markup.rs VS PyO3

(Sailfish is fastest, but it's syntax is of the more traditional <%= msg %> flavour and Markup.rs is second-fastest with a Maud-like syntax but the author apparently doesn't have time to rewrite the syntax reference, so you have to follow a link from the open issue to an old version of the README.)

Maud or markup.rs for templating (I use the latter, and it is faster, but they're both fast and markup.rs is currently missing its full syntax documentation unless you dig through the revision history for the stale version. I'd recommend the former for you.)

For the sake of thoroughness, I should point out that Haml-like templating engines like Maud and markup.rs are even more concise.

https://maud.lambda.xyz/ or https://github.com/utkarshkukreti/markup.rs for server-side HTML templates that compile to Rust code

(eg. In Python, Genshi templates are too slow for me to feel comfortable using them, but they were the main way to get robust correctness checks for templates last time I evaluated my options. In Rust, Markup.rs or Maud are the second and third fastest templating solutions, as I remember, and they give even more well-formedness guarantees for HTML than Genshi.)

There are various templating solutions that use syntax derived from the host language, like Maud or markup.rs for Rust, the E factory API for lxml for Python, etc.

You can have similar features to Phoenix Live View by using Turbo from Hotwire with your favorite template engine in Rust. Contrary to what the video presentation on the Hotwire main page leaves you to believe, Hotwire works with any template engine from any language, not just Rails. Markup.rs and Turbo from Hotwire should compose quite nicely.

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.