Message queues are usually a core part of any distributed architecture, and the options are endless: Kafka, RabbitMQ, NATS, Redis Streams, SQS, ZeroMQ... and then there's the “just use Postgres” camp for simpler use cases.
I’m trying to make sense of the tradeoffs between:
- async fire-and-forget pub/sub vs. sync RPC-like point to point communication
- simple FIFO vs. priority queues and delay queues
- intelligent brokers (e.g. RabbitMQ, NATS with filters) vs. minimal brokers (e.g. Kafka’s client-driven model)
There's also a fair amount of ideology/emotional attachment - some folks root for underdogs written in their favorite programming language, others reflexively dismiss anything that's not "enterprise-grade". And of course, vendors are always in the mix trying to steer the conversation toward their own solution.
If you’ve built a production system in the last few years:
1. What queue did you choose?
2. What didn't work out?
3. Where did you regret adding complexity?
4. And if you stuck with a DB-based queue — did it scale?
I’d love to hear war stories, regrets, and opinions.
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.
inb4 "oh but you wont be taken seriously" well... datadog.
[0] https://www.inkmi.com/blog/how-i-made-inkmi-selfhealing
So when selecting a message that isn’t started. It also looks for in progress ones that have been going longer than the timeout.
The update sets status, start time, and attempt counter.
If attempt counter equals 3 when the update happens, it sets the message to failed. The return looks at the stats sees failed and raises a notification.
Then if it’s a fix like correcting data or something I just reset the state to have it reprocess.
Never needed to track workers or cleanup jobs etc.
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.
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 (:
AWS Step Functions or GCP Workflows if you are on the cloud.
It has been submitted quite a few times but I don't readily see any experiences (pro or con) https://news.ycombinator.com/from?site=github.com/temporalio
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.
Its not very complex and feels like we're running a lot of compute resources to just sync data between systems. Admittedly there isn't good separation of concerns so there is overlap that requires data syncs.
I've been looking at things like kafka, etc. thinking there might be some magic there that makes us use less compute or makes data syncs a little easier to deal with but wonder what scale of data throughput is a tipping point where a service like that is really needed. If it turns out its just a different service but same timeliness of data sync and similar compute resources I struggle with what benefits might be provided.
I'd love for almost like a levels.fyi style site where people could anonymously report things like this for the tech stacks being used, throughput of data, amount of compute in play, and ratings/comments on their overall solution ("would do again", "don't recommend", "overkill", "resume filler"). It feels much like other areas of technology where a use case comes out of a huge company and RDD (resume driven development) takes hold and now there are people out there doing the equivalent of souping up a 1997 honda accord like its a racecar but its only driving grandma to her appointments.
That said, my suspicion about any such aggregation project like that is that context is everything and trying to capture "this sucks" for all the input criteria which produced that outcome is going to be a wall of text that few will write and even fewer will read (ahem, LLM "tl;dr it for me" aside)
Architecture info: https://explore.fednow.org/resources/technical-overview-guid...
RabbitMQ is neat out of the box. But I went with ZeroMQ at the time.
ZeroMQ is cool but during current year I'd only use it to learn from their excellent documentation. Coming from Python, it taught me about Berkeley sockets and the process of building cross-language messaging patterns. After a few projects, it's like realizing I didn't need ZeroMQ to begin with I could make my own! If ZeroMQ's Hintjens were still with us I'd still be using it.
It's like the documented incremental process of designing a messaging queue to fit your problem domain, plus a thin wrapper easing some of lower level socket nastiness. At least that's my experience using it over the years. Me talking about it won't do it enough justice.
NATS does the lower level socket wrapper part very nicely. It's a but more modern too. Golang's designed to be like a slightly nicer C syntax, so it would make sense that it's high performance and sturdy. So it's similar to ZeroMQ there.
I'm not sure if either persist to disk out of the box. So either of these are going to be simpler and faster than Kafka.
The DB people are probably trying too hard to cater to the queues. Ideally I'd have normalized the data and modeled the relations such transactions don't lock up the whole table. Then I started questioning why I needed a queue at all when databases (sans SQLite which is fast enough as is) are made for pooling access to a database.
Kafka supports pipelining to a relational database but this part is where you kind of have to be experienced to not footgun and I'm not at that level. I think using it as a queue in that you're short-circuiting it from the relational database pipeline is non-standard for Kafka. I suspect that's where a lot of the Kafka hate is from. I could understand if the distributed transactions part is hell but at that point it's like why'd you skip the database then? Trying to get that free lunch I assume.
I have an alternative. Try inserting everything into a SQLite file. Running into concurrency issues? Use a second SQLite file. Two computers? send it over the network. More issues? Since it's SQL just switch to a real database that will pool the clients. Or switch to five of them. SQL is sorta cool that way. I assume that would avoid the reimplementing half of the JVM to sync across computers where you get Oracle Java showing up to sell you their database halfway into making your galactic scale software or the whatever.
I must be stressed today. Thanks for asking.
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!
Performance is at least as good as Kafka.
For simpler workload, beanstalkd could be a good fit, either.
For smaller projects of "job queues," I tend to use Amazon SQS or RabbitMQ.
But just for clarity, Kafka is not really a message queue -- it's a persistent structured log that can be used as a message queue. More specifically, you can replay messages by resetting the offset. In a queue, the idea is once you pop an item off the queue, it's no longer in the queue and therefore is gone once it's consumed, but with Kafka, you're leaving the message where it is and moving an offset instead. This means, for example, that you can have many many clients read from the same topic without issue.
SQS and other MQs don't have that persistence -- once you consume the message and ack, the message disappears and you can't "replay it" via the queue system. You have to re-submit the message to process it. This means you can really only have one client per topic, because once the message is consumed, it's no longer available to anyone else.
There are pros and cons to either mechanism, and there's significant overlap in the usage of the two systems, but they are designed to serve different purposes.
The analogy I tend to use is that Kafka is like reading a book. You read a page, you turn the page. But if you get confused, you can flip back and reread a previous page. An MQ like RabbitMQ or Sidekiq is more like the line at the grocery store: once the customer pays, they walk out and they're gone. You can't go back and re-process their cart.
Again, pros and cons to both approaches.
"What didn't work out?" -- I've learned in my career that, in general, I really like replayability, so Kafka is typically my first choice, unless I know that re-creating the messages are trivial, in which case I am more inclined to lean toward RabbitMQ or SQS. I've been bitten several times by MQs where I can't easily recreate the queue, and I lose critical messages.
"Where did you regret adding complexity?" -- Again, smaller systems that are just "job queues" (versus service-to-service async communication) don't need a whole lot of complexity. So I've learned that if it's a small system, go with an MQ first (any of them are fine), and go with Kafka only if you start scaling beyond a single simple system.
"And if you stuck with a DB-based queue -- did it scale?" -- I've done this in the past. It scales until it doesn't. Given my experience with MQs and Kafka, I feel it's a trivial amount of work to set up an MQ/Kafka, and I don't get anything extra by using a DB-based queue. I personally would avoid these, unless you have a compelling reason to use it (eg, your DB isn't huge, and you can save money).
It depends on your use case (or maybe what you mean by "client"). If I just have a bunch of messages that need to be processed by "some" client, then having the message disappear once a client has processed it is exactly what you want.
On the consumer side the duty cycle drives design. If it’s a steady flow then a polling listener is easy to right size. If the flow is episodic (long periods of idle with unpredictable spikes of high load) one option is to put a alarm on the queue that triggers when it goes from empty to non-empty, and handle that alarm by starting the processing machinery. That avoids the cost of constantly polling during dead time.
I have an idea of a project where even MySql/Maria is too much of admin burden.
I found it hard to shift mentally from MSK and its even triggers back to regular consumer spun up in containers etc. but that also it rather MSK than Kafka.
I am currently swapping out the whole pub/sub layer to MongoDB change streams, which I have found to be working really well. For queuing it attempts to lock on read so I can scale consumers with retry / backoff etc. Broadcast is simple and without locking, auto delete in Mongo.
I will have to see how it really scales and I'm sure I'm trading one problem for another but, it will definitely help to remove a moving part. Overall, app is rather low volume with the occasional spike. I would have stayed with Kafka were there be let's say >100rpm on the core functions.
Database functions can remain independent of stack or programming changes.
Complexity comes on it's own, often little need to pile it in from the start to tie ones hands early for relatively simple solutions.
I still would call that crazy, because of the mental tax of explaining to every new employee "wait, you're using IMAP for what?" but if it works for you, then great
Example: https://natsbyexample.com/examples/auth/callout/java
For those unfamiliar, it's a Lua library that gets executed in Redis using one of the various language bindings (which are essentially wrappers around calling the Lua methods).
With our multi-node redis setup it seems to be quite reliable.