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.
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.