arq VS faust

I need to process a lot of long running IO heavy jobs with background workers. I've been using ARQ for a while but decided to take a crack at writing my own distributed queue.

Lately I've been writing asynchronous python code and yes, the resource integration problem has come again. Because now from version 1.4 SQLAlchemy has become asynchronous a new boilerplate was created. Now, as a scheduler, I took a completely asynchronous Arq. Considering the specifics of the service, long I/O operations, it seems that the service turned out to be more optimal in asynchronous execution. I haven't measured the performance yet, but I think I'll write another post about it.

We have many similar ideas and paradigms, as one would expect from modern frameworks tackling the same problem. However, Faust has been deprecated and no longer managed or supported (there is a fork being maintained by the community at https://github.com/faust-streaming/faust). I guess Robinhood at some point decided not to spend any more resources on it, an often destiny of such side projects by large corporations. On the other hand, we hope to stay around for a long time and build to a framework and a community that loves it. This initial version was built using many libraries in order to have a working system and to gather feedback from the large community. We plan to replace eventually all, or at least most of dependancies, and replace then with Rust lib with Python bindings.

Just wanted to add that there is an actively maintained fork called faust-streaming, you can find it here: https://github.com/faust-streaming/faust

That's the basics yes. You have a pletora of things coming next. One is "Windowing" mentioned in the article, it's well explained and maybe it looks simple, but when you start with it, takes some time to wrap your mind around it.

The other things in kafka world are stateful transformations, which you would normally do using Java's Flink. The closest in python is Faust (the fork) [0]. What are stateful aggregations? something like doing SQL on top of a topic: group_by, count, reduce, and joins. So similar to SQL that you have kSQL [1].

Consumer groups IMO falls under basic usage, if you need to scale, take a look at it, and what are partitions and replicas, with that in mind, you'll be ok.

[0]: https://github.com/faust-streaming/faust

[1]: https://www.confluent.io/blog/ksql-streaming-sql-for-apache-...

The forked one, https://github.com/faust-streaming/faust, has been updated but still doesn’t seem to support joins.

Faust: Python framework

I’d like to use Python for this so I came across Faust but I’m not sure if it’s possible to create HTTP requests through Faust or if there are better alternatives.

There is a community fork which was stale for a while but got a new commit a couple days ago, this one might be usable, but is still quite risky (70 open issues etc.): https://github.com/faust-streaming/faust

There is a community which forked and actively develops faust, here’s the https://github.com/faust-streaming/faust

Hi, thanks for the answer, I'm actually using virtualenv with the requirements of the file associated with the example. However I wanted to tell you that due to covid the project is suspended but only temporarily, but there is an active fork https://github.com/faust-streaming/faust. But the example in the fork doesn't work either.