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Comment by int_19h | Hacker News Reader

Python replaced Perl in many niches, because Python was better suited to general tasks. For string processing, it wasn't as convenient, but there were so many other things where it was clearly a better choice. And being able to use a single "good enough" language for everything is itself a major convenience.

Julia is the opposite - it's better than Python in one particular narrow niche, and cannot replace it broadly. So it has to offer enough to justify going from one language to two.

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> Julia is the opposite - it's better than Python in one particular narrow niche, and cannot replace it broadly. So it has to offer enough to justify going from one language to two.

Absolutely disagree. I will claim Julia is a BETTER general purpose language than Python. I am not an academic or researcher. I use Julia over Python because I find it better is almost every regard apart from startup time ;-)

Julia outdo Python as a glue language. Integration with C/C++, Fortran, Python, R and the shell is awesome.

I have rewritten large bash shells as Julia code. Due to the need for having a language more broadly available on customer computers, I tried rewriting it in Python. I found that far more painful.

Package management is IMHO superior in Julia. REPL environment is superior. Expressiveness of language is superior. Nothing in Python matches the power of Julia macros. Multiple dispatch is insanely useful and dead easy to use. Python has nothing like it out of the box.

That you can more easily redefine types at runtime and full class hierarchies can offer some advantages at times. However I don't find that it makes up for the power of multiple dispatch and macros.

Don't make the mistake of thinking that Julia is only suitable for scientific work, because that is where it is currently used.

What truly makes the language general purpose isn't really language features. The vast majority of languages are "general purpose" by that metric - but you don't see people writing desktop apps in, say, R or PHP. It's the libraries. Python embraced "batteries included" a long time ago, and then there's everything on PyPI on top of that.

With a language like Julia, it's going to be a chicken-and-egg problem - so long as people are using it mostly for scientific computing, the stable and well-maintained libraries for it are mostly going to be about that.

I partly agree, but I think it is a bit more to it than that. Part of what made Perl, Python and Ruby so widely used compared to say LISP or Matlab is that they are great at text manipulation and interacting with the unix shell.

Julia is exactly the same. Anything people would use to write shell scripts, I would use Julia with instead and I have used many languages for this purpose: Python, Ruby and Go. Out of al these I find Julia to be the best language.

The fact that Julia is free, open source, available on all platforms and is good at text and shell stuff means it has a sort of Trojan horse ability to get into more wide usage.

I am not a scientific programmer, yet I use Julia for a whole bunch of stuff and find it very productive. Here are some examples of stuff I have written in Julia:

- Custom scripts for Swift and Objective-C app compilations.

- Various file conversion utilities.

- Code generators based on custom DSLs. Due to Julia's support for macros writing a DSL is easier in Julia than in Python, Go or whatever "script" alternative I might have used. - Various parsers. I find Julia very good at writing parsers. I have used this to create tools for manipulating user interface definition files for a larger C++ project.

- Editor plugins. I have written plugins in Julia for e.g. TextMate although I guess it would have worked on Sublime and other editors as well.

I cannot imagine anyone would have had a nice experience writing any of this stuff in R or Matlab.

Julia may never get big as a Web programming language, but for scientific programming, data science, data scraping/munging/preparation, scripting make up quite a large field together.

Julia may have some slow startup due to JITing but I found my Julia scripts to run much faster than the shell scripts they replaced and the Julia code was easier to read and faster to write.

Did you know that Java for instance, was originally designed to run on interactive TVs? But then, Java run on many type of devices and in broad range of use-cases (maybe with the exception of TVs :-)).

The original niche for which a programming language is designed may or may not be indicative for what it is used in the end. Julia strikes a very good balance between performance, flexibility (macros) and ease-of-use (default type inference). I won't be surprised if there are other niches outside of scientific computing where these characteristics are desirable.

The biggest obstacle I see for the adoption of Julia outside of the scientific computing realm are the latencies due to compilation. There are already 3-party solutions for this (like PackageCompiler{X,}.jl) but imho they are not robust yet enough for widespread adoption, but this might be about to change when different effort join forces [1].

[1] https://github.com/JuliaLang/PackageCompiler.jl/pull/304

"Julia is the opposite - it's better than Python in one particular narrow niche, and cannot replace it broadly. So it has to offer enough to justify going from one language to two."

How so? Julia is a fine general purpose language. I'd go so far as to say it's more appropriate than Python for a wide class of problems!

In many ways, Julia is a better python than python, if only for the lack of the (quite insane in the 2000s) program structure by text formatting. That is a misfeature.

Julia is fast out of the box. You don't need Julia + some-other-language to get performance. You can use your GPUs fairly trivially from within the language (though this is implemented as FFI, it still has an idiomatic feel to it).

As for the previous comments on Perl, I am reminded of Mark Twain's famous retort. Rumors of its demise are greatly exaggerated.

Program structure by text formatting is a misfeature, though. What you do need though to make sure code remains readable is strong convention. I think actually that's the most unique feature of Julia (as also alluded to in the post) - how much of julia's ecosystem that works because of social convention (and conversation).

Purely anecdotal evidence, but Julia's support for basic HTTPS server capabilities is immature at best. Sure, it is capable, but the amount of effort to build an web API is much higher than in a language like Python which has had many more years to mature its ecosystem.

That's not to say I dislike Julia. I think it's a wonderful language for numerical computing. Over the next few years I certainly hope to see wider adoption among data science and analytics communities.

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