bincode VS PyO3

Ermm... actually I meant something like this: playground, but then I realized it's basically (de)serialization, and I just found that we already have a crate for that: bincode.

One, figure out the bincode format (documented here: https://github.com/bincode-org/bincode/blob/trunk/docs/spec.md) and write your own parser. Maybe a one-off that specifically only handles this one data structure would be fairly straightforward.

Step 1: The Deserialize type requests data from the Deserializer with one of the deserialize_type methods. This gives it an opportunity to provide certain metadata about the type: structs provide a list of fields, enums provide a list of variants, tuples provide a length, etc. Some data formats (notably bincode) require this metadata to drive deserializing, as the wire format is not self-describing. Crucially, the Deserialize type also provides a visitor that is capable of receiving the requested data from the Deserializer.

Alternatively, give Bincode a try.

Like separate instructions? I was thinking if a instruction have unknown length I make sure I have some kind of header field that tells the data length of the instruction so receiver knows when next instruction starts. And I was planning on using Bincode with serde to serialize and dezerialize like structs and stuff.

Isn't this essentially bincode?

This is similar in practice to using abi_stable, and end-users will still receive compiled files, but your plugins will be sandboxed and a single build will work on all platforms. The downside is that it's a bit more work because WebAssembly's support for passing complex data types between the host and the WebAssembly code is in the preliminary stages, so you need to do something like using Serde to encode your data into something like Bincode or MessagePack (or JSON and friends) to hand it off between the host and the plugin.

What kind of access do you need to the data ? You should be able to make a safe api to the Vec class by iterating on in in chunks, and using a closure to translate data between u8 and other representations. ( f32, u32 has the fomr_ne_bytes() / to_ne_bytes() methods ) You could make a helper function that takes a format description ( i.e. "fffuucc" , and calculates the size of the chunk, and generates a closure for reading accessing the data, of the layout is completely dynamic. This closure could use an enum to wrap the different primitive types. ) Or if the layouts are known at compile time , you could use procedural macros to generate code for serializaion / deserialization inot the the [u8] , though https://crates.io/crates/bincode may already do that for you )

Apparently there's a lot of discussion going on about that (3 of the 4 open tickets on the bincode implementation are about it), for example this one.

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.