Redis serves as the caching layer for countless production applications, but many teams underutilize its capabilities. Beyond simple string caching, Redis provides sorted sets, streams, and probabilistic data structures enabling sophisticated caching patterns.
Advanced Data Structures
Sorted sets enable leaderboards and time-based expiration with score-based ordering. HyperLogLog provides cardinality estimation with minimal memory for unique visitor counting. Streams support event sourcing patterns with consumer groups for reliable processing. These structures solve problems requiring multiple cache operations with single commands.
- Use sorted sets for rate limiting with sliding window implementations
- Implement distributed locks with SETNX and appropriate timeouts
- Cache entire objects as hashes enabling partial updates
- Use Redis Streams for lightweight message queuing and event sourcing
- Consider Redis Cluster for datasets exceeding single-node memory
Cache Invalidation Strategies
Cache invalidation remains challenging regardless of technology. TTL-based expiration works for data where staleness is acceptable. Event-driven invalidation ensures freshness but requires reliable event delivery. Cache-aside patterns simplify consistency at the cost of cache misses after invalidation.