# CHAPTER 6 SUMMARY

## Key Concepts Introduced
1. **W-TinyLFU Algorithm** - 25% better hit rates than LRU - Used in chapters: 10
2. **Cache Configuration** - maxSize, maxAge, staleWhileRevalidate - Used in chapters: 7, 10, 11
3. **Cache Strategies by Data Type** - Static vs dynamic TTLs - Used in chapters: 10, 11
4. **Stale-While-Revalidate** - Serve old data while fetching - Used in chapters: 11
5. **Cache Warming** - Predictive and scheduled preloading - Used in chapters: 10, 11
6. **Cache Invalidation** - Tags, patterns, and relationships - Used in chapters: 8, 10
7. **Multi-Layer Caching** - Memory → Session → Local - Used in chapters: 11
8. **Cache Key Generation** - User/locale/version aware - Used in chapters: 10
9. **Cache Analytics** - Hit rates, eviction monitoring - Used in chapters: 12
10. **Cache Events** - hit, miss, evict tracking - Used in chapters: 12

## Code Patterns Established
```javascript
// Pattern 1: Cache configuration
tf.configure({
  cache: {
    maxSize: 100 * 1024 * 1024,
    algorithm: 'W-TinyLFU',
    staleWhileRevalidate: true
  }
})

// Pattern 2: Per-request caching
await tf.get(url, {
  cache: {
    maxAge: 60000,
    key: 'custom-key',
    tags: ['tag1', 'tag2']
  }
})

// Pattern 3: Cache invalidation
tf.cache.invalidate(url)
tf.cache.invalidatePattern('/api/users/*')
tf.cache.invalidateTag('content')

// Pattern 4: Cache warming
const endpoints = ['/api/config', '/api/user']
await Promise.all(endpoints.map(url => tf.get(url)))
```

## Performance Metrics
- Cache hits: <1ms response time
- Network requests: 200-500ms
- W-TinyLFU: 15-25% better hit rate than LRU
- Memory usage: Efficient sketch data structures

## Advanced Patterns Introduced
1. **Layered Cache Architecture** - L1/L2/L3 cache levels
2. **Predictive Warming** - Based on navigation patterns
3. **Time-Based Strategies** - Different TTLs by time of day
4. **Relationship Warming** - Preload related endpoints
5. **Cache-First Architecture** - Offline-first with Service Workers
6. **Smart Key Generation** - Context-aware cache keys

## Building Blocks for Next Chapter
- Learned: Caching fundamentals and performance
- Mastered: Cache strategies and invalidation
- Ready for: Type safety and inference

## Weather Buddy App Status
- Version 6.0: Lightning fast with intelligent caching
- Features: Cache indicators, performance stats, controls
- Visual: Shows cache status (fresh/stale/miss)
- Analytics: Real-time hit rate and time saved
- Next: Type safety and auto-completion (Chapter 7)

## Best Practices Established
1. Cache appropriate data types
2. Set reasonable TTLs
3. Invalidate after mutations
4. Monitor cache performance
5. Warm cache proactively
6. Handle offline scenarios
7. Use stale-while-revalidate

## Common Mistakes to Avoid
- Caching sensitive/real-time data
- Forgetting invalidation
- Too short/long TTLs
- Not warming cache
- Ignoring cache size limits
- Not monitoring performance