Comment by spectramax | Hacker News Reader

Comment by spectramax | Hacker News Reader

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Julia doesn't feel production ready at all. Its fine to mess around in notebooks but I would never recommend it for production use. Not even at a gunpoint.

Debugger support is almost non-existent. Using Atom/Juno IDE is a D-grade experience. Julia offers little help to debug problems - errors are almost always without failure - completely tangent to what the real issue is.

Julia takes forever to start, syntax was wonderful pre 0.4 days, now the syntax just looks absolutely jarring to my eyes. Julia's speedups can be offset by using many many far better technologies - Numba, Numpy, Cython, PyPy etc.

Julia's ecosystem of libraries is a deserted land - that's expected for a new language. I hope it improves, but the core Julia experience needs to improve first.

On the other hand, I've been following Rust development and the developers made absolutely sure from the get-go to focus on debugging/errors that show what the actual problem is, provide a stack trace and figure out where the problem started. Julia absolutely sucks at this.

> Not even at a gunpoint

Really? I'd recommend assembler at gunpoint; even at stick-point for that matter. You either spin a mean hyperbole or you're one serious programmer.

I've had the same two complaints as you when I tried Julia ~2 years ago. I was told that the startup situation improved to the point of it not being a problem anymore, but even then people were just reusing Julia's processes. Don't know about the state of errors, but I'd expect noticable improvements there as well, as with most new languages.

I'm anxious to one day come back to Julia, due to its focus on numerical computation, but at the moment it's somewhat counter-balanced by Python ecosystem's maturity.

Just wanted to jump in here and say that for numerical computing Julia’s ecosystem of libraries are absolutely fantastic! DifferentialEquations.jl alone was worth switching to Julia!

Although I spent a long time optimising numerical code with Numba the speedup I got (whilst significant) wasn’t really comparable to the speed of a Julia implementation.

I love some of their libraries as you mentioned. Just that when the language itself is painful to write code and debug it, the whole value proposition is diminished.

The core developers are of Julia are very smart folks, they want to develop a great language that's fast and easy to use. They missed the opportunity to restrict syntax, provide useful exception message (just look at Rust! it is a thing of art when you get an exception, it is beautiful), and generally provide good documentation.

For example, just creating a Julia local registry requires significant overhead and time investment. Spinning up a registry should not take more than 30 mins.

All these aspects of Julia are prohibitive and in my opinion, Julia should not be used in any company or production use until perhaps version 2. People who have dealt with Julia issues will tell you the truth - not the academicians or researchers. The people that maintain infrastructure/maintenance support for Julia apps are almost ready to quit their jobs. It sucks so bad.

>The people that maintain infrastructure/maintenance support for Julia apps are almost ready to quit their jobs. It sucks so bad.

Any specific examples?

To which version of Julia are you referring? All of those things are much improved now.

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