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duthaho
117 lines
4.0 KiB
Markdown
117 lines
4.0 KiB
Markdown
---
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name: performance-optimization
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argument-hint: "[file or function]"
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description: >
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Use when analyzing or optimizing code performance — including profiling, benchmarking, fixing N+1 queries, reducing bundle size, eliminating memory leaks, or improving algorithm complexity. Trigger for keywords like "slow", "performance", "optimize", "profiling", "memory leak", "bundle size", "N+1", "re-render", "benchmark", "latency", "throughput", or any request to make code faster. Also activate when investigating production performance issues or when code review flags performance concerns.
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---
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# Performance Optimization
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## When to Use
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- Profiling slow code to find bottlenecks
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- Fixing N+1 query problems
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- Reducing JavaScript bundle size
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- Eliminating memory leaks
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- Improving algorithm complexity
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- Benchmarking before/after optimization
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- Investigating production latency issues
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## When NOT to Use
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- Premature optimization — profile first, optimize second
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- Caching strategy design — use `caching`
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- Database schema/index design — use `databases`
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- Code structure improvement — use `refactoring`
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---
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## Quick Reference
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| Topic | Reference | Key content |
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|-------|-----------|-------------|
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| Profiling tools | `references/profiling.md` | Python (cProfile, py-spy, Scalene) and JS/TS (DevTools, Lighthouse, clinic.js) |
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| Anti-patterns | `references/anti-patterns.md` | N+1 queries, unnecessary re-renders, event loop blocking, memory leaks |
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---
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## Optimization Workflow
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1. **Measure first** — profile to find the actual bottleneck
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2. **Set a target** — "reduce p95 latency from 500ms to 100ms"
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3. **Optimize the hot path** — fix the #1 bottleneck, not everything
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4. **Benchmark before/after** — prove the improvement with numbers
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5. **Check for regressions** — ensure correctness wasn't sacrificed
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---
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## Profiling Quick Start
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### Python
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```bash
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# CPU profiling
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python -m cProfile -o output.prof script.py
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# Visualize: pip install snakeviz && snakeviz output.prof
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# Live profiling (attach to running process)
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py-spy top --pid 12345
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# Line-by-line profiling
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kernprof -lv script.py # requires @profile decorator
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```
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### JavaScript/TypeScript
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```bash
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# Bundle analysis
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npx webpack-bundle-analyzer stats.json
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# or: ANALYZE=true next build
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# Node.js profiling
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node --prof app.js
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clinic doctor -- node app.js
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# Benchmarking
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npx vitest bench
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```
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---
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## Common Anti-Patterns
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| Anti-Pattern | Detection | Fix |
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|-------------|-----------|-----|
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| N+1 queries | `django-debug-toolbar`, `prisma.$on('query')` | `select_related`/`joinedload`/`include` |
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| Unnecessary re-renders | React DevTools Profiler | `useMemo`, `useCallback`, `React.memo` |
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| Blocking event loop | `clinic doctor`, high event loop lag | `worker_threads`, async variants |
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| Memory leaks | Heap snapshots, growing `process.memoryUsage()` | Remove listeners, clear refs, bound caches |
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| Unbounded lists | No pagination, full table scans | Cursor pagination, `LIMIT` |
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| Heavy imports | Bundle analyzer showing large deps | Tree-shaking, `import { x }`, code splitting |
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---
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## Best Practices
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1. **Profile before optimizing** — intuition about bottlenecks is often wrong.
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2. **Optimize the hot path** — 80% of time is spent in 20% of code.
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3. **Measure, don't guess** — use benchmarks with statistical significance.
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4. **Set clear targets** — "faster" is not measurable; "p95 < 100ms" is.
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5. **Avoid premature optimization** — correctness and readability come first.
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## Common Pitfalls
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1. **Optimizing cold paths** — spending time on code that runs once.
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2. **Micro-benchmarking without context** — 10ns vs 20ns doesn't matter if the DB call takes 50ms.
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3. **Sacrificing readability** — an unreadable optimization is a future bug.
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4. **Caching without invalidation** — stale data is worse than slow data.
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5. **Ignoring algorithmic complexity** — no amount of micro-optimization fixes O(n^2) on large inputs.
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---
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## Related Skills
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- `systematic-debugging` — Investigating slow paths with root-cause rigor
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- `testing` — Benchmarking and perf regression tests
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- `devops` — Deploy-time perf checks
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