pg8000 VS datafusion-ballista

As others have said you can use a custom compiled version of the lib, lambda layer or use lambda images, however, if you're not committed to psycogp2 I've found pg8000 a much easier library to work with in Lambda. You can just install it as any other library without any problems.

When people say "pure X", to me, it normally means they didn't involve an FFI or external compiler. This is an often beneficial thing since it simplifies your build process.

For example, here [0] is a "pure Python postgres driver" and the implication is that it doesn't use libpg.

Or see also this discussion [1].

[0] https://github.com/tlocke/pg8000

[1] https://www.reddit.com/r/learnpython/comments/nktut1/eli5_th...

I agree that information shouldn't be duplicated, but in one of my projects I've taken the opposite approach and made the FAQ the only place that certain information is presented. It's for the library https://github.com/tlocke/pg8000 and I've called them 'Examples' rather than a FAQ, but each time I get a question that isn't covered by the examples I add in a new example. I'd be interested to hear what people think of this approach.

Not super on topic because this is all immature and not integrated with one another yet, but there is a scaled-out rust data-frames-on-arrow implementation called ballista that could maybe? form the backend of a polars scale out approach: https://github.com/apache/arrow-ballista

> Is Rust's compile-time GC about something other than performance somehow?

AFAIK, memory safety and language features as RAII is also available in C++, for instance. About the reasons for slow compilation, take a look at https://www.reddit.com/r/rust/comments/xna9mb/why_are_rust_p...

Not having a GC is also about not having a runtime as you mention (e.g. nice for creating Python extensions and embedded systems programming) and also more runtime deterministic performance: on that, if I'm not mistaken that was the reason for Discourse switching to Rust and also, e.g.: "the choice of Rust as the main execution language avoids the overhead of GC pauses and results in deterministic processing times" https://github.com/apache/arrow-ballista/blob/main/README.md

Ballista (Arrow-Rust), which is largely inspired by Apache Spark, there are some interesting differences.

https://github.com/jorgecarleitao/arrow2 https://github.com/apache/arrow-datafusion https://github.com/apache/arrow-ballista https://github.com/pola-rs/polars https://github.com/duckdb/duckdb

Source: https://github.com/apache/arrow-ballista

Can you explain how this might differ from something like https://github.com/apache/arrow-ballista

I've seen several variants of "next-gen" spark, but nowhere have I really seen the different tradeoffs/advantages/disadvantages between them.

Rust has amazing integration with Python through PyO3 [1] so see it like a safe alternative for high performance calculations. The ecosystem itself is starting to come together exciting projects like Polars [2] (Pandas alternative), nalgebra [3], Datafusion [4] and Ballista [5]

[1] https://github.com/PyO3/pyo3

[2] https://github.com/pola-rs/polars/

[3] https://docs.rs/nalgebra/latest/nalgebra/

[4] https://github.com/apache/arrow-datafusion

[5] https://github.com/apache/arrow-ballista