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Julia adoption keeps climbing

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Re: Julia adoption keeps climbing

#161

Post by oxinabox »

superbcarrot wrote: post #4

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 And when you do find help, some of it is out of date because the language has changed over the past few years.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…

I remember in ~2005 people said almost the exact same thing about Python and Perl.

Re: Julia adoption keeps climbing

#162

Post by pjmlp »

oblio wrote: post #155
pjmlp wrote: post #33

Earlier quoted context omitted.

First Go needs to offer comparable stacks to .NET and Java offerings, not only their platforms languages, but also their guest ones. And yes, there are ways to AOT compile as well.

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.

Re: Julia adoption keeps climbing

#163

Post by amkkma »

KenoFischer wrote: post #36
bsdubernerd wrote: post #10

Earlier quoted context omitted.

In my tests I frequently switch between cpython, pypy and julia (depending on the libraries/task I want to perform) and I haven't found the JIT overhead to be worse than pypy on average. Count me as one of the 1-based index haters, but I do love multiple dispatch and the language in general. As a language for explorative tools and analysis is on par of python (strict preference between the two according to taste). To…

Agreed on error handling actually. It doesn't quite have the feel that it should in a modern language. I think error handling and lifetimes/mutability are two of the top things we're looking at for a fundamental remodel in 2.0.

What about traits or multiple inheritance?

Re: Julia adoption keeps climbing

#164

Post by oxinabox »

physicsguy wrote: post #41
alephnil wrote: post #11

Earlier quoted context omitted.

Julia has the focus on scientific and numerical computing, and is overtaking the python/numpy combo in that niche. In addition to being considerably faster than python, it also has quite some innovative libraries in the area. This can also extend into machine learning, where python has been the go to language, despite its limitations. For other areas, like web programming, there is no sign of Julia replacing Python i…

It's not overtaking at all. It's seen growth in some areas. The issue with regards to web programming/other programming is important, because sometimes it's useful to make a website/build another tool as a scientist. Python can do both easily.

there are so many tools coming up around this in julia that it is arguably a problem. THere was a whole session last juliacon that was just on web-dashboard tools like Dash.jl and Stipple.jl and several others. And there was another half-session worth of other talks about web related things.

Re: Julia adoption keeps climbing

#165

Post by goatlover »

ryndbfsrw wrote: post #157
laplacesdemon48 wrote: post #125

I've been using R nonstop for pretty much 5+ years. I'm happy that there's established competition coming from Python and new competition coming from Julia. Having these languages compete over similar types of programmers pushes each one to be better, which is awesome. I'm not a die-hard R person, I'd be more than happy to switch under the right circumstances. But...I think one thing gets overlooked way too often. Fo…

If we removed dplyr, then R scripts would absolutely scream so I find the speed argument for 'why switch to X' unconvincing. If users cared so deeply about speed, almost no one would be using tidyverse instead we'd all be using base-R or data.table. Multiple dispatch? Hmm is this really a problem that I'm going to come across in the real-world when 90% of our time is spent ingesting a poorly-formatted csv, doing some…

> If the goal of Julia is to replace R/Python then their priorities feel way off the mark

There's a lot more to scientific computing than wrangling tabular data. Julia is competing in that overall space with R/Python/Fortran/Java/C++. If R or Pandas is better at data wrangling, then Julia won't win out there. But so be it. No PL is best at everything.

Re: Julia adoption keeps climbing

#166

Post by veddox »

laplacesdemon48 wrote: post #125

I've been using R nonstop for pretty much 5+ years. I'm happy that there's established competition coming from Python and new competition coming from Julia. Having these languages compete over similar types of programmers pushes each one to be better, which is awesome. I'm not a die-hard R person, I'd be more than happy to switch under the right circumstances. But...I think one thing gets overlooked way too often. Fo…

I deeply loath R for its terrible type idiosyncracies, syntax, and slowness.

However, even I must admit that it is incredibly good at what it was meant to do - analyse and display data. (And yes, the tidyverse is a huge improvement of the syntax, although it's telling that they basically reinvented the language to do so.)

As an ecological modeller, I create my actual simulation models in Julia, because it is a much, much better language for any real programming. But I still analyse the output in R.

Re: Julia adoption keeps climbing

#167

Post by fnord123 »

alephnil wrote: post #11
superbcarrot wrote: post #4

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…

Julia has the focus on scientific and numerical computing, and is overtaking the python/numpy combo in that niche. In addition to being considerably faster than python, it also has quite some innovative libraries in the area. This can also extend into machine learning, where python has been the go to language, despite its limitations. For other areas, like web programming, there is no sign of Julia replacing Python i…

>Julia has the focus on scientific and numerical computing, and is overtaking the python/numpy combo in that niche\

I agree. In fact if Julia hasn't overtaken Python in numerical computing by January 2022 I will consider it a huge failure.

Re: Julia adoption keeps climbing

#168

Post by oxinabox »

chalst wrote: post #97
ptero wrote: post #37

Earlier quoted context omitted.

I think your "ecosystem of python libraries" is the key point. Python got a lot of mileage for a mass adoption from ML. Its libraries provided an "easy ML" for masses at the time ML got popular in science and job market, which quickly brought it into mainstream and built up its network effect. A similar enabler in a new field could help Julia burst in as a general language. My 2c.

Autodiff is a place where there is a gulf between Julia and Python, one that I think can't be bridged well: JuliaDiff is astonishingly flexible and performant. https://www.juliadiff.org/

Urg that website is so incredibly out of date. Julia has amazing things for autodiff. But like not the things listed on that website.

Also python is still doing great with Jax and PyTorch.

Re: Julia adoption keeps climbing

#169

Post by oblio »

amelius wrote: post #76

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).

I can't say that I like or use Julia, but just regarding your comment, and because I've seen similar comments about other languages around:

> 2. Julia uses an "end" keyword everywhere, which is imho too verbose (and the corresponding "begin" is missing so it's inconsistent).

This is an absolutely childish approach to comparing or selecting programming languages.

In my eyes, it says a lot about the maturity of software development as a discipline that a big chunk of our debates are at this level.

We should discuss about quality, breadth and depth of standard libraries, quality of implementation of the most common interpreters/compilers, etc.

I don't want to fault you personally, OP, I think this approach is quite widespread, unfortunately, one could say that it's part of our software development culture at this point.

Re: Julia adoption keeps climbing

#170

Post by laplacesdemon48 »

CoolGuySteve wrote: post #141
laplacesdemon48 wrote: post #125

I've been using R nonstop for pretty much 5+ years. I'm happy that there's established competition coming from Python and new competition coming from Julia. Having these languages compete over similar types of programmers pushes each one to be better, which is awesome. I'm not a die-hard R person, I'd be more than happy to switch under the right circumstances. But...I think one thing gets overlooked way too often. Fo…

When it comes to data wrangling, one huge advantage of Julia over tidyverse/R dataframes/Pandas is that you can write a damn for loop and it won't be brutally slow. It's so much simpler and faster to use a loop that says "pick this row only if this and that and this other thing are sometimes true" vs having to construct an algebra of column filters to do the same.

I think that is absolutely a fair criticism. Personally, I rarely run into an issue where I absolutely am bottlenecked by a slow loop. But this sort of thing drew me to Julia in the first place.

There was also an R update in ~2017 that introduced some JIT speed-ups for loops, which made a noticeable difference.

If this is a problem you run into often, I suggest converting your object to a data.table. You can pass a function row-wise over the object very quickly:

https://stackoverflow.com/questions/25431307/r-data-table-ap...

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