RE2 VS compile-time-regular-expressions

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

CTRE (https://github.com/hanickadot/compile-time-regular-expressions) ranges::views (filter, transform, etc.) (C++20) str.find() + str.substr() freopen to stdin + cin >> extraction Parser libraries

There are suggestions that should be replaced by the high performance ctre implementation: https://github.com/hanickadot/compile-time-regular-expressions

operator"" can be used in a similar way to expression templates (DSLs), where the type of the resulting expression is dependent on the string contents. For example ctre makes use of this to build efficient regular expression parsers, and kumi uses this in conjunction with operator[] to make tuple indexing quite elegant

Check out https://github.com/hanickadot/compile-time-regular-expressions anything is possible 😂

Yes to regex, no to std::regex. Better to use CTRE. Something like "^Hello [0-9]+ how are you" should allow checking if there's a match

You could compare your implementation with https://github.com/hanickadot/compile-time-regular-expressions and see if there are any ideas you can copy.

std::compile_time_regex would be a nice addition. Something similar to ctre https://github.com/hanickadot/compile-time-regular-expressions Simply letting the compiler generate all the regex parsing machinery at compile time.... And benefitting from compiler optimizations, vectorization, etc...

ctre