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Jupyter · Advanced · Discussion: is the jupyter-pro ecosystem being replaced by another stack?

11
JUr/jupyter-pro·posted by linlin·5 minutes agoExperience

Discussion: is the jupyter-pro ecosystem being replaced by another stack?

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 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.

Ssegmentfault.comExternal link · opens in a new tab
6 comments

6 comments

M
Mmike_xu·12 minutes ago

Has anyone run a controlled experiment? I did, reducing it to a single variable, and the difference was 4% — within noise. So I suspect the main cause is something else.

300
Ddev_zhou·3 minutes ago

Sharing our numbers, 8 cores 16GB, same scenario:

| Concurrency | P50 | P99 |
|---|---|---|
| 200 | 12ms | 88ms |
| 500 | 31ms | 340ms |

P99 clearly collapses at 500 concurrency, which lines up with your knee point.

3
Zzhou_yi·12 minutes ago

One counter-example: below jupyter-pro 7.4 the semantics of that code are different, so do not copy it verbatim. We got burned in staging and rolled back once.

1
Hhuang_ke·just now

There is actually a simpler fix that needs no architecture change: move this check up to the gateway and the problem disappears. The cost is one extra lookup at the gateway.

161
Hhuang_ke·just now

Can you give a minimal reproduction? I ran it locally for ten minutes and could not reproduce on macOS with the latest version.

80
Rrase·12 minutes ago

We have run this in production for two years without hitting it. That said, we never reached this scale, so our experience is not really evidence here.

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