java-cache-redis

Prevent cache stampede in Redis by implementing singleflight or locking mechanisms that allow only one request to rebuild an expired key while others wait, protecting databases from thundering herd loads during heavy read traffic.

Handle Redis cache invalidation using explicit, time-based, or version-based strategies. Version-based invalidation enables bulk-safe clearing across multi-tenant environments, while explicit invalidation targets specific keys to maintain data correctness.

TTL with jitter improves Redis caching by adding randomized offsets to expiration times, preventing mass key expiration simultaneously. This distributes cache refreshes evenly, reducing sudden database load spikes and improving application stability.

Yes, cache-aside and write-through patterns can be used together with Redis in Java. Cache-aside handles read fallbacks to the database, while write-through synchronizes updates to both cache and database, ensuring consistency across read and write operations.

When Redis goes down, implement robust failure handling with fallback to direct database reads. Monitor hit rate, latency, and failure modes through observability metrics to ensure the Java backend remains operational during cache outages.

Negative caching is needed when your Java service faces heavy read loads for non-existent data. It stores empty results in Redis with a TTL, preventing repeated database queries for missing keys and reducing unnecessary backend load.