# Caching Strategies

## Overview

Caching is a critical technique for improving application performance. This guide covers caching patterns, strategies, and best practices.

## Caching Layers

### Browser Cache

**Client-Side Caching:**
- Cache-Control headers
- ETags
- Last-Modified
- Reduce server load

### CDN Cache

**Edge Caching:**
- Geographic distribution
- Static content
- Reduce latency
- Offload origin

### Application Cache

**In-Memory Cache:**
- Redis, Memcached
- Frequently accessed data
- Reduce database load
- Fast access

### Database Cache

**Query Result Cache:**
- Query result caching
- Connection pooling
- Reduce query execution

## Cache Patterns

### Cache-Aside (Lazy Loading)

**Application-Managed:**
1. Check cache
2. If miss, query database
3. Store in cache
4. Return data

**Example:**
```python
def get_user(user_id):
    # Check cache
    user = cache.get(f"user:{user_id}")
    if user:
        return user
    
    # Query database
    user = db.query(User).filter(id=user_id).first()
    
    # Store in cache
    cache.set(f"user:{user_id}", user, ttl=3600)
    return user
```

**Pros:**
- Simple to implement
- Flexible
- Cache failures don't affect app

**Cons:**
- Cache miss penalty
- Possible stale data
- Manual invalidation

### Write-Through

**Synchronous Write:**
- Write to cache and database
- Consistent data
- Higher write latency

**Example:**
```python
def update_user(user_id, data):
    # Update database
    user = db.update(User, user_id, data)
    
    # Update cache
    cache.set(f"user:{user_id}", user, ttl=3600)
    return user
```

**Pros:**
- Always consistent
- No stale data
- Simple invalidation

**Cons:**
- Higher write latency
- Writes even if not read

### Write-Back (Write-Behind)

**Asynchronous Write:**
- Write to cache immediately
- Write to database asynchronously
- Lower write latency

**Pros:**
- Fast writes
- Batch database writes
- Better write performance

**Cons:**
- Risk of data loss
- More complex
- Consistency issues

### Refresh-Ahead

**Proactive Refresh:**
- Refresh before expiration
- Background refresh
- Always fresh data

**Pros:**
- Always fresh
- Low latency
- Good user experience

**Cons:**
- Wasted refreshes
- More complex
- Resource usage

## Cache Invalidation

### Time-Based (TTL)

**Expiration:**
- Set time-to-live
- Automatic expiration
- Simple to implement

**Example:**
```python
cache.set("key", value, ttl=3600)  # Expires in 1 hour
```

### Event-Based

**Invalidate on Events:**
- Invalidate on updates
- Event-driven
- Always fresh

**Example:**
```python
def update_user(user_id, data):
    # Update database
    user = db.update(User, user_id, data)
    
    # Invalidate cache
    cache.delete(f"user:{user_id}")
    return user
```

### Version-Based

**Version Keys:**
- Include version in key
- Increment on update
- Old versions expire naturally

**Example:**
```python
def get_user(user_id, version):
    key = f"user:{user_id}:v{version}"
    return cache.get(key)
```

## Cache Strategies

### LRU (Least Recently Used)

**Eviction Policy:**
- Evict least recently used
- Good for temporal locality
- Simple to implement

### LFU (Least Frequently Used)

**Eviction Policy:**
- Evict least frequently used
- Good for frequency patterns
- More complex

### FIFO (First In First Out)

**Eviction Policy:**
- Evict oldest entries
- Simple
- May evict frequently used

### Random

**Eviction Policy:**
- Random eviction
- Simple
- Not optimal

## Cache Sizing

### Size Limits

**Memory Bounded:**
- Set maximum size
- Use eviction policies
- Monitor memory usage

**Example:**
```python
from cachetools import LRUCache

cache = LRUCache(maxsize=1000)
```

### Memory Management

**Monitor Usage:**
- Track cache size
- Monitor hit rate
- Adjust size dynamically

## Distributed Caching

### Cache Consistency

**Multi-Node Caching:**
- Invalidate across nodes
- Event propagation
- Consistent state

**Strategies:**
- Cache invalidation messages
- Version vectors
- Distributed locks

### Cache Sharding

**Partition Cache:**
- Shard by key hash
- Distribute load
- Scale horizontally

## Cache Warming

### Pre-Load Cache

**Proactive Loading:**
- Load frequently accessed data
- Reduce cold starts
- Better performance

**Example:**
```python
def warm_cache():
    popular_users = db.query(User).filter(popular=True).all()
    for user in popular_users:
        cache.set(f"user:{user.id}", user, ttl=3600)
```

## Best Practices

1. **Cache Hot Data**: Cache frequently accessed data
2. **Set Appropriate TTL**: Balance freshness and performance
3. **Monitor Hit Rate**: Track cache effectiveness
4. **Handle Cache Misses**: Graceful degradation
5. **Invalidate Properly**: Keep data fresh
6. **Size Appropriately**: Balance memory and performance
7. **Use Multiple Layers**: Browser, CDN, application cache
8. **Test Cache Behavior**: Include in tests

## Common Mistakes

### Over-Caching

**Problem:**
- Caching everything
- Memory waste
- Low hit rate

**Solution:**
- Cache selectively
- Monitor effectiveness
- Remove unused caches

### Under-Caching

**Problem:**
- Not caching enough
- Missed opportunities
- Poor performance

**Solution:**
- Identify hot paths
- Cache frequently accessed data
- Monitor performance

### Stale Data

**Problem:**
- Outdated cache
- Wrong invalidation
- User sees old data

**Solution:**
- Proper invalidation
- Appropriate TTL
- Version-based cache

## References

- [Caching Strategies](https://aws.amazon.com/caching/)
- [Cache Patterns](https://docs.microsoft.com/en-us/azure/architecture/patterns/cache-aside)

