arniesmtpbufferserver VS oban

My guess is that many people are implementing queuing mechanisms just for sending email.

The Linux file system makes a perfectly good basis for a message queue since file moves are atomic.

You can see how this works in Arnie SMTP buffer server, a super simple queue just for emails, no database at all, just the file system.

https://github.com/bootrino/arniesmtpbufferserver

A practical applications of atomic mv is building simple file based queuing mechanisms.

For example I wrote this SMTP buffer server which moves things to different directories as a simple form of message queue.

https://github.com/bootrino/arniesmtpbufferserver

Caveat I think this needs examination from the perspective of fsync - i.e. I suspect the code should be fsyncing at certain points but not sure.

I actually wrote (in Rust) a simple file based message queue using atomic mv. It instantly maxed out the SSD performance at about 30,000 messages/second.

Yeah I was using Celery for sending emails - nothing else.

And it was such a nightmare to configure and debug and such overkill for email buffering that in a fit of frustration I wrote the Arnie SMTP buffering server and ditched Celery.

https://github.com/bootrino/arniesmtpbufferserver

It's only 100 lines of code:

https://github.com/bootrino/arniesmtpbufferserver/blob/maste...

Here's the 100 lines of code:

https://github.com/bootrino/arniesmtpbufferserver/blob/master/arniesmtpbufferserver.py

Here's the github repo:

https://github.com/bootrino/arniesmtpbufferserver

It's MIT licensed.

Arnie is a server that has the single purpose of buffering outbound SMTP emails.

A typical web SAAS needs to send emails such as signup/signin/forgot password etc.

The web page code itself should not directly write this to an SMTP server. Instead they should be decoupled. There's a few reasons for this. One is, if there is an error in sending the email, then the whole thing simply falls over if that send was executed by the web page code - there's no chance to resend because the web request has completed. Also, execution of an SMTP request by a web page slows the response time down of that page, whilst the code goes through the process of connecting to the server and sending the email. So when you send SMTP email from your web application, the most performant and safest way to do it is to buffer them for sending. The buffering server will then queue them and send them and handle things like retries if the target SMTP server is down or throttled.

There's a few ways to solve this problem - you can set up a local email server and configure it for relaying. Or in the Python world people often use Celery. Complexity is the down side of using either Celery or an email server configured for relaying - both of these solutions have many more features than needed and can be complex to configure/run/troubleshoot.

Arnie is intended for small scale usage - for example a typical web server for a simple SAAS application. Large scale email traffic would require parallel sends to the SMTP server.

Arnie sequentially sends emails - it does not attempt to send email to the SMTP server in parallel. It probably could do fairly easily by spawning email send tasks, but SMTP parallelisation was not the goal in writing Arnie.

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