workers-rs VS pyodide

- The speed of the Python interpreter running in WebAssembly

Today, Python cold starts are slower than cold starts for a JavaScript Worker of equivalent size. A basic "Hello World" Worker written in JavaScript has a near zero cold start time, while a Python Worker has a cold start under 1 second.

That's because we still need to load Pyodide into your Worker on-demand when a request comes in. The blog post describes what we're working on to reduce this — making Pyodide already available upfront.

Once a Python Worker has gone through a cold start though, the differences are more on the margins — maybe a handful milliseconds, depending on what happens during the request.

- There is a slight cost (think — microseconds not milliseconds) to crossing the "bridge" between JavaScript and WebAssembly — for example, by performing I/O or async operations. This difference tends to be minimal — generally something measured in microseconds not milliseconds. People with performance sensitive Workers already write them in Rust https://github.com/cloudflare/workers-rs, which also relies on bridging between JavaScript and WebAssembly.

- The Python interpreter that Pyodide provides, that runs in WebAssembly, isn't as fast as the years and years of optimization that have gone into making JavaScript fast in V8. But it's still relatively early days for Pyodide, compared to the JS engine in V8 — there are parts of its code where we think there are big perf gains to be had. We're looking forward to upstreaming performance improvements, and there are WebAssembly proposals that help here too.

Not yet, but we're working on that https://github.com/cloudflare/workers-rs/pull/324

I'm also in the Workers org but I have had a bit of interaction with Rust. There's some Rust in the Workers runtime using lol-html for HTMLRewriter as well as some tooling and there's the full blown workers-rs framework that I work on, but that's about it for the Rust I work on regularly.

I'm an engineer at Cloudflare working on Workers (and a maintainer of workers-rs) and I'd love to help whoever ends up maintaining this get that PR rewriting it in Rust across the line.

GitHub - cloudflare/workers-rs: Write Cloudflare Workers in 100% Rust via WebAssembly

I never said it was, but thankfully nowadays there are plenty of other tools that are fast enough to keep the dev cycle quick. Personally esbuild is my go-to when I need a bundler but I've grown really fond of SWC native api, we used to use it at work for our wasm build tool for our workers-rs framework.

There's actually a rust framework for Workers https://github.com/cloudflare/workers-rs

I'm a maintainer of the of the Cloudflare workers-rs project to allow you to write serverless functions in Rust running as WASM in our V8-based runtime. There's certainly some rough spots (doesn't have complete parity with our default JS runtime apis), but if you're concerned with cold start times and you don't need a full containerized environment I think it's a solid choice.

Most likely, it should, we just haven't had the time to fully implement it or add a library to wrap the FFI. Please let us know you need a feature by opening an issue.

I may be biased, as the original project author, but I’d recommend using Cloudflare Workers https://github.com/cloudflare/workers-rs - totally free their with very generous limits.

We implemented an in-browser Python editor/interpreter built on Pyodide over at Comet (our users are data scientists who need to build custom visualizations quite often, and the most familiar language for most of them is Python).

One of the issues you'll run into is that Pyodide only works by default with packages that have pure Python wheels available. The team has developed support for some libraries with C dependencies (like scikit-learn, I believe), but frameworks like PyTorch are particularly thorny (see this issue: https://github.com/pyodide/pyodide/issues/1625 )

We ended up rolling out a new version of our Python visualizations that runs off-browser, in order to support enough libraries/get the performance we need: https://www.comet.com/docs/v2/guides/comet-ui/experiment-man...

Thank you! Yes, one of the items in the Roadmap is support for Pyodide (https://github.com/pyodide/pyodide) for running in-browser python on the results of each of the code blocks! This should allow most ML libs to be usable in-browser! This is pretty high-up on our priority list.

If I understand correctly, WASM only makes sense for compiled languages, you can run the python interpreter in WASM of course[1], but that will be at a significant performance disadvantage to the native javascript interpreter, and it's also something that has to be loaded every time you load the website.

[1]: https://github.com/pyodide/pyodide

If you absolutely need something comparable to Sympy, then one option might be to figure out how to best call Sympy from Rust. e.g. - RustPython, although it seems like Sympy isn't supported yet - Pyodide, and figuring out how to run it outside of a web browser. Probably also not very easy. - PyPy, and having a pretty simple Python binary for every platform - ...

For running programming languages other than JavaScript in the browser there is Emscripten and WebAssembly. There is v86, where a Linux build is compiled to WASM. Folks have written QuickJS into a Linux build compiled to WASM, Node.js into the Linux buildroot https://github.com/cemalgnlts/now, so Python or CPython can be written to the image and loaded into the browser as WASM as well https://github.com/pyodide/pyodide.

There's a Python runtime that runs on WebAssembly (https://github.com/pyodide/pyodide). I have no idea what it's like, I've never used it.

Still in a quest to provide some tooling to quickly compose documentation websites: https://github.com/synw/docdundee . As I have tons of libs to document and was tired of managing restructured language for readthedocs I started with this, and now it has executable Python examples in the frontend via a Pyodide wrapper composable: usePython

This project's brand new and a lil hacky, but I've already reached out to the scikit-learn team, and they recommended that I experiment with using Pyodide as an alternative backend for the Python bridge.