caching-strategy

Design a caching strategy by evaluating what data to cache, selecting a cache topology like cache-aside, and setting eviction policies such as LRU or TTL. This approach intelligently stores frequently accessed data to reduce backend load and latency.

Prevent cache stampedes and thundering herd issues by applying thundering herd prevention patterns during your cache design. This Skill provides specific Redis configuration recommendations to mitigate these distributed system bottlenecks.

Choose between write-through and cache-aside caching patterns based on your data consistency requirements. Write-through offers immediate consistency, while cache-aside optimizes performance by loading data into the cache only on demand.

Handle cache invalidation and avoid stale data by implementing specific invalidation patterns that address performance optimization and data consistency trade-offs inherent in distributed systems.

You can apply this caching strategy to e-commerce platforms to determine optimal caching approaches for product catalogs, user sessions, and inventory data. This ensures a fast, responsive user experience while reducing backend load.

LRU and LFU eviction policies have limitations regarding which data they purge under memory constraints. LRU evicts the least recently used items, while LFU targets the least frequently used, requiring careful selection based on your specific access patterns.