Tide VS PyO3

Rust has several, production ready, REST API frame works.

Rust allows me to mainly only run the application to confirm things work from a business perspective.

For people starting out building stuff in rust - understand that there is a distinction of async code and libraries and can lead to confusing compiler errors if you don't realize there is a distinction. It's simple in hindsight but did cause me to waste hours barking up the wrong trees at first. Other wise just learn about `match` and Result/Option types asap, they're fundamental.

https://github.com/http-rs/tide tide is great to create an http server / routes

https://github.com/djc/askama I use this to template out HTML and it checks all my boxes, dynamic data, passing in functions, control flow.

https://github.com/launchbadge/sqlx sql interface for a variety of backend, async safe.

https://github.com/seanmonstar/reqwest http client to make requests

Rust is amazing, don't let the initial few speed bumps discourage you - building real things with rust is no more challenging today than any other modern language stack.

https://crates.io/crates/salvo - 581k all time and peak daily of ~2750 in last few months https://crates.io/crates/rocket - 2.68mil all-time / ~6200 daily https://crates.io/crates/actix-web - 9.8mil all-time / ~21k daily https://crates.io/crates/axum - 8.8mil all-time / 64k daily https://crates.io/crates/warp - 7.9mil all-time and 19k daily https://crates.io/crates/tide - 886k all-time / 2250 daily

Hi, guys, how you doing? I'm trying out this web framework Tide just to make a toy project and learn more about Rust. The create_payment_handler function is called by the framework whenever there is a POST request to /payment/ containing a JSON body with the payment information.

tide

I'm developing the backend of https://www.cozydate.com/ in Rust. Async Rust is not productive yet, so I tried rouille http server which lets me write non-async request handlers. Unfortunately, it uses an unbounded thread pool and falls down under load https://github.com/tiny-http/tiny-http/issues/221 . Then I tried Tide and a threadpool to call my non-async API handlers. This worked, but was really ugly, and I had issues with uploads after deploying to Heroku https://github.com/http-rs/tide/issues/878 .

You could also habe a look at tide apparently it is stable and production ready.

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.