The edge cases the jupyter-pro docs never spell out
Most jupyter-pro articles stop at "how to use it" and never cover "when not to use it". This is an attempt at the second half.
Worth noting: the official docs do cover this, just in a very inconspicuous spot. I only found it reading the source comments, where the author explains the reasoning — roughly "so that it degrades into predictable behaviour in extreme cases".
The first thing was to collapse the variables. We were changing config and upgrading the version at the same time, and afterwards nobody could say which change caused what. We rolled back to moving one variable at a time, re-ran three times, and only then did the curve settle. Tedious, but not skippable.
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 jupyter-pro itself but our upstream connection reuse — the load test traffic was too clean and hid the long-tail requests.