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Performance Optimization Examples

Real-world examples of performance bottlenecks and their optimizations across different layers.

Examples Overview

Algorithm Optimization

File: algorithm-optimization.md

Fix algorithmic bottlenecks:

  • Nested loops O(n²) → Map lookups O(n)
  • Inefficient array operations
  • Sorting and searching optimizations
  • Data structure selection (Array vs Set vs Map)
  • Before/after performance metrics

Use when: Profiling shows slow computational operations, CPU-intensive tasks.


Database Optimization

File: database-optimization.md

Optimize database queries and patterns:

  • N+1 query problem detection and fixes
  • Eager loading vs lazy loading
  • Query optimization with EXPLAIN ANALYZE
  • Index strategy (single, composite, partial)
  • Connection pooling
  • Query result caching

Use when: Database queries are slow, high database CPU usage, query timeouts.


Caching Optimization

File: caching-optimization.md

Implement effective caching strategies:

  • In-memory caching patterns
  • Redis distributed caching
  • HTTP caching headers
  • Cache invalidation strategies
  • Cache hit rate optimization
  • TTL tuning

Use when: Repeated expensive computations, external API calls, static data queries.


Frontend Optimization

File: frontend-optimization.md

Optimize React/frontend performance:

  • Bundle size reduction (code splitting, tree shaking)
  • React rendering optimization (memo, useMemo, useCallback)
  • Virtual scrolling for long lists
  • Image optimization (lazy loading, WebP, responsive images)
  • Web Vitals improvement (LCP, FID, CLS)

Use when: Slow page load, large bundle sizes, poor Web Vitals scores.


Backend Optimization

File: backend-optimization.md

Optimize server-side performance:

  • Async/parallel processing patterns
  • Stream processing for large data
  • Request batching and debouncing
  • Worker threads for CPU-intensive tasks
  • Memory leak prevention
  • Connection pooling

Use when: High server response times, memory leaks, CPU bottlenecks.


Quick Reference

Optimization Type Common Gains Typical Fixes
Algorithm 50-90% faster O(n²) → O(n), better data structures
Database 60-95% faster Indexes, eager loading, caching
Caching 80-99% faster Redis, in-memory, HTTP headers
Frontend 40-70% faster Code splitting, lazy loading, memoization
Backend 50-80% faster Async processing, streaming, pooling

Performance Impact Guide

High Impact (>50% improvement)

  • Fix N+1 queries
  • Add missing indexes
  • Implement caching layer
  • Fix O(n²) algorithms
  • Enable code splitting

Medium Impact (20-50% improvement)

  • Optimize React rendering
  • Add connection pooling
  • Implement lazy loading
  • Batch API requests
  • Optimize images

Low Impact (<20% improvement)

  • Minify assets
  • Enable gzip compression
  • Optimize CSS selectors
  • Reduce HTTP headers

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