ugrep
rebar
ugrep | rebar | |
---|---|---|
24 | 22 | |
2,435 | 197 | |
1.2% | - | |
9.1 | 8.5 | |
10 days ago | about 2 months ago | |
C++ | Python | |
BSD 3-clause "New" or "Revised" License | The Unlicense |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ugrep
- Ugrep – a more powerful, ultra fast, user-friendly, compatible grep
- The ugrep file pattern searcher
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Ripgrep is faster than {grep, ag, Git grep, ucg, pt, sift}
I switched from from ripgrep to ugrep and never looked back. It's just as fast, but also comes with fuzzy matching (which is super useful), a TUI (useful for code reviews), and can also search in PDFs, archives, etc.
The optional Google search syntax also very convenient.
https://ugrep.com
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ugrep 4.3.2 with updated TUI
Installation and user guide: ugrep.com (no ads, no cookies, just plain HTML in a GitHub page)
- New Ugrep 4.0
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ugrep 4.0 released + performance benchmarks
Not heard of ugrep? Read the wiki on GitHub.
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ripgrep is faster than {grep, ag, git grep, ucg, pt, sift}
These ripgrep announcements are getting a bit old, don't you think? Ripgrep hasn't improved or added new features since 2016. There are other fast alternatives with a lot more features, like ugrep and qgrep for example. Ugrep has fuzzy regex pattern search, archive search (even nested archives!), Boolean search queries like Google, interactive query TUI, hexdumps, and is compatible with GNU grep (ripgrep is not).
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ugrep vs. grep – What are the differences?
It's the first time I hear about the ugrep. Would be nice to compare it with ripgrep, since both provide benchmark tables listing their tool at the top :D. For my everyday use speed doesn't matter much, as well as interactive mode seems useless (YMMV). So I'm staying with ripgrep for now.
- ugrep 3.10 - yet another grep for you - this one is fast and has a ton of cool features: now outputs directory trees for your viewing pleasure
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Programming languages endorsed for server-side use at Meta
ugrep has full SSE/AVX support
https://github.com/Genivia/ugrep/blob/a3acf863803a755ff8da8c...
rebar
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Knuth–Morris–Pratt Illustrated
https://github.com/BurntSushi/rebar
For regex, you can't really distill it down to one single fastest algorithm.
It's somewhat similar even for substring search. But certainly, the fastest algorithms are going to be the ones that make use of SIMD in some way.
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Regex character "$" doesn't mean "end-of-string"
I'll add two notes to this:
* Finite automata based regex engines don't necessarily have to be slower than backtracking engines like PCRE. Go's regexp is in practice slower in a lot of cases, but this is more a property of its implementation than its concept. See: https://github.com/BurntSushi/rebar?tab=readme-ov-file#summa... --- Given "sufficient" implementation effort, backtrackers and finite automata engines can both perform very well, with one beating the other in some cases but not in others. It depends.
* Fun fact is that if you're iterating over all matches in a haystack (e.g., Go's `FindAll` routines), then you're susceptible to O(m * n^2) search time. This applies to all regex engines that implement some kind of leftmost match priority. See https://github.com/BurntSushi/rebar?tab=readme-ov-file#quadr... for a more detailed elaboration on this point.
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Re2c
They are extremely fast too: https://github.com/BurntSushi/rebar?tab=readme-ov-file#summa...
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C# Regex engine is now 3rd fastest in the world
I love the flourish of "in the world." I had never thought about it that way. Which makes me think if there are any regex engines that aren't in rebar that could conceivably by competitive with the top engines in rebar. I do maintained a WANTED list of engines[1], but none of them jump out to me except for maybe Nim's engine.
Of course, there's also the question of whether the benchmarks are representative enough to make such extrapolations. I don't have a good answer for that one. All models are wrong, but, some are useful.
[1]: https://github.com/BurntSushi/rebar/blob/96c6779b7e1cdd850b8...
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Ugrep – a more powerful, ultra fast, user-friendly, compatible grep
I'm the author of ripgrep and its regex engine.
Your claim is true to a first approximation. But greps are line oriented, and that means there are optimizations that can be done that are hard to do in a general regex library.
If you read my commentary in the ripgrep discussion above, you'll note that it isn't just about the benchmarks themselves being accurate, but the model they represent. Nevertheless, I linked the hypergrep benchmarks not because of Hyperscan, but because they were done by someone who isn't the author of either ripgrep or ugrep.
As for regex benchmarks, you'll want to check out rebar: https://github.com/BurntSushi/rebar
You can see my full thoughts around benchmark design and philosophy if you read the rebar documentation. Be warned though, you'll need some time.
There is a fork of ripgrep with Hyperscan support: https://sr.ht/~pierrenn/ripgrep/
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Translations of Russ Cox's Thompson NFA C Program to Rust
Before getting to your actual question, it might help to look at a regex benchmark that compares engines (perhaps JITs are not the fastest in all cases!): https://github.com/BurntSushi/rebar
In particular, the `regex-lite` engine is strictly just the PikeVM without any frills. No prefilters or literal optimizations. No other engines. Just the PikeVM.
As to your question, the PikeVM is, essentially, an NFA simulation. The PikeVM just refers to the layering of capture state on top of the NFA simulation. But you can peel back the capture state and you're still left with a slow NFA simulation. I mention this because you seem to compare the PikeVM with "big graph structures with NFAs/DFAs." But the PikeVM is using a big NFA graph structure.
At a very high level, the time complexity of a Thompson NFA simulation and a DFA hints strongly at the answer to your question: searching with a Thompson NFA has worst case O(m*n) time while a DFA has worst case O(n) time, where m is proportional to the size of the regex and n is proportional to the size of the haystack. That is, for each character of the haystack, the Thompson NFA is potentially doing up to `m` amount of work. And indeed, in practice, it really does need to do some work for each character.
A Thompson NFA simulation needs to keep track of every state it is simultaneously in at any given point. And in order to compute the transition function, you need to compute it for every state you're in. The epsilon transitions that are added as part of the Thompson NFA construction (and are, crucially, what make building a Thompson NFA so fast) exacerbate this. So what happens is that you wind up chasing epsilon transitions over and over for each character.
A DFA pre-computes these epsilon closures during powerset construction. Of course, that takes worst case O(2^m) time, which is why real DFAs aren't really used in general purpose engines. Instead, lazy DFAs are used.
As for things like V8, they are backtrackers. They don't need to keep track of every state they're simultaneously in because they don't mind taking a very long time to complete some searches. But in practice, this can make them much faster for some inputs.
Feel free to ask more questions. I'll stop here.
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Compile time regular expression in C++
I'd love for someone to add this to rebar[1] so that we can get a good sense of how well it does against other general purpose regex engines. It will be a little tricky to add (since the build step will require emitting a C++ program and compiling it), but it should be possible.
[1]: https://github.com/BurntSushi/rebar
- Stringzilla: Fastest string sort, search, split, and shuffle using SIMD
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Rust vs. Go in 2023
https://github.com/BurntSushi/rebar#summary-of-search-time-b...
Further, Go refusing to have macros means that many libraries use reflection instead, which often makes those parts of the Go program perform no better than Python and in some cases worse. Rust can just generate all of that at compile time with macros, and optimize them with LLVM like any other code. Some Go libraries go to enormous lengths to reduce reflection overhead, but that's hard to justify for most things, and hard to maintain even once done. The legendary https://github.com/segmentio/encoding seems to be abandoned now and progress on Go JSON in general seems to have died with https://github.com/go-json-experiment/json .
Many people claiming their projects are IO-bound are just assuming that's the case because most of the time is spent in their input reader. If they actually measured they'd see it's not even saturating a 100Mbps link, let alone 1-100Gbps, so by definition it is not IO-bound. Even if they didn't need more throughput than that, they still could have put those cycles to better use or at worst saved energy. Isn't that what people like to say about Go vs Python, that Go saves energy? Sure, but it still burns a lot more energy than it would if it had macros.
Rust can use state-of-the-art memory allocators like mimalloc, while Go is still stuck on an old fork of tcmalloc, and not just tcmalloc in its original C, but transpiled to Go so it optimizes much less than LLVM would optimize it. (Many people benchmarking them forget to even try substitute allocators in Rust, so they're actually underestimating just how much faster Rust is)
Finally, even Go Generics have failed to improve performance, and in many cases can make it unimaginably worse through -- I kid you not -- global lock contention hidden behind innocent type assertion syntax: https://planetscale.com/blog/generics-can-make-your-go-code-...
It's not even close. There are many reasons Go is a lot slower than Rust and many of them are likely to remain forever. Most of them have not seen meaningful progress in a decade or more. The GC has improved, which is great, but that's not even a factor on the Rust side.
- A Regex Barometer
What are some alternatives?
ripgrep - ripgrep recursively searches directories for a regex pattern while respecting your gitignore
Rebar3 - Erlang build tool that makes it easy to compile and test Erlang applications and releases.
blink - GUI of live indexed grep for source code. Fuzzy suggestion in auto complete. Files locator, search and replace. Index management for multiple projects.
cl-ppcre - Common Lisp regular expression library
website - The source code for the beyondgrep.com website
hypergrep - Recursively search directories for a regex pattern
so_stupid_search - It's my honor to drive you fucking fire faster, to have more time with your Family and Sunshine.This tool is for those who often want to search for a string Deeply into a directory in Recursive mode, but not with the great tools: grep, ack, ripgrep .........every thing should be Small, Thin, Fast, Lazy....without Think and Remember too much ...一个工具最大的价值不是它有多少功能,而是它能够让你以多快的速度达成所愿......
StringZilla - Up to 10x faster strings for C, C++, Python, Rust, and Swift, leveraging SWAR and SIMD on Arm Neon and x86 AVX2 & AVX-512-capable chips to accelerate search, sort, edit distances, alignment scores, etc 🦖
netctl - Profile based systemd network management
moar - Moar is a pager. It's designed to just do the right thing without any configuration.
altbox - Website for altbox.dev, the alternative toolbox for developers
bat - A cat(1) clone with wings.