pybind11 VS PyO3

I'm investigating using C++ to build a REST server, and would love to know of people's experiences with Crow-- or whether they would recommend something else as a "medium-level" abstraction C++ web server. As background, I started off experimenting with Python/FastAPI, which is great, but there is too much friction to translate from pybind11-exported C++ objects to the format that FastAPI expects, and, of course, there are inherent performance limitations using Python, which could impact scaling up if the project were to be successful.

I have a C++ function computing a large tensor which I would like to return to Python as a NumPy array via pybind11.

This is based on the Google Ink Stroke Modeler C++ library, and using pybind11 to make it available on python.

pybind11 makes it easy to call C++ from Python if you want to mix.

If you've never used Pybind before these pybind tests[1] and this repo[2] have good examples you can crib to get started (in addition to the docs). Once you handle passing/returning/creating the main data types (list, tuple, dict, set, numpy array) the first time, then it's mostly smooth sailing.

Pybind offers a lot of functionality, but core "good parts" I've found useful are (a) use a numpy array in Python and pass it to a C++ method to work on, (b) pass your python data structure to pybind and then do work on it in C++ (some copy overhead), and (c) Make a class/struct in C++ and expose it to Python (so no copying overhead and you can create nice cache-aware structs, etc.).

[1] https://github.com/pybind/pybind11/blob/master/tests/test_py...

I have a python module for which the logic is written in C++ and I use pybind11 to expose the objects and functions to Python.

Reading from Python requires a wrapper, using pybind11 this is fairly done.

PyO3 0.23.0 was a big release I’ve been tinkering with extensively and support for “free-threaded Python” is a headline feature and I imagine this NoGIL Python will be extremely nice for Rust interoperability so there is definitely interest in that crate. Also could be huge for queueing data for GPUs, api servers, bulk data fetching.

For whatever reason (maybe post 2to3 PTSD), Python community seems not extremely eager to jump on latest versions of Python and it often takes a long time for popular libraries to support the latest and greatest, so I’d recommend patience and baby steps

https://github.com/PyO3/pyo3/releases/tag/v0.23.0

🤖 Unlike the above languages Rust can also be used as a replacement of part of the application. Especially if there is a part that needs some speed-up or needs some memory saving. One could replace part of a Python or Node project by Rust and embed the code using PyO3 or napi respectively. This is also going to be the topic of the presentation of Aviram Hassan called Microdosing Rust to your organization.

The interop shop for Rust and Python is Pyo3. As the only game in town, Pyo3 uses the Foreign Function Interface (FFI) to allow your Rust code to make calls into the CPython binary. This works by agreeing on the Application Binary Interface (ABI), a concept I used during my career at AMD. Core software ftw!

Sorry, "FFI" was a shorthand for "mixing and matching two languages GC expectations, memory layouts, ..." and all the overhead associated with merging something opinionated, like Rust, with something dynamic, like Python. You almost certainly _can_ reduce that overhead further, but unless somebody has gone out of their way to do so, the default expectation for cross-language calls like that should be that somebody opted for maintainable code that has actually shipped instead of shaving off every last theoretical bit of overhead.

It's been a few years, so I really can't tell you exactly what the problem was (other than the general observation that you should try to do nontrivial amounts of work in your python extensions rather than trivial amounts), but PyO3 agrees with the general sentiment [0] [1], or at least did at roughly the same time I was working there.

[0] https://github.com/PyO3/pyo3/issues/679

[1] https://github.com/PyO3/pyo3/issues/1470

This blog post is meant to be an introduction to PyO3 by walking the reader through the build process of a non-trivial extension module in Rust using PyO3. Some familiarity with Python and Rust is recommended to get the most out of this post, basic understanding of Bitcoin concepts may be required to fully grasp the code samples.