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Edge Computing · Resources · Open-sourced a rate-limiting middleware for edge-computing-resources with token bucket and sliding window

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ECr/edge-computing-resources·posted by huang_ke·3 days agoPostmortem

Open-sourced a rate-limiting middleware for edge-computing-resources with token bucket and sliding window

Some background first. Our setup is edge-computing-resources plus three downstream services, seven figures of daily requests, peaking around nine in the evening.

Order of investigation, by return on effort: 1. Check downstream latency first — usually it is not your problem 2. Then pool hit rate and wait-queue length 3. Only then GC and allocation 4. Suspect the framework last

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.

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.

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 edge-computing-resources itself but our upstream connection reuse — the load test traffic was too clean and hid the long-tail requests.

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.

15 comments

15 comments

M
Kkite·12 minutes ago

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

271
Rrase·just now

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.

203
Aalice_devMod·3 minutes agoedited

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

202
Wwinter·12 minutes ago

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

224
Ttang_hao·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.

171
Rran_boMod·3 minutes agoedited

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

217
Mmike_xu·28 minutes ago

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

21
Llinlin·12 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.

109
Hhuang_keOP·1 hour ago

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

2
Mmike_xu·just now

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.

215
Ddev_zhou·2 hours ago

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

8
Kkite·just nowedited

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.

1
Nnikic·3 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.

8
NnikicOP·yesterday

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.

420
Llinlin·2 days agoedited

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

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