nextflow VS ploomber

> It's been a while since you can rerun/resume Nextflow pipelines

Yes, you can resume, but you need your whole upstream DAG to be present. Snakemake can rerun a job when only the dependencies of that job are present, which allows to neatly manage the disk usage, or archive an intermediate state of a project and rerun things from there.

> and yes, you can have dry runs in Nextflow

You have stubs, which really isn't the same thing.

> I have no idea what you're referring to with the 'arbitrary limit of 1000 parallel jobs' though

I was referring to this issue: https://github.com/nextflow-io/nextflow/issues/1871. Except, the discussion doesn't give the issue a full justice. Nextflow spans each job in a separate thread, and when it tries to span 1000+ condor jobs it die with a cryptic error message. The option of -Dnxf.pool.type=sync and -Dnxf.pool.maxThreads=N prevents the ability to resume and attempts to rerun the pipeline.

> As for deleting temporary files, there are features that allow you to do a few things related to that, and other features being implemented.

There are some hacks for this - but nothing I would feel safe to integrate into a production tool. They are implementing something - you're right - and it's been the case for several years now, so we'll see.

Snakemake has all that out of the box.

For now, I think that the best place to track this / get your voice heard is this GitHub Discussions post (which covers many things - error reporting is one of them). https://github.com/nextflow-io/nextflow/discussions/3107

We could spend the day pointing to things we wish were different, but that doesn't change the fact that Nextflow is the leader when it comes to workflow orchestration. And feel free to create a new issue in the GitHub repository if you wish to request a feature :)

The nextflow devs have been talking about this for a while on GitHub. Looks like they're implementing something along these lines using schema like they do for nf-core. GitHub discussion.

Groovy definitely scales up just fine I think but I never used it myself outside of little snippets embedded in my DSL, I know its considered by some to be "dead" so its interesting to see what other JVM-ecosystem users think of it.

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Documentation: https://jupysql.ploomber.io

Note that JupySQL is a fork of ipython-sql; which is no longer actively developed. Catherine, ipython-sql's creator, was kind enough to pass the project to us (check out ipython-sql's README).

We'd love to learn what you think and what features we can ship for JupySQL to be the best SQL client! Please let us know in the comments!

For those who don't know, Jupyter has a bash kernel: https://github.com/takluyver/bash_kernel

And you can run Jupyter notebooks from the CLI with Ploomber: https://github.com/ploomber/ploomber

Develop notebook-based pipelines

Fair point. MLflow has a lot of features to cover the end-to-end dev cycle. This SQLite tracker only covers the experiment tracking part.

We have another project to cover the orchestration/pipelines aspect: https://github.com/ploomber/ploomber and we have plans to work on the rest of features. For now, we're focusing on those two.

I recommend taking a look at the ploomber open source. It helps you structure your code and parameterize it in a way that's easier to maintain and test. Our blog has lots of resources about it from testing your code to building a data science platform on AWS.

Developing end-to-end data science infrastructure can get complex. For example, many of us might have struggled to try to integrate AWS services and deal with configuration, permissions, etc. At Ploomber, we’ve worked with many companies in a wide range of industries, such as energy, entertainment, computational chemistry, and genomics, so we are constantly looking for simple solutions to get them started with Data Science in the cloud.

If you like working locally with notebooks, you can run via the free tier of ploomber, that'll allow you to get the Ram/Compute you need for the bigger models as part of the free tier. Also, it has the historical executions so you don't need to remember what you executed an hour later!

It really depends on your use cases, I've seen a lot of those tools that lock you into a certain syntax, framework or weird language (for instance Groovy). If you'd like to use core python or Jupyter notebooks I'd recommend Ploomber, the community support is really strong, there's an emphasis on observability and you can deploy it on any executor like Slurm, AWS Batch or Airflow. In addition, there's a free managed compute (cloud edition) where you can run certain bioinformatics flows like Alphafold or Cripresso2

That's what we do for lineage with https://ploomber.io/