Tanstack · Ops · Open-sourced a rate-limiting middleware for tanstack-ops with token bucket and sliding window

I just read the tanstack-ops 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.

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

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

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

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.

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

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.

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.

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.

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

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.

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

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

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.

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

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.

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

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.

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

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.

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.

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.

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.

I just read the tanstack-ops 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.

I just read the tanstack-ops 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.

One counter-example: below tanstack-ops 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 tanstack-ops 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.

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.

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.

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.

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.

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.

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

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

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

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

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

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