Optimized string search routines for Rust. (by BurntSushi)

Rust-memchr Alternatives

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rust-memchr reviews and mentions

Posts with mentions or reviews of rust-memchr. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-05-21.
  • Regular Expression Matching Can Be Simple and Fast (2007)
    3 projects | | 21 May 2024
    As the author of ripgrep, I wouldn't necessarily buy this. I suppose I might agree with it in the extremes, but SIMD prefilters are quite exceptionally difficult to beat with scalar code in the common cases. Longer patterns are great for the SIMD algorithms in ripgrep because of its use of background frequency distribution heuristics. That is, the longer the pattern, the less likely your candidate detection is to produce a false positive in its hot path.

    I don't mean to 100% disagree with you, but I think it's misleading to suggest a sort of one dimensional view of things where, as the pattern gets larger, SIMD gets worse compared to sublinear search algorithms. There are other factors at play here, and, importantly, what "long" means in this context.

    In many practical circumstances, "short" might be a few bytes, while "long" is 16 bytes. But maybe your idea of "long" is actually much longer.

    If you're curious how your own algorithm stacks up to ripgrep's, you can plug your implementation into the `memchr` crate's benchmark harness:

    It uses rebar:

  • Memchr: Optimized string search routines for Rust
    1 project | | 13 Jan 2024
  • Ask HN: What's the fastest programming language with a large standard library?
    9 projects | | 26 Dec 2023
    That's what the `memchr` crate does. It uses `vshrn` just like in your links. And vpmaxq before even bothering with vshrn:
  • Rust-Cache
    1 project | | 4 Dec 2023
    I agree with everything you said, but I don't see how it leads the OP's formulation being silly or wrong or not useful. Here's another example (of my own) where you can pick an upper bound for `n` and base your complexity analysis around it. In this case, we're trying to provide an API guarantee that a search takes O(m+n) time despite wanting to use an O(mn) algorithm in some subset of cases. We can still meet the O(m+n) bound by reasoning that the O(mn) algorithm is only used in a finite set of cases, and thus collapses to O(1) time. Therefore, the O(m+n) time bound is preserved. And this isn't a trick either. That really is the scaling behavior of the implementation. See:

    > If an algorithm is defined as being unscalable (fixed input), what sense does it make to describe that it scales constantly with input size?

    I'll answer your question with another: in what cases does it make sense to describe the scaling behavior of algorithm with O(1)?

  • Rust memchr adds aarch64 SIMD with impressive speedups
    1 project | | 29 Aug 2023
  • Stringzilla: Fastest string sort, search, split, and shuffle using SIMD
    9 projects | | 29 Aug 2023
    Copying my feedback from reddit[1], where I discussed it in the context of the `memchr` crate.[2]

    I took a quick look at your library implementation and have some notes:

    * It doesn't appear to query CPUID, so I imagine the only way it uses AVX2 on x86-64 is if the user compiles with that feature enabled explicitly. (Or uses something like [`x86-64-v3`]( The `memchr` crate doesn't need that. It will use AVX2 even if the program isn't compiled with AVX2 enabled so long as the current CPU supports it.

    * Your substring routines have multiplicative worst case (that is, `O(m * n)`) running time. The `memchr` crate only uses SIMD for substring search for smallish needles. Otherwise it flips over to Two-Way with a SIMD prefilter. You'll be fine for short needles, but things could go very very badly for longer needles.

    * It seems quite likely that your [confirmation step]( is going to absolutely kill performance for even semi-frequently occurring candidates. The [`memchr` crate utilizes information from the vector step to limit where and when it calls `memcmp`]( Your code might do well in cases where matches are very rare. I took a quick peek at your benchmarks and don't see anything that obviously stresses this particular case. For substring search, the `memchr` crate uses a variant of the "[generic SIMD](" algorithm. Basically, it takes two bytes from the needle, looks for positions where those occur and then attempts to check whether that position corresponds to a match. It looks like your technique uses the first 4 bytes. I suspect that might be overkill. (I did try using 3 bytes from the needle and found that it was a bit slower in some cases.) That is, two bytes is usually enough predictive power to lower the false positive rate enough. Of course, one can write pathological inputs that cause either one to do better than the other. (The `memchr` crat benchmark suite has a [collection of pathological inputs](

    It would actually be possible to hook Stringzilla up to `memchr`'s benchmark suite if you were interested. :-)



  • Ripgrep now twice as fast on Apple Silicon with new aarch64 SIMD implementations
    1 project | | 28 Aug 2023
  • Regex Engine Internals as a Library
    5 projects | | 5 Jul 2023
    The current PR for ARM SIMD[1] uses a different instruction mix to achieve the same goals as movemask. I tested the PR and it has a significant speedup over the non-vectorized version.


  • Sneller Regex vs Ripgrep
    3 projects | | 18 May 2023
    And that is the primary reason why ripgrep doesn't bother with AVX-512. Not because of some lack of skill as this blog suggests:

    > Additionally, ripgrep uses AVX2 and does not take advantage of AVX-512 instruction sets, but this can be forgiven given the specialized skills required for handcrafting for SkylakeX and Icelake/Zen4 processors.

    Namely, I tried running sneller on my CPU, which is a pretty recent i9-12900K, and not even it supports AVX-512. That's because Intel has been dropping support for AVX-512 from its more recent consumer grade CPUs. ripgrep is running far more frequently on consumer grade CPUs, so supporting AVX-512 is probably not particularly advantageous. At least, it's not obvious to me that it's worth doing. And certainly, the skill argument isn't entirely wrong. I'd have to invest developer time to make it work.

    I think there are two other things worth highlighting from this blog.

    First is that sneller seems to do quite well with compressed data. This is definitely not ripgrep's strong suit. When you use ripgrep's -z/--search-zip flag, all it's doing is shelling out to your gzip/xz/whatever executable to do the decompression work, which is then streamed into ripgrep for searching. So if your search speed tanks when using -z/--search-zip, it's likely because your decompression tools are slow, not because of ripgrep. But it's a fair comparison from sneller's perspective, because it seems to integrate the two.

    Second is the issue of multi-threaded search. In ripgrep, the fundamental unit of work is "search a file." ripgrep has no support for more granular parallelism. That is, if you give it one file, it's limited to doing a single threaded search. ripgrep could do more granular parallelism, but it hasn't been obviously worth it to me. If most searches are on a directory tree, then parallelizing at the level of each file is almost certainly good enough. Making ripgrep's parallelism more fine grained is a fair bit of work too, and there would be a lot of fiddly stuff to get right. If I could run sneller easily, I'd probably try to see how it does in a more varied workload than what is presented in this blog. :-)

    And finally some corrections:

    > However, when using a single thread, ripgrep appears to be slightly faster.

    Not just slightly faster, over 2x faster!

    The single threaded results for Regex2 and Regex3 for Sneller are quite nice! I'd be interested in hearing more about what you're doing in the Regex2 case, since Sneller and ripgrep are about on par with the Regex3 case. Maybe a fail fast optimization?

    > The reason for this is that ripgrep uses the Boyer-Moore string search algorithm, which is a pattern matching algorithm that is often used for searching for substrings within larger strings. It is particularly efficient when the pattern being searched for is relatively long and the alphabet of characters being searched over is relatively small. Sneller does not use this substring search algorithm and as a result is slower than ripgrep with substrings. However, when long substrings are not present, Sneller outperforms ripgrep.

    ripgrep has never used Boyer-Moore. (Okay, some years ago, ripgrep could use Boyer-Moore in certain niche cases. But that hasn't been the case for a while and it was never the thing most commonly used). What ripgrep uses today is succinctly described here: (But it has always eschewed algorithms like Boyer-Moore in favor of more heuristic-y approaches based on a background frequency distribution of bytes.)

    I think I would also contest the claim that "long substrings" are the key here. ripgrep is plenty fast with short substrings too. You're correct that if you have no literals then ripgrep will get slower because it has to fall back to the regex engine. But I'd like to see more robust benchmarks there. Your Regex2 and Regex3 benchmarks raise more questions than it answers. :-)

    > Although the resulting .dot and .svg files may be somewhat clunky, we can still observe from the graph that the number of nodes and edges are small enough to use the branchless IceLake implementation. In this particular case, we only need 8 bits to encode the number of nodes and the number of distinct edges, enabling the tool to use (what we call) the 8-bit DFA implementation. For more details on how this works, see our post on regex implementations.

    So this is talking about the DFA graph for the regex `Sherlock [A-Z]\w+`. It's important to point out that, in ripgrep, `\w` is Unicode aware by default. Which makes it absolutely enormous. So I think the state graph you linked is probably only for the ASCII version of that regex.

    Indeed, reading your regex blog[1], it perhaps looks like a lot of the tricks you use won't work for Unicode, because Unicode tends to blow up finite automata.

    If I could run Sneller, I'd probably try to poke it to see what its Unicode support looks like. From a quick glance of the source code, it also looks like you build full DFAs. So I would also try to poke it to see what happens when handed a particularly a not-so-small regex. (Building a DFA can take quite some time.)

    Ah okay, I see, you put a max limit on the DFA:

    Overall this is a very cool project!


  • SIMD with Zig
    6 projects | | 1 May 2023
    Indeed. This is how ripgrep works. It's compiled for just plain `x86_64`, but it looks for whether things like AVX2 are enabled. And if so, uses vector algorithms for substring and multi-substring search. The nice thing about dealing with strings is that the "coarse" requirement is already somewhat natural to the domain.

    But, this functionality is absolutely critical. It doesn't even have to be automatic. Just the ability to compile functions with certain ISA extensions enabled, and then only call them when the requisite CPU features are enabled is enough.

    In a nutshell:

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Basic rust-memchr repo stats
about 1 month ago

BurntSushi/rust-memchr is an open source project licensed under The Unlicense which is not an OSI approved license.

The primary programming language of rust-memchr is Rust.

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