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Comment by mjfisher | Hacker News Reader

I see a lot of back and forth about postgres' suitability as a queuing system. I wonder if there's a couple of separable problems here. Postgres backed queues - even very scalable ones - might work well for background jobs in a monolithic app backed by a single DB. Things like backgrounding sending an email etc.

But usually when I reach for a queuing system, it's because I want to decouple a part of the architecture. And in that case, it's probably better to use a dedicated queueing system instead of postgres.

I wonder if the two cases are conflated in a lot of online discussion.

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We are very heavily using Postgres as a queuing system in production for many years already, not just some quiet background tasks but with millions of tasks in the queue at any given moment. I have yet so see any issues with that so I'm always a bit suspicious when people say they had to reach for something else unless you are at a crazy scale.

I know it's very hard to compare workloads, but famous recent example: https://openai.com/index/scaling-postgresql/

> It may sound surprising that a single-primary architecture can meet the demands of OpenAI’s scale; however, making this work in practice isn’t simple.

That post is about how Postgres doesn't scale for write-heavy workloads and that they had to move those workloads to Cosmos DB. For the rest of the remaining mostly-read workload they have a single primary with 50 read replicas.

The point is that Postgres scales a very long way. Once you arrive at OpenAI / ChatGPT scale there's no shame in reaching for a dedicated queuing system.

> scales a very long way

This ignores the fine print. It scales a very long way under specific circumstances with specific workloads. The more write-heavy your workload the less eloquently Postgres scales.

For OpenAI's use case you could swap Postgres with MySQL and it would scale just as well.

If your DB is write heavy, tune it for writes...

You can't tune your way out of this constraint. The heap table and MVCC implementation put a hard ceiling on what can be accomplished without reaching for another tool.

We are talking about Postgres as a queue here, which is heavy on tiny writes, reads and churning tables. The point is just that a RDMS like Postgres scales very far as a queue, it’s not a fight if Postgres or MySQL.

Nobody is arguing for using it for everything and forever but for most company sizes it’s perfectly fine to not reach for a dedicated queuing tool if you already have PG running.

> on tiny writes

Postgres doesn't do "tiny writes" - it writes whole pages multiple times on every update even if you're only changing 4 bytes, combined with even more writes later on when vacuuming tables. This is one of the reasons why the historical advice was to not build high-volume queues on top of Postgres and why "oh look another post about queues on Postgres" keeps soliciting comments like this.

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