lol-html VS PyO3

So I'm doing some web scraping in Rust, and so I will need to parse HTML. [scraper](https://docs.rs/scraper/latest/scraper/) (which uses [html5ever](https://github.com/servo/html5ever)) is doing fine except that it's the bottleneck of my application.

So I need a faster parser. I've tried [tl](https://docs.rs/tl/latest/tl/) which would've been perfect except that it doesn't actually work on the HTML I have. When I try to `query_selector` the elements I need, it returns nothing.

[Kuchiki](https://docs.rs/kuchiki/latest/kuchiki/) is abandonded.

I couldn't figure out how to get [lol-html](https://github.com/cloudflare/lol-html) to work for me (it's designed for re-writing HTML, whatever that means). It doesn't seem to have an API to extract the inner text of an element.

[html5gum](https://github.com/untitaker/html5gum) seems to be just an HTML tokenizer, or otherwise just too low-level. I have not yet tried [quick-xml](https://github.com/tafia/quick-xml/) but judging from the README, it's pretty low-level too. I mean, if these are the only options left then I will try them. Otherwise, I would love to use a parser that's faster but as ergonomic as `scraper` or `tl`.

At this point, I would be happy with an Lxml bridge/port of some sort. I don't need to mutate HTML, just parse and read data from it.

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.

Thanks again and happy to answer any questions! My current unreleased Rust projects include a web crawler that uses Tokio + Tokio Console + Reqwest with this crate for robots.txt and a fast text extraction library using lol-html that I am planning to sprinkle with some minimal ML to get Readability.js style intelligent extraction (with training in Python). See Fathom for an example of the ML approach I'll likely take.

I’d like to see a tool using lol-html [0] and their CSS selector API as a streaming HTML editor.

[0] https://github.com/cloudflare/lol-html

Cloudflare has open sourced lol-html, a "Low output latency streaming HTML parser/rewriter with CSS selector-based API". Is that what you are looking for?

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.