42 ms·

Performance comparison: counting words in Python, C/C++, Awk, Rust, and more

• lrem 4y ago
  This site makes Chrome (Android, current) stop responding.
  • mellosouls 4y ago
    Yeah, mine too. Ironic considering the subject...
• josefx 4y ago
  No need to decode the C++ names when you use valgrind/callgrind/kcachegrind for C, that works just as well for C++.
• mustache_kimono 4y ago
  ... (2021). Great read and fun project. See the discussion from when it was originally posted by the author.[0] [0]: https://news.ycombinator.com/item?id=26463967 https://news.ycombinator.com/item?id=26463967
• gary17the 4y ago
  Wow. Swift, touted as "safe by design and (...) runs lightning-fast"[1] is more of a screw-up than I thought. Almost twice as slow as Lua and behind even Pascal and Forth. [1] https://developer.apple.com/swift/ https://developer.apple.com/swift/
  • sk0g 4y ago
    It's a single implementation, I wouldn't put too much on it. I can definitely write a C++ implementation that's slower and less optimised than most of those results, particularly if I was using Boost. iPhone and Mac apps run pretty well in my experience, and Swift is definitely faster (in general) than Python at least. There were some serious considerations to port over ML libraries to Swift due to its ease of use, similar to Python, while providing much better execution speed.
    • fractalb 4y ago
      > There were some serious considerations to port over ML libraries to Swift due to its ease of use, similar to Python, while providing much better execution speed. Google abandoned that Swift work (if that's what you're referring to)
      • sk0g 4y ago
        Sure, but the fact that it was a project at all does imply Swift had a not-insignificant speedup vs Python, meanwhile in these benchmarks Python is about2x the speed of Swift.
      • PossiblyKyle 4y ago
        Google and abandoning things, name a more iconic duo
  • mayoff 4y ago
    Note that only an unoptimized Swift solution was provided, and Swift String operations (like `lowercased`) are fully Unicode-compliant.
  • machinekob 4y ago
    Swift is in the same or better performance bracket then C#/Java and unsafe usage can be close to performance of C (maybe 1.3-2x slower). I'm not sure about ranting for poor single test results :P
    • gernb 4y ago
      Latner himself (creator of Swift) said Swift had speed issues because of ARC that they were hoping to solve with better code analysis. Did they?
      • deleted 4y ago
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      • georgelyon 4y ago
        They have been improving this steadily (I would argue it is rarely an issue nowadays, and there have always been simple workarounds), but there’s no reason a solution to the problem mentioned in the article should need to use ARC, and should use structs/protocols instead (which don’t use reference counting). I’ve seen a lot of Benchmarks that rate Swift poorly because of this.
    • guenthert 4y ago
      No need for hand-waving, there are publicly available benchmark results: https://benchmarksgame-team.pages.debian.net/benchmarksgame/fastest/swift.html https://benchmarksgame-team.pages.debian.net/benchmarksgame/... It's not the only benchmark in town and it needs to be taken with a grain of salt, but so does every benchmark.
    • metaltyphoon 4y ago
      The optimized version of C# is not really optimized as there are things left in the table that could be much close to C++.
  • bhauer 4y ago
    I only skimmed the article, but I suspect they are including the time necessary to spin up the executable and any runtime environment prior to executing the relevant code. "Lightning fast" can mean a lot of things, and it might not mean "executables start quickly."
    • saagarjha 4y ago
      Swift executables start fairly quickly.
    • bornfreddy 4y ago
      If that is the case, it would explain why python is so slow. In my experience the runtime speed of python is good enough for most purposes, but startup time is definitely slow.
    • crummy 4y ago
      If that were the case, that makes Java's results fairly impressive, given the JVM's slow start time.
      • igouy 4y ago
        Maybe for tiny tiny programs "the JVM's slow start time" is only a few tenths of a second? https://benchmarksgame-team.pages.debian.net/benchmarksgame/sometimes-people-just-make-up-stuff.html#jvm-startup-time https://benchmarksgame-team.pages.debian.net/benchmarksgame/...
  • tialaramex 4y ago
    I don't know that I'd characterise it as a "screw up" based on one random person's solution to one particular problem. In particular the "simple" solutions, which is all that's offered for Swift, will be whatever was most idiomatic/ obvious to a programmer of potentially quite variable quality. It's unfortunate, I think, that there are "simple" solutions for some languages which have had performance revisions. If you needed an hour, or a friend, or even a community to "hint" how to improve it that wasn't the simple solution. [ For example I feel like the Rust might be faster asking for ASCII lowercase, and using the unstable (not order-preserving) sort algorithm, but even if I'm correct such a change would logically be made to the optimised Rust, not the simple ]
  • benibela 4y ago
    >even Pascal and Forth. Even? Pascal is touted as being as fast as C
    • gary17the 4y ago
      Perhaps bad phrasing on my part. I meant that in the are-they-still-even-maintaining-modern-optimizing-compilers-for-Pascal-and-Forth sense.
      • benibela 4y ago
        FreePascal maintains their own optimizer. It is one of the few languages that still have their own optimizer rather than using LLVM. Although they would probably better of using LLVM. They introduce a new optimization every month, and I usually notice the new optimization when my code starts crashing with the nightly compiler.´ But they have a bunch of reasons why they cannot use LLVM like "LLVM will almost certainly never support all targets that FPC supports (Gameboy Advance, OS/2, WinCE, ...), or at some point drop support for targets that FPC still supports (as already happened with Mac OS X for PowerPC/PowerPC64)." [1] Or "FPC's native code generators are much faster than LLVM's (even if you would neglect the overhead of FPC generating bitcode and the LLVM tool chain reading it back in), so especially while developing it may be more interesting to use FPC's own code generators" But the test of this thread mostly benchmarks the hashmap implementation. I got my own Pascal hash map, it is twice as fast than the one in their standard library. And, looking at the test code, it does a double hashing, first a get, then an insert. If it did a find entry and update it in-place, it would be twice as fast, too. Together that would be four times faster and as fast as the C version and still be simple [1] https://wiki.freepascal.org/LLVM#Frequently_Asked_Questions https://wiki.freepascal.org/LLVM#Frequently_Asked_Questions
• svat 4y ago
  See also a different comparison (on ~the same problem) at StackExchange: https://codegolf.stackexchange.com/questions/188133/bentleys-coding-challenge-k-most-frequent-words https://codegolf.stackexchange.com/questions/188133/bentleys... (Copying from my comment the last time this was posted: https://news.ycombinator.com/item?id=26467684 https://news.ycombinator.com/item?id=26467684) There's also a nice book "Exercises in Programming Style" about just this problem.
  • deleted 4y ago
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  • igouy 4y ago
    See also Doug Bagley's "The Great Computer Language Shootout" https://web.archive.org/web/20010616231931/http://www.bagley.org/~doug/shootout/bench/wordfreq/ https://web.archive.org/web/20010616231931/http://www.bagley...
• chrismorgan 4y ago
  > Incidentally, this problem set the scene for a wizard duel between two computer scientists several decades ago. In 1986, Jon Bentley asked Donald Knuth to show off “literate programming” with a solution to this problem, and he came up with an exquisite, ten-page Knuthian masterpiece. Then Doug McIlroy (the inventor of Unix pipelines) replied with a one-liner Unix shell version using tr, sort, and uniq. Since this writing and other linked resources present only one side of the affair, I will mention: the presentation in Pearls was quite unfair to Knuth, and the conclusions commonly made of it somewhere between moderately and entirely unsound. (That is, as conclusions from the paper. I speak of unsoundness of logic only: these conclusions may be supportable from other sources.) Knuth was challenged to demonstrate literate programming, and so that was what he demonstrated. He showed building something from the ground up assuming a very minimal environment, with problem analysis and all: of course that ended up more verbose than a Unix pipeline that glosses over problem analysis and starts with most of the tools you need to make a probably-acceptable solution! McIlroy himself admitted a few years later that he had been “a little unfair” to criticise on engineering grounds what had only been an illustration of technique. Even in the paper, Bentley did admit a degree of culpability for this mismatch in his “criticism of programs” paragraph. (“He admires the execution of the solution, but faults the problem on engineering grounds. (That is, of course, my responsibility as problem assigner; Knuth solved the problem he was given on grounds that are important to most engineers-the paychecks provided by their problem assigners.)”) But I do wish that Knuth had been given the opportunity to write a review of McIlroy’s review, for simultaneous publication.
  • mistrial9 4y ago
    1986 was very early days for modern operating systems. I suspect that the hardware environment(low resource) would be surprising to many phone owners here. So the efficacy had to do with the tools low-level bindings, too
    • drfuchs 4y ago
      Indeed. In 1986, Knuth was exclusively using the SAIL DEC10, running the WAITS OS. This environment limited him to approximately half a megabyte of data space (heap plus stack plus globals), and half a megabyte of code space. All statically linked, so all library and runtime stuff had to fit. Also of note is that most CPUs of the day didn't execute instructions orders of magnitude faster than bandwidth to main memory, as is the case today; it was early days for worrying about making algorithms "cache aware". So, for instance, there was no big penalty for "pointer chasing" in linked lists as there is today. Additionally, the severe memory size limits affected various time vs. space tradeoffs in algorithm design.
• deleted 4y ago
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• BiteCode_dev 4y ago
  In this type of blog post with comparisons including many languages, you usually see some unatural approach for your favorite language. However, in this case, the Python example is idiomatic. Since the articles calls for better approaches, I would suggest to take the opportunity to use the walrus operator and rsplit() for the optimized version. Something like this: reminding = "" c=Counter( ) while (chunk := sys.stdin.read(64 * 1024)): pre, post = chunk.lower().rsplit('\n', 1) c.update((reminding + pre ).split()) reminding = post It should not affect performance too much, and the code gets more expressive. However, the performances will be quite different depending of the python version you use. Interestingly, Python 3.11 beta, which comes with a lot performance tweaks, is slower for this exercice, while being reported to be faster on real life tasks.
  • eesmith 4y ago
    Your code assumes there is at least one newline in each 64K chunk, and assumes there are no words past the final newline. It will fail on "abc" and give the wrong answer for "abc\ndef". I prefer rpartition over rsplit to handle first case, and the loop-and-a-half construct instead of the while+walrus operator to handle the second, as in this modified version of your code: remaining = "" c=Counter( ) while True: chunk = sys.stdin.read(64 * 1024) if not chunk: if not remaining: break pre = post = "" else: pre, mid, post = chunk.lower().rpartition("\n") c.update((remaining + pre).split()) remaining = post
    • BiteCode_dev 4y ago
      Indeed, rpartition is better suited for this task.
• FpUser 4y ago
  I would not call it performance comparison at all. When Python call functions written in C it is not Python's performance. Write those functions using plain Python and then see the results. Sure for this basic example in the article it does not matter from a practical standpoint. But when you need to step away from canned cases suddenly Python's performance sucks big time.
  • __marvin_the__ 4y ago
    Python's `collections.Counter` is written in Python and is a subclass of the builtin `dict` type. I don't think it's comparable to something like using `pandas` to solve the problem. https://github.com/python/cpython/blob/main/Lib/collections/__init__.py https://github.com/python/cpython/blob/main/Lib/collections/...
  • kortex 4y ago
    Can we move past the whole "it is not really python to use libraries written in C", especially when talking pure stdlib python? Python is basically a DSL for C extensions. That is the whole point. It would be like criticizing any compiled language for essentially being a DSL for machine code, and not "Real Instructions". Python's ability to interop with pre-built, optimized libraries with a lightweight interface is arguably its greatest selling point. Everyone and their dog knows purely interpreted CPython is slow. It doesn't need to be pointed out every single time python performance is brought up unless literally discussing optimizing the CPython vm.
    • FpUser 4y ago
      I am not criticizing Python. It does well enough what it was made to do. It just make no sense to call that particular example a "language performance comparison". It is anything but.
      • kortex 4y ago
        But you are still wrong. As mentioned, Dicts are incredibly efficient data structures in Python (because they underpin everything) and the Counter class is pure python. That's 100% pure python. Saying dicts "don't count" because they are implemented in C would disqualify the entire language of CPython, as virtually everything under the hood is a PyObject struct pointer. It just so happens that "counting abstract objects" is the class of problem* CPython does basically all the time in normal VM execution anyways, so this task is easy and fast. * looking up a reference of an attribute with a string key underpins the meat and potatoes of python's execution model
• Twirrim 4y ago
  As they call out in the article, accurately counting words is a bit more complicated that splitting by space. You need to account for all sorts of punctuation etc. When I've been asked this in programming interviews, I've almost always been expected to produce something like the code in the article (and do), but usually I'd point to something like the NLTK library as a better approach. It's polished and highly capable, and handles probably just about every edge case there is.
• djhworld 4y ago
  EDIT: I think the use of the term "allocation" might be overloaded here from the original article, after thinking about this a bit more I think the author is referring to a different way of accessing the map. If you look in godbolt https://godbolt.org/z/Yf64rodW6 https://godbolt.org/z/Yf64rodW6 you can see that the pointer version uses CALL runtime.mapaccess2_fast64(SB) whereas the 'raw' version uses CALL runtime.mapassign_fast64(SB) When reading the Go solution this bit stood out to me > To reduce the allocations, we’ll use a map[string]*int instead of map[string]int so we only have to allocate once per unique word, instead of for every increment I just tried benchmarking this with a simple setup and I get zero allocations for both approaches, although the "pointer" approach is slightly faster package main import ( "testing" ) func BenchmarkIncrementMapRawInt(b *testing.B) { var data = make(map[int]int) b.ResetTimer() for i := 0; i < b.N; i++ { key := i % 1000 data[key]++ } } func BenchmarkIncrementMapPointerInt(b *testing.B) { var data = make(map[int]*int) b.ResetTimer() for i := 0; i < b.N; i++ { key := i % 1000 increment(data, key) } } func increment(counts map[int]*int, value int) { if p, ok := counts[value]; ok { *p++ return } n := 1 counts[value] = &n } $ go test -bench=. -benchmem . goos: darwin goarch: arm64 BenchmarkIncrementMapRawInt-8 112288002 10.57 ns/op 0 B/op 0 allocs/op BenchmarkIncrementMapPointerInt-8 139728302 8.586 ns/op 0 B/op 0 allocs/op
  • dan-robertson 4y ago
    Presumably this would allocate if bits weren’t “primitive”, eg if you had 128 bit ints implemented as a tuple of two ints.
• mustache_kimono 4y ago
  Re: the Rust optimized implementation, I was able to get ~20-25% better performance by rewriting the for loops as iterators, using Rust's byte range pattern matching, and a buffered writer, which seems crazy, but it's true. I chalked it up to some crazy ILP/SIMD tricks the compiler is doing. I even submitted a PR[0], but Ben decided he was tired of maintaining and decided to archive the project (which fair enough!). [0]: https://github.com/benhoyt/countwords/pull/115 https://github.com/benhoyt/countwords/pull/115
  • codeflo 4y ago
    Very nice. That’s in fact really clean, and (at least to me) indeed surprising. As rejection reasons go, “I wanted to name-drop person X” is interesting. But as you said, that’s the maintainer’s decision to make.
    • mustache_kimono 4y ago
      Can't win them all. I think I came along when they were just really sick of maintaining it. Ben and Andrew have all my respect.
    • deleted 4y ago
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  • grumpyprole 4y ago
    Iterators are potentially a much better design too as they allow for more modular code.
  • deleted 4y ago
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  • mustache_kimono 4y ago
    In case anyone is interested, I did an optimized, but much more simple, Rust implementation just today[0], which is faster than the optimized implementation on my machine. No indexing into arrays of bytes, no unsafe, etc., no "code golf" measures. Of course, credit where it's due -- these are simply tweaks to Andrew Gallant's code. Looks like idiomatic Rust, which I think is interesting. Shows there is more than one way to skin a cat. [0]: https://github.com/kimono-koans/countwords/blob/master/rust/fast-simple/main.rs https://github.com/kimono-koans/countwords/blob/master/rust/...
• codeflo 4y ago
  For those curious about Rust optimization, the “optimized” version makes three changes compared to the “idiomatic” version: * It uses byte strings instead of UTF-8 strings. In my opinion, that’s not an optimization, that’s changing the problem. Depending on the question you’re asking, only one of the two can be correct. * It uses a faster hash algorithm. It’s not the first time this came up in a benchmark article. Rust’s decision to use a DOS-safe hash by default (and not provide a fast algorithm in the std, like other languages do) really seems to hurt it in that kind of microbenchmark. * It uses get_mut+insert instead of the more convenient HashMap::entry method, because the latter would require redundantly allocating the key even in the repeat case. I’ve hit this problem in the past as well. Maybe the upcoming HashMap::raw_entry_mut will make this kind of optimization cleaner.
  • coldtea 4y ago
    >It uses byte strings instead of UTF-8 strings. In my opinion, that’s not an optimization, that’s changing the problem. Depending on the question you’re asking, only one of the two can be correct. For tons of questions, both can be correct.
  • tialaramex 4y ago
    The problem specified declares the words we're counting are ASCII: > ASCII: it’s okay to only support ASCII for the whitespace handling and lowercase operation UTF-8 (quite deliberately) is a superset of ASCII. So a UTF-8 solution is correct for ASCII, but a bytes-as-ASCII solution works fine in Rust if you only need ASCII. This is why Rust provides ASCII variants of a lot of functions on strings, and the same functions are available on byte slices [u8] where ASCII could be what you have (whereas their Unicode cousins are not available on byte slices).
    • codeflo 4y ago
      >> ASCII: it’s okay to only support ASCII for the whitespace handling and lowercase operation That's a somewhat specific list -- at least I didn't read that as a general "the program can assume that the input is only ASCII". But then, the author seems to have accepted solutions that crash on non-UTF8 sequences and ones that byte-compare them, so probably either behavior was meant to be fine. I just don't get that from this rule.
• yakubin 4y ago
  I like the OCaml version. It's both very easy to read and comes out really well in the benchmark. I also looked at the "simple" Zig version, which came out really well in the benchmark, and to me it didn't look simple at all. It seems you need to make a lot of low level details explicit. But IMHO AWK takes the crown here. :)
  • OskarS 4y ago
    > But IMHO AWK takes the crown here. :) Agreed! AWK is still the king of this stuff. For tasks like this, I kinda think AWK is nigh-unbeatable: so simple to write, so obvious what's going on (even if you've never seen any AWK program before, you're probably going to be able to figure out what's going on there), and decently performant. AWK is the bee's knees.
    • moffkalast 4y ago
      On the other hand, I'm rather surprised that the Go version is almost as long and opaque as C.
      • benhoyt 4y ago
        Are you looking at the "simple" or the "optimized" versions? For the optimized, yes, the Go one is very similar to the C. For the simple, idiomatic version, the Go version [1] is much simpler than the C one [2]: 40 very straight-forward LoC vs 93 rather more complex ones including pointer arithmetic, tricky manual memory management, and so on. [1] https://github.com/benhoyt/countwords/blob/c66dd01d868aa83dc30a9c95226575df1e5e1c5a/simple.go https://github.com/benhoyt/countwords/blob/c66dd01d868aa83dc... [2] https://github.com/benhoyt/countwords/blob/c66dd01d868aa83dc30a9c95226575df1e5e1c5a/simple.c https://github.com/benhoyt/countwords/blob/c66dd01d868aa83dc...
        • moffkalast 4y ago
          Yeah I meant the optimized versions.
  • 4y ago
• jerryjerryjerry 4y ago
  Our distributed NewSQL database project actually uses three of them, Go, Rust, and C++, for different layers. There are pros and cons for each of them, but it still challenge to maintain three languages in a one system development. Performance wise, C++ and Rust are good, coding wise Go is easiest but Rust is kind of having best practice.
• claytongulick 4y ago
  My takeaway is that unoptimized c++ is pretty close to the same performance as JavaScript. Wild.
  • igouy 4y ago
    fwiw https://benchmarksgame-team.pages.debian.net/benchmarksgame/fastest/node-gpp.html https://benchmarksgame-team.pages.debian.net/benchmarksgame/...
    • deleted 4y ago
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    • claytongulick 4y ago
      Yep, I don't have the energy tonight to inspect the source code, but how you write js matters a lot. Little things like hoisting local variables out of the prototype and closure chains can make a massive difference. I did some experiments with hot path js optimization several years ago and was pretty surprised at the performance you can squeeze out of the jitter. Js is notorious for being difficult to do good perf analysis on because of the warming and hot path analysis. From my experiments it can sometimes take many seconds from startup for the jitter to kick in on a tight loop.
• bsaul 4y ago
  surprised to see swift 4 times as slow as non-optimized go. Anyone has an explanation ? I know the swift string type is really complex, but i always assumed it was at least performing well..
  • deleted 4y ago
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  • msbarnett 4y ago
    Reading anything at all into the relative performance of these programs written by different people which are all doing radically different things seems like a gigantic waste of time. Some of these “simple” versions are custom-specifying allocators to minimize memory overhead and doing highly optimizable ASCII value manipulations and doing byte-equality on strings, rather than go through slower Unicode libraries. The “simple” Swift version is doing nothing at all to avoid generating many allocations/deallocations and running fully Unicode-aware algorithms to lower the input and perform Unicode-normalized string comparisons, it’s totally apples-to-oranges stuff.
    • bsaul 4y ago
      i was wondering if this was due to unicode indeed. I had hoped that the benchmarks required languages to perform the same kinds of string check, otherwise there's really no point indeed.
  • saagarjha 4y ago
    In case you miss it: https://news.ycombinator.com/item?id=32220587 https://news.ycombinator.com/item?id=32220587
• HarHarVeryFunny 4y ago
  This is a rather meaningless comparison, since the differences are going to be dominated by: 1) The choice of libraries/datatypes used for strings and word->count map 2) How the source file is split into words - probably library function again, although in C/C++ one could choose to implement a super-optimized low level version that would blow the others away IMO a performance comparison between languages is only meaningful if the runtime is not dominated by library functions, or if one admits it's really a standard library performance comparison, not a language comparison.
  • ashvardanian 4y ago
    Agreed. It’s a common desire to boil benchmarks down to a single number. Some of those languages, however, are far more complex then the others. And as any complex tool, it requires certain methodology. If you are to use heavy machinery like C++, forget iostreams and character-level processing on your hot paths.
  • kitd 4y ago
    I understand what you're saying. However if you look at it not from the point of the performance figures per se, but rather which language, when written idiomatically, can solve a realistic problem performantly, then I think it makes more sense.
    • HarHarVeryFunny 4y ago
      True - there's still value in these types of comparison, although the click-baity "C++ vs Python" title rather detracts from that. I'm not sure how much value there is in this specific benchmark though since in the real world you'd be using a utility to do this (e.g. linux wc - word count) and it's not obvious what's dominating the runtime (I'd guess reading file and/or splitting into words), so what you're takeaway should be (idiomatic X is good for what, exactly?) if wanting to extrapolate this to some other task. For that matter, in the real world people don't really choose language on a task specific basis... It's either use language X because the project demands it, or write some limited use utility in a scripting language if performance isn't a major concern.
  • 4y ago
• svnpenn 4y ago
  You included Awk in the title, but omitted Go, really?
  • hu3 4y ago
    Indeed. The original title is: > Performance comparison: counting words in Python, Go, C++, C, AWK, Forth, and Rust. Which means the submitter actively chose Go to be removed from the title. Judging from their post history they are aligned with Rust community.
    • yakubin 4y ago
      The original title is 2 characters over the HN title length limit. I suspect they dropped "Go" first, because it's the language which has a 2-letter name, and they they editorialised it further to hint that there are more languages.
      • hu3 4y ago
        Leaving Awk over the vastly more popular Go, hints a more probable and frequent behaviour: bias. Removing any of them would suffice.
        • e4m2 4y ago
          The author maintains an AWK implementation: https://github.com/benhoyt/goawk https://github.com/benhoyt/goawk
          • hu3 4y ago
            Ben Hoyt, isn't the same person as the HN submitter.
        • deleted 4y ago
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• fractalb 4y ago
  grep is indeed fast!
• jeffbee 4y ago
  Funny how you can take the "optimized" C++ and make it significantly faster (vs. gnu/linux standard libraries) with little effort. A lookup table for to_lower, instead of bit math, helps slightly. Building with the LLVM-libc string functions helps a lot, because the GNU std::string_view::operator== is calling an AVX-optmized memcmp via the PLT, which is a terrible strategy for short strings. LLVM-libc has an inlined bcmp (note: bcmp can be significantly faster than memcmp, but on GNU they are aliases) that is resolved for the target platform at build time instead of run time. Edit: Even if you ignore LLVM-libc, just slapping this into the optimized.cpp and replacing the critical `==` with `our_bcmp` makes it 10% faster. IFUNC calls to micro-optimized SIMD functions are counterproductive. It is far, far better that the compiler can see all the code at build time. int our_bcmp (const char* a, const char* b, size_t sz) { for (size_t i = 0; i < sz; i++) { if (a[i] != b[i]) return 1; } return 0; }
• fallingfrog 4y ago
  Your Perl implementation is almost as fast as c++? Something weird is going on here. Maybe you are bottlenecked on disk read speeds or something.
  • bjoli 4y ago
    Why is that surprising? The perl interpreter is fast and dispatches just about everything to heavily optimized C. Just like python does, it is only better at it. This is the kind of thing where perl really shines.
  • theonemind 4y ago
    Not really surprising. Perl approaches a domain specific language for text processing with a heavily-optimized 34 year old implementation.
  • wscott 4y ago
    This is what perl was made for and it is good at it. I was going to be very suspicious if Python or Ruby ended up faster. On this chart it it the fact that the simple C++ is so slow that is interesting.
• nr2x 4y ago
  Some of these efficiency gains are remarkable. Most of the code I write is just the quickest way to write a mundane function. Really curious if you went into a huge tech co and devoted ~10% of SWE time solely to optimization of code base what the energy savings would be.
  • jeffbee 4y ago
    You can't walk into a huge tech co and make a dent because huge tech co already has hundreds of engineers on that job full time. Just look at the papers coming out of huge tech co's. They will publish if their technique saves them 0.2%. That's how tight things are already wired in the big leagues. The real money is in huge non-tech co, or medium tech co. You could walk into a goldman sachs type of operation and point out savings in CPU time that would be 90-99%. But they're not going to care, because their corporate structures aren't wired to observe and act on such things.
    • saagarjha 4y ago
      If you make a dent on server bills on the order of several million dollars a year generally it's not too difficult to make a case that you should be allowed to continue.
      • deleted 4y ago
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• habibur 4y ago
  You need to measure memory usage with it, take the product of the two (memory and speed), and thus get a very good number on performance for comparison.
• anonymoushn 4y ago
  Recently there was a contest to optimize the performance of a similar program[0] and a Zoom discussion of the optimizations[1]. The programs are not comparable in the the following ways: - Case: TFA requires (at least) ASCII lowercasing but the contest problem required no lowercasing. - Ordering: TFA does not require sorting, but the contest problem required sorting. - Memory: TFA imposes a requirement phrased as "don't read the whole file into memory" and this sounds like it's a resource-saving constraint, but it's usually a constraint that requires the program to spend additional resources. You could just mmap the file and store pointers into the mapped region. It costs extra to do copies instead of no copies. - Text: TFA is unclear on what assumptions may be made about the lengths of words. For the contest problem, the Hungarian wikipedia input's longest word is around 80k. - Safe, Hashing, Stdlib: TFA imposes some restrictions on what constructs may be used that are not imposed in the contest problem. For the contest version of this problem, it seems like you can tokenize, hash, and count strings at around 1GB/s. Adapting a solution to solve TFA's problem (but not to conform to its Safe/Hashing/Stdlib requirements) would probably not carry too large of a penalty, since it's like 3 instructions to ASCII-lowercase 32 bytes and 1 string copy per unique string should take negligible time compared to the hash table lookups. So there is some room for the optimized solutions to go a little faster, if more optimizations are permitted. [0]: https://easyperf.net/blog/2022/05/28/Performance-analysis-and-tuning-contest-6 https://easyperf.net/blog/2022/05/28/Performance-analysis-an... [1]: https://www.youtube.com/watch?v=R_yX0XjdSBY https://www.youtube.com/watch?v=R_yX0XjdSBY
• gibsonf1 4y ago
  What code was used for Common Lisp? I'm guessing a well written version would be in the top contenders for speed.
  • Jtsummers 4y ago
    https://github.com/benhoyt/countwords/blob/master/simple.lisp https://github.com/benhoyt/countwords/blob/master/simple.lis... From Hoyt's repository itself.
    • gibsonf1 4y ago
      Which lisp compiler did you use?
      • Jtsummers 4y ago
        I haven't run it, just tracked down the source because I was similarly curious about the slow runtime. If I do run it it will be with SBCL. Looking at the repo, Hoyt did as well.
        • gibsonf1 4y ago
          That code is slow because it's working with a really long list instead of a very fast hash table
    • gibsonf1 4y ago
      Right, this came up a year ago with a version that ranks among the top here: https://news.ycombinator.com/item?id=26465857 https://news.ycombinator.com/item?id=26465857 (defmethod performance-count ((path-file string)) (let ((map (make-hash-table :test 'equal))) (with-open-file (stream path-file :direction :input :if-does-not-exist nil) (when stream (loop for line = (read-line stream nil 'end) until (eq line 'end) do (let ((split (split-string #\space (string-downcase line)))) (dolist (word split) (let ((index (gethash word map))) (if index (setf (gethash word map) (incf index)) (setf (gethash word map) 1)))))) (let ((keys (sort (alexandria:hash-table-keys map) (lambda(x y)(> (gethash x map)(gethash y map)))))) (dolist (key keys) (format t "~A ~A~%" key (gethash key map))))))))
• compumike 4y ago
  How about extending this to comparing programming language efficiency from a developer's standpoint, looking at bytes of code, versus the execution time? I took the source code file size from the repository, and the runtime from the blog post. Then I made an arbitrary overall "PAIN SCORE" (lower is better) by multiplying code size * runtime. I suggest this is a worthwhile metric simply because lower is better on both axes, but of course, in the "real world" there will be different economic costs to CPU time and developer time depending on the use case. Here's the sorted results, from least "pain" to most: LANGUAGE FILENAME CODE SIZE RUNTIME PAIN SCORE (LOWER IS BETTER) Shell optimized.sh 75 bytes 1.83 s 137.25 Crystal simple.cr 240 bytes 1.29 s 309.6 Nim simple.nim 424 bytes 0.77 s 326.48 Python simple.py 208 bytes 2.21 s 459.68 Ruby simple.rb 175 bytes 3.17 s 554.75 Go optimized.go 1514 bytes 0.40 s 605.6 Python optimized.py 464 bytes 1.33 s 617.12 Zig optimized.zig 2688 bytes 0.24 s 645.12 Go simple.go 688 bytes 1.12 s 770.56 Zig simple.zig 1394 bytes 0.55 s 766.7 Nim optimized.nim 1683 bytes 0.49 s 824.67 Shell simple.sh 60 bytes 14.81 s 888.6 Ruby optimized.rb 401 bytes 2.47 s 990.47 JavaScript simple.js 532 bytes 1.88 s 1000.16 C optimized.c 4360 bytes 0.23 s 1002.80 Rust optimized.rs 3065 bytes 0.43 s 1317.95 Swift simple.swift 317 bytes 4.23 s 1340.91 JavaScript optimized.js 1501 bytes 1.10 s 1651.1 C simple.c 2735 bytes 0.96 s 2625.6 Rust simple.rs 2239 bytes 1.38 s 3089.82 Sorting only by code size, the most concise implementations are: Shell, Ruby, Python, Crystal. Nobody was aiming to play code golf (i.e. minimize source code size), so these are fairly straightforward, idiomatic, readable implementations. I am definitely a Crystal fan, in fact this afternoon I'm continuing to implement suggestions from my recent Show HN https://news.ycombinator.com/item?id=32081943 https://news.ycombinator.com/item?id=32081943 comments. :)
  • igouy 4y ago
    > a worthwhile metric Previously — "Completely Random and Arbitrary Point System!, or CRAPS![TM]" https://web.archive.org/web/20010124100400/http://www.bagley.org/~doug/shootout/craps.shtml https://web.archive.org/web/20010124100400/http://www.bagley... Currently — fwiw https://benchmarksgame-team.pages.debian.net/benchmarksgame/box-plot-summary-charts.html#chart-fastest-simplest https://benchmarksgame-team.pages.debian.net/benchmarksgame/... > Sorting only by code size https://benchmarksgame-team.pages.debian.net/benchmarksgame/how-programs-are-measured.html#source-code https://benchmarksgame-team.pages.debian.net/benchmarksgame/...
• Karellen 4y ago
  Doesn't the Unix Shell implementation break the "threading" constraint, in that each program in the pipe will have a main thread each, and all the programs will run concurrently? Or should the "threading" constraint really have been stated as "you can use multiple threads, but only if they all have their own address space"? Alternatively, given the multi-core capabilities of even budget/mobile/small systems these days (even the Rasberry Pi 4 has a quad-core CPU), isn't restricting the implementation to a single thread a weird artificial limitation in 2022? Also, a GPU-based implementation would be interesting, and GPUs most of their power from their massively parallel core architecture.
  • slaymaker1907 4y ago
    If I were doing the benchmark, I would just run it on a VM with a single physical core. There are good reasons for comparing single threaded performance since it may not be necessary or desirable to scale that way. For example, suppose it's a wc API or something; limiting each request to a single thread can be better since you get fairer scheduling and less data sharing which is almost always better for multiprocessing. Even shared immutable data can cause problems since the CPU may not realize the data is actually immutable. I agree GPUs would probably do well on this problem, though it depends on how expensive the memory copying to/from the GPU ends up being. However, if you are going for all our performance, it seems like you could use SIMD like memchr or something.
    • Karellen 4y ago
      > If I were doing the benchmark, I would just run it on a VM with a single physical core. I think that's definitely an environment worth benchmarking on - but I don't think that it should be the only environment to benchmark on. Also, I don't think it's a good reason to limit implementations to a single thread, even if that is your benchmark environment. It can be worth seeing how well an implementation that's capable of taking advantage of multiple cores/CPUs, does when it's only given one core to work with. It's probably worth optimising for and benchmarking different threading strategies too - does your implementation create one thread per "work unit" and let the scheduler work them all out, or does it create one a one thread per core (or maybe, one thread per core, plus one), thread pool, and assign work units to each each thread until they're done? And how do those strategies work if they're only given a single core? The single core case is definitely worth testing, but it seems odd to limit implementations to a single thread because of it. If you think you can go faster with a threaded implementation, you should be able to try that out.
• githubholobeat 4y ago
  Nice 3rd place for Zig implementation. But is it missing `defer arena.deinit();`?
• deleted 4y ago
  [deleted]
• raffraffraff 4y ago
  I love using the UNIX shell (pipes and programs that do "one thing well") to the point where it hurts my ability to improve at other other languages. But sometimes all you need is a throwaway command to do something once, not a beautifully optimized function that will be written, improved, compiled, profiled, reoptimized and then executed a billion times. The utter tersity of a bash one-liner just tickles me.
  • SiVal 4y ago
    I'm with you with both shell pipelines and Perl "one-liners". The latter was a wonderful step up from the former back in the 1990s: a modern programming language designed around paving the cowpaths of shell pipelines. The power of both has always been inspiring. So many useful, one-off tasks can be accomplished with a single line in a terminal. So much potential power. But my problem with both has always been that I needed them often, but not quite often enough to remember them without looking things up (again). Over the years, as my memory of these commands has strengthened, I've needed them less often. We don't use computers the same way we did back then. It's like a spaced repetition system where the spacing is set to guarantee forgetting.
  • Izkata 4y ago
    > The utter tersity of a bash one-liner just tickles me. To anyone else confused, the repo has both "sh" and "bash" versions, and the bash one isn't a one liner - it's the sh one.
• staticassertion 4y ago
  It's a bit of a shame that Rust's stdlib doesn't have a "fast but less safe" hasher. It's one of the easiest performance optimizations in my experience because it's very rare that an attacker controls input to your hashmap in a way that can actually lead to a DOS. Not that it doesn't happen, it's just not something I've run into. Just today I was testing out the performance difference hashing some Scylla queries and got nearly 2x faster hashing moving to fxhash.
• benreesman 4y ago
  This was always my approach when I had do “leetcode whiteboard interviews”. Start with something not too far north of FizzBuzz but with a ton of scope to progressively enrich the problem until the clock runs out. At $BIG_CO du jour people tend to talk about how much “signal” an interview produces, and starting somewhere that a meaningful number of candidates can’t do anything with offers very little “signal”, likewise very little Shannon information. Great article!
• ribit 4y ago
  These days I tend to use R for these type problems. It’s slow, but vectors as first-class data types simplify things a lot readLines(file) |> strsplit(“ +”) |> unlist() |> table() |> sort(desc = TRUE)
• ppeetteerr 4y ago
  Curious if anyone knows why the swift version is slow? Is it the casting from subsequence to a string?
  • bhauer 4y ago
    As I wrote elsewhere in this thread (though I was downvoted), I suspect the reason is that this benchmark is measuring the total execution time of a single run of an executable. This would include any time spent by the executable to bootstrap its runtime environment, initiate a virtual machine, and whatever else it needs to do in addition to the relevant code at hand to process the string. Such a test will favor implementations that have extremely svelte boostrapping, allowing them to immediately begin executing the relevant code and return a result. I feel a more useful test would be for the relevant string processing code to be run tens to thousands of times within the process itself, so as to diminish the relative importance of boostrap/warmup code and/or runtime optimization performed by VMs. Unless, of course, part of the intent was specifically to measure the impact of the bootstrapping.
    • kmonsen 4y ago
      I was going to make a general comment about this too. It’s a huge penalty for Java too and has no correlation for how well it performs outside of toy benchmarks
      • jiggawatts 4y ago
        … unless you’re using Java in the same way, such as an Azure Function or AWS Lambda. Or simply as a command-line tool. IMHO, VM startup time should be included and the first few passes (before JIT kicks in) should also be included. Java has supposedly “nearly C-like performance” until you read the fine print. (This should apply to C# as well.)
      • igouy 4y ago
        > a huge penalty Is that just your assumption or have you measured that penalty for this tiny tiny program? Might "the JVM's slow start time" in this case be insignificant? https://benchmarksgame-team.pages.debian.net/benchmarksgame/sometimes-people-just-make-up-stuff.html#jvm-startup-time https://benchmarksgame-team.pages.debian.net/benchmarksgame/...
• osigurdson 4y ago
  C# really needs a GetOrAdd method on the regular Dictionary class (as exists on ConcurrentDictionary). This common pattern (from the optimized version) requires the hash function on "word" to be evaluated twice (once for TryGetValue and once for Add). Then, if there is a collision on the 32 bit hash then the equality check needs to be run twice as well. Not good as all of these are O(N) operations (where N is the size of "word"). I suspect the optimized version would be materially faster if they implemented their own dictionary that includes this. if (!counts.TryGetValue(word, out var wordCountRef)) { counts.Add(word, new Ref<int>(1)); }
• anonu 4y ago
  q solution in kdb, runs in about 2.5s on my machine vs simple.py which takes 4.7s. Rough interpolation with the results table puts this on par with Rust/Go/OCaml or other compiled languages. \t desc count each group `$lower " " vs raze read0 `:kjvbible_x10.txt It's also 1 line! I am sure q pros or k purists can optimize this even more... EDIT: Moving the lower earlier brings this down to 1.9s desc count each group `$ " " vs lower raze read0 `:kjvbible_x10.txt
• ijidak 4y ago
  > We usually think of I/O as expensive, but I/O isn’t the bottleneck here...The tokenization and hash table operations are the bottleneck by a long shot Interesting. This is something I'll have to keep in mind. Best practice is always been to consider I/O the slowest. But, it's true... times have changed. Maybe we shouldn't make this assumption anymore.
  • bjourne 4y ago
    He didn't clear the disk cache when profiling so of course the IO overhead was negligible. Had he flushed the cache between runs the numbers would have been very different.
    • igouy 4y ago
      Measurements of the similar k-nucleotide programs show that effect — N=250,000 compared to N=2,500,000 https://benchmarksgame-team.pages.debian.net/benchmarksgame/measurements/java.html https://benchmarksgame-team.pages.debian.net/benchmarksgame/...
  • FridgeSeal 4y ago
    Here’s a couple of articles on the performance of IO and slowness of other stuff that I find particularly interesting: https://itnext.io/modern-storage-is-plenty-fast-it-is-the-apis-that-are-bad-6a68319fbc1a https://itnext.io/modern-storage-is-plenty-fast-it-is-the-ap... https://gregoryszorc.com/blog/2021/04/06/surprisingly-slow/ https://gregoryszorc.com/blog/2021/04/06/surprisingly-slow/
• gxt 4y ago
  > a simple interview problem I've been asking different versions of that question since I started interviewing and it is a surprisingly good filter. But with articles like this, now I need new questions..
• unhammer 4y ago
  Hm, I tried comparing the simple.hs vs simple.py (with ghc 8.10.7 which I happened to have here, their stack.yaml uses 8.10.4) and it's 5.7s vs 1.9s, not quite as stark a difference. Maybe because I'm on Linux?
  • unhammer 4y ago
    Their slow timings seem to be because they enable the threaded/multicore runtime without actually using it. A performance footgun I suppose.
• lxe 4y ago
  > I tried running it using the PyPy optimizing compiler, but it’s significantly slower for some reason I wonder why that is?
• yb303 4y ago
  I had a 1 minute look at the "optimized" C code - it's calling malloc for every word.
  • Areading314 4y ago
    Doesn't malloc return a pointer? Sounds unsafe
    • yb303 4y ago
      That's not the point. It's perfectly safe and there's nothing else in C. The point is you don't have to call this function for every single word. It takes time and pointer increment doesn't
    • centilliard 4y ago
      Ooo... spooooky pointers... ancient prophecies speak of the Earth splitting in half and swallowing everyone who's ever thought about allocating memory.
  • Arnavion 4y ago
    >I had a 1 minute look at the "optimized" C code - it's calling malloc for every word. Neither simple.c nor optimized.c call malloc "for every word". They only call malloc when inserting a previously-unseen word into the counting map.
• yb303 4y ago
  Another 1 minute lookin... multiplication on every byte.. really? If this is supposed to be fast, get one big buffer for the words. Get another if you run out. The hash function can be the word itself for words up to length 8. Longer words would need a mult per 8 bytes. You can do branchless strchr. Roll your own memmove - just always copy 24 bytes and terminate at the length you already know. The whole point of using C or C++ is that you can do non idiomatic things when you have to.
• zX41ZdbW 4y ago
  I've checked with ClickHouse and the result is better than I expect... it runs in 0.043 sec. on my machine, which is faster than any other result. The code: SELECT arrayJoin(splitByChar(' ', lower(line))) AS word, count() AS c FROM file('kjvbible.txt', LineAsString) WHERE notEmpty(word) GROUP BY word ORDER BY c DESC FORMAT Null or: clickhouse-local --query "SELECT arrayJoin(splitByChar(' ', lower(line))) AS word, count() AS c FROM file('kjvbible.txt', LineAsString) WHERE notEmpty(word) GROUP BY word ORDER BY c DESC" > /dev/null It is using only a single thread.
  • zX41ZdbW 4y ago
    I forgot to multiply the file 10 times. When I do, the result is 0.209 sec. which is still better than every other result.
    • stoical 4y ago
      You are also using a language function to read the file. In the 'official' github implementations they have to accept the data line by line from stdin - stdin likely being slower than reading a file directly.
  • zX41ZdbW 4y ago
    https://github.com/ClickHouse/countwords/pull/1/files https://github.com/ClickHouse/countwords/pull/1/files
  • fauigerzigerk 4y ago
    >it runs in 0.043 sec. on my machine, which is faster than any other result. Did you run the other benchmarks on your machine as well?
    • zX41ZdbW 4y ago
      Yes (but only scripted, without compilation): `grep` | 0.03 | 0.03 | `grep` baseline; optimized sets `LC_ALL=C` `wc -w` | 0.18 | 0.25 | `wc` baseline; optimized sets `LC_ALL=C` SQL | 0.26 | | by Alexey Milovidov Perl | 1.22 | | by Charles Randall Python | 1.42 | 0.86 | Tcl | 5.30 | | by William Ross Shell | 9.66 | 1.79 | optimized does `LC_ALL=C sort -S 2G`
• thayne 4y ago
  I'm curious why go is faster than rust in the benchmarks. My best theory is that the program doesn't run long enough to trigger a GC, so rust is paying the cost of freeing memory, but go isn't. (Using an arena allocator in rust would probably produce similar behavior). Or maybe the difference is just noise? It's hard to tell without more details on how the benchmarks were run.
  • nestorD 4y ago
    Most of the time you see Rust lose on that kind of benchmark due to it not using buffered algorithm in the standard library (buffers are available and easy to use but they are an option you have to think about and search for).
  • mustache_kimono 4y ago
    The naive implementation is slower mostly because of the DOS resistant hasher in the Rust stdlib, and the smallish buffer a line represents.
  • burntsushi 4y ago
    Why not read the source code? :-) I wrote comments explaining things: https://github.com/benhoyt/countwords/blob/8553c8f600c40a4626e966bc7e7e804097e6e2f4/rust/simple/main.rs#L1-L16 https://github.com/benhoyt/countwords/blob/8553c8f600c40a462...
• kramerger 4y ago
  > When optimizing this, the first thing to do is compile with optimizations enabled (g++ -O2). I kind of like the fact that with Go you don’t have to worry about this – optimizations are always on. I think it is more fair to say that in Go optimizations are always OFF.
  • philosopher1234 4y ago
    The results indicate otherwise
• kramerger 4y ago
  For performance comparison between languages, this article is not very useful. Nothing against the author, this task is simply impossible :) Om the other hand the article is gold mine for learning performance analysis in different languages.
• RantyDave 4y ago
  Interesting, it confirms what I've suspected for a while: that by using Python and not C++ for "general text mashing" tasks I'm not losing that much performance. Like, 2x, 5x, perhaps 10x. But not 100x.
• zmmmmm 4y ago
  It seems like this is just measuring a single pass at reading a file with about 100k words in it. I ran the benchmark and it finished almost instantly. So languages with startup overhead and warmup time (eg: JIT etc) will totally underperform here. Makes sense then why Java is almost identical between optimised and non-optimised versions.
  • pjmlp 4y ago
    In what concerns Java, using AOT or JIT caches is also an option, however all these kind of benchmarks tend to reveal lack of knowledge of the Java ecosystem. Same applies to .NET by the way.
  • igouy 4y ago
    otoh the similar k-nucleotide program in the benchmarks game processes bigger files to amortize any startup overhead — https://benchmarksgame-team.pages.debian.net/benchmarksgame/measurements/java.html https://benchmarksgame-team.pages.debian.net/benchmarksgame/... otoh instead of asserting "will totally underperform" please measure and share your measurements! Sometimes "startup overhead" turns-out to be insignificant. https://benchmarksgame-team.pages.debian.net/benchmarksgame/sometimes-people-just-make-up-stuff.html#jvm-startup-time https://benchmarksgame-team.pages.debian.net/benchmarksgame/...
• samuell 4y ago
  Fun stuff! Did run a similar thing with a simple bioinformatics problem before (calculating the ratio of G and Cs against A+G+C+T), also with a whole bunch of contributors: https://github.com/samuell/gccontent-benchmark#readme https://github.com/samuell/gccontent-benchmark#readme Really hard - or impossible - to arrive at a definitive single number for one language, but the whole exercise is a lot of fun and quite informative IMO :)
  • igouy 4y ago
    That seems familiar — k-nucleotide https://benchmarksgame-team.pages.debian.net/benchmarksgame/description/knucleotide.html#knucleotide https://benchmarksgame-team.pages.debian.net/benchmarksgame/... https://benchmarksgame-team.pages.debian.net/benchmarksgame/performance/knucleotide.html https://benchmarksgame-team.pages.debian.net/benchmarksgame/...
• deleted 4y ago
  [deleted]
• gcanyon 4y ago
  My language of choice, LiveCode, is a superset of HyperTalk. The concept of "words" has been built-in (not a library) since the '80s. Hash tables since the '90s. A solution to every aspect of this problem but the line-by-line bit took just over a minute to write. Adding that took another minute. The trade-off is that LC is slower than almost any of the languages listed, and optimization is almost impossible -- the idiomatic way is the fastest (almost). And if you don't like the way LC parses words, there is almost no way to change it without going into the source code (a big pain).
• manv1 4y ago
  This is the executive version of this test: "I gave this problem to my team and it took them two weeks to figure out a solution."
• ncmncm 4y ago
  You can tell it will be BS just from the title treating C and C++ as if they were one language, not two very different languages. Other comments here reveal how, exactly.
  • burntsushi 4y ago
    I remember the last time this came up and you bloviated about a bunch of nonsense[1], and ended up not being able to deliver. Talk about bullshit. At least your comment here is shorter. [1]: https://news.ycombinator.com/item?id=26466672 https://news.ycombinator.com/item?id=26466672
• jcelerier 4y ago
  Got a bit better C++ version here which uses a couple libraries instead of std:: stuff - https://gist.github.com/jcelerier/74dfd473bccec8f1bd5d78be5aada569 https://gist.github.com/jcelerier/74dfd473bccec8f1bd5d78be5a... ; boost, fmt and https://github.com/martinus/robin-hood-hashing https://github.com/martinus/robin-hood-hashing $ g++ -I robin-hood-hashing/src/include -O2 -flto -std=c++20 -fno-exceptions -fno-unwind-tables -fno-asynchronous-unwind-tables -lfmt $ time ./a.out < kjvbible_x10.txt > /dev/null 0,19s user 0,01s system 99% cpu 0,197 total with the same build flags, optimize.cpp gives me 0,22s user 0,01s system 99% cpu 0,233 total
• Mlller 4y ago
  Taking it in a slightly different direction – because in my work chunking is nearly never necessary (too little text or too much memory) whereas Unicode characters and punctuation nearly always is – I would tend to something like: from collections import Counter from re import finditer word_counts = Counter() with open(PATH, encoding = 'utf-8') as file: doc = file.read() word_counts.update(match.group().lower() for match in finditer(r'\w+', doc)) In Python, this could be a fairly performant way of taking unicode into account, because it doesnʼt use Pythonʼs for-loop, and regular expressions are rather optimized compared with writing low-level-style Python. (Maybe it should use casefold instead of lower.)