huey VS Pipe

I've considered using Nextflow for bioinformatics pipelines but have yet to take the plunge. At work, I develop a proteomics pipeline that is composed of huey¹ tasks (Python library; simple alternative to Celery) which either use subprocess to call out to some external tool, or are just pure python. It runs in a worker container which is created by docker swarm, and all containers pull jobs from redis. For our scale, it works great. However, I don't have control over the resource utilization of individual steps, and in the past I've had issues with the pipeline blocking as a result of how I was chaining tasks together. I think something like Nextflow would remove these limitations, but one thing I think I would miss is the ability to debug individual pipeline steps locally with an interactive debugger. As far as I can tell, Nextflow has logging/tracing facilities but nothing quite like an interactive debugger. I'd be happy to be told I'm wrong, or even that I'm doing it wrong.

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¹ https://github.com/coleifer/huey/

Other options are DjangoQ and Huey, which tend to work ok. Of the two I prefer DjangoQ. Database backed, don't require the Redis/Celery rigmarole.

Funny, just this moment i finally switched from Celery to huey. And so far I don't regret. huey looks very promising, has good documentation and is well integrated into DJango. You should give it a try: https://github.com/coleifer/huey

huey – a little task queue for python

Huey -> Same again?

Huey: https://github.com/coleifer/huey

I just want to add that a couple celery alternatives worth looking at include huey and dramatiq.

huey

same, I ran into an issue cos of django-background-tasks. I am thinking to replace it with huey

The Apache Beam SDK for Python is another example. It has its own pipe expressions (|, >>, |>, etc.).

[1] https://github.com/JulienPalard/Pipe

For example, you can't do something like array.map(). You have to do map(array), which is really unergonomic in many cases. That's why there are things like Pipe for Python, or pipe-like operators in other languages.

Pipe – A Python library to use infix notation in Python

Yes it works with MPP engines as the package is not used for orchestration purposes. It is meant to encourage a coding paradigm: functional programming. The benefit of the package is that it provides functions that make it easy to apply functional programming to data processing tasks. Similar projects with a different focus are: fluentpy and Pipe