A high-performance drop-in replacement for polymarket-rs-client with latency-optimized data structures and zero-allocation hot paths. A 100% API-compatible drop-in replacement for polymarket-rs-client with identical method signatures.

Quick Start

Add to your Cargo.toml:

[dependencies]
polyfill-rs = "0.2.3"

Replace your imports:

// Before: use polymarket_rs_client::{ClobClient, Side, OrderType};
use polyfill_rs::{ClobClient, Side, OrderType};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = ClobClient::new("https://clob.polymarket.com");
    let markets = client.get_sampling_markets(None).await?;
    println!("Found {} markets", markets.data.len());
    Ok(())
}

Performance Comparison

Real-World API Performance (with network I/O)

End-to-end performance with Polymarket's API, including network latency, JSON parsing, and decompression:

Operation	polyfill-rs	polymarket-rs-client	Official Python Client
Fetch Markets	321.6 ms ± 92.9 ms	409.3 ms ± 137.6 ms	1.366 s ± 0.048 s

Performance vs Competition:

• 21.4% faster than polymarket-rs-client - 87.6ms improvement
• 32.5% more consistent than polymarket-rs-client
• 4.2x faster than Official Python Client

Benchmark Methodology: All benchmarks run side-by-side on the same machine, same network, same time using identical testing methodology (20 iterations, 100ms delay between requests, /simplified-markets endpoint). Best performance achieved with connection keep-alive enabled. See examples/side_by_side_benchmark.rs for the complete benchmark implementation.

Computational Performance (pure CPU, no I/O)

Operation	Performance	Notes
Order Book Updates (1000 ops)	159.6 µs ± 32 µs	6,260 updates/sec, zero-allocation
Spread/Mid Calculations	70 ns ± 77 ns	14.3M ops/sec, optimized BTreeMap
JSON Parsing (480KB)	~2.3 ms	SIMD-accelerated parsing (1.77x faster than serde_json)

Key Performance Optimizations:

The 21.4% performance improvement comes from SIMD-accelerated JSON parsing (1.77x faster than serde_json), HTTP/2 tuning with 512KB stream windows optimized for 469KB payloads, integrated DNS caching, connection keep-alive, and buffer pooling to reduce allocation overhead.

Benchmarking Methodology

Side-by-Side Testing: Both clients tested sequentially on identical infrastructure with the same network state, API endpoint, and parameters (20 iterations, 100ms delays). Side-by-side testing reveals polymarket-rs-client's claimed ±22.9ms variance understates actual ±137.6ms variance by 500%.

What We Measure:

• Real-world API performance with actual network I/O
• Statistical analysis with multiple runs (mean ± standard deviation)
• Connection establishment overhead and warm connection performance
• Variance analysis to measure consistency

Critical Path Optimizations

Fixed-point arithmetic eliminates floating-point pipeline stalls and decimal parsing overhead. Lock-free updates using compare-and-swap operations prevent mutex contention. Cache-aligned structures maintain 64-byte alignment for L1/L2 cache efficiency. SIMD-friendly data layouts enable batch price level processing.

Memory Architecture

Pre-allocated pools eliminate allocation latency spikes. Configurable book depth limiting prevents memory bloat. Hot data structures group frequently-accessed fields for cache line efficiency.

Architectural Principles

Price data converts to fixed-point at ingress boundaries while maintaining tick-aligned precision. The critical path uses integer arithmetic with branchless operations. Data converts back to IEEE 754 at egress for API compatibility. This enables deterministic execution with predictable instruction counts.

Measured Network Improvements

Optimization Technique	Performance Gain	Use Case
Optimized HTTP client	11% baseline improvement	Every API call
Connection pre-warming	70% faster subsequent requests	Application startup
Request parallelization	200% faster batch operations	Multi-market data fetching
Circuit breaker resilience	Better uptime during instability	Production trading systems