CheeseShop VS py2many

By comparison, PyO3 handles virtually all that boilerplate, so your Rust functions can accept and return many native Rust types and everything just works (for example). Or maybe I'm missing some fundamental difference with how JVM data are handled versus Python.

At work, I'm using PyO3 for a project that churns through a lot of data (step 1) and does some pattern mining (step 2). This is the second generation of the project and is on-demand compared with the large, batch project in Spark that it is replacing. The Rust+Python project has really good performance, and using Rust for the core logic is such a joy compared with Scala or Python that a lot of other pieces are written in.

Learning PyO3, I cobbled together a sample project[0] to demonstrate how some functionality works. It's a little outdated (uses PyO3 0.11.0 compared with the current 0.13.1) and doesn't show everything, but I think it's reasonably clear.

One thing I noticed is that passing very large data from Rust and into Python's memory space is a bit of a challenge. I haven't quite grokked who owns what when and how memory gets correctly dropped, but I think the issues I've had are with the amount of RAM used at any moment and not with any memory leaks.

[0] https://github.com/aeshirey/CheeseShop

> Another problem is that there are hundreds of built-in library functions that need to be compiled from Python from C

An approach I've advocated as one of the main authors of py2many is that all of the python builtin functions be written in a subset of python[1] and then compiled into native code. This has the benefit of avoiding GIL, problems with C-API among other things.

Do checkout the examples here[2] which work out of the box for many of the 8-9 supported backends.

[1] https://github.com/py2many/py2many/blob/main/doc/langspec.md

https://github.com/py2many/py2many/blob/main/doc/langspec.md

Reimplement a large enough, commonly used subset of python stdlib using this dialect and we may be in the business of writing cross platform apps (perhaps start with android and Ubuntu/Gnome)

For py2many, there is an informal specification here:

https://github.com/py2many/py2many/blob/main/doc/langspec.md

Would be great if all the authors of "python-like" languages get together and come up with a couple of specs.

I say a couple, because there are ones that support the python runtime (such as cython) and the ones which don't (like py2many).

It'd not fully solve your issue, but have you ever seen https://github.com/py2many/py2many ?

Cython doesn't consume python3 type hints and needs special type hints of its own. But it's certainly more mature than other players in the field.

What we need is a rpython suitable for app programming and a stdlib written in that dialect.

https://github.com/py2many/py2many/blob/main/doc/langspec.md

No intermediate AST. To understand the various stages of transpilation and separation of language specific and independent rewriters, this file is a good starting point:

https://github.com/adsharma/py2many/blob/main/py2many/cli.py...

Link to the test that's relevant for this discussion:

https://github.com/adsharma/py2many/blob/main/tests/cases/in...

This is an explicit deviation from python's bigint, which doesn't map very well to systemsey languages. The next logical step is to build on this to have dependent and refinement types.

Work in progress here:

https://github.com/adsharma/Typpete