Caching dramatically reduces latency and database load by storing frequently accessed data in fast memory. Core strategies: cache-aside (app checks cache first, falls back to DB), write-through (write to cache and DB simultaneously), read-through (cache fetches from DB on miss). Key concerns: cache invalidation (stale data), TTL design (too short = DB overwhelm, too long = stale), and cache stampede (many concurrent misses hitting DB at once). Use distributed caches (Redis) for multi-instance applications.
Preventing DB overload from concurrent cache misses.
When a high-traffic cache key expires, thousands of concurrent requests hit the DB simultaneously. Use Redis SETNX as a mutex — only one worker refreshes, others wait or get a slightly stale value.
TTL expiry is a safety net, not the primary invalidation strategy. When user data changes, immediately delete/update that user's cache key. Stale data served to users is a product bug.
A cache hit rate below 80% wastes infrastructure cost and provides minimal latency benefit. Low hit rate indicates: keys too granular, TTL too short, poor access pattern alignment, or insufficient cache size.
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