[](https://crates.io/crates/polyfill-rs)
[](https://docs.rs/polyfill-rs)
[](LICENSE)
A high-performance Polymarket Rust client with latency-optimized data structures and zero-allocation hot paths. An API-compatible drop-in replacement for `polymarket-rs-client` with identical method signatures.
At the time that this project was started, `polymarket-rs-client` was a Polymarket Rust Client with a few GitHub stars, but which seemed to be unmaintained. I took on the task of creating a Rust client which could beat the benchmarks quoted in the README.md of that project, with the added constraint of also maintaining zero alloc hot paths.
I also want to take a moment to clarify what zero-alloc means because I've now recieved double digit messages about this on twitter/x and telegram. In general, zero alloc means either zero alloc in hot paths (which can be a bit more arbitrary) or atlernatively it can mean zero alloc after init/warm-up, which is the objective of this repository. Succinctly that means that **the per-message handling loop never touches the heap**.
Notably order book paths that introduce new allocations by design:
- First time seeing a token/book (HashMap insert + key clone): `src/book.rs:~788`
- New price levels (BTreeMap node growth): `src/book.rs:~409`
## Quick Start
Add to your `Cargo.toml`:
```toml
[dependencies]
polyfill-rs = "0.3.0"
```
Replace your imports:
```rust
// 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:
| **Fetch Markets** | **321.6 ms ± 92.9 ms** | 409.3 ms ± 137.6 ms | 1.366 s ± 0.048 s |
**Performance vs polymarket-rs-client:**
- **21.4% faster**
- **32.5% more consistent**
- **4.2x faster** than Official Python Client
**Benchmark Methodology:** All benchmarks run side-by-side on the same machine, same network, same time using 20 iterations, 100ms delay between requests, /simplified-markets endpoint. Best performance achieved with connection keep-alive enabled. See `examples/side_by_side_benchmark.rs` in commit `a63a170`: https://github.com/floor-licker/polyfill-rs/blob/a63a170/examples/side_by_side_benchmark.rs for the complete benchmark implementation.
**Computational Performance (pure CPU, no I/O)**
| **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) |
| **WS `book` hot path (decode + apply)** | ~0.28 µs / 2.01 µs / 7.70 µs | 1 / 16 / 64 levels-per-side, ~3.7–4.0x faster vs serde decode+apply (see `benches/ws_hot_path.rs`) |
Run the WS hot-path benchmark locally with `cargo bench --bench ws_hot_path`.
**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.
### 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
| **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 |
