CLI Tools · Career · After reading the cli-tools-career core source I finally understand the trade-offs

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.