caching-patterns

Redis caching reduces database load by storing frequently accessed data in memory. This Skill implements distributed caching using ioredis and @upstash/redis, applying patterns like cache-aside and write-through to optimize web application performance and data retrieval latency.

Cache-aside and write-through are distinct caching strategies. Cache-aside loads data into the cache on demand, while write-through writes data to the cache and database simultaneously. This Skill implements both patterns to help manage data freshness and system scalability based on your specific requirements.

Cache invalidation in Next.js is handled through tag-based and event-based mechanisms. This Skill provides implementations for cache invalidation alongside Next.js caching utilities like unstable_cache and cache headers, ensuring data freshness without compromising server-side caching performance.

In-memory caching is limited in serverless environments due to stateless function execution. This Skill addresses serverless constraints by integrating @upstash/redis for distributed caching, while still offering local LRU and Map-based caches for fast, non-persistent data retrieval when applicable.

Monitoring cache performance requires instrumenting your caching layer to track hit rates and latency. This Skill provides patterns for instrumenting cache performance, allowing you to evaluate the effectiveness of your caching strategies and optimize data management accordingly.

Rate limiting using Redis controls API request frequency by tracking user requests in shared memory. This Skill includes examples of Redis rate limiting, leveraging its distributed nature to enforce limits consistently across multiple application instances.