Caching Strategies

Caching Strategies

Implementing caching strategies for web applications, APIs, and distributed systems — from in-memory through distributed cache, CDN, and cache invalidation patterns.

When to Use

• Reducing database load for frequently accessed data
• Improving API response times
• Implementing distributed caching for scalability
• Designing cache invalidation strategies
• Choosing between local, distributed, and CDN caching

Caching Patterns

CACHE_PATTERNS = {
    'cache_aside': 'App checks cache first, loads from DB on miss, populates cache',
    'read_through': 'Cache loads from DB automatically on miss',
    'write_through': 'Data written to cache and DB simultaneously',
    'write_behind': 'Data written to cache immediately, DB asynchronously',
    'write_around': 'Data written to DB directly, cache invalidated',
    'refresh_ahead': 'Cache proactively refreshes before expiration',
}

class CacheAside:
    """Cache-Aside pattern implementation."""
    def __init__(self, cache, db):
        self.cache = cache
        self.db = db
    
    def get(self, key: str) -> any:
        result = self.cache.get(key)
        if result is not None:
            return result
        result = self.db.query(key)
        self.cache.set(key, result, ttl=300)
        return result

Common Pitfalls

1. Stale data — cache invalidation is one of the hardest problems in CS
2. Cache stampede — many requests miss cache simultaneously, overloading DB
3. Thundering herd — multiple requests regenerate cache at same time; use locking
4. Memory overuse — caching too much data evicts useful data; set TTLs wisely
5. Distributed cache consistency — nodes can have different cached versions

Verification Checklist

• Cache hit ratio > 80% for hot data
• TTLs set appropriately for data freshness needs
• Cache stampede protection (mutex/lock on miss)
• Monitoring on cache hit/miss ratios
• Invalidation strategy defined for data updates