oban VS neoq

Oban, backed by PostgreSQL or SQLite, also provides a queue-based job processing system. Exq, on the other hand, is backed by Redis. It provides features similar to Flume, but without built-in rate limiting and batch processing capabilities.

In Elixir land Oban[0] uses Postgres as queue and seems to work quite well.

[0] - https://github.com/sorentwo/oban

I hear you on that, and can say that Postgres is incredibly capable at going beyond typical relational database workloads. One example are durable queues that are transactionally consistent with the rest of the database play a unique role in our architecture that would otherwise require more ceremony. More details here: https://getoban.pro

We are also working on shifting some workloads off of Postgres on to more appropriate systems as we scale, like logging. But we intentionally chose to minimize dependencies by pushing Postgres further to move faster, with migration plans ready as we continue to reach new levels of scale (e.g. using a dedicated log storage solution like elastic search or clickhouse).

You can actually have "background jobs" in very different ways in Elixir.

> I want background work to live on different compute capacity than http requests, both because they have very different resources usage

In Elixir, because of the way the BEAM works (the unit of parallelism is much cheaper and consume a low amount of memory), "incoming http requests" and related "workers" are not as expensive (a lot less actually) compared to other stacks (for instance Ruby and Python), where it is quite critical to release "http workers" and not hold the connection (which is what lead to the creation of background job tools like Resque, DelayedJob, Sidekiq, Celery...).

This means that you can actually hold incoming HTTP connections a lot longer without troubles.

A consequence of this is that implementing "reverse proxies", or anything calling third party servers _right in the middle_ of your own HTTP call, is usually perfectly acceptable (something I've done more than a couple of times, the latest one powering the reverse proxy behind https://transport.data.gouv.fr - code available at https://github.com/etalab/transport-site/tree/master/apps/un...).

As a consequence, what would be a bad pattern in Python or Ruby (holding the incoming HTTP connection) is not a problem with Elixir.

> because I want to have state or queues in front of background work so there's a well-defined process for retry, error handling, and back-pressure.

Unless you deal with immediate stuff like reverse proxying or cheap "one off async tasks" (like recording a metric), there also are solutions to have more "stateful" background works in Elixir, too.

A popular background job queue is https://github.com/sorentwo/oban (roughly similar to Sidekiq at al), which uses Postgres.

It handles retries, errors etc.

But it's not the only solution, as you have other tools dedicated to processing, such as Broadway (https://github.com/dashbitco/broadway), which handles back-pressure, fault-tolerance, batching etc natively.

You also have more simple options, such as flow (https://github.com/dashbitco/flow), gen_stage (https://github.com/elixir-lang/gen_stage), Task.async_stream (https://hexdocs.pm/elixir/1.12/Task.html#async_stream/5) etc.

It allows to use the "right tool for the job" quite easily.

It is also interesting to note there is no need to "go evented" if you need to fetch data from multiple HTTP servers: it can happen in the exact same process (even: in a background task attached to your HTTP server), as done here https://transport.data.gouv.fr/explore (if you zoom you will see vehicle moving in realtime, and ~80 data sources are being polled every 10 seconds & broadcasted to the visitors via pubsub & websockets).

Hi HN!

This is something I've been tinkering on for the past couple months. It's basically just an API/CLI for scheduling delayed or recurring jobs as HTTP requests.

I initially built it as a personal tool to save myself a bit of time on little side projects where I've needed scheduled/recurring alerts, but decided it could be a good opportunity to practice building out a nice landing page [0] and documentation [1]. And who knows, maybe someone else will find it useful ¯\_(ツ)_/¯

The tool relies heavily on Elixir's Oban [2] library for managing jobs, and Mintlify [3] for documentation. I also shamelessly stole most of the frontend design from Resend [4] because I'm a fan of the aesthetic and thought it would be good for my design chops to use their design as a guide. I also discovered Radix [5] UI while working on this, which ended up being immensely helpful for moving quickly on the frontend.

Anyways, I almost certainly spent a bit too much time on small UX details that are most likely utterly inconsequential, but it was a fun exercise in polish :)

All feedback is welcome!

[0] https://www.booper.dev/

[1] https://docs.booper.dev/

[2] https://github.com/sorentwo/oban

[3] https://mintlify.com/

[4] https://resend.com/

[5] https://www.radix-ui.com/

Idk about pgagent but any table is a resilient queue with the multiple locks available in pg along with some SELECT pg_advisory_lock or SELECT FOR UPDATE queries, and/or LISTEN/NOTIFY.

Several bg job libs are built around native locking functionality

> Relies upon Postgres integrity, session-level Advisory Locks to provide run-once safety and stay within the limits of schema.rb, and LISTEN/NOTIFY to reduce queuing latency.

https://github.com/bensheldon/good_job

> |> lock("FOR UPDATE SKIP LOCKED")

https://github.com/sorentwo/oban/blob/8acfe4dcfb3e55bbf233aa...

> Bad code in a specific part of the codebase bringing down the whole app, as in our November incident.

This is a non-issue if you're using a Elixir/Erlang monolith given its fault tolerant nature.

The noisy neighbour issue (resource hogging) is still something you need to manage though. If you use something like Oban[1] (for background job queues and cron jobs), you can set both local and global limits. Local being the current node, and global the cluster.

Operating in a shared cluster (vs split workload deployments) give you the benefit of being much more efficient with your hardware. I've heard many stories of massive infra savings due to moving to an Elixir/Erlang system.

1. https://github.com/sorentwo/oban

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