In this lesson, you will learn how to monitor the health of your key-value store by tracking eviction counts and command latency to ensure your caching layer remains performant.
When operating a high-performance database or cache, silence is not necessarily golden. If your eviction counts spike, your cache is essentially "churning," meaning your memory is too small for your working set, leading to frequent misses and increased database load. Similarly, monitoring command latency tells you exactly how much time your application spends waiting for network round-trips and memory operations.
Tracking Eviction Counts
Eviction happens when the database reaches its memory limit and must discard old keys to make room for new ones. In systems like Redis, you can monitor this via the evicted_keys metric. If you see a consistent upward trend, your memory-efficient data modeling (taught in lesson 13) is likely being undermined by an undersized cache.
You can visualize this in time-series tools like Prometheus or Grafana by calculating the rate of change:
-- Conceptual metric query
rate(redis_evicted_keys_total[5m])
If this value is high, you must either increase your memory allocation or reconsider your TTL strategy.
Measuring Command Latency
Latency is the "heartbeat" of your caching layer. We focus specifically on the duration of command execution. High latency often signals that your pipeline or batch execution (lesson 10) is inefficient, or that you are hitting the server with large value payloads (lesson 19) that create blocking operations.
To visualize this effectively, don't just look at the average. Always look at the p99 latency. While the average might look stable, a p99 spike indicates that 1% of your users are experiencing significant delays, which often correlates with garbage collection pauses or network buffer congestion.
By overlaying your eviction rate and your p99 command latency on a single dashboard, you can spot correlations instantly—for example, noticing that latency spikes occur exactly when the eviction process triggers to clear space.
Key Takeaway: Monitor the rate of evicted keys to assess cache hit quality and track p99 command latency to identify bottlenecks that impact your end-user experience.