Redis latency spikes only for one very popular value. How would you investigate and mitigate it?

Diagnose a hot key causing uneven Redis load

Explains hot-key behavior, traffic concentration, partition pressure, payload size, and mitigation options.

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I would check whether one key pattern receives a disproportionate amount of traffic or stores a very large value. A hot key can overload a Redis node, network bandwidth, serialization, or the app code that processes the cached value. I would look at Redis command latency, key size, endpoint correlation, app instance metrics, and whether spikes line up with a campaign, dashboard, or public page.

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Hot keys, traffic concentration, payload size, partition pressure, local caching, sharding, and endpoint behavior.

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Hot keys are easy to miss because the overall cache hit rate can look good while one key or endpoint hurts latency.
I would check whether one key pattern receives a disproportionate amount of traffic or stores a very large value. A hot key can overload a Redis node, network bandwidth, serialization, or the app code that processes the cached value. I would look at Redis command latency, key size, endpoint correlation, app instance metrics, and whether spikes line up with a campaign, dashboard, or public page.
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Mitigations depend on the data. If the value is small and safe to keep briefly, I might add a tiny local in-memory cache per instance to reduce Redis calls. If the value is huge, I would split it, compress carefully, or change the API shape. If many users need slightly different filtered views, the key design may be too broad or too narrow. For extreme cases, sharding or replicas can help, but I would not start there.

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Only checking overall cache hit rate.
Increasing Redis capacity without finding the hot key.
Adding local cache with no freshness boundary.
Ignoring large payload serialization cost.

Redis latency spikes only for one very popular value. How would you investigate and mitigate it?

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