Julia ranks in the top 5 most loved programming languages for 2022

First off we're ultimately talking about ordinal data here and even the Evan Miller post in another comment misses that ordinal data is fundamentally tricky. Everyone is talking about the p("most loved") but that makes assumptions that "most loved" has a consistent meaning.

The problem with ordinal data is that the only thing that's known about it is an ordering of the values, but the distance between values is undefined. That is the difference between a 5 star and a 4 star review is not necessarily that same a between a 2 and 1 star review. This means that you can't meaningfully average these.

However there is an even bigger problem, which is what I think parent is feeling here, and that is selection bias. It's very hard to compare the average rating of a film such as Star Wars and a film like Cannibal Holocaust. The first is a general audience film and the later is an extremely niche subset of gore horror. If you show Cannibal Holocaust to the general population it will receive wildly lower rating than Star Wars. However if you were to do a survey of all people who have a DVD of Star Wars and a DVD of Cannibal Holocaust, I wouldn't be at all surprised if Cannibal Holocaust was more loved. There are plenty of people that have Star Wars on their shelf and feel 'meh' about it, but almost no one who owns Cannibal Holocaust on DVD that doesn't feel like it is an essential horror classic.

The annoying truth is, there's really no statistical way to solve this. There are some solutions to better modeling ordinal data (you could for example, transform this problem into an ordinal regression problem). But ultimately "loved" is not really a measurable thing, so we're always looking for imperfect proxies.

fisher.test(matrix(c(786, 298, 22999, 11156), nrow = 2))

#> Fisher's Exact Test for Count Data

#>

#>data: matrix(c(786, 298, 22999, 11156), nrow = 2)

#>p-value = 0.0003266

#>alternative hypothesis: true odds ratio is not equal to 1

#>95 percent confidence interval:

#> 1.116046 1.469612

#>sample estimates:

#>odds ratio

#> 1.279435

(On the flip side, Matlab was a great tool and a significant achievement when introduced in the 70s. Cleve Moler did a lot of great work that advanced the state of art in numerical analysis and applied mathematics. We can celebrate that history while still trying to avoid Matlab whenever possible in 2022.)

Rust has nothing to match any of those three in 2022, maybe in a couple of years.

Delphi evolved from TurboPascal and TP was one of most universally and genuinely adored tools in the late 80s - early 90s. And it was in fact beautifully functional and super fast. Delphi retained most of that even if it acquired some bloat in the process, and it also inherited a lot of goodwill. It sort of dwindled into near non-existence since then, but the whiff of its awesomeness lingers.

I think this is not true, many of us (including myself) were really intrigued by Julia and really wanted to like it, but came away a bit underwhelmed.

It certainly has some neat ideas and people do very interesting stuff with it. That said it is certainly not the panacea of "write code which is as easy as python but gets you the speed of C".

What is causing the current (small) backlash is the community being so strongly fanboyish and are still selling this uncritical view. Compared Julia fans, Rust proponents sound like they hate their language.

The advocacy is becoming quite off putting.

Maybe I find it a little tone deaf. As someone that has been using Julia for a long time and still seen very little growth in the language (compared to Rust for example), and sees users on a regular basis complain about basic issues in Julia (tooling being the biggest one for me), I find it off-putting and evangelizing when prominent community members blatantly disregard information counter to the narrative they want to establish. I hold Logan to a much higher standard, and the tone in the blog post rubbed me the wrong way. Even on Slack, there's a discussion of how "little" Julia is in this survey, and for the public messaging to be different from what a lot of users are voicing feels off. I do feel my comment was a stronger reaction, but it was perhaps stemming from my frustrations after seeing the survey, seeing it discussed on slack, seeing it discussed at work, and then reading this post.

> I am also surprised by your "scathing indictment" comment. Julia released its first stable version 3 years ago. MATLAB/R/Haskell have existed for decades. Julia has 30% of the user-share of MATLAB (and same order of magnitude comparisons to R and Haskell). This seems like a pretty big achievement already, especially when you see where these other languages are on the "love" scale.

Perhaps. Maybe I was too strong here. Julia has come a long way, and I absolutely love using it over MATLAB. But scientists and researchers are still picking MATLAB over Julia today, and that is extremely troubling for the future of the language. People are dreaming that Julia will eat Python's lunch when Julia seems like it is barely catching up to MATLAB.

I think Julia will get there eventually. But maybe I need to adjust my expectations. I previously was hopeful that after Julia 1.0, adoption would skyrocket. It did increase, but not to the extent that I would have liked. After Julia 1.6, I was hopeful again. But I think I just need to temper my expectations. Maybe after like 10 years it might be a popular language, but right now it feels like it is such an extremely niche language, and that personally hurts that I've sunk so much time and effort into it.