Benchmark

Run benchmarks to measure and compare performance of code implementations.

Steps

1. Identify the target for benchmarking:
   • A specific function or module.
   • Two implementations to compare (before/after refactor).
   • An API endpoint under load.
2. Set up the benchmark:
   • Detect the runtime and available benchmarking tools.
   • Node.js: Use vitest bench or custom benchmark harness.
   • Python: Use pytest-benchmark or timeit.
   • Go: Use testing.B built-in benchmarks.
   • Rust: Use criterion or built-in #[bench].
3. Configure benchmark parameters:
   • Warm-up iterations to stabilize JIT and caches.
   • Measurement iterations (minimum 100 for statistical significance).
   • Input data size variations (small, medium, large).
4. Run benchmarks and collect results:
   • Operations per second.
   • Average time per operation.
   • Memory allocation per operation.
   • P50, P95, P99 latencies.
5. If comparing implementations, calculate relative performance difference.
6. Generate a summary with statistical confidence.

Format

Benchmark: <name>
Iterations: <N>

| Implementation | ops/sec | avg (ms) | P99 (ms) | mem (MB) |
|---------------|---------|----------|----------|----------|
| Original      | 10,000  | 0.10     | 0.25     | 2.1      |
| Optimized     | 25,000  | 0.04     | 0.08     | 1.8      |

Improvement: 2.5x faster, 14% less memory
Confidence: 95% (p < 0.05)

Rules

• Always include warm-up iterations before measurement.
• Run enough iterations for statistically significant results.
• Report standard deviation alongside averages.
• Benchmark on consistent hardware; note the environment.
• Disable garbage collection pauses during benchmarks where possible.