ECEdge Computing · Showcase
Community homeHot discussions Latest posts Best of Knowledge base Resources About

Edge Computing · Showcase · I built a small tool with edge-computing-show — code is open, feedback welcome

50
ECr/edge-computing-show·posted by li_ming·3 days agoOpen source

I built a small tool with edge-computing-show — code is open, feedback welcome

Short version: edge-computing-show 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.

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

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

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.

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.

25 comments

25 comments

M
Ddev_zhou·2 days 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.

389
Cchen_dev·just now

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.

309
Zzhou_yi·28 minutes ago

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

282
Oops_wang·28 minutes 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.

344
Mmike_xu·12 minutes ago

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

234
Zzhou_yiMod·1 hour ago

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

90
Cchen_dev·1 hour ago

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

372
Lli_ming·yesterday

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

146
Kkernel_panic·28 minutes ago

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

1
Hhuang_ke·1 hour ago

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

40
Sswoole_lee·5 hours ago

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

281
Llinlin·3 minutes ago

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

88
Bbob_chen·2 days 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.

83
Lli_ming·1 hour ago

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

6
Rran_bo·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.

517
Ttang_hao·2 days ago

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

108
Oops_wang·2 days ago

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

414
Hhuang_keOP·5 hours 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.

2
Rran_bo·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.

5
Aalice_dev·28 minutes agoedited

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

5
Wwinter·2 days ago

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

81
Kkite·yesterday

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

2
Kkernel_panic·3 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.

1
Aalice_dev·just now

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.

338
Ddev_zhou·2 days 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.

245

This is the post detail page /en/c/edge-computing-show/post/p5. Posts and comments are generated deterministically from a seeded PRNG, so the same post always renders the same content and the link can be shared, reloaded and indexed. In production this page reads MySQL for the post, Redis for hot-post caching, and fetches the whole comment tree in a single query on the path column.

See the database schema →