I've tried Julia and really liked it, but the user experience was pretty bad. The language really needs faster interactivity or a strong type checker. I found myself waiting after than compiler a lot more than in some AOT compiled language like Rust... I fear the language suffers from being overused by people who are familiar with Matlab and Python and draws too much inspiration from them, much like how Rust draws too much inspiration from C++.
What boggles my mind is that a language oriented to scientific programming has such a lousy time to first plot. I know it has been improving, it is still not acceptable. Not for me as a user, not acceptable for a language who wants to become mainstream.
It's a better time to first plot than matlab, which is one of the other major contenders. On my computer it is about 3 seconds, which is noticable, but far from disqualifing.
Julia oversells itself as a general purpose language which I find absolutely out of line. Their marketing needs to be a lot more humble until they figure out the kinks.
Also, my guess would be Python and not Matlab as it’s main competitors.
In what way is marketing julia as a general purpose programming language 'way out of line'?
People use julia to make webservers, write programming languages, create plotting libraries, do scientific analysis, do compiler research, make video games, do HPC, etc.
Julia has a design that's indeed strongly informed by scientific computing, but in order to actually meet the needs of the various people using it for scientific and technical purposes, it ended up needing to become a flexible enough language to be useful for anything.
Doing these things in julia is very different from doing these things in R or Matlab. The tooling and ecosystem for non-scientific applications in Julia is growing rapidly and is quite competent.
Julia is absolutely a general purpose language. It’s user base skews heavily towards scientific computing, but the demographics and ecosystem are broadening daily.
Having used Julia for 2+ years, I couldn't disagree more. Productionizing Julia code has been a total nightmare. The community library support has been growing but hasn't gone through the wringer. Just because things are improving doesn't provide a meaningful understanding against its competitors.
I don't see any reason to use Julia over Go for backend webservers. Rust or C++ for systems programming. And frankly, I prefer Python for scientific computing.
Julia also has a tiny standard library and lots of flaky external libs which make productionization of code a risky adventure which I have personally been bitten by.
Most people are allured by Julia's overhyped marketing which is a shame because the original paper by Stefan is pretty impressive. We're seeing some criticisms of Julia in this thread, rightfully so.
My advise to people who are subscribed to Julia's marketing is to listen to people that are complaining. No one wants to just complain, they're saying that because of many reasons. Be humble and try to listen, accept Julia's many shortcomings (error messages and stack traces, library support, startup time, IDE, debugging, etc.). Julia has many shortcomings that are only apparently after using it outside of the Jupyter Notebooks. Not accepting those makes you an annoying fanboy.
> I don't see any reason to use Julia over Go for backend webservers. Rust or C++ for systems programming. And frankly, I prefer Python for scientific computing.
Sure, I would never claim Julia is being the best language for webservers or systems programming. If someone came to me saying they wanted to do this in Julia, I'd probably tell them "if this is important, I'd probably look at a more established language for this purpose unless you have a good reason to want to use julia for this"
That doesn't make julia not a general purpose programming language. It just means it's not the best language for every imaginable purpose (no language is).
I personally prefer Julia very strongly for scientific computing to it's competitors, and because of the amount of time I've invested in it for that, I also do many other things in it and I find it quite nice for this.
It's totally fair that you prefer Python for scientific computing. Python has a great ecosystem and huge community with tonnes of investment! It's an incredibly stiff competitor. I prefer Julia, and think I have strong reasons to do so, but everyone's needs and desires and different.
> No one wants to just complain
This is an empirical claim about human psychology and it's false. But regardless, yes there are a lot of totally valid criticisms of julia in this thread! Just because these criticisms exist and some of them have good points doesn't make julia a bad language though.
Please consider the fact that not everybody has the same needs, desires and temperament as you. Every language has major probelms with it, but different people feel these problems differently.
For many people (for example, me), Julia is a gigantic breath of fresh air! For others, it's painful and clunky. I think there's a lot of good here that people should see and check out and think about, even if they decide it's not for them. Especially because these things improve every day.
Just a disclaimer in case anyone is suspicious about my affiliations: I have absolutely zero financial stake in Julia's success. I am not employed by anyone who would benefit from more people switching to julia. I'm a physics PhD student. I simply find julia very useful and pleasant to use and want to share that with others.
> I'm a physics PhD student. I simply find julia very useful and pleasant to use and want to share that with others.
User base tends to be scientists and not seasoned Software Engineers. No offense to either one, just that the community inspires the language and its mechanics. This is exactly the reason it is not a general purpose language. You just proved my point.
Glad you find it useful for your endeavors. I reckon DiffEq and other hardcore math is great in Julia.
> Most people are allured by Julia's overhyped marketing ...
Wikipedia says "Marketing refers to activities a company undertakes to promote the buying or selling of a product, service, or good." Julia is not a company; I think what you're calling "marketing" would better be labeled "user enthusiasm" :-)
Out of curiosity, what marketing material are you referring to? I.e. what is it that you find misleading, specifically?
If anything, I feel that the Julia website and manual focus more on technical computing than they ought to and could stand to spend more time on general computing matters for which the language is also well suited. I’ve been meaning to write a blog post entitled “Julia is a General Purpose Language” for a long time. Which I suspect you would take issue with, but that’s ok.
Short-comings are intrinsingly related to its general purposeness. Tiny standard library is one of them. Not being able to put things into production is another.
I guess I see general purpose languages such as Python and Go as rock solid. They have warts but they're well understood and wrinkles have been ironed out.
Video games in Julia?, come on. No one is going to make a video game in Julia (unless you consider the hang-man or sudoku a video game). Julia is already being oversold as a scientific programming language, so absurd claims that Julia can be used to develop video games are just going to damage even more its credibility.
Julia has a lot of main competitors. I would consider Julia a competitor to Fortran, C, Matlab, R, and python. If you look at DifferentialEquations.jl or the clima.jl package, these are packages that are competing with low level libraries that would traditionally be written in C or Fortran. It competes with these by offering comparable performance, while having much better quality of life features (like automatic PGO, a package system, metaprogramming, and not having to deal with make files). It competes with matlab by having an incredibly rich linear algebra library, while being free and not making you do dumb stuff like 1 function per file.
The plot is also very simple. No zoom, brush, save to file, label, title, regression etc that you need to process it in a GUI and save for a report.
I would really like to use Julia as a "Matlab or Octave but with nice string concatenation" but the UI is just lacking for one off calculations and data processing.
I'm afraid I don't understand your comment - both Plots.jl as well as Makie.jl (the two most commonly used plotting packages, as far as I know) support all of those things. Makie.jl does so natively, Plots.jl does so if the backend (e.g. Plotly, PyPlot) supports interactivity (the rest is available by default). Do you mind giving an example, such that this could be improved further?
Ye sorry I meant in the plot window (GUI), not the scripting. For data processing of lab measurements zooming and panning, brushing, "drag to select" etc in an easy way is really convenient since you don't know where in the plot interesting stuff will be in advance. Adding titles and labels, text arrows etc is a nice extra.
Matlab have quite good such capabilities, Octave is more limited (you can't add titles, labels, regression lines or brush away data points, get simple data statistics like sums or std devs like in Matlab, but you can zoom and pan, save to file etc).
Julia seems to launch a Qt-window with plot, so adding some menu bar with zoom and pan shouldn't add too much bloat.
E.g. exploring roots benefits from zooming a lot.
EDIT:
You seem to be able to switch "backend" of Plots to eg. PyPlot for some functionality I didn't know that.
Yes precisely - it's not the frontend that's doing the lifting here. The interactivity as you describe it depends on if the backend supports it, which not all do.
I agree that there's room for improvement here, but I also think that it won't happen in Plots.jl (and if it does, it'll depend on a backend that already provides interactivity). I think it'll be more likely to see something like this plop out of Makie.jl or something built on top of Makie.jl, as that has a lot of primitves for interactivity available already.
As a heavy octave user, I never felt a need to concatenate strings in any way. But I'd be happy if julia was an Octave but with fast loops, which it sort of is; but still not really there.
When you use Matlab/Simulink for c-code generation and use it for a lot of purposes Matlab was not really made for you might run into processing strings.
The most important part about the releases since 1.0 is that compile time has been significantly reduced, the article touches on basically all ways how this has been done - may I ask when you've last tried it?
A few months ago. First time to plot is noticeably better than it was a few versions ago, but still extremely slow compared to Python. The article gives a benchmark of 9 seconds. I mean, come on.
The main problem I had was simply that what any time I needed to modify a struct field, or anytime my program crashed, or any time the buggy IDE extension crashed, I needed to recompile everything. I also haven't found anyone particularly interested in basic things like interfaces, despite the language supporting type hierarchies (why can't we enforce contracts for types? The whole language is built around overloading...)
Overall this is very frustrating as the language is excellent is many regards, in particular multiple dispatch and the compilation model are just great. The "just ahed of time" compilation is one of those obvious-in-hindsight ideas IMO, better than full interpretation or full compilation for nearly all use cases, if only it could be cached between interpreter sessions or if you didn't need to restart all the time...
I know that there are plans to cache even more, but other than that I can basically only recommend to put distinct projects into proper projects (with a module and Project.toml). That will already cache precompiled code for that module, even between sessions. Having things in a script won't have that benefit. For experimenting with struct layouts, I've found that NamedTuples (https://docs.julialang.org/en/v1/base/base/#Core.NamedTuple) are amazing for prototyping, since they can be accessed via A.b just like structs but don't have the limitation of being const global.
The dynamism and flexibility combined with the compilation model is basically what leads down this path of recompilation, unfortunately. Since importing packages may change behaviour/invalidate some compiled method (that's what the SnoopCompile stuff in the article was about), it's nontrivial to just begin caching things left and right. You'd end up with an exponential explosion in the number of methods to cache, wasting huge amounts of disk space. That's not to say that there aren't more things that could be done, just that it's hard to do so.
I've read a bit on type invalidation and I know it's a hard problem (in fact it's hard for me to even wrap my head around it, lol). Still, it's unfortunate. One thing I would like to know is if the difficulties with invalidation are a symptom of the dynamic semantics, or of the compilation model.
Namedtuples are cool, but I'm not sure I understand the tradeoffs between using them and using structs. Can I just replace all structs in my project with named tuples, without having a performance hit?
Note that I didn't suggest replacing structs with NamedTuples entirely - only during prototyping, while you're figuring out what you want your struct to look like. Structs most definitely will be faster.
I mean, I could, it's just that its pretty hard to know in advance which structs I will have to modify... Most of the time I only need to do minor edits like add 1 field. By that point I need to recompile anyway if I am to switch to NamedTuples...
No one interested in interfaces? My impression is that interfaces is very commonly discussed, and is one of the most anticipated features in the language, though it may not come until v2.0.
OK, I was being hyperbolic. It's just that the interest is low compared to other features, as most people involved in the project are used to dynamic languages and don't feel the need. It's been in discussion for a long time, with no action so far. From what I've seen, the general attitude is a bit dismissive of the utility of static verification ("its different in our language because X" type attitude)
My impression with this is that it's mostly coming from a few academics that never work on code they haven't written themselves, and that have no understanding of use-cases other than their own...
I feel your pain on interfaces. As it stands, Julia simply doesn't encourage carefully thinking/documenting about what assumptions your code makes and just banging things together, hoping they work. Good luck dealing with any obscure MethodErros that result if they don't.
It still, at the end of the day, is a mostly academic language. So mostly small projects with very few people working on them. No need to architect bigger solutions/patterns, etc...
I'm not sure that's a fair characterization. Core team members have expressed serious interest in getting more static verification, interfaces and other type goodness into the language, but if you try to force the issue it turns into a Python 2->3 problem. Not to mention that there are few if any examples of how to fit type checking alongside multiple dispatch (e.g. C# punts with dynamic).
I personally find static types indispensable when working with a large codebase, but to say people don't care about this in the Julia community is just not correct.
One thing is the Core Team Members and their long term plans, which I'm not privy to. The other is the impression generated by interacting with others in the community online, where people disagree with the very premise that there is a problem.
I also see your point that not too much is known in this design space, but I also think that's why it would be good for the community to step up and experiment with this more. Figure out what works. I had plans to do that last summer but life intervened so I am stuck commentating from the sidelines. :P
Note that if you ask on slack, you often will get answers from the core team members. They're pretty open. The main place where plans aren't the most clear is when there are features that everyone knows would be good, but aren't on the top of any of the the main people's to-do list. The JuliaLang repo has over 1000 people who have contributed, so a lot of the time, a new feature is just the result of a community member making a random PR. Stack trace improvements for example, started as a package, got ported to Base, and got improved by a bunch of people contributing to design decisions.
When do you run into this kind of problem? Is it possible that you are over-typing your function signatures? The generally recommended style is to write generic code with loose type restrictions, or even none at all.
What do you even mean? It doesn't even catch that an argument's type doesn't match on a function call with types specified. It also can't catch trivial stuff like misspelling a struct field on a variable of known type.
If you want to be called a "super strong" type checker, you really have to catch that kind of simple issue at compile-time, _not_ when I run the code.
Technically it is catching the type error at compile time (but compile time is Just Ahead Of Time). If you want something that feels more like type checking in a statically compiled language, you should definitely check out https://github.com/aviatesk/JET.jl
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