Thank your for posting this. I disagreed with OP but couldn't _quite_ find the words to describe why. But your post covers what i was trying to say.

I was quite literally asked to implement an in-memory cache to avoid a "full table scan" caused by a join to a small DB table recently. Our architect saw "full table scans" in our database stats and assumed that must mean a performance problem. I feel like he thought he was making a data-driven profiling decision, but seemed to misunderstand that a full-table scan is faster for a small table than a lookup. That whole table is in RAM in the DB already.

So now we have a complex Redis PubSub cache invalidation strategy to save maybe a ms or two.

I would believe that we have performance problems in this chunk of code, and it's possible an in-memory cache may "fix" the issue, but if it does, then the root of the problem was more likely an N+1 query (that an in-memory cache bandaids over). But by focusing on this cache, suddenly we have a much more complex chunk of code that needs to be maintained than if we had just tracked down the N+1 query and fixed _that_