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Comment by Xcelerate | Hacker News Reader

As a data scientist, I’m constantly disappointed that Julia didn’t take off the way I was hoping (and thought) that it would when I was doing some combination of machine learning and HPC as a grad student back in 2013.

It was just so pleasant and enjoyable to program in, and there were many things I was able to do with the language that would have been much more difficult in other languages (as one example, I wrote a generated function to compute the irreducible matrix representations of compact groups; Julia generated a unique function with optimized bytecode for each distinct matrix dimension).

Like the post author, I also found Julia easy to use as a general purpose programming language. I’m still hoping adoption will gain momentum at some point. Python certainly took a while to get going.

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I feel the same way. As a bioinformatician, Julia is very close to the perfect language. It addresses real everyday problems I face using Python, our lingua Franca.

So why hasn't it gotten more popular in science and engineering? It seems to have stagnated in popularity since 2020 for no particular reason - the language itself has not stagnated, and it gets better every year. I'm honestly bewildered.

Well, more people don't use it because more people don't use it. Network effect and inertia.

Sure, but the same could be said about Python. In the early 2010s, Perl still reigned supreme in my field, yet Python usurped it, despite having no concrete advantages over Python except a better design.

And of course, Perl also replaced an existing language. It's not like people settled on Fortran in the 1950s and then never moved on.

Yes, but the situation seems to be different now. Perl wasn't as widely used, especially at universities, for ML, DL, Statistics, etc. as Python is now.

This has been said many times, but I think it is because of not having AOT compilation as an option. Due to this, not only do we have the "Time to first plot" problem, but apparently the memory used by the process is also large. I love Julia and use it for my data and computational work. But for CLI apps and for web apps I am learning Rust because the start up speed and memory usage for CLI and webapps matters.

I'm confused by this statement because AOT compilation is an option, and it's getting better over time.

https://julialang.github.io/PackageCompiler.jl/stable/sysima...

In Julia 1.9 and beyond, this process now also becomes modular with a native library being produced per package.

The difficulty is determining what exactly must be compiled since Julia's methods are polymorphic. There's are tools that exist to help with this. One of the latest that is SnoopPrecompile.jl.

It's not only a question of community but inertia in large companies. Where I am Python is allowed and I have it everwyhere but I'd have to jump into a lot of hoops to get a Julia permission.

I'm really curious what a new language would need to have to start stealing users from Python in the data domain. I was also a bit surprised that Julia didn't get more traction, as I felt it was a more enjoyable experience, but obviously it doesn't have the extensive community and ecosystem that Python has so it was fighting an uphill battle from the start. Interop with Python (like R's reticulate) would be helpful when targeting Python users, so they could still draw in their favourite packages, but Julia has that already.

In my new/current role, I primarily use Python but would prefer not to; however, it's a tough case to make against Python when it's become the default data language (along with SQL).

It already has interop with python via: https://juliapackages.com/p/pythoncall. It's pretty seamless for the most part I think.

> I'm really curious what a new language would need to have to start stealing users from Python

Backing of a large tech company. I tried to get something going at the one I worked at, but it went nowhere. To my surprise a coworker who was also one of the early top contributors to Julia mentioned to me that the language was unlikely to ever take off internally because the company had already invested so many resources into improving and building upon Python.

Queryverse is just sitting there like it wasn't the easiest way to sift into data. While the ML stacks are not as popular as python options yet, FluxML still abstracts away a lot of the ugly for scaling a problem.

Have a gloriously wonderful day =)

I'm quite hopeful that language models for code might actually lead to more people getting comfortable with multiple languages and especially some kind of hopping back and forth. I think the prospect of starting off your script in Python (which is a "comfort food" for many, including myself), using LLM to translate over to Julia, probably do some minor debugging, and then taking off from a starting point instead of a blank file could be a nice workflow for people who want to dip their toes in.

Not going to happen because the majority of Python users don't know Julia exists and see no reason to switch to anything else.

If you can use LLM's to translate your code from one language to another, why would you translate Python to Julia? Even the most ardent Julia supporters will admit that Julia code will be slower than optimized C and C++ code. Why not just use an LLM to rewrite your Python into those languages?

> Julia code will be slower than optimized C and C++ code

This isn't true. Optimized Julia will pretty much always tie optimized C/C++. This shouldn't be surprising. They use the same compiler and run on the same hardware and both let you use inline assembly where needed. Octavian often beats MKL at matmul, and the Julia math library is written in Julia and doesn't lose performance from doing so.

Rewriting to Julia instead of C/C++ has the benefit that the code is still readable and improvable by scientists who wrote the code in the first place.

Yeah, that's what I'm basically thinking: for scientists who already like Python because it's readable and mostly matches the mental model of what they care about, being able to catalyze the switch to a different, faster language that still reads nicely could really drive adoption. But it's a stretch for sure!

Same, I thought I had found the ideal language for ML/DS/generally interesting stuff and it simply lacked people, libraries, tools, etc. but that would come in time. It seems Pythons network effect is just too large to overcome for any language.

I think Julia started getting popular a little too early — I had a few bad experiences and crashes (and crashes were almost impossible to debug as they gave backtracked in the compiler code). There are only so many hours to try new things.

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