// ❌ O(n²) - Inefficient
function findDuplicates(arr) {
  const duplicates = [];
  for (let i = 0; i < arr.length; i++) {
    for (let j = i + 1; j < arr.length; j++) {
      if (arr[i] === arr[j]) duplicates.push(arr[i]);
    }
  }
  return duplicates;
}

// ✓ O(n) - Efficient
function findDuplicates(arr) {
  const seen = new Set();
  const duplicates = new Set();
  for (const item of arr) {
    if (seen.has(item)) duplicates.add(item);
    seen.add(item);
  }
  return Array.from(duplicates);
}

// ❌ Re-renders on every parent update
function ExpensiveComponent({ data }) {
  const processed = expensiveCalculation(data);
  return <div>{processed}</div>;
}

// ✓ Memoized, only re-renders when data changes
const ExpensiveComponent = React.memo(({ data }) => {
  const processed = useMemo(() => expensiveCalculation(data), [data]);
  return <div>{processed}</div>;
});

// ❌ N+1 queries
async function getPostsWithAuthors() {
  const posts = await db.posts.findAll();
  for (const post of posts) {
    post.author = await db.users.findById(post.authorId); // N queries
  }
  return posts;
}

// ✓ Single query with join
async function getPostsWithAuthors() {
  return await db.posts.findAll({
    include: [{ model: db.users, as: 'author' }]
  });
}

// ❌ Memory leak - event listener not cleaned up
useEffect(() => {
  window.addEventListener('scroll', handleScroll);
  // Missing cleanup!
}, []);

// ✓ Proper cleanup
useEffect(() => {
  window.addEventListener('scroll', handleScroll);
  return () => window.removeEventListener('scroll', handleScroll);
}, []);

@performance-profiler
@performance-profiler src/
@performance-profiler UserList.jsx
@performance-profiler --focus algorithms
@performance-profiler --include-bundle-size

# Performance Analysis Report

## Summary
- Files analyzed: 23
- Issues found: 18
- High priority: 4
- Medium priority: 9
- Low priority: 5
- Estimated improvement: 60% faster, 30% smaller bundle

## Critical Issues (4)

### 1. Inefficient Algorithm - src/utils/search.js:34
**Issue**: O(n²) search algorithm
**Current**: Linear search within loop (complexity: O(n²))
**Impact**: ~850ms for 1000 items
**Recommendation**: Use Map for O(1) lookups
**Expected improvement**: 99% faster (~8ms for 1000 items)

```javascript
// Current (slow)
function findMatches(items, queries) {
  return queries.map(q => items.find(i => i.id === q));
}

// Optimized
function findMatches(items, queries) {
  const itemMap = new Map(items.map(i => [i.id, i]));
  return queries.map(q => itemMap.get(q));
}

// Instead of
import _ from 'lodash';

// Use
import debounce from 'lodash/debounce';
import throttle from 'lodash/throttle';

## Optimization Techniques

### Frontend Performance
- **Memoization**: Cache expensive calculations
- **Virtualization**: Render only visible items
- **Lazy Loading**: Load code/images on demand
- **Code Splitting**: Break bundle into chunks
- **Debouncing/Throttling**: Limit function calls
- **Web Workers**: Offload CPU-intensive tasks

### Backend Performance
- **Caching**: Redis, in-memory caches
- **Query Optimization**: Indexes, joins, pagination
- **Connection Pooling**: Reuse database connections
- **Async Operations**: Non-blocking I/O
- **Batching**: Combine multiple operations

### General Optimizations
- **Algorithm Choice**: Pick right data structure
- **Early Returns**: Exit loops/functions early
- **Avoid Premature Optimization**: Profile first
- **Lazy Evaluation**: Compute only when needed

## Profiling Tools

- **JavaScript**: Chrome DevTools, React Profiler, Lighthouse
- **Node.js**: clinic.js, 0x, node --prof
- **Python**: cProfile, memory_profiler, py-spy
- **Database**: Query analyzers, EXPLAIN plans
- **Bundle**: webpack-bundle-analyzer, source-map-explorer

## Notes

- Always profile before optimizing
- Measure actual impact after changes
- Consider readability vs performance trade-offs
- Focus on bottlenecks, not micro-optimizations
- Test performance improvements with realistic data
- Document why optimizations were made