Performance

Performance Skill

Benchmarking and performance optimization for the Rust self-learning memory system.

Quick Reference

• Benchmarking — Criterion patterns and profiling
• Optimization — CPU/memory optimization strategies
• Profiling — perf, flamegraph, tokio-console

When to Use

• Benchmarking hot paths before/after changes
• Profiling CPU/memory bottlenecks
• Validating performance improvements
• Regression detection in CI

Key Metrics

Benchmarking Workflow

1. Criterion Benchmarks

# Run all benchmarks
cargo bench

# Run specific benchmark
cargo bench --bench retrieval_quality

# Compare baseline
cargo bench -- --save-baseline main
cargo bench -- --baseline main  # Compare against saved

2. Profiling Commands

# CPU profiling
perf record -g cargo bench
perf report

# Flamegraph
cargo flamegraph --bench retrieval_quality

# Tokio console (async)
RUSTFLAGS="--cfg tokio_unstable" cargo build
tokio-console

Performance Patterns

Hot Path Optimization

1. Avoid blocking in async: Use spawn_blocking for CPU-heavy ops
2. Lock contention: Use parking_lot::RwLock instead of std::sync::RwLock
3. Zero-copy: Use references where possible, avoid cloning
4. Batching: Group operations to reduce syscall overhead

Memory Optimization

1. Arena allocation: For frequent allocations/deallocations
2. Cache-friendly data: Keep related data contiguous
3. String interning: For repeated string values

Validation Checklist

Before claiming performance improvement:

• Benchmark shows measurable improvement
• No regression in other benchmarks
• Memory usage not increased significantly
• Tested with realistic data sizes

CI Integration

Quality gate docs/QUALITY_GATES.md includes performance regression check:

• Benchmarks must not degrade >10%
• cargo bench runs in nightly CI

Cross-References

• Testing: test-runner skill
• Debugging: debug-troubleshoot skill
• Quality Gates: docs/QUALITY_GATES.md
• Benchmark Location: benches/