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| ▲ | huijzer 4 minutes ago | parent | next [-] |
| To replace uses where you would use Matlab or R probably. I prefer Julia over Matlab or R. So data science. For production code however, it's not great since it has no static typing. Imagine having your production code crash mid-execution with the error "Function foo not found". Only dynamic languages can do that to you. |
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| ▲ | Certhas 7 minutes ago | parent | prev | next [-] |
| On top of what others have said: In many situations the alternative to Julia isn't Go but C++ (or maybe Rust though its library support is lacking). E.g. if you are writing high-ish* performance algorithmic code that should also run on GPU. Julia also heavily prioritizes composability of libraries. And though that can be a source of bugs at times, there are things we are doing in Julia with one or two PhD students that would be practically impossible (as in require an order of magnitude more implementation work) in any other language. |
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| ▲ | wolvesechoes 31 minutes ago | parent | prev | next [-] |
| It is highly interactive and dynamic, yet performant. And it is not only about scientific computing, for almost any application can take advantage of interactive, modifiable system, where you can explore your state at any point. In others, more static langs good debuggers help with this to lesser or larger extend, but it is not the same from my experience. So better question is: in which circumstances would you choose Julia over more mainstream-y alternative like Clojure? And here scientific and numerical angle comes to play. At the same time I think Julia is failed attempt, with unsolvable problems, but it is a different topic. |
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| ▲ | kryptiskt an hour ago | parent | prev | next [-] |
| Go is a total non-starter, it's not interactive at all. The competitors are things like Matlab, Mathematica, R or Python (with the right math libs). If you're weird you could use something like Haskell, APL or Lisp in this role, but you'd pay a hefty price in available libs. |
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| ▲ | Human-Cabbage 2 hours ago | parent | prev | next [-] |
| Julia is aimed at scientific computing. It competes against Python with numpy/scipy, R, etc. |
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| ▲ | ziotom78 an hour ago | parent [-] | | Correct, but I would add: Julia is better than Python+NumPy/SciPy when you need extreme speed in custom logic that can’t be easily vectorized. As Julia is JIT-compiled, if your code calls most of the functions just once it won’t provide a big advantage, as the time spent compiling functions can be significant (e.g., if you use some library heavily based on macros). To produce plots out of data files, Python and R are probably the best solutions. | | |
| ▲ | dgfl 29 minutes ago | parent | next [-] | | Disagree on the last statement. Makie is tremendously superior to matplotlib. I love ggplot but it is slow, as all of R is. And my work isn’t so heavy on statistics anyway. Makie has the best API I’ve seen (mostly matlab / matplotlib inspired), the easiest layout engine, the best system for live interactive plots (Observables are amazing), and the best performance for large data and exploration. It’s just a phenomenal visualization library for anything I do. I suggest everyone to give it a try. Matlab is the only one that comes close, but it has its own pros and cons. I could write about the topic in detail, as I’ve spent a lot of time trying almost everything that exists across the major languages. | | |
| ▲ | Certhas a minute ago | parent | next [-] | | I love Makie but for investigating our datasets Python is overall superior (I am not familiar enough with R), despite Julia having the superior Array Syntax and Makie having the better API. This is simply because of the brilliant library support available in scikit learn and the whole compilation overhead/TTFX issue. For these workflows it's a huge issue that restarting your interactive session takes minutes instead of seconds. | |
| ▲ | dan-robertson 21 minutes ago | parent | prev [-] | | I tried some Julia plotting libraries a few years ago and they had apis that were bad for interactively creating plots as well as often being buggy. I don’t have performance problems with ggplot so that’s what I tend to lean to. Matplotlib being bad isn’t much of a problem anymore as LLMs can translate from ggplot to matplotlib for you. |
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| ▲ | jey an hour ago | parent | prev [-] | | And I would further add: In addition to performance, Julia's language and semantics are much more ergonomic and natural for mathematical and algorithmic code. Even linear algebra in Python is syntactically painful. (Yes, they added the "@" operator for matmul, but this is still true). |
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| ▲ | setopt an hour ago | parent | prev | next [-] |
| Scientific computing. AFAIK, library support for that in Go is almost nonexistent. |
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| ▲ | markkitti an hour ago | parent | prev | next [-] |
| When I need to do serious math, I use Julia. |
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| ▲ | bandrami 26 minutes ago | parent | prev [-] |
| Math. Places you might use Wolfram or Sage. |
