cbindgen VS PyO3

Golang uses cgo to communicate with external C libraries and Rust uses FFI (Foreign function interfaces) to export C functions. Additionally, a library for automatically generating bindings can be used to generate the header files (.h). In our case, we'll only be exporting two C functions, so using cbindgen is overkill but we'll use it regardless because why not?  Firstly, we'll write the rust functions we want to export. We'll start by creating a new rust library:

[1]: https://github.com/mozilla/cbindgen

As a 2-step approach, you could also probably use https://github.com/mozilla/cbindgen and then Nim's native C FFI.

The tooling for the first kind -- calling Rust from another language -- is a bit less developed, and tends to rely on code generation that doesn't necessarily produce a natural C API. cbindgen, uniffi, cxx, and Diplomat all take this course.

There's also cbindgen for automating the generation of C headers once you've got your code in the right shape and you'll also want to read the Rustonomicon.

You can backport Rust standard library to C using https://github.com/eqrion/cbindgen .

Later we'll see what cbindgen essentially does is receive a configuration and a Rust library and then spit out a C header (.h) file. One might think that what cbindgen is doing might not be that special and can be done by hand. In which some cases that might be true if the project is simple enough. Though additionally as the cbindgen documentation states:

Fuck it, I'll take the bait. Rust allows for amazing C interop by allowing for exporting of C types as a dynamic library, like C. Unlike C, this still allows for Rust's advanced checking at compile time. librsvg is one such library that is being used in the GTK ecosystem that is also almost completely Rust. You can use tools like cbindgen to generate C bindings.

Or alternatively cbindgen to create the header file from the Rust source.

The easiest way to obtain the headers for the rust code is to use the cbingen library.

Integrating Rust into Python, Edward Wright, 2021-04-12 Examples for making rustpython run actual python code Calling Rust from Python using PyO3 Writing Python inside your Rust code — Part 1, 2020-04-17 RustPython, RustPython Rust for Python developers: Using Rust to optimize your Python code PyO3 (Rust bindings for Python) Musing About Pythonic Design Patterns In Rust, Teddy Rendahl, 2023-07-14

This story unfolds as a captivating journey where the agile Flounder, representing the Python programming language, navigates the vast seas of coding under the wise guidance of Sebastian, symbolizing Rust. Central to their adventure are three powerful tridents: cargo, PyO3, and maturin.

I would not recommend FFI + ctypes. Maintaining the bindings is tedious and error-prone. Also, Rust FFI/unsafe can be tricky even for experienced Rust devs.

Instead PyO3 [1] lets you "write a native Python module in Rust", and it works great. A much better choice IMO.

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

Same w/ Rust and Python, this is really neat because now each thread could have a GIL without doing exactly what you said. The pyO3 commit to allow subinterpreters was merged 21 days ago, so this might "just work" today: https://github.com/PyO3/pyo3/pull/3446

I expected someone to write a rust-based scripting language which tightly integrated with rust itself.

In reality, it seems like the python developers and toolchain are embracing rust enough to reduce the benefits to a new alternative.

https://github.com/PyO3/pyo3

Hey HN! I am one of the people working on Bytewax. Bytewax came out of our experience working with ML infrastructure at GitHub. We wanted to use Python because we could move fast, the team was very fluent in it, and the rest of our tooling was Python-native already. We didn't want to introduce JVM-based solutions into our stack because of the lack of experience and the friction we had trying to get Python-centric tooling working with existing solutions like Flink.

In our research, we found Timely Dataflow (https://timelydataflow.github.io/timely-dataflow/, https://news.ycombinator.com/item?id=24837031) and the Naiad project (https://www.microsoft.com/en-us/research/project/naiad/) as well as PyO3 (https://github.com/PyO3/pyo3) and we thought we found a match made in heaven :). Bytewax leverages both of these projects and builds on them to provide a clean API (at least we think so) and table stakes features like connectors, state recovery, and cloud-native scaling. It has been really cool to learn about the dataflow computation model, Rust, and how to wrangle the GIL with Rust and Python :P.

Would love to get your feedback :).

`pip install bytewax` to get started. We have a page of guides (https://www.bytewax.io/guides) with ready-to-run examples.

You can practice your Rust skills by writing performant and/or gluey extensions for higher-level language such as NodeJS (checkout napi-rs) and Python or complementing JS in the browser if you target Webassembly.

For instance, checkout Llama-node https://github.com/Atome-FE/llama-node for an involved Rust-based NodeJS extension. Python has PyO3, a Rust-Python extension toolset: https://github.com/PyO3/pyo3.

They can help you leverage your Rust for writing cool new stuff.