> However, the complexity of the algorithms, and, in particular, the presence of various special cases in the code which occur with very low but non-zero probability make it impossible to rule out the possibility of bugs remaining in the program.
Sounds like perhaps a nice testcase for formalization + AI?
I think this should include benchmarks zstd with larger windows and long range mode. I wouldn't be surprised if the window they used is smaller than an individual version tar file, which would prevent useful compression.
The benchmarks are disingenuous, to the point of looking cherry-picked. The block size for bzip3 is set to 512MB, but the window size for zstd is left to its default (8MB I believe for high levels). So in this corpus, which is made up of all versions of Perl source code concatenated, the window is too small to see all the identical files and just match them.
Also corpora made out of very long repetitions are pretty much the best case scenario for BWT-based compressors.
If we match the window size of zstd to that of bzip3 we get dramatically different results:
% gzcat *.gz | time zstd -T8 -16 | wc -c # baseline
2819113884
zstd -T8 -16 2054.50s user 3.47s system 783% cpu 4:22.80 total
% gzcat *.gz | time zstd -T8 -16 --long=29 | wc -c
196405076
zstd -T8 -16 --long=29 1083.06s user 2.41s system 783% cpu 2:18.55 total
Almost 15x smaller than the baseline, and more than 2x smaller than bzip3, also CPU time halves (since long matches are found earlier, so there's less work to do).
(the baseline number is slightly different because I don't have the exact Perl version set used by the author)
Also, in the benchmarks using lrzip, which would make the window size less relevant, zstd is not even compared.
Wow, that is widely disingenuous, I don't really think there is any excuse for that, I don't believe someone deep in compression algorithms wouldn't know they could adjust the block size, and 512GB is a huge block size for bzip3, as it needs to basically all be in memory so you can't pretend that's just 'the standard value'.
Depends - if parallel decompression of any bzip3 archive is possible I'd consider it fair, if it requires special flags on archive creation I'd consider it unfair. I didn't see any description about that on that page.
pbzip2 only can do parallel decompression on archives created with pbzip2, otherwise it'll fall back to single thread. There nowadays seems to be lbzip2, though, which claims to be able to add SMP support for standard bzip2 archives.
I'll need to try that next time I'm working with large archives - I learned about the pbzip2 limitations the hard way last time I was shuffling around a few multi-10GB archives, and was trying to speed things up fully utilising my 32 core threadripper.
I have not experimented with bzip3 recently, but more than a year ago I have done many tests with it.
Initially I was extremely impressed with it, because in a lot of tests it succeeded to compress hard-to-compress files, like movies, and in many cases it demonstrated a much better compromise between speed and compression ratio than zstd, i.e. depending on the command parameters I could make it either compress better than zstd at similar compression/decompression speed, or compress/decompress faster at a similar compression ratio.
Alas, the initial extremely favorable conclusion was short-lived, because trying later bzip3 on other data files gave worse results than zstd.
So the final conclusion was that the performance of bzip3 was somewhat unpredictable, being highly data dependent. For some files it provided outstanding compression ratio or speed, but for others it was inferior.
The problem was that without doing a compression there was no way to guess whether a file would be among those preferred by bzip3 or by zstd or by xz.
So now I would use it only for a file for which I want maximum compression and which I would compress once and decompress many times, so I can afford a very long compression time, during which I would test multiple compression algorithms, including bzip3 and zstd, with multiple parameter choices, and I would eventually choose the one that offers the best compromise between compression ratio and decompression time, for that particular file.
It certainly is a competitive compression algorithm, but unless it has changed since I last tested it, you cannot guess for which files it would win the compression competition.
Would a multi-stream archive format make sense at this point? I.e. store several compressed streams in the same file and use heuristics to decide where each file (or portion of file) goes.
But developing the heuristics for choosing the appropriate compression algorithm for a stream of data is likely to need a very long time for compression tests of a lot of diverse training data, similarly to the training of a specialized ML model that classifies patterns.
Such heuristics should provide not only algorithm selection, but also parameter selection, when given only some simple input, e.g. the relative importances of compression ratio, decompression speed and compression speed.
The benchmark is very rudimentary. It does not test different levels/settings apart from its own -b 256/512 (does it affect decompression?), it doesn't measure compression time and memory usage. It does not specify parallel vs single-threaded (it mentions parallel on the one decoding number but what about the others?).
The lrzip test is interesting but it omits for example zstd and doesn't even have (de-)compression timings.
A lot more numbers are needed to present a fair and informative comparison.
I don't want this to be a swipe against bzip3, I only want to point out the presented benchmarks could be a lot better.
There is a comparison with "zstd -19" on the Silesia corpus, showing better compression ratio for bzip3 (47.2 vs 53MB) while being ~5 times faster (and using only half the memory).
Even if the examples are highly cherry-picked, it is quite suprising to me that such pareto-dominance is possible at all.
edit: Tested it myself and found that it often also does slightly worse than zstd -19 in compression ratio but faster (it was slower in one case on "uncompressible" input).
Compression performance vs "zstd -19" seems to depends a lot on actual input data in a very unpredictable way. I'd assume the benchmarks that they show are definitely somewhat cherry-picked.
having used zstd, it has terrible defaults, optimized for speed and low-memory. You need to change it's params (not just level and dict size) to get high performance.
probably somebody should use a coding agent to do auto-research to optimize params for each compression algo, while matching one fixed goal - time, memory or size
It's a very strange algorithm completely different from most compression methods, that's what makes it interesting IMO. But it's probably not realistically competitive, since one needs to do more after the burrows-wheeler transform, and all that "more" has been ridiculously more optimized in zstd and other modern compression methods. Compressing bwt-transformed data is easier, but that doesn't mean it's easy to further than what's easy.
“Hi, tool author here.” A useful explanation of Burrows-Wheelers transform as used by bzip3: https://news.ycombinator.com/item?id=42902407
“bzip3 is not yet listed on the large text compression benchmark” It is now: https://mattmahoney.net/dc/text.html
(2 years ago, 176 comments) https://news.ycombinator.com/item?id=42899713
(4 years ago, 104 comments) https://news.ycombinator.com/item?id=31324439
Sounds like perhaps a nice testcase for formalization + AI?
If we match the window size of zstd to that of bzip3 we get dramatically different results:
Almost 15x smaller than the baseline, and more than 2x smaller than bzip3, also CPU time halves (since long matches are found earlier, so there's less work to do).(the baseline number is slightly different because I don't have the exact Perl version set used by the author)
Also, in the benchmarks using lrzip, which would make the window size less relevant, zstd is not even compared.
Sorry! That was a typo, it should have been 512MB (now fixed). Still huge.
The claim “stronger than bzip2” is strange. What does it even mean?
Also, comparing parallel decompression benchmarks with bzip2 instead of pbzip2 seems unfair.
pbzip2 only can do parallel decompression on archives created with pbzip2, otherwise it'll fall back to single thread. There nowadays seems to be lbzip2, though, which claims to be able to add SMP support for standard bzip2 archives.
I'll need to try that next time I'm working with large archives - I learned about the pbzip2 limitations the hard way last time I was shuffling around a few multi-10GB archives, and was trying to speed things up fully utilising my 32 core threadripper.
Initially I was extremely impressed with it, because in a lot of tests it succeeded to compress hard-to-compress files, like movies, and in many cases it demonstrated a much better compromise between speed and compression ratio than zstd, i.e. depending on the command parameters I could make it either compress better than zstd at similar compression/decompression speed, or compress/decompress faster at a similar compression ratio.
Alas, the initial extremely favorable conclusion was short-lived, because trying later bzip3 on other data files gave worse results than zstd.
So the final conclusion was that the performance of bzip3 was somewhat unpredictable, being highly data dependent. For some files it provided outstanding compression ratio or speed, but for others it was inferior.
The problem was that without doing a compression there was no way to guess whether a file would be among those preferred by bzip3 or by zstd or by xz.
So now I would use it only for a file for which I want maximum compression and which I would compress once and decompress many times, so I can afford a very long compression time, during which I would test multiple compression algorithms, including bzip3 and zstd, with multiple parameter choices, and I would eventually choose the one that offers the best compromise between compression ratio and decompression time, for that particular file.
It certainly is a competitive compression algorithm, but unless it has changed since I last tested it, you cannot guess for which files it would win the compression competition.
But developing the heuristics for choosing the appropriate compression algorithm for a stream of data is likely to need a very long time for compression tests of a lot of diverse training data, similarly to the training of a specialized ML model that classifies patterns.
Such heuristics should provide not only algorithm selection, but also parameter selection, when given only some simple input, e.g. the relative importances of compression ratio, decompression speed and compression speed.
The lrzip test is interesting but it omits for example zstd and doesn't even have (de-)compression timings.
A lot more numbers are needed to present a fair and informative comparison.
I don't want this to be a swipe against bzip3, I only want to point out the presented benchmarks could be a lot better.
how is this an honest benchmark:
Even if the examples are highly cherry-picked, it is quite suprising to me that such pareto-dominance is possible at all.
edit: Tested it myself and found that it often also does slightly worse than zstd -19 in compression ratio but faster (it was slower in one case on "uncompressible" input).
Compression performance vs "zstd -19" seems to depends a lot on actual input data in a very unpredictable way. I'd assume the benchmarks that they show are definitely somewhat cherry-picked.
probably somebody should use a coding agent to do auto-research to optimize params for each compression algo, while matching one fixed goal - time, memory or size