gleam VS PyO3

Want a friendly language for building safe systems at scale? Gleam is here for you. It features modern and familiar syntax, that's reliable and scalable. Gleam runs on an Erlang virtual machine, and can run plenty of concurrent tasks. It comes with a compiler, build tool, formatter, editor integrations, and package manager all built in so you can get started right away. Congrats to the team on shipping your first major version 🙌.

While I love Clojure, I have to agree about tooling. I recently started using Gleam* and was impressed at how easy it was to get up and running with the CLI tool. I think this is an important part of getting people to adopt a language.

* https://gleam.run/

If you use languages that compile to WASM (such as Gleam https://gleam.run), and can also run Postgres via WASM, then it opens very interesting offline scenarios with codebases which are similar on both the client and the server, for instance.

Recently, Gleam has gained more popularity, and a lot of developers (including me) are learning it. At the time of this writing, it has exceeded 14k stars on GitHub; it grew really fast for the last month.

Oh, strange that github had a date of 2016 on this one: https://github.com/gleam-lang/gleam/issues/2

I was just going by that, though I do remember checking out gleam 5 years ago or so.

Re: macros, I really do think they’re a big deal and all the other newer languages I’ve used, such as Rust have some kind of macros or powerful meta programming features.

For older languages, a few, like Ruby have enough meta programmability to make nice DSLs, but many others don’t. Given the choice, I’d much rather have Elixir/Clojure style macros than other meta-programming facilities I’ve seen so far.

I had been only following this language with some interest, I guess this was born in gitlab not sure if the creator(s) still work there. This is what I'd have wanted golang to be (albeit with GC when you do not have clear lifetimes).

But how would you differentiate yourself from https://gleam.run which can leverage the OTP, I'd be more interested if we can adapt Gleam to graalvm isolates so we can leverage the JVM ecosystem.

I don't think the implementation itself is at fault, but yes, I do think that the design of dialyzer makes it an (at times) faulty type checker. The unfortunate reality of a type checker that fails sometimes is that it makes it mostly useless because you can never trust that it'll do the job.

To be clear, I've had it fail in a function where I've literally specced that very function to return a `binary` but I'm returning an `integer` in one of the cases. This is a very shallow context but it can still fail. Now add more functions, maybe one more `case`.

I think an entire rethink of type checking on the BEAM had to be done and that's why eqWalizer[0] was created and why Elixir is looking to add an actual sound, well-developed type checker. Gleam[1] I would assume is just a Hindley-Milner system so that's completely solid. `purerl`[2] is just PureScript for the BEAM so that's also Hindley-Milner, meaning it's solid. `purerl` has some performance issues caused by it compiling down to closures everywhere but if you can pay that cost it's actually pretty fantastic. With that said my bet for the best statically typed experience right now on the BEAM would be `gleam`.

0 - https://github.com/WhatsApp/eqwalizer

1 - https://gleam.run

2 - https://github.com/purerl/purerl

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.