Edge Computing · Resources · Discussion: is the edge-computing-resources ecosystem being replaced by another stack?

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

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.

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

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

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.

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

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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

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.

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

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.

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.

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.

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.

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.

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.

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

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.

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

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

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.

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.

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

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.

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.

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.

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.

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

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

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