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Zhongwei Li
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skills/optimizing-performance/SKILL.md
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skills/optimizing-performance/SKILL.md
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name: Optimizing Performance
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description: Optimize performance with profiling, caching strategies, database query optimization, and bottleneck analysis. Use when improving response times, implementing caching layers, or scaling for high load.
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---
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# Optimizing Performance
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I help you identify and fix performance bottlenecks using language-specific profiling tools, optimization patterns, and best practices.
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## When to Use Me
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**Performance analysis:**
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- "Profile this code for bottlenecks"
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- "Analyze performance issues"
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- "Why is this slow?"
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**Optimization:**
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- "Optimize database queries"
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- "Improve response time"
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- "Reduce memory usage"
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**Scaling:**
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- "Implement caching strategy"
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- "Optimize for high load"
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- "Scale this service"
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## How I Work - Progressive Loading
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I load only the performance guidance relevant to your language:
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```yaml
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Language Detection:
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"Python project" → Load @languages/PYTHON.md
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"Rust project" → Load @languages/RUST.md
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"JavaScript/Node.js" → Load @languages/JAVASCRIPT.md
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"Go project" → Load @languages/GO.md
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"Any language" → Load @languages/GENERIC.md
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```
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**Don't load all files!** Start with language detection, then load specific guidance.
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## Core Principles
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### 1. Measure First
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**Never optimize without data.** Profile to find actual bottlenecks, don't guess.
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- Establish baseline metrics
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- Profile to identify hot paths
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- Focus on the 20% of code that takes 80% of time
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- Measure improvements after optimization
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### 2. Performance Budgets
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Set clear targets before optimizing:
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```yaml
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targets:
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api_response: "<200ms (p95)"
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page_load: "<2 seconds"
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database_query: "<50ms (p95)"
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cache_lookup: "<10ms"
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```
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### 3. Trade-offs
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Balance performance vs:
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- Code readability
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- Maintainability
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- Development time
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- Memory usage
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Premature optimization is the root of all evil. Optimize when:
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- Profiling shows clear bottleneck
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- Performance requirement not met
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- User experience degraded
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## Quick Wins (Language-Agnostic)
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### Database
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- Add indexes for frequently queried columns
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- Implement connection pooling
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- Use batch operations instead of loops
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- Cache expensive query results
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### Caching
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- Implement multi-level caching (L1: in-memory, L2: Redis, L3: database, L4: CDN)
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- Define cache invalidation strategy
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- Monitor cache hit rates
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### Network
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- Enable compression for responses
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- Use HTTP/2 or HTTP/3
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- Implement CDN for static assets
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- Configure appropriate timeouts
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## Language-Specific Guidance
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### Python
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**Load:** `@languages/PYTHON.md`
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**Quick reference:**
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- Profiling: `cProfile`, `py-spy`, `memory_profiler`
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- Patterns: Generators, async/await, list comprehensions
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- Anti-patterns: String concatenation in loops, GIL contention
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- Tools: `pytest-benchmark`, `locust`
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### Rust
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**Load:** `@languages/RUST.md`
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**Quick reference:**
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- Profiling: `cargo bench`, `flamegraph`, `perf`
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- Patterns: Zero-cost abstractions, iterator chains, preallocated collections
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- Anti-patterns: Unnecessary allocations, large enum variants
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- Tools: `criterion`, `rayon`, `parking_lot`
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### JavaScript/Node.js
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**Load:** `@languages/JAVASCRIPT.md`
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**Quick reference:**
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- Profiling: `clinic.js`, `0x`, Chrome DevTools
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- Patterns: Event loop optimization, worker threads, streaming
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- Anti-patterns: Blocking event loop, memory leaks, unnecessary re-renders
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- Tools: `autocannon`, `react-window`, `p-limit`
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### Go
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**Load:** `@languages/GO.md`
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**Quick reference:**
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- Profiling: `pprof`, `go test -bench`, `go tool trace`
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- Patterns: Goroutine pools, buffered channels, `sync.Pool`
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- Anti-patterns: Unlimited goroutines, defer in loops, lock contention
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- Tools: `benchstat`, `sync.Map`, `strings.Builder`
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### Generic Patterns
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**Load:** `@languages/GENERIC.md`
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**When to use:** Database optimization, caching strategies, load balancing, monitoring - applicable to any language.
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## Optimization Workflow
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### Phase 1: Baseline
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1. Define performance requirements
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2. Measure current performance
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3. Identify user-facing metrics (response time, throughput)
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### Phase 2: Profile
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1. Use language-specific profiling tools
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2. Identify hot paths (where time is spent)
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3. Find memory bottlenecks
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4. Check for resource leaks
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### Phase 3: Optimize
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1. Focus on biggest bottleneck first
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2. Apply language-specific optimizations
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3. Implement caching where appropriate
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4. Optimize database queries
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### Phase 4: Verify
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1. Re-profile to measure improvements
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2. Run performance regression tests
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3. Monitor in production
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4. Set up alerts for degradation
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## Common Bottlenecks
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### Database
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- Missing indexes
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- N+1 query problem
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- No connection pooling
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- Expensive joins
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→ **Load** `@languages/GENERIC.md` for DB optimization
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### Memory
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- Memory leaks
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- Excessive allocations
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- Large object graphs
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- No pooling
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→ **Load** language-specific file for memory management
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### Network
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- No compression
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- Chatty API calls
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- Synchronous external calls
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- No CDN
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→ **Load** `@languages/GENERIC.md` for network optimization
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### Concurrency
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- Lock contention
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- Excessive threading/goroutines
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- Blocking operations
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- Poor work distribution
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→ **Load** language-specific file for concurrency patterns
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## Success Criteria
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**Optimization complete when:**
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- ✅ Performance targets met
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- ✅ No regressions in functionality
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- ✅ Code remains maintainable
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- ✅ Improvements verified with profiling
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- ✅ Production metrics show improvement
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- ✅ Alerts configured for degradation
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## Next Steps
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- Use profiling tools to identify bottlenecks
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- Load language-specific guidance
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- Apply targeted optimizations
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- Set up monitoring and alerts
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---
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*Load language-specific files for detailed profiling tools, optimization patterns, and best practices*
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