Open Source · Blog · Two weeks with open-source-blog: what feels right and what drives me up the wall

20
OSr/open-source-blog·posted by linlin·1 hour agoInternals

Two weeks with open-source-blog: what feels right and what drives me up the wall

Short version: open-source-blog needs almost no tuning at small and medium scale — the point where it starts to hurt is much further out than most people assume. Full measurements below.

// Minimal reproduction: you must use a real long-tail distribution here.
// Uniform load-test traffic will never trigger this.
func (s *Server) handle(ctx context.Context) error {
    conn, err := s.pool.Acquire(ctx)
    if err != nil {
        return fmt.Errorf("acquire: %w", err)
    }
    defer conn.Release()

    return s.do(ctx, conn)
}

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.

The first thing was to collapse the variables. We were changing config and upgrading the version at the same time, and afterwards nobody could say which change caused what. We rolled back to moving one variable at a time, re-ran three times, and only then did the curve settle. Tedious, but not skippable.

On trade-offs, my view is this: if nobody on the team owns this area long-term, do not introduce a second mechanism. With two coexistence you first have to work out which one is even in play when things break, and that costs far more than the performance you saved.

6 comments

6 comments

M
Cchen_dev·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.

107
Llinlin·28 minutes agoedited

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.

7
Zzhu_zong·28 minutes 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.

6
Mmike_xu·28 minutes ago

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

1
Ddev_zhou·just nowedited

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.

4
Sslow_query·12 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.

7

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