jsmpeg VS rayon

>Today, there is a Python package for everything.

The same could be said about CPAN and NPM. Yet Perl is basically dead and JavaScript isn't used for any machine learning tasks as far as I'm aware. WebAssembly did help bring a niche array of audio and video codecs to the ecosystem[1][2], something I'm yet to see from Python.

I don't use Python, but with what little exposure I've had to it at work, its overall sluggish performance and need to set up a dozen virtualenvs -- only to dockerize everything in cursed ways when deploying -- makes me wonder how or why people bother with it at all beyond some 5-line script. Then again, Perl used to be THE glue language in the past and mod_perl was as big as FastAPI, and Perl users would also point out how CPAN was unparalleled in breadth and depth. I wonder if Python will follow a similar route as Perl. One can hope :-)

[1] https://github.com/phoboslab/jsmpeg

There's also mpegts over websockets if you don't need iphone support. https://github.com/phoboslab/jsmpeg

We will use jsmpeg to display the video on the page

Do some operations with .iter() then later use rayon to parallelize. So you can show how easy is to add a dependency and how easy is to parallelize.

rayon: Async CPU runtime for parallelism.

In the quest for more efficient solutions, the ONNX runtime emerged as a beacon of performance. The decision to transition from Typescript to Rust was an unconventional yet pivotal one. Driven by Rust's robust parallel processing capabilities using Rayon and seamless integration with ONNX through the ort crate, Repo-Query unlocked a realm of unparalleled efficiency. The result? A transformation from sluggish processing to, I have to say it, blazing-fast performance.

With a lot of rayon, blood, sweat and tears I finally managed to simulate a million buildings at 60fps :) Feel free to AMA, game is Combine And Conquer

(see https://github.com/rayon-rs/rayon/tree/master/src/iter/plumbing)

I did some very basic testing with this crate : https://crates.io/crates/rayon and it seems to work :

Sequentially, generating tree-sitter AST for each file and querying for the links of each file takes around 2.3 seconds. However, I randomly remembered this crate rayon, and I decided to test it. It ended up improving the performance (just by changing 2 lines of code) to 200-300ms by parallelizing the iterators and tree-sitter queries. MAJOR.

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.