Optimizing Code

The Optimization Hat

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

Golden Rules

• Measure First: Never optimize without a benchmark

• Profile Before Guessing: Find the actual bottleneck

• Optimize the Right Thing: Focus on the critical path

• 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