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My gripes, feel free to disagree: 1. Julia uses base-1 indexing. 2. Julia uses an "end" keyword everywhere, which is imho too verbose (and the corresponding "begin" is missing so it's inconsistent).
julia> (1:3)[begin+1:end] 2:3 it exists
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It's all about which "bubble" you're in. Many people posting here work for startups using micro services (for which Go is a decent fit) and for companies close to the whole Docker/Kubernetes ecosystem, which is based on Go. So naturally they assume Go is huge. My anecdata kind of tells me that Go is reasonably big, but it's not yet near .NET and Java, worldwide. But it could get there in a few years, I've seen/heard…
You don't need to write C or C++ when using a SQL RDMS, likewise .NET and Java shops don't need to write Go when using Docker/Kubernetes.
Look at the whole Cloud Native Foundation thing, I think most of their projects are developed using Go.
So if you're using that stack, it's easy to assume that all new development everywhere is in Go.
It will probably balance out once the newness wears off Go (I think this is already happening).
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> prototyped in Python, then ported to C++ This need to rewrite, of course, is what Julia is trying to avoid. My workflow is exactly the same, and I’d love to be able to write code in a high-level language like Python and then use that directly instead of having to rewrite. However, in my case the reason for rewriting isn’t just performance, but also to be able to build compiled binaries. Julia aims to be as high-lev…
Cython - in fact I think in 2021 if you want to write a pure C or pure C++ program, Cython is the best way to go, and just disable use of CPython. The “need to rewrite” is actually a sort of advantage with Cython. You only target small pieces of your program to be compiled to C or C++ for optimization, and the rest where runtime is already fast enough or otherwise doesn’t matter, you seamlessly write in plain Python.…
The problem with cython is that to really get the performance benefits your code looks almost like C.
I agree with you on the optimize the bits that matter, often the performance critical parts are very small fractions of the overall code base.
My gripes, feel free to disagree: 1. Julia uses base-1 indexing. 2. Julia uses an "end" keyword everywhere, which is imho too verbose (and the corresponding "begin" is missing so it's inconsistent).
Julia is a nice language, it's just tough to compete with Python. - The beginner experience in Julia is still much worse than it is in Python. Stuff that should work intuitively sometimes doesn't, and when you get a cryptic error message, it's difficult to find relevant help online. And when you do find help, some of it is out of date because the language has changed over the past few years. - You can squeeze a lot o…
This is an insightful and level-headed comment that applies equally to R. Although Julia is a growing alternative to Fortran/C/etc for long-running computations, it remains awkward and unpleasant for interactive analysis. Users familiar with Python/R/etc must weight the benefits of Julia against its slow library startup, its cryptic error messages, and its thin documentation. Also, the lack of a community repository…
Did you mean to write short-running scripts? If anything, the Julia dev workflow is biased towards interactive analysis in a REPL a la R or IPython/Jupyter. I don't mean to imply that there's no startup overhead, but how often are you restarting the REPL when doing EDA? Unless it's more than once every few minutes (which is a very odd workflow), then startup overhead is effectively amortized.
> A real strength of R (in comparison not just to Julia but also to python) is that such a repository exists
CRAN is certainly a cut above many other package repos here, but I'm not sure "trust their tools" can apply to all packages on there. Anecdotally, I've had a lot of issues with compiled dependencies and missing/out of date external assets on less well-trodden packages. There's a reason MRAN, Conda and JuliaBinaryWrappers exist after all.
For whatever reason, Julia package maintainers also seem more receptive to making their work compatible with other libraries as well. This goes beyond just multiple dispatch as well--imagine if tidyverse/non-tidyverse wasn't such a hard split.
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You absolutely can use regular jupyter notebooks for julia! Pluto has some advantages, like being stored as a normal julia file. The julia startup time issues affect both.
> being stored as a normal julia file. Oh, man, this is indeed a major feature. My main point of friction with jupyter notebooks is the stupid json ipynb format. Why can't it be just a regular language file with comments?
Have you ever used Jupyter notebooks? They contain code, rendered Markdown, images, plots, video players, widgets, etc.
How do you see a "regular language file with comments" supporting this, instead of the "stupid ipynb format"?
You can use plain text files with Jupyter, too.
My gripes, feel free to disagree: 1. Julia uses base-1 indexing. 2. Julia uses an "end" keyword everywhere, which is imho too verbose (and the corresponding "begin" is missing so it's inconsistent).
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Are many scientists using PyPy though? I never saw it in use in academia even though I'd tried it out personally.
I don't know. I also don't use pypy much, since python+numba is actually fast enough most of the time, and I always see pypy as a fallback to see if I can squeeze a little bit more performance before running a task.
Any recommendations on beginner (to Julia) books to get started?