Linux Community · Building an internal platform with linux from scratch: architecture and decision log

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.

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.

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

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

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.

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.

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

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

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.

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.

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

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

This is not a linux 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 linux 7.4 the semantics of that code are different, so do not copy it verbatim. We got burned in staging and rolled back once.

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

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

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

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.

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

This is not a linux 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.

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

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.

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

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.

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

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

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

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

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.

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

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

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.

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.

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

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.

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

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

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.

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

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.

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.

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 linux 7.4 the semantics of that code are different, so do not copy it verbatim. We got burned in staging and rolled back once.