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Jupyter · Advanced · Those easily-missed type details in jupyter-pro

57
JUr/jupyter-pro·posted by winter·6 hours agoOpen source

Those easily-missed type details in jupyter-pro

Short version: jupyter-pro needs almost no tuning at small and medium scale — the point where it starts to hurt is much further out than most people assume. Full measurements below.

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.

# Load test: do not jump straight to the max concurrency.
# Ramp it up, otherwise you miss the knee.
for c in 50 100 200 400 800; do
  wrk -t8 -c$c -d60s --latency http://127.0.0.1:8080/api/feed
  sleep 20
done

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".

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.

20 comments

20 comments

M
Ttang_hao·just now

A question: what changes in a container with a 512Mi memory limit? That is how we run it in production.

412
Zzhu_zong·1 hour ago

I see point 3 differently. The trade-off depends on your read/write ratio: read-heavy with little writing means caching actually widens the inconsistency window.

217
Rran_bo·2 days ago

A question: what changes in a container with a 512Mi memory limit? That is how we run it in production.

191
Sswoole_lee·12 minutes ago

Worth learning from this debugging approach. We went straight at the logs and took a much longer route.

15
Cchen_dev·5 hours ago

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.

12
Cchen_devMod·1 hour ago

I just read the jupyter-pro source — the author actually explains the reasoning in a comment, roughly "so that it degrades into predictable behaviour in extreme cases".

251
Bbob_chen·5 hours 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.

78
Lli_ming·2 days ago

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.

6
Wwinter·3 minutes ago

This matches what we see in production. We only hit it past 3k QPS; the earlier load tests showed nothing — the test traffic was too clean, with no long-tail requests.

1
RraseOP·2 hours 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.

450
Zzhou_yi·2 days ago

Thanks for sharing real numbers — far more useful than the articles that only cover concepts.

1
RraseOP·2 hours ago

I just read the jupyter-pro source — the author actually explains the reasoning in a comment, roughly "so that it degrades into predictable behaviour in extreme cases".

205
Zzhu_zongMod·12 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.

419
Mmike_xu·3 minutes ago

I see point 3 differently. The trade-off depends on your read/write ratio: read-heavy with little writing means caching actually widens the inconsistency window.

115
Kkernel_panic·just nowedited

Saved. I am reworking this area this week — this saves a lot of wrong turns.

424
Oops_wang·2 days ago

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

110
Oops_wang·3 minutes ago

Agreeing with the above. One addition: with this option enabled the GC count in your metrics doubles, so adjust the alert threshold at the same time or it will keep firing.

6
Sslow_queryOP·5 hours agoedited

This is not a jupyter-pro problem, it is a usage problem. The docs say this API is not thread-safe and you must lock around it yourself.

496
Aalice_dev·5 hours agoedited

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.

346
RraseOP·2 days 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.

53

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