3 ms·

When I last used it, the time to import even two modules was many seconds. I know that's JIT time, but that breaks the flow of developing with Julia like it's a

by rspeer 10y ago

When I last used it, the time to import even two modules was many seconds. I know that's JIT time, but that breaks the flow of developing with Julia like it's a dynamic language. And the JVM is popular, yes, but nobody would call it "extremely fast", particularly for interactive use, because of its startup time. The time to parse the data I needed was many minutes, slower than Python. Restarting a Jupyter kernel would break my flow every time. The benchmarks are all for numerical computing, where the hard work is typically offloaded to a BLAS library anyway, and don't involve strings or dictionaries. Let's see a JSON parser in there and not just a Mandelbrot set.

3JPLW 10y ago
When was the last time you used it? Module loading times improved significantly with precompilation in 0.4.
- rspeer 10y ago
  Yeah, it was 0.3. So that would address the importing problem, if the modules I'm importing use the precompilation system. How is string performance doing?
  - jernfrost 10y ago
    A LOT has happened since then. For such a young language which hasn't even reached version 1.0 yet you can expect a lot of changes still. You should seriously try out Julia again. I think it really works well now. I used to have a lot of issues with it before. Stuff would break, slow startup times etc. Today I don't really have any complaints. Ok I got a few small ones. The limits on the ability to redefine stuff as you develop in the REPL can be a bit cumbersome.
  - StefanKarpinski 10y ago
    0.3 was quite some time ago – string performance was certainly a problem back then (an acknowledged one). String performance on 0.6-alpha, released last week, is very good. It should now be at least as good as scripting languages that have traditionally focused on strings (Python, Ruby, Perl). There are a few more tricks we could do in the future that could make this even faster, but they involve a fair amount of GC trickery, so we'll have to see.
  - throwaway399 10y ago
    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.
  - catawbasam 10y ago
    String performance is better in 0.6. It is in alpha, so should get released fairly soon.
jamii 10y ago
> The benchmarks are all for numerical computing, where the hard work is typically offloaded to a BLAS library anyway, and don't involve strings or dictionaries. I built an in-memory relational query compiler in Julia which keeps up with Postgres on the Join Order Benchmark. http://scattered-thoughts.net/blog/2016/10/11/a-practical-relational-query-compiler-in-500-lines/ http://scattered-thoughts.net/blog/2016/10/11/a-practical-re... Last I checked there were still some limitations around stack-allocation of types containing pointers, which is occasionally painful, but other than that it gave me all the tools I could possibly want. https://github.com/JuliaLang/julia/pull/18632 https://github.com/JuliaLang/julia/pull/18632 The startup time is annoying, but I typically use Julia from Juno and I restart at most a couple of times per week. Ctrl-shift-enter recompiles the current module, which is usually all I want.

by rspeer 10y ago

3JPLW 10y ago

When was the last time you used it? Module loading times improved significantly with precompilation in 0.4.

rspeer 10y ago
Yeah, it was 0.3. So that would address the importing problem, if the modules I'm importing use the precompilation system. How is string performance doing?
- jernfrost 10y ago
  A LOT has happened since then. For such a young language which hasn't even reached version 1.0 yet you can expect a lot of changes still. You should seriously try out Julia again. I think it really works well now. I used to have a lot of issues with it before. Stuff would break, slow startup times etc. Today I don't really have any complaints. Ok I got a few small ones. The limits on the ability to redefine stuff as you develop in the REPL can be a bit cumbersome.
- StefanKarpinski 10y ago
  0.3 was quite some time ago – string performance was certainly a problem back then (an acknowledged one). String performance on 0.6-alpha, released last week, is very good. It should now be at least as good as scripting languages that have traditionally focused on strings (Python, Ruby, Perl). There are a few more tricks we could do in the future that could make this even faster, but they involve a fair amount of GC trickery, so we'll have to see.
- throwaway399 10y ago
  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.
- catawbasam 10y ago
  String performance is better in 0.6. It is in alpha, so should get released fairly soon.

jamii 10y ago

> The benchmarks are all for numerical computing, where the hard work is typically offloaded to a BLAS library anyway, and don't involve strings or dictionaries. I built an in-memory relational query compiler in Julia which keeps up with Postgres on the Join Order Benchmark. http://scattered-thoughts.net/blog/2016/10/11/a-practical-relational-query-compiler-in-500-lines/ http://scattered-thoughts.net/blog/2016/10/11/a-practical-re... Last I checked there were still some limitations around stack-allocation of types containing pointers, which is occasionally painful, but other than that it gave me all the tools I could possibly want. https://github.com/JuliaLang/julia/pull/18632 https://github.com/JuliaLang/julia/pull/18632 The startup time is annoying, but I typically use Julia from Juno and I restart at most a couple of times per week. Ctrl-shift-enter recompiles the current module, which is usually all I want.