CLI Tools · Career · Open-sourced a rate-limiting middleware for cli-tools-career with token bucket and sliding window

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

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.

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 just read the cli-tools-career 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.

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

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

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.

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.

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

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.

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.

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.

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

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.

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.

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

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.

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.

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.

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

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.

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.

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

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.

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.

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

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.

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

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

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

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.

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

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.

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

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 cli-tools-career 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.

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