Github-Ranking VS rayon

5th highest number of stars of any repo on GitHub 🙃

This explains the uneven distribution of Haskell applications, but this does not explain why the distribution is more even in other languages. But is that even the case? You mention Python, and Python happens to be THE language of choice for data science projects, so I would expect to also see an uneven distribution there. And Java happens to be THE language of choice for writing Android applications, so I would expect an uneven distribution there too. And Rust is a systems programming language, so I would expect games and other things that really need to run fast. Let's look at lists of popular projects by language:

The repo I chose was Github-Ranking, a repo to check the most starred and forked GitHub repos of the day. The link can be found here: https://github.com/EvanLi/Github-Ranking. I picked this repo because I've never explored the most popular repos before and this allowed me to see what a lot of people are working on.

Clocking in at 190K Github stars React's github ranking is easily ranked in the top 10.

Here's a few other tools that are written in Perl, sorted by GitHub popularity: https://github.com/EvanLi/Github-Ranking/blob/master/Top100/...

Actually, that repo has lists like this for most languages: https://github.com/EvanLi/Github-Ranking

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.