zlacker

>>bo0tzz+(OP)
For several projects I’ve opted for the even dumber approach, that works out of the box with every ORM/Query DSL framework in every language: using a normal table with SELECT FOR UPDATE SKIP LOCKED

https://www.pgcasts.com/episodes/the-skip-locked-feature-in-...

It’s not “web scale” but it easily extends to several thousand background jobs in my experience

>>aduffy+82
In my experience, a queue system is the worst thing to find out isn't scaling properly because once you find out your queue system can't architecturally scale, there's no easy fix to avoid data loss. You talk about "several thousand background jobs" but generally, queues are measured in terms of Little's Law [1] for which you need to be talking about rates; according to Little's Law namely average task enqueue rate per second and average task duration per second. Raw numbers don't mean that much.

In the beginning you can do a naive UPDATE ... SET, which locks way too much. While you can make your locking more efficient, doing UPDATE with SELECT subqueries for dequeues and SELECT FOR UPDATE SKIP LOCKED, eventually your dequeue queries will throttle each other's locks and your queue will grind to a halt. You can try to disable enqueues at that point to give your DB more breathing room but you'll have data loss on lost enqueues and it'll mostly be your dequeues locking each other out.

You can try very quickly to shard out your task tables to avoid locking and that may work but it's brittle to roll out across multiple workers and can result in data loss. You can of course drop a random subset of tasks but this will cause data loss. Any of these options is not only highly stressful in a production scenario but also very hard to recover from without a ground-up rearchitecture.

Is this kind of a nightmare production scenario really worth choosing Boring Technology? Maybe if you have a handful of customers and are confident you'll be working at tens of tasks per second forever. Having been in the hot seat for one of these I will always choose a real queue technology over a database when possible.

[1]: https://en.wikipedia.org/wiki/Little%27s_law

>>Karrot+sQ
If you find yourself in that situation, migrating to a more performant queuing solution is not that much of a leap. You already have an overall system architecture that scales well (async processing with a queue).

_Ideally_ the queuing technology is abstracted from the job-submitters/job-runners anyway. It's a bit more work if multiple services are just writing to the queue table directly.

I agree that the _moment_ the system comes to a screeching halt is definitely not fun.