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Comment by waldrews | Hacker News Reader

What's the GIL/threading story there?

I'm assuming you mean "how well does JVM concurrency play with Python concurrency"? Python concurrency works perfectly well on its own, Java/Clojure concurrency works very well on its own, trying to pass multithreaded information across the JVM boundary to Python while bypassing the GIL will result in a segfault (Edit: but there are "with-gil" wrappers you can use to prevent that, at a slight performance hit). In practice this tends not to be much of a problem as you setup a parallel workload on one side of the boundary or the other and pass information with a threadsafe queue. We do plenty of heavy parallel computations, data science, AI, fintech, etc.

There are certainly some leaky abstractions and there is a general expectation that you understand the quirks of Python and Clojure pretty well, so it's not for everyone. Knowing something about Java would probably help too but I've been using libpython-clj in production since 2017 years and I barely know anything about Java (compared to Python/Clojure).

This is pretty interesting, what's the benefit over using python so directly with java? I mean, is the overhead of having these as seperate services / processes too much? I'm not trying to provoke I'm genuinely curious about the use case.

Also, what's the dev workflow like? When I'm coding python I basically live inside the debugger (a.k.a the carmark method), do you use an IDE that understands both java and python? Whats the debugging experience like? Can you set a breakpoint and then evaluate python code and expressions inside the debugger like you can if it was just solely a python project using VSCode and the python debugger?

That's a really interesting line of questioning! We have a mode called "embedded mode" where you run the python application first, THEN initialize the JVM and Clojure via the Python "javabridge" package. From there, you can start your Clojure REPL and experience both Clojure's IDE integrated REPL experience or the Python debugger, depending on how you set it up. This also allows you to run maximally complex Python applications and is the recommended approach for training ML robustly.

I also tend to live inside the debugger for some things but for other things I really enjoy the Clojure/lisp style "in editor evaluation" (where the result appears right after your cursor when you evaluate the S-expression).

The usescasses question is a good one. Python has some pretty good libraries. For one project, we have a (Clojure) ring server and GCP cloud resources. Using the Python GCP secret manager to access protected cloud resources allows me to have the same code in dev and prod with minimal configuration.

Also sometimes it's just political. Maybe your workplace is a Clojure/Java only shop -- in that case, sometimes you can make the case Python is "just a library" and get some cool toys, in other circumstances where its Python only you can at least dev using your lisp REPL.

So if that kind of thing sounds fun to you (and you like emacs) you'll like this. If that sounds like hell to you, then it is!! I really tried hard to optimize around "fun" for the API, but it's also really performant, and great fun for hacking.

In particular I really love doing silly stuff with Python LLMs in the Clojure REPL.

So, tl;dr, I'd say it is really great if you are a certain kind of hacker who wants all the most fun toys and as an added bonus it also works in production.

Thank you for the very thorough answer :)

Oh sorry there are actually huge performance benefits over a services based approach, because you're using the same memory space instead of serializing. This is particularly enormous for ML and data science space because of the work Chris did on hyper efficient mapping zero copy mapping of numpy arrays to tech.ml.dataset tensors.

Not even GraalVM has that! Not yet, anyway.

So there's a lot of easy perfomance synergies over microservices, but I'm the kind of dev where I tend to prioritize fun over performance as long as it's "performant enough". Fortunately, Chris (author of libpython-clj) is an ex-Nvidia performance obsessed dev though so the performance there is on point.

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