Redis Monitoring: Gaining Fresh Perspective on Your Key-Value Store
Monitoring Redis databases in order to troubleshoot performance and other issues isn't always straightforward, but with the help of tools like eBPF, it's possible to gather insights in ways that would simply not have been possible using traditional approaches to Redis monitoring.
By storing data in memory, Redis key-value stores can read and write data much faster than databases that depend on conventional storage. That's part of the reason why many of the world's largest tech companies – such as Twitter, Snapchat and Craigslist – depend on Redis Servers, and clouds like GCP and Azure offer hosted data stores that use Redis protocol.
Unfortunately, though, Redis key-value stores don't always work the way they should. You may run into issues like slow performance due to low hit rates and poorly sharded data. Problems like these must be identified and fixed, otherwise , what's the point of paying for an in-memory key-value store if it's not living up to its full potential?
Monitoring Redis databases in order to troubleshoot performance and other problems isn't always as straightforward as you might like. But it's possible to do – especially with the help of tools like eBPF, which makes it possible to gather Redis monitoring insights in ways that would simply not have been possible using traditional approaches to Redis monitoring.
Redis is a single-threaded, high-throughput, low-latency, in-memory key-value store. That's a long (and overly hyphenated) way of saying that Redis uses in-memory data storage to deliver performance that’s hard to achieve using conventional databases.
If you're into databases and data structures, you might also enjoy knowing that Redis supports multiple types of data structures – including hashmaps, lists, sets, counters and more. That makes it a very flexible key-value store that is suited for many use cases, such as caching, pub/sub patterns, text search, graphs, atomic operations and rate limits, just to name a few.
On top of all of this, Redis also supports atomic operations, either through the use of transactions or by evaluating custom Lua scripts on requests.
To enhance performance and resiliency, Redis supports sharding, although that comes at the cost of losing atomicity (since single commands can't run across shards).
Alternatively, you can operate using a master/replica model that allows you to run multiple Redis nodes (each with a full copy of the data), achieving the benefits of more compute and memory resources without losing atomicity.
Oh, and in case you're wondering what happens to your in-memory data if your nodes shut down unexpectedly, Redis has a solution for that: You can configure persistent storage using the fsync feature, which syncs data to persistent files as backups.
Common Redis issues
Although Redis can do lots of cool things, it can also run into a lot of problems – just like any database.
Low hit rate
One common issue is what's known as low hit rate. This can cause poor performance on your Redis server due to TTL misses. You can check hit rate using the Redis CLI INFO command: