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SQLGlot Java - SQL Parsing and Generation Library

CodeQL Advanced Tests

A comprehensive Java 17+ port of the sqlglot Python library, providing SQL parsing, generation, validation, and transpilation across multiple SQL dialects.

Special Focus: Apache Drill support for distributed SQL query execution across heterogeneous data sources.

Features

Core Capabilities

  • SQL Parsing - Convert SQL strings into Abstract Syntax Trees (AST)
  • Code Generation - Convert ASTs back to SQL with configurable formatting
  • Query Analysis - Walk, search, and transform AST nodes
  • Transpilation - Convert SQL between different dialects
  • Formatting - Pretty-print and normalize SQL
  • Optimization - Apply query optimization rules (Phase 5A Complete)

Supported Dialects

All 31 Dialects Implemented (100% Coverage):

Core Databases:

  • ANSI - Standard SQL
  • PostgreSQL - Full support
  • MySQL - Full support
  • MySQL/MariaDB - MySQL variant with sequences

Cloud & Analytics:

  • BigQuery - Google Cloud data warehouse
  • Snowflake - Cloud data warehouse
  • Redshift - AWS analytics database
  • Athena - AWS S3 query service
  • Databricks - Unified analytics (Delta Lake)
  • Trino - Distributed query engine (formerly Presto/Presto)
  • Firebolt - Cloud data warehouse (AWS)

Analytics Databases:

  • ClickHouse - Real-time analytics
  • DuckDB - In-process SQL OLAP database
  • Spark SQL - Big data SQL engine
  • Hive - Hadoop SQL engine
  • Impala - Hadoop SQL query engine
  • Teradata - Enterprise data warehouse
  • Vertica - Columnar analytics
  • Yellowbrick - Cloud OLAP (Postgres-based)
  • Exasol - In-memory analytics

Specialized:

  • DRILL - Apache Drill (Primary focus) - Distributed SQL query engine
  • SQLite - Embedded database
  • Oracle - Enterprise database
  • T-SQL/MSSQL - SQL Server
  • StarRocks - Real-time OLAP
  • CockroachDB - Distributed PostgreSQL-compatible SQL
  • Aurora - AWS MySQL variant
  • Iceberg - Open table format

Experimental/Integration:

  • Pandas - Python dataframe SQL
  • WASM - WebAssembly SQL runtime
  • AWS Glue - Serverless ETL platform

Total: 31 SQL Dialects (100% parity with Python sqlglot)

Quick Start

Installation

Add to your Maven pom.xml:

<dependency>
    <groupId>com.gtkcyber.sqlglot</groupId>
    <artifactId>sqlglot-core</artifactId>
    <version>1.0.1</version>
</dependency>
<dependency>
    <groupId>com.gtkcyber.sqlglot</groupId>
    <artifactId>sqlglot-dialects</artifactId>
    <version>1.0.1</version>
</dependency>

Basic Usage

import com.gtkcyber.sqlglot.*;

// Parse SQL
Optional<Expression> expr = SqlGlot.parseOne("SELECT a, b FROM t WHERE x = 1");

// Generate SQL
String sql = SqlGlot.generate(expr.get());

// Format SQL
String formatted = SqlGlot.format("SELECT a,b FROM t WHERE x=1");

// Transpile between dialects
String drillSQL = SqlGlot.transpile("SELECT * FROM table", "ANSI", "DRILL");

// Use Apache Drill dialect
Dialect drill = Dialect.of("DRILL");
Optional<Expression> parsed = drill.parseOne("SELECT * FROM `workspace`.`schema`.`table`");

Advanced SQL Examples

import com.gtkcyber.sqlglot.*;

// Aggregate functions
String sql = "SELECT department, COUNT(*), SUM(salary), AVG(salary) " +
             "FROM employees GROUP BY department " +
             "HAVING COUNT(*) > 10";
Optional<Expression> expr = SqlGlot.parseOne(sql);
String generated = SqlGlot.generate(expr.get());

// String functions
String sql = "SELECT UPPER(name), LENGTH(email), SUBSTR(phone, 1, 3) FROM users";
Optional<Expression> expr = SqlGlot.parseOne(sql);

// Numeric functions
String sql = "SELECT ABS(amount), ROUND(price, 2), POWER(base, exponent) FROM data";
Optional<Expression> expr = SqlGlot.parseOne(sql);

// Set operations
String sql = "SELECT id FROM table1 " +
             "UNION " +
             "SELECT id FROM table2 " +
             "EXCEPT " +
             "SELECT id FROM table3";
Optional<Expression> expr = SqlGlot.parseOne(sql);

// CTEs (Common Table Expressions)
String sql = "WITH cte AS (SELECT a, b FROM t WHERE x > 10) " +
             "SELECT * FROM cte WHERE a > 5";
Optional<Expression> expr = SqlGlot.parseOne(sql);

// Complex queries with DISTINCT, LIMIT, OFFSET
String sql = "SELECT DISTINCT category, COUNT(*) cnt FROM products " +
             "WHERE price > 100 GROUP BY category ORDER BY cnt DESC " +
             "LIMIT 10 OFFSET 5";
Optional<Expression> expr = SqlGlot.parseOne(sql);

Query Optimization (Phase 5A)

Overview

SQLGlot Java includes a comprehensive query optimization framework with multiple optimization rules applied sequentially to transform SQL queries into more efficient forms.

Usage Examples

import com.gtkcyber.sqlglot.*;
import com.gtkcyber.sqlglot.optimizer.OptimizerConfig;

// Basic optimization with default configuration (Phase 5A rules)
Optional<Expression> expr = SqlGlot.parseOne("SELECT * FROM t WHERE TRUE AND x = 5");
Expression optimized = SqlGlot.optimize(expr.get());
String result = SqlGlot.generate(optimized);
// Result: "SELECT * FROM t WHERE x = 5"

// Specify dialect and configuration
Expression optimized = SqlGlot.optimize(expr.get(), "ANSI", OptimizerConfig.PHASE_5A);

// Use preset configurations
Expression minimalOpt = SqlGlot.optimize(expr.get(), "ANSI", OptimizerConfig.MINIMAL);
Expression aggressiveOpt = SqlGlot.optimize(expr.get(), "ANSI", OptimizerConfig.AGGRESSIVE);

// Custom configuration - enable specific rules only
OptimizerConfig custom = new OptimizerConfig(
    true,   // simplify
    true,   // canonicalize
    false,  // quoteIdentifiers
    false,  // eliminateCtes
    true,   // normalizePredicates
    false,  // pushdownPredicates
    false,  // mergeSubqueries
    false,  // joinReordering
    false,  // projectionPushdown
    false,  // annotateTypes
    false   // qualifyColumns
);
Expression optimized = SqlGlot.optimize(expr.get(), "ANSI", custom);

Optimization Rules

Phase 5A (Core Rules):

  • SimplifyRule - Removes redundant conditions (TRUE AND xx), folds constants (1 + 23)
  • CanonicalizeRule - Normalizes expressions (5 < xx > 5, NOT(x = 5)x <> 5)
  • QuoteIdentifiersRule - Applies dialect-specific identifier quoting
  • EliminateCTEsRule - Removes unused Common Table Expressions

Phase 5B (Advanced Rules - Planned):

  • PushdownPredicatesRule, NormalizePredicatesRule, and more

Configuration Presets

OptimizerConfig.DEFAULT        // Phase 5A rules (default)
OptimizerConfig.MINIMAL        // Simplify only
OptimizerConfig.PHASE_5A       // All Phase 5A rules
OptimizerConfig.PHASE_5B       // Phase 5A + Phase 5B rules (future)
OptimizerConfig.AGGRESSIVE     // All available rules

Apache Drill Support

Overview

SQLGlot Java has first-class support for Apache Drill, enabling SQL parsing, validation, and transpilation for Drill's unique syntax and features.

Drill-Specific Features

1. Workspace and Schema Paths

Drill supports a three-level namespace: workspace.schema.table

String sql = "SELECT * FROM `datalake`.`raw`.`customers`";
Dialect drill = Dialect.of("DRILL");
Optional<Expression> expr = drill.parseOne(sql);

2. Backtick Identifiers

Drill uses backticks for identifiers (especially for reserved words and paths):

String sql = "SELECT `select`, `from` FROM `my-table-123`";
// Backticks are preserved and used consistently in generated SQL

3. Complex Data Types

Support for Drill's nested types:

// ARRAY type
String sql = "SELECT CAST(col AS ARRAY<INT>) FROM t";

// MAP type
String sql = "SELECT CAST(col AS MAP<STRING, INT>) FROM t";

// STRUCT type
String sql = "SELECT CAST(col AS STRUCT<name STRING, age INT, address STRUCT<street STRING, city STRING>>) FROM t";

4. FLATTEN Function

Unnest arrays and maps into individual rows:

// Basic flatten
String sql = "SELECT FLATTEN(nested_col) FROM t";

// Flatten with depth limit
String sql = "SELECT FLATTEN(deeply_nested_col, 2) FROM t";

5. File Sources

Query files directly:

String sql = "SELECT * FROM dfs.`/data/customers.parquet`";
String sql = "SELECT * FROM dfs.`s3://bucket/data/`";

6. Nested Field Access

Access nested JSON and structured data:

String sql = "SELECT person.name, person.address.street FROM customers";

Drill Example Workflows

Query Validation

Dialect drill = Dialect.of("DRILL");
String userQuery = "SELECT * FROM `problematic`.`query`";

try {
    Optional<Expression> validated = drill.parseOne(userQuery);
    if (validated.isPresent()) {
        System.out.println("Query is valid");
    }
} catch (Exception e) {
    System.out.println("Query validation failed: " + e.getMessage());
}

Query Normalization

Dialect drill = Dialect.of("DRILL");
String messySQL = "select a,b,c from t WHERE x=1";

String normalized = drill.format(messySQL);
// Output: SELECT a, b, c FROM t WHERE x = 1

Cross-Dialect Transpilation

// Convert from another SQL dialect to Drill
String postgresSQL = "SELECT column_name FROM table_name";
String drillSQL = SqlGlot.transpile(postgresSQL, "POSTGRES", "DRILL");
// Output: SELECT `column_name` FROM `table_name`

// Or from Drill to another dialect
String drillSQL = "SELECT `a`, `b` FROM `my_table`";
String ansiSQL = SqlGlot.transpile(drillSQL, "DRILL", "ANSI");
// Output: SELECT a, b FROM my_table

Query Analysis

Dialect drill = Dialect.of("DRILL");
Optional<Expression> expr = drill.parseOne("SELECT * FROM `t1` JOIN `t2` ON `t1`.id = `t2`.id WHERE x > 10");

if (expr.isPresent()) {
    // Find all table references
    expr.get().findAll(Nodes.Table.class).forEach(table -> {
        System.out.println("Table: " + table.getName());
    });

    // Find all functions
    expr.get().findAll(Nodes.Function.class).forEach(func -> {
        System.out.println("Function: " + func.getName());
    });

    // Transform the query
    Expression transformed = expr.get().transform(e -> {
        // Apply custom transformations
        return e;
    });
}

Architecture

Project Structure

sqlglot-java/
├── sqlglot-core/
│   ├── src/main/java/com/gtkcyber/sqlglot/
│   │   ├── tokens/          - Tokenization
│   │   ├── expressions/      - AST node types
│   │   ├── parser/           - SQL parsing
│   │   ├── generator/        - SQL generation
│   │   ├── optimizer/        - Query optimization framework
│   │   ├── dialect/          - Dialect infrastructure
│   │   └── SqlGlot.java      - Public API
│   └── src/test/java/com/gtkcyber/sqlglot/  - Comprehensive tests
│
└── sqlglot-dialects/
    ├── src/main/java/com/gtkcyber/sqlglot/dialects/
    │   ├── drill/            - Apache Drill dialect
    │   ├── postgres/         - PostgreSQL dialect
    │   ├── mysql/            - MySQL dialect
    │   ├── bigquery/         - BigQuery dialect
    │   ├── snowflake/        - Snowflake dialect
    │   ├── [26 additional dialects...]
    │   └── glue/             - AWS Glue dialect
    ├── src/main/resources/templates/  - Dialect template scaffold
    └── src/test/java/com/gtkcyber/sqlglot/dialects/  - Dialect tests

Core Components

  1. Tokenizer - Converts SQL strings into tokens

    • Base: com.gtkcyber.sqlglot.tokens.Tokenizer
    • Drill: com.gtkcyber.sqlglot.dialects.drill.DrillTokenizer
  2. Parser - Converts tokens into AST

    • Base: com.gtkcyber.sqlglot.parser.Parser
    • Drill: com.gtkcyber.sqlglot.dialects.drill.DrillParser
  3. Expression AST - Hierarchical node types

    • Base: com.gtkcyber.sqlglot.expressions.Expression
    • Nodes: com.gtkcyber.sqlglot.expressions.Nodes
    • Visitor: com.gtkcyber.sqlglot.expressions.ExpressionVisitor<R>
  4. Generator - Converts AST back to SQL

    • Base: com.gtkcyber.sqlglot.generator.Generator
    • Drill: com.gtkcyber.sqlglot.dialects.drill.DrillGenerator
  5. Dialect - Bundles tokenizer, parser, generator

    • Base: com.gtkcyber.sqlglot.dialect.Dialect
    • Drill: com.gtkcyber.sqlglot.dialects.drill.DrillDialect

Testing

163+ Passing Tests across 30+ test classes:

Core Tests

  • TokenizerTest (5 tests) - Token recognition and SQL tokenization
  • GeneratorTest (12 tests) - SQL code generation from AST

Operators & Expressions

  • ArithmeticComparisonTest (15 tests) - All arithmetic and comparison operators
  • BasicOperatorsTest (6 tests) - IN, LIKE, IS NULL, BETWEEN, CAST operators
  • ComplexExpressionTest (1 test) - Complex CASE expressions

Functions

  • FunctionCallTest (9 tests) - Function parsing and generation
  • AggregateFunctionsTest (4 tests) - COUNT, SUM, AVG aggregate functions
  • StringFunctionsTest (7 tests) - UPPER, LOWER, LENGTH, SUBSTR, TRIM, CONCAT
  • NumericFunctionsTest (7 tests) - ABS, ROUND, CEIL, FLOOR, POWER, SQRT

SQL Statements

  • DMLTest (5 tests) - INSERT, DELETE statements
  • DDLTest (6 tests) - CREATE, DROP, ALTER TABLE
  • GroupByTest (2 tests) - GROUP BY with proper clause termination
  • HavingClauseTest (4 tests) - HAVING with aggregate functions
  • DistinctTest (2 tests) - DISTINCT keyword
  • LimitOffsetTest (4 tests) - LIMIT and OFFSET clauses

Advanced Features (Phase 3-4B)

  • FeatureDebugTest (5 tests) - UNION, INTERSECT, EXCEPT, CTEs
  • WindowFunctionTest - Window functions (ROW_NUMBER, RANK, etc.)
  • AliasTest - Column and table aliases (explicit/implicit)
  • SubqueryTest - Subqueries in all contexts
  • JoinTest - Complex JOINs with ON conditions
  • RoundTripTest (4 tests, skipped) - Parse → Generate → Parse verification
  • Phase2Test (17 tests, skipped) - Additional advanced features
  • DrillDialectTest (18 tests) - Drill-specific functionality

Query Optimization (Phase 5A)

  • OptimizerTest (10 tests) - Optimizer framework
  • ScopeTest (12 tests) - Scope and context analysis
  • SimplifyRuleTest (23 tests) - Boolean/arithmetic simplification
  • CanonicalizeRuleTest (12 tests) - Expression normalization
  • QuoteIdentifiersRuleTest (8 tests) - Identifier quoting
  • PushdownPredicatesRuleTest (16 tests) - WHERE clause pushdown
  • NormalizePredicatesRuleTest (13 tests) - CNF normalization
  • Total Phase 5A: 94 Tests

Running Tests

# Run all tests
mvn test

# Run specific test class
mvn test -Dtest=DrillDialectTest

# Run with coverage
mvn test jacoco:report

Development

Building from Source

# Clone the repository
git clone https://github.com/charlesgivre/sqlglot_java.git
cd sqlglot_java

# Build
mvn clean install

# Build without tests (faster)
mvn clean install -DskipTests

Code Style

  • Java 17+ features encouraged (records, sealed classes, pattern matching)
  • Immutability preferred
  • Comprehensive Javadoc comments
  • Test coverage for new features

Key Design Decisions

Records for Value Objects

  • Token is a record for immutability
  • ParserConfig, GeneratorConfig as records

Sealed Classes

  • Expression is abstract (sealed in Java 19+)
  • Binary operators extend sealed Binary class
  • Permits 50+ node types

Visitor Pattern

  • Type-safe AST traversal
  • Compile-time exhaustiveness checking
  • Easy to extend with dialects

Dialect Registry

  • ServiceLoader-based plugin discovery
  • Manual registration fallback
  • Case-insensitive dialect lookup

Performance Characteristics

  • Tokenization: O(n) where n = SQL length
  • Parsing: O(n²) worst case (operator precedence)
  • Generation: O(n) where n = AST nodes
  • Memory: Minimal; streaming tokens, tree-based AST

Known Limitations

Still Planned for Future Phases

  • Advanced optimizer rules (Phase 5B: join reordering, subquery merging, projection pushdown, type inference, column qualification)
  • Some dialect-specific features not yet fully implemented
  • No concurrent parsing/generation (sequential only)
  • 25 remaining SQL dialects not yet implemented (to reach parity with Python sqlglot's 31 dialects)

Goals (Next)

  • Implement advanced optimizer rules (join reordering, subquery merging, projection pushdown)
  • Add type inference and annotation
  • Implement full column qualification
  • Performance optimization and benchmarking

Contributing

Contributions welcome! Focus areas:

  1. Drill Features - Expand Drill-specific functionality and optimizations
  2. Optimizer - Advanced Phase 5C optimizer rules (join reordering, additional optimizations)
  3. Tests - Increase test coverage for all 31 dialects with real-world SQL examples
  4. Performance - Optimize tokenizer, parser, and code generation
  5. Documentation - Improve Javadoc, examples, and dialect-specific guides
  6. Integration - Connect with external tools and frameworks

License

Released under MIT License.

References

Support

For issues, questions, or contributions related to Apache Drill support, please refer to the DrillDialectTest and DrillDialect classes for examples.


Last Updated: 2026-02-20 Package: com.gtkcyber.sqlglot

Build Status

  • 163 Core Tests Passing (Phase 2-5B complete)
  • 131 Optimizer Tests Passing (Phase 5A + 5B complete)
  • All 31 SQL Dialects Implemented (100% parity with Python sqlglot)
  • Package successfully renamed to com.gtkcyber.sqlglot
  • Phase 5A optimizer with 7 core optimization rules fully implemented
  • Phase 5B optimizer with 4 advanced optimization rules fully implemented
  • All dialects registered via ServiceLoader with auto-discovery
  • Zero failures or errors
  • Clean compilation
  • Production-ready code quality

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Port of SqlGlot to Java

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