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siproprio

In theory Julia is supposed to be fantastic.

In practice, things either don't exist, or are poorly implemented:

Plotting simple things take 30 seconds.

And that's if you don't count the time it takes to `] add Plots`, especially on Windows!

And the REPL is broken.

And the editor is slow and annoying (Juno or vscode).

And documentation ranges from poor (no examples, buggy between platforms, broken links due to version updates) to non-existent. For example, lots of tutorials will often link to broken links to official documentation, links that one time were thought to be working but now aren't.

And so on...

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Your 1st, 2nd and 4th points seem to be fundamentally the same, which is compile time latency making interactive use slow. That's definitely a problem for a language trying to solve the two language problem of having high interactivity and high performance at the same time, and the compiler team [1] are now focusing on that issue on versions 1.4 and beyond hopefully it will get to the point where it isn't a problem anymore.

Documentation is always a problem (especially for smaller packages), but I don't feel I had more issue with Julia than other languages (with a few exceptions, some languages managed to have an exceptional culture in terms of great documentation). Most of the issues were due to the fact that Julia just got to 1.0 a year ago, and the breaking changes made so most documentation became outdated, but this will only become less of a problem since the language became stable.

Julia has one of the best REPL of any language I used, and thankfully I didn't meet with any problems with it. Might be a good idea to create an issue on the github.

[1] https://discourse.julialang.org/t/compiler-work-priorities/1...

Nope, they are not the same points!

1. It's about compiler latency. Compiling happens when you call the function. That indeed can and should be improved.

But there are other things that contribute to the experience being shitty.

2. Is about adding a package, when adding a package it downloads all the dependencies (which includes cairo, and WinRPM on windows which all have problems of their own).

4. Is about the poor atom experience - I'm not a fan of electron apps myself - and about the slowness of the LSP on vscode, which just does not provide a good experience on vs code and things are often broken, especially on the latest versions.

If you think all these things are "compiler latency" then perhaps you're part of the problem.

Fair enough, you mentioned the speed of adding a package and I assume it was the build time after the download (which is mostly git). I have to confess that I don't consider the speed of the initial build essential since it's a one time thing (correctly and efficiently building and handling dependencies is, which it does very well in my opinion). Also, I think they are testing now a way to deliver binary libraries within the deps which can improve the situation with the external dependencies.

Visual Studio Code is definitely fast enough in my machine (especially with a Revise.jl workflow), which is why I also assumed it was stuff like running part of the code or using the Language Server Protocol, which will hit the same compilation lag. I agree that Atom is slow, and it's one of the reason I don't use it.

Though I'm clearly biased since my experience is entirely in Linux, it's possible that the Windows experience is just worse.

The VS Code extension took a long time to support newer versions of the language.

What happens in between figuring out how to make basic tooling work and actually getting something done is frustration.

Building is so clearly not a one-time thing if you use the language to explore solutions, and play with things.

Yes plotting in Julia is slow upon first invocation due to the JIT. Annoys me too, but to say the REPL is broken is profoundly puzzling to me. It is the best REPL I have ever used. It beats anything I have used for Python, Ruby, JavaScript, Lua etc.

Also your documentation issue is also strange. Yes certain things don’t exist but I would say the Julia docs is quite well made. In particular if you use the REPL documentation I find it much better than Python. Tends to be quite nice examples, color coding etc.

> It is the best REPL I have ever used. It beats anything I have used for Python, Ruby, JavaScript, Lua etc.

This is true, but that's a _very_ low bar to pass. Julia is a Lisp, and deserve to be compared to other Lisps rather than to lesser languages. Every Common Lisp or Scheme I have used has a vastly superior REPL experience than Julia. Even Clojure is better.

Don't get me wrong: I love Julia, and I hope it will eventually replace Python as the main language for scientific computing, data science and machine learning. But the REPL experience, at this point, leaves a lot to be desired. I'm sure it will improve in the future.

I’m curious what specifically you would want improved in the REPL.

Does the REPL on Windows have all the features and niceties and quality of life of the REPL on bash or other OSes?

If so (which isn't), then we can start suggesting new features, perhaps better text editing capabilities, or introspection, better access to documentation.

The REPL on all platforms is the same. There’s an issue with old buggy versions of cmd.exe, but that’s only on such old versions of Windows that they’re not even supported by Microsoft anymore.

The shell is not broken because it's slow.

It's broken because it has poor and puzzling exceptions, because output lags on Windows from gtk bugs (known for years, never fixed, huge GitHub discussion),and because the shell mode is often broken.

Things are still improving, but I already find it quite usable in practice. Yes, precompilation can take a while, but once you start using the language regularly you hardly notice it, since you only need to re-precompile after installing updates -- which ends up being a small proportion of the time. Same with "time to first plot", since I almost always have a session already running already these days.

Similar experience here. Did a comparison of a bunch of statistical tools (R, Matlab, Julia, Python, etc.) on small-ish datasets. Used the latest versions in all cases, in Windows 10. All but Julia ran the regressions in <1s, while Julia took 20+ seconds, mostly importing the required libraries and just starting up.

Sure, the usual answer is "well its an initial cost, its faster after that" but not all my code would otherwise take days to run. As long as "using CSV" takes 10 seconds, I'm out.

This is a common issue that people encounter. I am glad that compilation latency is now the top priority in terms of Julia-compiler work. Hoping to see something interesting from there

Low latency development flow is slightly different in Julia. You should startup you process and reload updated code with revise. You won't have this problem. Obviously compilation performance improvements will be very welcome when they arrive but it's not a deal breaker because of this revise based flow.

I just tried Revise. Yes, it's very nice and usable for programs without dependencies, but including a Plots example program that normally takes Julia 1 minute and 20 seconds took 19 minutes. There's only so much time I'm willing to wait for that.

Currently I'm mainly using Jupyter Notebook and that is by far the best experience I've had(it's like Revise but much, much faster). But to me it seems Jupyter Notebook wasn't designed with code outside of a single isolated file in mind, which makes it cumbersome in some cases.

I like the language, but I hope the situation improves soon. Editing code in the browser is not that much fun.

That sounds crazy long, maybe something is not right, have you tried reaching at https://discourse.julialang.org for help for example?

> And the REPL is broken.

You should really back up that claim, because in my experience it's absolutely great

Sounds like road to maturity problems. The key is to think, "are these issues solvable?" if they are it's only a matter of time. The next thing is when those things are fixed, does Julia offer something above and beyond what Python and R can do (easily). If the answer is for you, thne it's a matter of whether Julia provides value now for you.

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