optimizing-code

name	optimizing-code
description	Improve code performance without changing behavior. Use when code fails latency/throughput requirements. Covers profiling, caching, and algorithmic optimization.

Optimizing Code

The Optimization Hat

When optimizing, you improve performance without changing behavior. Always measure before and after.

Golden Rules

1. Measure First: Never optimize without a benchmark
2. Profile Before Guessing: Find the actual bottleneck
3. Optimize the Right Thing: Focus on the critical path
4. Measure After: Verify the optimization worked

Workflows

• Benchmark: Establish baseline performance metrics
• Profile: Identify the actual bottleneck
• Hypothesize: What optimization will help?
• Implement: Make the change
• Measure: Verify improvement
• Document: Record the optimization and results

Common Optimizations

Algorithm Complexity

• Replace O(n²) with O(n log n) or O(n)
• Use appropriate data structures (Set for lookups, Map for key-value)

Caching

// Memoization
const cache = new Map<string, Result>();

function expensiveCalculation(input: string): Result {
  if (cache.has(input)) {
    return cache.get(input)!;
  }
  const result = /* expensive work */;
  cache.set(input, result);
  return result;
}

Database Queries

• Add indexes for frequently queried columns
• Avoid N+1 queries (use eager loading)
• Use pagination for large result sets

Memory

• Avoid creating unnecessary objects in loops
• Use streaming for large files
• Release references when done

Profiling Tools

# Node.js
node --prof app.js
node --prof-process isolate-*.log

# Python
python -m cProfile -s cumtime script.py

# Go
go test -bench=. -cpuprofile=cpu.prof
go tool pprof cpu.prof

Anti-Patterns to Avoid

• Premature optimization (no benchmark)
• Micro-optimizations (negligible impact)
• Optimizing cold paths
• Sacrificing readability for minor gains