I also stopped using after 0.3 and tried it recently again to speed up some NLP code in Python3 and it hasn't been faster than Python3. Back then I didn't like the poor support for pyjulia, slow string processing, people using unicode symbols as variables, module import system, lack of libraries for more obscure NLP algos, and startup time. I know these aren't major issues for the core users of Julia, but these were my concerns.
The way I wrote the current code in Python was abusing sets and dicts a lot to take advantage of those fast data structs. Rewriting in Julia was fun because it was different and because multiple dispatch is fun.
However, it was roughly the same speed as Python. Ended up sprinkling some Cython on top of Python and resulted in 10x speedup. Didn't take much time to add types/pass pointers instead of strings to functions. I am not at all familiar with C++/Cython.
I think even if they speed strings/dicts up by a lot, there seem to be lots of breaking changes between releases so I wouldn't try it for something big.
I think if Julia is to succeed in the near future in the same way Python is successful for data science, it needs to be more usable for general tasks. Things like web servers, fast JSON parsing, maybe static binaries or easy parallelism. Basically, some more selling points. So far, for me personally Python is faster and easier to read for most of the things I write. At least given comparable amount of work.
The way I wrote the current code in Python was abusing sets and dicts a lot to take advantage of those fast data structs. Rewriting in Julia was fun because it was different and because multiple dispatch is fun.
However, it was roughly the same speed as Python. Ended up sprinkling some Cython on top of Python and resulted in 10x speedup. Didn't take much time to add types/pass pointers instead of strings to functions. I am not at all familiar with C++/Cython.
I think even if they speed strings/dicts up by a lot, there seem to be lots of breaking changes between releases so I wouldn't try it for something big.
I think if Julia is to succeed in the near future in the same way Python is successful for data science, it needs to be more usable for general tasks. Things like web servers, fast JSON parsing, maybe static binaries or easy parallelism. Basically, some more selling points. So far, for me personally Python is faster and easier to read for most of the things I write. At least given comparable amount of work.