Llamaindex · Toolchain · Discussion: is the llamaindex-tools ecosystem being replaced by another stack?

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.

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.

One counter-example: below llamaindex-tools 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.

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.

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

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.

I just read the llamaindex-tools 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.

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

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.

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

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

I just read the llamaindex-tools 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.

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.

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

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

I just read the llamaindex-tools 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.

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.

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.

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.

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.

This is not a llamaindex-tools 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.

One counter-example: below llamaindex-tools 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 llamaindex-tools source — the author actually explains the reasoning in a comment, roughly "so that it degrades into predictable behaviour in extreme cases".

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.