A high hit rate
Where the backend sees very little, and a small drop changes that sharply
requests-per-second: 50000 hit-rate: 99 backend-latency-ms: 40
Redis Cache Hit Rate Calculator
Give the request rate and hit rate. The result shows what reaches the backend, at your hit rate and at several others, because that sensitivity is the thing worth planning for.
Paste below, or drop a file anywhere on this panel
Or drop a file anywhere on this panel. Nothing is uploaded: the analysis runs in this tab.
The answer appears here
Paste on the left and press Calculate. Nothing leaves this tab.
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Results
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Examples
Real input you can load into the tool above. Each one shows a different thing going wrong, because that is what the tool is for.
A degraded cache
The same traffic at 80%, which is four times the backend load of 95%
requests-per-second: 50000 hit-rate: 80 backend-latency-ms: 40
Common mistakes
These are the ones that fail silently. The config is accepted, nothing raises an error, and the consequence arrives later.
Sizing the backend for the steady-state hit rate
A restart, a flush or a mass eviction sends everything to the backend at once. The backend must survive a cold cache.
Instead:Size for the cold case, or add a stampede guard so only one request per key repopulates.
Setting the same TTL on a batch of keys
They expire together and produce a synchronised miss spike.
Instead:Jitter the TTLs by a random fraction.
Reporting the average latency
Hits and misses are orders of magnitude apart, so the average describes almost no real request.
Instead:Report percentiles. The p99 is roughly the backend latency once misses exceed 1%.
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