Redis Inventory: Analyzing memory usage in Redis by key pattern

”Redis Inventory” is a tool I developed as an open-source project to practice some coding skills and also solve a very practical problem. It collects memory usage information from Redis instance (or a cluster), detects key patterns, and displays memory usage in a hierarchical way.

Pretty much like lots of tools helping you to clean and organize your hard drive (for example “Disk Inventory X” or Unbuntu’s “Disk Usage Analyzer”) it even visualizes usage information in a similar fashion as the latest (sunburst diagram). But there are some nuances. Let’s look closer!

One can argue that, unlike hard disk, cache server is not persistent storage, so why bother analyzing its usage? Yeah, in theory, the cache is completely ephemeral and any application should be able to start and work with it in a “cold” state. But in reality, under the load, it is not always possible to flush the cache with no performance regression. Also, if there is a problem with how the app uses Redis, flushing would be only temporary mitigation as after some time the same problems will accumulate again. Sometimes, you just see general key count or memory consumption increase in Redis metrics, but it is not obvious where the problem is, so is hard to fix it in the code with no prior investigation.

The two most popular issues I’ve seen with cache were: cache key leak and forgetting to set TTLs or setting them too generously. Key leak happens when you accidentally add something too dynamic to the key, for example, timestamp or its hash. With TTLs you may rely on the application to delete them but miss that in some situations it will not happen and keys will stay in cache forever. Those issues are hard to track in big applications which are changing rapidly. Analysing all the code changes that can cause it with no hint for problematic keys can take days.

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