gopy VS truffleruby

I've used gopy[0] recently to access a go library in Python. It surprisingly Just Worked, but I was disappointed by some performance issues, like converting lists to slices.

[0] https://github.com/go-python/gopy

the heavy lifting is done in native libraries and you get to experiment fast using an easy language. the combo is quite hard to beat. Now there is a missed opportunity to write such libraries in Go, but as I read here and there Go is hard to integrate well as a library. There is gopy but it's light years away from PyO3 for instance, I don't think it'll ever gain traction, but who knows.

> So if you learn Go, you'll never be able to use it to interoperate with e.g. your Python program to speed it up.

Never done it myself, but:

https://www.ardanlabs.com/blog/2020/07/extending-python-with...

https://github.com/go-python/gopy

Check out gopy for an easy way to extend your Python code with Go.

I think it would be worth mentioning GraalVM and https://github.com/oracle/truffleruby in competitors section.

GitHub page has some info: https://github.com/oracle/truffleruby#current-status

My question is, how viable is TruffleRuby vs JRuby?

I wonder why GraalVM is not more often used for these speed critical cases: https://www.graalvm.org/python/

Is the problem the Oracle involvement? (Same for ruby https://www.graalvm.org/ruby/)

Looks like it’s still a WIP

https://github.com/oracle/truffleruby/commits?author=eregon

this comes at the cost of boxing ints inside Integer, though.

So, if you ignore for a moment primitives types, whenever you have generics, everything boils down to a single method accepting Objects and returning Objects. What the JVM does is to do runtime profiling of what actually you are passing to the generic method, and generate optimized routines for the "best case". In theory this is the best of the two worlds, because like in general you will have a single implementation of the method (avoiding duplication of the code), but if you use it in an hot spot you get the optimized code.

In a way, it is quite wasteful, because you throw away a lot of information at compile time, just to get it back (and maybe not all of it) at runtime through profiling, but in practice it works quite well.

A side effect of this is this makes the JVM a wonderful VM for running dynamic languages like Ruby and Python, because that information is _not_ there at compile time. In particular GraalVM/TruffleVM and exposes this functionality to dynamic language implementations, allowing very good performance (according to they website [1][2], Ruby and Python on TruffleVM are about 8x faster than the official implementation, and JS in line with V8)

[1] https://www.graalvm.org/ruby/

I opened a ticket some time ago about performance with Jekyll and liquid templates. At least in that case, yjit was way faster. I'm happy to retest though. Anything that would make my jekyll builds faster would help.

https://github.com/oracle/truffleruby/issues/2363

Here's a benchmark [1] done in Jan'22 against many ruby implementations, truffleRuby [2] seems to be way ahead in most, and at least ahead in all. Why truffleRuby isn't talk about much here?

[1] https://eregon.me/blog/2022/01/06/benchmarking-cruby-mjit-yj...

[2] https://github.com/oracle/truffleruby