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Open Source · Blog · Why does open-source-blog use twice the memory of comparable tools?

65
OSr/open-source-blog·posted by winter·3 days agoExperience

Why does open-source-blog use twice the memory of comparable tools?

Some background first. Our setup is open-source-blog plus three downstream services, seven figures of daily requests, peaking around nine in the evening.

Worth noting: the official docs do cover this, just in a very inconspicuous spot. I only found it reading the source comments, where the author explains the reasoning — roughly "so that it degrades into predictable behaviour in extreme cases".

-- The query that broke: a full scan over 20M rows.
-- A composite index took P99 from 1.8s down to 42ms.
SELECT id, title, created_at
  FROM posts
 WHERE community_id = ?
   AND status = 1
 ORDER BY score DESC
 LIMIT 20;

We also fixed monitoring along the way: replaced average-based alerts with percentiles and split them per endpoint. False alerts dropped by about seventy percent and the on-call rotation visibly cheered up.

What genuinely surprised me was the tail. The average looked great while P99 jumped by an order of magnitude past some threshold. The cause was not open-source-blog itself but our upstream connection reuse — the load test traffic was too clean and hid the long-tail requests.

36 comments

36 comments

M
Mmike_xu·3 minutes ago

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.

513
Aalice_dev·28 minutes ago

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.

427
Lli_ming·just nowedited

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

75
Zzhou_yi·just now

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.

392
Kkite·12 minutes ago

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.

226
Zzhou_yi·1 hour ago

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

69
Rran_bo·28 minutes ago

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

108
Wwinter·2 hours ago

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

1
Lli_ming·1 hour ago

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.

5
Wwinter·5 hours ago

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.

472
Sslow_query·12 minutes ago

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

15
Aalice_dev·2 days ago

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

493
Zzhu_zongOPMod·5 hours ago

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

58
Bbob_chenOPMod·28 minutes ago

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.

144
Lli_ming·2 days agoedited

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

6
Kkite·2 hours ago

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

371
Sswoole_lee·12 minutes ago

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

351
Hhuang_ke·2 days ago

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

322
Cchen_devOP·28 minutes agoedited

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.

291
Ddev_zhou·5 hours agoedited

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

75
Wwinter·2 days ago

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

405
Bbob_chen·yesterday

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.

75
Aalice_dev·2 days ago

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

228
Sswoole_lee·3 minutes ago

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

177
Cchen_devOP·yesterday

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

19
Sswoole_lee·28 minutes ago

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

12
Kkernel_panic·just now

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.

68
Ddev_zhou·2 days ago

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.

360
Wwinter·2 days ago

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.

62
Kkite·2 days ago

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

51
Hhuang_keOPMod·2 days agoedited

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.

293
Ttang_hao·2 days ago

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.

46
Kkite·2 hours ago

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.

26
Rran_bo·just now

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.

25
Cchen_dev·yesterday

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.

17
Zzhu_zong·2 days ago

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.

2

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