Caching Strategies and Eviction Rules

Caching improves latency and reduces load on backing stores by storing frequently accessed data closer to the consumer. Start by identifying what to cache: response bodies, computed results, or frequently accessed database rows. Determine cache granularity—fine-grained caches store individual records, while coarse-grained caches store aggregated results—based on consistency needs and reuse patterns.

Choose an eviction policy based on access patterns and memory constraints. Least Recently Used (LRU) works well for temporal locality, where recently used items are likely to be reused soon. Least Frequently Used (LFU) suits scenarios with skewed popularity distributions, such as popular products in an e-commerce catalog. Time-to-Live (TTL) eviction is useful when data becomes stale quickly, like session tokens or temporary tokens.

Introduce cache invalidation carefully: use write-through to update the cache on writes, or write-behind for asynchronous updates. Consider cache stampede prevention using a single writer or lock per key during cold cache scenarios. Validate cache hits and misses with metrics to tune placement, size, and eviction aggressiveness over time.