jsii VS PyO3

What about jsii? The technology behind AWS sdks: https://aws.github.io/jsii/

Is Stainless similar, different?

The libraries for other supported languages of CDK are built as wrappers for underlying JS or maybe TS code via https://github.com/aws/jsii

So all the core CDK code is written first in JS/TS and then stubs for the other languages are added

Unfortunately this is often done without consideration for how the other supported langs actually work, and artefacts of e.g. JS lack of support for kwargs leak through

This is why e.g. the typing in CDK Python is completely broken - pretty much uniformly the concrete types like "Resource" don't implement their corresponding interface like "IResource" (to a type checker)

(There are many other typing niggles like this but that's the most egregious and pervasive one)

At the end of the day, having to explicitly cast concrete types as their interface to satisfy type checker is a minor annoyance, albeit a stupid one that could have been avoided with more care in the core library.

I could live with that, but I encountered so many bugs and issues trying to use CDK on current project that it's now much clearer to me why every company I worked at previously was using Terraform.

Pretty sure some of those issues are ultimately CloudFormation ones. The cumbersome CF > CDK JS > CDK Python stack is great for obfuscating errors and making debugging hard or impossible though.

Pulumi do something similar, albeit with Go as the core language and Terraform underneath. From what I've seen with a little use they have a much more successful result though, Pulumi Python was not a complete mess, and deploys faster and more reliable with better error feedback. I guess they just took more care to get it right.

Every version of the CDK uses an interop layer and runs on top of the Typescript version

https://github.com/aws/jsii

And as far as TF supports services before CFT. Guess which is easier for an AWS employee to do - getting the CF service team to support a new service or just contribute to Terraform’s open source project?

I know of at least one service where the service team introduced the needed APIs and then an employee of AWS wrote the TF provider and contributed to the project before AWS’s own internal team added it to CFT.

Source: former AWS ProServe employee

To "meet developers where they are" is a beautiful tenet of AWS, and of the CDK, and inspired us to create awesome technology such as JSII and constructs.

Yeah both CDKTF and Pulumi piggyback on JSII, a class translator that supports the languages you've mentioned, which was used as a building base for CDK.

The reason we chose to start with compilation to JavaScript and not some other popular cloud language is because JavaScript is currently one of the most used ones, and in addition, we are using JSII to write our SDK in Typescript and then export it for consumption in other languages, such as Go, Python, Java and others.

The CDKTF CLI is implemented in TypeScript. Providers and resources are implemented in TypeScript as well. jsii is used to compile the providers and resources to the supported languages.

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.