Impressive compression benchmark. Four times smaller than z standard.
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:
bzip3 ... 12178M memory
zstd ... 687M memoryEven 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