What metrics would you monitor for Redis caching in production?

Measure cache health beyond hit rate

Explains cache observability across hit rate, latency, source load, memory, evictions, stale data, and fallback behavior.

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I would monitor hit rate, miss rate, Redis latency, Redis errors, timeouts, memory usage, evicted keys, expired keys, and command volume. I would also watch the source system, such as SQL, because cache misses or Redis outages can shift load back to the database. A cache metric only matters when it explains user latency, source pressure, or freshness.

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Hit rate, latency, errors, memory, evictions, source load, stale reads, key patterns, and endpoint correlation.

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A cache can have a high hit rate and still be unhealthy. Good observability connects cache behavior to user latency and source-system pressure.
I would monitor hit rate, miss rate, Redis latency, Redis errors, timeouts, memory usage, evicted keys, expired keys, and command volume. I would also watch the source system, such as SQL, because cache misses or Redis outages can shift load back to the database. A cache metric only matters when it explains user latency, source pressure, or freshness.
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I break metrics down by endpoint and normalized key pattern rather than recording raw keys that contain identifiers or create unbounded cardinality. For each path I compare hit, miss, stale-serve, rebuild, and fallback latency, plus payload size and serialization cost. Redis metrics are paired with source reads, database latency, and user response time so a high hit rate cannot hide an expensive hot key, failed invalidation, or slow deserialization.

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Looking only at Redis up/down.
Using hit rate without endpoint context.
Ignoring SQL load during cache misses.
No alert for evictions or stale-data incidents.

What metrics would you monitor for Redis caching in production?

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