Redis is a cache database that stores documents in memory. The data store has a key-value pair lookup with O(1) time complexity. This makes the cache fast and convenient because it does not have to deal with complex execution plans to get data. The cache service can be trusted to find a cache entry with a value in almost no time.
When datasets in cache begin to grow, it can be surprising to realize that any latency is not a Redis issue. In this take, I will show you several strategies to cache Redis data structures then show what you can do to pick the best approach.
Read on for the contenders and how they do. ProtoBuf’s results on small datasets surprised me.