Optimizing Code

The Optimization Hat

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