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@burntsushi Hi! First of all, thank you for taking the time to write this. I've been using ripgrep for quite some time, and it's an amazing piece of software.

@burntsushi Hi! First of all, thank you for taking the time to write this. I've been using ripgrep for quite some time, and it's an amazing piece of software. Having your comment here is truly an honor. > I'm not sure I totally get the motivation here to be honest This is primarily a small project I started to familiarize myself with Rust. I thought that exploring the basics of ripgrep and attempting to build something similar would be a good way to get started. > Also, the flags that it does support are overriding long-held custom that are likely to be confusing to users Noted. I'll consider making these changes to avoid potentially confusing anyone. > It's also pretty annoying to share screenshots of benchmarks instead of just showing a simple copyable command with a paste of the results. I've updated the documentation with the actual commands and included a copy of the results. > I also can't quite reproduce at least the curl benchmark I just ran the curl benchmark again on the same machine (my work laptop, an M3 Apple MacBook), and here are the results: $ hyperfine "rg '[A-Z]+_NOBODY' ." "gg '[A-Z]+_NOBODY'" "ggrep -rE '[A-Z]+_NOBODY' ." Benchmark 1: rg '[A-Z]+_NOBODY' . Time (mean ± σ): 38.5 ms ± 2.2 ms [User: 18.1 ms, System: 207.3 ms] Range (min … max): 33.8 ms … 42.8 ms 72 runs Benchmark 2: gg '[A-Z]+_NOBODY' Time (mean ± σ): 21.8 ms ± 0.8 ms [User: 15.4 ms, System: 53.1 ms] Range (min … max): 20.2 ms … 23.8 ms 115 runs Benchmark 3: ggrep -rE '[A-Z]+_NOBODY' . Time (mean ± σ): 73.3 ms ± 0.9 ms [User: 26.5 ms, System: 45.7 ms] Range (min … max): 70.8 ms … 75.6 ms 41 runs Summary gg '[A-Z]+_NOBODY' ran 1.77 ± 0.12 times faster than rg '[A-Z]+_NOBODY' . 3.36 ± 0.13 times faster than ggrep -rE '[A-Z]+_NOBODY' . > It looks like it's assuming that the `ArrayQueue` it uses is never full? I used a default maximum size for the queue (configurable via the --max-results argument) to pre-allocate it, as I thought this might improve performance. However, I'm currently not handling errors properly and just allowing the program to panic when the number of results exceeds the set limit. > So why doesn't it have the same performance profile as ripgrep? Given the differences in execution times between our benchmarks, I suspect that because ripgrep's (and, by extension, gg's) performance bottleneck is primarily disk I/O, variations in filesystems and underlying storage hardware could explain the significantly different results we're observing. What do you think?

burntsushi 2y ago
It's not disk I/O because we're using hyperfine for measuring. It does warm-up runs first, and unless your machine has a teeny amount of RAM, everything is in cache. You can put your corpus on a ramdisk (usually `/tmp` is on Linux and I believe always `/dev/shm`, IDK about macOS) to verify this. Since you're running on macOS, I'll do the same. I have an M2 mac mini. My previous benchmarks were on my Linux workstation. Your `curl` benchmark: $ hyperfine "rg '[A-Z]+_NOBODY' ." "gg '[A-Z]+_NOBODY'" Benchmark 1: rg '[A-Z]+_NOBODY' . Time (mean ± σ): 20.3 ms ± 0.7 ms [User: 18.6 ms, System: 96.0 ms] Range (min … max): 18.4 ms … 21.3 ms 126 runs Benchmark 2: gg '[A-Z]+_NOBODY' Time (mean ± σ): 17.9 ms ± 0.7 ms [User: 15.6 ms, System: 38.6 ms] Range (min … max): 17.0 ms … 19.9 ms 141 runs Summary gg '[A-Z]+_NOBODY' ran 1.13 ± 0.06 times faster than rg '[A-Z]+_NOBODY' . So slightly edged out by `gg` here, but not as big of a difference as you're seeing. What version of ripgrep are you using? Also, as I said before, these times are pretty short. Try a bigger corpus. For example, in my clone of Linux (also on my M2 mac mini): $ git remote -v origin git@github.com:BurntSushi/linux (fetch) origin git@github.com:BurntSushi/linux (push) $ git rev-parse HEAD 84e57d292203a45c96dbcb2e6be9dd80961d981a $ hyperfine "rg '[A-Z]+_NOBODY' ." "gg '[A-Z]+_NOBODY'" Benchmark 1: rg '[A-Z]+_NOBODY' . Time (mean ± σ): 343.3 ms ± 4.2 ms [User: 359.3 ms, System: 2243.3 ms] Range (min … max): 339.0 ms … 352.7 ms 10 runs Benchmark 2: gg '[A-Z]+_NOBODY' Time (mean ± σ): 351.1 ms ± 4.6 ms [User: 326.4 ms, System: 1059.1 ms] Range (min … max): 348.2 ms … 363.8 ms 10 runs Summary rg '[A-Z]+_NOBODY' . ran 1.02 ± 0.02 times faster than gg '[A-Z]+_NOBODY' It is very interesting that the differences are almost zero on macOS but quite a bit bigger on Linux. That might be worth investigating. IMO, if you're advertising "circumstantially faster than ripgrep," then you should be able to characterize the circumstances in which that occurs.
burntsushi 2y ago
Oh... I see the problem. It's probably the thread heuristic. When running gg and rg, make sure -T and -j, respectively, are set to the same number. Because I think gg always defaults to `4`. Where as ripgrep is probably defaulting to a higher number. On very small corpora, like curl, this can actually lead to overall slower times due to the overhead of starting the threads. This also explains why the times are faster on Linux. My Linux workstation has a lot more CPUs than my M2 mac mini. My mac mini has 8 logical CPUs while my Linux box has 24. ripgrep won't necessarily start one thread per core, but at 8 cores, it will indeed start one thread per core. Where as gg will start 4. You can see ripgrep's heuristic here: https://github.com/BurntSushi/ripgrep/blob/e0f1000df67f82ab0e735bad40e9b45b2d774ef0/crates/core/flags/hiargs.rs#L167-L173 https://github.com/BurntSushi/ripgrep/blob/e0f1000df67f82ab0... I suppose thread count heuristics are fair game for benchmarks, but in order to measure those better, you need a bigger variety of corpus sizes. Even with the Linux kernel, the difference between 4 and 8 threads for `gg` is not that big: $ hyperfine "gg -T4 '[A-Z]+_NOBODY'" "gg -T8 '[A-Z]+_NOBODY'" Benchmark 1: gg -T4 '[A-Z]+_NOBODY' Time (mean ± σ): 364.3 ms ± 2.5 ms [User: 331.1 ms, System: 1108.6 ms] Range (min … max): 360.8 ms … 369.1 ms 10 runs Benchmark 2: gg -T8 '[A-Z]+_NOBODY' Time (mean ± σ): 349.3 ms ± 3.1 ms [User: 454.2 ms, System: 2056.2 ms] Range (min … max): 345.4 ms … 355.8 ms 10 runs Summary gg -T8 '[A-Z]+_NOBODY' ran 1.04 ± 0.01 times faster than gg -T4 '[A-Z]+_NOBODY' But go to a bigger corpus and a difference becomes much more apparent: $ hyperfine "gg -T4 '[A-Z]+_NOBODY'" "gg -T8 '[A-Z]+_NOBODY'" Benchmark 1: gg -T4 '[A-Z]+_NOBODY' Time (mean ± σ): 16.777 s ± 0.351 s [User: 1.868 s, System: 12.301 s] Range (min … max): 16.376 s … 17.396 s 10 runs Benchmark 2: gg -T8 '[A-Z]+_NOBODY' Time (mean ± σ): 10.273 s ± 0.628 s [User: 1.931 s, System: 12.215 s] Range (min … max): 8.980 s … 11.066 s 10 runs Summary gg -T8 '[A-Z]+_NOBODY' ran 1.63 ± 0.11 times faster than gg -T4 '[A-Z]+_NOBODY' This is on a checkout of the Chromium repository. The increased variety of benchmarks is important here because you might have a simpler heuristic for thread count that does result in overall marginally faster times in some cases, but this obscures what you're giving up: substantially slower times in other cases. Moreover, the cases where 4 versus 8 threads results in faster times for 4 threads tend to have very small absolute differences. i.e., Not hugely perceptible by humans.

burntsushi 2y ago

It's not disk I/O because we're using hyperfine for measuring. It does warm-up runs first, and unless your machine has a teeny amount of RAM, everything is in cache. You can put your corpus on a ramdisk (usually `/tmp` is on Linux and I believe always `/dev/shm`, IDK about macOS) to verify this. Since you're running on macOS, I'll do the same. I have an M2 mac mini. My previous benchmarks were on my Linux workstation. Your `curl` benchmark: $ hyperfine "rg '[A-Z]+_NOBODY' ." "gg '[A-Z]+_NOBODY'" Benchmark 1: rg '[A-Z]+_NOBODY' . Time (mean ± σ): 20.3 ms ± 0.7 ms [User: 18.6 ms, System: 96.0 ms] Range (min … max): 18.4 ms … 21.3 ms 126 runs Benchmark 2: gg '[A-Z]+_NOBODY' Time (mean ± σ): 17.9 ms ± 0.7 ms [User: 15.6 ms, System: 38.6 ms] Range (min … max): 17.0 ms … 19.9 ms 141 runs Summary gg '[A-Z]+_NOBODY' ran 1.13 ± 0.06 times faster than rg '[A-Z]+_NOBODY' . So slightly edged out by `gg` here, but not as big of a difference as you're seeing. What version of ripgrep are you using? Also, as I said before, these times are pretty short. Try a bigger corpus. For example, in my clone of Linux (also on my M2 mac mini): $ git remote -v origin git@github.com:BurntSushi/linux (fetch) origin git@github.com:BurntSushi/linux (push) $ git rev-parse HEAD 84e57d292203a45c96dbcb2e6be9dd80961d981a $ hyperfine "rg '[A-Z]+_NOBODY' ." "gg '[A-Z]+_NOBODY'" Benchmark 1: rg '[A-Z]+_NOBODY' . Time (mean ± σ): 343.3 ms ± 4.2 ms [User: 359.3 ms, System: 2243.3 ms] Range (min … max): 339.0 ms … 352.7 ms 10 runs Benchmark 2: gg '[A-Z]+_NOBODY' Time (mean ± σ): 351.1 ms ± 4.6 ms [User: 326.4 ms, System: 1059.1 ms] Range (min … max): 348.2 ms … 363.8 ms 10 runs Summary rg '[A-Z]+_NOBODY' . ran 1.02 ± 0.02 times faster than gg '[A-Z]+_NOBODY' It is very interesting that the differences are almost zero on macOS but quite a bit bigger on Linux. That might be worth investigating. IMO, if you're advertising "circumstantially faster than ripgrep," then you should be able to characterize the circumstances in which that occurs.

burntsushi 2y ago

Oh... I see the problem. It's probably the thread heuristic. When running gg and rg, make sure -T and -j, respectively, are set to the same number. Because I think gg always defaults to `4`. Where as ripgrep is probably defaulting to a higher number. On very small corpora, like curl, this can actually lead to overall slower times due to the overhead of starting the threads. This also explains why the times are faster on Linux. My Linux workstation has a lot more CPUs than my M2 mac mini. My mac mini has 8 logical CPUs while my Linux box has 24. ripgrep won't necessarily start one thread per core, but at 8 cores, it will indeed start one thread per core. Where as gg will start 4. You can see ripgrep's heuristic here: https://github.com/BurntSushi/ripgrep/blob/e0f1000df67f82ab0e735bad40e9b45b2d774ef0/crates/core/flags/hiargs.rs#L167-L173 https://github.com/BurntSushi/ripgrep/blob/e0f1000df67f82ab0... I suppose thread count heuristics are fair game for benchmarks, but in order to measure those better, you need a bigger variety of corpus sizes. Even with the Linux kernel, the difference between 4 and 8 threads for `gg` is not that big: $ hyperfine "gg -T4 '[A-Z]+_NOBODY'" "gg -T8 '[A-Z]+_NOBODY'" Benchmark 1: gg -T4 '[A-Z]+_NOBODY' Time (mean ± σ): 364.3 ms ± 2.5 ms [User: 331.1 ms, System: 1108.6 ms] Range (min … max): 360.8 ms … 369.1 ms 10 runs Benchmark 2: gg -T8 '[A-Z]+_NOBODY' Time (mean ± σ): 349.3 ms ± 3.1 ms [User: 454.2 ms, System: 2056.2 ms] Range (min … max): 345.4 ms … 355.8 ms 10 runs Summary gg -T8 '[A-Z]+_NOBODY' ran 1.04 ± 0.01 times faster than gg -T4 '[A-Z]+_NOBODY' But go to a bigger corpus and a difference becomes much more apparent: $ hyperfine "gg -T4 '[A-Z]+_NOBODY'" "gg -T8 '[A-Z]+_NOBODY'" Benchmark 1: gg -T4 '[A-Z]+_NOBODY' Time (mean ± σ): 16.777 s ± 0.351 s [User: 1.868 s, System: 12.301 s] Range (min … max): 16.376 s … 17.396 s 10 runs Benchmark 2: gg -T8 '[A-Z]+_NOBODY' Time (mean ± σ): 10.273 s ± 0.628 s [User: 1.931 s, System: 12.215 s] Range (min … max): 8.980 s … 11.066 s 10 runs Summary gg -T8 '[A-Z]+_NOBODY' ran 1.63 ± 0.11 times faster than gg -T4 '[A-Z]+_NOBODY' This is on a checkout of the Chromium repository. The increased variety of benchmarks is important here because you might have a simpler heuristic for thread count that does result in overall marginally faster times in some cases, but this obscures what you're giving up: substantially slower times in other cases. Moreover, the cases where 4 versus 8 threads results in faster times for 4 threads tend to have very small absolute differences. i.e., Not hugely perceptible by humans.