wasmer-go VS PyO3

The next step is to create a Go project and run our wasm file with some runtime. For this, I chose wasmer-go.

However, there are other, more fleshed-out, libraries like wasmer-go that provides a runtime and help us navigate around these limitations. The wasmer-go documentation provides a good summary of these challenges:

wasmer-go

I did some research and found a WebAssembly runtime that can run Go code that has been compiled to WASM. It seems to me that one could implement a plugin system using this. I might try.

Thank you Syrus, appreciate your work with Wasmer. Congrats on the 3.0 release and Windows support! I just fixed guregu/trealla on WAPM to work with the latest changes. I think WAPM is very cool and I hope more people start doing releases on it.

These are the two issues I'm referring to:

https://github.com/wasmerio/wasmer-go/pull/200

https://github.com/wasmerio/wasmer-go/pull/286

Time to implement the other side of the story. I have found a WebAssembly runtime for Go. Wasmer-go is a complete and mature WebAssembly runtime for Go based on Wasmer.

If performance is your main concern, there's Wasmer-go, but if you'd rather avoid CGO dependencies, there's wazero.

See wasmer-go for server-side runtime.

I've been looking at wasmer-go, and it seems to be quite performant given that the runtime is written in Rust and invoked through CGo bindings. Is this what everyone is using?

Go: I don't know of anything higher-level than either exposing a C ABI from Rust and then calling it using cgo or using wasmer-go to embed a WebAssembly runtime in your Go program.

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.