mirrord VS maturin

So, you've been using mirrord to simplify your development process (if you haven’t, go here!). Naturally, you want the traffic from the app you're debugging to go through the cluster environment, so your app can communicate with its clustery pals. There is a problem though: your latest change adds some new columns to the database, and you don’t want to modify the database in the cluster and affect everyone else working on it. You do have a local instance of the database that you can modify, so your app can use that, but you still want it to talk to all the other components in the cluster. So what now? The new outgoing traffic filter feature is here to solve exactly this type of problem!

I had the pleasure of talking to Eyal @ CTO at Metalbear and the maintainer of Mirrord. I got some crazy insights.

If you want to take mirrord for a spin, check out the quick start guide. We’d love to hear about your experience or just general thoughts - chat us up on our Discord or open an issue or discussion on GitHub.

We're building an open-source tool called mirrord which lets you run a local process in the context of a pod in your cloud environment. We often get asked how mirrord is different from Telepresence and so we decided to write a short blog post about it, which we hope would be valuable to those interested in local Kubernetes development:

Hands-on tutorial of mirrord.dev with the creators and Rawkode!

if you are interested in k8s, iptables, hooking libc, asm etc https://github.com/metalbear-co/mirrord

I gave my first CNCF talk in Toronto yesterday, talking about https://github.com/metalbear-co/mirrord , how all the features work, and how it's engineered!

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.

-- Maturin on GitHub

My new favorite way to write very fast libraries for Python is to just use Rust and Maturin:

https://github.com/PyO3/maturin

It basically automates everything for you. If you use it with Github actions, it will compile wheels for you on each release for every platform and python version you want, and even upload them to PyPi (pip) for you. Everything feels very modern and well thought out. People really care about good tooling in the Rust world.

Python first, you will be able to experiment quickly with the notebooks. Then maybe write (or rewrite) some modules in Rust that you can expose as python modules, with py03 and maturin. Feel free to publish useful packages on both crates.io and pypi.org, so you can contribute to Python and Rust ecosystems.

Now if you really want to use Rust, you can rewrite only the part that are slowing down your consumer. It's easy by using Py03 and maturin. Maybe also rayon to parallelize.

It's relatively easy to extend Python with project like Py03[0] and Maturin[1]. Polars[2] is the perfect example of that.

It's not easy to push coworkers/companies to use an unfamiliar language. Rust isn't fast to learn. You need very good arguments and a good usecase to make it works.

I doubt that learning Rust will help you more that learning more about the data engineers tools, so this isn't really "worth" your time.

[0] -- https://pyo3.rs/v0.18.3/

[1] -- https://github.com/PyO3/maturin

[2] -- https://www.pola.rs/

In this case, PyO3/maturin does all the setup and getting the module into Python. They also have docs going into a lot more depth on this.

Lastly if you're willing to introduce Rust, I'd consider a gradual approach using native libraries built in rust with PYO3. Check the maturin guide that helps you to streamline the build process of native libraries : https://github.com/PyO3/maturin . From there you could try to find hotspots in your python app and replace those with a native implementation.