Caching Strategies Deep Dive
Caching stores computed results for reuse, avoiding repeated expensive operations. It's often the single most impactful performance optimization available. But caching introduces complexity — stale data, invalidation challenges, and consistency concerns. Understanding caching strategies helps you capture the benefits while managing the tradeoffs.
Caching Layers
Modern applications use multiple caching layers, each serving different purposes:
Browser cache stores assets on the user's device. Proper cache headers let browsers skip network requests entirely for repeat visits. This is the fastest cache — zero latency.
CDN cache stores content at edge locations worldwide. Users fetch cached content from nearby servers instead of your origin. CDNs excel at static assets but can also cache API responses.
Application cache using Redis or Memcached stores computed results in memory. Database query results, API responses, and session data commonly live here.
Database query cache stores query results within the database itself. This helps with repeated identical queries but doesn't reduce database load as effectively as application-level caching.
Caching Strategies
Cache-aside (lazy loading) is the most common pattern. Your application checks the cache first; on a miss, it fetches from the source and stores the result:
def get_user(user_id):
cached = redis.get(f"user:{user_id}")
if cached:
return json.loads(cached)
user = db.query(User).get(user_id)
redis.setex(f"user:{user_id}", 300, json.dumps(user))
return user
Write-through updates the cache whenever data changes. Writes go to both cache and database together, keeping them synchronized. This prevents stale reads but adds write latency.
Write-behind (write-back) writes to cache immediately and updates the database asynchronously. This improves write performance but risks data loss if the cache fails before persistence.
Cache Invalidation
"There are only two hard things in Computer Science: cache invalidation and naming things." This joke persists because invalidation genuinely is difficult.
TTL (time-to-live) expires entries automatically. Simple and reliable, but data can be stale until expiration. Choose TTLs based on how fresh data needs to be.
Event-based invalidation clears cache entries when underlying data changes. More complex to implement but ensures fresher data.
Versioning uses new cache keys when data changes, letting old entries expire naturally. This avoids explicit invalidation but consumes more cache space.
Common Pitfalls
Cache stampedes occur when many requests simultaneously miss the cache and hit the database. Use locking or request coalescing to prevent this.
Over-caching wastes memory on rarely-accessed data. Monitor cache hit rates and adjust what you cache accordingly.
Under-caching misses optimization opportunities. If you're repeatedly computing the same results, consider caching them.