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Comment by abdullahkhalids | Hacker News Reader

Python is just the more popular language. Julia array manipulation is mostly better (better syntax, better integration, larger standard library) or as good as python. Julia is also dynamically typed. It is also faster than Python, except for the jit issues.

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> It is also faster than Python, except for the jit issues.

I was intrigued by Julia a while ago, but didn't have time to properly learn it.

So just out of curiosity: what's the issues with jit and Julia ?

Julia's JIT compiles code when its first executed, so Julia has a noticable delay from you start the program and until it starts running. This is anywhere from a few hundred milliseconds for small scripts, to tens of seconds or even minutes for large packages.

I wonder why they don't just have an optional pre-compilation, so once you have a version you're happy with and want to run in production, you just have a fully compiled version of the code that you run.

Effectively, it does - one of the things recent releases of Julia have done is to add more precompilation caching on package install. Julia 1.10 feels considerably snappier than 1.0 as a result - that "first time to plot" is now only a couple of seconds thanks to this (and subsequent plots are, of course, much faster than that).

The "issue" is Julia is not Just-in-Time, but a "Just-Ahead-of-Time" language. This means code is compiled before getting executed, and this can get expensive for interactive use.

The famous "Time To First Plot" problem was about taking several minutes to do something like `using Plots; Plots.plot(sin)`.

But to be fair recent Julia releases improved a lot of it, the code above in Julia 1.10 takes 1.5s on my 3-year old laptop

Preaching to the choir here.

Julia’s threading API is really nice. One deficiency is that it can be tricky to maintain type stability across tasks / fetches.

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