caching-strategies

Cache-aside and read-through are common caching patterns used to reduce database load. This approach helps select between CDN, reverse proxy, application, and distributed caches based on access patterns and data locality.

To implement cache-aside with Redis, configure your application to check the cache first, fetch from the database on a miss, and then populate the cache with per-key TTLs. You must invalidate affected keys after writes to keep data fresh.

Use write-through caching when you need strong data consistency, as it writes to both cache and database synchronously. Use write-behind caching when low latency is critical, allowing asynchronous database updates after writing to the cache.

Prevent cache stampedes by implementing stampede protection mechanisms like request coalescing or locking. Design robust cache keys by ensuring they uniquely identify data entities and incorporate relevant parameters to avoid collisions and drift.

Best practices for cache invalidation involve defining appropriate TTLs and using event-driven invalidation to remove stale data. Eviction policies should be selected based on data access frequency to optimize memory usage and maintain high hit ratios.

Monitor cache performance by tracking hit and miss ratios, memory usage, and data drift across your serving layers. Observability is crucial for identifying inefficiencies in your caching strategies and ensuring reliable low-latency API responses.