Why this Ubuntu pattern gets slow in production: a source-level explanation
It took me two weeks of on-and-off digging and plenty of wrong turns. Writing the process down as it happened so the next person spends less time.
-- The query that broke: a full scan over 20M rows. -- A composite index took P99 from 1.8s down to 42ms. SELECT id, title, created_at FROM posts WHERE community_id = ? AND status = 1 ORDER BY score DESC LIMIT 20;
What genuinely surprised me was the tail. The average looked great while P99 jumped by an order of magnitude past some threshold. The cause was not Ubuntu itself but our upstream connection reuse — the load test traffic was too clean and hid the long-tail requests.
One last trap: in container environments remember to adjust the memory-related parameters in step. Otherwise the host limit and the process expectation disagree, and the symptom is intermittent, unreproducible failure.
We also fixed monitoring along the way: replaced average-based alerts with percentiles and split them per endpoint. False alerts dropped by about seventy percent and the on-call rotation visibly cheered up.
On trade-offs, my view is this: if nobody on the team owns this area long-term, do not introduce a second mechanism. With two coexistence you first have to work out which one is even in play when things break, and that costs far more than the performance you saved.