Why not: from Common Lisp to Julia
https://gist.github.com/digikar99/24decb414ddfa15a220b27f674...
I like Julia, but this is a point of frustration for me as well. Compilation time is fine for smaller scripts. But whenever I've tried using more powerful packages such as the SciML ones (DifferentialEquations.jl, DiffEqFlux.jl, Symbolics.jl), the precompilation time during the edit/debug cycle can be excruciating. It's a shame because the SciML packages are so amazing.
I'm waiting until Julia's static compilation process improves and is standardized. On-the-fly compilation that disappears every time you close the REPL will never be fast enough to give users a good experience when working with highly complex code.
EDIT: Just to be clear, I'm overall confident that things will improve. The folks at Julia have already made good progress in reducing precompilation times from where they used to be. Looking forward to seeing further advancements in the future.
Quote: "Coming very soon: a version of DifferentialEquations.jl that fully precompiles the solvers on Vector{Float64}, virtually eliminating the any #julialang #sciml JIT lag."
And agreed that the folks at SciML (and the rest of Julia) have put amazing efforts into reducing the compilation lag from where it used to be :) I'm optimistic that things will improve--it'll just take some time.
Also, if you take a look at a tutorial, say the tutorial video from 2018, https://youtu.be/KPEqYtEd-zY, you'll see that the code is still exactly the same an unbroken over the half decade. So no, compile times have only been worked on for about a year and code from half a decade ago still runs just fine.
I think passerbys should be made aware of the state of things in the language without spin from people making a living selling it. No personal offence to you, just please consider not overselling, it's damaging to people who jump in expecting a good experience.
> I'm not going to waste anymore time with digging into this to file an issue or prove a point. > No personal offence to you, just please consider not overselling, it's damaging to people who jump in expecting a good experience.
I'm sorry, but non-concrete information isn't helpful to anyone. It's not helpful to the devs (what tutorial needs to be updated where?) and it's not helpful to passerbys (something changed according to somebody, what does that even mean?). I would be happy to add a backwards compatibility patch if there was some more clear clue.
> I think passerbys should be made aware of the state of things in the language without spin from people making a living selling it.
The DiffEq/SciML ecosystem is free and open source software. There is nobody making a living from selling it.
It's a point of frustration for most of the community, it's just something we're willing to put up with (and work around) for the other benefits of the language. If the tradeoff doesn't seem worth it to you, it's totally fine to wait it out until it's in a more acceptable place for you.
There's a lot of focus on improvements surrounding this in the recent and upcoming versions, precisely because of this frustration, but it's still going to be a gradual process.
100% agreed on that. I've tried a Julia alias with `--compile=min --optimize=0` options passed in to try to say "please give me responsiveness over runtime performance", but it's still not quite the smooth flow I'd like it to be.
> Dynamic Binding
Beyond performance, it sounds like dynamic binding would have the same hard-to-debug action-at-a-distance problems that global variables often land you in, so I'm not sure it's worth it. (The specific case the author mentions would also lead to type stability problems, but that's maybe beside the point.)
> Structural editing
It's hard to process things from gifs, especially since I can't tell what the starting point of the gif is. It vaguely gives me the impression of the Emmet plugin for HTML development [1].
> I am aware julia has a --lisp mode, but I have never found any documentation for it. So, I don’t agree that all the things in julia are well-documented either :).
Afaik, the `--lisp` mode is intended to be sort of an easter egg, rather than a real mode for practical coding. I doubt many people use it other than Jeff himself. :)
The author doesn't say everything in Julia is well-documented by the way, or even mention Julia documentation. There's just complaints about the Lisp ecosystem's lack of documentation, and perhaps from that an implication that Julia has better docs, but I doubt the author would say all the things are well-documented - there's still quite a way to go for that to be the case.
Overall, the article left me more curious to explore Lisp, not less. It didn't feel particularly gloomy, and exposed me to many features of the language that make me really want to try it out. I hope there's more articles like this - in the sense of being intended for a general (non-lisp) audience, and talking about specific features rather than just "it expands your mind, it's programming like you've never done before" type statements.
This only happens for Segmentation fault type crashes which kill the entire Julia process, which shouldn't be common at all. Can you describe when you experience these issues?
I'd also be interested in your workload that was generating lots of seg faults (oom makes some sense if working with large data since Julia's runtime does add an unfortunate amount of memory overhead.
Segfaults happen all the time with FFI. But yea OOM is a killer. Julia runtime guzzles RAM, but pkg add any of the sciml stuff and watch your RAM explode. Doesn't take much data to lose a half hour of your life installing a package...