Distributed Caching Strategies with Redis
Cache Design Patterns for High-Performance Microservices
Practical guide to Redis in production microservices: when to cache, how to invalidate, handling cache stampedes, and the patterns that kept gaming sessions fast and payment idempotency checks reliable.
Cache-Aside Pattern
The most common pattern: check cache first, if miss, fetch from DB and populate cache. Simple but requires handling cache invalidation on writes. We used this for player session state in gaming: every game action checked Redis before hitting MongoDB, reducing DB load by ~70% during peak hours.
TTL Strategy: Your First Defense
Every cached value should have a TTL. The TTL should reflect how stale data is acceptable for that use case. Player session state: 30 minutes (auto-expires on inactivity). Idempotency keys: 24 hours (matches retry window). Configuration data: 1 hour (allow cache refresh without deployment). The danger is setting TTLs too long and serving stale data, or too short and generating cache stampedes.
Preventing Cache Stampedes
When a popular key expires, thousands of concurrent requests can hammer the DB simultaneously. We solved this with probabilistic early expiration: re-compute the cache slightly before TTL expiry with probability proportional to remaining TTL. Redis's SETNX (set if not exists) also provides a mutex-based approach for high-contention keys.
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