Redis Monitoring 101: Key Issues and Best Practices

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? … ### Large JSON keys Using large JSON keys instead of Redis hashes is another common Redis issue. It happens when you use a single key to hold a JSON value as a string, causing lookups in your apps to be very inefficient. A simple solution is to hold the data in a hash so you get a full lookup using a single field in O(1) complexity. … ### Poorly sharded data Redis clusters spread their data across many nodes. When you use a Redis cluster with a general-purpose hash instead of using multiple keys, your cluster can suffer a performance hit. This happens because the key is stored on a single node, and in a high-scale environment, the pressure will fall on that node instead of being distributed between all of the nodes in the cluster. The result is that the node becomes a performance bottleneck. As a real-world example, consider a cluster that stores user data in a hash, where the key is the user ID. An authentication server that performs a lot of lookups on the user ID will place heavy pressure on the node that stores the key. A solution would be to spread the hashed data to multiple keys across nodes, letting Redis's sharding algorithm distribute the pressure. … Here, we perform pipelined requests on three individual keys. The requests execute on a single node, and if one of the keys is not on that specific node, the commands with the keys that are on that node will return a response while the others will return the MOVED error. A quick fix is to use hashtags in the key structure, which means simply adding curly brackets around the part of the key that we want to hash by will cause the sharding algorithm to direct the values to the same node: … - **Multiple points of failure**: When something goes wrong in Redis, there are typically multiple potential causes. For example, high latency could stem from increased system load, lack of available memory, or poorly structured requests, to name just a few possibilities. To monitor and troubleshoot effectively, you need to be able to explore each potential root cause quickly. This requires the ability to correlate and analyze a variety of data points.