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Comment by barbarr | Hacker News Reader

I didn't find it too buggy personally, in fact it has an unexpected level of composability between libraries that I found exciting. Stuff "just works". But I felt it lacked performance in practical areas such as file I/O and one-off development in notebooks (e.g. plotting results), which is really important in the initial stages of model development.

(I also remember getting frustrated by frequent uninterruptible kernel hangs in Jupyter, but that might have been a skill issue on my part. But it was definitely a friction I don't encounter with python. When I was developing in Julia I remember feeling anxiety/dread about hitting enter on new cells, double and triple checking my code lest I initiate an uninterruptible error and have to restart my kernel and lose all my compilation progress, meaning I'll have to wait a long time again to run code and generate new graphs.)

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Julia does definitely need some love from devs with a strong understanding of IO performance. That said, for interactive use the compiler has gotten a bunch faster and better at caching results in the past few years. On Julia 1.10 (released about 6 months ago) the time to load Plots.jl and display a plot from scratch is 1.6 seconds on my laptop compared to 7.3 seconds in Julia 1.8 (2022)

I'm curious what kind of slow IO is a pain point for you -- I was surprised to read this comment because I normally think of Julia IO being pretty fast. I don't doubt there are cases where the Julia experience is slower than in other languages, I'm just curious what you're encountering since my experience is the opposite.

Tiny example (which blends Julia-the-language and Julia-the-ecosystem, for better and worse): I just timed reading the most recent CSV I generated in real life, a relatively small 14k rows x 19 columns. 10ms in Julia+CSV+DataFrames, 37ms in Python+Pandas...ie much faster in Julia but also not a pain point either way.

My use case was a program involving many calls to an external program that generated an XYZ file format to read in (computational chemistry). It's likely I was doing something wrong or inefficient, but I remember the whole process was rate-limited by this step in a way that Python wasn't.

IO is thread-safe by default, but that does slow it down. There's a keyword argument to turn that off (if you know you're running it single threaded) and right now it's a rather large overhead. It needs some GC work IIRC to reduce that overhead.

I’m jealous of your experience with its autograd if it “just worked” for you. It was a huge pain for me to get it to do anything non-trivial.

Did you ever try alternatives to Jupyter like Pluto.jl? I'm curious if there's the same sort of friction.

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