Cache hit rate: What it measures, what it doesn’t, and when to stop using it

Cache hit rate measures the share of requests served directly from a cache, but its meaning, calculation boundaries, and useful targets vary substantially across CPUs, databases, Redis, and CDNs. While high rates can reduce origin load and costs, they do not necessarily indicate low latency, high throughput, or reliability, especially when cumulative counters, multi-tier caches, invalidations, and operating-system page caches distort reporting. Even a 99% rate can produce poor tail latency when misses are much slower than hits or when user requests require multiple cache operations. Heavy dependence on a high hit rate also creates operational risk: cold caches, failures, deployments, or scaling events can redirect overwhelming traffic to an undersized backing store and trigger cascading outages. Adding memory may not improve results for workloads with little reuse, and alternative eviction policies can outperform LRU when one-time accesses dominate. Effective cache management therefore requires measuring recent behavior accurately, understanding workload characteristics, tuning TTLs and cache keys, planning warming and failure handling, and evaluating latency, throughput, and backing-store resilience alongside hit rate. The source also presents Aerospike’s in-memory-index and flash-storage architecture as an approach intended to make cache misses more predictable and reduce reliance on cache-hit ratios for real-time workloads.

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