[karbarcca]

As the primary author of CSV.jl, I can help clarify on the posted issues:

* #720: ended up not being an issue at all, but a misconfigured environment * #714: there was indeed a corner case when automatically detecting float values where the float started with '-' sign and only had a trailing decimal (e.g. '-123.'). Not super common, but indeed a bug * #749, #734 are related to a new "beta" feature (CSV.Chunks) which allows iterating over large CSV files in batches. I've been trying to track down the issues 2 people have reported, but haven't been able to reproduce on the same large file. Once we iron out some of those issues, we'll mark the feature as "out of beta".

I agree that Julia packages in general are still evolving and you might run into issues, but at the same time, I strongly believe it's reached a similar maturity in most ways with other language package ecosystems. For example, I use a lot of Javascript/Python frameworks/packages and at least for me, I tend to run into corner case bugs/issues as often as I do for the most common/mature Julia packages.

Compared with, say, data.table in R, or pandas in python, one of the things I enjoy most about Julia packages is that they're almost exclusively written in pure Julia. Having had to dive into data.table/pandas source C/C++ + language binding glue code is a huge pain when trying to track down bugs, so I feel like my knowledge of Julia "goes further" in that if I run into a package bug, it's relatively much easier to track down what's going on and even submit a pull request to fix!

[CJefferson]

I should have been a bit careful with this message, and mentioning issues that might be in beta. I don't want to specifically pick on csv.jl, but my experience of Julia has been every time I've worked on a significant program, we've hit an issue, either in core or a common library, certainly compared to Python (I avoid Javascript)

I agree that Julia is progressing quickly, but I think a lot of people (certainly myself) got burnt back in the earlier days of Julia when things were much less stable and changing rapidly and the language was (in many blogs) sold as ready for use.

[karbarcca]

No worries. The CSV.jl package has just been around for 5 years now, has some 700 issues opened/resolved, has built up a pretty extensive test suite, and is used in production by a number of companies, so I just wanted to hopefully clarify its level of maturity :D

I can definitely understand getting burnt in Julia early days (I was around back then as well), but since 1.0, the package ecosystem has matured quite a bit IMO; most of the really popular packages are very stable and work similarly to popular packages in other languages.

[zzleeper]

Same here. Burned quite a few times trying out Julia stuff.

Last time it was because cold start times were unbearable (compared to Python/R/Stata/Matlab). My goal was to compare how regressions in a few software packages behaved with difficult datasets, so I had to open each of those, run a snippet of code, and log the output. Here, Julia's cold start (and importing the CSV, GLM, etc libraries) took way longer than all of the other tools together.

[ViralBShah]

Wouldn't this statement be true for any new technology? I don't doubt what you say about being burnt when adopting in the early days. However, it is simultaneously true that people have successfully used Julia in several commercial applications and significant research codebases for many years now.

I routinely point people to the Julia Computing case studies for this reason: https://juliacomputing.com/case-studies/

We also request people to please file an issue against Julia or a package if it doesn't do what you expect it to, or post on discourse. That way, even though you may go ahead with a different tool, someone is likely to fix it by the time you come back the next time. :-)

[anthony_doan]

> Compared with, say, data.table in R, or pandas in python, one of the things I enjoy most about Julia packages is that they're almost exclusively written in pure Julia.

R is going to eventually use Julia in their package. It's one of the gluest language I've ever learned so far.

It's actually R's strength. It doesn't have to compete when it can assimilate stuff in the backend while leveraging their existing user. Just look at STAN.