Que VS neoq

> Can you define "low throughput"?

<1000 messages per minute

Not saying SKIP LOCKED can't work with that many. But you'll probably want to do something better.

FWIW, Que uses advisory locks [1]

[1] https://github.com/que-rb/que

Probably worth mentioning that aside from delayed_job there are at least two more modern alternatives backed by the DB: Que and good_job.

Good article. Sidekiq is a good, well respected too. However if you are starting out I would recommend not using it, and instead choosing a DB based queue system. We have great success with que, but there are others like good_job.

(not sure why this comment was dead, I vouched for it)

There are a lot of ways to implement a queue in an RDBMS and a lot of those ways are naive to locking behavior. That said, with PostgreSQL specifically, there are some techniques that result in an efficient queue without locking problems. The article doesn't really talk about their implementation so we can't know what they did, but one open source example is Que[1]. Que uses a combination of advisory locking rather than row-level locks and notification channels to great effect, as you can read in the README.

[1]: https://github.com/que-rb/que

https://github.com/que-rb/que

This one seems to be the most performant. By a lot too, from my understanding (haven't ran any benchmark myself, but the readme shows some good postgres knowledge)

Que seems like a good alternative if one doesn't want to use Reids. However, given that most apps need Redis (and have it within their infrastructure) nowadays, I still think that Sidekiq is the better option in the generic case.

Implementations that use advisory locks like https://github.com/que-rb/que are much more efficient (atleast when I last tested) and will easily reach 10k job/s on even very modest hardware.

There is a Go port of Que but you can also easily port it to any language you like. I have a currently non-OSS implementation in Rust that I might OSS someday when I have time to clean it up.

It’s also possible to use advisory locks to implement a job queue in Postgres. See e.g. Que[1]. Note there are a fair number of corner cases, so studying Que is wise if trying to implement something like this, as well as some (a bit older) elaboration[2].

We implemented a similar design to Que for a specific use case in our application that has a known low volume of jobs and for a variety of reasons benefits from this design over other solutions.

[1]: https://github.com/que-rb/que

Do also take into consideration que-scheduler (disclaimer, am author). It is built on top of the robust que async job system.

I just want to commend OP - if they’re here - for choosing an int64 for job IDs, and MD5 for hashing the payload in Neoq, the job library linked [0] from the article.

Especially given the emphasis on YAGNI, you don’t need a UUID primary key, and all of its problems they bring for B+trees (that thing RDBMS is built on), nor do you need the collision resistance of SHA256 - the odds of you creating a dupe job hash with MD5 are vanishingly small.

As to the actual topic, it’s fine IFF you carefully monitor for accumulating dead tuples, and adjust auto-vacuum for that table as necessary. While not something you’d run into at the start, at a modest scale you may start to see issues. May. You may also opt to switch to Redis or something else before that point anyway.

[0]: https://github.com/acaloiaro/neoq

Neoq (https://github.com/acaloiaro/neoq) is a background job processor for Go.

Yes, another one. It began from my desire to have a robust Postgres-backed job processor. What I quickly realized was that the interface in front of the queue was what was really important. This allowed me to add both in-memory and Redis (provided by asynq) backends behind the same interface. Which allows dependent projects to switch between different backends in different settings/durable requirements. E.g. in-memory for testing/development, postgres when you're not running Google-scale jobs, and Redis for all the obvious use cases for a Redis-backed queue.

This allows me to swap out job queue backends without changing a line of job processor code.

I'm familiar with the theory that one shouldn't implement queues on Postgres, and to a large extent, I disagree with those theories. I'm confident you can point out a scenario in which one shouldn't, and I contend that those scenarios are the exception rather than the rule.

I created a background processor called Neoq (https://github.com/acaloiaro/neoq) that is likely to interest you.

This is exactly the thesis behind neoq: https://github.com/acaloiaro/neoq