Performance Optimization Analysis

# Performance Optimization Analysis

Analyze application or code performance, identify bottlenecks, and provide specific optimization recommendations with measurable improvements.

## Analysis Areas

### 1. Frontend Performance
- Render performance and FPS
- JavaScript bundle size
- CSS optimization
- Image and asset loading
- Network requests and waterfalls
- Core Web Vitals (LCP, FID, CLS)

### 2. Backend Performance
- API response times
- Database query performance
- Memory usage and leaks
- CPU utilization
- Caching effectiveness
- Concurrency and parallelization

### 3. Database Performance
- Query execution time
- Index usage and missing indexes
- N+1 query problems
- Connection pooling
- Query plan analysis
- Data model optimization

## Optimization Strategies

### Quick Wins (Low effort, high impact)
- Enable compression (gzip/brotli)
- Add caching headers
- Optimize images (format, compression, lazy loading)
- Minify and bundle assets
- Use CDN for static assets
- Add database indexes

### Code-Level Optimizations
- Remove unnecessary re-renders
- Implement memoization
- Use efficient data structures
- Optimize algorithms (Big O)
- Reduce memory allocations
- Batch operations

### Architecture Improvements
- Implement caching layers (Redis, Memcached)
- Add database read replicas
- Use queue systems for async tasks
- Implement CDN strategy
- Add load balancing
- Consider microservices architecture

## Output Format

For each optimization:

1. **Current State**: Metrics showing the problem
2. **Bottleneck**: What's causing the issue
3. **Impact**: Performance cost (time, resources)
4. **Solution**: Specific optimization technique
5. **Implementation**: Code or configuration changes
6. **Expected Improvement**: Projected performance gain
7. **Trade-offs**: Any downsides or considerations
8. **Priority**: Critical, High, Medium, Low

## Benchmarking

Provide before/after metrics:
- Response time (ms)
- Throughput (requests/sec)
- Memory usage (MB)
- CPU usage (%)
- Bundle size (KB)
- Time to Interactive (ms)
- Lighthouse score

## Example Analysis

Optimization #1: Database Query Optimization

Current State

• Query execution time: 2,350ms
• Records returned: 1,000
• Full table scan detected

Bottleneck Missing index on user_id column causing full table scan on 5M row table.

Solution Add composite index on frequently queried columns:

CREATE INDEX idx_orders_user_created 
ON orders(user_id, created_at DESC);

Expected Improvement

• Query time: 2,350ms → 45ms (98% faster)
• CPU usage: -85%
• Scalable to 50M+ rows

Priority: Critical

## Performance Checklist

### Frontend
- [ ] Bundle size under budget
- [ ] Code splitting implemented
- [ ] Images optimized and lazy loaded
- [ ] Fonts optimized
- [ ] Unnecessary JavaScript removed
- [ ] CSS critical path optimized
- [ ] Service worker for caching
- [ ] Resource hints (preload, prefetch)

### Backend
- [ ] API responses under 200ms
- [ ] Database queries optimized
- [ ] Caching implemented
- [ ] Rate limiting in place
- [ ] Async operations used
- [ ] Connection pooling configured
- [ ] Monitoring and alerts set up

### Best Practices
- [ ] Performance budgets defined
- [ ] Regular profiling scheduled
- [ ] Metrics tracked over time
- [ ] Load testing performed
- [ ] Edge cases tested
- [ ] Documentation updated