Cache stampede

When a popular key expires or the cache restarts, thousands of requests miss at the same moment and hit the database together. Request coalescing, early refresh and staggered TTLs prevent it.

Where it shows up in real systems

  • In-memory cache

    The hit ratio depends on how much of the working set fits in memory, the TTL and the eviction policy. If the cache goes down or is flushed, every read falls on the database at once: plan for that stampede. Write-behind is fast but loses writes if a node dies without a replica.

    In production: Redis (Amazon ElastiCache, Azure Cache), Valkey, Memcached, Dragonfly

See it break, then fix it

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