That may be fun in a trivial setup such as op’s but when millions of customers or billions of transactions are affected it’s a nightmare. A competent engineer runs queries against a local and then a uat db, verifies results and then on prod. But if you must do it in prod then it must be limited in scope.
We're on the same page with the best approach, I just don't consider corrupting an unpredictable subset of my database much of an improvement. It's not closer to correct, it's just still incorrect.
This can also bite you if your dataset is larger than the buffer pool (or whatever other RDBMS calls it), and the particular table you’re querying isn’t commonly accessed.
Turns out when you start loading millions of rows of useless data into memory, the useful data has to get kicked out, and that makes query latency skyrocket.
