Ask HN: What's your go-to message queue in 2025?

I played with most message queues and I go with RabbitMQ in production.

Mostly because it has been very reliable for years in production at a previous company, and doesn’t require babysitting. Its recent versions also has new features that make it is a descent alternative to Kafka if you don’t need to scale to the moon.

And the logo is a rabbit.

Kafka is fairly different from the rest of these — it’s persistent and designed for high read throughput to multiple simultaneous clients at the same time, as some other commenters have pointed out.

We wanted replayability and multiple clients on the same topic, so we evaluated Kafka, but we determined it was too operationally complex for our needs. Persistence was also unnecessary as the data stream already had a separate archiving system and existing clients only needed about 24hr max of context. AWS Kinesis ended up being simpler for our needs and I have nothing but good things to say about it for the most part. Streaming client support in Elixir was not as good as Kafka but writing our own adapter wasn’t too hard.

I got tired of the pricing and/or complexity of running message queues/event brokers, so decided to play around with implementing my own. It utilizes S3 as the source of truth, which makes it orders of magnitude easier to manage and cheaper to run. There's an ongoing blog series on the implementation: https://github.com/micvbang/simple-event-broker

NATS.io because I'm using Go, and I can just embed it for one server [0], one binary to deploy with Systemd, but able to split it out when scaling the MVP.

[0] https://www.inkmi.com/blog/how-i-made-inkmi-selfhealing

SQS is great if you're already on AWS - it works and gets out of your way.

Kafka is a great tool with lots of very useful properties (not just queues, it can be your primary datastore), but it's not operationally simple. If you're going to use it you should fully commit to building your whole system on it and accept that you will need to invest in ops at least a little. It's not a good fit for a "side" feature on the edge of your system.

Postgres. Doing ~ 70k messages/second average. Nothing huge but don’t need anything dedicated yet.

I would highlight a distinction between Queues and Streams, as I think this is an important factor in making this choice.

In the case of a queue, you put an item in the queue, and then something removes it later. There is a single flow of items. They are put in. They are taken out.

In the case of a stream, you put an item in the queue, then it can be removed multiple times by any other process that cares to do so. This may be called 'fan out'.

This is an important distinction and really effects how one designs software that uses these systems. Queues work just fine for, say, background jobs. A user signs up, and you put a task in the 'send_registration_email' queue.[1]

However, what if some _other_ system then cares about user sign ups? Well, you have to add another queue, and the user sign-up code needs to be aware of it. For example, a 'add_user_to_crm' queue.

The result here is that choosing a queue early on leads to a tight-coupling of services down the road.

The alternative is to choose streams. In this case, instead of saying what _should_ happen, you say what _did_ happen (past tense). Here you replace 'send_registration_email' and 'add_user_to_crm' with a single stream called 'used_registered'. Each service that cares about this fact is then free to subscribe to that steam and get its own copy of the events (it does so via a 'consumer group', or something of a similar name).

This results in a more loosely coupled system, where you potentially also have access to an event history should you need it (if you configure your broker to keep the events around).

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This is where Postgresql and SQS tend to fall down. I've yet to hear of an implementation of streams in Postgresql[2]. And SQS is inherently a queue.

I therefore normally reach for Redis Steams, but mostly because it is what I am familiar with.

Note: This line of thinking leads into Domain Driven Design, CQRS, and Event Sourcing. Each of which is interesting and certainly has useful things to offer, although I would advise against simply consuming any of them wholesale.

[1] Although this is my go-to example, I'm actually unconvinced that email sending should be done via a queue. Email is just a sequence of queues anyway.

[2] If you know of one please tell me!

I am using Beanstalkd, it is small and fast and you just apt-get it on Debian.

However, I have noticed that oftentimes devs are using queues where Workflow Engines would be a better fit.

If your message processing time is in tens of seconds – talk to your local Workflow Engine professional (:

Sidekiq, Sidekiq, Sidekiq (or just Postgres if Im dealing with something trivial)

I have so far gotten by plenty well writing my own queue systems to fit the needs of the consuming application. Normally the only place where I need queue systems is in distributed systems with rapid fire transmissions to ensure messages hit the network in time sequence order. The additional benefit is that network traffic is saved in order when the current network socket fails so that nothing is lost but time.

The US Federal Reserve uses IBM MQ for the FedNow interbank settlement service that went live last year.

Architecture info: https://explore.fednow.org/resources/technical-overview-guid...

Another option to consider: Cloudflare Workers. They have a simple queue but you'll need to patch it with a Worker anyway. This means you can programatically manage the queue through the worker and also it makes it easy to send/receive HTTP requests.