, "output_rows": 1.3107200000000000e+05, @@ -993,9 +993,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 64, - "real_time": 1.1162739046994830e+07, - "cpu_time": 1.1038912562500004e+07, + "iterations": 63, + "real_time": 1.0878575555536719e+07, + "cpu_time": 1.0770946809523810e+07, "time_unit": "ns", "model_cost": 1.3107200000000000e+07, "output_rows": 1.3107200000000000e+05, @@ -1013,8 +1013,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.2138599588600604e+07, - "cpu_time": 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"output_rows": 2.6214400000000000e+05, @@ -1181,8 +1181,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 4.3888034953807639e+06, - "cpu_time": 4.2992318461600589e+06, + "real_time": 6.6404529515532206e+05, + "cpu_time": 6.3235548813659884e+05, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -1200,8 +1200,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.7113876756223478e-01, - "cpu_time": 1.6943170025722620e-01, + "real_time": 2.9365475013176764e-02, + "cpu_time": 2.8225558810381718e-02, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -1217,9 +1217,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 9876, - "real_time": 7.0365239469032807e+04, - "cpu_time": 7.0085761036856959e+04, + "iterations": 9645, + "real_time": 7.3610573354408436e+04, + "cpu_time": 7.3291684707102002e+04, "time_unit": "ns", "model_cost": 1.0240000000000000e+05, "output_rows": 5.1200000000000000e+02, @@ -1235,9 +1235,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 9876, - "real_time": 7.0987971344471502e+04, - "cpu_time": 7.0724434690157708e+04, + "iterations": 9645, + "real_time": 7.4202260965681271e+04, + "cpu_time": 7.3951352825298120e+04, "time_unit": "ns", "model_cost": 1.0240000000000000e+05, "output_rows": 5.1200000000000000e+02, @@ -1253,9 +1253,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 9876, - "real_time": 6.9922420007443376e+04, - "cpu_time": 6.9626919603078073e+04, + "iterations": 9645, + "real_time": 7.4259590149575626e+04, + "cpu_time": 7.4026318818040207e+04, "time_unit": "ns", "model_cost": 1.0240000000000000e+05, "output_rows": 5.1200000000000000e+02, @@ -1273,8 +1273,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 7.0425210273649223e+04, - 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+1383,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 5438, - "real_time": 1.2535870117730908e+05, - "cpu_time": 1.2493645770503869e+05, + "iterations": 4705, + "real_time": 1.3328246121179988e+05, + "cpu_time": 1.3284171179596151e+05, "time_unit": "ns", "model_cost": 2.0480000000000000e+05, "output_rows": 1.0240000000000000e+03, @@ -1403,8 +1403,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.2548200036764139e+05, - "cpu_time": 1.2505435785215166e+05, + "real_time": 1.3753263981627868e+05, + "cpu_time": 1.3699446900460505e+05, "time_unit": "ns", "model_cost": 2.0480000000000000e+05, "output_rows": 1.0240000000000000e+03, @@ -1422,8 +1422,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.2535870117730911e+05, - "cpu_time": 1.2493645770503870e+05, + "real_time": 1.3930782720651457e+05, + "cpu_time": 1.3868528990435688e+05, "time_unit": "ns", "model_cost": 2.0480000000000000e+05, "output_rows": 1.0240000000000000e+03, @@ -1441,8 +1441,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.2854768661543000e+02, - "cpu_time": 3.2190071984842018e+02, + "real_time": 3.6973564847743728e+03, + "cpu_time": 3.6170013919012345e+03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -1460,8 +1460,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 2.6182853768097411e-03, - "cpu_time": 2.5740863843305216e-03, + "real_time": 2.6883483729487356e-02, + "cpu_time": 2.6402535943109127e-02, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -1477,9 +1477,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 2896, - "real_time": 2.4423689675277474e+05, - "cpu_time": 2.4295935911602259e+05, + "iterations": 2692, + "real_time": 2.5931452748671529e+05, + "cpu_time": 2.5830611961367010e+05, "time_unit": "ns", "model_cost": 4.0960000000000000e+05, "output_rows": 2.0480000000000000e+03, @@ -1495,9 +1495,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 2896, - "real_time": 2.4357472582939221e+05, - "cpu_time": 2.4244804558010976e+05, + "iterations": 2692, + "real_time": 2.6544695654128061e+05, + "cpu_time": 2.6433167161961424e+05, "time_unit": "ns", "model_cost": 4.0960000000000000e+05, "output_rows": 2.0480000000000000e+03, @@ -1513,9 +1513,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 2896, - "real_time": 2.4557083322093674e+05, - "cpu_time": 2.4441006284530324e+05, + "iterations": 2692, + "real_time": 2.6003982577532434e+05, + "cpu_time": 2.5906983543833590e+05, "time_unit": "ns", "model_cost": 4.0960000000000000e+05, "output_rows": 2.0480000000000000e+03, @@ -1533,8 +1533,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.4446081860103455e+05, - "cpu_time": 2.4327248918047853e+05, + "real_time": 2.6160043660110669e+05, + "cpu_time": 2.6056920889054006e+05, "time_unit": "ns", "model_cost": 4.0960000000000000e+05, "output_rows": 2.0480000000000000e+03, @@ -1552,8 +1552,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.4423689675277472e+05, - "cpu_time": 2.4295935911602259e+05, + "real_time": 2.6003982577532428e+05, + "cpu_time": 2.5906983543833590e+05, "time_unit": "ns", "model_cost": 4.0960000000000000e+05, "output_rows": 2.0480000000000000e+03, @@ -1571,8 +1571,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.0167187050747069e+03, - "cpu_time": 1.0177994719893127e+03, + "real_time": 3.3508657298542607e+03, + "cpu_time": 3.2806873983889450e+03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -1590,8 +1590,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", 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4.0960000000000000e+03, @@ -1643,9 +1643,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 1470, - "real_time": 4.8689427823192585e+05, - "cpu_time": 4.8413452653061232e+05, + "iterations": 1361, + "real_time": 5.0875262967940397e+05, + "cpu_time": 5.0666673034533591e+05, "time_unit": "ns", "model_cost": 8.1920000000000000e+05, "output_rows": 4.0960000000000000e+03, @@ -1663,8 +1663,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 4.7969575396877801e+05, - "cpu_time": 4.7734947256235854e+05, + "real_time": 5.1383255301919294e+05, + "cpu_time": 5.1151141660543793e+05, "time_unit": "ns", "model_cost": 8.1920000000000000e+05, "output_rows": 4.0960000000000000e+03, @@ -1682,8 +1682,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 4.7748832381132449e+05, - "cpu_time": 4.7539816870748275e+05, + "real_time": 5.1609085009533918e+05, + "cpu_time": 5.1370937252020527e+05, "time_unit": "ns", "model_cost": 8.1920000000000000e+05, "output_rows": 4.0960000000000000e+03, @@ -1701,8 +1701,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 6.3875863923375291e+03, - "cpu_time": 6.0501935607376499e+03, + "real_time": 4.4083501218641932e+03, + "cpu_time": 4.2016173482983295e+03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -1720,8 +1720,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.3315911886835834e-02, - "cpu_time": 1.2674557967480070e-02, + "real_time": 8.5793515727282683e-03, + "cpu_time": 8.2141223282594118e-03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -1737,9 +1737,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 687, - "real_time": 9.9092686463762855e+05, - "cpu_time": 9.8521732751091616e+05, + "iterations": 643, + "real_time": 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3.2768000000000000e+04, @@ -2015,9 +2015,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 161, - "real_time": 4.4736977763689468e+06, - "cpu_time": 4.4500819565217569e+06, + "iterations": 139, + "real_time": 4.6125408992750850e+06, + "cpu_time": 4.5875623956834106e+06, "time_unit": "ns", "model_cost": 6.5536000000000000e+06, "output_rows": 3.2768000000000000e+04, @@ -2033,9 +2033,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 161, - "real_time": 4.5514333539827503e+06, - "cpu_time": 4.5265054906832362e+06, + "iterations": 139, + "real_time": 4.6494261149347182e+06, + "cpu_time": 4.6240235035970937e+06, "time_unit": "ns", "model_cost": 6.5536000000000000e+06, "output_rows": 3.2768000000000000e+04, @@ -2053,8 +2053,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 4.4363837494352432e+06, - "cpu_time": 4.4134838095238237e+06, + "real_time": 4.6021331822481761e+06, + "cpu_time": 4.5763759568345072e+06, "time_unit": "ns", "model_cost": 6.5536000000000000e+06, "output_rows": 3.2768000000000000e+04, @@ -2072,8 +2072,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 4.4736977763689468e+06, - "cpu_time": 4.4500819565217569e+06, + "real_time": 4.6125408992750859e+06, + "cpu_time": 4.5875623956834106e+06, "time_unit": "ns", "model_cost": 6.5536000000000000e+06, "output_rows": 3.2768000000000000e+04, @@ -2091,8 +2091,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.3755621452827789e+05, - "cpu_time": 1.3509148336150381e+05, + "real_time": 5.3264937027895743e+04, + "cpu_time": 5.4114984013475019e+04, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -2110,8 +2110,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 3.1006383193471251e-02, - "cpu_time": 3.0608809093168283e-02, + "real_time": 1.1573966879827547e-02, + "cpu_time": 1.1824855414830584e-02, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -2127,9 +2127,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 69, - "real_time": 9.6964141739063319e+06, - "cpu_time": 9.6437748550724182e+06, + "iterations": 73, + "real_time": 9.5998669040791243e+06, + "cpu_time": 9.5496207534246780e+06, "time_unit": "ns", "model_cost": 1.3107200000000000e+07, "output_rows": 6.5536000000000000e+04, @@ -2145,9 +2145,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 69, - "real_time": 9.9056520726214554e+06, - "cpu_time": 9.8435164347825982e+06, + "iterations": 73, + "real_time": 1.0333870123347830e+07, + "cpu_time": 1.0266777383561656e+07, "time_unit": "ns", "model_cost": 1.3107200000000000e+07, "output_rows": 6.5536000000000000e+04, @@ -2163,9 +2163,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 69, - 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1.3107200000000000e+05, @@ -2275,9 +2275,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 35, - "real_time": 2.0028071428532712e+07, - "cpu_time": 1.9914758771428507e+07, + "iterations": 34, + "real_time": 2.0398640734663997e+07, + "cpu_time": 2.0281736617647123e+07, "time_unit": "ns", "model_cost": 2.6214400000000000e+07, "output_rows": 1.3107200000000000e+05, @@ -2293,9 +2293,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 35, - "real_time": 2.0220994971376579e+07, - "cpu_time": 2.0097260742857210e+07, + "iterations": 34, + "real_time": 2.0609233647358932e+07, + "cpu_time": 2.0480860647058982e+07, "time_unit": "ns", "model_cost": 2.6214400000000000e+07, "output_rows": 1.3107200000000000e+05, @@ -2313,8 +2313,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.0011647723870173e+07, - "cpu_time": 1.9895345057142861e+07, + "real_time": 2.0746153950812202e+07, + "cpu_time": 2.0612936431372628e+07, "time_unit": "ns", "model_cost": 2.6214400000000000e+07, "output_rows": 1.3107200000000000e+05, @@ -2332,8 +2332,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.0028071428532712e+07, - "cpu_time": 1.9914758771428507e+07, + "real_time": 2.0609233647358935e+07, + "cpu_time": 2.0480860647058982e+07, "time_unit": "ns", "model_cost": 2.6214400000000000e+07, "output_rows": 1.3107200000000000e+05, @@ -2351,8 +2351,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.1802354340405521e+05, - "cpu_time": 2.1228935411897779e+05, + "real_time": 4.3254389361598721e+05, + "cpu_time": 4.1337731502374797e+05, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -2370,8 +2370,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.0894832170366147e-02, - "cpu_time": 1.0670302701925810e-02, + "real_time": 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3.2768000000000000e+04, @@ -3055,9 +3055,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 249, - "real_time": 3.2144210803468362e+06, - "cpu_time": 3.1946812168674762e+06, + "iterations": 223, + "real_time": 3.1441380807528519e+06, + "cpu_time": 3.1264916681614448e+06, "time_unit": "ns", "model_cost": 7.2089600000000000e+05, "output_rows": 3.2768000000000000e+04, @@ -3073,9 +3073,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 249, - "real_time": 3.0210174939943007e+06, - "cpu_time": 3.0067343172690705e+06, + "iterations": 223, + "real_time": 3.1486676008526166e+06, + "cpu_time": 3.1306725739910351e+06, "time_unit": "ns", "model_cost": 7.2089600000000000e+05, "output_rows": 3.2768000000000000e+04, @@ -3093,8 +3093,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.0635825194383524e+06, - "cpu_time": 3.0466744310575617e+06, + "real_time": 3.1219361389876497e+06, + "cpu_time": 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1.3107200000000000e+05, @@ -3315,9 +3315,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 50, - "real_time": 1.3940036760177465e+07, - "cpu_time": 1.3849617119999919e+07, + "iterations": 49, + "real_time": 1.4854621898254609e+07, + "cpu_time": 1.4734450653061284e+07, "time_unit": "ns", "model_cost": 2.8835840000000000e+06, "output_rows": 1.3107200000000000e+05, @@ -3333,9 +3333,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 50, - "real_time": 1.3394919459824450e+07, - "cpu_time": 1.3302785679999972e+07, + "iterations": 49, + "real_time": 1.4192063694021532e+07, + "cpu_time": 1.4077397938775642e+07, "time_unit": "ns", "model_cost": 2.8835840000000000e+06, "output_rows": 1.3107200000000000e+05, @@ -3353,8 +3353,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.3727593486643551e+07, - "cpu_time": 1.3637235786666652e+07, + "real_time": 1.4333160068058932e+07, + "cpu_time": 1.4219076190476254e+07, "time_unit": "ns", "model_cost": 2.8835840000000000e+06, "output_rows": 1.3107200000000000e+05, @@ -3372,8 +3372,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.3847824239928739e+07, - "cpu_time": 1.3759304560000060e+07, + "real_time": 1.4192063694021530e+07, + "cpu_time": 1.4077397938775642e+07, "time_unit": "ns", "model_cost": 2.8835840000000000e+06, "output_rows": 1.3107200000000000e+05, @@ -3391,8 +3391,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.9177010352796857e+05, - "cpu_time": 2.9314116908150271e+05, + "real_time": 4.6717689762875438e+05, + "cpu_time": 4.6115746861519286e+05, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -3410,8 +3410,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 2.1254279113950333e-02, - "cpu_time": 2.1495644254249213e-02, + "real_time": 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1.0240000000000000e+03, @@ -3632,8 +3632,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.1432597097089760e+05, - "cpu_time": 1.1395518596549467e+05, + "real_time": 1.1911438107141195e+05, + "cpu_time": 1.1872463614663183e+05, "time_unit": "ns", "model_cost": 1.2390400000000000e+05, "output_rows": 1.0240000000000000e+03, @@ -3651,8 +3651,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 5.8898294096836742e+02, - "cpu_time": 6.0417909074353395e+02, + "real_time": 1.1548347164198301e+03, + "cpu_time": 1.1653044664218460e+03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -3670,8 +3670,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 5.1592564642189455e-03, - "cpu_time": 5.3095933382833983e-03, + "real_time": 9.6982071366080967e-03, + "cpu_time": 9.8229704749476093e-03, "time_unit": "ns", "model_cost": 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4.0960000000000000e+03, @@ -3892,8 +3892,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 6.0084273609896225e+05, - "cpu_time": 5.9806915538195160e+05, + "real_time": 6.1954984601551632e+05, + "cpu_time": 6.1700765120593796e+05, "time_unit": "ns", "model_cost": 5.8572800000000000e+05, "output_rows": 4.0960000000000000e+03, @@ -3911,8 +3911,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.8894463778379418e+03, - "cpu_time": 3.6075368089105755e+03, + "real_time": 1.7605003411703383e+04, + "cpu_time": 1.7229347832970187e+04, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -3930,8 +3930,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 6.4823335176006436e-03, - "cpu_time": 6.0383436574528134e-03, + "real_time": 2.8214879933095228e-02, + "cpu_time": 2.7729505639523571e-02, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -3947,9 +3947,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 521, - "real_time": 1.3522237620067080e+06, - "cpu_time": 1.3461664817658223e+06, + "iterations": 486, + "real_time": 1.3865247675301048e+06, + "cpu_time": 1.3793727798353981e+06, "time_unit": "ns", "model_cost": 1.2615680000000000e+06, "output_rows": 8.1920000000000000e+03, @@ -3965,9 +3965,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 521, - "real_time": 1.3507929174859619e+06, - "cpu_time": 1.3444507408829364e+06, + "iterations": 486, + "real_time": 1.3822861172813624e+06, + "cpu_time": 1.3755845164608986e+06, "time_unit": "ns", "model_cost": 1.2615680000000000e+06, "output_rows": 8.1920000000000000e+03, @@ -3983,9 +3983,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 521, - "real_time": 1.3568369097893275e+06, - "cpu_time": 1.3512398905950070e+06, + "iterations": 486, + 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3.5302454527982595e+03, + "real_time": 6.5690348797831539e+03, + "cpu_time": 6.2780001070338121e+03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -4060,8 +4060,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 2.3339815356521244e-03, - "cpu_time": 2.6202649083493334e-03, + "real_time": 4.7327501765713591e-03, + "cpu_time": 4.5461973797376962e-03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -4077,9 +4077,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 213, - "real_time": 3.4479459952901909e+06, - "cpu_time": 3.4295087887324127e+06, + "iterations": 223, + "real_time": 3.1400983946945001e+06, + "cpu_time": 3.1244889417040586e+06, "time_unit": "ns", "model_cost": 2.7033600000000000e+06, "output_rows": 1.6384000000000000e+04, @@ -4095,9 +4095,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - 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@@ -4152,8 +4152,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.2806659108186313e+06, - "cpu_time": 3.2638859014084544e+06, + "real_time": 3.1220474663595650e+06, + "cpu_time": 3.1057233139013243e+06, "time_unit": "ns", "model_cost": 2.7033600000000000e+06, "output_rows": 1.6384000000000000e+04, @@ -4171,8 +4171,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.0243181979663535e+05, - "cpu_time": 1.0085950357350880e+05, + "real_time": 1.0847046197162557e+04, + "cpu_time": 1.1609985680904889e+04, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -4190,8 +4190,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 3.0758773459710530e-02, - "cpu_time": 3.0439364489453891e-02, + "real_time": 3.4681700999824876e-03, + "cpu_time": 3.7317244354075031e-03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -4208,8 +4208,8 @@ "repetition_index": 0, "threads": 1, "iterations": 84, - "real_time": 8.3151214047185406e+06, - "cpu_time": 8.2699810476190643e+06, + "real_time": 8.4118565712179542e+06, + "cpu_time": 8.3578090238095541e+06, "time_unit": "ns", "model_cost": 5.7671680000000000e+06, "output_rows": 3.2768000000000000e+04, @@ -4226,8 +4226,8 @@ "repetition_index": 1, "threads": 1, "iterations": 84, - "real_time": 8.3280912379753618e+06, - "cpu_time": 8.2805903452379452e+06, + "real_time": 8.9663329882092681e+06, + "cpu_time": 8.9157921547619402e+06, "time_unit": "ns", "model_cost": 5.7671680000000000e+06, "output_rows": 3.2768000000000000e+04, @@ -4244,8 +4244,8 @@ "repetition_index": 2, "threads": 1, "iterations": 84, - "real_time": 8.3322243928185860e+06, - "cpu_time": 8.2800271785714580e+06, + "real_time": 8.3241173096688557e+06, + "cpu_time": 8.2769622023808975e+06, "time_unit": "ns", "model_cost": 5.7671680000000000e+06, "output_rows": 3.2768000000000000e+04, @@ -4263,8 +4263,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 8.3251456785041615e+06, - "cpu_time": 8.2768661904761540e+06, + "real_time": 8.5674356230320241e+06, + "cpu_time": 8.5168544603174627e+06, "time_unit": "ns", "model_cost": 5.7671680000000000e+06, "output_rows": 3.2768000000000000e+04, @@ -4282,8 +4282,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 8.3280912379753618e+06, - "cpu_time": 8.2800271785714589e+06, + "real_time": 8.4118565712179542e+06, + "cpu_time": 8.3578090238095531e+06, "time_unit": "ns", "model_cost": 5.7671680000000000e+06, "output_rows": 3.2768000000000000e+04, @@ -4301,8 +4301,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 8.9238607658521032e+03, - "cpu_time": 5.9693536753735389e+03, + "real_time": 3.4822963036378776e+05, + "cpu_time": 3.4784697090858850e+05, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -4320,8 +4320,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.0719164697494540e-03, - "cpu_time": 7.2120939713175814e-04, + "real_time": 4.0645724775291499e-02, + "cpu_time": 4.0842187984931548e-02, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -4337,9 +4337,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 32, - "real_time": 2.1748032593677636e+07, - "cpu_time": 2.1608460312499743e+07, + "iterations": 33, + "real_time": 2.1202786454302967e+07, + "cpu_time": 2.1049975939394187e+07, "time_unit": "ns", "model_cost": 1.2255232000000000e+07, "output_rows": 6.5536000000000000e+04, @@ -4355,9 +4355,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 32, - "real_time": 2.4151544531378023e+07, - "cpu_time": 2.3988753593749747e+07, + "iterations": 33, + "real_time": 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@@ -4523,8 +4523,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 6.3230431259230316e+07, - "cpu_time": 6.2728449111111842e+07, + "real_time": 6.1087202605311602e+07, + "cpu_time": 6.0619245848485112e+07, "time_unit": "ns", "model_cost": 2.5952256000000000e+07, "output_rows": 1.3107200000000000e+05, @@ -4542,8 +4542,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 6.2867419556520574e+07, - "cpu_time": 6.2387320888889670e+07, + "real_time": 6.1025747727729708e+07, + "cpu_time": 6.0603653181818686e+07, "time_unit": "ns", "model_cost": 2.5952256000000000e+07, "output_rows": 1.3107200000000000e+05, @@ -4561,8 +4561,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.1853172940385167e+06, - "cpu_time": 1.1681693359981047e+06, + "real_time": 3.1895579368725367e+05, + "cpu_time": 2.6298346611344855e+05, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -4580,8 +4580,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.8745994142899115e-02, - "cpu_time": 1.8622640166488236e-02, + "real_time": 5.2213193612424495e-03, + "cpu_time": 4.3382833691260874e-03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -4598,8 +4598,8 @@ "repetition_index": 0, "threads": 1, "iterations": 4, - "real_time": 1.8396407225009170e+08, - "cpu_time": 1.8265281450000259e+08, + "real_time": 1.8493113650038141e+08, + "cpu_time": 1.8345952199999970e+08, "time_unit": "ns", "model_cost": 5.4788096000000000e+07, "output_rows": 2.6214400000000000e+05, @@ -4616,8 +4616,8 @@ "repetition_index": 1, "threads": 1, "iterations": 4, - "real_time": 1.7066075975162676e+08, - "cpu_time": 1.6965518974999937e+08, + "real_time": 1.7545055224763927e+08, + "cpu_time": 1.7413243025000113e+08, "time_unit": "ns", "model_cost": 5.4788096000000000e+07, "output_rows": 2.6214400000000000e+05, @@ -4634,8 +4634,8 @@ "repetition_index": 2, "threads": 1, "iterations": 4, - "real_time": 1.7532372624918935e+08, - "cpu_time": 1.7414484524999806e+08, + "real_time": 1.7636709049838829e+08, + "cpu_time": 1.7498345849999809e+08, "time_unit": "ns", "model_cost": 5.4788096000000000e+07, "output_rows": 2.6214400000000000e+05, @@ -4653,8 +4653,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.7664951941696927e+08, - "cpu_time": 1.7548428316666669e+08, + "real_time": 1.7891625974880299e+08, + "cpu_time": 1.7752513691666630e+08, "time_unit": "ns", "model_cost": 5.4788096000000000e+07, "output_rows": 2.6214400000000000e+05, @@ -4672,8 +4672,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.7532372624918935e+08, - "cpu_time": 1.7414484524999806e+08, + "real_time": 1.7636709049838826e+08, + "cpu_time": 1.7498345849999806e+08, "time_unit": "ns", "model_cost": 5.4788096000000000e+07, "output_rows": 2.6214400000000000e+05, @@ -4691,8 +4691,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 6.7500241851975797e+06, - "cpu_time": 6.6015250310010808e+06, + "real_time": 5.2291555096189724e+06, + "cpu_time": 5.1569135157117592e+06, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -4710,8 +4710,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 3.8211392861276929e-02, - "cpu_time": 3.7618896187593419e-02, + "real_time": 2.9226832245211616e-02, + "cpu_time": 2.9048920086919932e-02, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -4727,9 +4727,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 2553, - "real_time": 2.7610328006356570e+05, - "cpu_time": 2.7519259851155733e+05, + "iterations": 2445, + "real_time": 2.8716317914352054e+05, + "cpu_time": 2.8604619795501052e+05, "time_unit": "ns", "model_cost": 5.2224000000000000e+05, "output_rows": 1.0240000000000000e+03, @@ -4745,9 +4745,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 2553, - "real_time": 2.7262658205952554e+05, - "cpu_time": 2.7173017234625900e+05, + "iterations": 2445, + "real_time": 2.8396120695121097e+05, + "cpu_time": 2.8304634928425285e+05, "time_unit": "ns", "model_cost": 5.2224000000000000e+05, "output_rows": 1.0240000000000000e+03, @@ -4763,9 +4763,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 2553, - "real_time": 2.7537817547831591e+05, - "cpu_time": 2.7442027692910330e+05, + "iterations": 2445, + "real_time": 2.8444466093966475e+05, + "cpu_time": 2.8348736768915888e+05, "time_unit": "ns", "model_cost": 5.2224000000000000e+05, "output_rows": 1.0240000000000000e+03, @@ -4783,8 +4783,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 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2.0480000000000000e+03, @@ -4893,9 +4893,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 1309, - "real_time": 5.3507712069984933e+05, - "cpu_time": 5.3296460656990192e+05, + "iterations": 1222, + "real_time": 5.5669726266692590e+05, + "cpu_time": 5.5466473240589036e+05, "time_unit": "ns", "model_cost": 1.0444800000000000e+06, "output_rows": 2.0480000000000000e+03, @@ -4913,8 +4913,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 5.4724497504577169e+05, - "cpu_time": 5.4490866870384198e+05, + "real_time": 5.5498230877950729e+05, + "cpu_time": 5.5282407201308932e+05, "time_unit": "ns", "model_cost": 1.0444800000000000e+06, "output_rows": 2.0480000000000000e+03, @@ -4932,8 +4932,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 5.4224300917248323e+05, - "cpu_time": 5.3978409702062304e+05, + "real_time": 5.5435413666471152e+05, + "cpu_time": 5.5191627414075134e+05, "time_unit": "ns", "model_cost": 1.0444800000000000e+06, "output_rows": 2.0480000000000000e+03, @@ -4951,8 +4951,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.5295082025622289e+04, - "cpu_time": 1.5170038160467988e+04, + "real_time": 1.5027910139513822e+03, + "cpu_time": 1.5941079230850594e+03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -4970,8 +4970,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 2.7949241606728334e-02, - "cpu_time": 2.7839597774343555e-02, + "real_time": 2.7078178712691839e-03, + "cpu_time": 2.8835718337665212e-03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -4987,9 +4987,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 624, - "real_time": 1.1554927099356204e+06, - "cpu_time": 1.1490566618589582e+06, + "iterations": 557, + "real_time": 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8.1920000000000000e+03, @@ -5153,9 +5153,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 289, - "real_time": 2.4716337058861549e+06, - "cpu_time": 2.4597143321799492e+06, + "iterations": 283, + "real_time": 2.4070393357034312e+06, + "cpu_time": 2.3969821272084555e+06, "time_unit": "ns", "model_cost": 4.1779200000000000e+06, "output_rows": 8.1920000000000000e+03, @@ -5173,8 +5173,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.4262640692106732e+06, - "cpu_time": 2.4143701545559363e+06, + "real_time": 2.4328332650639750e+06, + "cpu_time": 2.4208724181389767e+06, "time_unit": "ns", "model_cost": 4.1779200000000000e+06, "output_rows": 8.1920000000000000e+03, @@ -5192,8 +5192,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.4226861937913341e+06, - "cpu_time": 2.4111745121107069e+06, + "real_time": 2.4422395089147580e+06, + "cpu_time": 2.4291331908127326e+06, "time_unit": "ns", "model_cost": 4.1779200000000000e+06, "output_rows": 8.1920000000000000e+03, @@ -5211,8 +5211,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 4.3690710874705655e+04, - "cpu_time": 4.3833808828086476e+04, + "real_time": 2.2609295119535480e+04, + "cpu_time": 2.1015092277346706e+04, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -5230,8 +5230,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.8007401349729987e-02, - "cpu_time": 1.8155380501772570e-02, + "real_time": 9.2934010086963090e-03, + "cpu_time": 8.6807929735933254e-03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -5248,8 +5248,8 @@ "repetition_index": 0, "threads": 1, "iterations": 115, - "real_time": 5.9126056608004989e+06, - "cpu_time": 5.8719608000000995e+06, + "real_time": 5.8015779738881337e+06, + "cpu_time": 5.7644407304347502e+06, "time_unit": "ns", "model_cost": 8.3558400000000000e+06, "output_rows": 1.6384000000000000e+04, @@ -5266,8 +5266,8 @@ "repetition_index": 1, "threads": 1, "iterations": 115, - "real_time": 5.8262068087376812e+06, - "cpu_time": 5.7936811130435010e+06, + "real_time": 5.9679846782945907e+06, + "cpu_time": 5.9246701478260970e+06, "time_unit": "ns", "model_cost": 8.3558400000000000e+06, "output_rows": 1.6384000000000000e+04, @@ -5284,8 +5284,8 @@ "repetition_index": 2, "threads": 1, "iterations": 115, - "real_time": 5.6979218607960753e+06, - "cpu_time": 5.6638630260869646e+06, + "real_time": 5.7739583216101415e+06, + "cpu_time": 5.7303521043478344e+06, "time_unit": "ns", "model_cost": 8.3558400000000000e+06, "output_rows": 1.6384000000000000e+04, @@ -5303,8 +5303,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 5.8122447767780842e+06, - "cpu_time": 5.7765016463768547e+06, + "real_time": 5.8478403245976204e+06, + "cpu_time": 5.8064876608695602e+06, "time_unit": "ns", "model_cost": 8.3558400000000000e+06, "output_rows": 1.6384000000000000e+04, @@ -5322,8 +5322,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 5.8262068087376812e+06, - "cpu_time": 5.7936811130434992e+06, + "real_time": 5.8015779738881337e+06, + "cpu_time": 5.7644407304347502e+06, "time_unit": "ns", "model_cost": 8.3558400000000000e+06, "output_rows": 1.6384000000000000e+04, @@ -5341,8 +5341,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.0802077230060748e+05, - "cpu_time": 1.0510719020649804e+05, + "real_time": 1.0496051920575833e+05, + "cpu_time": 1.0375853591175134e+05, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -5360,8 +5360,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.8585034947631179e-02, - "cpu_time": 1.8195647926876904e-02, + "real_time": 1.7948595272730957e-02, + "cpu_time": 1.7869414691258093e-02, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -5378,8 +5378,8 @@ "repetition_index": 0, "threads": 1, "iterations": 42, - "real_time": 1.6630917809379753e+07, - "cpu_time": 1.6511792023809422e+07, + "real_time": 1.7012846880658928e+07, + "cpu_time": 1.6855069785714179e+07, "time_unit": "ns", "model_cost": 1.6711680000000000e+07, "output_rows": 3.2768000000000000e+04, @@ -5396,8 +5396,8 @@ "repetition_index": 1, "threads": 1, "iterations": 42, - "real_time": 1.7047736643040221e+07, - "cpu_time": 1.6938945928571466e+07, + "real_time": 1.6986629000582200e+07, + "cpu_time": 1.6833719857143052e+07, "time_unit": "ns", "model_cost": 1.6711680000000000e+07, "output_rows": 3.2768000000000000e+04, @@ -5414,8 +5414,8 @@ "repetition_index": 2, "threads": 1, "iterations": 42, - "real_time": 1.6628109761984976e+07, - "cpu_time": 1.6514790285714502e+07, + "real_time": 1.6635681499749247e+07, + "cpu_time": 1.6495768880952189e+07, "time_unit": "ns", "model_cost": 1.6711680000000000e+07, "output_rows": 3.2768000000000000e+04, @@ -5433,8 +5433,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.6768921404801646e+07, - "cpu_time": 1.6655176079365127e+07, + "real_time": 1.6878385793663453e+07, + "cpu_time": 1.6728186174603136e+07, "time_unit": "ns", "model_cost": 1.6711680000000000e+07, "output_rows": 3.2768000000000000e+04, @@ -5452,8 +5452,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.6630917809379751e+07, - "cpu_time": 1.6514790285714500e+07, + "real_time": 1.6986629000582200e+07, + "cpu_time": 1.6833719857143048e+07, "time_unit": "ns", "model_cost": 1.6711680000000000e+07, "output_rows": 3.2768000000000000e+04, @@ -5471,8 +5471,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.4146516123056287e+05, - "cpu_time": 2.4575647068308786e+05, + "real_time": 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@@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 4595, - "real_time": 1.5607431381918039e+05, - "cpu_time": 1.5553370359086202e+05, + "iterations": 4351, + "real_time": 1.6147040611531536e+05, + "cpu_time": 1.6097874235807825e+05, "time_unit": "ns", "lhs_rows": 1.0240000000000000e+03, "model_cost": 4.0448000000000000e+05, @@ -5935,9 +5935,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 4595, - "real_time": 1.5534168661352131e+05, - "cpu_time": 1.5474480087051066e+05, + "iterations": 4351, + "real_time": 1.6108330728386005e+05, + "cpu_time": 1.6054411238795571e+05, "time_unit": "ns", "lhs_rows": 1.0240000000000000e+03, "model_cost": 4.0448000000000000e+05, @@ -5956,8 +5956,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.5509763336922001e+05, - "cpu_time": 1.5456490047152757e+05, + "real_time": 1.6072204757467113e+05, + "cpu_time": 1.6018541155289928e+05, "time_unit": "ns", "lhs_rows": 1.0240000000000000e+03, "model_cost": 4.0448000000000000e+05, @@ -5976,8 +5976,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.5534168661352128e+05, - "cpu_time": 1.5474480087051066e+05, + "real_time": 1.6108330728386005e+05, + "cpu_time": 1.6054411238795568e+05, "time_unit": "ns", "lhs_rows": 1.0240000000000000e+03, "model_cost": 4.0448000000000000e+05, @@ -5996,8 +5996,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.1188515182333529e+03, - "cpu_time": 1.0701549912465023e+03, + "real_time": 9.8025551342853771e+02, + "cpu_time": 1.0210820132656331e+03, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -6016,8 +6016,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 7.2138529384897440e-03, - "cpu_time": 6.9236611156983600e-03, + "real_time": 6.0990730781544647e-03, + "cpu_time": 6.3743758146691974e-03, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -6034,9 +6034,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 2348, - "real_time": 3.0235873679778207e+05, - "cpu_time": 3.0108759497444675e+05, + "iterations": 2275, + "real_time": 3.0990704790443426e+05, + "cpu_time": 3.0870987472527445e+05, "time_unit": "ns", "lhs_rows": 2.0480000000000000e+03, "model_cost": 8.0896000000000000e+05, @@ -6053,9 +6053,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 2348, - "real_time": 3.0332614863656298e+05, - "cpu_time": 3.0183868398636742e+05, + "iterations": 2275, + "real_time": 3.1020442197856668e+05, + "cpu_time": 3.0886639120879053e+05, "time_unit": "ns", "lhs_rows": 2.0480000000000000e+03, "model_cost": 8.0896000000000000e+05, @@ -6072,9 +6072,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 2348, - "real_time": 2.9839076746278559e+05, - "cpu_time": 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"model_cost": 1.6179200000000000e+06, @@ -6250,8 +6250,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 5.9918259553447273e+05, - "cpu_time": 5.9631605569836881e+05, + "real_time": 6.0889281392120186e+05, + "cpu_time": 6.0564358988256357e+05, "time_unit": "ns", "lhs_rows": 4.0960000000000000e+03, "model_cost": 1.6179200000000000e+06, @@ -6270,8 +6270,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.5855468131494076e+03, - "cpu_time": 2.8077102951815864e+03, + "real_time": 3.5295835733039253e+03, + "cpu_time": 3.4212808262568647e+03, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -6290,8 +6290,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 4.3145675372674962e-03, - "cpu_time": 4.7101629619195375e-03, + "real_time": 5.7961595584411019e-03, + "cpu_time": 5.6482797575765440e-03, "time_unit": "ns", "lhs_rows": 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- "real_time": 3.0184472081303708e+06, - "cpu_time": 2.9960085020408714e+06, + "iterations": 239, + "real_time": 2.9391373807728039e+06, + "cpu_time": 2.9150854393304875e+06, "time_unit": "ns", "lhs_rows": 1.6384000000000000e+04, "model_cost": 6.4716800000000000e+06, @@ -6483,9 +6483,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 245, - "real_time": 2.8937891387613490e+06, - "cpu_time": 2.8721630530612296e+06, + "iterations": 239, + "real_time": 2.9765875355163240e+06, + "cpu_time": 2.9521477949791085e+06, "time_unit": "ns", "lhs_rows": 1.6384000000000000e+04, "model_cost": 6.4716800000000000e+06, @@ -6504,8 +6504,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.9410913550781184e+06, - "cpu_time": 2.9191584068027455e+06, + "real_time": 2.9613613793165311e+06, + "cpu_time": 2.9369713375174254e+06, "time_unit": "ns", "lhs_rows": 1.6384000000000000e+04, "model_cost": 6.4716800000000000e+06, @@ -6524,8 +6524,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.9110377183426358e+06, - "cpu_time": 2.8893036653061341e+06, + "real_time": 2.9683592216604655e+06, + "cpu_time": 2.9436807782426812e+06, "time_unit": "ns", "lhs_rows": 1.6384000000000000e+04, "model_cost": 6.4716800000000000e+06, @@ -6544,8 +6544,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 6.7544980386957672e+04, - "cpu_time": 6.7103673525487204e+04, + "real_time": 1.9681358456234837e+04, + "cpu_time": 1.9420787772128646e+04, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -6564,8 +6564,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 2.2965957949702520e-02, - "cpu_time": 2.2987335448843824e-02, + "real_time": 6.6460508986502702e-03, + "cpu_time": 6.6125220644967959e-03, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -6582,9 +6582,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 96, - "real_time": 7.0209723021434909e+06, - "cpu_time": 6.9715293124999655e+06, + "iterations": 89, + "real_time": 7.0090569438690096e+06, + "cpu_time": 6.9495575842696512e+06, "time_unit": "ns", "lhs_rows": 3.2768000000000000e+04, "model_cost": 1.2943360000000000e+07, @@ -6601,9 +6601,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 96, - "real_time": 6.9082949791360684e+06, - "cpu_time": 6.8592047291666046e+06, + "iterations": 89, + "real_time": 7.0018265955542540e+06, + "cpu_time": 6.9391039213483091e+06, "time_unit": "ns", "lhs_rows": 3.2768000000000000e+04, "model_cost": 1.2943360000000000e+07, @@ -6620,9 +6620,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 96, - "real_time": 6.8923519479540111e+06, - "cpu_time": 6.8369164583333610e+06, + "iterations": 89, + "real_time": 7.1400920896440372e+06, + "cpu_time": 7.0809575168538187e+06, "time_unit": "ns", "lhs_rows": 3.2768000000000000e+04, "model_cost": 1.2943360000000000e+07, @@ -6641,8 +6641,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 6.9405397430778565e+06, - "cpu_time": 6.8892168333333097e+06, + "real_time": 7.0503252096890993e+06, + "cpu_time": 6.9898730074905930e+06, "time_unit": "ns", "lhs_rows": 3.2768000000000000e+04, "model_cost": 1.2943360000000000e+07, @@ -6661,8 +6661,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 6.9082949791360684e+06, - "cpu_time": 6.8592047291666046e+06, + "real_time": 7.0090569438690096e+06, + "cpu_time": 6.9495575842696512e+06, "time_unit": "ns", "lhs_rows": 3.2768000000000000e+04, "model_cost": 1.2943360000000000e+07, @@ -6681,8 +6681,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 7.0111286389714558e+04, - "cpu_time": 7.2150536547591357e+04, + "real_time": 7.7824411573982274e+04, + "cpu_time": 7.9054479010299154e+04, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -6701,8 +6701,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.0101705196579273e-02, - "cpu_time": 1.0472966418837730e-02, + "real_time": 1.1038414436121326e-02, + "cpu_time": 1.1309859124133102e-02, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -6720,8 +6720,8 @@ "repetition_index": 0, "threads": 1, "iterations": 45, - "real_time": 1.5705191377977220e+07, - "cpu_time": 1.5604051044444291e+07, + "real_time": 1.5253418999620609e+07, + "cpu_time": 1.5129156622222379e+07, "time_unit": "ns", "lhs_rows": 6.5536000000000000e+04, "model_cost": 2.5886720000000000e+07, @@ -6739,8 +6739,8 @@ "repetition_index": 1, "threads": 1, "iterations": 45, - "real_time": 1.6109410555671072e+07, - "cpu_time": 1.5980047333333246e+07, + "real_time": 1.6748759844469735e+07, + "cpu_time": 1.6605307111111263e+07, "time_unit": "ns", "lhs_rows": 6.5536000000000000e+04, "model_cost": 2.5886720000000000e+07, @@ -6758,8 +6758,8 @@ "repetition_index": 2, "threads": 1, "iterations": 45, - "real_time": 1.5683112288954565e+07, - "cpu_time": 1.5545816355555706e+07, + "real_time": 1.5615015200132295e+07, + "cpu_time": 1.5477257888888907e+07, "time_unit": "ns", "lhs_rows": 6.5536000000000000e+04, "model_cost": 2.5886720000000000e+07, @@ -6778,8 +6778,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.5832571407534286e+07, - "cpu_time": 1.5709971577777749e+07, + "real_time": 1.5872398014740877e+07, + "cpu_time": 1.5737240540740849e+07, "time_unit": "ns", "lhs_rows": 6.5536000000000000e+04, "model_cost": 2.5886720000000000e+07, @@ -6798,8 +6798,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.5705191377977220e+07, - "cpu_time": 1.5604051044444293e+07, + "real_time": 1.5615015200132297e+07, + "cpu_time": 1.5477257888888909e+07, "time_unit": "ns", "lhs_rows": 6.5536000000000000e+04, "model_cost": 2.5886720000000000e+07, @@ -6818,8 +6818,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.4000376455143033e+05, - "cpu_time": 2.3569791057976629e+05, + "real_time": 7.8018939720458270e+05, + "cpu_time": 7.7165296663051203e+05, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -6838,8 +6838,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.5158861967122985e-02, - "cpu_time": 1.5003076830079598e-02, + "real_time": 4.9153845340824488e-02, + "cpu_time": 4.9033562436365065e-02, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -6856,9 +6856,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 2101, - "real_time": 3.3416093574730022e+05, - "cpu_time": 3.3247805854354991e+05, + 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"model_cost": 1.1468800000000000e+06, @@ -7031,9 +7031,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 638, - "real_time": 1.0666206363569596e+06, - "cpu_time": 1.0614965830721152e+06, + "iterations": 602, + "real_time": 1.0905021494985451e+06, + "cpu_time": 1.0857482823920376e+06, "time_unit": "ns", "lhs_rows": 1.2800000000000000e+02, "model_cost": 1.1468800000000000e+06, @@ -7052,8 +7052,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.0728873913252994e+06, - "cpu_time": 1.0677762925809880e+06, + "real_time": 1.1163784413013693e+06, + "cpu_time": 1.1104206616832868e+06, "time_unit": "ns", "lhs_rows": 1.2800000000000000e+02, "model_cost": 1.1468800000000000e+06, @@ -7072,8 +7072,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.0697958761887101e+06, - "cpu_time": 1.0647440956112919e+06, + "real_time": 1.1155049069927682e+06, + "cpu_time": 1.1096449867109756e+06, "time_unit": "ns", "lhs_rows": 1.2800000000000000e+02, "model_cost": 1.1468800000000000e+06, @@ -7092,8 +7092,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 8.2585380979169531e+03, - "cpu_time": 8.2261951181809727e+03, + "real_time": 2.6323931475417834e+04, + "cpu_time": 2.5069218549400928e+04, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -7112,8 +7112,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 7.6974882589639490e-03, - "cpu_time": 7.7040436047675562e-03, + "real_time": 2.3579756202324959e-02, + "cpu_time": 2.2576325724521819e-02, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -7130,9 +7130,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 183, - "real_time": 3.8669555846543200e+06, - "cpu_time": 3.8495006338798408e+06, + "iterations": 174, + "real_time": 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3.9432495956227290e+06, - "cpu_time": 3.9241914134790543e+06, + "real_time": 3.9716907682667132e+06, + "cpu_time": 3.9552652854406107e+06, "time_unit": "ns", "lhs_rows": 2.5600000000000000e+02, "model_cost": 4.5875200000000000e+06, @@ -7209,8 +7209,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.8868669891072172e+06, - "cpu_time": 3.8704164426229154e+06, + "real_time": 3.9745058967054808e+06, + "cpu_time": 3.9582554597700764e+06, "time_unit": "ns", "lhs_rows": 2.5600000000000000e+02, "model_cost": 4.5875200000000000e+06, @@ -7229,8 +7229,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.1533182399950705e+05, - "cpu_time": 1.1174504259200160e+05, + "real_time": 4.2788726164343469e+04, + "cpu_time": 4.0949693988036655e+04, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -7249,8 +7249,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", 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-7305,9 +7305,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 47, - "real_time": 1.4809973680964135e+07, - "cpu_time": 1.4757937468084734e+07, + "iterations": 45, + "real_time": 1.5210718466227667e+07, + "cpu_time": 1.5150257066666959e+07, "time_unit": "ns", "lhs_rows": 5.1200000000000000e+02, "model_cost": 1.8350080000000000e+07, @@ -7326,8 +7326,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.4965493652572462e+07, - "cpu_time": 1.4909916262411112e+07, + "real_time": 1.5476134273922071e+07, + "cpu_time": 1.5410250533333451e+07, "time_unit": "ns", "lhs_rows": 5.1200000000000000e+02, "model_cost": 1.8350080000000000e+07, @@ -7346,8 +7346,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.5020642595853597e+07, - "cpu_time": 1.4962434297872350e+07, + "real_time": 1.5283883489125097e+07, + "cpu_time": 1.5218577688888974e+07, "time_unit": "ns", "lhs_rows": 5.1200000000000000e+02, "model_cost": 1.8350080000000000e+07, @@ -7366,8 +7366,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.3656905001064815e+05, - "cpu_time": 1.3369394250985849e+05, + "real_time": 3.9803556694830203e+05, + "cpu_time": 3.9264331052454625e+05, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -7386,8 +7386,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 9.1255960666003742e-03, - "cpu_time": 8.9667802391962294e-03, + "real_time": 2.5719314649460524e-02, + "cpu_time": 2.5479359318346658e-02, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -7405,8 +7405,8 @@ "repetition_index": 0, "threads": 1, "iterations": 12, - "real_time": 5.7287003583041944e+07, - "cpu_time": 5.7123170416666366e+07, + "real_time": 5.8646104082678594e+07, + "cpu_time": 5.8442293750000358e+07, "time_unit": "ns", "lhs_rows": 1.0240000000000000e+03, "model_cost": 7.3400320000000000e+07, @@ -7424,8 +7424,8 @@ "repetition_index": 1, "threads": 1, "iterations": 12, - "real_time": 5.7607890082484424e+07, - "cpu_time": 5.7430033666664809e+07, + "real_time": 5.8599149750079960e+07, + "cpu_time": 5.8381791666666538e+07, "time_unit": "ns", "lhs_rows": 1.0240000000000000e+03, "model_cost": 7.3400320000000000e+07, @@ -7443,8 +7443,8 @@ "repetition_index": 2, "threads": 1, "iterations": 12, - "real_time": 5.8178159334299080e+07, - "cpu_time": 5.7757600499999262e+07, + "real_time": 5.8865837333238840e+07, + "cpu_time": 5.8643922416666307e+07, "time_unit": "ns", "lhs_rows": 1.0240000000000000e+03, "model_cost": 7.3400320000000000e+07, @@ -7463,8 +7463,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 5.7691017666608483e+07, - "cpu_time": 5.7436934861110143e+07, + "real_time": 5.8703697055332460e+07, + "cpu_time": 5.8489335944444396e+07, "time_unit": "ns", "lhs_rows": 1.0240000000000000e+03, "model_cost": 7.3400320000000000e+07, @@ -7483,8 +7483,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 5.7607890082484424e+07, - "cpu_time": 5.7430033666664809e+07, + "real_time": 5.8646104082678594e+07, + "cpu_time": 5.8442293750000358e+07, "time_unit": "ns", "lhs_rows": 1.0240000000000000e+03, "model_cost": 7.3400320000000000e+07, @@ -7503,8 +7503,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 4.5135605643520114e+05, - "cpu_time": 3.1727133895765297e+05, + "real_time": 1.4236670828581837e+05, + "cpu_time": 1.3725107856626957e+05, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -7523,8 +7523,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 7.8236799191782284e-03, - "cpu_time": 5.5238208606510023e-03, + "real_time": 2.4251744852053780e-03, + "cpu_time": 2.3466000485393838e-03, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -7542,8 +7542,8 @@ "repetition_index": 0, "threads": 1, "iterations": 3, - "real_time": 2.2982852066343185e+08, - "cpu_time": 2.2906543199999872e+08, + "real_time": 2.3236395166410756e+08, + "cpu_time": 2.3157243633334208e+08, "time_unit": "ns", "lhs_rows": 2.0480000000000000e+03, "model_cost": 2.9360128000000000e+08, @@ -7561,8 +7561,8 @@ "repetition_index": 1, "threads": 1, "iterations": 3, - "real_time": 2.2727892999925339e+08, - "cpu_time": 2.2636837700000000e+08, + "real_time": 2.3338789967237973e+08, + "cpu_time": 2.3263515900000206e+08, "time_unit": "ns", "lhs_rows": 2.0480000000000000e+03, "model_cost": 2.9360128000000000e+08, @@ -7580,8 +7580,8 @@ "repetition_index": 2, "threads": 1, "iterations": 3, - "real_time": 2.3285727399828222e+08, - "cpu_time": 2.3186330033333507e+08, + "real_time": 2.3317426699213684e+08, + "cpu_time": 2.3234727733333215e+08, "time_unit": "ns", "lhs_rows": 2.0480000000000000e+03, "model_cost": 2.9360128000000000e+08, @@ -7600,8 +7600,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.2998824155365583e+08, - "cpu_time": 2.2909903644444457e+08, + "real_time": 2.3297537277620801e+08, + "cpu_time": 2.3218495755555877e+08, "time_unit": "ns", "lhs_rows": 2.0480000000000000e+03, "model_cost": 2.9360128000000000e+08, @@ -7620,8 +7620,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.2982852066343185e+08, - "cpu_time": 2.2906543199999872e+08, + "real_time": 2.3317426699213684e+08, + "cpu_time": 2.3234727733333215e+08, "time_unit": "ns", "lhs_rows": 2.0480000000000000e+03, "model_cost": 2.9360128000000000e+08, @@ -7640,8 +7640,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.7925997770830602e+06, - "cpu_time": 2.7476157944364916e+06, + "real_time": 5.4017271567849617e+05, + "cpu_time": 5.4964138241714903e+05, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -7660,8 +7660,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.2142358923299790e-02, - "cpu_time": 1.1993135532469928e-02, + "real_time": 2.3185829010235193e-03, + "cpu_time": 2.3672566397227833e-03, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -7678,9 +7678,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 5194, - "real_time": 1.3266751886856742e+05, - "cpu_time": 1.3154591259145027e+05, + "iterations": 5331, + "real_time": 1.2478490208398430e+05, + "cpu_time": 1.2380987919714779e+05, "time_unit": "ns", "lhs_rows": 3.2000000000000000e+01, "model_cost": 1.0649600000000000e+05, @@ -7697,9 +7697,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 5194, - "real_time": 1.3130809915363468e+05, - "cpu_time": 1.3010532345013291e+05, + "iterations": 5331, + "real_time": 1.3249372256814223e+05, + "cpu_time": 1.3128989307821999e+05, "time_unit": "ns", "lhs_rows": 3.2000000000000000e+01, "model_cost": 1.0649600000000000e+05, @@ -7716,9 +7716,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 5194, - "real_time": 1.2884908432706572e+05, - "cpu_time": 1.2773638679244889e+05, + "iterations": 5331, + "real_time": 1.2903942881183306e+05, + "cpu_time": 1.2802380322640981e+05, "time_unit": "ns", "lhs_rows": 3.2000000000000000e+01, "model_cost": 1.0649600000000000e+05, @@ -7737,8 +7737,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.3094156744975592e+05, - "cpu_time": 1.2979587427801067e+05, + "real_time": 1.2877268448798654e+05, + "cpu_time": 1.2770785850059253e+05, "time_unit": "ns", "lhs_rows": 3.2000000000000000e+01, "model_cost": 1.0649600000000000e+05, @@ -7757,8 +7757,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.3130809915363471e+05, - "cpu_time": 1.3010532345013291e+05, + "real_time": 1.2903942881183304e+05, + "cpu_time": 1.2802380322640981e+05, "time_unit": "ns", "lhs_rows": 3.2000000000000000e+01, "model_cost": 1.0649600000000000e+05, @@ -7777,8 +7777,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.9354249415575671e+03, - "cpu_time": 1.9235230167356876e+03, + "real_time": 3.8613265486061105e+03, + "cpu_time": 3.7500023623445318e+03, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -7797,8 +7797,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.4780829183980987e-02, - "cpu_time": 1.4819600603141516e-02, + "real_time": 2.9985602645150582e-02, + "cpu_time": 2.9363912341597465e-02, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -7815,9 +7815,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - 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4.2598400000000000e+05, @@ -7874,8 +7874,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.8863887774830993e+05, - "cpu_time": 2.8676136132533400e+05, + "real_time": 3.0136899474759947e+05, + "cpu_time": 2.9817651888131257e+05, "time_unit": "ns", "lhs_rows": 6.4000000000000000e+01, "model_cost": 4.2598400000000000e+05, @@ -7894,8 +7894,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.8854454884625011e+05, - "cpu_time": 2.8670788217651437e+05, + "real_time": 3.0098525041786098e+05, + "cpu_time": 2.9877492376489966e+05, "time_unit": "ns", "lhs_rows": 6.4000000000000000e+01, "model_cost": 4.2598400000000000e+05, @@ -7914,8 +7914,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.2018865442581614e+03, - "cpu_time": 3.1710055504633001e+03, + "real_time": 4.5408664067224045e+03, + "cpu_time": 6.1905436789447258e+03, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -7934,8 +7934,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.1093053608149672e-02, - "cpu_time": 1.1057994479478561e-02, + "real_time": 1.5067463759918104e-02, + "cpu_time": 2.0761338626428986e-02, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -7952,9 +7952,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 796, - "real_time": 8.8112485174870060e+05, - "cpu_time": 8.7488517964825244e+05, + "iterations": 721, + "real_time": 9.5932074615327525e+05, + "cpu_time": 9.5193887933424360e+05, "time_unit": "ns", "lhs_rows": 1.2800000000000000e+02, "model_cost": 1.7039360000000000e+06, @@ -7971,9 +7971,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 796, - "real_time": 8.5841894094790122e+05, - "cpu_time": 8.5318432914572989e+05, + "iterations": 721, + "real_time": 9.7721735229127982e+05, + "cpu_time": 9.6936163938975334e+05, "time_unit": "ns", "lhs_rows": 1.2800000000000000e+02, "model_cost": 1.7039360000000000e+06, @@ -7990,9 +7990,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 796, - "real_time": 9.5851955778578564e+05, - "cpu_time": 9.5112967964822904e+05, + "iterations": 721, + "real_time": 9.4006388486445660e+05, + "cpu_time": 9.3281144521494245e+05, "time_unit": "ns", "lhs_rows": 1.2800000000000000e+02, "model_cost": 1.7039360000000000e+06, @@ -8011,8 +8011,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 8.9935445016079571e+05, - "cpu_time": 8.9306639614740352e+05, + "real_time": 9.5886732776967064e+05, + "cpu_time": 9.5137065464631317e+05, "time_unit": "ns", "lhs_rows": 1.2800000000000000e+02, "model_cost": 1.7039360000000000e+06, @@ -8031,8 +8031,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 8.8112485174870060e+05, - "cpu_time": 8.7488517964825255e+05, + "real_time": 9.5932074615327513e+05, + "cpu_time": 9.5193887933424360e+05, "time_unit": "ns", "lhs_rows": 1.2800000000000000e+02, "model_cost": 1.7039360000000000e+06, @@ -8051,8 +8051,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 5.2481159157654816e+04, - "cpu_time": 5.1441621244921822e+04, + "real_time": 1.8580883365218095e+04, + "cpu_time": 1.8281721281819497e+04, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -8071,8 +8071,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 5.8354255264174981e-02, - "cpu_time": 5.7601116184458033e-02, + "real_time": 1.9377950240975786e-02, + "cpu_time": 1.9216192125051421e-02, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -8089,9 +8089,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 116, - "real_time": 5.0812632845040280e+06, - "cpu_time": 5.0276586034481227e+06, + "iterations": 109, + "real_time": 5.6205549447716074e+06, + "cpu_time": 5.5457994587156754e+06, "time_unit": "ns", "lhs_rows": 2.5600000000000000e+02, "model_cost": 6.8157440000000000e+06, @@ -8108,9 +8108,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 116, - "real_time": 4.9862488448180277e+06, - "cpu_time": 4.9387460517241396e+06, + "iterations": 109, + "real_time": 5.9907739541980382e+06, + "cpu_time": 5.9113671743118977e+06, "time_unit": "ns", "lhs_rows": 2.5600000000000000e+02, "model_cost": 6.8157440000000000e+06, @@ -8127,9 +8127,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 116, - "real_time": 5.0431421034867028e+06, - "cpu_time": 4.9845031724138493e+06, + "iterations": 109, + "real_time": 5.0800898349459432e+06, + "cpu_time": 5.0185620366970822e+06, "time_unit": "ns", "lhs_rows": 2.5600000000000000e+02, "model_cost": 6.8157440000000000e+06, @@ -8148,8 +8148,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 5.0368847442695852e+06, - "cpu_time": 4.9836359425287042e+06, + "real_time": 5.5638062446385296e+06, + "cpu_time": 5.4919095565748857e+06, "time_unit": "ns", "lhs_rows": 2.5600000000000000e+02, "model_cost": 6.8157440000000000e+06, @@ -8168,8 +8168,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 5.0431421034867028e+06, - "cpu_time": 4.9845031724138493e+06, + "real_time": 5.6205549447716065e+06, + "cpu_time": 5.5457994587156763e+06, "time_unit": "ns", "lhs_rows": 2.5600000000000000e+02, "model_cost": 6.8157440000000000e+06, @@ -8188,8 +8188,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 4.7815288825736119e+04, - "cpu_time": 4.4462619460212882e+04, + "real_time": 4.5798657457352517e+05, + "cpu_time": 4.4883554293573764e+05, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -8208,8 +8208,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 9.4930281817814290e-03, - "cpu_time": 8.9217230096571790e-03, + "real_time": 8.2315334940870108e-02, + "cpu_time": 8.1726681459710862e-02, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -8226,9 +8226,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 28, - "real_time": 2.5102633143043410e+07, - "cpu_time": 2.4885775642856751e+07, + "iterations": 26, + "real_time": 2.6842513768665060e+07, + "cpu_time": 2.6538571653845750e+07, "time_unit": "ns", "lhs_rows": 5.1200000000000000e+02, "model_cost": 2.7262976000000000e+07, @@ -8245,9 +8245,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 28, - "real_time": 2.5126405178265747e+07, - "cpu_time": 2.4882487535715014e+07, + "iterations": 26, + "real_time": 2.6442388577449422e+07, + "cpu_time": 2.6176888538461622e+07, "time_unit": "ns", "lhs_rows": 5.1200000000000000e+02, "model_cost": 2.7262976000000000e+07, @@ -8264,9 +8264,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 28, - "real_time": 2.5236648392560061e+07, - "cpu_time": 2.4974445571428660e+07, + "iterations": 26, + "real_time": 2.6032183076425169e+07, + "cpu_time": 2.5784397423077639e+07, "time_unit": "ns", "lhs_rows": 5.1200000000000000e+02, "model_cost": 2.7262976000000000e+07, @@ -8285,8 +8285,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.5155228904623073e+07, - "cpu_time": 2.4914236250000145e+07, + "real_time": 2.6439028474179883e+07, + "cpu_time": 2.6166619205128338e+07, "time_unit": "ns", "lhs_rows": 5.1200000000000000e+02, "model_cost": 2.7262976000000000e+07, @@ -8305,8 +8305,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.5126405178265747e+07, - "cpu_time": 2.4885775642856747e+07, + "real_time": 2.6442388577449422e+07, + "cpu_time": 2.6176888538461622e+07, "time_unit": "ns", "lhs_rows": 5.1200000000000000e+02, "model_cost": 2.7262976000000000e+07, @@ -8325,8 +8325,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 7.1506133838634225e+04, - "cpu_time": 5.2168713826543702e+04, + "real_time": 4.0517579569474870e+05, + "cpu_time": 3.7719197631191387e+05, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -8345,8 +8345,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 2.8425952357560421e-03, - "cpu_time": 2.0939318911107861e-03, + "real_time": 1.5324912414631259e-02, + "cpu_time": 1.4415006132622164e-02, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -8364,8 +8364,8 @@ "repetition_index": 0, "threads": 1, "iterations": 5, - "real_time": 1.2901079159928486e+08, - "cpu_time": 1.2789042899999571e+08, + "real_time": 1.2198015240137465e+08, + "cpu_time": 1.2094263579999733e+08, "time_unit": "ns", "lhs_rows": 1.0240000000000000e+03, "model_cost": 1.0905190400000000e+08, @@ -8383,8 +8383,8 @@ "repetition_index": 1, "threads": 1, "iterations": 5, - "real_time": 1.2328294980106875e+08, - "cpu_time": 1.2223155800000426e+08, + "real_time": 1.2660427120281383e+08, + "cpu_time": 1.2563009380000381e+08, "time_unit": "ns", "lhs_rows": 1.0240000000000000e+03, "model_cost": 1.0905190400000000e+08, @@ -8402,8 +8402,8 @@ "repetition_index": 2, "threads": 1, "iterations": 5, - "real_time": 1.2491676739882679e+08, - "cpu_time": 1.2392164259999844e+08, + "real_time": 1.2643557180417702e+08, + "cpu_time": 1.2531702260000089e+08, "time_unit": "ns", "lhs_rows": 1.0240000000000000e+03, "model_cost": 1.0905190400000000e+08, @@ -8422,8 +8422,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.2573683626639344e+08, - "cpu_time": 1.2468120986666612e+08, + "real_time": 1.2500666513612181e+08, + "cpu_time": 1.2396325073333400e+08, "time_unit": "ns", "lhs_rows": 1.0240000000000000e+03, "model_cost": 1.0905190400000000e+08, @@ -8442,8 +8442,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.2491676739882678e+08, - "cpu_time": 1.2392164259999843e+08, + "real_time": 1.2643557180417700e+08, + "cpu_time": 1.2531702260000087e+08, "time_unit": "ns", "lhs_rows": 1.0240000000000000e+03, "model_cost": 1.0905190400000000e+08, @@ -8462,8 +8462,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.9506656243997463e+06, - "cpu_time": 2.9048945029181428e+06, + "real_time": 2.6223938247274985e+06, + "cpu_time": 2.6206085792593178e+06, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -8482,8 +8482,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 2.3466994335282088e-02, - "cpu_time": 2.3298574869658646e-02, + "real_time": 2.0978032026307807e-02, + "cpu_time": 2.1140205373419027e-02, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, diff --git a/research/benchmark-results/query.json b/research/benchmark-results/query.json index bc4cee9..2fe2b6b 100644 --- a/research/benchmark-results/query.json +++ b/research/benchmark-results/query.json @@ -1,6 +1,6 @@ { "context": { - "date": "2026-06-02T01:16:13+03:00", + "date": "2026-06-09T23:45:24+03:00", "host_name": "nixos", "executable": "./build-release/bin/benchmarks", "num_cpus": 8, @@ -32,7 +32,7 @@ "num_sharing": 8 } ], - "load_avg": [0.375977,0.381836,0.45752], + "load_avg": [0.406738,0.518066,0.863281], "library_version": "v1.9.0", "library_build_type": "release", "json_schema_version": 1 @@ -47,9 +47,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 34521, - "real_time": 2.0956768459664425e+04, - "cpu_time": 2.0850844152834507e+04, + "iterations": 35883, + "real_time": 1.9347125825426523e+04, + "cpu_time": 1.9274702784048157e+04, "time_unit": "ns" }, { @@ -61,9 +61,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 34521, - "real_time": 2.4071903450188511e+04, - "cpu_time": 2.3901399727701977e+04, + "iterations": 35883, + "real_time": 1.9958099769192188e+04, + "cpu_time": 1.9870254744586575e+04, "time_unit": "ns" }, { @@ -75,9 +75,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 34521, - "real_time": 2.2447170504518679e+04, - "cpu_time": 2.2296074881955916e+04, + "iterations": 35883, + "real_time": 1.9839301117551120e+04, + "cpu_time": 1.9744612713541232e+04, "time_unit": "ns" }, { @@ -91,8 +91,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.2491947471457202e+04, - "cpu_time": 2.2349439587497462e+04, + "real_time": 1.9714842237389941e+04, + "cpu_time": 1.9629856747391983e+04, "time_unit": "ns" }, { @@ -106,8 +106,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.2447170504518683e+04, - "cpu_time": 2.2296074881955912e+04, + "real_time": 1.9839301117551116e+04, + "cpu_time": 1.9744612713541228e+04, "time_unit": "ns" }, { @@ -121,8 +121,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.5580501387545544e+03, - "cpu_time": 1.5259777759478854e+03, + "real_time": 3.2394413041203410e+02, + "cpu_time": 3.1392233633584686e+02, "time_unit": "ns" }, { @@ -136,8 +136,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 6.9271464408840328e-02, - "cpu_time": 6.8278122588878470e-02, + "real_time": 1.6431484792592550e-02, + "cpu_time": 1.5992084933454977e-02, "time_unit": "ns" }, { @@ -149,9 +149,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 34621, - "real_time": 2.1451438086390688e+04, - "cpu_time": 2.1330684237890302e+04, + "iterations": 35706, + "real_time": 1.9395967988571840e+04, + "cpu_time": 1.9322896347952723e+04, "time_unit": "ns" }, { @@ -163,9 +163,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 34621, - "real_time": 2.0835122295628222e+04, - "cpu_time": 2.0741762340775836e+04, + "iterations": 35706, + "real_time": 1.9952500560384295e+04, + "cpu_time": 1.9850908390746659e+04, "time_unit": "ns" }, { @@ -177,9 +177,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 34621, - "real_time": 1.9800852661814268e+04, - "cpu_time": 1.9729751278125987e+04, + "iterations": 35706, + "real_time": 1.9349337225960488e+04, + "cpu_time": 1.9260752674620504e+04, "time_unit": "ns" }, { @@ -193,8 +193,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.0695804347944391e+04, - "cpu_time": 2.0600732618930710e+04, + "real_time": 1.9565935258305544e+04, + "cpu_time": 1.9478185804439960e+04, "time_unit": "ns" }, { @@ -208,8 +208,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.0835122295628222e+04, - "cpu_time": 2.0741762340775840e+04, + "real_time": 1.9395967988571840e+04, + "cpu_time": 1.9322896347952723e+04, "time_unit": "ns" }, { @@ -223,8 +223,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 8.3406545238738693e+02, - "cpu_time": 8.0973058621220582e+02, + "real_time": 3.3558628783648948e+02, + "cpu_time": 3.2427928362951928e+02, "time_unit": "ns" }, { @@ -238,8 +238,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 4.0301185610610510e-02, - "cpu_time": 3.9305912133829508e-02, + "real_time": 1.7151558737476476e-02, + "cpu_time": 1.6648330952649670e-02, "time_unit": "ns" }, { @@ -251,9 +251,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 34006, - "real_time": 1.9905235928914706e+04, - "cpu_time": 1.9806883432335464e+04, + "iterations": 35747, + "real_time": 1.9461075754495792e+04, + "cpu_time": 1.9392605757126468e+04, "time_unit": "ns" }, { @@ -265,9 +265,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 34006, - "real_time": 2.0232451567379769e+04, - "cpu_time": 2.0150346409457165e+04, + "iterations": 35747, + "real_time": 2.0039857134594396e+04, + "cpu_time": 1.9956820963996979e+04, "time_unit": "ns" }, { @@ -279,9 +279,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 34006, - "real_time": 1.9979601540792919e+04, - "cpu_time": 1.9905176204199270e+04, + "iterations": 35747, + "real_time": 1.9761389738938429e+04, + "cpu_time": 1.9648120653481416e+04, "time_unit": "ns" }, { @@ -295,8 +295,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.0039096345695798e+04, - "cpu_time": 1.9954135348663967e+04, + "real_time": 1.9754107542676207e+04, + "cpu_time": 1.9665849124868284e+04, "time_unit": "ns" }, { @@ -310,8 +310,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.9979601540792919e+04, - "cpu_time": 1.9905176204199270e+04, + "real_time": 1.9761389738938433e+04, + "cpu_time": 1.9648120653481416e+04, "time_unit": "ns" }, { @@ -325,8 +325,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.7152912980545207e+02, - "cpu_time": 1.7688824871481089e+02, + "real_time": 2.8945940004436528e+02, + "cpu_time": 2.8252508550457100e+02, "time_unit": "ns" }, { @@ -340,8 +340,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 8.5597237942465817e-03, - "cpu_time": 8.8647413492990296e-03, + "real_time": 1.4653124643521634e-02, + "cpu_time": 1.4366279518910081e-02, "time_unit": "ns" }, { @@ -353,9 +353,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 34063, - "real_time": 1.9751227049826088e+04, - "cpu_time": 1.9676696679681751e+04, + "iterations": 34985, + "real_time": 1.9576876347119727e+04, + "cpu_time": 1.9499444018865241e+04, "time_unit": "ns" }, { @@ -367,9 +367,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 34063, - "real_time": 1.9815156415895137e+04, - "cpu_time": 1.9738392008924649e+04, + "iterations": 34985, + "real_time": 2.0412169873552859e+04, + "cpu_time": 2.0322958525082195e+04, "time_unit": "ns" }, { @@ -381,9 +381,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 34063, - "real_time": 1.9429818571364620e+04, - "cpu_time": 1.9358088336317978e+04, + "iterations": 34985, + "real_time": 1.9421935715485401e+04, + "cpu_time": 1.9353340631699339e+04, "time_unit": "ns" }, { @@ -397,8 +397,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.9665400679028615e+04, - "cpu_time": 1.9591059008308122e+04, + "real_time": 1.9803660645385997e+04, + "cpu_time": 1.9725247725215591e+04, "time_unit": "ns" }, { @@ -412,8 +412,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.9751227049826088e+04, - "cpu_time": 1.9676696679681751e+04, + "real_time": 1.9576876347119727e+04, + "cpu_time": 1.9499444018865244e+04, "time_unit": "ns" }, { @@ -427,8 +427,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.0650892971011589e+02, - "cpu_time": 2.0410310854664775e+02, + "real_time": 5.3264834591507758e+02, + "cpu_time": 5.2276208751519619e+02, "time_unit": "ns" }, { @@ -442,8 +442,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.0501130034453817e-02, - "cpu_time": 1.0418176396696689e-02, + "real_time": 2.6896458965488181e-02, + "cpu_time": 2.6502181102999685e-02, "time_unit": "ns" }, { @@ -455,9 +455,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 9523, - "real_time": 6.7734826314987717e+04, - "cpu_time": 6.6976087892470867e+04, + "iterations": 9891, + "real_time": 6.9485813061832043e+04, + "cpu_time": 6.8783745020725837e+04, "time_unit": "ns" }, { @@ -469,9 +469,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 9523, - "real_time": 7.4560993069410833e+04, - "cpu_time": 7.3580527459834018e+04, + "iterations": 9891, + "real_time": 6.9380505410659636e+04, + "cpu_time": 6.8705246486705015e+04, "time_unit": "ns" }, { @@ -483,9 +483,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 9523, - "real_time": 7.2185913472089000e+04, - "cpu_time": 7.1298075606426384e+04, + "iterations": 9891, + "real_time": 7.2888390554632861e+04, + "cpu_time": 7.0598849358002219e+04, "time_unit": "ns" }, { @@ -499,8 +499,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 7.1493910952162521e+04, - "cpu_time": 7.0618230319577080e+04, + "real_time": 7.0584903009041518e+04, + "cpu_time": 6.9362613621811019e+04, "time_unit": "ns" }, { @@ -514,8 +514,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 7.2185913472089000e+04, - "cpu_time": 7.1298075606426384e+04, + "real_time": 6.9485813061832057e+04, + "cpu_time": 6.8783745020725837e+04, "time_unit": "ns" }, { @@ -529,8 +529,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.4652977874184539e+03, - "cpu_time": 3.3542954118975481e+03, + "real_time": 1.9955734964841440e+03, + "cpu_time": 1.0713307619682653e+03, "time_unit": "ns" }, { @@ -544,8 +544,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 4.8469831084455968e-02, - "cpu_time": 4.7499001273721468e-02, + "real_time": 2.8271959178416985e-02, + "cpu_time": 1.5445363229960325e-02, "time_unit": "ns" }, { @@ -557,9 +557,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 8590, - "real_time": 7.2206211525310428e+04, - "cpu_time": 7.1370953317811305e+04, + "iterations": 16294, + "real_time": 4.5328077084222074e+04, + "cpu_time": 4.5118617589296737e+04, "time_unit": "ns" }, { @@ -571,9 +571,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 8590, - "real_time": 7.1837302561354969e+04, - "cpu_time": 7.0986034225844036e+04, + "iterations": 16294, + "real_time": 4.2265272677276589e+04, + "cpu_time": 4.2094951086289366e+04, "time_unit": "ns" }, { @@ -585,9 +585,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 8590, - "real_time": 6.9535109429968230e+04, - "cpu_time": 6.8780370430733412e+04, + "iterations": 16294, + "real_time": 4.1824865902154910e+04, + "cpu_time": 4.1692916656437970e+04, "time_unit": "ns" }, { @@ -601,8 +601,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 7.1192874505544547e+04, - "cpu_time": 7.0379119324796251e+04, + "real_time": 4.3139405221217858e+04, + "cpu_time": 4.2968828444008017e+04, "time_unit": "ns" }, { @@ -616,8 +616,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 7.1837302561354954e+04, - "cpu_time": 7.0986034225844036e+04, + "real_time": 4.2265272677276589e+04, + "cpu_time": 4.2094951086289366e+04, "time_unit": "ns" }, { @@ -631,8 +631,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.4474675264996595e+03, - "cpu_time": 1.3978695205702243e+03, + "real_time": 1.9081936287554665e+03, + "cpu_time": 1.8725925738392880e+03, "time_unit": "ns" }, { @@ -646,8 +646,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 2.0331634823748123e-02, - "cpu_time": 1.9861992221288299e-02, + "real_time": 4.4233192807603502e-02, + "cpu_time": 4.3580256703517832e-02, "time_unit": "ns" }, { @@ -659,9 +659,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 9950, - "real_time": 7.1095941407224585e+04, - "cpu_time": 7.0266699899497558e+04, + "iterations": 10101, + "real_time": 6.7519916445501847e+04, + "cpu_time": 6.6872900306900425e+04, "time_unit": "ns" }, { @@ -673,9 +673,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 9950, - "real_time": 7.4232385930226155e+04, - "cpu_time": 7.3228391859296491e+04, + "iterations": 10101, + "real_time": 6.7762344916422226e+04, + "cpu_time": 6.7088890208890210e+04, "time_unit": "ns" }, { @@ -687,9 +687,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 9950, - "real_time": 7.8242160603649754e+04, - "cpu_time": 7.7059992261306659e+04, + "iterations": 10101, + "real_time": 7.1656405504046284e+04, + "cpu_time": 7.0902481140481046e+04, "time_unit": "ns" }, { @@ -703,8 +703,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 7.4523495980366817e+04, - "cpu_time": 7.3518361340033574e+04, + "real_time": 6.8979555621990105e+04, + "cpu_time": 6.8288090552090536e+04, "time_unit": "ns" }, { @@ -718,8 +718,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 7.4232385930226155e+04, - "cpu_time": 7.3228391859296491e+04, + "real_time": 6.7762344916422226e+04, + "cpu_time": 6.7088890208890210e+04, "time_unit": "ns" }, { @@ -733,8 +733,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.5819926014453490e+03, - "cpu_time": 3.4059164703608490e+03, + "real_time": 2.3213868396231469e+03, + "cpu_time": 2.2667027861942097e+03, "time_unit": "ns" }, { @@ -748,8 +748,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 4.8065278665791834e-02, - "cpu_time": 4.6327426350105498e-02, + "real_time": 3.3653258834319140e-02, + "cpu_time": 3.3193237178965433e-02, "time_unit": "ns" }, { @@ -761,9 +761,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 9612, - "real_time": 7.8121309405331005e+04, - "cpu_time": 7.6963363503953427e+04, + "iterations": 16451, + "real_time": 4.3826071910359613e+04, + "cpu_time": 4.3656422588292517e+04, "time_unit": "ns" }, { @@ -775,9 +775,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 9612, - "real_time": 6.8386549001906576e+04, - "cpu_time": 6.7663309821056770e+04, + "iterations": 16451, + "real_time": 4.5471952586603991e+04, + "cpu_time": 4.5253403805239861e+04, "time_unit": "ns" }, { @@ -789,9 +789,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 9612, - "real_time": 6.5304516334599408e+04, - "cpu_time": 6.4723655326674838e+04, + "iterations": 16451, + "real_time": 4.3340157679465607e+04, + "cpu_time": 4.3146676615403223e+04, "time_unit": "ns" }, { @@ -805,8 +805,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 7.0604124913945663e+04, - "cpu_time": 6.9783442883894997e+04, + "real_time": 4.4212727392143075e+04, + "cpu_time": 4.4018834336311869e+04, "time_unit": "ns" }, { @@ -820,8 +820,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 6.8386549001906576e+04, - "cpu_time": 6.7663309821056770e+04, + "real_time": 4.3826071910359613e+04, + "cpu_time": 4.3656422588292531e+04, "time_unit": "ns" }, { @@ -835,8 +835,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 6.6899759602507065e+03, - "cpu_time": 6.3893534273306313e+03, + "real_time": 1.1172574581177582e+03, + "cpu_time": 1.0991276401882440e+03, "time_unit": "ns" }, { @@ -850,8 +850,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 9.4753330182969372e-02, - "cpu_time": 9.1559733416437653e-02, + "real_time": 2.5270041547274080e-02, + "cpu_time": 2.4969485375071716e-02, "time_unit": "ns" }, { @@ -863,9 +863,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 8005, - "real_time": 8.7787281075910752e+04, - "cpu_time": 8.6833757276702265e+04, + "iterations": 7826, + "real_time": 8.9939901991683058e+04, + "cpu_time": 8.8865522616917951e+04, "time_unit": "ns" }, { @@ -877,9 +877,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 8005, - "real_time": 9.4056740163364142e+04, - "cpu_time": 9.2897929419112756e+04, + "iterations": 7826, + "real_time": 9.3378252363479318e+04, + "cpu_time": 9.2188841809353500e+04, "time_unit": "ns" }, { @@ -891,9 +891,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 8005, - "real_time": 8.4392947408685955e+04, - "cpu_time": 8.3606973141786482e+04, + "iterations": 7826, + "real_time": 8.7275915154285205e+04, + "cpu_time": 8.6283555839509107e+04, "time_unit": "ns" }, { @@ -907,8 +907,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 8.8745656215986950e+04, - "cpu_time": 8.7779553279200496e+04, + "real_time": 9.0198023169815875e+04, + "cpu_time": 8.9112640088593515e+04, "time_unit": "ns" }, { @@ -922,8 +922,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 8.7787281075910738e+04, - "cpu_time": 8.6833757276702279e+04, + "real_time": 8.9939901991683058e+04, + "cpu_time": 8.8865522616917951e+04, "time_unit": "ns" }, { @@ -937,8 +937,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 4.9026609900321755e+03, - "cpu_time": 4.7171352212497422e+03, + "real_time": 3.0593462962911394e+03, + "cpu_time": 2.9603886366515130e+03, "time_unit": "ns" }, { @@ -952,8 +952,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 5.5243954454516651e-02, - "cpu_time": 5.3738428199172378e-02, + "real_time": 3.3918108055775310e-02, + "cpu_time": 3.3220748860188297e-02, "time_unit": "ns" }, { @@ -965,9 +965,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 8132, - "real_time": 8.8670200074989538e+04, - "cpu_time": 8.7742495450073635e+04, + "iterations": 8124, + "real_time": 9.1373874814580195e+04, + "cpu_time": 9.0240014524864659e+04, "time_unit": "ns" }, { @@ -979,9 +979,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 8132, - "real_time": 9.5884095057348080e+04, - "cpu_time": 9.4661022503689237e+04, + "iterations": 8124, + "real_time": 1.0381180330036678e+05, + "cpu_time": 1.0038200763170891e+05, "time_unit": "ns" }, { @@ -993,9 +993,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 8132, - "real_time": 9.4811416625880011e+04, - "cpu_time": 9.3695866945401081e+04, + "iterations": 8124, + "real_time": 9.5242334689878713e+04, + "cpu_time": 9.3994281265386206e+04, "time_unit": "ns" }, { @@ -1009,8 +1009,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 9.3121903919405871e+04, - "cpu_time": 9.2033128299721327e+04, + "real_time": 9.6809337601608553e+04, + "cpu_time": 9.4872101140653249e+04, "time_unit": "ns" }, { @@ -1024,8 +1024,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 9.4811416625880011e+04, - "cpu_time": 9.3695866945401067e+04, + "real_time": 9.5242334689878728e+04, + "cpu_time": 9.3994281265386206e+04, "time_unit": "ns" }, { @@ -1039,8 +1039,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.8924169988747799e+03, - "cpu_time": 3.7470026689048532e+03, + "real_time": 6.3653075219044285e+03, + "cpu_time": 5.1276633903641432e+03, "time_unit": "ns" }, { @@ -1054,8 +1054,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 4.1799156106640034e-02, - "cpu_time": 4.0713629299898517e-02, + "real_time": 6.5750966586498608e-02, + "cpu_time": 5.4048169363953388e-02, "time_unit": "ns" }, { @@ -1067,9 +1067,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 8535, - "real_time": 8.7415463152392287e+04, - "cpu_time": 8.6224500761569769e+04, + "iterations": 6458, + "real_time": 1.0016481913773292e+05, + "cpu_time": 9.8046890987921797e+04, "time_unit": "ns" }, { @@ -1081,9 +1081,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 8535, - "real_time": 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"time_unit": "ns" }, { @@ -1141,8 +1141,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.5982358124065668e+03, - "cpu_time": 3.4098217861917587e+03, + "real_time": 8.3979543239963441e+03, + "cpu_time": 8.2917655663595015e+03, "time_unit": "ns" }, { @@ -1156,8 +1156,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 4.2605052595263558e-02, - "cpu_time": 4.0847445301051367e-02, + "real_time": 8.7889033489661622e-02, + "cpu_time": 8.8416220483496566e-02, "time_unit": "ns" }, { @@ -1169,9 +1169,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 8615, - "real_time": 7.9806666743270805e+04, - "cpu_time": 7.9028190133488184e+04, + "iterations": 6858, + "real_time": 9.3030761591598537e+04, + "cpu_time": 9.1602852435112174e+04, "time_unit": "ns" }, { @@ -1183,9 +1183,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 8615, - "real_time": 8.0841487405394131e+04, - "cpu_time": 8.0040353105049435e+04, + "iterations": 6858, + "real_time": 8.6511492128064332e+04, + "cpu_time": 8.5318596821230691e+04, "time_unit": "ns" }, { @@ -1197,9 +1197,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 8615, - "real_time": 8.0566300986022805e+04, - "cpu_time": 7.9794792571096725e+04, + "iterations": 6858, + "real_time": 8.4088126856397226e+04, + "cpu_time": 8.2806301691455490e+04, "time_unit": "ns" }, { @@ -1213,8 +1213,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 8.0404818378229247e+04, - "cpu_time": 7.9621111936544781e+04, + "real_time": 8.7876793525353365e+04, + "cpu_time": 8.6575916982599461e+04, "time_unit": "ns" }, { @@ -1228,8 +1228,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 8.0566300986022805e+04, - "cpu_time": 7.9794792571096739e+04, + "real_time": 8.6511492128064347e+04, + "cpu_time": 8.5318596821230705e+04, "time_unit": "ns" }, { @@ -1243,8 +1243,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 5.3597660877733244e+02, - "cpu_time": 5.2796040795162992e+02, + "real_time": 4.6250097222475179e+03, + "cpu_time": 4.5310558082604975e+03, "time_unit": "ns" }, { @@ -1258,8 +1258,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 6.6659762385889018e-03, - "cpu_time": 6.6309097563520070e-03, + "real_time": 5.2630615395783127e-02, + "cpu_time": 5.2336215037389389e-02, "time_unit": "ns" } ] diff --git a/research/benchmark-results/ssb-sf001.json b/research/benchmark-results/ssb-sf001.json index 46f78a1..20ab4e5 100644 --- a/research/benchmark-results/ssb-sf001.json +++ b/research/benchmark-results/ssb-sf001.json @@ -1,6 +1,6 @@ { "context": { - "date": "2026-06-02T01:16:41+03:00", + "date": "2026-06-09T23:45:52+03:00", "host_name": "nixos", "executable": "./build-release/bin/benchmarks", "num_cpus": 8, @@ -32,7 +32,7 @@ "num_sharing": 8 } ], - "load_avg": [0.800293,0.489258,0.491211], + "load_avg": [1.00439,0.640625,0.89502], "library_version": "v1.9.0", "library_build_type": "release", "json_schema_version": 1 @@ -47,11 +47,11 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 74, - "real_time": 9.5399927162656542e+06, - "cpu_time": 9.4592325405405406e+06, + "iterations": 76, + "real_time": 9.9512121447141413e+06, + "cpu_time": 9.8711035526315793e+06, "time_unit": "ns", - "execution_us": 9.5399293783783778e+03, + "execution_us": 9.9511413026315786e+03, "includes_order_by": 1.0000000000000000e+00 }, { @@ -63,11 +63,11 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 74, - "real_time": 9.5605493782361895e+06, - "cpu_time": 9.4841847567567565e+06, + "iterations": 76, + "real_time": 1.0357855052509541e+07, + "cpu_time": 1.0266127460526315e+07, "time_unit": "ns", - "execution_us": 9.5604816216216223e+03, + "execution_us": 1.0357739236842106e+04, "includes_order_by": 1.0000000000000000e+00 }, { @@ -79,11 +79,11 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 74, - "real_time": 9.5072076622494254e+06, - "cpu_time": 9.4303343648648672e+06, + "iterations": 76, + "real_time": 1.1278557158029320e+07, + "cpu_time": 1.1154365052631577e+07, "time_unit": "ns", - "execution_us": 9.5070937837837828e+03, + "execution_us": 1.1278490815789473e+04, "includes_order_by": 1.0000000000000000e+00 }, { @@ -97,10 +97,10 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 9.5359165855837557e+06, - "cpu_time": 9.4579172207207214e+06, + "real_time": 1.0529208118417667e+07, + "cpu_time": 1.0430532021929821e+07, "time_unit": "ns", - "execution_us": 9.5358349279279282e+03, + "execution_us": 1.0529123785087719e+04, "includes_order_by": 1.0000000000000000e+00 }, { @@ -114,10 +114,10 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 9.5399927162656542e+06, - "cpu_time": 9.4592325405405406e+06, + "real_time": 1.0357855052509541e+07, + "cpu_time": 1.0266127460526315e+07, "time_unit": "ns", - "execution_us": 9.5399293783783778e+03, + "execution_us": 1.0357739236842106e+04, "includes_order_by": 1.0000000000000000e+00 }, { @@ -131,10 +131,10 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.6903453256290217e+04, - "cpu_time": 2.6949280628643854e+04, + "real_time": 6.8006073331009818e+05, + "cpu_time": 6.5723790533056122e+05, "time_unit": "ns", - "execution_us": 2.6928399517925090e+01, + "execution_us": 6.8006887890355438e+02, "includes_order_by": 0.0000000000000000e+00 }, { @@ -148,10 +148,10 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 2.8212760687276171e-03, - "cpu_time": 2.8493885069751368e-03, + "real_time": 6.4588022732738795e-02, + "cpu_time": 6.3010966645684230e-02, "time_unit": "ns", - "execution_us": 2.8239162822606082e-03, + "execution_us": 6.4589313677433285e-02, "includes_order_by": 0.0000000000000000e+00 }, { @@ -163,14 +163,14 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 54, - "real_time": 1.0037977036888523e+07, - "cpu_time": 9.9676256296296343e+06, + "iterations": 72, + "real_time": 1.1711527986335568e+07, + "cpu_time": 1.1595316222222218e+07, "time_unit": "ns", - "execution_us": 1.0037876462962964e+04, + "execution_us": 1.1711456208333333e+04, "includes_order_by": 1.0000000000000000e+00, - "optimizer_us": 8.7225999999999999e+02, - "plan_cost": 8.3660300000000000e+05 + "optimizer_us": 5.3586760000000004e+03, + "plan_cost": 8.3622300000000000e+05 }, { "name": "SSB/q2.1/Interpreted/Optimized/real_time", @@ -181,14 +181,14 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 54, - "real_time": 9.9924028518553637e+06, - "cpu_time": 9.9165896666666660e+06, + "iterations": 72, + "real_time": 1.1374830111081893e+07, + "cpu_time": 1.1181104888888886e+07, "time_unit": "ns", - "execution_us": 9.9923292222222226e+03, + "execution_us": 1.1374768583333333e+04, "includes_order_by": 1.0000000000000000e+00, - "optimizer_us": 7.8481600000000003e+02, - "plan_cost": 8.3660300000000000e+05 + "optimizer_us": 5.5903590000000004e+03, + "plan_cost": 8.3622300000000000e+05 }, { "name": "SSB/q2.1/Interpreted/Optimized/real_time", @@ -199,14 +199,14 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 54, - "real_time": 1.0025189036915198e+07, - "cpu_time": 9.9565894629629608e+06, + "iterations": 72, + "real_time": 1.0174952805553202e+07, + "cpu_time": 1.0039267958333332e+07, "time_unit": "ns", - "execution_us": 1.0025109962962963e+04, + "execution_us": 1.0174888694444446e+04, "includes_order_by": 1.0000000000000000e+00, - "optimizer_us": 7.9701099999999997e+02, - "plan_cost": 8.3660300000000000e+05 + "optimizer_us": 5.6177420000000002e+03, + "plan_cost": 8.3622300000000000e+05 }, { "name": "SSB/q2.1/Interpreted/Optimized/real_time_mean", @@ -219,13 +219,13 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.0018522975219695e+07, - "cpu_time": 9.9469349197530858e+06, + "real_time": 1.1087103634323554e+07, + "cpu_time": 1.0938563023148144e+07, "time_unit": "ns", - "execution_us": 1.0018438549382716e+04, + "execution_us": 1.1087037828703704e+04, "includes_order_by": 1.0000000000000000e+00, - "optimizer_us": 8.1802900000000000e+02, - "plan_cost": 8.3660300000000000e+05 + "optimizer_us": 5.5222590000000000e+03, + "plan_cost": 8.3622300000000000e+05 }, { "name": "SSB/q2.1/Interpreted/Optimized/real_time_median", @@ -238,13 +238,13 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.0025189036915200e+07, - "cpu_time": 9.9565894629629608e+06, + "real_time": 1.1374830111081891e+07, + "cpu_time": 1.1181104888888886e+07, "time_unit": "ns", - "execution_us": 1.0025109962962963e+04, + "execution_us": 1.1374768583333333e+04, "includes_order_by": 1.0000000000000000e+00, - "optimizer_us": 7.9701099999999997e+02, - "plan_cost": 8.3660300000000000e+05 + "optimizer_us": 5.5903590000000004e+03, + "plan_cost": 8.3622300000000000e+05 }, { "name": "SSB/q2.1/Interpreted/Optimized/real_time_stddev", @@ -257,12 +257,12 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.3506996177584002e+04, - "cpu_time": 2.6852840303399800e+04, + "real_time": 8.0768540634329349e+05, + "cpu_time": 8.0587931315157900e+05, "time_unit": "ns", - "execution_us": 2.3495076168409312e+01, + "execution_us": 8.0768290284891270e+02, "includes_order_by": 0.0000000000000000e+00, - "optimizer_us": 4.7359587487644710e+01, + "optimizer_us": 1.4232710770968617e+02, "plan_cost": 0.0000000000000000e+00 }, { @@ -276,12 +276,12 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 2.3463534730346343e-03, - "cpu_time": 2.6996095299743222e-03, + "real_time": 7.2849089625432378e-02, + "cpu_time": 7.3673233993000756e-02, "time_unit": "ns", - "execution_us": 2.3451834387761910e-03, + "execution_us": 7.2849296207673081e-02, "includes_order_by": 0.0000000000000000e+00, - "optimizer_us": 5.7894753716120954e-02, + "optimizer_us": 2.5773348861342103e-02, "plan_cost": 0.0000000000000000e+00 }, { @@ -293,11 +293,11 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 76, - "real_time": 8.7039228421513382e+06, - "cpu_time": 8.6439320526315812e+06, + "iterations": 83, + "real_time": 8.2656341567719691e+06, + "cpu_time": 8.2090268915662710e+06, "time_unit": "ns", - "execution_us": 8.7038643815789474e+03, + "execution_us": 8.2655834216867461e+03, "includes_order_by": 0.0000000000000000e+00 }, { @@ -309,11 +309,11 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 76, - "real_time": 8.6583044605121315e+06, - "cpu_time": 8.5988706052631680e+06, + "iterations": 83, + "real_time": 8.4240290358051080e+06, + "cpu_time": 8.3510533855421618e+06, "time_unit": "ns", - 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"aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 9.8855726515981276e+06, - "cpu_time": 9.8219928131313007e+06, + "real_time": 9.5533078619545065e+06, + "cpu_time": 9.4917103380952645e+06, "time_unit": "ns", - "execution_us": 9.8854904646464638e+03, + "execution_us": 9.5532349761904770e+03, "includes_order_by": 1.0000000000000000e+00 }, { @@ -2820,10 +2820,10 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 9.9354656971020941e+06, - "cpu_time": 9.8686349090908114e+06, + "real_time": 9.5405256712443326e+06, + "cpu_time": 9.4795176000001151e+06, "time_unit": "ns", - "execution_us": 9.9353701515151515e+03, + "execution_us": 9.5404531571428579e+03, "includes_order_by": 1.0000000000000000e+00 }, { @@ -2837,10 +2837,10 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.2611277094789948e+05, - "cpu_time": 1.2156726806552755e+05, + "real_time": 6.9032337217831679e+04, + "cpu_time": 7.2718625460879979e+04, "time_unit": "ns", - "execution_us": 1.2611946247567755e+02, + "execution_us": 6.9026377247445780e+01, "includes_order_by": 0.0000000000000000e+00 }, { @@ -2854,10 +2854,10 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.2757254980824176e-02, - "cpu_time": 1.2377047140881716e-02, + "real_time": 7.2260140901298647e-03, + "cpu_time": 7.6612773536737207e-03, "time_unit": "ns", - "execution_us": 1.2758037947304618e-02, + "execution_us": 7.2254453511800126e-03, "includes_order_by": 0.0000000000000000e+00 }, { @@ -2869,14 +2869,14 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 50, - "real_time": 1.3572907519992443e+07, - "cpu_time": 1.3484294879999936e+07, + "iterations": 54, + "real_time": 1.2863843851610674e+07, + "cpu_time": 1.2800047574074034e+07, "time_unit": "ns", - "execution_us": 1.3572817620000002e+04, + "execution_us": 1.2863761981481481e+04, "includes_order_by": 1.0000000000000000e+00, - "optimizer_us": 3.9196149999999998e+03, - "plan_cost": 8.4317300000000000e+05 + "optimizer_us": 7.3953727099999995e+05, + "plan_cost": 8.4279300000000000e+05 }, { "name": "SSB/q4.1/Interpreted/Optimized/real_time", @@ -2887,14 +2887,14 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 50, - "real_time": 1.3961699460051022e+07, - "cpu_time": 1.3863721519999929e+07, + "iterations": 54, + "real_time": 1.3123589462742934e+07, + "cpu_time": 1.3043514370370386e+07, "time_unit": "ns", - "execution_us": 1.3961627600000000e+04, + "execution_us": 1.3123442092592593e+04, "includes_order_by": 1.0000000000000000e+00, - "optimizer_us": 3.8879760000000001e+03, - "plan_cost": 8.4317300000000000e+05 + "optimizer_us": 7.4747992000000004e+05, + "plan_cost": 8.4279300000000000e+05 }, { "name": "SSB/q4.1/Interpreted/Optimized/real_time", @@ -2905,14 +2905,14 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 50, - "real_time": 1.3328604919952340e+07, - "cpu_time": 1.3238763319999976e+07, + "iterations": 54, + "real_time": 1.3179273685198966e+07, + "cpu_time": 1.3086517425925855e+07, "time_unit": "ns", - "execution_us": 1.3328537479999999e+04, + "execution_us": 1.3179186222222223e+04, "includes_order_by": 1.0000000000000000e+00, - "optimizer_us": 4.1023909999999996e+03, - "plan_cost": 8.4317300000000000e+05 + "optimizer_us": 7.1924559499999997e+05, + "plan_cost": 8.4279300000000000e+05 }, { "name": "SSB/q4.1/Interpreted/Optimized/real_time_mean", @@ -2925,13 +2925,13 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.3621070633331932e+07, - "cpu_time": 1.3528926573333280e+07, + "real_time": 1.3055568999850856e+07, + "cpu_time": 1.2976693123456756e+07, "time_unit": "ns", - "execution_us": 1.3620994233333333e+04, + "execution_us": 1.3055463432098764e+04, "includes_order_by": 1.0000000000000000e+00, - "optimizer_us": 3.9699939999999997e+03, - "plan_cost": 8.4317300000000000e+05 + "optimizer_us": 7.3542092866666673e+05, + "plan_cost": 8.4279300000000000e+05 }, { "name": "SSB/q4.1/Interpreted/Optimized/real_time_median", @@ -2944,13 +2944,13 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.3572907519992441e+07, - "cpu_time": 1.3484294879999936e+07, + "real_time": 1.3123589462742932e+07, + "cpu_time": 1.3043514370370382e+07, "time_unit": "ns", - "execution_us": 1.3572817620000002e+04, + "execution_us": 1.3123442092592593e+04, "includes_order_by": 1.0000000000000000e+00, - "optimizer_us": 3.9196149999999998e+03, - "plan_cost": 8.4317300000000000e+05 + "optimizer_us": 7.3953727099999995e+05, + "plan_cost": 8.4279300000000000e+05 }, { "name": "SSB/q4.1/Interpreted/Optimized/real_time_stddev", @@ -2963,12 +2963,12 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.1928347638208332e+05, - "cpu_time": 3.1486057068819489e+05, + "real_time": 1.6835700908612020e+05, + "cpu_time": 1.5448318123455989e+05, "time_unit": "ns", - "execution_us": 3.1928281282363724e+02, + "execution_us": 1.6834172573571180e+02, "includes_order_by": 0.0000000000000000e+00, - "optimizer_us": 1.1574532727933205e+02, + "optimizer_us": 1.4560305034755940e+04, "plan_cost": 0.0000000000000000e+00 }, { @@ -2982,12 +2982,12 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 2.3440409713519083e-02, - "cpu_time": 2.3273137671455262e-02, + "real_time": 1.2895417203803484e-02, + "cpu_time": 1.1904664752787831e-02, "time_unit": "ns", - "execution_us": 2.3440492474645316e-02, + "execution_us": 1.2894350829539998e-02, "includes_order_by": 0.0000000000000000e+00, - "optimizer_us": 2.9155038339940074e-02, + "optimizer_us": 1.9798600321524806e-02, "plan_cost": 0.0000000000000000e+00 }, { @@ -2999,11 +2999,11 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 73, - "real_time": 9.4941860821962431e+06, - "cpu_time": 9.4339479178081937e+06, + "iterations": 64, + "real_time": 9.6015775779960677e+06, + "cpu_time": 9.4240870624999758e+06, "time_unit": "ns", - "execution_us": 9.4941211917808214e+03, + "execution_us": 9.6015101718749993e+03, "includes_order_by": 1.0000000000000000e+00 }, { @@ -3015,11 +3015,11 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 73, - "real_time": 9.5839359040439054e+06, - "cpu_time": 9.5257411917808335e+06, + "iterations": 64, + "real_time": 9.4633789062754661e+06, + "cpu_time": 9.4057306249999423e+06, "time_unit": "ns", - "execution_us": 9.5838616438356166e+03, + "execution_us": 9.4633035624999993e+03, "includes_order_by": 1.0000000000000000e+00 }, { @@ -3031,11 +3031,11 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 73, - "real_time": 9.4410957671280894e+06, - "cpu_time": 9.3849140273972545e+06, + "iterations": 64, + "real_time": 9.4466778596142828e+06, + "cpu_time": 9.3827825468750801e+06, "time_unit": "ns", - "execution_us": 9.4410327945205481e+03, + "execution_us": 9.4466047187500008e+03, "includes_order_by": 1.0000000000000000e+00 }, { @@ -3049,10 +3049,10 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 9.5064059177894127e+06, - "cpu_time": 9.4482010456620920e+06, + "real_time": 9.5038781146286055e+06, + "cpu_time": 9.4042000781249981e+06, "time_unit": "ns", - "execution_us": 9.5063385433789954e+03, + "execution_us": 9.5038061510416665e+03, "includes_order_by": 1.0000000000000000e+00 }, { @@ -3066,10 +3066,10 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 9.4941860821962412e+06, - "cpu_time": 9.4339479178081919e+06, + "real_time": 9.4633789062754661e+06, + "cpu_time": 9.4057306249999423e+06, "time_unit": "ns", - "execution_us": 9.4941211917808214e+03, + "execution_us": 9.4633035624999993e+03, "includes_order_by": 1.0000000000000000e+00 }, { @@ -3083,10 +3083,10 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 7.2199857790542941e+04, - "cpu_time": 7.1487315639248089e+04, + "real_time": 8.5021292019139873e+04, + "cpu_time": 2.0694750133421130e+04, "time_unit": "ns", - "execution_us": 7.2193959645042938e+01, + "execution_us": 8.5025111648877925e+01, "includes_order_by": 0.0000000000000000e+00 }, { @@ -3100,10 +3100,10 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 7.5948637597553856e-03, - "cpu_time": 7.5662356562649271e-03, + "real_time": 8.9459577441626682e-03, + "cpu_time": 2.2005859043300185e-03, "time_unit": "ns", - "execution_us": 7.5942971434911511e-03, + "execution_us": 8.9464273889423487e-03, "includes_order_by": 0.0000000000000000e+00 }, { @@ -3115,14 +3115,14 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 105, - "real_time": 6.6341793333690260e+06, - "cpu_time": 6.5972472380952323e+06, + "iterations": 106, + "real_time": 6.2337937923521679e+06, + "cpu_time": 6.1994316603774205e+06, "time_unit": "ns", - "execution_us": 6.6341253999999999e+03, + "execution_us": 6.2337179245283014e+03, "includes_order_by": 1.0000000000000000e+00, - "optimizer_us": 8.7711900000000003e+02, - "plan_cost": 8.3660300000000000e+05 + "optimizer_us": 5.2146959999999999e+03, + "plan_cost": 8.3622300000000000e+05 }, { "name": "SSB/q2.3/Interpreted/Optimized/real_time", @@ -3133,14 +3133,14 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 105, - "real_time": 7.0254607809363818e+06, - "cpu_time": 6.9796631619047793e+06, + "iterations": 106, + "real_time": 6.4182148961886270e+06, + "cpu_time": 6.3774029433961688e+06, "time_unit": "ns", - "execution_us": 7.0253927428571433e+03, + "execution_us": 6.4181481415094340e+03, "includes_order_by": 1.0000000000000000e+00, - "optimizer_us": 8.4759100000000001e+02, - "plan_cost": 8.3660300000000000e+05 + "optimizer_us": 5.6024849999999997e+03, + "plan_cost": 8.3622300000000000e+05 }, { "name": "SSB/q2.3/Interpreted/Optimized/real_time", @@ -3151,14 +3151,14 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 105, - "real_time": 6.8238354951886106e+06, - "cpu_time": 6.7844589619047903e+06, + "iterations": 106, + "real_time": 6.2254421887392141e+06, + "cpu_time": 6.1920818396226596e+06, "time_unit": "ns", - "execution_us": 6.8237815047619042e+03, + "execution_us": 6.2253759528301889e+03, "includes_order_by": 1.0000000000000000e+00, - "optimizer_us": 8.3170299999999997e+02, - "plan_cost": 8.3660300000000000e+05 + "optimizer_us": 5.3557669999999998e+03, + "plan_cost": 8.3622300000000000e+05 }, { "name": "SSB/q2.3/Interpreted/Optimized/real_time_mean", @@ -3171,13 +3171,13 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 6.8278252031646715e+06, - "cpu_time": 6.7871231206349349e+06, + "real_time": 6.2924836257600030e+06, + "cpu_time": 6.2563054811320826e+06, "time_unit": "ns", - "execution_us": 6.8277665492063479e+03, + "execution_us": 6.2924140062893075e+03, "includes_order_by": 1.0000000000000000e+00, - "optimizer_us": 8.5213766666666663e+02, - "plan_cost": 8.3660300000000000e+05 + "optimizer_us": 5.3909826666666668e+03, + "plan_cost": 8.3622300000000000e+05 }, { "name": "SSB/q2.3/Interpreted/Optimized/real_time_median", @@ -3190,13 +3190,13 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 6.8238354951886097e+06, - "cpu_time": 6.7844589619047912e+06, + "real_time": 6.2337937923521670e+06, + "cpu_time": 6.1994316603774205e+06, "time_unit": "ns", - "execution_us": 6.8237815047619042e+03, + "execution_us": 6.2337179245283014e+03, "includes_order_by": 1.0000000000000000e+00, - "optimizer_us": 8.4759100000000001e+02, - "plan_cost": 8.3660300000000000e+05 + "optimizer_us": 5.3557669999999998e+03, + "plan_cost": 8.3622300000000000e+05 }, { "name": "SSB/q2.3/Interpreted/Optimized/real_time_stddev", @@ -3209,12 +3209,12 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.9567123224914467e+05, - "cpu_time": 1.9122188159851517e+05, + "real_time": 1.0896651592772501e+05, + "cpu_time": 1.0493784584879056e+05, "time_unit": "ns", - "execution_us": 1.9566410971631640e+02, + "execution_us": 1.0896881064158120e+02, "includes_order_by": 0.0000000000000000e+00, - "optimizer_us": 2.3046852221800975e+01, + "optimizer_us": 1.9627833429681101e+02, "plan_cost": 0.0000000000000000e+00 }, { @@ -3228,12 +3228,12 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 2.8657914698585391e-02, - "cpu_time": 2.8174217293501287e-02, + "real_time": 1.7316932773832061e-02, + "cpu_time": 1.6773133307710859e-02, "time_unit": "ns", - "execution_us": 2.8657117712828098e-02, + "execution_us": 1.7317489048347134e-02, "includes_order_by": 0.0000000000000000e+00, - "optimizer_us": 2.7045925938180931e-02, + "optimizer_us": 3.6408637614517340e-02, "plan_cost": 0.0000000000000000e+00 } ] diff --git a/research/benchmarks.ipynb b/research/benchmarks.ipynb index f475c98..4eeae6b 100644 --- a/research/benchmarks.ipynb +++ b/research/benchmarks.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "b5291871", "metadata": {}, "outputs": [ @@ -10,7 +10,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2026-06-02T01:16:13+03:00\n", + "2026-06-09T23:45:24+03:00\n", "Running ./build-release/bin/benchmarks\n", "Run on (8 X 4200 MHz CPU s)\n", "CPU Caches:\n", @@ -18,59 +18,59 @@ " L1 Instruction 32 KiB (x4)\n", " L2 Unified 1280 KiB (x4)\n", " L3 Unified 8192 KiB (x1)\n", - "Load Average: 0.38, 0.38, 0.46\n", + "Load Average: 0.41, 0.52, 0.86\n", "***WARNING*** CPU scaling is enabled, the benchmark real time measurements may be noisy and will incur extra overhead.\n", "----------------------------------------------------------------------------------------------------------------------------------------------------\n", "Benchmark Time CPU Iterations\n", "----------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 22492 ns 22349 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 22447 ns 22296 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 1558 ns 1526 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 6.93 % 6.83 % \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 20696 ns 20601 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 20835 ns 20742 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 834 ns 810 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 4.03 % 3.93 % \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 20039 ns 19954 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 19980 ns 19905 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 172 ns 177 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 0.86 % 0.89 % \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 19665 ns 19591 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 19751 ns 19677 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 207 ns 204 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_cv 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Caches:\n", @@ -98,115 +98,115 @@ " L1 Instruction 32 KiB (x4)\n", " L2 Unified 1280 KiB (x4)\n", " L3 Unified 8192 KiB (x1)\n", - "Load Average: 0.80, 0.49, 0.49\n", + "Load Average: 1.00, 0.64, 0.90\n", "***WARNING*** CPU scaling is enabled, the benchmark real time measurements may be noisy and will incur extra overhead.\n", "----------------------------------------------------------------------------------------------------------\n", "Benchmark Time CPU Iterations UserCounters...\n", "----------------------------------------------------------------------------------------------------------\n", - "\u001b[0;32mSSB/q2.1/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 9535917 ns 9457917 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.53583k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 9539993 ns 9459233 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.53993k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 26903 ns 26949 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=26.9284\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 0.28 % 0.28 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.28%\u001b[m includes_order_by=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 10018523 ns 9946935 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=10.0184k\u001b[m includes_order_by=1\u001b[m optimizer_us=818.029\u001b[m plan_cost=836.603k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 10025189 ns 9956589 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=10.0251k\u001b[m includes_order_by=1\u001b[m optimizer_us=797.011\u001b[m plan_cost=836.603k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Optimized/real_time_stddev 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includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 4.17 % 4.11 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=4.17%\u001b[m includes_order_by=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 7216482 ns 7178427 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.21641k\u001b[m includes_order_by=0\u001b[m optimizer_us=419.653\u001b[m plan_cost=654.297k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 7171734 ns 7135746 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.17166k\u001b[m includes_order_by=0\u001b[m optimizer_us=404.975\u001b[m plan_cost=654.297k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 378775 ns 369597 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=378.779\u001b[m includes_order_by=0\u001b[m optimizer_us=40.6977\u001b[m plan_cost=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 5.25 % 5.15 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=5.25%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=9.70%\u001b[m plan_cost=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 8755501 ns 8695939 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.75534k\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 8634674 ns 8580077 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.63449k\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 313421 ns 306308 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=313.48\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 3.58 % 3.52 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=3.58%\u001b[m includes_order_by=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 6842163 ns 6813610 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.8421k\u001b[m includes_order_by=0\u001b[m optimizer_us=225.618\u001b[m plan_cost=654.297k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 6857864 ns 6828042 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.8578k\u001b[m includes_order_by=0\u001b[m optimizer_us=225.797\u001b[m plan_cost=654.297k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 47976 ns 47617 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=47.9819\u001b[m includes_order_by=0\u001b[m optimizer_us=2.58966\u001b[m plan_cost=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 0.70 % 0.70 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.70%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=1.15%\u001b[m plan_cost=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 38770551 ns 38545894 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=38.7704k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 38937311 ns 38712070 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=38.9372k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 403708 ns 387320 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=403.68\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 1.04 % 1.00 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=1.04%\u001b[m includes_order_by=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 6165384 ns 6131574 ns 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"\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 6752747 ns 6702527 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.75267k\u001b[m includes_order_by=1\u001b[m optimizer_us=1.76762k\u001b[m plan_cost=817.083k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 547461 ns 516050 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=547.457\u001b[m includes_order_by=0\u001b[m optimizer_us=55.8846\u001b[m plan_cost=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 8.36 % 7.95 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.36%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=3.22%\u001b[m plan_cost=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 9839082 ns 9773621 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.83901k\u001b[m includes_order_by=1\u001b[m\n", - 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"\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 7876 ns 7272 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.88332\u001b[m includes_order_by=0\u001b[m optimizer_us=12.9813\u001b[m plan_cost=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 0.11 % 0.10 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.11%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=6.39%\u001b[m plan_cost=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 41168218 ns 40861121 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=41.168k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 39951647 ns 39624417 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=39.9514k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 2772999 ns 2719430 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=2.77305k\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 6.74 % 6.66 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.74%\u001b[m includes_order_by=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 7331045 ns 7263440 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.33098k\u001b[m includes_order_by=1\u001b[m optimizer_us=1.77516k\u001b[m plan_cost=816.743k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 7250476 ns 7179529 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.25042k\u001b[m includes_order_by=1\u001b[m optimizer_us=1.45913k\u001b[m plan_cost=816.743k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 560067 ns 542530 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=560.073\u001b[m includes_order_by=0\u001b[m optimizer_us=631.618\u001b[m plan_cost=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 7.64 % 7.47 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.64%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=35.58%\u001b[m plan_cost=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 10611863 ns 10523713 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=10.6118k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 10571693 ns 10485617 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=10.5716k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 700693 ns 680849 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=700.727\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 6.60 % 6.47 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.60%\u001b[m includes_order_by=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 7990721 ns 7922960 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.99066k\u001b[m includes_order_by=1\u001b[m optimizer_us=1.30379k\u001b[m plan_cost=841.703k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 7968509 ns 7901081 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.96844k\u001b[m includes_order_by=1\u001b[m optimizer_us=1.07317k\u001b[m plan_cost=841.703k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 412217 ns 399538 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=412.219\u001b[m includes_order_by=0\u001b[m optimizer_us=435.934\u001b[m plan_cost=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 5.16 % 5.04 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=5.16%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=33.44%\u001b[m plan_cost=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 41047048 ns 40776811 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=41.0468k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 40607061 ns 40351481 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=40.6066k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 1022743 ns 1007568 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=1.02279k\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 2.49 % 2.47 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=2.49%\u001b[m includes_order_by=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 7329905 ns 7261047 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.32985k\u001b[m includes_order_by=1\u001b[m optimizer_us=1.56312k\u001b[m plan_cost=816.743k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 7444323 ns 7376360 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.44426k\u001b[m includes_order_by=1\u001b[m optimizer_us=1.60169k\u001b[m plan_cost=816.743k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 377349 ns 367470 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=377.353\u001b[m includes_order_by=0\u001b[m optimizer_us=243.913\u001b[m plan_cost=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 5.15 % 5.06 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=5.15%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=15.60%\u001b[m plan_cost=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 11383627 ns 11273121 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=11.3835k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 11182684 ns 11075830 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=11.1826k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 462093 ns 441146 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=462.11\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 4.06 % 3.91 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=4.06%\u001b[m includes_order_by=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 13753622 ns 13641036 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=13.7534k\u001b[m includes_order_by=1\u001b[m optimizer_us=6.6919k\u001b[m plan_cost=843.203k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 13773635 ns 13660823 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=13.7735k\u001b[m includes_order_by=1\u001b[m optimizer_us=6.40666k\u001b[m plan_cost=843.203k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 1461768 ns 1423624 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=1.46187k\u001b[m includes_order_by=0\u001b[m optimizer_us=859.324\u001b[m plan_cost=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 10.63 % 10.44 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=10.63%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=12.84%\u001b[m plan_cost=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 9885573 ns 9821993 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.88549k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 9935466 ns 9868635 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.93537k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 126113 ns 121567 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=126.119\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 1.28 % 1.24 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=1.28%\u001b[m includes_order_by=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 13621071 ns 13528927 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=13.621k\u001b[m includes_order_by=1\u001b[m optimizer_us=3.96999k\u001b[m plan_cost=843.173k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 13572908 ns 13484295 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=13.5728k\u001b[m includes_order_by=1\u001b[m optimizer_us=3.91961k\u001b[m plan_cost=843.173k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 319283 ns 314861 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=319.283\u001b[m includes_order_by=0\u001b[m optimizer_us=115.745\u001b[m plan_cost=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 2.34 % 2.33 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=2.34%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=2.92%\u001b[m plan_cost=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 9506406 ns 9448201 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.50634k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 9494186 ns 9433948 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.49412k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 72200 ns 71487 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=72.194\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 0.76 % 0.76 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.76%\u001b[m includes_order_by=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 6827825 ns 6787123 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.82777k\u001b[m includes_order_by=1\u001b[m optimizer_us=852.138\u001b[m plan_cost=836.603k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 6823835 ns 6784459 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.82378k\u001b[m includes_order_by=1\u001b[m optimizer_us=847.591\u001b[m plan_cost=836.603k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 195671 ns 191222 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=195.664\u001b[m includes_order_by=0\u001b[m optimizer_us=23.0469\u001b[m plan_cost=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 2.87 % 2.82 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=2.87%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=2.70%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[0;32mSSB/q2.1/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 10529208 ns 10430532 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=10.5291k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 10357855 ns 10266127 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=10.3577k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 680061 ns 657238 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=680.069\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 6.46 % 6.30 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.46%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 11087104 ns 10938563 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=11.087k\u001b[m includes_order_by=1\u001b[m optimizer_us=5.52226k\u001b[m plan_cost=836.223k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 11374830 ns 11181105 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=11.3748k\u001b[m includes_order_by=1\u001b[m optimizer_us=5.59036k\u001b[m plan_cost=836.223k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 807685 ns 805879 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=807.683\u001b[m includes_order_by=0\u001b[m optimizer_us=142.327\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 7.28 % 7.37 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.28%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=2.58%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 8696587 ns 8625517 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.69653k\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 8424029 ns 8351053 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.42397k\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 614383 ns 602582 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=614.378\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 7.06 % 6.99 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.06%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 8481006 ns 8356025 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.48094k\u001b[m includes_order_by=0\u001b[m optimizer_us=464.225\u001b[m plan_cost=654.297k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 8430650 ns 8195103 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.43059k\u001b[m includes_order_by=0\u001b[m optimizer_us=463.328\u001b[m plan_cost=654.297k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 413000 ns 423645 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=413.003\u001b[m includes_order_by=0\u001b[m optimizer_us=5.17612\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 4.87 % 5.07 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=4.87%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=1.12%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 10591798 ns 10470366 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=10.5917k\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 10967922 ns 10832180 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=10.9678k\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 778749 ns 750058 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=778.722\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 7.35 % 7.16 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.35%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 8042493 ns 7973771 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.04242k\u001b[m includes_order_by=0\u001b[m optimizer_us=389.801\u001b[m plan_cost=654.297k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 7975113 ns 7919581 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.97505k\u001b[m includes_order_by=0\u001b[m optimizer_us=373.936\u001b[m plan_cost=654.297k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 716234 ns 709722 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=716.23\u001b[m includes_order_by=0\u001b[m optimizer_us=40.4184\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 8.91 % 8.90 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.91%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=10.37%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 41079134 ns 40719159 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=41.0788k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 41251745 ns 40951561 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=41.2516k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 1772659 ns 1741817 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=1.77282k\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 4.32 % 4.28 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=4.32%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 6184696 ns 6137487 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.18463k\u001b[m includes_order_by=1\u001b[m optimizer_us=97.5624k\u001b[m plan_cost=816.714k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 6169728 ns 6119638 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.16966k\u001b[m includes_order_by=1\u001b[m optimizer_us=96.677k\u001b[m plan_cost=816.714k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 35201 ns 43807 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=35.2042\u001b[m includes_order_by=0\u001b[m optimizer_us=2.64896k\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 0.57 % 0.71 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.57%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=2.72%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 39726018 ns 39369045 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=39.7258k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 39289269 ns 38939516 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=39.289k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 1152565 ns 1067549 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=1.15258k\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 2.90 % 2.71 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=2.90%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 6226768 ns 6189600 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.2267k\u001b[m includes_order_by=1\u001b[m optimizer_us=46.7043k\u001b[m plan_cost=816.714k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 6217583 ns 6181985 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.2175k\u001b[m includes_order_by=1\u001b[m optimizer_us=46.7191k\u001b[m plan_cost=816.714k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 74863 ns 74631 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=74.8587\u001b[m includes_order_by=0\u001b[m optimizer_us=496.521\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 1.20 % 1.21 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=1.20%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=1.06%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 10159185 ns 10079207 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=10.1591k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 10047677 ns 9977358 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=10.0476k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 345553 ns 338814 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=345.524\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 3.40 % 3.36 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=3.40%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 6886022 ns 6843094 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.88595k\u001b[m includes_order_by=1\u001b[m optimizer_us=592.57k\u001b[m plan_cost=841.353k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 6878252 ns 6836928 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.87819k\u001b[m includes_order_by=1\u001b[m optimizer_us=594.328k\u001b[m plan_cost=841.353k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 13616 ns 11041 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=13.5977\u001b[m includes_order_by=0\u001b[m optimizer_us=8.90421k\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 0.20 % 0.16 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.20%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=1.50%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 8560502 ns 8501831 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.56041k\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 8474498 ns 8421813 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.47444k\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 236679 ns 232363 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=236.611\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 2.76 % 2.73 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=2.76%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 7188918 ns 7145194 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.18887k\u001b[m includes_order_by=0\u001b[m optimizer_us=386.236\u001b[m plan_cost=705.057k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 7065500 ns 7025301 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.06544k\u001b[m includes_order_by=0\u001b[m optimizer_us=278.406\u001b[m plan_cost=705.057k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 220209 ns 217250 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=220.214\u001b[m includes_order_by=0\u001b[m optimizer_us=189.749\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 3.06 % 3.04 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=3.06%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=49.13%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 38735087 ns 38507394 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=38.7349k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 38800745 ns 38599089 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=38.8006k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 794694 ns 787590 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=794.706\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 2.05 % 2.05 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=2.05%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 6717959 ns 6675562 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.71791k\u001b[m includes_order_by=1\u001b[m optimizer_us=44.7762k\u001b[m plan_cost=816.374k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 6691263 ns 6649855 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.69122k\u001b[m includes_order_by=1\u001b[m optimizer_us=44.9583k\u001b[m plan_cost=816.374k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 223132 ns 219030 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=223.132\u001b[m includes_order_by=0\u001b[m optimizer_us=1.38305k\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 3.32 % 3.28 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=3.32%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=3.09%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 9318865 ns 9262092 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.31879k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 9317950 ns 9264243 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.31784k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 43983 ns 43352 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=43.9888\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 0.47 % 0.47 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.47%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 6690732 ns 6651178 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.69067k\u001b[m includes_order_by=1\u001b[m optimizer_us=13.2656k\u001b[m plan_cost=841.323k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 6611059 ns 6573641 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.611k\u001b[m includes_order_by=1\u001b[m optimizer_us=12.9576k\u001b[m plan_cost=841.323k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 178965 ns 175813 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=178.964\u001b[m includes_order_by=0\u001b[m optimizer_us=613.922\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 2.67 % 2.64 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=2.67%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=4.63%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 38097914 ns 37891700 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=38.0977k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 38067251 ns 37869328 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=38.0671k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 714779 ns 706378 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=714.755\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 1.88 % 1.86 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=1.88%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 6528813 ns 6491827 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.52875k\u001b[m includes_order_by=1\u001b[m optimizer_us=44.0747k\u001b[m plan_cost=816.374k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 6443385 ns 6408645 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.44333k\u001b[m includes_order_by=1\u001b[m optimizer_us=43.7956k\u001b[m plan_cost=816.374k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 189025 ns 188359 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=189.037\u001b[m includes_order_by=0\u001b[m optimizer_us=772.954\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 2.90 % 2.90 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=2.90%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=1.75%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 10061357 ns 9987626 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=10.0613k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 9866919 ns 9808790 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.86683k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 371172 ns 350939 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=371.183\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 3.69 % 3.51 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=3.69%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 11892791 ns 11823670 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=11.8927k\u001b[m includes_order_by=1\u001b[m optimizer_us=1.70535M\u001b[m plan_cost=842.823k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 12032078 ns 11957276 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=12.032k\u001b[m includes_order_by=1\u001b[m optimizer_us=1.70999M\u001b[m plan_cost=842.823k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 367685 ns 356358 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=367.683\u001b[m includes_order_by=0\u001b[m optimizer_us=14.1195k\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 3.09 % 3.01 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=3.09%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=0.83%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 9553308 ns 9491710 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.55323k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 9540526 ns 9479518 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.54045k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 69032 ns 72719 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=69.0264\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 0.72 % 0.77 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.72%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 13055569 ns 12976693 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=13.0555k\u001b[m includes_order_by=1\u001b[m optimizer_us=735.421k\u001b[m plan_cost=842.793k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 13123589 ns 13043514 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=13.1234k\u001b[m includes_order_by=1\u001b[m optimizer_us=739.537k\u001b[m plan_cost=842.793k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 168357 ns 154483 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=168.342\u001b[m includes_order_by=0\u001b[m optimizer_us=14.5603k\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 1.29 % 1.19 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=1.29%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=1.98%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 9503878 ns 9404200 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.50381k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 9463379 ns 9405731 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.4633k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 85021 ns 20695 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=85.0251\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 0.89 % 0.22 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.89%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 6292484 ns 6256305 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.29241k\u001b[m includes_order_by=1\u001b[m optimizer_us=5.39098k\u001b[m plan_cost=836.223k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Optimized/real_time_median 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./build-release/bin/benchmarks\n", "Run on (8 X 4200 MHz CPU s)\n", "CPU Caches:\n", @@ -233,267 +233,267 @@ " L1 Instruction 32 KiB (x4)\n", " L2 Unified 1280 KiB (x4)\n", " L3 Unified 8192 KiB (x1)\n", - "Load Average: 1.43, 0.74, 0.58\n", + "Load Average: 1.62, 0.92, 0.97\n", "***WARNING*** CPU scaling is enabled, the benchmark real time measurements may be noisy and will incur extra overhead.\n", "----------------------------------------------------------------------------------------------------------------------\n", "Benchmark Time CPU Iterations UserCounters...\n", "----------------------------------------------------------------------------------------------------------------------\n", - "\u001b[0;32mOperatorCost/SeqScan/1024/real_time_mean \u001b[m\u001b[0;33m 38473 ns 38210 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=102.4k\u001b[m output_rows=1.024k\u001b[m plan_cost=102.4k\u001b[m rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/1024/real_time_median \u001b[m\u001b[0;33m 38428 ns 38240 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=102.4k\u001b[m output_rows=1.024k\u001b[m plan_cost=102.4k\u001b[m rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/1024/real_time_stddev \u001b[m\u001b[0;33m 753 ns 851 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/1024/real_time_cv \u001b[m\u001b[0;33m 1.96 % 2.23 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/2048/real_time_mean \u001b[m\u001b[0;33m 69707 ns 69380 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=204.8k\u001b[m output_rows=2.048k\u001b[m plan_cost=204.8k\u001b[m rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/2048/real_time_median \u001b[m\u001b[0;33m 69583 ns 69238 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=204.8k\u001b[m output_rows=2.048k\u001b[m plan_cost=204.8k\u001b[m rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/2048/real_time_stddev \u001b[m\u001b[0;33m 909 ns 889 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/2048/real_time_cv \u001b[m\u001b[0;33m 1.30 % 1.28 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/4096/real_time_mean \u001b[m\u001b[0;33m 154163 ns 152385 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=409.6k\u001b[m output_rows=4.096k\u001b[m plan_cost=409.6k\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/4096/real_time_median \u001b[m\u001b[0;33m 153859 ns 152316 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=409.6k\u001b[m output_rows=4.096k\u001b[m plan_cost=409.6k\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/4096/real_time_stddev \u001b[m\u001b[0;33m 901 ns 563 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/4096/real_time_cv \u001b[m\u001b[0;33m 0.58 % 0.37 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/8192/real_time_mean \u001b[m\u001b[0;33m 390148 ns 385602 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=819.2k\u001b[m output_rows=8.192k\u001b[m plan_cost=819.2k\u001b[m rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/8192/real_time_median \u001b[m\u001b[0;33m 399998 ns 395298 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=819.2k\u001b[m output_rows=8.192k\u001b[m plan_cost=819.2k\u001b[m rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/8192/real_time_stddev \u001b[m\u001b[0;33m 30561 ns 29527 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/8192/real_time_cv \u001b[m\u001b[0;33m 7.83 % 7.66 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/16384/real_time_mean \u001b[m\u001b[0;33m 924368 ns 914961 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.6384M\u001b[m output_rows=16.384k\u001b[m plan_cost=1.6384M\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/16384/real_time_median \u001b[m\u001b[0;33m 931261 ns 921625 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.6384M\u001b[m output_rows=16.384k\u001b[m plan_cost=1.6384M\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/16384/real_time_stddev \u001b[m\u001b[0;33m 17627 ns 17412 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/16384/real_time_cv \u001b[m\u001b[0;33m 1.91 % 1.90 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/32768/real_time_mean \u001b[m\u001b[0;33m 1901611 ns 1885331 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.2768M\u001b[m output_rows=32.768k\u001b[m plan_cost=3.2768M\u001b[m rows=32.768k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/32768/real_time_median \u001b[m\u001b[0;33m 1933730 ns 1916091 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.2768M\u001b[m output_rows=32.768k\u001b[m plan_cost=3.2768M\u001b[m rows=32.768k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/32768/real_time_stddev \u001b[m\u001b[0;33m 90232 ns 87205 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/32768/real_time_cv \u001b[m\u001b[0;33m 4.75 % 4.63 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/65536/real_time_mean \u001b[m\u001b[0;33m 4965998 ns 4909459 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.5536M\u001b[m output_rows=65.536k\u001b[m plan_cost=6.5536M\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/65536/real_time_median \u001b[m\u001b[0;33m 4998141 ns 4946201 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.5536M\u001b[m output_rows=65.536k\u001b[m plan_cost=6.5536M\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/65536/real_time_stddev \u001b[m\u001b[0;33m 155421 ns 163668 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/65536/real_time_cv \u001b[m\u001b[0;33m 3.13 % 3.33 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/131072/real_time_mean \u001b[m\u001b[0;33m 12138600 ns 11998804 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=13.1072M\u001b[m output_rows=131.072k\u001b[m plan_cost=13.1072M\u001b[m rows=131.072k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/131072/real_time_median \u001b[m\u001b[0;33m 11162739 ns 11038913 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=13.1072M\u001b[m output_rows=131.072k\u001b[m plan_cost=13.1072M\u001b[m rows=131.072k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/131072/real_time_stddev \u001b[m\u001b[0;33m 1830952 ns 1805064 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/131072/real_time_cv \u001b[m\u001b[0;33m 15.08 % 15.04 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/262144/real_time_mean \u001b[m\u001b[0;33m 25644707 ns 25374424 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.2144M\u001b[m output_rows=262.144k\u001b[m plan_cost=26.2144M\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/262144/real_time_median \u001b[m\u001b[0;33m 23244999 ns 23024292 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.2144M\u001b[m output_rows=262.144k\u001b[m plan_cost=26.2144M\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/262144/real_time_stddev \u001b[m\u001b[0;33m 4388803 ns 4299232 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/262144/real_time_cv \u001b[m\u001b[0;33m 17.11 % 16.94 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/1024/real_time_mean \u001b[m\u001b[0;33m 70425 ns 70146 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=102.4k\u001b[m output_rows=512\u001b[m plan_cost=204.8k\u001b[m rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/1024/real_time_median \u001b[m\u001b[0;33m 70365 ns 70086 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=102.4k\u001b[m output_rows=512\u001b[m plan_cost=204.8k\u001b[m rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/1024/real_time_stddev \u001b[m\u001b[0;33m 535 ns 551 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/1024/real_time_cv \u001b[m\u001b[0;33m 0.76 % 0.79 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/2048/real_time_mean \u001b[m\u001b[0;33m 125482 ns 125054 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=204.8k\u001b[m output_rows=1.024k\u001b[m plan_cost=409.6k\u001b[m rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/2048/real_time_median \u001b[m\u001b[0;33m 125359 ns 124936 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=204.8k\u001b[m output_rows=1.024k\u001b[m plan_cost=409.6k\u001b[m rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/2048/real_time_stddev \u001b[m\u001b[0;33m 329 ns 322 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/2048/real_time_cv \u001b[m\u001b[0;33m 0.26 % 0.26 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/4096/real_time_mean \u001b[m\u001b[0;33m 244461 ns 243272 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=409.6k\u001b[m output_rows=2.048k\u001b[m plan_cost=819.2k\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/4096/real_time_median \u001b[m\u001b[0;33m 244237 ns 242959 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=409.6k\u001b[m output_rows=2.048k\u001b[m plan_cost=819.2k\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/4096/real_time_stddev \u001b[m\u001b[0;33m 1017 ns 1018 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/4096/real_time_cv \u001b[m\u001b[0;33m 0.42 % 0.42 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/8192/real_time_mean \u001b[m\u001b[0;33m 479696 ns 477349 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=819.2k\u001b[m output_rows=4.096k\u001b[m plan_cost=1.6384M\u001b[m rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/8192/real_time_median \u001b[m\u001b[0;33m 477488 ns 475398 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=819.2k\u001b[m output_rows=4.096k\u001b[m plan_cost=1.6384M\u001b[m rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/8192/real_time_stddev \u001b[m\u001b[0;33m 6388 ns 6050 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/8192/real_time_cv \u001b[m\u001b[0;33m 1.33 % 1.27 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/16384/real_time_mean \u001b[m\u001b[0;33m 993383 ns 988008 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.6384M\u001b[m output_rows=8.192k\u001b[m plan_cost=3.2768M\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/16384/real_time_median \u001b[m\u001b[0;33m 990927 ns 985217 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.6384M\u001b[m output_rows=8.192k\u001b[m plan_cost=3.2768M\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/16384/real_time_stddev \u001b[m\u001b[0;33m 5275 ns 5696 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/16384/real_time_cv \u001b[m\u001b[0;33m 0.53 % 0.58 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/32768/real_time_mean \u001b[m\u001b[0;33m 2178959 ns 2166464 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.2768M\u001b[m output_rows=16.384k\u001b[m plan_cost=6.5536M\u001b[m rows=32.768k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/32768/real_time_median \u001b[m\u001b[0;33m 2129820 ns 2118195 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.2768M\u001b[m output_rows=16.384k\u001b[m plan_cost=6.5536M\u001b[m rows=32.768k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/32768/real_time_stddev \u001b[m\u001b[0;33m 101295 ns 99833 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/32768/real_time_cv \u001b[m\u001b[0;33m 4.65 % 4.61 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/65536/real_time_mean \u001b[m\u001b[0;33m 4436384 ns 4413484 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.5536M\u001b[m output_rows=32.768k\u001b[m plan_cost=13.1072M\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/65536/real_time_median \u001b[m\u001b[0;33m 4473698 ns 4450082 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.5536M\u001b[m output_rows=32.768k\u001b[m plan_cost=13.1072M\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/65536/real_time_stddev \u001b[m\u001b[0;33m 137556 ns 135091 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/65536/real_time_cv \u001b[m\u001b[0;33m 3.10 % 3.06 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/131072/real_time_mean \u001b[m\u001b[0;33m 9775161 ns 9717069 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=13.1072M\u001b[m output_rows=65.536k\u001b[m plan_cost=26.2144M\u001b[m rows=131.072k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/131072/real_time_median \u001b[m\u001b[0;33m 9723418 ns 9663916 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=13.1072M\u001b[m output_rows=65.536k\u001b[m plan_cost=26.2144M\u001b[m rows=131.072k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/131072/real_time_stddev \u001b[m\u001b[0;33m 113812 ns 109969 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/131072/real_time_cv \u001b[m\u001b[0;33m 1.16 % 1.13 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/262144/real_time_mean \u001b[m\u001b[0;33m 20011648 ns 19895345 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.2144M\u001b[m output_rows=131.072k\u001b[m plan_cost=52.4288M\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/262144/real_time_median \u001b[m\u001b[0;33m 20028071 ns 19914759 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.2144M\u001b[m output_rows=131.072k\u001b[m plan_cost=52.4288M\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/262144/real_time_stddev \u001b[m\u001b[0;33m 218024 ns 212289 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/262144/real_time_cv \u001b[m\u001b[0;33m 1.09 % 1.07 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/1024/real_time_mean \u001b[m\u001b[0;33m 94356 ns 94028 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=22.528k\u001b[m output_rows=1.024k\u001b[m plan_cost=124.928k\u001b[m rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/1024/real_time_median \u001b[m\u001b[0;33m 94592 ns 94279 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=22.528k\u001b[m output_rows=1.024k\u001b[m plan_cost=124.928k\u001b[m rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/1024/real_time_stddev \u001b[m\u001b[0;33m 520 ns 549 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/1024/real_time_cv \u001b[m\u001b[0;33m 0.55 % 0.58 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/2048/real_time_mean \u001b[m\u001b[0;33m 176410 ns 175840 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=45.056k\u001b[m output_rows=2.048k\u001b[m plan_cost=249.856k\u001b[m rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/2048/real_time_median \u001b[m\u001b[0;33m 176334 ns 175777 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=45.056k\u001b[m output_rows=2.048k\u001b[m plan_cost=249.856k\u001b[m rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/2048/real_time_stddev \u001b[m\u001b[0;33m 553 ns 490 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/2048/real_time_cv \u001b[m\u001b[0;33m 0.31 % 0.28 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/4096/real_time_mean \u001b[m\u001b[0;33m 351209 ns 349742 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=90.112k\u001b[m output_rows=4.096k\u001b[m plan_cost=499.712k\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/4096/real_time_median \u001b[m\u001b[0;33m 350576 ns 349336 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=90.112k\u001b[m output_rows=4.096k\u001b[m plan_cost=499.712k\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/4096/real_time_stddev \u001b[m\u001b[0;33m 6628 ns 6489 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/4096/real_time_cv \u001b[m\u001b[0;33m 1.89 % 1.86 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/8192/real_time_mean \u001b[m\u001b[0;33m 688010 ns 684898 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=180.224k\u001b[m output_rows=8.192k\u001b[m plan_cost=0.999424M\u001b[m rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/8192/real_time_median \u001b[m\u001b[0;33m 687349 ns 684331 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=180.224k\u001b[m output_rows=8.192k\u001b[m plan_cost=0.999424M\u001b[m rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/8192/real_time_stddev \u001b[m\u001b[0;33m 6078 ns 6140 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/8192/real_time_cv \u001b[m\u001b[0;33m 0.88 % 0.90 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/16384/real_time_mean \u001b[m\u001b[0;33m 1408705 ns 1401811 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=360.448k\u001b[m output_rows=16.384k\u001b[m plan_cost=1.99885M\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/16384/real_time_median \u001b[m\u001b[0;33m 1405017 ns 1397537 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=360.448k\u001b[m output_rows=16.384k\u001b[m plan_cost=1.99885M\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/16384/real_time_stddev \u001b[m\u001b[0;33m 8952 ns 8860 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/16384/real_time_cv \u001b[m\u001b[0;33m 0.64 % 0.63 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/32768/real_time_mean \u001b[m\u001b[0;33m 3063583 ns 3046674 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=720.896k\u001b[m output_rows=32.768k\u001b[m plan_cost=3.9977M\u001b[m rows=32.768k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/32768/real_time_median \u001b[m\u001b[0;33m 3021017 ns 3006734 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=720.896k\u001b[m output_rows=32.768k\u001b[m plan_cost=3.9977M\u001b[m rows=32.768k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/32768/real_time_stddev \u001b[m\u001b[0;33m 134698 ns 132627 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/32768/real_time_cv \u001b[m\u001b[0;33m 4.40 % 4.35 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/65536/real_time_mean \u001b[m\u001b[0;33m 6409382 ns 6374643 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.44179M\u001b[m output_rows=65.536k\u001b[m plan_cost=7.99539M\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/65536/real_time_median \u001b[m\u001b[0;33m 6414271 ns 6383091 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.44179M\u001b[m output_rows=65.536k\u001b[m plan_cost=7.99539M\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/65536/real_time_stddev \u001b[m\u001b[0;33m 25434 ns 28186 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/65536/real_time_cv \u001b[m\u001b[0;33m 0.40 % 0.44 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/131072/real_time_mean \u001b[m\u001b[0;33m 13727593 ns 13637236 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.88358M\u001b[m output_rows=131.072k\u001b[m plan_cost=15.9908M\u001b[m rows=131.072k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/131072/real_time_median \u001b[m\u001b[0;33m 13847824 ns 13759305 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.88358M\u001b[m output_rows=131.072k\u001b[m plan_cost=15.9908M\u001b[m rows=131.072k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/131072/real_time_stddev \u001b[m\u001b[0;33m 291770 ns 293141 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/131072/real_time_cv \u001b[m\u001b[0;33m 2.13 % 2.15 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/262144/real_time_mean \u001b[m\u001b[0;33m 33247794 ns 33037538 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=5.76717M\u001b[m output_rows=262.144k\u001b[m plan_cost=31.9816M\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/262144/real_time_median \u001b[m\u001b[0;33m 33275241 ns 33069358 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=5.76717M\u001b[m output_rows=262.144k\u001b[m plan_cost=31.9816M\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/262144/real_time_stddev \u001b[m\u001b[0;33m 180580 ns 170487 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/262144/real_time_cv \u001b[m\u001b[0;33m 0.54 % 0.52 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/1024/real_time_mean \u001b[m\u001b[0;33m 114160 ns 113790 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=123.904k\u001b[m output_rows=1.024k\u001b[m plan_cost=226.304k\u001b[m rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/1024/real_time_median \u001b[m\u001b[0;33m 114326 ns 113955 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=123.904k\u001b[m output_rows=1.024k\u001b[m plan_cost=226.304k\u001b[m rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/1024/real_time_stddev \u001b[m\u001b[0;33m 589 ns 604 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/1024/real_time_cv \u001b[m\u001b[0;33m 0.52 % 0.53 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/2048/real_time_mean \u001b[m\u001b[0;33m 266928 ns 266080 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=270.336k\u001b[m output_rows=2.048k\u001b[m plan_cost=475.136k\u001b[m rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/2048/real_time_median \u001b[m\u001b[0;33m 266698 ns 265906 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=270.336k\u001b[m output_rows=2.048k\u001b[m plan_cost=475.136k\u001b[m rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/2048/real_time_stddev \u001b[m\u001b[0;33m 743 ns 705 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/2048/real_time_cv \u001b[m\u001b[0;33m 0.28 % 0.26 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/4096/real_time_mean \u001b[m\u001b[0;33m 600007 ns 597438 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=585.728k\u001b[m output_rows=4.096k\u001b[m plan_cost=995.328k\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/4096/real_time_median \u001b[m\u001b[0;33m 600843 ns 598069 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=585.728k\u001b[m output_rows=4.096k\u001b[m plan_cost=995.328k\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/4096/real_time_stddev \u001b[m\u001b[0;33m 3889 ns 3608 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/4096/real_time_cv \u001b[m\u001b[0;33m 0.65 % 0.60 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/8192/real_time_mean \u001b[m\u001b[0;33m 1353285 ns 1347286 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.26157M\u001b[m output_rows=8.192k\u001b[m plan_cost=2.08077M\u001b[m rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/8192/real_time_median \u001b[m\u001b[0;33m 1352224 ns 1346166 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.26157M\u001b[m output_rows=8.192k\u001b[m plan_cost=2.08077M\u001b[m rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/8192/real_time_stddev \u001b[m\u001b[0;33m 3159 ns 3530 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/8192/real_time_cv \u001b[m\u001b[0;33m 0.23 % 0.26 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/16384/real_time_mean \u001b[m\u001b[0;33m 3330166 ns 3313456 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.70336M\u001b[m output_rows=16.384k\u001b[m plan_cost=4.34176M\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/16384/real_time_median \u001b[m\u001b[0;33m 3280666 ns 3263886 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.70336M\u001b[m output_rows=16.384k\u001b[m plan_cost=4.34176M\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/16384/real_time_stddev \u001b[m\u001b[0;33m 102432 ns 100860 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/16384/real_time_cv \u001b[m\u001b[0;33m 3.08 % 3.04 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/32768/real_time_mean \u001b[m\u001b[0;33m 8325146 ns 8276866 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=5.76717M\u001b[m output_rows=32.768k\u001b[m plan_cost=9.04397M\u001b[m rows=32.768k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/32768/real_time_median \u001b[m\u001b[0;33m 8328091 ns 8280027 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=5.76717M\u001b[m output_rows=32.768k\u001b[m plan_cost=9.04397M\u001b[m rows=32.768k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/32768/real_time_stddev \u001b[m\u001b[0;33m 8924 ns 5969 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/32768/real_time_cv \u001b[m\u001b[0;33m 0.11 % 0.07 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/65536/real_time_mean \u001b[m\u001b[0;33m 22543761 ns 22399862 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.2552M\u001b[m output_rows=65.536k\u001b[m plan_cost=18.8088M\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/65536/real_time_median \u001b[m\u001b[0;33m 21748033 ns 21608460 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.2552M\u001b[m output_rows=65.536k\u001b[m plan_cost=18.8088M\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/65536/real_time_stddev \u001b[m\u001b[0;33m 1392405 ns 1376023 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/65536/real_time_cv \u001b[m\u001b[0;33m 6.18 % 6.14 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/131072/real_time_mean \u001b[m\u001b[0;33m 63230431 ns 62728449 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=25.9523M\u001b[m output_rows=131.072k\u001b[m plan_cost=39.0595M\u001b[m rows=131.072k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/131072/real_time_median \u001b[m\u001b[0;33m 62867420 ns 62387321 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=25.9523M\u001b[m output_rows=131.072k\u001b[m plan_cost=39.0595M\u001b[m rows=131.072k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/131072/real_time_stddev \u001b[m\u001b[0;33m 1185317 ns 1168169 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/131072/real_time_cv \u001b[m\u001b[0;33m 1.87 % 1.86 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/262144/real_time_mean \u001b[m\u001b[0;33m 176649519 ns 175484283 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=54.7881M\u001b[m output_rows=262.144k\u001b[m plan_cost=81.0025M\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/262144/real_time_median \u001b[m\u001b[0;33m 175323726 ns 174144845 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=54.7881M\u001b[m output_rows=262.144k\u001b[m plan_cost=81.0025M\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/262144/real_time_stddev \u001b[m\u001b[0;33m 6750024 ns 6601525 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/262144/real_time_cv \u001b[m\u001b[0;33m 3.82 % 3.76 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - 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"\u001b[m\u001b[0;32mOperatorCost/Aggregation/2048/real_time_mean \u001b[m\u001b[0;33m 547245 ns 544909 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.04448M\u001b[m output_rows=2.048k\u001b[m plan_cost=1.24928M\u001b[m rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/2048/real_time_median \u001b[m\u001b[0;33m 542243 ns 539784 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.04448M\u001b[m output_rows=2.048k\u001b[m plan_cost=1.24928M\u001b[m rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/2048/real_time_stddev \u001b[m\u001b[0;33m 15295 ns 15170 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/2048/real_time_cv \u001b[m\u001b[0;33m 2.79 % 2.78 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/4096/real_time_mean \u001b[m\u001b[0;33m 1141244 ns 1135478 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.08896M\u001b[m output_rows=4.096k\u001b[m plan_cost=2.49856M\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/4096/real_time_median \u001b[m\u001b[0;33m 1143881 ns 1138252 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.08896M\u001b[m output_rows=4.096k\u001b[m plan_cost=2.49856M\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/4096/real_time_stddev \u001b[m\u001b[0;33m 15733 ns 15158 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/4096/real_time_cv \u001b[m\u001b[0;33m 1.38 % 1.33 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/8192/real_time_mean \u001b[m\u001b[0;33m 2426264 ns 2414370 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=4.17792M\u001b[m output_rows=8.192k\u001b[m plan_cost=4.99712M\u001b[m rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/8192/real_time_median \u001b[m\u001b[0;33m 2422686 ns 2411175 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=4.17792M\u001b[m output_rows=8.192k\u001b[m plan_cost=4.99712M\u001b[m rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/8192/real_time_stddev \u001b[m\u001b[0;33m 43691 ns 43834 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/8192/real_time_cv \u001b[m\u001b[0;33m 1.80 % 1.82 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/16384/real_time_mean \u001b[m\u001b[0;33m 5812245 ns 5776502 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=8.35584M\u001b[m output_rows=16.384k\u001b[m plan_cost=9.99424M\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/16384/real_time_median \u001b[m\u001b[0;33m 5826207 ns 5793681 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=8.35584M\u001b[m output_rows=16.384k\u001b[m plan_cost=9.99424M\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/16384/real_time_stddev \u001b[m\u001b[0;33m 108021 ns 105107 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/16384/real_time_cv \u001b[m\u001b[0;33m 1.86 % 1.82 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/32768/real_time_mean \u001b[m\u001b[0;33m 16768921 ns 16655176 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=16.7117M\u001b[m output_rows=32.768k\u001b[m plan_cost=19.9885M\u001b[m rows=32.768k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/32768/real_time_median \u001b[m\u001b[0;33m 16630918 ns 16514790 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=16.7117M\u001b[m output_rows=32.768k\u001b[m plan_cost=19.9885M\u001b[m rows=32.768k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/32768/real_time_stddev \u001b[m\u001b[0;33m 241465 ns 245756 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/32768/real_time_cv \u001b[m\u001b[0;33m 1.44 % 1.48 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/65536/real_time_mean \u001b[m\u001b[0;33m 52625470 ns 52271134 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=33.4234M\u001b[m output_rows=65.536k\u001b[m plan_cost=39.977M\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/65536/real_time_median \u001b[m\u001b[0;33m 52315635 ns 51975856 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=33.4234M\u001b[m output_rows=65.536k\u001b[m plan_cost=39.977M\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/65536/real_time_stddev \u001b[m\u001b[0;33m 2766613 ns 2683321 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/65536/real_time_cv \u001b[m\u001b[0;33m 5.26 % 5.13 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/131072/real_time_mean \u001b[m\u001b[0;33m 129696984 ns 128861029 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=66.8467M\u001b[m output_rows=131.072k\u001b[m plan_cost=79.9539M\u001b[m rows=131.072k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/131072/real_time_median \u001b[m\u001b[0;33m 128465716 ns 127653219 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=66.8467M\u001b[m output_rows=131.072k\u001b[m plan_cost=79.9539M\u001b[m rows=131.072k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/131072/real_time_stddev \u001b[m\u001b[0;33m 3404042 ns 3352711 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/131072/real_time_cv \u001b[m\u001b[0;33m 2.62 % 2.60 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/262144/real_time_mean \u001b[m\u001b[0;33m 300294351 ns 298535917 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=133.693M\u001b[m output_rows=262.144k\u001b[m plan_cost=159.908M\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/262144/real_time_median \u001b[m\u001b[0;33m 298677499 ns 297021558 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=133.693M\u001b[m output_rows=262.144k\u001b[m plan_cost=159.908M\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/262144/real_time_stddev \u001b[m\u001b[0;33m 11210695 ns 10988337 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/262144/real_time_cv \u001b[m\u001b[0;33m 3.73 % 3.68 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/1024/1024/real_time_mean \u001b[m\u001b[0;33m 155098 ns 154565 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=404.48k\u001b[m output_rows=1.024k\u001b[m plan_cost=609.28k\u001b[m rhs_rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/1024/1024/real_time_median \u001b[m\u001b[0;33m 155342 ns 154745 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=404.48k\u001b[m output_rows=1.024k\u001b[m plan_cost=609.28k\u001b[m rhs_rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/1024/1024/real_time_stddev \u001b[m\u001b[0;33m 1119 ns 1070 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/1024/1024/real_time_cv \u001b[m\u001b[0;33m 0.72 % 0.69 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/2048/2048/real_time_mean \u001b[m\u001b[0;33m 301359 ns 300041 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=2.048k\u001b[m model_cost=808.96k\u001b[m output_rows=2.048k\u001b[m plan_cost=1.21856M\u001b[m rhs_rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/2048/2048/real_time_median \u001b[m\u001b[0;33m 302359 ns 301088 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=2.048k\u001b[m model_cost=808.96k\u001b[m output_rows=2.048k\u001b[m plan_cost=1.21856M\u001b[m rhs_rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/2048/2048/real_time_stddev \u001b[m\u001b[0;33m 2615 ns 2492 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/2048/2048/real_time_cv \u001b[m\u001b[0;33m 0.87 % 0.83 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/4096/4096/real_time_mean \u001b[m\u001b[0;33m 599260 ns 596096 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=4.096k\u001b[m model_cost=1.61792M\u001b[m output_rows=4.096k\u001b[m plan_cost=2.43712M\u001b[m rhs_rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/4096/4096/real_time_median \u001b[m\u001b[0;33m 599183 ns 596316 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=4.096k\u001b[m model_cost=1.61792M\u001b[m output_rows=4.096k\u001b[m plan_cost=2.43712M\u001b[m rhs_rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/4096/4096/real_time_stddev \u001b[m\u001b[0;33m 2586 ns 2808 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/4096/4096/real_time_cv \u001b[m\u001b[0;33m 0.43 % 0.47 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/8192/8192/real_time_mean \u001b[m\u001b[0;33m 1274549 ns 1266452 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=8.192k\u001b[m model_cost=3.23584M\u001b[m output_rows=8.192k\u001b[m plan_cost=4.87424M\u001b[m rhs_rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/8192/8192/real_time_median \u001b[m\u001b[0;33m 1234395 ns 1226034 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=8.192k\u001b[m model_cost=3.23584M\u001b[m output_rows=8.192k\u001b[m plan_cost=4.87424M\u001b[m rhs_rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/8192/8192/real_time_stddev \u001b[m\u001b[0;33m 78831 ns 77036 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/8192/8192/real_time_cv \u001b[m\u001b[0;33m 6.18 % 6.08 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/16384/16384/real_time_mean \u001b[m\u001b[0;33m 2941091 ns 2919158 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=16.384k\u001b[m model_cost=6.47168M\u001b[m output_rows=16.384k\u001b[m plan_cost=9.74848M\u001b[m rhs_rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/16384/16384/real_time_median \u001b[m\u001b[0;33m 2911038 ns 2889304 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=16.384k\u001b[m model_cost=6.47168M\u001b[m output_rows=16.384k\u001b[m plan_cost=9.74848M\u001b[m rhs_rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/16384/16384/real_time_stddev \u001b[m\u001b[0;33m 67545 ns 67104 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/16384/16384/real_time_cv \u001b[m\u001b[0;33m 2.30 % 2.30 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/32768/32768/real_time_mean \u001b[m\u001b[0;33m 6940540 ns 6889217 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=32.768k\u001b[m model_cost=12.9434M\u001b[m output_rows=32.768k\u001b[m plan_cost=19.497M\u001b[m rhs_rows=32.768k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/32768/32768/real_time_median \u001b[m\u001b[0;33m 6908295 ns 6859205 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=32.768k\u001b[m model_cost=12.9434M\u001b[m output_rows=32.768k\u001b[m plan_cost=19.497M\u001b[m rhs_rows=32.768k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/32768/32768/real_time_stddev \u001b[m\u001b[0;33m 70111 ns 72151 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/32768/32768/real_time_cv \u001b[m\u001b[0;33m 1.01 % 1.05 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/65536/65536/real_time_mean \u001b[m\u001b[0;33m 15832571 ns 15709972 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=65.536k\u001b[m model_cost=25.8867M\u001b[m output_rows=65.536k\u001b[m plan_cost=38.9939M\u001b[m rhs_rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/65536/65536/real_time_median \u001b[m\u001b[0;33m 15705191 ns 15604051 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=65.536k\u001b[m model_cost=25.8867M\u001b[m output_rows=65.536k\u001b[m plan_cost=38.9939M\u001b[m rhs_rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/65536/65536/real_time_stddev \u001b[m\u001b[0;33m 240004 ns 235698 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/65536/65536/real_time_cv \u001b[m\u001b[0;33m 1.52 % 1.50 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/64/64/real_time_mean \u001b[m\u001b[0;33m 341299 ns 339396 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=64\u001b[m model_cost=286.72k\u001b[m output_rows=64\u001b[m plan_cost=299.52k\u001b[m rhs_rows=64\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/64/64/real_time_median \u001b[m\u001b[0;33m 340857 ns 338876 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=64\u001b[m model_cost=286.72k\u001b[m output_rows=64\u001b[m plan_cost=299.52k\u001b[m rhs_rows=64\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/64/64/real_time_stddev \u001b[m\u001b[0;33m 7369 ns 7193 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/64/64/real_time_cv \u001b[m\u001b[0;33m 2.16 % 2.12 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/128/128/real_time_mean \u001b[m\u001b[0;33m 1072887 ns 1067776 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=128\u001b[m model_cost=1.14688M\u001b[m output_rows=128\u001b[m plan_cost=1.17248M\u001b[m rhs_rows=128\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/128/128/real_time_median \u001b[m\u001b[0;33m 1069796 ns 1064744 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=128\u001b[m model_cost=1.14688M\u001b[m output_rows=128\u001b[m plan_cost=1.17248M\u001b[m rhs_rows=128\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/128/128/real_time_stddev \u001b[m\u001b[0;33m 8259 ns 8226 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/128/128/real_time_cv \u001b[m\u001b[0;33m 0.77 % 0.77 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/256/256/real_time_mean \u001b[m\u001b[0;33m 3943250 ns 3924191 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=256\u001b[m model_cost=4.58752M\u001b[m output_rows=256\u001b[m plan_cost=4.63872M\u001b[m rhs_rows=256\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/256/256/real_time_median \u001b[m\u001b[0;33m 3886867 ns 3870416 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=256\u001b[m model_cost=4.58752M\u001b[m output_rows=256\u001b[m plan_cost=4.63872M\u001b[m rhs_rows=256\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/256/256/real_time_stddev \u001b[m\u001b[0;33m 115332 ns 111745 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/256/256/real_time_cv \u001b[m\u001b[0;33m 2.92 % 2.85 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/512/512/real_time_mean \u001b[m\u001b[0;33m 14965494 ns 14909916 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=512\u001b[m model_cost=18.3501M\u001b[m output_rows=512\u001b[m plan_cost=18.4525M\u001b[m rhs_rows=512\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/512/512/real_time_median \u001b[m\u001b[0;33m 15020643 ns 14962434 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=512\u001b[m model_cost=18.3501M\u001b[m output_rows=512\u001b[m plan_cost=18.4525M\u001b[m rhs_rows=512\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/512/512/real_time_stddev \u001b[m\u001b[0;33m 136569 ns 133694 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/512/512/real_time_cv \u001b[m\u001b[0;33m 0.91 % 0.90 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/1024/1024/real_time_mean \u001b[m\u001b[0;33m 57691018 ns 57436935 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=73.4003M\u001b[m output_rows=1.024k\u001b[m plan_cost=73.6051M\u001b[m rhs_rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/1024/1024/real_time_median \u001b[m\u001b[0;33m 57607890 ns 57430034 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=73.4003M\u001b[m output_rows=1.024k\u001b[m plan_cost=73.6051M\u001b[m rhs_rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/1024/1024/real_time_stddev \u001b[m\u001b[0;33m 451356 ns 317271 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/1024/1024/real_time_cv \u001b[m\u001b[0;33m 0.78 % 0.55 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/2048/2048/real_time_mean \u001b[m\u001b[0;33m 229988242 ns 229099036 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=2.048k\u001b[m model_cost=293.601M\u001b[m output_rows=2.048k\u001b[m plan_cost=294.011M\u001b[m rhs_rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/2048/2048/real_time_median \u001b[m\u001b[0;33m 229828521 ns 229065432 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=2.048k\u001b[m model_cost=293.601M\u001b[m output_rows=2.048k\u001b[m plan_cost=294.011M\u001b[m rhs_rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/2048/2048/real_time_stddev \u001b[m\u001b[0;33m 2792600 ns 2747616 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/2048/2048/real_time_cv \u001b[m\u001b[0;33m 1.21 % 1.20 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/32/32/real_time_mean \u001b[m\u001b[0;33m 130942 ns 129796 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=32\u001b[m model_cost=106.496k\u001b[m output_rows=1.024k\u001b[m plan_cost=112.896k\u001b[m rhs_rows=32\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/32/32/real_time_median \u001b[m\u001b[0;33m 131308 ns 130105 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=32\u001b[m model_cost=106.496k\u001b[m output_rows=1.024k\u001b[m plan_cost=112.896k\u001b[m rhs_rows=32\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/32/32/real_time_stddev \u001b[m\u001b[0;33m 1935 ns 1924 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/32/32/real_time_cv \u001b[m\u001b[0;33m 1.48 % 1.48 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/64/64/real_time_mean \u001b[m\u001b[0;33m 288639 ns 286761 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=64\u001b[m model_cost=425.984k\u001b[m output_rows=4.096k\u001b[m plan_cost=438.784k\u001b[m rhs_rows=64\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/64/64/real_time_median \u001b[m\u001b[0;33m 288545 ns 286708 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=64\u001b[m model_cost=425.984k\u001b[m output_rows=4.096k\u001b[m plan_cost=438.784k\u001b[m rhs_rows=64\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/64/64/real_time_stddev \u001b[m\u001b[0;33m 3202 ns 3171 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/64/64/real_time_cv \u001b[m\u001b[0;33m 1.11 % 1.11 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/128/128/real_time_mean \u001b[m\u001b[0;33m 899354 ns 893066 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=128\u001b[m model_cost=1.70394M\u001b[m output_rows=16.384k\u001b[m plan_cost=1.72954M\u001b[m rhs_rows=128\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/128/128/real_time_median \u001b[m\u001b[0;33m 881125 ns 874885 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=128\u001b[m model_cost=1.70394M\u001b[m output_rows=16.384k\u001b[m plan_cost=1.72954M\u001b[m rhs_rows=128\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/128/128/real_time_stddev \u001b[m\u001b[0;33m 52481 ns 51442 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/128/128/real_time_cv \u001b[m\u001b[0;33m 5.84 % 5.76 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/256/256/real_time_mean \u001b[m\u001b[0;33m 5036885 ns 4983636 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=256\u001b[m model_cost=6.81574M\u001b[m output_rows=65.536k\u001b[m plan_cost=6.86694M\u001b[m rhs_rows=256\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/256/256/real_time_median \u001b[m\u001b[0;33m 5043142 ns 4984503 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=256\u001b[m model_cost=6.81574M\u001b[m output_rows=65.536k\u001b[m plan_cost=6.86694M\u001b[m rhs_rows=256\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/256/256/real_time_stddev \u001b[m\u001b[0;33m 47815 ns 44463 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/256/256/real_time_cv \u001b[m\u001b[0;33m 0.95 % 0.89 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/512/512/real_time_mean \u001b[m\u001b[0;33m 25155229 ns 24914236 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=512\u001b[m model_cost=27.263M\u001b[m output_rows=262.144k\u001b[m plan_cost=27.3654M\u001b[m rhs_rows=512\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/512/512/real_time_median \u001b[m\u001b[0;33m 25126405 ns 24885776 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=512\u001b[m model_cost=27.263M\u001b[m output_rows=262.144k\u001b[m plan_cost=27.3654M\u001b[m rhs_rows=512\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/512/512/real_time_stddev \u001b[m\u001b[0;33m 71506 ns 52169 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/512/512/real_time_cv \u001b[m\u001b[0;33m 0.28 % 0.21 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/1024/1024/real_time_mean \u001b[m\u001b[0;33m 125736836 ns 124681210 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=109.052M\u001b[m output_rows=1.04858M\u001b[m plan_cost=109.257M\u001b[m rhs_rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/1024/1024/real_time_median \u001b[m\u001b[0;33m 124916767 ns 123921643 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=109.052M\u001b[m output_rows=1.04858M\u001b[m plan_cost=109.257M\u001b[m rhs_rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/1024/1024/real_time_stddev \u001b[m\u001b[0;33m 2950666 ns 2904895 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/1024/1024/real_time_cv \u001b[m\u001b[0;33m 2.35 % 2.33 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[0;32mOperatorCost/SeqScan/1024/real_time_mean \u001b[m\u001b[0;33m 39153 ns 39016 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=102.4k\u001b[m output_rows=1.024k\u001b[m plan_cost=102.4k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/1024/real_time_median \u001b[m\u001b[0;33m 39303 ns 39169 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=102.4k\u001b[m output_rows=1.024k\u001b[m plan_cost=102.4k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/1024/real_time_stddev \u001b[m\u001b[0;33m 267 ns 265 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/1024/real_time_cv \u001b[m\u001b[0;33m 0.68 % 0.68 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/2048/real_time_mean \u001b[m\u001b[0;33m 72348 ns 72079 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=204.8k\u001b[m output_rows=2.048k\u001b[m plan_cost=204.8k\u001b[m rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/2048/real_time_median \u001b[m\u001b[0;33m 71402 ns 71146 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=204.8k\u001b[m output_rows=2.048k\u001b[m plan_cost=204.8k\u001b[m rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/2048/real_time_stddev \u001b[m\u001b[0;33m 1791 ns 1734 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/2048/real_time_cv \u001b[m\u001b[0;33m 2.48 % 2.41 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/4096/real_time_mean \u001b[m\u001b[0;33m 158147 ns 156559 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=409.6k\u001b[m output_rows=4.096k\u001b[m plan_cost=409.6k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/4096/real_time_median \u001b[m\u001b[0;33m 157558 ns 155985 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=409.6k\u001b[m output_rows=4.096k\u001b[m plan_cost=409.6k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/4096/real_time_stddev \u001b[m\u001b[0;33m 2396 ns 2315 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/4096/real_time_cv \u001b[m\u001b[0;33m 1.51 % 1.48 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/8192/real_time_mean \u001b[m\u001b[0;33m 358960 ns 356073 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=819.2k\u001b[m output_rows=8.192k\u001b[m plan_cost=819.2k\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/8192/real_time_median \u001b[m\u001b[0;33m 357023 ns 354120 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=819.2k\u001b[m output_rows=8.192k\u001b[m plan_cost=819.2k\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/8192/real_time_stddev \u001b[m\u001b[0;33m 4165 ns 4140 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/8192/real_time_cv \u001b[m\u001b[0;33m 1.16 % 1.16 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/16384/real_time_mean \u001b[m\u001b[0;33m 797004 ns 790772 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.6384M\u001b[m output_rows=16.384k\u001b[m plan_cost=1.6384M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/16384/real_time_median \u001b[m\u001b[0;33m 796509 ns 790000 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.6384M\u001b[m output_rows=16.384k\u001b[m plan_cost=1.6384M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/16384/real_time_stddev \u001b[m\u001b[0;33m 5029 ns 5064 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/16384/real_time_cv \u001b[m\u001b[0;33m 0.63 % 0.64 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/32768/real_time_mean \u001b[m\u001b[0;33m 1990887 ns 1971823 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.2768M\u001b[m output_rows=32.768k\u001b[m plan_cost=3.2768M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/32768/real_time_median \u001b[m\u001b[0;33m 1989976 ns 1972159 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.2768M\u001b[m output_rows=32.768k\u001b[m plan_cost=3.2768M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/32768/real_time_stddev \u001b[m\u001b[0;33m 26044 ns 24709 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/32768/real_time_cv \u001b[m\u001b[0;33m 1.31 % 1.25 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/65536/real_time_mean \u001b[m\u001b[0;33m 4947678 ns 4898438 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.5536M\u001b[m output_rows=65.536k\u001b[m plan_cost=6.5536M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/65536/real_time_median \u001b[m\u001b[0;33m 4843216 ns 4796793 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.5536M\u001b[m output_rows=65.536k\u001b[m plan_cost=6.5536M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/65536/real_time_stddev \u001b[m\u001b[0;33m 274098 ns 266515 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/65536/real_time_cv \u001b[m\u001b[0;33m 5.54 % 5.44 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/131072/real_time_mean \u001b[m\u001b[0;33m 10909127 ns 10799682 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=13.1072M\u001b[m output_rows=131.072k\u001b[m plan_cost=13.1072M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/131072/real_time_median \u001b[m\u001b[0;33m 10878576 ns 10770947 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=13.1072M\u001b[m output_rows=131.072k\u001b[m plan_cost=13.1072M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/131072/real_time_stddev \u001b[m\u001b[0;33m 76933 ns 76956 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/131072/real_time_cv \u001b[m\u001b[0;33m 0.71 % 0.71 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/262144/real_time_mean \u001b[m\u001b[0;33m 22613130 ns 22403648 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.2144M\u001b[m output_rows=262.144k\u001b[m plan_cost=26.2144M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/262144/real_time_median \u001b[m\u001b[0;33m 22384763 ns 22194274 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.2144M\u001b[m output_rows=262.144k\u001b[m plan_cost=26.2144M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/262144/real_time_stddev \u001b[m\u001b[0;33m 664045 ns 632355 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/262144/real_time_cv \u001b[m\u001b[0;33m 2.94 % 2.82 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/1024/real_time_mean \u001b[m\u001b[0;33m 74024 ns 73756 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=102.4k\u001b[m output_rows=512\u001b[m plan_cost=204.8k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/1024/real_time_median \u001b[m\u001b[0;33m 74202 ns 73951 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=102.4k\u001b[m output_rows=512\u001b[m plan_cost=204.8k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/1024/real_time_stddev \u001b[m\u001b[0;33m 359 ns 404 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/1024/real_time_cv \u001b[m\u001b[0;33m 0.49 % 0.55 % 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plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/4096/real_time_mean \u001b[m\u001b[0;33m 261600 ns 260569 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=409.6k\u001b[m output_rows=2.048k\u001b[m plan_cost=819.2k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/4096/real_time_median \u001b[m\u001b[0;33m 260040 ns 259070 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=409.6k\u001b[m output_rows=2.048k\u001b[m plan_cost=819.2k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/4096/real_time_stddev \u001b[m\u001b[0;33m 3351 ns 3281 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/4096/real_time_cv \u001b[m\u001b[0;33m 1.28 % 1.26 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/8192/real_time_mean \u001b[m\u001b[0;33m 513833 ns 511511 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=819.2k\u001b[m output_rows=4.096k\u001b[m plan_cost=1.6384M\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/8192/real_time_median \u001b[m\u001b[0;33m 516091 ns 513709 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=819.2k\u001b[m output_rows=4.096k\u001b[m plan_cost=1.6384M\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/8192/real_time_stddev \u001b[m\u001b[0;33m 4408 ns 4202 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/8192/real_time_cv \u001b[m\u001b[0;33m 0.86 % 0.82 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/16384/real_time_mean \u001b[m\u001b[0;33m 1066718 ns 1061351 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.6384M\u001b[m output_rows=8.192k\u001b[m plan_cost=3.2768M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/16384/real_time_median \u001b[m\u001b[0;33m 1050933 ns 1045549 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.6384M\u001b[m output_rows=8.192k\u001b[m plan_cost=3.2768M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/16384/real_time_stddev \u001b[m\u001b[0;33m 34164 ns 33246 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/16384/real_time_cv \u001b[m\u001b[0;33m 3.20 % 3.13 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/32768/real_time_mean \u001b[m\u001b[0;33m 2189729 ns 2178064 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.2768M\u001b[m output_rows=16.384k\u001b[m plan_cost=6.5536M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/32768/real_time_median \u001b[m\u001b[0;33m 2177674 ns 2166723 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.2768M\u001b[m output_rows=16.384k\u001b[m plan_cost=6.5536M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/32768/real_time_stddev \u001b[m\u001b[0;33m 40807 ns 39296 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/32768/real_time_cv \u001b[m\u001b[0;33m 1.86 % 1.80 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/65536/real_time_mean \u001b[m\u001b[0;33m 4602133 ns 4576376 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.5536M\u001b[m output_rows=32.768k\u001b[m plan_cost=13.1072M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/65536/real_time_median \u001b[m\u001b[0;33m 4612541 ns 4587562 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.5536M\u001b[m output_rows=32.768k\u001b[m plan_cost=13.1072M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/65536/real_time_stddev \u001b[m\u001b[0;33m 53265 ns 54115 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/65536/real_time_cv \u001b[m\u001b[0;33m 1.16 % 1.18 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/131072/real_time_mean \u001b[m\u001b[0;33m 9965140 ns 9905109 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=13.1072M\u001b[m output_rows=65.536k\u001b[m plan_cost=26.2144M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/131072/real_time_median \u001b[m\u001b[0;33m 9961682 ns 9898928 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=13.1072M\u001b[m output_rows=65.536k\u001b[m plan_cost=26.2144M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/131072/real_time_stddev \u001b[m\u001b[0;33m 367014 ns 358618 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/131072/real_time_cv \u001b[m\u001b[0;33m 3.68 % 3.62 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/262144/real_time_mean \u001b[m\u001b[0;33m 20746154 ns 20612936 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.2144M\u001b[m output_rows=131.072k\u001b[m plan_cost=52.4288M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/262144/real_time_median \u001b[m\u001b[0;33m 20609234 ns 20480861 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.2144M\u001b[m output_rows=131.072k\u001b[m plan_cost=52.4288M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/262144/real_time_stddev \u001b[m\u001b[0;33m 432544 ns 413377 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/262144/real_time_cv \u001b[m\u001b[0;33m 2.08 % 2.01 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/1024/real_time_mean \u001b[m\u001b[0;33m 102501 ns 102103 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=22.528k\u001b[m output_rows=1.024k\u001b[m plan_cost=124.928k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/1024/real_time_median \u001b[m\u001b[0;33m 102476 ns 102063 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=22.528k\u001b[m output_rows=1.024k\u001b[m plan_cost=124.928k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/1024/real_time_stddev \u001b[m\u001b[0;33m 1037 ns 1019 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/1024/real_time_cv \u001b[m\u001b[0;33m 1.01 % 1.00 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/2048/real_time_mean \u001b[m\u001b[0;33m 192335 ns 191492 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=45.056k\u001b[m output_rows=2.048k\u001b[m plan_cost=249.856k\u001b[m rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/2048/real_time_median \u001b[m\u001b[0;33m 191735 ns 190906 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=45.056k\u001b[m output_rows=2.048k\u001b[m plan_cost=249.856k\u001b[m rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/2048/real_time_stddev \u001b[m\u001b[0;33m 6489 ns 6398 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/2048/real_time_cv \u001b[m\u001b[0;33m 3.37 % 3.34 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/4096/real_time_mean \u001b[m\u001b[0;33m 358122 ns 356961 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=90.112k\u001b[m output_rows=4.096k\u001b[m plan_cost=499.712k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/4096/real_time_median \u001b[m\u001b[0;33m 357740 ns 356476 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=90.112k\u001b[m output_rows=4.096k\u001b[m plan_cost=499.712k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/4096/real_time_stddev \u001b[m\u001b[0;33m 1726 ns 1799 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/4096/real_time_cv \u001b[m\u001b[0;33m 0.48 % 0.50 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/8192/real_time_mean \u001b[m\u001b[0;33m 730814 ns 727359 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=180.224k\u001b[m output_rows=8.192k\u001b[m plan_cost=0.999424M\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/8192/real_time_median \u001b[m\u001b[0;33m 730561 ns 727062 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=180.224k\u001b[m output_rows=8.192k\u001b[m plan_cost=0.999424M\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/8192/real_time_stddev \u001b[m\u001b[0;33m 2539 ns 2763 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/8192/real_time_cv \u001b[m\u001b[0;33m 0.35 % 0.38 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/16384/real_time_mean \u001b[m\u001b[0;33m 1526678 ns 1518388 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=360.448k\u001b[m output_rows=16.384k\u001b[m plan_cost=1.99885M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/16384/real_time_median \u001b[m\u001b[0;33m 1514234 ns 1505967 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=360.448k\u001b[m output_rows=16.384k\u001b[m plan_cost=1.99885M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/16384/real_time_stddev \u001b[m\u001b[0;33m 66490 ns 65574 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/16384/real_time_cv \u001b[m\u001b[0;33m 4.36 % 4.32 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/32768/real_time_mean \u001b[m\u001b[0;33m 3121936 ns 3104686 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=720.896k\u001b[m output_rows=32.768k\u001b[m plan_cost=3.9977M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/32768/real_time_median \u001b[m\u001b[0;33m 3144138 ns 3126492 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=720.896k\u001b[m output_rows=32.768k\u001b[m plan_cost=3.9977M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/32768/real_time_stddev \u001b[m\u001b[0;33m 42438 ns 41442 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/32768/real_time_cv \u001b[m\u001b[0;33m 1.36 % 1.33 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/65536/real_time_mean \u001b[m\u001b[0;33m 6595264 ns 6544746 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.44179M\u001b[m output_rows=65.536k\u001b[m plan_cost=7.99539M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/65536/real_time_median \u001b[m\u001b[0;33m 6608749 ns 6555081 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.44179M\u001b[m output_rows=65.536k\u001b[m plan_cost=7.99539M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/65536/real_time_stddev \u001b[m\u001b[0;33m 58317 ns 53393 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/65536/real_time_cv \u001b[m\u001b[0;33m 0.88 % 0.82 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/131072/real_time_mean \u001b[m\u001b[0;33m 14333160 ns 14219076 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.88358M\u001b[m output_rows=131.072k\u001b[m plan_cost=15.9908M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/131072/real_time_median \u001b[m\u001b[0;33m 14192064 ns 14077398 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.88358M\u001b[m output_rows=131.072k\u001b[m plan_cost=15.9908M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/131072/real_time_stddev \u001b[m\u001b[0;33m 467177 ns 461157 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/131072/real_time_cv \u001b[m\u001b[0;33m 3.26 % 3.24 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/262144/real_time_mean \u001b[m\u001b[0;33m 33368177 ns 33128753 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=5.76717M\u001b[m output_rows=262.144k\u001b[m plan_cost=31.9816M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/262144/real_time_median \u001b[m\u001b[0;33m 33354850 ns 33124116 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=5.76717M\u001b[m output_rows=262.144k\u001b[m plan_cost=31.9816M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/262144/real_time_stddev \u001b[m\u001b[0;33m 57832 ns 25519 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/262144/real_time_cv \u001b[m\u001b[0;33m 0.17 % 0.08 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/1024/real_time_mean \u001b[m\u001b[0;33m 119077 ns 118631 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=123.904k\u001b[m output_rows=1.024k\u001b[m plan_cost=226.304k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/1024/real_time_median \u001b[m\u001b[0;33m 119114 ns 118725 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=123.904k\u001b[m output_rows=1.024k\u001b[m plan_cost=226.304k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/1024/real_time_stddev \u001b[m\u001b[0;33m 1155 ns 1165 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/1024/real_time_cv \u001b[m\u001b[0;33m 0.97 % 0.98 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/2048/real_time_mean \u001b[m\u001b[0;33m 276292 ns 275137 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=270.336k\u001b[m output_rows=2.048k\u001b[m plan_cost=475.136k\u001b[m rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/2048/real_time_median \u001b[m\u001b[0;33m 275547 ns 274427 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=270.336k\u001b[m output_rows=2.048k\u001b[m plan_cost=475.136k\u001b[m rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/2048/real_time_stddev \u001b[m\u001b[0;33m 3680 ns 3546 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/2048/real_time_cv \u001b[m\u001b[0;33m 1.33 % 1.29 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/4096/real_time_mean \u001b[m\u001b[0;33m 623962 ns 621336 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=585.728k\u001b[m output_rows=4.096k\u001b[m plan_cost=995.328k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/4096/real_time_median \u001b[m\u001b[0;33m 619550 ns 617008 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=585.728k\u001b[m output_rows=4.096k\u001b[m plan_cost=995.328k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/4096/real_time_stddev \u001b[m\u001b[0;33m 17605 ns 17229 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/4096/real_time_cv \u001b[m\u001b[0;33m 2.82 % 2.77 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/8192/real_time_mean \u001b[m\u001b[0;33m 1387995 ns 1380934 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.26157M\u001b[m output_rows=8.192k\u001b[m plan_cost=2.08077M\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/8192/real_time_median \u001b[m\u001b[0;33m 1386525 ns 1379373 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.26157M\u001b[m output_rows=8.192k\u001b[m plan_cost=2.08077M\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/8192/real_time_stddev \u001b[m\u001b[0;33m 6569 ns 6278 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/8192/real_time_cv \u001b[m\u001b[0;33m 0.47 % 0.45 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/16384/real_time_mean \u001b[m\u001b[0;33m 3127599 ns 3111158 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.70336M\u001b[m output_rows=16.384k\u001b[m plan_cost=4.34176M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/16384/real_time_median \u001b[m\u001b[0;33m 3122047 ns 3105723 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.70336M\u001b[m output_rows=16.384k\u001b[m plan_cost=4.34176M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/16384/real_time_stddev 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3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/32768/real_time_cv \u001b[m\u001b[0;33m 4.06 % 4.08 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/65536/real_time_mean \u001b[m\u001b[0;33m 21204896 ns 21056618 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.2552M\u001b[m output_rows=65.536k\u001b[m plan_cost=18.8088M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/65536/real_time_median \u001b[m\u001b[0;33m 21202786 ns 21049976 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.2552M\u001b[m output_rows=65.536k\u001b[m plan_cost=18.8088M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/65536/real_time_stddev \u001b[m\u001b[0;33m 47868 ns 34465 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/65536/real_time_cv \u001b[m\u001b[0;33m 0.23 % 0.16 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/131072/real_time_mean \u001b[m\u001b[0;33m 61087203 ns 60619246 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=25.9523M\u001b[m output_rows=131.072k\u001b[m plan_cost=39.0595M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/131072/real_time_median \u001b[m\u001b[0;33m 61025748 ns 60603653 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=25.9523M\u001b[m output_rows=131.072k\u001b[m plan_cost=39.0595M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/131072/real_time_stddev \u001b[m\u001b[0;33m 318956 ns 262983 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/131072/real_time_cv \u001b[m\u001b[0;33m 0.52 % 0.43 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/262144/real_time_mean \u001b[m\u001b[0;33m 178916260 ns 177525137 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=54.7881M\u001b[m output_rows=262.144k\u001b[m plan_cost=81.0025M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/262144/real_time_median \u001b[m\u001b[0;33m 176367090 ns 174983458 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=54.7881M\u001b[m output_rows=262.144k\u001b[m plan_cost=81.0025M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/262144/real_time_stddev \u001b[m\u001b[0;33m 5229156 ns 5156914 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/262144/real_time_cv \u001b[m\u001b[0;33m 2.92 % 2.90 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/1024/real_time_mean \u001b[m\u001b[0;33m 285190 ns 284193 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=522.24k\u001b[m output_rows=1.024k\u001b[m plan_cost=624.64k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/1024/real_time_median \u001b[m\u001b[0;33m 284445 ns 283487 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=522.24k\u001b[m output_rows=1.024k\u001b[m plan_cost=624.64k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/1024/real_time_stddev \u001b[m\u001b[0;33m 1726 ns 1620 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/1024/real_time_cv 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3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/4096/real_time_mean \u001b[m\u001b[0;33m 1186394 ns 1179677 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.08896M\u001b[m output_rows=4.096k\u001b[m plan_cost=2.49856M\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/4096/real_time_median \u001b[m\u001b[0;33m 1161393 ns 1154736 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.08896M\u001b[m output_rows=4.096k\u001b[m plan_cost=2.49856M\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/4096/real_time_stddev \u001b[m\u001b[0;33m 52944 ns 51987 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/4096/real_time_cv \u001b[m\u001b[0;33m 4.46 % 4.41 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/8192/real_time_mean \u001b[m\u001b[0;33m 2432833 ns 2420872 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=4.17792M\u001b[m output_rows=8.192k\u001b[m plan_cost=4.99712M\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/8192/real_time_median \u001b[m\u001b[0;33m 2442240 ns 2429133 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=4.17792M\u001b[m output_rows=8.192k\u001b[m plan_cost=4.99712M\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/8192/real_time_stddev \u001b[m\u001b[0;33m 22609 ns 21015 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/8192/real_time_cv \u001b[m\u001b[0;33m 0.93 % 0.87 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/16384/real_time_mean \u001b[m\u001b[0;33m 5847840 ns 5806488 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=8.35584M\u001b[m output_rows=16.384k\u001b[m plan_cost=9.99424M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/16384/real_time_median \u001b[m\u001b[0;33m 5801578 ns 5764441 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=8.35584M\u001b[m output_rows=16.384k\u001b[m plan_cost=9.99424M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/16384/real_time_stddev \u001b[m\u001b[0;33m 104961 ns 103759 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/16384/real_time_cv \u001b[m\u001b[0;33m 1.79 % 1.79 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/32768/real_time_mean \u001b[m\u001b[0;33m 16878386 ns 16728186 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=16.7117M\u001b[m output_rows=32.768k\u001b[m plan_cost=19.9885M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/32768/real_time_median \u001b[m\u001b[0;33m 16986629 ns 16833720 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=16.7117M\u001b[m output_rows=32.768k\u001b[m plan_cost=19.9885M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/32768/real_time_stddev \u001b[m\u001b[0;33m 210596 ns 201562 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/32768/real_time_cv \u001b[m\u001b[0;33m 1.25 % 1.20 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/65536/real_time_mean \u001b[m\u001b[0;33m 53712474 ns 53266495 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=33.4234M\u001b[m output_rows=65.536k\u001b[m plan_cost=39.977M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/65536/real_time_median \u001b[m\u001b[0;33m 51282451 ns 50872196 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=33.4234M\u001b[m output_rows=65.536k\u001b[m plan_cost=39.977M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/65536/real_time_stddev \u001b[m\u001b[0;33m 4359336 ns 4263240 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/65536/real_time_cv \u001b[m\u001b[0;33m 8.12 % 8.00 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/131072/real_time_mean \u001b[m\u001b[0;33m 132381802 ns 131439592 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=66.8467M\u001b[m output_rows=131.072k\u001b[m plan_cost=79.9539M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/131072/real_time_median \u001b[m\u001b[0;33m 132670408 ns 131630071 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=66.8467M\u001b[m output_rows=131.072k\u001b[m plan_cost=79.9539M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/131072/real_time_stddev \u001b[m\u001b[0;33m 513538 ns 448055 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/131072/real_time_cv \u001b[m\u001b[0;33m 0.39 % 0.34 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/262144/real_time_mean \u001b[m\u001b[0;33m 312772495 ns 310749663 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=133.693M\u001b[m output_rows=262.144k\u001b[m plan_cost=159.908M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/262144/real_time_median \u001b[m\u001b[0;33m 312614458 ns 310482767 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=133.693M\u001b[m output_rows=262.144k\u001b[m plan_cost=159.908M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/262144/real_time_stddev \u001b[m\u001b[0;33m 21372055 ns 21016226 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/262144/real_time_cv \u001b[m\u001b[0;33m 6.83 % 6.76 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/1024/1024/real_time_mean \u001b[m\u001b[0;33m 160722 ns 160185 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=404.48k\u001b[m output_rows=1.024k\u001b[m plan_cost=609.28k\u001b[m rhs_rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/1024/1024/real_time_median \u001b[m\u001b[0;33m 161083 ns 160544 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=404.48k\u001b[m output_rows=1.024k\u001b[m plan_cost=609.28k\u001b[m rhs_rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/1024/1024/real_time_stddev \u001b[m\u001b[0;33m 980 ns 1021 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/1024/1024/real_time_cv \u001b[m\u001b[0;33m 0.61 % 0.64 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/2048/2048/real_time_mean \u001b[m\u001b[0;33m 309777 ns 308490 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=2.048k\u001b[m model_cost=808.96k\u001b[m output_rows=2.048k\u001b[m plan_cost=1.21856M\u001b[m rhs_rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/2048/2048/real_time_median \u001b[m\u001b[0;33m 309907 ns 308710 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=2.048k\u001b[m model_cost=808.96k\u001b[m output_rows=2.048k\u001b[m plan_cost=1.21856M\u001b[m rhs_rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/2048/2048/real_time_stddev \u001b[m\u001b[0;33m 506 ns 522 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/2048/2048/real_time_cv \u001b[m\u001b[0;33m 0.16 % 0.17 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/4096/4096/real_time_mean \u001b[m\u001b[0;33m 608952 ns 605721 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=4.096k\u001b[m model_cost=1.61792M\u001b[m output_rows=4.096k\u001b[m plan_cost=2.43712M\u001b[m rhs_rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/4096/4096/real_time_median \u001b[m\u001b[0;33m 608893 ns 605644 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=4.096k\u001b[m model_cost=1.61792M\u001b[m output_rows=4.096k\u001b[m plan_cost=2.43712M\u001b[m rhs_rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/4096/4096/real_time_stddev \u001b[m\u001b[0;33m 3530 ns 3421 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/4096/4096/real_time_cv \u001b[m\u001b[0;33m 0.58 % 0.56 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/8192/8192/real_time_mean \u001b[m\u001b[0;33m 1317737 ns 1309485 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=8.192k\u001b[m model_cost=3.23584M\u001b[m output_rows=8.192k\u001b[m plan_cost=4.87424M\u001b[m rhs_rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/8192/8192/real_time_median \u001b[m\u001b[0;33m 1316605 ns 1308784 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=8.192k\u001b[m model_cost=3.23584M\u001b[m output_rows=8.192k\u001b[m plan_cost=4.87424M\u001b[m rhs_rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/8192/8192/real_time_stddev \u001b[m\u001b[0;33m 59555 ns 59083 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/8192/8192/real_time_cv \u001b[m\u001b[0;33m 4.52 % 4.51 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/16384/16384/real_time_mean \u001b[m\u001b[0;33m 2961361 ns 2936971 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=16.384k\u001b[m model_cost=6.47168M\u001b[m output_rows=16.384k\u001b[m plan_cost=9.74848M\u001b[m rhs_rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/16384/16384/real_time_median \u001b[m\u001b[0;33m 2968359 ns 2943681 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=16.384k\u001b[m model_cost=6.47168M\u001b[m output_rows=16.384k\u001b[m plan_cost=9.74848M\u001b[m rhs_rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/16384/16384/real_time_stddev \u001b[m\u001b[0;33m 19681 ns 19421 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/16384/16384/real_time_cv \u001b[m\u001b[0;33m 0.66 % 0.66 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/32768/32768/real_time_mean \u001b[m\u001b[0;33m 7050325 ns 6989873 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=32.768k\u001b[m model_cost=12.9434M\u001b[m output_rows=32.768k\u001b[m plan_cost=19.497M\u001b[m rhs_rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/32768/32768/real_time_median \u001b[m\u001b[0;33m 7009057 ns 6949558 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=32.768k\u001b[m model_cost=12.9434M\u001b[m output_rows=32.768k\u001b[m plan_cost=19.497M\u001b[m rhs_rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/32768/32768/real_time_stddev \u001b[m\u001b[0;33m 77824 ns 79054 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/32768/32768/real_time_cv \u001b[m\u001b[0;33m 1.10 % 1.13 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/65536/65536/real_time_mean \u001b[m\u001b[0;33m 15872398 ns 15737241 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=65.536k\u001b[m model_cost=25.8867M\u001b[m output_rows=65.536k\u001b[m plan_cost=38.9939M\u001b[m rhs_rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/65536/65536/real_time_median \u001b[m\u001b[0;33m 15615015 ns 15477258 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=65.536k\u001b[m model_cost=25.8867M\u001b[m output_rows=65.536k\u001b[m plan_cost=38.9939M\u001b[m rhs_rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/65536/65536/real_time_stddev \u001b[m\u001b[0;33m 780189 ns 771653 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/65536/65536/real_time_cv \u001b[m\u001b[0;33m 4.92 % 4.90 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/64/64/real_time_mean \u001b[m\u001b[0;33m 355795 ns 353483 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=64\u001b[m model_cost=286.72k\u001b[m output_rows=64\u001b[m plan_cost=299.52k\u001b[m rhs_rows=64\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/64/64/real_time_median \u001b[m\u001b[0;33m 353326 ns 350916 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=64\u001b[m model_cost=286.72k\u001b[m output_rows=64\u001b[m plan_cost=299.52k\u001b[m rhs_rows=64\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/64/64/real_time_stddev \u001b[m\u001b[0;33m 4527 ns 4451 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/64/64/real_time_cv \u001b[m\u001b[0;33m 1.27 % 1.26 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/128/128/real_time_mean \u001b[m\u001b[0;33m 1116378 ns 1110421 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=128\u001b[m model_cost=1.14688M\u001b[m output_rows=128\u001b[m plan_cost=1.17248M\u001b[m rhs_rows=128\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/128/128/real_time_median \u001b[m\u001b[0;33m 1115505 ns 1109645 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=128\u001b[m model_cost=1.14688M\u001b[m output_rows=128\u001b[m plan_cost=1.17248M\u001b[m rhs_rows=128\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/128/128/real_time_stddev \u001b[m\u001b[0;33m 26324 ns 25069 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/128/128/real_time_cv \u001b[m\u001b[0;33m 2.36 % 2.26 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/256/256/real_time_mean \u001b[m\u001b[0;33m 3971691 ns 3955265 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=256\u001b[m model_cost=4.58752M\u001b[m output_rows=256\u001b[m plan_cost=4.63872M\u001b[m rhs_rows=256\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/256/256/real_time_median \u001b[m\u001b[0;33m 3974506 ns 3958255 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=256\u001b[m model_cost=4.58752M\u001b[m output_rows=256\u001b[m plan_cost=4.63872M\u001b[m rhs_rows=256\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/256/256/real_time_stddev \u001b[m\u001b[0;33m 42789 ns 40950 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/256/256/real_time_cv \u001b[m\u001b[0;33m 1.08 % 1.04 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/512/512/real_time_mean \u001b[m\u001b[0;33m 15476134 ns 15410251 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=512\u001b[m model_cost=18.3501M\u001b[m output_rows=512\u001b[m plan_cost=18.4525M\u001b[m rhs_rows=512\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/512/512/real_time_median \u001b[m\u001b[0;33m 15283883 ns 15218578 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=512\u001b[m model_cost=18.3501M\u001b[m output_rows=512\u001b[m plan_cost=18.4525M\u001b[m rhs_rows=512\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/512/512/real_time_stddev \u001b[m\u001b[0;33m 398036 ns 392643 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/512/512/real_time_cv \u001b[m\u001b[0;33m 2.57 % 2.55 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/1024/1024/real_time_mean \u001b[m\u001b[0;33m 58703697 ns 58489336 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=73.4003M\u001b[m output_rows=1.024k\u001b[m plan_cost=73.6051M\u001b[m rhs_rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/1024/1024/real_time_median \u001b[m\u001b[0;33m 58646104 ns 58442294 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=73.4003M\u001b[m output_rows=1.024k\u001b[m plan_cost=73.6051M\u001b[m rhs_rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/1024/1024/real_time_stddev \u001b[m\u001b[0;33m 142367 ns 137251 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/1024/1024/real_time_cv \u001b[m\u001b[0;33m 0.24 % 0.23 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/2048/2048/real_time_mean \u001b[m\u001b[0;33m 232975373 ns 232184958 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=2.048k\u001b[m model_cost=293.601M\u001b[m output_rows=2.048k\u001b[m plan_cost=294.011M\u001b[m rhs_rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/2048/2048/real_time_median \u001b[m\u001b[0;33m 233174267 ns 232347277 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=2.048k\u001b[m model_cost=293.601M\u001b[m output_rows=2.048k\u001b[m plan_cost=294.011M\u001b[m rhs_rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/2048/2048/real_time_stddev \u001b[m\u001b[0;33m 540173 ns 549641 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/2048/2048/real_time_cv \u001b[m\u001b[0;33m 0.23 % 0.24 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/32/32/real_time_mean \u001b[m\u001b[0;33m 128773 ns 127708 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=32\u001b[m model_cost=106.496k\u001b[m output_rows=1.024k\u001b[m plan_cost=112.896k\u001b[m rhs_rows=32\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/32/32/real_time_median \u001b[m\u001b[0;33m 129039 ns 128024 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=32\u001b[m model_cost=106.496k\u001b[m output_rows=1.024k\u001b[m plan_cost=112.896k\u001b[m rhs_rows=32\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/32/32/real_time_stddev \u001b[m\u001b[0;33m 3861 ns 3750 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/32/32/real_time_cv \u001b[m\u001b[0;33m 3.00 % 2.94 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/64/64/real_time_mean \u001b[m\u001b[0;33m 301369 ns 298177 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=64\u001b[m model_cost=425.984k\u001b[m output_rows=4.096k\u001b[m plan_cost=438.784k\u001b[m rhs_rows=64\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/64/64/real_time_median \u001b[m\u001b[0;33m 300985 ns 298775 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=64\u001b[m model_cost=425.984k\u001b[m output_rows=4.096k\u001b[m plan_cost=438.784k\u001b[m rhs_rows=64\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/64/64/real_time_stddev \u001b[m\u001b[0;33m 4541 ns 6191 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/64/64/real_time_cv \u001b[m\u001b[0;33m 1.51 % 2.08 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/128/128/real_time_mean \u001b[m\u001b[0;33m 958867 ns 951371 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=128\u001b[m model_cost=1.70394M\u001b[m output_rows=16.384k\u001b[m plan_cost=1.72954M\u001b[m rhs_rows=128\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/128/128/real_time_median \u001b[m\u001b[0;33m 959321 ns 951939 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=128\u001b[m model_cost=1.70394M\u001b[m output_rows=16.384k\u001b[m plan_cost=1.72954M\u001b[m rhs_rows=128\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/128/128/real_time_stddev \u001b[m\u001b[0;33m 18581 ns 18282 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/128/128/real_time_cv \u001b[m\u001b[0;33m 1.94 % 1.92 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/256/256/real_time_mean \u001b[m\u001b[0;33m 5563806 ns 5491910 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=256\u001b[m model_cost=6.81574M\u001b[m output_rows=65.536k\u001b[m plan_cost=6.86694M\u001b[m rhs_rows=256\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/256/256/real_time_median \u001b[m\u001b[0;33m 5620555 ns 5545799 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=256\u001b[m model_cost=6.81574M\u001b[m output_rows=65.536k\u001b[m plan_cost=6.86694M\u001b[m rhs_rows=256\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/256/256/real_time_stddev \u001b[m\u001b[0;33m 457987 ns 448836 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/256/256/real_time_cv \u001b[m\u001b[0;33m 8.23 % 8.17 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/512/512/real_time_mean \u001b[m\u001b[0;33m 26439028 ns 26166619 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=512\u001b[m model_cost=27.263M\u001b[m output_rows=262.144k\u001b[m plan_cost=27.3654M\u001b[m rhs_rows=512\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/512/512/real_time_median \u001b[m\u001b[0;33m 26442389 ns 26176889 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=512\u001b[m model_cost=27.263M\u001b[m output_rows=262.144k\u001b[m plan_cost=27.3654M\u001b[m rhs_rows=512\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/512/512/real_time_stddev \u001b[m\u001b[0;33m 405176 ns 377192 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/512/512/real_time_cv \u001b[m\u001b[0;33m 1.53 % 1.44 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/1024/1024/real_time_mean \u001b[m\u001b[0;33m 125006665 ns 123963251 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=109.052M\u001b[m output_rows=1.04858M\u001b[m plan_cost=109.257M\u001b[m rhs_rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/1024/1024/real_time_median \u001b[m\u001b[0;33m 126435572 ns 125317023 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=109.052M\u001b[m output_rows=1.04858M\u001b[m plan_cost=109.257M\u001b[m rhs_rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/1024/1024/real_time_stddev \u001b[m\u001b[0;33m 2622394 ns 2620609 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/1024/1024/real_time_cv \u001b[m\u001b[0;33m 2.10 % 2.11 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", "\u001b[m" ] } @@ -504,7 +504,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "bcfd11fc", "metadata": {}, "outputs": [ @@ -512,7 +512,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2026-06-02T01:20:14+03:00\n", + "2026-06-09T23:49:41+03:00\n", "Running ./build-release/bin/benchmarks\n", "Run on (8 X 4200 MHz CPU s)\n", "CPU Caches:\n", @@ -520,203 +520,203 @@ " L1 Instruction 32 KiB (x4)\n", " L2 Unified 1280 KiB (x4)\n", " L3 Unified 8192 KiB (x1)\n", - "Load Average: 1.60, 1.07, 0.73\n", + "Load Average: 2.21, 1.41, 1.15\n", "***WARNING*** CPU scaling is enabled, the benchmark real time measurements may be noisy and will incur extra overhead.\n", "------------------------------------------------------------------------------------------------------------------------------------------------\n", "Benchmark Time CPU Iterations UserCounters...\n", "------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\u001b[0;32mOperatorCostMatched/target_cost:640000/SeqScan/6400/real_time_mean \u001b[m\u001b[0;33m 320159 ns 317346 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640k\u001b[m output_rows=6.4k\u001b[m plan_cost=640k\u001b[m rows=6.4k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/SeqScan/6400/real_time_median \u001b[m\u001b[0;33m 319599 ns 316785 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640k\u001b[m output_rows=6.4k\u001b[m plan_cost=640k\u001b[m rows=6.4k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/SeqScan/6400/real_time_stddev \u001b[m\u001b[0;33m 1160 ns 1115 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/SeqScan/6400/real_time_cv \u001b[m\u001b[0;33m 0.36 % 0.35 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Filter/6400/real_time_mean \u001b[m\u001b[0;33m 409248 ns 407255 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640k\u001b[m output_rows=3.328k\u001b[m plan_cost=1.28M\u001b[m rows=6.4k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Filter/6400/real_time_median \u001b[m\u001b[0;33m 399719 ns 397882 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640k\u001b[m output_rows=3.328k\u001b[m plan_cost=1.28M\u001b[m rows=6.4k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Filter/6400/real_time_stddev \u001b[m\u001b[0;33m 18699 ns 18531 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Filter/6400/real_time_cv \u001b[m\u001b[0;33m 4.57 % 4.55 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Projection/29091/real_time_mean \u001b[m\u001b[0;33m 3097292 ns 3075629 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640.002k\u001b[m output_rows=29.091k\u001b[m plan_cost=3.5491M\u001b[m rows=29.091k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Projection/29091/real_time_median \u001b[m\u001b[0;33m 3113549 ns 3091459 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640.002k\u001b[m output_rows=29.091k\u001b[m plan_cost=3.5491M\u001b[m rows=29.091k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Projection/29091/real_time_stddev \u001b[m\u001b[0;33m 46014 ns 45803 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Projection/29091/real_time_cv \u001b[m\u001b[0;33m 1.49 % 1.49 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Sort/4476/real_time_mean \u001b[m\u001b[0;33m 714273 ns 707808 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640.068k\u001b[m output_rows=4.476k\u001b[m plan_cost=1.08767M\u001b[m rows=4.476k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Sort/4476/real_time_median \u001b[m\u001b[0;33m 704145 ns 697639 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640.068k\u001b[m output_rows=4.476k\u001b[m plan_cost=1.08767M\u001b[m rows=4.476k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Sort/4476/real_time_stddev \u001b[m\u001b[0;33m 21201 ns 20423 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Sort/4476/real_time_cv \u001b[m\u001b[0;33m 2.97 % 2.89 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Aggregation/1255/real_time_mean 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rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopJoin/96/96/real_time_cv \u001b[m\u001b[0;33m 0.30 % 0.42 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopCrossJoin/78/78/real_time_mean \u001b[m\u001b[0;33m 398744 ns 394565 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=78\u001b[m model_cost=632.736k\u001b[m output_rows=6.084k\u001b[m plan_cost=648.336k\u001b[m rhs_rows=78\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopCrossJoin/78/78/real_time_median \u001b[m\u001b[0;33m 399361 ns 393912 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=78\u001b[m model_cost=632.736k\u001b[m output_rows=6.084k\u001b[m plan_cost=648.336k\u001b[m rhs_rows=78\u001b[m\n", - 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model_cost=1M\u001b[m output_rows=10k\u001b[m plan_cost=1M\u001b[m rows=10k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/SeqScan/10000/real_time_stddev \u001b[m\u001b[0;33m 6767 ns 6945 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/SeqScan/10000/real_time_cv \u001b[m\u001b[0;33m 1.92 % 1.98 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Filter/10000/real_time_mean \u001b[m\u001b[0;33m 664349 ns 660903 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1M\u001b[m output_rows=5.12k\u001b[m plan_cost=2M\u001b[m rows=10k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Filter/10000/real_time_median \u001b[m\u001b[0;33m 655750 ns 652271 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1M\u001b[m output_rows=5.12k\u001b[m plan_cost=2M\u001b[m rows=10k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Filter/10000/real_time_stddev \u001b[m\u001b[0;33m 24445 ns 24189 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Filter/10000/real_time_cv \u001b[m\u001b[0;33m 3.68 % 3.66 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Projection/45455/real_time_mean \u001b[m\u001b[0;33m 5560017 ns 5518016 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.00001M\u001b[m output_rows=45.455k\u001b[m plan_cost=5.54551M\u001b[m rows=45.455k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Projection/45455/real_time_median \u001b[m\u001b[0;33m 5563214 ns 5519348 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.00001M\u001b[m output_rows=45.455k\u001b[m plan_cost=5.54551M\u001b[m rows=45.455k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Projection/45455/real_time_stddev \u001b[m\u001b[0;33m 17569 ns 19382 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Projection/45455/real_time_cv \u001b[m\u001b[0;33m 0.32 % 0.35 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Sort/6993/real_time_mean \u001b[m\u001b[0;33m 1181814 ns 1176185 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.999999M\u001b[m output_rows=6.993k\u001b[m plan_cost=1.6993M\u001b[m rows=6.993k\u001b[m\n", - 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plan_cost=1.19621M\u001b[m rows=1.961k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Aggregation/1961/real_time_median \u001b[m\u001b[0;33m 539330 ns 537405 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.00011M\u001b[m output_rows=1.961k\u001b[m plan_cost=1.19621M\u001b[m rows=1.961k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Aggregation/1961/real_time_stddev \u001b[m\u001b[0;33m 3827 ns 3777 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Aggregation/1961/real_time_cv \u001b[m\u001b[0;33m 0.71 % 0.70 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/HashJoin/2532/2532/real_time_mean \u001b[m\u001b[0;33m 408221 ns 406089 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=2.532k\u001b[m model_cost=1.00014M\u001b[m output_rows=2.532k\u001b[m plan_cost=1.50654M\u001b[m rhs_rows=2.532k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/HashJoin/2532/2532/real_time_median \u001b[m\u001b[0;33m 413762 ns 411413 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=2.532k\u001b[m model_cost=1.00014M\u001b[m output_rows=2.532k\u001b[m plan_cost=1.50654M\u001b[m rhs_rows=2.532k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/HashJoin/2532/2532/real_time_stddev \u001b[m\u001b[0;33m 10162 ns 9830 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/HashJoin/2532/2532/real_time_cv \u001b[m\u001b[0;33m 2.49 % 2.42 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - 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\u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/NestedLoopCrossJoin/98/98/real_time_mean \u001b[m\u001b[0;33m 600207 ns 596002 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=98\u001b[m model_cost=998.816k\u001b[m output_rows=9.604k\u001b[m plan_cost=1.01842M\u001b[m rhs_rows=98\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/NestedLoopCrossJoin/98/98/real_time_median \u001b[m\u001b[0;33m 600347 ns 596164 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=98\u001b[m model_cost=998.816k\u001b[m output_rows=9.604k\u001b[m plan_cost=1.01842M\u001b[m rhs_rows=98\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/NestedLoopCrossJoin/98/98/real_time_stddev \u001b[m\u001b[0;33m 1109 ns 1109 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/NestedLoopCrossJoin/98/98/real_time_cv \u001b[m\u001b[0;33m 0.18 % 0.19 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/SeqScan/32400/real_time_mean \u001b[m\u001b[0;33m 1284652 ns 1276040 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.24M\u001b[m output_rows=32.4k\u001b[m plan_cost=3.24M\u001b[m rows=32.4k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/SeqScan/32400/real_time_median \u001b[m\u001b[0;33m 1257372 ns 1248532 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.24M\u001b[m output_rows=32.4k\u001b[m plan_cost=3.24M\u001b[m rows=32.4k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/SeqScan/32400/real_time_stddev \u001b[m\u001b[0;33m 75695 ns 74938 ns 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\u001b[m\u001b[0;33m 45990 ns 44955 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Filter/32400/real_time_cv \u001b[m\u001b[0;33m 2.11 % 2.08 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Projection/147273/real_time_mean \u001b[m\u001b[0;33m 20058836 ns 19892307 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.24001M\u001b[m output_rows=147.273k\u001b[m plan_cost=17.9673M\u001b[m rows=147.273k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Projection/147273/real_time_median \u001b[m\u001b[0;33m 19959603 ns 19781221 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.24001M\u001b[m output_rows=147.273k\u001b[m plan_cost=17.9673M\u001b[m rows=147.273k\u001b[m\n", - 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plan_cost=5.20354M\u001b[m rows=19.636k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Sort/19636/real_time_stddev \u001b[m\u001b[0;33m 21162 ns 17617 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Sort/19636/real_time_cv \u001b[m\u001b[0;33m 0.51 % 0.42 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Aggregation/6353/real_time_mean \u001b[m\u001b[0;33m 1881536 ns 1872941 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.24003M\u001b[m output_rows=6.353k\u001b[m plan_cost=3.87533M\u001b[m rows=6.353k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Aggregation/6353/real_time_median \u001b[m\u001b[0;33m 1876677 ns 1867856 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.24003M\u001b[m output_rows=6.353k\u001b[m plan_cost=3.87533M\u001b[m rows=6.353k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Aggregation/6353/real_time_stddev \u001b[m\u001b[0;33m 19364 ns 18755 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Aggregation/6353/real_time_cv \u001b[m\u001b[0;33m 1.03 % 1.00 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/HashJoin/8203/8203/real_time_mean \u001b[m\u001b[0;33m 1306558 ns 1298432 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=8.203k\u001b[m model_cost=3.24018M\u001b[m output_rows=8.203k\u001b[m plan_cost=4.88078M\u001b[m rhs_rows=8.203k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/HashJoin/8203/8203/real_time_median \u001b[m\u001b[0;33m 1306181 ns 1297919 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=8.203k\u001b[m model_cost=3.24018M\u001b[m output_rows=8.203k\u001b[m plan_cost=4.88078M\u001b[m rhs_rows=8.203k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/HashJoin/8203/8203/real_time_stddev \u001b[m\u001b[0;33m 1248 ns 1251 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/HashJoin/8203/8203/real_time_cv \u001b[m\u001b[0;33m 0.10 % 0.10 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopJoin/215/215/real_time_mean \u001b[m\u001b[0;33m 2883331 ns 2862880 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=215\u001b[m model_cost=3.23575M\u001b[m output_rows=215\u001b[m plan_cost=3.27875M\u001b[m rhs_rows=215\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopJoin/215/215/real_time_median \u001b[m\u001b[0;33m 2883138 ns 2870337 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=215\u001b[m model_cost=3.23575M\u001b[m output_rows=215\u001b[m plan_cost=3.27875M\u001b[m rhs_rows=215\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopJoin/215/215/real_time_stddev \u001b[m\u001b[0;33m 25044 ns 36841 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopJoin/215/215/real_time_cv \u001b[m\u001b[0;33m 0.87 % 1.29 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopCrossJoin/177/177/real_time_mean \u001b[m\u001b[0;33m 1967699 ns 1948450 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=177\u001b[m model_cost=3.25822M\u001b[m output_rows=31.329k\u001b[m plan_cost=3.29362M\u001b[m rhs_rows=177\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopCrossJoin/177/177/real_time_median \u001b[m\u001b[0;33m 1961065 ns 1941733 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=177\u001b[m model_cost=3.25822M\u001b[m output_rows=31.329k\u001b[m plan_cost=3.29362M\u001b[m rhs_rows=177\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopCrossJoin/177/177/real_time_stddev \u001b[m\u001b[0;33m 26641 ns 26322 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - 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model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/SeqScan/67600/real_time_cv \u001b[m\u001b[0;33m 4.25 % 4.15 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Filter/67600/real_time_mean \u001b[m\u001b[0;33m 4921571 ns 4893957 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76M\u001b[m output_rows=33.808k\u001b[m plan_cost=13.52M\u001b[m rows=67.6k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Filter/67600/real_time_median \u001b[m\u001b[0;33m 4900259 ns 4872979 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76M\u001b[m output_rows=33.808k\u001b[m plan_cost=13.52M\u001b[m rows=67.6k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Filter/67600/real_time_stddev \u001b[m\u001b[0;33m 133893 ns 133878 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Filter/67600/real_time_cv \u001b[m\u001b[0;33m 2.72 % 2.74 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Projection/307273/real_time_mean \u001b[m\u001b[0;33m 41998731 ns 41673692 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76001M\u001b[m output_rows=307.273k\u001b[m plan_cost=37.4873M\u001b[m rows=307.273k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Projection/307273/real_time_median \u001b[m\u001b[0;33m 41714916 ns 41392995 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76001M\u001b[m output_rows=307.273k\u001b[m plan_cost=37.4873M\u001b[m rows=307.273k\u001b[m\n", - 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output_rows=38.409k\u001b[m plan_cost=10.6009M\u001b[m rows=38.409k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Sort/38409/real_time_stddev \u001b[m\u001b[0;33m 86829 ns 86031 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Sort/38409/real_time_cv \u001b[m\u001b[0;33m 0.85 % 0.84 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Aggregation/13255/real_time_mean \u001b[m\u001b[0;33m 4438157 ns 4412963 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76005M\u001b[m output_rows=13.255k\u001b[m plan_cost=8.08555M\u001b[m rows=13.255k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Aggregation/13255/real_time_median \u001b[m\u001b[0;33m 4443256 ns 4415243 ns 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5812015 ns 5788677 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=311\u001b[m model_cost=6.77047M\u001b[m output_rows=311\u001b[m plan_cost=6.83267M\u001b[m rhs_rows=311\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopJoin/311/311/real_time_median \u001b[m\u001b[0;33m 5776632 ns 5752852 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=311\u001b[m model_cost=6.77047M\u001b[m output_rows=311\u001b[m plan_cost=6.83267M\u001b[m rhs_rows=311\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopJoin/311/311/real_time_stddev \u001b[m\u001b[0;33m 65428 ns 65349 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopJoin/311/311/real_time_cv \u001b[m\u001b[0;33m 1.13 % 1.13 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopCrossJoin/255/255/real_time_mean \u001b[m\u001b[0;33m 5684609 ns 5595092 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=255\u001b[m model_cost=6.7626M\u001b[m output_rows=65.025k\u001b[m plan_cost=6.8136M\u001b[m rhs_rows=255\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopCrossJoin/255/255/real_time_median \u001b[m\u001b[0;33m 5585757 ns 5512270 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=255\u001b[m model_cost=6.7626M\u001b[m output_rows=65.025k\u001b[m plan_cost=6.8136M\u001b[m rhs_rows=255\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopCrossJoin/255/255/real_time_stddev \u001b[m\u001b[0;33m 283233 ns 295277 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - 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model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/SeqScan/122500/real_time_cv \u001b[m\u001b[0;33m 1.27 % 1.21 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Filter/122500/real_time_mean \u001b[m\u001b[0;33m 9183200 ns 9123227 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.25M\u001b[m output_rows=61.44k\u001b[m plan_cost=24.5M\u001b[m rows=122.5k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Filter/122500/real_time_median \u001b[m\u001b[0;33m 9274288 ns 9209433 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.25M\u001b[m output_rows=61.44k\u001b[m plan_cost=24.5M\u001b[m rows=122.5k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Filter/122500/real_time_stddev \u001b[m\u001b[0;33m 246032 ns 243070 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Filter/122500/real_time_cv \u001b[m\u001b[0;33m 2.68 % 2.66 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Projection/556818/real_time_mean \u001b[m\u001b[0;33m 76154538 ns 75642732 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.25M\u001b[m output_rows=556.818k\u001b[m plan_cost=67.9318M\u001b[m rows=556.818k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Projection/556818/real_time_median \u001b[m\u001b[0;33m 76053968 ns 75558307 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.25M\u001b[m output_rows=556.818k\u001b[m plan_cost=67.9318M\u001b[m rows=556.818k\u001b[m\n", - 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output_rows=65.536k\u001b[m plan_cost=18.8088M\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Sort/65536/real_time_stddev \u001b[m\u001b[0;33m 209379 ns 211435 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Sort/65536/real_time_cv \u001b[m\u001b[0;33m 0.97 % 0.98 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Aggregation/24020/real_time_mean \u001b[m\u001b[0;33m 10929372 ns 10855646 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.2502M\u001b[m output_rows=24.02k\u001b[m plan_cost=14.6522M\u001b[m rows=24.02k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Aggregation/24020/real_time_median \u001b[m\u001b[0;33m 11005949 ns 10931545 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.2502M\u001b[m output_rows=24.02k\u001b[m plan_cost=14.6522M\u001b[m rows=24.02k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Aggregation/24020/real_time_stddev \u001b[m\u001b[0;33m 184863 ns 178071 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Aggregation/24020/real_time_cv \u001b[m\u001b[0;33m 1.69 % 1.64 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/HashJoin/31013/31013/real_time_mean \u001b[m\u001b[0;33m 6579551 ns 6525622 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=31.013k\u001b[m model_cost=12.2501M\u001b[m output_rows=31.013k\u001b[m plan_cost=18.4527M\u001b[m rhs_rows=31.013k\u001b[m\n", - 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\u001b[m\u001b[0;33m 10203401 ns 10165100 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=418\u001b[m model_cost=12.2307M\u001b[m output_rows=418\u001b[m plan_cost=12.3143M\u001b[m rhs_rows=418\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopJoin/418/418/real_time_median \u001b[m\u001b[0;33m 10207017 ns 10169491 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=418\u001b[m model_cost=12.2307M\u001b[m output_rows=418\u001b[m plan_cost=12.3143M\u001b[m rhs_rows=418\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopJoin/418/418/real_time_stddev \u001b[m\u001b[0;33m 25817 ns 28217 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopJoin/418/418/real_time_cv \u001b[m\u001b[0;33m 0.25 % 0.28 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopCrossJoin/343/343/real_time_mean \u001b[m\u001b[0;33m 11246723 ns 11120161 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=343\u001b[m model_cost=12.2355M\u001b[m output_rows=117.649k\u001b[m plan_cost=12.3041M\u001b[m rhs_rows=343\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopCrossJoin/343/343/real_time_median \u001b[m\u001b[0;33m 11156642 ns 11021816 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=343\u001b[m model_cost=12.2355M\u001b[m output_rows=117.649k\u001b[m plan_cost=12.3041M\u001b[m rhs_rows=343\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopCrossJoin/343/343/real_time_stddev \u001b[m\u001b[0;33m 358953 ns 352800 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - 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3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/SeqScan/260100/real_time_cv \u001b[m\u001b[0;33m 0.41 % 0.42 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Filter/260100/real_time_mean \u001b[m\u001b[0;33m 20268578 ns 20143106 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=130.052k\u001b[m plan_cost=52.02M\u001b[m rows=260.1k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Filter/260100/real_time_median \u001b[m\u001b[0;33m 20226671 ns 20105167 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=130.052k\u001b[m plan_cost=52.02M\u001b[m rows=260.1k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Filter/260100/real_time_stddev 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"\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Projection/1182273/real_time_stddev \u001b[m\u001b[0;33m 1530329 ns 1447113 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Projection/1182273/real_time_cv \u001b[m\u001b[0;33m 0.97 % 0.92 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Sort/131364/real_time_mean \u001b[m\u001b[0;33m 66118518 ns 65605278 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.0101M\u001b[m output_rows=131.364k\u001b[m plan_cost=39.1465M\u001b[m rows=131.364k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Sort/131364/real_time_median \u001b[m\u001b[0;33m 65766383 ns 65262983 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.0101M\u001b[m output_rows=131.364k\u001b[m plan_cost=39.1465M\u001b[m rows=131.364k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Sort/131364/real_time_stddev \u001b[m\u001b[0;33m 1233820 ns 1224222 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Sort/131364/real_time_cv \u001b[m\u001b[0;33m 1.87 % 1.87 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Aggregation/51000/real_time_mean \u001b[m\u001b[0;33m 36824897 ns 36546612 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=51k\u001b[m plan_cost=31.11M\u001b[m rows=51k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Aggregation/51000/real_time_median \u001b[m\u001b[0;33m 37531672 ns 37260669 ns 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"\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/HashJoin/65848/65848/real_time_median \u001b[m\u001b[0;33m 15391909 ns 15276462 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=65.848k\u001b[m model_cost=26.01M\u001b[m output_rows=65.848k\u001b[m plan_cost=39.1796M\u001b[m rhs_rows=65.848k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/HashJoin/65848/65848/real_time_stddev \u001b[m\u001b[0;33m 395978 ns 386476 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/HashJoin/65848/65848/real_time_cv \u001b[m\u001b[0;33m 2.55 % 2.50 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopJoin/610/610/real_time_mean 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output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopCrossJoin/500/500/real_time_mean \u001b[m\u001b[0;33m 24928068 ns 24677118 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=500\u001b[m model_cost=26M\u001b[m output_rows=250k\u001b[m plan_cost=26.1M\u001b[m rhs_rows=500\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopCrossJoin/500/500/real_time_median \u001b[m\u001b[0;33m 24799747 ns 24566264 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=500\u001b[m model_cost=26M\u001b[m output_rows=250k\u001b[m plan_cost=26.1M\u001b[m rhs_rows=500\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopCrossJoin/500/500/real_time_stddev \u001b[m\u001b[0;33m 300738 ns 283592 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopCrossJoin/500/500/real_time_cv \u001b[m\u001b[0;33m 1.21 % 1.15 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[0;32mOperatorCostMatched/target_cost:640000/SeqScan/6400/real_time_mean \u001b[m\u001b[0;33m 316342 ns 313634 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640k\u001b[m output_rows=6.4k\u001b[m plan_cost=640k\u001b[m rows=6.4k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/SeqScan/6400/real_time_median \u001b[m\u001b[0;33m 317098 ns 314269 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640k\u001b[m output_rows=6.4k\u001b[m plan_cost=640k\u001b[m rows=6.4k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/SeqScan/6400/real_time_stddev \u001b[m\u001b[0;33m 2952 ns 3126 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/SeqScan/6400/real_time_cv \u001b[m\u001b[0;33m 0.93 % 1.00 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Filter/6400/real_time_mean \u001b[m\u001b[0;33m 410811 ns 409047 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640k\u001b[m output_rows=3.328k\u001b[m plan_cost=1.28M\u001b[m rows=6.4k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Filter/6400/real_time_median \u001b[m\u001b[0;33m 402156 ns 400612 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640k\u001b[m output_rows=3.328k\u001b[m plan_cost=1.28M\u001b[m rows=6.4k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Filter/6400/real_time_stddev \u001b[m\u001b[0;33m 15061 ns 14626 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Filter/6400/real_time_cv \u001b[m\u001b[0;33m 3.67 % 3.58 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Projection/29091/real_time_mean \u001b[m\u001b[0;33m 3125994 ns 3104770 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640.002k\u001b[m output_rows=29.091k\u001b[m plan_cost=3.5491M\u001b[m rows=29.091k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Projection/29091/real_time_median \u001b[m\u001b[0;33m 3137797 ns 3116535 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640.002k\u001b[m output_rows=29.091k\u001b[m plan_cost=3.5491M\u001b[m rows=29.091k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Projection/29091/real_time_stddev 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plan_cost=765.55k\u001b[m rows=1.255k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Aggregation/1255/real_time_stddev \u001b[m\u001b[0;33m 3908 ns 3846 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Aggregation/1255/real_time_cv \u001b[m\u001b[0;33m 1.12 % 1.10 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/HashJoin/1620/1620/real_time_mean \u001b[m\u001b[0;33m 250126 ns 249104 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.62k\u001b[m model_cost=639.9k\u001b[m output_rows=1.62k\u001b[m plan_cost=963.9k\u001b[m rhs_rows=1.62k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/HashJoin/1620/1620/real_time_median \u001b[m\u001b[0;33m 249492 ns 248365 ns 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rhs_rows=96\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopJoin/96/96/real_time_median \u001b[m\u001b[0;33m 666944 ns 663827 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=96\u001b[m model_cost=645.12k\u001b[m output_rows=96\u001b[m plan_cost=664.32k\u001b[m rhs_rows=96\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopJoin/96/96/real_time_stddev \u001b[m\u001b[0;33m 3379 ns 3351 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopJoin/96/96/real_time_cv \u001b[m\u001b[0;33m 0.51 % 0.50 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopCrossJoin/78/78/real_time_mean \u001b[m\u001b[0;33m 401858 ns 399119 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=78\u001b[m model_cost=632.736k\u001b[m output_rows=6.084k\u001b[m plan_cost=648.336k\u001b[m rhs_rows=78\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopCrossJoin/78/78/real_time_median \u001b[m\u001b[0;33m 396831 ns 394275 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=78\u001b[m model_cost=632.736k\u001b[m output_rows=6.084k\u001b[m plan_cost=648.336k\u001b[m rhs_rows=78\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopCrossJoin/78/78/real_time_stddev \u001b[m\u001b[0;33m 9520 ns 9122 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopCrossJoin/78/78/real_time_cv \u001b[m\u001b[0;33m 2.37 % 2.29 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/SeqScan/10000/real_time_mean \u001b[m\u001b[0;33m 344957 ns 343365 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1M\u001b[m output_rows=10k\u001b[m plan_cost=1M\u001b[m rows=10k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/SeqScan/10000/real_time_median \u001b[m\u001b[0;33m 344845 ns 343551 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1M\u001b[m output_rows=10k\u001b[m plan_cost=1M\u001b[m rows=10k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/SeqScan/10000/real_time_stddev \u001b[m\u001b[0;33m 627 ns 873 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/SeqScan/10000/real_time_cv \u001b[m\u001b[0;33m 0.18 % 0.25 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Filter/10000/real_time_mean \u001b[m\u001b[0;33m 639288 ns 636139 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1M\u001b[m output_rows=5.12k\u001b[m plan_cost=2M\u001b[m rows=10k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Filter/10000/real_time_median \u001b[m\u001b[0;33m 641247 ns 638152 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1M\u001b[m output_rows=5.12k\u001b[m plan_cost=2M\u001b[m rows=10k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Filter/10000/real_time_stddev \u001b[m\u001b[0;33m 5981 ns 5665 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Filter/10000/real_time_cv \u001b[m\u001b[0;33m 0.94 % 0.89 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Projection/45455/real_time_mean \u001b[m\u001b[0;33m 4977023 ns 4946997 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.00001M\u001b[m output_rows=45.455k\u001b[m plan_cost=5.54551M\u001b[m rows=45.455k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Projection/45455/real_time_median \u001b[m\u001b[0;33m 4972650 ns 4944757 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.00001M\u001b[m output_rows=45.455k\u001b[m plan_cost=5.54551M\u001b[m rows=45.455k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Projection/45455/real_time_stddev \u001b[m\u001b[0;33m 23603 ns 25476 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Projection/45455/real_time_cv 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output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Aggregation/1961/real_time_cv \u001b[m\u001b[0;33m 0.44 % 0.42 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/HashJoin/2532/2532/real_time_mean \u001b[m\u001b[0;33m 398480 ns 396620 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=2.532k\u001b[m model_cost=1.00014M\u001b[m output_rows=2.532k\u001b[m plan_cost=1.50654M\u001b[m rhs_rows=2.532k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/HashJoin/2532/2532/real_time_median \u001b[m\u001b[0;33m 393039 ns 391403 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=2.532k\u001b[m model_cost=1.00014M\u001b[m output_rows=2.532k\u001b[m plan_cost=1.50654M\u001b[m rhs_rows=2.532k\u001b[m\n", + 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rhs_rows=98\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/NestedLoopCrossJoin/98/98/real_time_median \u001b[m\u001b[0;33m 575840 ns 571979 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=98\u001b[m model_cost=998.816k\u001b[m output_rows=9.604k\u001b[m plan_cost=1.01842M\u001b[m rhs_rows=98\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/NestedLoopCrossJoin/98/98/real_time_stddev \u001b[m\u001b[0;33m 1501 ns 1458 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/NestedLoopCrossJoin/98/98/real_time_cv \u001b[m\u001b[0;33m 0.26 % 0.25 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/SeqScan/32400/real_time_mean 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plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Projection/147273/real_time_mean \u001b[m\u001b[0;33m 19539424 ns 19390815 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.24001M\u001b[m output_rows=147.273k\u001b[m plan_cost=17.9673M\u001b[m rows=147.273k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Projection/147273/real_time_median \u001b[m\u001b[0;33m 19513198 ns 19372676 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.24001M\u001b[m output_rows=147.273k\u001b[m plan_cost=17.9673M\u001b[m rows=147.273k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Projection/147273/real_time_stddev \u001b[m\u001b[0;33m 443369 ns 422122 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Projection/147273/real_time_cv \u001b[m\u001b[0;33m 2.27 % 2.18 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Sort/19636/real_time_mean \u001b[m\u001b[0;33m 4003506 ns 3985189 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.23994M\u001b[m output_rows=19.636k\u001b[m plan_cost=5.20354M\u001b[m rows=19.636k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Sort/19636/real_time_median \u001b[m\u001b[0;33m 3993834 ns 3973445 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.23994M\u001b[m output_rows=19.636k\u001b[m plan_cost=5.20354M\u001b[m rows=19.636k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Sort/19636/real_time_stddev \u001b[m\u001b[0;33m 36678 ns 37672 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Sort/19636/real_time_cv \u001b[m\u001b[0;33m 0.92 % 0.95 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Aggregation/6353/real_time_mean \u001b[m\u001b[0;33m 1891951 ns 1881946 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.24003M\u001b[m output_rows=6.353k\u001b[m plan_cost=3.87533M\u001b[m rows=6.353k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Aggregation/6353/real_time_median \u001b[m\u001b[0;33m 1864059 ns 1855129 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.24003M\u001b[m output_rows=6.353k\u001b[m plan_cost=3.87533M\u001b[m rows=6.353k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Aggregation/6353/real_time_stddev \u001b[m\u001b[0;33m 79166 ns 76712 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Aggregation/6353/real_time_cv \u001b[m\u001b[0;33m 4.18 % 4.08 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/HashJoin/8203/8203/real_time_mean \u001b[m\u001b[0;33m 1296747 ns 1289704 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=8.203k\u001b[m model_cost=3.24018M\u001b[m output_rows=8.203k\u001b[m plan_cost=4.88078M\u001b[m rhs_rows=8.203k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/HashJoin/8203/8203/real_time_median \u001b[m\u001b[0;33m 1290494 ns 1283508 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=8.203k\u001b[m model_cost=3.24018M\u001b[m output_rows=8.203k\u001b[m plan_cost=4.88078M\u001b[m rhs_rows=8.203k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/HashJoin/8203/8203/real_time_stddev \u001b[m\u001b[0;33m 15252 ns 15284 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/HashJoin/8203/8203/real_time_cv \u001b[m\u001b[0;33m 1.18 % 1.19 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopJoin/215/215/real_time_mean \u001b[m\u001b[0;33m 3015413 ns 2998763 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=215\u001b[m model_cost=3.23575M\u001b[m output_rows=215\u001b[m plan_cost=3.27875M\u001b[m rhs_rows=215\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopJoin/215/215/real_time_median \u001b[m\u001b[0;33m 2948122 ns 2933215 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=215\u001b[m model_cost=3.23575M\u001b[m output_rows=215\u001b[m plan_cost=3.27875M\u001b[m rhs_rows=215\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopJoin/215/215/real_time_stddev \u001b[m\u001b[0;33m 134096 ns 129989 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopJoin/215/215/real_time_cv \u001b[m\u001b[0;33m 4.45 % 4.33 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopCrossJoin/177/177/real_time_mean \u001b[m\u001b[0;33m 1969269 ns 1950102 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=177\u001b[m model_cost=3.25822M\u001b[m output_rows=31.329k\u001b[m plan_cost=3.29362M\u001b[m rhs_rows=177\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopCrossJoin/177/177/real_time_median \u001b[m\u001b[0;33m 1974125 ns 1956105 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=177\u001b[m model_cost=3.25822M\u001b[m output_rows=31.329k\u001b[m plan_cost=3.29362M\u001b[m rhs_rows=177\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopCrossJoin/177/177/real_time_stddev \u001b[m\u001b[0;33m 10036 ns 10656 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopCrossJoin/177/177/real_time_cv \u001b[m\u001b[0;33m 0.51 % 0.55 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/SeqScan/67600/real_time_mean \u001b[m\u001b[0;33m 3823546 ns 3788256 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76M\u001b[m output_rows=67.6k\u001b[m plan_cost=6.76M\u001b[m rows=67.6k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/SeqScan/67600/real_time_median \u001b[m\u001b[0;33m 3771835 ns 3738111 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76M\u001b[m output_rows=67.6k\u001b[m plan_cost=6.76M\u001b[m rows=67.6k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/SeqScan/67600/real_time_stddev \u001b[m\u001b[0;33m 138085 ns 136090 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/SeqScan/67600/real_time_cv \u001b[m\u001b[0;33m 3.61 % 3.59 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Filter/67600/real_time_mean \u001b[m\u001b[0;33m 5038810 ns 5006184 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76M\u001b[m output_rows=33.808k\u001b[m plan_cost=13.52M\u001b[m rows=67.6k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Filter/67600/real_time_median \u001b[m\u001b[0;33m 4977493 ns 4945750 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76M\u001b[m output_rows=33.808k\u001b[m plan_cost=13.52M\u001b[m rows=67.6k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Filter/67600/real_time_stddev \u001b[m\u001b[0;33m 229683 ns 228503 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Filter/67600/real_time_cv \u001b[m\u001b[0;33m 4.56 % 4.56 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Projection/307273/real_time_mean \u001b[m\u001b[0;33m 40927066 ns 40634020 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76001M\u001b[m output_rows=307.273k\u001b[m plan_cost=37.4873M\u001b[m rows=307.273k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Projection/307273/real_time_median \u001b[m\u001b[0;33m 40860570 ns 40567388 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76001M\u001b[m output_rows=307.273k\u001b[m plan_cost=37.4873M\u001b[m rows=307.273k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Projection/307273/real_time_stddev \u001b[m\u001b[0;33m 758964 ns 753831 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + 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output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Sort/38409/real_time_cv \u001b[m\u001b[0;33m 1.85 % 1.78 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Aggregation/13255/real_time_mean \u001b[m\u001b[0;33m 4512331 ns 4483187 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76005M\u001b[m output_rows=13.255k\u001b[m plan_cost=8.08555M\u001b[m rows=13.255k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Aggregation/13255/real_time_median \u001b[m\u001b[0;33m 4517228 ns 4488908 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76005M\u001b[m output_rows=13.255k\u001b[m plan_cost=8.08555M\u001b[m rows=13.255k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Aggregation/13255/real_time_stddev \u001b[m\u001b[0;33m 242531 ns 238082 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Aggregation/13255/real_time_cv \u001b[m\u001b[0;33m 5.37 % 5.31 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/HashJoin/17114/17114/real_time_mean \u001b[m\u001b[0;33m 3195742 ns 3168838 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=17.114k\u001b[m model_cost=6.76003M\u001b[m output_rows=17.114k\u001b[m plan_cost=10.1828M\u001b[m rhs_rows=17.114k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/HashJoin/17114/17114/real_time_median \u001b[m\u001b[0;33m 3199407 ns 3171066 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=17.114k\u001b[m model_cost=6.76003M\u001b[m output_rows=17.114k\u001b[m plan_cost=10.1828M\u001b[m rhs_rows=17.114k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/HashJoin/17114/17114/real_time_stddev \u001b[m\u001b[0;33m 92711 ns 91091 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/HashJoin/17114/17114/real_time_cv \u001b[m\u001b[0;33m 2.90 % 2.87 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopJoin/311/311/real_time_mean \u001b[m\u001b[0;33m 6023061 ns 5995465 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=311\u001b[m model_cost=6.77047M\u001b[m output_rows=311\u001b[m plan_cost=6.83267M\u001b[m rhs_rows=311\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopJoin/311/311/real_time_median 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output_rows=65.025k\u001b[m plan_cost=6.8136M\u001b[m rhs_rows=255\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopCrossJoin/255/255/real_time_median \u001b[m\u001b[0;33m 5243997 ns 5177842 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=255\u001b[m model_cost=6.7626M\u001b[m output_rows=65.025k\u001b[m plan_cost=6.8136M\u001b[m rhs_rows=255\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopCrossJoin/255/255/real_time_stddev \u001b[m\u001b[0;33m 364980 ns 356835 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopCrossJoin/255/255/real_time_cv \u001b[m\u001b[0;33m 6.97 % 6.89 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + 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plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Filter/122500/real_time_mean \u001b[m\u001b[0;33m 9281903 ns 9218854 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.25M\u001b[m output_rows=61.44k\u001b[m plan_cost=24.5M\u001b[m rows=122.5k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Filter/122500/real_time_median \u001b[m\u001b[0;33m 9174524 ns 9110491 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.25M\u001b[m output_rows=61.44k\u001b[m plan_cost=24.5M\u001b[m rows=122.5k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Filter/122500/real_time_stddev \u001b[m\u001b[0;33m 215303 ns 216576 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Filter/122500/real_time_cv \u001b[m\u001b[0;33m 2.32 % 2.35 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Projection/556818/real_time_mean \u001b[m\u001b[0;33m 74886058 ns 74388591 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.25M\u001b[m output_rows=556.818k\u001b[m plan_cost=67.9318M\u001b[m rows=556.818k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Projection/556818/real_time_median \u001b[m\u001b[0;33m 74916742 ns 74404628 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.25M\u001b[m output_rows=556.818k\u001b[m plan_cost=67.9318M\u001b[m rows=556.818k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Projection/556818/real_time_stddev \u001b[m\u001b[0;33m 68523 ns 73385 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + 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output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Sort/65536/real_time_cv \u001b[m\u001b[0;33m 2.27 % 2.18 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Aggregation/24020/real_time_mean \u001b[m\u001b[0;33m 10384281 ns 10310905 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.2502M\u001b[m output_rows=24.02k\u001b[m plan_cost=14.6522M\u001b[m rows=24.02k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Aggregation/24020/real_time_median \u001b[m\u001b[0;33m 10051196 ns 9984556 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.2502M\u001b[m output_rows=24.02k\u001b[m plan_cost=14.6522M\u001b[m rows=24.02k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Aggregation/24020/real_time_stddev \u001b[m\u001b[0;33m 594931 ns 586651 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Aggregation/24020/real_time_cv \u001b[m\u001b[0;33m 5.73 % 5.69 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/HashJoin/31013/31013/real_time_mean \u001b[m\u001b[0;33m 6415603 ns 6363475 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=31.013k\u001b[m model_cost=12.2501M\u001b[m output_rows=31.013k\u001b[m plan_cost=18.4527M\u001b[m rhs_rows=31.013k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/HashJoin/31013/31013/real_time_median \u001b[m\u001b[0;33m 6434016 ns 6382291 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=31.013k\u001b[m model_cost=12.2501M\u001b[m output_rows=31.013k\u001b[m plan_cost=18.4527M\u001b[m rhs_rows=31.013k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/HashJoin/31013/31013/real_time_stddev \u001b[m\u001b[0;33m 66815 ns 64167 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/HashJoin/31013/31013/real_time_cv \u001b[m\u001b[0;33m 1.04 % 1.01 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopJoin/418/418/real_time_mean \u001b[m\u001b[0;33m 10091080 ns 10051311 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=418\u001b[m model_cost=12.2307M\u001b[m output_rows=418\u001b[m plan_cost=12.3143M\u001b[m rhs_rows=418\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopJoin/418/418/real_time_median \u001b[m\u001b[0;33m 10123201 ns 10083856 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=418\u001b[m model_cost=12.2307M\u001b[m output_rows=418\u001b[m plan_cost=12.3143M\u001b[m rhs_rows=418\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopJoin/418/418/real_time_stddev \u001b[m\u001b[0;33m 65437 ns 68353 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopJoin/418/418/real_time_cv \u001b[m\u001b[0;33m 0.65 % 0.68 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopCrossJoin/343/343/real_time_mean \u001b[m\u001b[0;33m 10794051 ns 10661406 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=343\u001b[m model_cost=12.2355M\u001b[m output_rows=117.649k\u001b[m plan_cost=12.3041M\u001b[m rhs_rows=343\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopCrossJoin/343/343/real_time_median \u001b[m\u001b[0;33m 10805562 ns 10678400 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=343\u001b[m model_cost=12.2355M\u001b[m output_rows=117.649k\u001b[m plan_cost=12.3041M\u001b[m rhs_rows=343\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopCrossJoin/343/343/real_time_stddev \u001b[m\u001b[0;33m 58571 ns 53030 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopCrossJoin/343/343/real_time_cv \u001b[m\u001b[0;33m 0.54 % 0.50 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/SeqScan/260100/real_time_mean \u001b[m\u001b[0;33m 17073667 ns 16907574 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=260.1k\u001b[m plan_cost=26.01M\u001b[m rows=260.1k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/SeqScan/260100/real_time_median \u001b[m\u001b[0;33m 16767850 ns 16581281 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=260.1k\u001b[m plan_cost=26.01M\u001b[m rows=260.1k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/SeqScan/260100/real_time_stddev \u001b[m\u001b[0;33m 1427241 ns 1398924 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/SeqScan/260100/real_time_cv \u001b[m\u001b[0;33m 8.36 % 8.27 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Filter/260100/real_time_mean \u001b[m\u001b[0;33m 19234040 ns 19127109 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=130.052k\u001b[m plan_cost=52.02M\u001b[m rows=260.1k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Filter/260100/real_time_median \u001b[m\u001b[0;33m 19265841 ns 19167215 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=130.052k\u001b[m plan_cost=52.02M\u001b[m rows=260.1k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Filter/260100/real_time_stddev \u001b[m\u001b[0;33m 128928 ns 127286 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Filter/260100/real_time_cv \u001b[m\u001b[0;33m 0.67 % 0.67 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Projection/1182273/real_time_mean \u001b[m\u001b[0;33m 157051171 ns 155918245 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=1.18227M\u001b[m plan_cost=144.237M\u001b[m rows=1.18227M\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Projection/1182273/real_time_median \u001b[m\u001b[0;33m 156611403 ns 155561846 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=1.18227M\u001b[m plan_cost=144.237M\u001b[m rows=1.18227M\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Projection/1182273/real_time_stddev \u001b[m\u001b[0;33m 1135507 ns 1133326 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Projection/1182273/real_time_cv \u001b[m\u001b[0;33m 0.72 % 0.73 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Sort/131364/real_time_mean \u001b[m\u001b[0;33m 60817722 ns 60398176 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.0101M\u001b[m output_rows=131.364k\u001b[m plan_cost=39.1465M\u001b[m rows=131.364k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Sort/131364/real_time_median \u001b[m\u001b[0;33m 61052310 ns 60624938 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.0101M\u001b[m output_rows=131.364k\u001b[m plan_cost=39.1465M\u001b[m rows=131.364k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Sort/131364/real_time_stddev \u001b[m\u001b[0;33m 1963798 ns 1937882 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Sort/131364/real_time_cv \u001b[m\u001b[0;33m 3.23 % 3.21 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Aggregation/51000/real_time_mean \u001b[m\u001b[0;33m 35574296 ns 35336538 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=51k\u001b[m plan_cost=31.11M\u001b[m rows=51k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Aggregation/51000/real_time_median \u001b[m\u001b[0;33m 34202010 ns 33985413 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=51k\u001b[m plan_cost=31.11M\u001b[m rows=51k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Aggregation/51000/real_time_stddev \u001b[m\u001b[0;33m 2962023 ns 2918702 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Aggregation/51000/real_time_cv \u001b[m\u001b[0;33m 8.33 % 8.26 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/HashJoin/65848/65848/real_time_mean \u001b[m\u001b[0;33m 16676661 ns 16546743 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=65.848k\u001b[m model_cost=26.01M\u001b[m output_rows=65.848k\u001b[m plan_cost=39.1796M\u001b[m rhs_rows=65.848k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/HashJoin/65848/65848/real_time_median \u001b[m\u001b[0;33m 16172905 ns 16059889 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=65.848k\u001b[m model_cost=26.01M\u001b[m output_rows=65.848k\u001b[m plan_cost=39.1796M\u001b[m rhs_rows=65.848k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/HashJoin/65848/65848/real_time_stddev \u001b[m\u001b[0;33m 875878 ns 864388 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/HashJoin/65848/65848/real_time_cv \u001b[m\u001b[0;33m 5.25 % 5.22 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopJoin/610/610/real_time_mean \u001b[m\u001b[0;33m 21875298 ns 21785712 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=610\u001b[m model_cost=26.047M\u001b[m output_rows=610\u001b[m plan_cost=26.169M\u001b[m rhs_rows=610\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopJoin/610/610/real_time_median \u001b[m\u001b[0;33m 21873879 ns 21796083 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=610\u001b[m model_cost=26.047M\u001b[m output_rows=610\u001b[m plan_cost=26.169M\u001b[m rhs_rows=610\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopJoin/610/610/real_time_stddev \u001b[m\u001b[0;33m 190499 ns 184831 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopJoin/610/610/real_time_cv \u001b[m\u001b[0;33m 0.87 % 0.85 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopCrossJoin/500/500/real_time_mean \u001b[m\u001b[0;33m 23857713 ns 23609004 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=500\u001b[m model_cost=26M\u001b[m output_rows=250k\u001b[m plan_cost=26.1M\u001b[m rhs_rows=500\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopCrossJoin/500/500/real_time_median \u001b[m\u001b[0;33m 23866731 ns 23617933 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=500\u001b[m model_cost=26M\u001b[m output_rows=250k\u001b[m plan_cost=26.1M\u001b[m rhs_rows=500\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopCrossJoin/500/500/real_time_stddev \u001b[m\u001b[0;33m 168299 ns 160750 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopCrossJoin/500/500/real_time_cv \u001b[m\u001b[0;33m 0.71 % 0.68 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", "\u001b[m" ] } @@ -727,7 +727,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "b16b9fb4", "metadata": {}, "outputs": [], @@ -752,7 +752,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "d3172304", "metadata": {}, "outputs": [ @@ -774,48 +774,48 @@ " 11 CachedJitCompiledExpressionExecutor kComplex5 Optimized \n", " \n", " time_ms \n", - " 0 0.022492 \n", - " 1 0.020696 \n", - " 2 0.020039 \n", - " 3 0.019665 \n", - " 4 0.071494 \n", - " 5 0.071193 \n", - " 6 0.074523 \n", - " 7 0.070604 \n", - " 8 0.088746 \n", - " 9 0.093122 \n", - " 10 0.084456 \n", - " 11 0.080405 ,\n", + " 0 0.019715 \n", + " 1 0.019566 \n", + " 2 0.019754 \n", + " 3 0.019804 \n", + " 4 0.070585 \n", + " 5 0.043139 \n", + " 6 0.068980 \n", + " 7 0.044213 \n", + " 8 0.090198 \n", + " 9 0.096809 \n", + " 10 0.095552 \n", + " 11 0.087877 ,\n", " query executor mode time_ms\n", - " 0 q2.1 Interpreted Naive 9.535917\n", - " 1 q2.1 Interpreted Optimized 10.018523\n", - " 2 q1.3 Interpreted Naive 8.894810\n", - " 3 q1.3 Interpreted Optimized 7.216482\n", - " 4 q1.2 Interpreted Naive 8.755501\n", - " 5 q1.2 Interpreted Optimized 6.842163\n", - " 6 q3.3 Interpreted Naive 38.770551\n", - " 7 q3.3 Interpreted Optimized 6.165384\n", - " 8 q3.4 Interpreted Naive 38.014861\n", - " 9 q3.4 Interpreted Optimized 6.548677\n", - " 10 q4.3 Interpreted Naive 9.839082\n", - " 11 q4.3 Interpreted Optimized 6.726511\n", - " 12 q1.1 Interpreted Naive 8.337287\n", - " 13 q1.1 Interpreted Optimized 7.009090\n", - " 14 q3.2 Interpreted Naive 41.168218\n", - " 15 q3.2 Interpreted Optimized 7.331045\n", - " 16 q2.2 Interpreted Naive 10.611863\n", - " 17 q2.2 Interpreted Optimized 7.990721\n", - " 18 q3.1 Interpreted Naive 41.047048\n", - " 19 q3.1 Interpreted Optimized 7.329905\n", - " 20 q4.2 Interpreted Naive 11.383627\n", - " 21 q4.2 Interpreted Optimized 13.753622\n", - " 22 q4.1 Interpreted Naive 9.885573\n", - " 23 q4.1 Interpreted Optimized 13.621071\n", - " 24 q2.3 Interpreted Naive 9.506406\n", - " 25 q2.3 Interpreted Optimized 6.827825)" + " 0 q2.1 Interpreted Naive 10.529208\n", + " 1 q2.1 Interpreted Optimized 11.087104\n", + " 2 q1.3 Interpreted Naive 8.696587\n", + " 3 q1.3 Interpreted Optimized 8.481006\n", + " 4 q1.2 Interpreted Naive 10.591798\n", + " 5 q1.2 Interpreted Optimized 8.042493\n", + " 6 q3.3 Interpreted Naive 41.079134\n", + " 7 q3.3 Interpreted Optimized 6.184696\n", + " 8 q3.4 Interpreted Naive 39.726018\n", + " 9 q3.4 Interpreted Optimized 6.226768\n", + " 10 q4.3 Interpreted Naive 10.159185\n", + " 11 q4.3 Interpreted Optimized 6.886022\n", + " 12 q1.1 Interpreted Naive 8.560502\n", + " 13 q1.1 Interpreted Optimized 7.188918\n", + " 14 q3.2 Interpreted Naive 38.735087\n", + " 15 q3.2 Interpreted Optimized 6.717959\n", + " 16 q2.2 Interpreted Naive 9.318865\n", + " 17 q2.2 Interpreted Optimized 6.690732\n", + " 18 q3.1 Interpreted Naive 38.097914\n", + " 19 q3.1 Interpreted Optimized 6.528813\n", + " 20 q4.2 Interpreted Naive 10.061357\n", + " 21 q4.2 Interpreted Optimized 11.892791\n", + " 22 q4.1 Interpreted Naive 9.553308\n", + " 23 q4.1 Interpreted Optimized 13.055569\n", + " 24 q2.3 Interpreted Naive 9.503878\n", + " 25 q2.3 Interpreted Optimized 6.292484)" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -841,7 +841,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "f4e060f2", "metadata": {}, "outputs": [ @@ -849,71 +849,71 @@ "data": { "text/plain": [ "( operator input_rows model_cost time_ms\n", - " 0 SeqScan 1024 102400.0 0.038473\n", - " 1 SeqScan 2048 204800.0 0.069707\n", - " 2 SeqScan 4096 409600.0 0.154163\n", - " 3 SeqScan 8192 819200.0 0.390148\n", - " 4 SeqScan 16384 1638400.0 0.924368\n", + " 0 SeqScan 1024 102400.0 0.039153\n", + " 1 SeqScan 2048 204800.0 0.072348\n", + " 2 SeqScan 4096 409600.0 0.158147\n", + " 3 SeqScan 8192 819200.0 0.358960\n", + " 4 SeqScan 16384 1638400.0 0.797004\n", " .. ... ... ... ...\n", - " 59 NestedLoopCrossJoin 64 425984.0 0.288639\n", - " 60 NestedLoopCrossJoin 128 1703936.0 0.899354\n", - " 61 NestedLoopCrossJoin 256 6815744.0 5.036885\n", - " 62 NestedLoopCrossJoin 512 27262976.0 25.155229\n", - " 63 NestedLoopCrossJoin 1024 109051904.0 125.736836\n", + " 59 NestedLoopCrossJoin 64 425984.0 0.301369\n", + " 60 NestedLoopCrossJoin 128 1703936.0 0.958867\n", + " 61 NestedLoopCrossJoin 256 6815744.0 5.563806\n", + " 62 NestedLoopCrossJoin 512 27262976.0 26.439028\n", + " 63 NestedLoopCrossJoin 1024 109051904.0 125.006665\n", " \n", " [64 rows x 4 columns],\n", " target_cost operator input_rows model_cost time_ms\n", - " 0 640000 SeqScan 6400 640000.0 0.320159\n", - " 1 640000 Filter 6400 640000.0 0.409248\n", - " 2 640000 Projection 29091 640002.0 3.097292\n", - " 3 640000 Sort 4476 640068.0 0.714273\n", - " 4 640000 Aggregation 1255 640050.0 0.353749\n", - " 5 640000 HashJoin 1620 639900.0 0.251418\n", - " 6 640000 NestedLoopJoin 96 645120.0 0.676351\n", - " 7 640000 NestedLoopCrossJoin 78 632736.0 0.398744\n", - " 8 1000000 SeqScan 10000 1000000.0 0.352346\n", - " 9 1000000 Filter 10000 1000000.0 0.664349\n", - " 10 1000000 Projection 45455 1000010.0 5.560017\n", - " 11 1000000 Sort 6993 999999.0 1.181814\n", - " 12 1000000 Aggregation 1961 1000110.0 0.540896\n", - " 13 1000000 HashJoin 2532 1000140.0 0.408221\n", - " 14 1000000 NestedLoopJoin 120 1008000.0 1.006221\n", - " 15 1000000 NestedLoopCrossJoin 98 998816.0 0.600207\n", - " 16 3240000 SeqScan 32400 3240000.0 1.284652\n", - " 17 3240000 Filter 32400 3240000.0 2.176167\n", - " 18 3240000 Projection 147273 3240006.0 20.058836\n", - " 19 3240000 Sort 19636 3239940.0 4.184701\n", - " 20 3240000 Aggregation 6353 3240030.0 1.881536\n", - " 21 3240000 HashJoin 8203 3240185.0 1.306558\n", - " 22 3240000 NestedLoopJoin 215 3235750.0 2.883331\n", - " 23 3240000 NestedLoopCrossJoin 177 3258216.0 1.967699\n", - " 24 6760000 SeqScan 67600 6760000.0 3.804148\n", - " 25 6760000 Filter 67600 6760000.0 4.921571\n", - " 26 6760000 Projection 307273 6760006.0 41.998731\n", - " 27 6760000 Sort 38409 6759984.0 10.271929\n", - " 28 6760000 Aggregation 13255 6760050.0 4.438157\n", - " 29 6760000 HashJoin 17114 6760030.0 3.043218\n", - " 30 6760000 NestedLoopJoin 311 6770470.0 5.812015\n", - " 31 6760000 NestedLoopCrossJoin 255 6762600.0 5.684609\n", - " 32 12250000 SeqScan 122500 12250000.0 7.200013\n", - " 33 12250000 Filter 122500 12250000.0 9.183200\n", - " 34 12250000 Projection 556818 12249996.0 76.154538\n", - " 35 12250000 Sort 65536 12255232.0 21.631748\n", - " 36 12250000 Aggregation 24020 12250200.0 10.929372\n", - " 37 12250000 HashJoin 31013 12250135.0 6.579551\n", - " 38 12250000 NestedLoopJoin 418 12230680.0 10.203401\n", - " 39 12250000 NestedLoopCrossJoin 343 12235496.0 11.246723\n", - " 40 26010000 SeqScan 260100 26010000.0 17.194941\n", - " 41 26010000 Filter 260100 26010000.0 20.268578\n", - " 42 26010000 Projection 1182273 26010006.0 158.375198\n", - " 43 26010000 Sort 131364 26010072.0 66.118518\n", - " 44 26010000 Aggregation 51000 26010000.0 36.824897\n", - " 45 26010000 HashJoin 65848 26009960.0 15.544874\n", - " 46 26010000 NestedLoopJoin 610 26047000.0 21.482184\n", - " 47 26010000 NestedLoopCrossJoin 500 26000000.0 24.928068)" + " 0 640000 SeqScan 6400 640000.0 0.316342\n", + " 1 640000 Filter 6400 640000.0 0.410811\n", + " 2 640000 Projection 29091 640002.0 3.125994\n", + " 3 640000 Sort 4476 640068.0 0.701764\n", + " 4 640000 Aggregation 1255 640050.0 0.349396\n", + " 5 640000 HashJoin 1620 639900.0 0.250126\n", + " 6 640000 NestedLoopJoin 96 645120.0 0.668448\n", + " 7 640000 NestedLoopCrossJoin 78 632736.0 0.401858\n", + " 8 1000000 SeqScan 10000 1000000.0 0.344957\n", + " 9 1000000 Filter 10000 1000000.0 0.639288\n", + " 10 1000000 Projection 45455 1000010.0 4.977023\n", + " 11 1000000 Sort 6993 999999.0 1.141130\n", + " 12 1000000 Aggregation 1961 1000110.0 0.528994\n", + " 13 1000000 HashJoin 2532 1000140.0 0.398480\n", + " 14 1000000 NestedLoopJoin 120 1008000.0 0.988039\n", + " 15 1000000 NestedLoopCrossJoin 98 998816.0 0.576046\n", + " 16 3240000 SeqScan 32400 3240000.0 1.237620\n", + " 17 3240000 Filter 32400 3240000.0 2.123351\n", + " 18 3240000 Projection 147273 3240006.0 19.539424\n", + " 19 3240000 Sort 19636 3239940.0 4.003506\n", + " 20 3240000 Aggregation 6353 3240030.0 1.891951\n", + " 21 3240000 HashJoin 8203 3240185.0 1.296747\n", + " 22 3240000 NestedLoopJoin 215 3235750.0 3.015413\n", + " 23 3240000 NestedLoopCrossJoin 177 3258216.0 1.969269\n", + " 24 6760000 SeqScan 67600 6760000.0 3.823546\n", + " 25 6760000 Filter 67600 6760000.0 5.038810\n", + " 26 6760000 Projection 307273 6760006.0 40.927066\n", + " 27 6760000 Sort 38409 6759984.0 10.002418\n", + " 28 6760000 Aggregation 13255 6760050.0 4.512331\n", + " 29 6760000 HashJoin 17114 6760030.0 3.195742\n", + " 30 6760000 NestedLoopJoin 311 6770470.0 6.023061\n", + " 31 6760000 NestedLoopCrossJoin 255 6762600.0 5.240152\n", + " 32 12250000 SeqScan 122500 12250000.0 6.678741\n", + " 33 12250000 Filter 122500 12250000.0 9.281903\n", + " 34 12250000 Projection 556818 12249996.0 74.886058\n", + " 35 12250000 Sort 65536 12255232.0 20.096736\n", + " 36 12250000 Aggregation 24020 12250200.0 10.384281\n", + " 37 12250000 HashJoin 31013 12250135.0 6.415603\n", + " 38 12250000 NestedLoopJoin 418 12230680.0 10.091080\n", + " 39 12250000 NestedLoopCrossJoin 343 12235496.0 10.794051\n", + " 40 26010000 SeqScan 260100 26010000.0 17.073667\n", + " 41 26010000 Filter 260100 26010000.0 19.234040\n", + " 42 26010000 Projection 1182273 26010006.0 157.051171\n", + " 43 26010000 Sort 131364 26010072.0 60.817722\n", + " 44 26010000 Aggregation 51000 26010000.0 35.574296\n", + " 45 26010000 HashJoin 65848 26009960.0 16.676661\n", + " 46 26010000 NestedLoopJoin 610 26047000.0 21.875298\n", + " 47 26010000 NestedLoopCrossJoin 500 26000000.0 23.857713)" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -947,13 +947,13 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 8, "id": "2075a20f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -975,13 +975,13 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "c36bdd23", "metadata": {}, "outputs": [ { "data": { - "image/png": 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qZf3444+p1n/88UerRIkSNiRynoiICGvr1q3ux7lz57YWLFjgfrx7924rODjYjmiOkx265E7b2ywpKUmrVq3SkiVLVKFCBXXv3t3uSI5Fl+bQpTl0aRZ9mkOX5tClOXRpFn2aQ5fm0KU5dGkWfXruf3/lvFq1anK5XPr555/Vs2dP9e3bV23atLE7YqbXsmVLRUREaOrUqVq4cKEeffRRxcfHKzw8XJK0dOlS9enTRzt37rQ5aeaXHbpkaHubXLp0SZcuXVJISIjdURyPLs2hS3Po0iz6NIcuzaFLc+jSLPo0hy7NoUtz6NIs+rx14eHhOnfunK5cuSIfHx9Jcn8eHByc4twTJ07YETHT27p1qxo3bqzTp0/rypUrevXVV/XGG2+4j3fq1EnBwcGaPHmyjSmdITt0yRuRZYDp06erZ8+emjNnjiSpf//+Cg0NVY4cOXTvvfc6ez+N24wuzaFLc+jSLPo0hy7NoUtz6NIs+jSHLs2hS3Po0iz6NGv8+PGaMmWK/vvf/2rKlCkpPh83blyKD1xb5cqVtXPnTs2fP1/r169PMWSUpIcfflh9+/a1KZ2zpKXLfv362ZTOEHt3Z8h6hg0bZgUGBlqNGjWycuXKZT311FNW/vz5rZEjR1pvvfWWVbhwYeupp56yO6Yj0KU5dGkOXZpFn+bQpTl0aQ5dmkWf5tClOXRpDl2aRZ9A9jBz5kzrwoULqdYvXrxozZw504ZE5jC0NaxkyZLWxx9/bFmWZf3000+Wl5eX9emnn7qPf/XVV9Ydd9xhVzxHoUtz6NIcujSLPs2hS3Po0hy6NIs+zaFLc+jSHLo0iz4zztGjR61ff/3V2rZtW4oPeOa7776z+vfvb3Xr1s164oknUnwg7by8vKyjR4+mWv/rr78sLy8vGxKZw562hvn7+2vv3r0qUqSI+3FMTIxKly4tSTp8+LCKFSumS5cu2RnTEejSHLo0hy7Nok9z6NIcujSHLs2iT3Po0hy6NIcuzaJP8zZv3qzHH39cO3fu1L9HSS6XS0lJSTYlc54hQ4Zo6NChioqKUoECBeRyuVIcX7RokU3JnMfLy0tHjx5V3rx5U6xv27ZN0dHRjt5f2cfuAFnN5cuX5e/v737s5+cnX19f92MfHx9+kKURXZpDl+bQpVn0aQ5dmkOX5tClWfRpDl2aQ5fm0KVZ9GneE088oVKlSunDDz9Uvnz5Ug0akXaTJ0/WjBkz1KlTJ7ujOFaVKlXkcrnkcrnUqFEj95vjSVJSUpJiY2PVtGlTGxOmH0PbDLBjxw7Fx8dLkizL0u+//64zZ85Ikv766y87ozkOXZpDl+bQpVn0aQ5dmkOX5tClWfRpDl2aQ5fm0KVZ9GlWbGysFi5cqJIlS9odxfEuXbqkOnXq2B3D0dq0aSNJ2rp1q5o0aaKQkBD3MT8/P0VGRqpdu3Y2pTOD7REM8/LyksvlSvWrApLc6/zaQNrQpTl0aQ5dmkWf5tClOXRpDl2aRZ/m0KU5dGkOXZpFn+a1adNGnTp1cvwgLDPo27evQkJCNGDAALujON7MmTP18MMPp7izPqvgTlvDYmNj7Y6QZdClOXRpDl2aRZ/m0KU5dGkOXZpFn+bQpTl0aQ5dmkWf5k2bNk2PP/64fvvtN5UvXz7FdhOS1KpVK5uSOc+FCxc0ZcoUfffdd6pYsWKqLseOHWtTMucpV66ctm7dqpo1a6ZY//HHH+Xt7a2oqCibkqUfd9oCAAAAAADghpYsWaJOnTrp9OnTqY5x17JnoqOjr3vM5XJpxYoVtzGNs9WoUUOvvPKKHnzwwRTrCxcu1KhRo/Tjjz/alCz9GNoaduLECZ07d06FCxd2r23fvl1jxozR2bNn1aZNG3Xs2NHGhM5Bl+bQpTl0aRZ9mkOX5tClOXRpFn2aQ5fm0KU5dGkWfZoXGRmp+++/XwMGDFC+fPnsjgNIkkJCQhQTE6PixYunWI+NjVXFihWv+Y8MTuFld4Cs5tlnn01xG3tCQoLuuece/fTTT7p48aK6dOmi2bNn25jQOejSHLo0hy7Nok9z6NIcujSHLs2iT3Po0hy6NIcuzaJP844fP64XX3yRgS0yFX9/fx09ejTV+pEjR+Tj4/BdYS0YFRkZaa1cudL9ePTo0VaJEiWsy5cvux/XrFnTpnTOQpfm0KU5dGkWfZpDl+bQpTl0aRZ9mkOX5tClOXRpFn2a17lzZ2vq1Kl2x3CsBx54wDp16pT78xt9IO0eeughq379+tbJkyfda3///bdVv359q3379jYmSz/utDUsPj5exYoVcz9esWKFHnjgAfd0v1WrVtqzZ49d8RyFLs2hS3Po0iz6NIcuzaFLc+jSLPo0hy7NoUtz6NIs+jSvVKlS6t+/v7p06aK3335bEydOTPGBG8uRI4dcLpf78xt9IO3efvttHTx4UEWLFlV0dLSio6NVrFgxxcfH6+2337Y7Xro4/D7hzCcsLEwnT55U0aJFJUmbNm1St27d3MddLpcuXrxoVzxHoUtz6NIcujSLPs2hS3Po0hy6NIs+zaFLc+jSHLo0iz7NmzZtmkJCQrR69WqtXr06xTGXy6VevXrZlMwZpk+ffs3PkT6FChVSTEyM5syZo23btikwMFBPPPGEHnnkEfn6+todL12409awGjVqaOLEiUpOTtaCBQt0+vRpNWzY0H189+7dKlKkiI0JnYMuzaFLc+jSLPo0hy7NoUtz6NIs+jSHLs2hS3Po0iz6NC82Nva6H3/88Yfd8Rzp2LFjWrdunX744QcdO3bM7jiOFRwcrP/85z969913NWbMGHXu3NnxA1tJ7Glr2pYtW6zcuXNbfn5+lpeXl/X666+nOP7YY49ZTz75pE3pnIUuzaFLc+jSLPo0hy7NoUtz6NIs+jSHLs2hS3Po0iz6zFjJyclWcnKy3TEc68yZM9YTTzxheXt7Wy6Xy3K5XJaPj4/VtWtX6+zZs3bHc5xZs2ZZdevWtQoUKGDt37/fsizLGjt2rLV48WKbk6WPy7Isy+7BcVZz7NgxrV+/Xvnz51fNmjVTHFu6dKnKlSuXYm8dXB9dmkOX5tClWfRpDl2aQ5fm0KVZ9GkOXZpDl+bQpVn0ad6sWbM0evRo937ApUqV0ssvv6xOnTrZnMxZnnzySX333Xd65513VLduXUnSunXr1KtXL9177716//33bU7oHO+//74GDhyoF154QcOGDdP27dtVvHhxzZgxQzNnztTKlSvtjnjr7J4aZzUbN260vvrqqxRrM2fOtCIjI628efNaPXr0sC5cuGBTOmehS3Po0hy6NIs+zaFLc+jSHLo0iz7NoUtz6NIcujSLPs17++23raCgIOuVV16xPv/8c2vx4sXWyy+/bAUFBVljx461O56j5M6d21q5cmWq9RUrVlh58uS5/YEcrGzZstaiRYssy7KskJAQa9++fZZlWdavv/5q5c6d28Zk6cfQ1rCmTZtaI0eOdD+OiYmxfHx8rO7du1tvv/22lT9/fmvQoEH2BXQQujSHLs2hS7Po0xy6NIcuzaFLs+jTHLo0hy7NoUuz6NO8yMhIa+bMmanWZ8yYYUVGRtqQyLkCAwOtHTt2pFr/7bffrKCgIBsSOVdAQIB7S4T/Hdru3r3bCggIsDNaujG0NSx//vzWTz/95H786quvWnXr1nU/nj9/vlW2bFk7ojkOXZpDl+bQpVn0aQ5dmkOX5tClWfRpDl2aQ5fm0KVZ9Gmev7+/tWfPnlTru3fvtvz9/W1I5FwNGza02rdvb50/f969du7cOat9+/ZWo0aNbEzmPGXLlnXvXfu/Q9sJEyZYVatWtTNauvnYvT1DVvP3338rX7587serV69W06ZN3Y+rV6+ugwcP2hHNcejSHLo0hy7Nok9z6NIcujSHLs2iT3Po0hy6NIcuzaJP80qWLKn58+fr1VdfTbE+b9483XnnnTalcqYJEyaoadOmKly4sCpVqiSXy6WtW7cqICBAy5Ytszueo7z88st69tlndeHCBVmWpU2bNmnu3LkaMWKEpk2bZne8dPGyO0BWky9fPsXGxkqSLl26pC1btqh27dru46dPn5avr69d8RyFLs2hS3Po0iz6NIcuzaFLc+jSLPo0hy7NoUtz6NIs+jRvyJAhGjhwoJo2bao33nhDw4YNU9OmTTVkyBANHTrU7niOUr58ee3Zs0cjRoxQ5cqVVbFiRY0cOVJ79uzRXXfdZXc8R3niiSc0aNAgvfLKKzp37pw6duyoyZMna8KECXr44Yftjpcu3GlrWNOmTdWvXz+NGjVKixcvVlBQkO655x738ZiYGJUoUcLGhM5Bl+bQpTl0aRZ9mkOX5tClOXRpFn2aQ5fm0KU5dGkWfZrXrl07/fjjjxo3bpwWL14sy7JUrlw5bdq0SVWqVLE7nuMEBgaqR48edsfIEnr06KEePXror7/+UnJysiIiIuyOZIbd+zNkNQkJCdbdd99tuVwuKzQ01Fq4cGGK4w0bNrReffVVm9I5C12aQ5fm0KVZ9GkOXZpDl+bQpVn0aQ5dmkOX5tClWfSJzOjzzz+/6cfSpUutzZs3WxcvXrQ7LmzmsizLsntwnBWdOnVKISEh8vb2TrF+4sQJhYSEyM/Pz6ZkzkOX5tClOXRpFn2aQ5fm0KU5dGkWfZpDl+bQpTl0aRZ9pt+ff/6psWPHauDAgQoLC0tx7NSpUxo2bJj69OmTYg9hXJuXV9p3Kc2fP7/mzZuX4g5x/KNKlSpyuVxpOnfLli0ZnCbjMLQFAAAAAADANfXp00eJiYmaMmXKNY8/9dRTypEjh0aNGnWbk2VNlmXp6NGjGjZsmNavX+/ooWNGGTJkSJrPHTRoUAYmyVgMbQEAAAAAAHBN5cuX1+TJk3X33Xdf8/j69evVo0cPbd++/TYnc66TJ08qZ86c1zy2d+9elSxZUvv371eZMmV04cKF2xsOmUba78sGAAAAAABAthIbG6s77rjjuscLFy6s/fv3375AWUDz5s11/vz5VOu7du1SgwY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", 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" ] @@ -1002,13 +1002,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "4ee5fc7c", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1032,13 +1032,13 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "bbe140d5", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1063,13 +1063,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "id": "cde81524", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1093,7 +1093,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "id": "7b508249", "metadata": {}, "outputs": [ @@ -1202,7 +1202,7 @@ "9 HashJoin output row * width 10" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } diff --git a/research/cost-on-random.ipynb b/research/cost-on-random.ipynb index 65f1656..837c3ca 100644 --- a/research/cost-on-random.ipynb +++ b/research/cost-on-random.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 4, "id": "aed19ec2", "metadata": { "execution": { @@ -20,18 +20,22 @@ "collected=25 attempted=25 skipped=0\n", "collected=50 attempted=50 skipped=0\n", "collected=75 attempted=75 skipped=0\n", + "collected=99 attempted=100 skipped=0\n", "collected=100 attempted=100 skipped=0\n", "collected=125 attempted=125 skipped=0\n", "collected=150 attempted=150 skipped=0\n", "collected=175 attempted=175 skipped=0\n", + "collected=199 attempted=200 skipped=0\n", "collected=200 attempted=200 skipped=0\n", "collected=225 attempted=225 skipped=0\n", "collected=250 attempted=250 skipped=0\n", "collected=275 attempted=275 skipped=0\n", + "collected=299 attempted=300 skipped=0\n", "collected=300 attempted=300 skipped=0\n", "collected=325 attempted=325 skipped=0\n", "collected=350 attempted=350 skipped=0\n", "collected=375 attempted=375 skipped=0\n", + "collected=399 attempted=400 skipped=0\n", "collected=400 attempted=400 skipped=0\n", "done: collected=400 attempted=400 skipped=0 -> research/cost-stats.csv\n" ] @@ -47,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "id": "c50c4c78", "metadata": { "execution": { @@ -98,56 +102,56 @@ " 400.000000\n", " 4.000000e+02\n", " 4.000000e+02\n", - " 400.00000\n", + " 4.000000e+02\n", " \n", " \n", " mean\n", " 199.500000\n", - " 3.293322e+07\n", - " 3.063712e+04\n", - " 3446.78750\n", + " 8.429012e+06\n", + " 4.220586e+07\n", + " 1.402288e+04\n", " \n", " \n", " std\n", " 115.614301\n", - " 2.369086e+08\n", - " 2.124881e+05\n", - " 24278.01832\n", + " 7.494437e+07\n", + " 2.657503e+08\n", + " 1.405880e+05\n", " \n", " \n", " min\n", " 0.000000\n", " 3.660000e+02\n", - " 1.220000e+02\n", - " 0.00000\n", + " 3.660000e+02\n", + " 1.110000e+02\n", " \n", " \n", " 25%\n", " 99.750000\n", - " 2.987250e+03\n", - " 1.787500e+02\n", - " 2.00000\n", + " 2.425500e+03\n", + " 2.930000e+03\n", + " 1.927500e+02\n", " \n", " \n", " 50%\n", " 199.500000\n", - " 4.735800e+04\n", - " 3.300000e+02\n", - " 5.00000\n", + " 3.615500e+04\n", + " 5.414800e+04\n", + " 3.265000e+02\n", " \n", " \n", " 75%\n", " 299.250000\n", - " 6.809730e+05\n", - " 1.214250e+03\n", - " 30.00000\n", + " 6.801598e+05\n", + " 1.550466e+06\n", + " 1.315500e+03\n", " \n", " \n", " max\n", " 399.000000\n", + " 1.196362e+09\n", " 2.984422e+09\n", - " 2.956573e+06\n", - " 384231.00000\n", + " 2.615226e+06\n", " \n", " \n", "\n", @@ -155,17 +159,17 @@ ], "text/plain": [ " seed plan_cost exec_us rows\n", - "count 400.000000 4.000000e+02 4.000000e+02 400.00000\n", - "mean 199.500000 3.293322e+07 3.063712e+04 3446.78750\n", - "std 115.614301 2.369086e+08 2.124881e+05 24278.01832\n", - "min 0.000000 3.660000e+02 1.220000e+02 0.00000\n", - "25% 99.750000 2.987250e+03 1.787500e+02 2.00000\n", - "50% 199.500000 4.735800e+04 3.300000e+02 5.00000\n", - "75% 299.250000 6.809730e+05 1.214250e+03 30.00000\n", - "max 399.000000 2.984422e+09 2.956573e+06 384231.00000" + "count 400.000000 4.000000e+02 4.000000e+02 4.000000e+02\n", + "mean 199.500000 8.429012e+06 4.220586e+07 1.402288e+04\n", + "std 115.614301 7.494437e+07 2.657503e+08 1.405880e+05\n", + "min 0.000000 3.660000e+02 3.660000e+02 1.110000e+02\n", + "25% 99.750000 2.425500e+03 2.930000e+03 1.927500e+02\n", + "50% 199.500000 3.615500e+04 5.414800e+04 3.265000e+02\n", + "75% 299.250000 6.801598e+05 1.550466e+06 1.315500e+03\n", + "max 399.000000 1.196362e+09 2.984422e+09 2.615226e+06" ] }, - "execution_count": 2, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -183,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "id": "49d0ecdc", "metadata": { "execution": { @@ -196,7 +200,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -229,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "id": "226372f9", "metadata": { "execution": { @@ -242,7 +246,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -277,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "id": "5dd0a12c", "metadata": { "execution": { @@ -293,11 +297,11 @@ "output_type": "stream", "text": [ "samples : 400\n", - "cost range : 366 .. 2984422200\n", - "time range (ms) : 0.122 .. 2956.6\n", - "Pearson r (linear) : 0.967\n", - "log-log slope : 0.487 (1.0 == perfectly proportional)\n", - "log-log correlation : 0.899\n" + "cost range : 366 .. 1196362017\n", + "time range (ms) : 0.366 .. 2984422.2\n", + "Pearson r (linear) : 0.290\n", + "log-log slope : 1.091 (1.0 == perfectly proportional)\n", + "log-log correlation : 0.970\n" ] } ], diff --git a/research/cost-stats.csv b/research/cost-stats.csv index 0496566..8c74bae 100644 --- a/research/cost-stats.csv +++ b/research/cost-stats.csv @@ -1,1304 +1,1304 @@ seed,plan_cost,exec_us,rows,query -0,3466,255,3,"SELECT t1.price AS c2289 +0,2196,3466,198,"SELECT t1.price AS c2289 FROM regions AS t0 RIGHT JOIN books AS t1 ON t0.id = t1.id;" -1,56100,240,55,"SELECT t0.region_id AS c3748 +1,56100,56100,250,"SELECT t0.region_id AS c3748 FROM customers AS t0 WHERE t0.region_id = 7;" -2,110500,250,500,"SELECT t0.region_id, t0.id +2,110500,110500,296,"SELECT t0.region_id, t0.id FROM customers AS t0 ORDER BY t0.id DESC, t0.region_id DESC;" -3,610,122,5,"SELECT t0.id +3,610,610,122,"SELECT t0.id FROM departments AS t0;" -4,943,172,0,"SELECT t0.id, SUM(t0.id) +4,574,574,179,"SELECT t0.id, SUM(t0.id) FROM markets AS t0 WHERE t0.id IS NULL GROUP BY t0.id ORDER BY t0.id DESC;" -5,49760800,22867,3,"SELECT markets.region, markets.id AS c7685 +5,35285800,35285800,54434,"SELECT markets.region, markets.id AS c7685 FROM markets CROSS JOIN orders CROSS JOIN departments GROUP BY markets.region, markets.id;" -6,230694,628,4,"SELECT t1.id, t0.id AS c6716, t0.region_id +6,112653,440694,312,"SELECT t1.id, t0.id AS c6716, t0.region_id FROM customers AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id RIGHT JOIN departments AS t2 ON t0.id = t2.id WHERE t1.department_id <= 11;" -7,2593,293,1,"SELECT departments.id +7,2188,5724,193,"SELECT departments.id FROM departments RIGHT JOIN employees ON departments.id = employees.id WHERE (employees.id != 33 AND employees.department_id IN (34, 74)) GROUP BY departments.id ORDER BY departments.id DESC;" -8,552500,1154,1,"SELECT orders.customer_id, orders.id +8,552500,552500,1136,"SELECT orders.customer_id, orders.id FROM orders WHERE (orders.id = 117 AND orders.id > 60) ORDER BY orders.customer_id DESC, orders.id DESC;" -9,3939128,8455,2,"SELECT t0.customer_id AS c3674 +9,3939128,3939128,7819,"SELECT t0.customer_id AS c3674 FROM orders AS t0 CROSS JOIN departments AS t1 WHERE (t1.id >= 4 AND (t0.id IN (130) OR t1.id IS NULL));" -10,5532,325,3,"SELECT regions.id +10,4143,6932,235,"SELECT regions.id FROM books RIGHT JOIN employees ON books.id = employees.id JOIN regions ON books.id = regions.id WHERE books.price >= 6;" -11,16616,301,22,"SELECT employees.department_id +11,5889,16616,258,"SELECT employees.department_id FROM users FULL JOIN employees ON users.id = employees.id ORDER BY employees.department_id DESC;" -12,676416,975,3,"SELECT orders.id AS c2380, markets.region, markets.note +12,676416,1550366,903,"SELECT orders.id AS c2380, markets.region, markets.note FROM markets JOIN orders ON markets.id = orders.id;" -13,2390300,2814,15000,"SELECT markets.region, orders.customer_id AS c8705, markets.note +13,2390300,2390300,2752,"SELECT markets.region, orders.customer_id AS c8705, markets.note FROM markets CROSS JOIN orders;" -14,2076,143,10,"SELECT t0.department_id, t0.id AS c1827 +14,2076,2076,127,"SELECT t0.department_id, t0.id AS c1827 FROM employees AS t0 WHERE (t0.department_id BETWEEN 5 AND 34 OR t0.id NOT IN (2, 68, 69));" -15,422,142,0,"SELECT books.price +15,422,422,129,"SELECT books.price FROM books WHERE (books.id < 2 AND (books.price NOT BETWEEN 41 AND 55 OR books.price IN (57, 63, 77, 77)));" -16,16132,334,22,"SELECT t0.age AS c4948 +16,5405,16132,176,"SELECT t0.age AS c4948 FROM users AS t0 FULL JOIN employees AS t1 ON t0.id = t1.id;" -17,29255,307,4,"SELECT users.age, COUNT(departments.id), COUNT(*) +17,19505,29510,302,"SELECT users.age, COUNT(departments.id), COUNT(*) FROM regions JOIN users ON regions.id = users.id CROSS JOIN departments WHERE (users.age >= 52 OR regions.id IN (4, 9)) GROUP BY users.age;" -18,102500,273,436,"SELECT customers.region_id +18,102500,102500,311,"SELECT customers.region_id FROM customers WHERE customers.region_id != 8 ORDER BY customers.region_id ASC;" -19,3393,359,2,"SELECT t0.id +19,2736,4463,221,"SELECT t0.id FROM books AS t0 RIGHT JOIN regions AS t1 ON t0.id = t1.id JOIN markets AS t2 ON t1.id = t2.id WHERE t1.id < 3 ORDER BY t0.id ASC;" -20,49896040,29612,85,"SELECT orders.id AS c5497, orders.customer_id, departments.id AS c8484 +20,49896040,49896040,28905,"SELECT orders.id AS c5497, orders.customer_id, departments.id AS c8484 FROM departments CROSS JOIN users CROSS JOIN orders WHERE orders.id = 82;" -21,1220,146,10,"SELECT regions.id +21,1220,1220,268,"SELECT regions.id FROM regions;" -22,7436,167,11,"SELECT t0.department_id +22,3740,3740,171,"SELECT t0.department_id FROM employees AS t0 GROUP BY t0.department_id ORDER BY t0.department_id ASC;" -23,156962,489,10,"SELECT customers.region_id +23,109562,155822,514,"SELECT customers.region_id FROM customers LEFT JOIN books ON customers.id = books.id GROUP BY customers.region_id ORDER BY customers.region_id ASC;" -24,57976,440,4,"SELECT t2.id, COUNT(t0.id) AS c2763, COUNT(t2.id) +24,39522,56902,387,"SELECT t2.id, COUNT(t0.id) AS c2763, COUNT(t2.id) FROM regions AS t0 CROSS JOIN users AS t1 LEFT JOIN markets AS t2 ON t0.id = t2.id GROUP BY t2.id;" -25,68652,240,3,"SELECT customers.id +25,68652,155432,247,"SELECT customers.id FROM books JOIN customers ON books.id = customers.id ORDER BY customers.id DESC;" -26,4786826,149982,5489,"SELECT customers.region_id, employees.department_id +26,1417306,4786826,2866,"SELECT customers.region_id, employees.department_id FROM employees FULL JOIN orders ON employees.id = orders.id FULL JOIN customers ON employees.id = customers.id ORDER BY customers.region_id ASC;" -27,6471784,12004,2,"SELECT orders.customer_id, users.id +27,1323180,6466259,8053,"SELECT orders.customer_id, users.id FROM users RIGHT JOIN orders ON users.id = orders.id CROSS JOIN departments WHERE orders.customer_id IN (34, 321) GROUP BY orders.customer_id, users.id;" -28,3325,150,5,"SELECT departments.id AS c2364, SUM(departments.id) +28,1425,1425,325,"SELECT departments.id AS c2364, SUM(departments.id) FROM departments GROUP BY departments.id ORDER BY departments.id ASC;" -29,70066,376,5,"SELECT customers.id, markets.id +29,110161,226916,341,"SELECT customers.id, markets.id FROM customers FULL JOIN departments ON customers.id = departments.id JOIN markets ON customers.id = markets.id;" -30,32788,427,51,"SELECT markets.id AS c2619 +30,32788,32788,384,"SELECT markets.id AS c2619 FROM markets CROSS JOIN users CROSS JOIN books WHERE markets.region != 'AMERICA';" -31,4860,223,8,"SELECT t0.id, SUM(t0.id) +31,3125,3125,151,"SELECT t0.id, SUM(t0.id) FROM users AS t0 WHERE ((t0.id NOT IN (5) AND t0.age != 12) AND t0.age NOT BETWEEN 33 AND 64) GROUP BY t0.id;" -32,7436,190,11,"SELECT t0.id AS c8338, COUNT(t0.department_id) +32,3740,3740,183,"SELECT t0.id AS c8338, COUNT(t0.department_id) FROM employees AS t0 GROUP BY t0.id ORDER BY t0.id DESC;" -33,2705500,201810,1000,"SELECT orders.customer_id, customers.region_id +33,1760905,2705500,221424,"SELECT orders.customer_id, customers.region_id FROM departments RIGHT JOIN orders ON departments.id = orders.id CROSS JOIN customers WHERE departments.id BETWEEN 4 AND 96 ORDER BY customers.region_id DESC;" -34,34691744,33226,3,"SELECT markets.note AS c1833, markets.region, SUM(books.id) AS c1917, COUNT(*) +34,27634772,27634772,65216,"SELECT markets.note AS c1833, markets.region, SUM(books.id) AS c1917, COUNT(*) FROM orders CROSS JOIN markets CROSS JOIN books WHERE ((orders.id != 89 OR markets.note IS NOT NULL) OR (markets.id IN (22) AND books.id NOT BETWEEN 1 AND 3)) GROUP BY markets.note, markets.region ORDER BY markets.region DESC;" -35,7686500,17925,50000,"SELECT regions.id, orders.id +35,7686500,1755811500,17292,"SELECT regions.id, orders.id FROM orders CROSS JOIN regions JOIN customers ON regions.id = customers.id ORDER BY orders.id DESC, regions.id DESC;" -36,8426,254,51,"SELECT books.price AS c7084 +36,8426,8426,283,"SELECT books.price AS c7084 FROM books CROSS JOIN users;" -37,525797800,426043,1591,"SELECT orders.customer_id +37,1916300,525797800,2823,"SELECT orders.customer_id FROM customers CROSS JOIN markets FULL JOIN orders ON markets.id = orders.id WHERE orders.id < 95;" -38,4011762,5469,2,"SELECT t1.department_id, COUNT(*) +38,1310710,4010688,2504,"SELECT t1.department_id, COUNT(*) FROM regions AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id RIGHT JOIN orders AS t2 ON t0.id = t2.id WHERE ((t1.id = 3 OR t2.id = 185) OR (t1.id < 3 AND t2.id IS NULL)) GROUP BY t1.department_id ORDER BY t1.department_id DESC;" -39,433,153,0,"SELECT t0.id, t0.note, t0.region +39,433,433,141,"SELECT t0.id, t0.note, t0.region FROM markets AS t0 WHERE t0.note IS NULL ORDER BY t0.note DESC, t0.id DESC;" -40,1004300,621,500,"SELECT t1.id, t1.region_id AS c2127 +40,615800,615800,614,"SELECT t1.id, t1.region_id AS c2127 FROM books AS t0 CROSS JOIN customers AS t1 GROUP BY t1.id, t1.region_id;" -41,1485049368,499192,50000,"SELECT customers.region_id, orders.id AS c1616 +41,1196362017,1196362017,2615226,"SELECT customers.region_id, orders.id AS c1616 FROM orders CROSS JOIN customers WHERE ((orders.id >= 3 OR customers.region_id != 41) OR orders.id IN (73)) GROUP BY customers.region_id, orders.id;" -42,745022,1089,3,"SELECT t1.price, t0.region_id, t2.customer_id AS c2615 +42,745022,1705422,1024,"SELECT t1.price, t0.region_id, t2.customer_id AS c2615 FROM customers AS t0 JOIN books AS t1 ON t0.id = t1.id JOIN orders AS t2 ON t0.id = t2.id WHERE t2.id IS NOT NULL;" -43,422,148,0,"SELECT markets.region, markets.id AS c9574 +43,422,422,147,"SELECT markets.region, markets.id AS c9574 FROM markets WHERE ((markets.note IS NOT NULL OR markets.region != 'AMERICA') AND (markets.region IN ('EUROPE', 'EUROPE') AND markets.note IN ('North, South', 'North, South')));" -44,72448,263,6,"SELECT t1.id AS c8372, t1.age AS c1550 +44,72448,648028,274,"SELECT t1.id AS c8372, t1.age AS c1550 FROM customers AS t0 JOIN users AS t1 ON t0.id = t1.id WHERE t1.id > 11 ORDER BY t1.id ASC, t1.age DESC;" -45,6295,178,0,"SELECT t1.department_id AS c7018, t0.id AS c6686 +45,4560,11225,179,"SELECT t1.department_id AS c7018, t0.id AS c6686 FROM regions AS t0 JOIN employees AS t1 ON t0.id = t1.id WHERE t0.id >= 10 GROUP BY t1.department_id, t0.id;" -46,1122,156,1,"SELECT t0.id +46,1122,1122,152,"SELECT t0.id FROM regions AS t0 WHERE ((t0.id IS NOT NULL AND t0.id != 5) AND (t0.id > 8 AND t0.id > 9));" -47,436233,1138,1,"SELECT employees.id +47,110844,436233,363,"SELECT employees.id FROM customers FULL JOIN employees ON customers.id = employees.id WHERE employees.department_id = 32 ORDER BY employees.id ASC;" -48,1706896,142344,11,"SELECT customers.region_id AS c1201, SUM(customers.region_id) +48,1443652,175655822,2977,"SELECT customers.region_id AS c1201, SUM(customers.region_id) FROM orders FULL JOIN customers ON orders.id = customers.id FULL JOIN markets ON orders.id = markets.id GROUP BY customers.region_id;" -49,560554,1150,0,"SELECT orders.customer_id, orders.id +49,560554,560554,1733,"SELECT orders.customer_id, orders.id FROM orders WHERE ((orders.id = 130 OR orders.id <= 96) AND orders.id = 97) ORDER BY orders.customer_id DESC, orders.id DESC;" -50,1972,259,3,"SELECT markets.id +50,1417,1972,202,"SELECT markets.id FROM markets RIGHT JOIN departments ON markets.id = departments.id WHERE markets.id < 45;" -51,610,133,5,"SELECT t0.id +51,610,610,116,"SELECT t0.id FROM departments AS t0;" -52,7748300,4738,5000,"SELECT orders.id, COUNT(*) +52,4742675,5714300,3855,"SELECT orders.id, COUNT(*) FROM books CROSS JOIN orders WHERE books.price NOT IN (45, 55, 55, 66) GROUP BY orders.id;" -53,31865,316,3,"SELECT users.id, COUNT(employees.id) AS c5902 +53,30725,62340,430,"SELECT users.id, COUNT(employees.id) AS c5902 FROM employees CROSS JOIN users JOIN markets ON users.id = markets.id GROUP BY users.id ORDER BY users.id ASC;" -54,8460,165,15,"SELECT users.age, users.id AS c207, SUM(users.id) +54,5100,5100,191,"SELECT users.age, users.id AS c207, SUM(users.id) FROM users WHERE (users.age > 27 OR (users.age <= 123 AND users.age > 1)) GROUP BY users.age, users.id ORDER BY users.id ASC;" -55,1342,142,11,"SELECT t0.department_id AS c7732 +55,1342,1342,111,"SELECT t0.department_id AS c7732 FROM employees AS t0;" -56,69804832,17366,3,"SELECT books.id, SUM(orders.customer_id) AS c2549, COUNT(*) AS c1539 +56,53882332,53882332,55305,"SELECT books.id, SUM(orders.customer_id) AS c2549, COUNT(*) AS c1539 FROM books CROSS JOIN employees CROSS JOIN orders WHERE employees.id < 68 GROUP BY books.id;" -57,881250,1435,3001,"SELECT orders.id AS c9114 +57,881250,881250,1813,"SELECT orders.id AS c9114 FROM orders WHERE (orders.customer_id > 214 OR orders.customer_id BETWEEN 75 AND 84);" -58,1927703100,1595702,110,"SELECT employees.department_id, customers.region_id, COUNT(*), SUM(orders.customer_id) +58,4073600,1927083100,3996,"SELECT employees.department_id, customers.region_id, COUNT(*), SUM(orders.customer_id) FROM employees CROSS JOIN customers FULL JOIN orders ON customers.id = orders.id WHERE customers.id <= 149 GROUP BY employees.department_id, customers.region_id;" -59,316000,390,500,"SELECT customers.id, customers.region_id, COUNT(customers.region_id) +59,175500,175500,549,"SELECT customers.id, customers.region_id, COUNT(customers.region_id) FROM customers GROUP BY customers.id, customers.region_id;" -60,5702,286,17,"SELECT t0.id +60,3577,5702,189,"SELECT t0.id FROM markets AS t0 FULL JOIN users AS t1 ON t0.id = t1.id ORDER BY t0.id ASC;" -61,2493,292,5,"SELECT t1.region, t1.id, t1.note +61,2493,2493,269,"SELECT t1.region, t1.id, t1.note FROM departments AS t0 CROSS JOIN markets AS t1 WHERE ((t0.id < 43 OR t0.id >= 2) AND t1.note = 'Old World') ORDER BY t1.note DESC, t1.id ASC, t1.region ASC;" -62,70066,363,3,"SELECT departments.id AS c7718 +62,69881,156916,259,"SELECT departments.id AS c7718 FROM departments LEFT JOIN books ON departments.id = books.id JOIN customers ON departments.id = customers.id;" -63,10975,294,11,"SELECT users.age AS c5029, COUNT(*) AS c2096, SUM(users.age) +63,4771,9075,210,"SELECT users.age AS c5029, COUNT(*) AS c2096, SUM(users.age) FROM users FULL JOIN departments ON users.id = departments.id GROUP BY users.age ORDER BY users.age ASC;" -64,175610500,116231,500,"SELECT customers.region_id AS c6827, orders.id, customers.id +64,1494000,175610500,1644,"SELECT customers.region_id AS c6827, orders.id, customers.id FROM customers LEFT JOIN orders ON customers.id = orders.id ORDER BY customers.region_id ASC, customers.id ASC, orders.id DESC;" -65,2482,255,0,"SELECT markets.id AS c8656, markets.region +65,1558,2113,229,"SELECT markets.id AS c8656, markets.region FROM markets FULL JOIN departments ON markets.id = departments.id WHERE departments.id = 60 GROUP BY markets.id, markets.region;" -66,932,166,1,"SELECT markets.note AS c5953, SUM(markets.id), SUM(markets.id) +66,563,563,157,"SELECT markets.note AS c5953, SUM(markets.id), SUM(markets.id) FROM markets WHERE markets.region = 'EUROPE' GROUP BY markets.note;" -67,61000,234,500,"SELECT customers.region_id, customers.id AS c651 +67,61000,61000,400,"SELECT customers.region_id, customers.id AS c651 FROM customers;" -68,4015260,20935,5001,"SELECT users.id, regions.id AS c5610, orders.id AS c9918 +68,1311707,6465260,3026,"SELECT users.id, regions.id AS c5610, orders.id AS c9918 FROM users FULL JOIN orders ON users.id = orders.id FULL JOIN regions ON orders.id = regions.id ORDER BY users.id DESC;" -69,380850,2234,2380,"SELECT t0.id AS c499 +69,546522,548611,2460,"SELECT t0.id AS c499 FROM books AS t0 FULL JOIN users AS t1 ON t0.id = t1.id CROSS JOIN customers AS t2 WHERE ((t0.price > 66 OR t0.price NOT IN (55, 77, 93)) OR t2.id < 93) ORDER BY t0.id ASC;" -70,943,192,1,"SELECT markets.region, markets.note +70,574,574,354,"SELECT markets.region, markets.note FROM markets WHERE markets.id IN (2, 31) GROUP BY markets.region, markets.note ORDER BY markets.note ASC, markets.region ASC;" -71,10340,246,5,"SELECT departments.id AS c3175, SUM(departments.id), COUNT(books.price) +71,5300,5300,272,"SELECT departments.id AS c3175, SUM(departments.id), COUNT(books.price) FROM books CROSS JOIN departments GROUP BY departments.id;" -72,788,169,4,"SELECT t0.id +72,788,788,148,"SELECT t0.id FROM departments AS t0 WHERE t0.id >= 2 ORDER BY t0.id ASC;" -73,67647,262,0,"SELECT customers.id, customers.region_id +73,67647,155422,478,"SELECT customers.id, customers.region_id FROM customers JOIN markets ON customers.id = markets.id WHERE ((markets.id = 3 AND markets.region != 'AMERICA') AND (customers.id < 17 AND customers.region_id IS NULL));" -74,86272,601,3,"SELECT books.id AS c9785 +74,109996,226972,328,"SELECT books.id AS c9785 FROM customers RIGHT JOIN departments ON customers.id = departments.id RIGHT JOIN books ON departments.id = books.id WHERE books.price >= 55;" -75,15960,274,110,"SELECT t1.department_id AS c5193, t1.id +75,15960,15960,272,"SELECT t1.department_id AS c5193, t1.id FROM regions AS t0 CROSS JOIN employees AS t1;" -76,16988,294,17,"SELECT t0.id AS c3431 +76,6261,16988,270,"SELECT t0.id AS c3431 FROM users AS t0 LEFT JOIN employees AS t1 ON t0.id = t1.id WHERE ((t1.id != 67 AND t0.id IS NOT NULL) OR (t0.age NOT BETWEEN 33 AND 123 OR t0.age >= 22));" -77,339550000,200988,0,"SELECT t1.region_id AS c1177, t0.id AS c3278, COUNT(*), COUNT(t0.id) AS c3474 +77,315425000,315425000,206324,"SELECT t1.region_id AS c1177, t0.id AS c3278, COUNT(*), COUNT(t0.id) AS c3474 FROM orders AS t0 CROSS JOIN customers AS t1 WHERE (t0.id = 64 AND t0.customer_id > 397) GROUP BY t1.region_id, t0.id;" -78,61000,244,500,"SELECT t0.id, t0.region_id +78,61000,61000,748,"SELECT t0.id, t0.region_id FROM customers AS t0;" -79,2310,135,2,"SELECT t0.id AS c7963, t0.age AS c5437 +79,2310,2310,125,"SELECT t0.id AS c7963, t0.age AS c5437 FROM users AS t0 WHERE t0.age IN (5, 11, 63);" -80,6250,265,30,"SELECT regions.id AS c2137 +80,6250,6250,246,"SELECT regions.id AS c2137 FROM regions CROSS JOIN markets WHERE markets.note != 'AMERICA';" -81,7242,334,5,"SELECT markets.region AS c3832, users.age, departments.id +81,4658,9622,227,"SELECT markets.region AS c3832, users.age, departments.id FROM users LEFT JOIN departments ON users.id = departments.id RIGHT JOIN markets ON users.id = markets.id WHERE departments.id <= 5;" -82,1833,174,0,"SELECT users.age AS c4898, users.id AS c4014 +82,1833,1833,138,"SELECT users.age AS c4898, users.id AS c4014 FROM users WHERE ((users.id = 4 OR users.id BETWEEN 11 AND 16) AND (users.age = 22 AND users.id <= 57)) ORDER BY users.age DESC;" -83,4502,282,6,"SELECT t0.age AS c499, t1.price AS c7644, t0.id +83,3478,5692,198,"SELECT t0.age AS c499, t1.price AS c7644, t0.id FROM users AS t0 LEFT JOIN books AS t1 ON t0.id = t1.id WHERE t0.id < 7;" -84,4574,324,3,"SELECT books.price, regions.id, markets.id AS c8988 +84,3124,4574,221,"SELECT books.price, regions.id, markets.id AS c8988 FROM markets LEFT JOIN regions ON markets.id = regions.id LEFT JOIN books ON regions.id = books.id WHERE ((books.price BETWEEN 6 AND 77 OR markets.id NOT IN (1, 2, 2, 3)) OR books.price NOT IN (55));" -85,316000,330,500,"SELECT t0.id, t0.region_id +85,175500,175500,342,"SELECT t0.id, t0.region_id FROM customers AS t0 GROUP BY t0.id, t0.region_id;" -86,610000,1168,5000,"SELECT orders.customer_id, orders.id +86,610000,610000,1188,"SELECT orders.customer_id, orders.id FROM orders;" -87,6952,133,11,"SELECT employees.department_id, employees.id AS c6314 +87,3256,3256,169,"SELECT employees.department_id, employees.id AS c6314 FROM employees GROUP BY employees.department_id, employees.id;" -88,8275,273,5,"SELECT departments.id AS c2839, COUNT(*) +88,3612,6540,228,"SELECT departments.id AS c2839, COUNT(*) FROM employees RIGHT JOIN departments ON employees.id = departments.id GROUP BY departments.id ORDER BY departments.id DESC;" -89,1896,151,3,"SELECT t0.region AS c5477, t0.id AS c2794, COUNT(*) AS c9570 +89,822,822,155,"SELECT t0.region AS c5477, t0.id AS c2794, COUNT(*) AS c9570 FROM markets AS t0 GROUP BY t0.region, t0.id;" -90,208000,409,500,"SELECT customers.region_id, customers.id AS c6247, COUNT(*) AS c8943 +90,135000,135000,422,"SELECT customers.region_id, customers.id AS c6247, COUNT(*) AS c8943 FROM customers WHERE customers.region_id <= 40 GROUP BY customers.region_id, customers.id;" -91,610,132,5,"SELECT departments.id +91,610,610,115,"SELECT departments.id FROM departments;" -92,22030,249,3,"SELECT markets.region, markets.id +92,12280,12280,260,"SELECT markets.region, markets.id FROM markets CROSS JOIN regions GROUP BY markets.region, markets.id ORDER BY markets.region ASC;" -93,676086,907,3,"SELECT t0.id, t1.id AS c1156, t0.customer_id +93,676086,1550366,1339,"SELECT t0.id, t1.id AS c1156, t0.customer_id FROM orders AS t0 JOIN books AS t1 ON t0.id = t1.id;" -94,8782,324,0,"SELECT t1.price +94,5916,22032,1068,"SELECT t1.price FROM departments AS t0 CROSS JOIN books AS t1 RIGHT JOIN users AS t2 ON t1.id = t2.id WHERE (t2.id BETWEEN 45 AND 86 AND t1.id IN (2));" -95,7432,344,0,"SELECT t2.id +95,4669,7432,789,"SELECT t2.id FROM departments AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id CROSS JOIN books AS t2 WHERE ((t1.department_id IN (1, 5, 12, 34) AND t1.id IN (69)) AND t1.department_id <= 1);" -96,689824,912,16,"SELECT t1.id AS c5555, t0.id +96,684299,6455219,995,"SELECT t1.id AS c5555, t0.id FROM orders AS t0 JOIN users AS t1 ON t0.id = t1.id GROUP BY t1.id, t0.id;" -97,1643,148,0,"SELECT regions.id, SUM(regions.id), COUNT(regions.id) AS c9550 +97,1274,1274,172,"SELECT regions.id, SUM(regions.id), COUNT(regions.id) AS c9550 FROM regions WHERE ((regions.id < 2 AND regions.id = 32) AND (regions.id <= 2 AND regions.id != 3)) GROUP BY regions.id ORDER BY regions.id ASC;" -98,2482,253,0,"SELECT books.id +98,2482,2482,272,"SELECT books.id FROM books CROSS JOIN departments WHERE ((books.price IS NULL AND departments.id = 1) AND (books.price IS NULL OR books.price NOT IN (55, 77)));" -99,59312,262,0,"SELECT customers.id, SUM(customers.id), COUNT(customers.id) AS c8662 +99,58943,401263,308,"SELECT customers.id, SUM(customers.id), COUNT(customers.id) AS c8662 FROM regions JOIN customers ON regions.id = customers.id WHERE customers.region_id = 63 GROUP BY customers.id;" -100,3009,165,17,"SELECT users.age, users.id AS c7470 +100,3009,3009,157,"SELECT users.age, users.id AS c7470 FROM users ORDER BY users.id ASC;" -101,6624,358,50,"SELECT markets.id, departments.id AS c1258, markets.region +101,5266,6624,355,"SELECT markets.id, departments.id AS c1258, markets.region FROM employees LEFT JOIN markets ON employees.id = markets.id CROSS JOIN departments WHERE ((markets.id IN (3) AND markets.region = 'EUROPE') OR employees.id < 69);" -102,610000,1114,5000,"SELECT t0.customer_id, t0.id AS c8685 +102,610000,610000,1156,"SELECT t0.customer_id, t0.id AS c8685 FROM orders AS t0;" -103,696660,1089,11,"SELECT users.age AS c405 +103,686541,6467000,1466,"SELECT users.age AS c405 FROM users JOIN orders ON users.id = orders.id RIGHT JOIN regions ON orders.id = regions.id GROUP BY users.age ORDER BY users.age DESC;" -104,1222,134,0,"SELECT employees.id +104,1222,1222,135,"SELECT employees.id FROM employees WHERE employees.id = 14;" -105,1552826,2164,3,"SELECT markets.id, orders.customer_id, COUNT(*) +105,1306776,1551752,1478,"SELECT markets.id, orders.customer_id, COUNT(*) FROM orders RIGHT JOIN markets ON orders.id = markets.id LEFT JOIN books ON markets.id = books.id GROUP BY markets.id, orders.customer_id;" -106,10632,340,25,"SELECT books.price AS c2329, markets.note +106,10632,18447,831,"SELECT books.price AS c2329, markets.note FROM users CROSS JOIN books JOIN markets ON books.id = markets.id WHERE (markets.region = 'EUROPE' OR (users.age NOT IN (5, 22, 64, 123) AND users.age < 22)) ORDER BY markets.note ASC;" -107,1643,176,1,"SELECT regions.id +107,1274,1274,448,"SELECT regions.id FROM regions WHERE regions.id IN (5) GROUP BY regions.id ORDER BY regions.id ASC;" -108,102500,228,51,"SELECT customers.id AS c9166, customers.region_id +108,102500,102500,227,"SELECT customers.id AS c9166, customers.region_id FROM customers WHERE customers.id < 52 ORDER BY customers.region_id ASC, customers.id DESC;" -109,16132,273,22,"SELECT employees.id, users.age, employees.department_id +109,5405,16132,182,"SELECT employees.id, users.age, employees.department_id FROM employees FULL JOIN users ON employees.id = users.id;" -110,16522,270,0,"SELECT employees.department_id, employees.id, COUNT(users.id) AS c6401, COUNT(*) +110,5426,16153,222,"SELECT employees.department_id, employees.id, COUNT(users.id) AS c6401, COUNT(*) FROM employees FULL JOIN users ON employees.id = users.id WHERE ((employees.department_id = 31 AND employees.department_id NOT IN (6, 59, 72)) AND (employees.department_id NOT IN (1, 5, 6, 32) AND users.id = 5)) GROUP BY employees.department_id, employees.id;" -111,541846,1998,28,"SELECT orders.customer_id, orders.id +111,541846,541846,2127,"SELECT orders.customer_id, orders.id FROM orders WHERE ((orders.id IN (97, 99, 184, 198) OR orders.id BETWEEN 31 AND 58) AND (orders.customer_id IS NOT NULL AND orders.id BETWEEN 3 AND 65));" -112,1550943,2924,2,"SELECT markets.id, markets.note, COUNT(orders.id), SUM(orders.customer_id) +112,1305844,1550574,2813,"SELECT markets.id, markets.note, COUNT(orders.id), SUM(orders.customer_id) FROM markets RIGHT JOIN orders ON markets.id = orders.id WHERE ((orders.id < 188 AND orders.customer_id >= 193) OR (orders.customer_id BETWEEN 239 AND 436 OR markets.id = 1)) GROUP BY markets.id, markets.note ORDER BY markets.id DESC, markets.note DESC;" -113,422,154,1,"SELECT t0.region, t0.note AS c6295 +113,422,422,131,"SELECT t0.region, t0.note AS c6295 FROM markets AS t0 WHERE t0.id < 2;" -114,61000,219,500,"SELECT t0.id +114,61000,61000,210,"SELECT t0.id FROM customers AS t0;" -115,10020,278,10,"SELECT t1.id AS c2831, t0.id, t0.department_id +115,3922,10020,186,"SELECT t1.id AS c2831, t0.id, t0.department_id FROM employees AS t0 RIGHT JOIN regions AS t1 ON t0.id = t1.id;" -116,677083,1090,1,"SELECT t2.customer_id +116,677833,1551363,1025,"SELECT t2.customer_id FROM markets AS t0 LEFT JOIN books AS t1 ON t0.id = t1.id JOIN orders AS t2 ON t1.id = t2.id WHERE t2.customer_id > 244 ORDER BY t2.customer_id DESC;" -117,744,141,1,"SELECT t0.id AS c6139 +117,744,744,138,"SELECT t0.id AS c6139 FROM departments AS t0 WHERE t0.id >= 5;" -118,7052,335,0,"SELECT books.id AS c118 +118,4923,9763,250,"SELECT books.id AS c118 FROM departments RIGHT JOIN users ON departments.id = users.id FULL JOIN books ON departments.id = books.id WHERE ((users.id > 3 AND books.price = 77) AND users.age > 443) GROUP BY books.id;" -119,677024,1010,5,"SELECT orders.id, COUNT(departments.id) +119,676308,2251048,970,"SELECT orders.id, COUNT(departments.id) FROM departments JOIN orders ON departments.id = orders.id WHERE departments.id < 36 GROUP BY orders.id;" -120,1113982,1453,500,"SELECT orders.customer_id, employees.department_id +120,1377824,4786342,1518,"SELECT orders.customer_id, employees.department_id FROM employees JOIN customers ON employees.id = customers.id LEFT JOIN orders ON employees.id = orders.id;" -121,1222,133,0,"SELECT employees.id +121,1222,1222,151,"SELECT employees.id FROM employees WHERE ((employees.id > 68 OR employees.department_id = 5) AND (employees.id BETWEEN 2 AND 4 AND employees.department_id IN (3, 5, 5)));" -122,2847,295,0,"SELECT markets.note, employees.id +122,3217,5382,214,"SELECT markets.note, employees.id FROM employees LEFT JOIN markets ON employees.id = markets.id JOIN departments ON employees.id = departments.id WHERE ((departments.id != 36 OR markets.region IS NULL) AND departments.id = 4);" -123,544,147,1,"SELECT t0.id, t0.note, t0.region +123,544,544,145,"SELECT t0.id, t0.note, t0.region FROM markets AS t0 WHERE t0.id NOT BETWEEN 2 AND 21;" -124,65833,353,1,"SELECT books.price AS c5178 +124,59693,155433,328,"SELECT books.price AS c5178 FROM books RIGHT JOIN customers ON books.id = customers.id WHERE customers.id = 158 ORDER BY books.price DESC;" -125,1222,145,0,"SELECT employees.id AS c9929, employees.department_id +125,1222,1222,180,"SELECT employees.id AS c9929, employees.department_id FROM employees WHERE (employees.department_id = 35 AND employees.department_id < 32);" -126,4962,342,0,"SELECT books.price, books.id +126,3107,4593,243,"SELECT books.price, books.id FROM books FULL JOIN markets ON books.id = markets.id FULL JOIN regions ON books.id = regions.id WHERE (markets.region != 'EUROPE' AND (regions.id IS NULL AND books.price > 66)) GROUP BY books.price, books.id;" -127,81500,262,3,"SELECT t0.region_id +127,81500,81500,257,"SELECT t0.region_id FROM customers AS t0 WHERE t0.id IN (16, 32, 137) ORDER BY t0.region_id DESC;" -128,1288,159,2,"SELECT regions.id +128,1288,1288,345,"SELECT regions.id FROM regions WHERE regions.id BETWEEN 9 AND 10 ORDER BY regions.id DESC;" -129,1552892,3046,3,"SELECT orders.customer_id, COUNT(orders.id) AS c1702 +129,1306842,1551818,1390,"SELECT orders.customer_id, COUNT(orders.id) AS c1702 FROM markets LEFT JOIN books ON markets.id = books.id LEFT JOIN orders ON markets.id = orders.id GROUP BY orders.customer_id ORDER BY orders.customer_id DESC;" -130,680760,1036,5,"SELECT regions.id, orders.id AS c452, orders.customer_id +130,1308015,2255110,1326,"SELECT regions.id, orders.id AS c452, orders.customer_id FROM regions JOIN departments ON regions.id = departments.id LEFT JOIN orders ON departments.id = orders.id;" -131,4006320,8008,11,"SELECT regions.id AS c9060, COUNT(orders.customer_id), COUNT(orders.id) +131,1308440,4002960,2060,"SELECT regions.id AS c9060, COUNT(orders.customer_id), COUNT(orders.id) FROM orders FULL JOIN regions ON orders.id = regions.id GROUP BY regions.id;" -132,2522,247,0,"SELECT t0.id AS c4692, t1.department_id AS c2149, t1.id +132,2187,5572,201,"SELECT t0.id AS c4692, t1.department_id AS c2149, t1.id FROM departments AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id WHERE (t1.id IN (32, 66, 68, 80) AND (t1.id <= 26 OR t1.department_id <= 5));" -133,678982,931,11,"SELECT t1.id AS c8251, t0.customer_id, t0.id AS c2451 +133,678982,4351342,996,"SELECT t1.id AS c8251, t0.customer_id, t0.id AS c2451 FROM orders AS t0 JOIN employees AS t1 ON t0.id = t1.id;" -134,17111100,16205,55000,"SELECT orders.customer_id AS c6356 +134,17111100,17111100,20339,"SELECT orders.customer_id AS c6356 FROM employees CROSS JOIN orders ORDER BY orders.customer_id ASC;" -135,4015260,8754,5001,"SELECT users.id, regions.id AS c3154 +135,1311707,6465260,1861,"SELECT users.id, regions.id AS c3154 FROM orders FULL JOIN users ON orders.id = users.id RIGHT JOIN regions ON orders.id = regions.id ORDER BY users.id DESC, regions.id DESC;" -136,12682,387,4,"SELECT books.price +136,7798,11608,343,"SELECT books.price FROM regions CROSS JOIN markets LEFT JOIN books ON regions.id = books.id GROUP BY books.price ORDER BY books.price DESC;" -137,3160,137,5,"SELECT departments.id, SUM(departments.id), COUNT(*) AS c6998 +137,1425,1425,154,"SELECT departments.id, SUM(departments.id), COUNT(*) AS c6998 FROM departments GROUP BY departments.id;" -138,6320,137,10,"SELECT regions.id AS c7258 +138,2960,2960,148,"SELECT regions.id AS c7258 FROM regions GROUP BY regions.id;" -139,932,178,2,"SELECT markets.id +139,563,563,166,"SELECT markets.id FROM markets WHERE ((markets.note = 'Old World' OR markets.id >= 2) OR (markets.region = 'AMERICA' AND markets.region = 'EUROPE')) GROUP BY markets.id;" -140,422,152,3,"SELECT t0.price, t0.id AS c3105 +140,422,422,137,"SELECT t0.price, t0.id AS c3105 FROM books AS t0 WHERE t0.id IS NOT NULL;" -141,5272,358,0,"SELECT t1.id AS c8895, t1.note AS c8854, SUM(t1.id) +141,3365,4903,441,"SELECT t1.id AS c8895, t1.note AS c8854, SUM(t1.id) FROM books AS t0 RIGHT JOIN markets AS t1 ON t0.id = t1.id RIGHT JOIN employees AS t2 ON t0.id = t2.id WHERE t1.id > 24 GROUP BY t1.id, t1.note;" -142,87712,367,17,"SELECT t1.region_id AS c1630 +142,77205,661132,258,"SELECT t1.region_id AS c1630 FROM users AS t0 JOIN customers AS t1 ON t0.id = t1.id LEFT JOIN employees AS t2 ON t0.id = t2.id;" -143,422,158,1,"SELECT t0.id, t0.price +143,422,422,126,"SELECT t0.id, t0.price FROM books AS t0 WHERE t0.price <= 55;" -144,14944,273,10,"SELECT t1.department_id AS c3966, SUM(t1.id) AS c1392, COUNT(*) AS c3892 +144,9744,9744,291,"SELECT t1.department_id AS c3966, SUM(t1.id) AS c1392, COUNT(*) AS c3892 FROM books AS t0 CROSS JOIN employees AS t1 WHERE t1.department_id != 12 GROUP BY t1.department_id;" -145,3072,283,2,"SELECT t1.department_id AS c6376, t1.id AS c3596 +145,2444,3832,211,"SELECT t1.department_id AS c6376, t1.id AS c3596 FROM books AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id WHERE ((t0.id < 1 OR t1.id IS NOT NULL) AND (t0.id != 3 AND t1.department_id <= 35));" -146,3325,163,5,"SELECT departments.id +146,1590,1590,185,"SELECT departments.id FROM departments GROUP BY departments.id ORDER BY departments.id DESC;" -147,12750,343,110,"SELECT regions.id +147,9987,12750,336,"SELECT regions.id FROM departments FULL JOIN employees ON departments.id = employees.id CROSS JOIN regions;" -148,6250,268,20,"SELECT books.id, books.price AS c5525, regions.id +148,6250,6250,409,"SELECT books.id, books.price AS c5525, regions.id FROM books CROSS JOIN regions WHERE books.price < 69;" -149,102500,278,422,"SELECT t0.region_id AS c6318 +149,102500,102500,442,"SELECT t0.region_id AS c6318 FROM customers AS t0 WHERE t0.id > 78 ORDER BY t0.region_id DESC;" -150,1519000,1214,1040,"SELECT t0.id, t1.id AS c921 +150,1130500,1130500,1135,"SELECT t0.id, t1.id AS c921 FROM regions AS t0 CROSS JOIN customers AS t1 WHERE t1.region_id IN (5, 9, 51) GROUP BY t0.id, t1.id;" -151,4014888,11011,0,"SELECT regions.id, users.age, orders.id +151,1310829,4014888,2184,"SELECT regions.id, users.age, orders.id FROM regions RIGHT JOIN users ON regions.id = users.id FULL JOIN orders ON users.id = orders.id WHERE ((orders.customer_id BETWEEN 283 AND 359 AND users.id IN (10, 17, 17, 39)) OR (users.id IN (14, 16, 64, 75) AND users.id >= 81)) ORDER BY orders.id ASC, regions.id DESC, users.age ASC;" -152,12160,277,55,"SELECT departments.id, employees.department_id, employees.id +152,12160,12160,286,"SELECT departments.id, employees.department_id, employees.id FROM departments CROSS JOIN employees ORDER BY employees.id ASC;" -153,239300,1323,1500,"SELECT t1.id, t0.region_id, t0.id +153,239300,239300,1501,"SELECT t1.id, t0.region_id, t0.id FROM customers AS t0 CROSS JOIN books AS t1;" -154,5655,174,10,"SELECT regions.id, SUM(regions.id) AS c8443 +154,3226,3226,171,"SELECT regions.id, SUM(regions.id) AS c8443 FROM regions WHERE (regions.id >= 1 OR (regions.id >= 8 OR regions.id = 5)) GROUP BY regions.id ORDER BY regions.id ASC;" -155,681356,1097,2,"SELECT markets.region +155,1308138,1554232,1496,"SELECT markets.region FROM markets LEFT JOIN orders ON markets.id = orders.id JOIN employees ON markets.id = employees.id GROUP BY markets.region;" -156,4001220,3942,10,"SELECT t1.customer_id, t1.id AS c2544, t0.id AS c7170 +156,1307140,4001220,1259,"SELECT t1.customer_id, t1.id AS c2544, t0.id AS c7170 FROM regions AS t0 LEFT JOIN orders AS t1 ON t0.id = t1.id;" -157,675750,1912,4840,"SELECT t1.region_id, t2.id +157,141171,675750,1773,"SELECT t1.region_id, t2.id FROM users AS t0 RIGHT JOIN customers AS t1 ON t0.id = t1.id CROSS JOIN regions AS t2 WHERE t0.id IS NULL;" -158,5727,298,7,"SELECT t1.id, t0.id AS c3888, t2.id AS c5161 +158,4574,12322,208,"SELECT t1.id, t0.id AS c3888, t2.id AS c5161 FROM employees AS t0 FULL JOIN regions AS t1 ON t0.id = t1.id JOIN books AS t2 ON t0.id = t2.id WHERE t0.id IS NULL;" -159,2253028,2641,3,"SELECT orders.customer_id AS c3770, departments.id AS c4567, COUNT(*) AS c2771 +159,1307045,2251640,1418,"SELECT orders.customer_id AS c3770, departments.id AS c4567, COUNT(*) AS c2771 FROM departments LEFT JOIN orders ON departments.id = orders.id WHERE ((orders.customer_id NOT BETWEEN 115 AND 380 OR departments.id IS NULL) OR orders.id = 174) GROUP BY orders.customer_id, departments.id;" -160,610000,1128,5000,"SELECT t0.id, t0.customer_id AS c1926 +160,610000,610000,1199,"SELECT t0.id, t0.customer_id AS c1926 FROM orders AS t0;" -161,91868,354,33,"SELECT t1.region AS c961, t2.id +161,91868,1210558,361,"SELECT t1.region AS c961, t2.id FROM employees AS t0 CROSS JOIN markets AS t1 JOIN customers AS t2 ON t1.id = t2.id;" -162,1466,157,3,"SELECT employees.id, employees.department_id +162,1466,1466,150,"SELECT employees.id, employees.department_id FROM employees WHERE ((employees.id NOT IN (5, 67, 68) AND employees.department_id = 28) OR (employees.id > 4 AND employees.department_id > 32));" -163,948104,1813,2,"SELECT markets.region, COUNT(*), COUNT(*) +163,127054,1849979,485,"SELECT markets.region, COUNT(*), COUNT(*) FROM users CROSS JOIN markets LEFT JOIN customers ON markets.id = customers.id WHERE users.age >= 1 GROUP BY markets.region;" -164,433,184,3,"SELECT markets.id, markets.note AS c529, markets.region +164,433,433,151,"SELECT markets.id, markets.note AS c529, markets.region FROM markets WHERE (markets.region = 'EUROPE' OR markets.id <= 91) ORDER BY markets.region ASC, markets.id ASC, markets.note ASC;" -165,1896,151,3,"SELECT books.id, COUNT(books.price), SUM(books.id) +165,822,822,167,"SELECT books.id, COUNT(books.price), SUM(books.id) FROM books GROUP BY books.id;" -166,2328,149,9,"SELECT regions.id +166,2328,2328,153,"SELECT regions.id FROM regions WHERE regions.id NOT IN (2, 95) ORDER BY regions.id ASC;" -167,52196,252,0,"SELECT t0.id +167,52196,52196,244,"SELECT t0.id FROM customers AS t0 WHERE ((t0.region_id > 32 AND t0.id = 78) AND (t0.id >= 20 OR t0.id IS NULL));" -168,158668,1265,1,"SELECT t1.region AS c6909, t1.note, SUM(t2.id) +168,111678,157908,1393,"SELECT t1.region AS c6909, t1.note, SUM(t2.id) FROM customers AS t0 LEFT JOIN markets AS t1 ON t0.id = t1.id CROSS JOIN departments AS t2 WHERE ((t0.id = 89 OR t1.id BETWEEN 49 AND 71) AND t0.id IS NOT NULL) GROUP BY t1.region, t1.note ORDER BY t1.region ASC;" -169,2482,273,2,"SELECT departments.id +169,1558,2113,195,"SELECT departments.id FROM departments RIGHT JOIN markets ON departments.id = markets.id WHERE departments.id != 1 GROUP BY departments.id;" -170,2253325,2928,505,"SELECT t1.id AS c5737, t0.customer_id +170,1306995,2251590,3084,"SELECT t1.id AS c5737, t0.customer_id FROM orders AS t0 LEFT JOIN departments AS t1 ON t0.id = t1.id GROUP BY t1.id, t0.customer_id ORDER BY t0.customer_id DESC, t1.id ASC;" -171,37006,538,110,"SELECT employees.id AS c3715 +171,24626,37006,372,"SELECT employees.id AS c3715 FROM regions CROSS JOIN employees FULL JOIN books ON regions.id = books.id;" -172,436875,673,0,"SELECT t0.id, t0.department_id AS c3676, t1.region_id +172,111497,436875,360,"SELECT t0.id, t0.department_id AS c3676, t1.region_id FROM employees AS t0 FULL JOIN customers AS t1 ON t0.id = t1.id WHERE t0.department_id >= 57 ORDER BY t0.id DESC, t1.region_id ASC;" -173,27106700,19617,85000,"SELECT orders.customer_id +173,27106700,27106700,21320,"SELECT orders.customer_id FROM users CROSS JOIN orders ORDER BY orders.customer_id DESC;" -174,2475,163,3,"SELECT users.id, users.age +174,2475,2475,161,"SELECT users.id, users.age FROM users WHERE users.id IN (3, 11, 12) ORDER BY users.age DESC, users.id DESC;" -175,2292,433,1,"SELECT books.id, employees.id, employees.department_id +175,2273,3832,200,"SELECT books.id, employees.id, employees.department_id FROM employees RIGHT JOIN books ON employees.id = books.id WHERE books.price = 77;" -176,932,152,2,"SELECT books.price, SUM(books.price) AS c5357 +176,563,563,150,"SELECT books.price, SUM(books.price) AS c5357 FROM books WHERE books.price >= 66 GROUP BY books.price;" -177,3692,162,14,"SELECT users.id, users.age AS c4847 +177,3692,3692,179,"SELECT users.id, users.age AS c4847 FROM users WHERE (users.age < 16 OR users.age < 71) ORDER BY users.id ASC, users.age DESC;" -178,3009,140,17,"SELECT users.age AS c7380, users.id +178,3009,3009,158,"SELECT users.age AS c7380, users.id FROM users ORDER BY users.id DESC;" -179,358416,524,500,"SELECT customers.id, customers.region_id +179,221288,221288,370,"SELECT customers.id, customers.region_id FROM customers WHERE ((customers.id != 141 OR customers.region_id != 10) OR (customers.id NOT IN (197) OR customers.region_id IN (8))) GROUP BY customers.id, customers.region_id;" -180,365500,614,500,"SELECT customers.id AS c95, customers.region_id +180,225000,225000,809,"SELECT customers.id AS c95, customers.region_id FROM customers GROUP BY customers.id, customers.region_id ORDER BY customers.region_id DESC;" -181,4976800,3138,500,"SELECT customers.region_id AS c397, customers.id AS c5003, SUM(books.id) +181,3199300,3199300,3994,"SELECT customers.region_id AS c397, customers.id AS c5003, SUM(books.id) FROM customers CROSS JOIN books CROSS JOIN departments GROUP BY customers.region_id, customers.id;" -182,1111000,1136,500,"SELECT t1.id AS c6022, t0.id +182,970500,175675500,2055,"SELECT t1.id AS c6022, t0.id FROM orders AS t0 JOIN customers AS t1 ON t0.id = t1.id GROUP BY t1.id, t0.id;" -183,81600,287,1,"SELECT customers.id, COUNT(customers.id), COUNT(customers.id) +183,65900,65900,231,"SELECT customers.id, COUNT(customers.id), COUNT(customers.id) FROM customers WHERE customers.id = 24 GROUP BY customers.id;" -184,297800,441,500,"SELECT books.price AS c5308, customers.region_id AS c1694 +184,297800,297800,451,"SELECT books.price AS c5308, customers.region_id AS c1694 FROM books CROSS JOIN customers WHERE books.price <= 55;" -185,610,134,5,"SELECT t0.id AS c9527 +185,610,610,159,"SELECT t0.id AS c9527 FROM departments AS t0;" -186,14820,282,18,"SELECT users.id +186,5081,14820,206,"SELECT users.id FROM regions FULL JOIN users ON regions.id = users.id;" -187,15530,290,8,"SELECT users.id, regions.id, users.age AS c4755 +187,5791,15530,221,"SELECT users.id, regions.id, users.age AS c4755 FROM users LEFT JOIN regions ON users.id = regions.id WHERE ((regions.id != 31 AND users.age <= 64) OR users.id >= 66) ORDER BY users.age DESC;" -188,7942,327,0,"SELECT t2.price AS c5573, COUNT(*) +188,4810,7573,355,"SELECT t2.price AS c5573, COUNT(*) FROM employees AS t0 RIGHT JOIN departments AS t1 ON t0.id = t1.id CROSS JOIN books AS t2 WHERE (t2.price > 55 AND t0.id BETWEEN 66 AND 69) GROUP BY t2.price;" -189,57522,266,0,"SELECT markets.id AS c7660 +189,57522,155422,505,"SELECT markets.id AS c7660 FROM markets JOIN customers ON markets.id = customers.id WHERE (markets.note IS NULL AND (customers.id = 198 AND markets.region IN ('AMERICA', 'EUROPE')));" -190,3325,146,5,"SELECT departments.id +190,1590,1590,157,"SELECT departments.id FROM departments GROUP BY departments.id ORDER BY departments.id DESC;" -191,27819,351,55,"SELECT departments.id, employees.id +191,19044,21594,338,"SELECT departments.id, employees.id FROM departments JOIN regions ON departments.id = regions.id CROSS JOIN employees WHERE employees.id IS NOT NULL GROUP BY departments.id, employees.id ORDER BY departments.id DESC, employees.id ASC;" -192,1658,507,3,"SELECT books.id, markets.region +192,1658,1658,282,"SELECT books.id, markets.region FROM markets CROSS JOIN books WHERE markets.region = 'EUROPE';" -193,83620,1178,11,"SELECT customers.region_id, regions.id +193,114435,414820,288,"SELECT customers.region_id, regions.id FROM users JOIN regions ON users.id = regions.id LEFT JOIN customers ON regions.id = customers.id;" -194,5620982,10404,25000,"SELECT t2.id +194,5620982,5620982,9666,"SELECT t2.id FROM customers AS t0 CROSS JOIN markets AS t1 CROSS JOIN users AS t2 WHERE ((t1.id IN (3) OR t2.age != 1) OR (t1.id != 2 OR t2.id > 61));" -195,1553466,8004,10,"SELECT t0.id +195,1307400,1553466,2051,"SELECT t0.id FROM regions AS t0 FULL JOIN books AS t1 ON t0.id = t1.id LEFT JOIN orders AS t2 ON t1.id = t2.id;" -196,1006340,585,30,"SELECT customers.region_id, books.id, SUM(regions.id), SUM(customers.region_id) +196,617630,618900,790,"SELECT customers.region_id, books.id, SUM(regions.id), SUM(customers.region_id) FROM books JOIN regions ON books.id = regions.id CROSS JOIN customers GROUP BY customers.region_id, books.id;" -197,70841,337,3,"SELECT t0.price AS c8028, t2.id +197,70476,158466,399,"SELECT t0.price AS c8028, t2.id FROM books AS t0 JOIN customers AS t1 ON t0.id = t1.id JOIN regions AS t2 ON t1.id = t2.id;" -198,2074,129,17,"SELECT t0.age AS c2991 +198,2074,2074,125,"SELECT t0.age AS c2991 FROM users AS t0;" -199,30140,292,30,"SELECT t1.id, t0.id +199,30140,30140,291,"SELECT t1.id, t0.id FROM regions AS t0 CROSS JOIN users AS t1 WHERE (t1.age BETWEEN 33 AND 33 OR t1.id IN (12, 49, 95));" -200,2428,163,6,"SELECT employees.department_id +200,2428,2428,155,"SELECT employees.department_id FROM employees WHERE ((employees.id > 66 OR employees.id IN (33)) OR employees.department_id IN (6, 12, 12, 50)) ORDER BY employees.department_id DESC;" -201,610000,1297,5000,"SELECT orders.customer_id, orders.id +201,610000,610000,1150,"SELECT orders.customer_id, orders.id FROM orders;" -202,6401,205,6,"SELECT employees.id AS c3304, users.id AS c2088 +202,4879,16712,213,"SELECT employees.id AS c3304, users.id AS c2088 FROM users JOIN employees ON users.id = employees.id WHERE ((users.age IN (42, 64) OR users.id < 16) AND users.id != 99) GROUP BY employees.id, users.id;" -203,1896,159,3,"SELECT books.id, books.price +203,822,822,373,"SELECT books.id, books.price FROM books GROUP BY books.id, books.price;" -204,70446,345,3,"SELECT t0.id AS c8592, t1.region +204,69372,155822,320,"SELECT t0.id AS c8592, t1.region FROM customers AS t0 JOIN markets AS t1 ON t0.id = t1.id GROUP BY t0.id, t1.region;" -205,10416100,12344,1,"SELECT t1.id AS c8725, SUM(t1.id), COUNT(*) +205,9975800,9975800,11468,"SELECT t1.id AS c8725, SUM(t1.id), COUNT(*) FROM users AS t0 CROSS JOIN orders AS t1 WHERE (t0.age IN (33, 64) AND t1.id = 156) GROUP BY t1.id;" -206,61000,229,500,"SELECT t0.id AS c4257 +206,61000,61000,282,"SELECT t0.id AS c4257 FROM customers AS t0;" -207,8356600,3939,0,"SELECT t1.customer_id, t0.department_id +207,8356600,1931301600,3875,"SELECT t1.customer_id, t0.department_id FROM employees AS t0 CROSS JOIN orders AS t1 JOIN customers AS t2 ON t1.id = t2.id WHERE t0.department_id IS NULL;" -208,3776,263,3,"SELECT books.price AS c3720, employees.department_id, books.id +208,2388,3776,373,"SELECT books.price AS c3720, employees.department_id, books.id FROM employees RIGHT JOIN books ON employees.id = books.id;" -209,69182,369,2,"SELECT t2.region, t1.id, COUNT(t2.id) +209,69533,156493,322,"SELECT t2.region, t1.id, COUNT(t2.id) FROM books AS t0 JOIN customers AS t1 ON t0.id = t1.id FULL JOIN markets AS t2 ON t0.id = t2.id WHERE t0.id < 1 GROUP BY t2.region, t1.id;" -210,2390300,2862,15000,"SELECT orders.id, books.id AS c7933, orders.customer_id +210,2390300,2390300,2800,"SELECT orders.id, books.id AS c7933, orders.customer_id FROM books CROSS JOIN orders;" -211,2984422200,2956573,0,"SELECT t2.region_id, t2.id, t0.customer_id +211,26935700,2984422200,41119,"SELECT t2.region_id, t2.id, t0.customer_id FROM orders AS t0 CROSS JOIN users AS t1 LEFT JOIN customers AS t2 ON t1.id = t2.id WHERE t2.region_id >= 23;" -212,1551896,2851,3,"SELECT orders.id +212,1306062,1550822,1393,"SELECT orders.id FROM books LEFT JOIN orders ON books.id = orders.id GROUP BY orders.id;" -213,1620,151,2,"SELECT t0.id +213,1620,1620,142,"SELECT t0.id FROM regions AS t0 WHERE t0.id IN (4, 4, 9, 88) ORDER BY t0.id ASC;" -214,51596600,97240,247665,"SELECT orders.id, orders.customer_id, employees.id +214,51596600,51596600,95942,"SELECT orders.id, orders.customer_id, employees.id FROM orders CROSS JOIN employees CROSS JOIN departments WHERE orders.customer_id >= 52;" -215,805000,985,29,"SELECT t0.customer_id +215,805000,805000,1006,"SELECT t0.customer_id FROM orders AS t0 WHERE t0.id < 30;" -216,744,129,3,"SELECT departments.id +216,744,744,135,"SELECT departments.id FROM departments WHERE departments.id IN (1, 3, 4, 60);" -217,439260,709,3,"SELECT t0.id, t1.region_id +217,112147,437525,350,"SELECT t0.id, t1.region_id FROM employees AS t0 LEFT JOIN customers AS t1 ON t0.id = t1.id WHERE t1.region_id >= 8 GROUP BY t0.id, t1.region_id;" -218,1916,288,5,"SELECT books.price AS c5412, books.id, departments.id AS c9618 +218,1331,1916,188,"SELECT books.price AS c5412, books.id, departments.id AS c9618 FROM departments LEFT JOIN books ON departments.id = books.id;" -219,9485600,25795,45000,"SELECT t1.note, t0.customer_id AS c4395 +219,9485600,9485600,25727,"SELECT t1.note, t0.customer_id AS c4395 FROM orders AS t0 CROSS JOIN markets AS t1 CROSS JOIN books AS t2 WHERE t1.region IS NOT NULL;" -220,1931305120,1531952,45001,"SELECT customers.region_id, orders.id, employees.id +220,17063620,1931305120,29765,"SELECT customers.region_id, orders.id, employees.id FROM employees CROSS JOIN orders RIGHT JOIN customers ON employees.id = customers.id WHERE (customers.id IN (181) OR employees.id BETWEEN 3 AND 92) ORDER BY customers.region_id ASC;" -221,1133,157,0,"SELECT t0.id AS c9777 +221,1133,1133,142,"SELECT t0.id AS c9777 FROM regions AS t0 WHERE ((t0.id <= 5 OR t0.id IN (4, 12)) AND (t0.id = 9 AND t0.id NOT IN (2, 4, 5, 89))) ORDER BY t0.id ASC;" -222,6788,169,11,"SELECT t0.id, t0.department_id AS c2878, COUNT(t0.id), COUNT(*) AS c2633 +222,3764,3764,176,"SELECT t0.id, t0.department_id AS c2878, COUNT(t0.id), COUNT(*) AS c2633 FROM employees AS t0 WHERE ((t0.department_id IS NOT NULL OR t0.department_id IS NOT NULL) OR (t0.id > 26 OR t0.id = 52)) GROUP BY t0.id, t0.department_id;" -223,7431100,14977,55000,"SELECT t0.id +223,7431100,7431100,13982,"SELECT t0.id FROM orders AS t0 CROSS JOIN employees AS t1;" -224,1342,148,11,"SELECT employees.department_id +224,1342,1342,311,"SELECT employees.department_id FROM employees;" -225,1113982,1738,500,"SELECT orders.customer_id, customers.id AS c4775, employees.department_id AS c6986 +225,791024,4786342,1020,"SELECT orders.customer_id, customers.id AS c4775, employees.department_id AS c6986 FROM employees LEFT JOIN customers ON employees.id = customers.id JOIN orders ON employees.id = orders.id;" -226,3644,270,3,"SELECT books.id, regions.id, books.price AS c1520 +226,2374,3644,190,"SELECT books.id, regions.id, books.price AS c1520 FROM books LEFT JOIN regions ON books.id = regions.id WHERE ((regions.id < 10 OR books.price = 77) OR books.price > 60);" -227,1550422,6413,9,"SELECT orders.id, books.price, orders.customer_id AS c7568 +227,1305662,1550422,1899,"SELECT orders.id, books.price, orders.customer_id AS c7568 FROM orders FULL JOIN books ON orders.id = books.id WHERE orders.customer_id = 63;" -228,285106,1546,1000,"SELECT t1.id AS c1443, t2.region_id AS c2237 +228,283748,285106,1361,"SELECT t1.id AS c1443, t2.region_id AS c2237 FROM employees AS t0 LEFT JOIN markets AS t1 ON t0.id = t1.id CROSS JOIN customers AS t2 WHERE ((t1.id < 3 OR t1.region = 'AMERICA') AND (t2.region_id IS NULL OR t1.note != 'North, South'));" -229,228379,592,0,"SELECT departments.id +229,112350,228445,447,"SELECT departments.id FROM departments LEFT JOIN customers ON departments.id = customers.id CROSS JOIN markets WHERE markets.region IS NULL ORDER BY departments.id ASC;" -230,685010,1055,17,"SELECT departments.id, users.age AS c5139, orders.id +230,1309060,2258260,2184,"SELECT departments.id, users.age AS c5139, orders.id FROM departments JOIN users ON departments.id = users.id FULL JOIN orders ON users.id = orders.id;" -231,610,158,5,"SELECT departments.id +231,610,610,163,"SELECT departments.id FROM departments;" -232,656679,819,16,"SELECT customers.id, COUNT(*) AS c3789 +232,116575,651154,521,"SELECT customers.id, COUNT(*) AS c3789 FROM customers RIGHT JOIN users ON customers.id = users.id GROUP BY customers.id ORDER BY customers.id DESC;" -233,317325,812,1353,"SELECT departments.id +233,317095,317650,896,"SELECT departments.id FROM departments JOIN markets ON departments.id = markets.id CROSS JOIN customers WHERE (customers.region_id NOT IN (10, 10) AND (customers.id <= 80 OR departments.id >= 1));" -234,3466,261,10,"SELECT t0.note +234,2226,3466,203,"SELECT t0.note FROM markets AS t0 FULL JOIN regions AS t1 ON t0.id = t1.id;" -235,68764,248,1,"SELECT t1.region_id AS c5511 +235,68764,155544,271,"SELECT t1.region_id AS c5511 FROM books AS t0 JOIN customers AS t1 ON t0.id = t1.id WHERE (t0.price NOT BETWEEN 66 AND 94 OR (t0.id > 3 AND t1.region_id = 10));" -236,4366616,4378,17,"SELECT t1.department_id AS c2338, t0.customer_id +236,1311747,4366616,1382,"SELECT t1.department_id AS c2338, t0.customer_id FROM orders AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id RIGHT JOIN users AS t2 ON t1.id = t2.id ORDER BY t1.department_id ASC;" -237,669120,2379,5010,"SELECT regions.id +237,134541,669120,1920,"SELECT regions.id FROM users FULL JOIN customers ON users.id = customers.id CROSS JOIN regions;" -238,268722,690,0,"SELECT customers.region_id +238,103844,436222,814,"SELECT customers.region_id FROM customers LEFT JOIN employees ON customers.id = employees.id WHERE (employees.id IN (38) AND customers.region_id != 5);" -239,4425,166,6,"SELECT employees.id +239,2690,2690,155,"SELECT employees.id FROM employees WHERE employees.id > 5 GROUP BY employees.id ORDER BY employees.id DESC;" -240,169984,385,3,"SELECT t0.price AS c6143, SUM(t2.id) +240,95524,95524,401,"SELECT t0.price AS c6143, SUM(t2.id) FROM books AS t0 CROSS JOIN users AS t1 CROSS JOIN departments AS t2 GROUP BY t0.price;" -241,3875500,5272,25000,"SELECT customers.id, customers.region_id, departments.id +241,3759405,3875500,4164,"SELECT customers.id, customers.region_id, departments.id FROM customers RIGHT JOIN departments ON customers.id = departments.id CROSS JOIN orders;" -242,5320,321,0,"SELECT books.id +242,5320,5320,288,"SELECT books.id FROM books CROSS JOIN employees WHERE (employees.id != 33 AND (employees.id < 1 AND employees.department_id < 12));" -243,680223,1097,5,"SELECT employees.department_id, books.price AS c4475, books.id AS c4655 +243,680155,4353843,1006,"SELECT employees.department_id, books.price AS c4475, books.id AS c4655 FROM orders JOIN employees ON orders.id = employees.id RIGHT JOIN books ON employees.id = books.id WHERE ((orders.id >= 13 AND orders.customer_id != 233) OR (orders.id > 39 AND orders.customer_id <= 359)) ORDER BY books.id DESC;" -244,5632,333,0,"SELECT regions.id +244,4604,12322,221,"SELECT regions.id FROM regions RIGHT JOIN employees ON regions.id = employees.id LEFT JOIN markets ON employees.id = markets.id WHERE (regions.id NOT BETWEEN 2 AND 8 AND markets.note = 'Fast lane');" -245,433,172,1,"SELECT markets.note, markets.region, markets.id +245,433,433,251,"SELECT markets.note, markets.region, markets.id FROM markets WHERE ((markets.region != 'AMERICA' AND markets.region = 'AMERICA') OR markets.id < 2) ORDER BY markets.note DESC, markets.id ASC, markets.region DESC;" -246,1244,145,9,"SELECT regions.id AS c713 +246,1244,1244,130,"SELECT regions.id AS c713 FROM regions WHERE regions.id BETWEEN 2 AND 45;" -247,1551608,2262,3,"SELECT t0.id AS c7208 +247,1306088,1550848,1439,"SELECT t0.id AS c7208 FROM orders AS t0 RIGHT JOIN books AS t1 ON t0.id = t1.id WHERE t0.customer_id NOT IN (244, 365) GROUP BY t0.id ORDER BY t0.id ASC;" -248,6459284,8849,5000,"SELECT t1.id, COUNT(*), SUM(t0.id) AS c7387 +248,1311645,6455252,3301,"SELECT t1.id, COUNT(*), SUM(t0.id) AS c7387 FROM users AS t0 RIGHT JOIN orders AS t1 ON t0.id = t1.id WHERE ((t1.customer_id = 494 AND t1.customer_id != 145) OR (t0.age > 53 OR t1.customer_id IS NOT NULL)) GROUP BY t1.id;" -249,5692,274,3,"SELECT markets.region, users.age +249,3633,5692,210,"SELECT markets.region, users.age FROM markets LEFT JOIN users ON markets.id = users.id WHERE users.id < 17;" -250,401220,748,10,"SELECT customers.id, customers.region_id +250,110640,401220,381,"SELECT customers.id, customers.region_id FROM regions LEFT JOIN customers ON regions.id = customers.id;" -251,4001775,4600,42,"SELECT orders.customer_id, regions.id, orders.id AS c3346 +251,1307530,4001775,2243,"SELECT orders.customer_id, regions.id, orders.id AS c3346 FROM regions FULL JOIN orders ON regions.id = orders.id WHERE orders.id <= 42 ORDER BY regions.id ASC;" -252,7310933,21711,39,"SELECT orders.id AS c8852, orders.customer_id, books.price AS c8813 +252,4641438,7310933,7529,"SELECT orders.id AS c8852, orders.customer_id, books.price AS c8813 FROM books CROSS JOIN orders FULL JOIN departments ON orders.id = departments.id WHERE ((orders.customer_id = 163 OR orders.id IN (69, 96, 152)) AND (orders.id IS NOT NULL AND books.id IS NOT NULL)) ORDER BY orders.id DESC;" -253,121234,453,40,"SELECT customers.id +253,121234,3916834,377,"SELECT customers.id FROM regions CROSS JOIN employees JOIN customers ON employees.id = customers.id WHERE (employees.id IS NOT NULL AND customers.id >= 65);" -254,3776,262,11,"SELECT markets.id AS c3977, employees.department_id +254,2418,3776,173,"SELECT markets.id AS c3977, employees.department_id FROM markets FULL JOIN employees ON markets.id = employees.id;" -255,3160000,2415,5000,"SELECT orders.id, orders.customer_id AS c7286 +255,1975000,1975000,2385,"SELECT orders.id, orders.customer_id AS c7286 FROM orders GROUP BY orders.id, orders.customer_id;" -256,10520,274,9,"SELECT t0.id, t1.id AS c9218 +256,7496,7496,274,"SELECT t0.id, t1.id AS c9218 FROM markets AS t0 CROSS JOIN employees AS t1 WHERE t1.department_id IN (5, 12, 33) GROUP BY t0.id, t1.id;" -257,3936,183,3,"SELECT t1.price, t0.id AS c7828 +257,2652,3922,190,"SELECT t1.price, t0.id AS c7828 FROM regions AS t0 JOIN books AS t1 ON t0.id = t1.id GROUP BY t1.price, t0.id;" -258,75782,410,3,"SELECT customers.region_id +258,75739,651158,681,"SELECT customers.region_id FROM customers JOIN users ON customers.id = users.id RIGHT JOIN markets ON customers.id = markets.id GROUP BY customers.region_id ORDER BY customers.region_id DESC;" -259,110500,255,500,"SELECT customers.id +259,110500,110500,245,"SELECT customers.id FROM customers ORDER BY customers.id ASC;" -260,72814,282,118,"SELECT customers.region_id, customers.id +260,72814,72814,261,"SELECT customers.region_id, customers.id FROM customers WHERE ((customers.region_id IS NULL OR customers.id >= 198) AND customers.region_id <= 4);" -261,156962,566,3,"SELECT customers.region_id AS c4278, books.id +261,109628,155888,313,"SELECT customers.region_id AS c4278, books.id FROM books LEFT JOIN customers ON books.id = customers.id GROUP BY customers.region_id, books.id ORDER BY books.id ASC;" -262,15960,322,110,"SELECT t1.department_id AS c5186, t0.id, t1.id +262,15960,15960,264,"SELECT t1.department_id AS c5186, t0.id, t1.id FROM regions AS t0 CROSS JOIN employees AS t1;" -263,2973,330,5,"SELECT markets.id, departments.id, regions.id +263,3077,6483,244,"SELECT markets.id, departments.id, regions.id FROM regions LEFT JOIN departments ON regions.id = departments.id JOIN markets ON regions.id = markets.id WHERE markets.note != 'Old World' ORDER BY markets.id ASC, regions.id DESC;" -264,160636,939,500,"SELECT users.id AS c6920, markets.region AS c5238, users.age AS c1104 +264,112407,160636,449,"SELECT users.id AS c6920, markets.region AS c5238, users.age AS c1104 FROM markets RIGHT JOIN users ON markets.id = users.id RIGHT JOIN customers ON markets.id = customers.id;" -265,14820,312,17,"SELECT t1.id, t0.id AS c2693 +265,5081,14820,204,"SELECT t1.id, t0.id AS c2693 FROM users AS t0 LEFT JOIN regions AS t1 ON t0.id = t1.id;" -266,59400,248,1,"SELECT customers.id AS c604, customers.region_id +266,59400,59400,227,"SELECT customers.id AS c604, customers.region_id FROM customers WHERE customers.id = 135 ORDER BY customers.id DESC, customers.region_id DESC;" -267,422,139,0,"SELECT books.id +267,422,422,120,"SELECT books.id FROM books WHERE books.price IS NULL;" -268,107001400,33265,11,"SELECT employees.department_id, SUM(orders.customer_id) +268,76971400,76971400,122051,"SELECT employees.department_id, SUM(orders.customer_id) FROM orders CROSS JOIN books CROSS JOIN employees GROUP BY employees.department_id;" -269,366,135,3,"SELECT books.price AS c8205 +269,366,366,128,"SELECT books.price AS c8205 FROM books;" -270,610000,1139,5000,"SELECT orders.id AS c7922 +270,610000,610000,1109,"SELECT orders.id AS c7922 FROM orders;" -271,534702,1221,0,"SELECT orders.id AS c8902, departments.id AS c4368, orders.customer_id +271,534702,2250622,1173,"SELECT orders.id AS c8902, departments.id AS c4368, orders.customer_id FROM orders JOIN departments ON orders.id = departments.id WHERE ((orders.customer_id = 483 AND departments.id > 4) AND orders.id != 24);" -272,850932,2012,2,"SELECT orders.customer_id, orders.id, COUNT(*), COUNT(*) +272,850563,1550563,2028,"SELECT orders.customer_id, orders.id, COUNT(*), COUNT(*) FROM orders RIGHT JOIN books ON orders.id = books.id WHERE books.id != 1 GROUP BY orders.customer_id, orders.id;" -273,6952,177,11,"SELECT t0.id, SUM(t0.department_id), COUNT(t0.department_id) +273,3256,3256,156,"SELECT t0.id, SUM(t0.department_id), COUNT(t0.department_id) FROM employees AS t0 GROUP BY t0.id;" -274,2066,148,2,"SELECT t0.id AS c7269, t0.age +274,2066,2066,148,"SELECT t0.id AS c7269, t0.age FROM users AS t0 WHERE t0.age IN (33, 33);" -275,545750,1512,0,"SELECT orders.id AS c6877 +275,545750,545750,1457,"SELECT orders.id AS c6877 FROM orders WHERE ((orders.customer_id <= 56 OR orders.customer_id != 343) AND (orders.customer_id IN (52, 86) AND orders.customer_id >= 224));" -276,6766700,1560,11,"SELECT users.age AS c7960, SUM(users.id) +276,3536700,3536700,2753,"SELECT users.age AS c7960, SUM(users.id) FROM customers CROSS JOIN users GROUP BY users.age ORDER BY users.age ASC;" -277,1143,182,1,"SELECT t0.id +277,774,774,173,"SELECT t0.id FROM departments AS t0 WHERE ((t0.id IN (2, 10, 59) OR t0.id IS NULL) AND (t0.id != 5 AND t0.id IS NOT NULL)) GROUP BY t0.id ORDER BY t0.id DESC;" -278,8638,307,5,"SELECT t1.id AS c8751 +278,4334,8638,449,"SELECT t1.id AS c8751 FROM users AS t0 RIGHT JOIN departments AS t1 ON t0.id = t1.id WHERE (t0.age IS NULL OR (t1.id != 5 OR t1.id < 14));" -279,851732,1199,0,"SELECT regions.id, orders.id, COUNT(*), SUM(orders.customer_id) +279,851363,4001263,1051,"SELECT regions.id, orders.id, COUNT(*), SUM(orders.customer_id) FROM orders RIGHT JOIN regions ON orders.id = regions.id WHERE ((regions.id IN (8, 9, 22) AND regions.id <= 6) AND orders.id IN (39, 49, 104, 106)) GROUP BY regions.id, orders.id;" -280,6451822,7192,0,"SELECT orders.customer_id +280,1308743,6451822,2349,"SELECT orders.customer_id FROM users FULL JOIN orders ON users.id = orders.id WHERE ((users.age > 33 AND orders.customer_id < 84) AND (users.id = 15 OR orders.id IN (68, 128, 128, 198)));" -281,433,170,0,"SELECT t0.id, t0.note, t0.region +281,433,433,147,"SELECT t0.id, t0.note, t0.region FROM markets AS t0 WHERE ((t0.note = 'unknown' AND t0.id != 1) AND t0.id > 67) ORDER BY t0.id ASC, t0.note DESC;" -282,13612010,12919,9,"SELECT employees.id, orders.customer_id AS c5471, books.price +282,4643352,13612010,5295,"SELECT employees.id, orders.customer_id AS c5471, books.price FROM books CROSS JOIN orders LEFT JOIN employees ON orders.id = employees.id WHERE employees.id > 66;" -283,521733,2237,0,"SELECT books.id, markets.id, books.price AS c2723 +283,415563,521733,813,"SELECT books.id, markets.id, books.price AS c2723 FROM books CROSS JOIN customers FULL JOIN markets ON customers.id = markets.id WHERE ((markets.id > 55 AND customers.region_id NOT IN (8, 9, 10)) AND markets.region IN ('AMERICA', 'EUROPE')) ORDER BY books.id DESC, markets.id ASC;" -284,1962,176,2,"SELECT t0.region AS c1153, SUM(t0.id) AS c3569 +284,822,822,143,"SELECT t0.region AS c1153, SUM(t0.id) AS c3569 FROM markets AS t0 GROUP BY t0.region ORDER BY t0.region ASC;" -285,10836,413,0,"SELECT employees.id AS c5654, users.id +285,9478,10836,448,"SELECT employees.id AS c5654, users.id FROM markets FULL JOIN employees ON markets.id = employees.id CROSS JOIN users WHERE ((markets.note = 'Fast lane' AND employees.department_id NOT BETWEEN 11 AND 37) AND (users.id != 4 AND users.age <= 443));" -286,226143,997,3,"SELECT customers.id, COUNT(customers.id) AS c434, COUNT(*) +286,109668,225774,380,"SELECT customers.id, COUNT(customers.id) AS c434, COUNT(*) FROM customers FULL JOIN departments ON customers.id = departments.id WHERE departments.id BETWEEN 2 AND 4 GROUP BY customers.id ORDER BY customers.id DESC;" -287,2962,263,5,"SELECT t2.id, t1.id, t0.id +287,3181,5072,240,"SELECT t2.id, t1.id, t0.id FROM books AS t0 JOIN departments AS t1 ON t0.id = t1.id RIGHT JOIN regions AS t2 ON t1.id = t2.id WHERE t0.id IS NULL;" -288,5327,193,1,"SELECT regions.id AS c1673 +288,5252,15332,194,"SELECT regions.id AS c1673 FROM users JOIN regions ON users.id = regions.id WHERE (users.age IN (33, 47, 98) OR users.id > 54);" -289,2493,286,3,"SELECT departments.id, books.price AS c551 +289,2493,2493,854,"SELECT departments.id, books.price AS c551 FROM departments CROSS JOIN books WHERE departments.id = 5 ORDER BY departments.id DESC;" -290,680542,1100,6,"SELECT t2.region, t1.department_id, t0.customer_id AS c8493 +290,680174,4353832,1013,"SELECT t2.region, t1.department_id, t0.customer_id AS c8493 FROM orders AS t0 JOIN employees AS t1 ON t0.id = t1.id RIGHT JOIN markets AS t2 ON t1.id = t2.id WHERE t0.customer_id < 239;" -291,12324520,4720,10,"SELECT orders.id, COUNT(orders.id), SUM(regions.id) +291,12321160,68844660,4574,"SELECT orders.id, COUNT(orders.id), SUM(regions.id) FROM users CROSS JOIN orders JOIN regions ON orders.id = regions.id GROUP BY orders.id;" -292,1962,172,3,"SELECT t0.region, t0.id, SUM(t0.id), COUNT(*) +292,888,888,174,"SELECT t0.region, t0.id, SUM(t0.id), COUNT(*) FROM markets AS t0 GROUP BY t0.region, t0.id ORDER BY t0.region ASC, t0.id DESC;" -293,5692,311,0,"SELECT t0.age, t1.id +293,3603,5692,601,"SELECT t0.age, t1.id FROM users AS t0 FULL JOIN books AS t1 ON t0.id = t1.id WHERE (t0.age = 18 AND (t0.age != 123 AND t1.id <= 89));" -294,1564,165,3,"SELECT books.price AS c6589, books.id, COUNT(*) +294,848,848,176,"SELECT books.price AS c6589, books.id, COUNT(*) FROM books WHERE ((books.price > 77 OR books.id < 3) OR (books.price IN (49, 55, 55, 77) OR books.price IN (26, 66, 66, 68))) GROUP BY books.price, books.id;" -295,6105,182,5,"SELECT users.id, SUM(users.age), COUNT(users.id) +295,4370,9075,193,"SELECT users.id, SUM(users.age), COUNT(users.id) FROM users JOIN departments ON users.id = departments.id GROUP BY users.id;" -296,432,162,3,"SELECT t0.region, t0.id AS c2775, t0.note +296,432,432,179,"SELECT t0.region, t0.id AS c2775, t0.note FROM markets AS t0 ORDER BY t0.id DESC;" -297,694750,1166,21,"SELECT t2.department_id +297,685683,6467110,1086,"SELECT t2.department_id FROM orders AS t0 JOIN users AS t1 ON t0.id = t1.id FULL JOIN employees AS t2 ON t0.id = t2.id WHERE ((t2.id NOT BETWEEN 4 AND 6 OR t1.age != 21) OR t0.id <= 61);" -298,988,157,4,"SELECT departments.id +298,988,988,127,"SELECT departments.id FROM departments WHERE ((departments.id NOT IN (4, 4, 5, 7) OR departments.id NOT IN (2, 5)) OR (departments.id NOT BETWEEN 3 AND 27 OR departments.id IS NULL));" -299,3273,295,0,"SELECT books.price, employees.id AS c7128 +299,2455,3843,339,"SELECT books.price, employees.id AS c7128 FROM employees RIGHT JOIN books ON employees.id = books.id WHERE ((books.id > 68 OR books.id >= 52) AND (employees.department_id < 6 OR employees.department_id != 1)) ORDER BY books.price DESC;" -300,1276883,3244,4965,"SELECT orders.id AS c9121 +300,1306863,2251983,2076,"SELECT orders.id AS c9121 FROM orders LEFT JOIN departments ON orders.id = departments.id RIGHT JOIN books ON departments.id = books.id WHERE orders.customer_id <= 497 ORDER BY orders.id ASC;" -301,5241,175,3,"SELECT users.id, COUNT(markets.id), SUM(markets.id) +301,3967,6092,180,"SELECT users.id, COUNT(markets.id), SUM(markets.id) FROM users JOIN markets ON users.id = markets.id GROUP BY users.id;" -302,79260,419,15,"SELECT regions.id, customers.region_id, employees.id +302,75362,410460,975,"SELECT regions.id, customers.region_id, employees.id FROM regions FULL JOIN employees ON regions.id = employees.id JOIN customers ON regions.id = customers.id ORDER BY customers.region_id ASC, employees.id ASC, regions.id ASC;" -303,1832,150,8,"SELECT employees.id, employees.department_id +303,1832,1832,418,"SELECT employees.id, employees.department_id FROM employees WHERE ((employees.id <= 33 OR employees.id = 3) OR (employees.department_id NOT IN (5) AND employees.id = 67));" -304,1342,135,11,"SELECT employees.department_id AS c3770 +304,1342,1342,122,"SELECT employees.department_id AS c3770 FROM employees;" -305,2896,173,2,"SELECT regions.id AS c2143, COUNT(*), COUNT(*) +305,1822,1822,181,"SELECT regions.id AS c2143, COUNT(*), COUNT(*) FROM regions WHERE regions.id IN (3, 10, 89) GROUP BY regions.id;" -306,4531,199,5,"SELECT employees.id, employees.department_id, SUM(employees.id), SUM(employees.department_id) AS c8963 +306,3457,6272,196,"SELECT employees.id, employees.department_id, SUM(employees.id), SUM(employees.department_id) AS c8963 FROM employees JOIN departments ON employees.id = departments.id WHERE (departments.id <= 3 OR (employees.id = 68 OR employees.id <= 6)) GROUP BY employees.id, employees.department_id;" -307,678682,1225,3,"SELECT t0.note AS c4138 +307,1307657,1553663,2258,"SELECT t0.note AS c4138 FROM markets AS t0 JOIN regions AS t1 ON t0.id = t1.id RIGHT JOIN orders AS t2 ON t0.id = t2.id WHERE ((t2.customer_id = 298 OR t0.region = 'AMERICA') OR t0.region != 'AMERICA') GROUP BY t0.note;" -308,1808,156,1,"SELECT departments.id, COUNT(*) AS c9125 +308,1092,1092,182,"SELECT departments.id, COUNT(*) AS c9125 FROM departments WHERE (departments.id > 56 OR departments.id = 4) GROUP BY departments.id ORDER BY departments.id DESC;" -309,932,163,1,"SELECT t0.id AS c7541, COUNT(t0.id) +309,563,563,159,"SELECT t0.id AS c7541, COUNT(t0.id) FROM markets AS t0 WHERE (t0.note != 'Fast lane' AND (t0.id = 3 OR t0.note = 'Old World')) GROUP BY t0.id;" -310,7102108,12990,7,"SELECT t1.id, t2.id, SUM(t2.id) +310,1422102,7099420,1516,"SELECT t1.id, t2.id, SUM(t2.id) FROM customers AS t0 RIGHT JOIN users AS t1 ON t0.id = t1.id LEFT JOIN orders AS t2 ON t0.id = t2.id WHERE (t2.customer_id < 243 OR (t2.id >= 38 AND t2.customer_id = 302)) GROUP BY t1.id, t2.id ORDER BY t2.id ASC;" -311,1550433,2827,2,"SELECT t0.region, t1.customer_id AS c7183 +311,1305703,1550433,2695,"SELECT t0.region, t1.customer_id AS c7183 FROM markets AS t0 RIGHT JOIN orders AS t1 ON t0.id = t1.id WHERE ((t0.id <= 3 OR t0.note = 'Fast lane') AND (t0.note != 'North, South' OR t0.note = 'Fast lane')) ORDER BY t0.region ASC;" -312,681210,1215,5,"SELECT t1.id, t0.id +312,1308302,2255560,1538,"SELECT t1.id, t0.id FROM departments AS t0 LEFT JOIN orders AS t1 ON t0.id = t1.id JOIN employees AS t2 ON t1.id = t2.id;" -313,681466,1112,2,"SELECT employees.id, orders.id AS c4699, orders.customer_id +313,678803,2255816,930,"SELECT employees.id, orders.id AS c4699, orders.customer_id FROM departments JOIN orders ON departments.id = orders.id LEFT JOIN employees ON departments.id = employees.id WHERE employees.department_id NOT BETWEEN 5 AND 33;" -314,4228,179,13,"SELECT t0.age, t0.id +314,2840,2840,202,"SELECT t0.age, t0.id FROM users AS t0 WHERE (t0.age > 5 AND t0.id != 2) GROUP BY t0.age, t0.id;" -315,3354,192,5,"SELECT t0.id +315,2638,5548,212,"SELECT t0.id FROM regions AS t0 JOIN departments AS t1 ON t0.id = t1.id WHERE t1.id IS NOT NULL GROUP BY t0.id;" -316,1896,147,3,"SELECT books.id +316,822,822,200,"SELECT books.id FROM books GROUP BY books.id;" -317,1550366,2810,5000,"SELECT orders.id AS c7996, books.price +317,1305606,1550366,1943,"SELECT orders.id AS c7996, books.price FROM books FULL JOIN orders ON books.id = orders.id;" -318,8406,346,4,"SELECT books.price, books.id +318,4668,7332,216,"SELECT books.price, books.id FROM books FULL JOIN employees ON books.id = employees.id RIGHT JOIN regions ON employees.id = regions.id GROUP BY books.price, books.id;" -319,932,163,3,"SELECT books.id AS c9150, books.price, COUNT(books.id) +319,563,563,159,"SELECT books.id AS c9150, books.price, COUNT(books.id) FROM books WHERE ((books.price IN (76, 77) AND books.id BETWEEN 1 AND 96) OR books.price IN (55, 66, 77)) GROUP BY books.id, books.price;" -320,71482,247,11,"SELECT customers.region_id, employees.id +320,71482,436342,260,"SELECT customers.region_id, employees.id FROM customers JOIN employees ON customers.id = employees.id;" -321,808000,991,10,"SELECT t0.id AS c6071, COUNT(*), COUNT(*) AS c7241 +321,702625,702625,996,"SELECT t0.id AS c6071, COUNT(*), COUNT(*) AS c7241 FROM regions AS t0 CROSS JOIN customers AS t1 WHERE ((t1.id >= 59 OR t0.id IS NULL) AND t1.id = 126) GROUP BY t0.id;" -322,158466,723,500,"SELECT t1.region_id, t1.id, t2.price +322,111676,403466,440,"SELECT t1.region_id, t1.id, t2.price FROM regions AS t0 RIGHT JOIN customers AS t1 ON t0.id = t1.id LEFT JOIN books AS t2 ON t0.id = t2.id;" -323,90703,528,3,"SELECT t0.region_id +323,113190,650703,322,"SELECT t0.region_id FROM customers AS t0 RIGHT JOIN users AS t1 ON t0.id = t1.id RIGHT JOIN markets AS t2 ON t1.id = t2.id WHERE t1.id < 14 ORDER BY t0.region_id ASC;" -324,680894,1183,3,"SELECT orders.id AS c4187, regions.id +324,678469,2255244,1202,"SELECT orders.id AS c4187, regions.id FROM departments JOIN orders ON departments.id = orders.id LEFT JOIN regions ON departments.id = regions.id WHERE regions.id < 4;" -325,2992,402,4,"SELECT markets.note AS c7216, books.price +325,2991,5932,295,"SELECT markets.note AS c7216, books.price FROM departments CROSS JOIN markets LEFT JOIN books ON markets.id = books.id WHERE ((departments.id != 2 AND markets.note = 'Old World') AND (departments.id BETWEEN 1 AND 5 OR departments.id >= 41));" -326,2074,142,17,"SELECT t0.age +326,2074,2074,122,"SELECT t0.age FROM users AS t0;" -327,11679,179,11,"SELECT t0.age +327,6154,6154,204,"SELECT t0.age FROM users AS t0 GROUP BY t0.age ORDER BY t0.age ASC;" -328,44723198,91834,54310,"SELECT employees.department_id +328,16894318,44723198,32898,"SELECT employees.department_id FROM orders CROSS JOIN employees LEFT JOIN regions ON employees.id = regions.id WHERE ((employees.id <= 25 OR orders.id NOT BETWEEN 47 AND 184) OR orders.id IS NULL);" -329,3583,199,3,"SELECT t0.id +329,3583,5858,180,"SELECT t0.id FROM markets AS t0 JOIN users AS t1 ON t0.id = t1.id WHERE (t0.id != 2 OR t0.id != 1) ORDER BY t0.id ASC;" -330,131853,2259,0,"SELECT books.price, employees.id, COUNT(*) +330,111330,158984,597,"SELECT books.price, employees.id, COUNT(*) FROM customers LEFT JOIN books ON customers.id = books.id FULL JOIN employees ON customers.id = employees.id WHERE ((customers.id > 96 AND employees.department_id != 32) AND (employees.id > 5 OR books.id IS NOT NULL)) GROUP BY books.price, employees.id ORDER BY books.price DESC, employees.id DESC;" -331,1755762000,1746374,50490,"SELECT t2.region_id, t2.id +331,15545500,1755762000,23081,"SELECT t2.region_id, t2.id FROM orders AS t0 CROSS JOIN regions AS t1 FULL JOIN customers AS t2 ON t1.id = t2.id;" -332,1610,133,10,"SELECT regions.id AS c7728 +332,1610,1610,143,"SELECT regions.id AS c7728 FROM regions WHERE regions.id IS NOT NULL;" -333,2478160,315148,6,"SELECT departments.id, COUNT(*) +333,1415505,2476425,2558,"SELECT departments.id, COUNT(*) FROM orders FULL JOIN departments ON orders.id = departments.id LEFT JOIN customers ON departments.id = customers.id GROUP BY departments.id;" -334,1775,159,10,"SELECT regions.id AS c1480 +334,1775,1775,216,"SELECT regions.id AS c1480 FROM regions WHERE regions.id IS NOT NULL ORDER BY regions.id DESC;" -335,433,148,0,"SELECT books.price, books.id +335,433,433,195,"SELECT books.price, books.id FROM books WHERE (books.price < 44 AND (books.price IN (55, 55, 66, 66) AND books.price != 77)) ORDER BY books.price DESC, books.id DESC;" -336,1222,143,1,"SELECT t0.department_id +336,1222,1222,152,"SELECT t0.department_id FROM employees AS t0 WHERE ((t0.department_id = 12 AND t0.department_id < 35) AND (t0.id IN (4) OR t0.department_id != 31));" -337,2432,152,10,"SELECT users.id, users.age +337,2432,2432,132,"SELECT users.id, users.age FROM users WHERE users.age IN (1, 21, 33, 64);" -338,1732,155,9,"SELECT t0.id +338,1732,1732,220,"SELECT t0.id FROM regions AS t0 WHERE ((t0.id NOT BETWEEN 1 AND 15 OR t0.id < 10) AND (t0.id <= 40 OR t0.id != 3));" -339,744,145,1,"SELECT departments.id +339,744,744,137,"SELECT departments.id FROM departments WHERE ((departments.id <= 98 OR departments.id > 1) AND (departments.id IS NULL OR departments.id IN (3, 3, 30, 91)));" -340,155932,606,13,"SELECT customers.region_id AS c4276, markets.region AS c7002, COUNT(*), COUNT(*) AS c3968 +340,109333,155563,454,"SELECT customers.region_id AS c4276, markets.region AS c7002, COUNT(*), COUNT(*) AS c3968 FROM customers LEFT JOIN markets ON customers.id = markets.id WHERE (markets.note = 'Fast lane' OR customers.id IS NOT NULL) GROUP BY customers.region_id, markets.region;" -341,76376,408,16,"SELECT customers.id, SUM(customers.region_id) +341,112731,161092,473,"SELECT customers.id, SUM(customers.region_id) FROM markets JOIN users ON markets.id = users.id RIGHT JOIN customers ON users.id = customers.id GROUP BY customers.id;" -342,1222,161,0,"SELECT employees.department_id, employees.id +342,1222,1222,143,"SELECT employees.department_id, employees.id FROM employees WHERE ((employees.id >= 66 OR employees.id != 2) AND (employees.id > 69 AND employees.department_id = 33));" -343,3009,154,17,"SELECT users.age +343,3009,3009,151,"SELECT users.age FROM users ORDER BY users.age ASC;" -344,2122,341,3,"SELECT t1.price AS c605, t0.id AS c1651, t1.id +344,2101,3522,207,"SELECT t1.price AS c605, t0.id AS c1651, t1.id FROM regions AS t0 RIGHT JOIN books AS t1 ON t0.id = t1.id WHERE (t1.id != 3 OR t1.id = 3);" -345,155432,493,3,"SELECT books.id, customers.id AS c5626 +345,109172,155432,323,"SELECT books.id, customers.id AS c5626 FROM books LEFT JOIN customers ON books.id = customers.id ORDER BY customers.id DESC;" -346,29564,444,0,"SELECT t1.region, t2.id, COUNT(t1.id) +346,10087,29195,383,"SELECT t1.region, t2.id, COUNT(t1.id) FROM employees AS t0 CROSS JOIN markets AS t1 FULL JOIN regions AS t2 ON t1.id = t2.id WHERE t0.department_id = 36 GROUP BY t1.region, t2.id;" -347,6452676,5531,0,"SELECT users.age AS c6973 +347,1309597,6452676,2002,"SELECT users.age AS c6973 FROM users RIGHT JOIN orders ON users.id = orders.id WHERE users.id > 94;" -348,525100,1524,1187,"SELECT t1.id, t0.id +348,525100,525100,1405,"SELECT t1.id, t0.id FROM departments AS t0 CROSS JOIN customers AS t1 WHERE ((t1.id BETWEEN 28 AND 146 AND t1.id IN (46, 50, 104, 146)) OR (t0.id < 4 AND t1.region_id NOT IN (2, 2, 10, 12))) ORDER BY t1.id DESC;" -349,4208,350,5,"SELECT regions.id, departments.id AS c6050, COUNT(*), COUNT(*) +349,2832,5592,230,"SELECT regions.id, departments.id AS c6050, COUNT(*), COUNT(*) FROM regions RIGHT JOIN departments ON regions.id = departments.id WHERE departments.id IS NOT NULL GROUP BY regions.id, departments.id ORDER BY departments.id ASC;" -350,8110,276,11,"SELECT employees.department_id, departments.id AS c3106, COUNT(*) AS c7893 +350,3612,6375,207,"SELECT employees.department_id, departments.id AS c3106, COUNT(*) AS c7893 FROM departments RIGHT JOIN employees ON departments.id = employees.id GROUP BY employees.department_id, departments.id;" -351,32852932,60056,261,"SELECT t0.customer_id, t2.id AS c6006, t2.age +351,7680023,32852932,15314,"SELECT t0.customer_id, t2.id AS c6006, t2.age FROM orders AS t0 CROSS JOIN departments AS t1 FULL JOIN users AS t2 ON t1.id = t2.id WHERE (t1.id >= 3 AND (t0.id < 55 OR t0.id < 88));" -352,5464128,3748,80,"SELECT t1.customer_id, t1.id, t0.id +352,5464128,5464128,4017,"SELECT t1.customer_id, t1.id, t0.id FROM departments AS t0 CROSS JOIN orders AS t1 WHERE (t1.customer_id IN (426) OR (t1.customer_id IS NULL OR t0.id >= 57));" -353,61000,245,500,"SELECT t0.id, t0.region_id +353,61000,61000,225,"SELECT t0.id, t0.region_id FROM customers AS t0;" -354,3760,176,1,"SELECT t0.department_id, t0.id +354,2372,2372,163,"SELECT t0.department_id, t0.id FROM employees AS t0 WHERE (t0.department_id >= 31 AND (t0.department_id > 34 OR t0.id < 33)) GROUP BY t0.department_id, t0.id ORDER BY t0.department_id DESC;" -355,62200,362,2,"SELECT customers.id, customers.region_id +355,62200,62200,221,"SELECT customers.id, customers.region_id FROM customers WHERE customers.id IN (153, 183);" -356,5326,320,5,"SELECT employees.department_id, departments.id +356,3413,5326,220,"SELECT employees.department_id, departments.id FROM books LEFT JOIN employees ON books.id = employees.id FULL JOIN departments ON employees.id = departments.id;" -357,2870000,3657,4914,"SELECT orders.id AS c1456, orders.customer_id, COUNT(*), COUNT(orders.customer_id) +357,1940000,1940000,2539,"SELECT orders.id AS c1456, orders.customer_id, COUNT(*), COUNT(orders.customer_id) FROM orders WHERE (orders.id > 86 OR orders.customer_id IS NULL) GROUP BY orders.id, orders.customer_id;" -358,4260,153,0,"SELECT t0.department_id, t0.id AS c8666, COUNT(*) AS c1170, COUNT(*) +358,2525,2525,143,"SELECT t0.department_id, t0.id AS c8666, COUNT(*) AS c1170, COUNT(*) FROM employees AS t0 WHERE t0.id IS NULL GROUP BY t0.department_id, t0.id;" -359,6320,159,10,"SELECT regions.id, COUNT(regions.id) AS c1004, COUNT(regions.id) AS c9498 +359,2960,2960,139,"SELECT regions.id, COUNT(regions.id) AS c1004, COUNT(regions.id) AS c9498 FROM regions GROUP BY regions.id;" -360,366,145,3,"SELECT t0.id, t0.note AS c1949, t0.region +360,366,366,122,"SELECT t0.id, t0.note AS c1949, t0.region FROM markets AS t0;" -361,178001600,53741,11,"SELECT t2.department_id, COUNT(t2.id), SUM(t0.id) +361,130976600,130976600,209436,"SELECT t2.department_id, COUNT(t2.id), SUM(t0.id) FROM departments AS t0 CROSS JOIN orders AS t1 CROSS JOIN employees AS t2 GROUP BY t2.department_id;" -362,785551896,851162,3,"SELECT markets.id AS c6682, markets.note AS c3493 +362,785550822,785550822,812242,"SELECT markets.id AS c6682, markets.note AS c3493 FROM orders CROSS JOIN customers RIGHT JOIN markets ON customers.id = markets.id GROUP BY markets.id, markets.note;" -363,433,159,3,"SELECT books.price +363,433,433,150,"SELECT books.price FROM books WHERE books.price IS NOT NULL ORDER BY books.price DESC;" -364,69900,242,44,"SELECT customers.region_id AS c2372 +364,69900,69900,260,"SELECT customers.region_id AS c2372 FROM customers WHERE customers.region_id IN (4, 89) ORDER BY customers.region_id DESC;" -365,831472,1381,3,"SELECT t0.id AS c4468, t1.customer_id, t0.region +365,1373762,1705422,2392,"SELECT t0.id AS c4468, t1.customer_id, t0.region FROM markets AS t0 FULL JOIN orders AS t1 ON t0.id = t1.id JOIN customers AS t2 ON t0.id = t2.id WHERE t0.id <= 17;" -366,2310,178,1,"SELECT users.id, users.age +366,2310,2310,147,"SELECT users.id, users.age FROM users WHERE (users.id = 15 OR (users.id != 3 AND users.id IS NULL));" -367,2074,151,17,"SELECT t0.age +367,2074,2074,319,"SELECT t0.age FROM users AS t0;" -368,12062,286,49,"SELECT t0.id, t1.age +368,12062,12062,330,"SELECT t0.id, t1.age FROM books AS t0 CROSS JOIN users AS t1 WHERE (t1.id = 96 OR (t1.age != 18 OR t0.price <= 61));" -369,850932,1083,0,"SELECT t0.id AS c4185, SUM(t0.id) +369,850563,1550563,1025,"SELECT t0.id AS c4185, SUM(t0.id) FROM markets AS t0 LEFT JOIN orders AS t1 ON t0.id = t1.id WHERE ((t0.id BETWEEN 1 AND 7 OR t0.note != 'North, South') AND (t0.note != 'Old World' AND t0.note IN ('ASIA', 'Old World', 'unknown'))) GROUP BY t0.id;" -370,422,154,2,"SELECT markets.note AS c2495, markets.id, markets.region +370,422,422,131,"SELECT markets.note AS c2495, markets.id, markets.region FROM markets WHERE ((markets.note = 'North, South' AND markets.note != 'North, South') OR markets.note != 'Fast lane');" -371,15960,288,110,"SELECT regions.id +371,15960,15960,288,"SELECT regions.id FROM regions CROSS JOIN employees;" -372,1293750,1736,3214,"SELECT t0.id +372,1293750,1293750,1586,"SELECT t0.id FROM orders AS t0 WHERE (t0.id <= 95 OR t0.customer_id BETWEEN 55 AND 369) ORDER BY t0.id DESC;" -373,316000,380,500,"SELECT customers.id, COUNT(customers.region_id) +373,175500,175500,366,"SELECT customers.id, COUNT(customers.region_id) FROM customers GROUP BY customers.id;" -374,9978000,5935,0,"SELECT orders.id AS c9907, COUNT(*) AS c8627 +374,6653000,6653000,5857,"SELECT orders.id AS c9907, COUNT(*) AS c8627 FROM departments CROSS JOIN orders WHERE ((orders.id >= 79 OR orders.customer_id IN (63, 185, 226, 480)) AND departments.id IS NULL) GROUP BY orders.id ORDER BY orders.id ASC;" -375,957500,1679,4999,"SELECT t0.customer_id AS c3627, t0.id AS c1197 +375,957500,957500,1391,"SELECT t0.customer_id AS c3627, t0.id AS c1197 FROM orders AS t0 WHERE (t0.customer_id <= 56 OR t0.id > 1);" -376,10438,379,9,"SELECT t1.id, t0.id, t1.department_id +376,5325,14288,226,"SELECT t1.id, t0.id, t1.department_id FROM regions AS t0 FULL JOIN employees AS t1 ON t0.id = t1.id FULL JOIN departments AS t2 ON t0.id = t2.id WHERE t0.id NOT IN (3);" -377,98212,508,341,"SELECT customers.id, customers.region_id, SUM(customers.region_id), SUM(customers.id) +377,77608,77608,397,"SELECT customers.id, customers.region_id, SUM(customers.region_id), SUM(customers.id) FROM customers WHERE ((customers.region_id != 9 OR customers.id = 20) AND (customers.region_id >= 3 AND customers.id > 5)) GROUP BY customers.id, customers.region_id ORDER BY customers.id DESC, customers.region_id ASC;" -378,1896,155,3,"SELECT markets.region, markets.id AS c5516 +378,822,822,338,"SELECT markets.region, markets.id AS c5516 FROM markets GROUP BY markets.region, markets.id;" -379,1342,142,11,"SELECT employees.department_id, employees.id +379,1342,1342,168,"SELECT employees.department_id, employees.id FROM employees;" -380,1692500,2838,4329,"SELECT orders.customer_id +380,1692500,1692500,2768,"SELECT orders.customer_id FROM orders WHERE (orders.id < 86 OR (orders.customer_id IN (21, 71) OR orders.customer_id NOT BETWEEN 371 AND 436)) ORDER BY orders.customer_id ASC;" -381,10354,279,34,"SELECT t0.age AS c6869, t0.id +381,10354,10354,276,"SELECT t0.age AS c6869, t0.id FROM users AS t0 CROSS JOIN markets AS t1 WHERE t1.note != 'Old World';" -382,1564,211,3,"SELECT t0.id AS c3548, t0.note, SUM(t0.id) AS c5991, COUNT(*) +382,848,848,192,"SELECT t0.id AS c3548, t0.note, SUM(t0.id) AS c5991, COUNT(*) FROM markets AS t0 WHERE ((t0.note IN ('Fast lane', 'Fast lane', 'Old World') OR t0.region = 'AMERICA') OR t0.id NOT IN (1, 2, 3)) GROUP BY t0.id, t0.note;" -383,14597700,11406,8500,"SELECT customers.id, customers.region_id +383,14597700,14597700,11236,"SELECT customers.id, customers.region_id FROM regions CROSS JOIN customers CROSS JOIN users WHERE regions.id < 2;" -384,42520,429,9,"SELECT markets.id, books.id, COUNT(markets.id), COUNT(markets.id) +384,28390,28390,476,"SELECT markets.id, books.id, COUNT(markets.id), COUNT(markets.id) FROM regions CROSS JOIN books CROSS JOIN markets WHERE regions.id != 5 GROUP BY markets.id, books.id;" -385,2432,189,0,"SELECT t0.id AS c9611, t0.age AS c4657 +385,2432,2432,628,"SELECT t0.id AS c9611, t0.age AS c4657 FROM users AS t0 WHERE ((t0.id >= 3 OR t0.id IS NULL) AND t0.age IS NULL);" -386,1553776,2370,3,"SELECT markets.note, markets.region AS c394 +386,1307748,1553776,1919,"SELECT markets.note, markets.region AS c394 FROM markets LEFT JOIN orders ON markets.id = orders.id LEFT JOIN employees ON orders.id = employees.id;" -387,1832,254,0,"SELECT employees.id AS c1393 +387,1811,3832,187,"SELECT employees.id AS c1393 FROM employees LEFT JOIN books ON employees.id = books.id WHERE ((employees.id IS NULL AND books.id IS NOT NULL) AND (books.price = 55 AND employees.department_id = 12));" -388,3650500,11472,25000,"SELECT orders.customer_id AS c3285 +388,3650500,3650500,11216,"SELECT orders.customer_id AS c3285 FROM orders CROSS JOIN departments;" -389,679882,1202,2,"SELECT employees.id AS c6990 +389,1307562,1553832,1997,"SELECT employees.id AS c6990 FROM orders LEFT JOIN markets ON orders.id = markets.id JOIN employees ON orders.id = employees.id WHERE markets.id < 3;" -390,160080,1984,5000,"SELECT markets.note AS c1438, customers.region_id, markets.id AS c7869 +390,113850,160080,1935,"SELECT markets.note AS c1438, customers.region_id, markets.id AS c7869 FROM customers LEFT JOIN markets ON customers.id = markets.id CROSS JOIN regions;" -391,2253325,2732,5,"SELECT orders.customer_id AS c6961, departments.id +391,1306995,2251590,1312,"SELECT orders.customer_id AS c6961, departments.id FROM departments LEFT JOIN orders ON departments.id = orders.id GROUP BY orders.customer_id, departments.id ORDER BY departments.id DESC;" -392,63126,581,11,"SELECT employees.department_id AS c8803, SUM(users.id), SUM(employees.id) +392,43248,62366,417,"SELECT employees.department_id AS c8803, SUM(users.id), SUM(employees.id) FROM users CROSS JOIN employees LEFT JOIN markets ON employees.id = markets.id WHERE ((markets.id >= 18 OR users.id = 4) OR employees.id >= 2) GROUP BY employees.department_id ORDER BY employees.department_id ASC;" -393,291050000,530380,384231,"SELECT orders.customer_id, customers.id +393,291050000,291050000,536042,"SELECT orders.customer_id, customers.id FROM orders CROSS JOIN customers WHERE (orders.id IS NOT NULL AND (orders.customer_id <= 190 AND customers.region_id IN (1, 5, 8, 9)));" -394,1832,168,8,"SELECT t0.id +394,1832,1832,312,"SELECT t0.id FROM employees AS t0 WHERE t0.department_id NOT IN (6, 11, 32, 94);" -395,2390300,11567,15000,"SELECT orders.id, orders.customer_id, books.price AS c2061 +395,2390300,2390300,10589,"SELECT orders.id, orders.customer_id, books.price AS c2061 FROM orders CROSS JOIN books;" -396,422,160,0,"SELECT markets.region, markets.id, markets.note +396,422,422,141,"SELECT markets.region, markets.id, markets.note FROM markets WHERE markets.region IS NULL;" -397,316000,404,500,"SELECT t0.id, t0.region_id, SUM(t0.region_id) +397,175500,175500,531,"SELECT t0.id, t0.region_id, SUM(t0.region_id) FROM customers AS t0 GROUP BY t0.id, t0.region_id;" -398,70776,252,10,"SELECT regions.id, customers.id, customers.region_id +398,70776,401976,402,"SELECT regions.id, customers.id, customers.region_id FROM regions JOIN customers ON regions.id = customers.id WHERE (customers.id NOT BETWEEN 18 AND 150 OR regions.id IN (1, 9, 10, 63));" -399,1808,162,1,"SELECT departments.id AS c2818, COUNT(departments.id), COUNT(departments.id) +399,1092,1092,165,"SELECT departments.id AS c2818, COUNT(departments.id), COUNT(departments.id) FROM departments WHERE departments.id <= 1 GROUP BY departments.id diff --git a/research/fuzz/cost_stats.py b/research/fuzz/cost_stats.py index 190a4d7..62ec851 100644 --- a/research/fuzz/cost_stats.py +++ b/research/fuzz/cost_stats.py @@ -31,7 +31,8 @@ from research.query_generator import DIALECTS, QueryGenerator, load_schema, render_query -_STATS_RE = re.compile(r"STATS plan_cost=(-?\d+) exec_us=(-?\d+) rows=(\d+)") +#STATS plan_cost=2074 naive_plan_cost=2074 exec_us=133 rows=17 +_STATS_RE = re.compile(r"STATS plan_cost=(-?\d+) naive_plan_cost=(-?\d+) exec_us=(-?\d+) rows=(\d+)") def _run_ours(cli: str, data_dir: str, query: str, jit: bool) -> subprocess.CompletedProcess: @@ -88,6 +89,10 @@ def main() -> None: query = QueryGenerator(schema, rng).generate() sql = render_query(query, pg) + ";" + if attempted%100==0: + print(f"collected={collected} attempted={attempted} skipped={skipped}", + flush=True) + cost: int | None = None rows: int | None = None samples: list[int] = [] diff --git a/research/research.ipynb b/research/research.ipynb index 454f982..bfed507 100644 --- a/research/research.ipynb +++ b/research/research.ipynb @@ -10,7 +10,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2026-05-31T16:14:53+03:00\n", + "2026-06-09T23:39:57+03:00\n", "Running ./build-release/bin/benchmarks\n", "Run on (8 X 4200 MHz CPU s)\n", "CPU Caches:\n", @@ -18,81 +18,75 @@ " L1 Instruction 32 KiB (x4)\n", " L2 Unified 1280 KiB (x4)\n", " L3 Unified 8192 KiB (x1)\n", - "Load Average: 4.44, 2.58, 1.61\n", - "***WARNING*** CPU scaling is enabled, the benchmark real time measurements may be noisy and will incur extra overhead.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Load Average: 0.33, 0.62, 1.01\n", + "***WARNING*** CPU scaling is enabled, the benchmark real time measurements may be noisy and will incur extra overhead.\n", "---------------------------------------------------------------------------------------------------------------\n", "Benchmark Time CPU Iterations UserCounters...\n", "---------------------------------------------------------------------------------------------------------------\n", - "\u001b[0;32mOperatorCost/SeqScan/1024/real_time \u001b[m\u001b[0;33m 79997 ns 78049 ns \u001b[m\u001b[0;36m 8741\u001b[m model_cost=102.4k\u001b[m output_rows=1.024k\u001b[m plan_cost=102.4k\u001b[m rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/2048/real_time \u001b[m\u001b[0;33m 144761 ns 142224 ns 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model_cost=3.2768M\u001b[m output_rows=32.768k\u001b[m plan_cost=3.2768M\u001b[m rows=32.768k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/65536/real_time \u001b[m\u001b[0;33m 7848736 ns 7709842 ns \u001b[m\u001b[0;36m 101\u001b[m model_cost=6.5536M\u001b[m output_rows=65.536k\u001b[m plan_cost=6.5536M\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/131072/real_time \u001b[m\u001b[0;33m 14549342 ns 14285211 ns \u001b[m\u001b[0;36m 48\u001b[m model_cost=13.1072M\u001b[m output_rows=131.072k\u001b[m plan_cost=13.1072M\u001b[m rows=131.072k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/262144/real_time \u001b[m\u001b[0;33m 46805723 ns 44962657 ns \u001b[m\u001b[0;36m 24\u001b[m model_cost=26.2144M\u001b[m output_rows=262.144k\u001b[m plan_cost=26.2144M\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/1024/real_time \u001b[m\u001b[0;33m 110848 ns 109579 ns \u001b[m\u001b[0;36m 6051\u001b[m model_cost=102.4k\u001b[m output_rows=512\u001b[m plan_cost=204.8k\u001b[m rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/2048/real_time \u001b[m\u001b[0;33m 175851 ns 174124 ns \u001b[m\u001b[0;36m 4178\u001b[m model_cost=204.8k\u001b[m output_rows=1.024k\u001b[m plan_cost=409.6k\u001b[m rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/4096/real_time \u001b[m\u001b[0;33m 315093 ns 312199 ns \u001b[m\u001b[0;36m 2166\u001b[m model_cost=409.6k\u001b[m output_rows=2.048k\u001b[m plan_cost=819.2k\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/8192/real_time \u001b[m\u001b[0;33m 673538 ns 667479 ns \u001b[m\u001b[0;36m 1078\u001b[m model_cost=819.2k\u001b[m output_rows=4.096k\u001b[m plan_cost=1.6384M\u001b[m rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/16384/real_time \u001b[m\u001b[0;33m 1303801 ns 1294736 ns \u001b[m\u001b[0;36m 456\u001b[m model_cost=1.6384M\u001b[m output_rows=8.192k\u001b[m plan_cost=3.2768M\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/32768/real_time \u001b[m\u001b[0;33m 2952031 ns 2928449 ns \u001b[m\u001b[0;36m 256\u001b[m model_cost=3.2768M\u001b[m output_rows=16.384k\u001b[m plan_cost=6.5536M\u001b[m rows=32.768k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/65536/real_time \u001b[m\u001b[0;33m 7472369 ns 7365271 ns \u001b[m\u001b[0;36m 102\u001b[m model_cost=6.5536M\u001b[m output_rows=32.768k\u001b[m plan_cost=13.1072M\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/131072/real_time \u001b[m\u001b[0;33m 14015431 ns 13847255 ns \u001b[m\u001b[0;36m 58\u001b[m model_cost=13.1072M\u001b[m output_rows=65.536k\u001b[m plan_cost=26.2144M\u001b[m rows=131.072k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/262144/real_time \u001b[m\u001b[0;33m 27073700 ns 26782809 ns \u001b[m\u001b[0;36m 24\u001b[m model_cost=26.2144M\u001b[m output_rows=131.072k\u001b[m plan_cost=52.4288M\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/1024/real_time \u001b[m\u001b[0;33m 131940 ns 130699 ns \u001b[m\u001b[0;36m 5179\u001b[m model_cost=22.528k\u001b[m output_rows=1.024k\u001b[m plan_cost=124.928k\u001b[m rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/2048/real_time \u001b[m\u001b[0;33m 226190 ns 225141 ns \u001b[m\u001b[0;36m 2989\u001b[m model_cost=45.056k\u001b[m output_rows=2.048k\u001b[m plan_cost=249.856k\u001b[m rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/4096/real_time \u001b[m\u001b[0;33m 514393 ns 509783 ns \u001b[m\u001b[0;36m 1536\u001b[m model_cost=90.112k\u001b[m output_rows=4.096k\u001b[m plan_cost=499.712k\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/8192/real_time \u001b[m\u001b[0;33m 945676 ns 940030 ns \u001b[m\u001b[0;36m 718\u001b[m model_cost=180.224k\u001b[m output_rows=8.192k\u001b[m plan_cost=0.999424M\u001b[m rows=8.192k\u001b[m\n", - 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"\u001b[m\u001b[0;32mOperatorCost/Projection/262144/real_time \u001b[m\u001b[0;33m 41055491 ns 40717845 ns \u001b[m\u001b[0;36m 19\u001b[m model_cost=5.76717M\u001b[m output_rows=262.144k\u001b[m plan_cost=31.9816M\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/1024/real_time \u001b[m\u001b[0;33m 162085 ns 160916 ns \u001b[m\u001b[0;36m 4340\u001b[m model_cost=123.904k\u001b[m output_rows=1.024k\u001b[m plan_cost=226.304k\u001b[m rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/2048/real_time \u001b[m\u001b[0;33m 346371 ns 344873 ns \u001b[m\u001b[0;36m 2062\u001b[m model_cost=270.336k\u001b[m output_rows=2.048k\u001b[m plan_cost=475.136k\u001b[m rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/4096/real_time \u001b[m\u001b[0;33m 800255 ns 792542 ns \u001b[m\u001b[0;36m 883\u001b[m model_cost=585.728k\u001b[m output_rows=4.096k\u001b[m plan_cost=995.328k\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/8192/real_time \u001b[m\u001b[0;33m 1840524 ns 1830438 ns \u001b[m\u001b[0;36m 379\u001b[m model_cost=1.26157M\u001b[m output_rows=8.192k\u001b[m plan_cost=2.08077M\u001b[m rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/16384/real_time \u001b[m\u001b[0;33m 4673407 ns 4626060 ns \u001b[m\u001b[0;36m 164\u001b[m model_cost=2.70336M\u001b[m output_rows=16.384k\u001b[m plan_cost=4.34176M\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/32768/real_time \u001b[m\u001b[0;33m 10560526 ns 10441042 ns \u001b[m\u001b[0;36m 60\u001b[m model_cost=5.76717M\u001b[m output_rows=32.768k\u001b[m plan_cost=9.04397M\u001b[m rows=32.768k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/65536/real_time \u001b[m\u001b[0;33m 27512323 ns 27313637 ns \u001b[m\u001b[0;36m 26\u001b[m model_cost=12.2552M\u001b[m output_rows=65.536k\u001b[m plan_cost=18.8088M\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/131072/real_time \u001b[m\u001b[0;33m 76878972 ns 76256341 ns \u001b[m\u001b[0;36m 9\u001b[m model_cost=25.9523M\u001b[m output_rows=131.072k\u001b[m plan_cost=39.0595M\u001b[m rows=131.072k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/262144/real_time \u001b[m\u001b[0;33m 210729772 ns 208907082 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=54.7881M\u001b[m output_rows=262.144k\u001b[m plan_cost=81.0025M\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/1024/real_time \u001b[m\u001b[0;33m 367198 ns 365161 ns \u001b[m\u001b[0;36m 2007\u001b[m model_cost=522.24k\u001b[m output_rows=1.024k\u001b[m plan_cost=624.64k\u001b[m rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/2048/real_time \u001b[m\u001b[0;33m 714949 ns 711449 ns \u001b[m\u001b[0;36m 1030\u001b[m model_cost=1.04448M\u001b[m output_rows=2.048k\u001b[m plan_cost=1.24928M\u001b[m rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/4096/real_time \u001b[m\u001b[0;33m 1378878 ns 1372416 ns \u001b[m\u001b[0;36m 506\u001b[m model_cost=2.08896M\u001b[m output_rows=4.096k\u001b[m plan_cost=2.49856M\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/8192/real_time \u001b[m\u001b[0;33m 2970154 ns 2953446 ns \u001b[m\u001b[0;36m 233\u001b[m model_cost=4.17792M\u001b[m output_rows=8.192k\u001b[m plan_cost=4.99712M\u001b[m rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/16384/real_time \u001b[m\u001b[0;33m 7566779 ns 7487774 ns \u001b[m\u001b[0;36m 94\u001b[m model_cost=8.35584M\u001b[m output_rows=16.384k\u001b[m plan_cost=9.99424M\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/32768/real_time \u001b[m\u001b[0;33m 21294147 ns 21136412 ns \u001b[m\u001b[0;36m 28\u001b[m model_cost=16.7117M\u001b[m output_rows=32.768k\u001b[m plan_cost=19.9885M\u001b[m rows=32.768k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/65536/real_time \u001b[m\u001b[0;33m 58811825 ns 58334161 ns \u001b[m\u001b[0;36m 11\u001b[m model_cost=33.4234M\u001b[m output_rows=65.536k\u001b[m plan_cost=39.977M\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/131072/real_time \u001b[m\u001b[0;33m 145317421 ns 144445410 ns \u001b[m\u001b[0;36m 5\u001b[m model_cost=66.8467M\u001b[m output_rows=131.072k\u001b[m plan_cost=79.9539M\u001b[m rows=131.072k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/262144/real_time \u001b[m\u001b[0;33m 341980080 ns 339244361 ns \u001b[m\u001b[0;36m 2\u001b[m model_cost=133.693M\u001b[m output_rows=262.144k\u001b[m plan_cost=159.908M\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/1024/1024/real_time \u001b[m\u001b[0;33m 203598 ns 202661 ns \u001b[m\u001b[0;36m 3434\u001b[m lhs_rows=1.024k\u001b[m model_cost=141.312k\u001b[m output_rows=1.024k\u001b[m plan_cost=346.112k\u001b[m rhs_rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/2048/2048/real_time \u001b[m\u001b[0;33m 383152 ns 381209 ns \u001b[m\u001b[0;36m 1850\u001b[m lhs_rows=2.048k\u001b[m model_cost=282.624k\u001b[m output_rows=2.048k\u001b[m plan_cost=692.224k\u001b[m rhs_rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/4096/4096/real_time \u001b[m\u001b[0;33m 762752 ns 757843 ns \u001b[m\u001b[0;36m 903\u001b[m lhs_rows=4.096k\u001b[m model_cost=565.248k\u001b[m output_rows=4.096k\u001b[m plan_cost=1.38445M\u001b[m rhs_rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/8192/8192/real_time \u001b[m\u001b[0;33m 1662913 ns 1651698 ns \u001b[m\u001b[0;36m 461\u001b[m lhs_rows=8.192k\u001b[m model_cost=1.1305M\u001b[m output_rows=8.192k\u001b[m plan_cost=2.7689M\u001b[m rhs_rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/16384/16384/real_time \u001b[m\u001b[0;33m 4006295 ns 3972214 ns \u001b[m\u001b[0;36m 191\u001b[m lhs_rows=16.384k\u001b[m model_cost=2.26099M\u001b[m output_rows=16.384k\u001b[m plan_cost=5.53779M\u001b[m rhs_rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/32768/32768/real_time \u001b[m\u001b[0;33m 8204329 ns 8095465 ns \u001b[m\u001b[0;36m 82\u001b[m lhs_rows=32.768k\u001b[m model_cost=4.52198M\u001b[m output_rows=32.768k\u001b[m plan_cost=11.0756M\u001b[m rhs_rows=32.768k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/65536/65536/real_time \u001b[m\u001b[0;33m 16943075 ns 16813719 ns \u001b[m\u001b[0;36m 43\u001b[m lhs_rows=65.536k\u001b[m model_cost=9.04397M\u001b[m output_rows=65.536k\u001b[m plan_cost=22.1512M\u001b[m rhs_rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/64/64/real_time \u001b[m\u001b[0;33m 457941 ns 454351 ns \u001b[m\u001b[0;36m 1627\u001b[m lhs_rows=64\u001b[m model_cost=286.72k\u001b[m output_rows=64\u001b[m plan_cost=299.52k\u001b[m rhs_rows=64\u001b[m\n", - 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model_cost=73.4003M\u001b[m output_rows=1.024k\u001b[m plan_cost=73.6051M\u001b[m rhs_rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/2048/2048/real_time \u001b[m\u001b[0;33m 275065905 ns 274101250 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=2.048k\u001b[m model_cost=293.601M\u001b[m output_rows=2.048k\u001b[m plan_cost=294.011M\u001b[m rhs_rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/32/32/real_time \u001b[m\u001b[0;33m 161658 ns 160319 ns \u001b[m\u001b[0;36m 4263\u001b[m lhs_rows=32\u001b[m model_cost=106.496k\u001b[m output_rows=1.024k\u001b[m plan_cost=112.896k\u001b[m rhs_rows=32\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/64/64/real_time \u001b[m\u001b[0;33m 391432 ns 386106 ns \u001b[m\u001b[0;36m 1896\u001b[m lhs_rows=64\u001b[m model_cost=425.984k\u001b[m output_rows=4.096k\u001b[m plan_cost=438.784k\u001b[m rhs_rows=64\u001b[m\n", - 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lhs_rows=1.024k\u001b[m model_cost=109.052M\u001b[m output_rows=1.04858M\u001b[m plan_cost=109.257M\u001b[m rhs_rows=1.024k\u001b[m\n", + "\u001b[0;32mOperatorCost/SeqScan/1024/real_time \u001b[m\u001b[0;33m 37629 ns 37491 ns \u001b[m\u001b[0;36m 18331\u001b[m model_cost=102.4k\u001b[m output_rows=1.024k\u001b[m plan_cost=102.4k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/2048/real_time \u001b[m\u001b[0;33m 72904 ns 72542 ns \u001b[m\u001b[0;36m 10020\u001b[m model_cost=204.8k\u001b[m output_rows=2.048k\u001b[m plan_cost=204.8k\u001b[m rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/4096/real_time \u001b[m\u001b[0;33m 149078 ns 147494 ns \u001b[m\u001b[0;36m 4364\u001b[m model_cost=409.6k\u001b[m output_rows=4.096k\u001b[m plan_cost=409.6k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/8192/real_time \u001b[m\u001b[0;33m 342105 ns 339455 ns \u001b[m\u001b[0;36m 1927\u001b[m model_cost=819.2k\u001b[m output_rows=8.192k\u001b[m plan_cost=819.2k\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/16384/real_time \u001b[m\u001b[0;33m 944473 ns 935009 ns \u001b[m\u001b[0;36m 837\u001b[m model_cost=1.6384M\u001b[m output_rows=16.384k\u001b[m plan_cost=1.6384M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/32768/real_time \u001b[m\u001b[0;33m 2017875 ns 1998804 ns \u001b[m\u001b[0;36m 317\u001b[m model_cost=3.2768M\u001b[m output_rows=32.768k\u001b[m plan_cost=3.2768M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/65536/real_time \u001b[m\u001b[0;33m 4946438 ns 4895355 ns \u001b[m\u001b[0;36m 131\u001b[m model_cost=6.5536M\u001b[m output_rows=65.536k\u001b[m plan_cost=6.5536M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/131072/real_time \u001b[m\u001b[0;33m 11151818 ns 11034605 ns \u001b[m\u001b[0;36m 59\u001b[m model_cost=13.1072M\u001b[m output_rows=131.072k\u001b[m plan_cost=13.1072M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/262144/real_time \u001b[m\u001b[0;33m 23816470 ns 23575784 ns \u001b[m\u001b[0;36m 30\u001b[m model_cost=26.2144M\u001b[m output_rows=262.144k\u001b[m plan_cost=26.2144M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/1024/real_time \u001b[m\u001b[0;33m 73010 ns 72684 ns \u001b[m\u001b[0;36m 9776\u001b[m model_cost=102.4k\u001b[m output_rows=512\u001b[m plan_cost=204.8k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/2048/real_time \u001b[m\u001b[0;33m 132643 ns 132118 ns \u001b[m\u001b[0;36m 4789\u001b[m model_cost=204.8k\u001b[m output_rows=1.024k\u001b[m plan_cost=409.6k\u001b[m rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/4096/real_time \u001b[m\u001b[0;33m 254451 ns 253392 ns \u001b[m\u001b[0;36m 2735\u001b[m model_cost=409.6k\u001b[m output_rows=2.048k\u001b[m plan_cost=819.2k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/8192/real_time \u001b[m\u001b[0;33m 528183 ns 525175 ns \u001b[m\u001b[0;36m 1327\u001b[m model_cost=819.2k\u001b[m output_rows=4.096k\u001b[m plan_cost=1.6384M\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/16384/real_time \u001b[m\u001b[0;33m 1030577 ns 1025841 ns \u001b[m\u001b[0;36m 626\u001b[m model_cost=1.6384M\u001b[m output_rows=8.192k\u001b[m plan_cost=3.2768M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/32768/real_time \u001b[m\u001b[0;33m 2219699 ns 2207102 ns \u001b[m\u001b[0;36m 320\u001b[m model_cost=3.2768M\u001b[m output_rows=16.384k\u001b[m plan_cost=6.5536M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/65536/real_time \u001b[m\u001b[0;33m 4560355 ns 4535821 ns \u001b[m\u001b[0;36m 139\u001b[m model_cost=6.5536M\u001b[m output_rows=32.768k\u001b[m plan_cost=13.1072M\u001b[m rows=65.536k\u001b[m\n", + 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users 2010 May 30 19:02 operator-cost-smoke.json\n" + "-rw-r--r-- 1 st users 40322 Jun 9 23:41 operator-cost.json\n", + "-rw-r--r-- 1 st users 33223 May 31 16:03 operator-cost-matched-calibrated-smoke.json\n", + "-rw-r--r-- 1 st users 33305 May 31 16:16 operator-cost-matched.json\n", + "-rw-r--r-- 1 st users 6149 May 31 15:08 operator-cost-matched-plan-cost-smoke.json\n", + "-rw-r--r-- 1 st users 2010 May 30 19:02 operator-cost-smoke.json\n" ] } ], @@ -125,10 +119,1077 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "2d033649", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'context': {'date': '2026-06-09T23:39:57+03:00',\n", + " 'host_name': 'nixos',\n", + " 'executable': './build-release/bin/benchmarks',\n", + " 'num_cpus': 8,\n", + " 'mhz_per_cpu': 4200,\n", + " 'cpu_scaling_enabled': True,\n", + " 'caches': [{'type': 'Data', 'level': 1, 'size': 49152, 'num_sharing': 2},\n", + " {'type': 'Instruction', 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'output_rows': 1048576.0,\n", + " 'plan_cost': 109256704.0,\n", + " 'rhs_rows': 1024.0}]}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import json\n", "\n", @@ -179,7 +1240,7 @@ " Aggregation\n", " 1024\n", " NaN\n", - " 0.367198\n", + " 0.279850\n", " 522240.0\n", " \n", " \n", @@ -187,7 +1248,7 @@ " Aggregation\n", " 2048\n", " NaN\n", - " 0.714949\n", + " 0.547978\n", " 1044480.0\n", " \n", " \n", @@ -195,7 +1256,7 @@ " Aggregation\n", " 4096\n", " NaN\n", - " 1.378878\n", + " 1.112645\n", " 2088960.0\n", " \n", " \n", @@ -203,7 +1264,7 @@ " Aggregation\n", " 8192\n", " NaN\n", - " 2.970154\n", + " 2.393267\n", " 4177920.0\n", " \n", " \n", @@ -211,7 +1272,7 @@ " Aggregation\n", " 16384\n", " NaN\n", - " 7.566779\n", + " 6.024309\n", " 8355840.0\n", " \n", " \n", @@ -227,7 +1288,7 @@ " Sort\n", " 16384\n", " NaN\n", - " 4.673407\n", + " 3.425096\n", " 2703360.0\n", " \n", " \n", @@ -235,7 +1296,7 @@ " Sort\n", " 32768\n", " NaN\n", - " 10.560526\n", + " 8.128588\n", " 5767168.0\n", " \n", " \n", @@ -243,7 +1304,7 @@ " Sort\n", " 65536\n", " NaN\n", - " 27.512323\n", + " 21.036047\n", " 12255232.0\n", " \n", " \n", @@ -251,7 +1312,7 @@ " Sort\n", " 131072\n", " NaN\n", - " 76.878972\n", + " 68.694607\n", " 25952256.0\n", " \n", " \n", @@ -259,7 +1320,7 @@ " Sort\n", " 262144\n", " NaN\n", - " 210.729772\n", + " 195.667437\n", " 54788096.0\n", " \n", " \n", @@ -269,17 +1330,17 @@ ], "text/plain": [ " operator left_rows right_rows real_time_ms model_cost\n", - "36 Aggregation 1024 NaN 0.367198 522240.0\n", - "37 Aggregation 2048 NaN 0.714949 1044480.0\n", - "38 Aggregation 4096 NaN 1.378878 2088960.0\n", - "39 Aggregation 8192 NaN 2.970154 4177920.0\n", - "40 Aggregation 16384 NaN 7.566779 8355840.0\n", + "36 Aggregation 1024 NaN 0.279850 522240.0\n", + "37 Aggregation 2048 NaN 0.547978 1044480.0\n", + "38 Aggregation 4096 NaN 1.112645 2088960.0\n", + "39 Aggregation 8192 NaN 2.393267 4177920.0\n", + "40 Aggregation 16384 NaN 6.024309 8355840.0\n", ".. ... ... ... ... ...\n", - "31 Sort 16384 NaN 4.673407 2703360.0\n", - "32 Sort 32768 NaN 10.560526 5767168.0\n", - "33 Sort 65536 NaN 27.512323 12255232.0\n", - "34 Sort 131072 NaN 76.878972 25952256.0\n", - "35 Sort 262144 NaN 210.729772 54788096.0\n", + "31 Sort 16384 NaN 3.425096 2703360.0\n", + "32 Sort 32768 NaN 8.128588 5767168.0\n", + "33 Sort 65536 NaN 21.036047 12255232.0\n", + "34 Sort 131072 NaN 68.694607 25952256.0\n", + "35 Sort 262144 NaN 195.667437 54788096.0\n", "\n", "[64 rows x 5 columns]" ] @@ -315,7 +1376,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -347,7 +1408,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -469,15 +1530,15 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.426\n", - " -0.222\n", - " -0.149\n", - " -0.142\n", - " 0.126\n", - " 0.347\n", - " 0.440\n", - " 0.716\n", - " 0.910\n", + " -0.285\n", + " -0.211\n", + " -0.154\n", + " -0.117\n", + " 0.135\n", + " 0.384\n", + " 0.814\n", + " 0.966\n", + " 1.190\n", " \n", " \n", " HashJoin\n", @@ -485,13 +1546,13 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.718\n", - " -0.683\n", - " -0.713\n", - " -0.726\n", - " -0.673\n", - " -0.347\n", - " -0.063\n", + " 0.304\n", + " 0.337\n", + " 0.351\n", + " 0.403\n", + " 0.497\n", + " 0.577\n", + " 0.823\n", " NaN\n", " NaN\n", " \n", @@ -501,7 +1562,7 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.527\n", + " -0.653\n", " NaN\n", " NaN\n", " NaN\n", @@ -517,8 +1578,8 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.304\n", - " -0.315\n", + " -0.405\n", + " -0.442\n", " NaN\n", " NaN\n", " NaN\n", @@ -533,15 +1594,15 @@ " NaN\n", " NaN\n", " NaN\n", - " -2.116\n", - " -1.993\n", - " -2.153\n", - " -1.999\n", - " -1.768\n", - " -1.389\n", - " -1.083\n", - " -0.898\n", - " -1.023\n", + " -2.051\n", + " -2.026\n", + " -2.004\n", + " -1.916\n", + " -1.740\n", + " -1.443\n", + " -1.081\n", + " -0.891\n", + " -0.870\n", " \n", " \n", " SeqScan\n", @@ -549,15 +1610,15 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.086\n", - " -0.019\n", - " -0.195\n", - " -0.307\n", - " -0.291\n", - " 0.146\n", - " 0.394\n", - " 0.684\n", - " 0.392\n", + " 0.377\n", + " 0.389\n", + " 0.387\n", + " 0.319\n", + " 0.227\n", + " 0.483\n", + " 0.737\n", + " 0.861\n", + " 1.007\n", " \n", " \n", " Sort\n", @@ -565,15 +1626,15 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.619\n", - " -0.627\n", - " -0.722\n", - " -0.719\n", + " -0.546\n", " -0.647\n", - " -0.359\n", - " -0.245\n", - " -0.307\n", - " -0.407\n", + " -0.666\n", + " -0.628\n", + " -0.564\n", + " -0.349\n", + " -0.087\n", + " -0.276\n", + " -0.364\n", " \n", " \n", " Filter\n", @@ -582,15 +1643,15 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.426\n", - " 0.222\n", - " 0.149\n", - " 0.142\n", - " -0.126\n", - " -0.347\n", - " -0.440\n", - " -0.716\n", - " -0.910\n", + " 0.285\n", + " 0.211\n", + " 0.154\n", + " 0.117\n", + " -0.135\n", + " -0.384\n", + " -0.814\n", + " -0.966\n", + " -1.190\n", " \n", " \n", " HashJoin\n", @@ -598,13 +1659,13 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.293\n", - " -0.461\n", - " -0.565\n", - " -0.584\n", - " -0.799\n", - " -0.695\n", - " -0.503\n", + " 0.589\n", + " 0.547\n", + " 0.505\n", + " 0.521\n", + " 0.363\n", + " 0.193\n", + " 0.010\n", " NaN\n", " NaN\n", " \n", @@ -614,7 +1675,7 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.101\n", + " -0.369\n", " NaN\n", " NaN\n", " NaN\n", @@ -630,8 +1691,8 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.122\n", - " -0.094\n", + " -0.120\n", + " -0.231\n", " NaN\n", " NaN\n", " NaN\n", @@ -646,15 +1707,15 @@ " NaN\n", " NaN\n", " NaN\n", - " -1.690\n", - " -1.771\n", - " -2.004\n", + " -1.766\n", + " -1.816\n", + " -1.851\n", + " -1.799\n", + " -1.874\n", + " -1.827\n", + " -1.895\n", " -1.857\n", - " -1.894\n", - " -1.736\n", - " -1.523\n", - " -1.614\n", - " -1.933\n", + " -2.060\n", " \n", " \n", " SeqScan\n", @@ -662,15 +1723,15 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.339\n", - " 0.202\n", - " -0.046\n", - " -0.165\n", - " -0.417\n", - " -0.201\n", - " -0.046\n", - " -0.031\n", - " -0.518\n", + " 0.662\n", + " 0.600\n", + " 0.541\n", + " 0.436\n", + " 0.093\n", + " 0.099\n", + " -0.076\n", + " -0.105\n", + " -0.183\n", " \n", " \n", " Sort\n", @@ -678,15 +1739,15 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.194\n", - " -0.406\n", - " -0.574\n", - " -0.577\n", - " -0.773\n", - " -0.706\n", - " -0.685\n", - " -1.023\n", - " -1.317\n", + " -0.262\n", + " -0.437\n", + " -0.512\n", + " -0.510\n", + " -0.699\n", + " -0.733\n", + " -0.901\n", + " -1.242\n", + " -1.554\n", " \n", " \n", " HashJoin\n", @@ -695,13 +1756,13 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.718\n", - " 0.683\n", - " 0.713\n", - " 0.726\n", - " 0.673\n", - " 0.347\n", - " 0.063\n", + " -0.304\n", + " -0.337\n", + " -0.351\n", + " -0.403\n", + " -0.497\n", + " -0.577\n", + " -0.823\n", " NaN\n", " NaN\n", " \n", @@ -711,13 +1772,13 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.293\n", - " 0.461\n", - " 0.565\n", - " 0.584\n", - " 0.799\n", - " 0.695\n", - " 0.503\n", + " -0.589\n", + " -0.547\n", + " -0.505\n", + " -0.521\n", + " -0.363\n", + " -0.193\n", + " -0.010\n", " NaN\n", " NaN\n", " \n", @@ -727,7 +1788,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.192\n", + " -0.958\n", " NaN\n", " NaN\n", " NaN\n", @@ -743,8 +1804,8 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.415\n", - " 0.368\n", + " -0.709\n", + " -0.778\n", " NaN\n", " NaN\n", " NaN\n", @@ -759,13 +1820,13 @@ " NaN\n", " NaN\n", " NaN\n", - " -1.398\n", - " -1.310\n", - " -1.440\n", - " -1.273\n", - " -1.095\n", - " -1.042\n", - " -1.019\n", + " -2.355\n", + " -2.363\n", + " -2.356\n", + " -2.319\n", + " -2.237\n", + " -2.019\n", + " -1.905\n", " NaN\n", " NaN\n", " \n", @@ -775,13 +1836,13 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.632\n", - " 0.664\n", - " 0.518\n", - " 0.419\n", - " 0.382\n", - " 0.494\n", - " 0.458\n", + " 0.073\n", + " 0.052\n", + " 0.036\n", + " -0.084\n", + " -0.270\n", + " -0.093\n", + " -0.086\n", " NaN\n", " NaN\n", " \n", @@ -791,13 +1852,13 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.099\n", - " 0.056\n", - " -0.009\n", - " 0.007\n", - " 0.026\n", - " -0.011\n", - " -0.181\n", + " -0.851\n", + " -0.984\n", + " -1.017\n", + " -1.031\n", + " -1.062\n", + " -0.926\n", + " -0.911\n", " NaN\n", " NaN\n", " \n", @@ -808,7 +1869,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.527\n", + " 0.653\n", " NaN\n", " NaN\n", " NaN\n", @@ -824,7 +1885,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.101\n", + " 0.369\n", " NaN\n", " NaN\n", " NaN\n", @@ -840,7 +1901,7 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.192\n", + " 0.958\n", " NaN\n", " NaN\n", " NaN\n", @@ -852,11 +1913,11 @@ " \n", " \n", " NestedLoopJoin\n", - " -0.559\n", - " -0.194\n", - " -0.021\n", - " -0.026\n", - " 0.223\n", + " -0.59\n", + " -0.57\n", + " -0.094\n", + " 0.093\n", + " 0.248\n", " NaN\n", " NaN\n", " NaN\n", @@ -872,7 +1933,7 @@ " NaN\n", " NaN\n", " NaN\n", - " -1.589\n", + " -1.397\n", " NaN\n", " NaN\n", " NaN\n", @@ -888,7 +1949,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.441\n", + " 1.031\n", " NaN\n", " NaN\n", " NaN\n", @@ -904,7 +1965,7 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.092\n", + " 0.107\n", " NaN\n", " NaN\n", " NaN\n", @@ -921,8 +1982,8 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.304\n", - " 0.315\n", + " 0.405\n", + " 0.442\n", " NaN\n", " NaN\n", " NaN\n", @@ -937,8 +1998,8 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.122\n", - " 0.094\n", + " 0.120\n", + " 0.231\n", " NaN\n", " NaN\n", " NaN\n", @@ -953,8 +2014,8 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.415\n", - " -0.368\n", + " 0.709\n", + " 0.778\n", " NaN\n", " NaN\n", " NaN\n", @@ -965,11 +2026,11 @@ " \n", " \n", " NestedLoopCrossJoin\n", - " 0.559\n", - " 0.194\n", - " 0.021\n", - " 0.026\n", - " -0.223\n", + " 0.59\n", + " 0.57\n", + " 0.094\n", + " -0.093\n", + " -0.248\n", " NaN\n", " NaN\n", " NaN\n", @@ -985,8 +2046,8 @@ " NaN\n", " NaN\n", " NaN\n", - " -1.812\n", - " -1.678\n", + " -1.646\n", + " -1.585\n", " NaN\n", " NaN\n", " NaN\n", @@ -1001,8 +2062,8 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.217\n", - " 0.296\n", + " 0.782\n", + " 0.831\n", " NaN\n", " NaN\n", " NaN\n", @@ -1017,8 +2078,8 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.315\n", - " -0.312\n", + " -0.141\n", + " -0.206\n", " NaN\n", " NaN\n", " NaN\n", @@ -1034,15 +2095,15 @@ " NaN\n", " NaN\n", " NaN\n", - " 2.116\n", - " 1.993\n", - " 2.153\n", - " 1.999\n", - " 1.768\n", - " 1.389\n", - " 1.083\n", - " 0.898\n", - " 1.023\n", + " 2.051\n", + " 2.026\n", + " 2.004\n", + " 1.916\n", + " 1.740\n", + " 1.443\n", + " 1.081\n", + " 0.891\n", + " 0.870\n", " \n", " \n", " Filter\n", @@ -1050,15 +2111,15 @@ " NaN\n", " NaN\n", " NaN\n", - " 1.690\n", - " 1.771\n", - " 2.004\n", + " 1.766\n", + " 1.816\n", + " 1.851\n", + " 1.799\n", + " 1.874\n", + " 1.827\n", + " 1.895\n", " 1.857\n", - " 1.894\n", - " 1.736\n", - " 1.523\n", - " 1.614\n", - " 1.933\n", + " 2.060\n", " \n", " \n", " HashJoin\n", @@ -1066,13 +2127,13 @@ " NaN\n", " NaN\n", " NaN\n", - " 1.398\n", - " 1.310\n", - " 1.440\n", - " 1.273\n", - " 1.095\n", - " 1.042\n", - " 1.019\n", + " 2.355\n", + " 2.363\n", + " 2.356\n", + " 2.319\n", + " 2.237\n", + " 2.019\n", + " 1.905\n", " NaN\n", " NaN\n", " \n", @@ -1082,7 +2143,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 1.589\n", + " 1.397\n", " NaN\n", " NaN\n", " NaN\n", @@ -1098,8 +2159,8 @@ " NaN\n", " NaN\n", " NaN\n", - " 1.812\n", - " 1.678\n", + " 1.646\n", + " 1.585\n", " NaN\n", " NaN\n", " NaN\n", @@ -1114,15 +2175,15 @@ " NaN\n", " NaN\n", " NaN\n", - " 2.030\n", - " 1.973\n", - " 1.958\n", - " 1.691\n", - " 1.477\n", - " 1.535\n", - " 1.477\n", - " 1.582\n", - " 1.415\n", + " 2.428\n", + " 2.415\n", + " 2.392\n", + " 2.235\n", + " 1.967\n", + " 1.926\n", + " 1.819\n", + " 1.753\n", + " 1.877\n", " \n", " \n", " Sort\n", @@ -1130,15 +2191,15 @@ " NaN\n", " NaN\n", " NaN\n", - " 1.497\n", - " 1.365\n", - " 1.431\n", - " 1.280\n", - " 1.121\n", - " 1.030\n", - " 0.838\n", - " 0.591\n", - " 0.616\n", + " 1.504\n", + " 1.379\n", + " 1.338\n", + " 1.288\n", + " 1.175\n", + " 1.094\n", + " 0.994\n", + " 0.615\n", + " 0.506\n", " \n", " \n", " SeqScan\n", @@ -1147,15 +2208,15 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.086\n", - " 0.019\n", - " 0.195\n", - " 0.307\n", - " 0.291\n", - " -0.146\n", - " -0.394\n", - " -0.684\n", - " -0.392\n", + " -0.377\n", + " -0.389\n", + " -0.387\n", + " -0.319\n", + " -0.227\n", + " -0.483\n", + " -0.737\n", + " -0.861\n", + " -1.007\n", " \n", " \n", " Filter\n", @@ -1163,15 +2224,15 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.339\n", - " -0.202\n", - " 0.046\n", - " 0.165\n", - " 0.417\n", - " 0.201\n", - " 0.046\n", - " 0.031\n", - " 0.518\n", + " -0.662\n", + " -0.600\n", + " -0.541\n", + " -0.436\n", + " -0.093\n", + " -0.099\n", + " 0.076\n", + " 0.105\n", + " 0.183\n", " \n", " \n", " HashJoin\n", @@ -1179,13 +2240,13 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.632\n", - " -0.664\n", - " -0.518\n", - " -0.419\n", - " -0.382\n", - " -0.494\n", - " -0.458\n", + " -0.073\n", + " -0.052\n", + " -0.036\n", + " 0.084\n", + " 0.270\n", + " 0.093\n", + " 0.086\n", " NaN\n", " NaN\n", " \n", @@ -1195,7 +2256,7 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.441\n", + " -1.031\n", " NaN\n", " NaN\n", " NaN\n", @@ -1211,8 +2272,8 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.217\n", - " -0.296\n", + " -0.782\n", + " -0.831\n", " NaN\n", " NaN\n", " NaN\n", @@ -1227,15 +2288,15 @@ " NaN\n", " NaN\n", " NaN\n", - " -2.030\n", - " -1.973\n", - " -1.958\n", - " -1.691\n", - " -1.477\n", - " -1.535\n", - " -1.477\n", - " -1.582\n", - " -1.415\n", + " -2.428\n", + " -2.415\n", + " -2.392\n", + " -2.235\n", + " -1.967\n", + " -1.926\n", + " -1.819\n", + " -1.753\n", + " -1.877\n", " \n", " \n", " Sort\n", @@ -1243,15 +2304,15 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.533\n", - " -0.608\n", - " -0.528\n", - " -0.412\n", - " -0.356\n", - " -0.505\n", - " -0.639\n", - " -0.992\n", - " -0.799\n", + " -0.924\n", + " -1.036\n", + " -1.053\n", + " -0.947\n", + " -0.792\n", + " -0.832\n", + " -0.825\n", + " -1.138\n", + " -1.371\n", " \n", " \n", " Sort\n", @@ -1260,15 +2321,15 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.619\n", - " 0.627\n", - " 0.722\n", - " 0.719\n", + " 0.546\n", " 0.647\n", - " 0.359\n", - " 0.245\n", - " 0.307\n", - " 0.407\n", + " 0.666\n", + " 0.628\n", + " 0.564\n", + " 0.349\n", + " 0.087\n", + " 0.276\n", + " 0.364\n", " \n", " \n", " Filter\n", @@ -1276,15 +2337,15 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.194\n", - " 0.406\n", - " 0.574\n", - " 0.577\n", - " 0.773\n", - " 0.706\n", - " 0.685\n", - " 1.023\n", - " 1.317\n", + " 0.262\n", + " 0.437\n", + " 0.512\n", + " 0.510\n", + " 0.699\n", + " 0.733\n", + " 0.901\n", + " 1.242\n", + " 1.554\n", " \n", " \n", " HashJoin\n", @@ -1292,13 +2353,13 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.099\n", - " -0.056\n", - " 0.009\n", - " -0.007\n", - " -0.026\n", - " 0.011\n", - " 0.181\n", + " 0.851\n", + " 0.984\n", + " 1.017\n", + " 1.031\n", + " 1.062\n", + " 0.926\n", + " 0.911\n", " NaN\n", " NaN\n", " \n", @@ -1308,7 +2369,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.092\n", + " -0.107\n", " NaN\n", " NaN\n", " NaN\n", @@ -1324,8 +2385,8 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.315\n", - " 0.312\n", + " 0.141\n", + " 0.206\n", " NaN\n", " NaN\n", " NaN\n", @@ -1340,15 +2401,15 @@ " NaN\n", " NaN\n", " NaN\n", - " -1.497\n", - " -1.365\n", - " -1.431\n", - " -1.280\n", - " -1.121\n", - " -1.030\n", - " -0.838\n", - " -0.591\n", - " -0.616\n", + " -1.504\n", + " -1.379\n", + " -1.338\n", + " -1.288\n", + " -1.175\n", + " -1.094\n", + " -0.994\n", + " -0.615\n", + " -0.506\n", " \n", " \n", " SeqScan\n", @@ -1356,15 +2417,15 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.533\n", - " 0.608\n", - " 0.528\n", - " 0.412\n", - " 0.356\n", - " 0.505\n", - " 0.639\n", - " 0.992\n", - " 0.799\n", + " 0.924\n", + " 1.036\n", + " 1.053\n", + " 0.947\n", + " 0.792\n", + " 0.832\n", + " 0.825\n", + " 1.138\n", + " 1.371\n", " \n", " \n", "\n", @@ -1397,14 +2458,14 @@ "NestedLoopCrossJoin Aggregation NaN NaN NaN NaN \n", " Filter NaN NaN NaN NaN \n", " HashJoin NaN NaN NaN NaN \n", - " NestedLoopJoin -0.559 -0.194 -0.021 -0.026 \n", + " NestedLoopJoin -0.59 -0.57 -0.094 0.093 \n", " Projection NaN NaN NaN NaN \n", " SeqScan NaN NaN NaN NaN \n", " Sort NaN NaN NaN NaN \n", "NestedLoopJoin Aggregation NaN NaN NaN NaN \n", " Filter NaN NaN NaN NaN \n", " HashJoin NaN NaN NaN NaN \n", - " NestedLoopCrossJoin 0.559 0.194 0.021 0.026 \n", + " NestedLoopCrossJoin 0.59 0.57 0.094 -0.093 \n", " Projection NaN NaN NaN NaN \n", " SeqScan NaN NaN NaN NaN \n", " Sort NaN NaN NaN NaN \n", @@ -1432,86 +2493,86 @@ "\n", "row_count 1024 2048 4096 8192 \\\n", "a b \n", - "Aggregation Filter -0.426 -0.222 -0.149 -0.142 \n", - " HashJoin -0.718 -0.683 -0.713 -0.726 \n", - " NestedLoopCrossJoin -0.527 NaN NaN NaN \n", - " NestedLoopJoin -0.304 -0.315 NaN NaN \n", - " Projection -2.116 -1.993 -2.153 -1.999 \n", - " SeqScan -0.086 -0.019 -0.195 -0.307 \n", - " Sort -0.619 -0.627 -0.722 -0.719 \n", - "Filter Aggregation 0.426 0.222 0.149 0.142 \n", - " HashJoin -0.293 -0.461 -0.565 -0.584 \n", - " NestedLoopCrossJoin -0.101 NaN NaN NaN \n", - " NestedLoopJoin 0.122 -0.094 NaN NaN \n", - " Projection -1.690 -1.771 -2.004 -1.857 \n", - " SeqScan 0.339 0.202 -0.046 -0.165 \n", - " Sort -0.194 -0.406 -0.574 -0.577 \n", - "HashJoin Aggregation 0.718 0.683 0.713 0.726 \n", - " Filter 0.293 0.461 0.565 0.584 \n", - " NestedLoopCrossJoin 0.192 NaN NaN NaN \n", - " NestedLoopJoin 0.415 0.368 NaN NaN \n", - " Projection -1.398 -1.310 -1.440 -1.273 \n", - " SeqScan 0.632 0.664 0.518 0.419 \n", - " Sort 0.099 0.056 -0.009 0.007 \n", - "NestedLoopCrossJoin Aggregation 0.527 NaN NaN NaN \n", - " Filter 0.101 NaN NaN NaN \n", - " HashJoin -0.192 NaN NaN NaN \n", - " NestedLoopJoin 0.223 NaN NaN NaN \n", - " Projection -1.589 NaN NaN NaN \n", - " SeqScan 0.441 NaN NaN NaN \n", - " Sort -0.092 NaN NaN NaN \n", - "NestedLoopJoin Aggregation 0.304 0.315 NaN NaN \n", - " Filter -0.122 0.094 NaN NaN \n", - " HashJoin -0.415 -0.368 NaN NaN \n", - " NestedLoopCrossJoin -0.223 NaN NaN NaN \n", - " Projection -1.812 -1.678 NaN NaN \n", - " SeqScan 0.217 0.296 NaN NaN \n", - " Sort -0.315 -0.312 NaN NaN \n", - "Projection Aggregation 2.116 1.993 2.153 1.999 \n", - " Filter 1.690 1.771 2.004 1.857 \n", - " HashJoin 1.398 1.310 1.440 1.273 \n", - " NestedLoopCrossJoin 1.589 NaN NaN NaN \n", - " NestedLoopJoin 1.812 1.678 NaN NaN \n", - " SeqScan 2.030 1.973 1.958 1.691 \n", - " Sort 1.497 1.365 1.431 1.280 \n", - "SeqScan Aggregation 0.086 0.019 0.195 0.307 \n", - " Filter -0.339 -0.202 0.046 0.165 \n", - " HashJoin -0.632 -0.664 -0.518 -0.419 \n", - " NestedLoopCrossJoin -0.441 NaN NaN NaN \n", - " NestedLoopJoin -0.217 -0.296 NaN NaN \n", - " Projection -2.030 -1.973 -1.958 -1.691 \n", - " Sort -0.533 -0.608 -0.528 -0.412 \n", - "Sort Aggregation 0.619 0.627 0.722 0.719 \n", - " Filter 0.194 0.406 0.574 0.577 \n", - " HashJoin -0.099 -0.056 0.009 -0.007 \n", - " NestedLoopCrossJoin 0.092 NaN NaN NaN \n", - " NestedLoopJoin 0.315 0.312 NaN NaN \n", - " Projection -1.497 -1.365 -1.431 -1.280 \n", - " SeqScan 0.533 0.608 0.528 0.412 \n", + "Aggregation Filter -0.285 -0.211 -0.154 -0.117 \n", + " HashJoin 0.304 0.337 0.351 0.403 \n", + " NestedLoopCrossJoin -0.653 NaN NaN NaN \n", + " NestedLoopJoin -0.405 -0.442 NaN NaN \n", + " Projection -2.051 -2.026 -2.004 -1.916 \n", + " SeqScan 0.377 0.389 0.387 0.319 \n", + " Sort -0.546 -0.647 -0.666 -0.628 \n", + "Filter Aggregation 0.285 0.211 0.154 0.117 \n", + " HashJoin 0.589 0.547 0.505 0.521 \n", + " NestedLoopCrossJoin -0.369 NaN NaN NaN \n", + " NestedLoopJoin -0.120 -0.231 NaN NaN \n", + " Projection -1.766 -1.816 -1.851 -1.799 \n", + " SeqScan 0.662 0.600 0.541 0.436 \n", + " Sort -0.262 -0.437 -0.512 -0.510 \n", + "HashJoin Aggregation -0.304 -0.337 -0.351 -0.403 \n", + " Filter -0.589 -0.547 -0.505 -0.521 \n", + " NestedLoopCrossJoin -0.958 NaN NaN NaN \n", + " NestedLoopJoin -0.709 -0.778 NaN NaN \n", + " Projection -2.355 -2.363 -2.356 -2.319 \n", + " SeqScan 0.073 0.052 0.036 -0.084 \n", + " Sort -0.851 -0.984 -1.017 -1.031 \n", + "NestedLoopCrossJoin Aggregation 0.653 NaN NaN NaN \n", + " Filter 0.369 NaN NaN NaN \n", + " HashJoin 0.958 NaN NaN NaN \n", + " NestedLoopJoin 0.248 NaN NaN NaN \n", + " Projection -1.397 NaN NaN NaN \n", + " SeqScan 1.031 NaN NaN NaN \n", + " Sort 0.107 NaN NaN NaN \n", + "NestedLoopJoin Aggregation 0.405 0.442 NaN NaN \n", + " Filter 0.120 0.231 NaN NaN \n", + " HashJoin 0.709 0.778 NaN NaN \n", + " NestedLoopCrossJoin -0.248 NaN NaN NaN \n", + " Projection -1.646 -1.585 NaN NaN \n", + " SeqScan 0.782 0.831 NaN NaN \n", + " Sort -0.141 -0.206 NaN NaN \n", + "Projection Aggregation 2.051 2.026 2.004 1.916 \n", + " Filter 1.766 1.816 1.851 1.799 \n", + " HashJoin 2.355 2.363 2.356 2.319 \n", + " NestedLoopCrossJoin 1.397 NaN NaN NaN \n", + " NestedLoopJoin 1.646 1.585 NaN NaN \n", + " SeqScan 2.428 2.415 2.392 2.235 \n", + " Sort 1.504 1.379 1.338 1.288 \n", + "SeqScan Aggregation -0.377 -0.389 -0.387 -0.319 \n", + " Filter -0.662 -0.600 -0.541 -0.436 \n", + " HashJoin -0.073 -0.052 -0.036 0.084 \n", + " NestedLoopCrossJoin -1.031 NaN NaN NaN \n", + " NestedLoopJoin -0.782 -0.831 NaN NaN \n", + " Projection -2.428 -2.415 -2.392 -2.235 \n", + " Sort -0.924 -1.036 -1.053 -0.947 \n", + "Sort Aggregation 0.546 0.647 0.666 0.628 \n", + " Filter 0.262 0.437 0.512 0.510 \n", + " HashJoin 0.851 0.984 1.017 1.031 \n", + " NestedLoopCrossJoin -0.107 NaN NaN NaN \n", + " NestedLoopJoin 0.141 0.206 NaN NaN \n", + " Projection -1.504 -1.379 -1.338 -1.288 \n", + " SeqScan 0.924 1.036 1.053 0.947 \n", "\n", "row_count 16384 32768 65536 131072 \\\n", "a b \n", - "Aggregation Filter 0.126 0.347 0.440 0.716 \n", - " HashJoin -0.673 -0.347 -0.063 NaN \n", + "Aggregation Filter 0.135 0.384 0.814 0.966 \n", + " HashJoin 0.497 0.577 0.823 NaN \n", " NestedLoopCrossJoin NaN NaN NaN NaN \n", " NestedLoopJoin NaN NaN NaN NaN \n", - " Projection -1.768 -1.389 -1.083 -0.898 \n", - " SeqScan -0.291 0.146 0.394 0.684 \n", - " Sort -0.647 -0.359 -0.245 -0.307 \n", - "Filter Aggregation -0.126 -0.347 -0.440 -0.716 \n", - " HashJoin -0.799 -0.695 -0.503 NaN \n", + " Projection -1.740 -1.443 -1.081 -0.891 \n", + " SeqScan 0.227 0.483 0.737 0.861 \n", + " Sort -0.564 -0.349 -0.087 -0.276 \n", + "Filter Aggregation -0.135 -0.384 -0.814 -0.966 \n", + " HashJoin 0.363 0.193 0.010 NaN \n", " NestedLoopCrossJoin NaN NaN NaN NaN \n", " NestedLoopJoin NaN NaN NaN NaN \n", - " Projection -1.894 -1.736 -1.523 -1.614 \n", - " SeqScan -0.417 -0.201 -0.046 -0.031 \n", - " Sort -0.773 -0.706 -0.685 -1.023 \n", - "HashJoin Aggregation 0.673 0.347 0.063 NaN \n", - " Filter 0.799 0.695 0.503 NaN \n", + " Projection -1.874 -1.827 -1.895 -1.857 \n", + " SeqScan 0.093 0.099 -0.076 -0.105 \n", + " Sort -0.699 -0.733 -0.901 -1.242 \n", + "HashJoin Aggregation -0.497 -0.577 -0.823 NaN \n", + " Filter -0.363 -0.193 -0.010 NaN \n", " NestedLoopCrossJoin NaN NaN NaN NaN \n", " NestedLoopJoin NaN NaN NaN NaN \n", - " Projection -1.095 -1.042 -1.019 NaN \n", - " SeqScan 0.382 0.494 0.458 NaN \n", - " Sort 0.026 -0.011 -0.181 NaN \n", + " Projection -2.237 -2.019 -1.905 NaN \n", + " SeqScan -0.270 -0.093 -0.086 NaN \n", + " Sort -1.062 -0.926 -0.911 NaN \n", "NestedLoopCrossJoin Aggregation NaN NaN NaN NaN \n", " Filter NaN NaN NaN NaN \n", " HashJoin NaN NaN NaN NaN \n", @@ -1526,44 +2587,44 @@ " Projection NaN NaN NaN NaN \n", " SeqScan NaN NaN NaN NaN \n", " Sort NaN NaN NaN NaN \n", - "Projection Aggregation 1.768 1.389 1.083 0.898 \n", - " Filter 1.894 1.736 1.523 1.614 \n", - " HashJoin 1.095 1.042 1.019 NaN \n", + "Projection Aggregation 1.740 1.443 1.081 0.891 \n", + " Filter 1.874 1.827 1.895 1.857 \n", + " HashJoin 2.237 2.019 1.905 NaN \n", " NestedLoopCrossJoin NaN NaN NaN NaN \n", " NestedLoopJoin NaN NaN NaN NaN \n", - " SeqScan 1.477 1.535 1.477 1.582 \n", - " Sort 1.121 1.030 0.838 0.591 \n", - "SeqScan Aggregation 0.291 -0.146 -0.394 -0.684 \n", - " Filter 0.417 0.201 0.046 0.031 \n", - " HashJoin -0.382 -0.494 -0.458 NaN \n", + " SeqScan 1.967 1.926 1.819 1.753 \n", + " Sort 1.175 1.094 0.994 0.615 \n", + "SeqScan Aggregation -0.227 -0.483 -0.737 -0.861 \n", + " Filter -0.093 -0.099 0.076 0.105 \n", + " HashJoin 0.270 0.093 0.086 NaN \n", " NestedLoopCrossJoin NaN NaN NaN NaN \n", " NestedLoopJoin NaN NaN NaN NaN \n", - " Projection -1.477 -1.535 -1.477 -1.582 \n", - " Sort -0.356 -0.505 -0.639 -0.992 \n", - "Sort Aggregation 0.647 0.359 0.245 0.307 \n", - " Filter 0.773 0.706 0.685 1.023 \n", - " HashJoin -0.026 0.011 0.181 NaN \n", + " Projection -1.967 -1.926 -1.819 -1.753 \n", + " Sort -0.792 -0.832 -0.825 -1.138 \n", + "Sort Aggregation 0.564 0.349 0.087 0.276 \n", + " Filter 0.699 0.733 0.901 1.242 \n", + " HashJoin 1.062 0.926 0.911 NaN \n", " NestedLoopCrossJoin NaN NaN NaN NaN \n", " NestedLoopJoin NaN NaN NaN NaN \n", - " Projection -1.121 -1.030 -0.838 -0.591 \n", - " SeqScan 0.356 0.505 0.639 0.992 \n", + " Projection -1.175 -1.094 -0.994 -0.615 \n", + " SeqScan 0.792 0.832 0.825 1.138 \n", "\n", "row_count 262144 \n", "a b \n", - "Aggregation Filter 0.910 \n", + "Aggregation Filter 1.190 \n", " HashJoin NaN \n", " NestedLoopCrossJoin NaN \n", " NestedLoopJoin NaN \n", - " Projection -1.023 \n", - " SeqScan 0.392 \n", - " Sort -0.407 \n", - "Filter Aggregation -0.910 \n", + " Projection -0.870 \n", + " SeqScan 1.007 \n", + " Sort -0.364 \n", + "Filter Aggregation -1.190 \n", " HashJoin NaN \n", " NestedLoopCrossJoin NaN \n", " NestedLoopJoin NaN \n", - " Projection -1.933 \n", - " SeqScan -0.518 \n", - " Sort -1.317 \n", + " Projection -2.060 \n", + " SeqScan -0.183 \n", + " Sort -1.554 \n", "HashJoin Aggregation NaN \n", " Filter NaN \n", " NestedLoopCrossJoin NaN \n", @@ -1585,27 +2646,27 @@ " Projection NaN \n", " SeqScan NaN \n", " Sort NaN \n", - "Projection Aggregation 1.023 \n", - " Filter 1.933 \n", + "Projection Aggregation 0.870 \n", + " Filter 2.060 \n", " HashJoin NaN \n", " NestedLoopCrossJoin NaN \n", " NestedLoopJoin NaN \n", - " SeqScan 1.415 \n", - " Sort 0.616 \n", - "SeqScan Aggregation -0.392 \n", - " Filter 0.518 \n", + " SeqScan 1.877 \n", + " Sort 0.506 \n", + "SeqScan Aggregation -1.007 \n", + " Filter 0.183 \n", " HashJoin NaN \n", " NestedLoopCrossJoin NaN \n", " NestedLoopJoin NaN \n", - " Projection -1.415 \n", - " Sort -0.799 \n", - "Sort Aggregation 0.407 \n", - " Filter 1.317 \n", + " Projection -1.877 \n", + " Sort -1.371 \n", + "Sort Aggregation 0.364 \n", + " Filter 1.554 \n", " HashJoin NaN \n", " NestedLoopCrossJoin NaN \n", " NestedLoopJoin NaN \n", - " Projection -0.616 \n", - " SeqScan 0.799 " + " Projection -0.506 \n", + " SeqScan 1.371 " ] }, "execution_count": 7, @@ -1639,7 +2700,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2026-05-31T16:16:05+03:00\n", + "2026-06-09T23:41:04+03:00\n", "Running ./build-release/bin/benchmarks\n", "Run on (8 X 4200 MHz CPU s)\n", "CPU Caches:\n", @@ -1647,59 +2708,59 @@ " L1 Instruction 32 KiB (x4)\n", " L2 Unified 1280 KiB (x4)\n", " L3 Unified 8192 KiB (x1)\n", - "Load Average: 3.38, 2.81, 1.77\n", + "Load Average: 1.09, 0.80, 1.04\n", "***WARNING*** CPU scaling is enabled, the benchmark real time measurements may be noisy and will incur extra overhead.\n", "-----------------------------------------------------------------------------------------------------------------------------------------\n", "Benchmark Time CPU Iterations UserCounters...\n", "-----------------------------------------------------------------------------------------------------------------------------------------\n", - "\u001b[0;32mOperatorCostMatched/target_cost:640000/SeqScan/6400/real_time \u001b[m\u001b[0;33m 532418 ns 521056 ns \u001b[m\u001b[0;36m 1116\u001b[m model_cost=640k\u001b[m output_rows=6.4k\u001b[m plan_cost=640k\u001b[m rows=6.4k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Filter/6400/real_time \u001b[m\u001b[0;33m 508270 ns 501445 ns \u001b[m\u001b[0;36m 1089\u001b[m model_cost=640k\u001b[m output_rows=3.328k\u001b[m plan_cost=1.28M\u001b[m rows=6.4k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Projection/29091/real_time \u001b[m\u001b[0;33m 4068856 ns 4010747 ns \u001b[m\u001b[0;36m 156\u001b[m model_cost=640.002k\u001b[m output_rows=29.091k\u001b[m plan_cost=3.5491M\u001b[m rows=29.091k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Sort/4476/real_time \u001b[m\u001b[0;33m 997566 ns 988471 ns \u001b[m\u001b[0;36m 786\u001b[m model_cost=640.068k\u001b[m output_rows=4.476k\u001b[m plan_cost=1.08767M\u001b[m rows=4.476k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Aggregation/1255/real_time \u001b[m\u001b[0;33m 529187 ns 520615 ns 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rhs_rows=78\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/SeqScan/10000/real_time \u001b[m\u001b[0;33m 507202 ns 502389 ns \u001b[m\u001b[0;36m 1000\u001b[m model_cost=1M\u001b[m output_rows=10k\u001b[m plan_cost=1M\u001b[m rows=10k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Filter/10000/real_time \u001b[m\u001b[0;33m 861315 ns 854085 ns \u001b[m\u001b[0;36m 818\u001b[m model_cost=1M\u001b[m output_rows=5.12k\u001b[m plan_cost=2M\u001b[m rows=10k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Projection/45455/real_time \u001b[m\u001b[0;33m 6698145 ns 6632289 ns \u001b[m\u001b[0;36m 94\u001b[m model_cost=1.00001M\u001b[m output_rows=45.455k\u001b[m plan_cost=5.54551M\u001b[m rows=45.455k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Sort/6993/real_time \u001b[m\u001b[0;33m 1503435 ns 1491454 ns \u001b[m\u001b[0;36m 442\u001b[m model_cost=0.999999M\u001b[m output_rows=6.993k\u001b[m plan_cost=1.6993M\u001b[m rows=6.993k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Aggregation/1961/real_time \u001b[m\u001b[0;33m 702024 ns 697631 ns \u001b[m\u001b[0;36m 968\u001b[m model_cost=1.00011M\u001b[m output_rows=1.961k\u001b[m plan_cost=1.19621M\u001b[m rows=1.961k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/HashJoin/7246/7246/real_time \u001b[m\u001b[0;33m 1406982 ns 1398061 ns \u001b[m\u001b[0;36m 445\u001b[m lhs_rows=7.246k\u001b[m model_cost=0.999948M\u001b[m output_rows=7.246k\u001b[m plan_cost=2.44915M\u001b[m rhs_rows=7.246k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/NestedLoopJoin/120/120/real_time \u001b[m\u001b[0;33m 1204952 ns 1197240 ns \u001b[m\u001b[0;36m 548\u001b[m lhs_rows=120\u001b[m model_cost=1.008M\u001b[m output_rows=120\u001b[m plan_cost=1.032M\u001b[m rhs_rows=120\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/NestedLoopCrossJoin/98/98/real_time \u001b[m\u001b[0;33m 735577 ns 729661 ns \u001b[m\u001b[0;36m 851\u001b[m lhs_rows=98\u001b[m model_cost=998.816k\u001b[m output_rows=9.604k\u001b[m plan_cost=1.01842M\u001b[m rhs_rows=98\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/SeqScan/32400/real_time \u001b[m\u001b[0;33m 1552618 ns 1542405 ns \u001b[m\u001b[0;36m 398\u001b[m model_cost=3.24M\u001b[m output_rows=32.4k\u001b[m plan_cost=3.24M\u001b[m rows=32.4k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Filter/32400/real_time \u001b[m\u001b[0;33m 2615187 ns 2600696 ns \u001b[m\u001b[0;36m 253\u001b[m model_cost=3.24M\u001b[m output_rows=16.384k\u001b[m plan_cost=6.48M\u001b[m rows=32.4k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Projection/147273/real_time \u001b[m\u001b[0;33m 24615070 ns 24428312 ns \u001b[m\u001b[0;36m 28\u001b[m model_cost=3.24001M\u001b[m output_rows=147.273k\u001b[m plan_cost=17.9673M\u001b[m rows=147.273k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Sort/19636/real_time \u001b[m\u001b[0;33m 5716537 ns 5678245 ns \u001b[m\u001b[0;36m 122\u001b[m model_cost=3.23994M\u001b[m output_rows=19.636k\u001b[m plan_cost=5.20354M\u001b[m rows=19.636k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Aggregation/6353/real_time \u001b[m\u001b[0;33m 2528047 ns 2507421 ns \u001b[m\u001b[0;36m 253\u001b[m model_cost=3.24003M\u001b[m output_rows=6.353k\u001b[m plan_cost=3.87533M\u001b[m rows=6.353k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/HashJoin/23478/23478/real_time \u001b[m\u001b[0;33m 5300551 ns 5262456 ns \u001b[m\u001b[0;36m 116\u001b[m lhs_rows=23.478k\u001b[m model_cost=3.23996M\u001b[m output_rows=23.478k\u001b[m plan_cost=7.93556M\u001b[m rhs_rows=23.478k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopJoin/215/215/real_time \u001b[m\u001b[0;33m 3440769 ns 3423785 ns \u001b[m\u001b[0;36m 199\u001b[m lhs_rows=215\u001b[m model_cost=3.23575M\u001b[m output_rows=215\u001b[m plan_cost=3.27875M\u001b[m rhs_rows=215\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopCrossJoin/177/177/real_time \u001b[m\u001b[0;33m 2294554 ns 2274793 ns \u001b[m\u001b[0;36m 241\u001b[m lhs_rows=177\u001b[m model_cost=3.25822M\u001b[m output_rows=31.329k\u001b[m plan_cost=3.29362M\u001b[m rhs_rows=177\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/SeqScan/67600/real_time \u001b[m\u001b[0;33m 4220053 ns 4185107 ns \u001b[m\u001b[0;36m 169\u001b[m model_cost=6.76M\u001b[m output_rows=67.6k\u001b[m plan_cost=6.76M\u001b[m rows=67.6k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Filter/67600/real_time \u001b[m\u001b[0;33m 5592034 ns 5560841 ns 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"\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/HashJoin/48986/48986/real_time \u001b[m\u001b[0;33m 13564179 ns 13440945 ns \u001b[m\u001b[0;36m 51\u001b[m lhs_rows=48.986k\u001b[m model_cost=6.76007M\u001b[m output_rows=48.986k\u001b[m plan_cost=16.5573M\u001b[m rhs_rows=48.986k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopJoin/311/311/real_time \u001b[m\u001b[0;33m 7198310 ns 7163077 ns \u001b[m\u001b[0;36m 93\u001b[m lhs_rows=311\u001b[m model_cost=6.77047M\u001b[m output_rows=311\u001b[m plan_cost=6.83267M\u001b[m rhs_rows=311\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopCrossJoin/255/255/real_time \u001b[m\u001b[0;33m 6012240 ns 5936779 ns \u001b[m\u001b[0;36m 87\u001b[m lhs_rows=255\u001b[m model_cost=6.7626M\u001b[m output_rows=65.025k\u001b[m plan_cost=6.8136M\u001b[m rhs_rows=255\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/SeqScan/122500/real_time 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model_cost=639.9k\u001b[m output_rows=1.62k\u001b[m plan_cost=963.9k\u001b[m rhs_rows=1.62k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopJoin/96/96/real_time \u001b[m\u001b[0;33m 697766 ns 693468 ns \u001b[m\u001b[0;36m 1046\u001b[m lhs_rows=96\u001b[m model_cost=645.12k\u001b[m output_rows=96\u001b[m plan_cost=664.32k\u001b[m rhs_rows=96\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopCrossJoin/78/78/real_time \u001b[m\u001b[0;33m 399168 ns 395553 ns \u001b[m\u001b[0;36m 1705\u001b[m lhs_rows=78\u001b[m model_cost=632.736k\u001b[m output_rows=6.084k\u001b[m plan_cost=648.336k\u001b[m rhs_rows=78\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/SeqScan/10000/real_time \u001b[m\u001b[0;33m 380590 ns 377409 ns \u001b[m\u001b[0;36m 1982\u001b[m model_cost=1M\u001b[m output_rows=10k\u001b[m plan_cost=1M\u001b[m rows=10k\u001b[m\n", + 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'time_unit': 'ns',\n", + " 'model_cost': 26010072.0,\n", + " 'output_rows': 131364.0,\n", + " 'plan_cost': 39146472.0,\n", + " 'rows': 131364.0},\n", + " {'name': 'OperatorCostMatched/target_cost:26010000/Aggregation/51000/real_time',\n", + " 'family_index': 44,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCostMatched/target_cost:26010000/Aggregation/51000/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 21,\n", + " 'real_time': 34235903.47630948,\n", + " 'cpu_time': 33978390.333333306,\n", + " 'time_unit': 'ns',\n", + " 'model_cost': 26010000.0,\n", + " 'output_rows': 51000.0,\n", + " 'plan_cost': 31110000.0,\n", + " 'rows': 51000.0},\n", + " {'name': 'OperatorCostMatched/target_cost:26010000/HashJoin/65848/65848/real_time',\n", + " 'family_index': 45,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCostMatched/target_cost:26010000/HashJoin/65848/65848/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 47,\n", + " 'real_time': 15134376.276809564,\n", + " 'cpu_time': 15002296.74468074,\n", + " 'time_unit': 'ns',\n", + " 'lhs_rows': 65848.0,\n", + " 'model_cost': 26009960.0,\n", + " 'output_rows': 65848.0,\n", + " 'plan_cost': 39179560.0,\n", + " 'rhs_rows': 65848.0},\n", + " {'name': 'OperatorCostMatched/target_cost:26010000/NestedLoopJoin/610/610/real_time',\n", + " 'family_index': 46,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCostMatched/target_cost:26010000/NestedLoopJoin/610/610/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 31,\n", + " 'real_time': 21153396.581329647,\n", + " 'cpu_time': 21080711.03225805,\n", + " 'time_unit': 'ns',\n", + " 'lhs_rows': 610.0,\n", + " 'model_cost': 26047000.0,\n", + " 'output_rows': 610.0,\n", + " 'plan_cost': 26169000.0,\n", + " 'rhs_rows': 610.0},\n", + " {'name': 'OperatorCostMatched/target_cost:26010000/NestedLoopCrossJoin/500/500/real_time',\n", + " 'family_index': 47,\n", + " 'per_family_instance_index': 0,\n", + " 'run_name': 'OperatorCostMatched/target_cost:26010000/NestedLoopCrossJoin/500/500/real_time',\n", + " 'run_type': 'iteration',\n", + " 'repetitions': 1,\n", + " 'repetition_index': 0,\n", + " 'threads': 1,\n", + " 'iterations': 31,\n", + " 'real_time': 22172262.903393038,\n", + " 'cpu_time': 21945665.74193559,\n", + " 'time_unit': 'ns',\n", + " 'lhs_rows': 500.0,\n", + " 'model_cost': 26000000.0,\n", + " 'output_rows': 250000.0,\n", + " 'plan_cost': 26100000.0,\n", + " 'rhs_rows': 500.0}]}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import json\n", "results = None\n", @@ -1767,7 +3638,7 @@ " Aggregation\n", " 1255\n", " NaN\n", - " 0.529187\n", + " 0.348253\n", " 640050.0\n", " 765550.0\n", " 7.812500e-05\n", @@ -1778,7 +3649,7 @@ " Filter\n", " 6400\n", " NaN\n", - " 0.508270\n", + " 0.407352\n", " 640000.0\n", " 1280000.0\n", " 0.000000e+00\n", @@ -1787,12 +3658,12 @@ " 5\n", " 640000\n", " HashJoin\n", - " 4638\n", - " 4638.0\n", - " 1.026885\n", - " 640044.0\n", - " 1567644.0\n", - " 6.875000e-05\n", + " 1620\n", + " 1620.0\n", + " 0.246864\n", + " 639900.0\n", + " 963900.0\n", + " 1.562500e-04\n", " \n", " \n", " 7\n", @@ -1800,7 +3671,7 @@ " NestedLoopCrossJoin\n", " 78\n", " 78.0\n", - " 0.527338\n", + " 0.399168\n", " 632736.0\n", " 648336.0\n", " 1.135000e-02\n", @@ -1811,7 +3682,7 @@ " NestedLoopJoin\n", " 96\n", " 96.0\n", - " 0.875389\n", + " 0.697766\n", " 645120.0\n", " 664320.0\n", " 8.000000e-03\n", @@ -1822,7 +3693,7 @@ " Projection\n", " 29091\n", " NaN\n", - " 4.068856\n", + " 3.320695\n", " 640002.0\n", " 3549102.0\n", " 3.125000e-06\n", @@ -1833,7 +3704,7 @@ " SeqScan\n", " 6400\n", " NaN\n", - " 0.532418\n", + " 0.309954\n", " 640000.0\n", " 640000.0\n", " 0.000000e+00\n", @@ -1844,7 +3715,7 @@ " Sort\n", " 4476\n", " NaN\n", - " 0.997566\n", + " 0.720259\n", " 640068.0\n", " 1087668.0\n", " 1.062500e-04\n", @@ -1855,7 +3726,7 @@ " Aggregation\n", " 1961\n", " NaN\n", - " 0.702024\n", + " 0.531581\n", " 1000110.0\n", " 1196210.0\n", " 1.100000e-04\n", @@ -1866,7 +3737,7 @@ " Filter\n", " 10000\n", " NaN\n", - " 0.861315\n", + " 0.657861\n", " 1000000.0\n", " 2000000.0\n", " 0.000000e+00\n", @@ -1875,12 +3746,12 @@ " 13\n", " 1000000\n", " HashJoin\n", - " 7246\n", - " 7246.0\n", - " 1.406982\n", - " 999948.0\n", - " 2449148.0\n", - " 5.200000e-05\n", + " 2532\n", + " 2532.0\n", + " 0.396404\n", + " 1000140.0\n", + " 1506540.0\n", + " 1.400000e-04\n", " \n", " \n", " 15\n", @@ -1888,7 +3759,7 @@ " NestedLoopCrossJoin\n", " 98\n", " 98.0\n", - " 0.735577\n", + " 0.592521\n", " 998816.0\n", " 1018416.0\n", " 1.184000e-03\n", @@ -1899,7 +3770,7 @@ " NestedLoopJoin\n", " 120\n", " 120.0\n", - " 1.204952\n", + " 0.982848\n", " 1008000.0\n", " 1032000.0\n", " 8.000000e-03\n", @@ -1910,7 +3781,7 @@ " Projection\n", " 45455\n", " NaN\n", - " 6.698145\n", + " 5.492013\n", " 1000010.0\n", " 5545510.0\n", " 1.000000e-05\n", @@ -1921,7 +3792,7 @@ " SeqScan\n", " 10000\n", " NaN\n", - " 0.507202\n", + " 0.380590\n", " 1000000.0\n", " 1000000.0\n", " 0.000000e+00\n", @@ -1932,7 +3803,7 @@ " Sort\n", " 6993\n", " NaN\n", - " 1.503435\n", + " 1.165643\n", " 999999.0\n", " 1699299.0\n", " 1.000000e-06\n", @@ -1943,7 +3814,7 @@ " Aggregation\n", " 6353\n", " NaN\n", - " 2.528047\n", + " 1.831355\n", " 3240030.0\n", " 3875330.0\n", " 9.259259e-06\n", @@ -1954,7 +3825,7 @@ " Filter\n", " 32400\n", " NaN\n", - " 2.615187\n", + " 2.130491\n", " 3240000.0\n", " 6480000.0\n", " 0.000000e+00\n", @@ -1963,12 +3834,12 @@ " 21\n", " 3240000\n", " HashJoin\n", - " 23478\n", - " 23478.0\n", - " 5.300551\n", - " 3239964.0\n", - " 7935564.0\n", - " 1.111111e-05\n", + " 8203\n", + " 8203.0\n", + " 1.297606\n", + " 3240185.0\n", + " 4880785.0\n", + " 5.709877e-05\n", " \n", " \n", " 23\n", @@ -1976,7 +3847,7 @@ " NestedLoopCrossJoin\n", " 177\n", " 177.0\n", - " 2.294554\n", + " 1.823392\n", " 3258216.0\n", " 3293616.0\n", " 5.622222e-03\n", @@ -1987,7 +3858,7 @@ " NestedLoopJoin\n", " 215\n", " 215.0\n", - " 3.440769\n", + " 2.799041\n", " 3235750.0\n", " 3278750.0\n", " 1.311728e-03\n", @@ -1998,7 +3869,7 @@ " Projection\n", " 147273\n", " NaN\n", - " 24.615070\n", + " 19.355047\n", " 3240006.0\n", " 17967306.0\n", " 1.851852e-06\n", @@ -2009,7 +3880,7 @@ " SeqScan\n", " 32400\n", " NaN\n", - " 1.552618\n", + " 1.145653\n", " 3240000.0\n", " 3240000.0\n", " 0.000000e+00\n", @@ -2020,7 +3891,7 @@ " Sort\n", " 19636\n", " NaN\n", - " 5.716537\n", + " 4.048476\n", " 3239940.0\n", " 5203540.0\n", " 1.851852e-05\n", @@ -2031,7 +3902,7 @@ " Aggregation\n", " 13255\n", " NaN\n", - " 5.687200\n", + " 4.172917\n", " 6760050.0\n", " 8085550.0\n", " 7.396450e-06\n", @@ -2042,7 +3913,7 @@ " Filter\n", " 67600\n", " NaN\n", - " 5.592034\n", + " 4.628039\n", " 6760000.0\n", " 13520000.0\n", " 0.000000e+00\n", @@ -2051,12 +3922,12 @@ " 29\n", " 6760000\n", " HashJoin\n", - " 48986\n", - " 48986.0\n", - " 13.564179\n", - " 6760068.0\n", - " 16557268.0\n", - " 1.005917e-05\n", + " 17114\n", + " 17114.0\n", + " 2.975624\n", + " 6760030.0\n", + " 10182830.0\n", + " 4.437870e-06\n", " \n", " \n", " 31\n", @@ -2064,7 +3935,7 @@ " NestedLoopCrossJoin\n", " 255\n", " 255.0\n", - " 6.012240\n", + " 5.343956\n", " 6762600.0\n", " 6813600.0\n", " 3.846154e-04\n", @@ -2075,7 +3946,7 @@ " NestedLoopJoin\n", " 311\n", " 311.0\n", - " 7.198310\n", + " 5.785900\n", " 6770470.0\n", " 6832670.0\n", " 1.548817e-03\n", @@ -2086,7 +3957,7 @@ " Projection\n", " 307273\n", " NaN\n", - " 51.498743\n", + " 38.239999\n", " 6760006.0\n", " 37487306.0\n", " 8.875740e-07\n", @@ -2097,7 +3968,7 @@ " SeqScan\n", " 67600\n", " NaN\n", - " 4.220053\n", + " 3.559425\n", " 6760000.0\n", " 6760000.0\n", " 0.000000e+00\n", @@ -2108,7 +3979,7 @@ " Sort\n", " 38409\n", " NaN\n", - " 12.802465\n", + " 10.169495\n", " 6759984.0\n", " 10600884.0\n", " 2.366864e-06\n", @@ -2119,7 +3990,7 @@ " Aggregation\n", " 24020\n", " NaN\n", - " 15.047581\n", + " 10.917472\n", " 12250200.0\n", " 14652200.0\n", " 1.632653e-05\n", @@ -2130,7 +4001,7 @@ " Filter\n", " 122500\n", " NaN\n", - " 10.313976\n", + " 8.754703\n", " 12250000.0\n", " 24500000.0\n", " 0.000000e+00\n", @@ -2139,12 +4010,12 @@ " 37\n", " 12250000\n", " HashJoin\n", - " 88768\n", - " 88768.0\n", - " 25.706484\n", - " 12249984.0\n", - " 30003584.0\n", - " 1.306122e-06\n", + " 31013\n", + " 31013.0\n", + " 6.093819\n", + " 12250135.0\n", + " 18452735.0\n", + " 1.102041e-05\n", " \n", " \n", " 39\n", @@ -2152,7 +4023,7 @@ " NestedLoopCrossJoin\n", " 343\n", " 343.0\n", - " 12.634892\n", + " 10.467055\n", " 12235496.0\n", " 12304096.0\n", " 1.184000e-03\n", @@ -2163,7 +4034,7 @@ " NestedLoopJoin\n", " 418\n", " 418.0\n", - " 12.686905\n", + " 10.007416\n", " 12230680.0\n", " 12314280.0\n", " 1.577143e-03\n", @@ -2174,7 +4045,7 @@ " Projection\n", " 556818\n", " NaN\n", - " 90.203371\n", + " 74.966988\n", " 12249996.0\n", " 67931796.0\n", " 3.265306e-07\n", @@ -2185,7 +4056,7 @@ " SeqScan\n", " 122500\n", " NaN\n", - " 7.610143\n", + " 7.067011\n", " 12250000.0\n", " 12250000.0\n", " 0.000000e+00\n", @@ -2196,7 +4067,7 @@ " Sort\n", " 65536\n", " NaN\n", - " 26.495662\n", + " 19.754016\n", " 12255232.0\n", " 18808832.0\n", " 4.271020e-04\n", @@ -2207,7 +4078,7 @@ " Aggregation\n", " 51000\n", " NaN\n", - " 48.595095\n", + " 34.235903\n", " 26010000.0\n", " 31110000.0\n", " 0.000000e+00\n", @@ -2218,7 +4089,7 @@ " Filter\n", " 260100\n", " NaN\n", - " 26.934394\n", + " 19.593865\n", " 26010000.0\n", " 52020000.0\n", " 0.000000e+00\n", @@ -2227,12 +4098,12 @@ " 45\n", " 26010000\n", " HashJoin\n", - " 188478\n", - " 188478.0\n", - " 52.584297\n", - " 26009964.0\n", - " 63705564.0\n", - " 1.384083e-06\n", + " 65848\n", + " 65848.0\n", + " 15.134376\n", + " 26009960.0\n", + " 39179560.0\n", + " 1.537870e-06\n", " \n", " \n", " 47\n", @@ -2240,7 +4111,7 @@ " NestedLoopCrossJoin\n", " 500\n", " 500.0\n", - " 30.993358\n", + " 22.172263\n", " 26000000.0\n", " 26100000.0\n", " 3.844675e-04\n", @@ -2251,7 +4122,7 @@ " NestedLoopJoin\n", " 610\n", " 610.0\n", - " 25.448562\n", + " 21.153397\n", " 26047000.0\n", " 26169000.0\n", " 1.422530e-03\n", @@ -2262,7 +4133,7 @@ " Projection\n", " 1182273\n", " NaN\n", - " 202.987386\n", + " 159.555687\n", " 26010006.0\n", " 144237306.0\n", " 2.306805e-07\n", @@ -2273,7 +4144,7 @@ " SeqScan\n", " 260100\n", " NaN\n", - " 18.734465\n", + " 16.761908\n", " 26010000.0\n", " 26010000.0\n", " 0.000000e+00\n", @@ -2284,7 +4155,7 @@ " Sort\n", " 131364\n", " NaN\n", - " 74.202993\n", + " 56.860476\n", " 26010072.0\n", " 39146472.0\n", " 2.768166e-06\n", @@ -2295,59 +4166,59 @@ ], "text/plain": [ " target_cost operator left_rows right_rows real_time_ms \\\n", - "4 640000 Aggregation 1255 NaN 0.529187 \n", - "1 640000 Filter 6400 NaN 0.508270 \n", - "5 640000 HashJoin 4638 4638.0 1.026885 \n", - "7 640000 NestedLoopCrossJoin 78 78.0 0.527338 \n", - "6 640000 NestedLoopJoin 96 96.0 0.875389 \n", - "2 640000 Projection 29091 NaN 4.068856 \n", - "0 640000 SeqScan 6400 NaN 0.532418 \n", - "3 640000 Sort 4476 NaN 0.997566 \n", - "12 1000000 Aggregation 1961 NaN 0.702024 \n", - "9 1000000 Filter 10000 NaN 0.861315 \n", - "13 1000000 HashJoin 7246 7246.0 1.406982 \n", - "15 1000000 NestedLoopCrossJoin 98 98.0 0.735577 \n", - "14 1000000 NestedLoopJoin 120 120.0 1.204952 \n", - "10 1000000 Projection 45455 NaN 6.698145 \n", - "8 1000000 SeqScan 10000 NaN 0.507202 \n", - "11 1000000 Sort 6993 NaN 1.503435 \n", - "20 3240000 Aggregation 6353 NaN 2.528047 \n", - "17 3240000 Filter 32400 NaN 2.615187 \n", - "21 3240000 HashJoin 23478 23478.0 5.300551 \n", - "23 3240000 NestedLoopCrossJoin 177 177.0 2.294554 \n", - "22 3240000 NestedLoopJoin 215 215.0 3.440769 \n", - "18 3240000 Projection 147273 NaN 24.615070 \n", - "16 3240000 SeqScan 32400 NaN 1.552618 \n", - "19 3240000 Sort 19636 NaN 5.716537 \n", - "28 6760000 Aggregation 13255 NaN 5.687200 \n", - "25 6760000 Filter 67600 NaN 5.592034 \n", - "29 6760000 HashJoin 48986 48986.0 13.564179 \n", - "31 6760000 NestedLoopCrossJoin 255 255.0 6.012240 \n", - "30 6760000 NestedLoopJoin 311 311.0 7.198310 \n", - "26 6760000 Projection 307273 NaN 51.498743 \n", - "24 6760000 SeqScan 67600 NaN 4.220053 \n", - "27 6760000 Sort 38409 NaN 12.802465 \n", - "36 12250000 Aggregation 24020 NaN 15.047581 \n", - "33 12250000 Filter 122500 NaN 10.313976 \n", - "37 12250000 HashJoin 88768 88768.0 25.706484 \n", - "39 12250000 NestedLoopCrossJoin 343 343.0 12.634892 \n", - "38 12250000 NestedLoopJoin 418 418.0 12.686905 \n", - "34 12250000 Projection 556818 NaN 90.203371 \n", - "32 12250000 SeqScan 122500 NaN 7.610143 \n", - "35 12250000 Sort 65536 NaN 26.495662 \n", - "44 26010000 Aggregation 51000 NaN 48.595095 \n", - "41 26010000 Filter 260100 NaN 26.934394 \n", - "45 26010000 HashJoin 188478 188478.0 52.584297 \n", - "47 26010000 NestedLoopCrossJoin 500 500.0 30.993358 \n", - "46 26010000 NestedLoopJoin 610 610.0 25.448562 \n", - "42 26010000 Projection 1182273 NaN 202.987386 \n", - "40 26010000 SeqScan 260100 NaN 18.734465 \n", - "43 26010000 Sort 131364 NaN 74.202993 \n", + "4 640000 Aggregation 1255 NaN 0.348253 \n", + "1 640000 Filter 6400 NaN 0.407352 \n", + "5 640000 HashJoin 1620 1620.0 0.246864 \n", + "7 640000 NestedLoopCrossJoin 78 78.0 0.399168 \n", + "6 640000 NestedLoopJoin 96 96.0 0.697766 \n", + "2 640000 Projection 29091 NaN 3.320695 \n", + "0 640000 SeqScan 6400 NaN 0.309954 \n", + "3 640000 Sort 4476 NaN 0.720259 \n", + "12 1000000 Aggregation 1961 NaN 0.531581 \n", + "9 1000000 Filter 10000 NaN 0.657861 \n", + "13 1000000 HashJoin 2532 2532.0 0.396404 \n", + "15 1000000 NestedLoopCrossJoin 98 98.0 0.592521 \n", + "14 1000000 NestedLoopJoin 120 120.0 0.982848 \n", + "10 1000000 Projection 45455 NaN 5.492013 \n", + "8 1000000 SeqScan 10000 NaN 0.380590 \n", + "11 1000000 Sort 6993 NaN 1.165643 \n", + "20 3240000 Aggregation 6353 NaN 1.831355 \n", + "17 3240000 Filter 32400 NaN 2.130491 \n", + "21 3240000 HashJoin 8203 8203.0 1.297606 \n", + "23 3240000 NestedLoopCrossJoin 177 177.0 1.823392 \n", + "22 3240000 NestedLoopJoin 215 215.0 2.799041 \n", + "18 3240000 Projection 147273 NaN 19.355047 \n", + "16 3240000 SeqScan 32400 NaN 1.145653 \n", + "19 3240000 Sort 19636 NaN 4.048476 \n", + "28 6760000 Aggregation 13255 NaN 4.172917 \n", + "25 6760000 Filter 67600 NaN 4.628039 \n", + "29 6760000 HashJoin 17114 17114.0 2.975624 \n", + "31 6760000 NestedLoopCrossJoin 255 255.0 5.343956 \n", + "30 6760000 NestedLoopJoin 311 311.0 5.785900 \n", + "26 6760000 Projection 307273 NaN 38.239999 \n", + "24 6760000 SeqScan 67600 NaN 3.559425 \n", + "27 6760000 Sort 38409 NaN 10.169495 \n", + "36 12250000 Aggregation 24020 NaN 10.917472 \n", + "33 12250000 Filter 122500 NaN 8.754703 \n", + "37 12250000 HashJoin 31013 31013.0 6.093819 \n", + "39 12250000 NestedLoopCrossJoin 343 343.0 10.467055 \n", + "38 12250000 NestedLoopJoin 418 418.0 10.007416 \n", + "34 12250000 Projection 556818 NaN 74.966988 \n", + "32 12250000 SeqScan 122500 NaN 7.067011 \n", + "35 12250000 Sort 65536 NaN 19.754016 \n", + "44 26010000 Aggregation 51000 NaN 34.235903 \n", + "41 26010000 Filter 260100 NaN 19.593865 \n", + "45 26010000 HashJoin 65848 65848.0 15.134376 \n", + "47 26010000 NestedLoopCrossJoin 500 500.0 22.172263 \n", + "46 26010000 NestedLoopJoin 610 610.0 21.153397 \n", + "42 26010000 Projection 1182273 NaN 159.555687 \n", + "40 26010000 SeqScan 260100 NaN 16.761908 \n", + "43 26010000 Sort 131364 NaN 56.860476 \n", "\n", " model_cost plan_cost matching_error \n", "4 640050.0 765550.0 7.812500e-05 \n", "1 640000.0 1280000.0 0.000000e+00 \n", - "5 640044.0 1567644.0 6.875000e-05 \n", + "5 639900.0 963900.0 1.562500e-04 \n", "7 632736.0 648336.0 1.135000e-02 \n", "6 645120.0 664320.0 8.000000e-03 \n", "2 640002.0 3549102.0 3.125000e-06 \n", @@ -2355,7 +4226,7 @@ "3 640068.0 1087668.0 1.062500e-04 \n", "12 1000110.0 1196210.0 1.100000e-04 \n", "9 1000000.0 2000000.0 0.000000e+00 \n", - "13 999948.0 2449148.0 5.200000e-05 \n", + "13 1000140.0 1506540.0 1.400000e-04 \n", "15 998816.0 1018416.0 1.184000e-03 \n", "14 1008000.0 1032000.0 8.000000e-03 \n", "10 1000010.0 5545510.0 1.000000e-05 \n", @@ -2363,7 +4234,7 @@ "11 999999.0 1699299.0 1.000000e-06 \n", "20 3240030.0 3875330.0 9.259259e-06 \n", "17 3240000.0 6480000.0 0.000000e+00 \n", - "21 3239964.0 7935564.0 1.111111e-05 \n", + "21 3240185.0 4880785.0 5.709877e-05 \n", "23 3258216.0 3293616.0 5.622222e-03 \n", "22 3235750.0 3278750.0 1.311728e-03 \n", "18 3240006.0 17967306.0 1.851852e-06 \n", @@ -2371,7 +4242,7 @@ "19 3239940.0 5203540.0 1.851852e-05 \n", "28 6760050.0 8085550.0 7.396450e-06 \n", "25 6760000.0 13520000.0 0.000000e+00 \n", - "29 6760068.0 16557268.0 1.005917e-05 \n", + "29 6760030.0 10182830.0 4.437870e-06 \n", "31 6762600.0 6813600.0 3.846154e-04 \n", "30 6770470.0 6832670.0 1.548817e-03 \n", "26 6760006.0 37487306.0 8.875740e-07 \n", @@ -2379,7 +4250,7 @@ "27 6759984.0 10600884.0 2.366864e-06 \n", "36 12250200.0 14652200.0 1.632653e-05 \n", "33 12250000.0 24500000.0 0.000000e+00 \n", - "37 12249984.0 30003584.0 1.306122e-06 \n", + "37 12250135.0 18452735.0 1.102041e-05 \n", "39 12235496.0 12304096.0 1.184000e-03 \n", "38 12230680.0 12314280.0 1.577143e-03 \n", "34 12249996.0 67931796.0 3.265306e-07 \n", @@ -2387,7 +4258,7 @@ "35 12255232.0 18808832.0 4.271020e-04 \n", "44 26010000.0 31110000.0 0.000000e+00 \n", "41 26010000.0 52020000.0 0.000000e+00 \n", - "45 26009964.0 63705564.0 1.384083e-06 \n", + "45 26009960.0 39179560.0 1.537870e-06 \n", "47 26000000.0 26100000.0 3.844675e-04 \n", "46 26047000.0 26169000.0 1.422530e-03 \n", "42 26010006.0 144237306.0 2.306805e-07 \n", @@ -2425,7 +4296,7 @@ "outputs": [ { "data": { - "image/png": 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", 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y5ctTqVIlvbKuXbuSmJjIkSNHsl0vLS2NyZMnU65cOczMzDAxMcHMzIzz589n6vYA0KpVK73XFStWBNA7Xs/z+++/06BBA8qWLZttne3bt1OuXDlq1KihVx4WFoZSiu3btwNQo0YN4uPj6dKlC7/88gt37tzJcRy5kXHO9ujRAyOj//vXY2NjQ/v27dm3b1+mrh9Pvz/yuq3k5GSioqIICQl57pii+fPnU6VKFSwsLDAxMcHU1JRt27Zl+Tf+t3+f72lpabpxVL///julS5emUaNG2a67fft2rK2t6dChg155RrfKjO47Gd2cQkJCWLVqFf/8888zY/o3pVSmGJ/2ou97eNLlJSAgABsbG91xW7BgwXOPW4acnOsZnvfeio6O5t69e4SGhurtr1arpUmTJhw8eJAHDx7w4MEDDh48SLt27bCwsNBtz9bWlpYtWz43jr179/Lw4UO97q8AHh4evPPOO1l2u+rWrZve665duwKwY8cO4MlnqEajoXv37nqxu7q6UqlSpUyfoY6OjrzzzjuZ2snJ/7tnyfjceHrfOnbsiLW1daZ9q1ixYqaJG17mfBUFnyQW4rWh0Wjo1asXy5YtY/78+ZQuXZp69eplWffmzZvEx8djZmaGqamp3k9sbKzu4ikpKYl69eqxf/9+Pv/8cyIjIzl48CBr164F/m+gmUajYdu2bQQHBzN9+nSqVKlC4cKFGTx4MPfv33/hfXJzc8sUNzz54H067pUrV+rivnv3LgCurq6ZtplVWV5xcnLSe21mZoaVlZXeP9+M8pSUFN3rmzdvcvz48Uz7ZGtri1LquRez3bp1w9zcXNev//Tp0xw8eJBevXrp6pQoUYKtW7dSpEgR3n//fUqUKEGJEiX46quvcrRvzzqWGcc7K0OHDuWzzz6jTZs2/Prrr+zfv5+DBw9SqVKlLAcqOjs76702NzcHcjeo8fbt2xQrVuyZde7evZvp/AJwd3fXLQfo0aMHCxcu5PLly7Rv354iRYpQs2ZNtmzZkuN4ciKjvexi0mq1xMXF6ZVnVTcv24qLiyM9Pf25x3LWrFm899571KxZkzVr1rBv3z4OHjxIkyZNnvt3e/qcX7x4MZDzv6Grq2um6U2LFCmCiYmJbj/r169PRESE7kuJYsWKUaFCBb0xadmJiorKFOPTs3+96Pt+7dq1hISEULRoUZYtW8bevXt1Xwj9u96z5OQ4ZXjeeyvj87VDhw6Z9nnatGkopbh37x5xcXFotdoX/nx93vn39OeJiYlJptif/uy5efMmSilcXFwyxb5v375Mn6FZtZ3T/3fP2zcTE5NMibhGo8HV1TXTvmUVx8ucr6LgMzF0AELkRlhYGGPGjGH+/PlMmjQp23qFChXC2dmZTZs2Zbk8Y3ra7du3c/36dSIjI3Xf2gCZBqHBk7sBGVPenjt3jlWrVjFu3DgeP37M/PnzgSf/yJ4eVAnZX5g+fcFQqFAhAFavXo2Xl1e2+5fxTyg2NjbTsqzKDK1QoUJYWlqycOHCbJc/i6OjI61bt2bJkiV8/vnnLFq0CAsLC92g/gz16tWjXr16pKenc+jQIebMmcOQIUNwcXGhc+fOz2zjWcfy6X/6/7Zs2TJ69uzJ5MmT9crv3LmDg4PDM9t8UYULF+batWvPrOPs7MyNGzcylV+/fh3QP+a9evWiV69ePHjwgJ07dzJ27FhatGjBuXPnnnke5kbGMcwuJiMjIxwdHfXKX/R5ATltS6PRYGxs/NxjuWzZMoKCgpg3b55eeU6+VDh48KDe6+LFiwM5/xvu378fpZTesbh16xZpaWl6f8PWrVvTunVrHj16xL59+5gyZQpdu3bF29ub2rVrZ9tG1apVM8WYkXy+rGXLllG8eHFWrlypF39Wn5HZyclxyqmM4zVnzhxq1aqVZZ2MySA0Gs0Lf74+7/x7+vMuLS2Nu3fv6n3OPP3ZU6hQITQaDbt27dIlTP/2dFlW753c/L/LjrOzM2lpady+fVsvuVBKERsbq7sb8aw44MXPV1HwyR0L8VopWrQoH3/8MS1btiQ0NDTbei1atODu3bukp6dTrVq1TD++vr7A/33oPf2h/O233z4zjtKlS/Pf//4XPz8/vW4y3t7eHD9+XK/u9u3bSUpKytH+BQcHY2JiwsWLF7OMu1q1agD4+vri5ubGihUr9KaovXz5MtHR0Tlqy9zc/JVN/deiRQsuXryIs7NzlvuUkwf69erVi+vXr7Nx40aWLVtG27Zts71wNzY2pmbNmrqZTZ7VlSnDqVOnOHbsmF7Zjz/+iK2tLVWqVMl2PY1Gk+n8+e233/L19n7Tpk3ZsWPHM2eTatiwIadPn86070uWLEGj0dCgQYNM61hbW9O0aVNGjx7N48ePOXXqFPBid1We5uvrS9GiRfnxxx/1ztkHDx6wZs0a3exNeSGnbVlaWhIYGMjPP//8zLtmWf2Njx8/nuUMU097+lzPuFBs2rQp586d03UtyUrDhg1JSkrK9HyHjNnfGjZsmGkdc3NzAgMDmTZtGoBuNrns/oa2traZYjQzM3vufuWERqPBzMxM7+IyNjY206xQGfFldX7l5FzPqYCAABwcHDh9+nS2n69mZmZYW1tTo0YN1q5dq3dn5f79+/z666/Pbad27dpYWlqybNkyvfJr166xffv2LP9uy5cv13v9448/Auhmx2rRogVKKf75558s4/bz83tuXLn5f5fd+ZIR+9P7tmbNGh48eJDlvj1LduereH3JHQvx2pk6depz63Tu3Jnly5fTrFkzPvzwQ2rUqIGpqSnXrl1jx44dtG7dmrZt21KnTh0cHR35z3/+w9ixYzE1NWX58uWZLjCPHz/OBx98QMeOHSlVqhRmZmZs376d48ePM3LkSF29Hj168NlnnzFmzBgCAwM5ffo033zzDfb29jnaN29vbyZMmMDo0aP5+++/adKkCY6Ojty8eZMDBw5gbW3N+PHjMTIyYuLEifTt25e2bdvSr18/4uPjGTduXI67Qvn5+fHTTz+xcuVKfHx8sLCwyNE/pxcxZMgQ1qxZQ/369fnoo4+oWLEiWq2WK1eusHnzZoYNG0bNmjWfuY13332XYsWKMXDgQGJjY/W6QcGTPvDbt2+nefPmeHp6kpKSortD8qx+7Bnc3d1p1aoV48aNw83NjWXLlrFlyxamTZv2zAveFi1aEB4eTpkyZahYsSKHDx9mxowZOe6+8SImTJjA77//Tv369Rk1ahR+fn7Ex8ezadMmhg4dSpkyZfjoo49YsmQJzZs3Z8KECXh5efHbb78xd+5c3nvvPV2/5379+mFpaUlAQABubm7ExsYyZcoU7O3tdd8+Zoxj+e6777C1tcXCwoLixYs/807O04yMjJg+fTrdunWjRYsWDBgwgEePHjFjxgzi4+Nz9L7Oj7ZmzZpF3bp1qVmzJiNHjqRkyZLcvHmT9evX8+2332Jra0uLFi2YOHEiY8eOJTAwkLNnzzJhwgSKFy+e5ZiEnBgyZAgrV66kdevWjBw5kho1avDw4UOioqJo0aIFDRo0oGfPnvzvf/8jNDSUS5cu4efnx+7du5k8eTLNmjXTnddjxozh2rVrNGzYkGLFihEfH89XX32l13++RIkSWFpasnz5csqWLYuNjQ3u7u55dnciKxlTjQ4cOJAOHTpw9epVJk6ciJubG+fPn9er6+fnR2RkJL/++itubm7Y2tri6+ubo3M9p2xsbJgzZw6hoaHcu3ePDh06UKRIEW7fvs2xY8e4ffu27q7UxIkTadKkCY0bN2bYsGGkp6czbdo0rK2tuXfv3jPbcXBw4LPPPmPUqFH07NmTLl26cPfuXcaPH4+FhQVjx47Vq29mZsYXX3xBUlIS1atXJzo6ms8//5ymTZvqpm4OCAigf//+9OrVi0OHDlG/fn2sra25ceMGu3fvxs/Pj/fee++ZceX0/13G3wNg2rRpNG3aFGNjYypWrEjjxo0JDg5mxIgRJCYmEhAQwPHjxxk7diyVK1emR48ez/075OR8Fa8xw4wZFyJn/j0r1LNkNcNRamqqmjlzpqpUqZKysLBQNjY2qkyZMmrAgAHq/PnzunrR0dGqdu3aysrKShUuXFj17dtXHTlyRG8GlZs3b6qwsDBVpkwZZW1trWxsbFTFihXVl19+qTeV4aNHj9Qnn3yiPDw8lKWlpQoMDFRHjx7Ndlao7PYrIiJCNWjQQNnZ2Slzc3Pl5eWlOnTooDcdolJK/fDDD6pUqVLKzMxMlS5dWi1cuFCFhobmaFaoS5cuqXfffVfZ2trqpvZU6tmzQt2+fVtvG6Ghocra2jrTtgMDA1X58uX1ypKSktR///tf5evrq8zMzJS9vb3y8/NTH330kd5Uic8yatQoBSgPDw+92X6UejJrStu2bZWXl5cyNzdXzs7OKjAwUK1fv/652/Xy8lLNmzdXq1evVuXLl1dmZmbK29tbzZo1S69eVscmLi5O9enTRxUpUkRZWVmpunXrql27dqnAwEC9czKrmZKy22ZOXL16VfXu3Vu5uroqU1NT5e7urkJCQtTNmzd1dS5fvqy6du2qnJ2dlampqfL19VUzZszQO3aLFy9WDRo0UC4uLsrMzEy3nePHj+u1N3v2bFW8eHFlbGz83Hiz21elnpzbNWvWVBYWFsra2lo1bNhQ7dmzR69Odufb89p7etaqnLSllFKnT59WHTt2VM7OzrqpP8PCwnRTZz569EgNHz5cFS1aVFlYWKgqVaqoiIiILN9r5HBWKKWenDsffvih8vT0VKampqpIkSKqefPmetMU3717V/3nP/9Rbm5uysTERHl5ealPP/1Ub1rPDRs2qKZNm6qiRYsqMzMzVaRIEdWsWTO9aXWVejJbXpkyZZSpqWmO4syL9/3UqVOVt7e3Mjc3V2XLllXff/+9brv/dvToURUQEKCsrKwUoPfeed65ntv3VlRUlGrevLlycnJSpqamqmjRoqp58+aZ1l+/fr2qWLGi7pyYOnVqlrFn54cfftCtb29vr1q3bq03Zfi/j+Xx48dVUFCQsrS0VE5OTuq9995TSUlJmba5cOFCVbNmTWVtba0sLS1ViRIlVM+ePdWhQ4d0dbL6O2TIyf87pZ6c83379lWFCxdWGo1GASomJkYp9WQa5BEjRigvLy9lamqq3Nzc1HvvvZdpWu+Mz9Wn5fR8Fa8njVIF4FG/QghhQN7e3lSoUIENGzYYOhQhxFskLCyM1atX57i7rBAFnYyxEEIIIYQQQrw0GWMhhBAFhFKK9PT0Z9YxNjZ+4dmShBBCiPwkXaGEEKKACA8PzzQo/Wk7duzQzRQjhBBCFCSSWAghRAFx9+5dYmJinlnH19dX9xwWIYQQoiCRxEIIIYQQQgjx0mTwthBCCCGEEOKlyeBtQKvVcv36dWxtbWVQpBBCCCGEEP+fUor79+/j7u6OkdGz70lIYgFcv34dDw8PQ4chhBBCCCFEgXT16lWKFSv2zDqSWIBuIOTVq1exs7MzcDRCCCGEEEIUDImJiXh4eORo4hBJLEDX/cnOzk4SCyGEEEIIIZ6Sk+ECMnhbCCGEEEII8dIksRBCCCGEEEK8NEkshBBCCCGEEC9NxljkQnp6OqmpqYYOQwiDMDMze+40c0IIIYR4e0likQNKKWJjY4mPjzd0KEIYjJGREcWLF8fMzMzQoQghhBCiAJLEIgcykooiRYpgZWUlD9ETb52Mh0jeuHEDT09PeQ8IIYQQIhNJLJ4jPT1dl1Q4OzsbOhwhDKZw4cJcv36dtLQ0TE1NDR2OEEIIIQoY6TD9HBljKqysrAwciRCGldEFKj093cCRCCGEEKIgksQih6Trh3jbyXtACCGEEM8iiYUQQgghhBDipRk0sdi5cyctW7bE3d0djUZDREREpjpnzpyhVatW2NvbY2trS61atbhy5Ypu+aNHjxg0aBCFChXC2tqaVq1ace3atVe4F6IgGjduHP7+/oYOQwghhBDirWHQxOLBgwdUqlSJb775JsvlFy9epG7dupQpU4bIyEiOHTvGZ599hoWFha7OkCFDWLduHT/99BO7d+8mKSmJFi1aFLh+4Olaxd6Ld/nl6D/svXiXdK16ZW1HR0djbGxMkyZNXlmbr1JWSenw4cPZtm2bYQISQgghhMgDWm06V08d58yeKK6eOo5WW7Cub59m0FmhmjZtStOmTbNdPnr0aJo1a8b06dN1ZT4+PrrfExISWLBgAUuXLqVRo0YALFu2DA8PD7Zu3UpwcHD+BZ8Lm07eYPyvp7mRkKIrc7O3YGzLcjSp4Jbv7S9cuJBBgwbxww8/cOXKFTw9PfO1vdTUVIPPGmRjY4ONjY1BYxBCCCGEeFHn90ezPfw7ku7d0ZXZOBXinbD+lKpZx4CRZa/AjrHQarX89ttvlC5dmuDgYIoUKULNmjX1vpk+fPgwqampvPvuu7oyd3d3KlSoQHR0tAGizmzTyRu8t+yIXlIBEJuQwnvLjrDp5I18bf/BgwesWrWK9957jxYtWhAeHq63fP369ZQqVQpLS0saNGjA4sWL0Wg0eg8D/P777/Hw8MDKyoq2bdsya9YsHBwcdMszuh0tXLgQHx8fzM3NUUqRkJBA//79KVKkCHZ2drzzzjscO3ZMr/3PP/+cIkWKYGtrS9++fRk5cqReF6aDBw/SuHFjChUqhL29PYGBgRw5ckS33NvbG4C2bdui0Wh0r5/uCqXVapkwYQLFihXD3Nwcf39/Nm3apFt+6dIlNBoNa9eupUGDBlhZWVGpUiX27t37QsddCCGEEOJFnd8fzfpZk/WSCoCke3dYP2sy5/cXjOvcpxXYxOLWrVskJSUxdepUmjRpwubNm2nbti3t2rUjKioKePLgOjMzMxwdHfXWdXFxITY2NtttP3r0iMTERL2f3FBKkfw47bk/91NSGbv+FFl1esooG7f+NPdTUnO0PaVy331q5cqV+Pr64uvrS/fu3Vm0aJFuO5cuXaJDhw60adOGo0ePMmDAAEaPHq23/p49e/jPf/7Dhx9+yNGjR2ncuDGTJk3K1M6FCxdYtWoVa9as4ejRowA0b96c2NhYNm7cyOHDh6lSpQoNGzbk3r17ACxfvpxJkyYxbdo0Dh8+jKenJ/PmzdPb7v379wkNDWXXrl3s27ePUqVK0axZM+7fvw88STwAFi1axI0bN3Svn/bVV1/xxRdfMHPmTI4fP05wcDCtWrXi/PnzevVGjx7N8OHDOXr0KKVLl6ZLly6kpaXl8qgLIYQQQrwYrTad7eHfPbPOjsXfFchuUQX2AXlarRaA1q1b89FHHwHg7+9PdHQ08+fPJzAwMNt1lVLPnBpzypQpjB8//oVje5iaTrkxf7zw+hkUEJuYgt+4zTmqf3pCMFZmufuTLViwgO7duwPQpEkTkpKS2LZtG40aNWL+/Pn4+voyY8YMAHx9fTl58qRe4jBnzhyaNm3K8OHDAShdujTR0dFs2LBBr53Hjx+zdOlSChcuDMD27ds5ceIEt27dwtzcHICZM2cSERHB6tWr6d+/P3PmzKFPnz706tULgDFjxrB582aSkpJ0233nnXf02vn2229xdHQkKiqKFi1a6NpzcHDA1dU12+Mwc+ZMRowYQefOnQGYNm0aO3bsYPbs2fzvf//T1Rs+fDjNmzcHYPz48ZQvX54LFy5QpkyZHB1vIYQQQoiX8c+ZU5nuVDzt/t07/HPmFB7lK76iqHKmwN6xKFSoECYmJpQrV06vvGzZsrpZoVxdXXn8+DFxcXF6dW7duoWLi0u22/70009JSEjQ/Vy9ejXvd6AAOHv2LAcOHNBdTJuYmNCpUycWLlyoW169enW9dWrUqJFpG0+XPf0awMvLS3eRD0+6qSUlJeHs7Kwb72BjY0NMTAwXL17M8bZv3brFf/7zH0qXLo29vT329vYkJSXpzQz2PImJiVy/fp2AgAC98oCAAM6cOaNXVrHi/71B3dzcdDEIIYQQQrwKty7H5KheUnzc8yu9YgX2joWZmRnVq1fn7NmzeuXnzp3Dy8sLgKpVq2JqasqWLVsICQkB4MaNG5w8eVJvwPfTzM3Ndd+ivwhLU2NOT3j+wPADMfcIW5R115x/C+9VnRrFnXLUbm4sWLCAtLQ0ihYtqitTSmFqakpcXFyWd3ae7m6VkzoA1tbWeq+1Wi1ubm5ERkZmqvvv8RnP23ZYWBi3b99m9uzZeHl5YW5uTu3atXn8+HHmHX6OrNp6uuzfg84zlmXcPRNCCCGEyC+PkpPZv24lhzesy1F9GwfH51d6xQyaWCQlJXHhwgXd65iYGI4ePYqTkxOenp58/PHHdOrUifr169OgQQM2bdrEr7/+qrtYtbe3p0+fPgwbNgxnZ2ecnJwYPnw4fn5+ulmi8oNGo8lRl6R6pQrjZm9BbEJKluMsNICrvQX1ShXG2Chvn2qclpbGkiVL+OKLL/QGtwO0b9+e5cuXU6ZMGTZu3Ki37NChQ3qvy5Qpw4EDB55ZJytVqlQhNjYWExMT3YDqp/n6+nLgwAF69OiR7bZ37drF3LlzadasGQBXr17lzh3924OmpqbPnF7Yzs4Od3d3du/eTf369XXl0dHRWd59EUIIIYR4VbTadE7u2MKelctITogHwNjUlPTU1GzXsXUuRNGy5V9RhDln0MTi0KFDNGjQQPd66NChAISGhhIeHk7btm2ZP38+U6ZMYfDgwfj6+rJmzRrq1q2rW+fLL7/ExMSEkJAQHj58SMOGDQkPD8fYOHff7ucHYyMNY1uW471lR9CAXnKRkUaMbVkuz5MKgA0bNhAXF0efPn2wt7fXW9ahQwcWLFjA2rVrmTVrFiNGjKBPnz4cPXpUN2tUxrf1gwYNon79+syaNYuWLVuyfft2fv/992eOYQFo1KgRtWvXpk2bNkybNg1fX1+uX7/Oxo0badOmDdWqVWPQoEH069ePatWqUadOHVauXMnx48f1phQuWbIkS5cupVq1aiQmJvLxxx9jaWmp15a3tzfbtm0jICAAc3PzTIP5AT7++GPGjh1LiRIl8Pf3Z9GiRRw9epTly5e/yOEVQgghhHhpV04eI3Lx99y+cgkAR7eiBPboQ3paKr/OmpLteg1C+2NkZPhr3acZdIxFUFAQSqlMP/+eErV3796cP3+ehw8fcvToUVq3bq23DQsLC+bMmcPdu3dJTk7m119/xcPD4xXvSfaaVHBjXvcquNpb6JW72lswr3uVfHuOxYIFC2jUqFGmpAKe3LE4evQocXFxrF69mrVr11KxYkXmzZunmxUqo6tYQEAA8+fPZ9asWVSqVIlNmzbx0Ucf6T2kMCsajYaNGzdSv359evfuTenSpencuTOXLl3SjX/p1q0bn376KcOHD6dKlSrExMQQFhamt+2FCxcSFxdH5cqV6dGjB4MHD6ZIkSJ6bX3xxRds2bIFDw8PKleunGU8gwcPZtiwYQwbNgw/Pz82bdqkm2pXCCGEEOJVirvxDxEzJvLzxNHcvnIJc2trgnr2I3TmN5SoWoPSNQNoNXQUNk6F9NazdS5Eq6GjCuxzLDTqReYwfcMkJiZib29PQkICdnZ2estSUlKIiYmhePHiz72YfpZ0reJAzD1u3U+hiK0FNYo75cudipc1adIk5s+f/8wB7f369eOvv/5i165ded5+48aNcXV1ZenSpXm+bfFy8uq9IIQQQrytUpKS2Ld2BX9u+g1tehoaIyP8321O7Q5dsLS1y1Rfq01/MktUfBw2Do4ULVv+ld+peNZ18tMK7ODtN42xkYbaJZwNHUYmc+fOpXr16jg7O7Nnzx5mzJjBBx98oFdn5syZNG7cGGtra37//XcWL17M3LlzX7rt5ORk5s+fT3BwMMbGxqxYsYKtW7eyZcuWl962EEIIIURBoU1P5/jWTez5eTkp9588P624f1UCe/TFuVj2PW2MjIwL3JSyzyKJxVvu/PnzfP7559y7dw9PT0+GDRvGp59+qlfnwIEDTJ8+nfv37+Pj48PXX39N3759X7rtjO5Sn3/+OY8ePdKNocnPgfdCCCGEEK/SpaOHiVy6gLvXnkyV71TUg6CefSnuX9XAkeU96QrFq+kKJcTrTt4LQgghRM7dvXaVqKU/EHP0MAAWtnbU6diVSo2aYlQAJhnKKekKJYQQQgghhAE8vJ9I9M8/cmzLRpRWi5GxMZWbtKRWu85Y2NgYOrx8JYmFEEIIIYQQLyk9LZWjf2xk75ofefTgAQAlqtWkfrfeOLkXfc7abwZJLIQQQgghhHhBSin+PnKQqKULiLvxDwCFPL0J6tkXLz9/wwb3ikliIYQQQgghxAu4feUSkUt+4MqJowBY2TsQ0Kk7FRo0LpAPsMtvklgIIYQQQgiRC8kJ8exZtYwT2zajlBZjExOqNG9DzTYhmFtZGTo8g5HEQgghhBBCiBxIS03lz9/Xs2/tSh4/TAagdM0A6nXrhYOLq4GjMzxJLN5CQUFB+Pv7M3v2bAC8vb0ZMmQIQ4YMMWhcQgghhBAFkVKKCwf3ErVsIQk3YwEo4l2CBqH9KFaugoGjKzgksXhVtOlwORqSboKNC3jVgXzuexcWFsbixYszle/fv5+yZctmu55Go2HdunW0adMmH6MTQgghhCj4bsZcJHLJ91w7fRIAawdH6nYJpXz9d9AYGRk4uoJFEotX4fR62DQCEq//X5mdOzSZBuVa5WvTTZo0YdGiRXplhQsXxvgVPJglNTUVU1PTfG9HCCGEECKvJcXdY8/KpZyM3ApKYWJqRrWWbaneugNmFpaGDq9AkjQrv51eD6t66icVAIk3npSfXp+vzZubm+Pq6qr307Bhw2y7PXl7ewPQtm1bNBqN7jXAr7/+StWqVbGwsMDHx4fx48eTlpamW67RaJg/fz6tW7fG2tqazz//PB/3TAghhBAi76U+fsT+datYOGQAJ3dsAaUoExBIr9nzCejUQ5KKZ5A7Fi9CKUhNfn49bTr8/gmgstoIoHlyJ8MnKGfdokytQKPJXay5dPDgQYoUKcKiRYto0qSJ7s7GH3/8Qffu3fn666+pV68eFy9epH///gCMHTtWt/7YsWOZMmUKX3755Su5KyKEEEIIkReUUpzdu4tdP4aTePsWAK4lSxPUsx9FfbPvQi7+jyQWLyI1GSa758GG1JM7GVM9clZ91HUws85VCxs2bMDmX4+Pb9q06TPrFy5cGAAHBwdcXf9vdoNJkyYxcuRIQkNDAfDx8WHixIl88skneolF165d6d27d65iFEIIIYQwpNgL59ix5Aeunz0NgI1zIep3CaVMQKCMo8gFSSzecA0aNGDevHm619bW1nTp0iXX2zl8+DAHDx5k0qRJurL09HRSUlJITk7G6v/P2VytWrWXD1oIIYQQ4hW4f/cOu1cs5vSuHQCYmJtTo1UHqrVsi6m5hYGje/1IYvEiTK2e3D14nsvRsLzD8+t1W/1klqictJtL1tbWlCxZMtfrPU2r1TJ+/HjatWuXaZmFxf+98aytc3dHRQghhBDiVUt9lMLB9Ws5uH4NaY8fAVCu/jvU7dITW6dCBo7u9SWJxYvQaHLWJanEO09mf0q8QdbjLDRPlpd4J9+nns0NU1NT0tPT9cqqVKnC2bNn8yRJEUIIIYQwBKXVcmZPFLtWLCbp7h0A3EuXpUFoP1xLljZwdK8/SSzyk5HxkyllV/UENOgnF/9/EHaTqQUqqYAnM0Nt27aNgIAAzM3NcXR0ZMyYMbRo0QIPDw86duyIkZERx48f58SJEzL7kxBCCCEKvH/OniFyyffEXjgHgF3hItTv1ovSteqiyefJcd4WMholv5VrBSFLwM5Nv9zO/Ul5Pj/H4kV88cUXbNmyBQ8PDypXrgxAcHAwGzZsYMuWLVSvXp1atWoxa9YsvLy8DBytEEIIIUT2Em/fYsNX0/lpzMfEXjiHqYUldTv3pNes+fjWridJRR7SKKWy6qPzVklMTMTe3p6EhATs7Oz0lqWkpBATE0Px4sX1xhLkmgGevC1EXsqz94IQQgjxCjxOeciBiNUc3rCOtNTHoNFQIagxdTv3wNrB0dDhvTaedZ38NOkK9aoYGUPxeoaOQgghhBDijaa0Wk5FbWP3T0t4EB8HQLFyFQjq2Q+X4iUMHN2bTRILIYQQQgjxRrh2+iQ7lnzPrZiLADi4uFG/ey9KVq8tXZ5eAUkshBBCCCHEay3+Ziw7ly/k/P5oAMwsrajVvjOVm7TExNTUwNG9PSSxEEIIIYQQr6VHycnsX7eSIxt/IT0tDY3GiIqNgqnTsRtW9g6GDu+tI4mFEEIIIYR4rWi16ZzcvoXdK5fyMDEBAK+KlQns0YfCnt6GDe4tJomFEEIIIYR4bVw+cZTIJT9w58olABzdihLUsy/FK1eTcRQGJomFEEIIIYQo8O5d/4eoZQv4+/ABACysbajdoQuV3m2OsYlc0hYE8lcQQgghhBAFVkpSEvvWruDPTRvQpqejMTLC/93m1O7QBUvbZz9XQbxaklgIIYQQQogCR5uezrGtvxP984+k3E8EoHjlagR274NzMQ8DRyeyIomFeClhYWHEx8cTERGRZ9sMDw9nyJAhxMfH59k2hRBCCPH6iDl6mMglP3Dvn6sAOBfzJKhHH7z9qxo4MvEskli8IunadI7cOsLt5NsUtipMlSJVMDYyztc2s7voj4yMpEGDBsTFxeHg4JCvMQAEBQXh7+/P7Nmzc1S/U6dONGvWLH+DEkIIIUSBc/faFSKXLuDS0cMAWNjaERDSnYoNgzEyzt/rJvHyJLF4BbZe3srUA1O5mXxTV+Zi5cLIGiNp5NXIgJEVTJaWllhaWho6DCGEEEK8Ig/vJxL9848c27IRpdViZGxC5SYtqNW+MxbWNoYOT+SQkaEDeNNtvbyVoZFD9ZIKgFvJtxgaOZStl7caKLIn7t69S5cuXShWrBhWVlb4+fmxYsUKvTqrV6/Gz88PS0tLnJ2dadSoEQ8ePNCrM3PmTNzc3HB2dub9998nNTU12zbj4uLo2bMnjo6OWFlZ0bRpU86fP69bHh4erncnZdy4cfj7+7N06VK8vb2xt7enc+fO3L9/P28OghBCCCEMIj0tlcO//cKCD/tx9I8NKK2WEtVqEfbF/wjq2VeSiteM3LF4AUopHqY9fG69dG06Uw5MQaEyb+P/l009MJWarjVz1C3K0sQyz+dnTklJoWrVqowYMQI7Ozt+++03evTogY+PDzVr1uTGjRt06dKF6dOn07ZtW+7fv8+uXbtQ6v/2aceOHbi5ubFjxw4uXLhAp06d8Pf3p1+/flm2GRYWxvnz51m/fj12dnaMGDGCZs2acfr0aUxNTbNc5+LFi0RERLBhwwbi4uIICQlh6tSpTJo0KU+PhxBCCCHyn1KKv48cIGrpAuJuXAegsKc3QaH98KxQycDRiRdl0MRi586dzJgxg8OHD3Pjxg3WrVtHmzZtsqw7YMAAvvvuO7788kuGDBmiK3/06BHDhw9nxYoVPHz4kIYNGzJ37lyKFSuWb3E/THtIzR9r5sm2bibfpM5PdXJUd3/X/ViZWuVq+xs2bMDGRj/bT09P1/1etGhRhg8frns9aNAgNm3axM8//6xLLNLS0mjXrh1eXl4A+Pn56W3P0dGRb775BmNjY8qUKUPz5s3Ztm1blolFRkKxZ88e6tR5st/Lly/Hw8ODiIgIOnbsmOV+aLVawsPDsbW1BaBHjx5s27ZNEgshhBDiNXP7cgyRS37gysljAFjZOxDQqQcVGjTCKJ/Hn4r8ZdDE4sGDB1SqVIlevXrRvn37bOtFRESwf/9+3N3dMy0bMmQIv/76Kz/99BPOzs4MGzaMFi1acPjwYYxlkA8NGjRg3rx5emX79++ne/fuwJMkY+rUqaxcuZJ//vmHR48e8ejRI6ytrQGoVKkSDRs2xM/Pj+DgYN599106dOiAo6Ojbnvly5fXO9Zubm6cOHEiy3jOnDmDiYkJNWv+X2Lm7OyMr68vZ86cyXY/vL29dUlFRhu3bt3KxZEQQgghhCElJ8SzZ+UyTmzfjFJajE1MqNK8DTXbhGBulbsvTkXBZNDEomnTpjRt2vSZdf755x8++OAD/vjjD5o3b663LCEhgQULFrB06VIaNXoyCHrZsmV4eHiwdetWgoOD8yVuSxNL9nfd/9x6h28eZuC2gc+tN7fhXKq6PH/6NEuT3A9otra2pmTJknpl165d0/3+xRdf8OWXXzJ79mz8/PywtrZmyJAhPH78GABjY2O2bNlCdHQ0mzdvZs6cOYwePZr9+/dTvHhxgEzdlzQaDVqtNst4/t2F6unyZ3Xzyk0bQgghhCg40lJT+fP39exbu5LHD5MBKF0zgHrdeuHg4mrg6EReKtBjLLRaLT169ODjjz+mfPnymZYfPnyY1NRU3n33XV2Zu7s7FSpUIDo6OtvEIuNb+QyJiYm5ikuj0eSoS1Id9zq4WLlwK/lWluMsNGhwsXKhjnudfJ96Nju7du2idevWujsYWq2W8+fPU7Zs2f+LU6MhICCAgIAAxowZg5eXF+vWrWPo0KG5bq9cuXKkpaWxf/9+XVeou3fvcu7cOb02hRBCCPF6U0px/kA0O5cvIuFmLABFipegQWg/ipWtYODoRH4o0InFtGnTMDExYfDgwVkuj42NxczMTK9bDoCLiwuxsbHZbnfKlCmMHz8+T2PNirGRMSNrjGRo5FA0aPSSCw1Pvp0fUWOEwZIKgJIlS7JmzRqio6NxdHRk1qxZxMbG6i7y9+/fz7Zt23j33XcpUqQI+/fv5/bt2y+cBJQqVYrWrVvTr18/vv32W2xtbRk5ciRFixaldevWeblrQgghhDCQm39fIHLJD1w7cxIAa0cn6nUJpVy9BmiMZFLSN1WBTSwOHz7MV199xZEjR3I9E9LzutV8+umnet+2JyYm4uGRP4+Gb+TViFlBs7J8jsWIGiMM/hyLzz77jJiYGIKDg7GysqJ///60adOGhIQEAOzs7Ni5cyezZ88mMTERLy8vvvjii+d2Yfs3rVaLicn/nWqLFi3iww8/pEWLFjx+/Jj69euzcePGbGeEEkIIIcTrISnuHrt/WsKpqG2gFCamZlRr2ZbqrTtgZiHPqHrTaVR2nd5fMY1Gozcr1OzZsxk6dChG/8pq09PTMTIywsPDg0uXLrF9+3YaNmzIvXv39O5aVKpUiTZt2uT4rkRiYiL29vYkJCRgZ2entywlJYWYmBiKFy+OhYXFC++fIZ68XVCUKVOGvn376s0+JV4/efVeEEII8eZJffyII7/9wv51q0h9lAJAmYBA6nUNxa5QEQNHJ17Gs66Tn1Zg71j06NFDNyA7Q3BwMD169KBXr14AVK1aFVNTU7Zs2UJISAgAN27c4OTJk0yfPv2Vx/wsxkbGVHetbugwXqlbt27x+++/c/bsWRo2bGjocIQQQgiRx5RSnI3eyc4fw7l/5zYAbiV9CQrti3tpGTv5tjFoYpGUlMSFCxd0r2NiYjh69ChOTk54enri7OysV9/U1BRXV1d8fX0BsLe3p0+fPgwbNgxnZ2ecnJwYPnw4fn5+mZIS8eo1adKEuLg4vv76aypXrmzocIQQQgiRh25cOEvk4h+4fu7JdPE2zoWo3zWMMnXqyziKt5RBE4tDhw7RoEED3euMcQ+hoaGEh4fnaBtffvklJiYmhISE6B6QFx4eLs+wKACOHDli6BCEEEIIkcfu373DrhWLObNrBwAm5ubUaN2Bai3aYmouXWXfZgVmjIUhvYoxFkK87uS9IIQQb7fUlBQO/rqGg+vXkvb4ybT95QMbEtC5B7ZOhQwcncgvb8QYCyGEEEIIYXhKq+XM7kh2rVhM0r27ABQtU46gnv1wLVHKwNGJgkQSCyGEEEIIkaV/zp4hcvF3xF48D4Bd4SLU79ab0rUCcv04APHmk8RCCCGEEELoSbx9i53LF3F27y4ATC0sqdk2hKrNWmNiZmbg6ERBJYmFEEIIIYQA4PHDZA78sppDG9aRnpoKGg1+DRoT0KkH1g6Oz9+AeKtJYiGEEEII8ZbTatM5FbWNPT8t5UF8HAAe5fwICu1HEW8fA0cnXheSWIgCIygoCH9/f2bPnm3oUN5ITz/dXgghhAC4evoEkYt/4NaliwA4uLhRv0dvSlarJeMoRK7I00teEZWezoP9B0jY8BsP9h9Apafne5thYWFoNBqmTp2qVx4REZFnHxRBQUEMGTIkT7b1KredG48fP2b69OlUqlQJKysrChUqREBAAIsWLSI1NdUgMYWHh+Pg4JCrdW7cuEHTpk3zJyAhhBCvnfjYG6z/YjKrxn/KrUsXMbO0IrB7b0K/mEup6rUlqRC5JncsXoHEzZu5OXkKabGxujITV1dcRn2K3bvv5mvbFhYWTJs2jQEDBuDoKH0jc+vx48cEBwdz7NgxJk6cSEBAAHZ2duzbt4+ZM2dSuXJl/P39s1zPrIANbnN1dTV0CEIIIQqAR8kP2Ld2JX/+vp70tDQ0GiMqNmpCnZBuWNnZGzo88RqTOxb5LHHzZv75cIheUgGQdvMm/3w4hMTNm/O1/UaNGuHq6sqUKVOyrRMdHU39+vWxtLTEw8ODwYMH8+DBA93yuXPnUqpUKSwsLHBxcaFDhw7AkzsiUVFRfPXVV2g0GjQaDZcuXQLg9OnTNGvWDBsbG1xcXOjRowd37tzRbfPBgwf07NkTGxsb3Nzc+OKLL3K9b2vWrKF8+fKYm5vj7e2daRtxcXH07NkTR0dHrKysaNq0KefPn9ctz/jWPyIigtKlS2NhYUHjxo25evWqrs7s2bPZuXMn27Zt4/3338ff3x8fHx+6du3K/v37KVXqyfzdQUFBfPDBBwwdOpRChQrRuHFjAKKioqhRowbm5ua4ubkxcuRI0tLSdNtfvXo1fn5+WFpa4uzsTKNGjXTHPjIykho1amBtbY2DgwMBAQFcvnw52+Mxb948SpQogZmZGb6+vixdulRvuUajISIiAoBLly6h0WhYu3YtDRo0wMrKikqVKrF3795c/x2EEEK8HrTp6Rzb8jsLPuzPoV/Xkp6WhlfFyvSc/jWN+g6UpEK8NEksXoBSCm1y8nN/0u/f5+bnkyCrh5srBShuTppM+v37Odreizwk3djYmMmTJzNnzhyuXbuWafmJEycIDg6mXbt2HD9+nJUrV7J7924++OADAA4dOsTgwYOZMGECZ8+eZdOmTdSvXx+Ar776itq1a9OvXz9u3LjBjRs38PDw4MaNGwQGBuLv78+hQ4fYtGkTN2/eJCQkRNfuxx9/zI4dO1i3bh2bN28mMjKSw4cP53i/Dh8+TEhICJ07d+bEiROMGzeOzz77jPDwcF2dsLAwDh06xPr169m7dy9KKZo1a6bXfSk5OZlJkyaxePFi9uzZQ2JiIp07d9YtX758OY0aNaJy5cqZYjA1NcXa2lr3evHixZiYmLBnzx6+/fZb/vnnH5o1a0b16tU5duwY8+bNY8GCBXz++efAk65JXbp0oXfv3pw5c4bIyEjatWuHUoq0tDTatGlDYGAgx48fZ+/evfTv3z/b29Lr1q3jww8/ZNiwYZw8eZIBAwbQq1cvduzY8czjOHr0aIYPH87Ro0cpXbo0Xbp00Ut8hBBCvBkuHz/K0pEfsvWH//EwMQFH92K0HTGW9qMmUMjT29DhiTeEdIV6AerhQ85WqZoHG3py5+Jc9Ro5qu575DAaK6tcN9O2bVv8/f0ZO3YsCxYs0Fs2Y8YMunbtqhvLUKpUKb7++msCAwOZN28eV65cwdramhYtWmBra4uXl5fuItve3h4zMzOsrKz0utnMmzePKlWqMHnyZF3ZwoUL8fDw4Ny5c7i7u7NgwQKWLFmi+2Z/8eLFFCtWLMf7NGvWLBo2bMhnn30GQOnSpTl9+jQzZswgLCyM8+fPs379evbs2UOdOnWAJ0mCh4cHERERdOzYEYDU1FS++eYbatasqYujbNmyHDhwgBo1anD+/HmCgoJyFFPJkiWZPn267vXo0aPx8PDgm2++QaPRUKZMGa5fv86IESMYM2YMN27cIC0tjXbt2uHl5QWAn58fAPfu3SMhIYEWLVpQokQJAMqWLZtt2zNnziQsLIyBAwcCMHToUF13rQYNGmS73vDhw2nevDkA48ePp3z58ly4cIEyZcrkaJ+FEEIUbPeu/0PUsgX8ffgAABbWNtTu2JVKjZthbCKXgSJvyR2Lt8S0adNYvHgxp0+f1is/fPgw4eHh2NjY6H6Cg4PRarXExMTQuHFjvLy88PHxoUePHixfvpzk5ORntnX48GF27Niht82MC9WLFy9y8eJFHj9+TO3atXXrODk54evrm+P9OXPmDAEBAXplAQEBnD9/nvT0dM6cOYOJiYkuYQBwdnbG19eXM2fO6MpMTEyoVq2a7nWZMmVwcHDQ1VFK5Xjw2r+3kxFj7dr6g98CAgJISkri2rVrVKpUiYYNG+Ln50fHjh35/vvviYt7MsWfk5MTYWFhBAcH07JlS7766itu3LiR6+Px733NSsWKFXW/u7m5AXDr1q0c7a8QQoiCKyUpiR2Lv2fx8IH8ffgARsbGVG7akt5ff0+Vpq0kqRD5Qs6qF6CxtMT3yPO77SQfOsTV/gOeW8/ju2+xeuqiNLt2X1T9+vUJDg5m1KhRhIWF6cq1Wi0DBgxg8ODBmdbx9PTEzMyMI0eOEBkZyebNmxkzZgzjxo3j4MGD2c5KpNVqadmyJdOmTcu0zM3NTW+cw4vK6oL/313Fsus2ltV6WSUOGWWlS5d+7sV5hn93i3pejBqNBmNjY7Zs2UJ0dDSbN29mzpw5jB49mv3791O8eHEWLVrE4MGD2bRpEytXruS///0vW7ZsoVatWlm2n1Vbz0uKTE1NM62v1WpztL9CCCEKnvS0NI5v/Z3on38kJek+AD5VqlO/e2+ci3oYODrxppM7Fi9Ao9FgZGX13B/rgABMXF0hu4s7jQYTV1esAwJytL2XnfZt6tSp/Prrr0RHR+vKqlSpwqlTpyhZsmSmn4xZjUxMTGjUqBHTp0/n+PHjXLp0ie3btwNgZmZG+lNT52Zs09vbO9M2ra2tKVmyJKampuzbt0+3TlxcHOfOncvxvpQrV47du3frlUVHR1O6dGmMjY0pV64caWlp7N+/X7f87t27nDt3Tq9LUVpaGocOHdK9Pnv2LPHx8bo7LF27dmXr1q38+eefmWJIS0vTG+SeVYzR0dF6SU50dDS2trYULVoUeHIuBQQEMH78eP7880/MzMxYt26drn7lypX59NNPiY6OpkKFCvz4449ZtlW2bNksj8ezuk8JIYR4s8T8eYglH3/A9kXfkpJ0H+dinrQfNYG2I8ZKUiFeCbljkY80xsa4jPqUfz4c8iS5+Pe36P8/SXAZ9SkaY+NXEo+fnx/dunVjzpw5urIRI0ZQq1Yt3n//ffr164e1tTVnzpxhy5YtzJkzhw0bNvD3339Tv359HB0d2bhxI1qtVtdtydvbm/3793Pp0iVsbGxwcnLi/fff5/vvv6dLly58/PHHFCpUiAsXLvDTTz/x/fffY2NjQ58+ffj4449xdnbGxcWF0aNHY2SUOc+9ffs2R48e1StzdXVl2LBhVK9enYkTJ9KpUyf27t3LN998w9y5c4EnY0Vat25Nv379+Pbbb7G1tWXkyJEULVqU1q1b67ZlamrKoEGD+PrrrzE1NeWDDz6gVq1a1KjxZNzLkCFD+O2332jYsCETJ06kbt262NracujQIaZNm8aCBQuynG4WYODAgcyePZtBgwbxwQcfcPbsWcaOHcvQoUMxMjJi//79bNu2jXfffZciRYqwf/9+bt++TdmyZYmJieG7776jVatWuLu7c/bsWc6dO0fPnj2zbOvjjz8mJCSEKlWq0LBhQ3799VfWrl3L1q1bc3x+CCGEeD3dvXaFyKULuHT0SW8KC1s7AkK6U7FhMEav6BpDCACUUAkJCQpQCQkJmZY9fPhQnT59Wj18+PDFt//HH+pcYJA67VtG93MuMEgl/PHHy4T9XKGhoap169Z6ZZcuXVLm5ubq33/6AwcOqMaNGysbGxtlbW2tKlasqCZNmqSUUmrXrl0qMDBQOTo6KktLS1WxYkW1cuVK3bpnz55VtWrVUpaWlgpQMTExSimlzp07p9q2bascHByUpaWlKlOmjBoyZIjSarVKKaXu37+vunfvrqysrJSLi4uaPn26CgwMVB9++KFu24GBgU+mznrqZ+zYsUoppVavXq3KlSunTE1Nlaenp5oxY4bevt67d0/16NFD2dvbK0tLSxUcHKzOnTunW75o0SJlb2+v1qxZo3x8fJSZmZl655131KVLl/S2k5KSoqZMmaL8/PyUhYWFcnJyUgEBASo8PFylpqbqYv137BkiIyNV9erVlZmZmXJ1dVUjRozQrXP69GkVHBysChcurMzNzVXp0qXVnDlzlFJKxcbGqjZt2ig3NzdlZmamvLy81JgxY1R6erpSSqkFCxYoZ2dnvbbmzp2rfHx8lKmpqSpdurRasmSJ3nJArVu3TimlVExMjALUn3/+qVseFxenALVjx45M+6FU3rwXhBBC5J0HCfFq64K56ovOLdXMkOZqVpfWaseSH9TDpPuGDk28QZ51nfw0jVIvMIfpGyYxMRF7e3sSEhKws7PTW5aSkkJMTAzFixfHwsLihdtQ6ekkHzpM2u3bmBQujFW1qq/sToXIWnh4OEOGDCE+Pt7QoeTa1KlTWbZsGSdPnnxlbebVe0EIIcTLSU9L5egfv7F39QoeJT/pkluyei3qd++No6u7gaMTb5pnXSc/TbpCvSIaY2Osa+ZsWlkhspOcnMxff/3FokWLaNq0qaHDEUII8Qoppbh4+AA7ly0g7sZ1AAp7FSeoZz88K1R8ztpC5D9JLIR4jXz33XdMmDCBRo0aMWbMGEOHI4QQ4hW5fTmGyCU/cOXkMQCs7B0I6NSDCg0aYWQkPSBEwSBdoXg1XaGEeN3Je0EIIV69B/Fx7Fm1jJPbt6CUFmNTU6o2a02NNiGYv8BDc4XILekKJYQQQgjxGktLTeXIxl/Yv24ljx8+BKB0rbrU7xaGfRFXA0cnRNYksRBCCCGEKCCUUpw/EM3OZQtJuHUTABefkgT17EuxshUMHJ0QzyaJhRBCCCFEAXDz7wtELvmBa2eezPhn4+hE3S6hlKvXAE0Wz3oSoqCRxEIIIYQQwoCS7t1l909LObVzGyiFiakZ1Vq1p0ar9pjKmDbxGpHEQgghhBDCAFIfP+Lwr+s48MtqUh+lAFAmIJB6XUOxK1TEwNEJkXuSWAghhBBCvEJKKc5G72Tnj+Hcv3MbALeSvgSF9sO9dBkDRyfEi5PEQhQYQUFB+Pv7M3v2bEOH8lI0Gg3r1q2jTZs2hg5FCCFEAXPj/Fl2LPmeG+f+AsDWuTD1uoZSJiAQjUZj4OiEeDkyEugV0WoV/5yN49zBWP45G4dWm/+PDwkLC0Oj0TB16lS98oiIiDz78AoKCmLIkCF5sq1Xue2cCg8Px8HBIVfr3LhxQ56KLYQQQk/indtsnDOTH/87jBvn/sLE3JyAkO70+nIeZesGSVIh3ghyx+IVuPjnLXatPM+D+Ee6MmsHc+p1KkWJyvnbh9LCwoJp06YxYMAAHB0d87Ut8YSrq8wvLoQQ4onUlBQOrF/DoV/Xkvb4yXVA+cCG1O3cExsnZwNHJ0TekjsW+ezin7fY9O1JvaQC4EH8IzZ9e5KLf97K1/YbNWqEq6srU6ZMybZOdHQ09evXx9LSEg8PDwYPHsyDBw90y+fOnUupUqWwsLDAxcWFDh06AE/uiERFRfHVV1+h0WjQaDRcunQJgNOnT9OsWTNsbGxwcXGhR48e3LlzR7fNBw8e0LNnT2xsbHBzc+OLL77I9b6tWbOG8uXLY25ujre3d6ZtxMXF0bNnTxwdHbGysqJp06acP39etzzjbkRERASlS5fGwsKCxo0bc/Xq1We2O2/ePEqUKIGZmRm+vr4sXbpUb7lGoyEiIgKAS5cuodFoWLt2LQ0aNMDKyopKlSqxd+/eXO+vEEKI14fSajm9czsLh/Rn35oVpD1+RNEy5eg2+UuaDPxIkgrxRpLE4gUopUh9lP7cn0cP09i18twzt7Vr5XkePUzL0faUyn33KWNjYyZPnsycOXO4du1apuUnTpwgODiYdu3acfz4cVauXMnu3bv54IMPADh06BCDBw9mwoQJnD17lk2bNlG/fn0AvvrqK2rXrk2/fv24ceMGN27cwMPDgxs3bhAYGIi/vz+HDh1i06ZN3Lx5k5CQEF27H3/8MTt27GDdunVs3ryZyMhIDh8+nOP9Onz4MCEhIXTu3JkTJ04wbtw4PvvsM8LDw3V1wsLCOHToEOvXr2fv3r0opWjWrBmpqam6OsnJyUyaNInFixezZ88eEhMT6dy5c7btrlu3jg8//JBhw4Zx8uRJBgwYQK9evdixY8cz4x09ejTDhw/n6NGjlC5dmi5dupCWlpbj/RVCCPH6+Oev0ywfPYzf/zeLpLh72BV2oeVHI+k0bhquJUoZOjwh8o10hXoBaY+1fPdhVJ5s60H8I374aGeO6vb/KhBTc+Nct9G2bVv8/f0ZO3YsCxYs0Fs2Y8YMunbtqhvLUKpUKb7++msCAwOZN28eV65cwdramhYtWmBra4uXlxeVK1cGwN7eHjMzM6ysrPS6/8ybN48qVaowefJkXdnChQvx8PDg3LlzuLu7s2DBApYsWULjxo0BWLx4McWKFcvxPs2aNYuGDRvy2WefAVC6dGlOnz7NjBkzCAsL4/z586xfv549e/ZQp04dAJYvX46HhwcRERF07NgRgNTUVL755htq1qypi6Ns2bIcOHCAGjVqZGp35syZhIWFMXDgQACGDh3Kvn37mDlzJg0aNMg23uHDh9O8eXMAxo8fT/ny5blw4QJlysjsH0II8aZIuHWTnT+Gc27vLgDMLC2p2bYTVZq2wsTMzMDRCZH/5I7FW2LatGksXryY06dP65UfPnyY8PBwbGxsdD/BwcFotVpiYmJo3LgxXl5e+Pj40KNHD5YvX05ycvIz2zp8+DA7duzQ22bGBfTFixe5ePEijx8/pnbt2rp1nJyc8PX1zfH+nDlzhoCAAL2ygIAAzp8/T3p6OmfOnMHExESXMAA4Ozvj6+vLmTNndGUmJiZUq1ZN97pMmTI4ODjo1clJu9nVz1CxYkXd725ubgDcupW/3eCEEEK8Go8fJrNrxWIWDf3Pk6RCo8GvYTC9Z39HjdYdJKkQbw25Y/ECTMyM6P9V4HPrXT8fz4Zvjj23XosPKuFeyiFH7b6o+vXrExwczKhRowgLC9OVa7VaBgwYwODBgzOt4+npiZmZGUeOHCEyMpLNmzczZswYxo0bx8GDB7OdLUmr1dKyZUumTZuWaZmbm5veOIcXpZTKNIPGv7uKZddtLKv1spqJ41mzc2TV7vNm8zA1Nc20vlarfeY6QgghCjatNp1TkdvY/dMSkhPiAfAoX5Ggnn0p4u1j2OCEMABJLF6ARqPJUZckj3JOWDuYZxq4/W82juZ4lHPCyCj/p5mbOnUq/v7+lC5dWldWpUoVTp06RcmSJbNdz8TEhEaNGtGoUSPGjh2Lg4MD27dvp127dpiZmZGenq5Xv0qVKqxZswZvb29MTDKfYiVLlsTU1JR9+/bh6ekJPBlofe7cOQIDn5+wAZQrV47du3frlUVHR1O6dGmMjY0pV64caWlp7N+/X9cV6u7du5w7d46yZcvq1klLS+PQoUO6bk9nz54lPj4+2y5KZcuWZffu3fTs2VOv3X9vUwghxJvv6qnj7FjyA7cv/Q2Ag6sbgd37UKJaTZk6Vry1DNoVaufOnbRs2RJ3d3e9mXTgSd/3ESNG4Ofnh7W1Ne7u7vTs2ZPr16/rbePRo0cMGjSIQoUKYW1tTatWrbIcpGwIRkYa6nV69iCtuiGlXklSAeDn50e3bt2YM2eOrmzEiBHs3buX999/n6NHj+rGJgwaNAiADRs28PXXX3P06FEuX77MkiVL0Gq1um5L3t7e7N+/n0uXLnHnzh20Wi3vv/8+9+7do0uXLhw4cIC///6bzZs307t3b9LT07GxsaFPnz58/PHHbNu2jZMnTxIWFoaRUebT8fbt2xw9elTvJzY2lmHDhrFt2zYmTpzIuXPnWLx4Md988w3Dhw8HnowVad26Nf369WP37t0cO3aM7t27U7RoUVq3bq3bvqmpKYMGDWL//v0cOXKEXr16UatWrSzHV8CTQefh4eHMnz+f8+fPM2vWLNauXatrVwghxJstPvYGv8ycxKoJo7h96W/MrawJ7NGHsC/mUrJ6LUkqxFvNoInFgwcPqFSpEt98802mZcnJyRw5coTPPvuMI0eOsHbtWs6dO0erVq306g0ZMoR169bx008/sXv3bpKSkmjRokWmb9ENpUTlIjQZUAFrB3O9chtHc5oMqJDvz7F42sSJE/W6CVWsWJGoqCjOnz9PvXr1qFy5Mp999pluHICDgwNr167lnXfeoWzZssyfP58VK1ZQvnx54Mmg5Iw7BIULF+bKlSu4u7uzZ88e0tPTCQ4OpkKFCnz44YfY29vrkocZM2ZQv359WrVqRaNGjahbty5Vq1bNFO+PP/5I5cqV9X7mz59PlSpVWLVqFT/99BMVKlRgzJgxTJgwQa+b16JFi6hatSotWrSgdu3aKKXYuHGjXrckKysrRowYQdeuXalduzaWlpb89NNPuuVarVbvrkubNm346quvmDFjBuXLl+fbb79l0aJFBAUF5cnfRwghRMH0KPkBUcsWsmjoe1w4uBeNxohK7zan91ffUa1FW4xNTJ+/ESHecBr1InOY5gONRsO6deto06ZNtnUOHjxIjRo1uHz5Mp6eniQkJFC4cGGWLl1Kp06dALh+/ToeHh5s3LiR4ODgHLWdmJiIvb09CQkJ2NnZ6S1LSUkhJiaG4sWLY2Fh8cL7p9UqbpyP50HiI6ztzHEr5fDK7lSIrIWHhzNkyBDi4+OzrTN16lSWLVvGyZMnX11gBVRevReEEOJ1ok1P58T2P9izajkPExMA8KpYmaAefSjk6W3Y4IR4BZ51nfy012qMRUJCAhqNRjdo+PDhw6SmpvLuu+/q6ri7u1OhQgWio6OzTSwePXrEo0f/N+4hMTExX+OGJ92iivrKk69fF8nJyfz1118sWrSIpk2bGjocIYQQBnDp+J9ELfmBO1cvA+DoXoygnn0o7l9NujwJkYXXJrFISUlh5MiRdO3aVZctxcbGYmZmhqOj/gW7i4sLsbGx2W5rypQpjB8/Pl/jFa+37777jgkTJtCoUSPGjBlj6HCEEEK8QveuXyNq6QL+PnIQAAtrG2p37Ealxk0xzmJSEiHEE6/FuyM1NZXOnTuj1WqZO3fuc+s/b/rPTz/9lKFDh+peJyYm4uHhkSexitdHWFiY3piMfxsyZIjuoYFCCCHeDg+T7rNv9QqObv4NbXo6RsbG+L/bnFodumBpY2vo8IQo8Ap8YpGamkpISAgxMTFs375dr2+Xq6srjx8/Ji4uTu+uxa1bt3RTjGbF3Nwcc3PzbJcLIYQQ4u2RnpbGsS2/s3f1j6Qk3QfAp0p1Anv0wcm9mIGjE+L1UaATi4yk4vz58+zYsQNnZ2e95VWrVsXU1JQtW7YQEhICwI0bNzh58iTTp083RMhCCCGEeI3E/HmIyCU/cO/6k6nqC3l4EdizL94VKxs4MiFePwZNLJKSkrhw4YLudUxMDEePHsXJyQl3d3c6dOjAkSNH2LBhA+np6bpxE05OTpiZmWFvb0+fPn0YNmwYzs7OODk5MXz4cPz8/GjUqJGhdksIIYQQBdydq5eJWrqAS8eOAGBpa0dAp+74vROMkfHzH4IrhMjMoInFoUOHaNCgge51xriH0NBQxo0bx/r16wHw9/fXW2/Hjh265wZ8+eWXmJiYEBISwsOHD2nYsCHh4eEYy4eCEEIIIZ6SnJhA9M8/cnzr7yitFiNjE6o0a0XNtiFYWNsYOjwhXmsF5jkWhvQqnmMhxOtO3gtCiNdZeloqf27awL41P/Eo+QEAJavXpn73Xji6uhs4OiEKrjf2ORZCCCGEELmhlOLiof1ELVtAfOwNAAp7+xDUoy+eFSoaODoh3ixGhg5AvF7Cw8N1DyjMT97e3syePTvf2xFCCPHmunXpb1Z/PppfZn5OfOwNrOwdeHfAYLpP+VKSCiHygSQWr4hWm87VU8c5syeKq6eOo9Wm53ubYWFhaDQaNBoNpqam+Pj4MHz4cB48ePDC2+zUqRPnzp3LsxizS1QOHjxI//7986wdIYQQb48H8XFs/m4OS0d+yJWTxzE2NaVGm470+eo7/N55FyMjGYcpRH6QrlCvwPn90WwP/46ke3d0ZTZOhXgnrD+lamb/vI280KRJExYtWkRqaiq7du2ib9++PHjwgHnz5unVS01NxdTU9Lnbs7S0xNLSMr/C1SlcuHC+tyGEEOLNkvb4MUd+X8/+dSt5/PAhAKVr16N+1zDsi7gYODoh3nxyxyKfnd8fzfpZk/WSCoCke3dYP2sy5/dH52v75ubmuLq64uHhQdeuXenWrRsRERGMGzcOf39/Fi5ciI+PD+bm5iiluHLlCq1bt8bGxgY7OztCQkK4efOmbntZ3WH49ddfqVq1KhYWFvj4+DB+/HjS0tJ0y+Pj4+nfvz8uLi5YWFhQoUIFNmzYQGRkJL169SIhIUF3Z2XcuHFA5q5Qz4srY3+WLl2Kt7c39vb2dO7cmfv37+fLcRVCCFFwKKU4t2834cPeY9eP4Tx++BAXn1J0Gj+NlkNGSFIhxCsidyxegFKKtEePnltPq01n+6Jvn1lne/i3ePpVytFtWRNzczQaTY7jzIqlpSWpqakAXLhwgVWrVrFmzRrd9Lxt2rTB2tqaqKgo0tLSGDhwIJ06dSIyMjLL7f3xxx90796dr7/+mnr16nHx4kVdF6axY8ei1Wpp2rQp9+/fZ9myZZQoUYLTp09jbGxMnTp1mD17NmPGjOHs2bMA2NhknupPKZWjuC5evEhERAQbNmwgLi6OkJAQpk6dyqRJk17qmAkhhCi4bv59gR2Lv+efv04BYOPoRN0uoZSr1wCNkXx/KsSrJInFC0h79IivQzvkybaS7t3lm16dclR38OLVmL7ENJ8HDhzgxx9/pGHDhgA8fvyYpUuX6rodbdmyhePHjxMTE4OHhwcAS5cupXz58hw8eJDq1atn2uakSZMYOXIkoaGhAPj4+DBx4kQ++eQTxo4dy9atWzlw4ABnzpyhdOnSujoZ7O3t0Wg0uLq6Zhv31q1bcxSXVqslPDwcW1tbAHr06MG2bdsksRBCiDdQ0r277P5pKad2bgOlMDEzp1rLdtRo1f6l/lcKIV6cJBZvuA0bNmBjY0NaWhqpqam0bt2aOXPmMHfuXLy8vPTGMpw5cwYPDw/dxTtAuXLlcHBw4MyZM1kmFocPH+bgwYN6F+/p6emkpKSQnJzM0aNHKVasmC6peBE5jcvb21uXVAC4ublx69atF25XCCFEwZP6+BGHf13HgV9Wk/ooBYCydYOo2yUUu0IyPk8IQ5LE4gWYmJszePHq59a7duYka6eOe269diPHUaxshRy1m1sNGjRg3rx5mJqa4u7urjdA29raWq+uUirLrlbZlcOTuwTjx4+nXbt2mZZZWFjkyUDvnMb19OBzjUaDVqt96faFEEIYnlKKv6J3smt5OPfv3gbArZQvDUL741bK18DRCSFAEosXotFocnSb1atSZWycCmUauP1vts6F8KpUOd+mvrO2tqZkyZI5qluuXDmuXLnC1atXdXcHTp8+TUJCAmXLls1ynSpVqnD27Nls26hYsSLXrl3j3LlzWd61MDMzIz392VPvvkhcQggh3hzXz/1F5JLvuXH+yXg8W+fC1O8Whm+d+i899lAIkXckschHRkbGvBPWn/WzJmdbp0Fo/wIzn3ajRo2oWLEi3bp1Y/bs2bpB0oGBgVSrVi3LdcaMGUOLFi3w8PCgY8eOGBkZcfz4cU6cOMHnn39OYGAg9evXp3379syaNYuSJUvy119/odFoaNKkCd7e3iQlJbFt2zYqVaqElZUVVlZWLx2XEEKI11/indvs+jGcv/ZEAWBqbkGN1h2o2rItpma5v4svhMhfMl1CPitVsw6tho7CxqmQXrmtcyFaDR2V78+xyA2NRkNERASOjo7Ur1+fRo0a4ePjw8qVK7NdJzg4mA0bNrBlyxaqV69OrVq1mDVrFl5eXro6a9asoXr16nTp0oVy5crxySef6O5S1KlTh//85z906tSJwoULM3369DyJSwghxOsrNSWFPauWs+ij/+iSivKBjeg9+1tqte8sSYUQBZRGKaUMHYShJSYmYm9vT0JCAnZ2dnrLUlJSiImJoXjx4li8xCwTWm06/5w5RVJ8HDYOjhQtW77A3KnIjW+//ZaJEydy7do1Q4ciXrG8ei8IIUR2lFbL6V072L1iMUlx9wAoWqY8DUL74eKTs269Qoi89azr5KdJV6hXxMjIGI/yFQ0dxku5evUqGzdupHz58oYORQghxBvm2l+niFz8Azf/Pg+AfREX6nfvTakadWQchRCvCUksRI5VqVKFokWLEh4ebuhQhBBCvCESbsWyc3k45/btBsDM0pKabTtRpWkrTMzMDBydECI3JLEQOXb79m1DhyCEEOIN8fhhMvsjfubwbxGkp6aCRoPfO+8SENIdawdHQ4cnhHgBklgIIYQQ4pXRatM5FbmN3T8tITkhHgDPChUJ7NGXIt4+hg1OCPFSJLEQQgghxCtx5eRxIpf+wO1LfwPg4OpGYI++lKhaQ8ZRCPEGkMRCCCGEEPkqLvY6O5ct5MLBfQCYW1lTu0MX/IObY2xiauDohBB5RRILIYQQQuSLR8kP2Ld2JUc2rkebnobGyIiKjZpSp2NXrOzsDR2eECKPSWIhhBBCiDylTU/nxPY/2LNyGQ/vJwLgXakKQT374lzM08DRCSHyiyQWQgghhMgzl44dIXLJD9y9dgUAJ/diBPXsS/HK1QwcmRAiv0liIYQQQoiXdvefq+xctpC/jxwEwMLGljodu1KxUVOMTeRyQ4i3gZGhA3hbKK0i5WI8yUdvkXIxHqVV+d7mrVu3GDBgAJ6enpibm+Pq6kpwcDB79+7NszYePHjAiBEj8PHxwcLCgsKFCxMUFMSGDRvyrA0hhBAF18Ok+2wP/5YlH3/A30cOYmRsTJVmren91XdUbtJSkgoh3iLybn8FHp68Q/yvF0lPeKwrM7Y3w6FlCSwrFMq3dtu3b09qaiqLFy/Gx8eHmzdvsm3bNu7du5dnbfznP//hwIEDfPPNN5QrV467d+8SHR3N3bt386wNIYQQBU96WhrHtvzO3p+Xk/IgCQCfqjUI7N4bJ/diBo5OCGEIGqVU/n91XsAlJiZib29PQkICdnZ2estSUlKIiYmhePHiWFhY5HrbD0/e4e6yM9kud+5eNl+Si/j4eBwdHYmMjCQwMDDLOgkJCXz88cdERESQkpJCtWrV+PLLL6lUqZKuztSpU/nyyy9JTk4mJCSEwoULs2nTJo4ePQqAg4MDX331FaGhodnG8ujRIz777DNWrFjBrVu38PT0ZOTIkfTp04f09HT69+/P9u3biY2NxdPTk4EDB/Lhhx/q1g8LCyM+Pp66devyxRdf8PjxYzp37szs2bMxNZVpCl+Vl30vCCHeDEopYv48RNTSBdy7fg2AQh5eBPbsi3fFygaOTgiR1551nfw0uWPxApRSqFTt8+tpFXHrLz6zTtz6i5iVdEBj9PwHA2lMjXL8ACEbGxtsbGyIiIigVq1amJub68emFM2bN8fJyYmNGzdib2/Pt99+S8OGDTl37hxOTk6sWrWKsWPH8r///Y969eqxdOlSvv76a3x8/u/JqK6urmzcuJF27dpha2ubZSw9e/Zk7969fP3111SqVImYmBju3LkDgFarpVixYqxatYpChQoRHR1N//79cXNzIyQkRLeNHTt24Obmxo4dO7hw4QKdOnXC39+ffv365eh4CCGEeHl3rl4mcskPXD7+JwCWtnYEdOqB3zvvYmRsbODohBCGJncsyP0dC+3jdK6PiX7lcbpPqIORWc4/uNesWUO/fv14+PAhVapUITAwkM6dO1OxYkW2b99O27ZtuXXrll7SUbJkST755BP69+9PnTp1qFSpEvPmzdMtr1WrFikpKbo7Fjt37qRbt27cvHmTSpUqUbduXTp06EBAQAAA586dw9fXly1bttCoUaMcxf3+++9z8+ZNVq9eDTy5YxEZGcnFixcx/v//uEJCQjAyMuKnn37K8fEQL0fuWAjx9kpOTCB61XKOb92EUlqMjE2o0qwVtdp1wtzK2tDhCSHyUW7uWMjg7TdY+/btuX79OuvXryc4OJjIyEiqVKlCeHg4hw8fJikpCWdnZ93dDRsbG2JiYrh48cldljNnzlC7dm29bT79un79+vz9999s27aN9u3bc+rUKerVq8fEiRMBOHr0KMbGxtl2xwKYP38+1apVo3DhwtjY2PD9999z5coVvTrly5fXJRUAbm5u3Lp166WOjxBCiGdLT0vl0IZ1LPywP8e2bEQpLaVq1KHXrHkEdu8tSYUQQo90hXoBGlMj3CfUeW69RzEJ3F106rn1nHuVx7z4859AqjHNfR5oYWFB48aNady4MWPGjKFv376MHTuWgQMH4ubmRmRkZKZ1HBwcctWGqakp9erVo169eowcOZLPP/+cCRMmMGLECCwtLZ+57qpVq/joo4/44osvqF27Nra2tsyYMYP9+/dnauPfNBoNWu3zu6MJIYTIPaUUFw7tY+eyhcTH3gCgsLcPDXr2xaN8RQNHJ4QoqCSxeAEajQZNDrokWZRyxNjeTG82qKcZ25tjUcoxR2Ms8kK5cuWIiIigSpUqxMbGYmJigre3d5Z1y5Yty759++jZs6eubN++fTlqIy0tjZSUFPz8/NBqtURFRWXZFWrXrl3UqVOHgQMH6soy7pgIIYR49W5d+pvIJT9w9dRxAKzsHajbpSflAxtiZCTjKIQQ2ZPEIh9pjDQ4tCzxzFmhHFr65EtScffuXTp27Ejv3r2pWLEitra2HDp0iOnTp9O6dWsaNWpE7dq1adOmDdOmTcPX15fr16+zceNG2rRpQ7Vq1fjwww8JDQ2lWrVq1K1bl+XLl3Pq1Cm9wdtBQUF06dKFatWq4ezszOnTpxk1ahQNGjTAzs4OOzs7QkND6d27t27w9uXLl7l16xYhISGULFmSJUuW8Mcff1C8eHGWLl3KwYMHKV68eJ4fEyGEENl7EB/HnpVLObFjCyiFsakp1Vq0pUbrDphZWhk6PCHEa0ASi3xmWaEQzt3LZvEcC3McWvrk23MsbGxsqFmzJl9++SUXL14kNTUVDw8P+vXrx6hRo9BoNGzcuJHRo0fTu3dvbt++jaurK/Xr18fFxQWATp06cfHiRUaMGEFKSgrt27fnvffe448//tC1ExwczOLFixk1ahTJycm4u7vTokULxowZo6szb948Ro0axcCBA7l79y6enp6MGjUKePIcjKNHj9KpUyc0Gg1dunRh4MCB/P777/lyXIQQQuhLe/yYwxt/4UDEKh4/fAiAb+161Osahn0RFwNHJ4R4ncisUOTvcywyKK3iUUwC2vuPMbI1w7y4/Svr/pSXxo0bR0REhG5WKPH2kFmhhHizKKU4t28PO5cvIvH2TQBcS5QiqGc/ipYpZ+DohBAFhTzHogDSGGmwKOFg6DCEEEIIbv59gR2Lv+Ofv04DYOPoRL2uYZStG4TGSCaMFEK8GIN+euzcuZOWLVvi7u6ORqMhIiJCb7lSinHjxuHu7o6lpSVBQUGcOqU/y9KjR48YNGgQhQoVwtramlatWnHt2rVXuBdCCCHE6yHp3l02zf2SZZ8O4Z+/TmNiZk7tDl3oPfs7ytV/R5IKIcRLMegnyIMHD6hUqRLffPNNlsunT5/OrFmz+Oabbzh48CCurq40btyY+/fv6+oMGTKEdevW8dNPP7F7926SkpJo0aIF6enpr2o33irjxo2TblBCCPGaSX2Uwt41K1gwpD+norYBULZeA3rP/pY6HbthKt0bhRB5wKBdoZo2bUrTpk2zXKaUYvbs2YwePZp27doBsHjxYlxcXPjxxx8ZMGAACQkJLFiwgKVLl+qmMl22bBkeHh5s3bqV4ODgV7YvQgghREGjlOKvPVHs+nEx9+/eBsCtdBkahPbDraSvgaMTQrxpcp1YXLp0iV27dnHp0iWSk5MpXLgwlStXpnbt2nk6oDMmJobY2FjeffddXZm5uTmBgYFER0czYMAADh8+TGpqql4dd3d3KlSoQHR0tCQWQggh3lrXz/1F5JLvuXH+LAC2hQpTv2sYvnXqo9G8fpOHCCEKvhwnFj/++CNff/01Bw4coEiRIhQtWhRLS0vu3bvHxYsXsbCwoFu3bowYMQIvL6+XDiw2NhZAN/VpBhcXFy5fvqyrY2ZmhqOjY6Y6Getn5dGjRzx69Ej3OjEx8aXjFUIIIQqCxDu32fVjOH/tiQLA1NyCGm06UrVFG0zNzA0cnRDiTZajxKJKlSoYGRkRFhbGqlWr8PT01Fv+6NEj9u7dy08//US1atWYO3cuHTt2zJMAn/5WRSn13G9anldnypQpjB8/Pk/iE0IIIQqCxykPObh+DYd+XUfa40eg0VA+sCF1O/fExtHJ0OEJId4COUosJk6cSPPmzbNdbm5uTlBQEEFBQXz++efExMS8dGCurq7Ak7sSbm5uuvJbt27p7mK4urry+PFj4uLi9O5a3Lp1izp16mS77U8//ZShQ4fqXicmJuLh4fHSMQshhBCvmtJqOb1rB7tXLCYp7h4AxcpWIKhnX1x8Sho4OiHE2yRHicWzkoqnFSpUiEKFXv5p0sWLF8fV1ZUtW7ZQuXJlAB4/fkxUVBTTpk0DoGrVqpiamrJlyxZCQkIAuHHjBidPnmT69OnZbtvc3Bxzc7kdLIQQ4vV27a9TRC7+npt/XwDAvogL9bv3plSNOjKOQgjxyuV68PaRI0cwNTXFz88PgF9++YVFixZRrlw5xo0bh5mZWY63lZSUxIULF3SvY2JiOHr0KE5OTnh6ejJkyBAmT55MqVKlKFWqFJMnT8bKyoquXbsCYG9vT58+fRg2bBjOzs44OTkxfPhw/Pz8dLNEvc1u3brFZ599xu+//87NmzdxdHSkUqVKjBs3jtq1a7/wdoOCgvD392f27Nl5F6wQQogcS7gVy87l4ZzbtxsAM0tLarbtRJWmrTDJxf9hIYTIS7lOLAYMGMDIkSPx8/Pj77//pnPnzrRt25aff/6Z5OTkXF1sHjp0iAYNGuheZ3RPCg0NJTw8nE8++YSHDx8ycOBA4uLiqFmzJps3b8bW1la3zpdffomJiQkhISE8fPiQhg0bEh4ejrGxcW53LV9ptVouX75MUlISNjY2eHl5YZTPDyJq3749qampLF68GB8fH27evMm2bdu4d+/eC20vNTUVU1PTPI5SCCFETj1KTuZAxCoOb/yF9NRUNBoj/N55lzoh3bB2cHz+BoQQIh9plFIqNyvY29tz5MgRSpQowbRp09i+fTt//PEHe/bsoXPnzly9ejW/Ys03iYmJ2Nvbk5CQgJ2dnd6ylJQUYmJiKF68+AtPp3v69Gk2bdqkN/uUnZ0dTZo0oVy5ci8Ve3bi4+NxdHQkMjKSwMDALOtcuXKFQYMGsW3bNoyMjGjSpAlz5szRjWEZN24cERERDB48mM8//5xLly7Ro0cPlixZoredmJgYvL2982U/RMGRF+8FIcSL0WrTObljK3tWLiU5IR4AzwqVCOrZl8JexQ0bnBDijfas6+Sn5fqOhVIKrVYLwNatW2nRogUAHh4e3Llz5wXCfbOdPn2aVatWZSpPTExk1apVhISE5EtyYWNjg42NDREREdSqVSvTmBKlFG3atMHa2pqoqCjS0tIYOHAgnTp1IjIyUlfvwoULrFq1ijVr1mBsbIyXlxfnz5+nQoUKTJgwAYDChQvnefxCCCGeuHLyOJFLvuf25ScTozi6uVO/ex9KVK0h4yiEEAVKrhOLatWq8fnnn9OoUSOioqKYN28e8ORb66efOfGmUkqRmpr63HparZbff//9mXU2bdqEj49PjrpFmZqa5vifiImJCeHh4fTr14/58+dTpUoVAgMD6dy5MxUrVmTr1q0cP36cmJgY3YxYS5cupXz58hw8eJDq1asDTwbML126VC95MDMzw8rKSjdzlxBCiLwXF3udncsWcuHgPgDMra2p3b4L/sHNMTaRbqlCiIIn14nF7Nmz6datGxEREYwePZqSJZ9MZbd69epnTvH6JklNTWXy5Ml5sq3ExESmTp2ao7qjRo3K1eD49u3b07x5c3bt2sXevXvZtGkT06dP54cfftBNsfvvaXbLlSuHg4MDZ86c0SUWXl5eckdCCCFeoZQHSexbu5I/f/8VbXoaGiMjKjVuSu0OXbGyszd0eEIIka1cJxYVK1bkxIkTmcpnzJhR4AZMC7CwsKBx48Y0btyYMWPG0LdvX8aOHcvQoUOzvPvx9MMFra2tX2W4Qgjx1tKmp3N82x9Er1rGw/tPxuR5+1clqEcfnIt5PmdtIYQwvFwnFv+WlJSkG2+R4W2YNcjU1JRRo0Y9t97ly5dZvnz5c+t169YNLy+vHLX7ssqVK0dERATlypXjypUrXL16VXfX4vTp0yQkJFC2bNlnbsPMzIz09PSXjkUIIcQTl44dIXLJD9y9dgUAp6IeBPXoQ/HK1QwcmRBC5FyuE4uYmBg++OADIiMjSUlJ0ZVnfNP9NlxwajSaHHVJKlGiBHZ2dnqzQT3Nzs6OEiVK5PnUs3fv3qVjx4707t2bihUrYmtry6FDh5g+fTqtW7emUaNGVKxYkW7dujF79mzd4O3AwECqVXv2PzJvb2/279/PpUuXsLGxwcnJKd+nzhVCiDfR3X+usnPZQv4+chAACxtb6nTsSsVGTTE2eanv/oQQ4pXL9adWt27dAFi4cCEuLi4yI8UzZEzhmtWsUBmaNGmSLxflNjY21KxZky+//JKLFy+SmpqKh4cH/fr1Y9SoUWg0GiIiIhg0aBD169fXm272eYYPH05oaCjlypXj4cOHMt2sEELk0sOk++xd/SPHNm9Em56OkbExlZu0oFa7LljY2Bg6PCGEeCG5fo6FjY0Nhw8fxtfXN79ieuXexOdYCJHX5DkWQry89LQ0jm3ZyN6ffyTlQRIAPlVrENi9D07uRQ0cnRBCZJavz7GoXr06V69efaMSi/xWrlw5ypQp88qfvC2EEKJgUEoR8+chIpcuIO76NQAKeXgR1LMfXhX9DRucEELkkVwnFj/88AP/+c9/+Oeff6hQoUKmAcUVK1bMs+DeJEZGRhQvLk9HFUKIt82dK5eIXLqAy8f/BMDSzp6AkO74vfMuRjKbohDiDZLrxOL27dtcvHiRXr166co0Gs1bNXhbCCGEeJ7kxASiVy3n+NZNKKXF2MSEKs1aU7NtCOZWMpW3EOLNk+vEonfv3lSuXJkVK1bI4G0hhBDiKWmpqfy56Vf2r13Jo+QHAJSqUYf63Xrh4Opm4OiEECL/5DqxuHz5MuvXr9c9cVsIIYQQT8ZRXDi0j51LFxJ/8wYARbxLEBTaF49yfgaOTggh8l+uE4t33nmHY8eOvXWJRS4nzxLijSPvASGyd+vS30Qu/p6rp08AYO3gSN3OPSkX+A5GRjKOQgjxdsh1YtGyZUs++ugjTpw4gZ+fX6bB261atcqz4AqCjP1LTk7G0tLSwNEIYTiPHz8GwFgGmwqh8yA+jj0rl3JixxZQCmNTU6q1aEeN1u0xs7QydHhCCPFK5fo5Fs+aIvV1Hbz9vPl5b9y4QXx8PEWKFMHKykrGlYi3jlar5fr165iamuLp6SnvAfHWS3v8mMO/RbA/4mdSUx4C4Fu7HvW79cKucBEDRyeEEHknX59jodVqXziw15WrqysAt27dMnAkQhiOkZGRJBXiraeU4ty+PexcvojE2zcBcC1ZmqCe/SjqW9bA0QkhhGHlOrF4G2k0Gtzc3ChSpAipqamGDkcIgzAzM5OHOoq3WuzF80Qu+Z5//joNgI2TM/W6hlE2IBCNvDeEECJnicVPP/1E586dc7TBq1evcuXKFQICAl4qsILI2NhY+pcLIcRb5v69O+xesYTTO7cDYGJmTvVW7anesh2mFhYGjk4IIQqOHH3FMm/ePMqUKcO0adM4c+ZMpuUJCQls3LiRrl27UrVqVe7du5fngQohhBCvUuqjFPauXsHCIQN0SUW5eg3oPftb6nTsKkmFEEI8JUd3LKKiotiwYQNz5sxh1KhRWFtb4+LigoWFBXFxccTGxlK4cGF69erFyZMnKVJEBq4JIYR4PSmtlr/2RLFzxWKS7t4BwL10WYJC++JW0tfA0QkhRMGV61mh7t69y+7du7l06RIPHz6kUKFCVK5cmcqVK7+2/a9zM9pdCCHEm+v6ub+IXPw9Ny6cBcCucBHqdQ3Dt3Y9mbhACPFWytdZoZydnWnduvULByeEEEIUNIl3brHrx8X8tScKAFNzC2q2DaFK89aYmpkbODohhHg9yKxQQggh3lqPUx5y8JfVHPp1HWmpj0GjoUJQIwI69cDG0cnQ4QkhxGtFEgshhBBvHaXVcmrndnb/tIQHcU8mHClWrgJBPfri4lPSwNEJIcTrSRILIYQQb5VrZ04SueQHbv59AQB7F1cCu/WmZI3aMo5CCCFegiQWQggh3goJt2LZuWwR5/bvAcDM0pJa7TpTuWkrTExNDRydEEK8/l44sXj8+DExMTGUKFECExPJT4QQQhRMj5KT2R+xiiO/RZCeloZGY4Rfw3cJCOmOlb2DocMTQog3Rq4zguTkZAYNGsTixYsBOHfuHD4+PgwePBh3d3dGjhyZ50EKIYQQuaXVpnNyxxb2rFxGckI8AJ5+/gT17EthT2+DxiaEEG+iXD944tNPP+XYsWNERkZi8a+njjZq1IiVK1fmaXBCCCHEi7hy8hjLRnzIlu++ITkhHke3orT5ZAwdRk+UpEIIIfJJru9YREREsHLlSmrVqqU3yK1cuXJcvHgxT4MTQgghciPuxj9ELVvExUP7ADC3tqZ2+674BzfD2ETGUQghRH7KdWJx+/ZtihQpkqn8wYMHMpuGEEIIg0h5kMS+NT/x56YNaNPT0BgZUalxM+p07Iql7bOfFCuEECJv5DqxqF69Or/99huDBg0C0CUT33//PbVr187b6IQQQohn0Kanc3zrJvb8vJyU+4kAFPevSmCPPjgX8zRwdEII8XbJdWIxZcoUmjRpwunTp0lLS+Orr77i1KlT7N27l6ioqPyIUQghhMjk0tHDRC5dwN1rVwBwKupBUM++FPevauDIhBDi7ZTrxKJOnTrs2bOHmTNnUqJECTZv3kyVKlXYu3cvfn5++RGjEEIIoXP3n6tELV1AzJ+HALCwtaNOx65UatQUI2NjA0cnhBBvL41SShk6CENLTEzE3t6ehIQE7OykL64QQhRED+8nsnf1Co5u/g2l1WJkbEzlJi2o1a4LFjY2hg5PCCHeSLm5Ts71dLMZbt26xcmTJzl+/LjeT15LS0vjv//9L8WLF8fS0hIfHx8mTJiAVqvV1VFKMW7cONzd3bG0tCQoKIhTp07leSxCCCFevfS0NI5s/IWFH/bnz02/orRaSlSrSejMuQT17CdJhRBCFBC57gp1+PBhQkNDOXPmDE/f7NBoNKSnp+dZcADTpk1j/vz5LF68mPLly3Po0CF69eqFvb09H374IQDTp09n1qxZhIeHU7p0aT7//HMaN27M2bNnsbW1zdN4hBBCvBpKKf4+cpCoZQuJu34NgEKe3gT17IuXn79hgxNCCJFJrrtCVaxYkZIlSzJixAhcXFwyTTHr5eWVpwG2aNECFxcXFixYoCtr3749VlZWLF26FKUU7u7uDBkyhBEjRgDw6NEjXFxcmDZtGgMGDHhuG9IVSgghCpY7Vy4RuXQBl4//CYClnT11O/WgwjuNMTKScRRCCPGq5OY6Odd3LGJiYli7di0lS5Z84QBzo27dusyfP59z585RunRpjh07xu7du5k9e7YuntjYWN59913dOubm5gQGBhIdHZ1lYvHo0SMePXqke52YmJjv+yGEEOL5khMTiF61jONb/0ApLcYmJlRp1pqabUMwt7I2dHhCCCGeIdeJRcOGDTl27NgrSyxGjBhBQkICZcqUwdjYmPT0dCZNmkSXLl0AiI2NBcDFxUVvPRcXFy5fvpzlNqdMmcL48ePzN3AhhBA5lpaayp+bfmXfmp94/DAZgFI161C/W28cXFwNHJ0QQoicyHVi8cMPPxAaGsrJkyepUKECpqamestbtWqVZ8EBrFy5kmXLlvHjjz9Svnx5jh49ypAhQ3B3dyc0NFRX7+kuWUqpbJ8E/umnnzJ06FDd68TERDw8PPI0biGEEM+nlOLCwb3sXLaI+Js3ACjiXYIGof0oVq6CgaMTQgiRG7lOLKKjo9m9eze///57pmX5MXj7448/ZuTIkXTu3BkAPz8/Ll++zJQpUwgNDcXV9ck3WbGxsbi5uenWu3XrVqa7GBnMzc0xNzfP0ziFEELkzq1LfxO5+Huunj4BgLWDI3W7hFK+/jtojF540kIhhBAGkutP7sGDB9OjRw9u3LiBVqvV+8nrpAIgOTkZo6f+wRgbG+ummy1evDiurq5s2bJFt/zx48dERUVRp06dPI9HCCHEy3kQH8cf879m6cgPuXr6BCamZtRs24neX31HhaBGklQIIcRrKtd3LO7evctHH32U7d2AvNayZUsmTZqEp6cn5cuX588//2TWrFn07t0beHKXZMiQIUyePJlSpUpRqlQpJk+ejJWVFV27dn0lMQohhHi+tMePOfxbBPsjfiY15SEAvnXqU79rGHaFixg4OiGEEC8r14lFu3bt2LFjByVKlMiPeDKZM2cOn332GQMHDuTWrVu4u7szYMAAxowZo6vzySef8PDhQwYOHEhcXBw1a9Zk8+bN8gwLIYQoAJRSnNu3m53LF5F4+xYAriVLE9SzH0V9yxo4OiGEEHkl18+xmDRpErNnz6Z58+b4+fllGrw9ePDgPA3wVZDnWAghRP6IvXieHYu/5/rZ0wDYODlTv2sYZQICpcuTEEK8BnJznZzrxKJ48eLZb0yj4e+//87N5goESSyEEOLFaLXp/HPmFEnxcdg4OFK0bHmMjIy5f+8Ou1cs4fTO7QCYmJtTvWV7qrdsh6mFhYGjFkIIkVP5/oA8IYQQ4vz+aLaHf0fSvTu6MhsnZ4r6luPikQOk/f8HkZar14C6XUKxdS5kqFCFEEK8ArlOLIQQQojz+6NZP2typvKke3c5u3cXAO6ly9IgtB+uJUu/6vCEEEIYQI4Si6FDhzJx4kSsra31HiyXlVmzZuVJYEIIIQomrTad7eHfPbOOhY0tIeOmYGws318JIcTbIkef+H/++Sepqam634UQQry9/jlzSq/7U1ZSku5z/a/TeJSv+IqiEkIIYWg5Six27NiR5e9CCCHePkn37uasXnxcPkcihBCiIMn1XH+9e/fm/v37mcofPHige2idEEKIN9Odq5c58MuaHNW1cXDM52iEEEIUJLlOLBYvXszDhw8zlT98+P/au+/wqMr04ePfmclk0hPSGwQCoYRACL0IASm2FRQVEAFh1dWfuq5tLeu+KrsKq67oumLBRZp0EGxI7yDSklBCJ4SWQkJ6mcnMPO8fgYGQBBIgmZT7c10R5zll7vPkZM655zyliNmzZ9+WoIQQQtQtJcZitsybyZzXXyDjzKkbru/u40tIu/Y1H5gQQog6o8q96nJzc1FKoZQiLy8Pp6vGIbdYLKxYsQJ/f/8aCVIIIYT9nIzbxbrpX5F7IQ2All170CKmK2u/mVrpNgMe/xNara62QhRCCFEHVDmx8PLyQqPRoNFoaN26/NCBGo2GiRMn3tbghBBC2E/exQw2zvyGo79vA8Ddx487JzxNq249AXBx9yw3j4W7jy8DHv8TET162yVmIYQQ9lPlxGLDhg0opbjzzjtZunQp3t7etmWOjo6EhYURHBxcI0EKIYSoPVaLhfhVP7N14XeUFBeh0Wrpct8D9Hr4URydnG3rRfToTctuPSqceVsIIUTjU+XEIjY2FiidebtZs2ZoNJrrrv/ss8/yj3/8A19fmWlVCCHqi9TjR1nzv6mkJ50AICiiDYOefA7/5uEVrq/V6mRIWSGEEABolFKqJnbs4eFBfHw84eEVX4zqktzcXDw9PcnJycHDw8Pe4QghRK0zFhawdcEc4lf/AkphcHWl76Pj6TjwLjTaao/zIYQQooGozn1yjU2JWkP5ihBCiNtIKcWR37awcdY3FFyad6LdHf2JHfsErjJcrBBCiGqoscRCCCFE3ZadmsK6b7/kVMJeAJoEBTPwiWcJ69DJvoEJIYSolySxEEKIRsZcUsLuH5fy+7JFmEtM6PR6ejwwgm5DH8LB0dHe4QkhhKinJLEQQohG5EziftZ+M5WL588C0CwqmkFPPkuToBA7RyaEEKK+k8RCCCEagcLcHDZ/9y0HN60DwMXTi/7jnqRtn9gbjvInhBBCVEWNJRZjxoyREZaEEMLOlNXK/g1r2DJ3BsUF+aDRED3obu4Y9ThObm72Dk8IIUQDUqXEYt++fVXeYceOpeOZf/nllzcXkRBCiNsi4/Qp1vzvC84fSQTAL6wFg558juDWbe0cmRBCiIaoSolFp06d0Gg0lQ4he3mZRqPBYrHc1gCFEEJUT0lxMb99v4A9Py/DarGgNzjRe8RjdL5nKFqdzIothBCiZlQpsUhKSqrpOIQQQtwGJ/fuYt23X5J7IR2AVt16MmD803j4+tk5MiGEEA1dlRKLsLCwmo5DCCHELcjLzGDDzGkc27kdAHdfP+6c8Aytuvawc2RCCCEai5vuvJ2YmMjp06cxmUxlyocOHXrLQQkhhKgaq8VC3Mqf2bboO0qKi9BotXS57wF6PzwavZOTvcMTQgjRiFQ7sTh58iQPPvgg+/fvL9Pv4vJwhdLHQgghakfK8SOs/eYL0k+dACCodVsGP/U8fs2a2zcwIYQQjVK1E4u//OUvtGjRgrVr1xIeHs7OnTvJzMzklVde4d///ndNxCiEEOIqxQX5bF0wh4Q1K0ApnFzd6PvYeDoMGIJGq7V3eEIIIRqpaicWv/32G+vXr8fPzw+tVotWq+WOO+5g8uTJvPDCC8TFxdVEnEII0egppTiyfTMbZ/+PguwsACL7DiB27BO4eHrZNzghhBCNXrUTC4vFgtulSZV8fX05f/48bdq0ISwsjCNHjtz2AIUQQkBW6nnWTf+S5H2lX940CQ5l0BPP0iyqo50jE0IIIUpVO7GIiopi3759hIeH06NHDz788EMcHR2ZNm0a4eHhNRGjEEI0WuaSEnb9uITfly3CUlKCTq+nx4Mj6Db0YRz0enuHJ4QQQthUO7H4+9//TkFBAQDvvfcef/jDH+jbty8+Pj4sXLjwtgcohBCN1ekD+1g7/Quyzp8FIKxjDAOf+D+aBAbbOTIhhBCiPI2qbDrtarh48SJNmjSxjQxV3+Tm5uLp6UlOTg4eHh72DkcI0cgV5mSzac50ErdsAMDF04sBjz9Fm9796u3nrBBCiPqpOvfJNz2PxfHjxzlx4gT9+vXD29ub25CfCCFEo6asVvZvWM2WuTMpLsgHjYbowfdyx6ixOLm62Ts8IYQQ4rqqnVhkZmYyYsQINmzYgEaj4dixY4SHh/Pkk0/i5eXFxx9/XBNxCiFEg3bh9CnWfjOV80cPAeDXPJzBTz1HUKs2do5MCCGEqJpqD3j+0ksvodfrOX36NC4uLrbykSNHsnLlytsanBBCNHQlxcVs+u5b5rz+AuePHkLv5Ez/cU8xZtInklQIIYSoV6r9xGL16tWsWrWK0NDQMuUREREkJyfftsCEEKKhO7Hnd9Z9+xV5GRcAiOjemwHj/4S7j6+dIxNCCCGqr9pPLAoKCso8qbgsIyMDg8FwW4K61rlz5xgzZgw+Pj64uLjQqVMn9uzZY1uulOLdd98lODgYZ2dn+vfvz8GDB2skFiGEuFW5GRf44d/vsfzDf5KXcQEPP38eeO1thr7yN0kqhBBC1FvVTiz69evH7Nmzba81Gg1Wq5WPPvqIAQMG3NbgALKysujTpw96vZ5ff/2VxMREPv74Y7y8vGzrfPjhh0yZMoXPP/+cXbt2ERgYyODBg8nLy7vt8QghxM2yWizs/nkZM1/+P47v2oFWp6PbsIcZ/+8vaNmlu73DE0IIIW5JtYebTUxMpH///nTp0oX169czdOhQDh48yMWLF9m2bRstW7a8rQG+8cYbbNu2jS1btlS4XClFcHAwL774Iq+//joARqORgIAAPvjgA55++ukbvocMNyuEqGkpx46w5pvPuZCcBEBwm0gGP/ksvs2a2zcwIYQQ4jqqc59c7ScWkZGR7Nu3j+7duzN48GAKCgoYPnw4cXFxtz2pAPjxxx/p2rUrjzzyCP7+/sTExPDNN9/YliclJZGamsqQIUNsZQaDgdjYWLZv337b4xFCiOoozs9n7f+mMu//vcqF5CSc3NwZ8vQLjHr3X5JUCCGEaFCq1Xm7pKSEIUOG8PXXXzNx4sSaiqmMkydP8uWXX/Lyyy/zt7/9jZ07d/LCCy9gMBgYN24cqampAAQEBJTZLiAgoNLO5EajEaPRaHudm5tbcwcghGiUlFIc3raJjbP/R2FONgDtYwfSb8wfcfHwtG9wQgghRA2oVmKh1+s5cOBArc78arVa6dq1K5MmTQIgJiaGgwcP8uWXXzJu3DjbetfGpJSqNM7JkyfXWmIkhGh8slLOsXb6l5zeHw+Ad3Aog558lqbtO9o3MCGEEKIGVbsp1Lhx45g+fXpNxFKhoKAgIiMjy5S1a9eO06dPAxAYGAhge3JxWXp6ermnGJe9+eab5OTk2H7OnDlTA5ELIRobc0kJ2xfPY9Zfn+f0/ngc9I70GTmWsR/+V5IKIYQQDV6157EwmUz873//Y82aNXTt2hVXV9cyy6dMmXLbggPo06cPR44cKVN29OhRwsLCAGjRogWBgYGsWbOGmJgYW4ybNm3igw8+qHCfBoOhxobGFaKxsFqtJCcnk5+fj5ubG2FhYWi11f6uosFI3h/PuulfkpVyDoCwjjEMeuJZvAKD7ByZEEIIUTuqnVgcOHCAzp07A6U3+FeriSZSL730Er1792bSpEmMGDGCnTt3Mm3aNKZNm2Z7zxdffJFJkyYRERFBREQEkyZNwsXFhdGjR9/2eIQQpaPDrVy5skz/JA8PD+6+++5yTxgbuoLsLDbNmc6hrRsBcPVqwoDxf6J1zztqtdmoEEIIYW/VHm7WHn7++WfefPNNjh07RosWLXj55Zd56qmnbMuVUkycOJGvv/6arKwsevTowdSpU4mKiqrS/mW4WSGqLjExkUWLFlW6fMSIEY0iuVBWK/vWrWLL/JkYCwpAo6HTkPu4Y9RYDC6uN96BEEIIUQ9U5z65XiQWNU0SCyGqxmq18umnn153JDUPDw9efPHFBt0sKv3USdb+byopx0qbafq3aMngp54nsGWEnSMTQgghbq/q3CdXuymUEKLxSk5OvuHwzLm5uSQnJ9OiRYtaiqr2mIqL2L54HntX/ICyWnF0dqbPyLF0GnIfWp3O3uEJIYQQdiWJhRCiyvLz82/revXJ8V07WD/ja/IyLwDQukcf+o9/CndvXztHJoQQQtQNklgIIarM2dm5Suu5ubnVcCS1JzcjnfUzpnFi9w4APPwCGPjEM4THdLNzZEIIIUTdIomFEKJKjEYjW7ZsueF6Hh4etuGg6zOL2czeX39k++K5mI1GtDodXe8fTs/hI9EbnOwdnhBCCFHnSGIhhLihwsJCvvvuO86fP4+DgwNms7nSde++++5633H7/NFDrP1mKhdOnwIgpG0kg558Dt+m9T9hEkIIIWqKJBZCiOvKzc1lzpw5XLhwAWdnZ8aMGUNOTk6DnMeiOD+fLfNmsm/dSgCc3D2IfWwC7WMHoqnnyZIQQghR0ySxEEJU6uLFi8yePZvs7Gzc3d0ZO3Ys/v7+hISE0LZt2wYz87ZSikNbN7JpznQKc7IBaN9/EP0em4CLh6d9gxNCCCHqCUkshBAVSktLY86cOeTn59OkSRPGjRtHkyZNbMu1Wm2DGFL24vmzrJv+BacP7APAO6Qpg598jtDIqk2wKYQQQohSklgIIco5e/Ys3333HcXFxfj7+zN27Fjc3d3tHdZtZTaZ+H35Ynb9sBiL2YyD3pGeD42i6/0PonPQ2zs8IYQQot6RxEIIUcbJkydZsGABJpOJ0NBQRo8ejYuLi73Duq2S98WzdvpUslNTAGjeqQsD//h/eAUE2jkyIYQQov6SxEIIYXP48GEWL16MxWKhRYsWjBo1CoPBYO+wbpuC7Cw2zv4fh7dtAsCtiTcDxv+JiB590Gg0do5OCCGEqN8ksRBCAJCQkMDy5ctRStG2bVseeugh9PqG0SRIWa0krF3J1vmzMBYWoNFo6XTXffQZORZDA3saI4QQQtiLJBZCCHbu3MmKFSsAiI6OZujQoeh0OjtHdXuknzrJ2m+mknL8CAAB4a0Y9ORzBLaMsHNkQgghRMMiiYUQjZhSii1btrB+/XoAunfv3iAmuAMwFRexfdF37F3xE0pZcXR25o5R44geci9abcNImoQQQoi6RBILIRoppRSrV6/mt99+AyA2Npb+/fvX+74GSimO797B+hlfk5+ZAUDrXn0ZMO5J3Lx97BydEEII0XBJYiFEI2S1Wvnpp5+Ii4sD4K677qJXr152jurW5V5IZ92Mrzi5ZycAnv4BDHziWVp06mLnyIQQQoiGTxILIRoZs9nM999/T2JiIhqNhqFDhxITE2PvsG6JxWxmzy/L+W3pfMxGI1qdA92GDqfHgyPQG5zsHZ4QQgjRKEhiIUQjYjKZWLRoEcePH0er1fLwww8TGRlp77Buybkjh1j7zedknEkGILRdFIOefBaf0GZ2jkwIIYRoXCSxEKKRKCoqYt68eZw5cwa9Xs/IkSNp1aqVvcO6aUX5eWyZN5P961YB4OTuQf+xTxDZ7856309ECCGEqI8ksRCiEcjPz+e7774jNTUVg8HAY489RrNm9fMbfaUUh7ZsYOOc6RTl5gAQNWAw/R6bgLO7h52jE0IIIRovSSyEaOCys7OZM2cOmZmZuLq6MnbsWAIDA+0d1k3JPHeGdf/7gjOJ+wHwCW3GoCefJbRdlJ0jE0IIIYQkFkI0YBkZGcyePZvc3Fw8PT0ZN24cPj71b8jVEpORncsXs3P5EqwWMw6OBno+NIquf3gAnUPDmB1cCCGEqO8ksRCigUpJSeG7776joKAAHx8fxo0bh6enp73DqrZTCXtZN/1LstNSAGgR05WBf3wGT//6+dRFCCGEaKgksRCiATp9+jRz587FaDQSGBjI2LFjcXV1tXdY1ZKfdZGNs//Hke2bAXBr4s2ACU8T0b23dM4WQggh6iBJLIRoYI4fP86CBQswm800a9aM0aNH4+RUf+ZysFot7Fuzki3zZ2EqKkSj0RJz9x/oPWIMBhcXe4cnhBBCiEpIYiFEA3Lw4EGWLl2K1WqlVatWjBgxAkdHR3uHVWVpSSdY+83npJ44BkBAeASDn3qOgPD6OyyuEEII0VhIYiFEA7F3715++uknlFK0b9+eBx98EAeH+vEnbioqZNuiucT9+hNKWXF0duGOR8cRPfgetFqdvcMTQgghRBXUj7sOIcR1bd++ndWrVwPQuXNn/vCHP6DVau0c1Y0ppTi+8zfWz/ya/IuZALTp1Zf+jz+FWxNvO0cnhBBCiOqQxEKIekwpxYYNG9i8ubSDc+/evRk8eHC96Nyck57G+hlfcXLvLgA8AwIZ9Mf/o3mnLnaOTAghhBA3QxILIeopq9XKypUr2blzJwADBw7kjjvuqPNJhcVsZs8vy/ltyXzMJiNanQPdhz1E9wdHoHc02Ds8IYQQQtwkSSyEqIcsFgs//PAD+/btA+Dee++le/fudo7qxs4ePsjab6aSefY0AE0jOzDwyWfxCWlq58iEEEIIcasksRCinikpKWHJkiUcOXIEjUbDgw8+SMeOHe0d1nUV5eWyee5MDmwo7Qfi7O5B7NgniOx3Z51/wiKEEEKIqpHEQoh6xGg0smDBApKSktDpdIwYMYI2bdrYO6xKKaVI3LyeTXOmU5SXC0CHO4fQd/R4nN097BydEEIIIW4nSSyEqCcKCwuZO3cu586dw9HRkUcffZQWLVrYO6xKZZ49w9rpUzmbeAAA36ZhDHryOULaRto5MiGEEELUBEkshKgH8vLymDNnDunp6Tg7OzNmzBhCQkLsHVaFSkxGfv9+Ebt+XIrVYsbB0UCvhx+ly30PoKsn82oIIYQQovrq/kD3V5k8eTIajYYXX3zRVqaU4t133yU4OBhnZ2f69+/PwYMH7RekELdZVlYW3377Lenp6bi5uTFhwoQ6m1Scit/DrFef4/dlC7FazIR37sb4j7+g+7CHJakQQgghGrh6c6XftWsX06ZNK9dJ9cMPP2TKlCnMnDmT1q1b89577zF48GCOHDmCu7u7naIV4vZIT09nzpw55OXl0aRJE8aOHYu3d92bOC7/YiYbZv+Po79tAcDN24c7JzxNq269pHO2EEII0UjUiycW+fn5PPbYY3zzzTc0adLEVq6U4tNPP+Wtt95i+PDhREVFMWvWLAoLC5k3b54dIxbi1p07d44ZM2aQl5eHn58ff/zjH+tcUmG1Wohb+RMzXv4/jv62BY1GS5f7hjFhypdEdO8tSYUQQgjRiNSLxOK5557jvvvuY9CgQWXKk5KSSE1NZciQIbYyg8FAbGws27dvr+0whbhtkpKSmDVrFkVFRYSEhDBhwoQ69wQu7eRx5r31KutnfI2pqJDAVq15bPIn9B/3FI7OLvYOTwghhBC1rM43hVqwYAF79+5l165d5ZalpqYCEBAQUKY8ICCA5OTkSvdpNBoxGo2217m5ubcpWiFu3ZEjR1i0aBEWi4UWLVowatQoDIban5HaarVw7tBB8rOzcPNqQki79mi1OoyFhWxbNIf4lb+glBWDiyt3PPo4HQfdhVarq/U4hRBCCFE31OnE4syZM/zlL39h9erVODk5Vbretc0tlFLXbYIxefJkJk6ceNviFOJ22bdvH8uWLUMpRZs2bXj44YfR6/W1Hsex37ezfuY08i9m2MrcvH1o27sfh7dtIj/rIgBt+8TSf9yTuHo1qWxXQgghhGgkNEopZe8gKrN8+XIefPBBdLor34JaLBY0Gg1arZYjR47QqlUr9u7dS0xMjG2dYcOG4eXlxaxZsyrcb0VPLJo2bUpOTg4eHjJpl7CPXbt28csvvwDQsWNHhg0bVubcry3Hft/Oj1MmXXcdr8AgBj7xLM07xlx3PSGEEELUb7m5uXh6elbpPrlOP7EYOHAg+/fvL1M2YcIE2rZty+uvv054eDiBgYGsWbPGlliYTCY2bdrEBx98UOl+DQaDXZqWCFGZLVu2sG7dOgC6d+/O3XffjVZb+12grFYL62dOu+46js4ujP3gMxydnGspKiGEEELUB3U6sXB3dycqKqpMmaurKz4+PrbyF198kUmTJhEREUFERASTJk3CxcWF0aNH2yNkIapFKcXatWvZtm0bAP369WPAgAF2G03p3KGDZZo/VcRUVEjaiWM0bd/xuusJIYQQonGp04lFVbz22msUFRXx7LPPkpWVRY8ePVi9enWdG0FHiGtZrVZ++eUX9uzZA8CQIUPo3bu3XWPKu5hZpfXys7NqOBIhhBBC1Dd1uo9FbalO2zEhbgez2cyyZcs4ePAgGo2G+++/n86dO9stHovZzKEtG9i6cA4FlzpmX8+ItyfJEwshhBCiEWgwfSyEaIhMJhOLFi3i+PHjaLVaHnroIdq3b2+XWCxmM4mb1/P78kXkpJUO34xGA9f5vsHdx5eQdvaJVwghhBB1lyQWQtSi4uJi5s2bx+nTp3FwcGDUqFG0atWq1uOwmEs4uGkdvy9bTO6FNACcPTzpdv9wXL19+PW//6502wGP/0nmqxBCCCFEOZJYCFFLCgoK+O6770hJScFgMDB69GjCwsKqtK3FqtiZdJH0vGL83Z3o3sIbnbb6Hbwt5hIObFjLzh8Wk3shHQAXTy+63T+c6MH3or80X4xe71huHgt3H18GPP4nInrYtx+IEEIIIeomSSyEqAU5OTnMmTOHjIwMXFxcGDt2LEFBQVXaduWBFCb+lEhKTrGtLMjTiXfuj+TuqKrtw1xSwoENa9i5fDF5mRcAcPVqQrehD9Fx0N3oDWUnoIzo0ZuW3XpUOPO2EEIIIURFpPM20nlb1KzMzExmz55tO7/GjRuHr69vlbZdeSCF//tuL9f+kV5+VvHlmM7XTS7MJSUcWL+a339YTH5m6dMH1ybedB/6EB0G3Y3eUeZzEUIIIUTlpPO2EHVEamoqc+bMoaCgAB8fH8aOHYuXl1eVtrVYFRN/SiyXVAAoSpOLiT8lMjgysFyzKLPJxP71q9j5wxLyLw0h69bEm27DHqHDwCGSUAghhBDitpPEQogacubMGebOnUtxcTGBgYGMGTMGNze3Km+/M+limeZP11JASk4xO5Mu0qulDwAlJiP7161i1w9LyL80bKybtw/dH3iEDgOG4ODoeEvHJIQQQghRGUkshKgBJ06cYMGCBZSUlNC0aVNGjx6Ns7NztfaRnld5UnHteiUmI/vWrGTXj0souDR5nbuPH90feISoAYNx0OurfQxCCCGEENUhiYUQt1liYiJLly7FYrHQsmVLRo4cieNNPCnwd3e64ToO1hLMCRv53xe/UpiTDYC7rx89HhhB+/6DJKEQQgghRK2RxEKI2yguLo4ff/wRpRSRkZEMHz4cB4fq/5mZzFbWH06rdLmDtYSOeQfpkpvAqeRCADz8Aujx4CO0jx2IzkESCiGEEELULkkshLhNduzYwcqVKwGIiYnh/vvvR6vVVns/pzIKeGFBHPvO5tjKNJT2qdBbS+iQe4CYnHhcrKVNpTz9A+jx4Egi+92J7iaSGCGEEEKI20HuQoS4RUopNm7cyKZNmwDo1asXQ4YMQaOp/gR2y+LO8vdlBygwWfBy0fPhQx2xKsX7y+PxP7ubmJwEnC8lFI5N/BgwcjTt+g6QhEIIIYQQdid3I0LcAqvVyqpVq/j9998BuPPOO+nbt2+1k4p8o5m3lx/g+7hzAPRo4c2nozrhrVfEr/qZUUnLKM7PA8DJJ4B+jzxK+34D0OpkwjohhBBC1A2SWAhxkywWCz/99BPx8fEA3HvvvXTv3r3a+9l3NpsX5sdxKrMQrQZeHNSaJ3sEsW/VD3z/y3KKC/IBaBIUQs/hI2nbJ1YSCiGEEELUOZJYCHETzGYzS5Ys4fDhw2g0Gh544AGio6OrtQ+rVfG/rSf5cOURzFZFiJczHw+LQB3cyrcvLMdYUABAk+BQeg0fSZs+/dBqJaEQQgghRN0kiYUQ1WQ0Glm4cCEnT55Ep9PxyCOP0LZt22rtIz2vmFcWJbDlWAYAf2jryXDHJHZ/+DXGwtKEwjs4lJ4PjaJN776SUAghhBCizpPEQohqKCoqYu7cuZw9exa9Xs+jjz5KeHh4tfax8Ug6ry5OICPfhKfWxLN+5zFt3ERcUemwsT6hzeg5fCSte90hCYUQQggh6g1JLISoory8PObMmUN6ejrOzs489thjhIaGVnl7k9nKR6sO882WJJwsxdxnOUzrjHjyTxQBpQlFr4cfpXWPPmhuYphaIYQQQgh7ksRCiCrIyspizpw5XLx4ETc3N8aOHUtAQECVt0/KKOCF+XEcO51Kr5wEuhQcRGM2YQZ8mzWn10OjiOjeWxIKIYQQQtRbklgIcQMXLlxg9uzZ5OXl4eXlxbhx4/D29q7y9kv3nOW9pbtom7GX8XkH0FtLAPALa0Gvhx6lVbeeklAIIYQQot6TxEKI6zh//jzfffcdhYWF+Pn5MXbsWDw8PKq0bV5xCe8s/J3UbSsZkXsAR2UGwL95S3o+PIpWXXpIQiGEEEKIBkMSCyEqcerUKebNm4fJZCI4OJgxY8bg4uJSpW1/T0xmxlczaJ4eT8jlhKJFS3o9PJqWXbrf1KzcQgghhBB1mSQWQlTg6NGjLFq0CLPZTPPmzXn00UcxGAw33C7v4kW+/epbivZtJeJSQuEe2oKBo8cS3rmbJBRCCCGEaLAksRDiGvv372fZsmVYrVZat27NI488gl6vv+42+VkX2bRkIYnrV6G1mtEDxV4hDJswgfY9ekhCIYQQQogGTxILIa6ye/dufv75ZwA6dOjAAw88gE5X+VwS+Rcz2fnjEuLXrESZS9ACaU4BtL/vYcY+fBda6UMhhBBCiEZCEgshLtm6dStr164FoFu3btxzzz2VJgZ5mRns/GEJ+9evwlJSOsrTeUMgqS378e4zDxARULUO3kIIIYQQDYUkFqLRU0qxbt06tm7dCkDfvn258847K2y+lJtxgZ0/LOHA+lVYzKV9KM4bAtnZpBsD7+zD+/e2w0kvs2ULIYQQovGRxEI0alarlRUrVrB7924ABg8eTJ8+fcqtl5uRzs7li9m/fg1WS2lCkeIczA7PLhR4N+ejRzoxKLLqE+YJIYQQQjQ0kliIRstisbB8+XL2798PwP3330+XLl3KrJOTnsbO5Ys5sHGtLaEo9m3BCocOnHMOoVe4D5+M7ESgp1Otxy+EEEIIUZdIYiEapZKSEhYvXszRo0fRarUMHz6cqKgo2/Kc9FR+X7aIg5vWYbVYAGjSKpIftVHEl/ig02r46+DWPBPbEp1WRnwSQgghhJDEQjQ6xcXFzJ8/n+TkZBwcHBg5ciQREREAZKel8vuyhSRuXm9LKJpGRZPWsh+TDlgwlyhCmzjz2aMxdG7WpNy+lcVC4e49mC9cwMHPD5euXdBcZ1QpIYQQQoiGQhIL0agUFBQwd+5czp8/j8FgYPTo0YSFhZGVep7fv19E4pb1KKsVgLCOMbS95yEm7y1m275MAP7QMYhJwzvg4VR+Xovc1atJmzQZc2qqrcwhMJCAv72Jx5AhtXOAQgghhBB2IomFaDRyc3OZPXs2GRkZuLi4MGbMGJw1il+nTuHQ1o22hKJ5dGd6PfwoR6w+jFucwMUCE856HROHtueRrqEVjxa1ejXn/vIiKFWm3JyWVlr+n08luRBCCCFEgyaJhWgUMjMzmTNnDtnZ2Xh4eDD07iHEfz+fw1s3oVRpQtGiUxd6PTwa7xat+ODXI3y7bRcAkUEefPZoDK383Srct7JYSJs0GZRCoSHbqxVGRw8Mply8so+j0UDapMm4DxwozaKEEEII0WBJYiEavLS0NObMmUN+fj5eHh4005Tw4z/+Zksowjt3o+dDowhq1YYTF/J5Yup2ElNyAZjQpzlv3NMWg0PlCUHh7j2YU1NJ943mWKtHMDpd6XthKM4i4vhi/FMTKNy9B9ce3Wv2YIUQQggh7EQSC9GgnTlzhrlz51JcXIyzTkPJni0kmUtnyg7v0p1eDz1KYMsIlFIs2n2Gd344SFGJBW9XRz56uCMD2914bgrj8eOk+0ZzoP1T5ZcZvDjQ/imiDn5D8IULt/34hBBCCCHqijqfWEyePJnvv/+ew4cP4+zsTO/evfnggw9o06aNbR2lFBMnTmTatGlkZWXRo0cPpk6dSvv27e0YuahtVquV5ORk8vPzcXNzw2KxsHDBAkrMZrSF+ejOHENjtdCya096PTSKgPBWAOQWl/DWsgP8lHAegN4tS+emCPC4/twURfv2cXH2HHJW/Mqx7hNLC6/tf6HRgFIca/UwnX18b/sxi1pgtUDydshPA7cACOsNWmnSJoQQQlyrzicWmzZt4rnnnqNbt26YzWbeeusthgwZQmJiIq6urgB8+OGHTJkyhZkzZ9K6dWvee+89Bg8ezJEjR3B3d7fzEYiaYrVaOHfoIPnZWWTkF7LrYCK5uXnl1tPl5+B89gQRXXvQ6+FH8W8eblu293QWL8yP42xWETqthleGtObpfpXPTaFKSshbs4aLs+dQFB8PQLZXRJnmT+VoNBidvMnxaoWcjfVM4o+w8nXIPX+lzCMY7v4AIofaLy4hhBCiDtIodc0wNnXchQsX8Pf3Z9OmTfTr1w+lFMHBwbz44ou8/vrrABiNRgICAvjggw94+umnb7jP3NxcPD09ycnJwcPDo6YPQdwGx37fzvqZ08i/mEGJuxfFIS1LF1z7xEApmjs7cN+jY/ALa2ErtlgVX206wZQ1R7FYFU29nfnPqIrnpgAwZ2WRvXgJWfPm2YaTVXo9JUMe47BrLzLSzTeMefATkbTuFnhzByxqX+KPsGgccO1H5KVzbMRsSS6EEEI0eNW5T67zTyyulZOTA4C3tzcASUlJpKamMuSqoTwNBgOxsbFs3769wsTCaDRiNBptr3Nzc2s4anE7Hft9Oz9OmQSU3vIZA5qVLqhgGFg0Gi46uuLTNMxWlJZbzEsL49l+onRuivujg3n/wagK56YwHj9e2tzpxx9RxcUAWP1CyB74R06Zw8jOMELBjZMKAFcPQzWOUtiV1VL6pKJcUsGlMg2sfAPa3ifNooQQQohL6lVioZTi5Zdf5o477iAqKgqA1EvfHgcElO1kGxAQQHJycoX7mTx5MhMnTqzZYEWNsFotrJ85zfba4uKO0jted5vc3FySk5Np0aIF6w6l8eriBLIKS3DW6/jHsPY83KXs3BTKaiV/82ayZs+hYPt2W3lxh36kdXyQ5HQnzKlWwIjeoKN1jwBOxmVQlGeqNAa3JgaCIrxu+rhFLTu1rWzzp3IU5J4r7XvRom+thSWEEELUZfUqsXj++efZt28fW7duLbfs2knLlFIVTmQG8Oabb/Lyyy/bXufm5tK0adPbG6yoEecOHST/YobttdWh/FOGimTl5DLrx4PM3H4KgPbBpXNTtPS7MjeFtaCA7GXLyZozB9OlpNTiYCA39jHOeHcj44IVzgFY8QlxJapfCK17BOLo5EDTdums/PpApe9/x4gItJX02xB2phRkJcH5ODgfX/rv2d1V2zY/rUZDE0IIIeqTepNY/PnPf+bHH39k8+bNhIaG2soDA0vbrKemphIUFGQrT09PL/cU4zKDwYDBIM1S6qOcjHTb/5td3DH5hVRpu3+tTWZnRmmTlSfuaMFrd7exzU1hOnuWrO/mkr10Kda80s7fxb7NudB7LKdLgjEWWeGCFa1OQ8vO/kTFhhDU0rNM4toyxp+7n45iy8JjFGRfaWbn1sTAHSMiaBnjf8vHLm4DpSD79KUk4tJPSjwU59zc/txuPByxEEII0VjU+cRCKcWf//xnli1bxsaNG2nRokWZ5S1atCAwMJA1a9YQExMDgMlkYtOmTXzwwQf2CFnUAKUUR7ZvZvN3M7A66DEGNMXs4X15Yem/lTyhKlCO7MrQ4uPqyL8fiWZAW3+UUhTs3EnWnDnkrVsPVitWjZacdoNIaXsPqVlOkAtgxd3bifb9gmnXOxgXj8qbXYVH+xHsrifzWDbFSuHUwovgNk3kSYW9KAU5Z0sTh6sTiaKs8uvqDBAYBcExpT8BHWD+KMhLoeJ+FprS0aHCetfwQQghhBD1R51PLJ577jnmzZvHDz/8gLu7u61PhaenJ87Ozmg0Gl588UUmTZpEREQEERERTJo0CRcXF0aPHm3n6MXtkHr8KBtmfcO5o4cxeQdgahle2mFWKfRZ6eiKCigOblF6I3l1X4lL//m9pCl9WvkxZUQ0vgYN2d8v4+KcORgPHQLA6OhBRo9RnHWPpqAQyAI00CzShw6xITSL8rlhclB0IIPsn05gySntZ+EE6BIuYLy/Jc5RMn9FjVOqNAmwJRDxpf8WZpRfV6uHgPZXkojgGPBvB7prmtXd88GlUaE0lE0uLp0Ld/9LOm4LIYQQV6nzw81W1k9ixowZjB8/HrgyQd7XX39dZoK8yx28b0SGm62b8i9msmX+LBI3r8fs4o4xKAyrY+mkdbrCPAypp9EZiwAocffCGNCsTEfufOXIHnMzRg7pxR/bupOzcCFZCxZguXgRBeT4RZLe+RHOlfijrKXbOLnqadcniPZ9Q/D0c65SnEUHMsj87lCly33GtJPk4nbLS72SPFz+KUgvv57WAfwjLyUQnS4lEZHgUMWmkBXOYxFSmlTIULNCCCEagercJ9f5xKI2SGJRt5SYjOz5eTk7ly/GaLGUafbk6urK4MGDcSkuYMOsb8p05Hb18SU/cgC/pmopsDrg6BnAf7u64Ld6GTkrfoWSEsw6J9IjBnG++UByi68kIYHhHkTFhtKysx8O+qp/C62sitQPdtqeVFRE52kg8PVuaKRJ1M3Jv1C2P8T5uEtNlK6h0ZU+ebicQATFlD6Z0F9/BvUbkpm3hRBCNGINeh4L0XAppTi6Yyub584g58IFTD4BlPiFoDQaNBoN3bt3p3///jg7lz5JaNW9p23mbZPelclxRnYkZaO1WnjBcJ77437CNCOOHCDPNYS0rg9y3qUtZosGisHBUUvrHoFE9QvBr+nNzYltTMq5blIBYMkxYkzKwaml1029R11lsVrYm76XC4UX8HPxo7N/Z3S3esNdkAkpVzdniofcs+XX02jBr+2lBKJT6b+BUaCv2lOmatHqZEhZIYQQogoksRB1QtrJ42yYNY1zhxMxu3pgiuiI5dJQss2aNePee++1jQB2mVaro2n7jqxJTOOvSxIwZ+fw6NldjDr3O46Z6RRrHEgP7klq2z9w0XppRm0LNAl0ISo2lDY9AzE439yfgFIKU3IueetPV2l963XmuKiP1iav5V87/0Va4ZXhVgNcAnij+xsMChtUtZ0UXrz0BCL+SiKRU1F9asC39VV9IjpBYAdwdL31AxFCCCHEbSOJhbCr/KyLbJ0/m4Ob12HV6TE1jaDEzRMobfY0ZMgQOnbsWGFfm+ISC5NXHGL96p2MObGVwWf34Gg2UeTkw8nIkaQE9cZocQAraLUawmP8iOoXQnBrr0r77txIyYVCCuPSKYy/gOVicZW307pffxK/+mRt8lpe3vgy6prRktIL03l548tM6T+lfHJRlA0pCWWbNGWdqvgNfCKuNGcKjilNIgw390RJCCGEELVHEgthF2aTiT2/LOf3ZYswmYyYvAMw+4dipbTDfo8ePejfvz9OThW3jz+WmsPUD+fSZfdqRqUfQaEh0zuSlNb3cMGpOaABS+k8Eu37BtOuTzCunjc3d4kl30RRwgUK4tIpOZtvK9c46nBq743xaBbWAnOl2+s8DRhaeN7Ue9c1FquFf+38V7mkAkCh0KDhg98nM8CiR5e670oicfFkxTv0Di/bnCmoIzg1jLoSQgghGhtJLEStUkpx7PdtbPpuBrkX0jC7emBuEUmJRguUNnu67777Kp3c0JKfz7rPZqFZtoin89Ix6d041WwIqeGDKORK05imkd5E9QuheQcftDptteO0miwUH8qkMO4CxUcvwqVRo9CCU0QTXGL8cYr0Qeuou+GoUF73h9f7jtuZRZkkXEhgZdLKMs2frqVQpBals3fRI3QrNpZd6BVWdojXoGhw9qrZwIUQQghRaySxELUmLekEG2d9w9lDB7A6OGJp0Y5ip9JkwM3NjSFDhtChQ4cKmymVnDtHysw5ZC5eTGhxITke4exrfz8X/TphpTRxMLg40K536VCxXgEu1Y5PWRXGkzkUxqVTdCADZbTYlulD3XCJ8celox+6a5o1OUf54jOmXZl5LKD0SYXX/eH1bqhZs9XM8ezjJKQnEH8hnoQLCZzJO1OtfVxwD4AWna4aoakTuHjXRLhCCCGEqCMksRA1riA7i60LZnNg41oUYPYPxeQbhFWp6zZ7UkpRtHcvF2fNJnftWiwaPZn+3TjVdABGlysduf2bexDVL4SIrv44OFZ/VKKS1AIK4tIpikvHkntVYuBlKE0mYvzR+18/UXGO8sUp0gdjUg7WPBNad0cMLTzrxZOK7OJs9mXsIz49nn0X9rEvYx9F5qJy67UylRBsNrPZ5cYjL/k99C0EdquJcIUQQghRR0liIWqM2WRiz4of+H3ZIkqKizC7emBt3gajVYFShIWFce+995Zr9mQ1mchdsYKs2XMoTkwk3yWIcy0f5lxgT9CV9pNw0GuJ6BZAVGwI/mHVn3vEkmukMP4ChXHplKQU2Mo1Tg64dPTFJcYfxzCPaiUGGq2mzg8pa1VWTmSfKH0SkZ5AwoUETuWeKreem9VKB6ORTsUmoo1GOhiNeFgVFvcg7nJxIB1zBb0sQIOGAJcAOvt3rvFjEUIIIUTdIomFuO2UUhzbuZ3N331LTnoaVr0jtImmSKsHq6q02ZM5I4OsBaWzY5dkZnHBN5ozMS+R69nKto6HnzMd+5cOFevkqq9WXFajmaIDmRTGpWM8kY3tzlinwamtN64x/ji18Uajr36fjLoq15TLvgv7SLiQQEJ6Avsz9pFfUlBuveamEqKNRjoZjUQXmwgvKUHn6gfBPcs0Z9J5BPHGpVGhNFCmE7eG0t/l691fv/X5LIQQQghR70hiIW6r9FMn2TBrGmcTD6A0GggNp9jTF4vVikajoWfPnsTGxtqaPVmsij1rd2BdsgCP7Rsw6tw4F9yH823uoERfOsSoFYVLc3eGPNCK0DZNqjVUrLIoio9nURiXTvHBTFSJ1bbMMczjUr8JX7Qu1UtS6iKrsnIq5xQJFy71jUiP50RO+dGYXKxWOhhNdDQa6VRspKPRhJdTEwjufFXH6k7gEQwV1PWgsEFM6T+lwnksXu/+etXnsRBCCCFEg6JRSlXUoqFRqc5U5aJiBdlZbFs4h/0b1oBSWD2aYAkJp/jSt9jXNntSFgubZywha853tE47wcUmbTkX0o8Mn6jSWZWBPI0izVfHnyZE0y68SZVjUUpRci6/dL6JhAtY80tsyxx8nUuTiU5+OPjUwCzNtSjflM/+jP2liUR6HPvSE8gzl38a0aykhOhLTZqijUZa6dxwuHqI1+AY8AytMIm4nhqZeVsIIYQQdUp17pPliYW4KVarleTkZHJyckg5EM+xtb9QUlSEVe+IxjeUAq/SEYAMxSa6nDxBj1698AwIwJKbS/aSpZyfOZsmF/MoDurJbz3GUOzsZ9t3soOFeEcz/e8M44O72+LoULWmSeasYgrj0ymMS8ecfqXzsdbVAeeOfrh2DkAf6nbTk+PZk1KK5NzkS02a4klI3cWxvORy/RycrFbaG02lTZqMJjoqAz6B0RB21azVXmHVTiIqotPq6CYdtIUQQghxiSQWotoSExNZuXIlubm5tjJNSATOlhKK9M4onRaUBufCEFzym3HapQuGf86gxZKlFOzZQ47On3Mhg0lr2wWlLW2CVIzigKOFBIOZizqFt6sjr9/bDt0NOk9bi8wU7c+gIC4NU9KVeHDQ4hzpXTrfROsmaG5iLgt7Kiwp5EDGARLS40g4/xv7Mg+RZSkst15IidnWpCla6Wnt2x59+FVzRTRpcVuSCCGEEEKIG5HEQlRLYmIiixYtorTn85UbVqV3pFBfOr+D3uiJW24rHCylc1QYDV4caP8UWee3kNuuN3nuzWzbpemsxDmaOexooeSq+9+LBSZ2Jl2kV0ufcjEos5XiI1kUxqdTdCgTzJe+t9eAoYUnLp39cY7yRetUP05vpRRn88+SkBZP/NnN7EuP52hRGpZrnkc4WhXtTaWdq6MtGqKbtMGvadcrTZq8w0FbvxIoIYQQQjQc9ePOS9QJ+VkXWb5kCShV8bfgCjTKAY+sDmi56gZXowGlOBfSr/S1TsMBbQnxBjMpOnV1flJGel7xlV0rhel0XunkdfsuYC0025Y5BLhc6jfhj4OX4XYcauWsFkjeDvlp4BYAYb2hmv0Kis3FHMw4QMLpjcSf30FC3ikuWo3l1gswm0ufRJgV0W7NaRfUDX1Il9Ikwqdltd9XCCGEEKImSWIhbshcUkLcyp/Y8suPmAKbV960RgNKY8bsmIujyeuaZaXbNA81sbaZH78mpt7wff3dnSjJKCrthB2fjiXzSqKhdXfEpZNf6eR1Qa61028i8UdY+Trknr9S5hEMd38AkUMr3EQpRUr+eRKS15FwZgsJWUc4XJKF+Zr1HJQi0mgiusRCtHMw0f6dCGzaqzSJ8G0tSYQQQggh6jxJLESllFKc2P07m+ZMJzstBZOHd5W2s2pNlS5beTadn/It193eCw0PODkTvvIMaWfybOUaRy3O7X1x6eyPoaVX7c5qnfgjLBoH13aXzk0pLR8xGyKHYjQXc+j0JhJOrSPhwj4SilNJp/zx+pnNRJvMdHL0IdqnPe2a9sUQ0hX82oJO/iyFEEIIUf/IHYyo0IXTp9g46xtOH0gAwNE3AE3zNhgLyncgvpbW6ljpsgRXH1oHuHFnW3++3lQ6x4ICHIE+OHAXenrigEOxhpIzeaX9JiKalE5e194HraMdvrm3WkqfVFQw13SaTku8wZGEDa+QsOufHFJFlFzz9MRBKdqYSojWudPJsxXRIb0JCotFExAJuvo/f4YQQgghBEhiIa5RmJvD9kXfsW/tKpSyojE44d65F+ey8+BGSYUCrdWA3uSJj4MGJw0UK8g0K1CKEk0Jf3m0Iw90DkWn1dAp1JPFyw/TtUAxAD1uV3W20Ie4lfabiPZD5155olIrkrdD7nlKgMOOjiQ4GUqTCScDqQ5X/wkVg0aDt8VCNAai3ZoRHdCF9uFDcA6KAYca7v8hhBBCCGFHklgIACzmEuJW/syOpQswFhag0OAV05MLSktudmlzpLZhzdEeySDRkF+60bUtkTTQtLAld3jocb6qmVKRVbG/yEz7x2No0zWAkrQC8uPSiY67QFTBlW/szW56PLsE4NbZH32Aa00fcuWsFsg8Tsbp7SSc3UJCehzxQf4kOjpivGbUJa1StDaVlE4+F9CVTjFPEBrWH41j/Z58TwghhBCiuiSxaOSUUpzYs5NNc/5HdmoKAG7hbSjyC+ZsbmlC4ePmhd9ZJ7LP+mFxaIaHIYN8j+NYdVf6Unh4eNAmOIaO8bpyLYactBq6uepxPZlD2vZzlJy/Mju0xkmHSwc/XGL8cGzuWbv9JgDMRkg/RMn5vRw99xsJmQdJMF4gQe/AOf2lPw9HACcAPC0Woo2m0tGajEaijCZcLk9e/4e/QIu+tRu/EEIIIUQdIYlFI2O1KlKOZVOQa6SkKJ3ETYs4fSAeAEdvXxwjY0jJzILcPBx1ejwv+qNSw8lEAw7gqvLoGh1K++FDuZCVQn5+PulFMCshn/7xJhSgvaaPweVXhXvTSv9Hq8GpTZPS+Sba+qDR39rcCxarhb3pe7lQeAE/Fz86+3dGV9EoSsY8SD0Aqfu4eG43CRn7SChOI8FRz0GDI0VaLegAl9IkQqOglYMb0R7hdEreTXROJmHmkgpGx9WUjg4V1vuWjkMIIYQQoj6TxKIBs1otnDt0kPzsLNy8mmAs9mfr4uPkX8zBXLwdi3EfoNA4ONKke1/O5uZjycwCNLgUBOKc3xyUHq21hGCHNDo+0JEWgwfYhnZNNfkydetFVh5MJQYd/ty4+ZJrn2A87myGzvX2dFpem7yWf+38F2mFabayAJcA3oh+jkE6T0jZh+V8PMcvJJBQnE68wUCCkyOn9XowAAZ323buGj0d3cOIDuhCdLP+dPTvhJujW+lC26hQGso+krmUZtz9LxkSVgghhBCNmkYpVX6om0YmNzcXT09PcnJy8PDwsHc4t8Wx37ezfuY08i9mXCnUuKF1aIbVfAKUEQVYvNtR7OuJ0pXOrKA3euGW2xIHiyuuBSmE++YS/eRgPNu3su3mbFYhn649xvd7z2JV4Az8y9eHLhklN4zLe1QbXDr535ZjXJu8lpc3voy6tu3VpVN6cEEhuTod+w2OFFYwI3W4kz+d/DsRHdKbaP9OtPBsgVZznacnFc5jEVKaVFQyj4UQQgghRH1WnftkSSxoeInFsd+38+OUSdddx+oSgjGwOWZD6YzPWrMTbnnhOBW6E5iZQJtIJyKeeRjH0FDbNhn5Rj5ff5x5v59GZ7HSGwdGurvRrkihMVftNPK9H5w8bnLWaqsVLp6ElHgsKQnclfITacpc+YR9V3HVGejgE0WnoG5E+0XTwbcDngbPqr+3LYZbn3lbCCGEEKK+qM59sjSFamCsVgvrZk7D7OKOctCjMZegK8yz9Quw6hww+jfD7OUNGMGqxbWgGc4FoTRN2YJXhAOGP42kbZcIdJc6UucWlzBt00kWbk2ic4mWiRjoqdGjV0CeFQBtEwOqwIwyVTb5nUKnzcKwejxoSre57qzVZhNcOAyp+yBlH6QkkJ9+gMOaEg45OrLV2Yk0F+cbJhVj243lgYgHaOnZsuJ+F9Wl1UkHbSGEEEKICkhi0cBsW7OaNO9glP7K3A+aEhOGtDMovSNG32DQld5gexQH4JDbHJ21dH6F1T5N+cUhCJYeI2jtGd64py0ZFwpJ3HyaHiUaFuGC/nKKosDB1xnnDr44R/miD3al+GAmmd8dqiCq0qcZXrov0VxOKuDKrNXDv4EmYZCSUPqTuo+LGYc55KDhkKMjhxz1HDY4cjq4ajN/Xy3KN4rWTVpXezshhBBCCFE9kljUU9d2zA5p157Dh4+w7rffwaFsx2jloKc4JNz27b6v1Z1eJW0IwJMiN8X+IgspJQqNpXQCPC80dMuxYF5wlIHouIsrSYpDgAvOUb64dPDFIcDF1pEbwDnKF58x7cj+6QSWnCtD0eq0WXjpvsRZ99s1R1HaOyL1h6c5ZHDkkKMjhx31JBocSQ8NqPC4g1wDaevdDg9HD3448cMN68nPxe+G6wghhBBCiFsniUU9Y7Uqdv6whj0/z6E4P8tW7urtw8XglqUdl69tHqS58pThjpI2tLGGoLn05MFJA91cdBzIzaXQzYf/4EIndOiuGlQ131NPYLcgXDv6ofU1XBra9SB+aeWHdnWO8sUp0gfjySys58+gTd+CYf//Q6OxYgVOOzjYkohDBj2HHR3J1lXcRKm5RxjtvCNp69OWdt7taOvdliZOTYDSIWZ3pOwgvTC9fOdtQIOGAJcAOvt3vplqFkIIIYQQ1SSJRT1yIi6dtd/+TH769+WW5RSbMFms1+9zoAEPXG1JBYBGo0EpRZSHBx00VzozH8bCRkrYiJmPRnSnbUuf0qFdl1YwtGun5xmk9y2dIyLtAJq0A+jSD5GstZQmED6eHL6URFQ0OpODUoS7BNIuuCftfNrRzrsdbbzb4KqvfPhanVbHG93f4OWNL6NBUya5uHx8r3d//fb0qxBCCCGEEDckiUU9cSIunV+/2ocxZ3WFy60OVZsXoghjubLLzZmSsPALJWyihJSrbtTT84orHdo1vSCVl7b9nRezsnGzKtuTiOMhfpgqmEXbYLXSxlRCW5OJdiYT7YwltCoxYRj3ZbU7RQ8KG8SU/lMqnMfi9e6vMyhsULX2J4QQQgghbp4kFvWA1arYsvAYVvNZUPlllimdDpOnHybvqs0N4Yyh0mWzMLKW0vksPMinneYMbbWn6bZ/GeMsu1CUfyKiNBpQik+9m5Tbn5vejbbebWh3ahft8rNoZzTRvKTkmpNOUzoXxE3OWj0obBADmg6o2szbQgghhBCixkhiUUdYrYqUY9kU5BpxdtNTYsiloCAfNzc3HIo9KMg2gipEgwZfp1Bw8uCCG+S7GuBy86LLU5JU1BxKgSsGAq1elcYwULuBUbodtNGeQe+QxUlHPSf1et7PM5Dm6gJU0szq0vtF+UbRM6gn7bxLmzOFuIeUTjhnm7W63Ial/9zirNU6rY5ugd1uenshhBBCCHHrJLGoA47vSWfT/CMU5ZsodD1Nkes5lNZsW+7soEdviCBU44OX/yMcd8wkXZtjW24wWtBkngarojikRenorlfnAJfyjZ4lEWgrSA4UVoq1FzkYtIiTjg6cdHSiQBtS7eMY224s94bfW35B5FAYMbuCWauDZdZqIYQQQogGQhILO9u25ChnNp5FZ8gg2/8o5qsSisuKSkoo8krkOA6YNKXLtUpDC6s/7cyh+Fs9SDWcxNspiJSSAnboj1JwVV8Kk85IlPY8zY13orCi4UoHaisKDRr+HfQ92z2u6iyttPg7h9DRvw0GnYFfkn654bFcd2jXyKHQ9j6ZtVoIIYQQooFqMInFF198wUcffURKSgrt27fn008/pW/fuj1D8skfjuO/MxWrRxbr9ImVr3jpIYMJM87KkUhzKG0swbhc7i+hgWDXVgD4KoWrs5lE52Ok6bIocigiw3CBn7QaNuVaeSbtEfzMV/pDZDpk8ZPPAjyaKYZ6/BEXTRBtfSK4r20HnC5NsmexWtidtvvWh3aVWauFEEIIIRqsBpFYLFy4kBdffJEvvviCPn368PXXX3PPPfeQmJhIs2bN7B1ehQr2XUC//Tw6jWKH/mhp4XVGir2sf0kkIVafCpets/zEJ+1XYbl6duurdnzeaSdLA7bRpbAlTU0eBFgy6KD2EZNjhY4/V3rTL0O7CiGEEEKIGyk/qUA9NGXKFJ544gmefPJJ2rVrx6effkrTpk358ssv7R1ahZRVcXH5cQDSdDkUaIxVSioAiimpdNnu0PQKkoor/i87l/cyM3mwaCddLWtpSjwOGlWlUZkuD+3q71J29KkAlwCm9J8iQ7sKIYQQQjRy9f6JhclkYs+ePbzxxhtlyocMGcL27dvtFNX1GZNy0BSaQaOpcF6J67necLGZ+tzrbutnqfhJRlVHZZKhXYUQQgghRGXqfWKRkZGBxWIhICCgTHlAQACpqakVbmM0GjEar9zQ5+Ze/4b8drPmmWz/f71EoQwFrqri4WKVUlzQ53DQ+XiFm2rQEODoQWdHMxTf2qhMMrSrEEIIIYSoSL1PLC7TXDtxm1Llyi6bPHkyEydOrI2wKqR1d7T9f6DVC1dlKB3FqZLmUAoFmsvDxZYdS1YBaDSc6qxF5VF5H4je76JrOkBGZRJCCCGEEDWi3vex8PX1RafTlXs6kZ6eXu4pxmVvvvkmOTk5tp8zZ87URqg2hhaeaD0cUUqhRUPPktalC8oPuASASWvisM82YnTT0ZFZZpmDpwHfMe0YPvy+G/eBuDwqU4eHS/+VpEIIIYQQQtwm9f6JhaOjI126dGHNmjU8+OCDtvI1a9YwbNiwCrcxGAwYDFVsglQDNFoNTYa2JOO7RJRStLD6M7CkQ7n5JwxWBzJc0lgTsIEpFzJwa+KN6xAHjM4dsOaZ0Lo7YmjhiUZb+lRC+kAIIYQQQgh7qfeJBcDLL7/M2LFj6dq1K7169WLatGmcPn2aZ555xt6hVco5yhffMZGkzT+Ag0VHC6s/YUY/UrXZFGHEipEFgas46X2UKc1GMqh/fwjrjUarw+k6+5U+EEIIIYQQwh4aRGIxcuRIMjMz+cc//kFKSgpRUVGsWLGCsLAwe4d2Xc5RvoT9M5bC4xc5sTGO7NQzmBwvUNDcjC7MwJNhz9E5sJs8cRBCCCGEEHWeRilVScv+xiM3NxdPT09ycnLw8PCwdzhCCCGEEELUCdW5T673nbeFEEIIIYQQ9ieJhRBCCCGEEOKWSWIhhBBCCCGEuGWSWAghhBBCCCFumSQWQgghhBBCiFsmiYUQQgghhBDilkliIYQQQgghhLhlklgIIYQQQgghbpkkFkIIIYQQQohbJomFEEIIIYQQ4pZJYiGEEEIIIYS4ZQ72DqAuUEoBkJuba+dIhBBCCCGEqDsu3x9fvl++HkksgLy8PACaNm1q50iEEEIIIYSoe/Ly8vD09LzuOhpVlfSjgbNarZw/fx53d3c0Gk2tvW9ubi5NmzblzJkzeHh41Nr7NjZSz7VD6rn2SF3XDqnn2iN1XTuknmtHQ6tnpRR5eXkEBwej1V6/F4U8sQC0Wi2hoaF2e38PD48GceLVdVLPtUPqufZIXdcOqefaI3VdO6Sea0dDqucbPam4TDpvCyGEEEIIIW6ZJBZCCCGEEEKIWyaJhR0ZDAbeeecdDAaDvUNp0KSea4fUc+2Ruq4dUs+1R+q6dkg9147GXM/SeVsIIYQQQghxy+SJhRBCCCGEEOKWSWIhhBBCCCGEuGWSWAghhBBCCCFumSQWNeiLL76gRYsWODk50aVLF7Zs2XLd9Tdt2kSXLl1wcnIiPDycr776qpYirf+qU9fff/89gwcPxs/PDw8PD3r16sWqVatqMdr6q7rn9GXbtm3DwcGBTp061WyADUh169poNPLWW28RFhaGwWCgZcuWfPvtt7UUbf1V3XqeO3cu0dHRuLi4EBQUxIQJE8jMzKylaOunzZs3c//99xMcHIxGo2H58uU33Eauh9VX3XqWa+HNu5lz+rKGfj2UxKKGLFy4kBdffJG33nqLuLg4+vbtyz333MPp06crXD8pKYl7772Xvn37EhcXx9/+9jdeeOEFli5dWsuR1z/VrevNmzczePBgVqxYwZ49exgwYAD3338/cXFxtRx5/VLder4sJyeHcePGMXDgwFqKtP67mboeMWIE69atY/r06Rw5coT58+fTtm3bWoy6/qluPW/dupVx48bxxBNPcPDgQRYvXsyuXbt48sknazny+qWgoIDo6Gg+//zzKq0v18ObU916lmvhzatuXV/WKK6HStSI7t27q2eeeaZMWdu2bdUbb7xR4fqvvfaaatu2bZmyp59+WvXs2bPGYmwoqlvXFYmMjFQTJ0683aE1KDdbzyNHjlR///vf1TvvvKOio6NrMMKGo7p1/euvvypPT0+VmZlZG+E1GNWt548++kiFh4eXKfvss89UaGhojcXY0ABq2bJl111Hroe3rir1XBG5FlZfdeq6MVwP5YlFDTCZTOzZs4chQ4aUKR8yZAjbt2+vcJvffvut3Pp33XUXu3fvpqSkpMZire9upq6vZbVaycvLw9vbuyZCbBButp5nzJjBiRMneOedd2o6xAbjZur6xx9/pGvXrnz44YeEhITQunVrXn31VYqKimoj5HrpZuq5d+/enD17lhUrVqCUIi0tjSVLlnDffffVRsiNhlwP7UOuhTWrsVwPHewdQEOUkZGBxWIhICCgTHlAQACpqakVbpOamlrh+mazmYyMDIKCgmos3vrsZur6Wh9//DEFBQWMGDGiJkJsEG6mno8dO8Ybb7zBli1bcHCQj5qqupm6PnnyJFu3bsXJyYlly5aRkZHBs88+y8WLF6WfRSVupp579+7N3LlzGTlyJMXFxZjNZoYOHcp///vf2gi50ZDroX3ItbDmNKbroTyxqEEajabMa6VUubIbrV9RuSivunV92fz583n33XdZuHAh/v7+NRVeg1HVerZYLIwePZqJEyfSunXr2gqvQanOOW21WtFoNMydO5fu3btz7733MmXKFGbOnClPLW6gOvWcmJjICy+8wNtvv82ePXtYuXIlSUlJPPPMM7URaqMi18PaJdfCmtPYrocNO22yE19fX3Q6XblvvdLT08t9C3NZYGBghes7ODjg4+NTY7HWdzdT15ctXLiQJ554gsWLFzNo0KCaDLPeq2495+XlsXv3buLi4nj++eeB0ptfpRQODg6sXr2aO++8s1Zir29u5pwOCgoiJCQET09PW1m7du1QSnH27FkiIiJqNOb66GbqefLkyfTp04e//vWvAHTs2BFXV1f69u3Le++9J9+k3yZyPaxdci2sWY3teihPLGqAo6MjXbp0Yc2aNWXK16xZQ+/evSvcplevXuXWX716NV27dkWv19dYrPXdzdQ1lH47M378eObNmyfto6uguvXs4eHB/v37iY+Pt/0888wztGnThvj4eHr06FFbodc7N3NO9+nTh/Pnz5Ofn28rO3r0KFqtltDQ0BqNt766mXouLCxEqy172dTpdMCVb9TFrZPrYe2Ra2HNa3TXQ/v0GW/4FixYoPR6vZo+fbpKTExUL774onJ1dVWnTp1SSin1xhtvqLFjx9rWP3nypHJxcVEvvfSSSkxMVNOnT1d6vV4tWbLEXodQb1S3rufNm6ccHBzU1KlTVUpKiu0nOzvbXodQL1S3nq/VkEfBuN2qW9d5eXkqNDRUPfzww+rgwYNq06ZNKiIiQj355JP2OoR6obr1PGPGDOXg4KC++OILdeLECbV161bVtWtX1b17d3sdQr2Ql5en4uLiVFxcnALUlClTVFxcnEpOTlZKyfXwdqluPcu18OZVt66v1ZCvh5JY1KCpU6eqsLAw5ejoqDp37qw2bdpkW/b444+r2NjYMutv3LhRxcTEKEdHR9W8eXP15Zdf1nLE9Vd16jo2NlYB5X4ef/zx2g+8nqnuOX21hvxBWhOqW9eHDh1SgwYNUs7Ozio0NFS9/PLLqrCwsJajrn+qW8+fffaZioyMVM7OziooKEg99thj6uzZs7Ucdf2yYcOG637myvXw9qhuPcu18ObdzDl9tYZ8PdQoJc9vhRBCCCGEELdG+lgIIYQQQgghbpkkFkIIIYQQQohbJomFEEIIIYQQ4pZJYiGEEEIIIYS4ZZJYCCGEEEIIIW6ZJBZCCCGEEEKIWyaJhRBCCCGEEOKWSWIhhBBCCCFEPbV582buv/9+goOD0Wg0LF++vNr7WLVqFT179sTd3R0/Pz8eeughkpKSqr0fSSyEEOU0b96cTz/91N5h2EW/fv2YN2+e7fXNfkjfrJkzZ+Ll5VVr7yfqphv9DZ46dQqNRkN8fDwAGzduRKPRkJ2dXek2cm7duocffpgpU6bYOwwhyigoKCA6OprPP//8prY/efIkw4YN48477yQ+Pp5Vq1aRkZHB8OHDq70vSSyEEA1adW6mfv75Z1JTUxk1alTNBtXA1efE9N1336VTp072DuOGmjZtSkpKClFRUfYOpd66mUTr7bff5v333yc3N7dmghLiJtxzzz289957lSYCJpOJ1157jZCQEFxdXenRowcbN260Ld+7dy8Wi4X33nuPli1b0rlzZ1599VUSEhIoKSmpViySWAghxCWfffYZEyZMQKuVj8ZrKaUwm821+p4mk6lW3+9WjB8/nnfffbfW3k+n0xEYGIiDg0OtvaeAjh070rx5c+bOnWvvUISosgkTJrBt2zYWLFjAvn37eOSRR7j77rs5duwYAF27dkWn0zFjxgwsFgs5OTnMmTOHIUOGoNfrq/VecvUUopHp378/zz//PM8//zxeXl74+Pjw97//HaVUpdtMmTKFDh064OrqStOmTXn22WfJz8+3Lb/8zd+qVato164dbm5u3H333aSkpFQ5rm+//Zb27dtjMBgICgri+eefty07ffo0w4YNw83NDQ8PD0aMGEFaWppteUJCAgMGDMDd3R0PDw+6dOnC7t272bhxIxMmTCAnJweNRoNGo6n05i8jI4O1a9cydOjQ68a5f/9+7rzzTpydnfHx8eFPf/pTmbq40bHcqC6r4kYxjB8/ngceeICJEyfi7++Ph4cHTz/9dJkbdaUUH374IeHh4Tg7OxMdHc2SJUtsyy83rVm1ahVdu3bFYDCwZcsWTpw4wbBhwwgICMDNzY1u3bqxdu1a23b9+/cnOTmZl156yVbnly1dutRWL82bN+fjjz8uc1zNmzfnvffeY/z48Xh6evLUU09VePxWq5UPPviAVq1aYTAYaNasGe+//36V62fjxo10794dV1dXvLy86NOnD8nJycycOZOJEyeSkJBgi33mzJnV+t1Ux48//kjXrl1xcnLC19e33LeNhYWF/PGPf8Td3Z1mzZoxbdo027Jrm0JVZObMmTRr1gwXFxcefPBBMjMzrxuPyWTi+eefJygoCCcnJ5o3b87kyZNty7Ozs/nTn/5EQEAATk5OREVF8fPPPwOQmZnJo48+SmhoKC4uLnTo0IH58+eX2X///v154YUXeO211/D29iYwMLBKyVhtfzZ88cUXRERE4OTkREBAAA8//HCZeIYOHVru2ISoq06cOMH8+fNZvHgxffv2pWXLlrz66qvccccdzJgxAyj97F29ejV/+9vfMBgMeHl5cfbsWRYsWFD9N1RCiEYlNjZWubm5qb/85S/q8OHD6rvvvlMuLi5q2rRptnXCwsLUJ598Ynv9ySefqPXr16uTJ0+qdevWqTZt2qj/+7//sy2fMWOG0uv1atCgQWrXrl1qz549ql27dmr06NFViumLL75QTk5O6tNPP1VHjhxRO3futL2/1WpVMTEx6o477lC7d+9WO3bsUJ07d1axsbG27du3b6/GjBmjDh06pI4ePaoWLVqk4uPjldFoVJ9++qny8PBQKSkpKiUlReXl5VUYw7Jly5Srq6uyWCxlygG1bNkypZRSBQUFKjg4WA0fPlzt379frVu3TrVo0UI9/vjjVTqWqtalp6dnpXVVlRgef/xx5ebmpkaOHKkOHDigfv75Z+Xn56f+9re/2db529/+ptq2batWrlypTpw4oWbMmKEMBoPauHGjUkqpDRs2KEB17NhRrV69Wh0/flxlZGSo+Ph49dVXX6l9+/apo0ePqrfeeks5OTmp5ORkpZRSmZmZKjQ0VP3jH/+w1blSSu3evVtptVr1j3/8Qx05ckTNmDFDOTs7qxkzZthiCgsLUx4eHuqjjz5Sx44dU8eOHauwDl577TXVpEkTNXPmTHX8+HG1ZcsW9c0331SpfkpKSpSnp6d69dVX1fHjx1ViYqKaOXOmSk5OVoWFheqVV15R7du3t8VeWFhY6e/iao8//rh65513qrSuUkr9/PPPSqfTqbffflslJiaq+Ph49f7775epC29vbzV16lR17NgxNXnyZKXVatWhQ4eUUkolJSUpQMXFxSmlrvy+srKylFJK7dixQ2k0GjV58mR15MgR9Z///Ed5eXld99z66KOPVNOmTdXmzZvVqVOn1JYtW9S8efOUUkpZLBbVs2dP1b59e7V69Wp14sQJ9dNPP6kVK1YopZQ6e/as+uijj1RcXJw6ceKE+uyzz5ROp1M7duyw7T82NlZ5eHiod999Vx09elTNmjVLaTQatXr16kpjqu3Phl27dimdTqfmzZunTp06pfbu3av+85//lIlpxYoVymAwqOLi4uv+joWwh6uvWUoptWjRIgUoV1fXMj8ODg5qxIgRSimlUlJSVEREhPrrX/+q9u7dqzZt2qRiY2PVwIEDldVqrd77386DEULUfbGxsapdu3ZlPixef/111a5dO9vraxOLay1atEj5+PjYXs+YMUMB6vjx47ayqVOnqoCAgCrFFBwcrN56660Kl61evVrpdDp1+vRpW9nBgwcVoHbu3KmUUsrd3V3NnDmzwu1vdKN+2SeffKLCw8PLlV/9IT1t2jTVpEkTlZ+fb1v+yy+/KK1Wq1JTU294LBWpqC6vF29VYnj88ceVt7e3KigosK3z5ZdfKjc3N2WxWFR+fr5ycnJS27dvL7PvJ554Qj366KNKqSs3qsuXL7/hMURGRqr//ve/ttcVnT+jR49WgwcPLlP217/+VUVGRpbZ7oEHHrjue+Xm5iqDwWBLJK51o/rJzMxUgC2ButY777yjoqOjrxtDRaqbWPTq1Us99thjlS4PCwtTY8aMsb22Wq3K399fffnll0qpGycWjz76qLr77rvL7HPkyJHXPbf+/Oc/qzvvvLPCG4lVq1YprVarjhw5UsUjVOree+9Vr7zyiu11bGysuuOOO8qs061bN/X6669Xuo/a/mxYunSp8vDwULm5uZXGlJCQoAB16tSpStcRwl6uTSwWLFigdDqdOnz4sO0Lm8s/l7/4+fvf/666dOlSZj9nzpxRgPrtt9+q9f7SFEqIRqhnz55lmqj06tWLY8eOYbFYKlx/w4YNDB48mJCQENzd3Rk3bhyZmZkUFBTY1nFxcaFly5a210FBQaSnp98wlvT0dM6fP8/AgQMrXH7o0CGaNm1K06ZNbWWRkZF4eXlx6NAhAF5++WWefPJJBg0axL/+9S9OnDhxw/e9VlFREU5OTtdd59ChQ0RHR+Pq6mor69OnD1arlSNHjtzwWKBqdXkrMVwWHR2Ni4uL7XWvXr3Iz8/nzJkzJCYmUlxczODBg3Fzc7P9zJ49u1zdde3atczrgoICXnvtNdvvwM3NjcOHD3P69Okbxt2nT58yZX369Cl33l37fhXtx2g0Xvd8uV79eHt7M378eO666y7uv/9+/vOf/1Sryd5lc+fOLVN3c+fOZdKkSeXKKhMfH3/d8wRK2/NfptFoCAwMrNLfFJTWQ69evcqUXfv6WuPHjyc+Pp42bdrwwgsvsHr16jLxhoaG0rp16wq3tVgsvP/++3Ts2BEfHx/c3NxYvXp1ufPi6mOC639O2OOzYfDgwYSFhREeHs7YsWOZO3cuhYWFZdZxdnYGKFcuRF0UExODxWIhPT2dVq1alfkJDAwESs9lnU5XZrvLr61Wa7XeTxILIcR1JScnc++99xIVFcXSpUvZs2cPU6dOBSgzWsS1Hbw0Gs11+21cdvkiXRmlVJkkqKLyd999l4MHD3Lfffexfv16IiMjWbZs2Q3f+2q+vr5kZWXdVCxQerw3Opaq1uWtxHAjGo3GdqH45ZdfiI+Pt/0kJiaW6WcBlLlBB/jrX//K0qVLef/999myZQvx8fF06NDhhh2tK4q7ovPj2ve71s2eL3ClfmbMmMFvv/1G7969WbhwIa1bt2bHjh3X3e+1hg4dWqbuhg4dyjPPPFOu7GaPAyr+m6rqRb4qf3vX6ty5M0lJSfzzn/+kqKiIESNG2PoX3Cjejz/+mE8++YTXXnuN9evXEx8fz1133VXuvKjOMdnjs8Hd3Z29e/cyf/58goKCePvtt4mOji4zjO/FixcB8PPzu258QtSW/Px82+cOQFJSEvHx8Zw+fZrWrVvz2GOPMW7cOL7//nuSkpLYtWsXH3zwAStWrADgvvvuY9euXfzjH//g2LFj7N27lwkTJhAWFkZMTEy1YpHEQohG6NqbqB07dhAREVHuGwuA3bt3Yzab+fjjj+nZsyetW7fm/Pnzty0Wd3d3mjdvzrp16ypcHhkZyenTpzlz5oytLDExkZycHNq1a2cra926NS+99BKrV69m+PDhtk5pjo6OlT6JuVpMTAypqanXTS4iIyOJj48v83Rh27ZtaLVaWrdufcNjuR11eaMYLktISKCoqMj2eseOHbi5uREaGkpkZCQGg4HTp0+X+wbr6m9/K7JlyxbGjx/Pgw8+SIcOHQgMDOTUqVNl1qmoziMjI9m6dWuZsu3bt9O6desKz7vKRERE4OzsfN3zpSr1ExMTw5tvvsn27duJioqyzV1S1fPF3d29TL25u7vj7e1drqwyHTt2rPQYbofIyMgK/85vxMPDg5EjR/LNN9+wcOFCli5dysWLF+nYsSNnz57l6NGjFW63ZcsWhg0bxpgxY4iOjiY8PNw24szNstdng4ODA4MGDeLDDz9k3759nDp1ivXr19uWHzhwgNDQUHx9fW/p+IS4XXbv3k1MTIwtCXj55ZeJiYnh7bffBkq/TBk3bhyvvPIKbdq0YejQofz++++2z/s777yTefPmsXz5cmJiYrj77rsxGAysXLmySl+CXE0SCyEaoTNnzvDyyy9z5MgR5s+fz3//+1/+8pe/VLhuy5YtMZvN/Pe//+XkyZPMmTOHr7766rbG8+677/Lxxx/z2Wef2b4t+e9//wvAoEGD6NixI4899hh79+5l586djBs3jtjYWLp27UpRURHPP/88GzduJDk5mW3btrFr1y7bjUXz5s3Jz89n3bp1ZGRkVNp8ISYmBj8/P7Zt21ZpnI899hhOTk48/vjjHDhwgA0bNvDnP/+ZsWPHEhAQcMNjuR11WZUYoHSEnyeeeILExER+/fVX3nnnHZ5//nm0Wi3u7u68+uqrvPTSS8yaNYsTJ04QFxfH1KlTmTVr1nXfv1WrVnz//ffEx8eTkJDA6NGjy33j3Lx5czZv3sy5c+fIyMgA4JVXXmHdunX885//5OjRo8yaNYvPP/+cV199tVrH7+TkxOuvv85rr71ma7q1Y8cOpk+fXqX6SUpK4s033+S3334jOTmZ1atXc/To0TLny+Vv+zIyMjAajdWKr6reeecd5s+fzzvvvMOhQ4fYv38/H3744W3b/wsvvMDKlSv58MMPOXr0KJ9//jkrV6687jaffPIJCxYs4PDhwxw9epTFixcTGBiIl5cXsbGx9OvXj4ceeog1a9aQlJTEr7/+attnq1atWLNmDdu3b+fQoUM8/fTTpKam3vJx1PZnw88//8xnn31GfHw8ycnJzJ49G6vVSps2bWwxbdmyhSFDhtzysQlxu/Tv3x9V2m+6zM/lUe30ej0TJ04kKSkJk8lESkoK33//PR06dLDtY9SoUezdu5f8/HzS09P54YcfaNu2bfWDqXavECFEvRYbG6ueffZZ9cwzzygPDw/VpEkT9cYbb5TpsHlt59spU6aooKAg5ezsrO666y41e/bsMh1FK+oEuWzZMlWdj5ivvvpKtWnTRun1ehUUFKT+/Oc/25YlJyeroUOHKldXV+Xu7q4eeeQRW0dlo9GoRo0apZo2baocHR1VcHCwev7551VRUZFt+2eeeUb5+Pgo4LodbN944w01atSoMmVc0xFu3759asCAAcrJyUl5e3urp556qtxIU9c7lpupy2vdKIbHH39cDRs2TL399tvKx8dHubm5qSeffLLMKDZWq1X95z//scXp5+en7rrrLrVp0yalVPnOwJclJSWpAQMGKGdnZ9W0aVP1+eefq9jYWPWXv/zFts5vv/2mOnbsqAwGQ5lzYMmSJSoyMlLp9XrVrFkz9dFHH5XZ940GDbjMYrGo9957T4WFhdn2NWnSpCrVT2pqqnrggQdUUFCQcnR0VGFhYertt9+2jQZWXFysHnroIeXl5aWAMqNWXU91O28rVdpRuFOnTsrR0VH5+vqq4cOH25ZVVBfR0dG297hR522llJo+fboKDQ1Vzs7O6v7771f//ve/bzgwQKdOnZSrq6vy8PBQAwcOVHv37rUtz8zMVBMmTFA+Pj7KyclJRUVFqZ9//tm2bNiwYcrNzU35+/urv//972rcuHFq2LBhtu2vPU+UUmrYsGFlRjSrSG1+NmzZskXFxsaqJk2aKGdnZ9WxY0e1cOFC2/pFRUXKw8Oj2h1ahWgsNErdRENMIUS91b9/fzp16lRvZ0auSWlpabRv3549e/YQFhZm73Bu2vjx48nOzmb58uX2DkWIBmXq1Kn88MMPZTq2CyGukKZQQghxSUBAANOnT7/hCEdCiMZJr9fbmmIJIcpzsHcAQoiGz83NrdJlv/76K3379q3FaK5v2LBh9g5BCFFH/elPf7J3CELUadIUSghR444fP17pspCQkGqPOiGEEEKIukcSCyGEEEIIIcQtkz4WQgghhBBCiFsmiYUQQgghhBDilkliIYQQQgghhLhlklgIIYQQQgghbpkkFkIIIYQQQohbJomFEEIIIYQQ4pZJYiGEEEIIIYS4ZZJYCCGEEEIIIW7Z/wcHR38dtZo3JgAAAABJRU5ErkJggg==", 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" ] @@ -2456,7 +4327,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -2555,514 +4426,514 @@ " \n", " Aggregation\n", " Filter\n", - " 0.552\n", - " 0.312\n", - " 0.478\n", - " 0.531\n", - " 0.888\n", - " 1.101\n", + " 0.359\n", + " 0.302\n", + " 0.364\n", + " 0.411\n", + " 0.733\n", + " 1.071\n", " \n", " \n", " HashJoin\n", - " 0.047\n", - " 0.021\n", - " -0.025\n", - " -0.149\n", - " 0.181\n", - " 0.637\n", + " 0.575\n", + " 0.525\n", + " 0.577\n", + " 0.571\n", + " 0.817\n", + " 1.048\n", " \n", " \n", " NestedLoopCrossJoin\n", - " -0.169\n", - " -0.206\n", - " -0.065\n", - " -0.220\n", - " 0.000\n", - " 0.277\n", + " -0.297\n", + " -0.267\n", + " -0.153\n", + " -0.412\n", + " -0.129\n", + " 0.262\n", " \n", " \n", " NestedLoopJoin\n", - " -0.651\n", - " -0.688\n", - " -0.479\n", - " -0.405\n", - " -0.011\n", - " 0.469\n", + " -0.834\n", + " -0.762\n", + " -0.592\n", + " -0.496\n", + " -0.090\n", + " 0.304\n", " \n", " \n", " Projection\n", - " -0.508\n", - " -0.718\n", - " -0.743\n", - " -0.668\n", - " -0.262\n", - " 0.103\n", + " -0.717\n", + " -0.798\n", + " -0.820\n", + " -0.680\n", + " -0.394\n", + " -0.006\n", " \n", " \n", " SeqScan\n", - " -0.180\n", - " 0.149\n", - " 0.307\n", - " 0.122\n", - " 0.501\n", - " 0.778\n", + " -0.056\n", + " 0.159\n", + " 0.292\n", + " -0.015\n", + " 0.257\n", + " 0.536\n", " \n", " \n", " Sort\n", - " -0.290\n", - " -0.409\n", - " -0.523\n", - " -0.541\n", - " -0.320\n", - " -0.188\n", + " -0.374\n", + " -0.434\n", + " -0.498\n", + " -0.620\n", + " -0.344\n", + " -0.279\n", " \n", " \n", " Filter\n", " Aggregation\n", - " -0.552\n", - " -0.312\n", - " -0.478\n", - " -0.531\n", - " -0.888\n", - " -1.101\n", + " -0.359\n", + " -0.302\n", + " -0.364\n", + " -0.411\n", + " -0.733\n", + " -1.071\n", " \n", " \n", " HashJoin\n", - " -0.504\n", - " -0.290\n", - " -0.502\n", - " -0.680\n", - " -0.707\n", - " -0.465\n", + " 0.216\n", + " 0.223\n", + " 0.213\n", + " 0.159\n", + " 0.083\n", + " -0.023\n", " \n", " \n", " NestedLoopCrossJoin\n", - " -0.721\n", + " -0.656\n", + " -0.569\n", " -0.517\n", - " -0.543\n", - " -0.751\n", - " -0.887\n", - " -0.824\n", + " -0.823\n", + " -0.862\n", + " -0.810\n", " \n", " \n", " NestedLoopJoin\n", - " -1.203\n", - " -0.999\n", + " -1.193\n", + " -1.063\n", " -0.956\n", - " -0.936\n", - " -0.899\n", - " -0.633\n", + " -0.907\n", + " -0.823\n", + " -0.767\n", " \n", " \n", " Projection\n", - " -1.059\n", - " -1.030\n", - " -1.220\n", - " -1.199\n", - " -1.150\n", - " -0.998\n", + " -1.076\n", + " -1.099\n", + " -1.184\n", + " -1.091\n", + " -1.127\n", + " -1.077\n", " \n", " \n", " SeqScan\n", - " -0.732\n", - " -0.162\n", - " -0.171\n", - " -0.409\n", - " -0.386\n", - " -0.323\n", + " -0.416\n", + " -0.143\n", + " -0.071\n", + " -0.426\n", + " -0.476\n", + " -0.536\n", " \n", " \n", " Sort\n", - " -0.841\n", - " -0.720\n", - " -1.000\n", - " -1.072\n", - " -1.207\n", - " -1.289\n", + " -0.733\n", + " -0.735\n", + " -0.862\n", + " -1.031\n", + " -1.078\n", + " -1.350\n", " \n", " \n", " HashJoin\n", " Aggregation\n", - " -0.047\n", - " -0.021\n", - " 0.025\n", - " 0.149\n", - " -0.181\n", - " -0.637\n", + " -0.575\n", + " -0.525\n", + " -0.577\n", + " -0.571\n", + " -0.817\n", + " -1.048\n", " \n", " \n", " Filter\n", - " 0.504\n", - " 0.290\n", - " 0.502\n", - " 0.680\n", - " 0.707\n", - " 0.465\n", + " -0.216\n", + " -0.223\n", + " -0.213\n", + " -0.159\n", + " -0.083\n", + " 0.023\n", " \n", " \n", " NestedLoopCrossJoin\n", - " -0.217\n", - " -0.227\n", - " -0.041\n", - " -0.071\n", - " -0.180\n", - " -0.359\n", + " -0.872\n", + " -0.792\n", + " -0.730\n", + " -0.982\n", + " -0.946\n", + " -0.787\n", " \n", " \n", " NestedLoopJoin\n", - " -0.698\n", - " -0.709\n", - " -0.454\n", - " -0.256\n", - " -0.192\n", - " -0.168\n", + " -1.409\n", + " -1.287\n", + " -1.169\n", + " -1.066\n", + " -0.907\n", + " -0.744\n", " \n", " \n", " Projection\n", - " -0.555\n", - " -0.740\n", - " -0.718\n", - " -0.519\n", - " -0.443\n", - " -0.533\n", + " -1.291\n", + " -1.322\n", + " -1.397\n", + " -1.250\n", + " -1.210\n", + " -1.054\n", " \n", " \n", " SeqScan\n", - " -0.227\n", - " 0.128\n", - " 0.331\n", - " 0.271\n", - " 0.321\n", - " 0.142\n", + " -0.631\n", + " -0.366\n", + " -0.285\n", + " -0.585\n", + " -0.560\n", + " -0.513\n", " \n", " \n", " Sort\n", - " -0.337\n", - " -0.430\n", - " -0.498\n", - " -0.392\n", - " -0.500\n", - " -0.825\n", + " -0.949\n", + " -0.958\n", + " -1.075\n", + " -1.190\n", + " -1.161\n", + " -1.327\n", " \n", " \n", " NestedLoopCrossJoin\n", " Aggregation\n", - " 0.169\n", - " 0.206\n", - " 0.065\n", - " 0.220\n", - " -0.000\n", - " -0.277\n", + " 0.297\n", + " 0.267\n", + " 0.153\n", + " 0.412\n", + " 0.129\n", + " -0.262\n", " \n", " \n", " Filter\n", - " 0.721\n", + " 0.656\n", + " 0.569\n", " 0.517\n", - " 0.543\n", - " 0.751\n", - " 0.887\n", - " 0.824\n", + " 0.823\n", + " 0.862\n", + " 0.810\n", " \n", " \n", " HashJoin\n", - " 0.217\n", - " 0.227\n", - " 0.041\n", - " 0.071\n", - " 0.180\n", - " 0.359\n", + " 0.872\n", + " 0.792\n", + " 0.730\n", + " 0.982\n", + " 0.946\n", + " 0.787\n", " \n", " \n", " NestedLoopJoin\n", - " -0.482\n", - " -0.482\n", - " -0.413\n", - " -0.185\n", - " -0.011\n", - " 0.191\n", + " -0.537\n", + " -0.495\n", + " -0.439\n", + " -0.084\n", + " 0.039\n", + " 0.043\n", " \n", " \n", " Projection\n", - " -0.338\n", - " -0.512\n", - " -0.677\n", - " -0.448\n", - " -0.262\n", - " -0.174\n", + " -0.420\n", + " -0.531\n", + " -0.667\n", + " -0.268\n", + " -0.265\n", + " -0.267\n", " \n", " \n", " SeqScan\n", - " -0.011\n", - " 0.355\n", - " 0.372\n", - " 0.342\n", - " 0.501\n", - " 0.501\n", + " 0.241\n", + " 0.426\n", + " 0.445\n", + " 0.397\n", + " 0.386\n", + " 0.274\n", " \n", " \n", " Sort\n", - " -0.121\n", - " -0.203\n", - " -0.457\n", - " -0.321\n", - " -0.320\n", - " -0.465\n", + " -0.077\n", + " -0.167\n", + " -0.345\n", + " -0.208\n", + " -0.215\n", + " -0.540\n", " \n", " \n", " NestedLoopJoin\n", " Aggregation\n", - " 0.651\n", - " 0.688\n", - " 0.479\n", - " 0.405\n", - " 0.011\n", - " -0.469\n", + " 0.834\n", + " 0.762\n", + " 0.592\n", + " 0.496\n", + " 0.090\n", + " -0.304\n", " \n", " \n", " Filter\n", - " 1.203\n", - " 0.999\n", + " 1.193\n", + " 1.063\n", " 0.956\n", - " 0.936\n", - " 0.899\n", - " 0.633\n", + " 0.907\n", + " 0.823\n", + " 0.767\n", " \n", " \n", " HashJoin\n", - " 0.698\n", - " 0.709\n", - " 0.454\n", - " 0.256\n", - " 0.192\n", - " 0.168\n", + " 1.409\n", + " 1.287\n", + " 1.169\n", + " 1.066\n", + " 0.907\n", + " 0.744\n", " \n", " \n", " NestedLoopCrossJoin\n", - " 0.482\n", - " 0.482\n", - " 0.413\n", - " 0.185\n", - " 0.011\n", - " -0.191\n", + " 0.537\n", + " 0.495\n", + " 0.439\n", + " 0.084\n", + " -0.039\n", + " -0.043\n", " \n", " \n", " Projection\n", - " 0.143\n", - " -0.030\n", - " -0.264\n", - " -0.263\n", - " -0.251\n", - " -0.365\n", + " 0.118\n", + " -0.036\n", + " -0.228\n", + " -0.184\n", + " -0.304\n", + " -0.310\n", " \n", " \n", " SeqScan\n", - " 0.471\n", - " 0.837\n", - " 0.786\n", - " 0.527\n", - " 0.512\n", - " 0.310\n", + " 0.778\n", + " 0.921\n", + " 0.884\n", + " 0.481\n", + " 0.347\n", + " 0.231\n", " \n", " \n", " Sort\n", - " 0.361\n", - " 0.279\n", - " -0.044\n", - " -0.136\n", - " -0.309\n", - " -0.657\n", + " 0.460\n", + " 0.328\n", + " 0.094\n", + " -0.124\n", + " -0.255\n", + " -0.583\n", " \n", " \n", " Projection\n", " Aggregation\n", - " 0.508\n", - " 0.718\n", - " 0.743\n", - " 0.668\n", - " 0.262\n", - " -0.103\n", + " 0.717\n", + " 0.798\n", + " 0.820\n", + " 0.680\n", + " 0.394\n", + " 0.006\n", " \n", " \n", " Filter\n", - " 1.059\n", - " 1.030\n", - " 1.220\n", - " 1.199\n", - " 1.150\n", - " 0.998\n", + " 1.076\n", + " 1.099\n", + " 1.184\n", + " 1.091\n", + " 1.127\n", + " 1.077\n", " \n", " \n", " HashJoin\n", - " 0.555\n", - " 0.740\n", - " 0.718\n", - " 0.519\n", - " 0.443\n", - " 0.533\n", + " 1.291\n", + " 1.322\n", + " 1.397\n", + " 1.250\n", + " 1.210\n", + " 1.054\n", " \n", " \n", " NestedLoopCrossJoin\n", - " 0.338\n", - " 0.512\n", - " 0.677\n", - " 0.448\n", - " 0.262\n", - " 0.174\n", + " 0.420\n", + " 0.531\n", + " 0.667\n", + " 0.268\n", + " 0.265\n", + " 0.267\n", " \n", " \n", " NestedLoopJoin\n", - " -0.143\n", - " 0.030\n", - " 0.264\n", - " 0.263\n", - " 0.251\n", - " 0.365\n", + " -0.118\n", + " 0.036\n", + " 0.228\n", + " 0.184\n", + " 0.304\n", + " 0.310\n", " \n", " \n", " SeqScan\n", - " 0.328\n", - " 0.867\n", - " 1.049\n", - " 0.790\n", - " 0.763\n", - " 0.675\n", + " 0.660\n", + " 0.956\n", + " 1.113\n", + " 0.665\n", + " 0.650\n", + " 0.541\n", " \n", " \n", " Sort\n", - " 0.218\n", - " 0.309\n", - " 0.220\n", - " 0.127\n", - " -0.058\n", - " -0.291\n", + " 0.343\n", + " 0.364\n", + " 0.322\n", + " 0.060\n", + " 0.049\n", + " -0.273\n", " \n", " \n", " SeqScan\n", " Aggregation\n", - " 0.180\n", - " -0.149\n", - " -0.307\n", - " -0.122\n", - " -0.501\n", - " -0.778\n", + " 0.056\n", + " -0.159\n", + " -0.292\n", + " 0.015\n", + " -0.257\n", + " -0.536\n", " \n", " \n", " Filter\n", - " 0.732\n", - " 0.162\n", - " 0.171\n", - " 0.409\n", - " 0.386\n", - " 0.323\n", + " 0.416\n", + " 0.143\n", + " 0.071\n", + " 0.426\n", + " 0.476\n", + " 0.536\n", " \n", " \n", " HashJoin\n", - " 0.227\n", - " -0.128\n", - " -0.331\n", - " -0.271\n", - " -0.321\n", - " -0.142\n", + " 0.631\n", + " 0.366\n", + " 0.285\n", + " 0.585\n", + " 0.560\n", + " 0.513\n", " \n", " \n", " NestedLoopCrossJoin\n", - " 0.011\n", - " -0.355\n", - " -0.372\n", - " -0.342\n", - " -0.501\n", - " -0.501\n", + " -0.241\n", + " -0.426\n", + " -0.445\n", + " -0.397\n", + " -0.386\n", + " -0.274\n", " \n", " \n", " NestedLoopJoin\n", - " -0.471\n", - " -0.837\n", - " -0.786\n", - " -0.527\n", - " -0.512\n", - " -0.310\n", + " -0.778\n", + " -0.921\n", + " -0.884\n", + " -0.481\n", + " -0.347\n", + " -0.231\n", " \n", " \n", " Projection\n", - " -0.328\n", - " -0.867\n", - " -1.049\n", - " -0.790\n", - " -0.763\n", - " -0.675\n", + " -0.660\n", + " -0.956\n", + " -1.113\n", + " -0.665\n", + " -0.650\n", + " -0.541\n", " \n", " \n", " Sort\n", - " -0.110\n", - " -0.558\n", - " -0.830\n", - " -0.663\n", - " -0.821\n", - " -0.966\n", + " -0.318\n", + " -0.593\n", + " -0.791\n", + " -0.605\n", + " -0.601\n", + " -0.814\n", " \n", " \n", " Sort\n", " Aggregation\n", - " 0.290\n", - " 0.409\n", - " 0.523\n", - " 0.541\n", - " 0.320\n", - " 0.188\n", + " 0.374\n", + " 0.434\n", + " 0.498\n", + " 0.620\n", + " 0.344\n", + " 0.279\n", " \n", " \n", " Filter\n", - " 0.841\n", - " 0.720\n", - " 1.000\n", - " 1.072\n", - " 1.207\n", - " 1.289\n", + " 0.733\n", + " 0.735\n", + " 0.862\n", + " 1.031\n", + " 1.078\n", + " 1.350\n", " \n", " \n", " HashJoin\n", - " 0.337\n", - " 0.430\n", - " 0.498\n", - " 0.392\n", - " 0.500\n", - " 0.825\n", + " 0.949\n", + " 0.958\n", + " 1.075\n", + " 1.190\n", + " 1.161\n", + " 1.327\n", " \n", " \n", " NestedLoopCrossJoin\n", - " 0.121\n", - " 0.203\n", - " 0.457\n", - " 0.321\n", - " 0.320\n", - " 0.465\n", + " 0.077\n", + " 0.167\n", + " 0.345\n", + " 0.208\n", + " 0.215\n", + " 0.540\n", " \n", " \n", " NestedLoopJoin\n", - " -0.361\n", - " -0.279\n", - " 0.044\n", - " 0.136\n", - " 0.309\n", - " 0.657\n", + " -0.460\n", + " -0.328\n", + " -0.094\n", + " 0.124\n", + " 0.255\n", + " 0.583\n", " \n", " \n", " Projection\n", - " -0.218\n", - " -0.309\n", - " -0.220\n", - " -0.127\n", - " 0.058\n", - " 0.291\n", + " -0.343\n", + " -0.364\n", + " -0.322\n", + " -0.060\n", + " -0.049\n", + " 0.273\n", " \n", " \n", " SeqScan\n", - " 0.110\n", - " 0.558\n", - " 0.830\n", - " 0.663\n", - " 0.821\n", - " 0.966\n", + " 0.318\n", + " 0.593\n", + " 0.791\n", + " 0.605\n", + " 0.601\n", + " 0.814\n", " \n", " \n", "\n", @@ -3071,121 +4942,121 @@ "text/plain": [ "target_cost 640000 1000000 3240000 \\\n", "a b \n", - "Aggregation Filter 0.552 0.312 0.478 \n", - " HashJoin 0.047 0.021 -0.025 \n", - " NestedLoopCrossJoin -0.169 -0.206 -0.065 \n", - " NestedLoopJoin -0.651 -0.688 -0.479 \n", - " Projection -0.508 -0.718 -0.743 \n", - " SeqScan -0.180 0.149 0.307 \n", - " Sort -0.290 -0.409 -0.523 \n", - "Filter Aggregation -0.552 -0.312 -0.478 \n", - " HashJoin -0.504 -0.290 -0.502 \n", - " NestedLoopCrossJoin -0.721 -0.517 -0.543 \n", - " NestedLoopJoin -1.203 -0.999 -0.956 \n", - " Projection -1.059 -1.030 -1.220 \n", - " SeqScan -0.732 -0.162 -0.171 \n", - " Sort -0.841 -0.720 -1.000 \n", - "HashJoin Aggregation -0.047 -0.021 0.025 \n", - " Filter 0.504 0.290 0.502 \n", - " NestedLoopCrossJoin -0.217 -0.227 -0.041 \n", - " NestedLoopJoin -0.698 -0.709 -0.454 \n", - " Projection -0.555 -0.740 -0.718 \n", - " SeqScan -0.227 0.128 0.331 \n", - " Sort -0.337 -0.430 -0.498 \n", - "NestedLoopCrossJoin Aggregation 0.169 0.206 0.065 \n", - " Filter 0.721 0.517 0.543 \n", - " HashJoin 0.217 0.227 0.041 \n", - " NestedLoopJoin -0.482 -0.482 -0.413 \n", - " Projection -0.338 -0.512 -0.677 \n", - " SeqScan -0.011 0.355 0.372 \n", - " Sort -0.121 -0.203 -0.457 \n", - "NestedLoopJoin Aggregation 0.651 0.688 0.479 \n", - " Filter 1.203 0.999 0.956 \n", - " HashJoin 0.698 0.709 0.454 \n", - " NestedLoopCrossJoin 0.482 0.482 0.413 \n", - " Projection 0.143 -0.030 -0.264 \n", - " SeqScan 0.471 0.837 0.786 \n", - " Sort 0.361 0.279 -0.044 \n", - "Projection Aggregation 0.508 0.718 0.743 \n", - " Filter 1.059 1.030 1.220 \n", - " HashJoin 0.555 0.740 0.718 \n", - " NestedLoopCrossJoin 0.338 0.512 0.677 \n", - " NestedLoopJoin -0.143 0.030 0.264 \n", - " SeqScan 0.328 0.867 1.049 \n", - " Sort 0.218 0.309 0.220 \n", - "SeqScan Aggregation 0.180 -0.149 -0.307 \n", - " Filter 0.732 0.162 0.171 \n", - " HashJoin 0.227 -0.128 -0.331 \n", - " NestedLoopCrossJoin 0.011 -0.355 -0.372 \n", - " NestedLoopJoin -0.471 -0.837 -0.786 \n", - " Projection -0.328 -0.867 -1.049 \n", - " Sort -0.110 -0.558 -0.830 \n", - "Sort Aggregation 0.290 0.409 0.523 \n", - " Filter 0.841 0.720 1.000 \n", - " HashJoin 0.337 0.430 0.498 \n", - " NestedLoopCrossJoin 0.121 0.203 0.457 \n", - " NestedLoopJoin -0.361 -0.279 0.044 \n", - " Projection -0.218 -0.309 -0.220 \n", - " SeqScan 0.110 0.558 0.830 \n", + "Aggregation Filter 0.359 0.302 0.364 \n", + " HashJoin 0.575 0.525 0.577 \n", + " NestedLoopCrossJoin -0.297 -0.267 -0.153 \n", + " NestedLoopJoin -0.834 -0.762 -0.592 \n", + " Projection -0.717 -0.798 -0.820 \n", + " SeqScan -0.056 0.159 0.292 \n", + " Sort -0.374 -0.434 -0.498 \n", + "Filter Aggregation -0.359 -0.302 -0.364 \n", + " HashJoin 0.216 0.223 0.213 \n", + " NestedLoopCrossJoin -0.656 -0.569 -0.517 \n", + " NestedLoopJoin -1.193 -1.063 -0.956 \n", + " Projection -1.076 -1.099 -1.184 \n", + " SeqScan -0.416 -0.143 -0.071 \n", + " Sort -0.733 -0.735 -0.862 \n", + "HashJoin Aggregation -0.575 -0.525 -0.577 \n", + " Filter -0.216 -0.223 -0.213 \n", + " NestedLoopCrossJoin -0.872 -0.792 -0.730 \n", + " NestedLoopJoin -1.409 -1.287 -1.169 \n", + " Projection -1.291 -1.322 -1.397 \n", + " SeqScan -0.631 -0.366 -0.285 \n", + " Sort -0.949 -0.958 -1.075 \n", + "NestedLoopCrossJoin Aggregation 0.297 0.267 0.153 \n", + " Filter 0.656 0.569 0.517 \n", + " HashJoin 0.872 0.792 0.730 \n", + " NestedLoopJoin -0.537 -0.495 -0.439 \n", + " Projection -0.420 -0.531 -0.667 \n", + " SeqScan 0.241 0.426 0.445 \n", + " Sort -0.077 -0.167 -0.345 \n", + "NestedLoopJoin Aggregation 0.834 0.762 0.592 \n", + " Filter 1.193 1.063 0.956 \n", + " HashJoin 1.409 1.287 1.169 \n", + " NestedLoopCrossJoin 0.537 0.495 0.439 \n", + " Projection 0.118 -0.036 -0.228 \n", + " SeqScan 0.778 0.921 0.884 \n", + " Sort 0.460 0.328 0.094 \n", + "Projection Aggregation 0.717 0.798 0.820 \n", + " Filter 1.076 1.099 1.184 \n", + " HashJoin 1.291 1.322 1.397 \n", + " NestedLoopCrossJoin 0.420 0.531 0.667 \n", + " NestedLoopJoin -0.118 0.036 0.228 \n", + " SeqScan 0.660 0.956 1.113 \n", + " Sort 0.343 0.364 0.322 \n", + "SeqScan Aggregation 0.056 -0.159 -0.292 \n", + " Filter 0.416 0.143 0.071 \n", + " HashJoin 0.631 0.366 0.285 \n", + " NestedLoopCrossJoin -0.241 -0.426 -0.445 \n", + " NestedLoopJoin -0.778 -0.921 -0.884 \n", + " Projection -0.660 -0.956 -1.113 \n", + " Sort -0.318 -0.593 -0.791 \n", + "Sort Aggregation 0.374 0.434 0.498 \n", + " Filter 0.733 0.735 0.862 \n", + " HashJoin 0.949 0.958 1.075 \n", + " NestedLoopCrossJoin 0.077 0.167 0.345 \n", + " NestedLoopJoin -0.460 -0.328 -0.094 \n", + " Projection -0.343 -0.364 -0.322 \n", + " SeqScan 0.318 0.593 0.791 \n", "\n", "target_cost 6760000 12250000 26010000 \n", "a b \n", - "Aggregation Filter 0.531 0.888 1.101 \n", - " HashJoin -0.149 0.181 0.637 \n", - " NestedLoopCrossJoin -0.220 0.000 0.277 \n", - " NestedLoopJoin -0.405 -0.011 0.469 \n", - " Projection -0.668 -0.262 0.103 \n", - " SeqScan 0.122 0.501 0.778 \n", - " Sort -0.541 -0.320 -0.188 \n", - "Filter Aggregation -0.531 -0.888 -1.101 \n", - " HashJoin -0.680 -0.707 -0.465 \n", - " NestedLoopCrossJoin -0.751 -0.887 -0.824 \n", - " NestedLoopJoin -0.936 -0.899 -0.633 \n", - " Projection -1.199 -1.150 -0.998 \n", - " SeqScan -0.409 -0.386 -0.323 \n", - " Sort -1.072 -1.207 -1.289 \n", - "HashJoin Aggregation 0.149 -0.181 -0.637 \n", - " Filter 0.680 0.707 0.465 \n", - " NestedLoopCrossJoin -0.071 -0.180 -0.359 \n", - " NestedLoopJoin -0.256 -0.192 -0.168 \n", - " Projection -0.519 -0.443 -0.533 \n", - " SeqScan 0.271 0.321 0.142 \n", - " Sort -0.392 -0.500 -0.825 \n", - "NestedLoopCrossJoin Aggregation 0.220 -0.000 -0.277 \n", - " Filter 0.751 0.887 0.824 \n", - " HashJoin 0.071 0.180 0.359 \n", - " NestedLoopJoin -0.185 -0.011 0.191 \n", - " Projection -0.448 -0.262 -0.174 \n", - " SeqScan 0.342 0.501 0.501 \n", - " Sort -0.321 -0.320 -0.465 \n", - "NestedLoopJoin Aggregation 0.405 0.011 -0.469 \n", - " Filter 0.936 0.899 0.633 \n", - " HashJoin 0.256 0.192 0.168 \n", - " NestedLoopCrossJoin 0.185 0.011 -0.191 \n", - " Projection -0.263 -0.251 -0.365 \n", - " SeqScan 0.527 0.512 0.310 \n", - " Sort -0.136 -0.309 -0.657 \n", - "Projection Aggregation 0.668 0.262 -0.103 \n", - " Filter 1.199 1.150 0.998 \n", - " HashJoin 0.519 0.443 0.533 \n", - " NestedLoopCrossJoin 0.448 0.262 0.174 \n", - " NestedLoopJoin 0.263 0.251 0.365 \n", - " SeqScan 0.790 0.763 0.675 \n", - " Sort 0.127 -0.058 -0.291 \n", - "SeqScan Aggregation -0.122 -0.501 -0.778 \n", - " Filter 0.409 0.386 0.323 \n", - " HashJoin -0.271 -0.321 -0.142 \n", - " NestedLoopCrossJoin -0.342 -0.501 -0.501 \n", - " NestedLoopJoin -0.527 -0.512 -0.310 \n", - " Projection -0.790 -0.763 -0.675 \n", - " Sort -0.663 -0.821 -0.966 \n", - "Sort Aggregation 0.541 0.320 0.188 \n", - " Filter 1.072 1.207 1.289 \n", - " HashJoin 0.392 0.500 0.825 \n", - " NestedLoopCrossJoin 0.321 0.320 0.465 \n", - " NestedLoopJoin 0.136 0.309 0.657 \n", - " Projection -0.127 0.058 0.291 \n", - " SeqScan 0.663 0.821 0.966 " + "Aggregation Filter 0.411 0.733 1.071 \n", + " HashJoin 0.571 0.817 1.048 \n", + " NestedLoopCrossJoin -0.412 -0.129 0.262 \n", + " NestedLoopJoin -0.496 -0.090 0.304 \n", + " Projection -0.680 -0.394 -0.006 \n", + " SeqScan -0.015 0.257 0.536 \n", + " Sort -0.620 -0.344 -0.279 \n", + "Filter Aggregation -0.411 -0.733 -1.071 \n", + " HashJoin 0.159 0.083 -0.023 \n", + " NestedLoopCrossJoin -0.823 -0.862 -0.810 \n", + " NestedLoopJoin -0.907 -0.823 -0.767 \n", + " Projection -1.091 -1.127 -1.077 \n", + " SeqScan -0.426 -0.476 -0.536 \n", + " Sort -1.031 -1.078 -1.350 \n", + "HashJoin Aggregation -0.571 -0.817 -1.048 \n", + " Filter -0.159 -0.083 0.023 \n", + " NestedLoopCrossJoin -0.982 -0.946 -0.787 \n", + " NestedLoopJoin -1.066 -0.907 -0.744 \n", + " Projection -1.250 -1.210 -1.054 \n", + " SeqScan -0.585 -0.560 -0.513 \n", + " Sort -1.190 -1.161 -1.327 \n", + "NestedLoopCrossJoin Aggregation 0.412 0.129 -0.262 \n", + " Filter 0.823 0.862 0.810 \n", + " HashJoin 0.982 0.946 0.787 \n", + " NestedLoopJoin -0.084 0.039 0.043 \n", + " Projection -0.268 -0.265 -0.267 \n", + " SeqScan 0.397 0.386 0.274 \n", + " Sort -0.208 -0.215 -0.540 \n", + "NestedLoopJoin Aggregation 0.496 0.090 -0.304 \n", + " Filter 0.907 0.823 0.767 \n", + " HashJoin 1.066 0.907 0.744 \n", + " NestedLoopCrossJoin 0.084 -0.039 -0.043 \n", + " Projection -0.184 -0.304 -0.310 \n", + " SeqScan 0.481 0.347 0.231 \n", + " Sort -0.124 -0.255 -0.583 \n", + "Projection Aggregation 0.680 0.394 0.006 \n", + " Filter 1.091 1.127 1.077 \n", + " HashJoin 1.250 1.210 1.054 \n", + " NestedLoopCrossJoin 0.268 0.265 0.267 \n", + " NestedLoopJoin 0.184 0.304 0.310 \n", + " SeqScan 0.665 0.650 0.541 \n", + " Sort 0.060 0.049 -0.273 \n", + "SeqScan Aggregation 0.015 -0.257 -0.536 \n", + " Filter 0.426 0.476 0.536 \n", + " HashJoin 0.585 0.560 0.513 \n", + " NestedLoopCrossJoin -0.397 -0.386 -0.274 \n", + " NestedLoopJoin -0.481 -0.347 -0.231 \n", + " Projection -0.665 -0.650 -0.541 \n", + " Sort -0.605 -0.601 -0.814 \n", + "Sort Aggregation 0.620 0.344 0.279 \n", + " Filter 1.031 1.078 1.350 \n", + " HashJoin 1.190 1.161 1.327 \n", + " NestedLoopCrossJoin 0.208 0.215 0.540 \n", + " NestedLoopJoin 0.124 0.255 0.583 \n", + " Projection -0.060 -0.049 0.273 \n", + " SeqScan 0.605 0.601 0.814 " ] }, "execution_count": 13, @@ -3226,7 +5097,7 @@ "name": "python", "nbconvert_exporter": 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+1944,9 @@ \subsection{Дифференциальный анализ и тестирова повторений запросов SSB. Ускорение рассчитывается как отношение времени выполнения наивного плана ко времени выполнения оптимизированного плана. +Видно, что на стандартном наборе запросов оптимизатор дает улучшение в $1.2-1.5$ +раз, а на накоторых запросах в $6$ и более раз. + \begin{table}[H] \centering \caption{Результаты выполнения запросов Star Schema Benchmark.} @@ -1972,9 +1975,18 @@ \subsection{Дифференциальный анализ и тестирова \subsection{Калибровка стоимостной модели} Для калибровки коэффициентов в стоимостных формулах реализованы отдельные -бенчмарки физических операторов. Измеряется время выполнения последовательного -сканирования, фильтрации, проекции, сортировки, агрегации, хеш-соединения, -соединения вложенными циклами и декартова произведения. +бенчмарки физических операторов. +Измеряется время выполнения последовательного сканирования, фильтрации, +проекции, сортировки, агрегации, хеш-соединения, соединения вложенными циклами и +декартова произведения~\refImage{fig:operator-benchmark}. + +\begin{figure}[H] +\centering +\includegraphics[width=0.7\textwidth]{scaling.png} +\caption{Зависимость времени выполнения физических операторов от размера входных + данных.} +\label{fig:operator-benchmark} +\end{figure} При выполнении этих бенчмарков данные хранятся в оперативной памяти. Это позволяет уменьшить влияние файловой системы и измерять преимущественно @@ -1985,8 +1997,13 @@ \subsection{Калибровка стоимостной модели} стоимостью. Для этого размер входных отношений выбирается на основе стоимостной формулы оператора. Сопоставление расчетной стоимости с фактическим временем выполнения позволяет сравнить операторы между собой и скорректировать -коэффициенты модели. Благодаря этому оптимизатор принимает решения на основе -оценок, соответствующих характеристикам реализованного исполнителя. +коэффициенты модели~\refImage{fig:cost-model-calibration}. Благодаря этому +оптимизатор принимает решения на основе оценок, соответствующих характеристикам +реализованного исполнителя. + +Выведенные коэффициенты дополнительно валидируются на случайных запросах путем +сравнения полной стоимости плана и времени его +исполнения~\refImage{fig:cost-on-random-queries}. \anonsection{ЗАКЛЮЧЕНИЕ} @@ -2040,15 +2057,6 @@ \subsection{Калибровка стоимостной модели} \label{fig:cascades-tasks} \end{figure} -\begin{figure}[H] -\centering -\includegraphics[width=0.7\textwidth]{scaling.png} -\caption{Зависимость времени выполнения физических операторов от размера входных - данных.} -\label{fig:operator-benchmark} -\end{figure} - - \begin{figure}[H] \centering \begin{tikzpicture}[ @@ -2522,4 +2530,12 @@ \subsection{Калибровка стоимостной модели} \end{minted} \end{listing} +\begin{figure}[H] +\centering +\includegraphics[width=0.7\textwidth]{cost-on-random-queries.png} +\caption{Сравнение скорости выполнения и стоимости на случайных запросах.}% +\label{fig:cost-on-random-queries} +\end{figure} + + \end{document} From ea59a713c789becf744f9fbceef225830aa4ead3 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Wed, 10 Jun 2026 00:55:47 +0300 Subject: [PATCH 102/120] Update title --- report/vkr.pdf | Bin 920629 -> 1080097 bytes report/vkr.tex | 94 ++++++++++++++++++++++++++++++++++++++++++++++++- 2 files changed, 93 insertions(+), 1 deletion(-) diff --git a/report/vkr.pdf b/report/vkr.pdf index 9fa8361e7ecb1f697f1ce635c1fb6a1b35de9fed..7fcbf1707355ff26d582e2e16872f7cbea7ea79b 100644 GIT binary patch delta 515020 zcmce;1yGyOwgy_EMv=C(Kq*$BxVuX!ZbgGjA-KDRzd(T&ZPB7BF2Nmww54c(V8H{$ z-Q8d4Ip^Nqcki8fZ|2O+B$>>=|E&G(z2sYK?M$k^@~0n4u)KQvMw*3#l@I$>-%#r) zHU}j;rNcXGY++$+Hbt<#g{vhcKL;P>KOWc#{yDdwVq=SnV!OCHgH7ye{9oPVybWN3 z?alsi;ri3{*LnUDqv7QUrexDrd1nnab;V{=cYEjh=Zdt0vmG|u>(>sRlzQx#`#ISE zybQ3}u1EfSivycY$;8#!3UkLbFwFS%JLV2Gu#1D6vnkkxl9!U5kW%eGz;f{X6D#FE zzykjX7Ta@t5Wn{=eedsEH||~B-2U&V`7zS}wfKJ%>TA&d6Kny@)qfLej_Vozy|6j| 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#1\hspace{1em}% + \underline{\makebox[\dimexpr\linewidth-\lblwd-1em\relax][l]{\small #2}}% + \par +} + \begin{document} \sloppy @@ -61,6 +70,89 @@ }% } +\begin{titlepage} + \thispagestyle{empty} + \newpage + + \vspace*{-30pt} + \hspace{-45pt} + \begin{minipage}{0.17\textwidth} + \hspace*{-20pt}\centering + \includegraphics[width=1.3\textwidth]{emblem.png} + \end{minipage} + \begin{minipage}{0.82\textwidth}\small \textbf{ + \vspace*{-0.7ex} + \hspace*{-10pt}\centerline{Министерство науки и высшего образования Российской Федерации} + \vspace*{-0.7ex} + \centerline{Федеральное государственное автономное образовательное учреждение } + \vspace*{-0.7ex} + \centerline{высшего образования} + \vspace*{-0.7ex} + \centerline{<<Московский государственный технический университет} + \vspace*{-0.7ex} + \centerline{имени Н.Э. Баумана} + \vspace*{-0.7ex} + \centerline{(национальный исследовательский университет)>>} + \vspace*{-0.7ex} + \centerline{(МГТУ им. Н.Э. Баумана)}} + \end{minipage} + + \vspace{-2pt} + \hspace{-34.5pt}\rule{\textwidth}{2.5pt} + + \vspace*{-20.3pt} + \hspace{-34.5pt}\rule{\textwidth}{0.4pt} + +\titlefield{ФАКУЛЬТЕТ}{<<Информатика и системы управления>>} +\titlefield{КАФЕДРА}{<<Теоретическая информатика и компьютерные технологии>>} + + +\vspace{3em} +\begin{center} + {\Large\bfseries РАСЧЕТНО-ПОЯСНИТЕЛЬНАЯ ЗАПИСКА}\\[1.5ex] + {\Large\bfseries\itshape К ВЫПУСКНОЙ КВАЛИФИКАЦИОННОЙ РАБОТЕ НА ТЕМУ:}\\[1.5ex] + {\large\bfseries\itshape <<Автоматическая оптимизация планов выполнения SQL-запросов>>} +\end{center} + +\vspace{\fill} + + + \newlength{\ML} + \settowidth{\ML}{«\underline{\hspace{0.7cm}}» \underline{\hspace{2cm}}} + + \noindent Студент \underline{\text{ИУ9-81Б}} \hfill \underline{ \hspace{4cm}}\quad + \raisebox{0.42ex}{\underline{\parbox{4cm}{\centering Старовойтов А. И.}}} + + \vspace{-0.5ex} + \noindent\hspace{9ex}\scriptsize{(Группа)}\normalsize\hspace{170pt}\hspace{2ex}\scriptsize{(Подпись, дата)}\normalsize\hspace{30pt}\hspace{6ex}\scriptsize{(Фамилия И.О.)}\normalsize + + \bigskip + + \noindent Научный руководитель \hfill \underline{\hspace{4cm}}\quad + \raisebox{0.3ex}{\underline{\parbox{4cm}{\centering Непейвода А. Н.}}} + + \vspace{-0.5ex} + \noindent\hspace{13.5ex}\normalsize\hspace{170pt}\hspace{2ex}\scriptsize{(Подпись, дата)}\normalsize\hspace{30pt}\hspace{6ex}\scriptsize{(Фамилия И.О.)}\normalsize + + \bigskip + + \noindent Нормоконтролёр \hfill \underline{\hspace{4cm}}\quad + \raisebox{0.15ex}{\underline{\parbox{4cm}{\centering Алексеев С. М.}}} + + \vspace{-0.5ex} + \noindent\hspace{13.5ex}\normalsize\hspace{170pt}\hspace{2ex}\scriptsize{(Подпись, дата)}\normalsize\hspace{30pt}\hspace{6ex}\scriptsize{(Фамилия И.О.)}\normalsize + + \vfill + + %\vspace{\fill} + + + + \begin{center} + \textsl{2026} + \end{center} +\end{titlepage} + \setlength{\tabcolsep}{3pt} \setcounter{page}{2} %---------------------------------------------------------------------------- From 07935e1c83c7e420f96639d5a4465939b683d640 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Wed, 10 Jun 2026 02:07:18 +0300 Subject: [PATCH 103/120] Fix --- report/vkr.tex | 103 ++++++++++++++++++++++++------------------------- 1 file changed, 51 insertions(+), 52 deletions(-) diff --git a/report/vkr.tex b/report/vkr.tex index efffa67..f829f83 100644 --- a/report/vkr.tex +++ b/report/vkr.tex @@ -64,12 +64,13 @@ \ensuremath{% \begin{array}{c} #1 \\[1pt] - \xRightarrow{\,#2\,} \\[1pt] + \xRightarrow{\,{\small #2}\,} \\[1pt] #3 \end{array}% }% } + \begin{titlepage} \thispagestyle{empty} \newpage @@ -154,7 +155,9 @@ \end{titlepage} \setlength{\tabcolsep}{3pt} -\setcounter{page}{2} +\newpage +\thispagestyle{empty} + %---------------------------------------------------------------------------- % ОТСЮДА --- СОБСТВЕННО ТЕКСТ %---------------------------------------------------------------------------- @@ -185,6 +188,7 @@ \section*{АННОТАЦИЯ} оптимизации запросов и итеративного развития набора правил оптимизатора. \newpage +\setcounter{page}{2} \renewcommand\contentsname{\hfill{\normalfont{СОДЕРЖАНИЕ}}\hfill} \tableofcontents \newpage @@ -192,11 +196,11 @@ \section*{АННОТАЦИЯ} В эпоху стремительного роста объемов данных системы управления базами данных (СУБД) являются важной частью информационных систем. По данным аналитических -исследований, объем мирового рынка СУБД ежегодно увеличивается, что обусловлено -как расширением круга решаемых задач, так и усложнением структуры обрабатываемых -данных и увеличением их объема. Реляционные базы данных, работающие с различными -диалектами языка SQL, продолжают занимать доминирующее положение в сегменте -обработки транзакций и аналитических запросов. +исследований\cite{DBReport}, объем мирового рынка СУБД ежегодно увеличивается, +что обусловлено как расширением круга решаемых задач, так и усложнением +структуры обрабатываемых данных и увеличением их объема. Реляционные базы +данных, работающие с различными диалектами языка SQL, продолжают занимать +доминирующее положение в сегменте обработки транзакций и аналитических запросов. Одним из определяющих факторов конкурентоспособности между различными реализациями СУБД является качество оптимизатора запросов. Оптимизатор @@ -252,10 +256,10 @@ \section*{АННОТАЦИЯ} \section{Исследовательская часть} Система управления базами данных представляет собой программный комплекс, -обеспечивающий хранение, извлечение и модификацию структурированных -данных. Несмотря на разнообразие существующих реализаций, архитектура -большинства реляционных СУБД включает ряд типовых компонентов, -взаимодействие которых обеспечивает выполнение пользовательских запросов. +обеспечивающий хранение, извлечение и модификацию структурированных данных. +Несмотря на разнообразие существующих реализаций, архитектура большинства +реляционных СУБД включает ряд типовых компонентов, взаимодействие которых +обеспечивает выполнение пользовательских запросов\cite{petrov2019database}. К основным компонентам СУБД относятся~\refImage{fig::dbms_arch}: @@ -388,7 +392,8 @@ \subsection{Реляционная алгебра} R \subseteq \text{dom}(A_1) \times \text{dom}(A_2) \times \ldots \times \text{dom}(A_n). \] -Среди основных операторов реляционной алгебры выделяют следующие. +Среди основных операторов реляционной алгебры выделяют +следующие\cite{silberschatz2020database}. \begin{enumerate} \item \emph{Фильтрация} \(\Sel{p}{R}\) --- возвращает подмножество кортежей @@ -768,29 +773,29 @@ \subsection{Обзор архитектур оптимизаторов} ограниченной расширяемостью, ориентацией на левосторонние деревья соединений и независимой оптимизацией вложенных подзапросов. -Volcano~\cite{GraefeMcKenna1993} развивает стоимостный подход System~R, но -делает оптимизатор расширяемым за счет правил и структуры Memo. В отличие от -System~R, пространство эквивалентных выражений задается не жестко встроенным -алгоритмом, а набором правил трансформации и реализации. Поиск выполняется -нисходящим динамическим программированием и может учитывать требуемые физические -свойства. Преимущество Volcano состоит в модульности и возможности добавлять -новые преобразования и операторы без переписывания всего оптимизатора. Основной -недостаток заключается в разделении поиска на фазу генерации и фазу стоимостного -анализа: предварительное раскрытие всех классов эквивалентности может приводить -к избыточной работе. - -Cascades~\cite{Graefe1995} является развитием Volcano и сохраняет его ключевые -идеи: правила, Memo, физические свойства и стоимостный поиск. Главное отличие -Cascades состоит в том, что генерация альтернатив и стоимостная оптимизация не -разделяются на две независимые фазы, а чередуются через систему задач. Благодаря -этому оптимизатор может порождать только те альтернативы, которые нужны для -текущего поиска, раньше применять отсечения по стоимости и удобнее -распараллеливать независимые ветви. Недостатком Cascades является более сложная -реализация, так как требуется управлять состоянием групп, задачами поиска, -правилами и физическими свойствами. +Volcano~\cite{GraefeMcKenna1993,graefe2002volcano,Begoli2018Calcite} развивает +стоимостный подход System~R, но делает оптимизатор расширяемым за счет правил и +структуры Memo. В отличие от System~R, пространство эквивалентных выражений +задается не жестко встроенным алгоритмом, а набором правил трансформации и +реализации. Поиск выполняется нисходящим динамическим программированием и может +учитывать требуемые физические свойства. Преимущество Volcano состоит в +модульности и возможности добавлять новые преобразования и операторы без +переписывания всего оптимизатора. Основной недостаток заключается в разделении +поиска на фазу генерации и фазу стоимостного анализа: предварительное раскрытие +всех классов эквивалентности может приводить к избыточной работе. \subsection{Архитектура Cascades} +Cascades~\cite{Graefe1995,XuColumbia1998} является развитием Volcano и +сохраняет его ключевые идеи: правила, Memo, физические свойства и стоимостный +поиск. Главное отличие Cascades состоит в том, что генерация альтернатив и +стоимостная оптимизация не разделяются на две независимые фазы, а чередуются +через систему задач. Благодаря этому оптимизатор может порождать только те +альтернативы, которые нужны для текущего поиска, раньше применять отсечения по +стоимости и удобнее распараллеливать независимые ветви. Недостатком Cascades +является более сложная реализация, так как требуется управлять состоянием групп, +задачами поиска, правилами и физическими свойствами. + В следующих разделах подробно рассматривается основные элементы архитектуры оптимизаторов Cascades. @@ -1079,11 +1084,9 @@ \subsection{Дифференциальный анализ физических запросов. Следующим шагом сгенерированные запросы транспилируются в диалекты SQL целевых -промышленных СУБД. В качестве эталонных систем рассматриваются PostgreSQL, -DuckDB, Umbra и Microsoft SQL Server. Эти системы являются зрелыми, имеют -документированный формат вывода планов и представляют разные архитектурные -подходы. Затем для получения физических планов исполняется аналог -\texttt{EXPLAIN ANALYZE}. +промышленных СУБД. В качестве эталона рассматривается Microsoft SQL Server. Эта +система является зрелой и имеет машиночитаемый формат вывода планов. Затем для +получения физических планов исполняется аналог \texttt{EXPLAIN ANALYZE}. Полученные физические планы промышленных СУБД конвертируются в формат сериализованного плана разрабатываемого оптимизатора. Современные СУБД @@ -1180,9 +1183,10 @@ \subsection{Модули синтаксического анализа и пос Модуль синтаксического анализа SQL-запросов отвечает за лексический и синтаксический разбор поступающих на вход SQL-запросов. В качестве основы для -грамматики выбран диалект PostgreSQL, так как он широко используется на практике -и имеет открытый исходный код. Использование готовой грамматики из официального -репозитория~\cite{PostgresBisonGrammar} гарантирует совместимость. +грамматики выбран диалект PostgreSQL\cite{PostgresDocs}, так как он широко +используется на практике и имеет открытый исходный код. Использование готовой +грамматики из официального репозитория~\cite{PostgresBisonGrammar} гарантирует +совместимость. В рамках работы поддерживается подмножество диалекта PostgreSQL, достаточное для исследования оптимизации запросов на чтение данных. В него входят операторы @@ -1497,7 +1501,7 @@ \subsubsection{Правила трансформации и реализации предотвращает повторное выполнение одного правила над одним выражением и ограничивает образование циклов при заполнении Memo. -\subsubsection{Стоимостная модель} +\subsubsection{Алгоритм поиска} Стоимость физического плана зависит от количества обрабатываемых кортежей. Для получения этой величины используется класс \verb|CardinalityEstimates|. Оценка @@ -1507,8 +1511,6 @@ \subsubsection{Стоимостная модель} операторов. Фрагмент реализации стоимостной модели приведен в листинге~\ref{lst:optimizer-cost}. -\subsubsection{Алгоритм поиска} - Класс \verb|Optimizer| реализует алгоритм поиска. При создании объекта исходное логическое дерево помещается в Memo. Поиск запускается для корневой группы и требуемых физических свойств результата. @@ -1753,9 +1755,8 @@ \subsection{Руководство пользователя} Все команды далее выполняются из корневого каталога проекта. -\subsubsection{Сборка программы} - -Для выполнения конфигурации и сборки следует запустить команды на листинге~\ref{lst:build}. +Для выполнения конфигурации и сборки следует запустить команды на +листинге~\ref{lst:build}. \begin{listing}[H] \caption{Конфигурация и сборка проекта.} @@ -1770,8 +1771,6 @@ \subsubsection{Сборка программы} \texttt{build/bin/sql}. Для повторной сборки также можно использовать цель \texttt{make build}. -\subsubsection{Запуск программы} - Пользователь должен создать отдельную директорию и поместить в нее CSV-файлы. Имя каждого файла без расширения считается именем таблицы. В заголовке каждого файла для столбца указывается имя и тип через двоеточие. Поддерживаются типы @@ -2036,8 +2035,8 @@ \subsection{Дифференциальный анализ и тестирова повторений запросов SSB. Ускорение рассчитывается как отношение времени выполнения наивного плана ко времени выполнения оптимизированного плана. -Видно, что на стандартном наборе запросов оптимизатор дает улучшение в $1.2-1.5$ -раз, а на накоторых запросах в $6$ и более раз. +Видно, что на стандартном наборе запросов оптимизатор дает на $20-50\%$ раз, а +на накоторых запросах в $6$ и более раз. \begin{table}[H] \centering @@ -2386,7 +2385,7 @@ \subsection{Калибровка стоимостной модели} \endlastfoot 1 & Коммутативность соединения & - \(\TransRule{\InnerJoin{R}{\theta}{S}}{\text{внутреннее или полное внешнее соединение}}{\InnerJoin{S}{\theta}{R}}\) & + \(\TransRule{\InnerJoin{R}{\theta}{S}}{\text{\scriptsize внутреннее или полное внешнее соединение}}{\InnerJoin{S}{\theta}{R}}\) & Перестановка входов соединения. Позволяет рассмотреть оба порядка вычисления. \\ \hline @@ -2501,7 +2500,7 @@ \subsection{Калибровка стоимостной модели} \hline 2 & Сканирование по индексу & - \(\TransRule{\LogicalGet}{\text{есть совместимый индекс}}{\IndexScan}\) & + \(\TransRule{\LogicalGet}{\text{\scriptsize есть совместимый индекс}}{\IndexScan}\) & Индекс должен быть совместим с предикатом или требуемым порядком. \\ \hline From 815e14c9200d23f63250a583687ea9f90447d679 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Wed, 10 Jun 2026 03:02:02 +0300 Subject: [PATCH 104/120] Fix --- report/vkr.tex | 35 +++++++++++++++++++++++++++++++---- 1 file changed, 31 insertions(+), 4 deletions(-) diff --git a/report/vkr.tex b/report/vkr.tex index f829f83..f9a7168 100644 --- a/report/vkr.tex +++ b/report/vkr.tex @@ -15,6 +15,33 @@ \par } +\renewcommand{\sectionfont}{\normalsize} % Сбиваем стиль оглавления в стандартный +\renewcommand{\cftsecleader}{\cftdotfill{\cftdotsep}} % Точки в оглавлении напротив разделов + +\renewcommand{\cftsecfont}{\normalfont\large} % Переключение на times в содержании +\renewcommand{\cftsubsecfont}{\normalfont\large} % Переключение на times в содержании + +\setlength{\parindent}{1.25cm} +\setlength{\cftsecindent}{0pt}% Убираем отступы в содержании для \section +\setlength{\cftsubsecindent}{0pt}% Убираем отступы в содержании для \subsection +\setlength{\cftsubsubsecindent}{0pt}% Убираем отступы в содержании для \subsubsection + +\renewcommand{\anonsection}[1]{\cleardoublepage + \phantomsection + \addcontentsline{toc}{section}{#1}% + \section*{#1}\vspace*{2.5ex}% +} + +\usepackage{scrlayer-scrpage} +\clearpairofpagestyles +\ohead{} +\chead{} +\cfoot*{\thepage} % page number centered in footer on all pages +\pagestyle{scrplain} +% tune vertical position inside the 2 cm bottom margin +\setlength{\footskip}{1.25cm} + + \begin{document} \sloppy @@ -1740,7 +1767,7 @@ \subsection{Модуль исполнения физических планов} узлов плана. Для реализации каналов используется -\verb|boost::asio::experimental::concurrent_channel|. Данный класс обеспечивает +boost::asio::experimental::concurrent\_\-channel. Данный класс обеспечивает потокобезопасность, буферизацию, асинхронную передачу данных и совместимость с корутинами. Такой подход позволяет единообразно организовать взаимодействие между операторами независимо от конкретных алгоритмов для их реализации. @@ -2132,7 +2159,7 @@ \subsection{Калибровка стоимостной модели} \renewcommand\refname{СПИСОК ИСПОЛЬЗОВАННЫХ ИСТОЧНИКОВ} \clearpage {\catcode`"\active\def"{\relax} - \addcontentsline{toc}{section}{\protect\numberline{}\refname}% + \addcontentsline{toc}{section}{\refname}% \printbibliography } \newpage @@ -2510,12 +2537,12 @@ \subsection{Калибровка стоимостной модели} \hline 4 & Хеш-соединение & - \(\TransRule{\InnerJoin{R}{\theta}{S}}{\theta\ \text{содержит равенство по ключам}}{\HashJoin}\) & + \(\TransRule{\InnerJoin{R}{\theta}{S}}{\theta\ \text{\scriptsize содержит равенство по ключам}}{\HashJoin}\) & Хеш-таблица строится по ключам равенства. \\ \hline 5 & Соединение слиянием & - \(\TransRule{\InnerJoin{R}{\theta}{S}}{\text{входы упорядочены по ключам соединения}}{\MergeJoin}\) & + \(\TransRule{\InnerJoin{R}{\theta}{S}}{\text{\scriptsize входы упорядочены по ключам соединения}}{\MergeJoin}\) & Требуемый порядок может быть обеспечен явным оператором \Sort{}. \\ \hline From 8483013fb14734fc30a4a5b85ca1c3b56d2f91f4 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Wed, 10 Jun 2026 03:12:10 +0300 Subject: [PATCH 105/120] Done --- report/vkr.pdf | Bin 1080097 -> 1081868 bytes report/vkr.tex | 510 +++++++++++++++++++++++++++++++++++++++++++++---- 2 files changed, 477 insertions(+), 33 deletions(-) diff --git a/report/vkr.pdf b/report/vkr.pdf index 7fcbf1707355ff26d582e2e16872f7cbea7ea79b..2682c3f8f2b5000e1d54c27b1a82768541be8a44 100644 GIT binary patch delta 212628 zcmZsCV{o8LwCyAldt%$RZQGgHw(-TbZQHhOb7ChGYm#~AoLjG6y{h}8t9$ou{Oh&X z>b(n|g`$Ink;oK9#p#&nIbg`X&zDwl!@cN`G41F{p0=j4CbT&a4k?~mVXUUX4d~a?EhnM z{A;8MVS=MH5ZitSVM$BqheT;pmIVv@o+Jl{+B60o2AY(Qh|**TC-6OK0|l+A1(6gC z^IS$dP~D-@gIA)SyNS z;4#N}eVAi68hharYQXIA`7_D?~j zoY}wYZvOCTK+4x7?Q0@_A5L*jWP^FaO1+&aL)t_L9{K2G@jj`1xyHu`d*bWrjk-8z 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Z#r1SHw?N?l@$m`p@S@PtN~_4A{2w9A13dr$ diff --git a/report/vkr.tex b/report/vkr.tex index f9a7168..4630b3f 100644 --- a/report/vkr.tex +++ b/report/vkr.tex @@ -1,51 +1,490 @@ % !TeX TXS-program:bibliography = txs:///biber % chktex-file 1 -\documentclass[fontsize=14pt, russian]{scrartcl} -\input{header.tex} -\addbibresource{biblio.bib} +\documentclass[14pt, russian]{scrartcl} +\let\counterwithout\relax +\let\counterwithin\relax + +\usepackage{float} +\usepackage{xcolor} +\usepackage{extsizes} +\usepackage{subfig} +\usepackage[export]{adjustbox} +\usepackage{tocvsec2} % глубина разделов в оглавлении +\usepackage[subfigure]{tocloft} % сетка из картинок +\usepackage[newfloat]{minted} % можно менять место, где располагать объект +\captionsetup[listing]{position=top} % настрокйка подписи + +\usepackage{tikz} +\usetikzlibrary{automata,patterns, positioning, arrows,shadows,shapes,datavisualization} +\usetikzlibrary{decorations.pathreplacing} +\usetikzlibrary{arrows.meta,patterns.meta,graphs} + +% команды ниже просто добавляют перед текстам нечто +\AtBeginEnvironment{figure}{\vspace{0.5cm}} +\AtBeginEnvironment{table}{\vspace{0.5cm}} +\AtBeginEnvironment{listing}{\vspace{0.5cm}} +\AtBeginEnvironment{algorithm}{\vspace{0.5cm}} +\AtBeginEnvironment{minted}{\vspace{-0.5cm}} + +\usepackage{fancyvrb} +\usepackage{ulem,bm,mathrsfs,ifsym} %зачеркивания, особо жирный стиль и RSFS начертание +\usepackage{sectsty} % переопределение стилей подразделов + +% для полей и разметки страницы +\usepackage{pdflscape} %альбомные страницы +\usepackage{geometry} % настройка размеров страниц +\geometry{a4paper, tmargin=2cm, bmargin=2cm, lmargin=3cm, rmargin=1.5cm} % тоже самое, но лучше + +% математика +\usepackage{amsthm,amsfonts,amsmath,amssymb,amscd} % дополнения от AMS +\usepackage{mathtools} % добавляет окружение multlined +\usepackage[perpage]{footmisc} % нумперация сносок на каждой странице +\KOMAoptions{fontsize=14pt} % изменение параметров страницы + +\makeatletter % для команды с символом @ в имени не в стилевом пакете, а прямо в тексте документа +\def\showfontsize{\f@size{} point} +\makeatother + +% про стили и языки +\usepackage{cmap} % улучшает поиск кирилицы в pdf +\usepackage[T2A]{fontenc} % поддержка русских букв +\usepackage[utf8]{inputenc} % кодировка utf8 +\usepackage[english, main=russian]{babel} % языки: русский, английский +\usepackage{tempora} % шрифт times + +% оформление текста +\usepackage{indentfirst} % красная строка + +% таблицы +\usepackage{longtable} % длинные таблицы +\usepackage{multirow, makecell, array} % улучшенное форматирование таблиц +\usepackage{booktabs} % возможность оформления таблиц в классическом книжном стиле (при правильном использовании не противоречит ГОСТ) + +% общее форматирование +\usepackage{soulutf8} % переносоустойчивые подчёркивания и зачёркивания +\usepackage{icomma} % запятая в десятичных дробях + +% картинки +\usepackage{graphicx} % работа с графикой +\usepackage{wrapfig} % картинки могут «обтекаться» текстом + +% списки +\usepackage{enumitem} + +% подписи +\usepackage{caption} % помогает настраивать подписи к рисункам и таблицам +%% Использование: +%\begin{table}[h!]\ContinuedFloat - чтобы не переключать счетчик +%\captionsetup{labelformat=continued} %должен стоять до самого caption +%\caption{} +% либо ручками \caption*{Продолжение таблицы~\ref{...}.} :) + +% интервалы +\addto\captionsrussian{ + \renewcommand{\listingname}{Листинг} +} -\usepackage{svg} +%счётчики +\usepackage[figure, table, section]{totalcount} % счётчик рисунков и таблиц +\DeclareTotalCounter{lstlisting} % считать листинги тоже +\usepackage{totcount} % создание счётчиков на основе последнего номера подсчитываемого элемента +\usepackage{totpages} % счётчик страниц, совместимый с hyperref (ссылается на номер последней страницы) + +% интервалы +% linespread-реализация ближе к реализации полуторного интервала в ворде. +% setspace реализация заточена под шрифты 10, 11, 12pt, под остальные кегли хуже, но всё же ближе к типографской классике. +\linespread{1.35} % Полуторный интервал (ГОСТ Р 7.0.11-2011, 5.3.6) +%\renewcommand{\@biblabel}[1]{#1} + +% гиперссылки +\usepackage{hyperref} + +% выравнивание и переносы +\sloppy % оберег от переполнений +\clubpenalty=10000 % запрет разрыва страницы после первой строки абзаца +\widowpenalty=10000 % запрет разрыва страницы после последней строки абзаца + +% какая-то магия +\makeatletter % малые заглавные, small caps shape +\let\@@scshape=\scshape +\renewcommand{\scshape}{% + \ifnum\strcmp{\f@series}{bx}=\z@ + \usefont{T1}{cmr}{bx}{sc}% + \else + \ifnum\strcmp{\f@shape}{it}=\z@ + \fontshape{scsl}\selectfont + \else + \@@scshape + \fi + \fi} +\makeatother + +% подписи +%\captionsetup{% +%singlelinecheck=off, % многострочные подписи, например у таблиц +%skip=2pt, % вертикальная отбивка между подписью и содержимым рисунка или таблицы определяется ключом +%justification=centering, % центрирование подписей, заданных командой \caption +%} + +% пакеты +\usepackage{ifthen} % добавляет ifthenelse +% инициализирование переменных, не трогать! (опять магия) +\newcounter{intvl} +\newcounter{otstup} +\newcounter{contnumeq} +\newcounter{contnumfig} +\newcounter{contnumtab} +\newcounter{pgnum} +\newcounter{bibliosel} +\newcounter{chapstyle} +\newcounter{headingdelim} +\newcounter{headingalign} +\newcounter{headingsize} +\newcounter{tabcap} +\newcounter{tablaba} +\newcounter{tabtita} +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Область упрощённого управления оформлением %%% + +% интервал между заголовками и между заголовком и текстом +% заголовки отделяют от текста сверху и снизу тремя интервалами (ГОСТ Р 7.0.11-2011, 5.3.5) +\setcounter{intvl}{3} % коэффициент кратности к размеру шрифта + +% отступы у заголовков в тексте +\setcounter{otstup}{0} % 0 - без отступа; 1 - абзацный отступ + +% нумерация формул, таблиц и рисунков +\setcounter{contnumeq}{1} % нумерация формул: 0 - пораздельно, 1 - сквозная нумерация по всему документу +\setcounter{contnumfig}{1} % нумерация рисунков: 0 - пораздельно, 1 - сквозная нумерация по всему документу +\setcounter{contnumtab}{1} % нумерация таблиц: 0 - пораздельно, 1 - сквозная нумерация по всему документу + +% оглавление +\setcounter{pgnum}{0} % 0 - номера страниц никак не обозначены, 1 - стр. над номерами страниц + +% библиография +\setcounter{bibliosel}{1} % 0 - встроенная реализация с загрузкой файла через движок bibtex8, 1 - реализация пакетом biblatex через движок biber + +% текст и форматирование заголовков +\setcounter{chapstyle}{0} % 0 - разделы только под номером, 1 - разделы с названием "Глава" перед номером +\setcounter{headingdelim}{0} % 0 - номер отделен пропуском в 1em или \quad, 1 - номера разделов и приложений отделены точкой с пробелом, подразделы пропуском без точки, 2 - номера разделов, подразделов и приложений отделены точкой с пробелом + +% выравнивание заголовков в тексте +\setcounter{headingalign}{0} % 0 - по центру, 1 - по левому краю + +% размеры заголовков в тексте +\setcounter{headingsize}{0} % 0 - по ГОСТ, все всегда 14 пт; 1 - пропорционально изменяющийся размер в зависимости от базового шрифта + +% подпись таблиц +\setcounter{tabcap}{0} % 0 - по ГОСТ, номер таблицы и название разделены тире, выровнены по левому краю, при необходимости на нескольких строках, 1 - подпись таблицы не по ГОСТ, на двух и более строках, дальнейшие настройки: +% выравнивание первой строки, с подписью и номером +\setcounter{tablaba}{2} % 0 - по левому краю, 1 - по центру, 2 - по правому краю +% выравнивание строк с самим названием таблицы +\setcounter{tabtita}{1} % 0 - по левому краю, 1 - по центру, 2 - по правому краю + +% картинки +\DeclareCaptionLabelSeparator*{emdash}{~-- } % (ГОСТ 2.105, 4.3.1) +\captionsetup[figure]{labelsep=emdash,font=onehalfspacing,position=bottom} + +% таблицы +\ifthenelse{\equal{\thetabcap}{0}}{ + \newcommand{\tabcapalign}{\raggedright} % по левому краю страницы или аналога parbox +} -\newlength{\lblwd} -\newcommand{\titlefield}[2]{% - \par\addvspace{1.6ex}% - \settowidth{\lblwd}{\small #1}% - \noindent\small #1\hspace{1em}% - \underline{\makebox[\dimexpr\linewidth-\lblwd-1em\relax][l]{\small #2}}% - \par +\ifthenelse{\equal{\thetablaba}{0} \AND \equal{\thetabcap}{1}}{ + \newcommand{\tabcapalign}{\raggedright} % по левому краю страницы или аналога parbox +} + +\ifthenelse{\equal{\thetablaba}{1} \AND \equal{\thetabcap}{1}}{ + \newcommand{\tabcapalign}{\centering} % по центру страницы или аналога parbox +} + +\ifthenelse{\equal{\thetablaba}{2} \AND \equal{\thetabcap}{1}}{% + \newcommand{\tabcapalign}{\raggedleft} % по правому краю страницы или аналога parbox +} + +\ifthenelse{\equal{\thetabtita}{0} \AND \equal{\thetabcap}{1}}{% + \newcommand{\tabtitalign}{\raggedright} % по левому краю страницы или аналога parbox +} + +\ifthenelse{\equal{\thetabtita}{1} \AND \equal{\thetabcap}{1}}{% + \newcommand{\tabtitalign}{\centering} % по центру страницы или аналога parbox +} + +\ifthenelse{\equal{\thetabtita}{2} \AND \equal{\thetabcap}{1}}{% + \newcommand{\tabtitalign}{\raggedleft} % по правому краю страницы или аналога parbox +} + +\DeclareCaptionFormat{tablenocaption}{\tabcapalign #1\strut} % наименование таблицы отсутствует +\ifthenelse{\equal{\thetabcap}{0}}{ + \DeclareCaptionFormat{tablecaption}{\tabcapalign #1#2#3} + \captionsetup[table]{labelsep=emdash} % тире как разделитель идентификатора с номером от наименования +}{ + \DeclareCaptionFormat{tablecaption}{\tabcapalign #1#2\par % идентификатор таблицы на отдельной строке + \tabtitalign{#3}} % наименование таблицы строкой ниже + \captionsetup[table]{labelsep=space} % пробельный разделитель идентификатора с номером от наименования +} +\captionsetup[table]{format=tablecaption, singlelinecheck=off, font=onehalfspacing, position=top, skip=-5pt} % многострочные наименования +\DeclareCaptionLabelFormat{continued}{Продолжение таблицы~#2} +\setlength{\belowcaptionskip}{.2cm} +\setlength{\intextsep}{0ex} + +% подписи подрисунков +\renewcommand{\thesubfigure}{\asbuk{subfigure}} % буквенные номера подрисунков +\captionsetup[subfigure]{font={normalsize}, % шрифт подписи названий подрисунков (не отличается от основного) + labelformat=brace, % формат обозначения подрисунка + justification=centering, % выключение подписей +} + +% гиперссылки +\definecolor{linkcolor}{rgb}{0.0,0,0} +\definecolor{citecolor}{rgb}{0,0.0,0} +\definecolor{urlcolor}{rgb}{0,0,0} +\hypersetup{ + linktocpage=true, % ссылки с номера страницы в оглавлении, списке таблиц и списке рисунков + plainpages=true, % что-то про нумерацию в арабской форме + colorlinks, % ссылки отображаются раскрашенным текстом + linkcolor={linkcolor}, % цвет ссылок типа ref, eqref и подобных + citecolor={citecolor}, % цвет ссылок-цитат + urlcolor={urlcolor}, % цвет гиперссылок + pdflang={ru}, } +\urlstyle{same} +% ещё что-то +\setlength{\parindent}{2.5em} % абзацный отступ. должен быть одинаковым по всему тексту (ГОСТ Р 7.0.11-2011, 5.3.7). + +% списки +% используем дефис для ненумерованных списков (ГОСТ 2.105-95, 4.1.7) +%\renewcommand{\labelitemi}{\normalfont\bfseries~{--}} +\renewcommand{\labelitemi}{\bfseries~{--}} +\setlist{nosep, % единый стиль для всех списков (пакет enumitem), без дополнительных интервалов + labelindent=\parindent,leftmargin=*% каждый пункт, подпункт и перечисление с абзацного отступа (ГОСТ 2.105-95, 4.1.8) +} +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\usepackage{ragged2e} % выравнивание по горизонтали +\usepackage[explicit]{titlesec} % явное указание заголовка +\usepackage{placeins} % не даеёт объектам «утекать» за барьеры +\usepackage{xparse} % позволяет создавать более крутые команды, чем с \newcommand +\usepackage{csquotes} % многоязычное что-то +\usepackage{listingsutf8} % поддержка файлов с многобайтовыми кодировками +\usepackage{url} % пакеты расширений +\usepackage{algorithm, algorithmicx} % что-то св]занное с кодом +\usepackage[noend]{algpseudocode} % для норм форматирования кода +\usepackage{blkarray} % для работы с чем0то математическим +\usepackage{chngcntr} +\usepackage{tabularx} +\usepackage[backend=biber, + bibstyle=gost-numeric, + citestyle=gost-numeric, + sorting=none]{biblatex} +\setcounter{biburllcpenalty}{7000} +\setcounter{biburlucpenalty}{8000} +\newcommand*\template[1]{\text{<}#1\text{>}} +\addbibresource{biblio.bib} % добавление библиографии + +% нечто для нормального вида +\titleformat{name=\section,numberless}[block]{\normalfont\normalsize\centering}{}{0em}{#1} +\titleformat{\section}[block]{\normalfont\normalsize\bfseries\raggedright}{}{0em}{\thesection\hspace{0.25em}#1} +\titleformat{\subsection}[block]{\normalfont\normalsize\bfseries\raggedright}{}{0em}{\thesubsection\hspace{0.25em}#1} +\titleformat{name=\subsection,numberless}[block]{\normalfont\normalsize\bfseries\raggedright}{}{0em}{#1} +\titleformat{\subsubsection}[block]{\normalfont\normalsize\bfseries\raggedright}{}{0em}{\thesubsubsection\hspace{0.25em}#1} +\titleformat{name=\subsubsection,numberless}[block]{\normalfont\normalsize\bfseries\raggedright}{}{0em}{#1} + +\newcounter{subsubsubsection}[subsubsection] +\renewcommand{\thesubsubsubsection}{\thesubsubsection.\arabic{subsubsubsection}} +\setcounter{secnumdepth}{4} +\setcounter{tocdepth}{4} +\titleclass{\subsubsubsection}{straight}[\subsubsection] +\titleformat{\subsubsubsection}[block]{\normalfont\normalsize\bfseries\raggedright}{}{0em}{\thesubsubsubsection\hspace{0.25em}#1} +\titlespacing*{\subsubsubsection}{0pt}{3.25ex plus 1ex minus .2ex}{1.5ex plus .2ex} +\makeatletter +\newcommand*\l@subsubsubsection{\@dottedtocline{4}{0pt}{5em}} +\makeatother + +\let\Algorithm\algorithm % копирование одной команды в другую +\renewcommand\algorithm[1][]{\Algorithm[#1]\setstretch{1.5}} +%\renewcommand{\listingscaption}{Листинг} + +% работа с шрифтами, формулами +\usepackage{pifont} +\usepackage{calc} +\usepackage{suffix} +\usepackage{csquotes} +\DeclareQuoteStyle{russian} + {\guillemotleft}{\guillemotright}[0.025em] + {\quotedblbase}{\textquotedblleft} +\ExecuteQuoteOptions{style=russian} +\newcommand{\enq}[1]{\enquote{#1}} +\newcommand{\eng}[1]{\begin{english}#1\end{english}} + +% создание всяких разных счётчиков +\newcounter{cTheorem} +\newcounter{cDefinition} +\newcounter{cConsequent} +\newcounter{cExample} +\newcounter{cLemma} +\newcounter{cConjecture} +\newtheorem{Theorem}{Теорема}[cTheorem] +\newtheorem{Definition}{Определение}[cDefinition] +\newtheorem{Consequent}{Следствие}[cConsequent] +\newtheorem{Example}{Пример}[cExample] +\newtheorem{Lemma}{Лемма}[cLemma] +\newtheorem{Conjecture}{Гипотеза}[cConjecture] + +% счётчик - с арабской цифрой +\renewcommand{\theTheorem}{\arabic{Theorem}} +\renewcommand{\theDefinition}{\arabic{Definition}} +\renewcommand{\theConsequent}{\arabic{Consequent}} +\renewcommand{\theExample}{\arabic{Example}} +\renewcommand{\theLemma}{\arabic{Lemma}} +\renewcommand{\theConjecture}{\arabic{Conjecture}} +% \makeatletter +\NewDocumentCommand{\Newline}{}{\text{\\}} +\newcommand{\sequence}[2]{\ensuremath \left(#1,\ \dots,\ #2\right)} + +% переопределяем цвета +\definecolor{mygreen}{rgb}{0,0.6,0} +\definecolor{mygray}{rgb}{0.5,0.5,0.5} +\definecolor{mymauve}{rgb}{0.58,0,0.82} +\renewcommand{\listalgorithmname}{Список алгоритмов} +\floatname{algorithm}{Листинг} +\renewcommand{\lstlistingname}{Листинг} +\renewcommand{\thealgorithm}{\arabic{algorithm}} + +% работа с листингами, рисунками, параграфами +\newcommand{\refAlgo}[1]{(листинг \ref{#1})} +\newcommand{\refImage}[1]{(рисунок \ref{#1})} +\renewcommand{\theenumi}{\arabic{enumi}.} % меняем везде перечисления на цифра.цифра +\renewcommand{\labelenumi}{\arabic{enumi}.} % меняем везде перечисления на цифра.цифра +\renewcommand{\theenumii}{\arabic{enumii}} % меняем везде перечисления на цифра.цифра +\renewcommand{\labelenumii}{(\arabic{enumii})} % меняем везде перечисления на цифра.цифра +\renewcommand{\theenumiii}{\roman{enumiii}} % меняем везде перечисления на цифра.цифра +\renewcommand{\labelenumiii}{(\roman{enumiii})} % меняем везде перечисления на цифра.цифра +%\newfontfamily\AnkaCoder[Path=src/fonts/]{AnkaCoder-r.ttf} +\renewcommand{\labelitemi}{--} +\renewcommand{\labelitemii}{--} + +%\usepackage{courier} + +% оформление языков +\lstdefinelanguage{Refal}{ + alsodigit = {.,<,>}, + morekeywords = [1]{ENTRY}, + morekeywords = [2]{Go, Put, Get, Open, Close, Arg, Add, Sub, Mul, Div, Symb, Explode, Implode}, + %keyword4 + morekeywords = [3]{<,>}, + %keyword5 + morekeywords = [4]{e.,t.,s.}, + sensitive = true, + morecomment = [l]{*}, + morecomment = [s]{/*}{*/}, + commentstyle = \color{mygreen}, + morestring = [b]", + morestring = [b]', + stringstyle = \color{purple} +} + +\makeatletter % для норм обозначения @ +\def\p@subsection{} +\def\p@subsubsection{\thesection\,\thesubsection\,} +\makeatother + +% какая-то лютая магия +\newcommand{\prog}[1]{{\ttfamily\small#1}} +\lstset{ % + backgroundcolor=\color{white}, % выбор background, нужно \usepackage{color} or \usepackage{xcolor} + basicstyle=\ttfamily\footnotesize, + %basicstyle=\footnotesize\AnkaCoder, % размер букв для кода + breakatwhitespace=false, % sets if automatic breaks shoulbd only happen at whitespace + breaklines=true, % sets automatic line breaking + captionpos=top, % sets the caption-position to bottom + commentstyle=\color{mygreen}, % comment style + deletekeywords={...}, % if you want to delete keywords from the given language + escapeinside={\%*}{*)}, % if you want to add LaTeX within your code + extendedchars=true, % lets you use non-ASCII characters; for 8-bits encodings only, does not work with UTF-8 + inputencoding=utf8, + frame=single, % adds a frame around the code + keepspaces=true, % keeps spaces in text, useful for keeping indentation of code (possibly needs columns=flexible) + keywordstyle=\bf, % keyword style + language=Refal, % the language of the code + morekeywords={<,>,$ENTRY,Go,Arg, Open, Close, e., s., t., Get, Put}, + numbers=left, % where to put the line-numbers; possible values are (none, left, right) + numbersep=5pt, % how far the line-numbers are from the code + xleftmargin=25pt, + xrightmargin=25pt, + numberstyle=\small\color{black}, % the style that is used for the line-numbers + rulecolor=\color{black}, % if not set, the frame-color may be changed on line-breaks within not-black text (e.g. comments (green here)) + showspaces=false, % show spaces everywhere adding particular underscores; it overrides 'showstringspaces' + showstringspaces=false, % underline spaces within strings only + showtabs=false, % show tabs within strings adding particular underscores + stepnumber=1, % the step between two line-numbers. If it's 1, each line will be numbered + stringstyle=\color{mymauve}, % string literal style + tabsize=8, % sets default tabsize to 8 spaces + title=\lstname % show the filename of files included with \lstinputlisting; also try caption instead of title +} +% какие-то переобъявления и новые команды +\newcommand{\anonsection}[1]{\cleardoublepage +\phantomsection % якроь в опр месте +\addcontentsline{toc}{section}{#1} +\section*{#1}\vspace*{2.5ex} % по госту положены 3 пустые строки после заголовка ненумеруемого раздела +} +\newcommand{\anonsubsection}[1]{\subsection*{#1}} +\newcommand{\anonsubsubsection}[1]{\subsubsection*{#1}} +\newcommand{\sectionbreak}{\clearpage} \renewcommand{\sectionfont}{\normalsize} % Сбиваем стиль оглавления в стандартный \renewcommand{\cftsecleader}{\cftdotfill{\cftdotsep}} % Точки в оглавлении напротив разделов \renewcommand{\cftsecfont}{\normalfont\large} % Переключение на times в содержании \renewcommand{\cftsubsecfont}{\normalfont\large} % Переключение на times в содержании -\setlength{\parindent}{1.25cm} \setlength{\cftsecindent}{0pt}% Убираем отступы в содержании для \section \setlength{\cftsubsecindent}{0pt}% Убираем отступы в содержании для \subsection \setlength{\cftsubsubsecindent}{0pt}% Убираем отступы в содержании для \subsubsection -\renewcommand{\anonsection}[1]{\cleardoublepage - \phantomsection - \addcontentsline{toc}{section}{#1}% - \section*{#1}\vspace*{2.5ex}% -} +% опять магия, я устала +\usepackage{caption} +%\captionsetup[table]{justification=raggedleft} +%\captionsetup[figure]{justification=centering,labelsep=endash} +\usepackage{amsmath} % \bar (матрицы и проч. ...) +\usepackage{amsfonts} % \mathbb (символ для множества действительных чисел и проч. ...) +\usepackage{mathtools} % \abs, \norm + \DeclarePairedDelimiter\abs{\lvert}{\rvert} % операция модуля + \DeclarePairedDelimiter\norm{\lVert}{\rVert} % операция нормы +\DeclareTextCommandDefault{\textvisiblespace}{% + \mbox{\kern.06em\vrule \@height.3ex}% + \vbox{\hrule \@width.3em}% + \hbox{\vrule \@height.3ex}} +\newsavebox{\spacebox} +\begin{lrbox}{\spacebox} +\verb*! ! +\end{lrbox} +\newcommand{\aspace}{\usebox{\spacebox}} +\DeclareTotalCounter{listing} + +% снова магия +\makeatletter +\renewcommand*{\p@subsubsection}{} +\makeatother + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\makeatletter +\AddToHook{begindocument/before}{\@ifpackageloaded{minted}{\removefromtoclist[float]{lol}}{}} +\makeatother +\newlength{\lblwd} -\usepackage{scrlayer-scrpage} -\clearpairofpagestyles -\ohead{} -\chead{} -\cfoot*{\thepage} % page number centered in footer on all pages -\pagestyle{scrplain} -% tune vertical position inside the 2 cm bottom margin -\setlength{\footskip}{1.25cm} +% Поле титульного листа: метка + значение на подчёркивающей линии +\newcommand{\titlefield}[2]{% + \par\addvspace{1.6ex}% + \settowidth{\lblwd}{\small #1}% + \noindent\small #1\hspace{1em}% + \underline{\makebox[\dimexpr\linewidth-\lblwd-1em\relax][l]{\small #2}}% + \par +} +\begin{document} +\sloppy % избегаем переполнения -\begin{document} -\sloppy +\def\figurename{Рисунок} % переименуем рис в рисунок -\def\figurename{Рисунок} \newcommand{\sqlkw}[1]{\texttt{#1}} \newcommand{\SELECT}{\sqlkw{SELECT}} @@ -91,13 +530,17 @@ \ensuremath{% \begin{array}{c} #1 \\[1pt] - \xRightarrow{\,{\small #2}\,} \\[1pt] + \xRightarrow{\,#2\,} \\[1pt] #3 \end{array}% }% } +%---------------------------------- +% АННОТАЦИЯ (до титульного листа) +%---------------------------------- +% начала магии title \begin{titlepage} \thispagestyle{empty} \newpage @@ -180,14 +623,12 @@ \textsl{2026} \end{center} \end{titlepage} +% конец магии title \setlength{\tabcolsep}{3pt} \newpage \thispagestyle{empty} -%---------------------------------------------------------------------------- -% ОТСЮДА --- СОБСТВЕННО ТЕКСТ -%---------------------------------------------------------------------------- \section*{АННОТАЦИЯ} Работа посвящена разработке оптимизатора SQL-запросов для модельной реляционной @@ -216,7 +657,10 @@ \section*{АННОТАЦИЯ} \newpage \setcounter{page}{2} -\renewcommand\contentsname{\hfill{\normalfont{СОДЕРЖАНИЕ}}\hfill} +%---------------------------------------------------------------------------- +% ОТСЮДА --- СОБСТВЕННО ТЕКСТ +%---------------------------------------------------------------------------- +\renewcommand\contentsname{\hfill{\normalfont{СОДЕРЖАНИЕ}}\hfill} %Оглавление \tableofcontents \newpage \anonsection{ВВЕДЕНИЕ} From d911f88df879ef0d79805ab930972bd7416e24bb Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Wed, 10 Jun 2026 22:31:29 +0300 Subject: [PATCH 106/120] Implement remaining transformation rules and support constraints --- include/stewkk/sql/logic/executor/plan.hpp | 18 +- .../sql/logic/optimizer/logical_expr.hpp | 17 ++ .../stewkk/sql/logic/optimizer/optimizer.hpp | 4 +- .../sql/logic/optimizer/physical_expr.hpp | 17 ++ include/stewkk/sql/logic/optimizer/rule.hpp | 12 +- include/stewkk/sql/logic/optimizer/rules.hpp | 9 +- .../sql/logic/optimizer/rules_applier.hpp | 7 +- .../sql/logic/optimizer/schema_catalog.hpp | 30 +++ .../aggregation_join_transpose.hpp | 13 ++ .../aggregation_pushdown_through_join.hpp | 13 ++ .../cross_join_to_join.hpp | 13 ++ .../filter_lift_through_join.hpp | 13 ++ .../transformation_rules/filter_merge.hpp | 4 +- .../filter_pushdown_through_join.hpp | 4 +- .../filter_pushdown_through_projection.hpp | 4 +- .../transformation_rules/filter_split.hpp | 4 +- .../filter_to_join_predicate.hpp | 13 ++ .../transformation_rules/in_to_or_chain.hpp | 4 +- .../join_associativity.hpp | 4 +- .../join_commutativity.hpp | 4 +- .../outer_join_to_inner.hpp | 13 ++ .../transformation_rules/predicate_utils.hpp | 6 + .../projection_pushdown_through_join.hpp | 13 ++ report/vkr.pdf | Bin 1081868 -> 0 bytes src/stewkk/sql/CMakeLists.txt | 7 + src/stewkk/sql/logic/executor/executor.cpp | 10 + src/stewkk/sql/logic/executor/plan.cpp | 8 + .../sql/logic/executor/plan_serializer.cpp | 66 +++++++ .../implement_aggregation.cpp | 13 +- .../sql/logic/optimizer/cardinality.cpp | 14 ++ src/stewkk/sql/logic/optimizer/memo.cpp | 24 +++ src/stewkk/sql/logic/optimizer/optimizer.cpp | 56 +++++- .../sql/logic/optimizer/optimizer_test.cpp | 19 ++ .../sql/logic/optimizer/reachability.cpp | 22 +++ src/stewkk/sql/logic/optimizer/rule.cpp | 5 +- src/stewkk/sql/logic/optimizer/rule_test.cpp | 187 +++++++++++++++++- src/stewkk/sql/logic/optimizer/rules.cpp | 9 +- .../sql/logic/optimizer/rules_applier.cpp | 12 +- .../logic/optimizer/rules_applier_test.cpp | 9 +- .../sql/logic/optimizer/schema_catalog.cpp | 89 +++++++++ .../aggregation_join_transpose.cpp | 114 +++++++++++ .../aggregation_pushdown_through_join.cpp | 113 +++++++++++ .../cross_join_to_join.cpp | 36 ++++ .../filter_lift_through_join.cpp | 57 ++++++ .../transformation_rules/filter_merge.cpp | 4 +- .../filter_pushdown_through_join.cpp | 4 +- .../filter_pushdown_through_projection.cpp | 29 +-- .../transformation_rules/filter_split.cpp | 4 +- .../filter_to_join_predicate.cpp | 44 +++++ .../transformation_rules/in_to_or_chain.cpp | 4 +- .../join_associativity.cpp | 4 +- .../join_commutativity.cpp | 4 +- .../outer_join_to_inner.cpp | 56 ++++++ .../transformation_rules/predicate_utils.cpp | 71 +++++++ .../projection_pushdown_through_join.cpp | 109 ++++++++++ src/stewkk/sql/main.cpp | 3 +- 56 files changed, 1361 insertions(+), 84 deletions(-) create mode 100644 include/stewkk/sql/logic/transformation_rules/aggregation_join_transpose.hpp create mode 100644 include/stewkk/sql/logic/transformation_rules/aggregation_pushdown_through_join.hpp create mode 100644 include/stewkk/sql/logic/transformation_rules/cross_join_to_join.hpp create mode 100644 include/stewkk/sql/logic/transformation_rules/filter_lift_through_join.hpp create mode 100644 include/stewkk/sql/logic/transformation_rules/filter_to_join_predicate.hpp create mode 100644 include/stewkk/sql/logic/transformation_rules/outer_join_to_inner.hpp create mode 100644 include/stewkk/sql/logic/transformation_rules/projection_pushdown_through_join.hpp delete mode 100644 report/vkr.pdf create mode 100644 src/stewkk/sql/logic/transformation_rules/aggregation_join_transpose.cpp create mode 100644 src/stewkk/sql/logic/transformation_rules/aggregation_pushdown_through_join.cpp create mode 100644 src/stewkk/sql/logic/transformation_rules/cross_join_to_join.cpp create mode 100644 src/stewkk/sql/logic/transformation_rules/filter_lift_through_join.cpp create mode 100644 src/stewkk/sql/logic/transformation_rules/filter_to_join_predicate.cpp create mode 100644 src/stewkk/sql/logic/transformation_rules/outer_join_to_inner.cpp create mode 100644 src/stewkk/sql/logic/transformation_rules/projection_pushdown_through_join.cpp diff --git a/include/stewkk/sql/logic/executor/plan.hpp b/include/stewkk/sql/logic/executor/plan.hpp index 41b3a6f..821271e 100644 --- a/include/stewkk/sql/logic/executor/plan.hpp +++ b/include/stewkk/sql/logic/executor/plan.hpp @@ -120,6 +120,22 @@ struct PhysicalStreamAggregation { bool operator==(const PhysicalStreamAggregation&) const; }; +struct PhysicalPartialAggregation { + std::shared_ptr source; + std::vector group_by; + std::vector aggregates; + + bool operator==(const PhysicalPartialAggregation&) const; +}; + +struct PhysicalFinalAggregation { + std::shared_ptr source; + std::vector group_by; + std::vector aggregates; + + bool operator==(const PhysicalFinalAggregation&) const; +}; + struct PlanNodeMetadata { std::int64_t cardinality = 0; std::int64_t local_cost = 0; @@ -127,7 +143,7 @@ struct PlanNodeMetadata { bool operator==(const PlanNodeMetadata&) const = default; }; -using PhysicalPlanAlternative = std::variant; +using PhysicalPlanAlternative = std::variant; struct PhysicalPlanNode { PhysicalPlanAlternative node; diff --git a/include/stewkk/sql/logic/optimizer/logical_expr.hpp b/include/stewkk/sql/logic/optimizer/logical_expr.hpp index 8e2af14..43f681d 100644 --- a/include/stewkk/sql/logic/optimizer/logical_expr.hpp +++ b/include/stewkk/sql/logic/optimizer/logical_expr.hpp @@ -34,6 +34,22 @@ struct Aggregation { bool operator==(const Aggregation&) const = default; }; +struct PartialAggregation { + utils::NotNull source; + std::vector group_by; + std::vector aggregates; + + bool operator==(const PartialAggregation&) const = default; +}; + +struct FinalAggregation { + utils::NotNull source; + std::vector group_by; + std::vector aggregates; + + bool operator==(const FinalAggregation&) const = default; +}; + struct CrossJoin { utils::NotNull lhs; utils::NotNull rhs; @@ -55,6 +71,7 @@ struct Join { struct LogicalExpr { std::variant root_operator; utils::NotNull group; }; diff --git a/include/stewkk/sql/logic/optimizer/optimizer.hpp b/include/stewkk/sql/logic/optimizer/optimizer.hpp index c5ecc04..bd5fb2c 100644 --- a/include/stewkk/sql/logic/optimizer/optimizer.hpp +++ b/include/stewkk/sql/logic/optimizer/optimizer.hpp @@ -27,7 +27,8 @@ class Optimizer { public: Optimizer(const Operator& expr, Rules&& rules, CardinalityEstimates cardinality, SchemaCatalog schema, - PropertySet required = PropertySet::Any()); + PropertySet required = PropertySet::Any(), + ConstraintCatalog constraints = {}); PhysicalPlanNode Optimize(); PhysicalPlanNode OptimizeExhaustive(); @@ -69,6 +70,7 @@ class Optimizer { utils::NotNull root_; CardinalityEstimates cardinality_; SchemaCatalog schema_; + ConstraintCatalog constraints_; PropertySet global_required_; std::unordered_map local_cost_; std::unordered_map winner_; diff --git a/include/stewkk/sql/logic/optimizer/physical_expr.hpp b/include/stewkk/sql/logic/optimizer/physical_expr.hpp index 9dd2dba..17d6266 100644 --- a/include/stewkk/sql/logic/optimizer/physical_expr.hpp +++ b/include/stewkk/sql/logic/optimizer/physical_expr.hpp @@ -103,6 +103,22 @@ struct StreamAggregation { bool operator==(const StreamAggregation&) const = default; }; +struct PartialAggregation { + utils::NotNull source; + std::vector group_by; + std::vector aggregates; + + bool operator==(const PartialAggregation&) const = default; +}; + +struct FinalAggregation { + utils::NotNull source; + std::vector group_by; + std::vector aggregates; + + bool operator==(const FinalAggregation&) const = default; +}; + } // namespace physical struct PhysicalExpr { @@ -110,6 +126,7 @@ struct PhysicalExpr { physical::NestedLoopJoin, physical::NestedLoopCrossJoin, physical::HashJoin, physical::MergeJoin, physical::Sort, physical::Aggregation, physical::StreamAggregation, + physical::PartialAggregation, physical::FinalAggregation, physical::IndexSeek> root_operator; utils::NotNull group; bool is_enforcer = false; diff --git a/include/stewkk/sql/logic/optimizer/rule.hpp b/include/stewkk/sql/logic/optimizer/rule.hpp index 872c2f6..fa074cc 100644 --- a/include/stewkk/sql/logic/optimizer/rule.hpp +++ b/include/stewkk/sql/logic/optimizer/rule.hpp @@ -6,19 +6,25 @@ #include #include #include +#include namespace stewkk::sql { class Memo; +struct RuleContext { + SchemaCatalog& schema; + ConstraintCatalog& constraints; +}; + class TransformationRule { public: - virtual bool IsApplicable(utils::NotNull expr) = 0; - utils::NotNull Apply(utils::NotNull expr, Memo& memo); + virtual bool IsApplicable(utils::NotNull expr, RuleContext& ctx) = 0; + utils::NotNull Apply(utils::NotNull expr, Memo& memo, RuleContext& ctx); virtual ~TransformationRule() = default; private: - virtual LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo) = 0; + virtual LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo, RuleContext& ctx) = 0; }; class ImplementationRule { diff --git a/include/stewkk/sql/logic/optimizer/rules.hpp b/include/stewkk/sql/logic/optimizer/rules.hpp index 8b9a31c..fc336f8 100644 --- a/include/stewkk/sql/logic/optimizer/rules.hpp +++ b/include/stewkk/sql/logic/optimizer/rules.hpp @@ -11,6 +11,13 @@ #include #include #include +#include +#include +#include +#include +#include +#include +#include #include #include #include @@ -29,7 +36,7 @@ struct Rules { std::array, NImplementation> implementation_rules; }; -Rules<7, 9> MakeMainRules(IndexCatalog indexes = {}); +Rules<14, 9> MakeMainRules(IndexCatalog indexes = {}); Rules<0, 6> MakeNaiveRules(); } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/rules_applier.hpp b/include/stewkk/sql/logic/optimizer/rules_applier.hpp index 4d0bbf7..8834ecc 100644 --- a/include/stewkk/sql/logic/optimizer/rules_applier.hpp +++ b/include/stewkk/sql/logic/optimizer/rules_applier.hpp @@ -22,8 +22,11 @@ class RulesApplier { public: explicit RulesApplier(Rules rules); - bool IsApplicable(TransformationRuleId rule, utils::NotNull expr); - utils::NotNull Apply(TransformationRuleId rule, utils::NotNull expr, Memo& memo); + bool IsApplicable(TransformationRuleId rule, utils::NotNull expr, + RuleContext& ctx); + utils::NotNull Apply(TransformationRuleId rule, + utils::NotNull expr, Memo& memo, + RuleContext& ctx); bool IsApplicable(ImplementationRuleId rule, utils::NotNull expr); std::vector> Apply(ImplementationRuleId rule, utils::NotNull expr, Memo& memo); diff --git a/include/stewkk/sql/logic/optimizer/schema_catalog.hpp b/include/stewkk/sql/logic/optimizer/schema_catalog.hpp index b7f5528..c5c1e5e 100644 --- a/include/stewkk/sql/logic/optimizer/schema_catalog.hpp +++ b/include/stewkk/sql/logic/optimizer/schema_catalog.hpp @@ -2,6 +2,7 @@ #include #include +#include #include #include #include @@ -21,6 +22,18 @@ struct IndexInfo { std::string file; }; +struct UniqueKeyInfo { + std::string table; + std::string column; +}; + +struct ForeignKeyInfo { + std::string from_table; + std::string from_column; + std::string to_table; + std::string to_column; +}; + class IndexCatalog { public: IndexCatalog(std::vector indexes = {}); @@ -31,12 +44,27 @@ class IndexCatalog { std::vector indexes_; }; +class ConstraintCatalog { +public: + ConstraintCatalog(std::vector unique_keys = {}, + std::vector foreign_keys = {}); + + bool IsUnique(const Attribute& attr) const; + bool HasForeignKey(const Attribute& from, const Attribute& to) const; + +private: + std::vector unique_keys_; + std::vector foreign_keys_; +}; + class SchemaCatalog { public: SchemaCatalog(std::unordered_map tables = {}); Schema GetSchema(utils::NotNull group); std::int64_t GetWidth(utils::NotNull group); + std::optional ResolveBaseAttribute(const Attribute& attr, + utils::NotNull group); private: Schema Derive(const LogicalOperator& op); @@ -49,6 +77,8 @@ SchemaCatalog LoadSchemaFromCsvDir(const std::filesystem::path& dir); IndexCatalog LoadIndexCatalogFromCsvDir(const std::filesystem::path& dir); +ConstraintCatalog LoadConstraintCatalogFromCsvDir(const std::filesystem::path& dir); + std::unordered_map LoadTableSizesFromCsvDir( const std::filesystem::path& dir); diff --git a/include/stewkk/sql/logic/transformation_rules/aggregation_join_transpose.hpp b/include/stewkk/sql/logic/transformation_rules/aggregation_join_transpose.hpp new file mode 100644 index 0000000..25e17c9 --- /dev/null +++ b/include/stewkk/sql/logic/transformation_rules/aggregation_join_transpose.hpp @@ -0,0 +1,13 @@ +#pragma once + +#include + +namespace stewkk::sql { + +class AggregationJoinTranspose : public TransformationRule { + public: + bool IsApplicable(utils::NotNull expr, RuleContext& ctx) override; + LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo, RuleContext& ctx) override; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/transformation_rules/aggregation_pushdown_through_join.hpp b/include/stewkk/sql/logic/transformation_rules/aggregation_pushdown_through_join.hpp new file mode 100644 index 0000000..c29e77a --- /dev/null +++ b/include/stewkk/sql/logic/transformation_rules/aggregation_pushdown_through_join.hpp @@ -0,0 +1,13 @@ +#pragma once + +#include + +namespace stewkk::sql { + +class AggregationPushdownThroughJoin : public TransformationRule { + public: + bool IsApplicable(utils::NotNull expr, RuleContext& ctx) override; + LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo, RuleContext& ctx) override; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/transformation_rules/cross_join_to_join.hpp b/include/stewkk/sql/logic/transformation_rules/cross_join_to_join.hpp new file mode 100644 index 0000000..a54ece0 --- /dev/null +++ b/include/stewkk/sql/logic/transformation_rules/cross_join_to_join.hpp @@ -0,0 +1,13 @@ +#pragma once + +#include + +namespace stewkk::sql { + +class CrossJoinToJoin : public TransformationRule { + public: + bool IsApplicable(utils::NotNull expr, RuleContext& ctx) override; + LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo, RuleContext& ctx) override; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/transformation_rules/filter_lift_through_join.hpp b/include/stewkk/sql/logic/transformation_rules/filter_lift_through_join.hpp new file mode 100644 index 0000000..0f61ec2 --- /dev/null +++ b/include/stewkk/sql/logic/transformation_rules/filter_lift_through_join.hpp @@ -0,0 +1,13 @@ +#pragma once + +#include + +namespace stewkk::sql { + +class FilterLiftThroughJoin : public TransformationRule { + public: + bool IsApplicable(utils::NotNull expr, RuleContext& ctx) override; + LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo, RuleContext& ctx) override; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/transformation_rules/filter_merge.hpp b/include/stewkk/sql/logic/transformation_rules/filter_merge.hpp index dbbf035..e330c79 100644 --- a/include/stewkk/sql/logic/transformation_rules/filter_merge.hpp +++ b/include/stewkk/sql/logic/transformation_rules/filter_merge.hpp @@ -7,8 +7,8 @@ namespace stewkk::sql { class FilterMerge : public TransformationRule { public: - bool IsApplicable(utils::NotNull expr) override; - LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo) override; + bool IsApplicable(utils::NotNull expr, RuleContext& ctx) override; + LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo, RuleContext& ctx) override; }; } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/transformation_rules/filter_pushdown_through_join.hpp b/include/stewkk/sql/logic/transformation_rules/filter_pushdown_through_join.hpp index 9db2b4f..80d8c08 100644 --- a/include/stewkk/sql/logic/transformation_rules/filter_pushdown_through_join.hpp +++ b/include/stewkk/sql/logic/transformation_rules/filter_pushdown_through_join.hpp @@ -7,8 +7,8 @@ namespace stewkk::sql { class FilterPushdownThroughJoin : public TransformationRule { public: - bool IsApplicable(utils::NotNull expr) override; - LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo) override; + bool IsApplicable(utils::NotNull expr, RuleContext& ctx) override; + LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo, RuleContext& ctx) override; }; } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.hpp b/include/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.hpp index b22944d..a74d6f2 100644 --- a/include/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.hpp +++ b/include/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.hpp @@ -7,8 +7,8 @@ namespace stewkk::sql { class FilterPushdownThroughProjection : public TransformationRule { public: - bool IsApplicable(utils::NotNull expr) override; - LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo) override; + bool IsApplicable(utils::NotNull expr, RuleContext& ctx) override; + LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo, RuleContext& ctx) override; }; } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/transformation_rules/filter_split.hpp b/include/stewkk/sql/logic/transformation_rules/filter_split.hpp index 5eba6d8..7e36f8b 100644 --- a/include/stewkk/sql/logic/transformation_rules/filter_split.hpp +++ b/include/stewkk/sql/logic/transformation_rules/filter_split.hpp @@ -7,8 +7,8 @@ namespace stewkk::sql { class FilterSplit : public TransformationRule { public: - bool IsApplicable(utils::NotNull expr) override; - LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo) override; + bool IsApplicable(utils::NotNull expr, RuleContext& ctx) override; + LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo, RuleContext& ctx) override; }; } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/transformation_rules/filter_to_join_predicate.hpp b/include/stewkk/sql/logic/transformation_rules/filter_to_join_predicate.hpp new file mode 100644 index 0000000..ddd6de7 --- /dev/null +++ b/include/stewkk/sql/logic/transformation_rules/filter_to_join_predicate.hpp @@ -0,0 +1,13 @@ +#pragma once + +#include + +namespace stewkk::sql { + +class FilterToJoinPredicate : public TransformationRule { + public: + bool IsApplicable(utils::NotNull expr, RuleContext& ctx) override; + LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo, RuleContext& ctx) override; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/transformation_rules/in_to_or_chain.hpp b/include/stewkk/sql/logic/transformation_rules/in_to_or_chain.hpp index bbcd362..ae0c809 100644 --- a/include/stewkk/sql/logic/transformation_rules/in_to_or_chain.hpp +++ b/include/stewkk/sql/logic/transformation_rules/in_to_or_chain.hpp @@ -7,8 +7,8 @@ namespace stewkk::sql { class InToOrChain : public TransformationRule { public: - bool IsApplicable(utils::NotNull expr) override; - LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo) override; + bool IsApplicable(utils::NotNull expr, RuleContext& ctx) override; + LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo, RuleContext& ctx) override; }; } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/transformation_rules/join_associativity.hpp b/include/stewkk/sql/logic/transformation_rules/join_associativity.hpp index b4a2281..00c3f9c 100644 --- a/include/stewkk/sql/logic/transformation_rules/join_associativity.hpp +++ b/include/stewkk/sql/logic/transformation_rules/join_associativity.hpp @@ -7,8 +7,8 @@ namespace stewkk::sql { class JoinAssociativity : public TransformationRule { public: - bool IsApplicable(utils::NotNull expr) override; - LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo) override; + bool IsApplicable(utils::NotNull expr, RuleContext& ctx) override; + LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo, RuleContext& ctx) override; }; } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/transformation_rules/join_commutativity.hpp b/include/stewkk/sql/logic/transformation_rules/join_commutativity.hpp index ccaf13b..3004be5 100644 --- a/include/stewkk/sql/logic/transformation_rules/join_commutativity.hpp +++ b/include/stewkk/sql/logic/transformation_rules/join_commutativity.hpp @@ -7,8 +7,8 @@ namespace stewkk::sql { class JoinCommutativity : public TransformationRule { public: - bool IsApplicable(utils::NotNull expr) override; - LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo) override; + bool IsApplicable(utils::NotNull expr, RuleContext& ctx) override; + LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo, RuleContext& ctx) override; }; } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/transformation_rules/outer_join_to_inner.hpp b/include/stewkk/sql/logic/transformation_rules/outer_join_to_inner.hpp new file mode 100644 index 0000000..0465da6 --- /dev/null +++ b/include/stewkk/sql/logic/transformation_rules/outer_join_to_inner.hpp @@ -0,0 +1,13 @@ +#pragma once + +#include + +namespace stewkk::sql { + +class OuterJoinToInner : public TransformationRule { + public: + bool IsApplicable(utils::NotNull expr, RuleContext& ctx) override; + LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo, RuleContext& ctx) override; +}; + +} // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/transformation_rules/predicate_utils.hpp b/include/stewkk/sql/logic/transformation_rules/predicate_utils.hpp index a36f9ea..fce9ada 100644 --- a/include/stewkk/sql/logic/transformation_rules/predicate_utils.hpp +++ b/include/stewkk/sql/logic/transformation_rules/predicate_utils.hpp @@ -18,8 +18,14 @@ Expression AndConjuncts(const std::vector& conjs); void CollectAttrTables(const Expression& e, std::unordered_set& out); +void CollectAttributes(const Expression& e, std::vector& out); + std::unordered_set ExprTables(const Expression& e); std::unordered_set GroupTables(utils::NotNull g); +bool ExprUsesOnlyTables(const Expression& e, const std::unordered_set& tables); + +bool IsNullRejectingForTables(const Expression& e, const std::unordered_set& tables); + } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/transformation_rules/projection_pushdown_through_join.hpp b/include/stewkk/sql/logic/transformation_rules/projection_pushdown_through_join.hpp new file mode 100644 index 0000000..2c041b6 --- /dev/null +++ b/include/stewkk/sql/logic/transformation_rules/projection_pushdown_through_join.hpp @@ -0,0 +1,13 @@ +#pragma once + +#include + +namespace stewkk::sql { + +class ProjectionPushdownThroughJoin : public TransformationRule { + public: + bool IsApplicable(utils::NotNull expr, RuleContext& ctx) override; + LogicalOperator ApplyImpl(utils::NotNull expr, Memo& memo, RuleContext& ctx) override; +}; + +} // namespace stewkk::sql diff --git a/report/vkr.pdf b/report/vkr.pdf deleted file mode 100644 index 2682c3f8f2b5000e1d54c27b1a82768541be8a44..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1081868 zcmeFYbyQnl_9))=Q>92-ifao+N{hRtlmf+z26qeY5Fn&2R=jv|D=xt;SP2k3xJz+& zmxN#F%)B?h^=7U2U2m4W534pu&#SN+3n<2c;3?6h`9 zmN>$~IBcq5dsA9A4MihMQ)6cwHWe2m=RYlyc8)eUZ13OOxzlR1WBYTk|M}_Su-!oZ 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ssb_interpreted = ssb[ssb.executor == \"Interpreted\"].copy()\n", "\n", @@ -975,21 +234,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "c36bdd23", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "speedup = (wide[\"Naive\"] / wide[\"Optimized\"]).sort_index()\n", "ax = speedup.plot.bar(figsize=(14, 4))\n", @@ -1002,21 +250,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "4ee5fc7c", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "jit = synthetic[synthetic[\"mode\"] == \"Optimized\"].copy()\n", "jit[\"executor\"] = jit[\"executor\"].replace({\n", @@ -1032,21 +269,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "bbe140d5", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(9, 5))\n", "for name, group in matched.groupby(\"operator\"):\n", @@ -1093,120 +308,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "7b508249", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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operatorcoefficientvalue
0SeqScanrow100
1Filteroutput row100
2Projectionoutput row22
3Sortn * (floor(log2(n)) + 1)11
4Aggregationinput row510
5NestedLoopJoininput row pair70
6NestedLoopCrossJoininput row pair104
7HashJoinbuild row * width100
8HashJoinprobe row35
9HashJoinoutput row * width10
\n", - "
" - ], - "text/plain": [ - " operator coefficient value\n", - "0 SeqScan row 100\n", - "1 Filter output row 100\n", - "2 Projection output row 22\n", - "3 Sort n * (floor(log2(n)) + 1) 11\n", - "4 Aggregation input row 510\n", - "5 NestedLoopJoin input row pair 70\n", - "6 NestedLoopCrossJoin input row pair 104\n", - "7 HashJoin build row * width 100\n", - "8 HashJoin probe row 35\n", - "9 HashJoin output row * width 10" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "coefficients = pd.DataFrame([\n", " (\"SeqScan\", \"row\", 100),\n", diff --git a/src/stewkk/sql/logic/executor/executor.cpp b/src/stewkk/sql/logic/executor/executor.cpp index 0d25273..631af91 100644 --- a/src/stewkk/sql/logic/executor/executor.cpp +++ b/src/stewkk/sql/logic/executor/executor.cpp @@ -4,6 +4,7 @@ #include #include #include +#include #include #include @@ -14,6 +15,7 @@ #include #include +#include #include namespace stewkk::sql { @@ -1183,6 +1185,21 @@ int CompareNonNullValues(const Value& lhs, const Value& rhs, Type type) { std::unreachable(); } +std::optional FindHashJoinKey(const Expression& qual) { + std::vector conjuncts; + CollectConjuncts(qual, conjuncts); + for (const auto& conjunct : conjuncts) { + const auto* bin = std::get_if(&conjunct); + if (!bin || bin->binop != BinaryOp::kEq) continue; + if (!std::holds_alternative(*bin->lhs) + || !std::holds_alternative(*bin->rhs)) { + continue; + } + return *bin; + } + return std::nullopt; +} + } // namespace @@ -1199,15 +1216,12 @@ boost::asio::awaitable Executor::ExecuteHashJoin( if (join.type != JoinType::kInner) { throw std::logic_error{"HashJoin executor supports only Inner joins"}; } - const auto* bin = std::get_if(&join.qual); - if (!bin || bin->binop != BinaryOp::kEq) { - throw std::logic_error{"HashJoin qual must be an equality"}; + const auto bin = FindHashJoinKey(join.qual); + if (!bin) { + throw std::logic_error{"HashJoin qual must contain an equality between attributes"}; } const auto* a = std::get_if(bin->lhs.get()); const auto* b = std::get_if(bin->rhs.get()); - if (!a || !b) { - throw std::logic_error{"HashJoin qual must be `attr = attr`"}; - } auto exec = co_await boost::asio::this_coro::executor; auto [lhs_attrs_chan, lhs_tuples_chan] = co_await GetChannels(); @@ -1237,6 +1251,11 @@ boost::asio::awaitable Executor::ExecuteHashJoin( AttributesInfo out_attrs = lhs_attrs; std::ranges::copy(rhs_attrs, std::back_inserter(out_attrs)); + if (GetExpressionType(join.qual, out_attrs) != Type::kBool) { + throw std::logic_error{"hash join qual should return bool"}; + } + auto qual_executor = co_await expression_executor_.GetExpressionExecutor(join.qual, out_attrs); + co_await attr_chan.async_send(boost::system::error_code{}, out_attrs, boost::asio::use_awaitable); attr_chan.close(); @@ -1268,7 +1287,11 @@ boost::asio::awaitable Executor::ExecuteHashJoin( if (k.is_null) continue; auto range = build.equal_range(k.value.int_value); for (auto it = range.first; it != range.second; ++it) { - out_buf.push_back(ConcatTuples(it->second, rt)); + auto joined_tuple = ConcatTuples(it->second, rt); + if (!ApplyFilter(joined_tuple, out_attrs, qual_executor)) { + continue; + } + out_buf.push_back(std::move(joined_tuple)); if (out_buf.size() == kBufSize) { co_await tuples_chan.async_send(boost::system::error_code{}, std::move(out_buf), boost::asio::use_awaitable); diff --git a/src/stewkk/sql/logic/implementation_rules/implement_hash_join.cpp b/src/stewkk/sql/logic/implementation_rules/implement_hash_join.cpp index 012a1cd..59fb501 100644 --- a/src/stewkk/sql/logic/implementation_rules/implement_hash_join.cpp +++ b/src/stewkk/sql/logic/implementation_rules/implement_hash_join.cpp @@ -1,5 +1,10 @@ #include +#include +#include + +#include + namespace stewkk::sql { namespace { @@ -11,13 +16,19 @@ bool IsSimpleEquiJoin(const Expression& qual) { && std::holds_alternative(*bin->rhs); } +bool HasEquiJoinConjunct(const Expression& qual) { + std::vector conjuncts; + CollectConjuncts(qual, conjuncts); + return std::ranges::any_of(conjuncts, IsSimpleEquiJoin); +} + } // namespace bool ImplementHashJoin::IsApplicable(utils::NotNull expr) { if (!std::holds_alternative(expr->root_operator)) return false; const auto& join = std::get(expr->root_operator); if (join.type != JoinType::kInner) return false; - return IsSimpleEquiJoin(join.qual); + return HasEquiJoinConjunct(join.qual); } std::vector> ImplementHashJoin::Apply(utils::NotNull expr, Memo&) { diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index 86bbeb4..8ca0db6 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -16,6 +16,7 @@ #include #include #include +#include namespace stewkk::sql { @@ -43,10 +44,10 @@ bool AttrInSchema(const Attribute& attr, const Schema& schema) { }); } -std::optional ResolveJoinKeys(const Expression& qual, - utils::NotNull lhs, - utils::NotNull rhs, - SchemaCatalog& schema) { +std::optional ResolveSimpleJoinKeys(const Expression& qual, + utils::NotNull lhs, + utils::NotNull rhs, + SchemaCatalog& schema) { const auto* bin = std::get_if(&qual); if (!bin || bin->binop != BinaryOp::kEq) return std::nullopt; const auto* a = std::get_if(bin->lhs.get()); @@ -70,6 +71,19 @@ std::optional ResolveJoinKeys(const Expression& qual, return std::nullopt; } +std::optional ResolveJoinKeys(const Expression& qual, + utils::NotNull lhs, + utils::NotNull rhs, + SchemaCatalog& schema) { + std::vector conjuncts; + CollectConjuncts(qual, conjuncts); + for (const auto& conjunct : conjuncts) { + auto keys = ResolveSimpleJoinKeys(conjunct, lhs, rhs, schema); + if (keys) return keys; + } + return std::nullopt; +} + PropertySet SortOn(const Attribute& attr) { return PropertySet{SortProperty{SortOrder{{SortKey{attr.table, attr.name, Direction::kAsc}}}}}; } diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index be8360a..cbb9f75 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -282,6 +282,29 @@ TEST(OptimizerTest, PushesSelectiveFilterIntoHashBuildSide) { " (SeqScan lineorder lo))")); } +TEST(OptimizerTest, UsesHashJoinForConjunctiveEquiJoinPredicate) { + std::stringstream s{ + "SELECT * FROM lineorder AS lo " + "JOIN supplier AS s ON lo.suppkey = s.id AND s.region = 'AMERICA';"}; + Operator op = GetAST(s).value().op; + SchemaCatalog schema({ + {"lineorder", {Attribute{"lineorder", "suppkey"}, Attribute{"lineorder", "value"}}}, + {"supplier", {Attribute{"supplier", "id"}, Attribute{"supplier", "region"}}}, + }); + Optimizer optimizer(op, MakeMainRules(), CardinalityEstimates({ + {"lineorder", 6000}, + {"supplier", 100}, + }), std::move(schema)); + + auto got = optimizer.Optimize(); + + ASSERT_THAT( + Serialize(got), + Eq("(HashJoin Inner (and (= (attr lo suppkey) (attr s id))" + " (= (attr s region) (str \"AMERICA\")))" + " (SeqScan supplier s) (SeqScan lineorder lo))")); +} + TEST(OptimizerTest, UsesIndexSeekForIndexedIntegerPredicate) { std::stringstream s{"SELECT * FROM users WHERE users.id = 8;"}; Operator op = GetAST(s).value().op; diff --git a/src/stewkk/sql/logic/transformation_rules/filter_to_join_predicate.cpp b/src/stewkk/sql/logic/transformation_rules/filter_to_join_predicate.cpp index a3e14d3..447caf6 100644 --- a/src/stewkk/sql/logic/transformation_rules/filter_to_join_predicate.cpp +++ b/src/stewkk/sql/logic/transformation_rules/filter_to_join_predicate.cpp @@ -1,8 +1,10 @@ #include +#include #include #include +#include #include namespace stewkk::sql { @@ -19,12 +21,35 @@ const logical::Join* FindInnerJoin(utils::NotNull source) { return nullptr; } +bool UsesBothJoinSides(const Expression& expr, const logical::Join& join) { + const auto tables = ExprTables(expr); + const auto lhs_tables = GroupTables(join.lhs); + const auto rhs_tables = GroupTables(join.rhs); + + bool uses_lhs = false; + bool uses_rhs = false; + for (const auto& table : tables) { + uses_lhs = uses_lhs || lhs_tables.contains(table); + uses_rhs = uses_rhs || rhs_tables.contains(table); + } + return uses_lhs && uses_rhs; +} + +bool HasCrossSideConjunct(const Expression& predicate, const logical::Join& join) { + std::vector conjs; + CollectConjuncts(predicate, conjs); + return std::ranges::any_of(conjs, [&](const Expression& conj) { + return UsesBothJoinSides(conj, join); + }); +} + } // namespace bool FilterToJoinPredicate::IsApplicable(utils::NotNull expr, RuleContext&) { if (!std::holds_alternative(expr->root_operator)) return false; const auto& filter = std::get(expr->root_operator); - return FindInnerJoin(filter.source) != nullptr; + const auto* join = FindInnerJoin(filter.source); + return join != nullptr && HasCrossSideConjunct(filter.predicate, *join); } LogicalOperator FilterToJoinPredicate::ApplyImpl(utils::NotNull expr, @@ -35,10 +60,26 @@ LogicalOperator FilterToJoinPredicate::ApplyImpl(utils::NotNull ex throw std::runtime_error{"FilterToJoinPredicate requires an inner join below filter"}; } - std::vector conjs; - CollectConjuncts(join->qual, conjs); - CollectConjuncts(filter.predicate, conjs); - return logical::Join{join->lhs, join->rhs, join->type, AndConjuncts(conjs)}; + std::vector join_conjs; + std::vector rest_conjs; + CollectConjuncts(join->qual, join_conjs); + + std::vector filter_conjs; + CollectConjuncts(filter.predicate, filter_conjs); + for (const auto& conj : filter_conjs) { + if (UsesBothJoinSides(conj, *join)) { + join_conjs.push_back(conj); + } else { + rest_conjs.push_back(conj); + } + } + + auto new_join = logical::Join{join->lhs, join->rhs, join->type, AndConjuncts(join_conjs)}; + if (rest_conjs.empty()) { + return new_join; + } + auto new_join_group = memo.AddGroup(new_join)->group; + return logical::Filter{new_join_group, AndConjuncts(rest_conjs)}; } } // namespace stewkk::sql From 906f1eed9e274ad3e3e92d5d786db21559099c63 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sun, 14 Jun 2026 20:57:23 +0300 Subject: [PATCH 110/120] Fix --- .../stewkk/sql/logic/optimizer/optimizer.hpp | 2 + report/vkr.tex | 218 +- .../operator-cost-matched.json | 1618 ++++++------- research/benchmark-results/operator-cost.json | 2138 ++++++++--------- research/benchmark-results/query.json | 412 ++-- research/benchmark-results/ssb-sf001.json | 1434 +++++------ research/benchmarks.ipynb | 1131 ++++++++- src/stewkk/sql/logic/optimizer/optimizer.cpp | 36 + 8 files changed, 3977 insertions(+), 3012 deletions(-) diff --git a/include/stewkk/sql/logic/optimizer/optimizer.hpp b/include/stewkk/sql/logic/optimizer/optimizer.hpp index bd5fb2c..ad9b974 100644 --- a/include/stewkk/sql/logic/optimizer/optimizer.hpp +++ b/include/stewkk/sql/logic/optimizer/optimizer.hpp @@ -46,6 +46,7 @@ class Optimizer { void OptimizeInputs(utils::NotNull expr, PropertySet required, std::vector child_delivered, int64_t accum, Limit limit, size_t child_index = 0); std::int64_t LowerBoundCost(utils::NotNull group); + bool MarkOptimizeGroupRequested(const WinnerKey& key, Limit limit); void ApplyRule(TransformationRuleId rule, utils::NotNull expr, Limit limit); @@ -75,6 +76,7 @@ class Optimizer { std::unordered_map local_cost_; std::unordered_map winner_; std::unordered_set enforcers_added_; + std::unordered_map optimize_group_limits_; std::unordered_map> group_parents_; std::unordered_map lower_bounds_; }; diff --git a/report/vkr.tex b/report/vkr.tex index 18a9dcb..1c87985 100644 --- a/report/vkr.tex +++ b/report/vkr.tex @@ -1338,7 +1338,7 @@ \subsubsection{Алгоритм поиска} накапливается полная стоимость и обновляется информация о лучшем плане. Параметр \texttt{limit} используется для отсечения заведомо дорогих вариантов. -\subsection{Правила трансформации и реализации}\label{sec:transformation_rules} +\subsubsection{Правила трансформации и реализации}\label{sec:transformation_rules} Правило в оптимизаторе Cascades состоит из образца, условия применимости и алгоритма применения. Образец описывает форму логического выражения, к которому @@ -1468,103 +1468,6 @@ \subsubsection{Оценка стоимости} построенного плана уже превышает текущую лучшую, дальнейшее исследование по этой ветке не имеет смысла. -\subsection{Дифференциальный анализ физических планов} - -Множество правил трансформации и реализации, используемых в -оптимизаторах запросов, изучено довольно хорошо и описано в -академической литературе. Вместе с тем условия активации правил -существенно различаются от системы к системе. Многие реализации -оптимизаторов упускают возможности для оптимизации из-за слишком -строгих условий активации правил или отсутствия определенных правил -в их наборе. - -Данная проблема особенно актуальна при разработке нового оптимизатора: после -реализации базового набора правил возникает вопрос о полноте и корректности -этого набора по сравнению с устоявшимися и проверенными временем промышленными -системами. Такие системы аккумулировали обратную связь от пользователей и -уточняли условия активации своих правил в течение многих лет. Не имея такого -преимущества сложно добиться отличного качества построенных планов. - -Решением может быть поиск несоответствий между поведением реализуемой системы и -внешней эталонной системы путем применения дифференциального анализа планов. - -Первым шагом нужно сформировать несколько наборов данных, состоящих из схем -таблиц и их наполнения. Разные данные позволяют всесторонне исследовать правила -активации, потому что они зависят от кардинальности отношений, наличия индексов -и т.п. - -Далее, по заданным схемам генерируются случайные SQL-запросы различной структуры -и сложности. Такой подход исключает тривиальные планы и приближает условия -работы оптимизатора к реальным нагрузкам при дальнейшем построении планов -запросов. - -Следующим шагом сгенерированные запросы транспилируются в диалекты SQL целевых -промышленных СУБД. В качестве эталона рассматривается Microsoft SQL Server. Эта -система является зрелой и имеет машиночитаемый формат вывода планов. Затем для -получения физических планов исполняется аналог \texttt{EXPLAIN ANALYZE}. - -Полученные физические планы промышленных СУБД конвертируются в формат -сериализованного плана разрабатываемого оптимизатора. Современные СУБД -предоставляют вывод планов в виде XML или JSON, что позволяет легко реализовать -их разбор. Для конвертации используется отображение между физическими -операторами разрабатываемой системы и операторами эталонной СУБД. Например, -\texttt{Hash Match} в SQL Server отображается в \HashJoin{} в разрабатываемой -системе, \texttt{Clustered Index Scan} --- в \IndexScan{} и так далее. -Восстановление предикатов, ключей соединения и выражений проекции выполняется -путем разбора соответствующих полей плана, причем не нужно поддерживать весь -синтаксис предикатов, так как генератор запросов и схемы данных ограничивают -множество допустимых выражений в планах. - -Корректность конвертации выполняется по построению, но дополнительно проверяется -путем исполнения исходного плана на внешней системе и сконвертированного плана -на разрабатываемой системе с последующим сравнением полученных результатов при -фиксированной схеме и различных вариантах сгенерированных для нее данных. - -Конвертированные планы сравниваются с планами, порождаемыми разрабатываемым -оптимизатором на исходных запросах, с целью определить, мог ли существующий -набор правил разрабатываемого оптимизатора породить план, эквивалентный плану -промышленной системы. Стоимостные модели и реализации операторов в разных СУБД -различаются, поэтому план, оптимальный для эталонной системы, не обязательно -будет оптимальным для разрабатываемой. По этоу причине сравнение направлено на -исследование полноты пространства поиска разрабатываемого оптимизатора, и не -предполагает сравнение эффективности планов. - -Для корректности структурного сравнения необходимо полное исследование -пространства эквивалентных планов. Определение требуемых изменений лежит на -разработчике системы, поэтому найденный запрос и примеры планов сохраняются для -последующего анализа. В дальнейшем, полученные после конвертации ``эталонные'' -планы можно использовать как тесты для оптимизатора, которые будут успешно -выполняться после уточнения соответвующих правил активации. - -В случае невозможности построить ``эталонный'' план разрабатываемым -оптимизатором, предлагается искать ближайший план, т.е. план из пространства -поиска разрабатываемого оптимизатора, максимально близкий к плану промышленной -системы. При этом метрика расстояния между планами может быть определена как -расстояние редактирования деревьев. - -Анализ ближайшего плана может помочь упростить выявление трансформаций, которые -разрабатываемый план по какой-то причине не применил. - -Предлагаемый метод дифференциального анализа применим для сравнения как с -проприетарными системами, так и с СУБД с открытым исходным кодом, позволяя -уточнять условия применения правил. - -Систематическое применение этого подхода позволяет построить итеративный процесс -совершенствования оптимизатора: - -\begin{itemize} - \item генерация запросов; - \item запуск и сбор планов; - \item приведение к единому виду, сравнение и выявление расхождений; - \item добавление или корректировка правил; - \item повторное сравнение. -\end{itemize} - -Таким образом, дифференциальный анализ обеспечивает управляемое и измеримое -улучшение качества оптимизатора и позволяет новым или открытым оптимизаторам -быстрее достигать качества устоявшихся промышленных оптимизаторов. - -% {\color{red} TODO: описать как нормализовать и т.п.} \section{Конструкторская часть} @@ -1594,6 +1497,8 @@ \section{Конструкторская часть} постепенное улучшение качества итоговых планов путем уточнения и расширения пространства поиска. + + \subsection{Модули синтаксического анализа и построения логического представления} Модуль синтаксического анализа SQL-запросов отвечает за лексический и @@ -1779,6 +1684,123 @@ \subsection{Модуль стоимостной оптимизации план альтернативами. Результатом работы модуля является дерево физических операторов, которое передается в модуль исполнения. +\subsection{Дифференциальный анализ физических планов} + +Множество правил трансформации и реализации, используемых в +оптимизаторах запросов, изучено довольно хорошо и описано в +академической литературе. Вместе с тем условия активации правил +существенно различаются от системы к системе. Многие реализации +оптимизаторов упускают возможности для оптимизации из-за слишком +строгих условий активации правил или отсутствия определенных правил +в их наборе. + +Данная проблема особенно актуальна при разработке нового оптимизатора: после +реализации базового набора правил возникает вопрос о полноте и корректности +этого набора по сравнению с устоявшимися и проверенными временем промышленными +системами. Такие системы аккумулировали обратную связь от пользователей и +уточняли условия активации своих правил в течение многих лет. Не имея такого +преимущества сложно добиться отличного качества построенных планов. + +Решением может быть поиск несоответствий между поведением реализуемой системы и +внешней эталонной системы путем применения дифференциального анализа планов. + +Первым шагом нужно сформировать несколько наборов данных, состоящих из схем +таблиц и их наполнения. Разные данные позволяют всесторонне исследовать правила +активации, потому что они зависят от кардинальности отношений, наличия индексов +и т.п. + +Далее, по заданным схемам генерируются случайные SQL-запросы различной структуры +и сложности. Такой подход исключает тривиальные планы и приближает условия +работы оптимизатора к реальным нагрузкам при дальнейшем построении планов +запросов. + +Следующим шагом сгенерированные запросы транспилируются в диалекты SQL целевых +промышленных СУБД. В качестве эталона рассматривается Microsoft SQL Server. Эта +система является зрелой и имеет машиночитаемый формат вывода планов. Затем для +получения физических планов исполняется аналог \texttt{EXPLAIN ANALYZE}. + +Полученные физические планы промышленных СУБД конвертируются в формат +сериализованного плана разрабатываемого оптимизатора. Современные СУБД +предоставляют вывод планов в виде XML или JSON, что позволяет легко реализовать +их разбор. Для конвертации используется отображение между физическими +операторами разрабатываемой системы и операторами эталонной СУБД. Например, +\texttt{Hash Match} в SQL Server отображается в \HashJoin{} в разрабатываемой +системе, \texttt{Clustered Index Scan} --- в \IndexScan{} и так далее. +Восстановление предикатов, ключей соединения и выражений проекции выполняется +путем разбора соответствующих полей плана, причем не нужно поддерживать весь +синтаксис предикатов, так как генератор запросов и схемы данных ограничивают +множество допустимых выражений в планах. + +Для сериализации физических планов было решено использовать s-выражения, похожие +на выражения языка LISP. Этот формат прост с точки зрения реализации парсера, а +также удобен для чтения и изменения человеком. Пример сериализованного плана для +запроса \texttt{SELECT * FROM users WHERE users.age BETWEEN 18 AND 30} приведен +на листинге~\ref{lst:serialized-plan}. + +\begin{listing}[H] + \caption{Пример сериализованного плана.} + \label{lst:serialized-plan} + \begin{minted}[style=bw, breaklines, frame=single, fontsize = \footnotesize, linenos=false, xleftmargin = 1.5em]{text} +(PhysicalFilter + (and (>= (attr users age) 18) + (<= (attr users age) 30)) + (SeqScan users)) + \end{minted} +\end{listing} + +Корректность конвертации выполняется по построению, но дополнительно проверяется +путем исполнения исходного плана на внешней системе и сконвертированного плана +на разрабатываемой системе с последующим сравнением полученных результатов при +фиксированной схеме и различных вариантах сгенерированных для нее данных. + +Конвертированные планы сравниваются с планами, порождаемыми разрабатываемым +оптимизатором на исходных запросах, с целью определить, мог ли существующий +набор правил разрабатываемого оптимизатора породить план, эквивалентный плану +промышленной системы. Стоимостные модели и реализации операторов в разных СУБД +различаются, поэтому план, оптимальный для эталонной системы, не обязательно +будет оптимальным для разрабатываемой. По этоу причине сравнение направлено на +исследование полноты пространства поиска разрабатываемого оптимизатора, и не +предполагает сравнение эффективности планов. + +Для корректности структурного сравнения необходимо полное исследование +пространства эквивалентных планов. Определение требуемых изменений лежит на +разработчике системы, поэтому найденный запрос и примеры планов сохраняются для +последующего анализа. В дальнейшем, полученные после конвертации ``эталонные'' +планы можно использовать как тесты для оптимизатора, которые будут успешно +выполняться после уточнения соответвующих правил активации. + +В случае невозможности построить ``эталонный'' план разрабатываемым +оптимизатором, предлагается искать ближайший план, т.е. план из пространства +поиска разрабатываемого оптимизатора, максимально близкий к плану промышленной +системы. При этом метрика расстояния между планами может быть определена как +расстояние редактирования деревьев. + +Анализ ближайшего плана может помочь упростить выявление трансформаций, которые +разрабатываемый план по какой-то причине не применил. + +Предлагаемый метод дифференциального анализа применим для сравнения как с +проприетарными системами, так и с СУБД с открытым исходным кодом, позволяя +уточнять условия применения правил. + +Систематическое применение этого подхода позволяет построить итеративный процесс +совершенствования оптимизатора: + +\begin{itemize} + \item генерация запросов; + \item запуск и сбор планов; + \item приведение к единому виду, сравнение и выявление расхождений; + \item добавление или корректировка правил; + \item повторное сравнение. +\end{itemize} + +Таким образом, дифференциальный анализ обеспечивает управляемое и измеримое +улучшение качества оптимизатора и позволяет новым или открытым оптимизаторам +быстрее достигать качества устоявшихся промышленных оптимизаторов. + +% {\color{red} TODO: описать как нормализовать и т.п.} + + + \section{Технологическая часть} Программа написана на языке C++ с использованием стандарта C++23. Этот язык diff --git a/research/benchmark-results/operator-cost-matched.json b/research/benchmark-results/operator-cost-matched.json index e7e3666..748c8e3 100644 --- a/research/benchmark-results/operator-cost-matched.json +++ b/research/benchmark-results/operator-cost-matched.json @@ -1,6 +1,6 @@ { "context": { - "date": "2026-06-09T23:49:41+03:00", + "date": "2026-06-14T20:48:21+03:00", "host_name": "nixos", "executable": "./build-release/bin/benchmarks", "num_cpus": 8, @@ -32,7 +32,7 @@ "num_sharing": 8 } ], - "load_avg": [2.20801,1.40576,1.14795], + "load_avg": [1.43262,1.46533,1.78467], "library_version": "v1.9.0", "library_build_type": "release", "json_schema_version": 1 @@ -47,9 +47,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 2201, - "real_time": 3.1308477827712137e+05, - "cpu_time": 3.1023940345297597e+05, + "iterations": 2214, + "real_time": 3.0911023938454612e+05, + "cpu_time": 3.0619232971996389e+05, "time_unit": "ns", "model_cost": 6.4000000000000000e+05, "output_rows": 6.4000000000000000e+03, @@ -65,9 +65,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 2201, - "real_time": 3.1884279055965185e+05, - "cpu_time": 3.1639320490686060e+05, + "iterations": 2214, + "real_time": 3.1765129223070038e+05, + "cpu_time": 3.1451108536585368e+05, "time_unit": "ns", "model_cost": 6.4000000000000000e+05, "output_rows": 6.4000000000000000e+03, @@ -83,9 +83,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 2201, - "real_time": 3.1709757428868051e+05, - "cpu_time": 3.1426909904588806e+05, + "iterations": 2214, + "real_time": 3.5457537533911917e+05, + "cpu_time": 3.4965761698283651e+05, "time_unit": "ns", "model_cost": 6.4000000000000000e+05, "output_rows": 6.4000000000000000e+03, @@ -103,8 +103,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.1634171437515126e+05, - "cpu_time": 3.1363390246857487e+05, + "real_time": 3.2711230231812195e+05, + "cpu_time": 3.2345367735621799e+05, "time_unit": "ns", "model_cost": 6.4000000000000000e+05, "output_rows": 6.4000000000000000e+03, @@ -122,8 +122,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.1709757428868051e+05, - "cpu_time": 3.1426909904588811e+05, + "real_time": 3.1765129223070032e+05, + "cpu_time": 3.1451108536585368e+05, "time_unit": "ns", "model_cost": 6.4000000000000000e+05, "output_rows": 6.4000000000000000e+03, @@ -141,8 +141,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.9524853120924695e+03, - "cpu_time": 3.1256877806897505e+03, + "real_time": 2.4164078313638052e+04, + "cpu_time": 2.3071308216930705e+04, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -160,8 +160,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 9.3332152477086927e-03, - "cpu_time": 9.9660392453999273e-03, + "real_time": 7.3870894314876923e-02, + "cpu_time": 7.1328013351112357e-02, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -177,9 +177,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 1729, - "real_time": 4.2820256100733217e+05, - "cpu_time": 4.2593496992481180e+05, + "iterations": 1760, + "real_time": 4.1470032556803327e+05, + "cpu_time": 4.1158367443181819e+05, "time_unit": "ns", "model_cost": 6.4000000000000000e+05, "output_rows": 3.3280000000000000e+03, @@ -195,9 +195,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 1729, - "real_time": 4.0207458647550282e+05, - "cpu_time": 4.0061198843261990e+05, + "iterations": 1760, + "real_time": 4.1930176022850157e+05, + "cpu_time": 4.1661144374999992e+05, "time_unit": "ns", "model_cost": 6.4000000000000000e+05, "output_rows": 3.3280000000000000e+03, @@ -213,9 +213,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 1729, - "real_time": 4.0215632678001490e+05, - "cpu_time": 4.0059324117987277e+05, + "iterations": 1760, + "real_time": 4.3658621818229387e+05, + "cpu_time": 4.3417356250000041e+05, "time_unit": "ns", "model_cost": 6.4000000000000000e+05, "output_rows": 3.3280000000000000e+03, @@ -233,8 +233,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 4.1081115808761661e+05, - "cpu_time": 4.0904673317910143e+05, + "real_time": 4.2352943465960957e+05, + "cpu_time": 4.2078956022727280e+05, "time_unit": "ns", "model_cost": 6.4000000000000000e+05, "output_rows": 3.3280000000000000e+03, @@ -252,8 +252,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 4.0215632678001490e+05, - "cpu_time": 4.0061198843261978e+05, + "real_time": 4.1930176022850157e+05, + "cpu_time": 4.1661144374999986e+05, "time_unit": "ns", "model_cost": 6.4000000000000000e+05, "output_rows": 3.3280000000000000e+03, @@ -271,8 +271,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.5061452187829180e+04, - "cpu_time": 1.4625645050707279e+04, + "real_time": 1.1539193958084354e+04, + "cpu_time": 1.1860364821811641e+04, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -290,8 +290,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 3.6662714464578680e-02, - "cpu_time": 3.5755437861676870e-02, + "real_time": 2.7245317594887825e-02, + "cpu_time": 2.8185976894022118e-02, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -307,9 +307,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 224, - "real_time": 3.0960619286426147e+06, - "cpu_time": 3.0747937633928573e+06, + "iterations": 226, + "real_time": 3.0195558805306354e+06, + "cpu_time": 3.0009444867256628e+06, "time_unit": "ns", "model_cost": 6.4000200000000000e+05, "output_rows": 2.9091000000000000e+04, @@ -325,9 +325,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 224, - "real_time": 3.1441242409917838e+06, - "cpu_time": 3.1229820491071432e+06, + "iterations": 226, + "real_time": 2.9964330132742389e+06, + "cpu_time": 2.9785224601769918e+06, "time_unit": "ns", "model_cost": 6.4000200000000000e+05, "output_rows": 2.9091000000000000e+04, @@ -343,9 +343,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 224, - "real_time": 3.1377971116463803e+06, - "cpu_time": 3.1165352053571432e+06, + "iterations": 226, + "real_time": 2.8924311946842363e+06, + "cpu_time": 2.8764148097345126e+06, "time_unit": "ns", "model_cost": 6.4000200000000000e+05, "output_rows": 2.9091000000000000e+04, @@ -363,8 +363,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.1259944270935929e+06, - "cpu_time": 3.1047703392857146e+06, + "real_time": 2.9694733628297024e+06, + "cpu_time": 2.9519605855457224e+06, "time_unit": "ns", "model_cost": 6.4000200000000000e+05, "output_rows": 2.9091000000000000e+04, @@ -382,8 +382,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.1377971116463803e+06, - "cpu_time": 3.1165352053571437e+06, + "real_time": 2.9964330132742389e+06, + "cpu_time": 2.9785224601769918e+06, "time_unit": "ns", "model_cost": 6.4000200000000000e+05, "output_rows": 2.9091000000000000e+04, @@ -401,8 +401,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.6114631704802276e+04, - "cpu_time": 2.6159831331332960e+04, + "real_time": 6.7714758376275582e+04, + "cpu_time": 6.6378159064747015e+04, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -420,8 +420,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 8.3540237559164392e-03, - "cpu_time": 8.4256896557931282e-03, + "real_time": 2.2803625458942690e-02, + "cpu_time": 2.2486126471257015e-02, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -437,9 +437,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 996, - "real_time": 7.0582497386910755e+05, - "cpu_time": 7.0269638353413541e+05, + "iterations": 907, + "real_time": 7.0784833847735438e+05, + "cpu_time": 7.0478191951488447e+05, "time_unit": "ns", "model_cost": 6.4006800000000000e+05, "output_rows": 4.4760000000000000e+03, @@ -455,9 +455,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 996, - "real_time": 6.9997442669883172e+05, - "cpu_time": 6.9672412550200918e+05, + "iterations": 907, + "real_time": 7.1027558875225135e+05, + "cpu_time": 7.0695717530319712e+05, "time_unit": "ns", "model_cost": 6.4006800000000000e+05, "output_rows": 4.4760000000000000e+03, @@ -473,9 +473,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 996, - "real_time": 6.9949409538617241e+05, - "cpu_time": 6.9659677911646734e+05, + "iterations": 907, + "real_time": 7.0588639911670797e+05, + "cpu_time": 7.0260624255788233e+05, "time_unit": "ns", "model_cost": 6.4006800000000000e+05, "output_rows": 4.4760000000000000e+03, @@ -493,8 +493,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 7.0176449865137052e+05, - "cpu_time": 6.9867242938420398e+05, + "real_time": 7.0800344211543782e+05, + "cpu_time": 7.0478177912532119e+05, "time_unit": "ns", "model_cost": 6.4006800000000000e+05, "output_rows": 4.4760000000000000e+03, @@ -512,8 +512,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 6.9997442669883172e+05, - "cpu_time": 6.9672412550200929e+05, + "real_time": 7.0784833847735438e+05, + "cpu_time": 7.0478191951488436e+05, "time_unit": "ns", "model_cost": 6.4006800000000000e+05, "output_rows": 4.4760000000000000e+03, @@ -531,8 +531,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.5246664789629790e+03, - "cpu_time": 3.4854281696968096e+03, + "real_time": 2.1987017232992253e+03, + "cpu_time": 2.1754663760714229e+03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -550,8 +550,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 5.0225773542785001e-03, - "cpu_time": 4.9886442102328220e-03, + "real_time": 3.1054958104860925e-03, + "cpu_time": 3.0867233525408593e-03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -567,9 +567,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 1997, - "real_time": 3.4492794541993242e+05, - "cpu_time": 3.4377248823234811e+05, + "iterations": 1946, + "real_time": 3.4933276772805076e+05, + "cpu_time": 3.4805591058581707e+05, "time_unit": "ns", "model_cost": 6.4005000000000000e+05, "output_rows": 1.2550000000000000e+03, @@ -585,9 +585,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 1997, - "real_time": 3.5217452829173906e+05, - "cpu_time": 3.5094974411617417e+05, + "iterations": 1946, + "real_time": 3.5104961510741868e+05, + "cpu_time": 3.4970038848920772e+05, "time_unit": "ns", "model_cost": 6.4005000000000000e+05, "output_rows": 1.2550000000000000e+03, @@ -603,9 +603,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 1997, - "real_time": 3.5108648071568750e+05, - "cpu_time": 3.4975750125187810e+05, + "iterations": 1946, + "real_time": 3.5066883864314586e+05, + "cpu_time": 3.4928844039054483e+05, "time_unit": "ns", "model_cost": 6.4005000000000000e+05, "output_rows": 1.2550000000000000e+03, @@ -623,8 +623,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.4939631814245303e+05, - "cpu_time": 3.4815991120013344e+05, + "real_time": 3.5035040715953842e+05, + "cpu_time": 3.4901491315518977e+05, "time_unit": "ns", "model_cost": 6.4005000000000000e+05, "output_rows": 1.2550000000000000e+03, @@ -642,8 +642,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.5108648071568744e+05, - "cpu_time": 3.4975750125187816e+05, + "real_time": 3.5066883864314581e+05, + "cpu_time": 3.4928844039054483e+05, "time_unit": "ns", "model_cost": 6.4005000000000000e+05, "output_rows": 1.2550000000000000e+03, @@ -661,8 +661,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.9077778816701375e+03, - "cpu_time": 3.8460981508032892e+03, + "real_time": 9.0163195835342015e+02, + "cpu_time": 8.5568087220638881e+02, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -680,8 +680,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.1184370523552256e-02, - "cpu_time": 1.1046929951083395e-02, + "real_time": 2.5735148009770848e-03, + "cpu_time": 2.4517028927812877e-03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -697,9 +697,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 2600, - "real_time": 2.5277129883761515e+05, - "cpu_time": 2.5169727038461503e+05, + "iterations": 2052, + "real_time": 3.1039785721233365e+05, + "cpu_time": 3.0903469249512703e+05, "time_unit": "ns", "lhs_rows": 1.6200000000000000e+03, "model_cost": 6.3990000000000000e+05, @@ -716,9 +716,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 2600, - "real_time": 2.4949188269182251e+05, - "cpu_time": 2.4836474961538453e+05, + "iterations": 2052, + "real_time": 3.1106529385987530e+05, + "cpu_time": 3.0980652241715358e+05, "time_unit": "ns", "lhs_rows": 1.6200000000000000e+03, "model_cost": 6.3990000000000000e+05, @@ -735,9 +735,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - 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"real_time": 3.2289891982111921e-02, - "cpu_time": 3.2085110979793462e-02, + "real_time": 1.0239493211306075e-01, + "cpu_time": 1.0700416661210344e-01, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -5873,8 +5873,8 @@ "repetition_index": 0, "threads": 1, "iterations": 20, - "real_time": 3.8973592749971434e+07, - "cpu_time": 3.8685986850000374e+07, + "real_time": 3.1578403249841355e+07, + "cpu_time": 3.1374204650000341e+07, "time_unit": "ns", "model_cost": 2.6010000000000000e+07, "output_rows": 5.1000000000000000e+04, @@ -5891,8 +5891,8 @@ "repetition_index": 1, "threads": 1, "iterations": 20, - "real_time": 3.4202010498847812e+07, - "cpu_time": 3.3985413199999921e+07, + "real_time": 3.1751288499981456e+07, + "cpu_time": 3.1313289349999707e+07, "time_unit": "ns", "model_cost": 2.6010000000000000e+07, "output_rows": 5.1000000000000000e+04, @@ -5909,8 +5909,8 @@ "repetition_index": 2, "threads": 1, "iterations": 20, - "real_time": 3.3547285450913478e+07, - "cpu_time": 3.3338214049999945e+07, + "real_time": 3.0507513350130469e+07, + "cpu_time": 3.0061373449999709e+07, "time_unit": "ns", "model_cost": 2.6010000000000000e+07, "output_rows": 5.1000000000000000e+04, @@ -5928,8 +5928,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.5574296233244240e+07, - "cpu_time": 3.5336538033333413e+07, + "real_time": 3.1279068366651092e+07, + "cpu_time": 3.0916289149999917e+07, "time_unit": "ns", "model_cost": 2.6010000000000000e+07, "output_rows": 5.1000000000000000e+04, @@ -5947,8 +5947,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.4202010498847812e+07, - "cpu_time": 3.3985413199999921e+07, + "real_time": 3.1578403249841359e+07, + "cpu_time": 3.1313289349999707e+07, "time_unit": "ns", "model_cost": 2.6010000000000000e+07, "output_rows": 5.1000000000000000e+04, @@ -5966,8 +5966,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.9620227596312016e+06, - "cpu_time": 2.9187021459107138e+06, + "real_time": 6.7375454363427730e+05, + "cpu_time": 7.4100493184850633e+05, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -5985,8 +5985,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 8.3263003720736625e-02, - "cpu_time": 8.2597286218515920e-02, + "real_time": 2.1540109051093621e-02, + "cpu_time": 2.3968107176553188e-02, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -6002,9 +6002,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 43, - "real_time": 1.6169042418559277e+07, - "cpu_time": 1.6035586255813889e+07, + "iterations": 51, + "real_time": 1.5567576823563213e+07, + "cpu_time": 1.5447459196078412e+07, "time_unit": "ns", "lhs_rows": 6.5848000000000000e+04, "model_cost": 2.6009960000000000e+07, @@ -6021,9 +6021,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 43, - "real_time": 1.7688036069874976e+07, - "cpu_time": 1.7544754139534719e+07, + "iterations": 51, + "real_time": 1.3858204745095767e+07, + "cpu_time": 1.3798366549019760e+07, "time_unit": "ns", "lhs_rows": 6.5848000000000000e+04, "model_cost": 2.6009960000000000e+07, @@ -6040,9 +6040,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 43, - "real_time": 1.6172905348993966e+07, - "cpu_time": 1.6059888651162762e+07, + "iterations": 51, + "real_time": 1.3834395725508312e+07, + "cpu_time": 1.3780844490195885e+07, "time_unit": "ns", "lhs_rows": 6.5848000000000000e+04, "model_cost": 2.6009960000000000e+07, @@ -6061,8 +6061,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.6676661279142739e+07, - "cpu_time": 1.6546743015503792e+07, + "real_time": 1.4420059098055765e+07, + "cpu_time": 1.4342223411764687e+07, "time_unit": "ns", "lhs_rows": 6.5848000000000000e+04, "model_cost": 2.6009960000000000e+07, @@ -6081,8 +6081,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.6172905348993966e+07, - "cpu_time": 1.6059888651162764e+07, + "real_time": 1.3858204745095767e+07, + "cpu_time": 1.3798366549019760e+07, "time_unit": "ns", "lhs_rows": 6.5848000000000000e+04, "model_cost": 2.6009960000000000e+07, @@ -6101,8 +6101,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 8.7587839113336650e+05, - "cpu_time": 8.6438839902667922e+05, + "real_time": 9.9385080123668956e+05, + "cpu_time": 9.5720236096242710e+05, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -6121,8 +6121,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 5.2521207720925230e-02, - "cpu_time": 5.2239186782363979e-02, + "real_time": 6.8921409716738893e-02, + "cpu_time": 6.6740165278505556e-02, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -6140,8 +6140,8 @@ "repetition_index": 0, "threads": 1, "iterations": 34, - "real_time": 2.2066501999849122e+07, - "cpu_time": 2.1965138823529091e+07, + "real_time": 2.0726778235364370e+07, + "cpu_time": 2.0689765823529255e+07, "time_unit": "ns", "lhs_rows": 6.1000000000000000e+02, "model_cost": 2.6047000000000000e+07, @@ -6159,8 +6159,8 @@ "repetition_index": 1, "threads": 1, "iterations": 34, - "real_time": 2.1873878794448342e+07, - "cpu_time": 2.1796083499999952e+07, + "real_time": 2.0663114088272456e+07, + "cpu_time": 2.0627233588235203e+07, "time_unit": "ns", "lhs_rows": 6.1000000000000000e+02, "model_cost": 2.6047000000000000e+07, @@ -6178,8 +6178,8 @@ "repetition_index": 2, "threads": 1, "iterations": 34, - "real_time": 2.1685511941220336e+07, - "cpu_time": 2.1595912823529605e+07, + "real_time": 2.0601398117672514e+07, + "cpu_time": 2.0567957235294312e+07, "time_unit": "ns", "lhs_rows": 6.1000000000000000e+02, "model_cost": 2.6047000000000000e+07, @@ -6198,8 +6198,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.1875297578505937e+07, - "cpu_time": 2.1785711715686217e+07, + "real_time": 2.0663763480436448e+07, + "cpu_time": 2.0628318882352922e+07, "time_unit": "ns", "lhs_rows": 6.1000000000000000e+02, "model_cost": 2.6047000000000000e+07, @@ -6218,8 +6218,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.1873878794448342e+07, - "cpu_time": 2.1796083499999952e+07, + "real_time": 2.0663114088272456e+07, + "cpu_time": 2.0627233588235203e+07, "time_unit": "ns", "lhs_rows": 6.1000000000000000e+02, "model_cost": 2.6047000000000000e+07, @@ -6238,8 +6238,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.9049899187319237e+05, - "cpu_time": 1.8483138316086572e+05, + "real_time": 6.2692581385378056e+04, + "cpu_time": 6.0911546028532845e+04, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -6258,8 +6258,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 8.7084068771878763e-03, - "cpu_time": 8.4840644902035873e-03, + "real_time": 3.0339382003057029e-03, + "cpu_time": 2.9528119269399767e-03, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -6276,9 +6276,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 29, - "real_time": 2.4021322448569320e+07, - "cpu_time": 2.3765103241379101e+07, + "iterations": 41, + "real_time": 1.6635666585299497e+07, + "cpu_time": 1.6552195024390273e+07, "time_unit": "ns", "lhs_rows": 5.0000000000000000e+02, "model_cost": 2.6000000000000000e+07, @@ -6295,9 +6295,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 29, - "real_time": 2.3866731138570748e+07, - "cpu_time": 2.3617933034482863e+07, + "iterations": 41, + "real_time": 1.6714775658595210e+07, + "cpu_time": 1.6627896902439220e+07, "time_unit": "ns", "lhs_rows": 5.0000000000000000e+02, "model_cost": 2.6000000000000000e+07, @@ -6314,9 +6314,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 29, - "real_time": 2.3685086138169121e+07, - "cpu_time": 2.3443975241379227e+07, + "iterations": 41, + "real_time": 1.6558810463388132e+07, + "cpu_time": 1.6473782512195306e+07, "time_unit": "ns", "lhs_rows": 5.0000000000000000e+02, "model_cost": 2.6000000000000000e+07, @@ -6335,8 +6335,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.3857713241769727e+07, - "cpu_time": 2.3609003839080393e+07, + "real_time": 1.6636417569094280e+07, + "cpu_time": 1.6551291479674930e+07, "time_unit": "ns", "lhs_rows": 5.0000000000000000e+02, "model_cost": 2.6000000000000000e+07, @@ -6355,8 +6355,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.3866731138570752e+07, - "cpu_time": 2.3617933034482863e+07, + "real_time": 1.6635666585299497e+07, + "cpu_time": 1.6552195024390271e+07, "time_unit": "ns", "lhs_rows": 5.0000000000000000e+02, "model_cost": 2.6000000000000000e+07, @@ -6375,8 +6375,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.6829945322241614e+05, - "cpu_time": 1.6075010418015547e+05, + "real_time": 7.7985309587918309e+04, + "cpu_time": 7.7061168008186316e+04, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -6395,8 +6395,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 7.0542994425702105e-03, - "cpu_time": 6.8088473904207268e-03, + "real_time": 4.6876263633098981e-03, + "cpu_time": 4.6559006046638617e-03, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, diff --git a/research/benchmark-results/operator-cost.json b/research/benchmark-results/operator-cost.json index 6cbf34b..f574feb 100644 --- a/research/benchmark-results/operator-cost.json +++ b/research/benchmark-results/operator-cost.json @@ -1,6 +1,6 @@ { "context": { - "date": "2026-06-09T23:47:09+03:00", + "date": "2026-06-14T20:45:51+03:00", "host_name": "nixos", "executable": "./build-release/bin/benchmarks", "num_cpus": 8, @@ -32,7 +32,7 @@ "num_sharing": 8 } ], - "load_avg": [1.61963,0.920898,0.974609], + "load_avg": [1.35254,1.46924,1.84277], "library_version": "v1.9.0", "library_build_type": "release", "json_schema_version": 1 @@ -47,9 +47,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 18250, - "real_time": 3.8845030684980935e+04, - "cpu_time": 3.8709108164383571e+04, + "iterations": 18183, + "real_time": 3.9952357201905637e+04, + "cpu_time": 3.9780922180058289e+04, "time_unit": "ns", "model_cost": 1.0240000000000000e+05, "output_rows": 1.0240000000000000e+03, @@ -65,9 +65,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 18250, - "real_time": 3.9302844657843350e+04, - "cpu_time": 3.9168997315068496e+04, + "iterations": 18183, + "real_time": 3.9745107738011175e+04, + "cpu_time": 3.9613663476874004e+04, "time_unit": "ns", "model_cost": 1.0240000000000000e+05, "output_rows": 1.0240000000000000e+03, @@ -83,9 +83,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 18250, - "real_time": 3.9311777588222430e+04, - "cpu_time": 3.9168773041095883e+04, + "iterations": 18183, + "real_time": 3.9485971126900746e+04, + "cpu_time": 3.9359944178628393e+04, "time_unit": "ns", "model_cost": 1.0240000000000000e+05, "output_rows": 1.0240000000000000e+03, @@ -103,8 +103,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.9153217643682234e+04, - "cpu_time": 3.9015626173515986e+04, + "real_time": 3.9727812022272519e+04, + "cpu_time": 3.9584843278520224e+04, "time_unit": "ns", "model_cost": 1.0240000000000000e+05, "output_rows": 1.0240000000000000e+03, @@ -122,8 +122,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.9302844657843343e+04, - "cpu_time": 3.9168773041095883e+04, + "real_time": 3.9745107738011175e+04, + "cpu_time": 3.9613663476873997e+04, "time_unit": "ns", "model_cost": 1.0240000000000000e+05, "output_rows": 1.0240000000000000e+03, @@ -141,8 +141,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.6693510530977704e+02, - "cpu_time": 2.6545240631176927e+02, + "real_time": 2.3367359516357740e+02, + "cpu_time": 2.1196361078531322e+02, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -160,8 +160,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 6.8177054498827303e-03, - "cpu_time": 6.8037458922538020e-03, + "real_time": 5.8818641971164548e-03, + "cpu_time": 5.3546659082096262e-03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -177,9 +177,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 9649, - "real_time": 7.1402024148586672e+04, - "cpu_time": 7.1145830448751149e+04, + "iterations": 9450, + "real_time": 7.0023080740597041e+04, + "cpu_time": 6.9646964021164022e+04, "time_unit": "ns", "model_cost": 2.0480000000000000e+05, "output_rows": 2.0480000000000000e+03, @@ -195,9 +195,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 9649, - "real_time": 7.4413162504712483e+04, - "cpu_time": 7.4079988496217149e+04, + "iterations": 9450, + "real_time": 6.7391017036960286e+04, + "cpu_time": 6.7122223174603205e+04, "time_unit": "ns", "model_cost": 2.0480000000000000e+05, "output_rows": 2.0480000000000000e+03, @@ -213,9 +213,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 9649, - "real_time": 7.1228492588142806e+04, - "cpu_time": 7.1011361902787845e+04, + "iterations": 9450, + "real_time": 6.7115533862431737e+04, + "cpu_time": 6.6909787513227493e+04, "time_unit": "ns", "model_cost": 2.0480000000000000e+05, "output_rows": 2.0480000000000000e+03, @@ -233,8 +233,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 7.2347893080480644e+04, - "cpu_time": 7.2079060282585371e+04, + "real_time": 6.8176543879996345e+04, + "cpu_time": 6.7892991569664897e+04, "time_unit": "ns", "model_cost": 2.0480000000000000e+05, "output_rows": 2.0480000000000000e+03, @@ -252,8 +252,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 7.1402024148586672e+04, - "cpu_time": 7.1145830448751163e+04, + "real_time": 6.7391017036960300e+04, + "cpu_time": 6.7122223174603205e+04, "time_unit": "ns", "model_cost": 2.0480000000000000e+05, "output_rows": 2.0480000000000000e+03, @@ -271,8 +271,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.7906791020775183e+03, - "cpu_time": 1.7341585090601286e+03, + "real_time": 1.6050690103636371e+03, + "cpu_time": 1.5226939114672762e+03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -290,8 +290,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 2.4750950246547554e-02, - "cpu_time": 2.4059116507087832e-02, + "real_time": 2.3542833341462174e-02, + "cpu_time": 2.2427851185565187e-02, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -307,9 +307,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - 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"real_time": 1.5814652478906329e+05, - "cpu_time": 1.5655859870328000e+05, + "real_time": 1.6237682264582231e+05, + "cpu_time": 1.6077227074235803e+05, "time_unit": "ns", "model_cost": 4.0960000000000000e+05, "output_rows": 4.0960000000000000e+03, @@ -382,8 +382,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.5755818626769868e+05, - "cpu_time": 1.5598532494279181e+05, + "real_time": 1.6562274557530464e+05, + "cpu_time": 1.6383219903470456e+05, "time_unit": "ns", "model_cost": 4.0960000000000000e+05, "output_rows": 4.0960000000000000e+03, @@ -401,8 +401,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.3958237112342867e+03, - "cpu_time": 2.3152723519420706e+03, + "real_time": 5.9527179822873295e+03, + "cpu_time": 5.8585377938772517e+03, "time_unit": "ns", "model_cost": 0.0000000000000000e+00, "output_rows": 0.0000000000000000e+00, @@ -420,8 +420,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - 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"real_time": 9.5886732776967064e+05, - "cpu_time": 9.5137065464631317e+05, + "real_time": 9.0999000678507856e+05, + "cpu_time": 9.0279114341084834e+05, "time_unit": "ns", "lhs_rows": 1.2800000000000000e+02, "model_cost": 1.7039360000000000e+06, @@ -8031,8 +8031,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 9.5932074615327513e+05, - "cpu_time": 9.5193887933424360e+05, + "real_time": 8.9480302035342390e+05, + "cpu_time": 8.8767159738371044e+05, "time_unit": "ns", "lhs_rows": 1.2800000000000000e+02, "model_cost": 1.7039360000000000e+06, @@ -8051,8 +8051,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.8580883365218095e+04, - "cpu_time": 1.8281721281819497e+04, + "real_time": 3.0725437451581263e+04, + "cpu_time": 2.9804183747610597e+04, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -8071,8 +8071,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.9377950240975786e-02, - "cpu_time": 1.9216192125051421e-02, + "real_time": 3.3764587767432477e-02, + "cpu_time": 3.3013376310944940e-02, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -8089,9 +8089,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 109, - "real_time": 5.6205549447716074e+06, - "cpu_time": 5.5457994587156754e+06, + "iterations": 120, + "real_time": 5.2447048416676503e+06, + "cpu_time": 5.1699889416667586e+06, "time_unit": "ns", "lhs_rows": 2.5600000000000000e+02, "model_cost": 6.8157440000000000e+06, @@ -8108,9 +8108,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 109, - "real_time": 5.9907739541980382e+06, - "cpu_time": 5.9113671743118977e+06, + "iterations": 120, + "real_time": 5.0897273416618798e+06, + "cpu_time": 5.0246979666667366e+06, "time_unit": "ns", "lhs_rows": 2.5600000000000000e+02, "model_cost": 6.8157440000000000e+06, @@ -8127,9 +8127,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 109, - "real_time": 5.0800898349459432e+06, - "cpu_time": 5.0185620366970822e+06, + "iterations": 120, + "real_time": 4.7818925250188233e+06, + "cpu_time": 4.7253736416666452e+06, "time_unit": "ns", "lhs_rows": 2.5600000000000000e+02, "model_cost": 6.8157440000000000e+06, @@ -8148,8 +8148,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 5.5638062446385296e+06, - "cpu_time": 5.4919095565748857e+06, + "real_time": 5.0387749027827838e+06, + "cpu_time": 4.9733535166667132e+06, "time_unit": "ns", "lhs_rows": 2.5600000000000000e+02, "model_cost": 6.8157440000000000e+06, @@ -8168,8 +8168,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 5.6205549447716065e+06, - "cpu_time": 5.5457994587156763e+06, + "real_time": 5.0897273416618798e+06, + "cpu_time": 5.0246979666667366e+06, "time_unit": "ns", "lhs_rows": 2.5600000000000000e+02, "model_cost": 6.8157440000000000e+06, @@ -8188,8 +8188,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 4.5798657457352517e+05, - "cpu_time": 4.4883554293573764e+05, + "real_time": 2.3557572748748618e+05, + "cpu_time": 2.2671100691828699e+05, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -8208,8 +8208,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 8.2315334940870108e-02, - "cpu_time": 8.1726681459710862e-02, + "real_time": 4.6752580147484624e-02, + "cpu_time": 4.5585138108226682e-02, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -8226,9 +8226,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 26, - "real_time": 2.6842513768665060e+07, - "cpu_time": 2.6538571653845750e+07, + "iterations": 31, + "real_time": 2.2470577064532630e+07, + "cpu_time": 2.2274796290322606e+07, "time_unit": "ns", "lhs_rows": 5.1200000000000000e+02, "model_cost": 2.7262976000000000e+07, @@ -8245,9 +8245,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 26, - "real_time": 2.6442388577449422e+07, - "cpu_time": 2.6176888538461622e+07, + "iterations": 31, + "real_time": 2.2671293516102575e+07, + "cpu_time": 2.2471355741935614e+07, "time_unit": "ns", "lhs_rows": 5.1200000000000000e+02, "model_cost": 2.7262976000000000e+07, @@ -8264,9 +8264,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 26, - "real_time": 2.6032183076425169e+07, - "cpu_time": 2.5784397423077639e+07, + "iterations": 31, + "real_time": 2.2575022580689795e+07, + "cpu_time": 2.2343392677418977e+07, "time_unit": "ns", "lhs_rows": 5.1200000000000000e+02, "model_cost": 2.7262976000000000e+07, @@ -8285,8 +8285,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.6439028474179883e+07, - "cpu_time": 2.6166619205128338e+07, + "real_time": 2.2572297720441666e+07, + "cpu_time": 2.2363181569892395e+07, "time_unit": "ns", "lhs_rows": 5.1200000000000000e+02, "model_cost": 2.7262976000000000e+07, @@ -8305,8 +8305,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.6442388577449422e+07, - "cpu_time": 2.6176888538461622e+07, + "real_time": 2.2575022580689799e+07, + "cpu_time": 2.2343392677418977e+07, "time_unit": "ns", "lhs_rows": 5.1200000000000000e+02, "model_cost": 2.7262976000000000e+07, @@ -8325,8 +8325,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 4.0517579569474870e+05, - "cpu_time": 3.7719197631191387e+05, + "real_time": 1.0038596580303139e+05, + "cpu_time": 9.9762742061406781e+04, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -8345,8 +8345,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.5324912414631259e-02, - "cpu_time": 1.4415006132622164e-02, + "real_time": 4.4473082468747078e-03, + "cpu_time": 4.4610263414270879e-03, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -8364,8 +8364,8 @@ "repetition_index": 0, "threads": 1, "iterations": 5, - "real_time": 1.2198015240137465e+08, - "cpu_time": 1.2094263579999733e+08, + "real_time": 1.1748234059996322e+08, + "cpu_time": 1.1633298520000041e+08, "time_unit": "ns", "lhs_rows": 1.0240000000000000e+03, "model_cost": 1.0905190400000000e+08, @@ -8383,8 +8383,8 @@ "repetition_index": 1, "threads": 1, "iterations": 5, - "real_time": 1.2660427120281383e+08, - "cpu_time": 1.2563009380000381e+08, + "real_time": 1.1784866999951191e+08, + "cpu_time": 1.1670695120000120e+08, "time_unit": "ns", "lhs_rows": 1.0240000000000000e+03, "model_cost": 1.0905190400000000e+08, @@ -8402,8 +8402,8 @@ "repetition_index": 2, "threads": 1, "iterations": 5, - "real_time": 1.2643557180417702e+08, - "cpu_time": 1.2531702260000089e+08, + "real_time": 1.1698665840012836e+08, + "cpu_time": 1.1589235359999748e+08, "time_unit": "ns", "lhs_rows": 1.0240000000000000e+03, "model_cost": 1.0905190400000000e+08, @@ -8422,8 +8422,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.2500666513612181e+08, - "cpu_time": 1.2396325073333400e+08, + "real_time": 1.1743922299986781e+08, + "cpu_time": 1.1631076333333302e+08, "time_unit": "ns", "lhs_rows": 1.0240000000000000e+03, "model_cost": 1.0905190400000000e+08, @@ -8442,8 +8442,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.2643557180417700e+08, - "cpu_time": 1.2531702260000087e+08, + "real_time": 1.1748234059996320e+08, + "cpu_time": 1.1633298520000039e+08, "time_unit": "ns", "lhs_rows": 1.0240000000000000e+03, "model_cost": 1.0905190400000000e+08, @@ -8462,8 +8462,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.6223938247274985e+06, - "cpu_time": 2.6206085792593178e+06, + "real_time": 4.3262032424765010e+05, + "cpu_time": 4.0775319864778483e+05, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, @@ -8482,8 +8482,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 2.0978032026307807e-02, - "cpu_time": 2.1140205373419027e-02, + "real_time": 3.6837805393870587e-03, + "cpu_time": 3.5057219724301180e-03, "time_unit": "ns", "lhs_rows": 0.0000000000000000e+00, "model_cost": 0.0000000000000000e+00, diff --git a/research/benchmark-results/query.json b/research/benchmark-results/query.json index 2fe2b6b..0d0ca17 100644 --- a/research/benchmark-results/query.json +++ b/research/benchmark-results/query.json @@ -1,6 +1,6 @@ { "context": { - "date": "2026-06-09T23:45:24+03:00", + "date": "2026-06-14T20:44:00+03:00", "host_name": "nixos", "executable": "./build-release/bin/benchmarks", "num_cpus": 8, @@ -32,7 +32,7 @@ "num_sharing": 8 } ], - "load_avg": [0.406738,0.518066,0.863281], + "load_avg": [0.801758,1.39307,1.86475], "library_version": "v1.9.0", "library_build_type": "release", "json_schema_version": 1 @@ -47,9 +47,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 35883, - "real_time": 1.9347125825426523e+04, - "cpu_time": 1.9274702784048157e+04, + "iterations": 30908, + "real_time": 1.9690791995604395e+04, + "cpu_time": 1.9364304225443251e+04, "time_unit": "ns" }, { @@ -61,9 +61,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 35883, - "real_time": 1.9958099769192188e+04, - "cpu_time": 1.9870254744586575e+04, + "iterations": 30908, + "real_time": 2.0620323508553727e+04, + "cpu_time": 2.0439727384495920e+04, "time_unit": "ns" }, { @@ -75,9 +75,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 35883, - "real_time": 1.9839301117551120e+04, - "cpu_time": 1.9744612713541232e+04, + "iterations": 30908, + "real_time": 2.1001014268052593e+04, + "cpu_time": 2.0805882004658986e+04, "time_unit": "ns" }, { @@ -91,8 +91,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.9714842237389941e+04, - "cpu_time": 1.9629856747391983e+04, + "real_time": 2.0437376590736905e+04, + "cpu_time": 2.0203304538199383e+04, "time_unit": "ns" }, { @@ -106,8 +106,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.9839301117551116e+04, - "cpu_time": 1.9744612713541228e+04, + "real_time": 2.0620323508553727e+04, + "cpu_time": 2.0439727384495916e+04, "time_unit": "ns" }, { @@ -121,8 +121,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.2394413041203410e+02, - "cpu_time": 3.1392233633584686e+02, + "real_time": 6.7399761265075608e+02, + "cpu_time": 7.4930530831593944e+02, "time_unit": "ns" }, { @@ -136,8 +136,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.6431484792592550e-02, - "cpu_time": 1.5992084933454977e-02, + "real_time": 3.2978675597544187e-02, + "cpu_time": 3.7088254889153946e-02, "time_unit": "ns" }, { @@ -149,9 +149,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 35706, - "real_time": 1.9395967988571840e+04, - "cpu_time": 1.9322896347952723e+04, + "iterations": 34131, + "real_time": 2.0186661773754931e+04, + "cpu_time": 1.9933065453693125e+04, "time_unit": "ns" }, { @@ -163,9 +163,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 35706, - "real_time": 1.9952500560384295e+04, - "cpu_time": 1.9850908390746659e+04, + "iterations": 34131, + "real_time": 2.0169296094510435e+04, + "cpu_time": 1.9849478626468608e+04, "time_unit": "ns" }, { @@ -177,9 +177,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 35706, - "real_time": 1.9349337225960488e+04, - "cpu_time": 1.9260752674620504e+04, + "iterations": 34131, + "real_time": 2.0091933315705788e+04, + "cpu_time": 1.9958257449239689e+04, "time_unit": "ns" }, { @@ -193,8 +193,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.9565935258305544e+04, - "cpu_time": 1.9478185804439960e+04, + "real_time": 2.0149297061323719e+04, + "cpu_time": 1.9913600509800472e+04, "time_unit": "ns" }, { @@ -208,8 +208,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.9395967988571840e+04, - "cpu_time": 1.9322896347952723e+04, + "real_time": 2.0169296094510435e+04, + "cpu_time": 1.9933065453693125e+04, "time_unit": "ns" }, { @@ -223,8 +223,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.3558628783648948e+02, - "cpu_time": 3.2427928362951928e+02, + "real_time": 5.0431549524075258e+01, + "cpu_time": 5.6941822078140824e+01, "time_unit": "ns" }, { @@ -238,8 +238,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.7151558737476476e-02, - "cpu_time": 1.6648330952649670e-02, + "real_time": 2.5028937421781275e-03, + "cpu_time": 2.8594438283582580e-03, "time_unit": "ns" }, { @@ -251,9 +251,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 35747, - "real_time": 1.9461075754495792e+04, - "cpu_time": 1.9392605757126468e+04, + "iterations": 35049, + "real_time": 2.0029725127609468e+04, + "cpu_time": 1.9744634625809576e+04, "time_unit": "ns" }, { @@ -265,9 +265,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 35747, - "real_time": 2.0039857134594396e+04, - "cpu_time": 1.9956820963996979e+04, + "iterations": 35049, + "real_time": 1.9694179577221064e+04, + "cpu_time": 1.9469350423692540e+04, "time_unit": "ns" }, { @@ -279,9 +279,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 35747, - "real_time": 1.9761389738938429e+04, - "cpu_time": 1.9648120653481416e+04, + "iterations": 35049, + "real_time": 1.9976324659756159e+04, + "cpu_time": 1.9649384005249784e+04, "time_unit": "ns" }, { @@ -295,8 +295,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.9754107542676207e+04, - "cpu_time": 1.9665849124868284e+04, + "real_time": 1.9900076454862228e+04, + "cpu_time": 1.9621123018250630e+04, "time_unit": "ns" }, { @@ -310,8 +310,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.9761389738938433e+04, - "cpu_time": 1.9648120653481416e+04, + "real_time": 1.9976324659756163e+04, + "cpu_time": 1.9649384005249784e+04, "time_unit": "ns" }, { @@ -325,8 +325,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.8945940004436528e+02, - "cpu_time": 2.8252508550457100e+02, + "real_time": 1.8029987703411811e+02, + "cpu_time": 1.3980114635986928e+02, "time_unit": "ns" }, { @@ -340,8 +340,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.4653124643521634e-02, - "cpu_time": 1.4366279518910081e-02, + "real_time": 9.0602605192536840e-03, + "cpu_time": 7.1250328653376729e-03, "time_unit": "ns" }, { @@ -353,9 +353,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 34985, - "real_time": 1.9576876347119727e+04, - "cpu_time": 1.9499444018865241e+04, + "iterations": 35368, + "real_time": 1.9960714940043279e+04, + "cpu_time": 1.9790182368242466e+04, "time_unit": "ns" }, { @@ -367,9 +367,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 34985, - "real_time": 2.0412169873552859e+04, - "cpu_time": 2.0322958525082195e+04, + "iterations": 35368, + "real_time": 1.9953514787403295e+04, + "cpu_time": 1.9870601136620662e+04, "time_unit": "ns" }, { @@ -381,9 +381,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 34985, - "real_time": 1.9421935715485401e+04, - "cpu_time": 1.9353340631699339e+04, + "iterations": 35368, + "real_time": 1.9325330383437165e+04, + "cpu_time": 1.9268540545125539e+04, "time_unit": "ns" }, { @@ -397,8 +397,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.9803660645385997e+04, - "cpu_time": 1.9725247725215591e+04, + "real_time": 1.9746520036961247e+04, + "cpu_time": 1.9643108016662885e+04, "time_unit": "ns" }, { @@ -412,8 +412,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.9576876347119727e+04, - "cpu_time": 1.9499444018865244e+04, + "real_time": 1.9953514787403295e+04, + "cpu_time": 1.9790182368242466e+04, "time_unit": "ns" }, { @@ -427,8 +427,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 5.3264834591507758e+02, - "cpu_time": 5.2276208751519619e+02, + "real_time": 3.6477870514374996e+02, + "cpu_time": 3.2686753835056970e+02, "time_unit": "ns" }, { @@ -442,8 +442,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 2.6896458965488181e-02, - "cpu_time": 2.6502181102999685e-02, + "real_time": 1.8473062821244581e-02, + "cpu_time": 1.6640316699032251e-02, "time_unit": "ns" }, { @@ -455,9 +455,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 9891, - "real_time": 6.9485813061832043e+04, - "cpu_time": 6.8783745020725837e+04, + "iterations": 10059, + "real_time": 6.7123694005437108e+04, + "cpu_time": 6.6452997017596266e+04, "time_unit": "ns" }, { @@ -469,9 +469,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 9891, - "real_time": 6.9380505410659636e+04, - "cpu_time": 6.8705246486705015e+04, + "iterations": 10059, + "real_time": 6.6603497464995104e+04, + "cpu_time": 6.5978846406203447e+04, "time_unit": "ns" }, { @@ -483,9 +483,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 9891, - "real_time": 7.2888390554632861e+04, - "cpu_time": 7.0598849358002219e+04, + "iterations": 10059, + "real_time": 6.6185145243245774e+04, + "cpu_time": 6.5579015806740179e+04, "time_unit": "ns" }, { @@ -499,8 +499,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 7.0584903009041518e+04, - "cpu_time": 6.9362613621811019e+04, + "real_time": 6.6637445571225995e+04, + "cpu_time": 6.6003619743513293e+04, "time_unit": "ns" }, { @@ -514,8 +514,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 6.9485813061832057e+04, - "cpu_time": 6.8783745020725837e+04, + "real_time": 6.6603497464995118e+04, + "cpu_time": 6.5978846406203447e+04, "time_unit": "ns" }, { @@ -529,8 +529,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.9955734964841440e+03, - "cpu_time": 1.0713307619682653e+03, + "real_time": 4.7019442807371064e+02, + "cpu_time": 4.3751694586045232e+02, "time_unit": "ns" }, { @@ -544,8 +544,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 2.8271959178416985e-02, - "cpu_time": 1.5445363229960325e-02, + "real_time": 7.0560091858734202e-03, + "cpu_time": 6.6286810868952479e-03, "time_unit": "ns" }, { @@ -557,9 +557,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 16294, - "real_time": 4.5328077084222074e+04, - "cpu_time": 4.5118617589296737e+04, + "iterations": 16520, + "real_time": 4.4866463377613101e+04, + "cpu_time": 4.4648068462469775e+04, "time_unit": "ns" }, { @@ -571,9 +571,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 16294, - "real_time": 4.2265272677276589e+04, - "cpu_time": 4.2094951086289366e+04, + "iterations": 16520, + "real_time": 4.2604707445555112e+04, + "cpu_time": 4.2458468644067827e+04, "time_unit": "ns" }, { @@ -585,9 +585,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 16294, - "real_time": 4.1824865902154910e+04, - "cpu_time": 4.1692916656437970e+04, + "iterations": 16520, + "real_time": 4.2180054963624643e+04, + "cpu_time": 4.2046813801452801e+04, "time_unit": "ns" }, { @@ -601,8 +601,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 4.3139405221217858e+04, - "cpu_time": 4.2968828444008017e+04, + "real_time": 4.3217075262264276e+04, + "cpu_time": 4.3051116969330134e+04, "time_unit": "ns" }, { @@ -616,8 +616,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 4.2265272677276589e+04, - "cpu_time": 4.2094951086289366e+04, + "real_time": 4.2604707445555105e+04, + "cpu_time": 4.2458468644067827e+04, "time_unit": "ns" }, { @@ -631,8 +631,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.9081936287554665e+03, - "cpu_time": 1.8725925738392880e+03, + "real_time": 1.4441064015139771e+03, + "cpu_time": 1.3982329852147041e+03, "time_unit": "ns" }, { @@ -646,8 +646,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 4.4233192807603502e-02, - "cpu_time": 4.3580256703517832e-02, + "real_time": 3.3415181215997812e-02, + "cpu_time": 3.2478436882620573e-02, "time_unit": "ns" }, { @@ -659,9 +659,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 10101, - "real_time": 6.7519916445501847e+04, - "cpu_time": 6.6872900306900425e+04, + "iterations": 10476, + "real_time": 6.6407572833197148e+04, + "cpu_time": 6.5812611970217578e+04, "time_unit": "ns" }, { @@ -673,9 +673,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 10101, - "real_time": 6.7762344916422226e+04, - "cpu_time": 6.7088890208890210e+04, + "iterations": 10476, + "real_time": 6.6131168671347084e+04, + "cpu_time": 6.5514603283696037e+04, "time_unit": "ns" }, { @@ -687,9 +687,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 10101, - "real_time": 7.1656405504046284e+04, - "cpu_time": 7.0902481140481046e+04, + "iterations": 10476, + "real_time": 6.5557157693897243e+04, + "cpu_time": 6.4923682321496744e+04, "time_unit": "ns" }, { @@ -703,8 +703,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 6.8979555621990105e+04, - "cpu_time": 6.8288090552090536e+04, + "real_time": 6.6031966399480487e+04, + "cpu_time": 6.5416965858470117e+04, "time_unit": "ns" }, { @@ -718,8 +718,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 6.7762344916422226e+04, - "cpu_time": 6.7088890208890210e+04, + "real_time": 6.6131168671347084e+04, + "cpu_time": 6.5514603283696029e+04, "time_unit": "ns" }, { @@ -733,8 +733,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 2.3213868396231469e+03, - "cpu_time": 2.2667027861942097e+03, + "real_time": 4.3379983327240069e+02, + "cpu_time": 4.5243649299935231e+02, "time_unit": "ns" }, { @@ -748,8 +748,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 3.3653258834319140e-02, - "cpu_time": 3.3193237178965433e-02, + "real_time": 6.5695428581968394e-03, + "cpu_time": 6.9161950124406639e-03, "time_unit": "ns" }, { @@ -761,9 +761,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 16451, - "real_time": 4.3826071910359613e+04, - "cpu_time": 4.3656422588292517e+04, + "iterations": 16429, + "real_time": 4.2655499908660488e+04, + "cpu_time": 4.2512722868099081e+04, "time_unit": "ns" }, { @@ -775,9 +775,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 16451, - "real_time": 4.5471952586603991e+04, - "cpu_time": 4.5253403805239861e+04, + "iterations": 16429, + "real_time": 4.3006030555641300e+04, + "cpu_time": 4.2847248219611705e+04, "time_unit": "ns" }, { @@ -789,9 +789,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 16451, - "real_time": 4.3340157679465607e+04, - "cpu_time": 4.3146676615403223e+04, + "iterations": 16429, + "real_time": 4.2671599427912239e+04, + "cpu_time": 4.2512723598514865e+04, "time_unit": "ns" }, { @@ -805,8 +805,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 4.4212727392143075e+04, - "cpu_time": 4.4018834336311869e+04, + "real_time": 4.2777709964071335e+04, + "cpu_time": 4.2624231562075220e+04, "time_unit": "ns" }, { @@ -820,8 +820,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 4.3826071910359613e+04, - "cpu_time": 4.3656422588292531e+04, + "real_time": 4.2671599427912239e+04, + "cpu_time": 4.2512723598514865e+04, "time_unit": "ns" }, { @@ -835,8 +835,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.1172574581177582e+03, - "cpu_time": 1.0991276401882440e+03, + "real_time": 1.9789521982826508e+02, + "cpu_time": 1.9313809089472591e+02, "time_unit": "ns" }, { @@ -850,8 +850,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 2.5270041547274080e-02, - "cpu_time": 2.4969485375071716e-02, + "real_time": 4.6261293555563323e-03, + "cpu_time": 4.5311805941522223e-03, "time_unit": "ns" }, { @@ -863,9 +863,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 7826, - "real_time": 8.9939901991683058e+04, - "cpu_time": 8.8865522616917951e+04, + "iterations": 8249, + "real_time": 8.2960084252814093e+04, + "cpu_time": 8.2177835737665257e+04, "time_unit": "ns" }, { @@ -877,9 +877,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 7826, - "real_time": 9.3378252363479318e+04, - "cpu_time": 9.2188841809353500e+04, + "iterations": 8249, + "real_time": 8.2548407685846934e+04, + "cpu_time": 8.1742332161474085e+04, "time_unit": "ns" }, { @@ -891,9 +891,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 7826, - "real_time": 8.7275915154285205e+04, - "cpu_time": 8.6283555839509107e+04, + "iterations": 8249, + "real_time": 8.1475008728372239e+04, + "cpu_time": 8.0718609164747511e+04, "time_unit": "ns" }, { @@ -907,8 +907,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 9.0198023169815875e+04, - "cpu_time": 8.9112640088593515e+04, + "real_time": 8.2327833555677746e+04, + "cpu_time": 8.1546259021295627e+04, "time_unit": "ns" }, { @@ -922,8 +922,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 8.9939901991683058e+04, - "cpu_time": 8.8865522616917951e+04, + "real_time": 8.2548407685846934e+04, + "cpu_time": 8.1742332161474100e+04, "time_unit": "ns" }, { @@ -937,8 +937,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 3.0593462962911394e+03, - "cpu_time": 2.9603886366515130e+03, + "real_time": 7.6671509604269932e+02, + "cpu_time": 7.4911217784934934e+02, "time_unit": "ns" }, { @@ -952,8 +952,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 3.3918108055775310e-02, - "cpu_time": 3.3220748860188297e-02, + "real_time": 9.3129511968048466e-03, + "cpu_time": 9.1863463369143677e-03, "time_unit": "ns" }, { @@ -965,9 +965,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 8124, - "real_time": 9.1373874814580195e+04, - "cpu_time": 9.0240014524864659e+04, + "iterations": 8491, + "real_time": 8.1564215168851792e+04, + "cpu_time": 8.0868939347544539e+04, "time_unit": "ns" }, { @@ -979,9 +979,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 8124, - "real_time": 1.0381180330036678e+05, - "cpu_time": 1.0038200763170891e+05, + "iterations": 8491, + "real_time": 8.2415557766973274e+04, + "cpu_time": 8.1676025556471272e+04, "time_unit": "ns" }, { @@ -993,9 +993,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 8124, - "real_time": 9.5242334689878713e+04, - "cpu_time": 9.3994281265386206e+04, + "iterations": 8491, + "real_time": 8.1876454834554199e+04, + "cpu_time": 8.1156611588741158e+04, "time_unit": "ns" }, { @@ -1009,8 +1009,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 9.6809337601608553e+04, - "cpu_time": 9.4872101140653249e+04, + "real_time": 8.1952075923459750e+04, + "cpu_time": 8.1233858830918994e+04, "time_unit": "ns" }, { @@ -1024,8 +1024,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 9.5242334689878728e+04, - "cpu_time": 9.3994281265386206e+04, + "real_time": 8.1876454834554184e+04, + "cpu_time": 8.1156611588741158e+04, "time_unit": "ns" }, { @@ -1039,8 +1039,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 6.3653075219044285e+03, - "cpu_time": 5.1276633903641432e+03, + "real_time": 4.3067965666014658e+02, + "cpu_time": 4.0905059525465964e+02, "time_unit": "ns" }, { @@ -1054,8 +1054,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 6.5750966586498608e-02, - "cpu_time": 5.4048169363953388e-02, + "real_time": 5.2552623201685056e-03, + "cpu_time": 5.0354692137186529e-03, "time_unit": "ns" }, { @@ -1067,9 +1067,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 6458, - "real_time": 1.0016481913773292e+05, - "cpu_time": 9.8046890987921797e+04, + "iterations": 8845, + "real_time": 7.8003122781477985e+04, + "cpu_time": 7.7279036404748433e+04, "time_unit": "ns" }, { @@ -1081,9 +1081,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 6458, - "real_time": 1.0063212589107969e+05, - "cpu_time": 9.9071416382781026e+04, + "iterations": 8845, + "real_time": 7.8157331147635239e+04, + "cpu_time": 7.7421407687959552e+04, "time_unit": "ns" }, { @@ -1095,9 +1095,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 6458, - "real_time": 8.5858419942888766e+04, - "cpu_time": 8.4224828120161445e+04, + "iterations": 8845, + "real_time": 7.7875304239552788e+04, + "cpu_time": 7.7212780214810453e+04, "time_unit": "ns" }, { @@ -1111,8 +1111,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 9.5551788323900444e+04, - "cpu_time": 9.3781045163621427e+04, + "real_time": 7.8011919389555333e+04, + "cpu_time": 7.7304408102506146e+04, "time_unit": "ns" }, { @@ -1126,8 +1126,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.0016481913773289e+05, - "cpu_time": 9.8046890987921797e+04, + "real_time": 7.8003122781478000e+04, + "cpu_time": 7.7279036404748433e+04, "time_unit": "ns" }, { @@ -1141,8 +1141,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 8.3979543239963441e+03, - "cpu_time": 8.2917655663595015e+03, + "real_time": 1.4121908318453850e+02, + "cpu_time": 1.0660275758583292e+02, "time_unit": "ns" }, { @@ -1156,8 +1156,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 8.7889033489661622e-02, - "cpu_time": 8.8416220483496566e-02, + "real_time": 1.8102244412082200e-03, + "cpu_time": 1.3789997259208940e-03, "time_unit": "ns" }, { @@ -1169,9 +1169,9 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 6858, - "real_time": 9.3030761591598537e+04, - "cpu_time": 9.1602852435112174e+04, + "iterations": 9102, + "real_time": 7.7994850362925325e+04, + "cpu_time": 7.7232681388705620e+04, "time_unit": "ns" }, { @@ -1183,9 +1183,9 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 6858, - "real_time": 8.6511492128064332e+04, - "cpu_time": 8.5318596821230691e+04, + "iterations": 9102, + "real_time": 7.7683349923208254e+04, + "cpu_time": 7.6985877059986873e+04, "time_unit": "ns" }, { @@ -1197,9 +1197,9 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 6858, - "real_time": 8.4088126856397226e+04, - "cpu_time": 8.2806301691455490e+04, + "iterations": 9102, + "real_time": 7.7163363656431247e+04, + "cpu_time": 7.6465104372665315e+04, "time_unit": "ns" }, { @@ -1213,8 +1213,8 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 8.7876793525353365e+04, - "cpu_time": 8.6575916982599461e+04, + "real_time": 7.7613854647521584e+04, + "cpu_time": 7.6894554273785921e+04, "time_unit": "ns" }, { @@ -1228,8 +1228,8 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 8.6511492128064347e+04, - "cpu_time": 8.5318596821230705e+04, + "real_time": 7.7683349923208239e+04, + "cpu_time": 7.6985877059986859e+04, "time_unit": "ns" }, { @@ -1243,8 +1243,8 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 4.6250097222475179e+03, - "cpu_time": 4.5310558082604975e+03, + "real_time": 4.2007705337852963e+02, + "cpu_time": 3.9185266025673900e+02, "time_unit": "ns" }, { @@ -1258,8 +1258,8 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 5.2630615395783127e-02, - "cpu_time": 5.2336215037389389e-02, + "real_time": 5.4123977643718750e-03, + "cpu_time": 5.0959741422199160e-03, "time_unit": "ns" } ] diff --git a/research/benchmark-results/ssb-sf001.json b/research/benchmark-results/ssb-sf001.json index 20ab4e5..24be094 100644 --- a/research/benchmark-results/ssb-sf001.json +++ b/research/benchmark-results/ssb-sf001.json @@ -1,6 +1,6 @@ { "context": { - "date": "2026-06-09T23:45:52+03:00", + "date": "2026-06-14T20:44:28+03:00", "host_name": "nixos", "executable": "./build-release/bin/benchmarks", "num_cpus": 8, @@ -32,7 +32,7 @@ "num_sharing": 8 } ], - "load_avg": [1.00439,0.640625,0.89502], + "load_avg": [1.58936,1.53076,1.89648], "library_version": "v1.9.0", "library_build_type": "release", "json_schema_version": 1 @@ -47,11 +47,11 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 76, - "real_time": 9.9512121447141413e+06, - "cpu_time": 9.8711035526315793e+06, + "iterations": 65, + "real_time": 9.8329785384591483e+06, + "cpu_time": 9.7378805384615380e+06, "time_unit": "ns", - "execution_us": 9.9511413026315786e+03, + "execution_us": 9.8328972153846153e+03, "includes_order_by": 1.0000000000000000e+00 }, { @@ -63,11 +63,11 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 76, - "real_time": 1.0357855052509541e+07, - "cpu_time": 1.0266127460526315e+07, + "iterations": 65, + "real_time": 9.0062370153296236e+06, + "cpu_time": 8.9505159692307673e+06, "time_unit": "ns", - "execution_us": 1.0357739236842106e+04, + "execution_us": 9.0061597846153854e+03, "includes_order_by": 1.0000000000000000e+00 }, { @@ -79,11 +79,11 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - 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"real_time": 9.4633789062754661e+06, - "cpu_time": 9.4057306249999423e+06, + "real_time": 8.6581378375285566e+06, + "cpu_time": 8.5737115625001080e+06, "time_unit": "ns", - "execution_us": 9.4633035624999993e+03, + "execution_us": 8.6580729000000010e+03, "includes_order_by": 1.0000000000000000e+00 }, { @@ -3083,10 +3083,10 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 8.5021292019139873e+04, - "cpu_time": 2.0694750133421130e+04, + "real_time": 9.7558601409023788e+04, + "cpu_time": 9.4695975679618903e+04, "time_unit": "ns", - "execution_us": 8.5025111648877925e+01, + "execution_us": 9.7844471210802425e+01, "includes_order_by": 0.0000000000000000e+00 }, { @@ -3100,10 +3100,10 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 8.9459577441626682e-03, - "cpu_time": 2.2005859043300185e-03, + "real_time": 1.1290615591380299e-02, + "cpu_time": 1.1040643198034361e-02, "time_unit": "ns", - "execution_us": 8.9464273889423487e-03, + "execution_us": 1.1324012300979829e-02, "includes_order_by": 0.0000000000000000e+00 }, { @@ -3115,13 +3115,13 @@ "repetitions": 3, "repetition_index": 0, "threads": 1, - "iterations": 106, - "real_time": 6.2337937923521679e+06, - "cpu_time": 6.1994316603774205e+06, + "iterations": 114, + "real_time": 6.0918677368215611e+06, + "cpu_time": 6.0557457368420251e+06, "time_unit": "ns", - "execution_us": 6.2337179245283014e+03, + "execution_us": 6.0918212894736844e+03, "includes_order_by": 1.0000000000000000e+00, - "optimizer_us": 5.2146959999999999e+03, + "optimizer_us": 4.4798770000000004e+03, "plan_cost": 8.3622300000000000e+05 }, { @@ -3133,13 +3133,13 @@ "repetitions": 3, "repetition_index": 1, "threads": 1, - "iterations": 106, - "real_time": 6.4182148961886270e+06, - "cpu_time": 6.3774029433961688e+06, + "iterations": 114, + "real_time": 6.0794830087775560e+06, + "cpu_time": 6.0390325350876907e+06, "time_unit": "ns", - "execution_us": 6.4181481415094340e+03, + "execution_us": 6.0794388245614027e+03, "includes_order_by": 1.0000000000000000e+00, - "optimizer_us": 5.6024849999999997e+03, + "optimizer_us": 4.5196109999999999e+03, "plan_cost": 8.3622300000000000e+05 }, { @@ -3151,13 +3151,13 @@ "repetitions": 3, "repetition_index": 2, "threads": 1, - "iterations": 106, - "real_time": 6.2254421887392141e+06, - "cpu_time": 6.1920818396226596e+06, + "iterations": 114, + "real_time": 6.0435537719361791e+06, + "cpu_time": 6.0123747456140062e+06, "time_unit": "ns", - "execution_us": 6.2253759528301889e+03, + "execution_us": 6.0435049736842111e+03, "includes_order_by": 1.0000000000000000e+00, - "optimizer_us": 5.3557669999999998e+03, + "optimizer_us": 4.6384939999999997e+03, "plan_cost": 8.3622300000000000e+05 }, { @@ -3171,12 +3171,12 @@ "aggregate_name": "mean", "aggregate_unit": "time", "iterations": 3, - "real_time": 6.2924836257600030e+06, - "cpu_time": 6.2563054811320826e+06, + "real_time": 6.0716348391784308e+06, + "cpu_time": 6.0357176725145727e+06, "time_unit": "ns", - "execution_us": 6.2924140062893075e+03, + "execution_us": 6.0715883625730985e+03, "includes_order_by": 1.0000000000000000e+00, - "optimizer_us": 5.3909826666666668e+03, + "optimizer_us": 4.5459939999999997e+03, "plan_cost": 8.3622300000000000e+05 }, { @@ -3190,12 +3190,12 @@ "aggregate_name": "median", "aggregate_unit": "time", "iterations": 3, - "real_time": 6.2337937923521670e+06, - "cpu_time": 6.1994316603774205e+06, + "real_time": 6.0794830087775551e+06, + "cpu_time": 6.0390325350876898e+06, "time_unit": "ns", - "execution_us": 6.2337179245283014e+03, + "execution_us": 6.0794388245614027e+03, "includes_order_by": 1.0000000000000000e+00, - "optimizer_us": 5.3557669999999998e+03, + "optimizer_us": 4.5196109999999999e+03, "plan_cost": 8.3622300000000000e+05 }, { @@ -3209,12 +3209,12 @@ "aggregate_name": "stddev", "aggregate_unit": "time", "iterations": 3, - "real_time": 1.0896651592772501e+05, - "cpu_time": 1.0493784584879056e+05, + "real_time": 2.5094922300718245e+04, + "cpu_time": 2.1874687550823292e+04, "time_unit": "ns", - "execution_us": 1.0896881064158120e+02, + "execution_us": 2.5096591560550149e+01, "includes_order_by": 0.0000000000000000e+00, - "optimizer_us": 1.9627833429681101e+02, + "optimizer_us": 8.2534145594462700e+01, "plan_cost": 0.0000000000000000e+00 }, { @@ -3228,12 +3228,12 @@ "aggregate_name": "cv", "aggregate_unit": "percentage", "iterations": 3, - "real_time": 1.7316932773832061e-02, - "cpu_time": 1.6773133307710859e-02, + "real_time": 4.1331409028073076e-03, + "cpu_time": 3.6242065546631111e-03, "time_unit": "ns", - "execution_us": 1.7317489048347134e-02, + "execution_us": 4.1334474707231932e-03, "includes_order_by": 0.0000000000000000e+00, - "optimizer_us": 3.6408637614517340e-02, + "optimizer_us": 1.8155357352971146e-02, "plan_cost": 0.0000000000000000e+00 } ] diff --git a/research/benchmarks.ipynb b/research/benchmarks.ipynb index 22b41b0..629f6fe 100644 --- a/research/benchmarks.ipynb +++ b/research/benchmarks.ipynb @@ -2,98 +2,76 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "b5291871", "metadata": {}, "outputs": [ { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mFailed to start the Kernel. \n", - "\u001b[1;31mFailed to find the URL of the launched Jupyter notebook server\n", - "\u001b[1;31m[W 2026-06-14 17:27:38.911 ServerApp] jupyterlab | error adding extension (enabled: True): Unknown scheme for proxy URL URL('socks://localhost:1081')\n", - "\u001b[1;31m Traceback (most recent call last):\n", - "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/manager.py\", line 365, in add_extension\n", - "\u001b[1;31m extpkg = ExtensionPackage(name=extension_name, enabled=enabled)\n", - "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/manager.py\", line 219, in __init__\n", - "\u001b[1;31m self._load_metadata()\n", - "\u001b[1;31m ~~~~~~~~~~~~~~~~~~~^^\n", - "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/manager.py\", line 228, in _load_metadata\n", - "\u001b[1;31m self.module, self.metadata = get_metadata(name, logger=self.log)\n", - "\u001b[1;31m ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^\n", - "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/utils.py\", line 76, in get_metadata\n", - "\u001b[1;31m return module, module._jupyter_server_extension_points()\n", - "\u001b[1;31m ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^\n", - "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/__init__.py\", line 20, in _jupyter_server_extension_points\n", - "\u001b[1;31m from .labapp import LabApp\n", - "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/labapp.py\", line 46, in \n", - "\u001b[1;31m from .extensions import MANAGERS as EXT_MANAGERS\n", - "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/extensions/__init__.py\", line 12, in \n", - "\u001b[1;31m from .pypi import PyPIExtensionManager\n", - "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/extensions/pypi.py\", line 76, in \n", - "\u001b[1;31m \"http://\": httpx.AsyncHTTPTransport(proxy=http_proxy_url),\n", - "\u001b[1;31m ~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^\n", - "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/httpx/_transports/default.py\", line 296, in __init__\n", - "\u001b[1;31m proxy = Proxy(url=proxy) if isinstance(proxy, (str, URL)) else proxy\n", - "\u001b[1;31m ~~~~~^^^^^^^^^^^\n", - "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/httpx/_config.py\", line 214, in __init__\n", - "\u001b[1;31m raise ValueError(f\"Unknown scheme for proxy URL {url!r}\")\n", - "\u001b[1;31m ValueError: Unknown scheme for proxy URL URL('socks://localhost:1081')\n", - "\u001b[1;31m[I 2026-06-14 17:27:38.917 ServerApp] jupyter_lsp | extension was successfully linked.\n", - "\u001b[1;31m[I 2026-06-14 17:27:38.923 ServerApp] jupyter_server_terminals | extension was successfully linked.\n", - "\u001b[1;31m[W 2026-06-14 17:27:38.926 JupyterNotebookApp] 'iopub_data_rate_limit' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.\n", - "\u001b[1;31m[W 2026-06-14 17:27:38.931 ServerApp] ServerApp.iopub_data_rate_limit config is deprecated in 2.0. Use ZMQChannelsWebsocketConnection.iopub_data_rate_limit.\n", - "\u001b[1;31m[I 2026-06-14 17:27:38.932 ServerApp] notebook | extension was successfully linked.\n", - "\u001b[1;31m[W 2026-06-14 17:27:41.699 ServerApp] jupyterlab | error adding extension (enabled: True): Unknown scheme for proxy URL URL('socks://localhost:1081')\n", - "\u001b[1;31m Traceback (most recent call last):\n", - "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/manager.py\", line 365, in add_extension\n", - "\u001b[1;31m extpkg = ExtensionPackage(name=extension_name, enabled=enabled)\n", - "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/manager.py\", line 219, in __init__\n", - "\u001b[1;31m self._load_metadata()\n", - "\u001b[1;31m ~~~~~~~~~~~~~~~~~~~^^\n", - "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/manager.py\", line 228, in _load_metadata\n", - "\u001b[1;31m self.module, self.metadata = get_metadata(name, logger=self.log)\n", - "\u001b[1;31m ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^\n", - "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/utils.py\", line 76, in get_metadata\n", - "\u001b[1;31m return module, module._jupyter_server_extension_points()\n", - "\u001b[1;31m ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^\n", - "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/__init__.py\", line 20, in _jupyter_server_extension_points\n", - "\u001b[1;31m from .labapp import LabApp\n", - "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/labapp.py\", line 46, in \n", - "\u001b[1;31m from .extensions import MANAGERS as EXT_MANAGERS\n", - "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/extensions/__init__.py\", line 12, in \n", - "\u001b[1;31m from .pypi import PyPIExtensionManager\n", - "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/extensions/pypi.py\", line 76, in \n", - "\u001b[1;31m \"http://\": httpx.AsyncHTTPTransport(proxy=http_proxy_url),\n", - "\u001b[1;31m ~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^\n", - "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/httpx/_transports/default.py\", line 296, in __init__\n", - "\u001b[1;31m proxy = Proxy(url=proxy) if isinstance(proxy, (str, URL)) else proxy\n", - "\u001b[1;31m ~~~~~^^^^^^^^^^^\n", - "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/httpx/_config.py\", line 214, in __init__\n", - "\u001b[1;31m raise ValueError(f\"Unknown scheme for proxy URL {url!r}\")\n", - "\u001b[1;31m ValueError: Unknown scheme for proxy URL URL('socks://localhost:1081')\n", - "\u001b[1;31m[I 2026-06-14 17:27:41.702 ServerApp] notebook_shim | extension was successfully linked.\n", - "\u001b[1;31m[I 2026-06-14 17:27:41.774 ServerApp] notebook_shim | extension was successfully loaded.\n", - "\u001b[1;31m[I 2026-06-14 17:27:41.778 ServerApp] jupyter_lsp | extension was successfully loaded.\n", - "\u001b[1;31m[I 2026-06-14 17:27:41.785 ServerApp] jupyter_server_terminals | extension was successfully loaded.\n", - "\u001b[1;31m[I 2026-06-14 17:27:41.816 ServerApp] notebook | extension was successfully loaded.\n", - "\u001b[1;31m[I 2026-06-14 17:27:41.819 ServerApp] The port 8888 is already in use, trying another port.\n", - "\u001b[1;31m[I 2026-06-14 17:27:41.821 ServerApp] Serving notebooks from local directory: /home/st/c/iu9-sql-compiler/research\n", - "\u001b[1;31m[I 2026-06-14 17:27:41.822 ServerApp] Jupyter Server 2.17.0 is running at:\n", - "\u001b[1;31m[I 2026-06-14 17:27:41.822 ServerApp] http://localhost:8889/tree?token=c5ff0bd91935fa65038e4a11fe748322a019a84f3a190f69\n", - "\u001b[1;31m[I 2026-06-14 17:27:41.822 ServerApp] http://127.0.0.1:8889/tree?token=c5ff0bd91935fa65038e4a11fe748322a019a84f3a190f69\n", - "\u001b[1;31m[I 2026-06-14 17:27:41.822 ServerApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).\n", - "\u001b[1;31m[C 2026-06-14 17:27:41.836 ServerApp] \n", - "\u001b[1;31m \n", - "\u001b[1;31m To access the server, open this file in a browser:\n", - "\u001b[1;31m file:///home/st/.local/share/jupyter/runtime/jpserver-29560-open.html\n", - "\u001b[1;31m Or copy and paste one of these URLs:\n", - "\u001b[1;31m http://localhost:8889/tree?token=c5ff0bd91935fa65038e4a11fe748322a019a84f3a190f69\n", - "\u001b[1;31m http://127.0.0.1:8889/tree?token=c5ff0bd91935fa65038e4a11fe748322a019a84f3a190f69\n", - "\u001b[1;31m[I 2026-06-14 17:27:42.864 ServerApp] Skipped non-installed server(s): basedpyright, bash-language-server, dockerfile-language-server-nodejs, javascript-typescript-langserver, jedi-language-server, julia-language-server, pyrefly, pyright, python-language-server, python-lsp-server, r-languageserver, sql-language-server, texlab, typescript-language-server, unified-language-server, vscode-css-languageserver-bin, vscode-html-languageserver-bin, vscode-json-languageserver-bin, yaml-language-server. \n", - "\u001b[1;31mView Jupyter log for further details." + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-06-14T20:44:00+03:00\n", + "Running ./build-release/bin/benchmarks\n", + "Run on (8 X 4200 MHz CPU s)\n", + "CPU Caches:\n", + " L1 Data 48 KiB (x4)\n", + " L1 Instruction 32 KiB (x4)\n", + " L2 Unified 1280 KiB (x4)\n", + " L3 Unified 8192 KiB (x1)\n", + "Load Average: 0.80, 1.39, 1.86\n", + "***WARNING*** CPU scaling is enabled, the benchmark real time measurements may be noisy and will incur extra overhead.\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------\n", + "Benchmark Time CPU Iterations\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 20437 ns 20203 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 20620 ns 20440 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 674 ns 749 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 3.30 % 3.71 % \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 20149 ns 19914 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 20169 ns 19933 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 50.4 ns 56.9 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 0.25 % 0.29 % \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 19900 ns 19621 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 19976 ns 19649 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 180 ns 140 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 0.91 % 0.71 % \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 19747 ns 19643 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 19954 ns 19790 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 365 ns 327 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 1.85 % 1.66 % \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 66637 ns 66004 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 66603 ns 65979 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 470 ns 438 ns \u001b[m\u001b[0;36m 3\u001b[m\n", + "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 0.71 % 0.66 % \u001b[m\u001b[0;36m 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"----------------------------------------------------------------------------------------------------------\n", + "Benchmark Time CPU Iterations UserCounters...\n", + "----------------------------------------------------------------------------------------------------------\n", + "\u001b[0;32mSSB/q2.1/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 9348855 ns 9279593 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.34877k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 9207349 ns 9150383 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.20727k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 431153 ns 409276 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=431.152\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 4.61 % 4.41 % \u001b[m\u001b[0;36m 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"\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 6730765 ns 6700780 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.7307k\u001b[m includes_order_by=0\u001b[m optimizer_us=1.03781k\u001b[m plan_cost=654.297k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 44835 ns 42238 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=44.8477\u001b[m includes_order_by=0\u001b[m optimizer_us=134.145\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 0.67 % 0.63 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.67%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=13.82%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 36699167 ns 36503054 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=36.699k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 36780937 ns 36587893 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=36.7808k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 702089 ns 689228 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=702.125\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 1.91 % 1.89 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=1.91%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 5963849 ns 5933470 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=5.96378k\u001b[m includes_order_by=1\u001b[m optimizer_us=68.2714k\u001b[m plan_cost=816.714k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 6011656 ns 5980110 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.01157k\u001b[m includes_order_by=1\u001b[m optimizer_us=67.7282k\u001b[m plan_cost=816.714k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 126319 ns 124387 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=126.32\u001b[m includes_order_by=0\u001b[m optimizer_us=2.54977k\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 2.12 % 2.10 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=2.12%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=3.73%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 34712294 ns 34551769 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=34.7121k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 34714216 ns 34550395 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=34.714k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 143927 ns 137031 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=143.838\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 0.41 % 0.40 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.41%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 5903691 ns 5871216 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=5.90363k\u001b[m includes_order_by=1\u001b[m optimizer_us=45.6028k\u001b[m plan_cost=816.714k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 5946191 ns 5911962 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=5.94614k\u001b[m includes_order_by=1\u001b[m optimizer_us=45.9131k\u001b[m plan_cost=816.714k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 120315 ns 119854 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=120.31\u001b[m includes_order_by=0\u001b[m optimizer_us=647.108\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 2.04 % 2.04 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=2.04%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=1.42%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 9196776 ns 9139066 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.1967k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 9174353 ns 9117973 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.17429k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 203688 ns 198949 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=203.661\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 2.21 % 2.18 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=2.21%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 6029881 ns 6001703 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.02982k\u001b[m includes_order_by=1\u001b[m optimizer_us=305.279k\u001b[m plan_cost=841.353k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 6022257 ns 5994458 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.02219k\u001b[m includes_order_by=1\u001b[m optimizer_us=304.816k\u001b[m plan_cost=841.353k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 26686 ns 26193 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=26.693\u001b[m includes_order_by=0\u001b[m optimizer_us=861.045\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 0.44 % 0.44 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.44%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=0.28%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 8408618 ns 8358138 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.40857k\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 8314836 ns 8264783 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.31478k\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 164683 ns 161781 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=164.68\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Naive/real_time_cv 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0.26 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.27%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=1.25%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 35087301 ns 34918098 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=35.0871k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 35089531 ns 34919402 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=35.0894k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 8513 ns 11166 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.50818\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 0.02 % 0.03 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.02%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 6043214 ns 6008341 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.04315k\u001b[m includes_order_by=1\u001b[m optimizer_us=26.122k\u001b[m plan_cost=816.374k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 5993226 ns 5955491 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=5.99315k\u001b[m includes_order_by=1\u001b[m optimizer_us=25.803k\u001b[m plan_cost=816.374k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 141650 ns 142416 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=141.654\u001b[m includes_order_by=0\u001b[m optimizer_us=1.46216k\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 2.34 % 2.37 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=2.34%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=5.60%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 8857568 ns 8799695 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.85751k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 8874801 ns 8812681 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.87474k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 76477 ns 65016 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=76.4718\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 0.86 % 0.74 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.86%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 6276747 ns 6238963 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.2767k\u001b[m includes_order_by=1\u001b[m optimizer_us=8.03571k\u001b[m plan_cost=841.323k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 6264807 ns 6227206 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.26476k\u001b[m includes_order_by=1\u001b[m optimizer_us=8.06958k\u001b[m plan_cost=841.323k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 43179 ns 37483 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=43.1773\u001b[m includes_order_by=0\u001b[m optimizer_us=129.841\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 0.69 % 0.60 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.69%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=1.62%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 37245934 ns 36895021 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=37.2456k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 37229172 ns 36945452 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=37.229k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 89624 ns 100110 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=89.8603\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 0.24 % 0.27 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.24%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 5914830 ns 5884658 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=5.91478k\u001b[m includes_order_by=1\u001b[m optimizer_us=25.0299k\u001b[m plan_cost=816.374k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 5917390 ns 5888825 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=5.91734k\u001b[m includes_order_by=1\u001b[m optimizer_us=24.9689k\u001b[m plan_cost=816.374k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 19819 ns 17179 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=19.8171\u001b[m includes_order_by=0\u001b[m optimizer_us=119.745\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 0.34 % 0.29 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.34%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=0.48%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 9171801 ns 9099329 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.17174k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 9138713 ns 9086817 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.13865k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 88047 ns 55888 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=88.0417\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 0.96 % 0.61 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.96%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 7149140 ns 7098640 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.14907k\u001b[m includes_order_by=1\u001b[m optimizer_us=3.73882M\u001b[m plan_cost=842.823k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 7141587 ns 7094454 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.14153k\u001b[m includes_order_by=1\u001b[m optimizer_us=3.74972M\u001b[m plan_cost=842.823k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 112055 ns 112431 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=112.041\u001b[m includes_order_by=0\u001b[m optimizer_us=34.6262k\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 1.57 % 1.58 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=1.57%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=0.93%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 9111124 ns 9042171 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.11106k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 9099306 ns 9033217 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.09925k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 75179 ns 68936 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=75.1935\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 0.83 % 0.76 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.83%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 9521586 ns 9462121 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.52152k\u001b[m includes_order_by=1\u001b[m optimizer_us=100.318k\u001b[m plan_cost=842.793k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 9558283 ns 9492243 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.55822k\u001b[m includes_order_by=1\u001b[m optimizer_us=100.417k\u001b[m plan_cost=842.793k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 113161 ns 113309 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=113.159\u001b[m includes_order_by=0\u001b[m optimizer_us=830.166\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 1.19 % 1.20 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=1.19%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=0.83%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 8640680 ns 8577034 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.64044k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 8658138 ns 8573712 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.65807k\u001b[m includes_order_by=1\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 97559 ns 94696 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=97.8445\u001b[m includes_order_by=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 1.13 % 1.10 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=1.13%\u001b[m includes_order_by=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 6071635 ns 6035718 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.07159k\u001b[m includes_order_by=1\u001b[m optimizer_us=4.54599k\u001b[m plan_cost=836.223k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 6079483 ns 6039033 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.07944k\u001b[m includes_order_by=1\u001b[m optimizer_us=4.51961k\u001b[m plan_cost=836.223k\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 25095 ns 21875 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=25.0966\u001b[m includes_order_by=0\u001b[m optimizer_us=82.5341\u001b[m plan_cost=0\u001b[m\n", + "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 0.41 % 0.36 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.41%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=1.82%\u001b[m plan_cost=0.00%\u001b[m\n", + "\u001b[m" + ] + } + ], "source": [ - "!cd .. && SSB_DATA_DIR=benchmarks/datasets/ssb/generated/sf001 timeout 180s ./build-release/bin/benchmarks --benchmark_filter='^SSB/' --benchmark_repetitions=3 --benchmark_display_aggregates_only=true --benchmark_out=research/benchmark-results/ssb-sf001.json --benchmark_out_format=json" + "!rm -f benchmark-results/ssb-sf001.json\n", + "!cd .. && SSB_DATA_DIR=benchmarks/datasets/ssb/generated/sf001 timeout 600s ./build-release/bin/benchmarks --benchmark_filter='^SSB/' --benchmark_repetitions=3 --benchmark_display_aggregates_only=true --benchmark_out=research/benchmark-results/ssb-sf001.json --benchmark_out_format=json" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "6f8e3873", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-06-14T20:45:51+03:00\n", + "Running ./build-release/bin/benchmarks\n", + "Run on (8 X 4200 MHz CPU s)\n", + "CPU Caches:\n", + " L1 Data 48 KiB (x4)\n", + " L1 Instruction 32 KiB (x4)\n", + " L2 Unified 1280 KiB (x4)\n", + " L3 Unified 8192 KiB (x1)\n", + "Load Average: 1.35, 1.47, 1.84\n", + "***WARNING*** CPU scaling is enabled, the benchmark real time measurements may be noisy and will incur extra overhead.\n", + "----------------------------------------------------------------------------------------------------------------------\n", + "Benchmark Time CPU Iterations UserCounters...\n", + "----------------------------------------------------------------------------------------------------------------------\n", + "\u001b[0;32mOperatorCost/SeqScan/1024/real_time_mean \u001b[m\u001b[0;33m 39728 ns 39585 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=102.4k\u001b[m output_rows=1.024k\u001b[m plan_cost=102.4k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/1024/real_time_median \u001b[m\u001b[0;33m 39745 ns 39614 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=102.4k\u001b[m output_rows=1.024k\u001b[m plan_cost=102.4k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/1024/real_time_stddev \u001b[m\u001b[0;33m 234 ns 212 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/1024/real_time_cv \u001b[m\u001b[0;33m 0.59 % 0.54 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/2048/real_time_mean \u001b[m\u001b[0;33m 68177 ns 67893 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=204.8k\u001b[m output_rows=2.048k\u001b[m plan_cost=204.8k\u001b[m rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/2048/real_time_median \u001b[m\u001b[0;33m 67391 ns 67122 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=204.8k\u001b[m output_rows=2.048k\u001b[m plan_cost=204.8k\u001b[m rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/2048/real_time_stddev \u001b[m\u001b[0;33m 1605 ns 1523 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/2048/real_time_cv \u001b[m\u001b[0;33m 2.35 % 2.24 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/4096/real_time_mean \u001b[m\u001b[0;33m 162377 ns 160772 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=409.6k\u001b[m output_rows=4.096k\u001b[m plan_cost=409.6k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/4096/real_time_median \u001b[m\u001b[0;33m 165623 ns 163832 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=409.6k\u001b[m output_rows=4.096k\u001b[m plan_cost=409.6k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/4096/real_time_stddev \u001b[m\u001b[0;33m 5953 ns 5859 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/4096/real_time_cv \u001b[m\u001b[0;33m 3.67 % 3.64 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/8192/real_time_mean \u001b[m\u001b[0;33m 360639 ns 357681 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=819.2k\u001b[m output_rows=8.192k\u001b[m plan_cost=819.2k\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/8192/real_time_median \u001b[m\u001b[0;33m 360693 ns 357881 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=819.2k\u001b[m output_rows=8.192k\u001b[m plan_cost=819.2k\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/8192/real_time_stddev \u001b[m\u001b[0;33m 1345 ns 1523 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/8192/real_time_cv \u001b[m\u001b[0;33m 0.37 % 0.43 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/16384/real_time_mean \u001b[m\u001b[0;33m 791876 ns 785262 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.6384M\u001b[m output_rows=16.384k\u001b[m plan_cost=1.6384M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/16384/real_time_median \u001b[m\u001b[0;33m 785508 ns 779556 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.6384M\u001b[m output_rows=16.384k\u001b[m plan_cost=1.6384M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/16384/real_time_stddev \u001b[m\u001b[0;33m 34264 ns 33251 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/16384/real_time_cv \u001b[m\u001b[0;33m 4.33 % 4.23 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/32768/real_time_mean \u001b[m\u001b[0;33m 1862931 ns 1843550 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.2768M\u001b[m output_rows=32.768k\u001b[m plan_cost=3.2768M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/32768/real_time_median \u001b[m\u001b[0;33m 1859691 ns 1838972 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.2768M\u001b[m output_rows=32.768k\u001b[m plan_cost=3.2768M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/32768/real_time_stddev \u001b[m\u001b[0;33m 17007 ns 17740 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/32768/real_time_cv \u001b[m\u001b[0;33m 0.91 % 0.96 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/65536/real_time_mean \u001b[m\u001b[0;33m 4594572 ns 4551454 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.5536M\u001b[m output_rows=65.536k\u001b[m plan_cost=6.5536M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/65536/real_time_median \u001b[m\u001b[0;33m 4602633 ns 4558259 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.5536M\u001b[m output_rows=65.536k\u001b[m plan_cost=6.5536M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/65536/real_time_stddev \u001b[m\u001b[0;33m 14120 ns 12475 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/65536/real_time_cv \u001b[m\u001b[0;33m 0.31 % 0.27 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/131072/real_time_mean \u001b[m\u001b[0;33m 10053307 ns 9966611 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=13.1072M\u001b[m output_rows=131.072k\u001b[m plan_cost=13.1072M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/131072/real_time_median \u001b[m\u001b[0;33m 10166987 ns 10076585 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=13.1072M\u001b[m output_rows=131.072k\u001b[m plan_cost=13.1072M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/131072/real_time_stddev \u001b[m\u001b[0;33m 291946 ns 286458 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/131072/real_time_cv \u001b[m\u001b[0;33m 2.90 % 2.87 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/262144/real_time_mean \u001b[m\u001b[0;33m 20673458 ns 20513325 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.2144M\u001b[m output_rows=262.144k\u001b[m plan_cost=26.2144M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/262144/real_time_median \u001b[m\u001b[0;33m 20544696 ns 20386411 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.2144M\u001b[m output_rows=262.144k\u001b[m plan_cost=26.2144M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/262144/real_time_stddev \u001b[m\u001b[0;33m 246412 ns 240540 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/SeqScan/262144/real_time_cv \u001b[m\u001b[0;33m 1.19 % 1.17 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/1024/real_time_mean \u001b[m\u001b[0;33m 70811 ns 70565 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=102.4k\u001b[m output_rows=512\u001b[m plan_cost=204.8k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/1024/real_time_median \u001b[m\u001b[0;33m 70326 ns 70110 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=102.4k\u001b[m output_rows=512\u001b[m plan_cost=204.8k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/1024/real_time_stddev \u001b[m\u001b[0;33m 2401 ns 2353 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/1024/real_time_cv \u001b[m\u001b[0;33m 3.39 % 3.34 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/2048/real_time_mean \u001b[m\u001b[0;33m 135097 ns 134476 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=204.8k\u001b[m output_rows=1.024k\u001b[m plan_cost=409.6k\u001b[m rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/2048/real_time_median \u001b[m\u001b[0;33m 135164 ns 134571 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=204.8k\u001b[m output_rows=1.024k\u001b[m plan_cost=409.6k\u001b[m rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/2048/real_time_stddev \u001b[m\u001b[0;33m 799 ns 724 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/2048/real_time_cv \u001b[m\u001b[0;33m 0.59 % 0.54 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/4096/real_time_mean \u001b[m\u001b[0;33m 262054 ns 260826 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=409.6k\u001b[m output_rows=2.048k\u001b[m plan_cost=819.2k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/4096/real_time_median \u001b[m\u001b[0;33m 261825 ns 260866 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=409.6k\u001b[m output_rows=2.048k\u001b[m plan_cost=819.2k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/4096/real_time_stddev \u001b[m\u001b[0;33m 1211 ns 1402 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/4096/real_time_cv \u001b[m\u001b[0;33m 0.46 % 0.54 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/8192/real_time_mean \u001b[m\u001b[0;33m 508853 ns 506511 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=819.2k\u001b[m output_rows=4.096k\u001b[m plan_cost=1.6384M\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/8192/real_time_median \u001b[m\u001b[0;33m 505867 ns 503676 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=819.2k\u001b[m output_rows=4.096k\u001b[m plan_cost=1.6384M\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/8192/real_time_stddev \u001b[m\u001b[0;33m 12890 ns 12652 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/8192/real_time_cv \u001b[m\u001b[0;33m 2.53 % 2.50 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/16384/real_time_mean \u001b[m\u001b[0;33m 1010537 ns 1005862 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.6384M\u001b[m output_rows=8.192k\u001b[m plan_cost=3.2768M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/16384/real_time_median \u001b[m\u001b[0;33m 1010307 ns 1005759 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.6384M\u001b[m output_rows=8.192k\u001b[m plan_cost=3.2768M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/16384/real_time_stddev \u001b[m\u001b[0;33m 5573 ns 5402 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/16384/real_time_cv \u001b[m\u001b[0;33m 0.55 % 0.54 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/32768/real_time_mean \u001b[m\u001b[0;33m 2094588 ns 2083947 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.2768M\u001b[m output_rows=16.384k\u001b[m plan_cost=6.5536M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/32768/real_time_median \u001b[m\u001b[0;33m 2094297 ns 2083675 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.2768M\u001b[m output_rows=16.384k\u001b[m plan_cost=6.5536M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/32768/real_time_stddev \u001b[m\u001b[0;33m 13883 ns 12947 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/32768/real_time_cv \u001b[m\u001b[0;33m 0.66 % 0.62 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/65536/real_time_mean \u001b[m\u001b[0;33m 4399525 ns 4374122 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.5536M\u001b[m output_rows=32.768k\u001b[m plan_cost=13.1072M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/65536/real_time_median \u001b[m\u001b[0;33m 4398467 ns 4372248 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.5536M\u001b[m output_rows=32.768k\u001b[m plan_cost=13.1072M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/65536/real_time_stddev \u001b[m\u001b[0;33m 9109 ns 9887 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/65536/real_time_cv \u001b[m\u001b[0;33m 0.21 % 0.23 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/131072/real_time_mean \u001b[m\u001b[0;33m 9087365 ns 9040882 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=13.1072M\u001b[m output_rows=65.536k\u001b[m plan_cost=26.2144M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/131072/real_time_median \u001b[m\u001b[0;33m 9081014 ns 9026851 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=13.1072M\u001b[m output_rows=65.536k\u001b[m plan_cost=26.2144M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/131072/real_time_stddev \u001b[m\u001b[0;33m 206801 ns 203954 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/131072/real_time_cv \u001b[m\u001b[0;33m 2.28 % 2.26 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/262144/real_time_mean \u001b[m\u001b[0;33m 19038369 ns 18931027 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.2144M\u001b[m output_rows=131.072k\u001b[m plan_cost=52.4288M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/262144/real_time_median \u001b[m\u001b[0;33m 18975293 ns 18872990 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.2144M\u001b[m output_rows=131.072k\u001b[m plan_cost=52.4288M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/262144/real_time_stddev \u001b[m\u001b[0;33m 211770 ns 211621 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Filter/262144/real_time_cv \u001b[m\u001b[0;33m 1.11 % 1.12 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/1024/real_time_mean \u001b[m\u001b[0;33m 98176 ns 97832 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=22.528k\u001b[m output_rows=1.024k\u001b[m plan_cost=124.928k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/1024/real_time_median \u001b[m\u001b[0;33m 98024 ns 97688 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=22.528k\u001b[m output_rows=1.024k\u001b[m plan_cost=124.928k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/1024/real_time_stddev \u001b[m\u001b[0;33m 435 ns 437 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/1024/real_time_cv \u001b[m\u001b[0;33m 0.44 % 0.45 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/2048/real_time_mean \u001b[m\u001b[0;33m 182809 ns 182208 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=45.056k\u001b[m output_rows=2.048k\u001b[m plan_cost=249.856k\u001b[m rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/2048/real_time_median \u001b[m\u001b[0;33m 183246 ns 182566 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=45.056k\u001b[m output_rows=2.048k\u001b[m plan_cost=249.856k\u001b[m rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/2048/real_time_stddev \u001b[m\u001b[0;33m 1840 ns 1824 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/2048/real_time_cv \u001b[m\u001b[0;33m 1.01 % 1.00 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/4096/real_time_mean \u001b[m\u001b[0;33m 356092 ns 354748 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=90.112k\u001b[m output_rows=4.096k\u001b[m plan_cost=499.712k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/4096/real_time_median \u001b[m\u001b[0;33m 356039 ns 354780 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=90.112k\u001b[m output_rows=4.096k\u001b[m plan_cost=499.712k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/4096/real_time_stddev \u001b[m\u001b[0;33m 186 ns 189 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/4096/real_time_cv \u001b[m\u001b[0;33m 0.05 % 0.05 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/8192/real_time_mean \u001b[m\u001b[0;33m 712488 ns 709400 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=180.224k\u001b[m output_rows=8.192k\u001b[m plan_cost=0.999424M\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/8192/real_time_median \u001b[m\u001b[0;33m 714174 ns 711185 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=180.224k\u001b[m output_rows=8.192k\u001b[m plan_cost=0.999424M\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/8192/real_time_stddev \u001b[m\u001b[0;33m 7591 ns 7419 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/8192/real_time_cv \u001b[m\u001b[0;33m 1.07 % 1.05 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/16384/real_time_mean \u001b[m\u001b[0;33m 1528563 ns 1512518 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=360.448k\u001b[m output_rows=16.384k\u001b[m plan_cost=1.99885M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/16384/real_time_median \u001b[m\u001b[0;33m 1516121 ns 1504982 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=360.448k\u001b[m output_rows=16.384k\u001b[m plan_cost=1.99885M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/16384/real_time_stddev \u001b[m\u001b[0;33m 64472 ns 52538 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/16384/real_time_cv \u001b[m\u001b[0;33m 4.22 % 3.47 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/32768/real_time_mean \u001b[m\u001b[0;33m 3053942 ns 3037121 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=720.896k\u001b[m output_rows=32.768k\u001b[m plan_cost=3.9977M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/32768/real_time_median \u001b[m\u001b[0;33m 3064120 ns 3046222 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=720.896k\u001b[m output_rows=32.768k\u001b[m plan_cost=3.9977M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/32768/real_time_stddev \u001b[m\u001b[0;33m 53559 ns 51365 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/32768/real_time_cv \u001b[m\u001b[0;33m 1.75 % 1.69 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/65536/real_time_mean \u001b[m\u001b[0;33m 6328806 ns 6290455 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.44179M\u001b[m output_rows=65.536k\u001b[m plan_cost=7.99539M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/65536/real_time_median \u001b[m\u001b[0;33m 6334730 ns 6294222 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.44179M\u001b[m output_rows=65.536k\u001b[m plan_cost=7.99539M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/65536/real_time_stddev \u001b[m\u001b[0;33m 45869 ns 43718 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/65536/real_time_cv \u001b[m\u001b[0;33m 0.72 % 0.69 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/131072/real_time_mean \u001b[m\u001b[0;33m 13279845 ns 13170453 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.88358M\u001b[m output_rows=131.072k\u001b[m plan_cost=15.9908M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/131072/real_time_median \u001b[m\u001b[0;33m 13400773 ns 13305887 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.88358M\u001b[m output_rows=131.072k\u001b[m plan_cost=15.9908M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/131072/real_time_stddev \u001b[m\u001b[0;33m 280345 ns 278079 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/131072/real_time_cv \u001b[m\u001b[0;33m 2.11 % 2.11 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/262144/real_time_mean \u001b[m\u001b[0;33m 31873201 ns 31604072 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=5.76717M\u001b[m output_rows=262.144k\u001b[m plan_cost=31.9816M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/262144/real_time_median \u001b[m\u001b[0;33m 31345011 ns 31017351 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=5.76717M\u001b[m output_rows=262.144k\u001b[m plan_cost=31.9816M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/262144/real_time_stddev \u001b[m\u001b[0;33m 1102243 ns 1112834 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Projection/262144/real_time_cv \u001b[m\u001b[0;33m 3.46 % 3.52 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/1024/real_time_mean \u001b[m\u001b[0;33m 120718 ns 120152 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=123.904k\u001b[m output_rows=1.024k\u001b[m plan_cost=226.304k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/1024/real_time_median \u001b[m\u001b[0;33m 120666 ns 119977 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=123.904k\u001b[m output_rows=1.024k\u001b[m plan_cost=226.304k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/1024/real_time_stddev \u001b[m\u001b[0;33m 349 ns 380 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/1024/real_time_cv \u001b[m\u001b[0;33m 0.29 % 0.32 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/2048/real_time_mean \u001b[m\u001b[0;33m 279420 ns 278255 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=270.336k\u001b[m output_rows=2.048k\u001b[m plan_cost=475.136k\u001b[m rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/2048/real_time_median \u001b[m\u001b[0;33m 279887 ns 278699 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=270.336k\u001b[m output_rows=2.048k\u001b[m plan_cost=475.136k\u001b[m rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/2048/real_time_stddev \u001b[m\u001b[0;33m 1189 ns 863 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/2048/real_time_cv \u001b[m\u001b[0;33m 0.43 % 0.31 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/4096/real_time_mean \u001b[m\u001b[0;33m 620420 ns 617927 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=585.728k\u001b[m output_rows=4.096k\u001b[m plan_cost=995.328k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/4096/real_time_median \u001b[m\u001b[0;33m 621271 ns 618789 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=585.728k\u001b[m output_rows=4.096k\u001b[m plan_cost=995.328k\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/4096/real_time_stddev \u001b[m\u001b[0;33m 3643 ns 3652 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/4096/real_time_cv \u001b[m\u001b[0;33m 0.59 % 0.59 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/8192/real_time_mean \u001b[m\u001b[0;33m 1392947 ns 1386803 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.26157M\u001b[m output_rows=8.192k\u001b[m plan_cost=2.08077M\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/8192/real_time_median \u001b[m\u001b[0;33m 1383591 ns 1377946 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.26157M\u001b[m output_rows=8.192k\u001b[m plan_cost=2.08077M\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/8192/real_time_stddev \u001b[m\u001b[0;33m 18634 ns 18302 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/8192/real_time_cv \u001b[m\u001b[0;33m 1.34 % 1.32 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/16384/real_time_mean \u001b[m\u001b[0;33m 3221082 ns 3204370 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.70336M\u001b[m output_rows=16.384k\u001b[m plan_cost=4.34176M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/16384/real_time_median \u001b[m\u001b[0;33m 3202222 ns 3186356 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.70336M\u001b[m output_rows=16.384k\u001b[m plan_cost=4.34176M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/16384/real_time_stddev \u001b[m\u001b[0;33m 73386 ns 71144 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/16384/real_time_cv \u001b[m\u001b[0;33m 2.28 % 2.22 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/32768/real_time_mean \u001b[m\u001b[0;33m 8046817 ns 7978826 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=5.76717M\u001b[m output_rows=32.768k\u001b[m plan_cost=9.04397M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/32768/real_time_median \u001b[m\u001b[0;33m 8025546 ns 7966259 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=5.76717M\u001b[m output_rows=32.768k\u001b[m plan_cost=9.04397M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/32768/real_time_stddev \u001b[m\u001b[0;33m 115887 ns 112807 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/32768/real_time_cv \u001b[m\u001b[0;33m 1.44 % 1.41 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/65536/real_time_mean \u001b[m\u001b[0;33m 20480156 ns 20289990 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.2552M\u001b[m output_rows=65.536k\u001b[m plan_cost=18.8088M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/65536/real_time_median \u001b[m\u001b[0;33m 20550580 ns 20332200 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.2552M\u001b[m output_rows=65.536k\u001b[m plan_cost=18.8088M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/65536/real_time_stddev \u001b[m\u001b[0;33m 181757 ns 168891 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/65536/real_time_cv \u001b[m\u001b[0;33m 0.89 % 0.83 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/131072/real_time_mean \u001b[m\u001b[0;33m 55388937 ns 54787197 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=25.9523M\u001b[m output_rows=131.072k\u001b[m plan_cost=39.0595M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/131072/real_time_median \u001b[m\u001b[0;33m 55356484 ns 54701727 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=25.9523M\u001b[m output_rows=131.072k\u001b[m plan_cost=39.0595M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/131072/real_time_stddev \u001b[m\u001b[0;33m 128703 ns 155072 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/131072/real_time_cv \u001b[m\u001b[0;33m 0.23 % 0.28 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/262144/real_time_mean \u001b[m\u001b[0;33m 168947981 ns 167426580 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=54.7881M\u001b[m output_rows=262.144k\u001b[m plan_cost=81.0025M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/262144/real_time_median \u001b[m\u001b[0;33m 168927098 ns 167324195 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=54.7881M\u001b[m output_rows=262.144k\u001b[m plan_cost=81.0025M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/262144/real_time_stddev \u001b[m\u001b[0;33m 7017901 ns 6982613 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Sort/262144/real_time_cv \u001b[m\u001b[0;33m 4.15 % 4.17 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/1024/real_time_mean \u001b[m\u001b[0;33m 288821 ns 287061 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=522.24k\u001b[m output_rows=1.024k\u001b[m plan_cost=624.64k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/1024/real_time_median \u001b[m\u001b[0;33m 288883 ns 287336 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=522.24k\u001b[m output_rows=1.024k\u001b[m plan_cost=624.64k\u001b[m rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/1024/real_time_stddev \u001b[m\u001b[0;33m 515 ns 728 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/1024/real_time_cv 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3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/4096/real_time_mean \u001b[m\u001b[0;33m 1115801 ns 1110007 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.08896M\u001b[m output_rows=4.096k\u001b[m plan_cost=2.49856M\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/4096/real_time_median \u001b[m\u001b[0;33m 1107251 ns 1101741 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.08896M\u001b[m output_rows=4.096k\u001b[m plan_cost=2.49856M\u001b[m rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/4096/real_time_stddev \u001b[m\u001b[0;33m 23287 ns 22665 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/4096/real_time_cv \u001b[m\u001b[0;33m 2.09 % 2.04 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/8192/real_time_mean \u001b[m\u001b[0;33m 2490977 ns 2478885 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=4.17792M\u001b[m output_rows=8.192k\u001b[m plan_cost=4.99712M\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/8192/real_time_median \u001b[m\u001b[0;33m 2471500 ns 2459821 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=4.17792M\u001b[m output_rows=8.192k\u001b[m plan_cost=4.99712M\u001b[m rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/8192/real_time_stddev \u001b[m\u001b[0;33m 78857 ns 77957 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/8192/real_time_cv \u001b[m\u001b[0;33m 3.17 % 3.14 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/16384/real_time_mean \u001b[m\u001b[0;33m 5541260 ns 5509669 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=8.35584M\u001b[m output_rows=16.384k\u001b[m plan_cost=9.99424M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/16384/real_time_median \u001b[m\u001b[0;33m 5562212 ns 5531078 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=8.35584M\u001b[m output_rows=16.384k\u001b[m plan_cost=9.99424M\u001b[m rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/16384/real_time_stddev \u001b[m\u001b[0;33m 71339 ns 68139 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/16384/real_time_cv \u001b[m\u001b[0;33m 1.29 % 1.24 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/32768/real_time_mean \u001b[m\u001b[0;33m 16564439 ns 16435384 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=16.7117M\u001b[m output_rows=32.768k\u001b[m plan_cost=19.9885M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/32768/real_time_median \u001b[m\u001b[0;33m 16591871 ns 16467307 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=16.7117M\u001b[m output_rows=32.768k\u001b[m plan_cost=19.9885M\u001b[m rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/32768/real_time_stddev \u001b[m\u001b[0;33m 83381 ns 88516 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/32768/real_time_cv \u001b[m\u001b[0;33m 0.50 % 0.54 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/65536/real_time_mean \u001b[m\u001b[0;33m 49221785 ns 48751030 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=33.4234M\u001b[m output_rows=65.536k\u001b[m plan_cost=39.977M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/65536/real_time_median \u001b[m\u001b[0;33m 50072441 ns 49631210 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=33.4234M\u001b[m output_rows=65.536k\u001b[m plan_cost=39.977M\u001b[m rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/65536/real_time_stddev \u001b[m\u001b[0;33m 2627450 ns 2694749 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/65536/real_time_cv \u001b[m\u001b[0;33m 5.34 % 5.53 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/131072/real_time_mean \u001b[m\u001b[0;33m 118590633 ns 117787971 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=66.8467M\u001b[m output_rows=131.072k\u001b[m plan_cost=79.9539M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/131072/real_time_median \u001b[m\u001b[0;33m 117502626 ns 116574037 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=66.8467M\u001b[m output_rows=131.072k\u001b[m plan_cost=79.9539M\u001b[m rows=131.072k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/131072/real_time_stddev \u001b[m\u001b[0;33m 2312608 ns 2306280 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/131072/real_time_cv \u001b[m\u001b[0;33m 1.95 % 1.96 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/262144/real_time_mean \u001b[m\u001b[0;33m 299699036 ns 297386710 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=133.693M\u001b[m output_rows=262.144k\u001b[m plan_cost=159.908M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/262144/real_time_median \u001b[m\u001b[0;33m 300178360 ns 296997108 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=133.693M\u001b[m output_rows=262.144k\u001b[m plan_cost=159.908M\u001b[m rows=262.144k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/262144/real_time_stddev \u001b[m\u001b[0;33m 2483568 ns 2344279 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/Aggregation/262144/real_time_cv \u001b[m\u001b[0;33m 0.83 % 0.79 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/1024/1024/real_time_mean \u001b[m\u001b[0;33m 203861 ns 202673 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=404.48k\u001b[m output_rows=1.024k\u001b[m plan_cost=609.28k\u001b[m rhs_rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/1024/1024/real_time_median \u001b[m\u001b[0;33m 203619 ns 202944 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=404.48k\u001b[m output_rows=1.024k\u001b[m plan_cost=609.28k\u001b[m rhs_rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/1024/1024/real_time_stddev \u001b[m\u001b[0;33m 2527 ns 1821 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/1024/1024/real_time_cv \u001b[m\u001b[0;33m 1.24 % 0.90 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/2048/2048/real_time_mean \u001b[m\u001b[0;33m 388318 ns 386320 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=2.048k\u001b[m model_cost=808.96k\u001b[m output_rows=2.048k\u001b[m plan_cost=1.21856M\u001b[m rhs_rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/2048/2048/real_time_median \u001b[m\u001b[0;33m 389129 ns 386872 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=2.048k\u001b[m model_cost=808.96k\u001b[m output_rows=2.048k\u001b[m plan_cost=1.21856M\u001b[m rhs_rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/2048/2048/real_time_stddev \u001b[m\u001b[0;33m 1477 ns 1399 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/2048/2048/real_time_cv \u001b[m\u001b[0;33m 0.38 % 0.36 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/4096/4096/real_time_mean \u001b[m\u001b[0;33m 792906 ns 785115 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=4.096k\u001b[m model_cost=1.61792M\u001b[m output_rows=4.096k\u001b[m plan_cost=2.43712M\u001b[m rhs_rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/4096/4096/real_time_median \u001b[m\u001b[0;33m 785729 ns 778319 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=4.096k\u001b[m model_cost=1.61792M\u001b[m output_rows=4.096k\u001b[m plan_cost=2.43712M\u001b[m rhs_rows=4.096k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/4096/4096/real_time_stddev \u001b[m\u001b[0;33m 25076 ns 25976 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/4096/4096/real_time_cv \u001b[m\u001b[0;33m 3.16 % 3.31 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/8192/8192/real_time_mean \u001b[m\u001b[0;33m 1562261 ns 1551754 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=8.192k\u001b[m model_cost=3.23584M\u001b[m output_rows=8.192k\u001b[m plan_cost=4.87424M\u001b[m rhs_rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/8192/8192/real_time_median \u001b[m\u001b[0;33m 1572901 ns 1563377 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=8.192k\u001b[m model_cost=3.23584M\u001b[m output_rows=8.192k\u001b[m plan_cost=4.87424M\u001b[m rhs_rows=8.192k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/8192/8192/real_time_stddev \u001b[m\u001b[0;33m 40016 ns 39588 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/8192/8192/real_time_cv \u001b[m\u001b[0;33m 2.56 % 2.55 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/16384/16384/real_time_mean \u001b[m\u001b[0;33m 3419899 ns 3380729 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=16.384k\u001b[m model_cost=6.47168M\u001b[m output_rows=16.384k\u001b[m plan_cost=9.74848M\u001b[m rhs_rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/16384/16384/real_time_median \u001b[m\u001b[0;33m 3441218 ns 3399788 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=16.384k\u001b[m model_cost=6.47168M\u001b[m output_rows=16.384k\u001b[m plan_cost=9.74848M\u001b[m rhs_rows=16.384k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/16384/16384/real_time_stddev \u001b[m\u001b[0;33m 39715 ns 34672 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/16384/16384/real_time_cv \u001b[m\u001b[0;33m 1.16 % 1.03 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/32768/32768/real_time_mean \u001b[m\u001b[0;33m 8103526 ns 7992137 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=32.768k\u001b[m model_cost=12.9434M\u001b[m output_rows=32.768k\u001b[m plan_cost=19.497M\u001b[m rhs_rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/32768/32768/real_time_median \u001b[m\u001b[0;33m 8067914 ns 7981352 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=32.768k\u001b[m model_cost=12.9434M\u001b[m output_rows=32.768k\u001b[m plan_cost=19.497M\u001b[m rhs_rows=32.768k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/32768/32768/real_time_stddev \u001b[m\u001b[0;33m 379774 ns 374953 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/32768/32768/real_time_cv \u001b[m\u001b[0;33m 4.69 % 4.69 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/65536/65536/real_time_mean \u001b[m\u001b[0;33m 18077552 ns 17898348 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=65.536k\u001b[m model_cost=25.8867M\u001b[m output_rows=65.536k\u001b[m plan_cost=38.9939M\u001b[m rhs_rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/65536/65536/real_time_median \u001b[m\u001b[0;33m 17416211 ns 17231467 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=65.536k\u001b[m model_cost=25.8867M\u001b[m output_rows=65.536k\u001b[m plan_cost=38.9939M\u001b[m rhs_rows=65.536k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/65536/65536/real_time_stddev \u001b[m\u001b[0;33m 1955871 ns 1906829 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/HashJoin/65536/65536/real_time_cv \u001b[m\u001b[0;33m 10.82 % 10.65 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/64/64/real_time_mean \u001b[m\u001b[0;33m 378714 ns 375733 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=64\u001b[m model_cost=286.72k\u001b[m output_rows=64\u001b[m plan_cost=299.52k\u001b[m rhs_rows=64\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/64/64/real_time_median \u001b[m\u001b[0;33m 375010 ns 371548 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=64\u001b[m model_cost=286.72k\u001b[m output_rows=64\u001b[m plan_cost=299.52k\u001b[m rhs_rows=64\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/64/64/real_time_stddev \u001b[m\u001b[0;33m 14355 ns 13693 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/64/64/real_time_cv \u001b[m\u001b[0;33m 3.79 % 3.64 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/128/128/real_time_mean \u001b[m\u001b[0;33m 1181625 ns 1174045 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=128\u001b[m model_cost=1.14688M\u001b[m output_rows=128\u001b[m plan_cost=1.17248M\u001b[m rhs_rows=128\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/128/128/real_time_median \u001b[m\u001b[0;33m 1150213 ns 1144394 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=128\u001b[m model_cost=1.14688M\u001b[m output_rows=128\u001b[m plan_cost=1.17248M\u001b[m rhs_rows=128\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/128/128/real_time_stddev \u001b[m\u001b[0;33m 59677 ns 56624 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/128/128/real_time_cv \u001b[m\u001b[0;33m 5.05 % 4.82 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/256/256/real_time_mean \u001b[m\u001b[0;33m 4128424 ns 4098841 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=256\u001b[m model_cost=4.58752M\u001b[m output_rows=256\u001b[m plan_cost=4.63872M\u001b[m rhs_rows=256\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/256/256/real_time_median \u001b[m\u001b[0;33m 4114884 ns 4087364 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=256\u001b[m model_cost=4.58752M\u001b[m output_rows=256\u001b[m plan_cost=4.63872M\u001b[m rhs_rows=256\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/256/256/real_time_stddev \u001b[m\u001b[0;33m 36060 ns 33710 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/256/256/real_time_cv \u001b[m\u001b[0;33m 0.87 % 0.82 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/512/512/real_time_mean \u001b[m\u001b[0;33m 15505186 ns 15412702 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=512\u001b[m model_cost=18.3501M\u001b[m output_rows=512\u001b[m plan_cost=18.4525M\u001b[m rhs_rows=512\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/512/512/real_time_median \u001b[m\u001b[0;33m 15601415 ns 15520219 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=512\u001b[m model_cost=18.3501M\u001b[m output_rows=512\u001b[m plan_cost=18.4525M\u001b[m rhs_rows=512\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/512/512/real_time_stddev \u001b[m\u001b[0;33m 272156 ns 273022 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/512/512/real_time_cv \u001b[m\u001b[0;33m 1.76 % 1.77 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/1024/1024/real_time_mean \u001b[m\u001b[0;33m 58136121 ns 57917496 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=73.4003M\u001b[m output_rows=1.024k\u001b[m plan_cost=73.6051M\u001b[m rhs_rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/1024/1024/real_time_median \u001b[m\u001b[0;33m 58150968 ns 57926298 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=73.4003M\u001b[m output_rows=1.024k\u001b[m plan_cost=73.6051M\u001b[m rhs_rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/1024/1024/real_time_stddev \u001b[m\u001b[0;33m 304440 ns 274278 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/1024/1024/real_time_cv \u001b[m\u001b[0;33m 0.52 % 0.47 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/2048/2048/real_time_mean \u001b[m\u001b[0;33m 233424874 ns 232578334 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=2.048k\u001b[m model_cost=293.601M\u001b[m output_rows=2.048k\u001b[m plan_cost=294.011M\u001b[m rhs_rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/2048/2048/real_time_median \u001b[m\u001b[0;33m 231674264 ns 230876970 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=2.048k\u001b[m model_cost=293.601M\u001b[m output_rows=2.048k\u001b[m plan_cost=294.011M\u001b[m rhs_rows=2.048k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/2048/2048/real_time_stddev \u001b[m\u001b[0;33m 3983608 ns 3922793 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/2048/2048/real_time_cv \u001b[m\u001b[0;33m 1.71 % 1.69 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/32/32/real_time_mean \u001b[m\u001b[0;33m 125882 ns 124938 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=32\u001b[m model_cost=106.496k\u001b[m output_rows=1.024k\u001b[m plan_cost=112.896k\u001b[m rhs_rows=32\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/32/32/real_time_median \u001b[m\u001b[0;33m 126031 ns 125025 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=32\u001b[m model_cost=106.496k\u001b[m output_rows=1.024k\u001b[m plan_cost=112.896k\u001b[m rhs_rows=32\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/32/32/real_time_stddev \u001b[m\u001b[0;33m 3179 ns 3035 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/32/32/real_time_cv \u001b[m\u001b[0;33m 2.53 % 2.43 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/64/64/real_time_mean \u001b[m\u001b[0;33m 279630 ns 277682 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=64\u001b[m model_cost=425.984k\u001b[m output_rows=4.096k\u001b[m plan_cost=438.784k\u001b[m rhs_rows=64\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/64/64/real_time_median \u001b[m\u001b[0;33m 277484 ns 275597 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=64\u001b[m model_cost=425.984k\u001b[m output_rows=4.096k\u001b[m plan_cost=438.784k\u001b[m rhs_rows=64\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/64/64/real_time_stddev \u001b[m\u001b[0;33m 7966 ns 7725 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/64/64/real_time_cv \u001b[m\u001b[0;33m 2.85 % 2.78 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/128/128/real_time_mean \u001b[m\u001b[0;33m 909990 ns 902791 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=128\u001b[m model_cost=1.70394M\u001b[m output_rows=16.384k\u001b[m plan_cost=1.72954M\u001b[m rhs_rows=128\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/128/128/real_time_median \u001b[m\u001b[0;33m 894803 ns 887672 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=128\u001b[m model_cost=1.70394M\u001b[m output_rows=16.384k\u001b[m plan_cost=1.72954M\u001b[m rhs_rows=128\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/128/128/real_time_stddev \u001b[m\u001b[0;33m 30725 ns 29804 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/128/128/real_time_cv \u001b[m\u001b[0;33m 3.38 % 3.30 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/256/256/real_time_mean \u001b[m\u001b[0;33m 5038775 ns 4973354 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=256\u001b[m model_cost=6.81574M\u001b[m output_rows=65.536k\u001b[m plan_cost=6.86694M\u001b[m rhs_rows=256\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/256/256/real_time_median \u001b[m\u001b[0;33m 5089727 ns 5024698 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=256\u001b[m model_cost=6.81574M\u001b[m output_rows=65.536k\u001b[m plan_cost=6.86694M\u001b[m rhs_rows=256\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/256/256/real_time_stddev \u001b[m\u001b[0;33m 235576 ns 226711 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/256/256/real_time_cv \u001b[m\u001b[0;33m 4.68 % 4.56 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/512/512/real_time_mean \u001b[m\u001b[0;33m 22572298 ns 22363182 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=512\u001b[m model_cost=27.263M\u001b[m output_rows=262.144k\u001b[m plan_cost=27.3654M\u001b[m rhs_rows=512\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/512/512/real_time_median \u001b[m\u001b[0;33m 22575023 ns 22343393 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=512\u001b[m model_cost=27.263M\u001b[m output_rows=262.144k\u001b[m plan_cost=27.3654M\u001b[m rhs_rows=512\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/512/512/real_time_stddev \u001b[m\u001b[0;33m 100386 ns 99763 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/512/512/real_time_cv \u001b[m\u001b[0;33m 0.44 % 0.45 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/1024/1024/real_time_mean \u001b[m\u001b[0;33m 117439223 ns 116310763 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=109.052M\u001b[m output_rows=1.04858M\u001b[m plan_cost=109.257M\u001b[m rhs_rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/1024/1024/real_time_median \u001b[m\u001b[0;33m 117482341 ns 116332985 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=109.052M\u001b[m output_rows=1.04858M\u001b[m plan_cost=109.257M\u001b[m rhs_rows=1.024k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/1024/1024/real_time_stddev \u001b[m\u001b[0;33m 432620 ns 407753 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/1024/1024/real_time_cv \u001b[m\u001b[0;33m 0.37 % 0.35 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m" + ] + } + ], "source": [ "!cd .. && timeout 300s ./build-release/bin/benchmarks --benchmark_filter='^OperatorCost/' --benchmark_repetitions=3 --benchmark_display_aggregates_only=true --benchmark_out=research/benchmark-results/operator-cost.json --benchmark_out_format=json" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "bcfd11fc", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-06-14T20:48:21+03:00\n", + "Running ./build-release/bin/benchmarks\n", + "Run on (8 X 4200 MHz CPU s)\n", + "CPU Caches:\n", + " L1 Data 48 KiB (x4)\n", + " L1 Instruction 32 KiB (x4)\n", + " L2 Unified 1280 KiB (x4)\n", + " L3 Unified 8192 KiB (x1)\n", + "Load Average: 1.43, 1.47, 1.78\n", + "***WARNING*** CPU scaling is enabled, the benchmark real time measurements may be noisy and will incur extra overhead.\n", + "------------------------------------------------------------------------------------------------------------------------------------------------\n", + "Benchmark Time CPU Iterations UserCounters...\n", + "------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\u001b[0;32mOperatorCostMatched/target_cost:640000/SeqScan/6400/real_time_mean \u001b[m\u001b[0;33m 327112 ns 323454 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640k\u001b[m output_rows=6.4k\u001b[m plan_cost=640k\u001b[m rows=6.4k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/SeqScan/6400/real_time_median \u001b[m\u001b[0;33m 317651 ns 314511 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640k\u001b[m output_rows=6.4k\u001b[m plan_cost=640k\u001b[m rows=6.4k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/SeqScan/6400/real_time_stddev \u001b[m\u001b[0;33m 24164 ns 23071 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/SeqScan/6400/real_time_cv \u001b[m\u001b[0;33m 7.39 % 7.13 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Filter/6400/real_time_mean \u001b[m\u001b[0;33m 423529 ns 420790 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640k\u001b[m output_rows=3.328k\u001b[m plan_cost=1.28M\u001b[m rows=6.4k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Filter/6400/real_time_median \u001b[m\u001b[0;33m 419302 ns 416611 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640k\u001b[m output_rows=3.328k\u001b[m plan_cost=1.28M\u001b[m rows=6.4k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Filter/6400/real_time_stddev \u001b[m\u001b[0;33m 11539 ns 11860 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Filter/6400/real_time_cv \u001b[m\u001b[0;33m 2.72 % 2.82 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Projection/29091/real_time_mean \u001b[m\u001b[0;33m 2969473 ns 2951961 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640.002k\u001b[m output_rows=29.091k\u001b[m plan_cost=3.5491M\u001b[m rows=29.091k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Projection/29091/real_time_median \u001b[m\u001b[0;33m 2996433 ns 2978522 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640.002k\u001b[m output_rows=29.091k\u001b[m plan_cost=3.5491M\u001b[m rows=29.091k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Projection/29091/real_time_stddev \u001b[m\u001b[0;33m 67715 ns 66378 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Projection/29091/real_time_cv \u001b[m\u001b[0;33m 2.28 % 2.25 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Sort/4476/real_time_mean \u001b[m\u001b[0;33m 708003 ns 704782 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640.068k\u001b[m output_rows=4.476k\u001b[m plan_cost=1.08767M\u001b[m rows=4.476k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Sort/4476/real_time_median \u001b[m\u001b[0;33m 707848 ns 704782 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640.068k\u001b[m output_rows=4.476k\u001b[m plan_cost=1.08767M\u001b[m rows=4.476k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Sort/4476/real_time_stddev \u001b[m\u001b[0;33m 2199 ns 2175 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Sort/4476/real_time_cv \u001b[m\u001b[0;33m 0.31 % 0.31 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Aggregation/1255/real_time_mean \u001b[m\u001b[0;33m 350350 ns 349015 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640.05k\u001b[m output_rows=1.255k\u001b[m plan_cost=765.55k\u001b[m rows=1.255k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Aggregation/1255/real_time_median \u001b[m\u001b[0;33m 350669 ns 349288 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640.05k\u001b[m output_rows=1.255k\u001b[m plan_cost=765.55k\u001b[m rows=1.255k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Aggregation/1255/real_time_stddev \u001b[m\u001b[0;33m 902 ns 856 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Aggregation/1255/real_time_cv \u001b[m\u001b[0;33m 0.26 % 0.25 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/HashJoin/1620/1620/real_time_mean \u001b[m\u001b[0;33m 308857 ns 307622 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.62k\u001b[m model_cost=639.9k\u001b[m output_rows=1.62k\u001b[m plan_cost=963.9k\u001b[m rhs_rows=1.62k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/HashJoin/1620/1620/real_time_median \u001b[m\u001b[0;33m 310398 ns 309035 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.62k\u001b[m model_cost=639.9k\u001b[m output_rows=1.62k\u001b[m plan_cost=963.9k\u001b[m rhs_rows=1.62k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/HashJoin/1620/1620/real_time_stddev \u001b[m\u001b[0;33m 3264 ns 3138 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/HashJoin/1620/1620/real_time_cv \u001b[m\u001b[0;33m 1.06 % 1.02 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopJoin/96/96/real_time_mean \u001b[m\u001b[0;33m 665762 ns 662460 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=96\u001b[m model_cost=645.12k\u001b[m output_rows=96\u001b[m plan_cost=664.32k\u001b[m rhs_rows=96\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopJoin/96/96/real_time_median \u001b[m\u001b[0;33m 665768 ns 662568 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=96\u001b[m model_cost=645.12k\u001b[m output_rows=96\u001b[m plan_cost=664.32k\u001b[m rhs_rows=96\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopJoin/96/96/real_time_stddev \u001b[m\u001b[0;33m 740 ns 743 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopJoin/96/96/real_time_cv \u001b[m\u001b[0;33m 0.11 % 0.11 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopCrossJoin/78/78/real_time_mean \u001b[m\u001b[0;33m 389871 ns 385981 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=78\u001b[m model_cost=632.736k\u001b[m output_rows=6.084k\u001b[m plan_cost=648.336k\u001b[m rhs_rows=78\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopCrossJoin/78/78/real_time_median 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rows=10k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/SeqScan/10000/real_time_median \u001b[m\u001b[0;33m 354684 ns 352806 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1M\u001b[m output_rows=10k\u001b[m plan_cost=1M\u001b[m rows=10k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/SeqScan/10000/real_time_stddev \u001b[m\u001b[0;33m 1338 ns 1283 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/SeqScan/10000/real_time_cv \u001b[m\u001b[0;33m 0.38 % 0.36 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Filter/10000/real_time_mean \u001b[m\u001b[0;33m 630249 ns 627282 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1M\u001b[m output_rows=5.12k\u001b[m 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"\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/NestedLoopCrossJoin/98/98/real_time_stddev \u001b[m\u001b[0;33m 16916 ns 16319 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/NestedLoopCrossJoin/98/98/real_time_cv \u001b[m\u001b[0;33m 2.90 % 2.82 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/SeqScan/32400/real_time_mean \u001b[m\u001b[0;33m 1134758 ns 1128664 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.24M\u001b[m output_rows=32.4k\u001b[m plan_cost=3.24M\u001b[m rows=32.4k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/SeqScan/32400/real_time_median \u001b[m\u001b[0;33m 1122532 ns 1116998 ns \u001b[m\u001b[0;36m 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2104733 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.24M\u001b[m output_rows=16.384k\u001b[m plan_cost=6.48M\u001b[m rows=32.4k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Filter/32400/real_time_stddev \u001b[m\u001b[0;33m 75915 ns 75555 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Filter/32400/real_time_cv \u001b[m\u001b[0;33m 3.53 % 3.53 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Projection/147273/real_time_mean \u001b[m\u001b[0;33m 19626122 ns 19478967 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.24001M\u001b[m output_rows=147.273k\u001b[m plan_cost=17.9673M\u001b[m rows=147.273k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Projection/147273/real_time_median \u001b[m\u001b[0;33m 19715132 ns 19566701 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.24001M\u001b[m output_rows=147.273k\u001b[m plan_cost=17.9673M\u001b[m rows=147.273k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Projection/147273/real_time_stddev \u001b[m\u001b[0;33m 314025 ns 313346 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Projection/147273/real_time_cv \u001b[m\u001b[0;33m 1.60 % 1.61 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Sort/19636/real_time_mean \u001b[m\u001b[0;33m 3898469 ns 3882409 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.23994M\u001b[m output_rows=19.636k\u001b[m plan_cost=5.20354M\u001b[m rows=19.636k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Sort/19636/real_time_median \u001b[m\u001b[0;33m 3918521 ns 3902628 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.23994M\u001b[m output_rows=19.636k\u001b[m plan_cost=5.20354M\u001b[m rows=19.636k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Sort/19636/real_time_stddev \u001b[m\u001b[0;33m 48064 ns 46087 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Sort/19636/real_time_cv \u001b[m\u001b[0;33m 1.23 % 1.19 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Aggregation/6353/real_time_mean \u001b[m\u001b[0;33m 1853008 ns 1843719 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.24003M\u001b[m output_rows=6.353k\u001b[m plan_cost=3.87533M\u001b[m rows=6.353k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Aggregation/6353/real_time_median \u001b[m\u001b[0;33m 1844847 ns 1836541 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.24003M\u001b[m output_rows=6.353k\u001b[m plan_cost=3.87533M\u001b[m rows=6.353k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Aggregation/6353/real_time_stddev \u001b[m\u001b[0;33m 34380 ns 34282 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Aggregation/6353/real_time_cv \u001b[m\u001b[0;33m 1.86 % 1.86 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/HashJoin/8203/8203/real_time_mean \u001b[m\u001b[0;33m 1603236 ns 1594598 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=8.203k\u001b[m model_cost=3.24018M\u001b[m output_rows=8.203k\u001b[m plan_cost=4.88078M\u001b[m rhs_rows=8.203k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/HashJoin/8203/8203/real_time_median \u001b[m\u001b[0;33m 1607029 ns 1598368 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=8.203k\u001b[m model_cost=3.24018M\u001b[m output_rows=8.203k\u001b[m plan_cost=4.88078M\u001b[m rhs_rows=8.203k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/HashJoin/8203/8203/real_time_stddev \u001b[m\u001b[0;33m 9814 ns 9376 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/HashJoin/8203/8203/real_time_cv \u001b[m\u001b[0;33m 0.61 % 0.59 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopJoin/215/215/real_time_mean \u001b[m\u001b[0;33m 2910806 ns 2897804 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=215\u001b[m model_cost=3.23575M\u001b[m output_rows=215\u001b[m plan_cost=3.27875M\u001b[m rhs_rows=215\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopJoin/215/215/real_time_median \u001b[m\u001b[0;33m 2924068 ns 2910697 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=215\u001b[m model_cost=3.23575M\u001b[m output_rows=215\u001b[m plan_cost=3.27875M\u001b[m rhs_rows=215\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopJoin/215/215/real_time_stddev \u001b[m\u001b[0;33m 23345 ns 22420 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopJoin/215/215/real_time_cv \u001b[m\u001b[0;33m 0.80 % 0.77 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopCrossJoin/177/177/real_time_mean \u001b[m\u001b[0;33m 1943488 ns 1924736 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=177\u001b[m model_cost=3.25822M\u001b[m output_rows=31.329k\u001b[m plan_cost=3.29362M\u001b[m rhs_rows=177\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopCrossJoin/177/177/real_time_median \u001b[m\u001b[0;33m 1913052 ns 1894678 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=177\u001b[m model_cost=3.25822M\u001b[m output_rows=31.329k\u001b[m plan_cost=3.29362M\u001b[m rhs_rows=177\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopCrossJoin/177/177/real_time_stddev \u001b[m\u001b[0;33m 65998 ns 64519 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopCrossJoin/177/177/real_time_cv \u001b[m\u001b[0;33m 3.40 % 3.35 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/SeqScan/67600/real_time_mean \u001b[m\u001b[0;33m 3382780 ns 3353599 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76M\u001b[m output_rows=67.6k\u001b[m plan_cost=6.76M\u001b[m rows=67.6k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/SeqScan/67600/real_time_median \u001b[m\u001b[0;33m 3386841 ns 3356555 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76M\u001b[m output_rows=67.6k\u001b[m plan_cost=6.76M\u001b[m rows=67.6k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/SeqScan/67600/real_time_stddev \u001b[m\u001b[0;33m 63869 ns 60408 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/SeqScan/67600/real_time_cv \u001b[m\u001b[0;33m 1.89 % 1.80 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Filter/67600/real_time_mean \u001b[m\u001b[0;33m 4670764 ns 4640027 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76M\u001b[m output_rows=33.808k\u001b[m plan_cost=13.52M\u001b[m rows=67.6k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Filter/67600/real_time_median \u001b[m\u001b[0;33m 4712211 ns 4676999 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76M\u001b[m output_rows=33.808k\u001b[m plan_cost=13.52M\u001b[m rows=67.6k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Filter/67600/real_time_stddev \u001b[m\u001b[0;33m 184523 ns 180474 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Filter/67600/real_time_cv \u001b[m\u001b[0;33m 3.95 % 3.89 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Projection/307273/real_time_mean \u001b[m\u001b[0;33m 41478841 ns 41112802 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76001M\u001b[m output_rows=307.273k\u001b[m plan_cost=37.4873M\u001b[m rows=307.273k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Projection/307273/real_time_median \u001b[m\u001b[0;33m 41540129 ns 41133354 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76001M\u001b[m output_rows=307.273k\u001b[m plan_cost=37.4873M\u001b[m rows=307.273k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Projection/307273/real_time_stddev \u001b[m\u001b[0;33m 138078 ns 177153 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Projection/307273/real_time_cv \u001b[m\u001b[0;33m 0.33 % 0.43 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Sort/38409/real_time_mean \u001b[m\u001b[0;33m 10126082 ns 10063562 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.75998M\u001b[m output_rows=38.409k\u001b[m plan_cost=10.6009M\u001b[m rows=38.409k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Sort/38409/real_time_median \u001b[m\u001b[0;33m 10310140 ns 10245214 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.75998M\u001b[m output_rows=38.409k\u001b[m plan_cost=10.6009M\u001b[m rows=38.409k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Sort/38409/real_time_stddev \u001b[m\u001b[0;33m 323868 ns 315894 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Sort/38409/real_time_cv \u001b[m\u001b[0;33m 3.20 % 3.14 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Aggregation/13255/real_time_mean \u001b[m\u001b[0;33m 4259250 ns 4235870 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76005M\u001b[m output_rows=13.255k\u001b[m plan_cost=8.08555M\u001b[m rows=13.255k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Aggregation/13255/real_time_median \u001b[m\u001b[0;33m 4275696 ns 4251838 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76005M\u001b[m output_rows=13.255k\u001b[m plan_cost=8.08555M\u001b[m rows=13.255k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Aggregation/13255/real_time_stddev \u001b[m\u001b[0;33m 49296 ns 48716 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Aggregation/13255/real_time_cv \u001b[m\u001b[0;33m 1.16 % 1.15 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/HashJoin/17114/17114/real_time_mean \u001b[m\u001b[0;33m 3770392 ns 3743836 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=17.114k\u001b[m model_cost=6.76003M\u001b[m output_rows=17.114k\u001b[m plan_cost=10.1828M\u001b[m rhs_rows=17.114k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/HashJoin/17114/17114/real_time_median \u001b[m\u001b[0;33m 3738229 ns 3712971 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=17.114k\u001b[m model_cost=6.76003M\u001b[m output_rows=17.114k\u001b[m plan_cost=10.1828M\u001b[m rhs_rows=17.114k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/HashJoin/17114/17114/real_time_stddev \u001b[m\u001b[0;33m 99152 ns 95508 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/HashJoin/17114/17114/real_time_cv \u001b[m\u001b[0;33m 2.63 % 2.55 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopJoin/311/311/real_time_mean \u001b[m\u001b[0;33m 5772993 ns 5750595 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=311\u001b[m model_cost=6.77047M\u001b[m output_rows=311\u001b[m plan_cost=6.83267M\u001b[m rhs_rows=311\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopJoin/311/311/real_time_median \u001b[m\u001b[0;33m 5730610 ns 5708990 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=311\u001b[m model_cost=6.77047M\u001b[m output_rows=311\u001b[m plan_cost=6.83267M\u001b[m rhs_rows=311\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopJoin/311/311/real_time_stddev \u001b[m\u001b[0;33m 83980 ns 82214 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopJoin/311/311/real_time_cv \u001b[m\u001b[0;33m 1.45 % 1.43 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopCrossJoin/255/255/real_time_mean \u001b[m\u001b[0;33m 5119950 ns 5056736 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=255\u001b[m model_cost=6.7626M\u001b[m output_rows=65.025k\u001b[m plan_cost=6.8136M\u001b[m rhs_rows=255\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopCrossJoin/255/255/real_time_median \u001b[m\u001b[0;33m 5104559 ns 5039196 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=255\u001b[m model_cost=6.7626M\u001b[m output_rows=65.025k\u001b[m plan_cost=6.8136M\u001b[m rhs_rows=255\u001b[m\n", + 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plan_cost=67.9318M\u001b[m rows=556.818k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Projection/556818/real_time_median \u001b[m\u001b[0;33m 74892891 ns 74148114 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.25M\u001b[m output_rows=556.818k\u001b[m plan_cost=67.9318M\u001b[m rows=556.818k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Projection/556818/real_time_stddev \u001b[m\u001b[0;33m 1413431 ns 1341941 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Projection/556818/real_time_cv \u001b[m\u001b[0;33m 1.89 % 1.81 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Sort/65536/real_time_mean \u001b[m\u001b[0;33m 20585367 ns 20385385 ns 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output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/HashJoin/31013/31013/real_time_mean \u001b[m\u001b[0;33m 7152001 ns 7086308 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=31.013k\u001b[m model_cost=12.2501M\u001b[m output_rows=31.013k\u001b[m plan_cost=18.4527M\u001b[m rhs_rows=31.013k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/HashJoin/31013/31013/real_time_median \u001b[m\u001b[0;33m 7141940 ns 7077427 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=31.013k\u001b[m model_cost=12.2501M\u001b[m output_rows=31.013k\u001b[m plan_cost=18.4527M\u001b[m rhs_rows=31.013k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/HashJoin/31013/31013/real_time_stddev \u001b[m\u001b[0;33m 44807 ns 43542 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/HashJoin/31013/31013/real_time_cv \u001b[m\u001b[0;33m 0.63 % 0.61 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopJoin/418/418/real_time_mean \u001b[m\u001b[0;33m 10945706 ns 10888305 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=418\u001b[m model_cost=12.2307M\u001b[m output_rows=418\u001b[m plan_cost=12.3143M\u001b[m rhs_rows=418\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopJoin/418/418/real_time_median \u001b[m\u001b[0;33m 10932178 ns 10886830 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=418\u001b[m model_cost=12.2307M\u001b[m output_rows=418\u001b[m plan_cost=12.3143M\u001b[m rhs_rows=418\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopJoin/418/418/real_time_stddev \u001b[m\u001b[0;33m 113420 ns 97491 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopJoin/418/418/real_time_cv \u001b[m\u001b[0;33m 1.04 % 0.90 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopCrossJoin/343/343/real_time_mean \u001b[m\u001b[0;33m 10279043 ns 10138684 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=343\u001b[m model_cost=12.2355M\u001b[m output_rows=117.649k\u001b[m plan_cost=12.3041M\u001b[m rhs_rows=343\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopCrossJoin/343/343/real_time_median \u001b[m\u001b[0;33m 9569170 ns 9452603 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=343\u001b[m model_cost=12.2355M\u001b[m output_rows=117.649k\u001b[m plan_cost=12.3041M\u001b[m rhs_rows=343\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopCrossJoin/343/343/real_time_stddev \u001b[m\u001b[0;33m 1404724 ns 1368081 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopCrossJoin/343/343/real_time_cv \u001b[m\u001b[0;33m 13.67 % 13.49 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/SeqScan/260100/real_time_mean \u001b[m\u001b[0;33m 13336847 ns 13225191 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=260.1k\u001b[m plan_cost=26.01M\u001b[m rows=260.1k\u001b[m\n", + 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output_rows=130.052k\u001b[m plan_cost=52.02M\u001b[m rows=260.1k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Filter/260100/real_time_median \u001b[m\u001b[0;33m 18564079 ns 18476515 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=130.052k\u001b[m plan_cost=52.02M\u001b[m rows=260.1k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Filter/260100/real_time_stddev \u001b[m\u001b[0;33m 204348 ns 192770 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Filter/260100/real_time_cv \u001b[m\u001b[0;33m 1.10 % 1.04 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Projection/1182273/real_time_mean \u001b[m\u001b[0;33m 184939680 ns 181161386 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=1.18227M\u001b[m plan_cost=144.237M\u001b[m rows=1.18227M\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Projection/1182273/real_time_median \u001b[m\u001b[0;33m 185574441 ns 179442372 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=1.18227M\u001b[m plan_cost=144.237M\u001b[m rows=1.18227M\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Projection/1182273/real_time_stddev \u001b[m\u001b[0;33m 7745905 ns 7726947 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Projection/1182273/real_time_cv \u001b[m\u001b[0;33m 4.19 % 4.27 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Sort/131364/real_time_mean \u001b[m\u001b[0;33m 88475598 ns 87190889 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.0101M\u001b[m output_rows=131.364k\u001b[m plan_cost=39.1465M\u001b[m rows=131.364k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Sort/131364/real_time_median \u001b[m\u001b[0;33m 86036433 ns 85027458 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.0101M\u001b[m output_rows=131.364k\u001b[m plan_cost=39.1465M\u001b[m rows=131.364k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Sort/131364/real_time_stddev \u001b[m\u001b[0;33m 9059453 ns 9329788 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Sort/131364/real_time_cv \u001b[m\u001b[0;33m 10.24 % 10.70 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Aggregation/51000/real_time_mean \u001b[m\u001b[0;33m 31279068 ns 30916289 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=51k\u001b[m plan_cost=31.11M\u001b[m rows=51k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Aggregation/51000/real_time_median \u001b[m\u001b[0;33m 31578403 ns 31313289 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=51k\u001b[m plan_cost=31.11M\u001b[m rows=51k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Aggregation/51000/real_time_stddev \u001b[m\u001b[0;33m 673755 ns 741005 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Aggregation/51000/real_time_cv \u001b[m\u001b[0;33m 2.15 % 2.40 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/HashJoin/65848/65848/real_time_mean \u001b[m\u001b[0;33m 14420059 ns 14342223 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=65.848k\u001b[m model_cost=26.01M\u001b[m output_rows=65.848k\u001b[m plan_cost=39.1796M\u001b[m rhs_rows=65.848k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/HashJoin/65848/65848/real_time_median \u001b[m\u001b[0;33m 13858205 ns 13798367 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=65.848k\u001b[m model_cost=26.01M\u001b[m output_rows=65.848k\u001b[m plan_cost=39.1796M\u001b[m rhs_rows=65.848k\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/HashJoin/65848/65848/real_time_stddev \u001b[m\u001b[0;33m 993851 ns 957202 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/HashJoin/65848/65848/real_time_cv \u001b[m\u001b[0;33m 6.89 % 6.67 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopJoin/610/610/real_time_mean \u001b[m\u001b[0;33m 20663763 ns 20628319 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=610\u001b[m model_cost=26.047M\u001b[m output_rows=610\u001b[m plan_cost=26.169M\u001b[m rhs_rows=610\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopJoin/610/610/real_time_median \u001b[m\u001b[0;33m 20663114 ns 20627234 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=610\u001b[m model_cost=26.047M\u001b[m output_rows=610\u001b[m plan_cost=26.169M\u001b[m rhs_rows=610\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopJoin/610/610/real_time_stddev \u001b[m\u001b[0;33m 62693 ns 60912 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopJoin/610/610/real_time_cv \u001b[m\u001b[0;33m 0.30 % 0.30 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopCrossJoin/500/500/real_time_mean \u001b[m\u001b[0;33m 16636418 ns 16551291 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=500\u001b[m model_cost=26M\u001b[m output_rows=250k\u001b[m plan_cost=26.1M\u001b[m rhs_rows=500\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopCrossJoin/500/500/real_time_median \u001b[m\u001b[0;33m 16635667 ns 16552195 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=500\u001b[m model_cost=26M\u001b[m output_rows=250k\u001b[m plan_cost=26.1M\u001b[m rhs_rows=500\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopCrossJoin/500/500/real_time_stddev \u001b[m\u001b[0;33m 77985 ns 77061 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", + "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopCrossJoin/500/500/real_time_cv \u001b[m\u001b[0;33m 0.47 % 0.47 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", + "\u001b[m" + ] + } + ], "source": [ "!cd .. && timeout 300s ./build-release/bin/benchmarks --benchmark_filter='^OperatorCostMatched/' --benchmark_repetitions=3 --benchmark_display_aggregates_only=true --benchmark_out=research/benchmark-results/operator-cost-matched.json --benchmark_out_format=json" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "b16b9fb4", "metadata": {}, "outputs": [], @@ -149,8 +743,17 @@ "RESULTS = Path(\"benchmark-results\")\n", "UNIT_TO_MS = {\"ns\": 1e-6, \"us\": 1e-3, \"ms\": 1.0, \"s\": 1e3}\n", "\n", + "def load_benchmark_result(filename):\n", + " path = RESULTS / filename\n", + " try:\n", + " return json.loads(path.read_text())\n", + " except json.JSONDecodeError as exc:\n", + " raise RuntimeError(\n", + " f\"{path} is not valid JSON. The benchmark run probably timed out or was interrupted; rerun the cell that creates it.\"\n", + " ) from exc\n", + "\n", "def mean_rows(filename):\n", - " data = json.loads((RESULTS / filename).read_text())\n", + " data = load_benchmark_result(filename)\n", " return [row for row in data[\"benchmarks\"] if row.get(\"aggregate_name\") == \"mean\"]\n", "\n", "def time_ms(row):\n", @@ -159,10 +762,74 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "d3172304", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "( executor query mode \\\n", + " 0 InterpretedExpressionExecutor kSimpleSelectSmall Naive \n", + " 1 InterpretedExpressionExecutor kSimpleSelectSmall Optimized \n", + " 2 CachedJitCompiledExpressionExecutor kSimpleSelectSmall Naive \n", + " 3 CachedJitCompiledExpressionExecutor kSimpleSelectSmall Optimized \n", + " 4 InterpretedExpressionExecutor kJoinSmall Naive \n", + " 5 InterpretedExpressionExecutor kJoinSmall Optimized \n", + " 6 CachedJitCompiledExpressionExecutor kJoinSmall Naive \n", + " 7 CachedJitCompiledExpressionExecutor kJoinSmall Optimized \n", + " 8 InterpretedExpressionExecutor kComplex5 Naive \n", + " 9 InterpretedExpressionExecutor kComplex5 Optimized \n", + " 10 CachedJitCompiledExpressionExecutor kComplex5 Naive \n", + " 11 CachedJitCompiledExpressionExecutor kComplex5 Optimized \n", + " \n", + " time_ms \n", + " 0 0.020437 \n", + " 1 0.020149 \n", + " 2 0.019900 \n", + " 3 0.019747 \n", + " 4 0.066637 \n", + " 5 0.043217 \n", + " 6 0.066032 \n", + " 7 0.042778 \n", + " 8 0.082328 \n", + " 9 0.081952 \n", + " 10 0.078012 \n", + " 11 0.077614 ,\n", + " query executor mode time_ms\n", + " 0 q2.1 Interpreted Naive 9.348855\n", + " 1 q2.1 Interpreted Optimized 8.476780\n", + " 2 q1.3 Interpreted Naive 8.327581\n", + " 3 q1.3 Interpreted Optimized 6.667715\n", + " 4 q1.2 Interpreted Naive 8.315161\n", + " 5 q1.2 Interpreted Optimized 6.738973\n", + " 6 q3.3 Interpreted Naive 36.699167\n", + " 7 q3.3 Interpreted Optimized 5.963849\n", + " 8 q3.4 Interpreted Naive 34.712294\n", + " 9 q3.4 Interpreted Optimized 5.903691\n", + " 10 q4.3 Interpreted Naive 9.196776\n", + " 11 q4.3 Interpreted Optimized 6.029881\n", + " 12 q1.1 Interpreted Naive 8.408618\n", + " 13 q1.1 Interpreted Optimized 6.369086\n", + " 14 q3.2 Interpreted Naive 35.087301\n", + " 15 q3.2 Interpreted Optimized 6.043214\n", + " 16 q2.2 Interpreted Naive 8.857568\n", + " 17 q2.2 Interpreted Optimized 6.276747\n", + " 18 q3.1 Interpreted Naive 37.245934\n", + " 19 q3.1 Interpreted Optimized 5.914830\n", + " 20 q4.2 Interpreted Naive 9.171801\n", + " 21 q4.2 Interpreted Optimized 7.149140\n", + " 22 q4.1 Interpreted Naive 9.111124\n", + " 23 q4.1 Interpreted Optimized 9.521586\n", + " 24 q2.3 Interpreted Naive 8.640680\n", + " 25 q2.3 Interpreted Optimized 6.071635)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "synthetic_pattern = re.compile(\n", " r\"BM_SQL<(?P[^,]+), (?P[^,]+), PlannerMode::k(?P[^>]+)>/real_time_mean\"\n", @@ -184,10 +851,83 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "f4e060f2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "( operator input_rows model_cost time_ms\n", + " 0 SeqScan 1024 102400.0 0.039728\n", + " 1 SeqScan 2048 204800.0 0.068177\n", + " 2 SeqScan 4096 409600.0 0.162377\n", + " 3 SeqScan 8192 819200.0 0.360639\n", + " 4 SeqScan 16384 1638400.0 0.791876\n", + " .. ... ... ... ...\n", + " 59 NestedLoopCrossJoin 64 425984.0 0.279630\n", + " 60 NestedLoopCrossJoin 128 1703936.0 0.909990\n", + " 61 NestedLoopCrossJoin 256 6815744.0 5.038775\n", + " 62 NestedLoopCrossJoin 512 27262976.0 22.572298\n", + " 63 NestedLoopCrossJoin 1024 109051904.0 117.439223\n", + " \n", + " [64 rows x 4 columns],\n", + " target_cost operator input_rows model_cost time_ms\n", + " 0 640000 SeqScan 6400 640000.0 0.327112\n", + " 1 640000 Filter 6400 640000.0 0.423529\n", + " 2 640000 Projection 29091 640002.0 2.969473\n", + " 3 640000 Sort 4476 640068.0 0.708003\n", + " 4 640000 Aggregation 1255 640050.0 0.350350\n", + " 5 640000 HashJoin 1620 639900.0 0.308857\n", + " 6 640000 NestedLoopJoin 96 645120.0 0.665762\n", + " 7 640000 NestedLoopCrossJoin 78 632736.0 0.389871\n", + " 8 1000000 SeqScan 10000 1000000.0 0.355274\n", + " 9 1000000 Filter 10000 1000000.0 0.630249\n", + " 10 1000000 Projection 45455 1000010.0 4.981809\n", + " 11 1000000 Sort 6993 999999.0 1.141898\n", + " 12 1000000 Aggregation 1961 1000110.0 0.533838\n", + " 13 1000000 HashJoin 2532 1000140.0 0.485912\n", + " 14 1000000 NestedLoopJoin 120 1008000.0 1.018585\n", + " 15 1000000 NestedLoopCrossJoin 98 998816.0 0.582844\n", + " 16 3240000 SeqScan 32400 3240000.0 1.134758\n", + " 17 3240000 Filter 32400 3240000.0 2.151576\n", + " 18 3240000 Projection 147273 3240006.0 19.626122\n", + " 19 3240000 Sort 19636 3239940.0 3.898469\n", + " 20 3240000 Aggregation 6353 3240030.0 1.853008\n", + " 21 3240000 HashJoin 8203 3240185.0 1.603236\n", + " 22 3240000 NestedLoopJoin 215 3235750.0 2.910806\n", + " 23 3240000 NestedLoopCrossJoin 177 3258216.0 1.943488\n", + " 24 6760000 SeqScan 67600 6760000.0 3.382780\n", + " 25 6760000 Filter 67600 6760000.0 4.670764\n", + " 26 6760000 Projection 307273 6760006.0 41.478841\n", + " 27 6760000 Sort 38409 6759984.0 10.126082\n", + " 28 6760000 Aggregation 13255 6760050.0 4.259250\n", + " 29 6760000 HashJoin 17114 6760030.0 3.770392\n", + " 30 6760000 NestedLoopJoin 311 6770470.0 5.772993\n", + " 31 6760000 NestedLoopCrossJoin 255 6762600.0 5.119950\n", + " 32 12250000 SeqScan 122500 12250000.0 6.994680\n", + " 33 12250000 Filter 122500 12250000.0 8.849196\n", + " 34 12250000 Projection 556818 12249996.0 74.688525\n", + " 35 12250000 Sort 65536 12255232.0 20.585367\n", + " 36 12250000 Aggregation 24020 12250200.0 10.093168\n", + " 37 12250000 HashJoin 31013 12250135.0 7.152001\n", + " 38 12250000 NestedLoopJoin 418 12230680.0 10.945706\n", + " 39 12250000 NestedLoopCrossJoin 343 12235496.0 10.279043\n", + " 40 26010000 SeqScan 260100 26010000.0 13.336847\n", + " 41 26010000 Filter 260100 26010000.0 18.594129\n", + " 42 26010000 Projection 1182273 26010006.0 184.939680\n", + " 43 26010000 Sort 131364 26010072.0 88.475598\n", + " 44 26010000 Aggregation 51000 26010000.0 31.279068\n", + " 45 26010000 HashJoin 65848 26009960.0 14.420059\n", + " 46 26010000 NestedLoopJoin 610 26047000.0 20.663763\n", + " 47 26010000 NestedLoopCrossJoin 500 26000000.0 16.636418)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "operator = []\n", "for row in mean_rows(\"operator-cost.json\"):\n", @@ -217,10 +957,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "2075a20f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "ssb_interpreted = ssb[ssb.executor == \"Interpreted\"].copy()\n", "\n", @@ -234,10 +985,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "c36bdd23", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "speedup = (wide[\"Naive\"] / wide[\"Optimized\"]).sort_index()\n", "ax = speedup.plot.bar(figsize=(14, 4))\n", @@ -250,10 +1012,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "4ee5fc7c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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s2KGJEyeqffv2KlmyZKr7AyWpUKFCsrOzU2xsbKpt9xdTKVKkSFofj1XuH2PIkCFq27Ztmn3+fg9dt27d9M0332jdunUqUaKEdu3apRkzZlj0//LLL9W5c2dNmDDBov3SpUsPvC9JkpycnJSYmJiq/f4v4/ctX75ct27d0rJly+Tv729u/7fPmvP09Mz2z/xB7y0p3fe3s7NToUKFLNr/+b190LGtOa/0/u6kVWDY0oEDB9SmTRs1aNBAn3/+eartRYoUUZUqVTR+/Pg0979f5Hp6euqXX35JtT2zC688qH9cXJxKly6d7j7Z8V3Oly+fwsPDFR4ermvXrumnn37S0KFD1bRpU505cybHrMIKwDqM5AHIs3799VeFhYWpc+fO2rx5s6pUqaJ27dqlGuGQpGXLllmMBNy4cUMrVqxQvXr10l0U476WLVvKMAydO3cu1ehicHCwgoKCUu1jMplUtWpVffTRR/Lw8NDevXtT9XF0dFSDBg30/vvvS5LFioV/5+Liotq1a2vZsmUW/0KfkpKiL7/8Uo899liWPFetXLlyKlOmjPbv35/meQYHB8vV1dXcv0mTJipWrJjmzp2ruXPnysnJSa+88kqqz+GfhfGqVasyNU21ZMmSunDhgnmEUZLu3r2rNWvWpHoPybLYNgwjzV/0/znK8SBPPfWUNmzYkGpV0gULFsjZ2TlbH7lQrlw5FStWTF999ZUMwzC337p1S0uXLjWvuPkwrD2vRYsWWWQ4ffq0tm3bZrEK6L8ZtcwKMTExat68uQIDA7V06VLlz58/VZ+WLVvq4MGDKlWqVJrf7ftFXqNGjXTjxg3zCrH3ffXVV1ZlWrhwocXrbdu26fTp0xaf2z9Z811+GB4eHnrhhRf0+uuv68qVK+YVUAHkPozkAciVDh48mGp1TUkqVaqUvLy8dOvWLb300ksKCAjQ9OnT5eDgoK+//lo1atRQt27dtHz5cov97O3tFRoaqvDwcKWkpOj9999XfHy8xowZk2GWunXr6tVXX1W3bt20e/du1a9fXy4uLoqNjdWWLVsUFBSkPn36aOXKlZo+fbratGmjwMBAGYahZcuW6dq1a+bVPkeOHKmzZ8/qqaee0mOPPaZr167p448/Vv78+R/4jLSJEycqNDRUjRo10sCBA+Xg4KDp06fr4MGDWrRoUaZHcTLy6aefqnnz5mratKm6du2qYsWK6cqVKzp8+LD27t2rb775xtzX3t5enTt31pQpU+Tm5qa2bdvK3d3d4ngtW7bUvHnzVL58eVWpUkV79uzRBx98kKmHjrdr104jR47Uyy+/rLffflsJCQmaOnVqqnsoQ0ND5eDgoFdeeUWDBg1SQkKCZsyYkWaxHxQUpGXLlmnGjBmqWbOm7Ozs0n0e46hRo8z3cY0cOVKFCxfWwoULtWrVKk2aNCnVuWYlOzs7TZo0SR06dFDLli312muvKTExUR988IGuXbum995776GPbe15XbhwQc8995x69eql69eva9SoUXJyctKQIUPMfe7/Q8f777+v5s2by97eXlWqVDFPtcxuzZs317Vr1zRt2jT99ttvFtvuXzPGjh2rdevWqU6dOgoLC1O5cuWUkJCgU6dOKTIyUjNnztRjjz2mzp0766OPPlLnzp01fvx4lSlTRpGRkan+cSEju3fvVs+ePfXiiy/qzJkzGjZsmIoVK2ae+pwWa77LmdWqVSvzc0e9vLx0+vRpRUREyN/fX2XKlHno4wKwMVut+AIAD+NBq2tKMj7//HPDMAyjY8eOhrOzs3lFvPu++eYbQ5Lx0UcfGYbxf6s2vv/++8aYMWOMxx57zHBwcDCqV69urFmzxmLf+6sUXrx4Mc1sc+bMMWrXrm24uLgYBQoUMEqVKmV07tzZ2L17t2EYhvH7778br7zyilGqVCmjQIEChru7u1GrVi1j3rx55mOsXLnSaN68uVGsWDHDwcHB8Pb2Nlq0aGFs3rzZ3CetlSYNwzA2b95sNG7c2Pz+TzzxhLFixYo0P79du3ZZtN9f0TAqKurBfwCGYezfv9946aWXDG9vbyN//vyGr6+v0bhxY2PmzJmp+h49etT8Z7Nu3bpU269evWr06NHD8Pb2NpydnY0nn3zS2Lx5s9GgQQOjQYMGGZ5zZGSkUa1aNaNAgQJGYGCgMW3atDRX11yxYoVRtWpVw8nJyShWrJjx9ttvGz/++GOqc75y5YrxwgsvGB4eHobJZLI4jv6xuqZhGMavv/5qtGrVynB3dzccHByMqlWrpsqY1uqYDzqnf0pvf8MwjOXLlxu1a9c2nJycDBcXF+Opp54ytm7datEno+9tWqw5ry+++MIICwszvLy8DEdHR6NevXrm7/x9iYmJRs+ePQ0vLy/z53ry5EnDMNJfXfOf55vedzet8/vnMR90zfj7eV28eNEICwszAgICjPz58xuFCxc2atasaQwbNsy4efOmud/Zs2eN559/3ihYsKDh6upqPP/888a2bdusWl1z7dq1RqdOnQwPDw+jQIECRosWLYxjx45Z9E1rdc3MfpcbNGhgVKpUKdX7//OYH374oVGnTh2jSJEihoODg1GiRAmjR48exqlTpx54HgByNpNh/G2OBQD8x5w6dUoBAQH64IMPNHDgQFvHAXKNjRs3qlGjRvrmm2/0wgsv2DpOrjFv3jx169ZNu3btSneUGAD+Le7JAwAAAIA8hCIPAAAAAPIQpmsCAAAAQB7CSB4AAAAA5CEUeQAAAACQh1DkAQAAAEAewsPQ05CSkqLz58/L1dU1yx4gDAAAAAD/hmEYunHjhvz8/GRnl/54HUVeGs6fP6/ixYvbOgYAAAAApHLmzBk99thj6W6nyEuDq6urpL8+PDc3NxunAQAAAAApPj5exYsXN9cr6aHIS8P9KZpubm4UeQAAAABylIxuKWPhFQAAAADIQyjyAAAAACAPocgDAAAAgDyEe/IAAACAHCo5OVn37t2zdQw8Ivnz55e9vf2/Pg5FHgAAAJDDGIahuLg4Xbt2zdZR8Ih5eHjI19f3Xz2vmyIPAAAAyGHuF3je3t5ydnb+V7/wI3cwDEO3b9/WhQsXJElFixZ96GPZvMibPn26PvjgA8XGxqpSpUqKiIhQvXr10u2/adMmhYeH67fffpOfn58GDRqk3r17W/SJiIjQjBkzFBMToyJFiuiFF17QxIkT5eTklN2ng/+C0e62TpCzjL5u6wQAAOQpycnJ5gLP09PT1nHwCBUoUECSdOHCBXl7ez/01E2bLryyZMkS9e/fX8OGDVN0dLTq1aun5s2bKyYmJs3+J0+eVIsWLVSvXj1FR0dr6NChCgsL09KlS819Fi5cqMGDB2vUqFE6fPiwZs+erSVLlmjIkCGP6rQAAACAh3b/HjxnZ2cbJ4Et3P9z/zf3Ytp0JG/KlCnq0aOHevbsKemvEbg1a9ZoxowZmjhxYqr+M2fOVIkSJRQRESFJqlChgnbv3q3Jkyfr+eeflyRt375ddevWVfv27SVJJUuW1CuvvKJffvnl0ZwUAAAAkAWYovnflBV/7jYbybt796727NmjJk2aWLQ3adJE27ZtS3Of7du3p+rftGlT7d6921zpPvnkk9qzZ4+5qDtx4oQiIyP1zDPPpJslMTFR8fHxFj8AAAAAkBvZbCTv0qVLSk5Olo+Pj0W7j4+P4uLi0twnLi4uzf5JSUm6dOmSihYtqpdfflkXL17Uk08+KcMwlJSUpD59+mjw4MHpZpk4caLGjBnz708KAAAAAGzM5g9D/+dwpGEYDxyiTKv/39s3btyo8ePHa/r06dq7d6+WLVumlStXaty4cekec8iQIbp+/br558yZMw97OgAAAAAeoYYNG6p///62jpGj2Gwkr0iRIrK3t081anfhwoVUo3X3+fr6ptk/X7585pWHRowYoU6dOpnv8wsKCtKtW7f06quvatiwYbKzS13XOjo6ytHRMStOCwAAAEAudPfuXTk4ONg6Rpaw2Uieg4ODatasqXXr1lm0r1u3TnXq1Elzn5CQkFT9165dq+DgYOXPn1+SdPv27VSFnL29vQzDMI/6AQAAAMgcwzA0adIkBQYGqkCBAqpataq+/fZbGYahp59+Ws2aNTP/nn3t2jWVKFFCw4YNM+8/d+5cVahQQU5OTipfvrymT59ucfyzZ8/q5ZdfVuHCheXi4qLg4GDt3LlTktS1a1e1adPGon///v3VsGFD8/ZNmzbp448/lslkkslk0qlTpyT99ei1WrVqydHRUUWLFtXgwYOVlJRkPk7Dhg31xhtvKDw8XEWKFFFoaGgWf3K2Y9PVNcPDw9WpUycFBwcrJCREn332mWJiYszPvRsyZIjOnTunBQsWSJJ69+6tadOmKTw8XL169dL27ds1e/ZsLVq0yHzMVq1aacqUKapevbpq166t48ePa8SIEWrduvVDP2cCAAAA+K8aPny4li1bphkzZqhMmTL6+eef1bFjR3l5eWn+/PkKCgrS1KlT1a9fP/Xu3Vs+Pj4aPXq0JOnzzz/XqFGjNG3aNFWvXl3R0dHq1auXXFxc1KVLF928eVMNGjRQsWLF9MMPP8jX11d79+5VSkpKprJ9/PHHOnr0qCpXrqyxY8dKkry8vHTu3Dm1aNFCXbt21YIFC/T777+rV69ecnJyMmeTpPnz56tPnz7aunVrnhoQsmmR165dO12+fFljx45VbGysKleurMjISPn7+0uSYmNjLZ6ZFxAQoMjISA0YMECffPKJ/Pz8NHXqVPPjE6S/voQmk0nDhw/XuXPn5OXlpVatWmn8+PGP/PzykpKDV9k6Qo5xysnWCQAAAB6NW7duacqUKdqwYYNCQkIkSYGBgdqyZYs+/fRTffXVV/r000/VqVMn/fnnn1qxYoWio6PNs+zGjRunDz/8UG3btpX01+/zhw4d0qeffqouXbroq6++0sWLF7Vr1y4VLlxYklS6dOlM53N3d5eDg4OcnZ3l6+trbp8+fbqKFy+uadOmyWQyqXz58jp//rzeeecdjRw50jzzr3Tp0po0aVKWfFY5iU2LPEnq27ev+vbtm+a2efPmpWpr0KCB9u7dm+7x8uXLp1GjRmnUqFFZFREAAAD4Tzp06JASEhJSTWW8e/euqlevLkl68cUX9d1332nixImaMWOGypYtK0m6ePGizpw5ox49eqhXr17mfZOSkuTu7i5J2rdvn6pXr24u8LLK4cOHFRISYrFoY926dXXz5k2dPXtWJUqUkCQFBwdn6fvmFDYv8gAAAADkTPenTa5atUrFihWz2HZ/4cLbt29rz549sre317Fjx1Lt+/nnn6t27doW+96/japAgQIPfH87O7tU0yjvPx/7QdJasf+fq/JLkouLS4bHyo0o8gAAAACkqWLFinJ0dFRMTIwaNGiQZp+33npLdnZ2+vHHH9WiRQs988wzaty4sXx8fFSsWDGdOHFCHTp0SHPfKlWqaNasWbpy5Uqao3leXl46ePCgRdu+ffvM00GlvxZ0TE5OTpV76dKlFsXetm3b5OrqmqpYzYso8gAAAACkydXVVQMHDtSAAQOUkpKiJ598UvHx8dq2bZsKFiyoIkWKaM6cOdq+fbtq1KihwYMHq0uXLjpw4IAKFSqk0aNHKywsTG5ubmrevLkSExO1e/duXb16VeHh4XrllVc0YcIEtWnTRhMnTlTRokUVHR0tPz8/hYSEqHHjxvrggw+0YMEChYSE6Msvv9TBgwfNU0UlqWTJktq5c6dOnTqlggULqnDhwurbt68iIiL05ptv6o033tCRI0c0atQohYeHp/lItbwm758hAAAAgIc2btw4jRw5UhMnTlSFChXUtGlTrVixQiVLllSPHj00evRo1ahRQ5I0atQo+fn5mVfL79mzp2bNmqV58+YpKChIDRo00Lx58xQQECDpr1G4tWvXytvbWy1atFBQUJDee+8983TOpk2basSIERo0aJAef/xx3bhxQ507d7bIN3DgQNnb26tixYry8vJSTEyMihUrpsjISP3yyy+qWrWqevfurR49emj48OGP8JOzHZORl9YKzSLx8fFyd3fX9evX5ebmZus4OQKra/6fU07tbR0hZxl93dYJAADIUxISEnTy5EkFBATIyYllvf9rHvTnn9k6hZE8AAAAAMhDKPIAAAAAIA+xusg7c+aMzp49a379yy+/qH///vrss8+yNBgAAAAAwHpWF3nt27dXVFSUJCkuLk6hoaH65ZdfNHToUI0dOzbLAwIAAAAAMs/qIu/gwYOqVauWJOnrr79W5cqVtW3bNn311VeaN29eVucDAAAAAFjB6iLv3r175qfb//TTT2rdurUkqXz58oqNjc3adAAAAAAAq1hd5FWqVEkzZ87U5s2btW7dOjVr1kySdP78eXl6emZ5QAAAAABA5lld5L3//vv69NNP1bBhQ73yyiuqWrWqJOmHH34wT+MEAAAAANhGPmt3aNiwoS5duqT4+HgVKlTI3P7qq6/K2dk5S8MBAAAAAKzzUM/Js7e3tyjwJKlkyZLy9vbOklAAAAAAkJaGDRuqf//+WX7c0aNHq1q1all+XFuweiTv8uXLGjlypKKionThwgWlpKRYbL9y5UqWhQMAAADwl5KDVz3S9zv13jNW7xMXF6fx48dr1apVOnfunLy9vVWtWjX1799fTz31VDakfHROnTqlgIAARUdHq1q1ahavly9frjFjxjxw/5MnT6pkyZKPJKvVRV7Hjh31xx9/qEePHvLx8ZHJZMqOXAAAAABykVOnTqlu3bry8PDQpEmTVKVKFd27d09r1qzR66+/rt9//93WEbPNwIED1bt3b/Prxx9/XK+++qp69eplbvPy8npkeawu8rZs2aItW7aYF1wBAAAAgL59+8pkMumXX36Ri4uLub1SpUrq3r27+fWUKVM0d+5cnThxQoULF1arVq00adIkFSxY0Nxn69atGjp0qHbt2iVHR0fVqlVLixcvNt8ylpKSokGDBmnWrFlycHBQ7969NXr0aPP+169f19tvv63ly5crISFBwcHB+uijjyxqmPfee08fffSRbt++rZdeeulfFWEFCxa0yG9vby9XV1f5+vo+9DH/DavvyStfvrzu3LmTHVkAAAAA5EJXrlzR6tWr9frrr1sUePd5eHiY/9vOzk5Tp07VwYMHNX/+fG3YsEGDBg0yb9+3b5+eeuopVapUSdu3b9eWLVvUqlUrJScnm/vMnz9fLi4u2rlzpyZNmqSxY8dq3bp1kiTDMPTMM88oLi5OkZGR2rNnj2rUqKGnnnrKfGvZ119/rVGjRmn8+PHavXu3ihYtqunTp2fTp/PoWT2SN336dA0ePFgjR45U5cqVlT9/fovtbm5uWRYOAAAAQM53/PhxGYah8uXLZ9j374umBAQEaNy4cerTp4+5yJo0aZKCg4Mtiq5KlSpZHKNKlSoaNWqUJKlMmTKaNm2a1q9fr9DQUEVFRenXX3/VhQsX5OjoKEmaPHmyli9frm+//VavvvqqIiIi1L17d/Xs2VOS9O677+qnn35SQkLCv/occgqrizwPDw9dv35djRs3tmg3DEMmk8miwgYAAACQ9xmGIUmZWq8jKipKEyZM0KFDhxQfH6+kpCQlJCTo1q1bcnFx0b59+/Tiiy8+8BhVqlSxeF20aFFduHBBkrRnzx7dvHlTnp6eFn3u3LmjP/74Q5J0+PBhi3voJCkkJERRUVEZ5s8NrC7yOnToIAcHB3311VcsvAIAAABAZcqUkclk0uHDh9WmTZt0+50+fVotWrRQ7969NW7cOBUuXFhbtmxRjx49dO/ePUlSgQIFMny/f84mNJlM5lX/U1JSVLRoUW3cuDHVfn+fNpqXWV3kHTx4UNHR0SpXrlx25AEAAACQyxQuXFhNmzbVJ598orCwsFT35V27dk0eHh7avXu3kpKS9OGHH8rO7q/lQb7++muLvlWqVNH69eszfCRBemrUqKG4uDjly5cv3UcWVKhQQTt27FDnzp3NbTt27Hio98uJrF54JTg4WGfOnMmOLAAAAAByqenTpys5OVm1atXS0qVLdezYMR0+fFhTp05VSEiIJKlUqVJKSkrS//73P504cUJffPGFZs6caXGcIUOGaNeuXerbt68OHDig33//XTNmzNClS5cylePpp59WSEiI2rRpozVr1ujUqVPatm2bhg8frt27d0uS+vXrpzlz5mjOnDk6evSoRo0apd9++y1rPxAbsrrIe/PNN9WvXz/NmzdPe/bs0YEDByx+AAAAAPz3BAQEaO/evWrUqJHeeustVa5cWaGhoVq/fr1mzJghSapWrZqmTJmi999/X5UrV9bChQs1ceJEi+OULVtWa9eu1f79+1WrVi2FhITo+++/V758mZuEaDKZFBkZqfr166t79+4qW7asXn75ZZ06dUo+Pj6SpHbt2mnkyJF65513VLNmTZ0+fVp9+vR54HHvTwfNbA5bMhn375LMpPvDqhYHMZny1MIr8fHxcnd31/Xr11kt9P8rOXiVrSPkGKec2ts6Qs4y+rqtEwAAkKckJCTo5MmTCggIkJOTk63j4P/bsWOHQkJCdPHiRRUpUiTb3udBf/6ZrVOsLkNPnjxpfVIAAAAAyIWSkpJ06tQpffDBB6patWq2FnhZxeoiz9/fPztyAAAAAECOc/DgQdWpU0fVqlXTggULbB0nU3L+hFIAAAAAsJFq1arp9u3bto5hFasXXgEAAAAA5FwUeQAAAACQh9i8yJs+fbp55ZiaNWtq8+bND+y/adMm1axZU05OTgoMDEz1XA3pr4ctvv766ypatKicnJxUoUIFRUZGZtcpAAAAAECOYdMib8mSJerfv7+GDRum6Oho1atXT82bN1dMTEya/U+ePKkWLVqoXr16io6O1tChQxUWFqalS5ea+9y9e1ehoaE6deqUvv32Wx05ckSff/65ihUr9qhOCwAAAABsJksXXgkICFDjxo01duzYTBVVU6ZMUY8ePdSzZ09JUkREhNasWaMZM2akeiiiJM2cOVMlSpRQRESEJKlChQravXu3Jk+erOeff16SNGfOHF25ckXbtm1T/vz5JbEiKAAAAID/jiwdyevSpYtSUlJUv379DPvevXtXe/bsUZMmTSzamzRpom3btqW5z/bt21P1b9q0qXbv3q179+5Jkn744QeFhITo9ddfl4+PjypXrqwJEybkiYe0AwAAAEBGsnQkb/To0Znue+nSJSUnJ8vHx8ei3cfHR3FxcWnuExcXl2b/pKQkXbp0SUWLFtWJEye0YcMGdejQQZGRkTp27Jhef/11JSUlaeTIkWkeNzExUYmJiebX8fHxmT4PAAAAAMisefPmqX///rp27Vq2vcdDF3l3797VyZMnVapUKeXL9/C1oslksnhtGEaqtoz6/709JSVF3t7e+uyzz2Rvb6+aNWvq/Pnz+uCDD9It8iZOnKgxY8Y89DkAAAAA2W60+yN+v+tW79K1a1ddu3ZNy5cvz1R/k8mk7777Tm3atLH6vR6lR1GYZSWrp2vevn1bPXr0kLOzsypVqmReJCUsLEzvvfdepo9TpEgR2dvbpxq1u3DhQqrRuvt8fX3T7J8vXz55enpKkooWLaqyZcvK3t7e3KdChQqKi4vT3bt30zzukCFDdP36dfPPmTNnMn0eAAAAAGzr/q1b1kpOTlZKSkoWp7E9q4u8IUOGaP/+/dq4caOcnJzM7U8//bSWLFmS6eM4ODioZs2aWrdunUX7unXrVKdOnTT3CQkJSdV/7dq1Cg4ONi+yUrduXR0/ftziD+vo0aMqWrSoHBwc0jyuo6Oj3NzcLH4AAAAAPLyGDRsqLCxMgwYNUuHCheXr62txe1fJkiUlSc8995xMJpP5tSStWLHC4rFpY8aMUVJSknm7yWTSzJkz9eyzz8rFxUXvvvuuNm7cKJPJpFWrVqlq1apycnJS7dq19euvv5r3mzdvnjw8PLRy5UpVrFhRjo6OOn36tO7evatBgwapWLFicnFxUe3atbVx40ZJ0saNG9WtWzddv35dJpNJJpPJfB4P2u/v71miRAk5Ozvrueee0+XLl7PyY06T1UXe8uXLNW3aND355JMWUycrVqyoP/74w6pjhYeHa9asWZozZ44OHz6sAQMGKCYmRr1795b0V0HZuXNnc//evXvr9OnTCg8P1+HDhzVnzhzNnj1bAwcONPfp06ePLl++rH79+uno0aNatWqVJkyYoNdff93aUwUAAADwL8yfP18uLi7auXOnJk2apLFjx5oHbXbt2iVJmjt3rmJjY82v16xZo44dOyosLEyHDh3Sp59+qnnz5mn8+PEWxx41apSeffZZ/frrr+revbu5/e2339bkyZO1a9cueXt7q3Xr1hYjfbdv39bEiRM1a9Ys/fbbb/L29la3bt20detWLV68WAcOHNCLL76oZs2a6dixY6pTp44iIiLk5uam2NhYxcbGmuuPB+0nSTt37lT37t3Vt29f7du3T40aNdK7776bfR/4/2f1zXQXL16Ut7d3qvZbt2498F66tLRr106XL1/W2LFjFRsbq8qVKysyMtL8yIPY2FiLZ+YFBAQoMjJSAwYM0CeffCI/Pz9NnTrV/PgESSpevLjWrl2rAQMGqEqVKipWrJj69eund955x9pTBQAAAPAvVKlSRaNGjZIklSlTRtOmTdP69esVGhoqLy8vSZKHh4d8fX3N+4wfP16DBw9Wly5dJEmBgYEaN26cBg0aZD6WJLVv396iuDt58qSkv4q/0NBQSX8VmY899pi+++47vfTSS5L+mto5ffp0Va1aVZL0xx9/aNGiRTp79qz8/PwkSQMHDtTq1as1d+5cTZgwQe7u7jKZTBY5M7Pfxx9/rKZNm2rw4MGSpLJly2rbtm1avXp1Vn3EabK6yHv88ce1atUqvfnmm5L+b8GTzz//XCEhIVYH6Nu3r/r27Zvmtnnz5qVqa9Cggfbu3fvAY4aEhGjHjh1WZwEAAACQdapUqWLxumjRorpw4cID99mzZ4927dplMXKXnJyshIQE3b59W87OzpKk4ODgNPf/e01SuHBhlStXTocPHza3OTg4WOTau3evDMNQ2bJlLY6TmJhoXvcjLZnZ7/Dhw3ruuedS5ctxRd7EiRPVrFkzHTp0SElJSfr444/122+/afv27dq0aVN2ZAQAAACQC91fN+M+k8mU4UInKSkpGjNmjNq2bZtq29/XBHFxccl0jr/POCxQoIDF65SUFNnb22vPnj0WizdKUsGCBR+YM6P97j8J4FGzusirU6eOtm7dqsmTJ6tUqVJau3atatSooe3btysoKCg7MgIAAADIg/Lnz6/k5GSLtho1aujIkSMqXbr0Qx1zx44dKlGihCTp6tWrOnr0qMqXL59u/+rVqys5OVkXLlxQvXr10uzj4OCQKmdm9qtYsWKqGYaPYsbhQz3gLigoSPPnz8/qLAAAAAD+Q0qWLKn169erbt26cnR0VKFChTRy5Ei1bNlSxYsX14svvig7OzsdOHBAv/76a6YWLRk7dqw8PT3l4+OjYcOGqUiRIg98Dl/ZsmXVoUMHde7cWR9++KGqV6+uS5cuacOGDQoKClKLFi1UsmRJ3bx5U+vXr1fVqlXl7Oycqf3CwsJUp04dTZo0SW3atNHatWuzfaqm9BCra9534cIFHTx4UAcOHLD4AQAAAIDM+PDDD7Vu3ToVL15c1atXlyQ1bdpUK1eu1Lp16/T444/riSee0JQpU8yLM2bkvffeU79+/VSzZk3Fxsbqhx9+SPdRavfNnTtXnTt31ltvvaVy5cqpdevW2rlzp4oXLy7pr9mMvXv3Vrt27eTl5aVJkyZlar8nnnhCs2bN0v/+9z9Vq1ZNa9eu1fDhwx/248o0k2HlRNE9e/aoS5cuOnz4cKo5piaTKdUwZm4UHx8vd3d3Xb9+nWfm/X8lB6+ydYQc45RTe1tHyFlGX7d1AgAA8pSEhASdPHlSAQEBFveg4cE2btyoRo0a6erVq/Lw8LB1nIf2oD//zNYpVk/X7Natm8qWLavZs2fLx8fH6scmAAAAAACyj9VF3smTJ7Vs2bKHvhESAAAAAJB9rL4n76mnntL+/fuzIwsAAAAAPJSGDRvKMIxcPVUzq1g9kjdr1ix16dJFBw8eVOXKlVM9+6J169ZZFg4AAAAAYB2ri7xt27Zpy5Yt+vHHH1NtyysLrwAAAABAbmX1dM2wsDB16tRJsbGxSklJsfihwAMAAACyRkpKiq0jwAay4s/d6pG8y5cva8CAAfLx8fnXbw4AAADAkoODg+zs7HT+/Hl5eXnJwcGBFe3/AwzD0N27d3Xx4kXZ2dll+Gy/B7G6yGvbtq2ioqJUqlSph35TAAAAAGmzs7NTQECAYmNjdf78eVvHwSPm7OysEiVKyM7O6kmXZlYXeWXLltWQIUO0ZcsWBQUFpVp4JSws7KHDAAAAAPhrNK9EiRJKSkrilqj/EHt7e+XLl+9fj9yaDMMwrNkhICAg/YOZTDpx4sS/CpQTZPZJ8v8lJQevsnWEHOOUU3tbR8hZRl+3dQIAAID/hMzWKQ/1MHQAAAAAQM708BM9AQAAAAA5TqZG8sLDwzVu3Di5uLgoPDz8gX2nTJmSJcEAAAAAANbLVJEXHR2te/fumf8bAAAAAJAzZarIi4qKSvO/AQAAAAA5i9X35HXv3l03btxI1X7r1i117949S0IBAAAAAB6O1UXe/PnzdefOnVTtd+7c0YIFC7IkFAAAAADg4WT6EQrx8fEyDEOGYejGjRtycnIyb0tOTlZkZKS8vb2zJSQAAAAAIHMyXeR5eHjIZDLJZDKpbNmyqbabTCaNGTMmS8MBAAAAAKyT6SIvKipKhmGocePGWrp0qQoXLmze5uDgIH9/f/n5+WVLSAAAAABA5mS6yGvQoIEk6eTJkypRooRMJlO2hQIAAAAAPJxMF3n3+fv7Z0cOAAAAAEAWsHp1TQAAAABAzkWRBwAAAAB5CEUeAAAAAOQhVt+TBwAArDTa3dYJcpbR122dAADyNKuLvD///FMDBw7U+vXrdeHCBRmGYbE9OTk5y8IBAHKvkoNX2TpCjnHKydYJAAD/JVZP1+zatav27t2rESNG6Ntvv9WyZcssfqw1ffp0BQQEyMnJSTVr1tTmzZsf2H/Tpk2qWbOmnJycFBgYqJkzZ6bbd/HixTKZTGrTpo3VuQAAAAAgN7J6JG/Lli3avHmzqlWr9q/ffMmSJerfv7+mT5+uunXr6tNPP1Xz5s116NAhlShRIlX/kydPqkWLFurVq5e+/PJLbd26VX379pWXl5eef/55i76nT5/WwIEDVa9evX+dEwAAAAByC6tH8ooXL55qiubDmjJlinr06KGePXuqQoUKioiIUPHixTVjxow0+8+cOVMlSpRQRESEKlSooJ49e6p79+6aPHmyRb/k5GR16NBBY8aMUWBgYJZkBQAAAIDcwOoiLyIiQoMHD9apU6f+1RvfvXtXe/bsUZMmTSzamzRpom3btqW5z/bt21P1b9q0qXbv3q179+6Z28aOHSsvLy/16NHjX2UEAAAAgNzG6uma7dq10+3bt1WqVCk5Ozsrf/78FtuvXLmSqeNcunRJycnJ8vHxsWj38fFRXFxcmvvExcWl2T8pKUmXLl1S0aJFtXXrVs2ePVv79u3L9DklJiYqMTHR/Do+Pj7T+wIAAABATmJ1kRcREZGlAUwmk8VrwzBStWXU/377jRs31LFjR33++ecqUqRIpjNMnDhRY8aMsSI1AAAAAORMVhd5Xbp0yZI3LlKkiOzt7VON2l24cCHVaN19vr6+afbPly+fPD099dtvv+nUqVNq1aqVeXtKSookKV++fDpy5IhKlSqV6rhDhgxReHi4+XV8fLyKFy/+0OcGAAAAALbyUA9DT05O1vLly3X48GGZTCZVrFhRrVu3lr29faaP4eDgoJo1a2rdunV67rnnzO3r1q3Ts88+m+Y+ISEhWrFihUXb2rVrFRwcrPz586t8+fL69ddfLbYPHz5cN27c0Mcff5xu4ebo6ChHR8dMZwcAAACAnMrqIu/48eNq0aKFzp07p3LlyskwDB09elTFixfXqlWr0hwpS094eLg6deqk4OBghYSE6LPPPlNMTIx69+4t6a8RtnPnzmnBggWSpN69e2vatGkKDw9Xr169tH37ds2ePVuLFi2SJDk5Oaly5coW7+Hh4SFJqdoBAAAAIC+yusgLCwtTqVKltGPHDhUuXFiSdPnyZXXs2FFhYWFatWpVpo/Vrl07Xb58WWPHjlVsbKwqV66syMhI+fv7S5JiY2MVExNj7h8QEKDIyEgNGDBAn3zyifz8/DR16tRUz8gDAAAAgP8qk2HlQ+9cXFy0Y8cOBQUFWbTv379fdevW1c2bN7M0oC3Ex8fL3d1d169fl5ubm63j5AglB2e+eM/rTjm1t3WEnGX0dVsnQA7FdeP/cN34B64bAPBQMlunWP2cPEdHR924cSNV+82bN+Xg4GDt4QAAAAAAWcjqIq9ly5Z69dVXtXPnThmGIcMwtGPHDvXu3VutW7fOjowAAAAAgEyyusibOnWqSpUqpZCQEDk5OcnJyUl169ZV6dKl9fHHH2dHRgAAAABAJlm98IqHh4e+//57HTt2TL///rsMw1DFihVVunTp7MgHAAAAALDCQz0nT5LKlCmjMmXKZGUWAAAAAMC/lKkiLzw8XOPGjZOLi4vCw8Mf2HfKlClZEgwAAAAAYL1MFXnR0dG6d++e+b8BAAAAADlTpoq8qKioNP8bAAAAAJCzWL26Zvfu3dN8Tt6tW7fUvXv3LAkFAAAAAHg4Vhd58+fP1507d1K137lzRwsWLMiSUAAAAACAh5Pp1TXj4+PNDz+/ceOGnJyczNuSk5MVGRkpb2/vbAkJAAAAAMicTBd5Hh4eMplMMplMKlu2bKrtJpNJY8aMydJwAAAAAADrZLrIi4qKkmEYaty4sZYuXarChQubtzk4OMjf319+fn7ZEhIAAAAAkDmZLvIaNGggSTp58qRKlCghk8mUbaEAAAAAAA8n00XefadPn9bp06fT3V6/fv1/FQgAAAAA8PCsLvIaNmyYqu3vo3rJycn/KhAAAAAA4OFZ/QiFq1evWvxcuHBBq1ev1uOPP661a9dmR0YAAAAAQCZZPZLn7u6eqi00NFSOjo4aMGCA9uzZkyXBAAAAAADWs3okLz1eXl46cuRIVh0OAAAAAPAQrB7JO3DggMVrwzAUGxur9957T1WrVs2yYAAAAAAA61ld5FWrVk0mk0mGYVi0P/HEE5ozZ06WBQMAAAAAWM/qIu/kyZMWr+3s7OTl5SUnJ6csCwUAAAAAeDhWF3n+/v7ZkQMAAAAAkAWsXnglLCxMU6dOTdU+bdo09e/fPysyAQAAAAAektVF3tKlS1W3bt1U7XXq1NG3336bJaEAAAAAAA/H6iLv8uXLaT4rz83NTZcuXcqSUAAAAACAh2N1kVe6dGmtXr06VfuPP/6owMDALAkFAAAAAHg4Vi+8Eh4erjfeeEMXL15U48aNJUnr16/Xhx9+qIiIiKzOBwAAAACwgtVFXvfu3ZWYmKjx48dr3LhxkqSSJUtqxowZ6ty5c5YHBAAAAABkntVFniT16dNHffr00cWLF1WgQAEVLFgwq3MBAAAAAB6C1ffkSVJSUpJ++uknLVu2TIZhSJLOnz+vmzdvZmk4AAAAAIB1rB7JO336tJo1a6aYmBglJiYqNDRUrq6umjRpkhISEjRz5szsyAkAAAAAyASrR/L69eun4OBgXb16VQUKFDC3P/fcc1q/fr3VAaZPn66AgAA5OTmpZs2a2rx58wP7b9q0STVr1pSTk5MCAwNTFZWff/656tWrp0KFCqlQoUJ6+umn9csvv1idCwAAAAByI6uLvC1btmj48OFycHCwaPf399e5c+esOtaSJUvUv39/DRs2TNHR0apXr56aN2+umJiYNPufPHlSLVq0UL169RQdHa2hQ4cqLCxMS5cuNffZuHGjXnnlFUVFRWn79u0qUaKEmjRpYnU2AAAAAMiNrC7yUlJSlJycnKr97NmzcnV1tepYU6ZMUY8ePdSzZ09VqFBBERERKl68uGbMmJFm/5kzZ6pEiRKKiIhQhQoV1LNnT3Xv3l2TJ08291m4cKH69u2ratWqqXz58vr888+VkpLyUKOMAAAAAJDbWF3khYaGWjwPz2Qy6ebNmxo1apRatGiR6ePcvXtXe/bsUZMmTSzamzRpom3btqW5z/bt21P1b9q0qXbv3q179+6luc/t27d17949FS5cON0siYmJio+Pt/gBAAAAgNzI6oVXPvroIzVq1EgVK1ZUQkKC2rdvr2PHjqlIkSJatGhRpo9z6dIlJScny8fHx6Ldx8dHcXFxae4TFxeXZv+kpCRdunRJRYsWTbXP4MGDVaxYMT399NPpZpk4caLGjBmT6ewAAABAthrtbusEOcvo67ZOkKtYXeT5+flp3759Wrx4sfbs2aOUlBT16NFDHTp0sFiIJbNMJpPFa8MwUrVl1D+tdkmaNGmSFi1apI0bN8rJySndYw4ZMkTh4eHm1/Hx8SpevHim8gMAACBrlBy8ytYRcoxT6f/qCmTI6iLvzz//lI+Pj7p166Zu3bpZbDtw4ICqVKmSqeMUKVJE9vb2qUbtLly4kGq07j5fX980++fLl0+enp4W7ZMnT9aECRP0008/ZZjJ0dFRjo6OmcoNAAAAADmZ1ffkBQUF6YcffkjVPnnyZNWuXTvTx3FwcFDNmjW1bt06i/Z169apTp06ae4TEhKSqv/atWsVHBys/Pnzm9s++OADjRs3TqtXr1ZwcHCmMwEAAABAbmd1kffOO++oXbt26t27t+7cuaNz586pcePG+uCDD7RkyRKrjhUeHq5Zs2Zpzpw5Onz4sAYMGKCYmBj17t1b0l/TKDt37mzu37t3b50+fVrh4eE6fPiw5syZo9mzZ2vgwIHmPpMmTdLw4cM1Z84clSxZUnFxcYqLi9PNmzetPVUAAAAAyHWsnq751ltv6emnn1bHjh1VpUoVXblyRU888YQOHDiQ7jTL9LRr106XL1/W2LFjFRsbq8qVKysyMlL+/v6SpNjYWItn5gUEBCgyMlIDBgzQJ598Ij8/P02dOlXPP/+8uc/06dN19+5dvfDCCxbvNWrUKI0ePdra0wUAAACAXMXqIk+SAgMDValSJfNDyF966SWrC7z7+vbtq759+6a5bd68eanaGjRooL1796Z7vFOnTj1UDgAAAADIC6yerrl161ZVqVJFx48f14EDBzRjxgy9+eabeumll3T16tXsyAgAAAAAyCSri7zGjRurXbt22r59uypUqKCePXsqOjpaZ8+eVVBQUHZkBAAAAABkktXTNdeuXasGDRpYtJUqVUpbtmzR+PHjsywYAAAAAMB6Vo/k/bPAMx/Izk4jRoz414EAAAAAAA8v00VeixYtdP36dfPr8ePH69q1a+bXly9fVsWKFbM0HAAAAADAOpku8tasWaPExETz6/fff19Xrlwxv05KStKRI0eyNh0AAAAAwCqZLvIMw3jgawAAAACA7Vl9Tx4AAAAAIOfKdJFnMplkMplStQEAAAAAco5MP0LBMAx17dpVjo6OkqSEhAT17t1bLi4ukmRxvx4AAAAAwDYyXeR16dLF4nXHjh1T9encufO/TwQAAAAAeGiZLvLmzp2bnTkAAAAAAFmAhVcAAAAAIA+hyAMAAACAPIQiDwAAAADyEIo8AAAAAMhDKPIAAAAAIA+hyAMAAACAPIQiDwAAAADyEIo8AAAAAMhDKPIAAAAAIA+hyAMAAACAPIQiDwAAAADyEIo8AAAAAMhDKPIAAAAAIA+hyAMAAACAPIQiDwAAAADyEIo8AAAAAMhDKPIAAAAAIA+hyAMAAACAPIQiDwAAAADyEJsXedOnT1dAQICcnJxUs2ZNbd68+YH9N23apJo1a8rJyUmBgYGaOXNmqj5Lly5VxYoV5ejoqIoVK+q7777LrvgAAAAAkKPYtMhbsmSJ+vfvr2HDhik6Olr16tVT8+bNFRMTk2b/kydPqkWLFqpXr56io6M1dOhQhYWFaenSpeY+27dvV7t27dSpUyft379fnTp10ksvvaSdO3c+qtMCAAAAAJuxaZE3ZcoU9ejRQz179lSFChUUERGh4sWLa8aMGWn2nzlzpkqUKKGIiAhVqFBBPXv2VPfu3TV58mRzn4iICIWGhmrIkCEqX768hgwZoqeeekoRERGP6KwAAAAAwHZsVuTdvXtXe/bsUZMmTSzamzRpom3btqW5z/bt21P1b9q0qXbv3q179+49sE96xwQAAACAvCSfrd740qVLSk5Olo+Pj0W7j4+P4uLi0twnLi4uzf5JSUm6dOmSihYtmm6f9I4pSYmJiUpMTDS/vn79uiQpPj7eqnPKy1ISb9s6Qo4RbzJsHSFn4e8J0sF14/9w3fgHrhtIB9eN/8N14x+4bkj6v/rEMB78/bBZkXefyWSyeG0YRqq2jPr/s93aY06cOFFjxoxJ1V68ePH0g+M/y93WAXKa9/hEgIzwt+QfuG4AGeJvyT9w3bBw48YNubun/5nYrMgrUqSI7O3tU42wXbhwIdVI3H2+vr5p9s+XL588PT0f2Ce9Y0rSkCFDFB4ebn6dkpKiK1euyNPT84HFIf574uPjVbx4cZ05c0Zubm62jgMgF+C6AcBaXDeQHsMwdOPGDfn5+T2wn82KPAcHB9WsWVPr1q3Tc889Z25ft26dnn322TT3CQkJ0YoVKyza1q5dq+DgYOXPn9/cZ926dRowYIBFnzp16qSbxdHRUY6OjhZtHh4e1p4S/kPc3Ny46AKwCtcNANbiuoG0PGgE7z6bTtcMDw9Xp06dFBwcrJCQEH322WeKiYlR7969Jf01wnbu3DktWLBAktS7d29NmzZN4eHh6tWrl7Zv367Zs2dr0aJF5mP269dP9evX1/vvv69nn31W33//vX766Sdt2bLFJucIAAAAAI+STYu8du3a6fLlyxo7dqxiY2NVuXJlRUZGyt/fX5IUGxtr8cy8gIAARUZGasCAAfrkk0/k5+enqVOn6vnnnzf3qVOnjhYvXqzhw4drxIgRKlWqlJYsWaLatWs/8vMDAAAAgEfNZGS0NAsAs8TERE2cOFFDhgxJNcUXANLCdQOAtbhu4N+iyAMAAACAPMRmD0MHAAAAAGQ9ijwAAAAAyEMo8gAAAAAgD6HIAwAAAIA8hCIPAAAAAPIQijwAAAAAyENs+jB0AADyivDw8Ez3nTJlSjYmAZBbcN1AdqHIA9Jx9+5dOTg4mF//8ccf+t///qdjx46paNGi6tOnj2rWrGnDhABykujo6Ez1M5lM2ZwEQG7BdQPZhYehA+mwt7dXbGysvL29tW/fPtWtW1dly5bV448/rn379mn//v3avHmzatWqZeuoAAAAgBlFHpAOOzs7xcXFydvbW61atZKTk5O+/vpr87+mde/eXbGxsfrxxx9tnBQAAAD4P0zXBDJh3759Wrx4scV0iX79+qlp06Y2TAUgJ2nbtm2m+y5btiwbkwDILbhuILtQ5AHpMJlM5qLO3t5ebm5uFtvd3Nx0/fp1W0QDkAO5u7vbOgKAXIbrBrILRR6QDsMwVLZsWZlMJt28eVO//vqrgoKCzNuPHTsmX19fGyYEkJPMnTvX1hEA5DJcN5BdKPKAdPzzwluqVCmL1zt27NBzzz33KCMBAAAAGWLhFQAAssG3336rr7/+WjExMbp7967Ftr1799ooFYCcjOsGsoqdrQMAOd1vv/2W7rbVq1c/wiQAcoupU6eqW7du8vb2VnR0tGrVqiVPT0+dOHFCzZs3t3U8ADkQ1w1kJYo8IAPBwcH63//+Z9GWmJioN954g+maANI0ffp0ffbZZ5o2bZocHBw0aNAgrVu3TmFhYSzYBCBNXDeQlSjygAwsXLhQY8aMUfPmzRUXF6d9+/apevXq2rBhg7Zu3WrreAByoJiYGNWpU0eSVKBAAd24cUOS1KlTJy1atMiW0QDkUFw3kJUo8oAMtG3bVgcOHFBSUpIqV66skJAQNWzYUHv27FGNGjVsHQ9ADuTr66vLly9Lkvz9/bVjxw5J0smTJ8Wt8ADSwnUDWYkiD8iE5ORk3b17V8nJyUpOTpavr68cHR1tHQtADtW4cWOtWLFCktSjRw8NGDBAoaGhateuHdO8AaSJ6wayEqtrAhlYvHix+vTpo3r16mn27Nnat2+funXrJn9/f33xxRcKDAy0dUQAOUxKSopSUlKUL99fTyr6+uuvtWXLFpUuXVq9e/eWg4ODjRMCyGm4biArUeQBGXBxcdHkyZPVp08fc9vVq1f12muvafXq1YqPj7dhOgAAAMASRR6QgSNHjqhcuXJpbvviiy/UqVOnR5wIQG6QkJCgAwcO6MKFC0pJSbHY1rp1axulApCTcd1AVqHIAzIhKSlJGzdu1B9//KH27dvL1dVV58+fl5ubmwoWLGjreABymNWrV6tz5866dOlSqm0mk0nJyck2SAUgJ+O6gaxEkQdk4PTp02rWrJliYmKUmJioo0ePKjAwUP3791dCQoJmzpxp64gAcpjSpUuradOmGjlypHx8fGwdB0AuwHUDWYnVNYEM9OvXT8HBwbp69aoKFChgbn/uuee0fv16GyYDkFNduHBB4eHh/KIGINO4biArUeQBGdiyZYuGDx+ealUrf39/nTt3zkapAORkL7zwgjZu3GjrGAByEa4byEr5bB0AyOlSUlLSnAd/9uxZubq62iARgJxu2rRpevHFF7V582YFBQUpf/78FtvDwsJslAxATsV1A1mJe/KADLRr107u7u767LPP5OrqqgMHDsjLy0vPPvusSpQooblz59o6IoAcZtasWerdu7cKFCggT09PmUwm8zaTyaQTJ07YMB2AnIjrBrISRR6QgfPnz6tRo0ayt7fXsWPHFBwcrGPHjqlIkSL6+eef5e3tbeuIAHIYX19fhYWFafDgwbKz484IABnjuoGsRJEHZMKdO3e0aNEi7d27VykpKapRo4Y6dOhgsRALANxXuHBh7dq1S6VKlbJ1FAC5BNcNZCWKPAAAstiAAQPk5eWloUOH2joKgFyC6wayEguvAGn44YcfMt23devW2ZgEQG6UnJysSZMmac2aNapSpUqqBRSmTJlio2QAciquG8hKjOQBacjsXHiTyZTmypsA/tsaNWqU7jaTyaQNGzY8wjQAcgOuG8hKFHkAAAAAkIewdA8AANns9OnTOnTokFJSUmwdBUAuwXUD/wZFHpAJ69evV8uWLVWqVCmVLl1aLVu21E8//WTrWABymPnz5ysiIsKi7dVXX1VgYKCCgoJUuXJlnTlzxjbhAORIXDeQHSjygAxMmzZNzZo1k6urq/r166ewsDC5ubmpRYsWmjZtmq3jAchBZs6cKXd3d/Pr1atXa+7cuVqwYIF27dolDw8PjRkzxoYJAeQ0XDeQHbgnD8hAsWLFNGTIEL3xxhsW7Z988onGjx+v8+fP2ygZgJzG09NTGzduVFBQkCSpT58+unDhgpYuXSpJ2rhxo7p166aTJ0/aMiaAHITrBrIDI3lABuLj49WsWbNU7U2aNFF8fLwNEgHIqe7cuSM3Nzfz623btql+/frm14GBgYqLi7NFNAA5FNcNZAeKPCADrVu31nfffZeq/fvvv1erVq1skAhATuXv7689e/ZIki5duqTffvtNTz75pHl7XFycxbQsAOC6gezAw9CBDFSoUEHjx4/Xxo0bFRISIknasWOHtm7dqrfeektTp0419w0LC7NVTAA5QOfOnfX666/rt99+04YNG1S+fHnVrFnTvH3btm2qXLmyDRMCyGm4biA7cE8ekIGAgIBM9TOZTDpx4kQ2pwGQk6WkpGjUqFFauXKlfH19NWXKFFWoUMG8/cUXX1SzZs3Uo0cPG6YEkJNk5rrRtGlT9ezZ04YpkdtQ5AEAAABAHsI9eQAAZJO7d+/q7NmziomJsfgBgH8KDAzU5cuXU7Vfu3ZNgYGBNkiE3Ix78oAMGIahb7/9VlFRUbpw4YJSUlIsti9btsxGyQDkVEePHlWPHj20bds2i3bDMGQymZScnGyjZAByqlOnTqV5bUhMTNTZs2dtkAi5GUUekIF+/frps88+U6NGjeTj4yOTyWTrSAByuG7duilfvnxauXKlihYtynUDQLp++OEH83+vWbPGYiXN5ORkrV+/PtPrAwD3cU8ekIHChQvryy+/VIsWLWwdBUAu4eLioj179qh8+fK2jgIgh7Oz++vuKZPJpH/+Wp4/f36VLFlSH374oVq2bGmLeMilGMkDMuDu7s5ceABWqVixoi5dumTrGABygfu3gQQEBGjXrl0qUqSIjRMhL2AkD8jA/PnztXr1as2ZM0cFChSwdRwAucCGDRs0fPhwTZgwQUFBQcqfP7/Fdjc3NxslAwD8F1DkARm4ffu22rZtq61bt6pkyZKpflnbu3evjZIByKn+Pv3q71h4BUB6wsLCVLp0aYWFhVm0T5s2TcePH1dERIRtgiFXYromkIGuXbtqz5496tixIwuvAMiUqKgoW0cAkMssXbrUYhGW++rUqaP33nuPIg9WYSQPyICLi4vWrFmjJ5980tZRAABAHuXk5KSDBw+qdOnSFu3Hjx9X5cqVlZCQYKNkyI0YyQMyULx4ce6fAZChAwcOqHLlyrKzs9OBAwce2LdKlSqPKBWA3KJ06dJavXq13njjDYv2H3/8kQXgYDWKPCADH374oQYNGqSZM2eqZMmSto4DIIeqVq2a4uLi5O3trWrVqqW5HLok7skDkKbw8HC98cYbunjxoho3bixJWr9+vT788EOmasJqTNcEMlCoUCHdvn1bSUlJcnZ2TrXwypUrV2yUDEBOcvr0aZUoUUImk0mnT59+YF9/f/9HlApAbjJjxgyNHz9e58+flySVLFlSo0ePVufOnW2cDLkNRR6Qgfnz5z9we5cuXR5REgAA8F9w8eJFFShQQAULFrR1FORSFHkAAGSDP/74QxERETp8+LBMJpMqVKigfv36qVSpUraOBiCHSkpK0saNG/XHH3+offv2cnV11fnz5+Xm5kbBB6tQ5AGZkJycrOXLl5t/WatYsaJat24te3t7W0cDkAOtWbNGrVu3VrVq1VS3bl0ZhqFt27Zp//79WrFihUJDQ20dEUAOc/r0aTVr1kwxMTFKTEzU0aNHFRgYqP79+yshIUEzZ860dUTkIhR5QAaOHz+uFi1a6Ny5cypXrpwMw9DRo0dVvHhxrVq1in+VB5BK9erV1bRpU7333nsW7YMHD9batWu1d+9eGyUDkFO1adNGrq6umj17tjw9PbV//34FBgZq06ZN6tmzp44dO2briMhFKPKADLRo0UKGYWjhwoUqXLiwJOny5cvq2LGj7OzstGrVKhsnBJDTODk56ddff1WZMmUs2o8ePaoqVarwvCsAqRQpUkRbt25VuXLl5Orqai7yTp06pYoVK+r27du2johchEcoABnYtGmTduzYYS7wJMnT01Pvvfee6tata8NkAHIqLy8v7du3L1WRt2/fPnl7e9soFYCcLCUlJc3Hq5w9e1aurq42SITcjCIPyICjo6Nu3LiRqv3mzZtycHCwQSIAOV2vXr306quv6sSJE6pTp45MJpO2bNmi999/X2+99Zat4wHIgUJDQxUREaHPPvtM0l/P1Lx586ZGjRqlFi1a2DgdchumawIZ6Ny5s/bu3avZs2erVq1akqSdO3eqV69eqlmzpubNm2fbgAByHMMwFBERoQ8//ND8vCs/Pz+9/fbbCgsLk8lksnFCADnN+fPn1ahRI9nb2+vYsWMKDg7WsWPHVKRIEf3888/MAoBVKPKADFy7dk1dunTRihUrzA9CT0pKUuvWrTVv3jy5u7vbOCGAnOz+TACmWwHIyJ07d7Ro0SLt3btXKSkpqlGjhjp06KACBQrYOhpyGYo8IJOOHz+uw4cPyzAMVaxYUaVLl7Z1JAAAACAVijzgAeLj41WwYEHZ2dlZtKekpOjmzZtyc3OzUTIAOVH16tUznIqZL18++fr6KjQ0VK+99hr39gL/YT/88EOm+7Zu3TobkyCvocgD0vHdd9/pnXfe0b59++Ts7Gyx7fbt26pevbomT56sVq1a2SghgJxmzJgxGfZJSUnRhQsXtGzZMj3//POaPn36I0gGICf65z8ip8dkMqW58iaQHoo8IB1NmjTRSy+9pJ49e6a5fc6cOVqyZInWrFnziJMByOm+/PJLdezYMc1tb7/9tj744AP9/PPPeumllxQXF/eI0wEA8rrM/fMB8B908OBBNWzYMN3t9evX16+//vroAgHINd544w2tXLkyVXt4eLi+/PJLSVKNGjXUvn37Rx0NQC6QkJBg6wjI5SjygHRcvXpVSUlJ6W6/d++erl69+ggTAcgtFi9erI4dO+rnn382t7355ptavHixoqKiJEkFCxbUlClTbBURQA6TnJyscePGqVixYipYsKBOnDghSRoxYoRmz55t43TIbSjygHSULFlSu3fvTnf77t275e/v/wgTAcgtmjVrppkzZ6pNmzbavXu3+vbtq2XLlmnjxo0qX768reMByIHGjx+vefPmadKkSRYLMgUFBWnWrFk2TIbcKJ+tAwA5Vdu2bTVs2DCFhobKx8fHYltcXJyGDx+e7j03APDyyy/r6tWrevLJJ+Xl5aVNmzbx6BUA6VqwYIE+++wzPfXUU+rdu7e5vUqVKvr9999tmAy5EUUekI7Bgwfr+++/V5kyZdSxY0eVK1dOJpNJhw8f1sKFC1W8eHENHjzY1jEB5BDh4eFptnt7e6t69eoWq2gyTRPAP507dy7NfwhKSUnRvXv3bJAIuRlFHpAOV1dXbd26VUOGDNGSJUvM998VKlRIHTt21IQJE+Tq6mrjlAByiujo6DTbS5Uqpfj4ePP2jJ6jB+C/qVKlStq8eXOqW0G++eYbVa9e3UapkFtR5AEP4O7urjp16uiTTz7RpUuXZBiGvLy8zL+k3V8KHQDuL6gCAA9j1KhR6tSpk86dO6eUlBQtW7ZMR44c0YIFC9JcrRd4EJ6TB2TAw8NDX375pVq2bGnRPmDAAC1evFixsbE2SgYAAPKSNWvWaMKECdqzZ49SUlJUo0YNjRw5Uk2aNLF1NOQyFHlABlavXq2XX35ZP/zwg+rXry/pr6XQly1bpvXr17NSHgAAAHIUijwgExYvXqy+fftq7dq1mjNnjr7//ntFRUWpbNmyto4GAADymISEBC1ZskS3b9/W008/rTJlytg6EnIZijwgk2bMmKEBAwbIy8tLUVFRLIUOAAD+tbffflt3797Vxx9/LEm6e/euatWqpUOHDsnZ2VlJSUlat26dQkJCbJwUuQkLrwBpYCl0AADwKPz444+aMGGC+fXChQsVExOjY8eOqUSJEurevbveffddrVq1yoYpkdswkgekoVGjRpnqZzKZtGHDhmxOAwAA8io3Nzft3bvXPEPolVdekaurqz777DNJ0r59+9SiRQudP3/eljGRyzCSB6SBpdABAMCjYGdnp7+PuezYsUMjRowwv/bw8DA/qxfILDtbBwAAAAD+q8qXL68VK1ZIkn777TfFxMRYzCg6ffq0fHx8bBUPuRQjeQAAAICNvP3223rllVe0atUq/fbbb2rRooUCAgLM2yMjI1WrVi0bJkRuxEgeAAAAYCPPP/+8IiMjVaVKFQ0YMEBLliyx2O7s7Ky+ffvaKB1yKxZeAQAAAIA8hJE8AAAAIAfYvHmzOnbsqJCQEJ07d06S9MUXX2jLli02TobchiIPAAAAsLGlS5eqadOmKlCggKKjo5WYmChJunHjhsVz9IDMoMgDAAAAbOzdd9/VzJkz9fnnnyt//vzm9jp16mjv3r02TIbciCIPAAAAsLEjR46ofv36qdrd3Nx07dq1Rx8IuRpFHgAAAGBjRYsW1fHjx1O1b9myRYGBgTZIhNyMIg8AAACwsddee039+vXTzp07ZTKZdP78eS1cuFADBw7kEQqwGo9QAAAAAHKAYcOG6aOPPlJCQoIkydHRUQMHDtS4ceNsnAy5DUUeAAAAkEPcvn1bhw4dUkpKiipWrKiCBQvaOhJyIYo8AAAAAMhD8tk6AAAAAPBf1LZt20z3XbZsWTYmQV5DkQcAAADYgLu7u60jII9iuiYAAAAA5CGM5AEAAAA5xIULF3TkyBGZTCaVLVtW3t7eto6EXIjn5AEAAAA2Fh8fr06dOqlYsWJq0KCB6tevr2LFiqljx466fv26reMhl6HIAwAAAGysZ8+e2rlzp1auXKlr167p+vXrWrlypXbv3q1evXrZOh5yGe7JAwAAAGzMxcVFa9as0ZNPPmnRvnnzZjVr1ky3bt2yUTLkRozkAQAAADbm6emZ5mqb7u7uKlSokA0SITejyAMAAABsbPjw4QoPD1dsbKy5LS4uTm+//bZGjBhhw2TIjZiuCQAAANhY9erVdfz4cSUmJqpEiRKSpJiYGDk6OqpMmTIWfffu3WuLiMhFeIQCAAAAYGNt2rSxdQTkIYzkAQAAAEAewkgeAAAAkIPcvHlTKSkpFm1ubm42SoPciIVXAAAAABs7efKknnnmGbm4uJhX1CxUqJA8PDxYXRNWYyQPAAAAsLEOHTpIkubMmSMfHx+ZTCYbJ0Juxj15AAAAgI0VLFhQe/bsUbly5WwdBXkA0zUBAAAAG3v88cd15swZW8dAHsF0TQAAAMDGZs2apd69e+vcuXOqXLmy8ufPb7G9SpUqNkqG3IgiDwAAALCxixcv6o8//lC3bt3MbSaTSYZhyGQyKTk52YbpkNtwTx4AAABgYxUrVlSFChU0aNCgNBde8ff3t1Ey5EYUeQAAAICNubi4aP/+/SpdurStoyAPYOEVAAAAwMYaN26s/fv32zoG8gjuyQMAAABsrFWrVhowYIB+/fVXBQUFpVp4pXXr1jZKhtyI6ZoAAACAjdnZpT/BjoVXYC2KPAAAAADIQ7gnDwAAAADyEO7JAwAAAGxg6tSpevXVV+Xk5KSpU6c+sG9YWNgjSoW8gOmaAAAAgA0EBARo9+7d8vT0VEBAQLr9TCaTTpw48QiTIbejyAMAAACAPIR78gAAAIAcJikpSTdv3rR1DORSFHkAAACAjURGRuqLL76waBs/frwKFiwoDw8PNWnSRFevXrVROuRWFHkAAACAjUyePFnx8fHm19u2bdPIkSM1YsQIff311zpz5ozGjRtnw4TIjbgnDwAAALARb29vrVmzRtWrV5ckhYeH69ChQ1q9erWkv0b6+vXrp2PHjtkyJnIZRvIAAAAAG7lx44Y8PT3Nr7ds2aLGjRubX1eqVEnnz5+3RTTkYhR5AAAAgI34+fnp8OHDkqSbN29q//79qlu3rnn75cuX5ezsbKt4yKUo8gAAAAAbeeGFF9S/f3998cUX6tWrl3x9ffXEE0+Yt+/evVvlypWzYULkRvlsHQAAAAD4rxo1apTOnz+vsLAw+fr66ssvv5S9vb15+6JFi9SqVSsbJkRuxEgeAAAAYCPOzs764osvNHXqVB0+fFj16tWz2B4VFaVLly7ZKB1yK4o8AAAAwMbefPNNrVy5MlV7eHi4vvzySxskQm5GkQcAAADY2OLFi9WxY0f9/PPP5rY333xTixcvVlRUlA2TITfiOXkAAABADrB48WL17dtXa9eu1Zw5c/T9998rKipKZcuWtXU05DIsvAIAAADkAC+//LKuXr2qJ598Ul5eXtq0aZNKly5t61jIhRjJAwAAAGwgPDw8zfZvv/1W1atXV6lSpcxtU6ZMeVSxkAdQ5AEAAAA20KhRo0z1M5lM2rBhQzanQV5CkQcAAAAAeQirawIAAABAHkKRBwAAAAB5CEUeAAAAAOQhFHkAAAAAkIdQ5AEAAABAHkKRBwBAOs6cOaMePXrIz89PDg4O8vf3V79+/XT58mVbRwMAIF0UeQAApOHEiRMKDg7W0aNHtWjRIh0/flwzZ87U+vXrFRISoitXrmTbe9+9ezfbjg0AyPso8gAASMPrr78uBwcHrV27Vg0aNFCJEiXUvHlz/fTTTzp37pyGDRsm6a+HFC9fvtxiXw8PD82bN8/8+ty5c2rXrp0KFSokT09PPfvsszp16pR5e9euXdWmTRtNnDhRfn5+Klu2rMaOHaugoKBUuWrWrKmRI0dmxykDAPIIijwAAP7hypUrWrNmjfr27asCBQpYbPP19VWHDh20ZMkSGYaR4bFu376tRo0aqWDBgvr555+1ZcsWFSxYUM2aNbMYsVu/fr0OHz6sdevWaeXKlerevbsOHTqkXbt2mfscOHBA0dHR6tq1a5adKwAg78ln6wAAAOQ0x44dk2EYqlChQprbK1SooKtXr+rixYsZHmvx4sWys7PTrFmzZDKZJElz586Vh4eHNm7cqCZNmkiSXFxcNGvWLDk4OJj3bdq0qebOnavHH3/cvF+DBg0UGBj4b08RAJCHMZIHAICV7o/g/b0gS8+ePXt0/Phxubq6qmDBgipYsKAKFy6shIQE/fHHH+Z+QUFBqY7Xq1cvLVq0SAkJCbp3754WLlyo7t27Z+3JAADyHEbyAAD4h9KlS8tkMunQoUNq06ZNqu2///67vLy85OHhIZPJlGra5r1798z/nZKSopo1a2rhwoWpjuPl5WX+bxcXl1TbW7VqJUdHR3333XdydHRUYmKinn/++X9xZgCA/wKKPAAA/sHT01OhoaGaPn26BgwYYHFfXlxcnBYuXKjXX39d0l+FWmxsrHn7sWPHdPv2bfPrGjVqaMmSJfL29pabm5tVOfLly6cuXbpo7ty5cnR01MsvvyxnZ+d/eXYAgLyO6ZoAAKRh2rRpSkxMVNOmTfXzzz/rzJkzWr16tUJDQ1W2bFnzCpeNGzfWtGnTtHfvXu3evVu9e/dW/vz5zcfp0KGDihQpomeffVabN2/WyZMntWnTJvXr109nz57NMEfPnj21YcMG/fjjj0zVBABkCkUeAABpKFOmjHbt2qXAwEC99NJL8vf3V/PmzVW2bFlt3bpVBQsWlCR9+OGHKl68uOrXr6/27dtr4MCBFqNtzs7O+vnnn1WiRAm1bdtWFSpUUPfu3XXnzp1MjeyVKVNGderUUbly5VS7du1sO18AQN5hMjKz/jMAANCoUaM0ZcoUrV27ViEhIY/kPQ3DUPny5fXaa68pPDz8kbwnACB34548AAAyacyYMSpZsqR27typ2rVry84ueyfEXLhwQV988YXOnTunbt26Zet7AQDyDkbyAADIoUwmk4oUKaKPP/5Y7du3t3UcAEAuwUgeAAA5FP8OCwB4GCy8AgAAAAB5CEUeAAAAAOQhFHkAAAAAkIdQ5AEAAABAHkKRBwAAAAB5CEUeAAAAAOQhFHkAAAAAkIdQ5AEAAABAHkKRBwAAAAB5yP8Drfo+HNZ+z64AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "jit = synthetic[synthetic[\"mode\"] == \"Optimized\"].copy()\n", "jit[\"executor\"] = jit[\"executor\"].replace({\n", @@ -269,10 +1042,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "bbe140d5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots(figsize=(9, 5))\n", "for name, group in operator.groupby(\"operator\"):\n", @@ -289,10 +1073,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "cde81524", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots(figsize=(9, 5))\n", "for name, group in matched.groupby(\"operator\"):\n", @@ -308,10 +1103,120 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "7b508249", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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operatorcoefficientvalue
0SeqScanrow100
1Filteroutput row100
2Projectionoutput row22
3Sortn * (floor(log2(n)) + 1)11
4Aggregationinput row510
5NestedLoopJoininput row pair70
6NestedLoopCrossJoininput row pair104
7HashJoinbuild row * width100
8HashJoinprobe row35
9HashJoinoutput row * width10
\n", + "
" + ], + "text/plain": [ + " operator coefficient value\n", + "0 SeqScan row 100\n", + "1 Filter output row 100\n", + "2 Projection output row 22\n", + "3 Sort n * (floor(log2(n)) + 1) 11\n", + "4 Aggregation input row 510\n", + "5 NestedLoopJoin input row pair 70\n", + "6 NestedLoopCrossJoin input row pair 104\n", + "7 HashJoin build row * width 100\n", + "8 HashJoin probe row 35\n", + "9 HashJoin output row * width 10" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "coefficients = pd.DataFrame([\n", " (\"SeqScan\", \"row\", 100),\n", diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index 8ca0db6..dd75a5b 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -390,6 +390,27 @@ void Optimizer::SetExplored(utils::NotNull +bool Optimizer::MarkOptimizeGroupRequested( + const WinnerKey& key, Limit limit) { + auto it = optimize_group_limits_.find(key); + if (it == optimize_group_limits_.end()) { + optimize_group_limits_.emplace(key, limit); + return true; + } + + const auto& seen_limit = it->second; + if (!seen_limit) return false; + if (!limit) { + it->second = std::nullopt; + return true; + } + if (*seen_limit >= *limit) return false; + + it->second = limit; + return true; +} + template void Optimizer::OptimizeInputs( utils::NotNull expr, PropertySet required, @@ -533,6 +554,11 @@ void Optimizer::OptimizeGroup( return; } + if (!MarkOptimizeGroupRequested(key, limit)) { + Log("Skipping duplicate optimization request for group {}", group->GetId()); + return; + } + @@ -691,11 +717,21 @@ template void Optimizer::RunSearch(Limit limit) { Log("Starting optimization"); tasks_.emplace([this, limit]() { OptimizeGroup(root_->group, global_required_, limit); }); + std::size_t executed_tasks = 0; while (!tasks_.empty()) { auto next_task = std::move(tasks_.top()); tasks_.pop(); next_task(); + ++executed_tasks; + if (executed_tasks % 100000 == 0) { + Log("Optimizer progress: tasks={} pending={} groups={} explored_groups={} explored_exprs={} winners={}", + executed_tasks, tasks_.size(), memo_.GroupCount(), explored_groups_.size(), + explored_exprs_.size(), winner_.size()); + } } + Log("Optimizer search complete: tasks={} groups={} explored_groups={} explored_exprs={} winners={}", + executed_tasks, memo_.GroupCount(), explored_groups_.size(), explored_exprs_.size(), + winner_.size()); } template From 369a864253207fdcfc0843d5b8143c668aac0964 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 15 Jun 2026 00:45:15 +0300 Subject: [PATCH 111/120] Fix --- report/vkr.tex | 44 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 44 insertions(+) diff --git a/report/vkr.tex b/report/vkr.tex index 1c87985..626bff7 100644 --- a/report/vkr.tex +++ b/report/vkr.tex @@ -1497,7 +1497,51 @@ \section{Конструкторская часть} постепенное улучшение качества итоговых планов путем уточнения и расширения пространства поиска. +На рисунке~\ref{fig:project-pipeline} представлена диаграмма с процессом +обработки SQL-запроса запроса модельной СУБД. +\begin{figure}[H] +\centering +\begin{adjustbox}{max width=\textwidth} +\begin{tikzpicture}[ + scale=0.95, transform shape, + node/.style={rectangle, draw, rounded corners=2pt, + text width=3.0cm, minimum height=0.8cm, + align=center, font=\scriptsize}, + main/.style={node, fill=gray!8}, + io/.style={node, fill=gray!20}, + aux/.style={node, dashed}, + arrow/.style={-{Stealth[length=2.5mm]}, thick}, + auxarrow/.style={-{Stealth[length=2.5mm]}, thick, dashed} +] + \node[io] (sql) at (0, 0) {Генератор\\случайных\\запросов}; + \node[main] (parse) at (0, -2) {Разбор}; + \node[main] (optim) at (0, -4.5) {Оптимизация\\ и\\проверка\\ достижимости}; + \node[main] (exec) at (0, -6.65) {Исполнение}; + \node[io] (result) at (0, -8.2) {Результат}; + + \node[aux] (mssqlplan) at (5.0, -1.55) {MSSQL}; + \node[aux] (diff) at (5.0, -4.65) {Проверка\\корректности}; + \node[aux] (mssqlres) at (-5.0, -1.55) {MSSQL}; + \node[aux] (reach) at (-5.0, -4.5) {Конвертация\\в план\\модельной СУБД}; + + \draw[arrow] (sql) -- node[right, font=\scriptsize] {запрос} (parse); + \draw[arrow] (parse) -- node[right, font=\scriptsize] {реляционная алгебра} (optim); + \draw[arrow] (optim) -- node[right, font=\scriptsize] {план} (exec); + \draw[arrow] (exec) -- (result); + + \draw[auxarrow] (sql) -- node[above, font=\scriptsize] {запрос} (mssqlplan); + \draw[auxarrow] (mssqlplan) -- node[right, font=\scriptsize] {результат} (diff); + \draw[auxarrow] (result.east) -- ++(1.2,0) |- (diff.west); + + \draw[auxarrow] (sql) -- node[above, font=\scriptsize] {запрос} (mssqlres); + \draw[auxarrow] (mssqlres) -- node[left, font=\scriptsize] {план} (reach); + \draw[auxarrow] (reach.east) -- node[above, font=\scriptsize] {планы} (optim.west); +\end{tikzpicture} +\end{adjustbox} +\caption{Процесс обработки запроса в модельной СУБД.}% +\label{fig:project-pipeline} +\end{figure} \subsection{Модули синтаксического анализа и построения логического представления} From 9b26cc51d635470d90b3aeb2fda4018bd038ab46 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 15 Jun 2026 01:35:25 +0300 Subject: [PATCH 112/120] Fix --- .../transformation_rules/predicate_utils.hpp | 4 + research/converter.py | 15 +++ research/fuzz/diff_fuzz.py | 4 +- research/fuzz/reach_fuzz.py | 4 +- research/test_converter.py | 41 +++++++ .../sql/logic/optimizer/optimizer_test.cpp | 26 ++++ .../sql/logic/optimizer/reachability.cpp | 11 +- .../transformation_rules/predicate_utils.cpp | 111 ++++++++++++++++++ 8 files changed, 208 insertions(+), 8 deletions(-) diff --git a/include/stewkk/sql/logic/transformation_rules/predicate_utils.hpp b/include/stewkk/sql/logic/transformation_rules/predicate_utils.hpp index fce9ada..dd47c18 100644 --- a/include/stewkk/sql/logic/transformation_rules/predicate_utils.hpp +++ b/include/stewkk/sql/logic/transformation_rules/predicate_utils.hpp @@ -16,6 +16,10 @@ void CollectConjuncts(const Expression& e, std::vector& out); Expression AndConjuncts(const std::vector& conjs); +Expression CanonicalizePredicate(const Expression& e); + +bool EquivalentPredicate(const Expression& lhs, const Expression& rhs); + void CollectAttrTables(const Expression& e, std::unordered_set& out); void CollectAttributes(const Expression& e, std::vector& out); diff --git a/research/converter.py b/research/converter.py index f6c9d4c..c419561 100644 --- a/research/converter.py +++ b/research/converter.py @@ -265,6 +265,11 @@ def _convert_nested_loops(relop: ET.Element, logical_op: str) -> str: if nl is None: raise ValueError("NestedLoops element missing") lhs, rhs = _expect_two_relops(nl, "NestedLoops") + if _has_outer_references(nl) and _contains_index_seek(rhs): + raise NotImplementedError( + "nested loops with outer-reference index seek require unsupported " + "parameterized index lookup operator" + ) pred_elem = nl.find(f"{NS}Predicate/{NS}ScalarOperator") if pred_elem is None: @@ -318,6 +323,16 @@ def _expect_two_relops(parent: ET.Element, label: str) -> tuple[ET.Element, ET.E return children[0], children[1] +def _has_outer_references(nested_loops: ET.Element) -> bool: + return nested_loops.find(f"{NS}OuterReferences/{NS}ColumnReference") is not None + + +def _contains_index_seek(relop: ET.Element) -> bool: + if "Seek" in relop.get("PhysicalOp", ""): + return True + return any(_contains_index_seek(child) for child in relop.findall(f".//{NS}RelOp")) + + def _col_ref_to_attr(cr: ET.Element) -> str: table = _strip_sql_name(cr.get("Alias") or cr.get("Table")) col = cr.get("Column", "").strip("[]") diff --git a/research/fuzz/diff_fuzz.py b/research/fuzz/diff_fuzz.py index 1429b59..2e06156 100644 --- a/research/fuzz/diff_fuzz.py +++ b/research/fuzz/diff_fuzz.py @@ -182,8 +182,8 @@ def main() -> None: try: ours_proc = _run_ours(args.cli, args.data_dir, ours_sql, args.jit) except subprocess.TimeoutExpired: - print(f"DIVERGENCE seed={seed}: our CLI timed out\n--- query (ours):\n{ours_sql}") - sys.exit(1) + print(f"TIMEOUT seed={seed}: our CLI timed out\n--- query (ours):\n{ours_sql}", flush=True) + continue theirs = mssql.run(theirs_sql) diff --git a/research/fuzz/reach_fuzz.py b/research/fuzz/reach_fuzz.py index 1eb77b5..b732758 100644 --- a/research/fuzz/reach_fuzz.py +++ b/research/fuzz/reach_fuzz.py @@ -277,7 +277,9 @@ def main() -> None: converted = convert_plan(plan_xml) except NotImplementedError as e: skipped_convert += 1 - if seed % 25 == 0: + if "outer-reference index seek" in str(e): + print(f"seed={seed} converter skip: {e}", flush=True) + elif seed % 25 == 0: print(f"seed={seed} converter skip: {e}", flush=True) continue except Exception as e: diff --git a/research/test_converter.py b/research/test_converter.py index a484424..46a9211 100644 --- a/research/test_converter.py +++ b/research/test_converter.py @@ -190,6 +190,47 @@ def test_index_seek_prefix_eq_from_xml(): assert result == "(IndexSeek (= (attr t0 region_id) 7) customers t0)" +def test_nested_loops_outer_reference_index_seek_is_skipped_from_xml(): + plan = showplan(""" + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + """) + + with pytest.raises(NotImplementedError, match="outer-reference index seek"): + convert(plan) + + def test_filter_lt(extractor): plan = extract_plan(extractor, "SELECT * FROM Titles WHERE Titles.titleId < 5000") result = convert(plan) diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index cbb9f75..6e0a8b4 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -561,6 +561,32 @@ TEST(ReachabilityTest, InExpandsToOrChain) { ASSERT_THAT(result.reachable, IsTrue()); } +TEST(ReachabilityTest, NotInExpandedAndChainIgnoresComparisonOrder) { + std::stringstream s{"SELECT * FROM users WHERE users.id NOT IN (1, 2, 3);"}; + Expression ne3 = BinaryExpression{ + std::make_shared(Attribute{"users", "id"}), + BinaryOp::kNotEq, + std::make_shared(IntConst{3})}; + Expression ne2 = BinaryExpression{ + std::make_shared(Attribute{"users", "id"}), + BinaryOp::kNotEq, + std::make_shared(IntConst{2})}; + Expression ne1 = BinaryExpression{ + std::make_shared(Attribute{"users", "id"}), + BinaryOp::kNotEq, + std::make_shared(IntConst{1})}; + Expression and_chain = BinaryExpression{ + std::make_shared(BinaryExpression{ + std::make_shared(std::move(ne3)), + BinaryOp::kAnd, + std::make_shared(std::move(ne2))}), + BinaryOp::kAnd, + std::make_shared(std::move(ne1))}; + auto result = IsPlanReachable(s, PhysicalFilter{ + std::make_shared(SeqScan{"users"}), and_chain}, {}, MakeTestSchema()); + ASSERT_THAT(result.reachable, IsTrue()); +} + TEST(ReachabilityTest, OrderByReachableViaSortEnforcer) { diff --git a/src/stewkk/sql/logic/optimizer/reachability.cpp b/src/stewkk/sql/logic/optimizer/reachability.cpp index c8e4e65..8ce5cac 100644 --- a/src/stewkk/sql/logic/optimizer/reachability.cpp +++ b/src/stewkk/sql/logic/optimizer/reachability.cpp @@ -9,6 +9,7 @@ #include #include #include +#include #include namespace stewkk::sql { @@ -48,7 +49,7 @@ InternalMatch TryMatchExpr(utils::NotNull pe, return {false, depth + 1, std::format("IndexSeek alias '{}' != '{}'", op.alias.value_or(""), t->alias.value_or(""))}; - if (op.predicate != t->predicate) + if (!EquivalentPredicate(op.predicate, t->predicate)) return {false, depth + 1, std::format("IndexSeek predicate '{}' != '{}'", ToString(op.predicate), ToString(t->predicate))}; @@ -57,7 +58,7 @@ InternalMatch TryMatchExpr(utils::NotNull pe, [&](const physical::Filter& op) -> InternalMatch { const auto* t = std::get_if(&target.node); if (!t) return {false, depth, "type mismatch: expected Filter"}; - if (op.predicate != t->predicate) + if (!EquivalentPredicate(op.predicate, t->predicate)) return {false, depth + 1, std::format("Filter predicate '{}' != '{}'", ToString(op.predicate), ToString(t->predicate))}; @@ -79,7 +80,7 @@ InternalMatch TryMatchExpr(utils::NotNull pe, if (!t) return {false, depth, "type mismatch: expected NestedLoopJoin"}; if (op.type != t->type) return {false, depth + 1, "NestedLoopJoin join type mismatch"}; - if (op.qual != t->qual) + if (!EquivalentPredicate(op.qual, t->qual)) return {false, depth + 1, std::format("NestedLoopJoin qual '{}' != '{}'", ToString(op.qual), ToString(t->qual))}; @@ -103,7 +104,7 @@ InternalMatch TryMatchExpr(utils::NotNull pe, if (!t) return {false, depth, "type mismatch: expected HashJoin"}; if (op.type != t->type) return {false, depth + 1, "HashJoin join type mismatch"}; - if (op.qual != t->qual) + if (!EquivalentPredicate(op.qual, t->qual)) return {false, depth + 1, std::format("HashJoin qual '{}' != '{}'", ToString(op.qual), ToString(t->qual))}; @@ -118,7 +119,7 @@ InternalMatch TryMatchExpr(utils::NotNull pe, if (!t) return {false, depth, "type mismatch: expected MergeJoin"}; if (op.type != t->type) return {false, depth + 1, "MergeJoin join type mismatch"}; - if (op.qual != t->qual) + if (!EquivalentPredicate(op.qual, t->qual)) return {false, depth + 1, std::format("MergeJoin qual '{}' != '{}'", ToString(op.qual), ToString(t->qual))}; diff --git a/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp b/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp index 4cef467..343ed0a 100644 --- a/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp +++ b/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp @@ -1,6 +1,7 @@ #include #include +#include #include #include @@ -35,6 +36,116 @@ Expression AndConjuncts(const std::vector& conjs) { return acc; } +namespace { + +std::shared_ptr Share(Expression e) { + return std::make_shared(std::move(e)); +} + +std::string CanonicalKey(const Expression& e); + +std::string CanonicalKey(const BinaryExpression& e) { + return std::format("({} {} {})", static_cast(e.binop), + CanonicalKey(*e.lhs), CanonicalKey(*e.rhs)); +} + +std::string CanonicalKey(const UnaryExpression& e) { + return std::format("({} {})", static_cast(e.op), CanonicalKey(*e.child)); +} + +std::string CanonicalKey(const InExpression& e) { + std::string out = std::format("({} {}", e.negated ? "notin" : "in", CanonicalKey(*e.lhs)); + for (const auto& value : e.values) { + out += ' '; + out += CanonicalKey(value); + } + out += ')'; + return out; +} + +std::string CanonicalKey(const AggregateExpression& e) { + if (e.is_star) { + return std::format("({} *)", static_cast(e.function)); + } + return std::format("({} {})", static_cast(e.function), CanonicalKey(*e.argument)); +} + +std::string CanonicalKey(const Expression& e) { + return std::visit(utils::Overloaded{ + [](const BinaryExpression& b) { return CanonicalKey(b); }, + [](const Attribute& a) { return std::format("(attr {} {})", a.table, a.name); }, + [](const IntConst& i) { return std::format("(int {})", i); }, + [](const StringConst& s) { return std::format("(str {})", s); }, + [](const UnaryExpression& u) { return CanonicalKey(u); }, + [](const InExpression& i) { return CanonicalKey(i); }, + [](const AggregateExpression& a) { return CanonicalKey(a); }, + [](const Literal& l) { return std::format("(literal {})", static_cast(l)); }, + }, e); +} + +void CollectByOp(const Expression& e, BinaryOp op, std::vector& out) { + if (const auto* b = std::get_if(&e); b && b->binop == op) { + CollectByOp(*b->lhs, op, out); + CollectByOp(*b->rhs, op, out); + return; + } + out.push_back(CanonicalizePredicate(e)); +} + +Expression ChainByOp(std::vector exprs, BinaryOp op) { + std::ranges::sort(exprs, [](const Expression& lhs, const Expression& rhs) { + return CanonicalKey(lhs) < CanonicalKey(rhs); + }); + Expression acc = std::move(exprs.front()); + for (size_t i = 1; i < exprs.size(); ++i) { + acc = BinaryExpression{Share(std::move(acc)), op, Share(std::move(exprs[i]))}; + } + return acc; +} + +} // namespace + +Expression CanonicalizePredicate(const Expression& e) { + return std::visit(utils::Overloaded{ + [](const BinaryExpression& b) -> Expression { + if (b.binop == BinaryOp::kAnd || b.binop == BinaryOp::kOr) { + std::vector terms; + CollectByOp(*b.lhs, b.binop, terms); + CollectByOp(*b.rhs, b.binop, terms); + return ChainByOp(std::move(terms), b.binop); + } + return BinaryExpression{ + Share(CanonicalizePredicate(*b.lhs)), + b.binop, + Share(CanonicalizePredicate(*b.rhs)), + }; + }, + [](const UnaryExpression& u) -> Expression { + return UnaryExpression{u.op, Share(CanonicalizePredicate(*u.child))}; + }, + [](const InExpression& i) -> Expression { + std::vector values; + values.reserve(i.values.size()); + for (const auto& value : i.values) { + values.push_back(CanonicalizePredicate(value)); + } + std::ranges::sort(values, [](const Expression& lhs, const Expression& rhs) { + return CanonicalKey(lhs) < CanonicalKey(rhs); + }); + return InExpression{Share(CanonicalizePredicate(*i.lhs)), std::move(values), i.negated}; + }, + [](const AggregateExpression& a) -> Expression { + if (a.is_star || !a.argument) return a; + return AggregateExpression{a.function, Share(CanonicalizePredicate(*a.argument)), a.is_star}; + }, + [](const auto& leaf) -> Expression { return leaf; }, + }, e); +} + +bool EquivalentPredicate(const Expression& lhs, const Expression& rhs) { + return CanonicalizePredicate(lhs) == CanonicalizePredicate(rhs); +} + void CollectAttrTables(const Expression& e, std::unordered_set& out) { std::visit(utils::Overloaded{ [&](const Attribute& a) { out.insert(a.table); }, From 455b6499ac0089ef712d8758f0efaf30642a66cf Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 15 Jun 2026 15:51:35 +0300 Subject: [PATCH 113/120] Add stats collection --- .../sql/logic/optimizer/reachability.hpp | 1 + research/converter.py | 46 ++-- research/fuzz/reach_fuzz.py | 188 +++++++++++----- research/test_converter.py | 80 +++++++ .../sql/logic/optimizer/optimizer_test.cpp | 88 ++++++++ .../sql/logic/optimizer/reachability.cpp | 211 +++++++++++++++++- .../transformation_rules/predicate_utils.cpp | 164 ++++++++++++-- src/stewkk/sql/main.cpp | 4 +- 8 files changed, 689 insertions(+), 93 deletions(-) diff --git a/include/stewkk/sql/logic/optimizer/reachability.hpp b/include/stewkk/sql/logic/optimizer/reachability.hpp index ab16136..be064c0 100644 --- a/include/stewkk/sql/logic/optimizer/reachability.hpp +++ b/include/stewkk/sql/logic/optimizer/reachability.hpp @@ -15,6 +15,7 @@ namespace stewkk::sql { struct MatchResult { bool reachable; std::string mismatch; + int closest_distance = 0; }; MatchResult IsReachable(utils::NotNull root, const PhysicalPlanNode& target); diff --git a/research/converter.py b/research/converter.py index c419561..d0dc6e9 100644 --- a/research/converter.py +++ b/research/converter.py @@ -238,25 +238,35 @@ def _convert_seek_predicates(relop: ET.Element, table: str) -> str | None: if seek_preds is None: return None - conditions = [] - for range_elem in (seek_preds.findall(f".//{NS}Prefix") + - seek_preds.findall(f".//{NS}StartRange") + - seek_preds.findall(f".//{NS}EndRange")): - op = _SEEK_SCAN_TYPES.get(range_elem.get("ScanType", "")) - if not op: - continue - col_refs = range_elem.findall(f"{NS}RangeColumns/{NS}ColumnReference") - expr_elems = range_elem.findall(f"{NS}RangeExpressions/{NS}ScalarOperator") - for col_ref, expr_elem in zip(col_refs, expr_elems): - col = col_ref.get("Column", "").strip("[]") - col_table = _strip_sql_name(col_ref.get("Alias") or col_ref.get("Table") or table) - conditions.append(f"({op} (attr {col_table} {col}) {_convert_scalar(expr_elem)})") - - if not conditions: + alternatives = [] + for seek_keys in seek_preds.findall(f".//{NS}SeekKeys"): + conditions = [] + for range_elem in (seek_keys.findall(f"{NS}Prefix") + + seek_keys.findall(f"{NS}StartRange") + + seek_keys.findall(f"{NS}EndRange")): + op = _SEEK_SCAN_TYPES.get(range_elem.get("ScanType", "")) + if not op: + continue + col_refs = range_elem.findall(f"{NS}RangeColumns/{NS}ColumnReference") + expr_elems = range_elem.findall(f"{NS}RangeExpressions/{NS}ScalarOperator") + for col_ref, expr_elem in zip(col_refs, expr_elems): + col = col_ref.get("Column", "").strip("[]") + col_table = _strip_sql_name(col_ref.get("Alias") or col_ref.get("Table") or table) + conditions.append(f"({op} (attr {col_table} {col}) {_convert_scalar(expr_elem)})") + + if conditions: + alternatives.append(_fold_binary("and", conditions)) + + if not alternatives: return None - result = conditions[0] - for c in conditions[1:]: - result = f"(and {result} {c})" + return _fold_binary("or", alternatives) + +def _fold_binary(op: str, exprs: list[str]) -> str: + if not exprs: + raise ValueError("_fold_binary requires at least one expression") + result = exprs[0] + for expr in exprs[1:]: + result = f"({op} {result} {expr})" return result diff --git a/research/fuzz/reach_fuzz.py b/research/fuzz/reach_fuzz.py index b732758..95a0470 100644 --- a/research/fuzz/reach_fuzz.py +++ b/research/fuzz/reach_fuzz.py @@ -3,7 +3,7 @@ execution plan, convert that plan to the project's serialized format, then invoke our CLI in ``--check-reachable`` mode to confirm that our exhaustive search would have considered it. Stops at the first plan our optimizer cannot -reach. +reach, unless ``--collect-stats`` is used. The fuzzer only flags a divergence when the MS SQL plan converts cleanly but our optimizer cannot reach it. Coverage gaps in the XML→s-expr converter @@ -20,6 +20,7 @@ python -m research.fuzz.reach_fuzz \\ --cli build/bin/sql \\ --data-dir test/static/executor/test_data \\ + [--collect-stats N] \\ [--start-seed 0] """ from __future__ import annotations @@ -27,6 +28,7 @@ import argparse import os import random +import re import shutil import subprocess import sys @@ -45,6 +47,8 @@ render_query, ) +DISTANCE_RE = re.compile(r"\bdistance=(\d+)\b") + def _make_reachability_data_dir(source_dir: Path, schema: "Schema", tmp_dir: Path) -> Path: for path in source_dir.glob("*.csv"): @@ -221,17 +225,35 @@ def _run_reach_check( Path(plan_path).unlink(missing_ok=True) +def _parse_distance(proc: subprocess.CompletedProcess) -> int | None: + match = DISTANCE_RE.search(proc.stdout) + if match is None: + return None + return int(match.group(1)) + + def main() -> None: ap = argparse.ArgumentParser(description=__doc__) ap.add_argument("--cli", required=True, help="Path to our sql binary") ap.add_argument("--data-dir", required=True, help="CSV table directory (shared with MS SQL)") ap.add_argument("--start-seed", type=int, default=0) + ap.add_argument( + "--collect-stats", + type=int, + metavar="N", + help=( + "Check exactly N seeds, keep going after reachability divergences, " + "and print skipped/ok/divergent totals plus average closest-plan distance" + ), + ) ap.add_argument("--mssql-host", default="localhost") ap.add_argument("--mssql-port", type=int, default=1433) ap.add_argument("--mssql-user", default="sa") ap.add_argument("--mssql-password", default="Password123!") ap.add_argument("--mssql-database", default="fuzz") args = ap.parse_args() + if args.collect_stats is not None and args.collect_stats < 0: + ap.error("--collect-stats must be non-negative") source_data_dir = Path(args.data_dir) schema = load_schema(source_data_dir) @@ -255,66 +277,122 @@ def main() -> None: ms = DIALECTS["mssql"] skipped_convert = 0 + skipped_converter_errors = 0 skipped_mssql = 0 + ok = 0 + divergent = 0 + checked = 0 + distance_sum = 0 + distance_count = 0 + distance_missing = 0 + + seeds = ( + range(args.start_seed, args.start_seed + args.collect_stats) + if args.collect_stats is not None + else count(args.start_seed) + ) + collect_stats = args.collect_stats is not None - for seed in count(args.start_seed): - rng = random.Random(seed) - query = QueryGenerator(schema, rng).generate() - ours_sql = render_query(query, pg) + ";" - # OPTION (RECOMPILE) embeds literal values in the plan so the converter - # sees Const nodes instead of @P parameter references. - theirs_sql = render_query(query, ms) + " OPTION (RECOMPILE);" - - try: - plan_xml = mssql.get_plan(theirs_sql) - except Exception as e: - skipped_mssql += 1 - if seed % 25 == 0: - print(f"seed={seed} mssql refused: {e}", flush=True) - continue - - try: - converted = convert_plan(plan_xml) - except NotImplementedError as e: - skipped_convert += 1 - if "outer-reference index seek" in str(e): - print(f"seed={seed} converter skip: {e}", flush=True) - elif seed % 25 == 0: - print(f"seed={seed} converter skip: {e}", flush=True) - continue - except Exception as e: - print(f"seed={seed} converter error: {e}\n--- query:\n{theirs_sql}") - sys.exit(2) - - target_plan = _wrap_projection(converted, query) + try: + for seed in seeds: + checked += 1 + rng = random.Random(seed) + query = QueryGenerator(schema, rng).generate() + ours_sql = render_query(query, pg) + ";" + # OPTION (RECOMPILE) embeds literal values in the plan so the converter + # sees Const nodes instead of @P parameter references. + theirs_sql = render_query(query, ms) + " OPTION (RECOMPILE);" + + try: + plan_xml = mssql.get_plan(theirs_sql) + except Exception as e: + skipped_mssql += 1 + if seed % 25 == 0: + print(f"seed={seed} mssql refused: {e}", flush=True) + continue + + try: + converted = convert_plan(plan_xml) + except NotImplementedError as e: + skipped_convert += 1 + if "outer-reference index seek" in str(e): + print(f"seed={seed} converter skip: {e}", flush=True) + elif seed % 25 == 0: + print(f"seed={seed} converter skip: {e}", flush=True) + continue + except Exception as e: + print(f"seed={seed} converter error: {e}\n--- query:\n{theirs_sql}") + if collect_stats: + skipped_converter_errors += 1 + continue + sys.exit(2) + + target_plan = _wrap_projection(converted, query) + + try: + proc = _run_reach_check(args.cli, str(reach_data_dir), ours_sql, target_plan) + except subprocess.TimeoutExpired: + divergent += 1 + distance_missing += 1 + print(f"DIVERGENCE seed={seed}: reachability check timed out") + print(f"\n--- query (ours):\n{ours_sql}") + print(f"\n--- target plan:\n{target_plan}") + if collect_stats: + continue + sys.exit(1) + + distance = _parse_distance(proc) + if distance is None: + distance_missing += 1 + else: + distance_sum += distance + distance_count += 1 + + if proc.returncode == 0: + ok += 1 + if seed % 25 == 0: + print( + f"seed={seed} ok" + f" (skipped: {skipped_convert} convert, {skipped_mssql} mssql)", + flush=True, + ) + continue - try: - proc = _run_reach_check(args.cli, str(reach_data_dir), ours_sql, target_plan) - except subprocess.TimeoutExpired: - print(f"DIVERGENCE seed={seed}: reachability check timed out") + # Non-zero: either parse/optimizer error, or NOT REACHABLE. + divergent += 1 + print(f"DIVERGENCE seed={seed}: exit={proc.returncode}") print(f"\n--- query (ours):\n{ours_sql}") - print(f"\n--- target plan:\n{target_plan}") + print(f"\n--- query (mssql):\n{theirs_sql}") + print(f"\n--- mssql plan (converted):\n{target_plan}") + if proc.stdout: + print(f"\n--- stdout:\n{proc.stdout}", end="") + if proc.stderr: + print(f"\n--- stderr:\n{proc.stderr}", end="") + if collect_stats: + continue sys.exit(1) - - if proc.returncode == 0: - if seed % 25 == 0: - print( - f"seed={seed} ok" - f" (skipped: {skipped_convert} convert, {skipped_mssql} mssql)", - flush=True, + finally: + if collect_stats: + skipped = skipped_convert + skipped_converter_errors + skipped_mssql + average_distance = distance_sum / distance_count if distance_count else None + print( + "\nReachability fuzz stats:" + f"\n seeds: {checked}/{args.collect_stats}" + f"\n ok: {ok}" + f"\n skipped: {skipped}" + f"\n converter: {skipped_convert}" + f"\n converter_errors: {skipped_converter_errors}" + f"\n mssql: {skipped_mssql}" + f"\n divergent: {divergent}" + + ( + f"\n average_distance: {average_distance:.3f}" + if average_distance is not None + else "\n average_distance: n/a" ) - continue - - # Non-zero: either parse/optimizer error, or NOT REACHABLE. - print(f"DIVERGENCE seed={seed}: exit={proc.returncode}") - print(f"\n--- query (ours):\n{ours_sql}") - print(f"\n--- query (mssql):\n{theirs_sql}") - print(f"\n--- mssql plan (converted):\n{target_plan}") - if proc.stdout: - print(f"\n--- stdout:\n{proc.stdout}", end="") - if proc.stderr: - print(f"\n--- stderr:\n{proc.stderr}", end="") - sys.exit(1) + + f"\n distance_samples: {distance_count}" + + f"\n distance_missing: {distance_missing}", + flush=True, + ) if __name__ == "__main__": diff --git a/research/test_converter.py b/research/test_converter.py index 46a9211..5e83a6d 100644 --- a/research/test_converter.py +++ b/research/test_converter.py @@ -190,6 +190,86 @@ def test_index_seek_prefix_eq_from_xml(): assert result == "(IndexSeek (= (attr t0 region_id) 7) customers t0)" +def test_index_seek_bounded_range_from_xml(): + plan = showplan(""" + + + + + + + + + + + + + + + + + + + + + + + + + + + + """) + + result = convert(plan) + + assert result == ( + "(IndexSeek (and (> (attr customers region_id) 3)" + " (< (attr customers region_id) 8)) customers)" + ) + + +def test_index_seek_split_ranges_from_xml(): + plan = showplan(""" + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + """) + + result = convert(plan) + + assert result == ( + "(IndexSeek (or (< (attr customers region_id) 8)" + " (> (attr customers region_id) 8)) customers)" + ) + + def test_nested_loops_outer_reference_index_seek_is_skipped_from_xml(): plan = showplan(""" diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index 6e0a8b4..b7678a1 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -405,6 +405,7 @@ TEST(ReachabilityTest, SeqScanReachable) { std::stringstream s{"SELECT * FROM users;"}; auto result = IsPlanReachable(s, SeqScan{"users"}, {}, MakeTestSchema()); ASSERT_THAT(result.reachable, IsTrue()); + ASSERT_THAT(result.closest_distance, Eq(0)); } TEST(ReachabilityTest, SeqScanWrongTable) { @@ -412,6 +413,7 @@ TEST(ReachabilityTest, SeqScanWrongTable) { auto result = IsPlanReachable(s, SeqScan{"orders"}, {}, MakeTestSchema()); ASSERT_THAT(result.reachable, IsFalse()); ASSERT_THAT(result.mismatch, HasSubstr("users")); + ASSERT_THAT(result.closest_distance, Eq(1)); } TEST(ReachabilityTest, IndexSeekReachableWithIndexCatalog) { @@ -587,6 +589,91 @@ TEST(ReachabilityTest, NotInExpandedAndChainIgnoresComparisonOrder) { ASSERT_THAT(result.reachable, IsTrue()); } +TEST(ReachabilityTest, NotBetweenMatchesExpandedRangeDisjunction) { + std::stringstream s{"SELECT * FROM users WHERE users.id NOT BETWEEN 1 AND 3;"}; + Expression lt = BinaryExpression{ + std::make_shared(Attribute{"users", "id"}), + BinaryOp::kLt, + std::make_shared(IntConst{1})}; + Expression gt = BinaryExpression{ + std::make_shared(Attribute{"users", "id"}), + BinaryOp::kGt, + std::make_shared(IntConst{3})}; + Expression range = BinaryExpression{ + std::make_shared(std::move(lt)), + BinaryOp::kOr, + std::make_shared(std::move(gt))}; + + auto result = IsPlanReachable(s, PhysicalFilter{ + std::make_shared(SeqScan{"users"}), range}, {}, MakeTestSchema()); + ASSERT_THAT(result.reachable, IsTrue()); +} + +TEST(ReachabilityTest, NegatedConjunctionMatchesDeMorganForm) { + std::stringstream s{"SELECT * FROM users WHERE NOT (users.id = 1 AND users.age >= 30);"}; + Expression id_ne = BinaryExpression{ + std::make_shared(Attribute{"users", "id"}), + BinaryOp::kNotEq, + std::make_shared(IntConst{1})}; + Expression age_lt = BinaryExpression{ + std::make_shared(Attribute{"users", "age"}), + BinaryOp::kLt, + std::make_shared(IntConst{30})}; + Expression demorgan = BinaryExpression{ + std::make_shared(std::move(age_lt)), + BinaryOp::kOr, + std::make_shared(std::move(id_ne))}; + + auto result = IsPlanReachable(s, PhysicalFilter{ + std::make_shared(SeqScan{"users"}), demorgan}, {}, MakeTestSchema()); + ASSERT_THAT(result.reachable, IsTrue()); +} + +TEST(ReachabilityTest, ReversedComparisonOperandsMatch) { + std::stringstream s{"SELECT * FROM users WHERE 1 < users.id;"}; + Expression gt = BinaryExpression{ + std::make_shared(Attribute{"users", "id"}), + BinaryOp::kGt, + std::make_shared(IntConst{1})}; + + auto result = IsPlanReachable(s, PhysicalFilter{ + std::make_shared(SeqScan{"users"}), gt}, {}, MakeTestSchema()); + ASSERT_THAT(result.reachable, IsTrue()); +} + +TEST(ReachabilityTest, JoinQualNormalizesInAndNotBetween) { + std::stringstream s{ + "SELECT * FROM users CROSS JOIN orders " + "WHERE users.id IN (22) AND orders.id NOT BETWEEN 1 AND 3;"}; + Expression users_eq = BinaryExpression{ + std::make_shared(Attribute{"users", "id"}), + BinaryOp::kEq, + std::make_shared(IntConst{22})}; + Expression orders_lt = BinaryExpression{ + std::make_shared(Attribute{"orders", "id"}), + BinaryOp::kLt, + std::make_shared(IntConst{1})}; + Expression orders_gt = BinaryExpression{ + std::make_shared(Attribute{"orders", "id"}), + BinaryOp::kGt, + std::make_shared(IntConst{3})}; + Expression orders_range = BinaryExpression{ + std::make_shared(std::move(orders_lt)), + BinaryOp::kOr, + std::make_shared(std::move(orders_gt))}; + Expression qual = BinaryExpression{ + std::make_shared(std::move(users_eq)), + BinaryOp::kAnd, + std::make_shared(std::move(orders_range))}; + + auto result = IsPlanReachable(s, NestedLoopJoin{ + std::make_shared(SeqScan{"users"}), + std::make_shared(SeqScan{"orders"}), + JoinType::kInner, + qual}, {}, MakeTestSchema()); + ASSERT_THAT(result.reachable, IsTrue()); +} + TEST(ReachabilityTest, OrderByReachableViaSortEnforcer) { @@ -613,6 +700,7 @@ TEST(ReachabilityTest, OrderByWrongDirectionNotReachable) { SortOrder{{SortKey{"users", "id", Direction::kDesc}}}}; auto result = IsPlanReachable(s, target, {}, MakeTestSchema()); ASSERT_THAT(result.reachable, IsFalse()); + ASSERT_THAT(result.closest_distance, Eq(1)); } diff --git a/src/stewkk/sql/logic/optimizer/reachability.cpp b/src/stewkk/sql/logic/optimizer/reachability.cpp index 8ce5cac..3c5fcfb 100644 --- a/src/stewkk/sql/logic/optimizer/reachability.cpp +++ b/src/stewkk/sql/logic/optimizer/reachability.cpp @@ -1,6 +1,10 @@ #include +#include #include +#include +#include +#include #include #include @@ -16,6 +20,8 @@ namespace stewkk::sql { namespace { +constexpr int kInfiniteDistance = std::numeric_limits::max() / 4; + struct InternalMatch { bool ok; int depth; @@ -196,12 +202,215 @@ InternalMatch MatchGroup(Group* group, const PhysicalPlanNode& target, int depth return best; } +std::vector Children(const PhysicalExpr& expr) { + return std::visit(utils::Overloaded{ + [](const physical::SeqScan&) { return std::vector{}; }, + [](const physical::IndexSeek&) { return std::vector{}; }, + [](const physical::Filter& op) { return std::vector{op.source.get()}; }, + [](const physical::Projection& op) { return std::vector{op.source.get()}; }, + [](const physical::NestedLoopJoin& op) { + return std::vector{op.lhs.get(), op.rhs.get()}; + }, + [](const physical::NestedLoopCrossJoin& op) { + return std::vector{op.lhs.get(), op.rhs.get()}; + }, + [](const physical::HashJoin& op) { + return std::vector{op.lhs.get(), op.rhs.get()}; + }, + [](const physical::MergeJoin& op) { + return std::vector{op.lhs.get(), op.rhs.get()}; + }, + [](const physical::Sort& op) { return std::vector{op.input.get()}; }, + [](const physical::Aggregation& op) { return std::vector{op.source.get()}; }, + [](const physical::StreamAggregation& op) { return std::vector{op.source.get()}; }, + [](const physical::PartialAggregation& op) { return std::vector{op.source.get()}; }, + [](const physical::FinalAggregation& op) { return std::vector{op.source.get()}; }, + }, expr.root_operator); +} + +std::vector Children(const PhysicalPlanNode& node) { + return std::visit(utils::Overloaded{ + [](const SeqScan&) { return std::vector{}; }, + [](const IndexSeek&) { return std::vector{}; }, + [](const PhysicalFilter& op) { return std::vector{op.source.get()}; }, + [](const PhysicalProjection& op) { return std::vector{op.source.get()}; }, + [](const NestedLoopJoin& op) { + return std::vector{op.lhs.get(), op.rhs.get()}; + }, + [](const NestedLoopCrossJoin& op) { + return std::vector{op.lhs.get(), op.rhs.get()}; + }, + [](const HashJoin& op) { + return std::vector{op.lhs.get(), op.rhs.get()}; + }, + [](const MergeJoin& op) { + return std::vector{op.lhs.get(), op.rhs.get()}; + }, + [](const PhysicalSort& op) { return std::vector{op.source.get()}; }, + [](const PhysicalAggregation& op) { return std::vector{op.source.get()}; }, + [](const PhysicalStreamAggregation& op) { return std::vector{op.source.get()}; }, + [](const PhysicalPartialAggregation& op) { return std::vector{op.source.get()}; }, + [](const PhysicalFinalAggregation& op) { return std::vector{op.source.get()}; }, + }, node.node); +} + +int TargetTreeSize(const PhysicalPlanNode& node) { + int size = 1; + for (const auto* child : Children(node)) { + size += TargetTreeSize(*child); + } + return size; +} + +struct PairHash { + size_t operator()(const std::pair& p) const noexcept { + size_t h = std::hash{}(p.first); + h ^= std::hash{}(p.second) + 0x9e3779b9 + (h << 6) + (h >> 2); + return h; + } +}; + +class TreeDistance { + public: + int Distance(Group* group, const PhysicalPlanNode& target) { + auto key = std::pair{group, &target}; + if (auto it = distance_memo_.find(key); it != distance_memo_.end()) { + return it->second; + } + if (!distance_visiting_.insert(key).second) { + return kInfiniteDistance; + } + + int best = kInfiniteDistance; + for (auto pe : group->GetPhysicalExprs()) { + best = std::min(best, Distance(*pe, target)); + } + distance_visiting_.erase(key); + distance_memo_.emplace(key, best); + return best; + } + + private: + int MinTreeSize(Group* group) { + if (auto it = size_memo_.find(group); it != size_memo_.end()) { + return it->second; + } + if (!size_visiting_.insert(group).second) { + return kInfiniteDistance; + } + + int best = kInfiniteDistance; + for (auto pe : group->GetPhysicalExprs()) { + int size = 1; + for (auto* child : Children(*pe)) { + int child_size = MinTreeSize(child); + if (child_size >= kInfiniteDistance) { + size = kInfiniteDistance; + break; + } + size += child_size; + } + best = std::min(best, size); + } + + size_visiting_.erase(group); + size_memo_.emplace(group, best); + return best; + } + + int Distance(const PhysicalExpr& expr, const PhysicalPlanNode& target) { + const auto source_children = Children(expr); + const auto target_children = Children(target); + return LabelCost(expr, target) + ChildrenDistance(source_children, target_children); + } + + int ChildrenDistance(const std::vector& source_children, + const std::vector& target_children) { + std::vector> dp( + source_children.size() + 1, std::vector(target_children.size() + 1, 0)); + + for (size_t i = 1; i <= source_children.size(); ++i) { + dp[i][0] = dp[i - 1][0] + MinTreeSize(source_children[i - 1]); + } + for (size_t j = 1; j <= target_children.size(); ++j) { + dp[0][j] = dp[0][j - 1] + TargetTreeSize(*target_children[j - 1]); + } + + for (size_t i = 1; i <= source_children.size(); ++i) { + for (size_t j = 1; j <= target_children.size(); ++j) { + const int del = dp[i - 1][j] + MinTreeSize(source_children[i - 1]); + const int ins = dp[i][j - 1] + TargetTreeSize(*target_children[j - 1]); + const int sub = dp[i - 1][j - 1] + + Distance(source_children[i - 1], *target_children[j - 1]); + dp[i][j] = std::min({del, ins, sub}); + } + } + return dp[source_children.size()][target_children.size()]; + } + + static int LabelCost(const PhysicalExpr& expr, const PhysicalPlanNode& target) { + return std::visit(utils::Overloaded{ + [](const physical::SeqScan& op, const SeqScan& t) { + return op.table == t.table && op.alias == t.alias ? 0 : 1; + }, + [](const physical::IndexSeek& op, const IndexSeek& t) { + return op.table == t.table && op.alias == t.alias + && EquivalentPredicate(op.predicate, t.predicate) + ? 0 + : 1; + }, + [](const physical::Filter& op, const PhysicalFilter& t) { + return EquivalentPredicate(op.predicate, t.predicate) ? 0 : 1; + }, + [](const physical::Projection& op, const PhysicalProjection& t) { + return op.expressions == t.expressions ? 0 : 1; + }, + [](const physical::NestedLoopJoin& op, const NestedLoopJoin& t) { + return op.type == t.type && EquivalentPredicate(op.qual, t.qual) ? 0 : 1; + }, + [](const physical::NestedLoopCrossJoin&, const NestedLoopCrossJoin&) { + return 0; + }, + [](const physical::HashJoin& op, const HashJoin& t) { + return op.type == t.type && EquivalentPredicate(op.qual, t.qual) ? 0 : 1; + }, + [](const physical::MergeJoin& op, const MergeJoin& t) { + return op.type == t.type && EquivalentPredicate(op.qual, t.qual) ? 0 : 1; + }, + [](const physical::Sort& op, const PhysicalSort& t) { + return op.keys == t.keys ? 0 : 1; + }, + [](const physical::Aggregation& op, const PhysicalAggregation& t) { + return op.group_by == t.group_by && op.aggregates == t.aggregates ? 0 : 1; + }, + [](const physical::StreamAggregation& op, const PhysicalStreamAggregation& t) { + return op.group_by == t.group_by && op.aggregates == t.aggregates ? 0 : 1; + }, + [](const physical::PartialAggregation& op, const PhysicalPartialAggregation& t) { + return op.group_by == t.group_by && op.aggregates == t.aggregates ? 0 : 1; + }, + [](const physical::FinalAggregation& op, const PhysicalFinalAggregation& t) { + return op.group_by == t.group_by && op.aggregates == t.aggregates ? 0 : 1; + }, + [](const auto&, const auto&) { + return 1; + }, + }, expr.root_operator, target.node); + } + + std::unordered_map, int, PairHash> distance_memo_; + std::unordered_set, PairHash> distance_visiting_; + std::unordered_map size_memo_; + std::unordered_set size_visiting_; +}; + } // namespace MatchResult IsReachable(utils::NotNull root, const PhysicalPlanNode& target) { auto r = MatchGroup(root.get(), target, 0); - return {r.ok, r.reason}; + TreeDistance distance; + return {r.ok, r.reason, distance.Distance(root.get(), target)}; } MatchResult IsPlanReachable(std::istream& sql, const PhysicalPlanNode& target, diff --git a/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp b/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp index 343ed0a..35ae135 100644 --- a/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp +++ b/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp @@ -3,6 +3,7 @@ #include #include #include +#include #include #include @@ -43,6 +44,8 @@ std::shared_ptr Share(Expression e) { } std::string CanonicalKey(const Expression& e); +Expression NormalizePredicate(const Expression& e); +Expression NormalizeNegated(const Expression& e); std::string CanonicalKey(const BinaryExpression& e) { return std::format("({} {} {})", static_cast(e.binop), @@ -89,7 +92,7 @@ void CollectByOp(const Expression& e, BinaryOp op, std::vector& out) CollectByOp(*b->rhs, op, out); return; } - out.push_back(CanonicalizePredicate(e)); + out.push_back(NormalizePredicate(e)); } Expression ChainByOp(std::vector exprs, BinaryOp op) { @@ -103,9 +106,93 @@ Expression ChainByOp(std::vector exprs, BinaryOp op) { return acc; } -} // namespace +std::optional InvertComparison(BinaryOp op) { + switch (op) { + case BinaryOp::kGt: return BinaryOp::kLe; + case BinaryOp::kLt: return BinaryOp::kGe; + case BinaryOp::kLe: return BinaryOp::kGt; + case BinaryOp::kGe: return BinaryOp::kLt; + case BinaryOp::kNotEq: return BinaryOp::kEq; + case BinaryOp::kEq: return BinaryOp::kNotEq; + case BinaryOp::kOr: + case BinaryOp::kAnd: + case BinaryOp::kPlus: + case BinaryOp::kMinus: + case BinaryOp::kMul: + case BinaryOp::kDiv: + case BinaryOp::kMod: + case BinaryOp::kPow: + return std::nullopt; + } +} -Expression CanonicalizePredicate(const Expression& e) { +std::optional ReverseComparison(BinaryOp op) { + switch (op) { + case BinaryOp::kGt: return BinaryOp::kLt; + case BinaryOp::kLt: return BinaryOp::kGt; + case BinaryOp::kLe: return BinaryOp::kGe; + case BinaryOp::kGe: return BinaryOp::kLe; + case BinaryOp::kNotEq: return BinaryOp::kNotEq; + case BinaryOp::kEq: return BinaryOp::kEq; + case BinaryOp::kOr: + case BinaryOp::kAnd: + case BinaryOp::kPlus: + case BinaryOp::kMinus: + case BinaryOp::kMul: + case BinaryOp::kDiv: + case BinaryOp::kMod: + case BinaryOp::kPow: + return std::nullopt; + } +} + +std::optional InvertLiteral(Literal literal) { + switch (literal) { + case Literal::kTrue: return Literal::kFalse; + case Literal::kFalse: return Literal::kTrue; + case Literal::kNull: + case Literal::kUnknown: + return std::nullopt; + } +} + +Expression ExpandIn(const InExpression& in) { + Expression lhs = NormalizePredicate(*in.lhs); + std::vector values; + values.reserve(in.values.size()); + for (const auto& value : in.values) { + values.push_back(NormalizePredicate(value)); + } + std::ranges::sort(values, [](const Expression& lhs, const Expression& rhs) { + return CanonicalKey(lhs) < CanonicalKey(rhs); + }); + + if (values.empty()) { + return InExpression{Share(std::move(lhs)), {}, in.negated}; + } + + const BinaryOp leaf_op = in.negated ? BinaryOp::kNotEq : BinaryOp::kEq; + const BinaryOp join_op = in.negated ? BinaryOp::kAnd : BinaryOp::kOr; + + std::vector terms; + terms.reserve(values.size()); + for (auto& value : values) { + terms.push_back(BinaryExpression{Share(lhs), leaf_op, Share(std::move(value))}); + } + return ChainByOp(std::move(terms), join_op); +} + +Expression NormalizeBinary(const BinaryExpression& b) { + Expression lhs = NormalizePredicate(*b.lhs); + Expression rhs = NormalizePredicate(*b.rhs); + if (auto reversed = ReverseComparison(b.binop); + reversed && CanonicalKey(rhs) < CanonicalKey(lhs)) { + return BinaryExpression{Share(std::move(rhs)), *reversed, Share(std::move(lhs))}; + } + return BinaryExpression{Share(std::move(lhs)), b.binop, Share(std::move(rhs))}; +} + +Expression NormalizePredicate(const Expression& e) { return std::visit(utils::Overloaded{ [](const BinaryExpression& b) -> Expression { if (b.binop == BinaryOp::kAnd || b.binop == BinaryOp::kOr) { @@ -114,34 +201,77 @@ Expression CanonicalizePredicate(const Expression& e) { CollectByOp(*b.rhs, b.binop, terms); return ChainByOp(std::move(terms), b.binop); } - return BinaryExpression{ - Share(CanonicalizePredicate(*b.lhs)), - b.binop, - Share(CanonicalizePredicate(*b.rhs)), - }; + return NormalizeBinary(b); + }, + [](const UnaryExpression& u) -> Expression { + if (u.op == UnaryOp::kNot) { + return NormalizeNegated(*u.child); + } + return UnaryExpression{u.op, Share(NormalizePredicate(*u.child))}; + }, + [](const InExpression& i) -> Expression { + return ExpandIn(i); + }, + [](const AggregateExpression& a) -> Expression { + if (a.is_star || !a.argument) return a; + return AggregateExpression{a.function, Share(NormalizePredicate(*a.argument)), a.is_star}; + }, + [](const auto& leaf) -> Expression { return leaf; }, + }, e); +} + +Expression NormalizeNegated(const Expression& e) { + return std::visit(utils::Overloaded{ + [](const BinaryExpression& b) -> Expression { + if (b.binop == BinaryOp::kAnd || b.binop == BinaryOp::kOr) { + const BinaryOp op = b.binop == BinaryOp::kAnd ? BinaryOp::kOr : BinaryOp::kAnd; + std::vector terms; + CollectByOp(NormalizeNegated(*b.lhs), op, terms); + CollectByOp(NormalizeNegated(*b.rhs), op, terms); + return ChainByOp(std::move(terms), op); + } + if (auto inverted = InvertComparison(b.binop)) { + return BinaryExpression{ + Share(NormalizePredicate(*b.lhs)), + *inverted, + Share(NormalizePredicate(*b.rhs)), + }; + } + return UnaryExpression{UnaryOp::kNot, Share(NormalizePredicate(Expression{b}))}; }, [](const UnaryExpression& u) -> Expression { - return UnaryExpression{u.op, Share(CanonicalizePredicate(*u.child))}; + if (u.op == UnaryOp::kNot) { + return NormalizePredicate(*u.child); + } + return UnaryExpression{UnaryOp::kNot, Share(NormalizePredicate(Expression{u}))}; }, [](const InExpression& i) -> Expression { std::vector values; values.reserve(i.values.size()); for (const auto& value : i.values) { - values.push_back(CanonicalizePredicate(value)); + values.push_back(value); } - std::ranges::sort(values, [](const Expression& lhs, const Expression& rhs) { - return CanonicalKey(lhs) < CanonicalKey(rhs); - }); - return InExpression{Share(CanonicalizePredicate(*i.lhs)), std::move(values), i.negated}; + return ExpandIn(InExpression{Share(*i.lhs), std::move(values), !i.negated}); }, [](const AggregateExpression& a) -> Expression { - if (a.is_star || !a.argument) return a; - return AggregateExpression{a.function, Share(CanonicalizePredicate(*a.argument)), a.is_star}; + return UnaryExpression{UnaryOp::kNot, Share(NormalizePredicate(Expression{a}))}; + }, + [](Literal l) -> Expression { + if (auto inverted = InvertLiteral(l)) return *inverted; + return UnaryExpression{UnaryOp::kNot, Share(Expression{l})}; + }, + [](const auto& leaf) -> Expression { + return UnaryExpression{UnaryOp::kNot, Share(Expression{leaf})}; }, - [](const auto& leaf) -> Expression { return leaf; }, }, e); } +} // namespace + +Expression CanonicalizePredicate(const Expression& e) { + return NormalizePredicate(e); +} + bool EquivalentPredicate(const Expression& lhs, const Expression& rhs) { return CanonicalizePredicate(lhs) == CanonicalizePredicate(rhs); } diff --git a/src/stewkk/sql/main.cpp b/src/stewkk/sql/main.cpp index 0619c46..febe06d 100644 --- a/src/stewkk/sql/main.cpp +++ b/src/stewkk/sql/main.cpp @@ -232,10 +232,10 @@ int main(int argc, char** argv) { return kOptimizerError; } if (mr.reachable) { - std::cout << "REACHABLE\n"; + std::cout << "REACHABLE distance=" << mr.closest_distance << "\n"; return kOk; } - std::cout << "NOT REACHABLE: " << mr.mismatch << "\n"; + std::cout << "NOT REACHABLE distance=" << mr.closest_distance << ": " << mr.mismatch << "\n"; return kNotReachable; } From fcec0c026565167809a1bafa1bb4f664f57ed49e Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 15 Jun 2026 17:08:31 +0300 Subject: [PATCH 114/120] More stats --- .../sql/logic/optimizer/logical_expr.hpp | 10 + include/stewkk/sql/logic/optimizer/memo.hpp | 23 + .../stewkk/sql/logic/optimizer/optimizer.hpp | 25 + .../sql/logic/optimizer/physical_expr.hpp | 15 + include/stewkk/sql/logic/optimizer/rules.hpp | 3 + .../sql/logic/optimizer/rules_applier.hpp | 4 + report/reach-fuzz-cross-mssql.pdf | Bin 0 -> 13418 bytes report/reach-fuzz-cross-ours.pdf | Bin 0 -> 16941 bytes report/vkr.tex | 81 + research/rule-lineage-random-1000-samples.csv | 3204 +++++++++++++++++ research/rule-lineage-random-1000-summary.csv | 21 + .../sql/logic/executor/plan_serializer.cpp | 22 +- .../logic/executor/plan_serializer_test.cpp | 2 +- src/stewkk/sql/logic/optimizer/memo.cpp | 21 + src/stewkk/sql/logic/optimizer/optimizer.cpp | 119 + .../sql/logic/optimizer/optimizer_test.cpp | 59 +- src/stewkk/sql/logic/optimizer/rules.cpp | 36 + .../sql/logic/optimizer/rules_applier.cpp | 31 +- src/stewkk/sql/main.cpp | 28 + 19 files changed, 3690 insertions(+), 14 deletions(-) create mode 100644 report/reach-fuzz-cross-mssql.pdf create mode 100644 report/reach-fuzz-cross-ours.pdf create mode 100644 research/rule-lineage-random-1000-samples.csv create mode 100644 research/rule-lineage-random-1000-summary.csv diff --git a/include/stewkk/sql/logic/optimizer/logical_expr.hpp b/include/stewkk/sql/logic/optimizer/logical_expr.hpp index 43f681d..67050c1 100644 --- a/include/stewkk/sql/logic/optimizer/logical_expr.hpp +++ b/include/stewkk/sql/logic/optimizer/logical_expr.hpp @@ -1,5 +1,8 @@ #pragma once +#include +#include + #include #include @@ -74,6 +77,13 @@ struct LogicalExpr { logical::PartialAggregation, logical::FinalAggregation, logical::Join, logical::CrossJoin> root_operator; utils::NotNull group; + + struct Provenance { + size_t rule_id; + std::string_view rule_name; + LogicalExpr* source; + }; + std::optional provenance; }; } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/memo.hpp b/include/stewkk/sql/logic/optimizer/memo.hpp index b62a57d..4fef909 100644 --- a/include/stewkk/sql/logic/optimizer/memo.hpp +++ b/include/stewkk/sql/logic/optimizer/memo.hpp @@ -1,7 +1,9 @@ #pragma once #include +#include #include +#include #include #include @@ -10,6 +12,25 @@ namespace stewkk::sql { class Memo { public: + struct LogicalProvenance { + size_t rule_id; + std::string_view rule_name; + LogicalExpr* source; + }; + + class ScopedLogicalProvenance { + public: + ScopedLogicalProvenance(Memo& memo, LogicalProvenance provenance); + ~ScopedLogicalProvenance(); + + ScopedLogicalProvenance(const ScopedLogicalProvenance&) = delete; + ScopedLogicalProvenance& operator=(const ScopedLogicalProvenance&) = delete; + + private: + Memo& memo_; + std::optional previous_; + }; + size_t GroupCount() const; utils::NotNull AddGroup(LogicalOperator root_operator); LogicalExpr* GetGroup(LogicalOperator root_operator) const; @@ -19,9 +40,11 @@ class Memo { private: LogicalExpr* GetGroup(const std::string& key) const; + void SetProvenanceIfNew(utils::NotNull expr); std::deque groups_; std::unordered_map expr_index_; + std::optional current_provenance_; }; } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/optimizer.hpp b/include/stewkk/sql/logic/optimizer/optimizer.hpp index ad9b974..82ee638 100644 --- a/include/stewkk/sql/logic/optimizer/optimizer.hpp +++ b/include/stewkk/sql/logic/optimizer/optimizer.hpp @@ -4,6 +4,8 @@ #include #include #include +#include +#include #include #include #include @@ -22,6 +24,19 @@ namespace stewkk::sql { std::vector> GetChildren(utils::NotNull expr); std::vector> GetChildren(utils::NotNull expr); +enum class RuleLineageKind { + kTransformation, + kImplementation, + kEnforcer, +}; + +struct RuleLineageStat { + RuleLineageKind kind; + size_t rule_id; + std::string_view rule_name; + size_t count; +}; + template class Optimizer { public: @@ -35,6 +50,7 @@ class Optimizer { std::int64_t GetBestCost() const; utils::NotNull GetRootGroup() const; + std::vector GetSelectedPlanRuleLineage(); private: using Limit = std::optional; @@ -56,6 +72,15 @@ class Optimizer { void OptimizeGroup(utils::NotNull group, PropertySet required = PropertySet::Any(), Limit limit = std::nullopt); PhysicalPlanNode BuildOptimalPlan(Group* group, PropertySet required = PropertySet::Any()); + void CollectSelectedPlanRuleLineage( + Group* group, PropertySet required, + std::unordered_map& stats, + std::unordered_set& visited_physical, + std::unordered_set& visited_logical); + void CollectLogicalRuleLineage( + LogicalExpr* expr, + std::unordered_map& stats, + std::unordered_set& visited_logical); struct WinnerEntry { int64_t cost; diff --git a/include/stewkk/sql/logic/optimizer/physical_expr.hpp b/include/stewkk/sql/logic/optimizer/physical_expr.hpp index 17d6266..44f559b 100644 --- a/include/stewkk/sql/logic/optimizer/physical_expr.hpp +++ b/include/stewkk/sql/logic/optimizer/physical_expr.hpp @@ -3,6 +3,7 @@ #include #include #include +#include #include #include @@ -12,6 +13,7 @@ namespace stewkk::sql { struct Group; +struct LogicalExpr; namespace physical { @@ -122,6 +124,18 @@ struct FinalAggregation { } // namespace physical struct PhysicalExpr { + enum class ProvenanceKind { + kImplementation, + kEnforcer, + }; + + struct Provenance { + ProvenanceKind kind; + size_t rule_id; + std::string_view rule_name; + LogicalExpr* source; + }; + std::variant root_operator; utils::NotNull group; bool is_enforcer = false; + std::optional provenance; }; } // namespace stewkk::sql diff --git a/include/stewkk/sql/logic/optimizer/rules.hpp b/include/stewkk/sql/logic/optimizer/rules.hpp index fc336f8..5b97b50 100644 --- a/include/stewkk/sql/logic/optimizer/rules.hpp +++ b/include/stewkk/sql/logic/optimizer/rules.hpp @@ -2,6 +2,7 @@ #include #include +#include #include #include @@ -34,6 +35,8 @@ template struct Rules { std::array, NTransformation> transformation_rules; std::array, NImplementation> implementation_rules; + std::array transformation_rule_names; + std::array implementation_rule_names; }; Rules<14, 9> MakeMainRules(IndexCatalog indexes = {}); diff --git a/include/stewkk/sql/logic/optimizer/rules_applier.hpp b/include/stewkk/sql/logic/optimizer/rules_applier.hpp index 8834ecc..b511b24 100644 --- a/include/stewkk/sql/logic/optimizer/rules_applier.hpp +++ b/include/stewkk/sql/logic/optimizer/rules_applier.hpp @@ -1,6 +1,7 @@ #pragma once #include +#include #include #include @@ -31,6 +32,9 @@ class RulesApplier { bool IsApplicable(ImplementationRuleId rule, utils::NotNull expr); std::vector> Apply(ImplementationRuleId rule, utils::NotNull expr, Memo& memo); + std::string_view GetTransformationRuleName(TransformationRuleId rule) const; + std::string_view GetImplementationRuleName(ImplementationRuleId rule) const; + private: Rules rules_; std::unordered_map> applied_transformation_rules_; diff --git a/report/reach-fuzz-cross-mssql.pdf b/report/reach-fuzz-cross-mssql.pdf new file mode 100644 index 0000000000000000000000000000000000000000..e3a8b7f77895f939ef022fffc82d6a862fa12115 GIT binary patch literal 13418 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+ORDER BY orders.customer_id DESC, orders.id DESC;" +9,1351928,3939128,2497,2,transformation 0 JoinCommutativity: 1; transformation 2 FilterSplit: 2; transformation 5 FilterPushdownThroughJoin: 2; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT t0.customer_id AS c3674 +FROM orders AS t0 CROSS JOIN departments AS t1 +WHERE (t1.id >= 4 AND (t0.id IN (130) OR t1.id IS NULL));" +10,3639,6932,228,3,transformation 5 FilterPushdownThroughJoin: 3; transformation 10 ProjectionPushdownThroughJoin: 3; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 2; implementation 6 ImplementHashJoin: 2,"SELECT regions.id +FROM books RIGHT JOIN employees ON books.id = employees.id JOIN regions ON books.id = regions.id +WHERE books.price >= 6;" +11,5889,16616,226,22,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT employees.department_id +FROM users FULL JOIN employees ON users.id = employees.id +ORDER BY employees.department_id DESC;" +12,676416,1550366,1214,3,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1,"SELECT orders.id AS c2380, markets.region, markets.note +FROM markets JOIN orders ON markets.id = orders.id;" +13,2390300,2390300,2959,15000,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1,"SELECT markets.region, orders.customer_id AS c8705, markets.note +FROM markets CROSS JOIN orders;" +14,2076,2076,135,10,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT t0.department_id, t0.id AS c1827 +FROM employees AS t0 +WHERE (t0.department_id BETWEEN 5 AND 34 OR t0.id NOT IN (2, 68, 69));" +15,422,422,229,0,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT books.price +FROM books +WHERE (books.id < 2 AND (books.price NOT BETWEEN 41 AND 55 OR books.price IN (57, 63, 77, 77)));" +16,5405,16132,184,22,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.age AS c4948 +FROM users AS t0 FULL JOIN employees AS t1 ON t0.id = t1.id;" +17,14805,29510,354,4,transformation 0 JoinCommutativity: 1; transformation 6 InToOrChain: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT users.age, COUNT(departments.id), COUNT(*) +FROM regions JOIN users ON regions.id = users.id CROSS JOIN departments +WHERE (users.age >= 52 OR regions.id IN (4, 9)) +GROUP BY users.age;" +18,102500,102500,291,436,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT customers.region_id +FROM customers +WHERE customers.region_id != 8 +ORDER BY customers.region_id ASC;" +19,2736,4463,264,2,transformation 0 JoinCommutativity: 2; transformation 5 FilterPushdownThroughJoin: 4; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT t0.id +FROM books AS t0 RIGHT JOIN regions AS t1 ON t0.id = t1.id JOIN markets AS t2 ON t1.id = t2.id +WHERE t1.id < 3 +ORDER BY t0.id ASC;" +20,31196040,49896040,20899,85,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 5 ImplementCrossJoin: 1,"SELECT orders.id AS c5497, orders.customer_id, departments.id AS c8484 +FROM departments CROSS JOIN users CROSS JOIN orders +WHERE orders.id = 82;" +21,1220,1220,123,10,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT regions.id +FROM regions;" +22,3740,3740,220,11,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.department_id +FROM employees AS t0 +GROUP BY t0.department_id +ORDER BY t0.department_id ASC;" +23,109562,155822,402,10,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT customers.region_id +FROM customers LEFT JOIN books ON customers.id = books.id +GROUP BY customers.region_id +ORDER BY customers.region_id ASC;" +24,39522,56902,652,4,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t2.id, COUNT(t0.id) AS c2763, COUNT(t2.id) +FROM regions AS t0 CROSS JOIN users AS t1 LEFT JOIN markets AS t2 ON t0.id = t2.id +GROUP BY t2.id;" +25,68652,155432,263,3,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT customers.id +FROM books JOIN customers ON books.id = customers.id +ORDER BY customers.id DESC;" +26,1417306,4786826,4875,5489,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT customers.region_id, employees.department_id +FROM employees FULL JOIN orders ON employees.id = orders.id FULL JOIN customers ON employees.id = customers.id +ORDER BY customers.region_id ASC;" +27,1318590,6466259,4170,2,transformation 0 JoinCommutativity: 2; transformation 6 InToOrChain: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT orders.customer_id, users.id +FROM users RIGHT JOIN orders ON users.id = orders.id CROSS JOIN departments +WHERE orders.customer_id IN (34, 321) +GROUP BY orders.customer_id, users.id;" +28,1425,1425,156,5,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT departments.id AS c2364, SUM(departments.id) +FROM departments +GROUP BY departments.id +ORDER BY departments.id ASC;" +29,110161,226916,320,3,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 3,"SELECT customers.id, markets.id +FROM customers FULL JOIN departments ON customers.id = departments.id JOIN markets ON customers.id = markets.id;" +30,19986,32788,418,51,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 5 ImplementCrossJoin: 1,"SELECT markets.id AS c2619 +FROM markets CROSS JOIN users CROSS JOIN books +WHERE markets.region != 'AMERICA';" +31,3125,3125,190,8,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id, SUM(t0.id) +FROM users AS t0 +WHERE ((t0.id NOT IN (5) AND t0.age != 12) AND t0.age NOT BETWEEN 33 AND 64) +GROUP BY t0.id;" +32,3740,3740,166,11,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.id AS c8338, COUNT(t0.department_id) +FROM employees AS t0 +GROUP BY t0.id +ORDER BY t0.id DESC;" +33,878460,2705500,1374,1000,transformation 2 FilterSplit: 1; transformation 5 FilterPushdownThroughJoin: 4; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 6 ImplementHashJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT orders.customer_id, customers.region_id +FROM departments RIGHT JOIN orders ON departments.id = orders.id CROSS JOIN customers +WHERE departments.id BETWEEN 4 AND 96 +ORDER BY customers.region_id DESC;" +34,22645472,27634772,76328,3,transformation 0 JoinCommutativity: 1; transformation 6 InToOrChain: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 5 ImplementCrossJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT markets.note AS c1833, markets.region, SUM(books.id) AS c1917, COUNT(*) +FROM orders CROSS JOIN markets CROSS JOIN books +WHERE ((orders.id != 89 OR markets.note IS NOT NULL) OR (markets.id IN (22) AND books.id NOT BETWEEN 1 AND 3)) +GROUP BY markets.note, markets.region +ORDER BY markets.region DESC;" +35,7686500,1755811500,23921,50000,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 6 ImplementHashJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT regions.id, orders.id +FROM orders CROSS JOIN regions JOIN customers ON regions.id = customers.id +ORDER BY orders.id DESC, regions.id DESC;" +36,8426,8426,329,51,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1,"SELECT books.price AS c7084 +FROM books CROSS JOIN users;" +37,1916300,525797800,3304,1591,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT orders.customer_id +FROM customers CROSS JOIN markets FULL JOIN orders ON markets.id = orders.id +WHERE orders.id < 95;" +38,1310710,4010688,2615,2,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 6,"SELECT t1.department_id, COUNT(*) +FROM regions AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id RIGHT JOIN orders AS t2 ON t0.id = t2.id +WHERE ((t1.id = 3 OR t2.id = 185) OR (t1.id < 3 AND t2.id IS NULL)) +GROUP BY t1.department_id +ORDER BY t1.department_id DESC;" +39,433,433,401,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id, t0.note, t0.region +FROM markets AS t0 +WHERE t0.note IS NULL +ORDER BY t0.note DESC, t0.id DESC;" +40,615800,615800,692,500,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t1.id, t1.region_id AS c2127 +FROM books AS t0 CROSS JOIN customers AS t1 +GROUP BY t1.id, t1.region_id;" +41,917612117,1196362017,1308879,50000,transformation 0 JoinCommutativity: 1; transformation 6 InToOrChain: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT customers.region_id, orders.id AS c1616 +FROM orders CROSS JOIN customers +WHERE ((orders.id >= 3 OR customers.region_id != 41) OR orders.id IN (73)) +GROUP BY customers.region_id, orders.id;" +42,744958,1705422,1361,3,transformation 0 JoinCommutativity: 1; transformation 9 FilterLiftThroughJoin: 2; transformation 10 ProjectionPushdownThroughJoin: 3; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 2; implementation 6 ImplementHashJoin: 2,"SELECT t1.price, t0.region_id, t2.customer_id AS c2615 +FROM customers AS t0 JOIN books AS t1 ON t0.id = t1.id JOIN orders AS t2 ON t0.id = t2.id +WHERE t2.id IS NOT NULL;" +43,422,422,293,0,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT markets.region, markets.id AS c9574 +FROM markets +WHERE ((markets.note IS NOT NULL OR markets.region != 'AMERICA') AND (markets.region IN ('EUROPE', 'EUROPE') AND markets.note IN ('North, South', 'North, South')));" +44,72448,648028,243,6,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT t1.id AS c8372, t1.age AS c1550 +FROM customers AS t0 JOIN users AS t1 ON t0.id = t1.id +WHERE t1.id > 11 +ORDER BY t1.id ASC, t1.age DESC;" +45,4560,11225,172,0,transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t1.department_id AS c7018, t0.id AS c6686 +FROM regions AS t0 JOIN employees AS t1 ON t0.id = t1.id +WHERE t0.id >= 10 +GROUP BY t1.department_id, t0.id;" +46,1122,1122,164,1,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id +FROM regions AS t0 +WHERE ((t0.id IS NOT NULL AND t0.id != 5) AND (t0.id > 8 AND t0.id > 9));" +47,110844,436233,343,1,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT employees.id +FROM customers FULL JOIN employees ON customers.id = employees.id +WHERE employees.department_id = 32 +ORDER BY employees.id ASC;" +48,1443652,175655822,3485,11,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT customers.region_id AS c1201, SUM(customers.region_id) +FROM orders FULL JOIN customers ON orders.id = customers.id FULL JOIN markets ON orders.id = markets.id +GROUP BY customers.region_id;" +49,560554,560554,1253,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT orders.customer_id, orders.id +FROM orders +WHERE ((orders.id = 130 OR orders.id <= 96) AND orders.id = 97) +ORDER BY orders.customer_id DESC, orders.id DESC;" +50,1239,1972,489,3,transformation 5 FilterPushdownThroughJoin: 1; transformation 10 ProjectionPushdownThroughJoin: 3; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 2; implementation 6 ImplementHashJoin: 1,"SELECT markets.id +FROM markets RIGHT JOIN departments ON markets.id = departments.id +WHERE markets.id < 45;" +51,610,610,140,5,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id +FROM departments AS t0;" +52,3345275,5714300,8172,5000,transformation 0 JoinCommutativity: 1; transformation 2 FilterSplit: 3; transformation 5 FilterPushdownThroughJoin: 2; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT orders.id, COUNT(*) +FROM books CROSS JOIN orders +WHERE books.price NOT IN (45, 55, 55, 66) +GROUP BY orders.id;" +53,30725,62340,348,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT users.id, COUNT(employees.id) AS c5902 +FROM employees CROSS JOIN users JOIN markets ON users.id = markets.id +GROUP BY users.id +ORDER BY users.id ASC;" +54,5100,5100,178,15,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT users.age, users.id AS c207, SUM(users.id) +FROM users +WHERE (users.age > 27 OR (users.age <= 123 AND users.age > 1)) +GROUP BY users.age, users.id +ORDER BY users.id ASC;" +55,1342,1342,128,11,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.department_id AS c7732 +FROM employees AS t0;" +56,40022332,53882332,79727,3,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 5 ImplementCrossJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT books.id, SUM(orders.customer_id) AS c2549, COUNT(*) AS c1539 +FROM books CROSS JOIN employees CROSS JOIN orders +WHERE employees.id < 68 +GROUP BY books.id;" +57,881250,881250,1505,3001,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT orders.id AS c9114 +FROM orders +WHERE (orders.customer_id > 214 OR orders.customer_id BETWEEN 75 AND 84);" +58,4073600,1927083100,3920,110,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT employees.department_id, customers.region_id, COUNT(*), SUM(orders.customer_id) +FROM employees CROSS JOIN customers FULL JOIN orders ON customers.id = orders.id +WHERE customers.id <= 149 +GROUP BY employees.department_id, customers.region_id;" +59,175500,175500,469,500,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT customers.id, customers.region_id, COUNT(customers.region_id) +FROM customers +GROUP BY customers.id, customers.region_id;" +60,3577,5702,185,17,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.id +FROM markets AS t0 FULL JOIN users AS t1 ON t0.id = t1.id +ORDER BY t0.id ASC;" +61,1763,2493,272,5,transformation 0 JoinCommutativity: 1; transformation 2 FilterSplit: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT t1.region, t1.id, t1.note +FROM departments AS t0 CROSS JOIN markets AS t1 +WHERE ((t0.id < 43 OR t0.id >= 2) AND t1.note = 'Old World') +ORDER BY t1.note DESC, t1.id ASC, t1.region ASC;" +62,69881,156916,323,5,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT departments.id AS c7718 +FROM departments LEFT JOIN books ON departments.id = books.id JOIN customers ON departments.id = customers.id;" +63,4771,9075,197,11,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT users.age AS c5029, COUNT(*) AS c2096, SUM(users.age) +FROM users FULL JOIN departments ON users.id = departments.id +GROUP BY users.age +ORDER BY users.age ASC;" +64,1494000,175610500,1709,500,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT customers.region_id AS c6827, orders.id, customers.id +FROM customers LEFT JOIN orders ON customers.id = orders.id +ORDER BY customers.region_id ASC, customers.id ASC, orders.id DESC;" +65,1558,2113,203,0,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT markets.id AS c8656, markets.region +FROM markets FULL JOIN departments ON markets.id = departments.id +WHERE departments.id = 60 +GROUP BY markets.id, markets.region;" +66,563,563,234,1,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT markets.note AS c5953, SUM(markets.id), SUM(markets.id) +FROM markets +WHERE markets.region = 'EUROPE' +GROUP BY markets.note;" +67,61000,61000,262,500,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT customers.region_id, customers.id AS c651 +FROM customers;" +68,1311707,6465260,5389,5001,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT users.id, regions.id AS c5610, orders.id AS c9918 +FROM users FULL JOIN orders ON users.id = orders.id FULL JOIN regions ON orders.id = regions.id +ORDER BY users.id DESC;" +69,356822,548611,2595,2380,transformation 0 JoinCommutativity: 2; transformation 6 InToOrChain: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT t0.id AS c499 +FROM books AS t0 FULL JOIN users AS t1 ON t0.id = t1.id CROSS JOIN customers AS t2 +WHERE ((t0.price > 66 OR t0.price NOT IN (55, 77, 93)) OR t2.id < 93) +ORDER BY t0.id ASC;" +70,574,574,156,1,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT markets.region, markets.note +FROM markets +WHERE markets.id IN (2, 31) +GROUP BY markets.region, markets.note +ORDER BY markets.note ASC, markets.region ASC;" +71,5300,5300,256,5,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT departments.id AS c3175, SUM(departments.id), COUNT(books.price) +FROM books CROSS JOIN departments +GROUP BY departments.id;" +72,788,788,138,4,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id +FROM departments AS t0 +WHERE t0.id >= 2 +ORDER BY t0.id ASC;" +73,67449,155422,437,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 10 ProjectionPushdownThroughJoin: 3; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 2; implementation 6 ImplementHashJoin: 1,"SELECT customers.id, customers.region_id +FROM customers JOIN markets ON customers.id = markets.id +WHERE ((markets.id = 3 AND markets.region != 'AMERICA') AND (customers.id < 17 AND customers.region_id IS NULL));" +74,109996,226972,330,3,transformation 0 JoinCommutativity: 2; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 3,"SELECT books.id AS c9785 +FROM customers RIGHT JOIN departments ON customers.id = departments.id RIGHT JOIN books ON departments.id = books.id +WHERE books.price >= 55;" +75,15960,15960,395,110,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1,"SELECT t1.department_id AS c5193, t1.id +FROM regions AS t0 CROSS JOIN employees AS t1;" +76,6261,16988,208,17,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.id AS c3431 +FROM users AS t0 LEFT JOIN employees AS t1 ON t0.id = t1.id +WHERE ((t1.id != 67 AND t0.id IS NOT NULL) OR (t0.age NOT BETWEEN 33 AND 123 OR t0.age >= 22));" +77,60475000,315425000,1366,0,transformation 0 JoinCommutativity: 1; transformation 2 FilterSplit: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t1.region_id AS c1177, t0.id AS c3278, COUNT(*), COUNT(t0.id) AS c3474 +FROM orders AS t0 CROSS JOIN customers AS t1 +WHERE (t0.id = 64 AND t0.customer_id > 397) +GROUP BY t1.region_id, t0.id;" +78,61000,61000,263,500,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id, t0.region_id +FROM customers AS t0;" +79,2310,2310,601,2,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id AS c7963, t0.age AS c5437 +FROM users AS t0 +WHERE t0.age IN (5, 11, 63);" +80,3730,6250,275,30,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT regions.id AS c2137 +FROM regions CROSS JOIN markets +WHERE markets.note != 'AMERICA';" +81,3837,9622,233,3,transformation 0 JoinCommutativity: 3; transformation 1 JoinAssociativity: 3; transformation 5 FilterPushdownThroughJoin: 4; transformation 11 OuterJoinToInner: 2; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT markets.region AS c3832, users.age, departments.id +FROM users LEFT JOIN departments ON users.id = departments.id RIGHT JOIN markets ON users.id = markets.id +WHERE departments.id <= 5;" +82,1833,1833,147,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT users.age AS c4898, users.id AS c4014 +FROM users +WHERE ((users.id = 4 OR users.id BETWEEN 11 AND 16) AND (users.age = 22 AND users.id <= 57)) +ORDER BY users.age DESC;" +83,3478,5692,185,6,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.age AS c499, t1.price AS c7644, t0.id +FROM users AS t0 LEFT JOIN books AS t1 ON t0.id = t1.id +WHERE t0.id < 7;" +84,3124,4574,510,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT books.price, regions.id, markets.id AS c8988 +FROM markets LEFT JOIN regions ON markets.id = regions.id LEFT JOIN books ON regions.id = books.id +WHERE ((books.price BETWEEN 6 AND 77 OR markets.id NOT IN (1, 2, 2, 3)) OR books.price NOT IN (55));" +85,175500,175500,331,500,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id, t0.region_id +FROM customers AS t0 +GROUP BY t0.id, t0.region_id;" +86,610000,610000,1135,5000,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT orders.customer_id, orders.id +FROM orders;" +87,3256,3256,218,11,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT employees.department_id, employees.id AS c6314 +FROM employees +GROUP BY employees.department_id, employees.id;" +88,3612,6540,207,5,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT departments.id AS c2839, COUNT(*) +FROM employees RIGHT JOIN departments ON employees.id = departments.id +GROUP BY departments.id +ORDER BY departments.id DESC;" +89,822,822,176,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.region AS c5477, t0.id AS c2794, COUNT(*) AS c9570 +FROM markets AS t0 +GROUP BY t0.region, t0.id;" +90,135000,135000,585,500,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT customers.region_id, customers.id AS c6247, COUNT(*) AS c8943 +FROM customers +WHERE customers.region_id <= 40 +GROUP BY customers.region_id, customers.id;" +91,610,610,181,5,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT departments.id +FROM departments;" +92,12280,12280,358,3,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT markets.region, markets.id +FROM markets CROSS JOIN regions +GROUP BY markets.region, markets.id +ORDER BY markets.region ASC;" +93,675822,1550366,1009,3,transformation 0 JoinCommutativity: 1; transformation 10 ProjectionPushdownThroughJoin: 3; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 2; implementation 6 ImplementHashJoin: 1,"SELECT t0.id, t1.id AS c1156, t0.customer_id +FROM orders AS t0 JOIN books AS t1 ON t0.id = t1.id;" +94,4252,22032,305,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 3; transformation 6 InToOrChain: 1; transformation 8 CrossJoinToJoin: 1; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 2,"SELECT t1.price +FROM departments AS t0 CROSS JOIN books AS t1 RIGHT JOIN users AS t2 ON t1.id = t2.id +WHERE (t2.id BETWEEN 45 AND 86 AND t1.id IN (2));" +95,2646,7432,516,0,transformation 0 JoinCommutativity: 1; transformation 2 FilterSplit: 3; transformation 5 FilterPushdownThroughJoin: 4; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t2.id +FROM departments AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id CROSS JOIN books AS t2 +WHERE ((t1.department_id IN (1, 5, 12, 34) AND t1.id IN (69)) AND t1.department_id <= 1);" +96,684299,6455219,1022,16,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t1.id AS c5555, t0.id +FROM orders AS t0 JOIN users AS t1 ON t0.id = t1.id +GROUP BY t1.id, t0.id;" +97,1274,1274,178,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT regions.id, SUM(regions.id), COUNT(regions.id) AS c9550 +FROM regions +WHERE ((regions.id < 2 AND regions.id = 32) AND (regions.id <= 2 AND regions.id != 3)) +GROUP BY regions.id +ORDER BY regions.id ASC;" +98,1232,2482,250,0,transformation 0 JoinCommutativity: 1; transformation 2 FilterSplit: 5; transformation 5 FilterPushdownThroughJoin: 2; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT books.id +FROM books CROSS JOIN departments +WHERE ((books.price IS NULL AND departments.id = 1) AND (books.price IS NULL OR books.price NOT IN (55, 77)));" +99,58943,401263,262,0,transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT customers.id, SUM(customers.id), COUNT(customers.id) AS c8662 +FROM regions JOIN customers ON regions.id = customers.id +WHERE customers.region_id = 63 +GROUP BY customers.id;" +100,3009,3009,138,17,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT users.age, users.id AS c7470 +FROM users +ORDER BY users.id ASC;" +101,4056,6624,461,50,transformation 0 JoinCommutativity: 2; transformation 6 InToOrChain: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT markets.id, departments.id AS c1258, markets.region +FROM employees LEFT JOIN markets ON employees.id = markets.id CROSS JOIN departments +WHERE ((markets.id IN (3) AND markets.region = 'EUROPE') OR employees.id < 69);" +102,610000,610000,1304,5000,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.customer_id, t0.id AS c8685 +FROM orders AS t0;" +103,686541,6467000,1121,9,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT users.age AS c405 +FROM users JOIN orders ON users.id = orders.id RIGHT JOIN regions ON orders.id = regions.id +GROUP BY users.age +ORDER BY users.age DESC;" +104,1222,1222,196,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT employees.id +FROM employees +WHERE employees.id = 14;" +105,1306776,1551752,1464,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT markets.id, orders.customer_id, COUNT(*) +FROM orders RIGHT JOIN markets ON orders.id = markets.id LEFT JOIN books ON markets.id = books.id +GROUP BY markets.id, orders.customer_id;" +106,10392,18447,326,25,transformation 0 JoinCommutativity: 1; transformation 6 InToOrChain: 1; transformation 7 FilterToJoinPredicate: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 6 ImplementHashJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT books.price AS c2329, markets.note +FROM users CROSS JOIN books JOIN markets ON books.id = markets.id +WHERE (markets.region = 'EUROPE' OR (users.age NOT IN (5, 22, 64, 123) AND users.age < 22)) +ORDER BY markets.note ASC;" +107,1274,1274,159,1,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT regions.id +FROM regions +WHERE regions.id IN (5) +GROUP BY regions.id +ORDER BY regions.id ASC;" +108,102500,102500,239,51,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT customers.id AS c9166, customers.region_id +FROM customers +WHERE customers.id < 52 +ORDER BY customers.region_id ASC, customers.id DESC;" +109,5405,16132,208,22,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT employees.id, users.age, employees.department_id +FROM employees FULL JOIN users ON employees.id = users.id;" +110,3233,16153,330,0,transformation 5 FilterPushdownThroughJoin: 3; transformation 6 InToOrChain: 1; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT employees.department_id, employees.id, COUNT(users.id) AS c6401, COUNT(*) +FROM employees FULL JOIN users ON employees.id = users.id +WHERE ((employees.department_id = 31 AND employees.department_id NOT IN (6, 59, 72)) AND (employees.department_id NOT IN (1, 5, 6, 32) AND users.id = 5)) +GROUP BY employees.department_id, employees.id;" +111,541846,541846,2403,28,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT orders.customer_id, orders.id +FROM orders +WHERE ((orders.id IN (97, 99, 184, 198) OR orders.id BETWEEN 31 AND 58) AND (orders.customer_id IS NOT NULL AND orders.id BETWEEN 3 AND 65));" +112,1305844,1550574,3017,2,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT markets.id, markets.note, COUNT(orders.id), SUM(orders.customer_id) +FROM markets RIGHT JOIN orders ON markets.id = orders.id +WHERE ((orders.id < 188 AND orders.customer_id >= 193) OR (orders.customer_id BETWEEN 239 AND 436 OR markets.id = 1)) +GROUP BY markets.id, markets.note +ORDER BY markets.id DESC, markets.note DESC;" +113,422,422,175,1,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT t0.region, t0.note AS c6295 +FROM markets AS t0 +WHERE t0.id < 2;" +114,61000,61000,226,500,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id +FROM customers AS t0;" +115,3922,10020,204,10,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t1.id AS c2831, t0.id, t0.department_id +FROM employees AS t0 RIGHT JOIN regions AS t1 ON t0.id = t1.id;" +116,676779,1551363,1099,1,transformation 0 JoinCommutativity: 1; transformation 9 FilterLiftThroughJoin: 2; transformation 10 ProjectionPushdownThroughJoin: 3; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 2; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT t2.customer_id +FROM markets AS t0 LEFT JOIN books AS t1 ON t0.id = t1.id JOIN orders AS t2 ON t1.id = t2.id +WHERE t2.customer_id > 244 +ORDER BY t2.customer_id DESC;" +117,744,744,354,1,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id AS c6139 +FROM departments AS t0 +WHERE t0.id >= 5;" +118,3722,9763,328,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 5; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT books.id AS c118 +FROM departments RIGHT JOIN users ON departments.id = users.id FULL JOIN books ON departments.id = books.id +WHERE ((users.id > 3 AND books.price = 77) AND users.age > 443) +GROUP BY books.id;" +119,676308,2251048,1306,5,transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT orders.id, COUNT(departments.id) +FROM departments JOIN orders ON departments.id = orders.id +WHERE departments.id < 36 +GROUP BY orders.id;" +120,1377824,4786342,1550,11,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT orders.customer_id, employees.department_id +FROM employees JOIN customers ON employees.id = customers.id LEFT JOIN orders ON employees.id = orders.id;" +121,1222,1222,130,0,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT employees.id +FROM employees +WHERE ((employees.id > 68 OR employees.department_id = 5) AND (employees.id BETWEEN 2 AND 4 AND employees.department_id IN (3, 5, 5)));" +122,3184,5382,344,1,transformation 0 JoinCommutativity: 2; transformation 5 FilterPushdownThroughJoin: 2; transformation 7 FilterToJoinPredicate: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT markets.note, employees.id +FROM employees LEFT JOIN markets ON employees.id = markets.id JOIN departments ON employees.id = departments.id +WHERE ((departments.id != 36 OR markets.region IS NULL) AND departments.id = 4);" +123,544,544,168,1,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id, t0.note, t0.region +FROM markets AS t0 +WHERE t0.id NOT BETWEEN 2 AND 21;" +124,59693,155433,332,1,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT books.price AS c5178 +FROM books RIGHT JOIN customers ON books.id = customers.id +WHERE customers.id = 158 +ORDER BY books.price DESC;" +125,1222,1222,152,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT employees.id AS c9929, employees.department_id +FROM employees +WHERE (employees.department_id = 35 AND employees.department_id < 32);" +126,3107,4593,277,0,implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT books.price, books.id +FROM books FULL JOIN markets ON books.id = markets.id FULL JOIN regions ON books.id = regions.id +WHERE (markets.region != 'EUROPE' AND (regions.id IS NULL AND books.price > 66)) +GROUP BY books.price, books.id;" +127,81500,81500,286,3,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.region_id +FROM customers AS t0 +WHERE t0.id IN (16, 32, 137) +ORDER BY t0.region_id DESC;" +128,1288,1288,144,2,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT regions.id +FROM regions +WHERE regions.id BETWEEN 9 AND 10 +ORDER BY regions.id DESC;" +129,1306842,1551818,1376,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 5,"SELECT orders.customer_id, COUNT(orders.id) AS c1702 +FROM markets LEFT JOIN books ON markets.id = books.id LEFT JOIN orders ON markets.id = orders.id +GROUP BY orders.customer_id +ORDER BY orders.customer_id DESC;" +130,1308015,2255110,1379,5,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT regions.id, orders.id AS c452, orders.customer_id +FROM regions JOIN departments ON regions.id = departments.id LEFT JOIN orders ON departments.id = orders.id;" +131,1308440,4002960,2135,11,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT regions.id AS c9060, COUNT(orders.customer_id), COUNT(orders.id) +FROM orders FULL JOIN regions ON orders.id = regions.id +GROUP BY regions.id;" +132,2187,5572,196,0,transformation 5 FilterPushdownThroughJoin: 2; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.id AS c4692, t1.department_id AS c2149, t1.id +FROM departments AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id +WHERE (t1.id IN (32, 66, 68, 80) AND (t1.id <= 26 OR t1.department_id <= 5));" +133,678014,4351342,1007,11,transformation 0 JoinCommutativity: 1; transformation 10 ProjectionPushdownThroughJoin: 3; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 2; implementation 6 ImplementHashJoin: 1,"SELECT t1.id AS c8251, t0.customer_id, t0.id AS c2451 +FROM orders AS t0 JOIN employees AS t1 ON t0.id = t1.id;" +134,17111100,17111100,23355,55000,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT orders.customer_id AS c6356 +FROM employees CROSS JOIN orders +ORDER BY orders.customer_id ASC;" +135,1311707,6465260,2119,11,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT users.id, regions.id AS c3154 +FROM orders FULL JOIN users ON orders.id = users.id RIGHT JOIN regions ON orders.id = regions.id +ORDER BY users.id DESC, regions.id DESC;" +136,7798,11608,602,4,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT books.price +FROM regions CROSS JOIN markets LEFT JOIN books ON regions.id = books.id +GROUP BY books.price +ORDER BY books.price DESC;" +137,1425,1425,186,5,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT departments.id, SUM(departments.id), COUNT(*) AS c6998 +FROM departments +GROUP BY departments.id;" +138,2960,2960,198,10,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT regions.id AS c7258 +FROM regions +GROUP BY regions.id;" +139,563,563,179,2,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT markets.id +FROM markets +WHERE ((markets.note = 'Old World' OR markets.id >= 2) OR (markets.region = 'AMERICA' AND markets.region = 'EUROPE')) +GROUP BY markets.id;" +140,422,422,162,3,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT t0.price, t0.id AS c3105 +FROM books AS t0 +WHERE t0.id IS NOT NULL;" +141,2932,4903,508,0,transformation 0 JoinCommutativity: 2; transformation 5 FilterPushdownThroughJoin: 4; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t1.id AS c8895, t1.note AS c8854, SUM(t1.id) +FROM books AS t0 RIGHT JOIN markets AS t1 ON t0.id = t1.id RIGHT JOIN employees AS t2 ON t0.id = t2.id +WHERE t1.id > 24 +GROUP BY t1.id, t1.note;" +142,75995,661132,331,17,transformation 10 ProjectionPushdownThroughJoin: 7; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 4; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t1.region_id AS c1630 +FROM users AS t0 JOIN customers AS t1 ON t0.id = t1.id LEFT JOIN employees AS t2 ON t0.id = t2.id;" +143,422,422,171,1,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id, t0.price +FROM books AS t0 +WHERE t0.price <= 55;" +144,7022,9744,298,10,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t1.department_id AS c3966, SUM(t1.id) AS c1392, COUNT(*) AS c3892 +FROM books AS t0 CROSS JOIN employees AS t1 +WHERE t1.department_id != 12 +GROUP BY t1.department_id;" +145,2159,3832,197,2,transformation 5 FilterPushdownThroughJoin: 1; transformation 7 FilterToJoinPredicate: 1; transformation 9 FilterLiftThroughJoin: 2; transformation 10 ProjectionPushdownThroughJoin: 3; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 2; implementation 6 ImplementHashJoin: 1,"SELECT t1.department_id AS c6376, t1.id AS c3596 +FROM books AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id +WHERE ((t0.id < 1 OR t1.id IS NOT NULL) AND (t0.id != 3 AND t1.department_id <= 35));" +146,1590,1590,127,5,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT departments.id +FROM departments +GROUP BY departments.id +ORDER BY departments.id DESC;" +147,9987,12750,392,110,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT regions.id +FROM departments FULL JOIN employees ON departments.id = employees.id CROSS JOIN regions;" +148,3730,6250,284,20,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT books.id, books.price AS c5525, regions.id +FROM books CROSS JOIN regions +WHERE books.price < 69;" +149,102500,102500,393,422,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.region_id AS c6318 +FROM customers AS t0 +WHERE t0.id > 78 +ORDER BY t0.region_id DESC;" +150,810500,1130500,1199,1040,transformation 0 JoinCommutativity: 1; transformation 6 InToOrChain: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id, t1.id AS c921 +FROM regions AS t0 CROSS JOIN customers AS t1 +WHERE t1.region_id IN (5, 9, 51) +GROUP BY t0.id, t1.id;" +151,1310829,4014888,2354,0,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT regions.id, users.age, orders.id +FROM regions RIGHT JOIN users ON regions.id = users.id FULL JOIN orders ON users.id = orders.id +WHERE ((orders.customer_id BETWEEN 283 AND 359 AND users.id IN (10, 17, 17, 39)) OR (users.id IN (14, 16, 64, 75) AND users.id >= 81)) +ORDER BY orders.id ASC, regions.id DESC, users.age ASC;" +152,12160,12160,302,55,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT departments.id, employees.department_id, employees.id +FROM departments CROSS JOIN employees +ORDER BY employees.id ASC;" +153,239300,239300,1299,1500,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1,"SELECT t1.id, t0.region_id, t0.id +FROM customers AS t0 CROSS JOIN books AS t1;" +154,3226,3226,193,10,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT regions.id, SUM(regions.id) AS c8443 +FROM regions +WHERE (regions.id >= 1 OR (regions.id >= 8 OR regions.id = 5)) +GROUP BY regions.id +ORDER BY regions.id ASC;" +155,1308138,1554232,1515,2,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT markets.region +FROM markets LEFT JOIN orders ON markets.id = orders.id JOIN employees ON markets.id = employees.id +GROUP BY markets.region;" +156,1307140,4001220,1831,10,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t1.customer_id, t1.id AS c2544, t0.id AS c7170 +FROM regions AS t0 LEFT JOIN orders AS t1 ON t0.id = t1.id;" +157,126891,675750,1165,4840,transformation 0 JoinCommutativity: 2; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t1.region_id, t2.id +FROM users AS t0 RIGHT JOIN customers AS t1 ON t0.id = t1.id CROSS JOIN regions AS t2 +WHERE t0.id IS NULL;" +158,4574,12322,266,0,implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 3,"SELECT t1.id, t0.id AS c3888, t2.id AS c5161 +FROM employees AS t0 FULL JOIN regions AS t1 ON t0.id = t1.id JOIN books AS t2 ON t0.id = t2.id +WHERE t0.id IS NULL;" +159,1307045,2251640,1502,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT orders.customer_id AS c3770, departments.id AS c4567, COUNT(*) AS c2771 +FROM departments LEFT JOIN orders ON departments.id = orders.id +WHERE ((orders.customer_id NOT BETWEEN 115 AND 380 OR departments.id IS NULL) OR orders.id = 174) +GROUP BY orders.customer_id, departments.id;" +160,610000,610000,1315,5000,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id, t0.customer_id AS c1926 +FROM orders AS t0;" +161,91868,1210558,389,33,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 6 ImplementHashJoin: 1,"SELECT t1.region AS c961, t2.id +FROM employees AS t0 CROSS JOIN markets AS t1 JOIN customers AS t2 ON t1.id = t2.id;" +162,1466,1466,137,3,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT employees.id, employees.department_id +FROM employees +WHERE ((employees.id NOT IN (5, 67, 68) AND employees.department_id = 28) OR (employees.id > 4 AND employees.department_id > 32));" +163,122820,1849979,435,2,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT markets.region, COUNT(*), COUNT(*) +FROM users CROSS JOIN markets LEFT JOIN customers ON markets.id = customers.id +WHERE users.age >= 1 +GROUP BY markets.region;" +164,433,433,203,3,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT markets.id, markets.note AS c529, markets.region +FROM markets +WHERE (markets.region = 'EUROPE' OR markets.id <= 91) +ORDER BY markets.region ASC, markets.id ASC, markets.note ASC;" +165,822,822,182,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT books.id, COUNT(books.price), SUM(books.id) +FROM books +GROUP BY books.id;" +166,2328,2328,243,9,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT regions.id +FROM regions +WHERE regions.id NOT IN (2, 95) +ORDER BY regions.id ASC;" +167,52196,52196,305,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id +FROM customers AS t0 +WHERE ((t0.region_id > 32 AND t0.id = 78) AND (t0.id >= 20 OR t0.id IS NULL));" +168,110368,157908,560,1,transformation 0 JoinCommutativity: 2; transformation 2 FilterSplit: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t1.region AS c6909, t1.note, SUM(t2.id) +FROM customers AS t0 LEFT JOIN markets AS t1 ON t0.id = t1.id CROSS JOIN departments AS t2 +WHERE ((t0.id = 89 OR t1.id BETWEEN 49 AND 71) AND t0.id IS NOT NULL) +GROUP BY t1.region, t1.note +ORDER BY t1.region ASC;" +169,1392,2113,295,2,transformation 5 FilterPushdownThroughJoin: 2; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT departments.id +FROM departments RIGHT JOIN markets ON departments.id = markets.id +WHERE departments.id != 1 +GROUP BY departments.id;" +170,1306995,2251590,3048,505,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT t1.id AS c5737, t0.customer_id +FROM orders AS t0 LEFT JOIN departments AS t1 ON t0.id = t1.id +GROUP BY t1.id, t0.customer_id +ORDER BY t0.customer_id DESC, t1.id ASC;" +171,24626,37006,524,110,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT employees.id AS c3715 +FROM regions CROSS JOIN employees FULL JOIN books ON regions.id = books.id;" +172,111497,436875,382,0,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT t0.id, t0.department_id AS c3676, t1.region_id +FROM employees AS t0 FULL JOIN customers AS t1 ON t0.id = t1.id +WHERE t0.department_id >= 57 +ORDER BY t0.id DESC, t1.region_id ASC;" +173,27106700,27106700,22994,85000,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT orders.customer_id +FROM users CROSS JOIN orders +ORDER BY orders.customer_id DESC;" +174,2475,2475,139,3,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT users.id, users.age +FROM users +WHERE users.id IN (3, 11, 12) +ORDER BY users.age DESC, users.id DESC;" +175,2273,3832,203,1,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT books.id, employees.id, employees.department_id +FROM employees RIGHT JOIN books ON employees.id = books.id +WHERE books.price = 77;" +176,563,563,268,2,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT books.price, SUM(books.price) AS c5357 +FROM books +WHERE books.price >= 66 +GROUP BY books.price;" +177,3692,3692,175,14,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT users.id, users.age AS c4847 +FROM users +WHERE (users.age < 16 OR users.age < 71) +ORDER BY users.id ASC, users.age DESC;" +178,3009,3009,347,17,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT users.age AS c7380, users.id +FROM users +ORDER BY users.id DESC;" +179,221288,221288,659,500,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT customers.id, customers.region_id +FROM customers +WHERE ((customers.id != 141 OR customers.region_id != 10) OR (customers.id NOT IN (197) OR customers.region_id IN (8))) +GROUP BY customers.id, customers.region_id;" +180,225000,225000,394,500,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT customers.id AS c95, customers.region_id +FROM customers +GROUP BY customers.id, customers.region_id +ORDER BY customers.region_id DESC;" +181,3199300,3199300,3855,500,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT customers.region_id AS c397, customers.id AS c5003, SUM(books.id) +FROM customers CROSS JOIN books CROSS JOIN departments +GROUP BY customers.region_id, customers.id;" +182,970500,175675500,1144,500,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t1.id AS c6022, t0.id +FROM orders AS t0 JOIN customers AS t1 ON t0.id = t1.id +GROUP BY t1.id, t0.id;" +183,65900,65900,256,1,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT customers.id, COUNT(customers.id), COUNT(customers.id) +FROM customers +WHERE customers.id = 24 +GROUP BY customers.id;" +184,171800,297800,1278,500,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT books.price AS c5308, customers.region_id AS c1694 +FROM books CROSS JOIN customers +WHERE books.price <= 55;" +185,610,610,132,5,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id AS c9527 +FROM departments AS t0;" +186,5081,14820,190,18,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT users.id +FROM regions FULL JOIN users ON regions.id = users.id;" +187,5791,15530,209,8,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT users.id, regions.id, users.age AS c4755 +FROM users LEFT JOIN regions ON users.id = regions.id +WHERE ((regions.id != 31 AND users.age <= 64) OR users.id >= 66) +ORDER BY users.age DESC;" +188,3600,7573,313,0,transformation 0 JoinCommutativity: 2; transformation 2 FilterSplit: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t2.price AS c5573, COUNT(*) +FROM employees AS t0 RIGHT JOIN departments AS t1 ON t0.id = t1.id CROSS JOIN books AS t2 +WHERE (t2.price > 55 AND t0.id BETWEEN 66 AND 69) +GROUP BY t2.price;" +189,57522,155422,250,0,transformation 5 FilterPushdownThroughJoin: 3; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1,"SELECT markets.id AS c7660 +FROM markets JOIN customers ON markets.id = customers.id +WHERE (markets.note IS NULL AND (customers.id = 198 AND markets.region IN ('AMERICA', 'EUROPE')));" +190,1590,1590,222,5,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT departments.id +FROM departments +GROUP BY departments.id +ORDER BY departments.id DESC;" +191,14474,21594,411,55,transformation 0 JoinCommutativity: 2; transformation 1 JoinAssociativity: 3; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT departments.id, employees.id +FROM departments JOIN regions ON departments.id = regions.id CROSS JOIN employees +WHERE employees.id IS NOT NULL +GROUP BY departments.id, employees.id +ORDER BY departments.id DESC, employees.id ASC;" +192,1252,1658,322,3,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT books.id, markets.region +FROM markets CROSS JOIN books +WHERE markets.region = 'EUROPE';" +193,114435,414820,343,10,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT customers.region_id, regions.id +FROM users JOIN regions ON users.id = regions.id LEFT JOIN customers ON regions.id = customers.id;" +194,2490882,5620982,22803,25000,transformation 0 JoinCommutativity: 1; transformation 6 InToOrChain: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 5 ImplementCrossJoin: 1,"SELECT t2.id +FROM customers AS t0 CROSS JOIN markets AS t1 CROSS JOIN users AS t2 +WHERE ((t1.id IN (3) OR t2.age != 1) OR (t1.id != 2 OR t2.id > 61));" +195,1307400,1553466,1893,10,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 3,"SELECT t0.id +FROM regions AS t0 FULL JOIN books AS t1 ON t0.id = t1.id LEFT JOIN orders AS t2 ON t1.id = t2.id;" +196,617630,618900,807,30,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT customers.region_id, books.id, SUM(regions.id), SUM(customers.region_id) +FROM books JOIN regions ON books.id = regions.id CROSS JOIN customers +GROUP BY customers.region_id, books.id;" +197,70476,158466,310,3,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.price AS c8028, t2.id +FROM books AS t0 JOIN customers AS t1 ON t0.id = t1.id JOIN regions AS t2 ON t1.id = t2.id;" +198,2074,2074,158,17,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.age AS c2991 +FROM users AS t0;" +199,16360,30140,267,30,transformation 0 JoinCommutativity: 1; transformation 6 InToOrChain: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT t1.id, t0.id +FROM regions AS t0 CROSS JOIN users AS t1 +WHERE (t1.age BETWEEN 33 AND 33 OR t1.id IN (12, 49, 95));" +200,2428,2428,159,6,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT employees.department_id +FROM employees +WHERE ((employees.id > 66 OR employees.id IN (33)) OR employees.department_id IN (6, 12, 12, 50)) +ORDER BY employees.department_id DESC;" +201,610000,610000,1481,5000,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT orders.customer_id, orders.id +FROM orders;" +202,4879,16712,225,6,transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT employees.id AS c3304, users.id AS c2088 +FROM users JOIN employees ON users.id = employees.id +WHERE ((users.age IN (42, 64) OR users.id < 16) AND users.id != 99) +GROUP BY employees.id, users.id;" +203,822,822,256,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT books.id, books.price +FROM books +GROUP BY books.id, books.price;" +204,69372,155822,327,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id AS c8592, t1.region +FROM customers AS t0 JOIN markets AS t1 ON t0.id = t1.id +GROUP BY t0.id, t1.region;" +205,2016100,9975800,3442,1,transformation 0 JoinCommutativity: 1; transformation 2 FilterSplit: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t1.id AS c8725, SUM(t1.id), COUNT(*) +FROM users AS t0 CROSS JOIN orders AS t1 +WHERE (t0.age IN (33, 64) AND t1.id = 156) +GROUP BY t1.id;" +206,61000,61000,471,500,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id AS c4257 +FROM customers AS t0;" +207,1236600,1931301600,1356,0,transformation 0 JoinCommutativity: 1; transformation 1 JoinAssociativity: 2; transformation 5 FilterPushdownThroughJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 6 ImplementHashJoin: 1,"SELECT t1.customer_id, t0.department_id +FROM employees AS t0 CROSS JOIN orders AS t1 JOIN customers AS t2 ON t1.id = t2.id +WHERE t0.department_id IS NULL;" +208,2388,3776,246,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT books.price AS c3720, employees.department_id, books.id +FROM employees RIGHT JOIN books ON employees.id = books.id;" +209,69533,156493,311,0,implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t2.region, t1.id, COUNT(t2.id) +FROM books AS t0 JOIN customers AS t1 ON t0.id = t1.id FULL JOIN markets AS t2 ON t0.id = t2.id +WHERE t0.id < 1 +GROUP BY t2.region, t1.id;" +210,2390300,2390300,3375,15000,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1,"SELECT orders.id, books.id AS c7933, orders.customer_id +FROM books CROSS JOIN orders;" +211,12462200,2984422200,8506,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 6 ImplementHashJoin: 1,"SELECT t2.region_id, t2.id, t0.customer_id +FROM orders AS t0 CROSS JOIN users AS t1 LEFT JOIN customers AS t2 ON t1.id = t2.id +WHERE t2.region_id >= 23;" +212,1306062,1550822,1381,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT orders.id +FROM books LEFT JOIN orders ON books.id = orders.id +GROUP BY orders.id;" +213,1620,1620,151,2,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id +FROM regions AS t0 +WHERE t0.id IN (4, 4, 9, 88) +ORDER BY t0.id ASC;" +214,28496600,51596600,97726,247665,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 5 ImplementCrossJoin: 1,"SELECT orders.id, orders.customer_id, employees.id +FROM orders CROSS JOIN employees CROSS JOIN departments +WHERE orders.customer_id >= 52;" +215,805000,805000,1080,29,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT t0.customer_id +FROM orders AS t0 +WHERE t0.id < 30;" +216,744,744,164,3,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT departments.id +FROM departments +WHERE departments.id IN (1, 3, 4, 60);" +217,72665,437525,314,3,transformation 11 OuterJoinToInner: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id, t1.region_id +FROM employees AS t0 LEFT JOIN customers AS t1 ON t0.id = t1.id +WHERE t1.region_id >= 8 +GROUP BY t0.id, t1.region_id;" +218,1331,1916,204,5,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT books.price AS c5412, books.id, departments.id AS c9618 +FROM departments LEFT JOIN books ON departments.id = books.id;" +219,5705600,9485600,43305,45000,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 5 ImplementCrossJoin: 1,"SELECT t1.note, t0.customer_id AS c4395 +FROM orders AS t0 CROSS JOIN markets AS t1 CROSS JOIN books AS t2 +WHERE t1.region IS NOT NULL;" +220,17063620,1931305120,30660,45001,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT customers.region_id, orders.id, employees.id +FROM employees CROSS JOIN orders RIGHT JOIN customers ON employees.id = customers.id +WHERE (customers.id IN (181) OR employees.id BETWEEN 3 AND 92) +ORDER BY customers.region_id ASC;" +221,1133,1133,141,0,transformation 2 FilterSplit: 1; transformation 3 FilterMerge: 1; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id AS c9777 +FROM regions AS t0 +WHERE ((t0.id <= 5 OR t0.id IN (4, 12)) AND (t0.id = 9 AND t0.id NOT IN (2, 4, 5, 89))) +ORDER BY t0.id ASC;" +222,3764,3764,183,11,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id, t0.department_id AS c2878, COUNT(t0.id), COUNT(*) AS c2633 +FROM employees AS t0 +WHERE ((t0.department_id IS NOT NULL OR t0.department_id IS NOT NULL) OR (t0.id > 26 OR t0.id = 52)) +GROUP BY t0.id, t0.department_id;" +223,7431100,7431100,16252,55000,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1,"SELECT t0.id +FROM orders AS t0 CROSS JOIN employees AS t1;" +224,1342,1342,139,11,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT employees.department_id +FROM employees;" +225,790056,4786342,1089,11,transformation 0 JoinCommutativity: 1; transformation 10 ProjectionPushdownThroughJoin: 3; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 2; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT orders.customer_id, customers.id AS c4775, employees.department_id AS c6986 +FROM employees LEFT JOIN customers ON employees.id = customers.id JOIN orders ON employees.id = orders.id;" +226,2374,3644,253,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT books.id, regions.id, books.price AS c1520 +FROM books LEFT JOIN regions ON books.id = regions.id +WHERE ((regions.id < 10 OR books.price = 77) OR books.price > 60);" +227,1305662,1550422,1986,9,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT orders.id, books.price, orders.customer_id AS c7568 +FROM orders FULL JOIN books ON orders.id = books.id +WHERE orders.customer_id = 63;" +228,100831,285106,925,1000,transformation 0 JoinCommutativity: 1; transformation 2 FilterSplit: 1; transformation 5 FilterPushdownThroughJoin: 4; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 6 ImplementHashJoin: 1,"SELECT t1.id AS c1443, t2.region_id AS c2237 +FROM employees AS t0 LEFT JOIN markets AS t1 ON t0.id = t1.id CROSS JOIN customers AS t2 +WHERE ((t1.id < 3 OR t1.region = 'AMERICA') AND (t2.region_id IS NULL OR t1.note != 'North, South'));" +229,111140,228445,393,0,transformation 0 JoinCommutativity: 2; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT departments.id +FROM departments LEFT JOIN customers ON departments.id = customers.id CROSS JOIN markets +WHERE markets.region IS NULL +ORDER BY departments.id ASC;" +230,1309060,2258260,2139,5000,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT departments.id, users.age AS c5139, orders.id +FROM departments JOIN users ON departments.id = users.id FULL JOIN orders ON users.id = orders.id;" +231,610,610,147,5,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT departments.id +FROM departments;" +232,116575,651154,322,16,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT customers.id, COUNT(*) AS c3789 +FROM customers RIGHT JOIN users ON customers.id = users.id +GROUP BY customers.id +ORDER BY customers.id DESC;" +233,109845,317650,643,1353,transformation 0 JoinCommutativity: 1; transformation 2 FilterSplit: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT departments.id +FROM departments JOIN markets ON departments.id = markets.id CROSS JOIN customers +WHERE (customers.region_id NOT IN (10, 10) AND (customers.id <= 80 OR departments.id >= 1));" +234,2226,3466,165,10,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.note +FROM markets AS t0 FULL JOIN regions AS t1 ON t0.id = t1.id;" +235,68524,155544,239,1,transformation 7 FilterToJoinPredicate: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1,"SELECT t1.region_id AS c5511 +FROM books AS t0 JOIN customers AS t1 ON t0.id = t1.id +WHERE (t0.price NOT BETWEEN 66 AND 94 OR (t0.id > 3 AND t1.region_id = 10));" +236,1311747,4366616,1376,17,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT t1.department_id AS c2338, t0.customer_id +FROM orders AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id RIGHT JOIN users AS t2 ON t1.id = t2.id +ORDER BY t1.department_id ASC;" +237,134541,669120,1866,5010,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT regions.id +FROM users FULL JOIN customers ON users.id = customers.id CROSS JOIN regions;" +238,68974,436222,236,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 1; transformation 6 InToOrChain: 1; transformation 9 FilterLiftThroughJoin: 2; transformation 10 ProjectionPushdownThroughJoin: 3; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 2; implementation 6 ImplementHashJoin: 1,"SELECT customers.region_id +FROM customers LEFT JOIN employees ON customers.id = employees.id +WHERE (employees.id IN (38) AND customers.region_id != 5);" +239,2690,2690,170,6,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT employees.id +FROM employees +WHERE employees.id > 5 +GROUP BY employees.id +ORDER BY employees.id DESC;" +240,95524,95524,484,3,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.price AS c6143, SUM(t2.id) +FROM books AS t0 CROSS JOIN users AS t1 CROSS JOIN departments AS t2 +GROUP BY t0.price;" +241,3759405,3875500,4325,25000,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT customers.id, customers.region_id, departments.id +FROM customers RIGHT JOIN departments ON customers.id = departments.id CROSS JOIN orders;" +242,3038,5320,370,0,transformation 0 JoinCommutativity: 1; transformation 2 FilterSplit: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT books.id +FROM books CROSS JOIN employees +WHERE (employees.id != 33 AND (employees.id < 1 AND employees.department_id < 12));" +243,678835,4353843,959,0,transformation 0 JoinCommutativity: 2; transformation 1 JoinAssociativity: 2; transformation 5 FilterPushdownThroughJoin: 1; transformation 9 FilterLiftThroughJoin: 2; transformation 11 OuterJoinToInner: 2; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT employees.department_id, books.price AS c4475, books.id AS c4655 +FROM orders JOIN employees ON orders.id = employees.id RIGHT JOIN books ON employees.id = books.id +WHERE ((orders.id >= 13 AND orders.customer_id != 233) OR (orders.id > 39 AND orders.customer_id <= 359)) +ORDER BY books.id DESC;" +244,4057,12322,431,0,transformation 0 JoinCommutativity: 2; transformation 1 JoinAssociativity: 4; transformation 5 FilterPushdownThroughJoin: 2; transformation 9 FilterLiftThroughJoin: 2; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 6 ImplementHashJoin: 1,"SELECT regions.id +FROM regions RIGHT JOIN employees ON regions.id = employees.id LEFT JOIN markets ON employees.id = markets.id +WHERE (regions.id NOT BETWEEN 2 AND 8 AND markets.note = 'Fast lane');" +245,433,433,280,1,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT markets.note, markets.region, markets.id +FROM markets +WHERE ((markets.region != 'AMERICA' AND markets.region = 'AMERICA') OR markets.id < 2) +ORDER BY markets.note DESC, markets.id ASC, markets.region DESC;" +246,1244,1244,150,9,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT regions.id AS c713 +FROM regions +WHERE regions.id BETWEEN 2 AND 45;" +247,670078,1550848,1189,3,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 6 InToOrChain: 1; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id AS c7208 +FROM orders AS t0 RIGHT JOIN books AS t1 ON t0.id = t1.id +WHERE t0.customer_id NOT IN (244, 365) +GROUP BY t0.id +ORDER BY t0.id ASC;" +248,1311645,6455252,3403,5000,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT t1.id, COUNT(*), SUM(t0.id) AS c7387 +FROM users AS t0 RIGHT JOIN orders AS t1 ON t0.id = t1.id +WHERE ((t1.customer_id = 494 AND t1.customer_id != 145) OR (t0.age > 53 OR t1.customer_id IS NOT NULL)) +GROUP BY t1.id;" +249,3488,5692,177,3,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT markets.region, users.age +FROM markets LEFT JOIN users ON markets.id = users.id +WHERE users.id < 17;" +250,110640,401220,258,10,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT customers.id, customers.region_id +FROM regions LEFT JOIN customers ON regions.id = customers.id;" +251,1307530,4001775,1809,42,implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT orders.customer_id, regions.id, orders.id AS c3346 +FROM regions FULL JOIN orders ON regions.id = orders.id +WHERE orders.id <= 42 +ORDER BY regions.id ASC;" +252,4641438,7310933,6345,39,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT orders.id AS c8852, orders.customer_id, books.price AS c8813 +FROM books CROSS JOIN orders FULL JOIN departments ON orders.id = departments.id +WHERE ((orders.customer_id = 163 OR orders.id IN (69, 96, 152)) AND (orders.id IS NOT NULL AND books.id IS NOT NULL)) +ORDER BY orders.id DESC;" +253,81634,3916834,362,40,transformation 1 JoinAssociativity: 2; transformation 5 FilterPushdownThroughJoin: 1; transformation 9 FilterLiftThroughJoin: 2; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 6 ImplementHashJoin: 1,"SELECT customers.id +FROM regions CROSS JOIN employees JOIN customers ON employees.id = customers.id +WHERE (employees.id IS NOT NULL AND customers.id >= 65);" +254,2418,3776,166,11,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT markets.id AS c3977, employees.department_id +FROM markets FULL JOIN employees ON markets.id = employees.id;" +255,1975000,1975000,2178,5000,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT orders.id, orders.customer_id AS c7286 +FROM orders +GROUP BY orders.id, orders.customer_id;" +256,5474,7496,426,9,transformation 0 JoinCommutativity: 1; transformation 6 InToOrChain: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id, t1.id AS c9218 +FROM markets AS t0 CROSS JOIN employees AS t1 +WHERE t1.department_id IN (5, 12, 33) +GROUP BY t0.id, t1.id;" +257,2652,3922,247,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t1.price, t0.id AS c7828 +FROM regions AS t0 JOIN books AS t1 ON t0.id = t1.id +GROUP BY t1.price, t0.id;" +258,75739,651158,303,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT customers.region_id +FROM customers JOIN users ON customers.id = users.id RIGHT JOIN markets ON customers.id = markets.id +GROUP BY customers.region_id +ORDER BY customers.region_id DESC;" +259,110500,110500,248,500,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT customers.id +FROM customers +ORDER BY customers.id ASC;" +260,72814,72814,233,118,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT customers.region_id, customers.id +FROM customers +WHERE ((customers.region_id IS NULL OR customers.id >= 198) AND customers.region_id <= 4);" +261,109628,155888,306,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT customers.region_id AS c4278, books.id +FROM books LEFT JOIN customers ON books.id = customers.id +GROUP BY customers.region_id, books.id +ORDER BY books.id ASC;" +262,15960,15960,259,110,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1,"SELECT t1.department_id AS c5186, t0.id, t1.id +FROM regions AS t0 CROSS JOIN employees AS t1;" +263,3077,6483,228,2,transformation 0 JoinCommutativity: 2; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT markets.id, departments.id, regions.id +FROM regions LEFT JOIN departments ON regions.id = departments.id JOIN markets ON regions.id = markets.id +WHERE markets.note != 'Old World' +ORDER BY markets.id ASC, regions.id DESC;" +264,112407,160636,399,500,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT users.id AS c6920, markets.region AS c5238, users.age AS c1104 +FROM markets RIGHT JOIN users ON markets.id = users.id RIGHT JOIN customers ON markets.id = customers.id;" +265,5081,14820,202,17,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t1.id, t0.id AS c2693 +FROM users AS t0 LEFT JOIN regions AS t1 ON t0.id = t1.id;" +266,59400,59400,235,1,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT customers.id AS c604, customers.region_id +FROM customers +WHERE customers.id = 135 +ORDER BY customers.id DESC, customers.region_id DESC;" +267,422,422,127,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT books.id +FROM books +WHERE books.price IS NULL;" +268,76971400,76971400,140145,11,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT employees.department_id, SUM(orders.customer_id) +FROM orders CROSS JOIN books CROSS JOIN employees +GROUP BY employees.department_id;" +269,366,366,310,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT books.price AS c8205 +FROM books;" +270,610000,610000,1415,5000,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT orders.id AS c7922 +FROM orders;" +271,534702,2250622,1096,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 3; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1,"SELECT orders.id AS c8902, departments.id AS c4368, orders.customer_id +FROM orders JOIN departments ON orders.id = departments.id +WHERE ((orders.customer_id = 483 AND departments.id > 4) AND orders.id != 24);" +272,850563,1550563,1892,2,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT orders.customer_id, orders.id, COUNT(*), COUNT(*) +FROM orders RIGHT JOIN books ON orders.id = books.id +WHERE books.id != 1 +GROUP BY orders.customer_id, orders.id;" +273,3256,3256,150,11,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id, SUM(t0.department_id), COUNT(t0.department_id) +FROM employees AS t0 +GROUP BY t0.id;" +274,2066,2066,132,2,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id AS c7269, t0.age +FROM users AS t0 +WHERE t0.age IN (33, 33);" +275,545750,545750,1554,0,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT orders.id AS c6877 +FROM orders +WHERE ((orders.customer_id <= 56 OR orders.customer_id != 343) AND (orders.customer_id IN (52, 86) AND orders.customer_id >= 224));" +276,3536700,3536700,2615,11,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT users.age AS c7960, SUM(users.id) +FROM customers CROSS JOIN users +GROUP BY users.age +ORDER BY users.age ASC;" +277,774,774,191,1,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.id +FROM departments AS t0 +WHERE ((t0.id IN (2, 10, 59) OR t0.id IS NULL) AND (t0.id != 5 AND t0.id IS NOT NULL)) +GROUP BY t0.id +ORDER BY t0.id DESC;" +278,4334,8638,211,5,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t1.id AS c8751 +FROM users AS t0 RIGHT JOIN departments AS t1 ON t0.id = t1.id +WHERE (t0.age IS NULL OR (t1.id != 5 OR t1.id < 14));" +279,676493,4001263,1252,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 9 FilterLiftThroughJoin: 2; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT regions.id, orders.id, COUNT(*), SUM(orders.customer_id) +FROM orders RIGHT JOIN regions ON orders.id = regions.id +WHERE ((regions.id IN (8, 9, 22) AND regions.id <= 6) AND orders.id IN (39, 49, 104, 106)) +GROUP BY regions.id, orders.id;" +280,679538,6451822,1025,0,transformation 5 FilterPushdownThroughJoin: 1; transformation 7 FilterToJoinPredicate: 1; transformation 9 FilterLiftThroughJoin: 2; transformation 10 ProjectionPushdownThroughJoin: 3; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 2; implementation 6 ImplementHashJoin: 1,"SELECT orders.customer_id +FROM users FULL JOIN orders ON users.id = orders.id +WHERE ((users.age > 33 AND orders.customer_id < 84) AND (users.id = 15 OR orders.id IN (68, 128, 128, 198)));" +281,433,433,167,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id, t0.note, t0.region +FROM markets AS t0 +WHERE ((t0.note = 'unknown' AND t0.id != 1) AND t0.id > 67) +ORDER BY t0.id ASC, t0.note DESC;" +282,2588310,13612010,1854,9,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 6 ImplementHashJoin: 1,"SELECT employees.id, orders.customer_id AS c5471, books.price +FROM books CROSS JOIN orders LEFT JOIN employees ON orders.id = employees.id +WHERE employees.id > 66;" +283,68793,521733,358,0,transformation 0 JoinCommutativity: 2; transformation 1 JoinAssociativity: 2; transformation 3 FilterMerge: 1; transformation 5 FilterPushdownThroughJoin: 3; transformation 6 InToOrChain: 2; transformation 9 FilterLiftThroughJoin: 1; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 6 ImplementHashJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT books.id, markets.id, books.price AS c2723 +FROM books CROSS JOIN customers FULL JOIN markets ON customers.id = markets.id +WHERE ((markets.id > 55 AND customers.region_id NOT IN (8, 9, 10)) AND markets.region IN ('AMERICA', 'EUROPE')) +ORDER BY books.id DESC, markets.id ASC;" +284,822,822,143,2,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.region AS c1153, SUM(t0.id) AS c3569 +FROM markets AS t0 +GROUP BY t0.region +ORDER BY t0.region ASC;" +285,5364,10836,332,0,transformation 0 JoinCommutativity: 2; transformation 2 FilterSplit: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT employees.id AS c5654, users.id +FROM markets FULL JOIN employees ON markets.id = employees.id CROSS JOIN users +WHERE ((markets.note = 'Fast lane' AND employees.department_id NOT BETWEEN 11 AND 37) AND (users.id != 4 AND users.age <= 443));" +286,109668,225774,393,3,implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT customers.id, COUNT(customers.id) AS c434, COUNT(*) +FROM customers FULL JOIN departments ON customers.id = departments.id +WHERE departments.id BETWEEN 2 AND 4 +GROUP BY customers.id +ORDER BY customers.id DESC;" +287,3181,5072,278,7,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 3,"SELECT t2.id, t1.id, t0.id +FROM books AS t0 JOIN departments AS t1 ON t0.id = t1.id RIGHT JOIN regions AS t2 ON t1.id = t2.id +WHERE t0.id IS NULL;" +288,5252,15332,178,1,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT regions.id AS c1673 +FROM users JOIN regions ON users.id = regions.id +WHERE (users.age IN (33, 47, 98) OR users.id > 54);" +289,1883,2493,414,3,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT departments.id, books.price AS c551 +FROM departments CROSS JOIN books +WHERE departments.id = 5 +ORDER BY departments.id DESC;" +290,679184,4353832,1026,2,transformation 0 JoinCommutativity: 2; transformation 1 JoinAssociativity: 2; transformation 5 FilterPushdownThroughJoin: 1; transformation 9 FilterLiftThroughJoin: 2; transformation 11 OuterJoinToInner: 2; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t2.region, t1.department_id, t0.customer_id AS c8493 +FROM orders AS t0 JOIN employees AS t1 ON t0.id = t1.id RIGHT JOIN markets AS t2 ON t1.id = t2.id +WHERE t0.customer_id < 239;" +291,12321160,68844660,4406,10,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT orders.id, COUNT(orders.id), SUM(regions.id) +FROM users CROSS JOIN orders JOIN regions ON orders.id = regions.id +GROUP BY orders.id;" +292,888,888,175,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.region, t0.id, SUM(t0.id), COUNT(*) +FROM markets AS t0 +GROUP BY t0.region, t0.id +ORDER BY t0.region ASC, t0.id DESC;" +293,2292,5692,281,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 3; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT t0.age, t1.id +FROM users AS t0 FULL JOIN books AS t1 ON t0.id = t1.id +WHERE (t0.age = 18 AND (t0.age != 123 AND t1.id <= 89));" +294,848,848,165,3,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT books.price AS c6589, books.id, COUNT(*) +FROM books +WHERE ((books.price > 77 OR books.id < 3) OR (books.price IN (49, 55, 55, 77) OR books.price IN (26, 66, 66, 68))) +GROUP BY books.price, books.id;" +295,4370,9075,190,5,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT users.id, SUM(users.age), COUNT(users.id) +FROM users JOIN departments ON users.id = departments.id +GROUP BY users.id;" +296,432,432,144,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.region, t0.id AS c2775, t0.note +FROM markets AS t0 +ORDER BY t0.id DESC;" +297,685683,6467110,1084,22,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t2.department_id +FROM orders AS t0 JOIN users AS t1 ON t0.id = t1.id FULL JOIN employees AS t2 ON t0.id = t2.id +WHERE ((t2.id NOT BETWEEN 4 AND 6 OR t1.age != 21) OR t0.id <= 61);" +298,988,988,153,4,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT departments.id +FROM departments +WHERE ((departments.id NOT IN (4, 4, 5, 7) OR departments.id NOT IN (2, 5)) OR (departments.id NOT BETWEEN 3 AND 27 OR departments.id IS NULL));" +299,2455,3843,262,0,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT books.price, employees.id AS c7128 +FROM employees RIGHT JOIN books ON employees.id = books.id +WHERE ((books.id > 68 OR books.id >= 52) AND (employees.department_id < 6 OR employees.department_id != 1)) +ORDER BY books.price DESC;" +300,1126398,2251983,2203,3,transformation 0 JoinCommutativity: 2; transformation 5 FilterPushdownThroughJoin: 4; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 5,"SELECT orders.id AS c9121 +FROM orders LEFT JOIN departments ON orders.id = departments.id RIGHT JOIN books ON departments.id = books.id +WHERE orders.customer_id <= 497 +ORDER BY orders.id ASC;" +301,3967,6092,289,3,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT users.id, COUNT(markets.id), SUM(markets.id) +FROM users JOIN markets ON users.id = markets.id +GROUP BY users.id;" +302,74482,410460,402,10,transformation 0 JoinCommutativity: 1; transformation 10 ProjectionPushdownThroughJoin: 3; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 2; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT regions.id, customers.region_id, employees.id +FROM regions FULL JOIN employees ON regions.id = employees.id JOIN customers ON regions.id = customers.id +ORDER BY customers.region_id ASC, employees.id ASC, regions.id ASC;" +303,1832,1832,166,8,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT employees.id, employees.department_id +FROM employees +WHERE ((employees.id <= 33 OR employees.id = 3) OR (employees.department_id NOT IN (5) AND employees.id = 67));" +304,1342,1342,138,11,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT employees.department_id AS c3770 +FROM employees;" +305,1822,1822,173,2,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT regions.id AS c2143, COUNT(*), COUNT(*) +FROM regions +WHERE regions.id IN (3, 10, 89) +GROUP BY regions.id;" +306,3097,6272,186,5,transformation 0 JoinCommutativity: 1; transformation 7 FilterToJoinPredicate: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT employees.id, employees.department_id, SUM(employees.id), SUM(employees.department_id) AS c8963 +FROM employees JOIN departments ON employees.id = departments.id +WHERE (departments.id <= 3 OR (employees.id = 68 OR employees.id <= 6)) +GROUP BY employees.id, employees.department_id;" +307,1307657,1553663,2492,4,implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT t0.note AS c4138 +FROM markets AS t0 JOIN regions AS t1 ON t0.id = t1.id RIGHT JOIN orders AS t2 ON t0.id = t2.id +WHERE ((t2.customer_id = 298 OR t0.region = 'AMERICA') OR t0.region != 'AMERICA') +GROUP BY t0.note;" +308,1092,1092,179,1,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT departments.id, COUNT(*) AS c9125 +FROM departments +WHERE (departments.id > 56 OR departments.id = 4) +GROUP BY departments.id +ORDER BY departments.id DESC;" +309,563,563,166,1,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id AS c7541, COUNT(t0.id) +FROM markets AS t0 +WHERE (t0.note != 'Fast lane' AND (t0.id = 3 OR t0.note = 'Old World')) +GROUP BY t0.id;" +310,797661,7099420,1142,7,transformation 0 JoinCommutativity: 1; transformation 11 OuterJoinToInner: 2; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT t1.id, t2.id, SUM(t2.id) +FROM customers AS t0 RIGHT JOIN users AS t1 ON t0.id = t1.id LEFT JOIN orders AS t2 ON t0.id = t2.id +WHERE (t2.customer_id < 243 OR (t2.id >= 38 AND t2.customer_id = 302)) +GROUP BY t1.id, t2.id +ORDER BY t2.id ASC;" +311,675695,1550433,1021,2,transformation 5 FilterPushdownThroughJoin: 1; transformation 10 ProjectionPushdownThroughJoin: 3; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 2; implementation 6 ImplementHashJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.region, t1.customer_id AS c7183 +FROM markets AS t0 RIGHT JOIN orders AS t1 ON t0.id = t1.id +WHERE ((t0.id <= 3 OR t0.note = 'Fast lane') AND (t0.note != 'North, South' OR t0.note = 'Fast lane')) +ORDER BY t0.region ASC;" +312,1308302,2255560,1399,5,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT t1.id, t0.id +FROM departments AS t0 LEFT JOIN orders AS t1 ON t0.id = t1.id JOIN employees AS t2 ON t1.id = t2.id;" +313,678803,2255816,988,2,implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT employees.id, orders.id AS c4699, orders.customer_id +FROM departments JOIN orders ON departments.id = orders.id LEFT JOIN employees ON departments.id = employees.id +WHERE employees.department_id NOT BETWEEN 5 AND 33;" +314,2840,2840,185,13,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.age, t0.id +FROM users AS t0 +WHERE (t0.age > 5 AND t0.id != 2) +GROUP BY t0.age, t0.id;" +315,2638,5548,220,5,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id +FROM regions AS t0 JOIN departments AS t1 ON t0.id = t1.id +WHERE t1.id IS NOT NULL +GROUP BY t0.id;" +316,822,822,162,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT books.id +FROM books +GROUP BY books.id;" +317,1305606,1550366,1945,5000,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT orders.id AS c7996, books.price +FROM books FULL JOIN orders ON books.id = orders.id;" +318,4668,7332,236,4,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT books.price, books.id +FROM books FULL JOIN employees ON books.id = employees.id RIGHT JOIN regions ON employees.id = regions.id +GROUP BY books.price, books.id;" +319,563,563,148,3,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT books.id AS c9150, books.price, COUNT(books.id) +FROM books +WHERE ((books.price IN (76, 77) AND books.id BETWEEN 1 AND 96) OR books.price IN (55, 66, 77)) +GROUP BY books.id, books.price;" +320,70514,436342,469,11,transformation 0 JoinCommutativity: 1; transformation 10 ProjectionPushdownThroughJoin: 3; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 2; implementation 6 ImplementHashJoin: 1,"SELECT customers.region_id, employees.id +FROM customers JOIN employees ON customers.id = employees.id;" +321,495125,702625,868,10,transformation 0 JoinCommutativity: 1; transformation 2 FilterSplit: 1; transformation 7 FilterToJoinPredicate: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id AS c6071, COUNT(*), COUNT(*) AS c7241 +FROM regions AS t0 CROSS JOIN customers AS t1 +WHERE ((t1.id >= 59 OR t0.id IS NULL) AND t1.id = 126) +GROUP BY t0.id;" +322,111676,403466,462,500,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT t1.region_id, t1.id, t2.price +FROM regions AS t0 RIGHT JOIN customers AS t1 ON t0.id = t1.id LEFT JOIN books AS t2 ON t0.id = t2.id;" +323,112483,650703,353,3,transformation 0 JoinCommutativity: 2; transformation 5 FilterPushdownThroughJoin: 4; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT t0.region_id +FROM customers AS t0 RIGHT JOIN users AS t1 ON t0.id = t1.id RIGHT JOIN markets AS t2 ON t1.id = t2.id +WHERE t1.id < 14 +ORDER BY t0.region_id ASC;" +324,678284,2255244,928,3,transformation 0 JoinCommutativity: 2; transformation 1 JoinAssociativity: 3; transformation 5 FilterPushdownThroughJoin: 2; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT orders.id AS c4187, regions.id +FROM departments JOIN orders ON departments.id = orders.id LEFT JOIN regions ON departments.id = regions.id +WHERE regions.id < 4;" +325,1881,5932,317,4,transformation 0 JoinCommutativity: 2; transformation 2 FilterSplit: 4; transformation 5 FilterPushdownThroughJoin: 4; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT markets.note AS c7216, books.price +FROM departments CROSS JOIN markets LEFT JOIN books ON markets.id = books.id +WHERE ((departments.id != 2 AND markets.note = 'Old World') AND (departments.id BETWEEN 1 AND 5 OR departments.id >= 41));" +326,2074,2074,160,17,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.age +FROM users AS t0;" +327,6154,6154,269,11,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.age +FROM users AS t0 +GROUP BY t0.age +ORDER BY t0.age ASC;" +328,14356396,44723198,27321,54310,transformation 0 JoinCommutativity: 2; transformation 5 FilterPushdownThroughJoin: 2; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT employees.department_id +FROM orders CROSS JOIN employees LEFT JOIN regions ON employees.id = regions.id +WHERE ((employees.id <= 25 OR orders.id NOT BETWEEN 47 AND 184) OR orders.id IS NULL);" +329,3541,5858,367,3,transformation 5 FilterPushdownThroughJoin: 1; transformation 10 ProjectionPushdownThroughJoin: 4; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 3; implementation 6 ImplementHashJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id +FROM markets AS t0 JOIN users AS t1 ON t0.id = t1.id +WHERE (t0.id != 2 OR t0.id != 1) +ORDER BY t0.id ASC;" +330,104084,158984,1037,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 5; transformation 7 FilterToJoinPredicate: 1; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT books.price, employees.id, COUNT(*) +FROM customers LEFT JOIN books ON customers.id = books.id FULL JOIN employees ON customers.id = employees.id +WHERE ((customers.id > 96 AND employees.department_id != 32) AND (employees.id > 5 OR books.id IS NOT NULL)) +GROUP BY books.price, employees.id +ORDER BY books.price DESC, employees.id DESC;" +331,15545500,1755762000,24523,50490,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t2.region_id, t2.id +FROM orders AS t0 CROSS JOIN regions AS t1 FULL JOIN customers AS t2 ON t1.id = t2.id;" +332,1610,1610,139,10,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT regions.id AS c7728 +FROM regions +WHERE regions.id IS NOT NULL;" +333,1415505,2476425,3742,6,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT departments.id, COUNT(*) +FROM orders FULL JOIN departments ON orders.id = departments.id LEFT JOIN customers ON departments.id = customers.id +GROUP BY departments.id;" +334,1775,1775,152,10,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT regions.id AS c1480 +FROM regions +WHERE regions.id IS NOT NULL +ORDER BY regions.id DESC;" +335,433,433,131,0,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT books.price, books.id +FROM books +WHERE (books.price < 44 AND (books.price IN (55, 55, 66, 66) AND books.price != 77)) +ORDER BY books.price DESC, books.id DESC;" +336,1222,1222,133,1,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT t0.department_id +FROM employees AS t0 +WHERE ((t0.department_id = 12 AND t0.department_id < 35) AND (t0.id IN (4) OR t0.department_id != 31));" +337,2432,2432,210,10,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT users.id, users.age +FROM users +WHERE users.age IN (1, 21, 33, 64);" +338,1732,1732,138,9,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id +FROM regions AS t0 +WHERE ((t0.id NOT BETWEEN 1 AND 15 OR t0.id < 10) AND (t0.id <= 40 OR t0.id != 3));" +339,744,744,142,1,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT departments.id +FROM departments +WHERE ((departments.id <= 98 OR departments.id > 1) AND (departments.id IS NULL OR departments.id IN (3, 3, 30, 91)));" +340,109333,155563,539,13,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT customers.region_id AS c4276, markets.region AS c7002, COUNT(*), COUNT(*) AS c3968 +FROM customers LEFT JOIN markets ON customers.id = markets.id +WHERE (markets.note = 'Fast lane' OR customers.id IS NOT NULL) +GROUP BY customers.region_id, markets.region;" +341,112731,161092,479,500,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT customers.id, SUM(customers.region_id) +FROM markets JOIN users ON markets.id = users.id RIGHT JOIN customers ON users.id = customers.id +GROUP BY customers.id;" +342,1222,1222,145,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT employees.department_id, employees.id +FROM employees +WHERE ((employees.id >= 66 OR employees.id != 2) AND (employees.id > 69 AND employees.department_id = 33));" +343,3009,3009,139,17,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT users.age +FROM users +ORDER BY users.age ASC;" +344,2101,3522,188,3,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t1.price AS c605, t0.id AS c1651, t1.id +FROM regions AS t0 RIGHT JOIN books AS t1 ON t0.id = t1.id +WHERE (t1.id != 3 OR t1.id = 3);" +345,109172,155432,298,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT books.id, customers.id AS c5626 +FROM books LEFT JOIN customers ON books.id = customers.id +ORDER BY customers.id DESC;" +346,10087,29195,369,0,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t1.region, t2.id, COUNT(t1.id) +FROM employees AS t0 CROSS JOIN markets AS t1 FULL JOIN regions AS t2 ON t1.id = t2.id +WHERE t0.department_id = 36 +GROUP BY t1.region, t2.id;" +347,679596,6452676,958,0,transformation 5 FilterPushdownThroughJoin: 2; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1,"SELECT users.age AS c6973 +FROM users RIGHT JOIN orders ON users.id = orders.id +WHERE users.id > 94;" +348,347600,525100,2006,1187,transformation 0 JoinCommutativity: 1; transformation 6 InToOrChain: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT t1.id, t0.id +FROM departments AS t0 CROSS JOIN customers AS t1 +WHERE ((t1.id BETWEEN 28 AND 146 AND t1.id IN (46, 50, 104, 146)) OR (t0.id < 4 AND t1.region_id NOT IN (2, 2, 10, 12))) +ORDER BY t1.id DESC;" +349,2832,5592,209,5,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT regions.id, departments.id AS c6050, COUNT(*), COUNT(*) +FROM regions RIGHT JOIN departments ON regions.id = departments.id +WHERE departments.id IS NOT NULL +GROUP BY regions.id, departments.id +ORDER BY departments.id ASC;" +350,3612,6375,203,11,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT employees.department_id, departments.id AS c3106, COUNT(*) AS c7893 +FROM departments RIGHT JOIN employees ON departments.id = employees.id +GROUP BY employees.department_id, departments.id;" +351,7680023,32852932,14639,261,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.customer_id, t2.id AS c6006, t2.age +FROM orders AS t0 CROSS JOIN departments AS t1 FULL JOIN users AS t2 ON t1.id = t2.id +WHERE (t1.id >= 3 AND (t0.id < 55 OR t0.id < 88));" +352,2676728,5464128,4305,80,transformation 0 JoinCommutativity: 1; transformation 6 InToOrChain: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT t1.customer_id, t1.id, t0.id +FROM departments AS t0 CROSS JOIN orders AS t1 +WHERE (t1.customer_id IN (426) OR (t1.customer_id IS NULL OR t0.id >= 57));" +353,61000,61000,214,500,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id, t0.region_id +FROM customers AS t0;" +354,2372,2372,196,1,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.department_id, t0.id +FROM employees AS t0 +WHERE (t0.department_id >= 31 AND (t0.department_id > 34 OR t0.id < 33)) +GROUP BY t0.department_id, t0.id +ORDER BY t0.department_id DESC;" +355,62200,62200,258,2,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT customers.id, customers.region_id +FROM customers +WHERE customers.id IN (153, 183);" +356,3413,5326,234,5,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT employees.department_id, departments.id +FROM books LEFT JOIN employees ON books.id = employees.id FULL JOIN departments ON employees.id = departments.id;" +357,1940000,1940000,2422,4914,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT orders.id AS c1456, orders.customer_id, COUNT(*), COUNT(orders.customer_id) +FROM orders +WHERE (orders.id > 86 OR orders.customer_id IS NULL) +GROUP BY orders.id, orders.customer_id;" +358,2525,2525,167,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.department_id, t0.id AS c8666, COUNT(*) AS c1170, COUNT(*) +FROM employees AS t0 +WHERE t0.id IS NULL +GROUP BY t0.department_id, t0.id;" +359,2960,2960,174,10,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT regions.id, COUNT(regions.id) AS c1004, COUNT(regions.id) AS c9498 +FROM regions +GROUP BY regions.id;" +360,366,366,122,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id, t0.note AS c1949, t0.region +FROM markets AS t0;" +361,130976600,130976600,208219,11,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t2.department_id, COUNT(t2.id), SUM(t0.id) +FROM departments AS t0 CROSS JOIN orders AS t1 CROSS JOIN employees AS t2 +GROUP BY t2.department_id;" +362,785550822,785550822,807862,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 5 ImplementCrossJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT markets.id AS c6682, markets.note AS c3493 +FROM orders CROSS JOIN customers RIGHT JOIN markets ON customers.id = markets.id +GROUP BY markets.id, markets.note;" +363,433,433,178,3,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT books.price +FROM books +WHERE books.price IS NOT NULL +ORDER BY books.price DESC;" +364,69900,69900,225,44,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT customers.region_id AS c2372 +FROM customers +WHERE customers.region_id IN (4, 89) +ORDER BY customers.region_id DESC;" +365,1373762,1705422,1959,3,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.id AS c4468, t1.customer_id, t0.region +FROM markets AS t0 FULL JOIN orders AS t1 ON t0.id = t1.id JOIN customers AS t2 ON t0.id = t2.id +WHERE t0.id <= 17;" +366,2310,2310,171,1,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT users.id, users.age +FROM users +WHERE (users.id = 15 OR (users.id != 3 AND users.id IS NULL));" +367,2074,2074,172,17,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.age +FROM users AS t0;" +368,6428,12062,276,49,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT t0.id, t1.age +FROM books AS t0 CROSS JOIN users AS t1 +WHERE (t1.id = 96 OR (t1.age != 18 OR t0.price <= 61));" +369,850563,1550563,1020,0,transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id AS c4185, SUM(t0.id) +FROM markets AS t0 LEFT JOIN orders AS t1 ON t0.id = t1.id +WHERE ((t0.id BETWEEN 1 AND 7 OR t0.note != 'North, South') AND (t0.note != 'Old World' AND t0.note IN ('ASIA', 'Old World', 'unknown'))) +GROUP BY t0.id;" +370,422,422,147,2,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT markets.note AS c2495, markets.id, markets.region +FROM markets +WHERE ((markets.note = 'North, South' AND markets.note != 'North, South') OR markets.note != 'Fast lane');" +371,15960,15960,285,110,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1,"SELECT regions.id +FROM regions CROSS JOIN employees;" +372,1293750,1293750,1728,3214,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id +FROM orders AS t0 +WHERE (t0.id <= 95 OR t0.customer_id BETWEEN 55 AND 369) +ORDER BY t0.id DESC;" +373,175500,175500,352,500,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT customers.id, COUNT(customers.region_id) +FROM customers +GROUP BY customers.id;" +374,4689550,6653000,3011,0,transformation 0 JoinCommutativity: 1; transformation 2 FilterSplit: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT orders.id AS c9907, COUNT(*) AS c8627 +FROM departments CROSS JOIN orders +WHERE ((orders.id >= 79 OR orders.customer_id IN (63, 185, 226, 480)) AND departments.id IS NULL) +GROUP BY orders.id +ORDER BY orders.id ASC;" +375,957500,957500,1491,4999,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT t0.customer_id AS c3627, t0.id AS c1197 +FROM orders AS t0 +WHERE (t0.customer_id <= 56 OR t0.id > 1);" +376,5325,14288,305,9,implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 3,"SELECT t1.id, t0.id, t1.department_id +FROM regions AS t0 FULL JOIN employees AS t1 ON t0.id = t1.id FULL JOIN departments AS t2 ON t0.id = t2.id +WHERE t0.id NOT IN (3);" +377,77608,77608,570,341,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT customers.id, customers.region_id, SUM(customers.region_id), SUM(customers.id) +FROM customers +WHERE ((customers.region_id != 9 OR customers.id = 20) AND (customers.region_id >= 3 AND customers.id > 5)) +GROUP BY customers.id, customers.region_id +ORDER BY customers.id DESC, customers.region_id ASC;" +378,822,822,363,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT markets.region, markets.id AS c5516 +FROM markets +GROUP BY markets.region, markets.id;" +379,1342,1342,687,11,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT employees.department_id, employees.id +FROM employees;" +380,1692500,1692500,4349,4329,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT orders.customer_id +FROM orders +WHERE (orders.id < 86 OR (orders.customer_id IN (21, 71) OR orders.customer_id NOT BETWEEN 371 AND 436)) +ORDER BY orders.customer_id ASC;" +381,6120,10354,289,34,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT t0.age AS c6869, t0.id +FROM users AS t0 CROSS JOIN markets AS t1 +WHERE t1.note != 'Old World';" +382,848,848,182,3,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id AS c3548, t0.note, SUM(t0.id) AS c5991, COUNT(*) +FROM markets AS t0 +WHERE ((t0.note IN ('Fast lane', 'Fast lane', 'Old World') OR t0.region = 'AMERICA') OR t0.id NOT IN (1, 2, 3)) +GROUP BY t0.id, t0.note;" +383,7457700,14597700,5603,8500,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 5 ImplementCrossJoin: 1,"SELECT customers.id, customers.region_id +FROM regions CROSS JOIN customers CROSS JOIN users +WHERE regions.id < 2;" +384,20830,28390,456,9,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 5 ImplementCrossJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT markets.id, books.id, COUNT(markets.id), COUNT(markets.id) +FROM regions CROSS JOIN books CROSS JOIN markets +WHERE regions.id != 5 +GROUP BY markets.id, books.id;" +385,2432,2432,160,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id AS c9611, t0.age AS c4657 +FROM users AS t0 +WHERE ((t0.id >= 3 OR t0.id IS NULL) AND t0.age IS NULL);" +386,1307748,1553776,1822,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT markets.note, markets.region AS c394 +FROM markets LEFT JOIN orders ON markets.id = orders.id LEFT JOIN employees ON orders.id = employees.id;" +387,1692,3832,245,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 3; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT employees.id AS c1393 +FROM employees LEFT JOIN books ON employees.id = books.id +WHERE ((employees.id IS NULL AND books.id IS NOT NULL) AND (books.price = 55 AND employees.department_id = 12));" +388,3650500,3650500,11085,25000,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1,"SELECT orders.customer_id AS c3285 +FROM orders CROSS JOIN departments;" +389,677641,1553832,987,2,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 3; transformation 10 ProjectionPushdownThroughJoin: 4; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 3; implementation 6 ImplementHashJoin: 2,"SELECT employees.id AS c6990 +FROM orders LEFT JOIN markets ON orders.id = markets.id JOIN employees ON orders.id = employees.id +WHERE markets.id < 3;" +390,113850,160080,1873,5000,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT markets.note AS c1438, customers.region_id, markets.id AS c7869 +FROM customers LEFT JOIN markets ON customers.id = markets.id CROSS JOIN regions;" +391,1306995,2251590,1424,5,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT orders.customer_id AS c6961, departments.id +FROM departments LEFT JOIN orders ON departments.id = orders.id +GROUP BY orders.customer_id, departments.id +ORDER BY departments.id DESC;" +392,43248,62366,679,11,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT employees.department_id AS c8803, SUM(users.id), SUM(employees.id) +FROM users CROSS JOIN employees LEFT JOIN markets ON employees.id = markets.id +WHERE ((markets.id >= 18 OR users.id = 4) OR employees.id >= 2) +GROUP BY employees.department_id +ORDER BY employees.department_id ASC;" +393,93800000,291050000,224105,384231,transformation 0 JoinCommutativity: 1; transformation 2 FilterSplit: 2; transformation 5 FilterPushdownThroughJoin: 2; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT orders.customer_id, customers.id +FROM orders CROSS JOIN customers +WHERE (orders.id IS NOT NULL AND (orders.customer_id <= 190 AND customers.region_id IN (1, 5, 8, 9)));" +394,1832,1832,189,8,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id +FROM employees AS t0 +WHERE t0.department_id NOT IN (6, 11, 32, 94);" +395,2390300,2390300,10768,15000,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1,"SELECT orders.id, orders.customer_id, books.price AS c2061 +FROM orders CROSS JOIN books;" +396,422,422,131,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT markets.region, markets.id, markets.note +FROM markets +WHERE markets.region IS NULL;" +397,175500,175500,363,500,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id, t0.region_id, SUM(t0.region_id) +FROM customers AS t0 +GROUP BY t0.id, t0.region_id;" +398,69916,401976,271,10,transformation 6 InToOrChain: 1; transformation 7 FilterToJoinPredicate: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1,"SELECT regions.id, customers.id, customers.region_id +FROM regions JOIN customers ON regions.id = customers.id +WHERE (customers.id NOT BETWEEN 18 AND 150 OR regions.id IN (1, 9, 10, 63));" +399,1092,1092,246,1,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT departments.id AS c2818, COUNT(departments.id), COUNT(departments.id) +FROM departments +WHERE departments.id <= 1 +GROUP BY departments.id +ORDER BY departments.id DESC;" +400,2226,3466,284,10,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT markets.note AS c7054, markets.region, regions.id +FROM markets FULL JOIN regions ON markets.id = regions.id;" +401,112878,438256,453,500,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT customers.region_id, customers.id +FROM customers FULL JOIN employees ON customers.id = employees.id +GROUP BY customers.region_id, customers.id;" +402,1456314,175572814,2560,2194,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT orders.id, orders.customer_id AS c5129, customers.id AS c8733 +FROM customers FULL JOIN orders ON customers.id = orders.id +WHERE (orders.customer_id > 283 AND (orders.customer_id IS NOT NULL OR orders.customer_id IS NULL));" +403,1246000,1246000,1346,44,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id, COUNT(*), COUNT(t0.customer_id) AS c7688 +FROM orders AS t0 +WHERE t0.customer_id IN (15, 185, 359, 461) +GROUP BY t0.id;" +404,1122,1122,134,0,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT regions.id AS c6964 +FROM regions +WHERE (regions.id = 86 AND (regions.id IN (5, 9) AND regions.id < 9));" +405,610,610,130,5,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT departments.id +FROM departments;" +406,6451,16190,217,9,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT t1.id AS c2861, t0.id +FROM regions AS t0 LEFT JOIN users AS t1 ON t0.id = t1.id +WHERE t1.id IS NOT NULL +GROUP BY t1.id, t0.id +ORDER BY t0.id DESC;" +407,111830,405275,331,5,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT customers.id, customers.region_id, departments.id AS c3112 +FROM customers FULL JOIN regions ON customers.id = regions.id JOIN departments ON regions.id = departments.id +ORDER BY departments.id ASC, customers.id DESC, customers.region_id ASC;" +408,4788,10132,363,7,transformation 0 JoinCommutativity: 2; transformation 2 FilterSplit: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.id AS c868, t2.region +FROM users AS t0 RIGHT JOIN departments AS t1 ON t0.id = t1.id CROSS JOIN markets AS t2 +WHERE ((t1.id <= 8 OR t0.age IN (64)) AND (t2.id = 1 OR t1.id = 3));" +409,113508,650692,318,1,transformation 0 JoinCommutativity: 2; transformation 5 FilterPushdownThroughJoin: 2; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT users.age AS c3766, customers.id AS c5291, markets.id +FROM users LEFT JOIN customers ON users.id = customers.id LEFT JOIN markets ON customers.id = markets.id +WHERE markets.id NOT IN (1, 1, 1, 3);" +410,422,422,152,2,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT t0.region, t0.note, t0.id AS c8799 +FROM markets AS t0 +WHERE (t0.id IN (1) OR (t0.note IN ('AMERICA', 'Fast lane', 'North, South', 'unknown') AND t0.note != 'Old World'));" +411,822,822,137,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.price, t0.id, COUNT(*), SUM(t0.id) AS c6022 +FROM books AS t0 +GROUP BY t0.price, t0.id;" +412,3645,5814,228,2,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT users.age, books.price +FROM books LEFT JOIN users ON books.id = users.id +WHERE books.price NOT BETWEEN 66 AND 66;" +413,2398822,2721522,2134,1500,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT customers.id, books.price, COUNT(users.age) +FROM users CROSS JOIN customers RIGHT JOIN books ON users.id = books.id +GROUP BY customers.id, books.price;" +414,4551000,8751000,2981,920,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT regions.id +FROM orders CROSS JOIN regions +WHERE orders.id <= 92;" +415,6102,12200,244,6,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT t0.id, t1.id +FROM employees AS t0 JOIN regions AS t1 ON t0.id = t1.id +GROUP BY t0.id, t1.id +ORDER BY t0.id DESC;" +416,1122,1362,330,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT markets.note, books.id, markets.id +FROM books RIGHT JOIN markets ON books.id = markets.id +ORDER BY books.id DESC;" +417,785942,1705822,1166,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t1.price AS c5776, t0.id AS c5445, COUNT(t1.price) +FROM customers AS t0 RIGHT JOIN books AS t1 ON t0.id = t1.id JOIN orders AS t2 ON t1.id = t2.id +GROUP BY t1.price, t0.id;" +418,1306128,1550888,1442,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT t1.id, t0.customer_id, COUNT(*) +FROM orders AS t0 RIGHT JOIN books AS t1 ON t0.id = t1.id +GROUP BY t1.id, t0.customer_id +ORDER BY t0.customer_id DESC;" +419,112961,233260,333,5,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT t0.id AS c6883, t2.id +FROM departments AS t0 LEFT JOIN customers AS t1 ON t0.id = t1.id JOIN users AS t2 ON t1.id = t2.id;" +420,894875,894875,1916,2330,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id +FROM orders AS t0 +WHERE (t0.customer_id >= 270 AND (t0.customer_id IN (108, 315, 364) OR t0.id > 11)) +ORDER BY t0.id ASC;" +421,4298,6932,201,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 3,"SELECT markets.note, markets.region +FROM markets FULL JOIN regions ON markets.id = regions.id JOIN employees ON regions.id = employees.id +WHERE ((regions.id > 39 AND employees.department_id IS NOT NULL) OR (markets.region IS NOT NULL AND markets.id != 47));" +422,932,1363,208,1,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT t1.id AS c7037, t0.region AS c4401, t0.id AS c405 +FROM markets AS t0 LEFT JOIN books AS t1 ON t0.id = t1.id +WHERE t0.note = 'Old World' +ORDER BY t0.id DESC;" +423,2690,2690,255,15,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1,"SELECT markets.note, departments.id, markets.id +FROM departments CROSS JOIN markets;" +424,682435,4014820,964,10,transformation 0 JoinCommutativity: 1; transformation 10 ProjectionPushdownThroughJoin: 3; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 2; implementation 6 ImplementHashJoin: 2,"SELECT orders.customer_id AS c6591, users.age AS c6962, users.id AS c7318 +FROM users JOIN regions ON users.id = regions.id JOIN orders ON users.id = orders.id;" +425,112495,647074,334,501,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.id, t1.age, t0.region_id AS c768 +FROM customers AS t0 LEFT JOIN users AS t1 ON t0.id = t1.id;" +426,652500,652500,1144,102,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT orders.customer_id, orders.id +FROM orders +WHERE orders.id BETWEEN 43 AND 144;" +427,2676,2676,168,7,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT users.age, users.id AS c2712 +FROM users +WHERE users.id >= 11;" +428,1252,1658,481,3,transformation 0 JoinCommutativity: 1; transformation 6 InToOrChain: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT markets.id +FROM books CROSS JOIN markets +WHERE books.id IN (2);" +429,72226,160616,507,3,transformation 0 JoinCommutativity: 3; transformation 1 JoinAssociativity: 3; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.region_id, COUNT(*) +FROM customers AS t0 JOIN books AS t1 ON t0.id = t1.id CROSS JOIN departments AS t2 +WHERE (t2.id <= 1 OR t1.id IS NULL) +GROUP BY t0.region_id;" +430,1377,2113,260,2,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t0.id AS c2823, t1.id AS c3548, SUM(t1.price), COUNT(*) AS c5225 +FROM departments AS t0 RIGHT JOIN books AS t1 ON t0.id = t1.id +WHERE t1.price > 55 +GROUP BY t0.id, t1.id;" +431,1305662,1550422,1240,2,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT books.id, orders.id, books.price AS c8011 +FROM books LEFT JOIN orders ON books.id = orders.id +WHERE ((books.id > 1 OR books.price BETWEEN 66 AND 77) OR (books.price <= 66 AND orders.id = 187));" +432,14852,48496,400,0,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT users.id, books.id, COUNT(users.id) +FROM users CROSS JOIN books FULL JOIN employees ON users.id = employees.id +WHERE (books.id > 3 AND (users.age IS NOT NULL OR employees.id BETWEEN 30 AND 49)) +GROUP BY users.id, books.id;" +433,3028,3028,320,9,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id, t0.age +FROM users AS t0 +WHERE t0.age >= 64 +ORDER BY t0.id DESC;" +434,68783,155433,264,0,transformation 0 JoinCommutativity: 1; transformation 7 FilterToJoinPredicate: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT t1.note +FROM customers AS t0 JOIN markets AS t1 ON t0.id = t1.id +WHERE (t1.note IS NULL OR t0.id = 123) +ORDER BY t1.note DESC;" +435,3042,3042,218,9,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT users.id +FROM users +WHERE (users.id IS NULL OR users.age IN (5, 12, 33, 64));" +436,3547,14722,183,2,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1,"SELECT t1.id +FROM users AS t0 LEFT JOIN regions AS t1 ON t0.id = t1.id +WHERE t1.id = 8;" +437,3432,4918,250,4,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 5,"SELECT t1.id AS c9930, SUM(t0.id), COUNT(t1.id) AS c171 +FROM markets AS t0 FULL JOIN books AS t1 ON t0.id = t1.id RIGHT JOIN regions AS t2 ON t1.id = t2.id +GROUP BY t1.id +ORDER BY t1.id DESC;" +438,61346,61346,258,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT customers.id +FROM customers +WHERE ((customers.region_id NOT BETWEEN 2 AND 19 AND customers.id < 36) AND customers.id > 39);" +439,1306015,2250610,1289,5,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT departments.id AS c4906, orders.customer_id +FROM orders RIGHT JOIN departments ON orders.id = departments.id;" +440,225000,225000,369,500,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT customers.id, COUNT(*), COUNT(*) AS c6478 +FROM customers +GROUP BY customers.id +ORDER BY customers.id ASC;" +441,1220,1220,149,10,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT regions.id +FROM regions;" +442,3633,5692,185,0,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT markets.region AS c3227, users.age +FROM users FULL JOIN markets ON users.id = markets.id +WHERE (users.id IN (7, 15, 96) AND (users.age IN (33) OR markets.note != 'North, South'));" +443,366,366,149,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT books.price AS c5763 +FROM books;" +444,1278,1518,206,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT t1.price, t1.id +FROM markets AS t0 FULL JOIN books AS t1 ON t0.id = t1.id +WHERE ((t1.price >= 55 AND t0.id IS NULL) OR (t0.note != 'North, South' OR t0.region != 'EUROPE')) +ORDER BY t1.price DESC;" +445,3073124,4408024,2652,5000,transformation 0 JoinCommutativity: 1; transformation 6 InToOrChain: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 5 ImplementCrossJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT t2.id, t1.region_id, t1.id AS c770 +FROM regions AS t0 CROSS JOIN customers AS t1 CROSS JOIN books AS t2 +WHERE (t2.id > 34 OR t2.id IN (2)) +ORDER BY t1.id ASC;" +446,111332,436710,455,11,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.id, t1.region_id +FROM employees AS t0 FULL JOIN customers AS t1 ON t0.id = t1.id +WHERE t0.id != 14;" +447,2320,3126,261,2,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT t1.region, COUNT(t0.id), COUNT(*) AS c7336 +FROM books AS t0 CROSS JOIN markets AS t1 +WHERE (t1.id IS NOT NULL OR (t0.id <= 2 AND t0.price IS NOT NULL)) +GROUP BY t1.region +ORDER BY t1.region DESC;" +448,2778,4232,391,4,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT books.id, SUM(books.price) +FROM employees FULL JOIN books ON employees.id = books.id +GROUP BY books.id;" +449,1363,1363,160,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id, COUNT(*) AS c6748 +FROM employees AS t0 +WHERE ((t0.department_id <= 31 OR t0.id BETWEEN 5 AND 49) AND (t0.department_id IS NULL AND t0.department_id BETWEEN 5 AND 31)) +GROUP BY t0.id;" +450,5219,5219,162,16,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT users.id +FROM users +GROUP BY users.id;" +451,633,633,194,4,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT departments.id +FROM departments +WHERE ((departments.id BETWEEN 4 AND 4 OR departments.id <= 4) AND departments.id <= 47) +ORDER BY departments.id DESC;" +452,1310637,6454068,1484,5,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT users.id AS c3185 +FROM orders RIGHT JOIN users ON orders.id = users.id +WHERE (users.age IN (17, 34, 64) OR (users.id < 12 AND orders.id IS NULL)) +GROUP BY users.id +ORDER BY users.id ASC;" +453,1220,1220,129,10,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT regions.id +FROM regions;" +454,1311205,6455219,1428,16,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT t1.id, COUNT(*) +FROM orders AS t0 RIGHT JOIN users AS t1 ON t0.id = t1.id +GROUP BY t1.id;" +455,3072,4706,208,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 3,"SELECT employees.id AS c8673 +FROM books LEFT JOIN employees ON books.id = employees.id JOIN markets ON books.id = markets.id;" +456,44507,154580,433,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 3; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT t1.department_id, t0.age +FROM users AS t0 CROSS JOIN employees AS t1 RIGHT JOIN regions AS t2 ON t0.id = t2.id +WHERE ((t0.id IN (13, 79) OR t2.id >= 44) AND t2.id IS NULL) +ORDER BY t0.age DESC;" +457,957500,957500,1329,4999,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT orders.id +FROM orders +WHERE (orders.id != 33 OR orders.customer_id > 461);" +458,2074,2074,152,17,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.age, t0.id AS c1193 +FROM users AS t0;" +459,685169,19762099,1076,55,transformation 1 JoinAssociativity: 2; transformation 5 FilterPushdownThroughJoin: 1; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 6 ImplementHashJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id +FROM departments AS t0 CROSS JOIN employees AS t1 RIGHT JOIN orders AS t2 ON t1.id = t2.id +WHERE t1.id <= 91 +ORDER BY t0.id DESC;" +460,682089,6453009,897,17,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT t1.customer_id, t0.age, t1.id +FROM users AS t0 JOIN orders AS t1 ON t0.id = t1.id +ORDER BY t1.customer_id ASC;" +461,1308264,4003644,2072,0,transformation 0 JoinCommutativity: 2; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT regions.id AS c8461 +FROM orders LEFT JOIN regions ON orders.id = regions.id RIGHT JOIN books ON regions.id = books.id +WHERE books.id NOT BETWEEN 1 AND 14;" +462,60649,409922,390,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 3; transformation 9 FilterLiftThroughJoin: 2; transformation 10 ProjectionPushdownThroughJoin: 3; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 2; implementation 6 ImplementHashJoin: 2,"SELECT regions.id, employees.department_id AS c5017, customers.region_id +FROM customers JOIN regions ON customers.id = regions.id RIGHT JOIN employees ON regions.id = employees.id +WHERE (employees.id < 54 AND customers.id = 67);" +463,2074,2074,149,17,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT users.id AS c7578 +FROM users;" +464,1361,1916,208,5,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT departments.id, markets.id, markets.region +FROM departments LEFT JOIN markets ON departments.id = markets.id;" +465,3256,3256,419,11,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.department_id AS c3122, COUNT(t0.id) AS c9921 +FROM employees AS t0 +GROUP BY t0.department_id;" +466,61000,61000,241,500,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id +FROM customers AS t0;" +467,5889,16616,256,11,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT t0.age AS c4980 +FROM users AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id +ORDER BY t0.age DESC;" +468,3664,3664,167,9,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id +FROM regions AS t0 +WHERE (t0.id > 5 OR (t0.id NOT IN (1, 3, 9, 96) OR t0.id NOT IN (1, 5))) +GROUP BY t0.id;" +469,676872,1550822,939,3,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id AS c3150, COUNT(t1.customer_id) +FROM markets AS t0 JOIN orders AS t1 ON t0.id = t1.id +GROUP BY t0.id;" +470,2228125,2228125,2822,5000,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id, COUNT(t0.id), COUNT(t0.id) +FROM orders AS t0 +WHERE ((t0.customer_id IS NOT NULL OR t0.customer_id < 310) OR t0.id <= 5) +GROUP BY t0.id;" +471,348050982,1135550932,92238,1500,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 6 ImplementHashJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT customers.region_id +FROM customers CROSS JOIN orders JOIN departments ON orders.id = departments.id +WHERE departments.id NOT IN (3, 4, 33, 52) +ORDER BY customers.region_id DESC;" +472,1363,1363,159,2,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT employees.id +FROM employees +WHERE (employees.department_id != 19 AND employees.id IN (2, 5)) +GROUP BY employees.id;" +473,6650300,6650300,8463,3,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t1.price +FROM orders AS t0 CROSS JOIN books AS t1 +GROUP BY t1.price;" +474,822,822,164,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT books.price AS c6021, books.id AS c591 +FROM books +GROUP BY books.price, books.id;" +475,563,563,186,1,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT books.price, SUM(books.price) AS c2521 +FROM books +WHERE books.price = 66 +GROUP BY books.price;" +476,2690,2690,298,15,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1,"SELECT markets.region, markets.note +FROM markets CROSS JOIN departments;" +477,3322,5213,237,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT books.id, COUNT(books.price) +FROM regions LEFT JOIN books ON regions.id = books.id FULL JOIN departments ON regions.id = departments.id +WHERE books.id IS NOT NULL +GROUP BY books.id;" +478,1377,2113,241,3,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT books.id, books.price AS c3187, COUNT(books.price), SUM(departments.id) +FROM departments JOIN books ON departments.id = books.id +WHERE books.id <= 98 +GROUP BY books.id, books.price;" +479,3364,8164,334,0,transformation 0 JoinCommutativity: 1; transformation 2 FilterSplit: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT regions.id, departments.id +FROM departments CROSS JOIN regions +WHERE (departments.id >= 3 AND departments.id > 5);" +480,2979,5692,171,2,transformation 5 FilterPushdownThroughJoin: 1; transformation 6 InToOrChain: 1; transformation 10 ProjectionPushdownThroughJoin: 3; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 2; implementation 6 ImplementHashJoin: 1,"SELECT markets.id, markets.region, users.age +FROM markets JOIN users ON markets.id = users.id +WHERE (markets.note != 'ASIA' AND markets.id IN (1, 3, 89));" +481,4960163,9590890,14129,30000,transformation 0 JoinCommutativity: 2; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t1.id AS c5950, t2.id AS c7759 +FROM users AS t0 LEFT JOIN employees AS t1 ON t0.id = t1.id CROSS JOIN orders AS t2 +WHERE t1.id IS NOT NULL;" +482,942989,2566838,2592,5000,transformation 0 JoinCommutativity: 1; transformation 2 FilterSplit: 1; transformation 5 FilterPushdownThroughJoin: 4; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT employees.department_id AS c6048, markets.region, markets.note +FROM markets LEFT JOIN employees ON markets.id = employees.id CROSS JOIN orders +WHERE (markets.id <= 2 AND (employees.id = 68 OR markets.region != 'AMERICA'));" +483,1307418,2252623,1534,3,transformation 0 JoinCommutativity: 2; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t2.id, COUNT(*) AS c2015 +FROM orders AS t0 RIGHT JOIN departments AS t1 ON t0.id = t1.id CROSS JOIN books AS t2 +WHERE t1.id = 3 +GROUP BY t2.id;" +484,8367,19707,314,0,transformation 0 JoinCommutativity: 1; transformation 2 FilterSplit: 1; transformation 5 FilterPushdownThroughJoin: 4; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT t1.department_id +FROM departments AS t0 JOIN employees AS t1 ON t0.id = t1.id CROSS JOIN users AS t2 +WHERE t1.id BETWEEN 43 AND 48 +ORDER BY t1.department_id DESC;" +485,744,744,158,5,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT departments.id +FROM departments +WHERE departments.id <= 96;" +486,1120,1120,156,5,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT departments.id AS c4502 +FROM departments +WHERE ((departments.id IS NOT NULL OR departments.id <= 2) OR (departments.id > 3 OR departments.id IN (1, 5))) +ORDER BY departments.id ASC;" +487,7676202,8350932,11659,15000,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT t1.id AS c1414, t2.id AS c4044 +FROM orders AS t0 CROSS JOIN departments AS t1 RIGHT JOIN books AS t2 ON t1.id = t2.id +ORDER BY t1.id DESC;" +488,805000,805000,1406,4992,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT orders.customer_id, orders.id +FROM orders +WHERE orders.customer_id != 133;" +489,2140,2140,184,0,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT regions.id AS c266 +FROM regions +WHERE ((regions.id = 63 AND regions.id IN (1, 36)) OR (regions.id NOT IN (7) AND regions.id IS NULL)) +GROUP BY regions.id;" +490,5689,9112,367,11,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT markets.region AS c2686, users.age AS c2053, employees.department_id +FROM users RIGHT JOIN markets ON users.id = markets.id RIGHT JOIN employees ON markets.id = employees.id +ORDER BY users.age DESC, employees.department_id DESC, markets.region DESC;" +491,2824,10532,229,4,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 1; transformation 6 InToOrChain: 1; transformation 10 ProjectionPushdownThroughJoin: 3; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 2; implementation 6 ImplementHashJoin: 1,"SELECT employees.id, regions.id AS c47 +FROM regions LEFT JOIN employees ON regions.id = employees.id +WHERE employees.id NOT IN (3, 6, 6, 75);" +492,69422,405122,358,0,transformation 0 JoinCommutativity: 3; transformation 1 JoinAssociativity: 3; transformation 5 FilterPushdownThroughJoin: 5; transformation 6 InToOrChain: 1; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 6 ImplementHashJoin: 1,"SELECT regions.id AS c9972 +FROM customers LEFT JOIN regions ON customers.id = regions.id JOIN departments ON regions.id = departments.id +WHERE (departments.id IN (8, 31, 37) AND regions.id = 1);" +493,3644,5703,230,1,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT t1.age AS c4963, t0.note +FROM markets AS t0 FULL JOIN users AS t1 ON t0.id = t1.id +WHERE t0.region != 'AMERICA' +ORDER BY t0.note ASC;" +494,3647,5538,301,11,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 5,"SELECT regions.id, books.price, SUM(books.price) +FROM departments FULL JOIN books ON departments.id = books.id FULL JOIN regions ON books.id = regions.id +GROUP BY regions.id, books.price +ORDER BY books.price ASC;" +495,110392,226982,468,500,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 5,"SELECT t2.region +FROM departments AS t0 RIGHT JOIN customers AS t1 ON t0.id = t1.id LEFT JOIN markets AS t2 ON t0.id = t2.id +ORDER BY t2.region ASC;" +496,6093,18632,221,17,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT employees.department_id, books.price +FROM users LEFT JOIN employees ON users.id = employees.id LEFT JOIN books ON users.id = books.id +ORDER BY books.price ASC;" +497,4370,9075,188,5,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t1.age, t1.id, SUM(t0.id) AS c2929 +FROM departments AS t0 JOIN users AS t1 ON t0.id = t1.id +GROUP BY t1.age, t1.id;" +498,2024,3522,169,3,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 1; transformation 6 InToOrChain: 1; transformation 10 ProjectionPushdownThroughJoin: 3; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 2; implementation 6 ImplementHashJoin: 1,"SELECT regions.id AS c3913, markets.region +FROM regions JOIN markets ON regions.id = markets.id +WHERE ((markets.note = 'Fast lane' AND markets.id IN (1, 3, 51)) OR markets.region IN ('AMERICA', 'EUROPE', 'EUROPE'));" +499,1640,1640,244,4,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT departments.id, COUNT(*), COUNT(departments.id) +FROM departments +WHERE ((departments.id IN (2, 58, 99) OR departments.id >= 2) OR departments.id >= 2) +GROUP BY departments.id;" +500,3039,8394,362,4,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 3; transformation 6 InToOrChain: 2; transformation 7 FilterToJoinPredicate: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT users.id, users.age AS c9399, departments.id +FROM users JOIN departments ON users.id = departments.id +WHERE (users.age NOT IN (21, 22, 123) AND (departments.id < 4 OR users.id <= 15));" +501,4641630,7311075,7972,15000,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT t1.id, t0.id, t0.note +FROM markets AS t0 CROSS JOIN orders AS t1 LEFT JOIN departments AS t2 ON t1.id = t2.id +ORDER BY t1.id ASC, t0.id DESC, t0.note DESC;" +502,910557480,3235555219,1646785,5000,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t2.id AS c3228, t0.id +FROM orders AS t0 CROSS JOIN customers AS t1 FULL JOIN users AS t2 ON t0.id = t2.id +GROUP BY t2.id, t0.id;" +503,1048,1048,174,5,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id, SUM(t0.id), COUNT(*) +FROM departments AS t0 +WHERE t0.id IS NOT NULL +GROUP BY t0.id;" +504,8676000,12876000,4179,6910,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id, t0.customer_id +FROM orders AS t0 CROSS JOIN regions AS t1 +WHERE t0.customer_id <= 74 +ORDER BY t0.id ASC, t0.customer_id DESC;" +505,588,588,156,3,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT markets.id, markets.region, markets.note +FROM markets +WHERE ((markets.id <= 3 OR markets.note != 'unknown') OR markets.id IN (2, 68)) +ORDER BY markets.note ASC;" +506,5558,5558,346,33,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1,"SELECT markets.region, employees.id AS c412, employees.department_id +FROM markets CROSS JOIN employees;" +507,3612,6540,214,11,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT employees.id, COUNT(departments.id), COUNT(employees.department_id) +FROM departments RIGHT JOIN employees ON departments.id = employees.id +GROUP BY employees.id +ORDER BY employees.id DESC;" +508,4300,12463,243,2,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 4; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT t1.department_id, SUM(t2.price) AS c7329 +FROM regions AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id RIGHT JOIN books AS t2 ON t1.id = t2.id +WHERE (t1.id BETWEEN 3 AND 14 OR t1.department_id IN (1, 3, 33)) +GROUP BY t1.department_id;" +509,107641,416025,432,8,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 4; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 5,"SELECT users.id, users.age, COUNT(*), COUNT(users.age) +FROM regions RIGHT JOIN customers ON regions.id = customers.id RIGHT JOIN users ON regions.id = users.id +WHERE customers.id != 8 +GROUP BY users.id, users.age;" +510,8645,29395,414,18,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 6 ImplementHashJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT markets.note, employees.id AS c4181 +FROM markets CROSS JOIN regions LEFT JOIN employees ON regions.id = employees.id +WHERE employees.department_id < 54 +ORDER BY markets.note DESC;" +511,1833,1833,172,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id, t0.age +FROM users AS t0 +WHERE ((t0.id = 3 AND t0.id IS NULL) AND t0.id BETWEEN 6 AND 95) +ORDER BY t0.age DESC;" +512,3009,3009,153,17,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id AS c74, t0.age +FROM users AS t0 +ORDER BY t0.id ASC, t0.age DESC;" +513,1342,1342,123,11,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.department_id AS c4862, t0.id +FROM employees AS t0;" +514,1480714,175597214,2113,153,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT customers.region_id AS c5690, customers.id, orders.customer_id AS c2293 +FROM customers RIGHT JOIN orders ON customers.id = orders.id +WHERE ((orders.id IS NULL OR customers.id = 97) OR orders.customer_id <= 14);" +515,822,822,151,2,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT markets.region, COUNT(markets.id), COUNT(*) +FROM markets +GROUP BY markets.region;" +516,4314,6876,225,3,transformation 0 JoinCommutativity: 1; transformation 10 ProjectionPushdownThroughJoin: 3; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 2; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT regions.id AS c4858, markets.note, markets.id +FROM employees LEFT JOIN markets ON employees.id = markets.id JOIN regions ON markets.id = regions.id;" +517,5471,15210,204,11,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t1.age AS c8314, t1.id, t0.id +FROM regions AS t0 FULL JOIN users AS t1 ON t0.id = t1.id +WHERE t0.id IS NOT NULL;" +518,1306180,2250775,2519,5000,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT orders.customer_id +FROM orders LEFT JOIN departments ON orders.id = departments.id +ORDER BY orders.customer_id DESC;" +519,3794,7253,267,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 3; transformation 10 ProjectionPushdownThroughJoin: 3; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 2; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT t2.id, t0.id +FROM users AS t0 JOIN books AS t1 ON t0.id = t1.id LEFT JOIN departments AS t2 ON t0.id = t2.id +WHERE t1.id IS NULL +ORDER BY t2.id DESC, t0.id DESC;" +520,422,422,151,2,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT books.price +FROM books +WHERE (books.price != 55 OR (books.price BETWEEN 66 AND 66 AND books.id < 1));" +521,422,422,143,2,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT books.id AS c5535, books.price +FROM books +WHERE ((books.id < 3 OR books.id IS NULL) AND books.id IS NOT NULL);" +522,1342,1342,131,11,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id +FROM employees AS t0;" +523,10286,11526,343,170,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t1.id, t2.id +FROM markets AS t0 FULL JOIN regions AS t1 ON t0.id = t1.id CROSS JOIN users AS t2;" +524,544,544,131,2,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT books.price +FROM books +WHERE books.price NOT IN (77);" +525,152440,477818,448,187,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT customers.region_id AS c7276, users.age, employees.department_id +FROM customers RIGHT JOIN employees ON customers.id = employees.id CROSS JOIN users +ORDER BY employees.department_id ASC;" +526,2320,2320,131,11,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id +FROM employees AS t0 +WHERE ((t0.id != 2 AND t0.department_id < 3) OR (t0.department_id NOT IN (1, 33) OR t0.department_id NOT IN (31)));" +527,20391700,27531700,21735,16,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT users.id, users.age, COUNT(*) AS c3042, COUNT(users.age) +FROM orders CROSS JOIN users +WHERE orders.id IS NOT NULL +GROUP BY users.id, users.age;" +528,563,563,181,1,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id, t0.region AS c5933 +FROM markets AS t0 +WHERE ((t0.id != 1 AND t0.region = 'AMERICA') AND (t0.id IS NOT NULL OR t0.region = 'EUROPE')) +GROUP BY t0.id, t0.region;" +529,1415835,2476590,3712,5000,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 5,"SELECT orders.id, orders.customer_id, COUNT(*) +FROM departments FULL JOIN orders ON departments.id = orders.id FULL JOIN customers ON orders.id = customers.id +GROUP BY orders.id, orders.customer_id +ORDER BY orders.id DESC, orders.customer_id DESC;" +530,920550000,920550000,1297662,2500000,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT orders.customer_id AS c9140, customers.region_id, customers.id +FROM customers CROSS JOIN orders +ORDER BY customers.region_id ASC, customers.id ASC, orders.customer_id DESC;" +531,570413,2255572,1095,0,transformation 5 FilterPushdownThroughJoin: 4; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT orders.customer_id, orders.id AS c4291, employees.department_id +FROM departments JOIN orders ON departments.id = orders.id LEFT JOIN employees ON orders.id = employees.id +WHERE orders.id IN (99);" +532,633,633,143,1,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id +FROM departments AS t0 +WHERE (t0.id < 2 AND (t0.id IN (1, 1, 5) OR t0.id >= 5)) +ORDER BY t0.id ASC;" +533,7775500,7775500,7589,25000,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id AS c1841, t1.id +FROM departments AS t0 CROSS JOIN orders AS t1 +ORDER BY t0.id ASC;" +534,5903,17315,226,6,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT users.age AS c2013, COUNT(*) AS c86, COUNT(employees.department_id) +FROM employees JOIN users ON employees.id = users.id +WHERE users.id != 10 +GROUP BY users.age +ORDER BY users.age ASC;" +535,1220,1220,126,10,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id +FROM regions AS t0;" +536,16891862,17771532,23633,55000,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT orders.customer_id +FROM employees CROSS JOIN orders FULL JOIN markets ON orders.id = markets.id +ORDER BY orders.customer_id ASC;" +537,121968,668932,851,5,transformation 0 JoinCommutativity: 2; transformation 2 FilterSplit: 5; transformation 5 FilterPushdownThroughJoin: 4; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t1.region_id, t0.age, SUM(t0.id), COUNT(t2.id) AS c2707 +FROM users AS t0 LEFT JOIN customers AS t1 ON t0.id = t1.id CROSS JOIN regions AS t2 +WHERE ((t1.region_id >= 6 OR t0.age != 22) AND (t0.age >= 64 AND t0.age IN (22, 64))) +GROUP BY t1.region_id, t0.age;" +538,932,1363,195,2,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT books.id, markets.region +FROM markets RIGHT JOIN books ON markets.id = books.id +WHERE markets.region = 'AMERICA' +ORDER BY markets.region DESC, books.id DESC;" +539,11604,23544,239,22,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT regions.id +FROM employees CROSS JOIN regions +WHERE regions.id NOT BETWEEN 2 AND 9;" +540,678650,1554258,1074,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t1.price +FROM orders AS t0 JOIN books AS t1 ON t0.id = t1.id FULL JOIN employees AS t2 ON t0.id = t2.id +WHERE ((t0.customer_id > 224 AND t2.id NOT IN (2, 66, 67)) OR t0.customer_id < 119) +GROUP BY t1.price;" +541,7022,9744,836,11,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t1.id, COUNT(t1.department_id) AS c9704, COUNT(t1.department_id) +FROM books AS t0 CROSS JOIN employees AS t1 +WHERE t0.id <= 2 +GROUP BY t1.id;" +542,115640,650219,432,10,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT customers.region_id AS c2902 +FROM customers LEFT JOIN users ON customers.id = users.id +GROUP BY customers.region_id;" +543,3449,5392,225,11,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 5,"SELECT books.price AS c9867, employees.department_id +FROM departments RIGHT JOIN books ON departments.id = books.id FULL JOIN employees ON departments.id = employees.id +ORDER BY employees.department_id DESC, books.price DESC;" +544,3332,5282,327,15,transformation 0 JoinCommutativity: 3; transformation 1 JoinAssociativity: 3; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT markets.note AS c8307, departments.id, books.price AS c1104 +FROM markets JOIN books ON markets.id = books.id CROSS JOIN departments +WHERE (departments.id >= 24 OR (markets.id BETWEEN 1 AND 2 OR books.id <= 96)) +ORDER BY departments.id DESC, books.price ASC, markets.note DESC;" +545,574,574,419,2,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.price AS c637, t0.id AS c2803 +FROM books AS t0 +WHERE t0.id IN (1, 2, 24) +GROUP BY t0.price, t0.id +ORDER BY t0.id ASC, t0.price ASC;" +546,366,366,131,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.note, t0.id AS c5805 +FROM markets AS t0;" +547,159512,159512,395,329,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT customers.id, customers.region_id, COUNT(customers.id), COUNT(customers.id) AS c8210 +FROM customers +WHERE (customers.id < 119 OR (customers.region_id <= 6 AND customers.region_id != 10)) +GROUP BY customers.id, customers.region_id;" +548,433,433,143,2,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT books.id AS c7062, books.price AS c5593 +FROM books +WHERE books.price <= 66 +ORDER BY books.price DESC;" +549,515777,1057204,2573,7000,transformation 0 JoinCommutativity: 2; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT customers.id AS c529 +FROM employees RIGHT JOIN users ON employees.id = users.id CROSS JOIN customers +WHERE ((employees.department_id != 65 AND customers.region_id >= 8) OR users.id < 15);" +550,112495,647074,363,501,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT users.id +FROM users FULL JOIN customers ON users.id = customers.id;" +551,125278,1217026,451,33,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t0.id AS c8412, t1.price AS c8263, COUNT(t1.id) +FROM employees AS t0 CROSS JOIN books AS t1 LEFT JOIN customers AS t2 ON t0.id = t2.id +GROUP BY t0.id, t1.price;" +552,61000,61000,264,500,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT customers.id +FROM customers;" +553,2585,3973,222,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t1.department_id, t0.price, COUNT(*) AS c1800 +FROM books AS t0 FULL JOIN employees AS t1 ON t0.id = t1.id +WHERE (t1.department_id IS NOT NULL AND (t0.price IS NOT NULL OR t0.id IN (3))) +GROUP BY t1.department_id, t0.price;" +554,686187,175725622,1888,0,transformation 0 JoinCommutativity: 2; transformation 1 JoinAssociativity: 2; transformation 5 FilterPushdownThroughJoin: 3; transformation 6 InToOrChain: 1; transformation 11 OuterJoinToInner: 2; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 2,"SELECT customers.id +FROM orders JOIN customers ON orders.id = customers.id RIGHT JOIN departments ON customers.id = departments.id +WHERE (orders.id IN (22, 50, 61, 149) AND orders.id IS NULL);" +555,563,563,171,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.note +FROM markets AS t0 +WHERE (t0.id < 1 AND (t0.region = 'AMERICA' AND t0.id BETWEEN 2 AND 3)) +GROUP BY t0.note;" +556,23036435700,23036435700,24496249,10,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT customers.region_id +FROM users CROSS JOIN customers CROSS JOIN orders +GROUP BY customers.region_id;" +557,110500,110500,396,500,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT customers.region_id, customers.id +FROM customers +ORDER BY customers.region_id ASC;" +558,3577,5636,167,17,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.region +FROM markets AS t0 FULL JOIN users AS t1 ON t0.id = t1.id;" +559,1220,1220,126,10,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT regions.id +FROM regions;" +560,672828,12055320,1633,27,transformation 0 JoinCommutativity: 1; transformation 2 FilterSplit: 1; transformation 5 FilterPushdownThroughJoin: 5; transformation 6 InToOrChain: 1; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 6 ImplementHashJoin: 1,"SELECT employees.department_id, employees.id AS c3726, books.id +FROM books CROSS JOIN employees RIGHT JOIN orders ON employees.id = orders.id +WHERE (employees.department_id NOT IN (1, 35, 39, 88) AND (orders.id IS NOT NULL AND orders.customer_id != 133));" +561,111672,438832,491,2,implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 3,"SELECT t2.note AS c4598, t0.department_id AS c1378, t0.id AS c1288 +FROM employees AS t0 FULL JOIN customers AS t1 ON t0.id = t1.id FULL JOIN markets AS t2 ON t0.id = t2.id +WHERE t2.region = 'AMERICA';" +562,1310198,6455692,1863,0,transformation 0 JoinCommutativity: 2; transformation 7 FilterToJoinPredicate: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t1.age, t2.price +FROM orders AS t0 LEFT JOIN users AS t1 ON t0.id = t1.id JOIN books AS t2 ON t1.id = t2.id +WHERE ((t1.age = 123 AND t2.id IS NULL) OR (t0.customer_id = 354 AND t0.customer_id = 252));" +563,3110874,5210874,2149,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 5 ImplementCrossJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.id, COUNT(*), COUNT(*) +FROM markets AS t0 CROSS JOIN orders AS t1 RIGHT JOIN books AS t2 ON t1.id = t2.id +WHERE t2.price <= 42 +GROUP BY t0.id +ORDER BY t0.id DESC;" +564,671553,4351363,1635,0,transformation 5 FilterPushdownThroughJoin: 3; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id AS c6853 +FROM employees AS t0 RIGHT JOIN orders AS t1 ON t0.id = t1.id +WHERE ((t1.id IS NULL AND t1.customer_id < 172) AND t0.id != 67) +GROUP BY t0.id;" +565,89730,161130,646,765,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 5 ImplementCrossJoin: 1,"SELECT departments.id, users.id AS c5132 +FROM users CROSS JOIN regions CROSS JOIN departments +WHERE regions.id <= 9;" +566,68272,155422,286,2,transformation 5 FilterPushdownThroughJoin: 2; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1,"SELECT markets.id, markets.region, markets.note AS c6111 +FROM markets RIGHT JOIN customers ON markets.id = customers.id +WHERE markets.id IN (2, 3, 23, 41);" +567,610000,610000,1279,5000,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT orders.customer_id, orders.id +FROM orders;" +568,366,366,122,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT books.price, books.id AS c5367 +FROM books;" +569,8406,23335,962,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t2.age +FROM books AS t0 CROSS JOIN departments AS t1 FULL JOIN users AS t2 ON t0.id = t2.id +WHERE ((t1.id IS NOT NULL OR t2.id = 4) AND (t0.price BETWEEN 66 AND 77 OR t0.price IS NULL)) +GROUP BY t2.age +ORDER BY t2.age ASC;" +570,3764,3764,176,11,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.department_id AS c7134, t0.id AS c6407, SUM(t0.department_id), COUNT(*) +FROM employees AS t0 +WHERE (t0.id IN (1, 42, 51) OR (t0.department_id >= 3 OR t0.department_id <= 34)) +GROUP BY t0.department_id, t0.id;" +571,366,366,125,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT markets.id, markets.note AS c5448, markets.region +FROM markets;" +572,2085,2085,151,9,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT regions.id +FROM regions +WHERE ((regions.id IN (7, 90) AND regions.id != 9) OR regions.id NOT IN (2, 2, 29)) +ORDER BY regions.id DESC;" +573,260498,522188,1486,1500,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT t1.id, t2.price, COUNT(*) +FROM markets AS t0 CROSS JOIN customers AS t1 JOIN books AS t2 ON t0.id = t2.id +GROUP BY t1.id, t2.price +ORDER BY t2.price ASC, t1.id ASC;" +574,253000,463000,399,0,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT t0.region_id, t0.id +FROM customers AS t0 CROSS JOIN departments AS t1 +WHERE t1.id < 1;" +575,2337,3832,213,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.price +FROM books AS t0 JOIN employees AS t1 ON t0.id = t1.id +WHERE t1.id >= 33;" +576,118847,653426,419,51,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT books.price, customers.id AS c8754, users.age AS c3420 +FROM users LEFT JOIN customers ON users.id = customers.id CROSS JOIN books;" +577,1308754,6451833,1921,6,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT t0.customer_id, t1.id, t0.id AS c4362 +FROM orders AS t0 FULL JOIN users AS t1 ON t0.id = t1.id +WHERE t1.age = 64 +ORDER BY t1.id DESC, t0.customer_id ASC;" +578,2525500,4625500,12855,25000,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT departments.id, orders.id +FROM departments CROSS JOIN orders +WHERE departments.id != 45;" +579,5041,15375,514,9,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT users.id, regions.id +FROM regions JOIN users ON regions.id = users.id +WHERE users.id <= 8 +ORDER BY users.id DESC, regions.id DESC;" +580,3134,4880,300,20,transformation 0 JoinCommutativity: 2; transformation 6 InToOrChain: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT regions.id, markets.note AS c9073 +FROM markets FULL JOIN regions ON markets.id = regions.id CROSS JOIN books +WHERE books.price IN (66, 77, 77);" +581,110137,157124,401,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 5,"SELECT t1.id +FROM books AS t0 FULL JOIN customers AS t1 ON t0.id = t1.id RIGHT JOIN departments AS t2 ON t0.id = t2.id +WHERE t1.region_id IN (9, 64) +GROUP BY t1.id +ORDER BY t1.id DESC;" +582,61000,61000,241,500,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT customers.region_id AS c8490 +FROM customers;" +583,3759405,3875500,347832,2500000,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t1.id, t2.id, t2.customer_id +FROM customers AS t0 LEFT JOIN departments AS t1 ON t0.id = t1.id CROSS JOIN orders AS t2;" +584,69714,156902,422,6,transformation 10 ProjectionPushdownThroughJoin: 7; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 4; implementation 4 ImplementJoin: 1; implementation 6 ImplementHashJoin: 1,"SELECT markets.region +FROM markets JOIN customers ON markets.id = customers.id CROSS JOIN books +WHERE ((books.id IN (2, 3, 3, 63) AND markets.region != 'ASIA') OR (customers.region_id IS NOT NULL AND markets.id > 81));" +585,92456,92456,284,313,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT customers.region_id, customers.id AS c9727 +FROM customers +WHERE ((customers.region_id IN (6) OR customers.id IS NULL) OR (customers.id = 66 OR customers.region_id BETWEEN 5 AND 60));" +586,4783,7022,267,4,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 5,"SELECT t0.region, t1.price, SUM(t1.id), SUM(t2.age) +FROM markets AS t0 LEFT JOIN books AS t1 ON t0.id = t1.id RIGHT JOIN users AS t2 ON t1.id = t2.id +GROUP BY t0.region, t1.price;" +587,1317032,1561792,29744,85000,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT users.id +FROM orders FULL JOIN books ON orders.id = books.id CROSS JOIN users +ORDER BY users.id DESC;" +588,87300,1807800,399,50,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 6 ImplementHashJoin: 1,"SELECT t2.region_id AS c6587, t1.id AS c973, t0.id +FROM regions AS t0 CROSS JOIN departments AS t1 JOIN customers AS t2 ON t1.id = t2.id;" +589,1033750,1033750,1719,5000,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT orders.customer_id +FROM orders +WHERE (orders.customer_id >= 10 OR (orders.id >= 28 OR orders.id != 171));" +590,544,544,150,2,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT books.id AS c8304 +FROM books +WHERE ((books.price >= 77 OR books.id != 1) OR books.id > 1);" +591,2620,4452,252,0,transformation 0 JoinCommutativity: 2; transformation 5 FilterPushdownThroughJoin: 4; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT markets.region, markets.note, regions.id AS c1044 +FROM regions RIGHT JOIN markets ON regions.id = markets.id RIGHT JOIN books ON regions.id = books.id +WHERE markets.id = 45;" +592,2676,2676,167,16,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT users.id, users.age +FROM users +WHERE users.id != 12;" +593,1306015,2250610,2076,5000,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT orders.id AS c6161 +FROM orders FULL JOIN departments ON orders.id = departments.id;" +594,1963,1963,148,1,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT users.id, SUM(users.age) +FROM users +WHERE users.age = 18 +GROUP BY users.id;" +595,1308995,6452074,2266,5001,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT users.age, orders.customer_id AS c5702 +FROM orders FULL JOIN users ON orders.id = users.id;" +596,68894,225744,283,4,transformation 11 OuterJoinToInner: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1,"SELECT customers.id, departments.id +FROM departments LEFT JOIN customers ON departments.id = customers.id +WHERE customers.region_id < 10;" +597,1425,1425,450,5,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id, COUNT(*) +FROM departments AS t0 +GROUP BY t0.id;" +598,5219,5219,206,16,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT users.age AS c5631, users.id, COUNT(users.id) +FROM users +GROUP BY users.age, users.id;" +599,9921,24850,440,6,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT departments.id AS c5260, COUNT(books.id) AS c2969, SUM(departments.id) +FROM books CROSS JOIN departments RIGHT JOIN users ON books.id = users.id +GROUP BY departments.id;" +600,27106700,27106700,26255,85000,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT users.age, orders.id +FROM orders CROSS JOIN users +ORDER BY orders.id ASC, users.age ASC;" +601,366,366,119,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.price +FROM books AS t0;" +602,4771,9075,244,16,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT users.id, COUNT(*), SUM(departments.id) +FROM departments FULL JOIN users ON departments.id = users.id +GROUP BY users.id +ORDER BY users.id ASC;" +603,698250,698250,2540,4964,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT orders.customer_id, orders.id AS c3643 +FROM orders +WHERE ((orders.id >= 36 OR orders.customer_id IN (32, 70, 315)) AND orders.id != 113);" +604,1342,1342,210,11,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT employees.department_id AS c8290, employees.id +FROM employees;" +605,1610,1610,165,1,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT regions.id +FROM regions +WHERE regions.id <= 1;" +606,1306757,1552138,3197,4809,implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT orders.id AS c7144, books.id, orders.customer_id +FROM books FULL JOIN departments ON books.id = departments.id RIGHT JOIN orders ON books.id = orders.id +WHERE (orders.id IS NULL OR (books.price = 55 OR orders.id >= 193)) +ORDER BY orders.id DESC, orders.customer_id DESC;" +607,120050,410630,1115,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t2.id, COUNT(t2.price) +FROM customers AS t0 LEFT JOIN regions AS t1 ON t0.id = t1.id CROSS JOIN books AS t2 +GROUP BY t2.id +ORDER BY t2.id ASC;" +608,2030,2030,146,7,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT employees.department_id +FROM employees +WHERE ((employees.department_id >= 6 AND employees.department_id > 34) OR employees.id < 22) +ORDER BY employees.department_id ASC;" +609,421490,421730,839,1500,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT markets.note, books.id AS c2320, customers.region_id AS c7927 +FROM markets RIGHT JOIN books ON markets.id = books.id CROSS JOIN customers +ORDER BY books.id ASC, customers.region_id DESC, markets.note DESC;" +610,2192,5122,171,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 9 FilterLiftThroughJoin: 2; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1,"SELECT regions.id, departments.id +FROM regions LEFT JOIN departments ON regions.id = departments.id +WHERE (regions.id < 5 AND departments.id = 97);" +611,1421966,7100219,2935,11,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT customers.region_id AS c888 +FROM orders FULL JOIN users ON orders.id = users.id LEFT JOIN customers ON orders.id = customers.id +GROUP BY customers.region_id;" +612,1342,1342,147,11,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT employees.id +FROM employees;" +613,2585,5110,191,10,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t1.id +FROM departments AS t0 FULL JOIN regions AS t1 ON t0.id = t1.id;" +614,2257,3832,231,3,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1,"SELECT markets.id, markets.region +FROM employees JOIN markets ON employees.id = markets.id +WHERE markets.note != 'EUROPE';" +615,2960,2960,162,10,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id +FROM regions AS t0 +GROUP BY t0.id;" +616,3290,3290,164,3,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT users.age, users.id +FROM users +WHERE users.id IN (3, 8, 11) +GROUP BY users.age, users.id +ORDER BY users.age DESC;" +617,6476,20350,243,6,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 3,"SELECT t0.age +FROM users AS t0 JOIN employees AS t1 ON t0.id = t1.id FULL JOIN departments AS t2 ON t0.id = t2.id;" +618,2378,5713,275,4,transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t1.id +FROM departments AS t0 JOIN employees AS t1 ON t0.id = t1.id +WHERE t0.id BETWEEN 2 AND 95 +GROUP BY t1.id;" +619,935433,1705433,1969,1,transformation 0 JoinCommutativity: 2; transformation 5 FilterPushdownThroughJoin: 4; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 2; enforcer 0 SortEnforcer: 1,"SELECT customers.id AS c7618, orders.customer_id +FROM books LEFT JOIN orders ON books.id = orders.id LEFT JOIN customers ON books.id = customers.id +WHERE books.id < 2 +ORDER BY customers.id ASC;" +620,1660,1660,147,10,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT regions.id AS c894 +FROM regions +ORDER BY regions.id DESC;" +621,1307580,4001660,3319,5000,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT regions.id, orders.customer_id AS c8455 +FROM orders LEFT JOIN regions ON orders.id = regions.id +ORDER BY regions.id ASC;" +622,2658,3948,185,3,transformation 6 InToOrChain: 1; transformation 7 FilterToJoinPredicate: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT books.id, books.price AS c9301, COUNT(books.price) +FROM books JOIN regions ON books.id = regions.id +WHERE (regions.id NOT IN (3) OR books.id <= 3) +GROUP BY books.id, books.price;" +623,2425,2425,146,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id, SUM(t0.id) AS c6358, COUNT(*) +FROM regions AS t0 +WHERE t0.id IS NULL +GROUP BY t0.id;" +624,422,422,509,1,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT t0.price, t0.id AS c4296 +FROM books AS t0 +WHERE (t0.id < 2 OR (t0.id = 81 OR t0.price IN (21, 30)));" +625,3774,5833,242,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT users.age, markets.id +FROM markets RIGHT JOIN users ON markets.id = users.id +WHERE markets.note IS NOT NULL +GROUP BY users.age, markets.id;" +626,112495,647074,307,501,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t1.age +FROM customers AS t0 FULL JOIN users AS t1 ON t0.id = t1.id;" +627,72910,647310,229,9,transformation 0 JoinCommutativity: 1; transformation 6 InToOrChain: 1; transformation 7 FilterToJoinPredicate: 1; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1,"SELECT t0.id, t1.age AS c4295, t0.region_id +FROM customers AS t0 LEFT JOIN users AS t1 ON t0.id = t1.id +WHERE ((t1.id IN (4, 8) AND t1.id > 35) OR (t1.id <= 8 AND t0.region_id > 2));" +628,109136,155366,391,500,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT markets.region, markets.note, customers.region_id +FROM customers LEFT JOIN markets ON customers.id = markets.id;" +629,1975000,1975000,2290,5000,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT orders.id, orders.customer_id, COUNT(*) +FROM orders +GROUP BY orders.id, orders.customer_id;" +630,1361,1916,176,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT markets.note AS c3373, markets.region +FROM departments RIGHT JOIN markets ON departments.id = markets.id;" +631,1309320,4003400,1610,10,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT t0.id, t1.id +FROM orders AS t0 RIGHT JOIN regions AS t1 ON t0.id = t1.id +GROUP BY t0.id, t1.id +ORDER BY t0.id ASC, t1.id DESC;" +632,9998,13520,356,0,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT t1.region, t1.note +FROM employees AS t0 CROSS JOIN markets AS t1 +WHERE t1.id NOT BETWEEN 1 AND 83 +GROUP BY t1.region, t1.note +ORDER BY t1.region DESC, t1.note DESC;" +633,610000,610000,1334,5000,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT orders.id +FROM orders;" +634,1875,1875,156,11,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT employees.department_id +FROM employees +WHERE employees.id != 49 +ORDER BY employees.department_id DESC;" +635,679260,4002960,999,10,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT regions.id, SUM(orders.id) AS c5946, COUNT(orders.id) +FROM orders JOIN regions ON orders.id = regions.id +GROUP BY regions.id;" +636,422,422,184,1,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id AS c1836, t0.price +FROM books AS t0 +WHERE t0.price > 66;" +637,2083,5583,289,1,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 6 InToOrChain: 1; transformation 7 FilterToJoinPredicate: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT departments.id, employees.department_id AS c5334, employees.id +FROM employees JOIN departments ON employees.id = departments.id +WHERE ((departments.id > 3 OR employees.id IN (5)) AND (employees.department_id IS NOT NULL AND employees.id IN (4, 4, 96))) +ORDER BY employees.id ASC, employees.department_id ASC, departments.id ASC;" +638,445582248,445582248,398994,55000,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT employees.id, orders.id +FROM employees CROSS JOIN users CROSS JOIN orders +GROUP BY employees.id, orders.id;" +639,7453072,65202822,2907,10,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 6 ImplementHashJoin: 1,"SELECT t0.id, t1.id AS c9098 +FROM regions AS t0 CROSS JOIN orders AS t1 LEFT JOIN users AS t2 ON t1.id = t2.id +WHERE t2.age IN (88);" +640,4420,4420,171,8,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.age, SUM(t0.age) AS c7571 +FROM users AS t0 +WHERE t0.id < 13 +GROUP BY t0.age +ORDER BY t0.age ASC;" +641,1734,1734,444,9,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1,"SELECT t1.region, t0.id, t0.price +FROM books AS t0 CROSS JOIN markets AS t1;" +642,4298,6932,230,0,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 3,"SELECT markets.region +FROM markets RIGHT JOIN regions ON markets.id = regions.id LEFT JOIN employees ON regions.id = employees.id +WHERE (employees.department_id >= 25 OR (regions.id != 8 AND markets.note = 'unknown'));" +643,6213,17132,248,17,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 5,"SELECT t0.id AS c6788, t2.id +FROM regions AS t0 RIGHT JOIN users AS t1 ON t0.id = t1.id FULL JOIN markets AS t2 ON t0.id = t2.id +ORDER BY t0.id ASC, t2.id DESC;" +644,678796,4352076,896,10,transformation 0 JoinCommutativity: 1; transformation 6 InToOrChain: 1; transformation 7 FilterToJoinPredicate: 1; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1,"SELECT employees.id, employees.department_id +FROM orders LEFT JOIN employees ON orders.id = employees.id +WHERE ((employees.id NOT IN (2, 4, 20) AND employees.department_id > 3) OR (orders.id NOT IN (150, 198) AND employees.department_id NOT IN (3)));" +645,1048,1048,228,2,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT departments.id, SUM(departments.id) AS c664 +FROM departments +WHERE departments.id < 3 +GROUP BY departments.id;" +646,31399500,59194500,89761,190000,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 5 ImplementCrossJoin: 1,"SELECT markets.region +FROM orders CROSS JOIN users CROSS JOIN markets +WHERE (users.id < 5 OR markets.region != 'EUROPE');" +647,682544,6455844,1136,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT orders.customer_id +FROM orders JOIN users ON orders.id = users.id RIGHT JOIN books ON orders.id = books.id +WHERE books.price BETWEEN 1 AND 6 +GROUP BY orders.customer_id +ORDER BY orders.customer_id DESC;" +648,3917,22032,339,2,transformation 1 JoinAssociativity: 2; transformation 2 FilterSplit: 2; transformation 5 FilterPushdownThroughJoin: 3; transformation 6 InToOrChain: 1; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 6 ImplementHashJoin: 1,"SELECT departments.id, users.age AS c8349, markets.region AS c8908 +FROM departments CROSS JOIN markets RIGHT JOIN users ON markets.id = users.id +WHERE ((markets.region = 'AMERICA' AND markets.note = 'North, South') AND (departments.id NOT BETWEEN 1 AND 3 OR departments.id IN (38, 78, 83)));" +649,3053,5816,232,5,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT departments.id +FROM employees RIGHT JOIN departments ON employees.id = departments.id +WHERE ((employees.department_id = 1 OR departments.id >= 99) OR (employees.id != 40 AND departments.id <= 85));" +650,2558,5738,280,4,transformation 5 FilterPushdownThroughJoin: 2; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT employees.department_id AS c5479, employees.id AS c3162, departments.id +FROM departments LEFT JOIN employees ON departments.id = employees.id +WHERE ((departments.id IN (2) OR departments.id NOT IN (51)) AND departments.id <= 4) +ORDER BY employees.department_id DESC, employees.id DESC;" +651,622,622,147,1,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT departments.id AS c1103 +FROM departments +WHERE ((departments.id <= 1 OR departments.id != 1) AND (departments.id = 50 OR departments.id = 4));" +652,6154,6154,254,16,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT users.id, COUNT(users.age) AS c4429 +FROM users +GROUP BY users.id +ORDER BY users.id ASC;" +653,424011,426070,2060,8500,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT markets.region +FROM users FULL JOIN markets ON users.id = markets.id CROSS JOIN customers +ORDER BY markets.region ASC;" +654,1308980,4005925,3762,510,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT regions.id AS c4375, orders.customer_id AS c4532 +FROM regions FULL JOIN orders ON regions.id = orders.id LEFT JOIN departments ON orders.id = departments.id +GROUP BY regions.id, orders.customer_id;" +655,1342,1342,148,11,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.department_id AS c8831 +FROM employees AS t0;" +656,4261,6566,233,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 3,"SELECT t1.note AS c9009 +FROM users AS t0 LEFT JOIN markets AS t1 ON t0.id = t1.id JOIN books AS t2 ON t0.id = t2.id;" +657,684100,4007800,1066,50,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 6 ImplementHashJoin: 1,"SELECT t2.id AS c86, t0.id AS c971 +FROM orders AS t0 JOIN regions AS t1 ON t0.id = t1.id CROSS JOIN departments AS t2;" +658,433,433,135,0,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.region AS c1228, t0.note, t0.id +FROM markets AS t0 +WHERE (t0.id IN (76) AND (t0.id <= 2 AND t0.region != 'AMERICA')) +ORDER BY t0.note ASC, t0.id ASC, t0.region ASC;" +659,10312,30045,345,7,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT t2.id +FROM markets AS t0 CROSS JOIN regions AS t1 FULL JOIN employees AS t2 ON t1.id = t2.id +WHERE t0.id > 2 +GROUP BY t2.id;" +660,2960,2960,168,10,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id, SUM(t0.id) +FROM regions AS t0 +GROUP BY t0.id;" +661,1366,1366,143,4,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id AS c6502 +FROM regions AS t0 +WHERE (t0.id <= 4 AND (t0.id >= 3 OR t0.id <= 6));" +662,3421000,3421000,6445,25500,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 2,"SELECT t0.id, t2.id, t1.price AS c6596 +FROM customers AS t0 CROSS JOIN books AS t1 CROSS JOIN users AS t2;" +663,75082,442664,404,27,transformation 0 JoinCommutativity: 2; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 6 ImplementHashJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT customers.id AS c1872 +FROM customers JOIN employees ON customers.id = employees.id CROSS JOIN books +WHERE employees.department_id >= 5 +ORDER BY customers.id ASC;" +664,1311267,6464820,2817,5001,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 3,"SELECT orders.id, regions.id AS c6643, users.id +FROM users FULL JOIN orders ON users.id = orders.id FULL JOIN regions ON orders.id = regions.id;" +665,6079,9502,273,4,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT t1.note, COUNT(t2.id), COUNT(*) AS c2705 +FROM employees AS t0 FULL JOIN markets AS t1 ON t0.id = t1.id LEFT JOIN users AS t2 ON t1.id = t2.id +GROUP BY t1.note;" +666,4550,11225,224,2,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t0.id, t1.id AS c8913, COUNT(*) AS c1414 +FROM regions AS t0 JOIN employees AS t1 ON t0.id = t1.id +WHERE t1.department_id <= 3 +GROUP BY t0.id, t1.id;" +667,1775,1775,167,7,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT regions.id AS c2245 +FROM regions +WHERE regions.id > 3 +ORDER BY regions.id DESC;" +668,68586,155366,317,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1,"SELECT books.price, books.id, customers.region_id +FROM customers JOIN books ON customers.id = books.id;" +669,1308995,6452074,1967,5001,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t1.customer_id +FROM users AS t0 FULL JOIN orders AS t1 ON t0.id = t1.id;" +670,1386309,7099068,1898,16,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT users.id AS c9255, users.age, COUNT(*), COUNT(customers.id) +FROM orders RIGHT JOIN users ON orders.id = users.id JOIN customers ON orders.id = customers.id +WHERE customers.id != 40 +GROUP BY users.id, users.age;" +671,4657,10060,215,11,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 3,"SELECT employees.department_id, employees.id AS c3646 +FROM departments FULL JOIN regions ON departments.id = regions.id RIGHT JOIN employees ON departments.id = employees.id;" +672,118952,118952,280,357,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.region_id +FROM customers AS t0 +WHERE (t0.id < 13 OR t0.region_id BETWEEN 4 AND 53) +ORDER BY t0.region_id DESC;" +673,16893483,44722363,23746,11,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t1.id +FROM orders AS t0 CROSS JOIN employees AS t1 LEFT JOIN regions AS t2 ON t0.id = t2.id +WHERE ((t0.id > 126 AND t2.id = 3) OR t0.customer_id = 248) +GROUP BY t1.id;" +674,797300,797300,1370,1500,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT customers.id, books.price, COUNT(customers.region_id), COUNT(*) +FROM customers CROSS JOIN books +GROUP BY customers.id, books.price +ORDER BY customers.id ASC, books.price DESC;" +675,1325000,1325000,1421,5000,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT orders.customer_id, orders.id AS c1243 +FROM orders +ORDER BY orders.id DESC, orders.customer_id DESC;" +676,5252,5252,178,11,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT users.age, users.id, COUNT(users.age), COUNT(*) +FROM users +WHERE users.age NOT BETWEEN 61 AND 64 +GROUP BY users.age, users.id;" +677,637302,175655422,1118,0,transformation 0 JoinCommutativity: 2; transformation 1 JoinAssociativity: 3; transformation 5 FilterPushdownThroughJoin: 3; transformation 9 FilterLiftThroughJoin: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 2,"SELECT books.id +FROM customers JOIN orders ON customers.id = orders.id JOIN books ON customers.id = books.id +WHERE orders.customer_id = 89;" +678,1975000,1975000,2350,5000,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id AS c1600, SUM(t0.customer_id) +FROM orders AS t0 +GROUP BY t0.id;" +679,6711,18875,234,5,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 5,"SELECT t1.age AS c5457, t0.id, t2.id AS c6810 +FROM regions AS t0 LEFT JOIN users AS t1 ON t0.id = t1.id RIGHT JOIN departments AS t2 ON t1.id = t2.id +ORDER BY t1.age DESC, t2.id DESC, t0.id DESC;" +680,39066,56446,395,51,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t2.id, t0.age +FROM users AS t0 CROSS JOIN regions AS t1 RIGHT JOIN markets AS t2 ON t1.id = t2.id;" +681,1914392,3124292,2550,3067,transformation 0 JoinCommutativity: 1; transformation 2 FilterSplit: 5; transformation 5 FilterPushdownThroughJoin: 2; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT orders.id +FROM orders CROSS JOIN books +WHERE ((orders.customer_id < 310 AND books.id >= 3) AND (books.price IN (77) OR books.id < 3)) +GROUP BY orders.id +ORDER BY orders.id DESC;" +682,111672,438832,513,10,implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 3,"SELECT t2.id, t1.region_id AS c4049 +FROM employees AS t0 FULL JOIN customers AS t1 ON t0.id = t1.id LEFT JOIN markets AS t2 ON t0.id = t2.id +WHERE ((t2.note = 'Old World' OR t0.id >= 2) AND t1.id <= 131);" +683,3184,5024,313,3,transformation 0 JoinCommutativity: 1; transformation 2 FilterSplit: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.price, SUM(t1.id), SUM(t1.id) +FROM books AS t0 CROSS JOIN departments AS t1 +WHERE t1.id NOT IN (5, 8, 17, 45) +GROUP BY t0.price;" +684,2682,3922,217,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT markets.region AS c7251 +FROM regions FULL JOIN markets ON regions.id = markets.id +GROUP BY markets.region;" +685,113430,648009,451,501,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT t0.region_id, t0.id, t1.age AS c7926 +FROM customers AS t0 LEFT JOIN users AS t1 ON t0.id = t1.id +ORDER BY t0.region_id ASC, t0.id ASC;" +686,671327,1551972,1261,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT markets.region AS c1187, markets.id, orders.id +FROM markets RIGHT JOIN departments ON markets.id = departments.id LEFT JOIN orders ON markets.id = orders.id +WHERE orders.customer_id BETWEEN 324 AND 365;" +687,2422,6822,282,9,transformation 0 JoinCommutativity: 1; transformation 2 FilterSplit: 3; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT regions.id, departments.id +FROM departments CROSS JOIN regions +WHERE ((regions.id >= 2 AND departments.id IN (2, 3, 5)) AND (departments.id != 1 AND departments.id IN (4, 4, 5)));" +688,3510,5393,248,2,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 5,"SELECT t1.id +FROM markets AS t0 RIGHT JOIN departments AS t1 ON t0.id = t1.id FULL JOIN employees AS t2 ON t0.id = t2.id +WHERE (t1.id < 3 OR (t1.id <= 1 AND t2.id IN (3, 6, 33, 85))) +ORDER BY t1.id ASC;" +689,5828,12772,274,0,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 3,"SELECT t2.id AS c3, t0.id +FROM departments AS t0 LEFT JOIN regions AS t1 ON t0.id = t1.id FULL JOIN users AS t2 ON t0.id = t2.id +WHERE t2.age = 51;" +690,1122700,1122700,2265,8500,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1,"SELECT users.id, customers.id +FROM users CROSS JOIN customers;" +691,110296,226916,340,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT customers.region_id AS c2257, customers.id AS c5252 +FROM departments LEFT JOIN customers ON departments.id = customers.id JOIN books ON customers.id = books.id;" +692,633,633,281,3,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT departments.id +FROM departments +WHERE (departments.id != 5 AND departments.id > 1) +ORDER BY departments.id ASC;" +693,2358,3843,176,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT markets.region AS c9465, markets.id +FROM employees JOIN markets ON employees.id = markets.id +WHERE employees.id > 67 +ORDER BY markets.region DESC, markets.id ASC;" +694,6686,18875,247,5,transformation 0 JoinCommutativity: 2; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT regions.id AS c1390, users.age AS c184 +FROM users RIGHT JOIN regions ON users.id = regions.id JOIN departments ON users.id = departments.id +ORDER BY users.age DESC;" +695,2424,3973,237,2,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT markets.note, employees.department_id AS c9332, SUM(employees.id) AS c3951 +FROM employees RIGHT JOIN markets ON employees.id = markets.id +WHERE markets.region = 'AMERICA' +GROUP BY markets.note, employees.department_id;" +696,1342,1342,145,11,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT employees.department_id, employees.id +FROM employees;" +697,366,366,130,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.price +FROM books AS t0;" +698,3898,16153,220,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT employees.department_id, COUNT(employees.department_id) AS c7934, COUNT(*) AS c8534 +FROM users LEFT JOIN employees ON users.id = employees.id +WHERE ((employees.id IS NULL AND employees.id >= 66) AND employees.id NOT IN (2, 6, 33, 68)) +GROUP BY employees.department_id;" +699,3922,10460,181,6,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT regions.id +FROM employees JOIN regions ON employees.id = regions.id +ORDER BY regions.id ASC;" +700,1922,1922,170,5,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT employees.id, COUNT(employees.department_id) AS c6900 +FROM employees +WHERE ((employees.id < 5 AND employees.id <= 6) OR (employees.id = 67 AND employees.department_id >= 31)) +GROUP BY employees.id;" +701,3315,5079,445,3,transformation 0 JoinCommutativity: 2; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT t1.id, t0.note AS c5073 +FROM markets AS t0 LEFT JOIN employees AS t1 ON t0.id = t1.id CROSS JOIN books AS t2 +WHERE t0.region = 'EUROPE' +ORDER BY t1.id DESC, t0.note DESC;" +702,1425,1425,166,5,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id, COUNT(*) AS c4016, COUNT(t0.id) +FROM departments AS t0 +GROUP BY t0.id;" +703,2690000,2690000,1857,500,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.customer_id AS c8628, SUM(t0.customer_id) +FROM orders AS t0 +GROUP BY t0.customer_id +ORDER BY t0.customer_id DESC;" +704,690944,68842944,1034,51,transformation 0 JoinCommutativity: 1; transformation 1 JoinAssociativity: 3; transformation 5 FilterPushdownThroughJoin: 1; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 6 ImplementHashJoin: 1,"SELECT users.id +FROM orders CROSS JOIN users RIGHT JOIN regions ON orders.id = regions.id +WHERE orders.customer_id BETWEEN 56 AND 310;" +705,1331,1916,185,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.id, t0.price AS c5080, t1.id +FROM books AS t0 LEFT JOIN departments AS t1 ON t0.id = t1.id;" +706,677417,1553644,1000,2,transformation 5 FilterPushdownThroughJoin: 2; transformation 6 InToOrChain: 1; transformation 10 ProjectionPushdownThroughJoin: 6; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 3; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT markets.region, orders.customer_id AS c9499, orders.id AS c2512 +FROM markets JOIN orders ON markets.id = orders.id JOIN regions ON orders.id = regions.id +WHERE markets.note NOT IN ('AMERICA', 'Old World');" +707,1293750,1293750,2125,5000,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT orders.customer_id, orders.id +FROM orders +WHERE (orders.customer_id IS NOT NULL OR (orders.customer_id > 52 AND orders.customer_id <= 48)) +ORDER BY orders.id ASC, orders.customer_id DESC;" +708,2277500,2277500,2754,4991,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.customer_id, t0.id +FROM orders AS t0 +WHERE (t0.customer_id > 257 OR t0.customer_id NOT IN (88, 404)) +GROUP BY t0.customer_id, t0.id;" +709,69108,156778,387,1,transformation 0 JoinCommutativity: 2; transformation 1 JoinAssociativity: 3; transformation 5 FilterPushdownThroughJoin: 3; transformation 6 InToOrChain: 1; transformation 7 FilterToJoinPredicate: 1; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t2.id +FROM markets AS t0 JOIN books AS t1 ON t0.id = t1.id RIGHT JOIN customers AS t2 ON t0.id = t2.id +WHERE ((t0.id IS NOT NULL OR t2.region_id < 10) AND t0.region NOT IN ('AMERICA')) +GROUP BY t2.id;" +710,678892,6452676,986,13,transformation 5 FilterPushdownThroughJoin: 1; transformation 10 ProjectionPushdownThroughJoin: 3; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 2; implementation 6 ImplementHashJoin: 1,"SELECT t1.id, t1.customer_id, t0.id +FROM users AS t0 RIGHT JOIN orders AS t1 ON t0.id = t1.id +WHERE t0.id <= 13;" +711,17610163,22240890,39324,5500,transformation 0 JoinCommutativity: 2; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT orders.customer_id, users.age +FROM users LEFT JOIN employees ON users.id = employees.id CROSS JOIN orders +WHERE users.age IS NOT NULL +GROUP BY orders.customer_id, users.age +ORDER BY orders.customer_id DESC, users.age DESC;" +712,3092,16012,211,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 3; transformation 6 InToOrChain: 1; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT t1.id AS c8042, t0.id +FROM users AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id +WHERE ((t0.age BETWEEN 4 AND 64 AND t1.department_id < 3) AND (t0.age = 33 AND t1.department_id IN (2, 5, 31, 48)));" +713,1361,1916,266,5,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT markets.id, markets.note AS c3697 +FROM markets RIGHT JOIN departments ON markets.id = departments.id;" +714,65900,65900,279,1,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT customers.id AS c1736, customers.region_id AS c1547, COUNT(customers.id) AS c74 +FROM customers +WHERE customers.id = 166 +GROUP BY customers.id, customers.region_id;" +715,115207,660260,563,501,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT t0.id, t2.id, t1.id AS c5704 +FROM customers AS t0 FULL JOIN users AS t1 ON t0.id = t1.id FULL JOIN regions AS t2 ON t0.id = t2.id +ORDER BY t2.id ASC, t0.id DESC;" +716,4024,4024,167,15,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT users.age +FROM users +WHERE (users.age >= 443 OR users.id NOT BETWEEN 13 AND 14) +ORDER BY users.age DESC;" +717,763,763,154,2,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT departments.id +FROM departments +WHERE (departments.id IS NOT NULL AND (departments.id BETWEEN 4 AND 37 OR departments.id >= 4)) +GROUP BY departments.id;" +718,26362,26362,270,187,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1,"SELECT users.age, users.id +FROM employees CROSS JOIN users;" +719,1494000,175610500,2992,5000,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT t1.id AS c3382, t1.customer_id, t0.region_id AS c6097 +FROM customers AS t0 RIGHT JOIN orders AS t1 ON t0.id = t1.id +ORDER BY t1.customer_id DESC;" +720,683600,5753350,1064,15,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 6 ImplementHashJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT t2.id, t1.price, t1.id +FROM departments AS t0 CROSS JOIN books AS t1 JOIN orders AS t2 ON t1.id = t2.id +ORDER BY t1.price DESC, t1.id DESC;" +721,1660,1660,131,10,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id AS c5795 +FROM regions AS t0 +ORDER BY t0.id DESC;" +722,113611,234075,442,14,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT departments.id, users.age AS c5964, COUNT(users.age) +FROM departments RIGHT JOIN customers ON departments.id = customers.id LEFT JOIN users ON customers.id = users.id +GROUP BY departments.id, users.age;" +723,112808,233272,542,500,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 3,"SELECT t1.id AS c5700, t2.age, t2.id +FROM departments AS t0 FULL JOIN customers AS t1 ON t0.id = t1.id FULL JOIN users AS t2 ON t1.id = t2.id +WHERE (t1.id != 184 AND (t1.region_id IS NOT NULL OR t1.region_id >= 82));" +724,3178,5124,322,30,transformation 0 JoinCommutativity: 2; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT regions.id, markets.note +FROM markets FULL JOIN regions ON markets.id = regions.id CROSS JOIN books +WHERE books.id < 37;" +725,1822,1822,145,0,transformation 2 FilterSplit: 1; transformation 3 FilterMerge: 1; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id AS c8444 +FROM users AS t0 +WHERE ((t0.age IN (10, 33, 64, 88) AND t0.age IN (21, 33, 92)) AND (t0.id IS NULL AND t0.age < 64));" +726,22101000,22101000,22796,50000,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT orders.id, regions.id AS c2828, COUNT(*) +FROM orders CROSS JOIN regions +GROUP BY orders.id, regions.id;" +727,4022,5942,337,9,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT markets.id AS c2746, books.price, departments.id +FROM departments CROSS JOIN markets RIGHT JOIN books ON departments.id = books.id +ORDER BY markets.id DESC;" +728,2074,2074,155,17,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT users.id +FROM users;" +729,1308299,4005244,1463,0,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 3,"SELECT orders.customer_id AS c7513 +FROM orders RIGHT JOIN regions ON orders.id = regions.id FULL JOIN departments ON regions.id = departments.id +WHERE orders.id IS NULL;" +730,102133,155563,488,356,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t0.id, t1.id, COUNT(t0.id), COUNT(t0.id) +FROM customers AS t0 LEFT JOIN markets AS t1 ON t0.id = t1.id +WHERE t0.region_id >= 4 +GROUP BY t0.id, t1.id;" +731,868050300,1498050300,984190,2500000,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 5 ImplementCrossJoin: 1,"SELECT orders.id, customers.id +FROM customers CROSS JOIN orders CROSS JOIN markets +WHERE markets.region != 'AMERICA';" +732,83440,423880,351,0,transformation 0 JoinCommutativity: 3; transformation 1 JoinAssociativity: 3; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 6 ImplementHashJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT regions.id +FROM customers JOIN regions ON customers.id = regions.id CROSS JOIN employees +WHERE employees.department_id IS NULL +ORDER BY regions.id ASC;" +733,76799,650219,275,11,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT users.age, COUNT(customers.region_id), SUM(users.age) AS c9006 +FROM users JOIN customers ON users.id = customers.id +GROUP BY users.age;" +734,1308580,4005110,1832,5,transformation 0 JoinCommutativity: 2; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT orders.id +FROM orders LEFT JOIN regions ON orders.id = regions.id JOIN departments ON regions.id = departments.id;" +735,2235,3445,264,0,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT departments.id, books.id AS c2211, books.price +FROM departments CROSS JOIN books +WHERE books.price IS NULL +ORDER BY books.price DESC, departments.id ASC;" +736,69042,155822,271,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t1.id AS c7269, t1.price AS c9813 +FROM customers AS t0 JOIN books AS t1 ON t0.id = t1.id +GROUP BY t1.id, t1.price;" +737,1342,1342,158,11,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.department_id, t0.id AS c7129 +FROM employees AS t0;" +738,74400,74400,297,282,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id, t0.region_id +FROM customers AS t0 +WHERE ((t0.region_id NOT IN (8, 10) AND t0.id BETWEEN 50 AND 141) OR (t0.region_id > 6 AND t0.region_id != 1));" +739,805000,805000,1274,4999,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT orders.customer_id, orders.id +FROM orders +WHERE orders.id != 185;" +740,3581,5472,245,4,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT t1.id, COUNT(t0.id) AS c1028, SUM(t2.id) +FROM books AS t0 LEFT JOIN regions AS t1 ON t0.id = t1.id RIGHT JOIN departments AS t2 ON t0.id = t2.id +GROUP BY t1.id;" +741,433,433,172,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT books.id AS c935, books.price +FROM books +WHERE ((books.price BETWEEN 26 AND 66 AND books.price < 66) AND (books.price BETWEEN 66 AND 91 AND books.price IS NOT NULL)) +ORDER BY books.id ASC, books.price DESC;" +742,109192,155422,483,0,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT markets.note +FROM markets FULL JOIN customers ON markets.id = customers.id +WHERE markets.id < 1;" +743,4529,7294,364,6,transformation 0 JoinCommutativity: 2; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t2.id +FROM markets AS t0 LEFT JOIN users AS t1 ON t0.id = t1.id CROSS JOIN books AS t2 +WHERE ((t0.note = 'Fast lane' AND t1.id != 6) OR t0.note != 'Old World');" +744,850433,1550433,1943,2,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT t1.region, t1.note +FROM orders AS t0 RIGHT JOIN markets AS t1 ON t0.id = t1.id +WHERE t1.id != 3 +ORDER BY t1.region ASC, t1.note ASC;" +745,652500,652500,1159,35,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT orders.id AS c7997 +FROM orders +WHERE orders.customer_id BETWEEN 477 AND 480;" +746,2797,5560,173,5,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT employees.id, departments.id +FROM departments LEFT JOIN employees ON departments.id = employees.id;" +747,110640,401220,327,500,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT customers.region_id AS c5955, customers.id AS c1522 +FROM customers LEFT JOIN regions ON customers.id = regions.id;" +748,744,744,131,1,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT departments.id AS c497 +FROM departments +WHERE departments.id >= 5;" +749,6821,16560,306,12,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT users.age AS c5325, COUNT(*), SUM(users.age) +FROM users FULL JOIN regions ON users.id = regions.id +GROUP BY users.age +ORDER BY users.age ASC;" +750,69552,401122,228,0,transformation 7 FilterToJoinPredicate: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1,"SELECT customers.id AS c5621, regions.id, customers.region_id +FROM regions JOIN customers ON regions.id = customers.id +WHERE (customers.id = 154 OR regions.id = 74);" +751,404006,2238756,903,55,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT departments.id, customers.id, SUM(employees.department_id) +FROM customers CROSS JOIN departments JOIN employees ON customers.id = employees.id +GROUP BY departments.id, customers.id;" +752,5505,15620,308,4,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t1.id AS c7811, t0.id +FROM books AS t0 CROSS JOIN departments AS t1 RIGHT JOIN employees AS t2 ON t0.id = t2.id +WHERE t2.id > 33;" +753,168180,648180,491,0,transformation 0 JoinCommutativity: 1; transformation 2 FilterSplit: 3; transformation 5 FilterPushdownThroughJoin: 2; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.region_id AS c1319 +FROM customers AS t0 CROSS JOIN regions AS t1 +WHERE ((t0.region_id NOT IN (6) OR t1.id IN (8)) AND (t0.id = 53 AND t0.id > 175)) +GROUP BY t0.region_id;" +754,6154,6154,166,16,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT users.id AS c8201 +FROM users +GROUP BY users.id +ORDER BY users.id DESC;" +755,22333,33348,410,5,transformation 0 JoinCommutativity: 2; transformation 6 InToOrChain: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t0.id, COUNT(t1.id) AS c3083 +FROM regions AS t0 RIGHT JOIN departments AS t1 ON t0.id = t1.id CROSS JOIN users AS t2 +WHERE (t2.id < 15 OR (t2.id BETWEEN 5 AND 78 OR t0.id IN (9))) +GROUP BY t0.id;" +756,2292,5692,251,0,transformation 0 JoinCommutativity: 1; transformation 3 FilterMerge: 1; transformation 5 FilterPushdownThroughJoin: 4; transformation 6 InToOrChain: 3; transformation 7 FilterToJoinPredicate: 2; transformation 9 FilterLiftThroughJoin: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT users.id AS c8257, markets.note AS c8225, markets.region AS c1435 +FROM markets JOIN users ON markets.id = users.id +WHERE ((markets.region IN ('unknown') OR users.age BETWEEN 5 AND 33) AND (users.id IN (13) AND markets.id IN (96)));" +757,8530,8530,298,55,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1,"SELECT t1.id +FROM departments AS t0 CROSS JOIN employees AS t1;" +758,968798,1507498,2197,1500,transformation 0 JoinCommutativity: 1; transformation 6 InToOrChain: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT customers.region_id, regions.id AS c2521 +FROM customers CROSS JOIN regions +WHERE ((regions.id >= 8 OR regions.id IN (10, 11, 23)) OR (customers.region_id > 10 AND customers.region_id > 93)) +ORDER BY customers.region_id DESC;" +759,152102,152102,426,500,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT customers.region_id AS c6281, customers.id +FROM customers +WHERE ((customers.id >= 24 OR customers.id IN (10, 19, 31, 38)) OR (customers.id >= 31 OR customers.id != 127)) +ORDER BY customers.id ASC;" +760,80500,80500,305,500,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT customers.region_id AS c9204 +FROM customers +WHERE customers.region_id <= 59;" +761,1875,1875,172,11,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT employees.department_id, employees.id AS c1116 +FROM employees +WHERE employees.id != 13 +ORDER BY employees.department_id DESC, employees.id DESC;" +762,734412,175936222,1099,0,transformation 0 JoinCommutativity: 2; transformation 1 JoinAssociativity: 3; transformation 5 FilterPushdownThroughJoin: 4; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 2,"SELECT t0.id AS c87, t2.id, t1.region_id +FROM orders AS t0 LEFT JOIN customers AS t1 ON t0.id = t1.id JOIN employees AS t2 ON t0.id = t2.id +WHERE (t2.id BETWEEN 5 AND 67 AND t1.region_id = 1);" +763,11939,46176,287,33,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT books.price, users.id, employees.id +FROM books CROSS JOIN employees LEFT JOIN users ON employees.id = users.id;" +764,5864,15252,292,21,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT books.price, employees.id +FROM departments CROSS JOIN books RIGHT JOIN employees ON departments.id = employees.id;" +765,2522,2522,163,12,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.age AS c3911, t0.id, COUNT(t0.age) +FROM users AS t0 +WHERE ((t0.age IS NOT NULL AND t0.age > 18) AND (t0.id < 2 OR t0.age IS NOT NULL)) +GROUP BY t0.age, t0.id;" +766,2072,5583,250,1,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 3; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT t0.department_id +FROM employees AS t0 RIGHT JOIN departments AS t1 ON t0.id = t1.id +WHERE (t1.id <= 79 AND t0.department_id = 6) +ORDER BY t0.department_id DESC;" +767,1956040,3862300,685207,2499996,transformation 0 JoinCommutativity: 2; transformation 6 InToOrChain: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT customers.id +FROM customers LEFT JOIN books ON customers.id = books.id CROSS JOIN orders +WHERE ((books.id < 2 OR orders.id NOT IN (36, 91, 181, 188)) OR customers.id != 3);" +768,432,432,147,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT markets.note +FROM markets +ORDER BY markets.note DESC;" +769,774,774,177,1,transformation 2 FilterSplit: 1; transformation 3 FilterMerge: 1; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT departments.id, COUNT(departments.id), COUNT(departments.id) AS c9545 +FROM departments +WHERE ((departments.id IN (1, 4, 5, 74) AND departments.id > 4) AND (departments.id BETWEEN 4 AND 19 OR departments.id IN (2, 3, 49))) +GROUP BY departments.id +ORDER BY departments.id DESC;" +770,5550,9750,302,0,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT regions.id +FROM departments CROSS JOIN regions +WHERE regions.id IS NULL;" +771,68894,225744,255,5,transformation 11 OuterJoinToInner: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1,"SELECT customers.id +FROM departments LEFT JOIN customers ON departments.id = customers.id +WHERE customers.region_id >= 4;" +772,1460000,1460000,2111,2996,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT orders.id, orders.customer_id, SUM(orders.customer_id) AS c6858 +FROM orders +WHERE orders.customer_id >= 207 +GROUP BY orders.id, orders.customer_id;" +773,2074,2074,152,17,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT users.id, users.age AS c2495 +FROM users;" +774,2311,5692,183,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT users.age, users.id AS c8300 +FROM books LEFT JOIN users ON books.id = users.id +WHERE users.age = 5;" +775,254300,380300,669,387,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT customers.region_id +FROM books CROSS JOIN customers +WHERE customers.id <= 129 +ORDER BY customers.region_id DESC;" +776,2196,3466,174,10,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT regions.id AS c6446, books.price AS c1473, books.id AS c4530 +FROM regions FULL JOIN books ON regions.id = books.id;" +777,1822,1822,131,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT users.age, users.id +FROM users +WHERE users.age = 86;" +778,610000,610000,1117,5000,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.customer_id +FROM orders AS t0;" +779,3318,6933,241,4,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT t1.id, t2.note +FROM departments AS t0 FULL JOIN employees AS t1 ON t0.id = t1.id LEFT JOIN markets AS t2 ON t0.id = t2.id +WHERE t0.id NOT IN (2, 2, 2, 63) +ORDER BY t2.note DESC;" +780,1975000,1975000,1807,500,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT orders.customer_id, COUNT(orders.id), COUNT(*) AS c1649 +FROM orders +GROUP BY orders.customer_id;" +781,1342,1342,138,11,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT employees.id, employees.department_id +FROM employees;" +782,70978,230548,348,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 11 OuterJoinToInner: 2; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT customers.region_id, departments.id AS c1329, COUNT(customers.region_id) +FROM customers RIGHT JOIN departments ON customers.id = departments.id LEFT JOIN regions ON departments.id = regions.id +WHERE customers.region_id >= 60 +GROUP BY customers.region_id, departments.id;" +783,819128,7170288,747,44,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 6 InToOrChain: 2; transformation 7 FilterToJoinPredicate: 2; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 6 ImplementHashJoin: 1,"SELECT employees.id, customers.region_id, users.id +FROM employees CROSS JOIN customers LEFT JOIN users ON employees.id = users.id +WHERE ((customers.id < 45 OR users.age IN (33, 64, 64, 443)) AND users.id IN (4, 11, 15, 73));" +784,1236,1972,175,1,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.id, t0.price +FROM books AS t0 RIGHT JOIN departments AS t1 ON t0.id = t1.id +WHERE t0.price IN (77);" +785,136783,671362,1650,5511,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT users.id +FROM customers LEFT JOIN users ON customers.id = users.id CROSS JOIN employees;" +786,585048,1550433,1412,0,transformation 5 FilterPushdownThroughJoin: 3; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.price, t0.id, t1.customer_id +FROM books AS t0 JOIN orders AS t1 ON t0.id = t1.id +WHERE ((t1.id >= 39 AND t1.customer_id BETWEEN 319 AND 371) AND t0.price = 66) +ORDER BY t0.id DESC, t0.price ASC;" +787,1308995,6452074,2794,5001,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT orders.customer_id, users.id AS c419, orders.id +FROM users RIGHT JOIN orders ON users.id = orders.id;" +788,6599,17548,265,15,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 5,"SELECT users.id +FROM users LEFT JOIN regions ON users.id = regions.id LEFT JOIN books ON regions.id = books.id +WHERE ((users.age <= 18 AND users.id < 6) OR (books.price NOT IN (9, 55, 77, 77) OR users.age NOT IN (11, 18))) +GROUP BY users.id;" +789,2454,3842,203,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT t1.id, t0.price +FROM books AS t0 LEFT JOIN employees AS t1 ON t0.id = t1.id +ORDER BY t0.price DESC;" +790,1308138,1554232,4448,12,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT t1.department_id, COUNT(t1.id), COUNT(t1.id) AS c7952 +FROM markets AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id RIGHT JOIN orders AS t2 ON t1.id = t2.id +GROUP BY t1.department_id;" +791,2908,4903,359,1,transformation 0 JoinCommutativity: 3; transformation 1 JoinAssociativity: 3; transformation 5 FilterPushdownThroughJoin: 4; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.department_id AS c4790, t2.id, COUNT(t0.id) AS c1515, COUNT(t0.department_id) +FROM employees AS t0 JOIN markets AS t1 ON t0.id = t1.id RIGHT JOIN books AS t2 ON t0.id = t2.id +WHERE t1.note = 'North, South' +GROUP BY t0.department_id, t2.id;" +792,1586400,16287420,4020,10,transformation 0 JoinCommutativity: 1; transformation 2 FilterSplit: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 2,"SELECT t1.region, t2.customer_id AS c3350, t1.note +FROM regions AS t0 CROSS JOIN markets AS t1 CROSS JOIN orders AS t2 +WHERE (t1.region IN ('EUROPE') AND t2.id IN (117));" +793,4779,10724,374,51,transformation 0 JoinCommutativity: 2; transformation 2 FilterSplit: 2; transformation 5 FilterPushdownThroughJoin: 4; transformation 6 InToOrChain: 1; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT t1.price, t0.region +FROM markets AS t0 LEFT JOIN books AS t1 ON t0.id = t1.id CROSS JOIN users AS t2 +WHERE ((t1.price = 77 OR t1.price < 77) AND (t0.id IN (2) OR t0.region != 'EUROPE')) +ORDER BY t0.region ASC, t1.price ASC;" +794,2004,3214,265,0,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT t0.id, t1.region AS c4531, t1.note +FROM departments AS t0 CROSS JOIN markets AS t1 +WHERE t0.id IS NULL;" +795,5833,16866,231,11,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.age, t1.department_id AS c5680, t1.id +FROM users AS t0 RIGHT JOIN employees AS t1 ON t0.id = t1.id +WHERE (t1.department_id IS NOT NULL OR t1.department_id IS NULL);" +796,6057,18566,247,3,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 3,"SELECT markets.note, users.id +FROM users FULL JOIN employees ON users.id = employees.id JOIN markets ON users.id = markets.id;" +797,114500,405080,1485,1500,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT books.price +FROM regions RIGHT JOIN customers ON regions.id = customers.id CROSS JOIN books;" +798,2474,3832,411,5,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.note AS c2770, t0.region AS c8751 +FROM markets AS t0 FULL JOIN employees AS t1 ON t0.id = t1.id +WHERE (t1.department_id BETWEEN 5 AND 6 OR t1.department_id <= 11);" +799,1660,1660,189,10,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT regions.id +FROM regions +ORDER BY regions.id ASC;" +800,26882,26882,638,153,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 2,"SELECT users.age AS c9040, users.id, markets.note AS c4935 +FROM users CROSS JOIN markets CROSS JOIN books;" +801,1342,1342,165,11,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id, t0.department_id +FROM employees AS t0;" +802,4536,7186,242,17,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 3,"SELECT t2.age, t1.note, t1.id +FROM departments AS t0 RIGHT JOIN markets AS t1 ON t0.id = t1.id FULL JOIN users AS t2 ON t1.id = t2.id;" +803,1640,1640,173,4,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT departments.id, COUNT(departments.id), COUNT(*) AS c7527 +FROM departments +WHERE departments.id NOT IN (1, 65) +GROUP BY departments.id;" +804,4744,8768,317,48,transformation 0 JoinCommutativity: 1; transformation 2 FilterSplit: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT books.price AS c5825, users.id, books.id +FROM users CROSS JOIN books +WHERE (users.id < 17 AND books.price != 83);" +805,27408,37348,347,7,transformation 0 JoinCommutativity: 1; transformation 6 InToOrChain: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT employees.department_id, employees.id AS c1965, SUM(employees.department_id) +FROM employees CROSS JOIN regions +WHERE ((employees.department_id IN (64) OR employees.department_id >= 11) OR (regions.id = 32 AND employees.department_id != 5)) +GROUP BY employees.department_id, employees.id +ORDER BY employees.id ASC, employees.department_id DESC;" +806,1062,1493,237,1,transformation 5 FilterPushdownThroughJoin: 2; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t0.id +FROM books AS t0 LEFT JOIN markets AS t1 ON t0.id = t1.id +WHERE t1.region != 'AMERICA' +GROUP BY t0.id +ORDER BY t0.id ASC;" +807,317000,937000,1960,4960,transformation 0 JoinCommutativity: 1; transformation 2 FilterSplit: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT t1.id, t0.region_id AS c3707, t0.id +FROM customers AS t0 CROSS JOIN regions AS t1 +WHERE t0.id NOT IN (33, 42, 91, 200);" +808,678304,6451944,985,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 3; transformation 9 FilterLiftThroughJoin: 3; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1,"SELECT t0.age, t1.id, t0.id +FROM users AS t0 RIGHT JOIN orders AS t1 ON t0.id = t1.id +WHERE (t0.id < 4 AND (t1.id >= 151 AND t0.id IS NULL));" +809,1220,1220,150,10,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id +FROM regions AS t0;" +810,174981,303070,1785,1500,transformation 0 JoinCommutativity: 2; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT books.id, users.id, users.age +FROM users LEFT JOIN books ON users.id = books.id CROSS JOIN customers +WHERE books.price <= 95;" +811,633,633,149,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT departments.id +FROM departments +WHERE departments.id = 13 +ORDER BY departments.id DESC;" +812,1660,1660,136,10,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT regions.id +FROM regions +ORDER BY regions.id ASC;" +813,366,366,118,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT books.price AS c8764, books.id +FROM books;" +814,70867,230432,352,1,transformation 0 JoinCommutativity: 1; transformation 10 ProjectionPushdownThroughJoin: 3; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 2; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT t2.region_id, t2.id, t1.id +FROM departments AS t0 FULL JOIN regions AS t1 ON t0.id = t1.id JOIN customers AS t2 ON t1.id = t2.id +WHERE ((t1.id = 8 OR t2.region_id > 57) OR t1.id IS NULL) +ORDER BY t2.region_id DESC, t2.id DESC;" +815,563,563,172,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.note +FROM markets AS t0 +WHERE ((t0.id > 7 AND t0.region != 'EUROPE') AND t0.id < 2) +GROUP BY t0.note;" +816,80500,80500,223,500,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT customers.id +FROM customers +WHERE customers.region_id != 78;" +817,840200,840200,1459,31,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id AS c573, t0.customer_id +FROM orders AS t0 +WHERE (t0.customer_id IN (178, 239) OR t0.customer_id = 84) +ORDER BY t0.customer_id DESC;" +818,4254550,7107050,7951,5098,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT orders.id, orders.customer_id +FROM orders CROSS JOIN regions +WHERE ((orders.customer_id = 337 OR regions.id = 86) OR (regions.id = 10 AND orders.id != 156));" +819,109515,225610,511,500,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.id, t1.id, t1.region_id +FROM departments AS t0 RIGHT JOIN customers AS t1 ON t0.id = t1.id;" +820,1034588,1199375,3651,4960,transformation 2 FilterSplit: 2; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT orders.id, orders.customer_id, COUNT(orders.id) AS c5012, SUM(orders.customer_id) +FROM orders +WHERE ((orders.id != 129 OR orders.customer_id BETWEEN 72 AND 352) AND orders.customer_id NOT IN (191, 289, 289, 404)) +GROUP BY orders.id, orders.customer_id;" +821,5866,12760,247,10,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 3,"SELECT departments.id, users.id AS c1842, users.age +FROM users FULL JOIN departments ON users.id = departments.id RIGHT JOIN regions ON departments.id = regions.id;" +822,2960,2960,203,10,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id, COUNT(t0.id), SUM(t0.id) +FROM regions AS t0 +GROUP BY t0.id;" +823,318040,693140,1917,1,transformation 0 JoinCommutativity: 1; transformation 2 FilterSplit: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT employees.department_id, COUNT(employees.department_id) +FROM employees CROSS JOIN customers +WHERE (customers.id != 157 AND (customers.id NOT BETWEEN 13 AND 55 AND employees.department_id = 34)) +GROUP BY employees.department_id;" +824,4927,22342,314,3,transformation 0 JoinCommutativity: 2; transformation 1 JoinAssociativity: 3; transformation 5 FilterPushdownThroughJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 6 ImplementHashJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT t1.note, t0.id, t2.id +FROM departments AS t0 CROSS JOIN markets AS t1 JOIN users AS t2 ON t0.id = t2.id +WHERE t0.id IN (1, 33) +ORDER BY t0.id DESC, t1.note ASC;" +825,4099,6092,224,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT users.age, markets.note, COUNT(markets.id) +FROM markets LEFT JOIN users ON markets.id = users.id +GROUP BY users.age, markets.note +ORDER BY users.age ASC;" +826,3473,5326,262,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT employees.department_id, markets.note AS c1405, departments.id +FROM markets LEFT JOIN employees ON markets.id = employees.id LEFT JOIN departments ON employees.id = departments.id;" +827,2347,3832,230,3,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT markets.id, employees.id +FROM markets JOIN employees ON markets.id = employees.id +WHERE employees.department_id IS NOT NULL;" +828,1048,1048,249,4,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT departments.id, COUNT(*) AS c4059, SUM(departments.id) AS c8530 +FROM departments +WHERE ((departments.id > 3 OR departments.id IN (2)) OR departments.id IN (3)) +GROUP BY departments.id;" +829,422,422,164,2,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT markets.id, markets.region, markets.note +FROM markets +WHERE (markets.note != 'AMERICA' AND markets.region = 'AMERICA');" +830,3256,3256,151,11,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id +FROM employees AS t0 +GROUP BY t0.id;" +831,1975000,1975000,1793,5000,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT orders.id +FROM orders +GROUP BY orders.id;" +832,677748,1553466,960,3,transformation 0 JoinCommutativity: 1; transformation 10 ProjectionPushdownThroughJoin: 6; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 3; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT orders.customer_id AS c5347, markets.id AS c3605, orders.id +FROM orders JOIN markets ON orders.id = markets.id JOIN regions ON markets.id = regions.id;" +833,8121,22900,335,15,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT t0.note, t0.id +FROM markets AS t0 CROSS JOIN departments AS t1 LEFT JOIN users AS t2 ON t1.id = t2.id +ORDER BY t0.note ASC;" +834,744100,744100,1011,5500,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1,"SELECT customers.id, employees.id, customers.region_id +FROM employees CROSS JOIN customers;" +835,3413,6866,221,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 3,"SELECT employees.id, books.price, departments.id +FROM employees FULL JOIN departments ON employees.id = departments.id JOIN books ON departments.id = books.id;" +836,1278,1518,229,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT books.id, markets.note +FROM books JOIN markets ON books.id = markets.id +WHERE (books.id > 1 OR markets.note != 'unknown') +ORDER BY books.id ASC, markets.note ASC;" +837,3786,5144,300,9,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT books.price, markets.id, books.id AS c9062 +FROM markets LEFT JOIN employees ON markets.id = employees.id CROSS JOIN books;" +838,1313696,1558426,19341,85000,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT markets.region, markets.id AS c8264 +FROM orders FULL JOIN markets ON orders.id = markets.id CROSS JOIN users;" +839,112105,405110,489,500,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT regions.id +FROM customers LEFT JOIN regions ON customers.id = regions.id FULL JOIN departments ON regions.id = departments.id;" +840,1220,1220,149,10,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT regions.id +FROM regions;" +841,111333,403533,398,0,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT markets.note, customers.id, regions.id +FROM regions FULL JOIN customers ON regions.id = customers.id JOIN markets ON customers.id = markets.id +WHERE ((customers.region_id = 41 OR regions.id IS NULL) AND (customers.id IN (32, 65, 97, 184) OR markets.id <= 13)) +ORDER BY markets.note DESC, customers.id ASC, regions.id DESC;" +842,1822,1822,165,1,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT t0.age, t0.id AS c599 +FROM users AS t0 +WHERE (t0.id IN (5) AND t0.age < 88);" +843,15036300,31386300,34689,149880,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 5 ImplementCrossJoin: 1,"SELECT books.id, orders.id, books.price +FROM books CROSS JOIN orders CROSS JOIN regions +WHERE orders.id NOT BETWEEN 50 AND 53;" +844,2988,4903,368,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 4; transformation 7 FilterToJoinPredicate: 1; transformation 11 OuterJoinToInner: 2; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT books.price, COUNT(employees.department_id) AS c475 +FROM employees RIGHT JOIN books ON employees.id = books.id RIGHT JOIN markets ON books.id = markets.id +WHERE (employees.id < 3 AND (employees.department_id IS NULL OR markets.note = 'unknown')) +GROUP BY books.price;" +845,422,422,183,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT markets.note, markets.region AS c5409 +FROM markets +WHERE markets.id IS NULL;" +846,54514,54514,277,26,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT customers.region_id, customers.id +FROM customers +WHERE (customers.region_id = 6 AND customers.id NOT BETWEEN 29 AND 200);" +847,2046000,2046000,1327,10,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT customers.region_id, COUNT(customers.region_id) +FROM regions CROSS JOIN customers +GROUP BY customers.region_id;" +848,67736,89716,348,16,transformation 0 JoinCommutativity: 1; transformation 6 InToOrChain: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT t1.id, t1.age AS c2807 +FROM regions AS t0 CROSS JOIN users AS t1 +WHERE ((t0.id NOT IN (10) OR t1.age = 21) OR t1.id != 15) +GROUP BY t1.id, t1.age +ORDER BY t1.age ASC;" +849,422,422,126,1,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT books.id AS c2855, books.price AS c862 +FROM books +WHERE books.id IN (3);" +850,3381,5636,175,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1,"SELECT users.age, books.price, books.id +FROM users JOIN books ON users.id = books.id;" +851,1319042,4017922,1570,10,transformation 0 JoinCommutativity: 2; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT orders.id +FROM regions LEFT JOIN orders ON regions.id = orders.id CROSS JOIN departments +WHERE ((departments.id <= 8 AND orders.id <= 71) OR regions.id != 7) +GROUP BY orders.id +ORDER BY orders.id DESC;" +852,5210,29176,464,15,transformation 0 JoinCommutativity: 3; transformation 1 JoinAssociativity: 3; transformation 2 FilterSplit: 1; transformation 5 FilterPushdownThroughJoin: 4; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT employees.id AS c5743, regions.id AS c3685 +FROM books CROSS JOIN employees RIGHT JOIN regions ON employees.id = regions.id +WHERE (employees.department_id != 5 AND employees.id IS NOT NULL);" +853,3730,6250,272,10,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT t1.id, t0.id AS c1844 +FROM markets AS t0 CROSS JOIN regions AS t1 +WHERE t0.region != 'AMERICA';" +854,681154,6452074,926,17,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1,"SELECT users.id AS c8433, orders.id, users.age AS c5978 +FROM orders JOIN users ON orders.id = users.id;" +855,822,822,148,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.note, t0.region +FROM markets AS t0 +GROUP BY t0.note, t0.region;" +856,1222,1222,220,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT employees.department_id +FROM employees +WHERE (employees.department_id > 35 AND employees.id = 2);" +857,5837,15576,238,10,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT regions.id AS c3241, users.id, users.age +FROM regions LEFT JOIN users ON regions.id = users.id +WHERE ((users.age != 64 OR users.age >= 33) OR regions.id > 9);" +858,113694,234290,520,393,implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT customers.id +FROM customers FULL JOIN departments ON customers.id = departments.id FULL JOIN users ON customers.id = users.id +WHERE ((users.id != 8 OR departments.id IS NOT NULL) OR customers.id > 122) +GROUP BY customers.id;" +859,4668,7332,274,10,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT regions.id, books.id, COUNT(*) AS c2626 +FROM employees LEFT JOIN books ON employees.id = books.id RIGHT JOIN regions ON employees.id = regions.id +GROUP BY regions.id, books.id;" +860,788,788,190,2,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT departments.id +FROM departments +WHERE departments.id <= 2 +ORDER BY departments.id ASC;" +861,54051700,54051700,46969,500,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT t1.customer_id +FROM users AS t0 CROSS JOIN orders AS t1 +GROUP BY t1.customer_id +ORDER BY t1.customer_id DESC;" +862,1308778,4353954,1550,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT t0.department_id +FROM employees AS t0 LEFT JOIN orders AS t1 ON t0.id = t1.id RIGHT JOIN markets AS t2 ON t1.id = t2.id +WHERE ((t2.note != 'Old World' OR t0.department_id <= 12) OR (t0.department_id != 35 OR t1.id NOT BETWEEN 4 AND 78));" +863,1319320,18364979,1814,32,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT users.id, markets.id +FROM markets CROSS JOIN users LEFT JOIN orders ON markets.id = orders.id +WHERE markets.id > 1 +GROUP BY users.id, markets.id;" +864,3589,5844,218,0,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT t1.id, t1.price AS c2144 +FROM users AS t0 JOIN books AS t1 ON t0.id = t1.id +WHERE (t0.age IS NULL OR t0.age = 21) +GROUP BY t1.id, t1.price +ORDER BY t1.id ASC, t1.price DESC;" +865,12026,12026,322,33,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT books.price, employees.id AS c7767, COUNT(books.price) AS c6906, COUNT(*) +FROM books CROSS JOIN employees +GROUP BY books.price, employees.id;" +866,1875,1875,156,11,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.department_id, t0.id +FROM employees AS t0 +WHERE t0.department_id IS NOT NULL +ORDER BY t0.department_id ASC, t0.id DESC;" +867,2475,2475,146,1,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT users.age AS c9309, users.id +FROM users +WHERE users.age IN (45, 75, 88) +ORDER BY users.id DESC, users.age DESC;" +868,3377,8272,260,1,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 3; transformation 9 FilterLiftThroughJoin: 3; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1,"SELECT users.id +FROM departments FULL JOIN users ON departments.id = users.id +WHERE (departments.id >= 3 AND (users.age = 11 OR users.id > 15));" +869,110128,401488,277,2,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT customers.id AS c2844 +FROM regions LEFT JOIN customers ON regions.id = customers.id +WHERE regions.id IN (6, 9, 32, 41);" +870,3042,3042,142,16,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT users.age +FROM users +WHERE (users.age IN (3, 50, 69, 95) OR users.id != 13);" +871,111030,401610,321,1,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT regions.id AS c476, customers.region_id AS c8704, customers.id +FROM customers RIGHT JOIN regions ON customers.id = regions.id +WHERE (customers.region_id >= 66 OR regions.id = 6);" +872,97942,671362,480,187,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 6 ImplementHashJoin: 1,"SELECT users.age, users.id AS c6703 +FROM users JOIN customers ON users.id = customers.id CROSS JOIN employees;" +873,366,366,125,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT markets.id, markets.region +FROM markets;" +874,6031,12760,219,17,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT t2.id AS c1600, t1.age AS c5206 +FROM departments AS t0 RIGHT JOIN users AS t1 ON t0.id = t1.id LEFT JOIN regions AS t2 ON t0.id = t2.id;" +875,678740,1552690,1047,15,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 6 ImplementHashJoin: 1,"SELECT markets.id +FROM markets JOIN orders ON markets.id = orders.id CROSS JOIN departments;" +876,677111,1551916,1030,5,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT books.id, orders.customer_id, books.price +FROM books JOIN orders ON books.id = orders.id FULL JOIN departments ON orders.id = departments.id;" +877,1220,1220,128,10,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT regions.id +FROM regions;" +878,3215,5194,317,3,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 4; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 3,"SELECT markets.note AS c9740, departments.id +FROM markets JOIN regions ON markets.id = regions.id LEFT JOIN departments ON markets.id = departments.id +WHERE (markets.note != 'North, South' OR markets.region IS NOT NULL);" +879,683000,683000,1259,3,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT t0.customer_id +FROM orders AS t0 +WHERE t0.id IN (24, 100, 169);" +880,110500,110500,284,500,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT customers.id +FROM customers +ORDER BY customers.id DESC;" +881,4422165,2185552525,29179,55000,transformation 0 JoinCommutativity: 1; transformation 1 JoinAssociativity: 2; transformation 5 FilterPushdownThroughJoin: 1; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT orders.id, employees.id AS c6235, SUM(employees.department_id) +FROM orders CROSS JOIN customers RIGHT JOIN employees ON customers.id = employees.id +WHERE customers.id <= 83 +GROUP BY orders.id, employees.id;" +882,610000,610000,1363,5000,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.customer_id AS c4872, t0.id AS c6904 +FROM orders AS t0;" +883,228608,3911463,755,0,transformation 0 JoinCommutativity: 1; transformation 1 JoinAssociativity: 2; transformation 5 FilterPushdownThroughJoin: 1; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT customers.id AS c9594, departments.id +FROM customers CROSS JOIN users RIGHT JOIN departments ON users.id = departments.id +WHERE customers.region_id = 58 +GROUP BY customers.id, departments.id;" +884,748788,4401488,1078,0,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 2,"SELECT regions.id AS c6190 +FROM customers JOIN regions ON customers.id = regions.id JOIN orders ON customers.id = orders.id +WHERE orders.id IN (23, 82, 167, 183);" +885,4876,15210,221,15,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.id, t1.age AS c5455, t1.id AS c7095 +FROM regions AS t0 RIGHT JOIN users AS t1 ON t0.id = t1.id +WHERE t1.age >= 11;" +886,1732,1732,163,3,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id +FROM regions AS t0 +WHERE (t0.id IS NULL OR (t0.id > 7 AND t0.id IS NOT NULL));" +887,5293,12168,184,6,transformation 7 FilterToJoinPredicate: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT employees.id AS c2880, employees.department_id, COUNT(regions.id) AS c3583, SUM(employees.department_id) +FROM regions JOIN employees ON regions.id = employees.id +WHERE ((employees.department_id >= 22 OR regions.id IS NOT NULL) OR (employees.id >= 2 AND employees.id < 66)) +GROUP BY employees.id, employees.department_id;" +888,1220,1220,135,10,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT regions.id AS c6441 +FROM regions;" +889,8756,12584,373,12,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t2.id AS c1119, t1.note AS c6695, COUNT(*) AS c7579, COUNT(*) +FROM employees AS t0 CROSS JOIN markets AS t1 LEFT JOIN books AS t2 ON t0.id = t2.id +GROUP BY t2.id, t1.note;" +890,563,563,192,1,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id, t0.note, COUNT(*) AS c5980 +FROM markets AS t0 +WHERE (t0.region != 'AMERICA' AND (t0.note NOT IN ('AMERICA') OR t0.region != 'AMERICA')) +GROUP BY t0.id, t0.note;" +891,9222,17622,267,20,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 6 ImplementHashJoin: 1,"SELECT t2.region, t1.id +FROM regions AS t0 CROSS JOIN departments AS t1 LEFT JOIN markets AS t2 ON t1.id = t2.id +WHERE ((t2.note NOT IN ('North, South', 'North, South') OR t2.region IS NOT NULL) AND (t2.id IN (1, 3) AND t2.region IN ('AMERICA', 'EUROPE')));" +892,366,366,122,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT markets.id, markets.note +FROM markets;" +893,945706300,945706300,794257,7500000,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 2,"SELECT t0.region, t2.customer_id +FROM markets AS t0 CROSS JOIN customers AS t1 CROSS JOIN orders AS t2;" +894,4121,8425,210,5,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT users.id, users.age, departments.id +FROM users RIGHT JOIN departments ON users.id = departments.id +ORDER BY users.id DESC, departments.id DESC;" +895,422822,424210,1309,5500,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT employees.department_id, customers.region_id, customers.id +FROM books RIGHT JOIN employees ON books.id = employees.id CROSS JOIN customers +ORDER BY employees.department_id DESC;" +896,110500,110500,300,500,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.region_id +FROM customers AS t0 +ORDER BY t0.region_id DESC;" +897,422,422,162,1,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT books.id +FROM books +WHERE (books.id = 3 OR books.price = 77);" +898,1092,1972,321,0,transformation 5 FilterPushdownThroughJoin: 3; transformation 6 InToOrChain: 1; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT t0.id, t1.price, t1.id AS c28 +FROM departments AS t0 LEFT JOIN books AS t1 ON t0.id = t1.id +WHERE ((t0.id >= 4 OR t0.id IN (42, 48)) AND (t0.id BETWEEN 3 AND 56 AND t1.id = 2));" +899,61000,61000,244,500,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT customers.region_id +FROM customers;" +900,2647,7063,365,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 5; transformation 6 InToOrChain: 1; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT employees.id AS c9569 +FROM employees JOIN departments ON employees.id = departments.id FULL JOIN books ON employees.id = books.id +WHERE ((books.price BETWEEN 55 AND 77 OR books.id IN (2, 2)) AND (books.id = 2 AND employees.id = 1)) +GROUP BY employees.id +ORDER BY employees.id ASC;" +901,1056,1296,206,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT books.id, books.price +FROM markets RIGHT JOIN books ON markets.id = books.id;" +902,15960,15960,288,110,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1,"SELECT t1.department_id AS c4915 +FROM regions AS t0 CROSS JOIN employees AS t1;" +903,91125,91125,393,196,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.region_id, t0.id AS c4393 +FROM customers AS t0 +WHERE t0.region_id BETWEEN 2 AND 5 +GROUP BY t0.region_id, t0.id;" +904,14262,47796,546,0,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT markets.id AS c1939 +FROM markets CROSS JOIN users FULL JOIN employees ON markets.id = employees.id +WHERE ((markets.id < 2 OR users.id < 33) AND users.age = 91);" +905,1188612,2251972,2182,0,transformation 0 JoinCommutativity: 2; transformation 5 FilterPushdownThroughJoin: 5; transformation 6 InToOrChain: 1; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 3,"SELECT departments.id, markets.region AS c1481, orders.customer_id +FROM orders LEFT JOIN departments ON orders.id = departments.id FULL JOIN markets ON orders.id = markets.id +WHERE ((orders.id = 16 OR orders.customer_id != 311) AND markets.note IN ('ASIA'));" +906,822,822,157,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT markets.region, markets.note, COUNT(*), COUNT(markets.id) +FROM markets +GROUP BY markets.region, markets.note;" +907,115472,1107972,422,20,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 6 InToOrChain: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t1.id AS c6763, t0.id +FROM regions AS t0 CROSS JOIN books AS t1 LEFT JOIN customers AS t2 ON t0.id = t2.id +WHERE t1.id IN (1, 1, 3, 97) +GROUP BY t1.id, t0.id;" +908,16034,17304,337,44,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT t0.price, t2.department_id AS c7096, COUNT(*) AS c2148 +FROM books AS t0 FULL JOIN regions AS t1 ON t0.id = t1.id CROSS JOIN employees AS t2 +GROUP BY t0.price, t2.department_id +ORDER BY t2.department_id DESC, t0.price DESC;" +909,1610,1610,194,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT regions.id +FROM regions +WHERE regions.id >= 66;" +910,2309766,6019600,3273,5000,transformation 0 JoinCommutativity: 1; transformation 2 FilterSplit: 1; transformation 5 FilterPushdownThroughJoin: 4; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t1.id +FROM users AS t0 JOIN regions AS t1 ON t0.id = t1.id CROSS JOIN orders AS t2 +WHERE (t0.id < 11 AND t1.id = 7);" +911,1307140,4001220,1382,10,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT regions.id +FROM regions LEFT JOIN orders ON regions.id = orders.id;" +912,72243,160692,335,1,implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT markets.id AS c8092 +FROM markets JOIN customers ON markets.id = customers.id LEFT JOIN users ON markets.id = users.id +WHERE (users.id = 17 OR markets.region != 'AMERICA');" +913,366,366,123,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT books.price, books.id +FROM books;" +914,366,366,124,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT markets.region AS c7952 +FROM markets;" +915,433,433,145,3,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT markets.id, markets.note +FROM markets +WHERE (markets.note IS NOT NULL OR markets.id IN (1, 3, 3)) +ORDER BY markets.id ASC;" +916,5055,12333,307,2,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 5,"SELECT employees.department_id, markets.note, markets.id +FROM employees LEFT JOIN regions ON employees.id = regions.id FULL JOIN markets ON regions.id = markets.id +WHERE (markets.region = 'AMERICA' OR (markets.note = 'North, South' AND markets.region != 'unknown')) +ORDER BY markets.note ASC, markets.id ASC;" +917,419320,1259700,1857,1501,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT books.id, customers.id, COUNT(books.id) AS c1217, SUM(customers.id) +FROM customers CROSS JOIN books RIGHT JOIN regions ON books.id = regions.id +GROUP BY books.id, customers.id +ORDER BY customers.id ASC;" +918,26767433,27192133,32958,17,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT t2.price, t0.age, t0.id +FROM users AS t0 CROSS JOIN orders AS t1 FULL JOIN books AS t2 ON t1.id = t2.id +WHERE t2.price = 77 +ORDER BY t0.id ASC;" +919,64542,156983,335,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 5; transformation 6 InToOrChain: 1; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT departments.id +FROM markets LEFT JOIN departments ON markets.id = departments.id FULL JOIN customers ON markets.id = customers.id +WHERE ((markets.note = 'ASIA' AND markets.region = 'EUROPE') AND customers.region_id IN (2, 7)) +ORDER BY departments.id DESC;" +920,1311328,4366665,1984,17,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT t2.age, t0.customer_id AS c4744 +FROM orders AS t0 FULL JOIN employees AS t1 ON t0.id = t1.id JOIN users AS t2 ON t0.id = t2.id +WHERE t0.customer_id IS NOT NULL +ORDER BY t2.age ASC, t0.customer_id ASC;" +921,1309378,4353256,3596,511,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t0.customer_id, t1.id, COUNT(t1.department_id), SUM(t0.id) AS c8528 +FROM orders AS t0 LEFT JOIN employees AS t1 ON t0.id = t1.id +GROUP BY t0.customer_id, t1.id;" +922,3009,3009,141,17,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT users.age +FROM users +ORDER BY users.age DESC;" +923,2690,2690,249,15,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1,"SELECT t1.note AS c7666 +FROM departments AS t0 CROSS JOIN markets AS t1;" +924,677986,4353954,1037,2,transformation 0 JoinCommutativity: 2; transformation 1 JoinAssociativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT orders.customer_id, books.price AS c537 +FROM orders JOIN employees ON orders.id = employees.id JOIN books ON employees.id = books.id +WHERE (employees.department_id NOT IN (1, 5, 6, 14) OR (books.price NOT IN (55, 66, 93) OR employees.id > 3));" +925,432,432,211,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT books.id AS c1708, books.price AS c6613 +FROM books +ORDER BY books.id ASC, books.price ASC;" +926,114285,414844,412,0,implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 3,"SELECT users.age, regions.id AS c873 +FROM regions JOIN users ON regions.id = users.id FULL JOIN customers ON regions.id = customers.id +WHERE regions.id IN (9, 9);" +927,6045,6630,272,30,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t1.price AS c9588, t0.id, t2.id +FROM departments AS t0 JOIN books AS t1 ON t0.id = t1.id CROSS JOIN regions AS t2;" +928,2226,3466,216,10,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.id +FROM regions AS t0 LEFT JOIN markets AS t1 ON t0.id = t1.id;" +929,227750,227750,490,404,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT customers.region_id, customers.id AS c650, COUNT(customers.region_id) +FROM customers +WHERE ((customers.region_id NOT IN (1, 9, 46) OR customers.id = 28) OR (customers.id NOT IN (135) AND customers.id BETWEEN 140 AND 147)) +GROUP BY customers.region_id, customers.id +ORDER BY customers.region_id ASC;" +930,71454,440694,454,3,transformation 0 JoinCommutativity: 2; transformation 5 FilterPushdownThroughJoin: 4; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT departments.id, customers.id AS c5398 +FROM customers LEFT JOIN employees ON customers.id = employees.id LEFT JOIN departments ON employees.id = departments.id +WHERE employees.department_id < 6;" +931,11673302,17248202,24718,50000,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT orders.id, orders.customer_id, regions.id AS c3962 +FROM regions CROSS JOIN orders +WHERE (orders.customer_id != 353 OR (regions.id != 58 OR orders.customer_id = 281)) +ORDER BY orders.id DESC, orders.customer_id ASC, regions.id DESC;" +932,1603000,1751193000,2148,10,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 6 ImplementHashJoin: 1,"SELECT customers.id, orders.id, customers.region_id +FROM customers CROSS JOIN regions JOIN orders ON customers.id = orders.id +WHERE orders.customer_id IN (97, 268);" +933,3667,5498,271,0,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 4,"SELECT t1.note, t1.id, COUNT(t2.id) AS c841 +FROM departments AS t0 LEFT JOIN markets AS t1 ON t0.id = t1.id JOIN regions AS t2 ON t0.id = t2.id +WHERE (t2.id IS NULL OR (t1.id NOT BETWEEN 1 AND 3 AND t1.note != 'Old World')) +GROUP BY t1.note, t1.id;" +934,4370,8492,308,33,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT t1.price, t1.id, t0.department_id AS c8063 +FROM employees AS t0 CROSS JOIN books AS t1 +WHERE (t0.department_id >= 1 OR (t1.price <= 77 OR t0.department_id NOT BETWEEN 18 AND 33));" +935,9698,24047,378,51,transformation 0 JoinCommutativity: 2; transformation 6 InToOrChain: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT t2.id +FROM users AS t0 FULL JOIN employees AS t1 ON t0.id = t1.id CROSS JOIN books AS t2 +WHERE ((t2.price IN (13, 66) AND t1.department_id > 53) OR (t0.id > 4 OR t0.id != 58)) +ORDER BY t2.id ASC;" +936,1775,1775,170,7,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT regions.id +FROM regions +WHERE regions.id <= 7 +ORDER BY regions.id DESC;" +937,1306862,2251982,1792,5,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 5,"SELECT orders.id AS c9311, books.price, orders.customer_id +FROM departments LEFT JOIN orders ON departments.id = orders.id LEFT JOIN books ON orders.id = books.id +ORDER BY orders.id ASC, books.price ASC, orders.customer_id ASC;" +938,4212,8516,207,5,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT users.age, departments.id, users.id +FROM users RIGHT JOIN departments ON users.id = departments.id +WHERE (departments.id != 3 OR (users.id < 5 AND users.id != 17));" +939,633,633,166,1,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id +FROM departments AS t0 +WHERE t0.id = 5 +ORDER BY t0.id ASC;" +940,892,1352,289,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 3; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1,"SELECT t0.note AS c4575 +FROM markets AS t0 JOIN books AS t1 ON t0.id = t1.id +WHERE ((t0.id >= 1 AND t0.region IS NULL) AND t1.price != 66);" +941,1307140,4001220,2081,5000,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT orders.id, orders.customer_id, regions.id +FROM regions RIGHT JOIN orders ON regions.id = orders.id;" +942,3922,10020,172,15,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT employees.department_id, regions.id, employees.id +FROM employees FULL JOIN regions ON employees.id = regions.id;" +943,1317110,12057028,2701,4997,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT t1.id, t1.note +FROM employees AS t0 CROSS JOIN markets AS t1 FULL JOIN orders AS t2 ON t1.id = t2.id +WHERE (t1.region IS NULL OR t1.region = 'unknown');" +944,805000,805000,1368,2978,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT orders.customer_id AS c1298, orders.id +FROM orders +WHERE orders.customer_id <= 300;" +945,1309930,6453009,3132,5001,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT users.age, orders.customer_id, orders.id +FROM orders LEFT JOIN users ON orders.id = users.id +ORDER BY users.age ASC, orders.customer_id DESC, orders.id ASC;" +946,422,422,168,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT markets.region, markets.id +FROM markets +WHERE (markets.region = 'AMERICA' AND (markets.note IS NULL AND markets.id <= 21));" +947,422,422,135,2,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT markets.region +FROM markets +WHERE markets.region != 'EUROPE';" +948,1308242,4003532,1324,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 5,"SELECT orders.customer_id +FROM orders RIGHT JOIN regions ON orders.id = regions.id RIGHT JOIN books ON orders.id = books.id +ORDER BY orders.customer_id DESC;" +949,1309963,6453042,2038,47,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT users.age AS c940, orders.id +FROM orders FULL JOIN users ON orders.id = users.id +WHERE (orders.customer_id IN (84, 108, 233, 290) OR users.age > 64);" +950,1826,1826,149,11,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT employees.department_id AS c483 +FROM employees +ORDER BY employees.department_id DESC;" +951,422,422,139,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT markets.id, markets.note, markets.region AS c2312 +FROM markets +WHERE (markets.id = 1 AND markets.region = 'EUROPE');" +952,563,563,174,1,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT books.id, SUM(books.price) AS c4950 +FROM books +WHERE books.id > 2 +GROUP BY books.id;" +953,1875,1875,153,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.department_id AS c600 +FROM employees AS t0 +WHERE t0.id IS NULL +ORDER BY t0.department_id DESC;" +954,822,822,386,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT books.price, books.id AS c2022 +FROM books +GROUP BY books.price, books.id;" +955,4594,14044,222,0,transformation 0 JoinCommutativity: 2; transformation 5 FilterPushdownThroughJoin: 4; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 3,"SELECT employees.department_id, employees.id AS c9494, departments.id AS c9282 +FROM regions RIGHT JOIN employees ON regions.id = employees.id JOIN departments ON employees.id = departments.id +WHERE employees.department_id >= 33;" +956,113102,410020,348,11,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 3,"SELECT customers.region_id AS c272, regions.id +FROM regions RIGHT JOIN customers ON regions.id = customers.id RIGHT JOIN employees ON customers.id = employees.id;" +957,2383,5263,190,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 3; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t0.id, t1.id, COUNT(*) AS c5678 +FROM departments AS t0 LEFT JOIN regions AS t1 ON t0.id = t1.id +WHERE (t1.id >= 8 AND (t0.id IS NULL AND t1.id NOT BETWEEN 3 AND 10)) +GROUP BY t0.id, t1.id;" +958,113219,647798,312,6,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT users.age, users.id AS c4390, customers.id AS c5582 +FROM users LEFT JOIN customers ON users.id = customers.id +WHERE (users.age <= 22 OR customers.region_id = 1);" +959,2388,3776,175,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT books.price AS c3041 +FROM employees JOIN books ON employees.id = books.id;" +960,2690,2690,311,15,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1,"SELECT t1.id, t1.price +FROM departments AS t0 CROSS JOIN books AS t1;" +961,5785,9268,261,7,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; enforcer 0 SortEnforcer: 4,"SELECT employees.id AS c7515 +FROM books RIGHT JOIN users ON books.id = users.id FULL JOIN employees ON users.id = employees.id +WHERE (employees.department_id >= 12 OR books.price > 55) +ORDER BY employees.id DESC;" +962,3993,8698,189,0,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t1.id AS c6095, COUNT(*) +FROM users AS t0 JOIN departments AS t1 ON t0.id = t1.id +WHERE t0.age IS NULL +GROUP BY t1.id;" +963,417240,1257520,798,1500,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT markets.note, markets.id +FROM markets CROSS JOIN customers LEFT JOIN regions ON customers.id = regions.id;" +964,4729,8792,367,1,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 1; transformation 10 ProjectionPushdownThroughJoin: 3; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 2; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT users.id, users.age +FROM regions FULL JOIN markets ON regions.id = markets.id JOIN users ON regions.id = users.id +WHERE markets.note = 'Old World';" +965,2074,2074,172,17,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id, t0.age +FROM users AS t0;" +966,1705750,1705750,1999,4998,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT orders.id, orders.customer_id +FROM orders +WHERE ((orders.id < 52 OR orders.id NOT IN (71, 82)) OR orders.id = 92) +ORDER BY orders.id ASC, orders.customer_id DESC;" +967,74906,661500,338,6,transformation 0 JoinCommutativity: 2; transformation 1 JoinAssociativity: 2; transformation 5 FilterPushdownThroughJoin: 2; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 2,"SELECT employees.id +FROM customers JOIN users ON customers.id = users.id LEFT JOIN employees ON users.id = employees.id +WHERE employees.department_id <= 31;" +968,563,563,188,1,transformation 2 FilterSplit: 1; transformation 3 FilterMerge: 1; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT markets.region, markets.note, COUNT(markets.id) AS c1532 +FROM markets +WHERE ((markets.region IN ('AMERICA', 'EUROPE') OR markets.region NOT IN ('AMERICA')) AND (markets.note IN ('AMERICA', 'AMERICA', 'North, South', 'unknown') AND markets.id <= 2)) +GROUP BY markets.region, markets.note;" +969,70410,401610,299,1,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1,"SELECT customers.id, customers.region_id +FROM customers JOIN regions ON customers.id = regions.id +WHERE ((customers.region_id IN (5) AND customers.region_id > 5) OR customers.region_id <= 2);" +970,69760,401610,288,10,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1,"SELECT t0.id, t1.id AS c5601 +FROM customers AS t0 LEFT JOIN regions AS t1 ON t0.id = t1.id +WHERE t1.id != 53;" +971,1307948,4351826,1402,11,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT t0.department_id, t1.customer_id AS c5553, t0.id +FROM employees AS t0 LEFT JOIN orders AS t1 ON t0.id = t1.id +ORDER BY t0.id DESC, t1.customer_id DESC, t0.department_id DESC;" +972,1398700,2124700,13080,5500,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 5 ImplementCrossJoin: 1,"SELECT t2.id +FROM customers AS t0 CROSS JOIN markets AS t1 CROSS JOIN employees AS t2 +WHERE t1.note = 'North, South';" +973,366,366,123,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT books.id, books.price AS c662 +FROM books;" +974,2610,3998,194,11,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT employees.id, books.id +FROM books RIGHT JOIN employees ON books.id = employees.id +WHERE ((books.id IS NULL OR employees.id IS NOT NULL) OR books.id BETWEEN 2 AND 14) +ORDER BY employees.id DESC, books.id ASC;" +975,6650300,6650300,4507,5000,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT orders.id, COUNT(books.id), COUNT(books.id) +FROM books CROSS JOIN orders +GROUP BY orders.id;" +976,3323,10063,227,0,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 3; transformation 9 FilterLiftThroughJoin: 3; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT employees.department_id, employees.id AS c8812, COUNT(*), COUNT(*) +FROM employees RIGHT JOIN regions ON employees.id = regions.id +WHERE (regions.id >= 9 AND (employees.id < 2 AND employees.department_id >= 31)) +GROUP BY employees.department_id, employees.id;" +977,1306838,1551778,1363,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 5,"SELECT t1.customer_id AS c4052, t0.id AS c3598, COUNT(*), COUNT(*) AS c317 +FROM books AS t0 LEFT JOIN orders AS t1 ON t0.id = t1.id LEFT JOIN markets AS t2 ON t1.id = t2.id +WHERE ((t2.id NOT IN (61) OR t2.note IN ('Fast lane', 'Fast lane')) OR t0.price NOT IN (55, 55, 66, 77)) +GROUP BY t1.customer_id, t0.id;" +978,5325,7850,315,15,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT regions.id +FROM departments LEFT JOIN regions ON departments.id = regions.id CROSS JOIN books +ORDER BY regions.id ASC;" +979,6925,11125,263,50,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; enforcer 0 SortEnforcer: 1,"SELECT t1.id, t0.id +FROM departments AS t0 CROSS JOIN regions AS t1 +WHERE t0.id <= 5 +ORDER BY t0.id DESC, t1.id DESC;" +980,109202,155432,376,500,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; enforcer 0 SortEnforcer: 3,"SELECT markets.region +FROM markets RIGHT JOIN customers ON markets.id = customers.id +ORDER BY markets.region ASC;" +981,366,366,128,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id, t0.price +FROM books AS t0;" +982,7846500,7846500,14033,25000,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 2; enforcer 0 SortEnforcer: 1,"SELECT t1.id, t0.id, t2.id +FROM customers AS t0 CROSS JOIN regions AS t1 CROSS JOIN departments AS t2 +ORDER BY t0.id ASC, t2.id DESC;" +983,5923,9192,258,3,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 2; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 5,"SELECT markets.id +FROM regions RIGHT JOIN markets ON regions.id = markets.id JOIN users ON regions.id = users.id +GROUP BY markets.id +ORDER BY markets.id ASC;" +984,74556,452315,349,2,transformation 0 JoinCommutativity: 3; transformation 1 JoinAssociativity: 3; transformation 5 FilterPushdownThroughJoin: 4; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT t2.id AS c3607, COUNT(t0.region_id) +FROM customers AS t0 JOIN employees AS t1 ON t0.id = t1.id RIGHT JOIN users AS t2 ON t1.id = t2.id +WHERE t1.id >= 5 +GROUP BY t2.id;" +985,3018,5998,236,5,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t1.id, t0.department_id, COUNT(*) +FROM employees AS t0 RIGHT JOIN departments AS t1 ON t0.id = t1.id +WHERE t0.department_id <= 12 +GROUP BY t1.id, t0.department_id;" +986,210758,210758,487,500,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t0.id AS c6190 +FROM customers AS t0 +WHERE ((t0.id IN (2, 24, 27, 41) OR t0.id NOT IN (75, 109)) OR t0.region_id IN (1, 5, 5)) +GROUP BY t0.id;" +987,15668,45964,305,4,transformation 0 JoinCommutativity: 1; implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1; implementation 7 ImplementMergeJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 3,"SELECT t0.id, COUNT(*) +FROM books AS t0 CROSS JOIN users AS t1 RIGHT JOIN regions AS t2 ON t0.id = t2.id +GROUP BY t0.id +ORDER BY t0.id ASC;" +988,68692,155892,325,2,transformation 0 JoinCommutativity: 1; transformation 5 FilterPushdownThroughJoin: 2; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 2; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 2,"SELECT t0.region_id +FROM customers AS t0 JOIN markets AS t1 ON t0.id = t1.id +WHERE (t1.note != 'Fast lane' OR (t1.note NOT IN ('Fast lane') AND t1.region != 'unknown')) +GROUP BY t0.region_id +ORDER BY t0.region_id DESC;" +989,610,610,137,5,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT t0.id +FROM departments AS t0;" +990,11510800,11510800,37851,75000,implementation 0 ImplementTable: 3; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 2,"SELECT books.price, orders.id, departments.id +FROM books CROSS JOIN orders CROSS JOIN departments;" +991,2132,2132,140,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT users.age +FROM users +WHERE ((users.id NOT BETWEEN 13 AND 16 OR users.id = 34) AND users.id BETWEEN 14 AND 15) +ORDER BY users.age ASC;" +992,1098532,1098532,2037,5000,transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT orders.customer_id AS c1752 +FROM orders +WHERE ((orders.id BETWEEN 69 AND 86 OR orders.customer_id > 243) OR (orders.customer_id NOT IN (224) OR orders.id IS NOT NULL));" +993,99532,99532,341,396,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT t0.region_id +FROM customers AS t0 +WHERE ((t0.region_id BETWEEN 3 AND 5 OR t0.id IS NULL) OR t0.id >= 148);" +994,1133,1133,142,0,transformation 2 FilterSplit: 1; transformation 3 FilterMerge: 1; transformation 6 InToOrChain: 1; implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1; enforcer 0 SortEnforcer: 1,"SELECT regions.id AS c494 +FROM regions +WHERE ((regions.id = 100 OR regions.id NOT IN (5)) AND regions.id IN (29)) +ORDER BY regions.id ASC;" +995,366,366,146,3,implementation 0 ImplementTable: 1; implementation 3 ImplementProjection: 1,"SELECT markets.id, markets.region +FROM markets;" +996,1900450,2614450,2199,418,transformation 0 JoinCommutativity: 1; transformation 8 CrossJoinToJoin: 1; implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT t1.id, t1.region_id AS c505, COUNT(*) +FROM users AS t0 CROSS JOIN customers AS t1 +WHERE t1.id >= 83 +GROUP BY t1.id, t1.region_id;" +997,12910,12910,509,85,implementation 0 ImplementTable: 2; implementation 3 ImplementProjection: 1; implementation 5 ImplementCrossJoin: 1,"SELECT t0.id, t1.id, t0.age AS c6537 +FROM users AS t0 CROSS JOIN departments AS t1;" +998,2798,2798,157,0,implementation 0 ImplementTable: 1; implementation 1 ImplementFilter: 1; implementation 3 ImplementProjection: 1,"SELECT users.id AS c9941 +FROM users +WHERE ((users.id >= 12 OR users.age < 22) AND (users.age IS NULL OR users.id IS NULL));" +999,70213,403663,443,3,transformation 0 JoinCommutativity: 4; transformation 1 JoinAssociativity: 3; transformation 5 FilterPushdownThroughJoin: 3; transformation 6 InToOrChain: 1; transformation 7 FilterToJoinPredicate: 2; transformation 9 FilterLiftThroughJoin: 4; transformation 11 OuterJoinToInner: 1; implementation 0 ImplementTable: 3; implementation 1 ImplementFilter: 2; implementation 3 ImplementProjection: 1; implementation 4 ImplementJoin: 1; implementation 6 ImplementHashJoin: 1; implementation 8 ImplementAggregation: 1; enforcer 0 SortEnforcer: 1,"SELECT markets.note +FROM customers RIGHT JOIN regions ON customers.id = regions.id LEFT JOIN markets ON customers.id = markets.id +WHERE ((markets.id != 52 OR customers.id IN (129, 173)) AND (markets.region != 'ASIA' AND customers.region_id > 1)) +GROUP BY markets.note;" diff --git a/research/rule-lineage-random-1000-summary.csv b/research/rule-lineage-random-1000-summary.csv new file mode 100644 index 0000000..fdb56bc --- /dev/null +++ b/research/rule-lineage-random-1000-summary.csv @@ -0,0 +1,21 @@ +kind,rule_id,rule_name,total_lineage_occurrences,queries_with_rule,query_frequency_percent,max_occurrences_in_one_query +transformation,0,JoinCommutativity,594,517,51.70,4 +transformation,1,JoinAssociativity,75,29,2.90,4 +transformation,2,FilterSplit,70,42,4.20,5 +transformation,3,FilterMerge,7,7,0.70,1 +transformation,5,FilterPushdownThroughJoin,469,191,19.10,5 +transformation,6,InToOrChain,158,153,15.30,3 +transformation,7,FilterToJoinPredicate,27,23,2.30,2 +transformation,8,CrossJoinToJoin,82,82,8.20,1 +transformation,9,FilterLiftThroughJoin,40,19,1.90,4 +transformation,10,ProjectionPushdownThroughJoin,106,30,3.00,7 +transformation,11,OuterJoinToInner,101,91,9.10,2 +implementation,0,ImplementTable,1977,1000,100.00,3 +implementation,1,ImplementFilter,531,487,48.70,2 +implementation,3,ImplementProjection,1038,1000,100.00,4 +implementation,4,ImplementJoin,156,153,15.30,2 +implementation,5,ImplementCrossJoin,157,145,14.50,2 +implementation,6,ImplementHashJoin,171,162,16.20,2 +implementation,7,ImplementMergeJoin,493,394,39.40,2 +implementation,8,ImplementAggregation,319,319,31.90,1 +enforcer,0,SortEnforcer,1494,706,70.60,6 diff --git a/src/stewkk/sql/logic/executor/plan_serializer.cpp b/src/stewkk/sql/logic/executor/plan_serializer.cpp index e219df0..deff5ae 100644 --- a/src/stewkk/sql/logic/executor/plan_serializer.cpp +++ b/src/stewkk/sql/logic/executor/plan_serializer.cpp @@ -661,9 +661,9 @@ struct DotBuilder { int EmitAlternative(const SeqScan& n, const std::optional& metadata) { if (n.alias) { - return Emit(std::format("SeqScan\\n{} AS {}", n.table, *n.alias), metadata); + return Emit(std::format("SeqScan\n{} AS {}", n.table, *n.alias), metadata); } - return Emit(std::format("SeqScan\\n{}", n.table), metadata); + return Emit(std::format("SeqScan\n{}", n.table), metadata); } int EmitAlternative(const PhysicalFilter& n, @@ -700,7 +700,7 @@ struct DotBuilder { const std::optional& metadata) { int lhs = EmitNode(*n.lhs); int rhs = EmitNode(*n.rhs); - int id = Emit(std::format("NL {}\\nON {}", ToString(n.type), ToString(n.qual)), + int id = Emit(std::format("NL {}\nON {}", ToString(n.type), ToString(n.qual)), metadata); EmitEdge(lhs, id); EmitEdge(rhs, id); @@ -708,14 +708,14 @@ struct DotBuilder { } int EmitAlternative(const IndexSeek& n, const std::optional& metadata) { - return Emit(std::format("IndexSeek\\n{}\\n{}", n.table, ToString(n.predicate)), + return Emit(std::format("IndexSeek\n{}\n{}", n.table, ToString(n.predicate)), metadata); } int EmitAlternative(const HashJoin& n, const std::optional& metadata) { int lhs = EmitNode(*n.lhs); int rhs = EmitNode(*n.rhs); - int id = Emit(std::format("Hash {}\\nON {}", ToString(n.type), ToString(n.qual)), + int id = Emit(std::format("Hash {}\nON {}", ToString(n.type), ToString(n.qual)), metadata); EmitEdge(lhs, id); EmitEdge(rhs, id); @@ -725,7 +725,7 @@ struct DotBuilder { int EmitAlternative(const MergeJoin& n, const std::optional& metadata) { int lhs = EmitNode(*n.lhs); int rhs = EmitNode(*n.rhs); - int id = Emit(std::format("Merge {}\\nON {}", ToString(n.type), ToString(n.qual)), + int id = Emit(std::format("Merge {}\nON {}", ToString(n.type), ToString(n.qual)), metadata); EmitEdge(lhs, id); EmitEdge(rhs, id); @@ -743,7 +743,7 @@ struct DotBuilder { keys += k.column; keys += k.dir == Direction::kAsc ? " asc" : " desc"; } - int id = Emit(std::format("Sort\\n{}", keys), metadata); + int id = Emit(std::format("Sort\n{}", keys), metadata); EmitEdge(src, id); return id; } @@ -761,7 +761,7 @@ struct DotBuilder { if (!group_by.empty()) group_by += ", "; group_by += ToString(e); } - int id = Emit(std::format("HashAgg\\nGROUP BY {}\\n{}", group_by, aggs), + int id = Emit(std::format("HashAgg\nGROUP BY {}\n{}", group_by, aggs), metadata); EmitEdge(src, id); return id; @@ -780,7 +780,7 @@ struct DotBuilder { if (!group_by.empty()) group_by += ", "; group_by += ToString(e); } - int id = Emit(std::format("StreamAgg\\nGROUP BY {}\\n{}", group_by, aggs), + int id = Emit(std::format("StreamAgg\nGROUP BY {}\n{}", group_by, aggs), metadata); EmitEdge(src, id); return id; @@ -799,7 +799,7 @@ struct DotBuilder { if (!group_by.empty()) group_by += ", "; group_by += ToString(e); } - int id = Emit(std::format("PartialAgg\\nGROUP BY {}\\n{}", group_by, aggs), + int id = Emit(std::format("PartialAgg\nGROUP BY {}\n{}", group_by, aggs), metadata); EmitEdge(src, id); return id; @@ -818,7 +818,7 @@ struct DotBuilder { if (!group_by.empty()) group_by += ", "; group_by += ToString(e); } - int id = Emit(std::format("FinalAgg\\nGROUP BY {}\\n{}", group_by, aggs), + int id = Emit(std::format("FinalAgg\nGROUP BY {}\n{}", group_by, aggs), metadata); EmitEdge(src, id); return id; diff --git a/src/stewkk/sql/logic/executor/plan_serializer_test.cpp b/src/stewkk/sql/logic/executor/plan_serializer_test.cpp index ed59687..2867e5d 100644 --- a/src/stewkk/sql/logic/executor/plan_serializer_test.cpp +++ b/src/stewkk/sql/logic/executor/plan_serializer_test.cpp @@ -267,7 +267,7 @@ TEST(PlanSerializerDotTest, RendersMetadataWhenPresent) { auto dot = SerializeDot(plan); - EXPECT_THAT(dot, ::testing::HasSubstr("SeqScan\\\\nusers\\ncard=42\\ncost=4200")); + EXPECT_THAT(dot, ::testing::HasSubstr("SeqScan\\nusers\\ncard=42\\ncost=4200")); } } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/memo.cpp b/src/stewkk/sql/logic/optimizer/memo.cpp index f041b94..8393763 100644 --- a/src/stewkk/sql/logic/optimizer/memo.cpp +++ b/src/stewkk/sql/logic/optimizer/memo.cpp @@ -76,6 +76,16 @@ size_t Memo::GroupCount() const { return groups_.size(); } +Memo::ScopedLogicalProvenance::ScopedLogicalProvenance( + Memo& memo, LogicalProvenance provenance) + : memo_(memo), previous_(memo.current_provenance_) { + memo_.current_provenance_ = provenance; +} + +Memo::ScopedLogicalProvenance::~ScopedLogicalProvenance() { + memo_.current_provenance_ = previous_; +} + LogicalExpr* Memo::GetGroup(LogicalOperator root_operator) const { auto key = ToKey(root_operator); return GetGroup(key); @@ -96,6 +106,7 @@ utils::NotNull Memo::AddGroup(LogicalOperator root_operator) { } auto& group = groups_.emplace_back(Group(groups_.size())); auto expr = group.AddLogicalExpr(std::move(root_operator)); + SetProvenanceIfNew(expr); expr_index_[key] = expr; return expr; } @@ -107,10 +118,20 @@ utils::NotNull Memo::AddLogicalExprToGroup(utils::NotNull return g; } auto expr = group->AddLogicalExpr(std::move(root_operator)); + SetProvenanceIfNew(expr); expr_index_[key] = expr; return expr; } +void Memo::SetProvenanceIfNew(utils::NotNull expr) { + if (!current_provenance_) return; + expr->provenance = LogicalExpr::Provenance{ + .rule_id = current_provenance_->rule_id, + .rule_name = current_provenance_->rule_name, + .source = current_provenance_->source, + }; +} + utils::NotNull Memo::Populate(const Operator& op) { return std::visit(utils::Overloaded{ [this](const Table& t) { diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index dd75a5b..b0ab890 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -5,6 +5,7 @@ #include #include #include +#include #include @@ -270,6 +271,52 @@ int64_t CalcCost(utils::NotNull expr, CardinalityEstimates& cardi }, expr->root_operator); } +char RuleLineageKindCode(RuleLineageKind kind) { + switch (kind) { + case RuleLineageKind::kTransformation: + return 't'; + case RuleLineageKind::kImplementation: + return 'i'; + case RuleLineageKind::kEnforcer: + return 'e'; + } + return '?'; +} + +int RuleLineageKindOrder(RuleLineageKind kind) { + switch (kind) { + case RuleLineageKind::kTransformation: + return 0; + case RuleLineageKind::kImplementation: + return 1; + case RuleLineageKind::kEnforcer: + return 2; + } + return 3; +} + +void AddRuleLineageStat(std::unordered_map& stats, + RuleLineageKind kind, size_t rule_id, + std::string_view rule_name) { + std::string key; + key.reserve(rule_name.size() + 32); + key.push_back(RuleLineageKindCode(kind)); + key.push_back(':'); + key += std::to_string(rule_id); + key.push_back(':'); + key += rule_name; + + auto [it, _] = stats.emplace( + std::move(key), + RuleLineageStat{ + .kind = kind, + .rule_id = rule_id, + .rule_name = rule_name, + .count = 0, + }); + ++it->second.count; +} + } // namespace std::vector> GetChildren(utils::NotNull expr) { @@ -577,6 +624,12 @@ void Optimizer::OptimizeGroup( auto op = enforcer->TryBuild(group, required, schema_); if (!op) continue; auto enf_expr = group->AddPhysicalExpr(*op, true); + enf_expr->provenance = PhysicalExpr::Provenance{ + .kind = PhysicalExpr::ProvenanceKind::kEnforcer, + .rule_id = 0, + .rule_name = "SortEnforcer", + .source = nullptr, + }; auto lc = CalcCost(enf_expr, cardinality_, schema_); Log("Enforcer local cost for group {}: {}", group->GetId(), lc); local_cost_[enf_expr.get()] = lc; @@ -713,6 +766,72 @@ PhysicalPlanNode Optimizer::BuildOptimalPlan(G return plan; } +template +void Optimizer::CollectLogicalRuleLineage( + LogicalExpr* expr, std::unordered_map& stats, + std::unordered_set& visited_logical) { + if (!expr || !visited_logical.insert(expr).second) return; + + if (!expr->provenance) return; + AddRuleLineageStat(stats, RuleLineageKind::kTransformation, + expr->provenance->rule_id, expr->provenance->rule_name); + CollectLogicalRuleLineage(expr->provenance->source, stats, visited_logical); +} + +template +void Optimizer::CollectSelectedPlanRuleLineage( + Group* group, PropertySet required, + std::unordered_map& stats, + std::unordered_set& visited_physical, + std::unordered_set& visited_logical) { + WinnerKey key{group, required}; + auto it = winner_.find(key); + if (it == winner_.end() || !it->second.plan) { + throw std::runtime_error{"no optimal plan for group"}; + } + + auto* best_expr = it->second.plan; + const bool first_visit = visited_physical.insert(best_expr).second; + if (first_visit && best_expr->provenance) { + const auto& provenance = *best_expr->provenance; + auto kind = provenance.kind == PhysicalExpr::ProvenanceKind::kEnforcer + ? RuleLineageKind::kEnforcer + : RuleLineageKind::kImplementation; + AddRuleLineageStat(stats, kind, provenance.rule_id, provenance.rule_name); + CollectLogicalRuleLineage(provenance.source, stats, visited_logical); + } + + utils::NotNull best_expr_nn{best_expr}; + auto children = GetChildren(best_expr_nn); + for (size_t i = 0; i < children.size(); ++i) { + CollectSelectedPlanRuleLineage( + children[i].get(), RequiredInputProps(best_expr_nn, required, i, schema_), + stats, visited_physical, visited_logical); + } +} + +template +std::vector Optimizer::GetSelectedPlanRuleLineage() { + std::unordered_map stats; + std::unordered_set visited_physical; + std::unordered_set visited_logical; + CollectSelectedPlanRuleLineage( + root_->group.get(), global_required_, stats, visited_physical, visited_logical); + + std::vector result; + result.reserve(stats.size()); + for (const auto& [_, stat] : stats) { + result.push_back(stat); + } + std::ranges::sort(result, [](const RuleLineageStat& lhs, const RuleLineageStat& rhs) { + if (RuleLineageKindOrder(lhs.kind) != RuleLineageKindOrder(rhs.kind)) { + return RuleLineageKindOrder(lhs.kind) < RuleLineageKindOrder(rhs.kind); + } + return lhs.rule_id < rhs.rule_id; + }); + return result; +} + template void Optimizer::RunSearch(Limit limit) { Log("Starting optimization"); diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index b7678a1..a04f77f 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -50,6 +50,16 @@ SchemaCatalog MakeTestSchema() { }); } +size_t RuleLineageCount(const std::vector& stats, + RuleLineageKind kind, std::string_view rule_name) { + for (const auto& stat : stats) { + if (stat.kind == kind && stat.rule_name == rule_name) { + return stat.count; + } + } + return 0; +} + TEST(CardinalityEstimatesTest, AppliesFilterHeuristics) { ASSERT_THAT(EstimateCardinality("SELECT * FROM users WHERE users.id = 1;", {{"users", 1000}}), Eq(100)); @@ -111,7 +121,7 @@ TEST(OptimizerTest, Simple) { ASSERT_THAT(SerializeDot(got), Eq("digraph G { rankdir=BT;\n" - " n0 [label=\"SeqScan\\\\nusers\\ncard=10\\ncost=1000\"]\n" + " n0 [label=\"SeqScan\\nusers\\ncard=10\\ncost=1000\"]\n" "}\n")); ASSERT_THAT(optimizer.GetBestCost(), Eq(1000)); } @@ -282,6 +292,53 @@ TEST(OptimizerTest, PushesSelectiveFilterIntoHashBuildSide) { " (SeqScan lineorder lo))")); } +TEST(OptimizerTest, TracksSelectedPlanRuleLineage) { + std::stringstream s{ + "SELECT * FROM lineorder AS lo " + "JOIN supplier AS s ON lo.suppkey = s.id " + "WHERE s.region = 'AMERICA';"}; + Operator op = GetAST(s).value().op; + SchemaCatalog schema({ + {"lineorder", {Attribute{"lineorder", "suppkey"}, Attribute{"lineorder", "value"}}}, + {"supplier", {Attribute{"supplier", "id"}, Attribute{"supplier", "region"}}}, + }); + Optimizer optimizer(op, MakeMainRules(), CardinalityEstimates({ + {"lineorder", 6000}, + {"supplier", 100}, + }), std::move(schema)); + + auto got = optimizer.Optimize(); + auto stats = optimizer.GetSelectedPlanRuleLineage(); + + ASSERT_THAT(Serialize(got), + HasSubstr("(PhysicalFilter (= (attr s region) (str \"AMERICA\")) " + "(SeqScan supplier s))")); + EXPECT_THAT(RuleLineageCount(stats, RuleLineageKind::kTransformation, + "FilterPushdownThroughJoin"), Gt(0)); + EXPECT_THAT(RuleLineageCount(stats, RuleLineageKind::kImplementation, + "ImplementTable"), Eq(2)); + EXPECT_THAT(RuleLineageCount(stats, RuleLineageKind::kImplementation, + "ImplementFilter"), Eq(1)); + EXPECT_THAT(RuleLineageCount(stats, RuleLineageKind::kImplementation, + "ImplementHashJoin"), Eq(1)); +} + +TEST(OptimizerTest, TracksSelectedPlanEnforcerLineage) { + std::stringstream s{"SELECT * FROM users ORDER BY users.id;"}; + auto parsed = GetAST(s).value(); + SchemaCatalog schema({{"users", {Attribute{"users", "id"}, Attribute{"users", "age"}}}}); + PropertySet required{SortProperty{*parsed.required_order}}; + Optimizer optimizer(parsed.op, MakeMainRules(), {}, std::move(schema), std::move(required)); + + auto got = optimizer.Optimize(); + auto stats = optimizer.GetSelectedPlanRuleLineage(); + + ASSERT_THAT(Serialize(got), Eq("(Sort (keys users.id Asc) (SeqScan users))")); + EXPECT_THAT(RuleLineageCount(stats, RuleLineageKind::kEnforcer, "SortEnforcer"), Eq(1)); + EXPECT_THAT(RuleLineageCount(stats, RuleLineageKind::kImplementation, + "ImplementTable"), Eq(1)); +} + TEST(OptimizerTest, UsesHashJoinForConjunctiveEquiJoinPredicate) { std::stringstream s{ "SELECT * FROM lineorder AS lo " diff --git a/src/stewkk/sql/logic/optimizer/rules.cpp b/src/stewkk/sql/logic/optimizer/rules.cpp index 204386e..48dc4d5 100644 --- a/src/stewkk/sql/logic/optimizer/rules.cpp +++ b/src/stewkk/sql/logic/optimizer/rules.cpp @@ -33,6 +33,33 @@ Rules<14, 9> MakeMainRules(IndexCatalog indexes) { std::make_unique(), std::make_unique(), }, + .transformation_rule_names = { + "JoinCommutativity", + "JoinAssociativity", + "FilterSplit", + "FilterMerge", + "FilterPushdownThroughProjection", + "FilterPushdownThroughJoin", + "InToOrChain", + "FilterToJoinPredicate", + "CrossJoinToJoin", + "FilterLiftThroughJoin", + "ProjectionPushdownThroughJoin", + "OuterJoinToInner", + "AggregationPushdownThroughJoin", + "AggregationJoinTranspose", + }, + .implementation_rule_names = { + "ImplementTable", + "ImplementFilter", + "ImplementIndexSeek", + "ImplementProjection", + "ImplementJoin", + "ImplementCrossJoin", + "ImplementHashJoin", + "ImplementMergeJoin", + "ImplementAggregation", + }, }; } @@ -47,6 +74,15 @@ Rules<0, 6> MakeNaiveRules() { std::make_unique(), std::make_unique(), }, + .transformation_rule_names = {}, + .implementation_rule_names = { + "ImplementTable", + "ImplementFilter", + "ImplementProjection", + "ImplementJoin", + "ImplementCrossJoin", + "ImplementAggregation", + }, }; } diff --git a/src/stewkk/sql/logic/optimizer/rules_applier.cpp b/src/stewkk/sql/logic/optimizer/rules_applier.cpp index 5d0758e..1b81376 100644 --- a/src/stewkk/sql/logic/optimizer/rules_applier.cpp +++ b/src/stewkk/sql/logic/optimizer/rules_applier.cpp @@ -17,6 +17,14 @@ template utils::NotNull RulesApplier::Apply( TransformationRuleId rule, utils::NotNull expr, Memo& memo, RuleContext& ctx) { applied_transformation_rules_[expr.get()][rule.value] = 1; + Memo::ScopedLogicalProvenance provenance{ + memo, + Memo::LogicalProvenance{ + .rule_id = rule.value, + .rule_name = rules_.transformation_rule_names[rule.value], + .source = expr.get(), + }, + }; return rules_.transformation_rules[rule.value]->Apply(expr, memo, ctx); } @@ -29,7 +37,28 @@ bool RulesApplier::IsApplicable(Implementation template std::vector> RulesApplier::Apply(ImplementationRuleId rule, utils::NotNull expr, Memo& memo) { applied_implementation_rules_[expr.get()][rule.value] = 1; - return rules_.implementation_rules[rule.value]->Apply(expr, memo); + auto result = rules_.implementation_rules[rule.value]->Apply(expr, memo); + for (auto physical_expr : result) { + physical_expr->provenance = PhysicalExpr::Provenance{ + .kind = PhysicalExpr::ProvenanceKind::kImplementation, + .rule_id = rule.value, + .rule_name = rules_.implementation_rule_names[rule.value], + .source = expr.get(), + }; + } + return result; +} + +template +std::string_view RulesApplier::GetTransformationRuleName( + TransformationRuleId rule) const { + return rules_.transformation_rule_names[rule.value]; +} + +template +std::string_view RulesApplier::GetImplementationRuleName( + ImplementationRuleId rule) const { + return rules_.implementation_rule_names[rule.value]; } template class RulesApplier<14, 9>; diff --git a/src/stewkk/sql/main.cpp b/src/stewkk/sql/main.cpp index febe06d..74ae0cd 100644 --- a/src/stewkk/sql/main.cpp +++ b/src/stewkk/sql/main.cpp @@ -175,6 +175,31 @@ void PrintRelation(const Relation& rel, bool preserve_order) { for (const auto& r : rows) std::cout << r << '\n'; } +std::string_view RuleLineageKindName(RuleLineageKind kind) { + switch (kind) { + case RuleLineageKind::kTransformation: + return "transformation"; + case RuleLineageKind::kImplementation: + return "implementation"; + case RuleLineageKind::kEnforcer: + return "enforcer"; + } + return "unknown"; +} + +void PrintRuleLineageStats(const std::vector& stats) { + std::cerr << "Selected plan rule lineage:\n"; + if (stats.empty()) { + std::cerr << " \n"; + return; + } + for (const auto& stat : stats) { + std::cerr << " " << RuleLineageKindName(stat.kind) << ' ' + << stat.rule_id << ' ' << stat.rule_name + << ": " << stat.count << '\n'; + } +} + boost::asio::awaitable> RunQuery(const std::string& data_dir, const PhysicalPlanNode& plan) { CsvDirSequentialScanner seq_scan{data_dir}; @@ -264,6 +289,9 @@ int main(int argc, char** argv) { LoadConstraintCatalogFromCsvDir(args.data_dir)); plan = optimizer.Optimize(); plan_cost = optimizer.GetBestCost(); + if (args.stats) { + PrintRuleLineageStats(optimizer.GetSelectedPlanRuleLineage()); + } PropertySet naive_required = parsed.required_order ? PropertySet{SortProperty{*parsed.required_order}} From b2a1810f6d4791f0390f0081a079193985f688c1 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 15 Jun 2026 18:08:16 +0300 Subject: [PATCH 115/120] Done? --- .../stewkk/sql/logic/executor/executor.hpp | 2 +- report/naive-vs-optimizer.png | Bin 25109 -> 27856 bytes report/optimizer-speedup.png | Bin 28446 -> 18338 bytes report/vkr.tex | 142 +- research/benchmark-results/ssb-sf01.json | 3240 +++++++++++++++++ research/benchmarks.ipynb | 12 +- src/stewkk/sql/logic/executor/executor.cpp | 54 +- src/stewkk/sql/logic/optimizer/rule_test.cpp | 30 + .../aggregation_pushdown_through_join.cpp | 33 +- 9 files changed, 3412 insertions(+), 101 deletions(-) create mode 100644 research/benchmark-results/ssb-sf01.json diff --git a/include/stewkk/sql/logic/executor/executor.hpp b/include/stewkk/sql/logic/executor/executor.hpp index 0cfc999..8549848 100644 --- a/include/stewkk/sql/logic/executor/executor.hpp +++ b/include/stewkk/sql/logic/executor/executor.hpp @@ -77,7 +77,7 @@ class Executor { TuplesChannel& tuples_chan); boost::asio::awaitable ExecuteMergeJoin(const MergeJoin& join, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan); - boost::asio::awaitable ExecuteHashAggregate(const PhysicalAggregation& agg, + boost::asio::awaitable ExecuteHashAggregate(PhysicalAggregation agg, AttributesInfoChannel& attr_chan, TuplesChannel& tuples_chan); boost::asio::awaitable ExecuteStreamAggregate(const PhysicalStreamAggregation& agg, diff --git a/report/naive-vs-optimizer.png b/report/naive-vs-optimizer.png index ed0bc7a05323f292206b5b3f5563f7dd9a4c2c34..8a320751ea674a9dd56d90ae5216750fdbf36326 100644 GIT binary patch literal 27856 zcmeIbXINF~vMo#p+pKMym0$ox5K$2k5KvKZfr6lb62(AHk~3->T2#=b7yv;8B!fuK zW>Cq3NH&m@A~}DffMs{@v-iGdpL@UOxj*iDIuO>H^PO*~8Z~NEadE%A%*<&^rm?ZH z&1CG^sl>)M$%KvVhvq52;3uvo#*gtoF{@pNtPYszTG{GYoM79hV`Xk^W@T(}beYWw 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\item разбиение конъюнкции на последовательность фильтров; + \item разбиение фильтра на конъюнкты; \item объединение последовательных фильтров; \item проталкивание фильтра через проекцию; \item проталкивание фильтра во входы соединения; - \item преобразование выражения \verb|IN| в цепочку сравнений. + \item преобразование выражения \verb|IN| в цепочку сравнений; + \item перенос фильтра в предикат соединения; + \item превращение декартова произведения в соединение; + \item поднятие фильтра над соединением; + \item проталкивание проекции через соединение; + \item преобразование внешнего соединения во внутреннее; + \item проталкивание агрегации ниже соединения; + \item перестановка агрегации и соединения. \end{itemize} И следующие правила реализации: \begin{itemize} \item последовательное сканирование таблицы; \item фильтрация; + \item сканирование по индексу; \item проекция; - \item хэш-агрегация; - \item потоковая агрегация; - \item декартово произведение вложенными циклами; \item соединение вложенными циклами; + \item декартово произведение вложенными циклами; \item хеш-соединение; - \item соединение слиянием. + \item соединение слиянием; + \item хэш-агрегация; + \item потоковая агрегация. \end{itemize} Данный набор правил все основные алгоритмы, реализующие операторы реляционной @@ -2397,20 +2405,6 @@ \subsection{Руководство пользователя} формат проекта и запускает программу в режиме \verb|--check-reachable|, как показано на листинге~\ref{lst:reach-fuzz}. -\begin{listing}[H] - \caption{Запуск фаззеров.} - \label{lst:reach-fuzz} - \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} -docker compose up -d -python -m research.fuzz.reach_fuzz \ - --cli build/bin/sql \ - --data-dir test/static/executor/test_data -python -m research.fuzz.diff_fuzz \ - --cli build/bin/sql \ - --data-dir test/static/executor/test_data - \end{minted} -\end{listing} - % \begin{figure}[H] % \centering % \includegraphics[width=0.8\textwidth]{reach-fuzz.png} @@ -2573,10 +2567,10 @@ \subsection{Дифференциальный анализ и тестирова \label{fig:reach-fuzz-cross-mssql} \end{figure} -Модельная СУБД для того же запроса строит другой достижимый план, представленный -на рисунке~\ref{fig:reach-fuzz-cross-ours}. В нем фильтр по таблице -\verb|markets| используется как предикат внутреннего соединения, а не как -отдельный оператор \verb|PhysicalFilter| над чтением этой таблицы. +Модельная СУБД для того же запроса строит план, представленный на +рисунке~\ref{fig:reach-fuzz-cross-ours}. В нем фильтр по таблице \verb|markets| +используется как предикат внутреннего соединения, а не как отдельный оператор +\verb|PhysicalFilter| над чтением этой таблицы. \begin{figure}[H] \centering @@ -2628,8 +2622,8 @@ \subsection{Дифференциальный анализ и тестирова трех повторений запросов SSB. На рисунке~\ref{fig:benchmark-speedup} приведено относительное ускорение -запросов на том же наборе данных. Видно, что оптимизатор дает ускорение на -$20-50\%$, а на накоторых запросах в $6$ и более раз. +запросов на том же наборе данных. Видно, что оптимизатор дает ускорение в $2$ и +более раз а на накоторых запросах в $30$ раз. \begin{table}[H] \centering @@ -2639,23 +2633,80 @@ \subsection{Дифференциальный анализ и тестирова \hline Запрос & \texttt{Naive}, мс & \texttt{Optimized}, мс & Ускорение \\ \hline - \texttt{q1.1} & 8.11 & 6.51 & 1.25 \\ - \texttt{q1.2} & 8.20 & 6.59 & 1.24 \\ - \texttt{q1.3} & 8.00 & 6.58 & 1.22 \\ - \texttt{q2.1} & 8.80 & 9.33 & 0.94 \\ - \texttt{q2.2} & 8.84 & 6.18 & 1.43 \\ - \texttt{q2.3} & 8.68 & 5.80 & 1.50 \\ - \texttt{q3.1} & 35.67 & 5.90 & 6.04 \\ - \texttt{q3.2} & 35.88 & 5.58 & 6.43 \\ - \texttt{q3.3} & 38.05 & 5.86 & 6.49 \\ - \texttt{q3.4} & 37.12 & 5.77 & 6.43 \\ - \texttt{q4.1} & 9.06 & 12.21 & 0.74 \\ - \texttt{q4.2} & 9.05 & 11.12 & 0.81 \\ - \texttt{q4.3} & 9.28 & 6.31 & 1.47 \\ + \texttt{q1.1} & 172.82 & 61.51 & 2.81 \\ + \texttt{q1.2} & 167.64 & 63.25 & 2.65 \\ + \texttt{q1.3} & 166.89 & 64.17 & 2.60 \\ + \texttt{q2.1} & 301.34 & 65.05 & 4.63 \\ + \texttt{q2.2} & 300.50 & 58.72 & 5.12 \\ + \texttt{q2.3} & 303.55 & 58.67 & 5.17 \\ + \texttt{q3.1} & 1629.95 & 63.39 & 25.71 \\ + \texttt{q3.2} & 1590.02 & 56.05 & 28.37 \\ + \texttt{q3.3} & 1583.15 & 54.40 & 29.10 \\ + \texttt{q3.4} & 1587.09 & 54.16 & 29.30 \\ + \texttt{q4.1} & 351.49 & 161.92 & 2.17 \\ + \texttt{q4.2} & 354.18 & 163.33 & 2.17 \\ + \texttt{q4.3} & 350.95 & 59.86 & 5.86 \\ \hline \end{tabular} \end{table} +Дополнительно была собрана статистика по правилам, которые участвовали в +создании оптимальных планов на 1000 случайных запросах +(таблица~\ref{tbl:rule-lineage-random}). Это реализовано через восстановление +цепочки правил, через которую были получены выражения, вошедшие в итоговый план. +Это позволяет оценить, какие правила чаще входят в оптимальные планы, что может +быть полезно для определения подмножеств правил для многоэтапной оптимизации. + +\begin{table}[H] + \centering + \scriptsize + \setlength{\tabcolsep}{3pt} + \caption{Правила в происхождении выбранных планов на 1000 случайных запросах.} + \label{tbl:rule-lineage-random} + \begin{tabular}{|p{2.6cm}|p{5.0cm}|r|r|r|r|} + \hline + Тип & Правило & Всего & Запросов & Доля, \% & Максимум \\ + \hline + Трансформация & \texttt{JoinCommutativity} & 594 & 517 & 51.70 & 4 \\ + Трансформация & \texttt{JoinAssociativity} & 75 & 29 & 2.90 & 4 \\ + Трансформация & \texttt{FilterSplit} & 70 & 42 & 4.20 & 5 \\ + Трансформация & \texttt{FilterMerge} & 7 & 7 & 0.70 & 1 \\ + Трансформация & \makecell[l]{\texttt{FilterPushdown}\\\texttt{ThroughJoin}} & 469 & 191 & 19.10 & 5 \\ + Трансформация & \texttt{InToOrChain} & 158 & 153 & 15.30 & 3 \\ + Трансформация & \texttt{FilterToJoinPredicate} & 27 & 23 & 2.30 & 2 \\ + Трансформация & \texttt{CrossJoinToJoin} & 82 & 82 & 8.20 & 1 \\ + Трансформация & \texttt{FilterLiftThroughJoin} & 40 & 19 & 1.90 & 4 \\ + Трансформация & \makecell[l]{\texttt{ProjectionPushdown}\\\texttt{ThroughJoin}} & 106 & 30 & 3.00 & 7 \\ + Трансформация & \texttt{OuterJoinToInner} & 101 & 91 & 9.10 & 2 \\ + Реализация & \texttt{ImplementTable} & 1977 & 1000 & 100.00 & 3 \\ + Реализация & \texttt{ImplementFilter} & 531 & 487 & 48.70 & 2 \\ + Реализация & \texttt{ImplementProjection} & 1038 & 1000 & 100.00 & 4 \\ + Реализация & \texttt{ImplementJoin} & 156 & 153 & 15.30 & 2 \\ + Реализация & \texttt{ImplementCrossJoin} & 157 & 145 & 14.50 & 2 \\ + Реализация & \texttt{ImplementHashJoin} & 171 & 162 & 16.20 & 2 \\ + Реализация & \texttt{ImplementMergeJoin} & 493 & 394 & 39.40 & 2 \\ + Реализация & \texttt{ImplementAggregation} & 319 & 319 & 31.90 & 1 \\ + Обеспечение свойства & \texttt{SortEnforcer} & 1494 & 706 & 70.60 & 6 \\ + \hline + \end{tabular} +\end{table} + +В таблице~\ref{tbl:rule-lineage-random} столбец <<Всего>> обозначает суммарное +число уникальных выражений в происхождении выбранных планов, полученных данным +правилом. Столбец <<Запросов>> показывает, в скольких запросах правило +встретилось хотя бы один раз, а <<Максимум>>~--- максимальное число таких +выражений для одного запроса. Наиболее часто среди логических преобразований +встречается коммутативность соединения, что показывает важность выбора +правильного порядка соединений. Правило проталкивания фильтра через соединение +встречается реже, но его появление в $19.10\%$ запросов показывает, что оно +реально участвует в построении выбранных планов, а не только расширяет +пространство поиска. Высокая частота \texttt{SortEnforcer} объясняется +генерацией запросов с \texttt{ORDER BY} и выбором физических операторов, +требующих отсортированный вход, например \texttt{MergeJoin} и потоковой +агрегации. Часть правил не встретилась ни в одном из планов, но это не является +признаком того, что их можно удалить, так как на других данных или при другой +конфигурации генератора запросов эти правила могут дать более оптимальные планы. + \subsection{Калибровка стоимостной модели} Для калибровки коэффициентов в стоимостных формулах реализованы отдельные @@ -3243,5 +3294,18 @@ \subsection{Калибровка стоимостной модели} \label{fig:cost-on-random-queries} \end{figure} +\begin{listing}[H] + \caption{Запуск фаззеров.} + \label{lst:reach-fuzz} + \begin{minted}[style=bw, breaklines, frame=single, fontsize=\footnotesize]{text} +docker compose up -d +python -m research.fuzz.reach_fuzz \ + --cli build/bin/sql \ + --data-dir test/static/executor/test_data +python -m research.fuzz.diff_fuzz \ + --cli build/bin/sql \ + --data-dir test/static/executor/test_data + \end{minted} +\end{listing} \end{document} diff --git a/research/benchmark-results/ssb-sf01.json b/research/benchmark-results/ssb-sf01.json new 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"cell_type": "code", - "execution_count": 16, + "execution_count": 25, "id": "d3172304", "metadata": {}, "outputs": [ @@ -825,7 +825,7 @@ " 25 q2.3 Interpreted Optimized 6.071635)" ] }, - "execution_count": 16, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -851,7 +851,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 26, "id": "f4e060f2", "metadata": {}, "outputs": [ @@ -923,7 +923,7 @@ " 47 26010000 NestedLoopCrossJoin 500 26000000.0 16.636418)" ] }, - "execution_count": 17, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -957,7 +957,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 27, "id": "2075a20f", "metadata": {}, "outputs": [ diff --git a/src/stewkk/sql/logic/executor/executor.cpp b/src/stewkk/sql/logic/executor/executor.cpp index 631af91..5df06ca 100644 --- a/src/stewkk/sql/logic/executor/executor.cpp +++ b/src/stewkk/sql/logic/executor/executor.cpp @@ -1097,16 +1097,6 @@ boost::asio::awaitable Executor::ExecuteJoin(const Nes namespace { -size_t FindAttrIndex(const AttributesInfo& attrs, const Attribute& a) { - auto it = std::find_if(attrs.begin(), attrs.end(), [&](const AttributeInfo& ai) { - return ai.table == a.table && ai.name == a.name; - }); - if (it == attrs.end()) { - throw std::runtime_error{"hash join key attribute not found: " + a.table + "." + a.name}; - } - return static_cast(it - attrs.begin()); -} - bool AttrMatches(const AttributeInfo& ai, const Attribute& a) { return ai.name == a.name && (a.table.empty() || ai.table == a.table); } @@ -1185,21 +1175,6 @@ int CompareNonNullValues(const Value& lhs, const Value& rhs, Type type) { std::unreachable(); } -std::optional FindHashJoinKey(const Expression& qual) { - std::vector conjuncts; - CollectConjuncts(qual, conjuncts); - for (const auto& conjunct : conjuncts) { - const auto* bin = std::get_if(&conjunct); - if (!bin || bin->binop != BinaryOp::kEq) continue; - if (!std::holds_alternative(*bin->lhs) - || !std::holds_alternative(*bin->rhs)) { - continue; - } - return *bin; - } - return std::nullopt; -} - } // namespace @@ -1216,13 +1191,6 @@ boost::asio::awaitable Executor::ExecuteHashJoin( if (join.type != JoinType::kInner) { throw std::logic_error{"HashJoin executor supports only Inner joins"}; } - const auto bin = FindHashJoinKey(join.qual); - if (!bin) { - throw std::logic_error{"HashJoin qual must contain an equality between attributes"}; - } - const auto* a = std::get_if(bin->lhs.get()); - const auto* b = std::get_if(bin->rhs.get()); - auto exec = co_await boost::asio::this_coro::executor; auto [lhs_attrs_chan, lhs_tuples_chan] = co_await GetChannels(); auto lhs_task = SpawnExecutor(exec, *join.lhs, lhs_attrs_chan, lhs_tuples_chan); @@ -1231,23 +1199,9 @@ boost::asio::awaitable Executor::ExecuteHashJoin( auto lhs_attrs = co_await lhs_attrs_chan.async_receive(boost::asio::use_awaitable); auto rhs_attrs = co_await rhs_attrs_chan.async_receive(boost::asio::use_awaitable); - - - auto lhs_has = [&](const Attribute& attr) { - return std::any_of(lhs_attrs.begin(), lhs_attrs.end(), [&](const AttributeInfo& ai) { - return ai.table == attr.table && ai.name == attr.name; - }); - }; - const Attribute* lhs_attr = nullptr; - const Attribute* rhs_attr = nullptr; - if (lhs_has(*a)) { lhs_attr = a; rhs_attr = b; } - else if (lhs_has(*b)) { lhs_attr = b; rhs_attr = a; } - else { - throw std::runtime_error{"HashJoin qual: neither side of equality found in lhs attrs"}; - } - - size_t lhs_key_idx = FindAttrIndex(lhs_attrs, *lhs_attr); - size_t rhs_key_idx = FindAttrIndex(rhs_attrs, *rhs_attr); + auto keys = ResolveExecutorJoinKeys(join.qual, lhs_attrs, rhs_attrs); + size_t lhs_key_idx = FindAttrIndexFlexible(lhs_attrs, keys.lhs); + size_t rhs_key_idx = FindAttrIndexFlexible(rhs_attrs, keys.rhs); AttributesInfo out_attrs = lhs_attrs; std::ranges::copy(rhs_attrs, std::back_inserter(out_attrs)); @@ -1473,7 +1427,7 @@ boost::asio::awaitable Executor::ExecuteMergeJoin( template boost::asio::awaitable Executor::ExecuteHashAggregate( - const PhysicalAggregation& agg, AttributesInfoChannel& out_attr_chan, + PhysicalAggregation agg, AttributesInfoChannel& out_attr_chan, TuplesChannel& out_tuples_chan) { Log("Executing hash aggregate"); auto close_on_fail = boost::scope::make_scope_fail( diff --git a/src/stewkk/sql/logic/optimizer/rule_test.cpp b/src/stewkk/sql/logic/optimizer/rule_test.cpp index ff9e24b..a4e7f8e 100644 --- a/src/stewkk/sql/logic/optimizer/rule_test.cpp +++ b/src/stewkk/sql/logic/optimizer/rule_test.cpp @@ -246,6 +246,36 @@ TEST(TransformationRulesTest, PushesPartialAggregationBelowJoin) { new_join.lhs->GetLogicalExprs()[0]->root_operator)); } +TEST(TransformationRulesTest, PartialAggregationBelowJoinKeepsOnlyLeftGroupBy) { + Memo memo; + auto a = memo.AddGroup(logical::Table{"a"})->group; + auto b = memo.AddGroup(logical::Table{"b"})->group; + auto join = memo.AddGroup(logical::Join{ + a, b, JoinType::kInner, Eq(Attribute{"a", "id"}, Attribute{"b", "aid"})})->group; + auto agg = memo.AddGroup(logical::Aggregation{ + join, + {Attribute{"a", "region"}, Attribute{"b", "brand"}}, + {Sum(Attribute{"a", "x"})}}); + SchemaCatalog schema; + ConstraintCatalog constraints; + RuleContext ctx{schema, constraints}; + AggregationPushdownThroughJoin rule; + + auto result = rule.Apply(agg, memo, ctx); + + const auto& final = std::get(result->root_operator); + const auto& new_join = std::get( + final.source->GetLogicalExprs()[0]->root_operator); + const auto& partial = std::get( + new_join.lhs->GetLogicalExprs()[0]->root_operator); + EXPECT_THAT(partial.group_by, ::testing::ElementsAre( + Expression{Attribute{"a", "region"}}, + Expression{Attribute{"a", "id"}})); + EXPECT_THAT(final.group_by, ::testing::ElementsAre( + Expression{Attribute{"a", "region"}}, + Expression{Attribute{"b", "brand"}})); +} + TEST(TransformationRulesTest, TransposesAggregationAndJoinWithConstraints) { Memo memo; auto a = memo.AddGroup(logical::Table{"a"})->group; diff --git a/src/stewkk/sql/logic/transformation_rules/aggregation_pushdown_through_join.cpp b/src/stewkk/sql/logic/transformation_rules/aggregation_pushdown_through_join.cpp index 01ec4d1..d584af7 100644 --- a/src/stewkk/sql/logic/transformation_rules/aggregation_pushdown_through_join.cpp +++ b/src/stewkk/sql/logic/transformation_rules/aggregation_pushdown_through_join.cpp @@ -58,10 +58,33 @@ bool AggregatesUseOnlyLeft(const logical::Aggregation& agg, }); } +bool GroupByCanBeReconstructedAfterJoin(const logical::Aggregation& agg, + const std::unordered_set& lhs_tables, + const std::unordered_set& rhs_tables) { + return std::ranges::all_of(agg.group_by, [&](const Expression& expr) { + return ExprUsesOnlyTables(expr, lhs_tables) || ExprUsesOnlyTables(expr, rhs_tables); + }); +} + bool ContainsExpr(const std::vector& exprs, const Expression& needle) { return std::ranges::any_of(exprs, [&](const Expression& e) { return e == needle; }); } +std::vector PartialGroupBy(const logical::Aggregation& agg, + const std::unordered_set& lhs_tables, + const Attribute& join_key) { + std::vector out; + for (const auto& expr : agg.group_by) { + if (ExprUsesOnlyTables(expr, lhs_tables)) { + out.push_back(expr); + } + } + if (!ContainsExpr(out, Expression{join_key})) { + out.push_back(join_key); + } + return out; +} + std::vector FinalAggregates(const std::vector& aggregates) { std::vector out; out.reserve(aggregates.size()); @@ -78,7 +101,9 @@ bool CanApply(const logical::Aggregation& agg) { const auto* join = FindInnerJoin(agg.source); if (join == nullptr || !EquiJoinKeys(*join)) return false; auto lhs_tables = GroupTables(join->lhs); - return AggregatesUseOnlyLeft(agg, lhs_tables); + auto rhs_tables = GroupTables(join->rhs); + return AggregatesUseOnlyLeft(agg, lhs_tables) + && GroupByCanBeReconstructedAfterJoin(agg, lhs_tables, rhs_tables); } } // namespace @@ -98,10 +123,8 @@ LogicalOperator AggregationPushdownThroughJoin::ApplyImpl(utils::NotNulllhs})) { - partial_group_by.push_back(keys->lhs); - } + auto lhs_tables = GroupTables(join->lhs); + auto partial_group_by = PartialGroupBy(agg, lhs_tables, keys->lhs); auto partial = memo.AddGroup( logical::PartialAggregation{join->lhs, partial_group_by, agg.aggregates})->group; From fa35de010615726936dabf15c05782be5910515c Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Mon, 15 Jun 2026 21:05:55 +0300 Subject: [PATCH 116/120] Fix all --- report/reach-fuzz-cross-mssql.pdf | Bin 13418 -> 12456 bytes report/reach-fuzz-cross-ours.pdf | Bin 16941 -> 15315 bytes report/vkr.pdf | Bin 0 -> 1118222 bytes report/vkr.tex | 135 +++++++++++++++++------------- research/benchmarks.ipynb | 72 ++++++++-------- 5 files changed, 114 insertions(+), 93 deletions(-) create mode 100644 report/vkr.pdf diff --git a/report/reach-fuzz-cross-mssql.pdf b/report/reach-fuzz-cross-mssql.pdf index 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Executor::Execute(const Physica boost::asio::awaitable operator()(const PhysicalStreamAggregation& agg) const { return executor.ExecuteStreamAggregate(agg, attr_chan, tuples_chan); } + boost::asio::awaitable operator()(const PhysicalPartialAggregation& agg) const { + return executor.ExecuteHashAggregate( + PhysicalAggregation{agg.source, agg.group_by, agg.aggregates}, + attr_chan, tuples_chan); + } + boost::asio::awaitable operator()(const PhysicalFinalAggregation& agg) const { + return executor.ExecuteHashAggregate( + PhysicalAggregation{agg.source, agg.group_by, agg.aggregates}, + attr_chan, tuples_chan); + } boost::asio::awaitable operator()(const MergeJoin& join) const { return executor.ExecuteMergeJoin(join, attr_chan, tuples_chan); } diff --git a/src/stewkk/sql/logic/executor/plan.cpp b/src/stewkk/sql/logic/executor/plan.cpp index 5fddf24..55c82d0 100644 --- a/src/stewkk/sql/logic/executor/plan.cpp +++ b/src/stewkk/sql/logic/executor/plan.cpp @@ -51,4 +51,12 @@ bool PhysicalStreamAggregation::operator==(const PhysicalStreamAggregation& othe return *source == *other.source && group_by == other.group_by && aggregates == other.aggregates; } +bool PhysicalPartialAggregation::operator==(const PhysicalPartialAggregation& other) const { + return *source == *other.source && group_by == other.group_by && aggregates == other.aggregates; +} + +bool PhysicalFinalAggregation::operator==(const PhysicalFinalAggregation& other) const { + return *source == *other.source && group_by == other.group_by && aggregates == other.aggregates; +} + } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/executor/plan_serializer.cpp b/src/stewkk/sql/logic/executor/plan_serializer.cpp index 819fdea..e219df0 100644 --- a/src/stewkk/sql/logic/executor/plan_serializer.cpp +++ b/src/stewkk/sql/logic/executor/plan_serializer.cpp @@ -237,6 +237,34 @@ std::string SerializeNode(const PhysicalPlanNode& node) { return std::format("(StreamAggregate (group_by {}) (aggs {}) {})", group_by_str, aggs_str, SerializeNode(*n.source)); } + std::string operator()(const PhysicalPartialAggregation& n) const { + std::string group_by_str; + for (const auto& e : n.group_by) { + if (!group_by_str.empty()) group_by_str += ' '; + group_by_str += SerializeExpr(e); + } + std::string aggs_str; + for (const auto& e : n.aggregates) { + if (!aggs_str.empty()) aggs_str += ' '; + aggs_str += SerializeExpr(e); + } + return std::format("(PartialAggregate (group_by {}) (aggs {}) {})", + group_by_str, aggs_str, SerializeNode(*n.source)); + } + std::string operator()(const PhysicalFinalAggregation& n) const { + std::string group_by_str; + for (const auto& e : n.group_by) { + if (!group_by_str.empty()) group_by_str += ' '; + group_by_str += SerializeExpr(e); + } + std::string aggs_str; + for (const auto& e : n.aggregates) { + if (!aggs_str.empty()) aggs_str += ' '; + aggs_str += SerializeExpr(e); + } + return std::format("(FinalAggregate (group_by {}) (aggs {}) {})", + group_by_str, aggs_str, SerializeNode(*n.source)); + } }; return std::visit(Visitor{}, node.node); } @@ -757,6 +785,44 @@ struct DotBuilder { EmitEdge(src, id); return id; } + + int EmitAlternative(const PhysicalPartialAggregation& n, + const std::optional& metadata) { + int src = EmitNode(*n.source); + std::string aggs; + for (const auto& e : n.aggregates) { + if (!aggs.empty()) aggs += ", "; + aggs += ToString(e); + } + std::string group_by; + for (const auto& e : n.group_by) { + if (!group_by.empty()) group_by += ", "; + group_by += ToString(e); + } + int id = Emit(std::format("PartialAgg\\nGROUP BY {}\\n{}", group_by, aggs), + metadata); + EmitEdge(src, id); + return id; + } + + int EmitAlternative(const PhysicalFinalAggregation& n, + const std::optional& metadata) { + int src = EmitNode(*n.source); + std::string aggs; + for (const auto& e : n.aggregates) { + if (!aggs.empty()) aggs += ", "; + aggs += ToString(e); + } + std::string group_by; + for (const auto& e : n.group_by) { + if (!group_by.empty()) group_by += ", "; + group_by += ToString(e); + } + int id = Emit(std::format("FinalAgg\\nGROUP BY {}\\n{}", group_by, aggs), + metadata); + EmitEdge(src, id); + return id; + } }; } // namespace diff --git a/src/stewkk/sql/logic/implementation_rules/implement_aggregation.cpp b/src/stewkk/sql/logic/implementation_rules/implement_aggregation.cpp index 8164a5c..cf8a586 100644 --- a/src/stewkk/sql/logic/implementation_rules/implement_aggregation.cpp +++ b/src/stewkk/sql/logic/implementation_rules/implement_aggregation.cpp @@ -5,10 +5,21 @@ namespace stewkk::sql { bool ImplementAggregation::IsApplicable(utils::NotNull expr) { - return std::holds_alternative(expr->root_operator); + return std::holds_alternative(expr->root_operator) + || std::holds_alternative(expr->root_operator) + || std::holds_alternative(expr->root_operator); } std::vector> ImplementAggregation::Apply(utils::NotNull expr, Memo&) { + if (auto* partial = std::get_if(&expr->root_operator)) { + return {expr->group->AddPhysicalExpr( + physical::PartialAggregation{partial->source, partial->group_by, partial->aggregates})}; + } + if (auto* final = std::get_if(&expr->root_operator)) { + return {expr->group->AddPhysicalExpr( + physical::FinalAggregation{final->source, final->group_by, final->aggregates})}; + } + auto& agg = std::get(expr->root_operator); std::vector> result{ expr->group->AddPhysicalExpr( diff --git a/src/stewkk/sql/logic/optimizer/cardinality.cpp b/src/stewkk/sql/logic/optimizer/cardinality.cpp index 9097843..4f4ed0e 100644 --- a/src/stewkk/sql/logic/optimizer/cardinality.cpp +++ b/src/stewkk/sql/logic/optimizer/cardinality.cpp @@ -109,6 +109,20 @@ int64_t CardinalityEstimates::GetCardinality(const LogicalOperator& op) { } return source_cardinality; }, + [this](const logical::PartialAggregation& a) -> int64_t { + auto source_cardinality = GetCardinality(a.source); + if (a.group_by.empty()) { + return 1; + } + return source_cardinality; + }, + [this](const logical::FinalAggregation& a) -> int64_t { + auto source_cardinality = GetCardinality(a.source); + if (a.group_by.empty()) { + return 1; + } + return source_cardinality; + }, [this](const logical::CrossJoin& j) -> int64_t { return GetCardinality(j.lhs) * GetCardinality(j.rhs); }, diff --git a/src/stewkk/sql/logic/optimizer/memo.cpp b/src/stewkk/sql/logic/optimizer/memo.cpp index 2515a79..f041b94 100644 --- a/src/stewkk/sql/logic/optimizer/memo.cpp +++ b/src/stewkk/sql/logic/optimizer/memo.cpp @@ -36,6 +36,30 @@ std::string ToKey(const LogicalOperator& op) { return "Aggregation(" + group_by + ";" + aggregates + ";" + std::to_string(a.source->GetId()) + ")"; }, + [](const logical::PartialAggregation& a) { + std::string group_by; + for (const auto& e : a.group_by) { + group_by += ToString(e) + ","; + } + std::string aggregates; + for (const auto& e : a.aggregates) { + aggregates += ToString(e) + ","; + } + return "PartialAggregation(" + group_by + ";" + aggregates + ";" + + std::to_string(a.source->GetId()) + ")"; + }, + [](const logical::FinalAggregation& a) { + std::string group_by; + for (const auto& e : a.group_by) { + group_by += ToString(e) + ","; + } + std::string aggregates; + for (const auto& e : a.aggregates) { + aggregates += ToString(e) + ","; + } + return "FinalAggregation(" + group_by + ";" + aggregates + ";" + + std::to_string(a.source->GetId()) + ")"; + }, [](const logical::CrossJoin& j) { return "CrossJoin(" + std::to_string(j.lhs->GetId()) + "," + std::to_string(j.rhs->GetId()) + ")"; }, diff --git a/src/stewkk/sql/logic/optimizer/optimizer.cpp b/src/stewkk/sql/logic/optimizer/optimizer.cpp index 02abba3..86bbeb4 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer.cpp @@ -110,6 +110,8 @@ PropertySet RequiredInputProps(utils::NotNull expr, [&](const physical::Sort&) { return PropertySet::Any(); }, [&](const physical::Aggregation&) { return PropertySet::Any(); }, [&](const physical::StreamAggregation& a) { return SortOnGroupBy(a.group_by); }, + [&](const physical::PartialAggregation&) { return PropertySet::Any(); }, + [&](const physical::FinalAggregation&) { return PropertySet::Any(); }, }, expr->root_operator); } @@ -134,6 +136,8 @@ PropertySet DeriveOutputProps(utils::NotNull expr, [&](const physical::Sort& s) { return PropertySet{SortProperty{s.keys}}; }, [&](const physical::Aggregation&) { return PropertySet::Any(); }, [&](const physical::StreamAggregation& a) { return SortOnGroupBy(a.group_by); }, + [&](const physical::PartialAggregation&) { return PropertySet::Any(); }, + [&](const physical::FinalAggregation&) { return PropertySet::Any(); }, }, expr->root_operator); } @@ -172,6 +176,12 @@ int64_t LowerBoundLocalCost(utils::NotNull expr, CardinalityEstima [&](const logical::Aggregation& a) -> int64_t { return 510 * cardinality.GetCardinality(a.source); }, + [&](const logical::PartialAggregation& a) -> int64_t { + return 510 * cardinality.GetCardinality(a.source); + }, + [&](const logical::FinalAggregation& a) -> int64_t { + return 510 * cardinality.GetCardinality(a.source); + }, [&](const logical::CrossJoin& j) -> int64_t { return 104 * cardinality.GetCardinality(j.lhs) * cardinality.GetCardinality(j.rhs); }, @@ -237,6 +247,12 @@ int64_t CalcCost(utils::NotNull expr, CardinalityEstimates& cardi [&](const physical::StreamAggregation& a) -> int64_t { return 130 * cardinality.GetCardinality(a.source); }, + [&](const physical::PartialAggregation& a) -> int64_t { + return 510 * cardinality.GetCardinality(a.source); + }, + [&](const physical::FinalAggregation& a) -> int64_t { + return 510 * cardinality.GetCardinality(a.source); + }, }, expr->root_operator); } @@ -256,6 +272,12 @@ std::vector> GetChildren(utils::NotNull exp [](const logical::Aggregation& a) -> std::vector> { return {a.source}; }, + [](const logical::PartialAggregation& a) -> std::vector> { + return {a.source}; + }, + [](const logical::FinalAggregation& a) -> std::vector> { + return {a.source}; + }, [](const logical::CrossJoin& j) -> std::vector> { return {j.lhs, j.rhs}; }, @@ -300,15 +322,23 @@ std::vector> GetChildren(utils::NotNull ex [](const physical::StreamAggregation& a) -> std::vector> { return {a.source}; }, + [](const physical::PartialAggregation& a) -> std::vector> { + return {a.source}; + }, + [](const physical::FinalAggregation& a) -> std::vector> { + return {a.source}; + }, }, expr->root_operator); } template Optimizer::Optimizer( const Operator& expr, Rules&& rules, - CardinalityEstimates cardinality, SchemaCatalog schema, PropertySet required) + CardinalityEstimates cardinality, SchemaCatalog schema, PropertySet required, + ConstraintCatalog constraints) : memo_(), rules_applier_(std::move(rules)), root_(memo_.Populate(expr)), cardinality_(std::move(cardinality)), schema_(std::move(schema)), + constraints_(std::move(constraints)), global_required_(std::move(required)) { } @@ -398,7 +428,8 @@ template void Optimizer::ApplyRule( TransformationRuleId rule, utils::NotNull expr, Limit limit) { Log("Applying transformation rule {} to group {}", rule.value, expr->group->GetId()); - auto new_expr = rules_applier_.Apply(rule, expr, memo_); + RuleContext ctx{schema_, constraints_}; + auto new_expr = rules_applier_.Apply(rule, expr, memo_, ctx); tasks_.emplace([this, new_expr, limit]() { ExploreExpression(new_expr, limit); }); @@ -416,7 +447,8 @@ template void Optimizer::TryRules( utils::NotNull expr, Limit limit) { for (size_t rule = 0; rule < NTransformation; rule++) { - if (!rules_applier_.IsApplicable(TransformationRuleId{rule}, expr)) { + RuleContext ctx{schema_, constraints_}; + if (!rules_applier_.IsApplicable(TransformationRuleId{rule}, expr, ctx)) { continue; } tasks_.emplace([this, expr, rule, limit]() { @@ -616,6 +648,22 @@ PhysicalPlanNode Optimizer::BuildOptimalPlan(G .aggregates = op.aggregates, }; }, + [this, best_expr_nn, required](const physical::PartialAggregation& op) -> PhysicalPlanNode { + return PhysicalPartialAggregation{ + .source = std::make_shared( + BuildOptimalPlan(op.source.get(), RequiredInputProps(best_expr_nn, required, 0, schema_))), + .group_by = op.group_by, + .aggregates = op.aggregates, + }; + }, + [this, best_expr_nn, required](const physical::FinalAggregation& op) -> PhysicalPlanNode { + return PhysicalFinalAggregation{ + .source = std::make_shared( + BuildOptimalPlan(op.source.get(), RequiredInputProps(best_expr_nn, required, 0, schema_))), + .group_by = op.group_by, + .aggregates = op.aggregates, + }; + }, }, best_expr->root_operator); plan.metadata = PlanNodeMetadata{ @@ -675,7 +723,7 @@ utils::NotNull Optimizer::GetRootGroup return root_->group; } -template class Optimizer<7, 9>; +template class Optimizer<14, 9>; template class Optimizer<0, 6>; } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index 03a23c1..be8360a 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -1,5 +1,7 @@ #include +#include +#include #include #include #include @@ -83,6 +85,23 @@ TEST(SchemaCatalogTest, MissingTableSchemaThrows) { ASSERT_THROW(schema.GetWidth(root->group), std::runtime_error); } +TEST(ConstraintCatalogTest, LoadsUniqueAndForeignKeyMetadata) { + auto dir = std::filesystem::temp_directory_path() / "iu9_sql_constraints_test"; + std::filesystem::create_directories(dir); + { + std::ofstream out{dir / "constraints.meta"}; + out << "unique departments id\n"; + out << "foreign_key users department_id departments id\n"; + } + + auto catalog = LoadConstraintCatalogFromCsvDir(dir); + + EXPECT_TRUE(catalog.IsUnique(Attribute{"departments", "id"})); + EXPECT_TRUE(catalog.HasForeignKey(Attribute{"users", "department_id"}, + Attribute{"departments", "id"})); + std::filesystem::remove_all(dir); +} + TEST(OptimizerTest, Simple) { std::stringstream s{"SELECT * FROM users;"}; Operator op = GetAST(s).value().op; diff --git a/src/stewkk/sql/logic/optimizer/reachability.cpp b/src/stewkk/sql/logic/optimizer/reachability.cpp index c36000c..c8e4e65 100644 --- a/src/stewkk/sql/logic/optimizer/reachability.cpp +++ b/src/stewkk/sql/logic/optimizer/reachability.cpp @@ -159,6 +159,28 @@ InternalMatch TryMatchExpr(utils::NotNull pe, if (!child.ok) child.reason = "StreamAggregation.source: " + child.reason; return child; }, + [&](const physical::PartialAggregation& op) -> InternalMatch { + const auto* t = std::get_if(&target.node); + if (!t) return {false, depth, "type mismatch: expected PartialAggregate"}; + if (op.group_by != t->group_by) + return {false, depth + 1, "PartialAggregation group_by mismatch"}; + if (op.aggregates != t->aggregates) + return {false, depth + 1, "PartialAggregation aggregates mismatch"}; + auto child = MatchGroup(op.source.get(), *t->source, depth + 1); + if (!child.ok) child.reason = "PartialAggregation.source: " + child.reason; + return child; + }, + [&](const physical::FinalAggregation& op) -> InternalMatch { + const auto* t = std::get_if(&target.node); + if (!t) return {false, depth, "type mismatch: expected FinalAggregate"}; + if (op.group_by != t->group_by) + return {false, depth + 1, "FinalAggregation group_by mismatch"}; + if (op.aggregates != t->aggregates) + return {false, depth + 1, "FinalAggregation aggregates mismatch"}; + auto child = MatchGroup(op.source.get(), *t->source, depth + 1); + if (!child.ok) child.reason = "FinalAggregation.source: " + child.reason; + return child; + }, }, pe->root_operator); } diff --git a/src/stewkk/sql/logic/optimizer/rule.cpp b/src/stewkk/sql/logic/optimizer/rule.cpp index 81c387a..e3237ad 100644 --- a/src/stewkk/sql/logic/optimizer/rule.cpp +++ b/src/stewkk/sql/logic/optimizer/rule.cpp @@ -4,8 +4,9 @@ namespace stewkk::sql { -utils::NotNull TransformationRule::Apply(utils::NotNull expr, Memo& memo) { - return memo.AddLogicalExprToGroup(expr->group, ApplyImpl(expr, memo)); +utils::NotNull TransformationRule::Apply(utils::NotNull expr, + Memo& memo, RuleContext& ctx) { + return memo.AddLogicalExprToGroup(expr->group, ApplyImpl(expr, memo, ctx)); } } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/rule_test.cpp b/src/stewkk/sql/logic/optimizer/rule_test.cpp index 269f88e..ff9e24b 100644 --- a/src/stewkk/sql/logic/optimizer/rule_test.cpp +++ b/src/stewkk/sql/logic/optimizer/rule_test.cpp @@ -1,12 +1,46 @@ #include +#include +#include + #include #include #include #include +#include +#include +#include +#include +#include +#include namespace stewkk::sql { +namespace { + +Expression Eq(Attribute lhs, Attribute rhs) { + return BinaryExpression{ + std::make_shared(std::move(lhs)), + BinaryOp::kEq, + std::make_shared(std::move(rhs))}; +} + +Expression EqConst(Attribute lhs, IntConst rhs) { + return BinaryExpression{ + std::make_shared(std::move(lhs)), + BinaryOp::kEq, + std::make_shared(rhs)}; +} + +Expression Sum(Attribute attr) { + return AggregateExpression{ + AggregateFunction::kSum, + std::make_shared(std::move(attr)), + false}; +} + +} // namespace + class JoinCommutativityTest : public ::testing::Test { protected: void SetUp() override { @@ -18,13 +52,16 @@ class JoinCommutativityTest : public ::testing::Test { Group* a = nullptr; Group* b = nullptr; JoinCommutativity rule; + SchemaCatalog schema; + ConstraintCatalog constraints; }; TEST_F(JoinCommutativityTest, ReturnsSwappedJoin) { auto join_group = memo.AddGroup(logical::Join{a, b, JoinType::kInner, Literal::kTrue})->group; auto expr = join_group->GetLogicalExprs()[0]; - auto result = rule.Apply(expr, memo); + RuleContext ctx{schema, constraints}; + auto result = rule.Apply(expr, memo, ctx); const auto& join = std::get(result->root_operator); EXPECT_EQ(join.lhs.get(), b); @@ -35,7 +72,8 @@ TEST_F(JoinCommutativityTest, AddsNewJoinIntoGroup) { auto join_group = memo.AddGroup(logical::Join{a, b, JoinType::kInner, Literal::kTrue})->group; auto expr = join_group->GetLogicalExprs()[0]; - rule.Apply(expr, memo); + RuleContext ctx{schema, constraints}; + rule.Apply(expr, memo, ctx); EXPECT_EQ(join_group->GetLogicalExprs().size(), 2u); const auto& new_join = std::get(join_group->GetLogicalExprs()[1]->root_operator); @@ -60,16 +98,20 @@ class JoinAssociativityTest : public ::testing::Test { Group* ab = nullptr; Group* abc = nullptr; JoinAssociativity rule; + SchemaCatalog schema; + ConstraintCatalog constraints; }; TEST_F(JoinAssociativityTest, CreatesNewGroup) { - rule.Apply(abc->GetLogicalExprs()[0], memo); + RuleContext ctx{schema, constraints}; + rule.Apply(abc->GetLogicalExprs()[0], memo, ctx); EXPECT_EQ(memo.GroupCount(), 6u); } TEST_F(JoinAssociativityTest, ReturnsCorrectExpression) { - auto result = rule.Apply(abc->GetLogicalExprs()[0], memo); + RuleContext ctx{schema, constraints}; + auto result = rule.Apply(abc->GetLogicalExprs()[0], memo, ctx); const auto& outer = std::get(result->root_operator); EXPECT_EQ(outer.lhs.get(), a); @@ -86,14 +128,147 @@ TEST_F(JoinAssociativityTest, DoesNotApplyWhenInnerJoinIsOuter) { auto expr = memo.AddGroup( logical::Join{outer_ab, c, JoinType::kInner, Literal::kTrue}); - EXPECT_FALSE(rule.IsApplicable(expr)); + RuleContext ctx{schema, constraints}; + EXPECT_FALSE(rule.IsApplicable(expr, ctx)); } TEST_F(JoinAssociativityTest, DoesNotApplyWhenOuterJoinIsOuter) { auto expr = memo.AddGroup( logical::Join{ab, c, JoinType::kFull, Literal::kTrue}); - EXPECT_FALSE(rule.IsApplicable(expr)); + RuleContext ctx{schema, constraints}; + EXPECT_FALSE(rule.IsApplicable(expr, ctx)); +} + +TEST(TransformationRulesTest, MovesFilterIntoInnerJoinPredicate) { + Memo memo; + auto a = memo.AddGroup(logical::Table{"a"})->group; + auto b = memo.AddGroup(logical::Table{"b"})->group; + auto join = memo.AddGroup(logical::Join{a, b, JoinType::kInner, Literal::kTrue})->group; + auto filter = memo.AddGroup(logical::Filter{join, Eq(Attribute{"a", "id"}, Attribute{"b", "id"})}); + SchemaCatalog schema; + ConstraintCatalog constraints; + RuleContext ctx{schema, constraints}; + FilterToJoinPredicate rule; + + auto result = rule.Apply(filter, memo, ctx); + + const auto& got = std::get(result->root_operator); + EXPECT_EQ(got.type, JoinType::kInner); + EXPECT_EQ(got.qual, Eq(Attribute{"a", "id"}, Attribute{"b", "id"})); +} + +TEST(TransformationRulesTest, ConvertsFilteredCrossJoinToInnerJoin) { + Memo memo; + auto a = memo.AddGroup(logical::Table{"a"})->group; + auto b = memo.AddGroup(logical::Table{"b"})->group; + auto cross = memo.AddGroup(logical::CrossJoin{a, b})->group; + auto pred = Eq(Attribute{"a", "id"}, Attribute{"b", "id"}); + auto filter = memo.AddGroup(logical::Filter{cross, pred}); + SchemaCatalog schema; + ConstraintCatalog constraints; + RuleContext ctx{schema, constraints}; + CrossJoinToJoin rule; + + auto result = rule.Apply(filter, memo, ctx); + + const auto& got = std::get(result->root_operator); + EXPECT_EQ(got.type, JoinType::kInner); + EXPECT_EQ(got.qual, pred); +} + +TEST(TransformationRulesTest, ConvertsNullRejectedLeftJoinToInnerJoin) { + Memo memo; + auto a = memo.AddGroup(logical::Table{"a"})->group; + auto b = memo.AddGroup(logical::Table{"b"})->group; + auto join = memo.AddGroup(logical::Join{ + a, b, JoinType::kLeft, Eq(Attribute{"a", "id"}, Attribute{"b", "aid"})})->group; + auto filter = memo.AddGroup(logical::Filter{join, EqConst(Attribute{"b", "aid"}, 7)}); + SchemaCatalog schema; + ConstraintCatalog constraints; + RuleContext ctx{schema, constraints}; + OuterJoinToInner rule; + + auto result = rule.Apply(filter, memo, ctx); + + const auto& got_filter = std::get(result->root_operator); + const auto& got_join = std::get( + got_filter.source->GetLogicalExprs()[0]->root_operator); + EXPECT_EQ(got_join.type, JoinType::kInner); +} + +TEST(TransformationRulesTest, PushesProjectionBelowJoinInputs) { + Memo memo; + auto a = memo.AddGroup(logical::Table{"a"})->group; + auto b = memo.AddGroup(logical::Table{"b"})->group; + auto join = memo.AddGroup(logical::Join{ + a, b, JoinType::kInner, Eq(Attribute{"a", "id"}, Attribute{"b", "aid"})})->group; + auto projection = memo.AddGroup(logical::Projection{ + join, {Attribute{"a", "x"}}, {}}); + SchemaCatalog schema({ + {"a", {Attribute{"a", "id"}, Attribute{"a", "x"}, Attribute{"a", "unused"}}}, + {"b", {Attribute{"b", "aid"}, Attribute{"b", "unused"}}}, + }); + ConstraintCatalog constraints; + RuleContext ctx{schema, constraints}; + ProjectionPushdownThroughJoin rule; + + auto result = rule.Apply(projection, memo, ctx); + + const auto& got_projection = std::get(result->root_operator); + const auto& got_join = std::get( + got_projection.source->GetLogicalExprs()[0]->root_operator); + EXPECT_TRUE(std::holds_alternative( + got_join.lhs->GetLogicalExprs()[0]->root_operator)); + EXPECT_TRUE(std::holds_alternative( + got_join.rhs->GetLogicalExprs()[0]->root_operator)); +} + +TEST(TransformationRulesTest, PushesPartialAggregationBelowJoin) { + Memo memo; + auto a = memo.AddGroup(logical::Table{"a"})->group; + auto b = memo.AddGroup(logical::Table{"b"})->group; + auto join = memo.AddGroup(logical::Join{ + a, b, JoinType::kInner, Eq(Attribute{"a", "id"}, Attribute{"b", "aid"})})->group; + auto agg = memo.AddGroup(logical::Aggregation{ + join, {Attribute{"a", "id"}}, {Sum(Attribute{"a", "x"})}}); + SchemaCatalog schema; + ConstraintCatalog constraints; + RuleContext ctx{schema, constraints}; + AggregationPushdownThroughJoin rule; + + auto result = rule.Apply(agg, memo, ctx); + + const auto& final = std::get(result->root_operator); + const auto& new_join = std::get( + final.source->GetLogicalExprs()[0]->root_operator); + EXPECT_TRUE(std::holds_alternative( + new_join.lhs->GetLogicalExprs()[0]->root_operator)); +} + +TEST(TransformationRulesTest, TransposesAggregationAndJoinWithConstraints) { + Memo memo; + auto a = memo.AddGroup(logical::Table{"a"})->group; + auto b = memo.AddGroup(logical::Table{"b"})->group; + auto join = memo.AddGroup(logical::Join{ + a, b, JoinType::kInner, Eq(Attribute{"a", "id"}, Attribute{"b", "id"})})->group; + auto agg = memo.AddGroup(logical::Aggregation{ + join, {Attribute{"a", "id"}}, {Sum(Attribute{"a", "x"})}}); + SchemaCatalog schema; + ConstraintCatalog constraints( + {UniqueKeyInfo{.table = "b", .column = "id"}}, + {ForeignKeyInfo{.from_table = "a", .from_column = "id", + .to_table = "b", .to_column = "id"}}); + RuleContext ctx{schema, constraints}; + AggregationJoinTranspose rule; + + auto result = rule.Apply(agg, memo, ctx); + + const auto& projection = std::get(result->root_operator); + const auto& new_join = std::get( + projection.source->GetLogicalExprs()[0]->root_operator); + EXPECT_TRUE(std::holds_alternative( + new_join.lhs->GetLogicalExprs()[0]->root_operator)); } } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/rules.cpp b/src/stewkk/sql/logic/optimizer/rules.cpp index dadda82..204386e 100644 --- a/src/stewkk/sql/logic/optimizer/rules.cpp +++ b/src/stewkk/sql/logic/optimizer/rules.cpp @@ -4,7 +4,7 @@ namespace stewkk::sql { -Rules<7, 9> MakeMainRules(IndexCatalog indexes) { +Rules<14, 9> MakeMainRules(IndexCatalog indexes) { return { .transformation_rules = { std::make_unique(), @@ -14,6 +14,13 @@ Rules<7, 9> MakeMainRules(IndexCatalog indexes) { std::make_unique(), std::make_unique(), std::make_unique(), + std::make_unique(), + std::make_unique(), + std::make_unique(), + std::make_unique(), + std::make_unique(), + std::make_unique(), + std::make_unique(), }, .implementation_rules = { std::make_unique(), diff --git a/src/stewkk/sql/logic/optimizer/rules_applier.cpp b/src/stewkk/sql/logic/optimizer/rules_applier.cpp index 41a9186..5d0758e 100644 --- a/src/stewkk/sql/logic/optimizer/rules_applier.cpp +++ b/src/stewkk/sql/logic/optimizer/rules_applier.cpp @@ -7,15 +7,17 @@ RulesApplier::RulesApplier(Rules -bool RulesApplier::IsApplicable(TransformationRuleId rule, utils::NotNull expr) { +bool RulesApplier::IsApplicable( + TransformationRuleId rule, utils::NotNull expr, RuleContext& ctx) { return !applied_transformation_rules_[expr.get()][rule.value] && - rules_.transformation_rules[rule.value]->IsApplicable(expr); + rules_.transformation_rules[rule.value]->IsApplicable(expr, ctx); } template -utils::NotNull RulesApplier::Apply(TransformationRuleId rule, utils::NotNull expr, Memo& memo) { +utils::NotNull RulesApplier::Apply( + TransformationRuleId rule, utils::NotNull expr, Memo& memo, RuleContext& ctx) { applied_transformation_rules_[expr.get()][rule.value] = 1; - return rules_.transformation_rules[rule.value]->Apply(expr, memo); + return rules_.transformation_rules[rule.value]->Apply(expr, memo, ctx); } template @@ -30,7 +32,7 @@ std::vector> RulesApplierApply(expr, memo); } -template class RulesApplier<7, 9>; +template class RulesApplier<14, 9>; template class RulesApplier<0, 6>; } // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/optimizer/rules_applier_test.cpp b/src/stewkk/sql/logic/optimizer/rules_applier_test.cpp index d49d40e..699b4c3 100644 --- a/src/stewkk/sql/logic/optimizer/rules_applier_test.cpp +++ b/src/stewkk/sql/logic/optimizer/rules_applier_test.cpp @@ -13,10 +13,13 @@ TEST(RulesApplierTest, ChecksThatRuleAlreadyApplied) { RulesApplier applier(MakeMainRules()); constexpr TransformationRuleId kJoinCommutativity{0}; + SchemaCatalog schema; + ConstraintCatalog constraints; + RuleContext ctx{schema, constraints}; - EXPECT_TRUE(applier.IsApplicable(kJoinCommutativity, expr)); - applier.Apply(kJoinCommutativity, expr, memo); - EXPECT_FALSE(applier.IsApplicable(kJoinCommutativity, expr)); + EXPECT_TRUE(applier.IsApplicable(kJoinCommutativity, expr, ctx)); + applier.Apply(kJoinCommutativity, expr, memo, ctx); + EXPECT_FALSE(applier.IsApplicable(kJoinCommutativity, expr, ctx)); } TEST(RulesApplierTest, ApplyImplementationRule) { diff --git a/src/stewkk/sql/logic/optimizer/schema_catalog.cpp b/src/stewkk/sql/logic/optimizer/schema_catalog.cpp index e0a36fe..54ffb2c 100644 --- a/src/stewkk/sql/logic/optimizer/schema_catalog.cpp +++ b/src/stewkk/sql/logic/optimizer/schema_catalog.cpp @@ -22,6 +22,23 @@ bool IndexCatalog::HasSortedIndex(const std::string& table, const std::string& c }); } +ConstraintCatalog::ConstraintCatalog(std::vector unique_keys, + std::vector foreign_keys) + : unique_keys_(std::move(unique_keys)), foreign_keys_(std::move(foreign_keys)) {} + +bool ConstraintCatalog::IsUnique(const Attribute& attr) const { + return std::ranges::any_of(unique_keys_, [&](const UniqueKeyInfo& key) { + return key.table == attr.table && key.column == attr.name; + }); +} + +bool ConstraintCatalog::HasForeignKey(const Attribute& from, const Attribute& to) const { + return std::ranges::any_of(foreign_keys_, [&](const ForeignKeyInfo& fk) { + return fk.from_table == from.table && fk.from_column == from.name + && fk.to_table == to.table && fk.to_column == to.name; + }); +} + SchemaCatalog::SchemaCatalog(std::unordered_map tables) : tables_(std::move(tables)) {} @@ -38,6 +55,19 @@ std::int64_t SchemaCatalog::GetWidth(utils::NotNull group) { return std::max(1, GetSchema(group).size()); } +std::optional SchemaCatalog::ResolveBaseAttribute(const Attribute& attr, + utils::NotNull group) { + auto schema = GetSchema(group); + auto it = std::ranges::find_if(schema, [&](const Attribute& schema_attr) { + return schema_attr.name == attr.name + && (attr.table.empty() || attr.table == schema_attr.table); + }); + if (it == schema.end()) { + return std::nullopt; + } + return *it; +} + // TODO: refactor to remove duplicate logic: both executor and optimizer derive // attributes Schema SchemaCatalog::Derive(const LogicalOperator& op) { @@ -80,6 +110,32 @@ Schema SchemaCatalog::Derive(const LogicalOperator& op) { } return out; }, + [this](const logical::PartialAggregation& a) -> Schema { + GetSchema(a.source); + Schema out; + for (const auto& expr : a.group_by) { + if (const auto* attr = std::get_if(&expr)) { + out.push_back(*attr); + } + } + for (size_t i = 0; i < a.aggregates.size(); ++i) { + out.push_back(Attribute{"", std::format("__agg{}", i)}); + } + return out; + }, + [this](const logical::FinalAggregation& a) -> Schema { + GetSchema(a.source); + Schema out; + for (const auto& expr : a.group_by) { + if (const auto* attr = std::get_if(&expr)) { + out.push_back(*attr); + } + } + for (size_t i = 0; i < a.aggregates.size(); ++i) { + out.push_back(Attribute{"", std::format("__agg{}", i)}); + } + return out; + }, [this](const logical::CrossJoin& j) -> Schema { auto l = GetSchema(j.lhs); auto r = GetSchema(j.rhs); @@ -146,6 +202,39 @@ IndexCatalog LoadIndexCatalogFromCsvDir(const std::filesystem::path& dir) { return IndexCatalog{std::move(indexes)}; } +ConstraintCatalog LoadConstraintCatalogFromCsvDir(const std::filesystem::path& dir) { + std::vector unique_keys; + std::vector foreign_keys; + std::ifstream input{dir / "constraints.meta"}; + if (!input) { + return ConstraintCatalog{}; + } + + std::string line; + while (std::getline(input, line)) { + auto first = line.find_first_not_of(" \t\r\n"); + if (first == std::string::npos || line[first] == '#') { + continue; + } + + std::istringstream fields{line}; + std::string kind; + fields >> kind; + if (kind == "unique") { + UniqueKeyInfo key; + if (fields >> key.table >> key.column) { + unique_keys.push_back(std::move(key)); + } + } else if (kind == "foreign_key") { + ForeignKeyInfo fk; + if (fields >> fk.from_table >> fk.from_column >> fk.to_table >> fk.to_column) { + foreign_keys.push_back(std::move(fk)); + } + } + } + return ConstraintCatalog{std::move(unique_keys), std::move(foreign_keys)}; +} + std::unordered_map LoadTableSizesFromCsvDir( const std::filesystem::path& dir) { std::unordered_map sizes; diff --git a/src/stewkk/sql/logic/transformation_rules/aggregation_join_transpose.cpp b/src/stewkk/sql/logic/transformation_rules/aggregation_join_transpose.cpp new file mode 100644 index 0000000..6f8782f --- /dev/null +++ b/src/stewkk/sql/logic/transformation_rules/aggregation_join_transpose.cpp @@ -0,0 +1,114 @@ +#include + +#include +#include +#include +#include +#include + +#include +#include + +namespace stewkk::sql { + +namespace { + +struct JoinKeys { + Attribute lhs; + Attribute rhs; +}; + +const logical::Join* FindInnerJoin(utils::NotNull source) { + for (auto inner_expr : source->GetLogicalExprs()) { + if (const auto* j = std::get_if(&inner_expr->root_operator); + j != nullptr && j->type == JoinType::kInner) { + return j; + } + } + return nullptr; +} + +std::optional EquiJoinKeys(const logical::Join& join) { + const auto* bin = std::get_if(&join.qual); + if (bin == nullptr || bin->binop != BinaryOp::kEq) return std::nullopt; + const auto* l = std::get_if(bin->lhs.get()); + const auto* r = std::get_if(bin->rhs.get()); + if (l == nullptr || r == nullptr) return std::nullopt; + auto lhs_tables = GroupTables(join.lhs); + auto rhs_tables = GroupTables(join.rhs); + if (lhs_tables.contains(l->table) && rhs_tables.contains(r->table)) { + return JoinKeys{*l, *r}; + } + if (lhs_tables.contains(r->table) && rhs_tables.contains(l->table)) { + return JoinKeys{*r, *l}; + } + return std::nullopt; +} + +bool ContainsExpr(const std::vector& exprs, const Expression& needle) { + return std::ranges::any_of(exprs, [&](const Expression& e) { return e == needle; }); +} + +bool AggregateInputsOnLeft(const logical::Aggregation& agg, + const std::unordered_set& lhs_tables) { + return std::ranges::all_of(agg.aggregates, [&](const Expression& expr) { + const auto* a = std::get_if(&expr); + return a != nullptr + && (a->is_star || (a->argument && ExprUsesOnlyTables(*a->argument, lhs_tables))); + }); +} + +bool GroupByOnLeft(const logical::Aggregation& agg, + const std::unordered_set& lhs_tables) { + return std::ranges::all_of(agg.group_by, [&](const Expression& expr) { + return ExprUsesOnlyTables(expr, lhs_tables); + }); +} + +std::vector OutputProjection(const logical::Aggregation& agg) { + std::vector out = agg.group_by; + for (size_t i = 0; i < agg.aggregates.size(); ++i) { + out.push_back(Attribute{"", std::format("__agg{}", i)}); + } + return out; +} + +bool CanApply(const logical::Aggregation& agg, RuleContext& ctx) { + const auto* join = FindInnerJoin(agg.source); + if (join == nullptr) return false; + auto keys = EquiJoinKeys(*join); + if (!keys) return false; + auto lhs_tables = GroupTables(join->lhs); + if (!AggregateInputsOnLeft(agg, lhs_tables) || !GroupByOnLeft(agg, lhs_tables)) { + return false; + } + if (!ContainsExpr(agg.group_by, Expression{keys->lhs})) { + return false; + } + return ctx.constraints.IsUnique(keys->rhs) + && ctx.constraints.HasForeignKey(keys->lhs, keys->rhs); +} + +} // namespace + +bool AggregationJoinTranspose::IsApplicable(utils::NotNull expr, + RuleContext& ctx) { + if (!std::holds_alternative(expr->root_operator)) return false; + return CanApply(std::get(expr->root_operator), ctx); +} + +LogicalOperator AggregationJoinTranspose::ApplyImpl(utils::NotNull expr, + Memo& memo, RuleContext& ctx) { + const auto& agg = std::get(expr->root_operator); + if (!CanApply(agg, ctx)) { + throw std::runtime_error{"AggregationJoinTranspose requires unique/full inner equijoin"}; + } + const auto* join = FindInnerJoin(agg.source); + auto agg_lhs = memo.AddGroup( + logical::Aggregation{join->lhs, agg.group_by, agg.aggregates})->group; + auto new_join = memo.AddGroup( + logical::Join{agg_lhs, join->rhs, join->type, join->qual})->group; + return logical::Projection{new_join, OutputProjection(agg), {}}; +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/transformation_rules/aggregation_pushdown_through_join.cpp b/src/stewkk/sql/logic/transformation_rules/aggregation_pushdown_through_join.cpp new file mode 100644 index 0000000..01ec4d1 --- /dev/null +++ b/src/stewkk/sql/logic/transformation_rules/aggregation_pushdown_through_join.cpp @@ -0,0 +1,113 @@ +#include + +#include +#include +#include +#include +#include + +#include +#include + +namespace stewkk::sql { + +namespace { + +struct JoinKeys { + Attribute lhs; + Attribute rhs; +}; + +const logical::Join* FindInnerJoin(utils::NotNull source) { + for (auto inner_expr : source->GetLogicalExprs()) { + if (const auto* j = std::get_if(&inner_expr->root_operator); + j != nullptr && j->type == JoinType::kInner) { + return j; + } + } + return nullptr; +} + +std::optional EquiJoinKeys(const logical::Join& join) { + const auto* bin = std::get_if(&join.qual); + if (bin == nullptr || bin->binop != BinaryOp::kEq) return std::nullopt; + const auto* l = std::get_if(bin->lhs.get()); + const auto* r = std::get_if(bin->rhs.get()); + if (l == nullptr || r == nullptr) return std::nullopt; + auto lhs_tables = GroupTables(join.lhs); + auto rhs_tables = GroupTables(join.rhs); + if (lhs_tables.contains(l->table) && rhs_tables.contains(r->table)) { + return JoinKeys{*l, *r}; + } + if (lhs_tables.contains(r->table) && rhs_tables.contains(l->table)) { + return JoinKeys{*r, *l}; + } + return std::nullopt; +} + +bool IsSupportedAggregate(const Expression& expr) { + return std::holds_alternative(expr); +} + +bool AggregatesUseOnlyLeft(const logical::Aggregation& agg, + const std::unordered_set& lhs_tables) { + return std::ranges::all_of(agg.aggregates, [&](const Expression& expr) { + if (!IsSupportedAggregate(expr)) return false; + const auto& a = std::get(expr); + return a.is_star || (a.argument && ExprUsesOnlyTables(*a.argument, lhs_tables)); + }); +} + +bool ContainsExpr(const std::vector& exprs, const Expression& needle) { + return std::ranges::any_of(exprs, [&](const Expression& e) { return e == needle; }); +} + +std::vector FinalAggregates(const std::vector& aggregates) { + std::vector out; + out.reserve(aggregates.size()); + for (size_t i = 0; i < aggregates.size(); ++i) { + out.push_back(AggregateExpression{ + AggregateFunction::kSum, + std::make_shared(Attribute{"", std::format("__agg{}", i)}), + false}); + } + return out; +} + +bool CanApply(const logical::Aggregation& agg) { + const auto* join = FindInnerJoin(agg.source); + if (join == nullptr || !EquiJoinKeys(*join)) return false; + auto lhs_tables = GroupTables(join->lhs); + return AggregatesUseOnlyLeft(agg, lhs_tables); +} + +} // namespace + +bool AggregationPushdownThroughJoin::IsApplicable(utils::NotNull expr, + RuleContext&) { + if (!std::holds_alternative(expr->root_operator)) return false; + return CanApply(std::get(expr->root_operator)); +} + +LogicalOperator AggregationPushdownThroughJoin::ApplyImpl(utils::NotNull expr, + Memo& memo, RuleContext&) { + const auto& agg = std::get(expr->root_operator); + const auto* join = FindInnerJoin(agg.source); + auto keys = join == nullptr ? std::optional{} : EquiJoinKeys(*join); + if (join == nullptr || !keys) { + throw std::runtime_error{"AggregationPushdownThroughJoin requires an inner equijoin"}; + } + + auto partial_group_by = agg.group_by; + if (!ContainsExpr(partial_group_by, Expression{keys->lhs})) { + partial_group_by.push_back(keys->lhs); + } + + auto partial = memo.AddGroup( + logical::PartialAggregation{join->lhs, partial_group_by, agg.aggregates})->group; + auto new_join = memo.AddGroup( + logical::Join{partial, join->rhs, join->type, join->qual})->group; + return logical::FinalAggregation{new_join, agg.group_by, FinalAggregates(agg.aggregates)}; +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/transformation_rules/cross_join_to_join.cpp b/src/stewkk/sql/logic/transformation_rules/cross_join_to_join.cpp new file mode 100644 index 0000000..82620f3 --- /dev/null +++ b/src/stewkk/sql/logic/transformation_rules/cross_join_to_join.cpp @@ -0,0 +1,36 @@ +#include + +#include + +namespace stewkk::sql { + +namespace { + +const logical::CrossJoin* FindCrossJoin(utils::NotNull source) { + for (auto inner_expr : source->GetLogicalExprs()) { + if (const auto* j = std::get_if(&inner_expr->root_operator)) { + return j; + } + } + return nullptr; +} + +} // namespace + +bool CrossJoinToJoin::IsApplicable(utils::NotNull expr, RuleContext&) { + if (!std::holds_alternative(expr->root_operator)) return false; + const auto& filter = std::get(expr->root_operator); + return FindCrossJoin(filter.source) != nullptr; +} + +LogicalOperator CrossJoinToJoin::ApplyImpl(utils::NotNull expr, + Memo&, RuleContext&) { + const auto& filter = std::get(expr->root_operator); + const auto* cross = FindCrossJoin(filter.source); + if (cross == nullptr) { + throw std::runtime_error{"CrossJoinToJoin requires a cross join below filter"}; + } + return logical::Join{cross->lhs, cross->rhs, JoinType::kInner, filter.predicate}; +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/transformation_rules/filter_lift_through_join.cpp b/src/stewkk/sql/logic/transformation_rules/filter_lift_through_join.cpp new file mode 100644 index 0000000..ae7e40c --- /dev/null +++ b/src/stewkk/sql/logic/transformation_rules/filter_lift_through_join.cpp @@ -0,0 +1,57 @@ +#include + +#include +#include + +#include + +namespace stewkk::sql { + +namespace { + +struct LiftCandidate { + logical::Filter filter; + bool left; +}; + +std::optional FindCandidate(const logical::Join& join) { + const bool can_left = join.type == JoinType::kInner || join.type == JoinType::kLeft; + const bool can_right = join.type == JoinType::kInner || join.type == JoinType::kRight; + if (can_left) { + for (auto expr : join.lhs->GetLogicalExprs()) { + if (const auto* f = std::get_if(&expr->root_operator)) { + return LiftCandidate{*f, true}; + } + } + } + if (can_right) { + for (auto expr : join.rhs->GetLogicalExprs()) { + if (const auto* f = std::get_if(&expr->root_operator)) { + return LiftCandidate{*f, false}; + } + } + } + return std::nullopt; +} + +} // namespace + +bool FilterLiftThroughJoin::IsApplicable(utils::NotNull expr, RuleContext&) { + if (!std::holds_alternative(expr->root_operator)) return false; + return FindCandidate(std::get(expr->root_operator)).has_value(); +} + +LogicalOperator FilterLiftThroughJoin::ApplyImpl(utils::NotNull expr, + Memo& memo, RuleContext&) { + const auto& join = std::get(expr->root_operator); + auto candidate = FindCandidate(join); + if (!candidate) { + throw std::runtime_error{"FilterLiftThroughJoin requires a child filter"}; + } + auto new_lhs = candidate->left ? candidate->filter.source : join.lhs; + auto new_rhs = candidate->left ? join.rhs : candidate->filter.source; + auto new_join = memo.AddGroup(logical::Join{new_lhs, new_rhs, join.type, join.qual})->group; + return logical::Filter{new_join, candidate->filter.predicate}; +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/transformation_rules/filter_merge.cpp b/src/stewkk/sql/logic/transformation_rules/filter_merge.cpp index 4411a3e..d30bd82 100644 --- a/src/stewkk/sql/logic/transformation_rules/filter_merge.cpp +++ b/src/stewkk/sql/logic/transformation_rules/filter_merge.cpp @@ -20,13 +20,13 @@ const logical::Filter* FindInnerFilter(utils::NotNull source) { } // namespace -bool FilterMerge::IsApplicable(utils::NotNull expr) { +bool FilterMerge::IsApplicable(utils::NotNull expr, RuleContext&) { if (!std::holds_alternative(expr->root_operator)) return false; const auto& outer = std::get(expr->root_operator); return FindInnerFilter(outer.source) != nullptr; } -LogicalOperator FilterMerge::ApplyImpl(utils::NotNull expr, Memo&) { +LogicalOperator FilterMerge::ApplyImpl(utils::NotNull expr, Memo&, RuleContext&) { const auto& outer = std::get(expr->root_operator); const auto* inner = FindInnerFilter(outer.source); if (inner == nullptr) { diff --git a/src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_join.cpp b/src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_join.cpp index 23fcb6b..d8d2bfb 100644 --- a/src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_join.cpp +++ b/src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_join.cpp @@ -79,14 +79,14 @@ const logical::Join* FindPushableJoin(utils::NotNull source, } // namespace -bool FilterPushdownThroughJoin::IsApplicable(utils::NotNull expr) { +bool FilterPushdownThroughJoin::IsApplicable(utils::NotNull expr, RuleContext&) { if (!std::holds_alternative(expr->root_operator)) return false; const auto& f = std::get(expr->root_operator); return FindPushableJoin(f.source, f.predicate) != nullptr; } LogicalOperator FilterPushdownThroughJoin::ApplyImpl(utils::NotNull expr, - Memo& memo) { + Memo& memo, RuleContext&) { const auto& f = std::get(expr->root_operator); const auto* join = FindPushableJoin(f.source, f.predicate); if (join == nullptr) { diff --git a/src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.cpp b/src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.cpp index 1765de7..91f2128 100644 --- a/src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.cpp +++ b/src/stewkk/sql/logic/transformation_rules/filter_pushdown_through_projection.cpp @@ -11,31 +11,6 @@ namespace stewkk::sql { namespace { -void CollectAttributes(const Expression& e, std::vector& out) { - std::visit(utils::Overloaded{ - [&](const Attribute& a) { out.push_back(a); }, - [&](const BinaryExpression& b) { - CollectAttributes(*b.lhs, out); - CollectAttributes(*b.rhs, out); - }, - [&](const UnaryExpression& u) { CollectAttributes(*u.child, out); }, - [&](const InExpression& i) { - CollectAttributes(*i.lhs, out); - for (const auto& value : i.values) { - CollectAttributes(value, out); - } - }, - [&](const AggregateExpression& a) { - if (!a.is_star && a.argument) { - CollectAttributes(*a.argument, out); - } - }, - [&](const IntConst&) {}, - [&](const StringConst&) {}, - [&](const Literal&) {}, - }, e); -} - bool ProjectionPassesThrough(const std::vector& proj_exprs, const std::vector& needed) { return std::all_of(needed.begin(), needed.end(), [&](const Attribute& a) { @@ -62,14 +37,14 @@ const logical::Projection* FindPushableProjection(utils::NotNull source, } // namespace -bool FilterPushdownThroughProjection::IsApplicable(utils::NotNull expr) { +bool FilterPushdownThroughProjection::IsApplicable(utils::NotNull expr, RuleContext&) { if (!std::holds_alternative(expr->root_operator)) return false; const auto& f = std::get(expr->root_operator); return FindPushableProjection(f.source, f.predicate) != nullptr; } LogicalOperator FilterPushdownThroughProjection::ApplyImpl(utils::NotNull expr, - Memo& memo) { + Memo& memo, RuleContext&) { const auto& f = std::get(expr->root_operator); const auto* proj = FindPushableProjection(f.source, f.predicate); if (proj == nullptr) { diff --git a/src/stewkk/sql/logic/transformation_rules/filter_split.cpp b/src/stewkk/sql/logic/transformation_rules/filter_split.cpp index e61a54c..47eb0f1 100644 --- a/src/stewkk/sql/logic/transformation_rules/filter_split.cpp +++ b/src/stewkk/sql/logic/transformation_rules/filter_split.cpp @@ -17,13 +17,13 @@ size_t ConjunctCount(const Expression& e) { } // namespace -bool FilterSplit::IsApplicable(utils::NotNull expr) { +bool FilterSplit::IsApplicable(utils::NotNull expr, RuleContext&) { if (!std::holds_alternative(expr->root_operator)) return false; const auto& f = std::get(expr->root_operator); return ConjunctCount(f.predicate) >= 2; } -LogicalOperator FilterSplit::ApplyImpl(utils::NotNull expr, Memo& memo) { +LogicalOperator FilterSplit::ApplyImpl(utils::NotNull expr, Memo& memo, RuleContext&) { const auto& f = std::get(expr->root_operator); std::vector conjs; diff --git a/src/stewkk/sql/logic/transformation_rules/filter_to_join_predicate.cpp b/src/stewkk/sql/logic/transformation_rules/filter_to_join_predicate.cpp new file mode 100644 index 0000000..a3e14d3 --- /dev/null +++ b/src/stewkk/sql/logic/transformation_rules/filter_to_join_predicate.cpp @@ -0,0 +1,44 @@ +#include + +#include +#include + +#include + +namespace stewkk::sql { + +namespace { + +const logical::Join* FindInnerJoin(utils::NotNull source) { + for (auto inner_expr : source->GetLogicalExprs()) { + if (const auto* j = std::get_if(&inner_expr->root_operator); + j != nullptr && j->type == JoinType::kInner) { + return j; + } + } + return nullptr; +} + +} // namespace + +bool FilterToJoinPredicate::IsApplicable(utils::NotNull expr, RuleContext&) { + if (!std::holds_alternative(expr->root_operator)) return false; + const auto& filter = std::get(expr->root_operator); + return FindInnerJoin(filter.source) != nullptr; +} + +LogicalOperator FilterToJoinPredicate::ApplyImpl(utils::NotNull expr, + Memo& memo, RuleContext&) { + const auto& filter = std::get(expr->root_operator); + const auto* join = FindInnerJoin(filter.source); + if (join == nullptr) { + throw std::runtime_error{"FilterToJoinPredicate requires an inner join below filter"}; + } + + std::vector conjs; + CollectConjuncts(join->qual, conjs); + CollectConjuncts(filter.predicate, conjs); + return logical::Join{join->lhs, join->rhs, join->type, AndConjuncts(conjs)}; +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/transformation_rules/in_to_or_chain.cpp b/src/stewkk/sql/logic/transformation_rules/in_to_or_chain.cpp index 4fb62c8..d25dfdb 100644 --- a/src/stewkk/sql/logic/transformation_rules/in_to_or_chain.cpp +++ b/src/stewkk/sql/logic/transformation_rules/in_to_or_chain.cpp @@ -62,13 +62,13 @@ Expression ExpandIn(const InExpression& in) { } // namespace -bool InToOrChain::IsApplicable(utils::NotNull expr) { +bool InToOrChain::IsApplicable(utils::NotNull expr, RuleContext&) { if (!std::holds_alternative(expr->root_operator)) return false; const auto& f = std::get(expr->root_operator); return ContainsIn(f.predicate); } -LogicalOperator InToOrChain::ApplyImpl(utils::NotNull expr, Memo& memo) { +LogicalOperator InToOrChain::ApplyImpl(utils::NotNull expr, Memo&, RuleContext&) { const auto& f = std::get(expr->root_operator); return logical::Filter{f.source, Expand(f.predicate)}; } diff --git a/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp b/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp index c5a0e47..6c7b961 100644 --- a/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp +++ b/src/stewkk/sql/logic/transformation_rules/join_associativity.cpp @@ -8,7 +8,7 @@ namespace stewkk::sql { -bool JoinAssociativity::IsApplicable(utils::NotNull expr) { +bool JoinAssociativity::IsApplicable(utils::NotNull expr, RuleContext&) { if (!std::holds_alternative(expr->root_operator)) return false; const auto& outer = std::get(expr->root_operator); if (outer.type != JoinType::kInner) return false; @@ -19,7 +19,7 @@ bool JoinAssociativity::IsApplicable(utils::NotNull expr) { return false; } -LogicalOperator JoinAssociativity::ApplyImpl(utils::NotNull expr, Memo& memo) { +LogicalOperator JoinAssociativity::ApplyImpl(utils::NotNull expr, Memo& memo, RuleContext&) { const auto& outer = std::get(expr->root_operator); for (auto inner_expr : outer.lhs->GetLogicalExprs()) { if (!std::holds_alternative(inner_expr->root_operator)) continue; diff --git a/src/stewkk/sql/logic/transformation_rules/join_commutativity.cpp b/src/stewkk/sql/logic/transformation_rules/join_commutativity.cpp index 7776cb1..0cc9dbb 100644 --- a/src/stewkk/sql/logic/transformation_rules/join_commutativity.cpp +++ b/src/stewkk/sql/logic/transformation_rules/join_commutativity.cpp @@ -2,11 +2,11 @@ namespace stewkk::sql { -bool JoinCommutativity::IsApplicable(utils::NotNull expr) { +bool JoinCommutativity::IsApplicable(utils::NotNull expr, RuleContext&) { return std::holds_alternative(expr->root_operator); } -LogicalOperator JoinCommutativity::ApplyImpl(utils::NotNull expr, Memo&) { +LogicalOperator JoinCommutativity::ApplyImpl(utils::NotNull expr, Memo&, RuleContext&) { auto join = std::get(expr->root_operator); std::swap(join.lhs, join.rhs); if (join.type == JoinType::kLeft) { diff --git a/src/stewkk/sql/logic/transformation_rules/outer_join_to_inner.cpp b/src/stewkk/sql/logic/transformation_rules/outer_join_to_inner.cpp new file mode 100644 index 0000000..186b5d3 --- /dev/null +++ b/src/stewkk/sql/logic/transformation_rules/outer_join_to_inner.cpp @@ -0,0 +1,56 @@ +#include + +#include + +#include +#include + +namespace stewkk::sql { + +namespace { + +const logical::Join* FindReducibleJoin(utils::NotNull source, + const Expression& predicate) { + for (auto inner_expr : source->GetLogicalExprs()) { + const auto* join = std::get_if(&inner_expr->root_operator); + if (join == nullptr || join->type == JoinType::kInner) continue; + auto lhs_tables = GroupTables(join->lhs); + auto rhs_tables = GroupTables(join->rhs); + if (join->type == JoinType::kLeft + && IsNullRejectingForTables(predicate, rhs_tables)) { + return join; + } + if (join->type == JoinType::kRight + && IsNullRejectingForTables(predicate, lhs_tables)) { + return join; + } + if (join->type == JoinType::kFull + && IsNullRejectingForTables(predicate, lhs_tables) + && IsNullRejectingForTables(predicate, rhs_tables)) { + return join; + } + } + return nullptr; +} + +} // namespace + +bool OuterJoinToInner::IsApplicable(utils::NotNull expr, RuleContext&) { + if (!std::holds_alternative(expr->root_operator)) return false; + const auto& filter = std::get(expr->root_operator); + return FindReducibleJoin(filter.source, filter.predicate) != nullptr; +} + +LogicalOperator OuterJoinToInner::ApplyImpl(utils::NotNull expr, + Memo& memo, RuleContext&) { + const auto& filter = std::get(expr->root_operator); + const auto* join = FindReducibleJoin(filter.source, filter.predicate); + if (join == nullptr) { + throw std::runtime_error{"OuterJoinToInner requires a null-rejecting filter"}; + } + auto inner = memo.AddGroup( + logical::Join{join->lhs, join->rhs, JoinType::kInner, join->qual})->group; + return logical::Filter{inner, filter.predicate}; +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp b/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp index 84b7dbd..4cef467 100644 --- a/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp +++ b/src/stewkk/sql/logic/transformation_rules/predicate_utils.cpp @@ -1,5 +1,6 @@ #include +#include #include #include @@ -59,12 +60,80 @@ void CollectAttrTables(const Expression& e, std::unordered_set& out }, e); } +void CollectAttributes(const Expression& e, std::vector& out) { + std::visit(utils::Overloaded{ + [&](const Attribute& a) { out.push_back(a); }, + [&](const BinaryExpression& b) { + CollectAttributes(*b.lhs, out); + CollectAttributes(*b.rhs, out); + }, + [&](const UnaryExpression& u) { CollectAttributes(*u.child, out); }, + [&](const InExpression& i) { + CollectAttributes(*i.lhs, out); + for (const auto& value : i.values) { + CollectAttributes(value, out); + } + }, + [&](const AggregateExpression& a) { + if (!a.is_star && a.argument) { + CollectAttributes(*a.argument, out); + } + }, + [&](const IntConst&) {}, + [&](const StringConst&) {}, + [&](const Literal&) {}, + }, e); +} + std::unordered_set ExprTables(const Expression& e) { std::unordered_set out; CollectAttrTables(e, out); return out; } +bool ExprUsesOnlyTables(const Expression& e, const std::unordered_set& tables) { + auto expr_tables = ExprTables(e); + return std::all_of(expr_tables.begin(), expr_tables.end(), + [&](const auto& table) { return tables.contains(table); }); +} + +bool IsNullRejectingForTables(const Expression& e, + const std::unordered_set& tables) { + return std::visit(utils::Overloaded{ + [&](const Attribute& a) { return tables.contains(a.table); }, + [&](const BinaryExpression& b) { + if (b.binop == BinaryOp::kAnd) { + return IsNullRejectingForTables(*b.lhs, tables) + || IsNullRejectingForTables(*b.rhs, tables); + } + if (b.binop == BinaryOp::kOr) { + return IsNullRejectingForTables(*b.lhs, tables) + && IsNullRejectingForTables(*b.rhs, tables); + } + if (b.binop == BinaryOp::kEq || b.binop == BinaryOp::kNotEq + || b.binop == BinaryOp::kGt || b.binop == BinaryOp::kLt + || b.binop == BinaryOp::kGe || b.binop == BinaryOp::kLe) { + return IsNullRejectingForTables(*b.lhs, tables) + || IsNullRejectingForTables(*b.rhs, tables); + } + return false; + }, + [&](const UnaryExpression& u) { + if (u.op == UnaryOp::kIsNull) return false; + return IsNullRejectingForTables(*u.child, tables); + }, + [&](const InExpression& i) { + return IsNullRejectingForTables(*i.lhs, tables); + }, + [&](const AggregateExpression& a) { + return !a.is_star && a.argument && IsNullRejectingForTables(*a.argument, tables); + }, + [&](const IntConst&) { return false; }, + [&](const StringConst&) { return false; }, + [&](const Literal&) { return false; }, + }, e); +} + namespace { void CollectGroupTables(utils::NotNull g, std::unordered_set& out, @@ -77,6 +146,8 @@ void CollectGroupTables(utils::NotNull g, std::unordered_set + +#include +#include +#include + +#include +#include + +namespace stewkk::sql { + +namespace { + +const logical::Join* FindJoin(utils::NotNull source) { + for (auto inner_expr : source->GetLogicalExprs()) { + if (const auto* j = std::get_if(&inner_expr->root_operator)) { + return j; + } + } + return nullptr; +} + +bool ContainsAttr(const std::vector& attrs, const Attribute& attr) { + return std::ranges::any_of(attrs, [&](const Attribute& a) { return a == attr; }); +} + +std::vector NeededForSide(const std::vector& needed, + const Schema& schema) { + std::vector out; + for (const auto& attr : needed) { + auto it = std::ranges::find_if(schema, [&](const Attribute& schema_attr) { + return schema_attr.name == attr.name + && (attr.table.empty() || attr.table == schema_attr.table); + }); + if (it != schema.end() && !ContainsAttr(out, *it)) { + out.push_back(*it); + } + } + return out; +} + +std::vector AttrExpressions(const std::vector& attrs) { + std::vector out; + out.reserve(attrs.size()); + for (const auto& attr : attrs) { + out.push_back(attr); + } + return out; +} + +bool CanPush(const logical::Projection& projection, const logical::Join& join, + RuleContext& ctx) { + std::vector needed; + for (const auto& expr : projection.expressions) { + CollectAttributes(expr, needed); + } + CollectAttributes(join.qual, needed); + + auto lhs_schema = ctx.schema.GetSchema(join.lhs); + auto rhs_schema = ctx.schema.GetSchema(join.rhs); + auto lhs_needed = NeededForSide(needed, lhs_schema); + auto rhs_needed = NeededForSide(needed, rhs_schema); + return (!lhs_needed.empty() && lhs_needed.size() < lhs_schema.size()) + || (!rhs_needed.empty() && rhs_needed.size() < rhs_schema.size()); +} + +} // namespace + +bool ProjectionPushdownThroughJoin::IsApplicable(utils::NotNull expr, + RuleContext& ctx) { + if (!std::holds_alternative(expr->root_operator)) return false; + const auto& projection = std::get(expr->root_operator); + const auto* join = FindJoin(projection.source); + return join != nullptr && CanPush(projection, *join, ctx); +} + +LogicalOperator ProjectionPushdownThroughJoin::ApplyImpl(utils::NotNull expr, + Memo& memo, RuleContext& ctx) { + const auto& projection = std::get(expr->root_operator); + const auto* join = FindJoin(projection.source); + if (join == nullptr) { + throw std::runtime_error{"ProjectionPushdownThroughJoin requires a join below"}; + } + + std::vector needed; + for (const auto& e : projection.expressions) { + CollectAttributes(e, needed); + } + CollectAttributes(join->qual, needed); + + auto lhs_schema = ctx.schema.GetSchema(join->lhs); + auto rhs_schema = ctx.schema.GetSchema(join->rhs); + auto lhs_needed = NeededForSide(needed, lhs_schema); + auto rhs_needed = NeededForSide(needed, rhs_schema); + + auto lhs = join->lhs; + if (!lhs_needed.empty() && lhs_needed.size() < lhs_schema.size()) { + lhs = memo.AddGroup(logical::Projection{join->lhs, AttrExpressions(lhs_needed), {}})->group; + } + auto rhs = join->rhs; + if (!rhs_needed.empty() && rhs_needed.size() < rhs_schema.size()) { + rhs = memo.AddGroup(logical::Projection{join->rhs, AttrExpressions(rhs_needed), {}})->group; + } + + auto new_join = memo.AddGroup(logical::Join{lhs, rhs, join->type, join->qual})->group; + return logical::Projection{new_join, projection.expressions, projection.aliases}; +} + +} // namespace stewkk::sql diff --git a/src/stewkk/sql/main.cpp b/src/stewkk/sql/main.cpp index 172431e..0619c46 100644 --- a/src/stewkk/sql/main.cpp +++ b/src/stewkk/sql/main.cpp @@ -260,7 +260,8 @@ int main(int argc, char** argv) { : PropertySet::Any(); Optimizer optimizer(parsed.op, MakeMainRules(LoadIndexCatalogFromCsvDir(args.data_dir)), CardinalityEstimates{LoadTableSizesFromCsvDir(args.data_dir)}, - LoadSchemaFromCsvDir(args.data_dir), std::move(required)); + LoadSchemaFromCsvDir(args.data_dir), std::move(required), + LoadConstraintCatalogFromCsvDir(args.data_dir)); plan = optimizer.Optimize(); plan_cost = optimizer.GetBestCost(); From eead31c63c55496a462a79aee88a72222889c717 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sun, 14 Jun 2026 14:15:34 +0300 Subject: [PATCH 107/120] Fix --- report/vkr.tex | 33 ++++++++++++++++++--------------- 1 file changed, 18 insertions(+), 15 deletions(-) diff --git a/report/vkr.tex b/report/vkr.tex index 4630b3f..c5a149f 100644 --- a/report/vkr.tex +++ b/report/vkr.tex @@ -262,6 +262,7 @@ } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \usepackage{ragged2e} % выравнивание по горизонтали +\newcolumntype{L}[1]{>{\RaggedRight\arraybackslash}p{#1}} \usepackage[explicit]{titlesec} % явное указание заголовка \usepackage{placeins} % не даеёт объектам «утекать» за барьеры \usepackage{xparse} % позволяет создавать более крутые команды, чем с \newcommand @@ -1093,8 +1094,8 @@ \subsection{Поиск оптимального плана} op/.style={rectangle, draw, fill=gray!12, minimum width=4.25cm, minimum height=1.2cm, align=center, font=\small} ] - \node[op] (p1j2) at (-4.4, 2.9) {Hash Join\\\scriptsize \(10^6\) чтений}; - \node[op] (p1j1) at (-6.5, 1.2) {Hash Join\\\scriptsize \(1{,}26 \cdot 10^5\) чтений}; + \node[op] (p1j2) at (-4.4, 2.9) {Hash Join\\\footnotesize \(10^6\) чтений}; + \node[op] (p1j1) at (-6.5, 1.2) {Hash Join\\\footnotesize \(1{,}26 \cdot 10^5\) чтений}; \node[rel] (p1l) at (-2.3, 1.2) {\texttt{lineitem}}; \node[rel] (p1c) at (-7.8, -0.3) {\texttt{customer}}; \node[rel] (p1o) at (-4.4, -0.3) {\texttt{orders}}; @@ -1105,8 +1106,8 @@ \subsection{Поиск оптимального плана} \draw[->] (p1o) -- (p1j1); \node[font=\small\bfseries] at (-4.4, 4.0) {План \(P_1\)}; - \node[op] (p2j2) at (4.4, 2.9) {Nested Loop Join\\\scriptsize \(1{,}25 \cdot 10^{14}\) чтений}; - \node[op] (p2j1) at (2.3, 1.2) {Nested Loop Join\\\scriptsize \(1{,}25 \cdot 10^8\) чтений}; + \node[op] (p2j2) at (4.4, 2.9) {Nested Loop Join\\\footnotesize \(1{,}25 \cdot 10^{14}\) чтений}; + \node[op] (p2j1) at (2.3, 1.2) {Nested Loop Join\\\footnotesize \(1{,}25 \cdot 10^8\) чтений}; \node[rel] (p2l) at (6.5, 1.2) {\texttt{lineitem}}; \node[rel] (p2c) at (1.0, -0.3) {\texttt{customer}}; \node[rel] (p2o) at (4.4, -0.3) {\texttt{orders}}; @@ -1169,7 +1170,7 @@ \subsection{Поиск оптимального плана} \node[op] (p1proj) at (-3.5, 7.7) {\(\ProjectionOp{\texttt{ename}}\)}; \node[op] (p1sig2) at (-3.5, 6.2) {\(\SelOp{\texttt{dname}=\texttt{'Toy'}}\)}; \node[op] (p1sig1) at (-3.5, 4.7) {\(\SelOp{\texttt{Emp.did}=\texttt{Dept.did}}\)}; - \node[op] (p1cp) at (-3.5, 3.2) {\(\CProd{\texttt{Emp}}{\texttt{Dept}}\)\\\scriptsize \(50{,}050\) чтений}; + \node[op] (p1cp) at (-3.5, 3.2) {\(\CProd{\texttt{Emp}}{\texttt{Dept}}\)\\\footnotesize \(50{,}050\) чтений}; \node[rel] (p1emp) at (-5.2, 1.7) {\texttt{Emp}}; \node[rel] (p1dept) at (-1.8, 1.7) {\texttt{Dept}}; @@ -1181,9 +1182,9 @@ \subsection{Поиск оптимального плана} \node[font=\small\bfseries] at (3.5, 8.8) {План \(P_2\)}; \node[op] (p2proj) at (3.5, 7.7) {\(\ProjectionOp{\texttt{ename}}\)}; - \node[op] (p2join) at (3.5, 5.5) {Index NL Join\\\(\texttt{Emp.did}=\texttt{Dept.did}\)\\\scriptsize \(23\) чтения}; + \node[op] (p2join) at (3.5, 5.5) {Index NL Join\\\(\texttt{Emp.did}=\texttt{Dept.did}\)\\\footnotesize \(23\) чтения}; \node[rel] (p2emp) at (1.6, 3.5) {\texttt{Emp}}; - \node[op] (p2sig) at (5.4, 3.5) {\(\SelOp{\texttt{dname}=\texttt{'Toy'}}\)\\\scriptsize \(3\) чтения}; + \node[op] (p2sig) at (5.4, 3.5) {\(\SelOp{\texttt{dname}=\texttt{'Toy'}}\)\\\footnotesize \(3\) чтения}; \node[rel] (p2dept) at (5.4, 1.8) {\texttt{Dept}}; \draw[->] (p2emp) -- (p2join); @@ -2754,6 +2755,7 @@ \subsection{Калибровка стоимостной модели} \begin{algorithm}[H] \caption{Алгоритм поиска (часть 1).}% \label{alg:cascades-search} + \footnotesize \begin{algorithmic}[1] \Procedure{Optimize}{$root, required$} \State Push task $\Call{OptimizeGroup}{root.group, required, +\infty}$ @@ -2798,6 +2800,7 @@ \subsection{Калибровка стоимостной модели} \begin{algorithm}[H] \caption{Алгоритм поиска (часть 2).}% \label{alg:cascades-search-2} + \footnotesize \begin{algorithmic}[1] \Procedure{OptimizeGroup}{$group, required, limit$} \If{$winner[group, required]$ exists \textbf{or} $group$ is explored \textbf{and} $\Call{LowerBound}{group} \geq limit$} @@ -2838,8 +2841,8 @@ \subsection{Калибровка стоимостной модели} \end{algorithmic} \end{algorithm} -\begin{scriptsize} -\begin{longtable}{|c|p{2.6cm}|c|p{4.2cm}|} +\begin{footnotesize} +\begin{longtable}{|c|L{3.1cm}|c|L{3.7cm}|} \caption{Правила трансформации.}\label{tbl:transformation_rules}\\ \hline № & Название & Правило & Комментарий \\ @@ -2856,7 +2859,7 @@ \subsection{Калибровка стоимостной модели} \endlastfoot 1 & Коммутативность соединения & - \(\TransRule{\InnerJoin{R}{\theta}{S}}{\text{\scriptsize внутреннее или полное внешнее соединение}}{\InnerJoin{S}{\theta}{R}}\) & + \(\TransRule{\InnerJoin{R}{\theta}{S}}{\text{внутреннее или полное внешнее соединение}}{\InnerJoin{S}{\theta}{R}}\) & Перестановка входов соединения. Позволяет рассмотреть оба порядка вычисления. \\ \hline @@ -2949,7 +2952,7 @@ \subsection{Калибровка стоимостной модели} \end{tabular} \end{table} -\begin{longtable}{|c|p{2.8cm}|c|p{4.5cm}|} +\begin{longtable}{|c|L{3.4cm}|c|L{3.9cm}|} \caption{Правила реализации.}\label{tbl:implementation_rules}\\ \hline № & Название & Правило & Комментарий \\ @@ -2971,7 +2974,7 @@ \subsection{Калибровка стоимостной модели} \hline 2 & Сканирование по индексу & - \(\TransRule{\LogicalGet}{\text{\scriptsize есть совместимый индекс}}{\IndexScan}\) & + \(\TransRule{\LogicalGet}{\text{есть совместимый индекс}}{\IndexScan}\) & Индекс должен быть совместим с предикатом или требуемым порядком. \\ \hline @@ -2981,12 +2984,12 @@ \subsection{Калибровка стоимостной модели} \hline 4 & Хеш-соединение & - \(\TransRule{\InnerJoin{R}{\theta}{S}}{\theta\ \text{\scriptsize содержит равенство по ключам}}{\HashJoin}\) & + \(\TransRule{\InnerJoin{R}{\theta}{S}}{\theta\ \text{содержит равенство по ключам}}{\HashJoin}\) & Хеш-таблица строится по ключам равенства. \\ \hline 5 & Соединение слиянием & - \(\TransRule{\InnerJoin{R}{\theta}{S}}{\text{\scriptsize входы упорядочены по ключам соединения}}{\MergeJoin}\) & + \(\TransRule{\InnerJoin{R}{\theta}{S}}{\text{входы упорядочены по ключам соединения}}{\MergeJoin}\) & Требуемый порядок может быть обеспечен явным оператором \Sort{}. \\ \hline @@ -3000,7 +3003,7 @@ \subsection{Калибровка стоимостной модели} Эффективна при уже отсортированном входе. Может быть выгодна, если требуемый порядок нужен и родительскому оператору. \\ \hline \end{longtable} -\end{scriptsize} +\end{footnotesize} \begin{listing}[H] \caption{Вычисление локальной стоимости физических операторов.} From 1c9a7b757c9664dbb854415d05b09d13736354a0 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sun, 14 Jun 2026 14:33:43 +0300 Subject: [PATCH 108/120] Fix --- report/vkr.tex | 226 ++++++++++++++++++++++++------------------------- 1 file changed, 110 insertions(+), 116 deletions(-) diff --git a/report/vkr.tex b/report/vkr.tex index c5a149f..d81639d 100644 --- a/report/vkr.tex +++ b/report/vkr.tex @@ -628,7 +628,7 @@ \section*{#1}\vspace*{2.5ex} % по госту положены 3 пустые \setlength{\tabcolsep}{3pt} \newpage -\thispagestyle{empty} +% \thispagestyle{empty} \section*{АННОТАЦИЯ} @@ -657,7 +657,7 @@ \section*{АННОТАЦИЯ} оптимизации запросов и итеративного развития набора правил оптимизатора. \newpage -\setcounter{page}{2} +\setcounter{page}{5} %---------------------------------------------------------------------------- % ОТСЮДА --- СОБСТВЕННО ТЕКСТ %---------------------------------------------------------------------------- @@ -668,7 +668,7 @@ \section*{АННОТАЦИЯ} В эпоху стремительного роста объемов данных системы управления базами данных (СУБД) являются важной частью информационных систем. По данным аналитических -исследований\cite{DBReport}, объем мирового рынка СУБД ежегодно увеличивается, +исследований~\cite{DBReport}, объем мирового рынка СУБД ежегодно увеличивается, что обусловлено как расширением круга решаемых задач, так и усложнением структуры обрабатываемых данных и увеличением их объема. Реляционные базы данных, работающие с различными диалектами языка SQL, продолжают занимать @@ -697,7 +697,7 @@ \section*{АННОТАЦИЯ} итеративного улучшения путем сравнения с промышленными реализациями. Для достижения поставленной цели прежде всего требуется изучить архитектуру -оптимиазторов Cascades и на ее основе проработать собственную. +оптимизаторов Cascades и на ее основе проработать собственную. После выбора архитектурной основы необходимо формализовать поддерживаемое подмножество SQL и соответствующее ему подмножество реляционной алгебры. Эта @@ -715,7 +715,7 @@ \section*{АННОТАЦИЯ} Правила трансформации должны порождать логически эквивалентные планы, а правила реализации сопоставлять логическим операторам допустимые физические операторы. -Для оптимизации алгоритма поиска нужно реазивовать метод ветвей и границ. Этот +Для оптимизации алгоритма поиска нужно реализовать метод ветвей и границ. Этот метод должен использовать оценки стоимости для отсечения заведомо неоптимальных планов и уменьшения объема пространства поиска, исследуемого оптимизатором. @@ -731,7 +731,7 @@ \section{Исследовательская часть} обеспечивающий хранение, извлечение и модификацию структурированных данных. Несмотря на разнообразие существующих реализаций, архитектура большинства реляционных СУБД включает ряд типовых компонентов, взаимодействие которых -обеспечивает выполнение пользовательских запросов\cite{petrov2019database}. +обеспечивает выполнение пользовательских запросов~\cite{petrov2019database}. К основным компонентам СУБД относятся~\refImage{fig::dbms_arch}: @@ -803,31 +803,25 @@ \subsection{Процесс исполнения SQL-запроса} Процесс исполнения SQL-запроса в реляционной СУБД проходит несколько последовательных этапов. -\begin{enumerate} - \item \emph{Синтаксический анализ} --- запроса проходит лексический и - синтаксический разбор. Результатом данного этапа является синтаксическое - дерево, узлы которого соответствуют конструкциям языка SQL: операторам - \SELECT, \FROM, \WHERE, \JOIN, \GROUPBY, \ORDERBY{} и другим. На этом же - этапе выполняется семантический анализ: проверяется существование - указанных таблиц и столбцов, разрешаются имена объектов и определяются - типы выражений. - - \item \emph{Оптимизация} --- синтаксическое дерево преобразуется в логический - план запроса, выраженный в терминах реляционной алгебры. Оптимизатор - исследует пространство эквивалентных логических планов и для каждого из - них рассматривает возможные физические реализации операторов. С помощью - модели стоимости оптимизатор оценивает затраты на выполнение каждого - плана и выбирает план с наименьшей оценочной стоимостью. Результатом - этого этапа является физический план исполнения. Под этим понимается - дерево физических операторов с указанием конкретных алгоритмов - соединения, методов доступа к данным и порядка операций. - - \item \emph{Исполнение физического плана} --- движок выполнения реализует - физический план, порождая потоки кортежей. Данные извлекаются из - хранилища, обрабатываются операторами плана и формируют результирующий - набор, возвращаемый пользователю. -\end{enumerate} - +\emph{Синтаксический анализ} --- запрос проходит лексический и синтаксический +разбор. Результатом данного этапа является синтаксическое дерево, узлы которого +соответствуют конструкциям языка SQL: операторам \SELECT, \FROM, \WHERE, \JOIN, +\GROUPBY, \ORDERBY{} и другим. На этом же этапе выполняется семантический +анализ: проверяется существование указанных таблиц и столбцов, разрешаются имена +объектов и определяются типы выражений. + +\emph{Оптимизация} --- синтаксическое дерево преобразуется в логический план +запроса, выраженный в терминах реляционной алгебры. Оптимизатор исследует +пространство эквивалентных логических планов и для каждого из них рассматривает +возможные физические реализации операторов. С помощью модели стоимости +оптимизатор оценивает затраты на выполнение каждого плана и выбирает план с +наименьшей оценочной стоимостью. Результатом этого этапа является физический +план исполнения. Под этим понимается дерево физических операторов с указанием +конкретных алгоритмов соединения, методов доступа к данным и порядка операций. + +\emph{Исполнение физического плана} --- движок выполнения реализует физический +план, порождая потоки кортежей. Данные извлекаются из хранилища, обрабатываются +операторами плана и формируют результирующий набор, возвращаемый пользователю. Сформулируем задачу, которую решает оптимизатор: по запросу, представленному в форме дерева операторов реляционной алгебры, построить эквивалентный и @@ -865,40 +859,43 @@ \subsection{Реляционная алгебра} \] Среди основных операторов реляционной алгебры выделяют -следующие\cite{silberschatz2020database}. +следующие~\cite{silberschatz2020database}. -\begin{enumerate} - \item \emph{Фильтрация} \(\Sel{p}{R}\) --- возвращает подмножество кортежей - отношения \(R\), удовлетворяющих предикату \(p\). Например, - \(\Sel{\text{age} > 30}{\text{Employee}}\) вернет все кортежи из - отношения Employee, для которых значение атрибута age больше 30. - \item \emph{Проекция} \(\Projection{A_1, \ldots, A_k}{R}\) --- формирует новое - отношение, содержащее только перечисленные атрибуты. - \item \emph{Декартово произведение} \(\CProd{R}{S}\) --- формирует отношение, - каждый кортеж которого является конкатенацией кортежа из \(R\) и кортежа - из \(S\), при этом результат содержит \(|\CProd{R}{S}| = |R| \cdot |S|\) - элементов. - \item \emph{Соединение} \(\InnerJoin{R}{p}{S}\) --- комбинирует кортежи двух - отношений на основании предиката \(p\). Внутреннее соединение - определяется как - \(\InnerJoin{R}{R.a = S.b}{S} = \Sel{R.a = S.b}{\CProd{R}{S}}\). - \item \emph{Объединение} \(R \cup S\) --- возвращает все кортежи, - принадлежащие хотя бы одному из двух совместимых по схеме отношений. - \item \emph{Разность} \(R - S\) --- выбирает кортежи, принадлежащие \(R\), но - отсутствующие в \(S\). - \item \emph{Агрегация} \(\Agg{G}{f}{R}\) --- разбивает отношение \(R\) на - непересекающиеся множества по набору атрибутов \(G\) и вычисляет - агрегированные значения функции \(f\) по каждому такому множеству. - Примеры функций: \COUNT, \SUM, \AVG, \MIN, \MAX. - \item \emph{Apply} \(\Apply{R}{S(t)}\) --- для каждого кортежа \(t\) - отношения \(R\) вычисляет параметризованное им отношение \(S(t)\) и - конкатенирует \(t\) с каждым кортежем результата: +\emph{Фильтрация} \(\Sel{p}{R}\) --- возвращает подмножество кортежей отношения +\(R\), удовлетворяющих предикату \(p\). Например, +\(\Sel{\text{age} > 30}{\text{Employee}}\) вернет все кортежи из отношения +Employee, для которых значение атрибута age больше 30. + +\emph{Проекция} \(\Projection{A_1, \ldots, A_k}{R}\) --- формирует новое +отношение, содержащее только перечисленные атрибуты. + +\emph{Декартово произведение} \(\CProd{R}{S}\) --- формирует отношение, каждый +кортеж которого является конкатенацией кортежа из \(R\) и кортежа из \(S\), при +этом результат содержит \(|\CProd{R}{S}| = |R| \cdot |S|\) элементов. + +\emph{Соединение} \(\InnerJoin{R}{p}{S}\) --- комбинирует кортежи двух отношений +на основании предиката \(p\). Внутреннее соединение определяется как +\(\InnerJoin{R}{R.a = S.b}{S} = \Sel{R.a = S.b}{\CProd{R}{S}}\). + +\emph{Объединение} \(R \cup S\) --- возвращает все кортежи, принадлежащие хотя +бы одному из двух совместимых по схеме отношений. + +\emph{Разность} \(R - S\) --- выбирает кортежи, принадлежащие \(R\), но +отсутствующие в \(S\). + +\emph{Агрегация} \(\Agg{G}{f}{R}\) --- разбивает отношение \(R\) на +непересекающиеся множества по набору атрибутов \(G\) и вычисляет агрегированные +значения функции \(f\) по каждому такому множеству. Примеры функций: \COUNT, +\SUM, \AVG, \MIN, \MAX. + +\emph{Apply} \(\Apply{R}{S(t)}\) --- для каждого кортежа \(t\) отношения \(R\) +вычисляет параметризованное им отношение \(S(t)\) и конкатенирует \(t\) с каждым +кортежем результата: \[ \Apply{R}{S(t)} = \bigcup_{t \in R} \{t\} \times S(t). \] - В отличие от соединения, правая сторона зависит от текущего кортежа - левой и используется для представления коррелированных подзапросов. -\end{enumerate} +\noindent{} В отличие от соединения, правая сторона зависит от текущего кортежа +левой и используется для представления коррелированных подзапросов. \subsection{Преобразование синтаксического дерева в реляционную алгебру}\label{sec:translation} @@ -910,40 +907,38 @@ \subsection{Преобразование синтаксического дере правил вывода. Если для всех поддеревьев известны алгебраические представления, то алгебраическое представление составной конструкции выражается через них. -\begin{enumerate} - \item \emph{Базовый случай.} Для ссылки на таблицу \(T\) выполняется - \(\Tr{T} = T\). - \item \emph{Блок \texttt{SELECT-FROM-WHERE}.} Пусть \(\bar{T} = T_1, \ldots, - T_n\) --- ссылки на таблицы или вложенные подзапросы, \(p\) --- - предикат фильтрации, \(\bar{a} = a_1, \ldots, a_k\) --- список - выражений проекции. Тогда +\emph{Базовый случай.} Для ссылки на таблицу \(T\) выполняется \(\Tr{T} = T\). + +\emph{Блок \texttt{SELECT-FROM-WHERE}.} Пусть \(\bar{T} = T_1, \ldots, T_n\) --- +ссылки на таблицы или вложенные подзапросы, \(p\) --- предикат фильтрации, +\(\bar{a} = a_1, \ldots, a_k\) --- список выражений проекции. Тогда \[ \frac{\Tr{T_1} = E_1 \quad \cdots \quad \Tr{T_n} = E_n} {\Tr{\SELECT\ \bar{a}\ \FROM\ \bar{T}\ \WHERE\ p} = \Projection{\bar{a}}{\Sel{p}{E_1 \times \cdots \times E_n}}}. \] - \item \emph{Группировка.} Пусть \(\bar{g} = g_1, \ldots, g_m\) --- список - группирующих атрибутов, \(\bar{f} = f_1, \ldots, f_l\) --- список - агрегирующих функций. Тогда - \[ - \frac{\Tr{T_1} = E_1 \quad \cdots \quad \Tr{T_n} = E_n} - {\Tr{\SELECT\ \bar{g}, \bar{f}\ \FROM\ \bar{T}\ \WHERE\ p\ \GROUPBY\ \bar{g}} - = \Agg{\bar{g}}{\bar{f}}{\Sel{p}{E_1 \times \cdots \times E_n}}}. - \] - \item \emph{Вложенные подзапросы.} Пусть \(Q\) --- внешний блок, а \(Q'(t)\) - --- коррелированный подзапрос, зависящий от кортежа \(t\) из - \(\Tr{Q}\). Тогда - \[ - \frac{\Tr{Q} = E \quad \Tr{Q'(t)} = E'(t)} - {\Tr{Q\ \text{с подзапросом}\ Q'} = \Apply{E}{E'(t)}}, - \] - где \(\mathcal{A}\) --- оператор Apply, который впоследствии может быть - удалён процедурой декорреляции. - \item \emph{Сортировка.} Конструкция \ORDERBY{} не выражается в виде - операторов реляционной алгебры. Вместо этого она задает требуемое - физическое свойство упорядоченности результата, учитываемое на этапе - выбора физического плана. -\end{enumerate} + +\emph{Группировка.} Пусть \(\bar{g} = g_1, \ldots, g_m\) --- список +группирующих атрибутов, \(\bar{f} = f_1, \ldots, f_l\) --- список +агрегирующих функций. Тогда +\[ + \frac{\Tr{T_1} = E_1 \quad \cdots \quad \Tr{T_n} = E_n} + {\Tr{\SELECT\ \bar{g}, \bar{f}\ \FROM\ \bar{T}\ \WHERE\ p\ \GROUPBY\ \bar{g}} + = \Agg{\bar{g}}{\bar{f}}{\Sel{p}{E_1 \times \cdots \times E_n}}}. +\] +\emph{Вложенные подзапросы.} Пусть \(Q\) --- внешний блок, а \(Q'(t)\) +--- коррелированный подзапрос, зависящий от кортежа \(t\) из +\(\Tr{Q}\). Тогда +\[ + \frac{\Tr{Q} = E \quad \Tr{Q'(t)} = E'(t)} + {\Tr{Q\ \text{с подзапросом}\ Q'} = \Apply{E}{E'(t)}}, +\] +\noindent{} где \(\mathcal{A}\) --- оператор Apply, который впоследствии может быть +удалён процедурой декорреляции. +\emph{Сортировка.} Конструкция \ORDERBY{} не выражается в виде +операторов реляционной алгебры. Вместо этого она задает требуемое +физическое свойство упорядоченности результата, учитываемое на этапе +выбора физического плана. Приведём пример применения этих правил к SQL-запросу из листинга~\ref{lst:correlated_subquery}. Трансляция этого запроса в реляционную @@ -973,7 +968,7 @@ \subsection{Преобразование синтаксического дере \[ \Tr{Q'(e)} = \Agg{\varnothing}{\AVG(\text{salary})}{\Sel{\text{dept} = e.\text{dept}}{\text{Employee}}}. \] - Подзапрос коррелирован, так как зависит от атрибута \(e.\text{dept}\) + \noindent{} Подзапрос коррелирован, так как зависит от атрибута \(e.\text{dept}\) внешнего кортежа. \item По правилу вложенных подзапросов результат \(\Tr{Q'(e)}\) присоединяется к внешнему блоку оператором \(\mathcal{A}\), добавляя к каждому кортежу @@ -987,7 +982,7 @@ \subsection{Преобразование синтаксического дере \[ \Projection{e.\text{name}}{\Sel{e.\text{salary} > c}{\Apply{\text{Employee}\;e}{\Agg{\varnothing}{\text{AVG}(\text{salary}) \to c}{\Sel{\text{dept} = e.\text{dept}}{\text{Employee}}}}}}. \] -Соответствующее дерево операторов изображено на +\noindent{} Соответствующее дерево операторов изображено на рисунке~\ref{fig:correlated_subquery_tree}. \begin{figure}[!htb]\centering @@ -1122,23 +1117,22 @@ \subsection{Поиск оптимального плана} \label{fig::join_plan_comparison} \end{figure} -\begin{itemize} - \item План \(P_1\): - \(\NJoin{(\NJoin{\texttt{customer}}{\texttt{orders}})}{\texttt{lineitem}}\) - с реализацией обоих соединений через Hash Join, для чего сначала - полностью считываются \(P_C + P_O = 1{,}26 \cdot 10^5\) страниц для - построения хеш-таблицы, после чего промежуточный результат размером - \(10^7\) строк соединяется с \texttt{lineitem}, требуя считывания ещё % - \(P_L = 10^6\) страниц. В сумме --- порядка \(1{,}126 \cdot 10^6\) - операций чтения с диска. - \item План \(P_2\): тот же запрос, но реализованный как соединение с помощью - вложенных циклов без использования индексов: для каждой строки - \texttt{customer} полностью читается таблица \texttt{orders}, для каждой - результирующей пары --- \texttt{lineitem}. Совокупный объём ввода-вывода - оценивается величиной - \(P_C \cdot P_O \cdot P_L \approx 1{,}25 \cdot 10^{14}\) операций - чтения, что на восемь порядков превышает \(P_1\). -\end{itemize} +План \(P_1\): +\(\NJoin{(\NJoin{\texttt{customer}}{\texttt{orders}})}{\texttt{lineitem}}\) +с реализацией обоих соединений через Hash Join, для чего сначала +полностью считываются \(P_C + P_O = 1{,}26 \cdot 10^5\) страниц для +построения хеш-таблицы, после чего промежуточный результат размером +\(10^7\) строк соединяется с \texttt{lineitem}, требуя считывания ещё % +\(P_L = 10^6\) страниц. В сумме --- порядка \(1{,}126 \cdot 10^6\) +операций чтения с диска. + +План \(P_2\): тот же запрос, но реализованный как соединение с помощью +вложенных циклов без использования индексов: для каждой строки +\texttt{customer} полностью читается таблица \texttt{orders}, для каждой +результирующей пары --- \texttt{lineitem}. Совокупный объём ввода-вывода +оценивается величиной +\(P_C \cdot P_O \cdot P_L \approx 1{,}25 \cdot 10^{14}\) операций +чтения, что на восемь порядков превышает \(P_1\). На еще одном примере запроса из листинга~\ref{lst:logical_transform_query} рассмотрим влияние логических преобразований. Отношение \texttt{Emp} содержит @@ -1354,7 +1348,7 @@ \subsection{Правила трансформации и реализации}\l \[ r : \TransRule{pattern(e)}{condition(e)}{substitute(e)}, \] -где \(e\) --- исходное выражение, \(pattern\) --- структурный образец, +\noindent{} где \(e\) --- исходное выражение, \(pattern\) --- структурный образец, \(condition\) --- предикат применимости, \(substitute\) --- выражение-замена. Для правил трансформации результатом применения является новое логическое выражение в группе, а для правил реализации физическое выражение в той же @@ -1420,7 +1414,7 @@ \subsubsection{Метод ветвей и границ} C_{local}(e) + \sum_{i=1}^{k} LB(G_i, P_i) \geq C^*, \] -то выражение \(e\) может быть отсечено. Здесь \(LB\) обозначает нижнюю границу +\noindent{}то выражение \(e\) может быть отсечено. Здесь \(LB\) обозначает нижнюю границу стоимости оптимизации дочерней группы под требуемым свойством \(P_i\). Чем точнее нижняя граница, тем эффективнее отсечение. @@ -1461,7 +1455,7 @@ \subsubsection{Оценка кардинальности} \frac{|R| \cdot |S|}{\max(NDV(R.a), NDV(S.b))}, \] -где \(NDV\) --- число различных значений атрибута. Такая оценка удобна, но может +\noindent{}где \(NDV\) --- число различных значений атрибута. Такая оценка удобна, но может быть грубой, если данные имеют корреляции, сильный перекос или сложные зависимости между атрибутами. Экспериментальные исследования показывают, что ошибки кардинальности часто являются одной из основных причин выбора неудачных @@ -1483,7 +1477,7 @@ \subsubsection{Оценка стоимости} Cost(p) = w_{io} \cdot IO(p) + w_{cpu} \cdot CPU(p). \] -Коэффициенты \(w_{io}\), \(w_{cpu}\) зависят от конкретной аппаратной +\noindent{}Коэффициенты \(w_{io}\), \(w_{cpu}\) зависят от конкретной аппаратной конфигурации. Стоимость является аддитивной и вычисляется рекурсивно по дереву физического @@ -1655,7 +1649,7 @@ \subsection{Модули синтаксического анализа и пос Модуль синтаксического анализа SQL-запросов отвечает за лексический и синтаксический разбор поступающих на вход SQL-запросов. В качестве основы для -грамматики выбран диалект PostgreSQL\cite{PostgresDocs}, так как он широко +грамматики выбран диалект PostgreSQL~\cite{PostgresDocs}, так как он широко используется на практике и имеет открытый исходный код. Использование готовой грамматики из официального репозитория~\cite{PostgresBisonGrammar} гарантирует совместимость. @@ -2036,8 +2030,8 @@ \subsubsection{Метод ветвей и границ} \min(69(N_l + N_r), 70N_lN_r). \] -Если нижняя граница не меньше стоимости уже известного решения, исследование -ветви прекращается. Фрагмент реализации приведен в +\noindent{}Если нижняя граница не меньше стоимости уже известного решения, +исследование ветви прекращается. Фрагмент реализации приведен в листинге~\ref{lst:lower-bound}. \begin{listing}[H] From 64b4713c5c7850557cd63072dace709c26f04105 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Sun, 14 Jun 2026 17:42:02 +0300 Subject: [PATCH 109/120] Update --- report/vkr.tex | 164 +-- research/benchmarks.ipynb | 1117 ++--------------- src/stewkk/sql/logic/executor/executor.cpp | 37 +- .../implement_hash_join.cpp | 13 +- src/stewkk/sql/logic/optimizer/optimizer.cpp | 22 +- .../sql/logic/optimizer/optimizer_test.cpp | 23 + .../filter_to_join_predicate.cpp | 51 +- 7 files changed, 322 insertions(+), 1105 deletions(-) diff --git a/report/vkr.tex b/report/vkr.tex index d81639d..18a9dcb 100644 --- a/report/vkr.tex +++ b/report/vkr.tex @@ -926,6 +926,7 @@ \subsection{Преобразование синтаксического дере {\Tr{\SELECT\ \bar{g}, \bar{f}\ \FROM\ \bar{T}\ \WHERE\ p\ \GROUPBY\ \bar{g}} = \Agg{\bar{g}}{\bar{f}}{\Sel{p}{E_1 \times \cdots \times E_n}}}. \] + \emph{Вложенные подзапросы.} Пусть \(Q\) --- внешний блок, а \(Q'(t)\) --- коррелированный подзапрос, зависящий от кортежа \(t\) из \(\Tr{Q}\). Тогда @@ -935,6 +936,7 @@ \subsection{Преобразование синтаксического дере \] \noindent{} где \(\mathcal{A}\) --- оператор Apply, который впоследствии может быть удалён процедурой декорреляции. + \emph{Сортировка.} Конструкция \ORDERBY{} не выражается в виде операторов реляционной алгебры. Вместо этого она задает требуемое физическое свойство упорядоченности результата, учитываемое на этапе @@ -1358,27 +1360,6 @@ \subsection{Правила трансформации и реализации}\l тем самым определяют пространство планов, исследуемое оптимизатором. Наиболее важные из них представлены в таблице~\ref{tbl:transformation_rules}. -% TODO: может это вообще убрать? + нигде не описываю как выводятся свойства - -\begin{Definition} -Null-отбрасывающим называется предикат, который при замене всех атрибутов -соответствующей стороны на \texttt{NULL} принимает значение, отличное от -\texttt{TRUE}. -\end{Definition} - -\begin{Definition} -Конъюнкты \(\theta_1 \land \theta_2\) называются перераспределяемыми, если их -можно разбить на две группы \(\theta'_1\) и \(\theta'_2\) такие, что атрибуты -\(\theta'_2\) принадлежат \(attrs(S) \cup attrs(T)\), а конъюнкты с атрибутами -из \(attrs(R)\) остаются в \(\theta'_1\). -\end{Definition} - -\begin{Definition} -Функция агрегации называется разделяемой, если она представима композицией -частичного агрегата и финального комбинатора. Например, \SUM{} раскладывается в -сумму частичных сумм, \AVG{} --- в пару \SUM{} и \COUNT{}. -\end{Definition} - Правила реализации переводят логические операторы в физические. Используемые в настоящей работе правила собраны в таблице~\ref{tbl:implementation_rules}. @@ -1393,48 +1374,6 @@ \subsection{Правила трансформации и реализации}\l сортировкой может конкурировать с планом на основе соединения слиянием, которое сохраняет порядок результата. -\subsubsection{Метод ветвей и границ} - -% актуализировать: формулы, алгос и у меня другая селективность... - -Метод ветвей и границ используется для сокращения пространства поиска и, как -следствие, неасимптотического улучшения скорости работы алгоритма. В контексте -Cascades ветвями являются альтернативные выражения и комбинации физических -реализаций дочерних групп, а границей является текущая лучшая стоимость плана -для заданной группы и требуемых физических свойств. Если нижняя оценка стоимости -частично построенного плана уже превышает текущую лучшую, дальнейшее -исследование по этой ветке не имеет смысла. - -Пусть для группы \(G\) и требуемого свойства \(P\) уже найден план стоимости -\(C^*\). Рассматривается физическое выражение \(e\), имеющее локальную стоимость -\(C_{local}(e)\) и дочерние группы \(G_1, \ldots, G_k\). Если даже минимально -возможная оценка дочерних групп приводит к неравенству - -\[ - C_{local}(e) + \sum_{i=1}^{k} LB(G_i, P_i) \geq C^*, -\] - -\noindent{}то выражение \(e\) может быть отсечено. Здесь \(LB\) обозначает нижнюю границу -стоимости оптимизации дочерней группы под требуемым свойством \(P_i\). Чем -точнее нижняя граница, тем эффективнее отсечение. - -На практике в качестве \(LB(G_i, P_i)\) принимается лучшая стоимость, найденная -для пары \((G_i, P_i)\) на текущий момент работы алгоритма. Поскольку -оптимизация группы является рекурсивным процессом, к моменту отсечения для -\((G_i, P_i)\) уже могут быть найдены некоторые планы, и их минимальная -стоимость служит нижней границей. Если же группа \(G_i\) под свойством \(P_i\) -ещё не рассматривалась, используется тривиальная граница \(LB = 0\), или, при -условии наличия минимальной стоимости на кортеж, можно принимать -\(LB(G_i, P_i) = |G_i| \cdot \mathit{minCostPerRow}\). - -Требуемые свойства дочерних групп \(P_i\) определяются оператором выражения \(e\) -совместно с требованием родителя \(P\). Каждый физический оператор описывает, какие -свойства он ожидает от своих входов: например, оператор сортирующего слияния требует -упорядоченности обоих входов по соответствующим ключам, тогда как хеш-соединение не -предъявляет требований к порядку. Таким образом, при спуске по дереву поиска каждый -оператор транслирует глобальное требование \(P\) в конкретные требования \(P_1, -\ldots, P_k\) к своим дочерним группам. - \subsubsection{Оценка кардинальности} Оценка кардинальности является процессом предсказания числа кортежей, @@ -1517,7 +1456,17 @@ \subsubsection{Оценка стоимости} \(\Sort(R)\) & \(2 P(R) \cdot \lceil \log_M P(R) \rceil\) & \(|R| \cdot \log_2 |R|\) \end{longtable} -Для операторов \(\FilterOp\) и \(\ProjOp\) стоимость ввода-вывода равна \(0\), потому что это конвейерные операторы, которые не используют диск. +Для операторов \(\FilterOp\) и \(\ProjOp\) стоимость ввода-вывода равна \(0\), +потому что это конвейерные операторы, которые не используют диск. + +На оценке стоимости исполнения также основывается метод ветвей и границ. Он +используется для сокращения пространства поиска и, как следствие, +неасимптотического улучшения скорости работы алгоритма. В контексте Cascades +ветвями являются альтернативные выражения и комбинации физических реализаций +дочерних групп, а границей является текущая лучшая стоимость плана для заданной +группы и требуемых физических свойств. Если нижняя оценка стоимости частично +построенного плана уже превышает текущую лучшую, дальнейшее исследование по этой +ветке не имеет смысла. \subsection{Дифференциальный анализ физических планов} @@ -1762,15 +1711,45 @@ \subsection{Модуль стоимостной оптимизации план дифференциального сравнения планов для последующего расширения покрытия правилами трансформации. -Выбор лучшего плана выполняется на основе стоимостной модели. Стоимость -физического оператора рассчитывается с учетом оценок кардинальности входных и -выходных групп. Формулы для вычисления стоимости откалиброваны по результатам -бенчмарков и представлены в табилце~\ref{tbl:cost-model-coefficients}. -Кардинальность базовых отношений берется из описания входных таблиц, а для -фильтров, соединений, агрегаций и проекций оценивается эвристически. Чтобы -сократить перебор, оптимизатор использует метод ветвей и границ. Если локальная -стоимость или нижняя оценка стоимости поддерева уже превышает текущий лучший -результат, соответствующая ветвь пространства поиска не рассматривается дальше. +Выбор лучшего плана выполняется на основе стоимостной модели. Пусть для группы +\(G\) и требуемого свойства \(P\) уже найден план стоимости \(C^*\). +Рассматривается физическое выражение \(e\), имеющее локальную стоимость +\(C_{local}(e)\) и дочерние группы \(G_1, \ldots, G_k\). Если даже минимально +возможная оценка дочерних групп приводит к неравенству + +\[ + C_{local}(e) + \sum_{i=1}^{k} LB(G_i, P_i) \geq C^*, +\] + +\noindent{}то выражение \(e\) может быть отсечено. Здесь \(LB\) обозначает +нижнюю границу стоимости оптимизации дочерней группы под требуемым свойством +\(P_i\). Чем точнее нижняя граница, тем эффективнее отсечение. При этом \(C^{*}\) +должна быть вычислена в том же запуске оптимизатора, а значит использует оценки +дочерних групп не хуже чем \(LB(G_{i}, P_{i}), i=1, \ldots{} ,k\). + +В качестве \(LB(G_i, P_i)\) принимается лучшая стоимость, найденная для пары +\((G_i, P_i)\) на текущий момент работы алгоритма. Поскольку оптимизация группы +является рекурсивным процессом, к моменту отсечения для \((G_i, P_i)\) уже могут +быть найдены некоторые планы, и их минимальная стоимость служит нижней границей. +Если же группа \(G_i\) под свойством \(P_i\) ещё не рассматривалась, +используется минимальная из стоимостей физических операторов, которые реализуют +логический оператор из группы \(G_{i}\). + +Требуемые свойства дочерних групп \(P_i\) определяются оператором выражения +\(e\) совместно с требованием родителя \(P\). Каждый физический оператор +описывает, какие свойства он ожидает от своих входов: например, оператор +сортирующего слияния требует упорядоченности обоих входов по соответствующим +ключам, тогда как хеш-соединение не предъявляет требований к порядку. Таким +образом, при спуске по дереву поиска каждый оператор транслирует глобальное +требование \(P\) в конкретные требования \(P_1, \ldots, P_k\) к своим дочерним +группам. + +Стоимость физического оператора рассчитывается с учетом оценок кардинальности +входных и выходных групп. Формулы для вычисления стоимости откалиброваны по +результатам бенчмарков и представлены в +табилце~\ref{tbl:cost-model-coefficients}. Кардинальность базовых отношений +берется из описания входных таблиц, а для фильтров, соединений, агрегаций и +проекций оценивается эвристически. Для оценки кардинальности промежуточных результатов используется коэффициент селективности \(sel(p)\), показывающий долю кортежей, удовлетворяющих предикату @@ -1793,12 +1772,12 @@ \subsection{Модуль стоимостной оптимизации план условий соединения применяется селективность \(1\), чтобы не занижать стоимость плана при отсутствии надежной информации. -Отдельно учитываются физические свойства результата. В данной работе таким +Также учитываются физические свойства результата. В данной работе таким свойством является порядок строк, требуемый предложением \ORDERBY{}. Если выбранный план не обеспечивает требуемую сортировку, оптимизатор может добавить -обеспечивающий физический оператор сортировки и сравнить его стоимость с -другими альтернативами. Результатом работы модуля является дерево физических -операторов, которое передается в модуль исполнения. +обеспечивающий физический оператор сортировки и сравнить его стоимость с другими +альтернативами. Результатом работы модуля является дерево физических операторов, +которое передается в модуль исполнения. \section{Технологическая часть} @@ -2497,12 +2476,13 @@ \subsection{Дифференциальный анализ и тестирова \label{fig:benchmark-speedup} \end{figure} -В таблице~\ref{tbl:ssb-benchmark} приведено среднее время выполнения трех -повторений запросов SSB. Ускорение рассчитывается как отношение времени -выполнения наивного плана ко времени выполнения оптимизированного плана. +В таблице~\ref{tbl:ssb-benchmark} и на +рисунке~\ref{fig:benchmark-naive-optimized} приведено среднее время выполнения +трех повторений запросов SSB. -Видно, что на стандартном наборе запросов оптимизатор дает на $20-50\%$ раз, а -на накоторых запросах в $6$ и более раз. +На рисунке~\ref{fig:benchmark-speedup} приведено относительное ускорение +запросов на том же наборе данных. Видно, что оптимизатор дает ускорение на +$20-50\%$, а на накоторых запросах в $6$ и более раз. \begin{table}[H] \centering @@ -2835,6 +2815,26 @@ \subsection{Калибровка стоимостной модели} \end{algorithmic} \end{algorithm} +\begin{Definition} +Null-отбрасывающим называется предикат, который при замене всех атрибутов +соответствующей стороны на \texttt{NULL} принимает значение, отличное от +\texttt{TRUE}. +\end{Definition} + +\begin{Definition} +Конъюнкты \(\theta_1 \land \theta_2\) называются перераспределяемыми, если их +можно разбить на две группы \(\theta'_1\) и \(\theta'_2\) такие, что атрибуты +\(\theta'_2\) принадлежат \(attrs(S) \cup attrs(T)\), а конъюнкты с атрибутами +из \(attrs(R)\) остаются в \(\theta'_1\). +\end{Definition} + +\begin{Definition} +Функция агрегации называется разделяемой, если она представима композицией +частичного агрегата и финального комбинатора. Например, \SUM{} раскладывается в +сумму частичных сумм, \AVG{} --- в пару \SUM{} и \COUNT{}. +\end{Definition} + + \begin{footnotesize} \begin{longtable}{|c|L{3.1cm}|c|L{3.7cm}|} \caption{Правила трансформации.}\label{tbl:transformation_rules}\\ diff --git a/research/benchmarks.ipynb b/research/benchmarks.ipynb index 4eeae6b..22b41b0 100644 --- a/research/benchmarks.ipynb +++ b/research/benchmarks.ipynb @@ -2,76 +2,98 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "b5291871", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "2026-06-09T23:45:24+03:00\n", - "Running ./build-release/bin/benchmarks\n", - "Run on (8 X 4200 MHz CPU s)\n", - "CPU Caches:\n", - " L1 Data 48 KiB (x4)\n", - " L1 Instruction 32 KiB (x4)\n", - " L2 Unified 1280 KiB (x4)\n", - " L3 Unified 8192 KiB (x1)\n", - "Load Average: 0.41, 0.52, 0.86\n", - "***WARNING*** CPU scaling is enabled, the benchmark real time measurements may be noisy and will incur extra overhead.\n", - "----------------------------------------------------------------------------------------------------------------------------------------------------\n", - "Benchmark Time CPU Iterations\n", - "----------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 19715 ns 19630 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 19839 ns 19745 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 324 ns 314 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 1.64 % 1.60 % \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 19566 ns 19478 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 19396 ns 19323 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 336 ns 324 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 1.72 % 1.66 % \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 19754 ns 19666 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 19761 ns 19648 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 289 ns 283 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 1.47 % 1.44 % \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 19804 ns 19725 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 19577 ns 19499 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 533 ns 523 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 2.69 % 2.65 % \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 70585 ns 69363 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 69486 ns 68784 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 1996 ns 1071 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 2.83 % 1.54 % \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 43139 ns 42969 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 42265 ns 42095 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 1908 ns 1873 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 4.42 % 4.36 % \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 68980 ns 68288 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 67762 ns 67089 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 2321 ns 2267 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 3.37 % 3.32 % \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 44213 ns 44019 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 43826 ns 43656 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 1117 ns 1099 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 2.53 % 2.50 % \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 90198 ns 89113 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 89940 ns 88866 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 3059 ns 2960 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 3.39 % 3.32 % \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 96809 ns 94872 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 95242 ns 93994 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 6365 ns 5128 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 6.58 % 5.40 % \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 95552 ns 93781 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 100165 ns 98047 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 8398 ns 8292 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 8.79 % 8.84 % \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_mean \u001b[m\u001b[0;33m 87877 ns 86576 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_median \u001b[m\u001b[0;33m 86511 ns 85319 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_stddev \u001b[m\u001b[0;33m 4625 ns 4531 ns \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m\u001b[0;32mBM_SQL/real_time_cv \u001b[m\u001b[0;33m 5.26 % 5.23 % \u001b[m\u001b[0;36m 3\u001b[m\n", - "\u001b[m" + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mFailed to start the Kernel. \n", + "\u001b[1;31mFailed to find the URL of the launched Jupyter notebook server\n", + "\u001b[1;31m[W 2026-06-14 17:27:38.911 ServerApp] jupyterlab | error adding extension (enabled: True): Unknown scheme for proxy URL URL('socks://localhost:1081')\n", + "\u001b[1;31m Traceback (most recent call last):\n", + "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/manager.py\", line 365, in add_extension\n", + "\u001b[1;31m extpkg = ExtensionPackage(name=extension_name, enabled=enabled)\n", + "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/manager.py\", line 219, in __init__\n", + "\u001b[1;31m self._load_metadata()\n", + "\u001b[1;31m ~~~~~~~~~~~~~~~~~~~^^\n", + "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/manager.py\", line 228, in _load_metadata\n", + "\u001b[1;31m self.module, self.metadata = get_metadata(name, logger=self.log)\n", + "\u001b[1;31m ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^\n", + "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/utils.py\", line 76, in get_metadata\n", + "\u001b[1;31m return module, module._jupyter_server_extension_points()\n", + "\u001b[1;31m ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^\n", + "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/__init__.py\", line 20, in _jupyter_server_extension_points\n", + "\u001b[1;31m from .labapp import LabApp\n", + "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/labapp.py\", line 46, in \n", + "\u001b[1;31m from .extensions import MANAGERS as EXT_MANAGERS\n", + "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/extensions/__init__.py\", line 12, in \n", + "\u001b[1;31m from .pypi import PyPIExtensionManager\n", + "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/extensions/pypi.py\", line 76, in \n", + "\u001b[1;31m \"http://\": httpx.AsyncHTTPTransport(proxy=http_proxy_url),\n", + "\u001b[1;31m ~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^\n", + "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/httpx/_transports/default.py\", line 296, in __init__\n", + "\u001b[1;31m proxy = Proxy(url=proxy) if isinstance(proxy, (str, URL)) else proxy\n", + "\u001b[1;31m ~~~~~^^^^^^^^^^^\n", + "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/httpx/_config.py\", line 214, in __init__\n", + "\u001b[1;31m raise ValueError(f\"Unknown scheme for proxy URL {url!r}\")\n", + "\u001b[1;31m ValueError: Unknown scheme for proxy URL URL('socks://localhost:1081')\n", + "\u001b[1;31m[I 2026-06-14 17:27:38.917 ServerApp] jupyter_lsp | extension was successfully linked.\n", + "\u001b[1;31m[I 2026-06-14 17:27:38.923 ServerApp] jupyter_server_terminals | extension was successfully linked.\n", + "\u001b[1;31m[W 2026-06-14 17:27:38.926 JupyterNotebookApp] 'iopub_data_rate_limit' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.\n", + "\u001b[1;31m[W 2026-06-14 17:27:38.931 ServerApp] ServerApp.iopub_data_rate_limit config is deprecated in 2.0. Use ZMQChannelsWebsocketConnection.iopub_data_rate_limit.\n", + "\u001b[1;31m[I 2026-06-14 17:27:38.932 ServerApp] notebook | extension was successfully linked.\n", + "\u001b[1;31m[W 2026-06-14 17:27:41.699 ServerApp] jupyterlab | error adding extension (enabled: True): Unknown scheme for proxy URL URL('socks://localhost:1081')\n", + "\u001b[1;31m Traceback (most recent call last):\n", + "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/manager.py\", line 365, in add_extension\n", + "\u001b[1;31m extpkg = ExtensionPackage(name=extension_name, enabled=enabled)\n", + "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/manager.py\", line 219, in __init__\n", + "\u001b[1;31m self._load_metadata()\n", + "\u001b[1;31m ~~~~~~~~~~~~~~~~~~~^^\n", + "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/manager.py\", line 228, in _load_metadata\n", + "\u001b[1;31m self.module, self.metadata = get_metadata(name, logger=self.log)\n", + "\u001b[1;31m ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^\n", + "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyter_server/extension/utils.py\", line 76, in get_metadata\n", + "\u001b[1;31m return module, module._jupyter_server_extension_points()\n", + "\u001b[1;31m ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^\n", + "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/__init__.py\", line 20, in _jupyter_server_extension_points\n", + "\u001b[1;31m from .labapp import LabApp\n", + "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/labapp.py\", line 46, in \n", + "\u001b[1;31m from .extensions import MANAGERS as EXT_MANAGERS\n", + "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/extensions/__init__.py\", line 12, in \n", + "\u001b[1;31m from .pypi import PyPIExtensionManager\n", + "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/jupyterlab/extensions/pypi.py\", line 76, in \n", + "\u001b[1;31m \"http://\": httpx.AsyncHTTPTransport(proxy=http_proxy_url),\n", + "\u001b[1;31m ~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^\n", + "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/httpx/_transports/default.py\", line 296, in __init__\n", + "\u001b[1;31m proxy = Proxy(url=proxy) if isinstance(proxy, (str, URL)) else proxy\n", + "\u001b[1;31m ~~~~~^^^^^^^^^^^\n", + "\u001b[1;31m File \"/nix/store/mvrqpz0dm2101n2r2m2s3nmrji30gf56-python3-3.13.13-env/lib/python3.13/site-packages/httpx/_config.py\", line 214, in __init__\n", + "\u001b[1;31m raise ValueError(f\"Unknown scheme for proxy URL {url!r}\")\n", + "\u001b[1;31m ValueError: Unknown scheme for proxy URL URL('socks://localhost:1081')\n", + "\u001b[1;31m[I 2026-06-14 17:27:41.702 ServerApp] notebook_shim | extension was successfully linked.\n", + "\u001b[1;31m[I 2026-06-14 17:27:41.774 ServerApp] notebook_shim | extension was successfully loaded.\n", + "\u001b[1;31m[I 2026-06-14 17:27:41.778 ServerApp] jupyter_lsp | extension was successfully loaded.\n", + "\u001b[1;31m[I 2026-06-14 17:27:41.785 ServerApp] jupyter_server_terminals | extension was successfully loaded.\n", + "\u001b[1;31m[I 2026-06-14 17:27:41.816 ServerApp] notebook | extension was successfully loaded.\n", + "\u001b[1;31m[I 2026-06-14 17:27:41.819 ServerApp] The port 8888 is already in use, trying another port.\n", + "\u001b[1;31m[I 2026-06-14 17:27:41.821 ServerApp] Serving notebooks from local directory: /home/st/c/iu9-sql-compiler/research\n", + "\u001b[1;31m[I 2026-06-14 17:27:41.822 ServerApp] Jupyter Server 2.17.0 is running at:\n", + "\u001b[1;31m[I 2026-06-14 17:27:41.822 ServerApp] http://localhost:8889/tree?token=c5ff0bd91935fa65038e4a11fe748322a019a84f3a190f69\n", + "\u001b[1;31m[I 2026-06-14 17:27:41.822 ServerApp] http://127.0.0.1:8889/tree?token=c5ff0bd91935fa65038e4a11fe748322a019a84f3a190f69\n", + "\u001b[1;31m[I 2026-06-14 17:27:41.822 ServerApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).\n", + "\u001b[1;31m[C 2026-06-14 17:27:41.836 ServerApp] \n", + "\u001b[1;31m \n", + "\u001b[1;31m To access the server, open this file in a browser:\n", + "\u001b[1;31m file:///home/st/.local/share/jupyter/runtime/jpserver-29560-open.html\n", + "\u001b[1;31m Or copy and paste one of these URLs:\n", + "\u001b[1;31m http://localhost:8889/tree?token=c5ff0bd91935fa65038e4a11fe748322a019a84f3a190f69\n", + "\u001b[1;31m http://127.0.0.1:8889/tree?token=c5ff0bd91935fa65038e4a11fe748322a019a84f3a190f69\n", + "\u001b[1;31m[I 2026-06-14 17:27:42.864 ServerApp] Skipped non-installed server(s): basedpyright, bash-language-server, dockerfile-language-server-nodejs, javascript-typescript-langserver, jedi-language-server, julia-language-server, pyrefly, pyright, python-language-server, python-lsp-server, r-languageserver, sql-language-server, texlab, typescript-language-server, unified-language-server, vscode-css-languageserver-bin, vscode-html-languageserver-bin, vscode-json-languageserver-bin, yaml-language-server. \n", + "\u001b[1;31mView Jupyter log for further details." ] } ], @@ -82,652 +104,37 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "94b2cd75", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2026-06-09T23:45:52+03:00\n", - "Running ./build-release/bin/benchmarks\n", - "Run on (8 X 4200 MHz CPU s)\n", - "CPU Caches:\n", - " L1 Data 48 KiB (x4)\n", - " L1 Instruction 32 KiB (x4)\n", - " L2 Unified 1280 KiB (x4)\n", - " L3 Unified 8192 KiB (x1)\n", - "Load Average: 1.00, 0.64, 0.90\n", - "***WARNING*** CPU scaling is enabled, the benchmark real time measurements may be noisy and will incur extra overhead.\n", - "----------------------------------------------------------------------------------------------------------\n", - "Benchmark Time CPU Iterations UserCounters...\n", - "----------------------------------------------------------------------------------------------------------\n", - "\u001b[0;32mSSB/q2.1/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 10529208 ns 10430532 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=10.5291k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 10357855 ns 10266127 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=10.3577k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 680061 ns 657238 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=680.069\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 6.46 % 6.30 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.46%\u001b[m includes_order_by=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 11087104 ns 10938563 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=11.087k\u001b[m includes_order_by=1\u001b[m optimizer_us=5.52226k\u001b[m plan_cost=836.223k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 11374830 ns 11181105 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=11.3748k\u001b[m includes_order_by=1\u001b[m optimizer_us=5.59036k\u001b[m plan_cost=836.223k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 807685 ns 805879 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=807.683\u001b[m includes_order_by=0\u001b[m optimizer_us=142.327\u001b[m plan_cost=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.1/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 7.28 % 7.37 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.28%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=2.58%\u001b[m plan_cost=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 8696587 ns 8625517 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.69653k\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 8424029 ns 8351053 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.42397k\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 614383 ns 602582 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=614.378\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 7.06 % 6.99 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.06%\u001b[m includes_order_by=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 8481006 ns 8356025 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.48094k\u001b[m includes_order_by=0\u001b[m optimizer_us=464.225\u001b[m plan_cost=654.297k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 8430650 ns 8195103 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.43059k\u001b[m includes_order_by=0\u001b[m optimizer_us=463.328\u001b[m plan_cost=654.297k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 413000 ns 423645 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=413.003\u001b[m includes_order_by=0\u001b[m optimizer_us=5.17612\u001b[m plan_cost=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.3/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 4.87 % 5.07 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=4.87%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=1.12%\u001b[m plan_cost=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 10591798 ns 10470366 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=10.5917k\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 10967922 ns 10832180 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=10.9678k\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 778749 ns 750058 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=778.722\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 7.35 % 7.16 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.35%\u001b[m includes_order_by=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 8042493 ns 7973771 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.04242k\u001b[m includes_order_by=0\u001b[m optimizer_us=389.801\u001b[m plan_cost=654.297k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 7975113 ns 7919581 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.97505k\u001b[m includes_order_by=0\u001b[m optimizer_us=373.936\u001b[m plan_cost=654.297k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 716234 ns 709722 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=716.23\u001b[m includes_order_by=0\u001b[m optimizer_us=40.4184\u001b[m plan_cost=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.2/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 8.91 % 8.90 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.91%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=10.37%\u001b[m plan_cost=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 41079134 ns 40719159 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=41.0788k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 41251745 ns 40951561 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=41.2516k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 1772659 ns 1741817 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=1.77282k\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 4.32 % 4.28 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=4.32%\u001b[m includes_order_by=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 6184696 ns 6137487 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.18463k\u001b[m includes_order_by=1\u001b[m optimizer_us=97.5624k\u001b[m plan_cost=816.714k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 6169728 ns 6119638 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.16966k\u001b[m includes_order_by=1\u001b[m optimizer_us=96.677k\u001b[m plan_cost=816.714k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 35201 ns 43807 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=35.2042\u001b[m includes_order_by=0\u001b[m optimizer_us=2.64896k\u001b[m plan_cost=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.3/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 0.57 % 0.71 % 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"\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 6226768 ns 6189600 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.2267k\u001b[m includes_order_by=1\u001b[m optimizer_us=46.7043k\u001b[m plan_cost=816.714k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 6217583 ns 6181985 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.2175k\u001b[m includes_order_by=1\u001b[m optimizer_us=46.7191k\u001b[m plan_cost=816.714k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 74863 ns 74631 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=74.8587\u001b[m includes_order_by=0\u001b[m optimizer_us=496.521\u001b[m plan_cost=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.4/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 1.20 % 1.21 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=1.20%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=1.06%\u001b[m plan_cost=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 10159185 ns 10079207 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=10.1591k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 10047677 ns 9977358 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=10.0476k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 345553 ns 338814 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=345.524\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 3.40 % 3.36 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=3.40%\u001b[m includes_order_by=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 6886022 ns 6843094 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.88595k\u001b[m includes_order_by=1\u001b[m optimizer_us=592.57k\u001b[m plan_cost=841.353k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 6878252 ns 6836928 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.87819k\u001b[m includes_order_by=1\u001b[m optimizer_us=594.328k\u001b[m plan_cost=841.353k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 13616 ns 11041 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=13.5977\u001b[m includes_order_by=0\u001b[m optimizer_us=8.90421k\u001b[m plan_cost=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.3/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 0.20 % 0.16 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.20%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=1.50%\u001b[m plan_cost=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 8560502 ns 8501831 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.56041k\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 8474498 ns 8421813 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=8.47444k\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 236679 ns 232363 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=236.611\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 2.76 % 2.73 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=2.76%\u001b[m includes_order_by=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 7188918 ns 7145194 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=7.18887k\u001b[m includes_order_by=0\u001b[m optimizer_us=386.236\u001b[m plan_cost=705.057k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q1.1/Interpreted/Optimized/real_time_median 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38599089 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=38.8006k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 794694 ns 787590 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=794.706\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 2.05 % 2.05 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=2.05%\u001b[m includes_order_by=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 6717959 ns 6675562 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.71791k\u001b[m includes_order_by=1\u001b[m optimizer_us=44.7762k\u001b[m plan_cost=816.374k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 6691263 ns 6649855 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.69122k\u001b[m includes_order_by=1\u001b[m optimizer_us=44.9583k\u001b[m plan_cost=816.374k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 223132 ns 219030 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=223.132\u001b[m includes_order_by=0\u001b[m optimizer_us=1.38305k\u001b[m plan_cost=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.2/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 3.32 % 3.28 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=3.32%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=3.09%\u001b[m plan_cost=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 9318865 ns 9262092 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.31879k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 9317950 ns 9264243 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.31784k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 43983 ns 43352 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=43.9888\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 0.47 % 0.47 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.47%\u001b[m includes_order_by=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 6690732 ns 6651178 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.69067k\u001b[m includes_order_by=1\u001b[m optimizer_us=13.2656k\u001b[m plan_cost=841.323k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 6611059 ns 6573641 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.611k\u001b[m includes_order_by=1\u001b[m optimizer_us=12.9576k\u001b[m plan_cost=841.323k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 178965 ns 175813 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=178.964\u001b[m includes_order_by=0\u001b[m optimizer_us=613.922\u001b[m plan_cost=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.2/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 2.67 % 2.64 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=2.67%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=4.63%\u001b[m plan_cost=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 38097914 ns 37891700 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=38.0977k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 38067251 ns 37869328 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=38.0671k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 714779 ns 706378 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=714.755\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 1.88 % 1.86 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=1.88%\u001b[m includes_order_by=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 6528813 ns 6491827 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.52875k\u001b[m includes_order_by=1\u001b[m optimizer_us=44.0747k\u001b[m plan_cost=816.374k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 6443385 ns 6408645 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.44333k\u001b[m includes_order_by=1\u001b[m optimizer_us=43.7956k\u001b[m plan_cost=816.374k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 189025 ns 188359 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=189.037\u001b[m includes_order_by=0\u001b[m optimizer_us=772.954\u001b[m plan_cost=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q3.1/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 2.90 % 2.90 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=2.90%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=1.75%\u001b[m plan_cost=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 10061357 ns 9987626 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=10.0613k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 9866919 ns 9808790 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.86683k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 371172 ns 350939 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=371.183\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 3.69 % 3.51 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=3.69%\u001b[m includes_order_by=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 11892791 ns 11823670 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=11.8927k\u001b[m includes_order_by=1\u001b[m optimizer_us=1.70535M\u001b[m plan_cost=842.823k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 12032078 ns 11957276 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=12.032k\u001b[m includes_order_by=1\u001b[m optimizer_us=1.70999M\u001b[m plan_cost=842.823k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 367685 ns 356358 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=367.683\u001b[m includes_order_by=0\u001b[m optimizer_us=14.1195k\u001b[m plan_cost=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.2/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 3.09 % 3.01 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=3.09%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=0.83%\u001b[m plan_cost=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 9553308 ns 9491710 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.55323k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 9540526 ns 9479518 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.54045k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 69032 ns 72719 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=69.0264\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 0.72 % 0.77 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.72%\u001b[m includes_order_by=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 13055569 ns 12976693 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=13.0555k\u001b[m includes_order_by=1\u001b[m optimizer_us=735.421k\u001b[m plan_cost=842.793k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 13123589 ns 13043514 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=13.1234k\u001b[m includes_order_by=1\u001b[m optimizer_us=739.537k\u001b[m plan_cost=842.793k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 168357 ns 154483 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=168.342\u001b[m includes_order_by=0\u001b[m optimizer_us=14.5603k\u001b[m plan_cost=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q4.1/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 1.29 % 1.19 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=1.29%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=1.98%\u001b[m plan_cost=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Naive/real_time_mean \u001b[m\u001b[0;33m 9503878 ns 9404200 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.50381k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Naive/real_time_median \u001b[m\u001b[0;33m 9463379 ns 9405731 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=9.4633k\u001b[m includes_order_by=1\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Naive/real_time_stddev \u001b[m\u001b[0;33m 85021 ns 20695 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=85.0251\u001b[m includes_order_by=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Naive/real_time_cv \u001b[m\u001b[0;33m 0.89 % 0.22 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=0.89%\u001b[m includes_order_by=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Optimized/real_time_mean \u001b[m\u001b[0;33m 6292484 ns 6256305 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.29241k\u001b[m includes_order_by=1\u001b[m optimizer_us=5.39098k\u001b[m plan_cost=836.223k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Optimized/real_time_median \u001b[m\u001b[0;33m 6233794 ns 6199432 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=6.23372k\u001b[m includes_order_by=1\u001b[m optimizer_us=5.35577k\u001b[m plan_cost=836.223k\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Optimized/real_time_stddev \u001b[m\u001b[0;33m 108967 ns 104938 ns \u001b[m\u001b[0;36m 3\u001b[m execution_us=108.969\u001b[m includes_order_by=0\u001b[m optimizer_us=196.278\u001b[m plan_cost=0\u001b[m\n", - "\u001b[m\u001b[0;32mSSB/q2.3/Interpreted/Optimized/real_time_cv \u001b[m\u001b[0;33m 1.73 % 1.68 % \u001b[m\u001b[0;36m 3\u001b[m execution_us=1.73%\u001b[m includes_order_by=0.00%\u001b[m optimizer_us=3.64%\u001b[m plan_cost=0.00%\u001b[m\n", - "\u001b[m" - ] - } - ], + "outputs": [], "source": [ "!cd .. && SSB_DATA_DIR=benchmarks/datasets/ssb/generated/sf001 timeout 180s ./build-release/bin/benchmarks --benchmark_filter='^SSB/' --benchmark_repetitions=3 --benchmark_display_aggregates_only=true --benchmark_out=research/benchmark-results/ssb-sf001.json --benchmark_out_format=json" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "6f8e3873", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2026-06-09T23:47:09+03:00\n", - "Running ./build-release/bin/benchmarks\n", - "Run on (8 X 4200 MHz CPU s)\n", - "CPU Caches:\n", - " L1 Data 48 KiB (x4)\n", - " L1 Instruction 32 KiB (x4)\n", - " L2 Unified 1280 KiB (x4)\n", - " L3 Unified 8192 KiB (x1)\n", - "Load Average: 1.62, 0.92, 0.97\n", - "***WARNING*** CPU scaling is enabled, the benchmark real time measurements may be noisy and will incur extra overhead.\n", - "----------------------------------------------------------------------------------------------------------------------\n", - "Benchmark Time CPU Iterations UserCounters...\n", - "----------------------------------------------------------------------------------------------------------------------\n", - "\u001b[0;32mOperatorCost/SeqScan/1024/real_time_mean \u001b[m\u001b[0;33m 39153 ns 39016 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=102.4k\u001b[m output_rows=1.024k\u001b[m plan_cost=102.4k\u001b[m rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/1024/real_time_median \u001b[m\u001b[0;33m 39303 ns 39169 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=102.4k\u001b[m output_rows=1.024k\u001b[m plan_cost=102.4k\u001b[m rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/1024/real_time_stddev \u001b[m\u001b[0;33m 267 ns 265 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/1024/real_time_cv \u001b[m\u001b[0;33m 0.68 % 0.68 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/2048/real_time_mean \u001b[m\u001b[0;33m 72348 ns 72079 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=204.8k\u001b[m output_rows=2.048k\u001b[m plan_cost=204.8k\u001b[m rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/2048/real_time_median \u001b[m\u001b[0;33m 71402 ns 71146 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=204.8k\u001b[m output_rows=2.048k\u001b[m plan_cost=204.8k\u001b[m rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/2048/real_time_stddev \u001b[m\u001b[0;33m 1791 ns 1734 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/2048/real_time_cv \u001b[m\u001b[0;33m 2.48 % 2.41 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/4096/real_time_mean \u001b[m\u001b[0;33m 158147 ns 156559 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=409.6k\u001b[m output_rows=4.096k\u001b[m plan_cost=409.6k\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/4096/real_time_median \u001b[m\u001b[0;33m 157558 ns 155985 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=409.6k\u001b[m output_rows=4.096k\u001b[m plan_cost=409.6k\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/4096/real_time_stddev \u001b[m\u001b[0;33m 2396 ns 2315 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/4096/real_time_cv \u001b[m\u001b[0;33m 1.51 % 1.48 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/8192/real_time_mean \u001b[m\u001b[0;33m 358960 ns 356073 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=819.2k\u001b[m output_rows=8.192k\u001b[m plan_cost=819.2k\u001b[m rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/8192/real_time_median \u001b[m\u001b[0;33m 357023 ns 354120 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=819.2k\u001b[m output_rows=8.192k\u001b[m plan_cost=819.2k\u001b[m rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/8192/real_time_stddev \u001b[m\u001b[0;33m 4165 ns 4140 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/8192/real_time_cv \u001b[m\u001b[0;33m 1.16 % 1.16 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/16384/real_time_mean \u001b[m\u001b[0;33m 797004 ns 790772 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.6384M\u001b[m output_rows=16.384k\u001b[m plan_cost=1.6384M\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/16384/real_time_median \u001b[m\u001b[0;33m 796509 ns 790000 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.6384M\u001b[m output_rows=16.384k\u001b[m plan_cost=1.6384M\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/16384/real_time_stddev \u001b[m\u001b[0;33m 5029 ns 5064 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/16384/real_time_cv \u001b[m\u001b[0;33m 0.63 % 0.64 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/32768/real_time_mean \u001b[m\u001b[0;33m 1990887 ns 1971823 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.2768M\u001b[m output_rows=32.768k\u001b[m plan_cost=3.2768M\u001b[m rows=32.768k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/32768/real_time_median \u001b[m\u001b[0;33m 1989976 ns 1972159 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.2768M\u001b[m output_rows=32.768k\u001b[m plan_cost=3.2768M\u001b[m rows=32.768k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/32768/real_time_stddev \u001b[m\u001b[0;33m 26044 ns 24709 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/32768/real_time_cv \u001b[m\u001b[0;33m 1.31 % 1.25 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/65536/real_time_mean \u001b[m\u001b[0;33m 4947678 ns 4898438 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.5536M\u001b[m output_rows=65.536k\u001b[m plan_cost=6.5536M\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/65536/real_time_median \u001b[m\u001b[0;33m 4843216 ns 4796793 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.5536M\u001b[m output_rows=65.536k\u001b[m plan_cost=6.5536M\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/65536/real_time_stddev \u001b[m\u001b[0;33m 274098 ns 266515 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/65536/real_time_cv \u001b[m\u001b[0;33m 5.54 % 5.44 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/131072/real_time_mean \u001b[m\u001b[0;33m 10909127 ns 10799682 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=13.1072M\u001b[m output_rows=131.072k\u001b[m plan_cost=13.1072M\u001b[m rows=131.072k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/131072/real_time_median \u001b[m\u001b[0;33m 10878576 ns 10770947 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=13.1072M\u001b[m output_rows=131.072k\u001b[m plan_cost=13.1072M\u001b[m rows=131.072k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/131072/real_time_stddev \u001b[m\u001b[0;33m 76933 ns 76956 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/131072/real_time_cv \u001b[m\u001b[0;33m 0.71 % 0.71 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/262144/real_time_mean \u001b[m\u001b[0;33m 22613130 ns 22403648 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.2144M\u001b[m output_rows=262.144k\u001b[m plan_cost=26.2144M\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/262144/real_time_median \u001b[m\u001b[0;33m 22384763 ns 22194274 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.2144M\u001b[m output_rows=262.144k\u001b[m plan_cost=26.2144M\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/262144/real_time_stddev \u001b[m\u001b[0;33m 664045 ns 632355 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/SeqScan/262144/real_time_cv \u001b[m\u001b[0;33m 2.94 % 2.82 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/1024/real_time_mean \u001b[m\u001b[0;33m 74024 ns 73756 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=102.4k\u001b[m output_rows=512\u001b[m plan_cost=204.8k\u001b[m rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/1024/real_time_median 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model_cost=204.8k\u001b[m output_rows=1.024k\u001b[m plan_cost=409.6k\u001b[m rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/2048/real_time_stddev \u001b[m\u001b[0;33m 3697 ns 3617 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/2048/real_time_cv \u001b[m\u001b[0;33m 2.69 % 2.64 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/4096/real_time_mean \u001b[m\u001b[0;33m 261600 ns 260569 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=409.6k\u001b[m output_rows=2.048k\u001b[m plan_cost=819.2k\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/4096/real_time_median \u001b[m\u001b[0;33m 260040 ns 259070 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=409.6k\u001b[m output_rows=2.048k\u001b[m plan_cost=819.2k\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/4096/real_time_stddev \u001b[m\u001b[0;33m 3351 ns 3281 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/4096/real_time_cv \u001b[m\u001b[0;33m 1.28 % 1.26 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/8192/real_time_mean \u001b[m\u001b[0;33m 513833 ns 511511 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=819.2k\u001b[m output_rows=4.096k\u001b[m plan_cost=1.6384M\u001b[m rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/8192/real_time_median \u001b[m\u001b[0;33m 516091 ns 513709 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=819.2k\u001b[m output_rows=4.096k\u001b[m plan_cost=1.6384M\u001b[m rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/8192/real_time_stddev \u001b[m\u001b[0;33m 4408 ns 4202 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/8192/real_time_cv \u001b[m\u001b[0;33m 0.86 % 0.82 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/16384/real_time_mean \u001b[m\u001b[0;33m 1066718 ns 1061351 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.6384M\u001b[m output_rows=8.192k\u001b[m plan_cost=3.2768M\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/16384/real_time_median \u001b[m\u001b[0;33m 1050933 ns 1045549 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.6384M\u001b[m output_rows=8.192k\u001b[m plan_cost=3.2768M\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/16384/real_time_stddev 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3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/32768/real_time_cv \u001b[m\u001b[0;33m 1.86 % 1.80 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/65536/real_time_mean \u001b[m\u001b[0;33m 4602133 ns 4576376 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.5536M\u001b[m output_rows=32.768k\u001b[m plan_cost=13.1072M\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/65536/real_time_median \u001b[m\u001b[0;33m 4612541 ns 4587562 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.5536M\u001b[m output_rows=32.768k\u001b[m plan_cost=13.1072M\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/65536/real_time_stddev \u001b[m\u001b[0;33m 53265 ns 54115 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/65536/real_time_cv \u001b[m\u001b[0;33m 1.16 % 1.18 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/131072/real_time_mean \u001b[m\u001b[0;33m 9965140 ns 9905109 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=13.1072M\u001b[m output_rows=65.536k\u001b[m plan_cost=26.2144M\u001b[m rows=131.072k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/131072/real_time_median \u001b[m\u001b[0;33m 9961682 ns 9898928 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=13.1072M\u001b[m output_rows=65.536k\u001b[m plan_cost=26.2144M\u001b[m rows=131.072k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/131072/real_time_stddev \u001b[m\u001b[0;33m 367014 ns 358618 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/131072/real_time_cv \u001b[m\u001b[0;33m 3.68 % 3.62 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/262144/real_time_mean \u001b[m\u001b[0;33m 20746154 ns 20612936 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.2144M\u001b[m output_rows=131.072k\u001b[m plan_cost=52.4288M\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/262144/real_time_median \u001b[m\u001b[0;33m 20609234 ns 20480861 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.2144M\u001b[m output_rows=131.072k\u001b[m plan_cost=52.4288M\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/262144/real_time_stddev \u001b[m\u001b[0;33m 432544 ns 413377 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Filter/262144/real_time_cv \u001b[m\u001b[0;33m 2.08 % 2.01 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/1024/real_time_mean \u001b[m\u001b[0;33m 102501 ns 102103 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=22.528k\u001b[m output_rows=1.024k\u001b[m plan_cost=124.928k\u001b[m rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/1024/real_time_median \u001b[m\u001b[0;33m 102476 ns 102063 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=22.528k\u001b[m output_rows=1.024k\u001b[m plan_cost=124.928k\u001b[m rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/1024/real_time_stddev \u001b[m\u001b[0;33m 1037 ns 1019 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/1024/real_time_cv \u001b[m\u001b[0;33m 1.01 % 1.00 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/2048/real_time_mean \u001b[m\u001b[0;33m 192335 ns 191492 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=45.056k\u001b[m output_rows=2.048k\u001b[m plan_cost=249.856k\u001b[m rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/2048/real_time_median \u001b[m\u001b[0;33m 191735 ns 190906 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=45.056k\u001b[m output_rows=2.048k\u001b[m plan_cost=249.856k\u001b[m rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/2048/real_time_stddev \u001b[m\u001b[0;33m 6489 ns 6398 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/2048/real_time_cv \u001b[m\u001b[0;33m 3.37 % 3.34 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/4096/real_time_mean \u001b[m\u001b[0;33m 358122 ns 356961 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=90.112k\u001b[m output_rows=4.096k\u001b[m plan_cost=499.712k\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/4096/real_time_median \u001b[m\u001b[0;33m 357740 ns 356476 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=90.112k\u001b[m output_rows=4.096k\u001b[m plan_cost=499.712k\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/4096/real_time_stddev \u001b[m\u001b[0;33m 1726 ns 1799 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/4096/real_time_cv \u001b[m\u001b[0;33m 0.48 % 0.50 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/8192/real_time_mean \u001b[m\u001b[0;33m 730814 ns 727359 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=180.224k\u001b[m output_rows=8.192k\u001b[m plan_cost=0.999424M\u001b[m rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/8192/real_time_median \u001b[m\u001b[0;33m 730561 ns 727062 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=180.224k\u001b[m output_rows=8.192k\u001b[m plan_cost=0.999424M\u001b[m rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/8192/real_time_stddev \u001b[m\u001b[0;33m 2539 ns 2763 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/8192/real_time_cv \u001b[m\u001b[0;33m 0.35 % 0.38 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/16384/real_time_mean \u001b[m\u001b[0;33m 1526678 ns 1518388 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=360.448k\u001b[m output_rows=16.384k\u001b[m plan_cost=1.99885M\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/16384/real_time_median \u001b[m\u001b[0;33m 1514234 ns 1505967 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=360.448k\u001b[m output_rows=16.384k\u001b[m plan_cost=1.99885M\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/16384/real_time_stddev \u001b[m\u001b[0;33m 66490 ns 65574 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/16384/real_time_cv \u001b[m\u001b[0;33m 4.36 % 4.32 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/32768/real_time_mean \u001b[m\u001b[0;33m 3121936 ns 3104686 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=720.896k\u001b[m output_rows=32.768k\u001b[m plan_cost=3.9977M\u001b[m rows=32.768k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/32768/real_time_median \u001b[m\u001b[0;33m 3144138 ns 3126492 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=720.896k\u001b[m output_rows=32.768k\u001b[m plan_cost=3.9977M\u001b[m rows=32.768k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/32768/real_time_stddev \u001b[m\u001b[0;33m 42438 ns 41442 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/32768/real_time_cv \u001b[m\u001b[0;33m 1.36 % 1.33 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/65536/real_time_mean \u001b[m\u001b[0;33m 6595264 ns 6544746 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.44179M\u001b[m output_rows=65.536k\u001b[m plan_cost=7.99539M\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/65536/real_time_median \u001b[m\u001b[0;33m 6608749 ns 6555081 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.44179M\u001b[m output_rows=65.536k\u001b[m plan_cost=7.99539M\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/65536/real_time_stddev \u001b[m\u001b[0;33m 58317 ns 53393 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/65536/real_time_cv \u001b[m\u001b[0;33m 0.88 % 0.82 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/131072/real_time_mean \u001b[m\u001b[0;33m 14333160 ns 14219076 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.88358M\u001b[m output_rows=131.072k\u001b[m plan_cost=15.9908M\u001b[m rows=131.072k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/131072/real_time_median \u001b[m\u001b[0;33m 14192064 ns 14077398 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.88358M\u001b[m output_rows=131.072k\u001b[m plan_cost=15.9908M\u001b[m rows=131.072k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/131072/real_time_stddev \u001b[m\u001b[0;33m 467177 ns 461157 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/131072/real_time_cv \u001b[m\u001b[0;33m 3.26 % 3.24 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/262144/real_time_mean \u001b[m\u001b[0;33m 33368177 ns 33128753 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=5.76717M\u001b[m output_rows=262.144k\u001b[m plan_cost=31.9816M\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/262144/real_time_median \u001b[m\u001b[0;33m 33354850 ns 33124116 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=5.76717M\u001b[m output_rows=262.144k\u001b[m plan_cost=31.9816M\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/262144/real_time_stddev \u001b[m\u001b[0;33m 57832 ns 25519 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Projection/262144/real_time_cv \u001b[m\u001b[0;33m 0.17 % 0.08 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/1024/real_time_mean \u001b[m\u001b[0;33m 119077 ns 118631 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=123.904k\u001b[m output_rows=1.024k\u001b[m plan_cost=226.304k\u001b[m rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/1024/real_time_median \u001b[m\u001b[0;33m 119114 ns 118725 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=123.904k\u001b[m output_rows=1.024k\u001b[m plan_cost=226.304k\u001b[m rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/1024/real_time_stddev \u001b[m\u001b[0;33m 1155 ns 1165 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/1024/real_time_cv \u001b[m\u001b[0;33m 0.97 % 0.98 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/2048/real_time_mean \u001b[m\u001b[0;33m 276292 ns 275137 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=270.336k\u001b[m output_rows=2.048k\u001b[m plan_cost=475.136k\u001b[m rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/2048/real_time_median \u001b[m\u001b[0;33m 275547 ns 274427 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=270.336k\u001b[m output_rows=2.048k\u001b[m plan_cost=475.136k\u001b[m rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/2048/real_time_stddev \u001b[m\u001b[0;33m 3680 ns 3546 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/2048/real_time_cv \u001b[m\u001b[0;33m 1.33 % 1.29 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/4096/real_time_mean \u001b[m\u001b[0;33m 623962 ns 621336 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=585.728k\u001b[m output_rows=4.096k\u001b[m plan_cost=995.328k\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/4096/real_time_median \u001b[m\u001b[0;33m 619550 ns 617008 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=585.728k\u001b[m output_rows=4.096k\u001b[m plan_cost=995.328k\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/4096/real_time_stddev \u001b[m\u001b[0;33m 17605 ns 17229 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/4096/real_time_cv \u001b[m\u001b[0;33m 2.82 % 2.77 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/8192/real_time_mean \u001b[m\u001b[0;33m 1387995 ns 1380934 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.26157M\u001b[m output_rows=8.192k\u001b[m plan_cost=2.08077M\u001b[m rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/8192/real_time_median \u001b[m\u001b[0;33m 1386525 ns 1379373 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.26157M\u001b[m output_rows=8.192k\u001b[m plan_cost=2.08077M\u001b[m rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/8192/real_time_stddev \u001b[m\u001b[0;33m 6569 ns 6278 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/8192/real_time_cv \u001b[m\u001b[0;33m 0.47 % 0.45 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/16384/real_time_mean \u001b[m\u001b[0;33m 3127599 ns 3111158 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.70336M\u001b[m output_rows=16.384k\u001b[m plan_cost=4.34176M\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/16384/real_time_median \u001b[m\u001b[0;33m 3122047 ns 3105723 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.70336M\u001b[m output_rows=16.384k\u001b[m plan_cost=4.34176M\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/16384/real_time_stddev \u001b[m\u001b[0;33m 10847 ns 11610 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/16384/real_time_cv \u001b[m\u001b[0;33m 0.35 % 0.37 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/32768/real_time_mean \u001b[m\u001b[0;33m 8567436 ns 8516854 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=5.76717M\u001b[m output_rows=32.768k\u001b[m plan_cost=9.04397M\u001b[m rows=32.768k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/32768/real_time_median \u001b[m\u001b[0;33m 8411857 ns 8357809 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=5.76717M\u001b[m output_rows=32.768k\u001b[m plan_cost=9.04397M\u001b[m rows=32.768k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/32768/real_time_stddev \u001b[m\u001b[0;33m 348230 ns 347847 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/32768/real_time_cv \u001b[m\u001b[0;33m 4.06 % 4.08 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/65536/real_time_mean \u001b[m\u001b[0;33m 21204896 ns 21056618 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.2552M\u001b[m output_rows=65.536k\u001b[m plan_cost=18.8088M\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/65536/real_time_median \u001b[m\u001b[0;33m 21202786 ns 21049976 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.2552M\u001b[m output_rows=65.536k\u001b[m plan_cost=18.8088M\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/65536/real_time_stddev \u001b[m\u001b[0;33m 47868 ns 34465 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/65536/real_time_cv \u001b[m\u001b[0;33m 0.23 % 0.16 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/131072/real_time_mean \u001b[m\u001b[0;33m 61087203 ns 60619246 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=25.9523M\u001b[m output_rows=131.072k\u001b[m plan_cost=39.0595M\u001b[m rows=131.072k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/131072/real_time_median \u001b[m\u001b[0;33m 61025748 ns 60603653 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=25.9523M\u001b[m output_rows=131.072k\u001b[m plan_cost=39.0595M\u001b[m rows=131.072k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/131072/real_time_stddev \u001b[m\u001b[0;33m 318956 ns 262983 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/131072/real_time_cv \u001b[m\u001b[0;33m 0.52 % 0.43 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/262144/real_time_mean \u001b[m\u001b[0;33m 178916260 ns 177525137 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=54.7881M\u001b[m output_rows=262.144k\u001b[m plan_cost=81.0025M\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/262144/real_time_median \u001b[m\u001b[0;33m 176367090 ns 174983458 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=54.7881M\u001b[m output_rows=262.144k\u001b[m plan_cost=81.0025M\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/262144/real_time_stddev \u001b[m\u001b[0;33m 5229156 ns 5156914 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Sort/262144/real_time_cv \u001b[m\u001b[0;33m 2.92 % 2.90 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/1024/real_time_mean \u001b[m\u001b[0;33m 285190 ns 284193 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=522.24k\u001b[m output_rows=1.024k\u001b[m plan_cost=624.64k\u001b[m rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/1024/real_time_median \u001b[m\u001b[0;33m 284445 ns 283487 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=522.24k\u001b[m output_rows=1.024k\u001b[m plan_cost=624.64k\u001b[m rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/1024/real_time_stddev \u001b[m\u001b[0;33m 1726 ns 1620 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/1024/real_time_cv \u001b[m\u001b[0;33m 0.61 % 0.57 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/2048/real_time_mean \u001b[m\u001b[0;33m 554982 ns 552824 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.04448M\u001b[m output_rows=2.048k\u001b[m plan_cost=1.24928M\u001b[m rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/2048/real_time_median \u001b[m\u001b[0;33m 554354 ns 551916 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.04448M\u001b[m output_rows=2.048k\u001b[m plan_cost=1.24928M\u001b[m rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/2048/real_time_stddev \u001b[m\u001b[0;33m 1503 ns 1594 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/2048/real_time_cv \u001b[m\u001b[0;33m 0.27 % 0.29 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/4096/real_time_mean \u001b[m\u001b[0;33m 1186394 ns 1179677 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.08896M\u001b[m output_rows=4.096k\u001b[m plan_cost=2.49856M\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/4096/real_time_median \u001b[m\u001b[0;33m 1161393 ns 1154736 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=2.08896M\u001b[m output_rows=4.096k\u001b[m plan_cost=2.49856M\u001b[m rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/4096/real_time_stddev \u001b[m\u001b[0;33m 52944 ns 51987 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/4096/real_time_cv \u001b[m\u001b[0;33m 4.46 % 4.41 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/8192/real_time_mean \u001b[m\u001b[0;33m 2432833 ns 2420872 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=4.17792M\u001b[m output_rows=8.192k\u001b[m plan_cost=4.99712M\u001b[m rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/8192/real_time_median \u001b[m\u001b[0;33m 2442240 ns 2429133 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=4.17792M\u001b[m output_rows=8.192k\u001b[m plan_cost=4.99712M\u001b[m rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/8192/real_time_stddev \u001b[m\u001b[0;33m 22609 ns 21015 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/8192/real_time_cv \u001b[m\u001b[0;33m 0.93 % 0.87 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/16384/real_time_mean \u001b[m\u001b[0;33m 5847840 ns 5806488 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=8.35584M\u001b[m output_rows=16.384k\u001b[m plan_cost=9.99424M\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/16384/real_time_median \u001b[m\u001b[0;33m 5801578 ns 5764441 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=8.35584M\u001b[m output_rows=16.384k\u001b[m plan_cost=9.99424M\u001b[m rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/16384/real_time_stddev \u001b[m\u001b[0;33m 104961 ns 103759 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/16384/real_time_cv \u001b[m\u001b[0;33m 1.79 % 1.79 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/32768/real_time_mean \u001b[m\u001b[0;33m 16878386 ns 16728186 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=16.7117M\u001b[m output_rows=32.768k\u001b[m plan_cost=19.9885M\u001b[m rows=32.768k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/32768/real_time_median \u001b[m\u001b[0;33m 16986629 ns 16833720 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=16.7117M\u001b[m output_rows=32.768k\u001b[m plan_cost=19.9885M\u001b[m rows=32.768k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/32768/real_time_stddev \u001b[m\u001b[0;33m 210596 ns 201562 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/32768/real_time_cv \u001b[m\u001b[0;33m 1.25 % 1.20 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/65536/real_time_mean \u001b[m\u001b[0;33m 53712474 ns 53266495 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=33.4234M\u001b[m output_rows=65.536k\u001b[m plan_cost=39.977M\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/65536/real_time_median \u001b[m\u001b[0;33m 51282451 ns 50872196 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=33.4234M\u001b[m output_rows=65.536k\u001b[m plan_cost=39.977M\u001b[m rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/65536/real_time_stddev \u001b[m\u001b[0;33m 4359336 ns 4263240 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/65536/real_time_cv \u001b[m\u001b[0;33m 8.12 % 8.00 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/131072/real_time_mean \u001b[m\u001b[0;33m 132381802 ns 131439592 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=66.8467M\u001b[m output_rows=131.072k\u001b[m plan_cost=79.9539M\u001b[m rows=131.072k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/131072/real_time_median \u001b[m\u001b[0;33m 132670408 ns 131630071 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=66.8467M\u001b[m output_rows=131.072k\u001b[m plan_cost=79.9539M\u001b[m rows=131.072k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/131072/real_time_stddev \u001b[m\u001b[0;33m 513538 ns 448055 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/131072/real_time_cv \u001b[m\u001b[0;33m 0.39 % 0.34 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/262144/real_time_mean \u001b[m\u001b[0;33m 312772495 ns 310749663 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=133.693M\u001b[m output_rows=262.144k\u001b[m plan_cost=159.908M\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/262144/real_time_median \u001b[m\u001b[0;33m 312614458 ns 310482767 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=133.693M\u001b[m output_rows=262.144k\u001b[m plan_cost=159.908M\u001b[m rows=262.144k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/262144/real_time_stddev \u001b[m\u001b[0;33m 21372055 ns 21016226 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/Aggregation/262144/real_time_cv \u001b[m\u001b[0;33m 6.83 % 6.76 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/1024/1024/real_time_mean \u001b[m\u001b[0;33m 160722 ns 160185 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=404.48k\u001b[m output_rows=1.024k\u001b[m plan_cost=609.28k\u001b[m rhs_rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/1024/1024/real_time_median \u001b[m\u001b[0;33m 161083 ns 160544 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=404.48k\u001b[m output_rows=1.024k\u001b[m plan_cost=609.28k\u001b[m rhs_rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/1024/1024/real_time_stddev \u001b[m\u001b[0;33m 980 ns 1021 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/1024/1024/real_time_cv \u001b[m\u001b[0;33m 0.61 % 0.64 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/2048/2048/real_time_mean \u001b[m\u001b[0;33m 309777 ns 308490 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=2.048k\u001b[m model_cost=808.96k\u001b[m output_rows=2.048k\u001b[m plan_cost=1.21856M\u001b[m rhs_rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/2048/2048/real_time_median \u001b[m\u001b[0;33m 309907 ns 308710 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=2.048k\u001b[m model_cost=808.96k\u001b[m output_rows=2.048k\u001b[m plan_cost=1.21856M\u001b[m rhs_rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/2048/2048/real_time_stddev \u001b[m\u001b[0;33m 506 ns 522 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/2048/2048/real_time_cv \u001b[m\u001b[0;33m 0.16 % 0.17 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/4096/4096/real_time_mean \u001b[m\u001b[0;33m 608952 ns 605721 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=4.096k\u001b[m model_cost=1.61792M\u001b[m output_rows=4.096k\u001b[m plan_cost=2.43712M\u001b[m rhs_rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/4096/4096/real_time_median \u001b[m\u001b[0;33m 608893 ns 605644 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=4.096k\u001b[m model_cost=1.61792M\u001b[m output_rows=4.096k\u001b[m plan_cost=2.43712M\u001b[m rhs_rows=4.096k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/4096/4096/real_time_stddev \u001b[m\u001b[0;33m 3530 ns 3421 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/4096/4096/real_time_cv \u001b[m\u001b[0;33m 0.58 % 0.56 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/8192/8192/real_time_mean \u001b[m\u001b[0;33m 1317737 ns 1309485 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=8.192k\u001b[m model_cost=3.23584M\u001b[m output_rows=8.192k\u001b[m plan_cost=4.87424M\u001b[m rhs_rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/8192/8192/real_time_median \u001b[m\u001b[0;33m 1316605 ns 1308784 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=8.192k\u001b[m model_cost=3.23584M\u001b[m output_rows=8.192k\u001b[m plan_cost=4.87424M\u001b[m rhs_rows=8.192k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/8192/8192/real_time_stddev \u001b[m\u001b[0;33m 59555 ns 59083 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/8192/8192/real_time_cv \u001b[m\u001b[0;33m 4.52 % 4.51 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/16384/16384/real_time_mean \u001b[m\u001b[0;33m 2961361 ns 2936971 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=16.384k\u001b[m model_cost=6.47168M\u001b[m output_rows=16.384k\u001b[m plan_cost=9.74848M\u001b[m rhs_rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/16384/16384/real_time_median \u001b[m\u001b[0;33m 2968359 ns 2943681 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=16.384k\u001b[m model_cost=6.47168M\u001b[m output_rows=16.384k\u001b[m plan_cost=9.74848M\u001b[m rhs_rows=16.384k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/16384/16384/real_time_stddev \u001b[m\u001b[0;33m 19681 ns 19421 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/16384/16384/real_time_cv \u001b[m\u001b[0;33m 0.66 % 0.66 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/32768/32768/real_time_mean \u001b[m\u001b[0;33m 7050325 ns 6989873 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=32.768k\u001b[m model_cost=12.9434M\u001b[m output_rows=32.768k\u001b[m plan_cost=19.497M\u001b[m rhs_rows=32.768k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/32768/32768/real_time_median \u001b[m\u001b[0;33m 7009057 ns 6949558 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=32.768k\u001b[m model_cost=12.9434M\u001b[m output_rows=32.768k\u001b[m plan_cost=19.497M\u001b[m rhs_rows=32.768k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/32768/32768/real_time_stddev \u001b[m\u001b[0;33m 77824 ns 79054 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/32768/32768/real_time_cv \u001b[m\u001b[0;33m 1.10 % 1.13 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/65536/65536/real_time_mean \u001b[m\u001b[0;33m 15872398 ns 15737241 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=65.536k\u001b[m model_cost=25.8867M\u001b[m output_rows=65.536k\u001b[m plan_cost=38.9939M\u001b[m rhs_rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/65536/65536/real_time_median \u001b[m\u001b[0;33m 15615015 ns 15477258 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=65.536k\u001b[m model_cost=25.8867M\u001b[m output_rows=65.536k\u001b[m plan_cost=38.9939M\u001b[m rhs_rows=65.536k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/65536/65536/real_time_stddev \u001b[m\u001b[0;33m 780189 ns 771653 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/HashJoin/65536/65536/real_time_cv \u001b[m\u001b[0;33m 4.92 % 4.90 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/64/64/real_time_mean \u001b[m\u001b[0;33m 355795 ns 353483 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=64\u001b[m model_cost=286.72k\u001b[m output_rows=64\u001b[m plan_cost=299.52k\u001b[m rhs_rows=64\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/64/64/real_time_median \u001b[m\u001b[0;33m 353326 ns 350916 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=64\u001b[m model_cost=286.72k\u001b[m output_rows=64\u001b[m plan_cost=299.52k\u001b[m rhs_rows=64\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/64/64/real_time_stddev \u001b[m\u001b[0;33m 4527 ns 4451 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/64/64/real_time_cv \u001b[m\u001b[0;33m 1.27 % 1.26 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/128/128/real_time_mean \u001b[m\u001b[0;33m 1116378 ns 1110421 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=128\u001b[m model_cost=1.14688M\u001b[m output_rows=128\u001b[m plan_cost=1.17248M\u001b[m rhs_rows=128\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/128/128/real_time_median \u001b[m\u001b[0;33m 1115505 ns 1109645 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=128\u001b[m model_cost=1.14688M\u001b[m output_rows=128\u001b[m plan_cost=1.17248M\u001b[m rhs_rows=128\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/128/128/real_time_stddev \u001b[m\u001b[0;33m 26324 ns 25069 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/128/128/real_time_cv \u001b[m\u001b[0;33m 2.36 % 2.26 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/256/256/real_time_mean \u001b[m\u001b[0;33m 3971691 ns 3955265 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=256\u001b[m model_cost=4.58752M\u001b[m output_rows=256\u001b[m plan_cost=4.63872M\u001b[m rhs_rows=256\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/256/256/real_time_median \u001b[m\u001b[0;33m 3974506 ns 3958255 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=256\u001b[m model_cost=4.58752M\u001b[m output_rows=256\u001b[m plan_cost=4.63872M\u001b[m rhs_rows=256\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/256/256/real_time_stddev \u001b[m\u001b[0;33m 42789 ns 40950 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/256/256/real_time_cv \u001b[m\u001b[0;33m 1.08 % 1.04 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/512/512/real_time_mean \u001b[m\u001b[0;33m 15476134 ns 15410251 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=512\u001b[m model_cost=18.3501M\u001b[m output_rows=512\u001b[m plan_cost=18.4525M\u001b[m rhs_rows=512\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/512/512/real_time_median \u001b[m\u001b[0;33m 15283883 ns 15218578 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=512\u001b[m model_cost=18.3501M\u001b[m output_rows=512\u001b[m plan_cost=18.4525M\u001b[m rhs_rows=512\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/512/512/real_time_stddev \u001b[m\u001b[0;33m 398036 ns 392643 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/512/512/real_time_cv \u001b[m\u001b[0;33m 2.57 % 2.55 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/1024/1024/real_time_mean \u001b[m\u001b[0;33m 58703697 ns 58489336 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=73.4003M\u001b[m output_rows=1.024k\u001b[m plan_cost=73.6051M\u001b[m rhs_rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/1024/1024/real_time_median \u001b[m\u001b[0;33m 58646104 ns 58442294 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=73.4003M\u001b[m output_rows=1.024k\u001b[m plan_cost=73.6051M\u001b[m rhs_rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/1024/1024/real_time_stddev \u001b[m\u001b[0;33m 142367 ns 137251 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/1024/1024/real_time_cv \u001b[m\u001b[0;33m 0.24 % 0.23 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/2048/2048/real_time_mean \u001b[m\u001b[0;33m 232975373 ns 232184958 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=2.048k\u001b[m model_cost=293.601M\u001b[m output_rows=2.048k\u001b[m plan_cost=294.011M\u001b[m rhs_rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/2048/2048/real_time_median \u001b[m\u001b[0;33m 233174267 ns 232347277 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=2.048k\u001b[m model_cost=293.601M\u001b[m output_rows=2.048k\u001b[m plan_cost=294.011M\u001b[m rhs_rows=2.048k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/2048/2048/real_time_stddev \u001b[m\u001b[0;33m 540173 ns 549641 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopJoin/2048/2048/real_time_cv \u001b[m\u001b[0;33m 0.23 % 0.24 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/32/32/real_time_mean \u001b[m\u001b[0;33m 128773 ns 127708 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=32\u001b[m model_cost=106.496k\u001b[m output_rows=1.024k\u001b[m plan_cost=112.896k\u001b[m rhs_rows=32\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/32/32/real_time_median \u001b[m\u001b[0;33m 129039 ns 128024 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=32\u001b[m model_cost=106.496k\u001b[m output_rows=1.024k\u001b[m plan_cost=112.896k\u001b[m rhs_rows=32\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/32/32/real_time_stddev \u001b[m\u001b[0;33m 3861 ns 3750 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/32/32/real_time_cv \u001b[m\u001b[0;33m 3.00 % 2.94 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/64/64/real_time_mean \u001b[m\u001b[0;33m 301369 ns 298177 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=64\u001b[m model_cost=425.984k\u001b[m output_rows=4.096k\u001b[m plan_cost=438.784k\u001b[m rhs_rows=64\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/64/64/real_time_median \u001b[m\u001b[0;33m 300985 ns 298775 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=64\u001b[m model_cost=425.984k\u001b[m output_rows=4.096k\u001b[m plan_cost=438.784k\u001b[m rhs_rows=64\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/64/64/real_time_stddev \u001b[m\u001b[0;33m 4541 ns 6191 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/64/64/real_time_cv \u001b[m\u001b[0;33m 1.51 % 2.08 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/128/128/real_time_mean \u001b[m\u001b[0;33m 958867 ns 951371 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=128\u001b[m model_cost=1.70394M\u001b[m output_rows=16.384k\u001b[m plan_cost=1.72954M\u001b[m rhs_rows=128\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/128/128/real_time_median \u001b[m\u001b[0;33m 959321 ns 951939 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=128\u001b[m model_cost=1.70394M\u001b[m output_rows=16.384k\u001b[m plan_cost=1.72954M\u001b[m rhs_rows=128\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/128/128/real_time_stddev \u001b[m\u001b[0;33m 18581 ns 18282 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/128/128/real_time_cv \u001b[m\u001b[0;33m 1.94 % 1.92 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/256/256/real_time_mean \u001b[m\u001b[0;33m 5563806 ns 5491910 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=256\u001b[m model_cost=6.81574M\u001b[m output_rows=65.536k\u001b[m plan_cost=6.86694M\u001b[m rhs_rows=256\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/256/256/real_time_median \u001b[m\u001b[0;33m 5620555 ns 5545799 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=256\u001b[m model_cost=6.81574M\u001b[m output_rows=65.536k\u001b[m plan_cost=6.86694M\u001b[m rhs_rows=256\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/256/256/real_time_stddev \u001b[m\u001b[0;33m 457987 ns 448836 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/256/256/real_time_cv \u001b[m\u001b[0;33m 8.23 % 8.17 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/512/512/real_time_mean \u001b[m\u001b[0;33m 26439028 ns 26166619 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=512\u001b[m model_cost=27.263M\u001b[m output_rows=262.144k\u001b[m plan_cost=27.3654M\u001b[m rhs_rows=512\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/512/512/real_time_median \u001b[m\u001b[0;33m 26442389 ns 26176889 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=512\u001b[m model_cost=27.263M\u001b[m output_rows=262.144k\u001b[m plan_cost=27.3654M\u001b[m rhs_rows=512\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/512/512/real_time_stddev \u001b[m\u001b[0;33m 405176 ns 377192 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/512/512/real_time_cv \u001b[m\u001b[0;33m 1.53 % 1.44 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/1024/1024/real_time_mean \u001b[m\u001b[0;33m 125006665 ns 123963251 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=109.052M\u001b[m output_rows=1.04858M\u001b[m plan_cost=109.257M\u001b[m rhs_rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/1024/1024/real_time_median \u001b[m\u001b[0;33m 126435572 ns 125317023 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.024k\u001b[m model_cost=109.052M\u001b[m output_rows=1.04858M\u001b[m plan_cost=109.257M\u001b[m rhs_rows=1.024k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/1024/1024/real_time_stddev \u001b[m\u001b[0;33m 2622394 ns 2620609 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCost/NestedLoopCrossJoin/1024/1024/real_time_cv \u001b[m\u001b[0;33m 2.10 % 2.11 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m" - ] - } - ], + "outputs": [], "source": [ "!cd .. && timeout 300s ./build-release/bin/benchmarks --benchmark_filter='^OperatorCost/' --benchmark_repetitions=3 --benchmark_display_aggregates_only=true --benchmark_out=research/benchmark-results/operator-cost.json --benchmark_out_format=json" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "bcfd11fc", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2026-06-09T23:49:41+03:00\n", - "Running ./build-release/bin/benchmarks\n", - "Run on (8 X 4200 MHz CPU s)\n", - "CPU Caches:\n", - " L1 Data 48 KiB (x4)\n", - " L1 Instruction 32 KiB (x4)\n", - " L2 Unified 1280 KiB (x4)\n", - " L3 Unified 8192 KiB (x1)\n", - "Load Average: 2.21, 1.41, 1.15\n", - "***WARNING*** CPU scaling is enabled, the benchmark real time measurements may be noisy and will incur extra overhead.\n", - "------------------------------------------------------------------------------------------------------------------------------------------------\n", - "Benchmark Time CPU Iterations UserCounters...\n", - "------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\u001b[0;32mOperatorCostMatched/target_cost:640000/SeqScan/6400/real_time_mean \u001b[m\u001b[0;33m 316342 ns 313634 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640k\u001b[m output_rows=6.4k\u001b[m plan_cost=640k\u001b[m rows=6.4k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/SeqScan/6400/real_time_median \u001b[m\u001b[0;33m 317098 ns 314269 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640k\u001b[m output_rows=6.4k\u001b[m plan_cost=640k\u001b[m rows=6.4k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/SeqScan/6400/real_time_stddev \u001b[m\u001b[0;33m 2952 ns 3126 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/SeqScan/6400/real_time_cv \u001b[m\u001b[0;33m 0.93 % 1.00 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Filter/6400/real_time_mean \u001b[m\u001b[0;33m 410811 ns 409047 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640k\u001b[m output_rows=3.328k\u001b[m plan_cost=1.28M\u001b[m rows=6.4k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Filter/6400/real_time_median \u001b[m\u001b[0;33m 402156 ns 400612 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640k\u001b[m output_rows=3.328k\u001b[m plan_cost=1.28M\u001b[m rows=6.4k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Filter/6400/real_time_stddev \u001b[m\u001b[0;33m 15061 ns 14626 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Filter/6400/real_time_cv \u001b[m\u001b[0;33m 3.67 % 3.58 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Projection/29091/real_time_mean \u001b[m\u001b[0;33m 3125994 ns 3104770 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640.002k\u001b[m output_rows=29.091k\u001b[m plan_cost=3.5491M\u001b[m rows=29.091k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Projection/29091/real_time_median \u001b[m\u001b[0;33m 3137797 ns 3116535 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640.002k\u001b[m output_rows=29.091k\u001b[m plan_cost=3.5491M\u001b[m rows=29.091k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Projection/29091/real_time_stddev \u001b[m\u001b[0;33m 26115 ns 26160 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Projection/29091/real_time_cv \u001b[m\u001b[0;33m 0.84 % 0.84 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Sort/4476/real_time_mean \u001b[m\u001b[0;33m 701764 ns 698672 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640.068k\u001b[m output_rows=4.476k\u001b[m plan_cost=1.08767M\u001b[m rows=4.476k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Sort/4476/real_time_median \u001b[m\u001b[0;33m 699974 ns 696724 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640.068k\u001b[m output_rows=4.476k\u001b[m plan_cost=1.08767M\u001b[m rows=4.476k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Sort/4476/real_time_stddev \u001b[m\u001b[0;33m 3525 ns 3485 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Sort/4476/real_time_cv \u001b[m\u001b[0;33m 0.50 % 0.50 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Aggregation/1255/real_time_mean \u001b[m\u001b[0;33m 349396 ns 348160 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640.05k\u001b[m output_rows=1.255k\u001b[m plan_cost=765.55k\u001b[m rows=1.255k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Aggregation/1255/real_time_median \u001b[m\u001b[0;33m 351086 ns 349758 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=640.05k\u001b[m output_rows=1.255k\u001b[m plan_cost=765.55k\u001b[m rows=1.255k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Aggregation/1255/real_time_stddev \u001b[m\u001b[0;33m 3908 ns 3846 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/Aggregation/1255/real_time_cv \u001b[m\u001b[0;33m 1.12 % 1.10 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/HashJoin/1620/1620/real_time_mean \u001b[m\u001b[0;33m 250126 ns 249104 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=1.62k\u001b[m model_cost=639.9k\u001b[m output_rows=1.62k\u001b[m plan_cost=963.9k\u001b[m rhs_rows=1.62k\u001b[m\n", - 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rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopCrossJoin/78/78/real_time_mean \u001b[m\u001b[0;33m 401858 ns 399119 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=78\u001b[m model_cost=632.736k\u001b[m output_rows=6.084k\u001b[m plan_cost=648.336k\u001b[m rhs_rows=78\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopCrossJoin/78/78/real_time_median \u001b[m\u001b[0;33m 396831 ns 394275 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=78\u001b[m model_cost=632.736k\u001b[m output_rows=6.084k\u001b[m plan_cost=648.336k\u001b[m rhs_rows=78\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopCrossJoin/78/78/real_time_stddev \u001b[m\u001b[0;33m 9520 ns 9122 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:640000/NestedLoopCrossJoin/78/78/real_time_cv \u001b[m\u001b[0;33m 2.37 % 2.29 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/SeqScan/10000/real_time_mean \u001b[m\u001b[0;33m 344957 ns 343365 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1M\u001b[m output_rows=10k\u001b[m plan_cost=1M\u001b[m rows=10k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/SeqScan/10000/real_time_median \u001b[m\u001b[0;33m 344845 ns 343551 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1M\u001b[m output_rows=10k\u001b[m plan_cost=1M\u001b[m rows=10k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/SeqScan/10000/real_time_stddev \u001b[m\u001b[0;33m 627 ns 873 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/SeqScan/10000/real_time_cv \u001b[m\u001b[0;33m 0.18 % 0.25 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Filter/10000/real_time_mean \u001b[m\u001b[0;33m 639288 ns 636139 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1M\u001b[m output_rows=5.12k\u001b[m plan_cost=2M\u001b[m rows=10k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Filter/10000/real_time_median \u001b[m\u001b[0;33m 641247 ns 638152 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1M\u001b[m output_rows=5.12k\u001b[m plan_cost=2M\u001b[m rows=10k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Filter/10000/real_time_stddev \u001b[m\u001b[0;33m 5981 ns 5665 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Filter/10000/real_time_cv \u001b[m\u001b[0;33m 0.94 % 0.89 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Projection/45455/real_time_mean \u001b[m\u001b[0;33m 4977023 ns 4946997 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.00001M\u001b[m output_rows=45.455k\u001b[m plan_cost=5.54551M\u001b[m rows=45.455k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Projection/45455/real_time_median \u001b[m\u001b[0;33m 4972650 ns 4944757 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=1.00001M\u001b[m output_rows=45.455k\u001b[m plan_cost=5.54551M\u001b[m rows=45.455k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Projection/45455/real_time_stddev \u001b[m\u001b[0;33m 23603 ns 25476 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Projection/45455/real_time_cv \u001b[m\u001b[0;33m 0.47 % 0.51 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Sort/6993/real_time_mean \u001b[m\u001b[0;33m 1141130 ns 1136359 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.999999M\u001b[m output_rows=6.993k\u001b[m plan_cost=1.6993M\u001b[m rows=6.993k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Sort/6993/real_time_median \u001b[m\u001b[0;33m 1136818 ns 1132508 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.999999M\u001b[m output_rows=6.993k\u001b[m plan_cost=1.6993M\u001b[m rows=6.993k\u001b[m\n", - 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plan_cost=1.19621M\u001b[m rows=1.961k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Aggregation/1961/real_time_stddev \u001b[m\u001b[0;33m 2349 ns 2195 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/Aggregation/1961/real_time_cv \u001b[m\u001b[0;33m 0.44 % 0.42 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/HashJoin/2532/2532/real_time_mean \u001b[m\u001b[0;33m 398480 ns 396620 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=2.532k\u001b[m model_cost=1.00014M\u001b[m output_rows=2.532k\u001b[m plan_cost=1.50654M\u001b[m rhs_rows=2.532k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/HashJoin/2532/2532/real_time_median \u001b[m\u001b[0;33m 393039 ns 391403 ns 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rhs_rows=120\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/NestedLoopJoin/120/120/real_time_median \u001b[m\u001b[0;33m 984337 ns 979219 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=120\u001b[m model_cost=1.008M\u001b[m output_rows=120\u001b[m plan_cost=1.032M\u001b[m rhs_rows=120\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/NestedLoopJoin/120/120/real_time_stddev \u001b[m\u001b[0;33m 18469 ns 18091 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/NestedLoopJoin/120/120/real_time_cv \u001b[m\u001b[0;33m 1.87 % 1.84 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:1000000/NestedLoopCrossJoin/98/98/real_time_mean 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output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/SeqScan/32400/real_time_mean \u001b[m\u001b[0;33m 1237620 ns 1228930 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.24M\u001b[m output_rows=32.4k\u001b[m plan_cost=3.24M\u001b[m rows=32.4k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/SeqScan/32400/real_time_median \u001b[m\u001b[0;33m 1199173 ns 1191634 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.24M\u001b[m output_rows=32.4k\u001b[m plan_cost=3.24M\u001b[m rows=32.4k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/SeqScan/32400/real_time_stddev \u001b[m\u001b[0;33m 78181 ns 76723 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/SeqScan/32400/real_time_cv \u001b[m\u001b[0;33m 6.32 % 6.24 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Filter/32400/real_time_mean \u001b[m\u001b[0;33m 2123351 ns 2112531 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.24M\u001b[m output_rows=16.384k\u001b[m plan_cost=6.48M\u001b[m rows=32.4k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Filter/32400/real_time_median \u001b[m\u001b[0;33m 2125920 ns 2114863 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.24M\u001b[m output_rows=16.384k\u001b[m plan_cost=6.48M\u001b[m rows=32.4k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Filter/32400/real_time_stddev \u001b[m\u001b[0;33m 18255 ns 16526 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Filter/32400/real_time_cv \u001b[m\u001b[0;33m 0.86 % 0.78 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Projection/147273/real_time_mean \u001b[m\u001b[0;33m 19539424 ns 19390815 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.24001M\u001b[m output_rows=147.273k\u001b[m plan_cost=17.9673M\u001b[m rows=147.273k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Projection/147273/real_time_median \u001b[m\u001b[0;33m 19513198 ns 19372676 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.24001M\u001b[m output_rows=147.273k\u001b[m plan_cost=17.9673M\u001b[m rows=147.273k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Projection/147273/real_time_stddev \u001b[m\u001b[0;33m 443369 ns 422122 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Projection/147273/real_time_cv \u001b[m\u001b[0;33m 2.27 % 2.18 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Sort/19636/real_time_mean \u001b[m\u001b[0;33m 4003506 ns 3985189 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.23994M\u001b[m output_rows=19.636k\u001b[m plan_cost=5.20354M\u001b[m rows=19.636k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Sort/19636/real_time_median \u001b[m\u001b[0;33m 3993834 ns 3973445 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.23994M\u001b[m output_rows=19.636k\u001b[m plan_cost=5.20354M\u001b[m rows=19.636k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Sort/19636/real_time_stddev \u001b[m\u001b[0;33m 36678 ns 37672 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Sort/19636/real_time_cv \u001b[m\u001b[0;33m 0.92 % 0.95 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Aggregation/6353/real_time_mean \u001b[m\u001b[0;33m 1891951 ns 1881946 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.24003M\u001b[m output_rows=6.353k\u001b[m plan_cost=3.87533M\u001b[m rows=6.353k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Aggregation/6353/real_time_median \u001b[m\u001b[0;33m 1864059 ns 1855129 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=3.24003M\u001b[m output_rows=6.353k\u001b[m plan_cost=3.87533M\u001b[m rows=6.353k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/Aggregation/6353/real_time_stddev \u001b[m\u001b[0;33m 79166 ns 76712 ns 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"\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/HashJoin/8203/8203/real_time_stddev \u001b[m\u001b[0;33m 15252 ns 15284 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/HashJoin/8203/8203/real_time_cv \u001b[m\u001b[0;33m 1.18 % 1.19 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopJoin/215/215/real_time_mean \u001b[m\u001b[0;33m 3015413 ns 2998763 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=215\u001b[m model_cost=3.23575M\u001b[m output_rows=215\u001b[m plan_cost=3.27875M\u001b[m rhs_rows=215\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopJoin/215/215/real_time_median \u001b[m\u001b[0;33m 2948122 ns 2933215 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=215\u001b[m model_cost=3.23575M\u001b[m output_rows=215\u001b[m plan_cost=3.27875M\u001b[m rhs_rows=215\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopJoin/215/215/real_time_stddev \u001b[m\u001b[0;33m 134096 ns 129989 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopJoin/215/215/real_time_cv \u001b[m\u001b[0;33m 4.45 % 4.33 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopCrossJoin/177/177/real_time_mean \u001b[m\u001b[0;33m 1969269 ns 1950102 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=177\u001b[m model_cost=3.25822M\u001b[m output_rows=31.329k\u001b[m plan_cost=3.29362M\u001b[m rhs_rows=177\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopCrossJoin/177/177/real_time_median \u001b[m\u001b[0;33m 1974125 ns 1956105 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=177\u001b[m model_cost=3.25822M\u001b[m output_rows=31.329k\u001b[m plan_cost=3.29362M\u001b[m rhs_rows=177\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopCrossJoin/177/177/real_time_stddev \u001b[m\u001b[0;33m 10036 ns 10656 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:3240000/NestedLoopCrossJoin/177/177/real_time_cv \u001b[m\u001b[0;33m 0.51 % 0.55 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/SeqScan/67600/real_time_mean \u001b[m\u001b[0;33m 3823546 ns 3788256 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76M\u001b[m output_rows=67.6k\u001b[m plan_cost=6.76M\u001b[m rows=67.6k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/SeqScan/67600/real_time_median \u001b[m\u001b[0;33m 3771835 ns 3738111 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76M\u001b[m output_rows=67.6k\u001b[m plan_cost=6.76M\u001b[m rows=67.6k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/SeqScan/67600/real_time_stddev \u001b[m\u001b[0;33m 138085 ns 136090 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/SeqScan/67600/real_time_cv \u001b[m\u001b[0;33m 3.61 % 3.59 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Filter/67600/real_time_mean \u001b[m\u001b[0;33m 5038810 ns 5006184 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76M\u001b[m output_rows=33.808k\u001b[m plan_cost=13.52M\u001b[m rows=67.6k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Filter/67600/real_time_median \u001b[m\u001b[0;33m 4977493 ns 4945750 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76M\u001b[m output_rows=33.808k\u001b[m plan_cost=13.52M\u001b[m rows=67.6k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Filter/67600/real_time_stddev \u001b[m\u001b[0;33m 229683 ns 228503 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Filter/67600/real_time_cv \u001b[m\u001b[0;33m 4.56 % 4.56 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Projection/307273/real_time_mean \u001b[m\u001b[0;33m 40927066 ns 40634020 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76001M\u001b[m output_rows=307.273k\u001b[m plan_cost=37.4873M\u001b[m rows=307.273k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Projection/307273/real_time_median \u001b[m\u001b[0;33m 40860570 ns 40567388 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76001M\u001b[m output_rows=307.273k\u001b[m plan_cost=37.4873M\u001b[m rows=307.273k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Projection/307273/real_time_stddev \u001b[m\u001b[0;33m 758964 ns 753831 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - 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output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Sort/38409/real_time_cv \u001b[m\u001b[0;33m 1.85 % 1.78 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Aggregation/13255/real_time_mean \u001b[m\u001b[0;33m 4512331 ns 4483187 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76005M\u001b[m output_rows=13.255k\u001b[m plan_cost=8.08555M\u001b[m rows=13.255k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Aggregation/13255/real_time_median \u001b[m\u001b[0;33m 4517228 ns 4488908 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=6.76005M\u001b[m output_rows=13.255k\u001b[m plan_cost=8.08555M\u001b[m rows=13.255k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Aggregation/13255/real_time_stddev \u001b[m\u001b[0;33m 242531 ns 238082 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/Aggregation/13255/real_time_cv \u001b[m\u001b[0;33m 5.37 % 5.31 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/HashJoin/17114/17114/real_time_mean \u001b[m\u001b[0;33m 3195742 ns 3168838 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=17.114k\u001b[m model_cost=6.76003M\u001b[m output_rows=17.114k\u001b[m plan_cost=10.1828M\u001b[m rhs_rows=17.114k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/HashJoin/17114/17114/real_time_median \u001b[m\u001b[0;33m 3199407 ns 3171066 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=17.114k\u001b[m model_cost=6.76003M\u001b[m output_rows=17.114k\u001b[m plan_cost=10.1828M\u001b[m rhs_rows=17.114k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/HashJoin/17114/17114/real_time_stddev \u001b[m\u001b[0;33m 92711 ns 91091 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/HashJoin/17114/17114/real_time_cv \u001b[m\u001b[0;33m 2.90 % 2.87 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopJoin/311/311/real_time_mean \u001b[m\u001b[0;33m 6023061 ns 5995465 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=311\u001b[m model_cost=6.77047M\u001b[m output_rows=311\u001b[m plan_cost=6.83267M\u001b[m rhs_rows=311\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopJoin/311/311/real_time_median 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output_rows=65.025k\u001b[m plan_cost=6.8136M\u001b[m rhs_rows=255\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopCrossJoin/255/255/real_time_median \u001b[m\u001b[0;33m 5243997 ns 5177842 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=255\u001b[m model_cost=6.7626M\u001b[m output_rows=65.025k\u001b[m plan_cost=6.8136M\u001b[m rhs_rows=255\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopCrossJoin/255/255/real_time_stddev \u001b[m\u001b[0;33m 364980 ns 356835 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:6760000/NestedLoopCrossJoin/255/255/real_time_cv \u001b[m\u001b[0;33m 6.97 % 6.89 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - 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plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Filter/122500/real_time_mean \u001b[m\u001b[0;33m 9281903 ns 9218854 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.25M\u001b[m output_rows=61.44k\u001b[m plan_cost=24.5M\u001b[m rows=122.5k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Filter/122500/real_time_median \u001b[m\u001b[0;33m 9174524 ns 9110491 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.25M\u001b[m output_rows=61.44k\u001b[m plan_cost=24.5M\u001b[m rows=122.5k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Filter/122500/real_time_stddev \u001b[m\u001b[0;33m 215303 ns 216576 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Filter/122500/real_time_cv \u001b[m\u001b[0;33m 2.32 % 2.35 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Projection/556818/real_time_mean \u001b[m\u001b[0;33m 74886058 ns 74388591 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.25M\u001b[m output_rows=556.818k\u001b[m plan_cost=67.9318M\u001b[m rows=556.818k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Projection/556818/real_time_median \u001b[m\u001b[0;33m 74916742 ns 74404628 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.25M\u001b[m output_rows=556.818k\u001b[m plan_cost=67.9318M\u001b[m rows=556.818k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Projection/556818/real_time_stddev \u001b[m\u001b[0;33m 68523 ns 73385 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - 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output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Sort/65536/real_time_cv \u001b[m\u001b[0;33m 2.27 % 2.18 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Aggregation/24020/real_time_mean \u001b[m\u001b[0;33m 10384281 ns 10310905 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.2502M\u001b[m output_rows=24.02k\u001b[m plan_cost=14.6522M\u001b[m rows=24.02k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Aggregation/24020/real_time_median \u001b[m\u001b[0;33m 10051196 ns 9984556 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=12.2502M\u001b[m output_rows=24.02k\u001b[m plan_cost=14.6522M\u001b[m rows=24.02k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Aggregation/24020/real_time_stddev \u001b[m\u001b[0;33m 594931 ns 586651 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/Aggregation/24020/real_time_cv \u001b[m\u001b[0;33m 5.73 % 5.69 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/HashJoin/31013/31013/real_time_mean \u001b[m\u001b[0;33m 6415603 ns 6363475 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=31.013k\u001b[m model_cost=12.2501M\u001b[m output_rows=31.013k\u001b[m plan_cost=18.4527M\u001b[m rhs_rows=31.013k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/HashJoin/31013/31013/real_time_median \u001b[m\u001b[0;33m 6434016 ns 6382291 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=31.013k\u001b[m model_cost=12.2501M\u001b[m output_rows=31.013k\u001b[m plan_cost=18.4527M\u001b[m rhs_rows=31.013k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/HashJoin/31013/31013/real_time_stddev \u001b[m\u001b[0;33m 66815 ns 64167 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/HashJoin/31013/31013/real_time_cv \u001b[m\u001b[0;33m 1.04 % 1.01 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopJoin/418/418/real_time_mean \u001b[m\u001b[0;33m 10091080 ns 10051311 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=418\u001b[m model_cost=12.2307M\u001b[m output_rows=418\u001b[m plan_cost=12.3143M\u001b[m rhs_rows=418\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopJoin/418/418/real_time_median \u001b[m\u001b[0;33m 10123201 ns 10083856 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=418\u001b[m model_cost=12.2307M\u001b[m output_rows=418\u001b[m plan_cost=12.3143M\u001b[m rhs_rows=418\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopJoin/418/418/real_time_stddev \u001b[m\u001b[0;33m 65437 ns 68353 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopJoin/418/418/real_time_cv \u001b[m\u001b[0;33m 0.65 % 0.68 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopCrossJoin/343/343/real_time_mean \u001b[m\u001b[0;33m 10794051 ns 10661406 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=343\u001b[m model_cost=12.2355M\u001b[m output_rows=117.649k\u001b[m plan_cost=12.3041M\u001b[m rhs_rows=343\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopCrossJoin/343/343/real_time_median \u001b[m\u001b[0;33m 10805562 ns 10678400 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=343\u001b[m model_cost=12.2355M\u001b[m output_rows=117.649k\u001b[m plan_cost=12.3041M\u001b[m rhs_rows=343\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopCrossJoin/343/343/real_time_stddev \u001b[m\u001b[0;33m 58571 ns 53030 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:12250000/NestedLoopCrossJoin/343/343/real_time_cv \u001b[m\u001b[0;33m 0.54 % 0.50 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/SeqScan/260100/real_time_mean \u001b[m\u001b[0;33m 17073667 ns 16907574 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=260.1k\u001b[m plan_cost=26.01M\u001b[m rows=260.1k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/SeqScan/260100/real_time_median \u001b[m\u001b[0;33m 16767850 ns 16581281 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=260.1k\u001b[m plan_cost=26.01M\u001b[m rows=260.1k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/SeqScan/260100/real_time_stddev \u001b[m\u001b[0;33m 1427241 ns 1398924 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/SeqScan/260100/real_time_cv \u001b[m\u001b[0;33m 8.36 % 8.27 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Filter/260100/real_time_mean \u001b[m\u001b[0;33m 19234040 ns 19127109 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=130.052k\u001b[m plan_cost=52.02M\u001b[m rows=260.1k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Filter/260100/real_time_median \u001b[m\u001b[0;33m 19265841 ns 19167215 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=130.052k\u001b[m plan_cost=52.02M\u001b[m rows=260.1k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Filter/260100/real_time_stddev \u001b[m\u001b[0;33m 128928 ns 127286 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Filter/260100/real_time_cv \u001b[m\u001b[0;33m 0.67 % 0.67 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Projection/1182273/real_time_mean \u001b[m\u001b[0;33m 157051171 ns 155918245 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=1.18227M\u001b[m plan_cost=144.237M\u001b[m rows=1.18227M\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Projection/1182273/real_time_median \u001b[m\u001b[0;33m 156611403 ns 155561846 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=1.18227M\u001b[m plan_cost=144.237M\u001b[m rows=1.18227M\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Projection/1182273/real_time_stddev \u001b[m\u001b[0;33m 1135507 ns 1133326 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Projection/1182273/real_time_cv \u001b[m\u001b[0;33m 0.72 % 0.73 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Sort/131364/real_time_mean \u001b[m\u001b[0;33m 60817722 ns 60398176 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.0101M\u001b[m output_rows=131.364k\u001b[m plan_cost=39.1465M\u001b[m rows=131.364k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Sort/131364/real_time_median \u001b[m\u001b[0;33m 61052310 ns 60624938 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.0101M\u001b[m output_rows=131.364k\u001b[m plan_cost=39.1465M\u001b[m rows=131.364k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Sort/131364/real_time_stddev \u001b[m\u001b[0;33m 1963798 ns 1937882 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Sort/131364/real_time_cv \u001b[m\u001b[0;33m 3.23 % 3.21 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Aggregation/51000/real_time_mean \u001b[m\u001b[0;33m 35574296 ns 35336538 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=51k\u001b[m plan_cost=31.11M\u001b[m rows=51k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Aggregation/51000/real_time_median \u001b[m\u001b[0;33m 34202010 ns 33985413 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=26.01M\u001b[m output_rows=51k\u001b[m plan_cost=31.11M\u001b[m rows=51k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Aggregation/51000/real_time_stddev \u001b[m\u001b[0;33m 2962023 ns 2918702 ns \u001b[m\u001b[0;36m 3\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/Aggregation/51000/real_time_cv \u001b[m\u001b[0;33m 8.33 % 8.26 % \u001b[m\u001b[0;36m 3\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/HashJoin/65848/65848/real_time_mean \u001b[m\u001b[0;33m 16676661 ns 16546743 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=65.848k\u001b[m model_cost=26.01M\u001b[m output_rows=65.848k\u001b[m plan_cost=39.1796M\u001b[m rhs_rows=65.848k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/HashJoin/65848/65848/real_time_median \u001b[m\u001b[0;33m 16172905 ns 16059889 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=65.848k\u001b[m model_cost=26.01M\u001b[m output_rows=65.848k\u001b[m plan_cost=39.1796M\u001b[m rhs_rows=65.848k\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/HashJoin/65848/65848/real_time_stddev \u001b[m\u001b[0;33m 875878 ns 864388 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/HashJoin/65848/65848/real_time_cv \u001b[m\u001b[0;33m 5.25 % 5.22 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopJoin/610/610/real_time_mean \u001b[m\u001b[0;33m 21875298 ns 21785712 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=610\u001b[m model_cost=26.047M\u001b[m output_rows=610\u001b[m plan_cost=26.169M\u001b[m rhs_rows=610\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopJoin/610/610/real_time_median \u001b[m\u001b[0;33m 21873879 ns 21796083 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=610\u001b[m model_cost=26.047M\u001b[m output_rows=610\u001b[m plan_cost=26.169M\u001b[m rhs_rows=610\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopJoin/610/610/real_time_stddev \u001b[m\u001b[0;33m 190499 ns 184831 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopJoin/610/610/real_time_cv \u001b[m\u001b[0;33m 0.87 % 0.85 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopCrossJoin/500/500/real_time_mean \u001b[m\u001b[0;33m 23857713 ns 23609004 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=500\u001b[m model_cost=26M\u001b[m output_rows=250k\u001b[m plan_cost=26.1M\u001b[m rhs_rows=500\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopCrossJoin/500/500/real_time_median \u001b[m\u001b[0;33m 23866731 ns 23617933 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=500\u001b[m model_cost=26M\u001b[m output_rows=250k\u001b[m plan_cost=26.1M\u001b[m rhs_rows=500\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopCrossJoin/500/500/real_time_stddev \u001b[m\u001b[0;33m 168299 ns 160750 ns \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0\u001b[m model_cost=0\u001b[m output_rows=0\u001b[m plan_cost=0\u001b[m rhs_rows=0\u001b[m\n", - "\u001b[m\u001b[0;32mOperatorCostMatched/target_cost:26010000/NestedLoopCrossJoin/500/500/real_time_cv \u001b[m\u001b[0;33m 0.71 % 0.68 % \u001b[m\u001b[0;36m 3\u001b[m lhs_rows=0.00%\u001b[m model_cost=0.00%\u001b[m output_rows=0.00%\u001b[m plan_cost=0.00%\u001b[m rhs_rows=0.00%\u001b[m\n", - "\u001b[m" - ] - } - ], + "outputs": [], "source": [ "!cd .. && timeout 300s ./build-release/bin/benchmarks --benchmark_filter='^OperatorCostMatched/' --benchmark_repetitions=3 --benchmark_display_aggregates_only=true --benchmark_out=research/benchmark-results/operator-cost-matched.json --benchmark_out_format=json" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "b16b9fb4", "metadata": {}, "outputs": [], @@ -752,74 +159,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "d3172304", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( executor query mode \\\n", - " 0 InterpretedExpressionExecutor kSimpleSelectSmall Naive \n", - " 1 InterpretedExpressionExecutor kSimpleSelectSmall Optimized \n", - " 2 CachedJitCompiledExpressionExecutor kSimpleSelectSmall Naive \n", - " 3 CachedJitCompiledExpressionExecutor kSimpleSelectSmall Optimized \n", - " 4 InterpretedExpressionExecutor kJoinSmall Naive \n", - " 5 InterpretedExpressionExecutor kJoinSmall Optimized \n", - " 6 CachedJitCompiledExpressionExecutor kJoinSmall Naive \n", - " 7 CachedJitCompiledExpressionExecutor kJoinSmall Optimized \n", - " 8 InterpretedExpressionExecutor kComplex5 Naive \n", - " 9 InterpretedExpressionExecutor kComplex5 Optimized \n", - " 10 CachedJitCompiledExpressionExecutor kComplex5 Naive \n", - " 11 CachedJitCompiledExpressionExecutor kComplex5 Optimized \n", - " \n", - " time_ms \n", - " 0 0.019715 \n", - " 1 0.019566 \n", - " 2 0.019754 \n", - " 3 0.019804 \n", - " 4 0.070585 \n", - " 5 0.043139 \n", - " 6 0.068980 \n", - " 7 0.044213 \n", - " 8 0.090198 \n", - " 9 0.096809 \n", - " 10 0.095552 \n", - " 11 0.087877 ,\n", - " query executor mode time_ms\n", - " 0 q2.1 Interpreted Naive 10.529208\n", - " 1 q2.1 Interpreted Optimized 11.087104\n", - " 2 q1.3 Interpreted Naive 8.696587\n", - " 3 q1.3 Interpreted Optimized 8.481006\n", - " 4 q1.2 Interpreted Naive 10.591798\n", - " 5 q1.2 Interpreted Optimized 8.042493\n", - " 6 q3.3 Interpreted Naive 41.079134\n", - " 7 q3.3 Interpreted Optimized 6.184696\n", - " 8 q3.4 Interpreted Naive 39.726018\n", - " 9 q3.4 Interpreted Optimized 6.226768\n", - " 10 q4.3 Interpreted Naive 10.159185\n", - " 11 q4.3 Interpreted Optimized 6.886022\n", - " 12 q1.1 Interpreted Naive 8.560502\n", - " 13 q1.1 Interpreted Optimized 7.188918\n", - " 14 q3.2 Interpreted Naive 38.735087\n", - " 15 q3.2 Interpreted Optimized 6.717959\n", - " 16 q2.2 Interpreted Naive 9.318865\n", - " 17 q2.2 Interpreted Optimized 6.690732\n", - " 18 q3.1 Interpreted Naive 38.097914\n", - " 19 q3.1 Interpreted Optimized 6.528813\n", - " 20 q4.2 Interpreted Naive 10.061357\n", - " 21 q4.2 Interpreted Optimized 11.892791\n", - " 22 q4.1 Interpreted Naive 9.553308\n", - " 23 q4.1 Interpreted Optimized 13.055569\n", - " 24 q2.3 Interpreted Naive 9.503878\n", - " 25 q2.3 Interpreted Optimized 6.292484)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "synthetic_pattern = re.compile(\n", " r\"BM_SQL<(?P[^,]+), (?P[^,]+), PlannerMode::k(?P[^>]+)>/real_time_mean\"\n", @@ -841,83 +184,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "f4e060f2", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( operator input_rows model_cost time_ms\n", - " 0 SeqScan 1024 102400.0 0.039153\n", - " 1 SeqScan 2048 204800.0 0.072348\n", - " 2 SeqScan 4096 409600.0 0.158147\n", - " 3 SeqScan 8192 819200.0 0.358960\n", - " 4 SeqScan 16384 1638400.0 0.797004\n", - " .. ... ... ... ...\n", - " 59 NestedLoopCrossJoin 64 425984.0 0.301369\n", - " 60 NestedLoopCrossJoin 128 1703936.0 0.958867\n", - " 61 NestedLoopCrossJoin 256 6815744.0 5.563806\n", - " 62 NestedLoopCrossJoin 512 27262976.0 26.439028\n", - " 63 NestedLoopCrossJoin 1024 109051904.0 125.006665\n", - " \n", - " [64 rows x 4 columns],\n", - " target_cost operator input_rows model_cost time_ms\n", - " 0 640000 SeqScan 6400 640000.0 0.316342\n", - " 1 640000 Filter 6400 640000.0 0.410811\n", - " 2 640000 Projection 29091 640002.0 3.125994\n", - " 3 640000 Sort 4476 640068.0 0.701764\n", - " 4 640000 Aggregation 1255 640050.0 0.349396\n", - " 5 640000 HashJoin 1620 639900.0 0.250126\n", - " 6 640000 NestedLoopJoin 96 645120.0 0.668448\n", - " 7 640000 NestedLoopCrossJoin 78 632736.0 0.401858\n", - " 8 1000000 SeqScan 10000 1000000.0 0.344957\n", - " 9 1000000 Filter 10000 1000000.0 0.639288\n", - " 10 1000000 Projection 45455 1000010.0 4.977023\n", - " 11 1000000 Sort 6993 999999.0 1.141130\n", - " 12 1000000 Aggregation 1961 1000110.0 0.528994\n", - " 13 1000000 HashJoin 2532 1000140.0 0.398480\n", - " 14 1000000 NestedLoopJoin 120 1008000.0 0.988039\n", - " 15 1000000 NestedLoopCrossJoin 98 998816.0 0.576046\n", - " 16 3240000 SeqScan 32400 3240000.0 1.237620\n", - " 17 3240000 Filter 32400 3240000.0 2.123351\n", - " 18 3240000 Projection 147273 3240006.0 19.539424\n", - " 19 3240000 Sort 19636 3239940.0 4.003506\n", - " 20 3240000 Aggregation 6353 3240030.0 1.891951\n", - " 21 3240000 HashJoin 8203 3240185.0 1.296747\n", - " 22 3240000 NestedLoopJoin 215 3235750.0 3.015413\n", - " 23 3240000 NestedLoopCrossJoin 177 3258216.0 1.969269\n", - " 24 6760000 SeqScan 67600 6760000.0 3.823546\n", - " 25 6760000 Filter 67600 6760000.0 5.038810\n", - " 26 6760000 Projection 307273 6760006.0 40.927066\n", - " 27 6760000 Sort 38409 6759984.0 10.002418\n", - " 28 6760000 Aggregation 13255 6760050.0 4.512331\n", - " 29 6760000 HashJoin 17114 6760030.0 3.195742\n", - " 30 6760000 NestedLoopJoin 311 6770470.0 6.023061\n", - " 31 6760000 NestedLoopCrossJoin 255 6762600.0 5.240152\n", - " 32 12250000 SeqScan 122500 12250000.0 6.678741\n", - " 33 12250000 Filter 122500 12250000.0 9.281903\n", - " 34 12250000 Projection 556818 12249996.0 74.886058\n", - " 35 12250000 Sort 65536 12255232.0 20.096736\n", - " 36 12250000 Aggregation 24020 12250200.0 10.384281\n", - " 37 12250000 HashJoin 31013 12250135.0 6.415603\n", - " 38 12250000 NestedLoopJoin 418 12230680.0 10.091080\n", - " 39 12250000 NestedLoopCrossJoin 343 12235496.0 10.794051\n", - " 40 26010000 SeqScan 260100 26010000.0 17.073667\n", - " 41 26010000 Filter 260100 26010000.0 19.234040\n", - " 42 26010000 Projection 1182273 26010006.0 157.051171\n", - " 43 26010000 Sort 131364 26010072.0 60.817722\n", - " 44 26010000 Aggregation 51000 26010000.0 35.574296\n", - " 45 26010000 HashJoin 65848 26009960.0 16.676661\n", - " 46 26010000 NestedLoopJoin 610 26047000.0 21.875298\n", - " 47 26010000 NestedLoopCrossJoin 500 26000000.0 23.857713)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "operator = []\n", "for row in mean_rows(\"operator-cost.json\"):\n", @@ -947,21 +217,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "2075a20f", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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HcmV?d00001 diff --git a/report/thesis.tex b/report/thesis.tex index c49cc15..2d54d16 100644 --- a/report/thesis.tex +++ b/report/thesis.tex @@ -347,7 +347,7 @@ \section{Цель оптимизатора} \begin{Example} Ассоциативность соединения: \[ - (R \bowtie S) \bowtie T = R \bowtie (S \bowtie T). + (R \bowtie S) \bowtie T \equiv R \bowtie (S \bowtie T). \] \end{Example} From ca605f4a94f44c14cfb75b97229790653c9404eb Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Tue, 5 May 2026 20:43:37 +0300 Subject: [PATCH 016/120] Wip --- flake.nix | 5 +- report/biblio.bib | 125 +++++++++-------- report/cascades.dot | 27 ++++ report/cascades.pdf | Bin 0 -> 16363 bytes report/thesis.tex | 329 ++++++++++++++++++++++++++++++++------------ 5 files changed, 340 insertions(+), 146 deletions(-) create mode 100644 report/cascades.dot create mode 100644 report/cascades.pdf diff --git a/flake.nix b/flake.nix index da6ce7d..2e59869 100644 --- a/flake.nix +++ b/flake.nix @@ -55,10 +55,11 @@ perl tex plantuml - inkscape + inkscape llvmPackages_21.llvm llvmPackages_21.llvm.dev - nodejs_20 + nodejs_20 + graphviz ]; nativeBuildInputs = [ diff --git a/report/biblio.bib b/report/biblio.bib index edb22c7..91f4984 100644 --- a/report/biblio.bib +++ b/report/biblio.bib @@ -1,70 +1,85 @@ - -@misc{DBReport, -title = {Исследование рынка СУБД}, -url = {https://www.mordorintelligence.com/industry-reports/database-market}, -language = {english}, -note = {(дата обращения: 17.01.2026)} +@inproceedings{Selinger1979, + author = {Selinger, P. Griffiths and Astrahan, M. M. and Chamberlin, D. D. and Lorie, R. A. and Price, T. G.}, + title = {Access path selection in a relational database management system}, + booktitle = {Proceedings of the 1979 ACM SIGMOD International Conference on Management of Data}, + series = {SIGMOD '79}, + pages = {23--34}, + year = {1979}, + doi = {10.1145/582095.582099}, + language = {english}, + note = {(дата обращения: 01.05.2026)} } -@misc{PostgresDocs, -title = {Документация PostgreSQL}, -url = {https://www.postgresql.org/docs/current/sql-select.html}, -language = {english}, -note = {(дата обращения: 17.01.2026)} +@article{Graefe1995, + author = {Graefe, Goetz}, + title = {The Cascades Framework for Query Optimization}, + journal = {IEEE Data Engineering Bulletin}, + volume = {18}, + number = {3}, + pages = {19--29}, + year = {1995}, + language = {english}, + note = {(дата обращения: 01.05.2026)} } -@misc{PostgresBisonGrammar, -title = {Грамматика PostgreSQL}, -url = {https://github.com/postgres/postgres/blob/master/src/backend/parser/gram.y}, -language = {english}, -note = {(дата обращения: 17.01.2026)} +@inproceedings{GraefeMcKenna1993, + author = {Graefe, Goetz and McKenna, William J.}, + title = {The {Volcano} Optimizer Generator: Extensibility and Efficient Search}, + booktitle = {Proceedings of IEEE 9th International Conference on Data Engineering}, + pages = {209--218}, + year = {1993}, + doi = {10.1109/ICDE.1993.344061}, + language = {english}, + note = {(дата обращения: 01.05.2026)} } -@misc{Antlr4PostgreSQL, -title = {Грамматика PostgreSQL для ANTLR4}, -url = {https://github.com/antlr/grammars-v4/blob/master/sql/postgresql/PostgreSQLParser.g4}, -language = {english}, -note = {(дата обращения: 17.01.2026)} +@article{DingNarasayya2024, + author = {Ding, Bailu and Narasayya, Vivek and Chaudhuri, Surajit}, + title = {Extensible Query Optimizers in Practice}, + journal = {Foundations and Trends in Databases}, + volume = {14}, + number = {3-4}, + pages = {186--402}, + year = {2024}, + doi = {10.1561/1900000077}, + language = {english}, + note = {(дата обращения: 01.05.2026)} } -@book{silberschatz2020database, - title={Database system concepts}, - author={Silberschatz, Abraham and Korth, Henry F and Sudarshan, Shashank and others}, - year={2020}, - publisher={Mcgraw-hill New York}, - language = {english}, - note = {(дата обращения: 17.01.2026)} +@inproceedings{Begoli2018Calcite, + author = {Begoli, Edmon and Camacho-Rodr{\'i}guez, Jes{\'u}s and Hyde, Julian + and Mior, Michael J. and Lemire, Daniel}, + title = {Apache {Calcite}: A foundational framework for optimized query + processing over heterogeneous data sources}, + booktitle = {Proceedings of the 2018 International Conference on Management of Data}, + series = {SIGMOD '18}, + pages = {221--230}, + year = {2018}, + doi = {10.1145/3183713.3190662} } -@inproceedings{pantilimonov2019machine, - title={Machine code caching in postgresql query jit-compiler}, - author={Pantilimonov, Michael and Buchatskiy, Ruben and Zhuykov, Roman and Sharygin, Eugene and Melnik, Dmitry}, - booktitle={2019 Ivannikov Memorial Workshop (IVMEM)}, - pages={18--25}, - year={2019}, - organization={IEEE}, - language = {english}, - note = {(дата обращения: 17.01.2026)} +@inproceedings{ArmbrustCatalyst2015, + author = {Armbrust, Michael and Xin, Reynold S. and Lian, Cheng and Huai, Yin + and Liu, Davies and Bradley, Joseph K. and Meng, Xiangrui and Kaftan, Tomer + and Franklin, Michael J. and Ghodsi, Ali and Zaharia, Matei}, + title = {Spark {SQL}: Relational data processing in {Spark}}, + booktitle = {Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data}, + pages = {1383--1394}, + year = {2015}, + doi = {10.1145/2723372.2742797} } -@book{petrov2019database, - title={Database Internals: A deep dive into how distributed data systems work}, - author={Petrov, Alex}, - year={2019}, - publisher={O'Reilly Media}, - language = {english}, - note = {(дата обращения: 17.01.2026)} +@phdthesis{XuColumbia1998, + author = {Xu, Yongwen}, + title = {Efficiency in the {Columbia} Database Query Optimizer}, + school = {Portland State University}, + year = {1998} } -@article{graefe2002volcano, - title={Volcano - an extensible and parallel query evaluation system}, - author={Graefe, Goetz}, - journal={IEEE Transactions on Knowledge and Data Engineering}, - volume={6}, - number={1}, - pages={120--135}, - year={2002}, - publisher={IEEE}, - language = {english}, - note = {(дата обращения: 17.01.2026)} +@misc{IntroToTheJoinOrderingProblem, +title = {Introduction to the Join Ordering Problem}, +url = {https://www.querifylabs.com/blog/introduction-to-the-join-ordering-problem}, +language = {english}, +note = {(дата обращения: 01.05.2026)} } + diff --git a/report/cascades.dot b/report/cascades.dot new file mode 100644 index 0000000..307d9f9 --- /dev/null +++ b/report/cascades.dot @@ -0,0 +1,27 @@ +digraph optimizer { + node [shape=box] + + Entry [shape=none, label=""] + OptGrp [label="Оптимизация группы\n(OptGrp)"] + OptExpr [label="Оптимизация выражения\n(OptExpr)"] + OptInputs [label="Оптимизация входных групп\n(OptInputs)"] + ApplyRule [label="Применение правила\n(ApplyRule)"] + ExplGrp [label="Исследование группы\n(ExplGrp)"] + ExplExpr [label="Исследование выражения\n(ExplExpr)"] + + Entry -> OptGrp [label="Выражение"] + + OptGrp -> OptExpr + OptExpr -> OptExpr + OptExpr -> OptGrp + OptExpr -> OptInputs + OptExpr -> ApplyRule + OptExpr -> ExplGrp + OptInputs -> OptInputs + OptInputs -> OptGrp + ApplyRule -> OptInputs + ExplGrp -> ApplyRule + ExplGrp -> ExplExpr + ExplExpr -> ExplGrp + ExplExpr -> OptGrp +} diff --git a/report/cascades.pdf b/report/cascades.pdf new file mode 100644 index 0000000000000000000000000000000000000000..14fa81da8ef0784067d65faf64fdcd9454b1e4fe GIT binary patch literal 16363 zcmd73bzIcl7VxXmA>AQ^(lHD}3`0tXgrrD!cQ?`@AtBw}-6;(c($Zbh(#-`u&vQKY zoO93pynnq6pPAoYyVu$=v%Y&%z7-N-WClWzDXaHNu8~;*Ab_Qg2{Jb~fJsu{!qCnL z!1f@LLk0i3@`3yPmWGW1d|q}aW=^eHn6g?;_KkL zGxK<&1rzvM5eJ=Xch1a-@oy 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(p2l) at (6.5, 1.2) {\texttt{lineitem}}; + \node[rel] (p2c) at (1.0, -0.3) {\texttt{customer}}; + \node[rel] (p2o) at (4.4, -0.3) {\texttt{orders}}; + + \draw[->] (p2j1) -- (p2j2); + \draw[->] (p2l) -- (p2j2); + \draw[->] (p2c) -- (p2j1); + \draw[->] (p2o) -- (p2j1); + \node[font=\small\bfseries] at (4.4, 4.0) {План \(P_2\)}; +\end{tikzpicture} +\caption{Сравнение планов для запроса из листинга~\ref{lst:join_query}.} +\label{fig::join_plan_comparison} +\end{figure} \begin{itemize} \item План \(P_1\): @@ -411,21 +454,97 @@ \section{Цель оптимизатора} чтения, что на восемь порядков превышает \(P_1\). \end{itemize} -{\color{red} TODO: пример с логическими трансформациями, аналогично визуализация - планов (tikz). Адаптировать абзац ниже.} +На еще одном примере запроса из листинга~\ref{lst:logical_transform_query} +рассмотрим влияние логических преобразований. Отношение \texttt{Emp} содержит +\(10{,}000\) кортежей, что эквивалентно \(1{,}000\) страницам. \texttt{Dept} +включает \(500\) записей и \(50\) страниц соответственно. Атрибут +\texttt{Emp.did} является внешним ключом к \texttt{Dept.did}, поэтому каждому +кортежу из \texttt{Dept} соответствует \(10{,}000 / 500 = 20\) из \texttt{Emp}. +Пусть на \texttt{Emp.did} и \texttt{Dept.dname} построены индексы, а в +промежуточную страницу помещается \(5\) кортежей. + +\begin{listing} + \caption{Пример запроса с проекцией, фильтрацией и соединением.} + \label{lst:logical_transform_query} + \begin{minted}[style=bw, breaklines, frame=single, fontsize = \footnotesize, linenos=false, xleftmargin = 1.5em]{SQL} + SELECT DISTINCT ename + FROM Emp E JOIN Dept D ON E.did = D.did + WHERE D.dname = 'Toy' + \end{minted} +\end{listing} + +\begin{figure}[!htb]\centering +\begin{tikzpicture}[ + rel/.style={rectangle, draw, rounded corners=2pt, minimum width=2.0cm, + minimum height=0.8cm, align=center, font=\small}, + op/.style={rectangle, draw, fill=gray!12, minimum width=4.5cm, + minimum height=1.2cm, align=center, font=\small} +] + \node[font=\small\bfseries] at (-3.5, 8.8) {План \(P_1\)}; + \node[op] (p1proj) at (-3.5, 7.7) {\(\pi_{\texttt{ename}}\)}; + \node[op] (p1sig2) at (-3.5, 6.2) {\(\sigma_{\texttt{dname}=\texttt{'Toy'}}\)}; + \node[op] (p1sig1) at (-3.5, 4.7) {\(\sigma_{\texttt{Emp.did}=\texttt{Dept.did}}\)}; + \node[op] (p1cp) at (-3.5, 3.2) {\(\texttt{Emp} \times \texttt{Dept}\)\\\scriptsize \(50{,}050\) чтений}; + \node[rel] (p1emp) at (-5.2, 1.7) {\texttt{Emp}}; + \node[rel] (p1dept) at (-1.8, 1.7) {\texttt{Dept}}; + + \draw[->] (p1emp) -- (p1cp); + \draw[->] (p1dept) -- (p1cp); + \draw[->] (p1cp) -- (p1sig1); + \draw[->] (p1sig1) -- (p1sig2); + \draw[->] (p1sig2) -- (p1proj); + + \node[font=\small\bfseries] at (3.5, 8.8) {План \(P_2\)}; + \node[op] (p2proj) at (3.5, 7.7) {\(\pi_{\texttt{ename}}\)}; + \node[op] (p2join) at (3.5, 5.5) {Index NL Join\\\(\texttt{Emp.did}=\texttt{Dept.did}\)\\\scriptsize \(23\) чтения}; + \node[rel] (p2emp) at (1.6, 3.5) {\texttt{Emp}}; + \node[op] (p2sig) at (5.4, 3.5) {\(\sigma_{\texttt{dname}=\texttt{'Toy'}}\)\\\scriptsize \(3\) чтения}; + \node[rel] (p2dept) at (5.4, 1.8) {\texttt{Dept}}; + + \draw[->] (p2emp) -- (p2join); + \draw[->] (p2sig) -- (p2join); + \draw[->] (p2join) -- (p2proj); + \draw[->] (p2dept) -- (p2sig); +\end{tikzpicture} +\caption{Сравнение планов для запроса из + листинга~\ref{lst:logical_transform_query}.} +\label{fig::logical_transform_plans} +\end{figure} + +Рассмотрим два плана~\refImage{fig::logical_transform_plans}: -Различие на восемь порядков в первом случае показывает, что разница между -удачным и неудачным планами может составлять сотни миллионов раз. Если -исполнение \(P_1\) на современной системе занимает порядка секунд, то наивный -\(P_2\) будет завершен через десятки лет. Более того, если в плане \(P_1\) -дополнительно использовать индекс по внешнему ключу при соединении с -\texttt{lineitem}, стоимость может снизиться ещё в несколько раз. +\begin{itemize} + \item План \(P_1\) соответствует прямому преобразованию запроса в реляционную + алгебру. Если рассматривать стоимость исполнения такого плана в модели + итераторов, то каждый оператор запрашивает у дочернего следующий кортеж + через вызов \texttt{next()}, поэтому \(\sigma\) и~\(\pi\) работают в + режиме конвейера и не инициируют обращений к диску. Вся стоимость + ввода-вывода сосредоточена в операторе~\(\times\), реализованном через + вложенные циклы с \texttt{Dept} в роли внешней таблицы: \texttt{Dept} + читается один раз, а \texttt{Emp} перечитывается полностью для каждой + страницы~\texttt{Dept}: + \[ + 50 + 50 \times 1{,}000 = 50{,}050 \text{ чтений.} + \] + + \item План \(P_2\) является оптимизированным вариантом, где применено правило + проталкивания предиката и декартово произведение заменено на соединение + по предикату. Причем для доступа к таблице \texttt{Dept} применяется + сканирование по индексу, что дает всего \(3\) чтения с диска. Соединение + реализуется через вложенные циклы с использованием индекса, что в итоге дает: + \[ + 3 + (3 + 20) = 26 \text{ чтений.} + \] +\end{itemize} -Этот пример наглядно демонстрирует, что оптимизатор отвечает не за доли -процентов производительности, а за принципиальную возможность исполнения сложных -аналитических запросов за приемлемое время. Именно поэтому разработка -качественного оптимизатора занимает центральное место в проектировании любой -реляционной СУБД. +Оба приведенных примера наглядно демонстрируют, что оптимизатор отвечает не за +доли процентов производительности, а за принципиальную возможность исполнения +сложных аналитических запросов за приемлемое время. В первом случае разница +между физическими планами составляет восемь порядков: если \(P_1\) завершается +за секунды, то наивный \(P_2\) потребует десятков лет. Во втором случае даже +единственное логическое преобразование, в виде протаскивания предиката, снижает +число чтений с диска в \(1{,}900\) раз. Именно поэтому качественный оптимизатор +является конкурентным преимуществом и одной из самых важных частей СУБД. \section{Обзор архитектур оптимизаторов} @@ -433,11 +552,9 @@ \section{Обзор архитектур оптимизаторов} \subsection{System R} -{\color{red} TODO: ссылка на статью по System R. Есть в лекциях Andy} - -Оптимизатор System~R, разработанный в IBM Research в конце 1970-х годов, стал -первым стоимостным оптимизатором запросов и заложил фундаментальные принципы, -используемые во всех последующих системах. +Оптимизатор System~R, разработанный в IBM Research в конце 1970-х +годов~\cite{Selinger1979}, стал первым стоимостным оптимизатором запросов и +заложил фундаментальные принципы, используемые во всех последующих системах. Архитектуру, аналогичную System~R, реализуют PostgreSQL и ранние версии IBM DB2. @@ -483,13 +600,10 @@ \subsection{System R} \subsection{Volcano} Volcano --- расширяемый, основанный на правилах, стоимостной оптимизатор -запросов, разработанный Грефе в 1993 году. Volcano ввел ряд концепций, ставших -основой для последующих расширяемых оптимизаторов: физические свойства -выражений, обеспечивающие правила, структуру данных Memo и нисходящий алгоритм -поиска на основе динамического программирования. - -{color{red} TODO: ссылка на статью (взять из лекций Andy). Добавить системы - использующие Volcano} +запросов, разработанный Грефе в 1993 году~\cite{GraefeMcKenna1993}. Volcano ввел +ряд концепций, ставших основой для последующих расширяемых оптимизаторов: +физические свойства выражений, обеспечивающие правила, структуру данных Memo и +нисходящий алгоритм поиска на основе динамического программирования. Volcano разделяет поиск на две фазы: @@ -519,7 +633,7 @@ \subsection{Volcano} \section{Архитектура Cascades} Cascades --- это расширяемая архитектура оптимизатора запросов, являющаяся -логическим продолжением идеи Volcano. +логическим продолжением идеи Volcano~\cite{Graefe1995}. Основными объектами Cascades являются: @@ -591,38 +705,102 @@ \subsection{Структура Memo} предоставляемые физические свойства, например сортировка выходного потока, и рассчитанная стоимость исполнения этого оператора. -На рисунке~\ref{fig:memo-structure} показан пример Memo для запроса с тремя -отношениями. Группа \(G_5\) представляет результат соединения трех отношений, а -внутри нее находятся два логически эквивалентных выражения с разной -ассоциацией соединений. Дочерние выражения ссылаются на группы, поэтому общие -подпланы не дублируются. +\begin{listing} + \caption{Пример запроса.} + \label{lst:query} + \begin{minted}[style=bw, breaklines, frame=single, fontsize = \footnotesize, linenos=false, xleftmargin = 1.5em]{SQL} + SELECT * FROM A JOIN B ON p_1 JOIN C ON p_2; + \end{minted} +\end{listing} + +На рисунке~\ref{fig:memo-structure} показан пример Memo для запроса из +листинга~\ref{lst:query}. Например, группа \(G_5\) представляет результат +соединения трех отношений, в ней находятся два логически эквивалентных выражения +с разной ассоциацией соединений и четыре физические реализации для них. Входными +данными для операторов являются группы группы, поэтому общие подвыражения не +дублируются. -{\color{red} TODO: поправить схему} -{\color{red} TODO: привести сам запрос} \begin{figure}[H] \centering \begin{tikzpicture}[ - group/.style={draw, rounded corners, align=left, minimum width=4.0cm, minimum height=1.5cm}, - expr/.style={draw, align=center, fill=white, minimum width=3.3cm, minimum height=0.6cm}, - arrow/.style={-{Stealth[length=2mm]}, thick} + scale=0.85, transform shape, + group/.style={draw, rounded corners, text width=4.8cm, inner sep=5pt}, + arrow/.style={-{Stealth[length=2.5mm]}, thick}, + altarrow/.style={-{Stealth[length=2.5mm]}, thick, dashed} ] - \node[group] (g1) at (0,0) {\textbf{Группа G1}\\Scan(A)}; - \node[group] (g2) at (5,0) {\textbf{Группа G2}\\Scan(B)}; - \node[group] (g3) at (10,0) {\textbf{Группа G3}\\Scan(C)}; - \node[group] (g4) at (2.5,-3) {\textbf{Группа G4}\\Join(G1,G2)\\HashJoin(G1,G2)}; - \node[group] (g6) at (7.5,-3) {\textbf{Группа G6}\\Join(G2,G3)\\HashJoin(G2,G3)}; - \node[group] (g5) at (5,-6) {\textbf{Группа G5}\\Join(G4,G3)\\Join(G1,G6)}; + \node[group] (g5) at (6,0) { + \textbf{Группа G5}\\[2pt] + \hrule\vspace{2pt} + \textit{Логические:}\\ + Join(G4,G3)\\ + Join(G1,G6)\\[2pt] + \hrule\vspace{2pt} + \textit{Физические:}\\ + HashJoin(G4,G3)\\ + NestedLoopJoin(G4,G3)\\ + HashJoin(G1,G6)\\ + NestedLoopJoin(G1,G6) + }; + \node[group] (g4) at (3,-6) { + \textbf{Группа G4}\\[2pt] + \hrule\vspace{2pt} + \textit{Логические:}\\ + Join(G1,G2)\\[2pt] + \hrule\vspace{2pt} + \textit{Физические:}\\ + HashJoin(G1,G2)\\ + NestedLoopJoin(G1,G2) + }; + \node[group] (g6) at (9,-6) { + \textbf{Группа G6}\\[2pt] + \hrule\vspace{2pt} + \textit{Логические:}\\ + Join(G2,G3)\\[2pt] + \hrule\vspace{2pt} + \textit{Физические:}\\ + HashJoin(G2,G3)\\ + NestedLoopJoin(G2,G3) + }; + \node[group] (g1) at (0,-12) { + \textbf{Группа G1}\\[2pt] + \hrule\vspace{2pt} + \textit{Логические:}\\ + Scan(A)\\[2pt] + \hrule\vspace{2pt} + \textit{Физические:}\\ + SeqScan(A) + }; + \node[group] (g2) at (6,-12) { + \textbf{Группа G2}\\[2pt] + \hrule\vspace{2pt} + \textit{Логические:}\\ + Scan(B)\\[2pt] + \hrule\vspace{2pt} + \textit{Физические:}\\ + SeqScan(B) + }; + \node[group] (g3) at (12,-12) { + \textbf{Группа G3}\\[2pt] + \hrule\vspace{2pt} + \textit{Логические:}\\ + Scan(C)\\[2pt] + \hrule\vspace{2pt} + \textit{Физические:}\\ + SeqScan(C) + }; + \draw[arrow] (g5) -- (g4); + \draw[arrow, rounded corners=5pt] + (g5.east) -- (15.5,0) -- (15.5,-12) -- (g3.east); + \draw[altarrow] (g5) -- (g6); + \draw[altarrow, rounded corners=5pt] + (g5.west) -- (-3.5,0) -- (-3.5,-12) -- (g1.west); \draw[arrow] (g4) -- (g1); \draw[arrow] (g4) -- (g2); \draw[arrow] (g6) -- (g2); \draw[arrow] (g6) -- (g3); - \draw[arrow] (g5) -- (g4); - \draw[arrow] (g5) -- (g6); - \draw[arrow] (g5) -- (g3); - \draw[arrow] (g5) -- (g1); \end{tikzpicture} - \caption{Пример структуры Memo с группами эквивалентных выражений} + \caption{Пример структуры Memo.} \label{fig:memo-structure} \end{figure} @@ -646,40 +824,11 @@ \subsection{Алгоритм поиска} На рисунке~\ref{fig:cascades-tasks} показана схема взаимодействия задач в оптимизаторе. -{\color{red} TODO: поправить рисунок} - \begin{figure}[H] - \centering - \begin{tikzpicture}[ - node distance=0.9cm and 1.1cm, - block/.style={draw, rounded corners, align=center, minimum width=3.4cm, minimum height=0.9cm}, - small/.style={draw, rounded corners, align=center, minimum width=2.9cm, minimum height=0.8cm}, - arrow/.style={-{Stealth[length=2mm]}, thick} - ] - \node[block] (root) {запрос\\к оптимизации}; - \node[block, right=of root] (memo) {вставка\\в Memo}; - \node[block, right=of memo] (optgroup) {задача\\оптимизации группы}; - \node[small, below left=of optgroup] (explore) {исследовать\\группу}; - \node[small, below=of optgroup] (implement) {реализовать\\выражение}; - \node[small, below right=of optgroup] (enforce) {обеспечить\\свойство}; - \node[small, below=of explore] (rules) {применить\\правила}; - \node[small, below=of implement] (cost) {оценить\\стоимость}; - \node[small, below=of enforce] (winner) {обновить\\победителя}; - - \draw[arrow] (root) -- (memo); - \draw[arrow] (memo) -- (optgroup); - \draw[arrow] (optgroup) -- (explore); - \draw[arrow] (optgroup) -- (implement); - \draw[arrow] (optgroup) -- (enforce); - \draw[arrow] (explore) -- (rules); - \draw[arrow] (rules) -| (memo); - \draw[arrow] (implement) -- (cost); - \draw[arrow] (cost) -- (winner); - \draw[arrow] (enforce) -- (winner); - \draw[arrow] (winner) -| (optgroup); - \end{tikzpicture} - \caption{Взаимодействие задач в оптимизаторе Cascades} - \label{fig:cascades-tasks} +\centering +\includegraphics[width=0.8\textwidth]{cascades.pdf} +\caption{Схема взаимодействия задач в оптимизаторе Cascades} +\label{fig:cascades-tasks} \end{figure} На листинге~\ref{alg:cascades-search} приведен псевдокод алгоритма поиска в @@ -915,6 +1064,8 @@ \subsection{Оценка кардинальности} ошибки кардинальности часто являются одной из основных причин выбора неудачных планов~\cite{leis2015}. +{\color{red} TODO: поправить ссылку} + Для повышения качества оценок используются гистограммы, статистики по многоколоночным ключам, сведения об уникальности, функциональные зависимости, ограничения на схему, а в современных исследованиях также модели машинного From 030283d8266d7bb3c44eb39eb6a40ca8026bb3d0 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Wed, 6 May 2026 03:58:04 +0300 Subject: [PATCH 017/120] Wip --- report/biblio.bib | 12 ++ report/thesis.tex | 178 +++++++++++------- .../sql/logic/optimizer/optimizer_test.cpp | 1 + 3 files changed, 126 insertions(+), 65 deletions(-) diff --git a/report/biblio.bib b/report/biblio.bib index 91f4984..901497b 100644 --- a/report/biblio.bib +++ b/report/biblio.bib @@ -83,3 +83,15 @@ @misc{IntroToTheJoinOrderingProblem note = {(дата обращения: 01.05.2026)} } +@article{leis2015good, + title={How good are query optimizers, really?}, + author={Leis, Viktor and Gubichev, Andrey and Mirchev, Atanas and Boncz, Peter and Kemper, Alfons and Neumann, Thomas}, + journal={Proceedings of the VLDB Endowment}, + volume={9}, + number={3}, + pages={204--215}, + year={2015}, + publisher={VLDB Endowment}, + language = {english}, + note = {(дата обращения: 01.05.2026)} +} diff --git a/report/thesis.tex b/report/thesis.tex index 1fbb10f..982891f 100644 --- a/report/thesis.tex +++ b/report/thesis.tex @@ -831,63 +831,10 @@ \subsection{Алгоритм поиска} \label{fig:cascades-tasks} \end{figure} -На листинге~\ref{alg:cascades-search} приведен псевдокод алгоритма поиска в +На листингах~\ref{alg:cascades-search}-\ref{alg:cascades-search-3} приведен псевдокод алгоритма поиска в архитектуре Cascades. -{color{red} TODO: поправить листинг, убрать в приложение} - -\begin{algorithm}[H] - \caption{Top-down Cascades search} - \label{alg:cascades-search} - \begin{algorithmic}[1] - \Function{Optimize}{$rootExpr, requiredProps$} - \State $rootGroup \gets Memo.Insert(rootExpr)$ - \State $upperBound \gets +\infty$ - \State \Return \Call{OptimizeGroup}{$rootGroup, requiredProps, upperBound$} - \EndFunction - \Statex - \Function{OptimizeGroup}{$group, props, bound$} - \If{$group$ has winner for $props$ with cost $\le bound$} - \State \Return stored winner - \EndIf - \State \Call{ExploreGroup}{$group$} - \State $bestPlan \gets null$ - \State $bestCost \gets bound$ - \ForAll{$expr \in group.expressions$} - \State $candidate \gets$ \Call{OptimizeExpression}{$expr, props, bestCost$} - \If{$candidate.cost < bestCost$} - \State $bestPlan \gets candidate$ - \State $bestCost \gets candidate.cost$ - \EndIf - \EndFor - \State \Call{StoreWinner}{$group, props, bestPlan, bestCost$} - \State \Return $bestPlan$ - \EndFunction - \Statex - \Function{ExploreGroup}{$group$} - \ForAll{$expr \in group.logicalExpressions$} - \ForAll{$rule \in MatchingRules(expr)$} - \If{\Call{IsApplicable}{$rule, expr$}} - \State $newExpr \gets$ \Call{ApplyRule}{$rule, expr$} - \State \Call{Memo.InsertIntoEquivalentGroup}{$newExpr, group$} - \EndIf - \EndFor - \EndFor - \EndFunction - \Statex - \Function{OptimizeExpression}{$expr, props, bound$} - \If{$expr$ is logical} - \State generate physical expressions using implementation rules - \EndIf - \State optimize children with derived required properties - \State prune expression if lower bound exceeds $bound$ - \State add local operator cost and enforcer cost if needed - \State \Return physical candidate - \EndFunction - \end{algorithmic} -\end{algorithm} - -{color{red} TODO: добавить описание алгоритма} +{\color{red} TODO: добавить описание алгоритма} \section{Правила трансформации и реализации} @@ -971,7 +918,7 @@ \subsection{Логические правила трансформации} атрибуты заказов, а ограничение внешнего ключа гарантирует существование соответствующего клиента. -{color{red}: дописать, мб примеры?} +{\color{red} TODO: дописать, мб примеры?} \subsection{Правила реализации} @@ -1000,7 +947,7 @@ \subsection{Правила реализации} Если требуемый порядок нужен также родительскому оператору, потоковая агрегация может оказаться выгодной даже учитывая затраты на сортировку. -{color{red} TODO: псевдокод алгоритмов} +% {\color{red} TODO: псевдокод алгоритмов} \subsection{Обеспечивающие операторы} @@ -1018,7 +965,7 @@ \subsection{Обеспечивающие операторы} \section{Метод ветвей и границ} Метод ветвей и границ используется для сокращения пространства поиска и, как -следствие, неасимптотическим улучшением скорости работы алгоритма. В контексте +следствие, неасимптотического улучшения скорости работы алгоритма. В контексте Cascades ветвями являются альтернативные выражения и комбинации физических реализаций дочерних групп, а границей является текущая лучшая стоимость плана для заданной группы и требуемых физических свойств. Если нижняя оценка стоимости @@ -1062,9 +1009,7 @@ \subsection{Оценка кардинальности} быть грубой, если данные имеют корреляции, сильный перекос или сложные зависимости между атрибутами. Экспериментальные исследования показывают, что ошибки кардинальности часто являются одной из основных причин выбора неудачных -планов~\cite{leis2015}. - -{\color{red} TODO: поправить ссылку} +планов~\cite{leis2015good}. Для повышения качества оценок используются гистограммы, статистики по многоколоночным ключам, сведения об уникальности, функциональные зависимости, @@ -1094,7 +1039,7 @@ \subsection{Оценка стоимости} При этом локальная стоимость зависит от кардинальности входов и физических свойств, а также конкретного физического оператора. -{\color{red} TODO: табличка формул для разных операторов} +% {\color{red} TODO: табличка формул для разных операторов} \section{Дифференциальный анализ физических планов} @@ -1106,8 +1051,6 @@ \section{Дифференциальный анализ физических пл строгих условий активации правил или отсутствия определенных правил в их наборе. -{\color{red} TODO: почему нет формализации?} - Данная проблема особенно актуальна при разработке нового оптимизатора: после реализации базового набора правил возникает вопрос о полноте и корректности этого набора по сравнению с устоявшимися и проверенными временем промышленными @@ -1185,7 +1128,7 @@ \subsection{Область применения} улучшение качества оптимизатора и позволяет новым или открытым оптимизаторам быстрее достигать качества устоявшихся промышленных оптимизаторов. -{\color{red} TODO: описать как нормализовать и т.п.} +% {\color{red} TODO: описать как нормализовать и т.п.} \renewcommand\refname{СПИСОК ИСПОЛЬЗОВАННЫХ ИСТОЧНИКОВ} \clearpage @@ -1194,4 +1137,109 @@ \subsection{Область применения} \printbibliography } \newpage + +\settocdepth{section} +\anonsection{ПРИЛОЖЕНИЕ А} +\vspace{-30pt} + +\begin{algorithm}[H] + \caption{Алгоритм поиска (часть 1).} + \label{alg:cascades-search} + \begin{algorithmic}[1] + + \Procedure{Optimize}{$root$} + \State Push task $\Call{OptimizeGroup}{root.group, \bot}$ onto task stack + \While{task stack is not empty} + \State Pop and execute next task + \EndWhile + \State \Return $best\_plan[root.group]$ + \EndProcedure + + \Procedure{OptimizeGroup}{$group, limit$} + \If{$best\_cost[group]$ exists \textbf{and} ($limit = \bot$ \textbf{or} $best\_cost[group] < limit$)} + \State \Return + \EndIf + \If{$group$ is not explored} + \State Push task $\Call{OptimizeGroup}{group, limit}$ onto task stack + \State Push task $\Call{ExploreGroup}{group, limit}$ onto task stack + \State \Return + \EndIf + \For{each logical expression $expr \in group$} + \State Push task $\Call{OptimizeExpression}{expr, limit}$ onto task stack + \EndFor + \EndProcedure + + \Procedure{ExploreGroup}{$group, limit$} + \State Mark $group$ as explored + \For{each logical expression $expr \in group$} + \State Push task $\Call{ExploreExpression}{expr, limit}$ onto task stack + \EndFor + \EndProcedure + \end{algorithmic} +\end{algorithm} + +\begin{algorithm}[H] + \caption{Алгоритм поиска (часть 2).} + \label{alg:cascades-search-2} + \begin{algorithmic}[1] + \Procedure{ExploreExpression}{$expr, limit$} + \For{each applicable transformation rule $r$} + \State Push task $\Call{ApplyTransformation}{r, expr, limit}$ onto task stack + \EndFor + \For{each child group $child \in \Call{Children}{expr}$} + \If{$child$ is not explored} + \State Push task $\Call{ExploreGroup}{child, limit}$ onto task stack + \EndIf + \EndFor + \EndProcedure + + \Procedure{OptimizeExpression}{$expr, limit$} + \For{each applicable implementation rule $r$} + \State Push task $\Call{ApplyImplementation}{r, expr, limit}$ onto task stack + \EndFor + \For{each child group $child \in \Call{Children}{expr}$} + \If{$child$ is not explored} + \State Push task $\Call{ExploreGroup}{child, limit}$ onto task stack + \EndIf + \EndFor + \EndProcedure + + \Procedure{ApplyTransformation}{$rule, expr, limit$} + \State $new\_expr \leftarrow \Call{Apply}{rule, expr}$ + \State Push task $\Call{ExploreExpression}{new\_expr, limit}$ onto task stack + \EndProcedure + + \Procedure{ApplyImplementation}{$rule, expr, limit$} + \State $phys \leftarrow \Call{Apply}{rule, expr}$ + \State $lc \leftarrow \Call{CalcLocalCost}{phys}$ + \If{$limit \neq \bot$ \textbf{and} $lc \geq limit$} + \State \Return + \EndIf + \State $child\_limit \leftarrow (limit = \bot) \mathbin{?} \bot \mathbin{:} limit - lc$ + \State Push task $\Call{OptimizeInputs}{phys, child\_limit, 0}$ onto task stack + \EndProcedure + \end{algorithmic} +\end{algorithm} + +\begin{algorithm}[H] + \caption{Алгоритм поиска (часть 3).} + \label{alg:cascades-search-3} + \begin{algorithmic}[1] + \Procedure{OptimizeInputs}{$expr, limit, i$} + \State $children \leftarrow \Call{Children}{expr}$ + \If{$i \geq |children|$} + \State $total \leftarrow local\_cost[expr] + accum\_child\_cost[expr]$ + \If{$best\_cost[expr.group]$ does not exist \textbf{or} $total < best\_cost[expr.group]$} + \State $best\_cost[expr.group] \leftarrow total$ + \State $best\_plan[expr.group] \leftarrow expr$ + \EndIf + \State \Return + \EndIf + \State $child \leftarrow children[i]$ + \State Push task: \textbf{if} $best\_cost[child]$ exists \textbf{then} accumulate child cost \textbf{and} push $\Call{OptimizeInputs}{expr, \ldots, i+1}$ + \State Push task $\Call{OptimizeGroup}{child, limit}$ onto task stack + \EndProcedure + \end{algorithmic} +\end{algorithm} + \end{document} diff --git a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp index 8a972eb..1119055 100644 --- a/src/stewkk/sql/logic/optimizer/optimizer_test.cpp +++ b/src/stewkk/sql/logic/optimizer/optimizer_test.cpp @@ -156,6 +156,7 @@ class Optimizer { if (!best_cost_.contains(child.get())) return; auto cc = best_cost_.at(child.get()); accum_child_cost_[expr.get()] += cc; + // FIXME add -LB(children[child_index+1..k]) Limit next = limit ? std::optional{*limit - local_cost_[expr.get()] - accum_child_cost_[expr.get()]} : std::nullopt; if (next && *next < 0) return; OptimizeInputs(expr, next, child_index + 1); From 8833c041cdc20619f524f649449d261783c56592 Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Wed, 6 May 2026 04:22:31 +0300 Subject: [PATCH 018/120] Wip --- report/thesis.tex | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/report/thesis.tex b/report/thesis.tex index 982891f..b3315ea 100644 --- a/report/thesis.tex +++ b/report/thesis.tex @@ -834,7 +834,6 @@ \subsection{Алгоритм поиска} На листингах~\ref{alg:cascades-search}-\ref{alg:cascades-search-3} приведен псевдокод алгоритма поиска в архитектуре Cascades. -{\color{red} TODO: добавить описание алгоритма} \section{Правила трансформации и реализации} @@ -985,6 +984,23 @@ \section{Метод ветвей и границ} стоимости оптимизации дочерней группы под требуемым свойством \(P_i\). Чем точнее нижняя граница, тем эффективнее отсечение. +На практике в качестве \(LB(G_i, P_i)\) принимается лучшая стоимость, найденная +для пары \((G_i, P_i)\) на текущий момент работы алгоритма. Поскольку +оптимизация группы является рекурсивным процессом, к моменту отсечения для +\((G_i, P_i)\) уже могут быть найдены некоторые планы, и их минимальная +стоимость служит нижней границей. Если же группа \(G_i\) под свойством \(P_i\) +ещё не рассматривалась, используется тривиальная граница \(LB = 0\), или, при +условии наличия минимальной стоимости на кортеж, можно принимать +\(LB(G_i, P_i) = |G_i| \cdot min_cost_per_row\). + +Требуемые свойства дочерних групп \(P_i\) определяются оператором выражения \(e\) +совместно с требованием родителя \(P\). Каждый физический оператор описывает, какие +свойства он ожидает от своих входов: например, оператор сортирующего слияния требует +упорядоченности обоих входов по соответствующим ключам, тогда как хеш-соединение не +предъявляет требований к порядку. Таким образом, при спуске по дереву поиска каждый +оператор транслирует глобальное требование \(P\) в конкретные требования \(P_1, +\ldots, P_k\) к своим дочерним группам. + \subsection{Оценка кардинальности} Оценка кардинальности является процессом предсказания числа кортежей, From 068388899614b4853ca540a57a3e17760cf2671b Mon Sep 17 00:00:00 2001 From: Alexandr Starovoytov Date: Wed, 6 May 2026 04:41:16 +0300 Subject: [PATCH 019/120] Done --- report/biblio.bib | 11 ----------- report/thesis.pdf | Bin 573217 -> 702601 bytes report/thesis.tex | 26 +++++++++++++++++++------- 3 files changed, 19 insertions(+), 18 deletions(-) diff --git a/report/biblio.bib b/report/biblio.bib index 901497b..9203646 100644 --- a/report/biblio.bib +++ b/report/biblio.bib @@ -58,17 +58,6 @@ @inproceedings{Begoli2018Calcite doi = {10.1145/3183713.3190662} } -@inproceedings{ArmbrustCatalyst2015, - author = {Armbrust, Michael and Xin, Reynold S. and Lian, Cheng and Huai, Yin - and Liu, Davies and Bradley, Joseph K. and Meng, Xiangrui and Kaftan, Tomer - and Franklin, Michael J. and Ghodsi, Ali and Zaharia, Matei}, - title = {Spark {SQL}: Relational data processing in {Spark}}, - booktitle = {Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data}, - pages = {1383--1394}, - year = {2015}, - doi = {10.1145/2723372.2742797} -} - @phdthesis{XuColumbia1998, author = {Xu, Yongwen}, title = {Efficiency in the {Columbia} Database Query Optimizer}, diff --git a/report/thesis.pdf b/report/thesis.pdf index c5566253fd684f5173cb6d41d3f1ea5bbc35f367..fe3913a016f2138fd34ee6ff852a04458b96ab1a 100644 GIT binary patch delta 351443 zcmZs>V{x3{iCe6QJ+rlQOf|+$7pbH!MTDqa_h~xp6vEC%ks$D2z8%kX zX5QxGrd&lWyf2!{m7kY4w~JSU$EO7SzF(iHTM`&suc16=7LI$QpOWjtte=EOe!kq_ zo(rY2W#N}xJ122G03mx8hTV}0U4*VS&6c#`nW-nuwDF63L~`T2)RlusDvgxR%hF{m#4Ud} zVB0%R0Y>XCUYjG|jopg(Pq6PRxLL4WL3cZ1L<1Y!^ z6#qX;=QEQNi$#C0gE%(0&wt7SRfcy&I7H`0;0d{8T=`XBvodO-RD+d79MF^{`h6?o 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