An article on Python vs Julia for scripting

For those whom don’t know, Julia is a very powerful new language, which aims to leverage a JIT compilation mechanism to generate very fast numerical/computational code in general from a well thought out language.

I’ve argued for a while that it feels like a better Python than Python. Python, for those whom aren’t aware, is a scripting language which has risen in popularity over the recent years. It is generally fairly easy to work in, with a few caveats.

Indentation is the killer for me. The language is tolerable though, IMO, not nearly as “simple” as people claim, with a number of lower level abstractions peaking through. I am fine with those. I am not fine with (and have never been fine with) structure by indentation. This isn’t its only issue. The global interpreter lock, the incompatibility between Python 2.x and 3.x. Python does have a very nice interface to C/C++ libraries though, which make extending it relatively easy.

Julia eschews this structure by indentation. It also tries hard to be convenient, and consistent. IMO it does a great job of it. We are experimenting with using it for more than basic analytics, and it is installed on every single machine we ship in /opt/scalable/bin/julia , and have been for years. As is Python3, and Perl 5.xx.

These tools are part of our analytics stack, which has a few different versions depending upon physical footprint requirements.

Julia has made interacting with the underlying system trivial, as it should be, with few abstractions peaking out from underneath the syntax. This article discusses the differences from a pragmatic viewpoint.

Overall I agree with the points made. Perl, my go-to scripting language, has some of the python issues (abstraction leakage). Perl6 is better. Much better. Really … been looking into it in depth … and it is pretty incredible. Julia is better, and much better at the stuff that you’d want to use Python for.

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