ai-engineer-agent/performance-optimizer

Performance Optimizer

Analyze and optimize $ARGUMENTS for performance:

Analysis Process

1. Identify the bottleneck — don't optimize without measuring
2. Profile first — use language-appropriate tools
3. Propose changes with expected impact
4. Verify improvement with benchmarks

Common Optimization Patterns

Algorithmic

• Replace O(n²) with O(n log n) or O(n) where possible
• Use appropriate data structures (HashMap for lookups, not array scan)
• Avoid unnecessary sorting — use partial sorts or heaps
• Cache computed results (memoization)

I/O & Network

• Batch database queries (avoid N+1)
• Use connection pooling
• Implement caching layers (in-memory, Redis)
• Use streaming for large data instead of loading all into memory
• Compress responses (gzip, brotli)
• Parallelize independent I/O operations

Memory

• Avoid unnecessary copies — use references/borrows/views
• Use generators/iterators for large sequences
• Release large objects early (set to null, use weak refs)
• Use appropriate collection types with size hints
• Pool expensive-to-create objects

Frontend

• Lazy load images and non-critical resources
• Code split routes and heavy components
• Debounce/throttle frequent events
• Use virtual scrolling for long lists
• Minimize re-renders (memoization, stable references)

Database

• Use EXPLAIN ANALYZE to understand query plans
• Add missing indexes for common query patterns
• Denormalize for read-heavy access patterns
• Use materialized views for expensive aggregations
• Optimize pagination (keyset > offset)

Profiling Tools by Language

• JavaScript/Node.js: Chrome DevTools, node --prof, clinic.js
• Python: cProfile, py-spy, memory_profiler
• Go: pprof, trace
• Rust: flamegraph, criterion
• Java: JProfiler, async-profiler, JMH
• C#/.NET: dotnet-trace, BenchmarkDotNet