Github-Ranking VS RE2

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

Looking at the benchmark where C++ is worst compared to other languages, it's depending on the library used. I would guess if they used Google's re2 Regex library instead of Boost's, the result would be different.

https://github.com/google/re2

https://github.com/greensoftwarelab/Energy-Languages/blob/ma...

That page says it just includes "some of the most common special characters", and following the link to the Examples page in turn includes a link to the full list.

Python uses backtracking, so this probably isn't O(n), especially with the ability to choose the dictionary.

But with there are non-backtracking matchers which would make this O(n). Here's re2 from https://github.com/google/re2 :

  >>> import re2

RE2 is a Google regular expression library

google's regex library: https://github.com/google/re2

I am currently working with Regex, specifically Re2, and was wondering if there is a real difference between the above expressions for repeated sub-regex.

I ported Google RE2, a regular expression library, to SPVM as Resource::Re2, and created SPVM::Regex, a wrapper for it.

I searched for I found that there is a Perl compatible regular expression called Google RE2. It is written in C++, and with Google RE2, I can use Perl-compatible regular expressions as a library.

Yes, but there is an interesting clarification here. RE2 has used the "caching" approach documented in the Ruby bug ticket linked for quite some time (since its birth?): https://github.com/google/re2/blob/954656f47fe8fb505d4818da1...

It is mentioned only briefly in Cox's article on regex matching in the wild. Look for the word "bitstate": https://swtch.com/~rsc/regexp/regexp3.html

I didn't know Perl had implemented this trick too.

The paper[1] cited in the Ruby bug ticket was published very recently. When I first read the Ruby bug ticket, I immediately wondered how they sidestepped the memory use problem. The paper's abstract seems to suggest there is some technique for doing so, as it rebuffs the idea of doing "full" memoization. Alas, I do not have access the paper. (Which is fucking ridiculous.)

[1]: https://ieeexplore.ieee.org/abstract/document/9519427