“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
And it comes in relatively well, in my opinion. I'm a compression amateur, but bzip3 is the first entry I recognize as a general purpose compression program.
FWIW, the Burrows-Wheelers transform is also used by bzip2, so this isn’t a new feature even though that quote kinda sounds like it is.
I ended up using gzip because it's best supported by the software I use and most likely to have support in software I adopt. But it gave the worst compression results of the options I tried. These bzip3 numbers certainly give me FOMO...
So I stripped out formatting, got rid of dupes, and tried out zstd, which was the hot new thing, along with the dictionary feature you describe, figuring it'd help. It didn't. I tried having one per book, one per multiple books, one for the whole archive.
It didn't work, or the gains were so marginal that I ended up scrapping the approach.
So it's not impossible that it can work, but stuff like regular json already compresses extremely well, I haven't found a scenario where it's a major boon.
When I studied at school, I used ZFS with lz4 enabled on my working machine. During that times I had a task of parsing Wikipedia's data. I had enough brain cells to find compressed dumps and download them with aria2 but not enough to leave the file compressed. I ran a decompressor. It'd been taking longer than I expected so I went out to walk a dog.
Imagine how fast me and the dog ran back 30 minutes later when I realized how cooked I was. I only had 10 GB left on my disks after I downloaded that 20 GB file. This decompressed file would have blown the machine up. I was terrified to find a frozen system with no storage space left.
Instead, the process finished and `df -h` reported 8 GB of the free space left. Files were decompressed. I could `less` them! That made no sense! Only many many minutes later I finally figured out to run a `zfs get compressratio` command which showed ZFS successfully and transparently recompressed everything on the fly. That was too impressive for that teenager and he never switched to a different file system.
Sun was a really cool company.
\[T]/
I don't know much about duckdb but it looks like it supports zstd too: https://duckdb.org/docs/lts/data/json/loading_json
Writing your files directly into a compressed stream and decompressing on the fly has become almost a standard workflow for any files I'm going to read and write sequentially anyways. No need for the data to ever exist uncompressed on the file system. Previous formats never did that for me because they either had too much overhead or too little gain, often both
Under most r/w workloads, using parquet/lance/vortex/native-duckdb, with their built-in columnar compression will result in more performance AND space savings. Non-solid compression. Then, the query engine can push down your query predicate to a column row group level, instead of forcing it to decompress the entire dataset to operate.
Practical example: duckdb has syntax - https://duckdb.org/docs/lts/data/multiple_files/overview - to glob multiple files at once, but that really only works if you're applying push down query predicates instead of re-decompressing your entire data set per SELECT. I would say for most dataset, even 20%+ size is worth not having to decompress (or even download!) the entire dataset, to figure out if something fits the predicate.
After all, if you have to download and decompress the dataset back again to operate, then the "space savings" are gone.
Here are my benchmarks for 2.3 GB of jsonl, on a laptop. Compressed size, compress time, decompress time; using defaults.
gzip 7.3% 21s 9s
bzip2 4.6% 251s 50s
bzip3 3.3% 82s 69s
zstd 6.9% 2s 3s
lzma 4.7% 51s 3sSince we kind of need a default "Need to compress something? Use this!" setting - would you prefer zlib over zstd, or something else for that role?
It's hard to understand what point you're trying to make. Can you clarify?
A go-to thing means it's a sensible default choice and has no little to no downsides (versus not using compression), it doesn't mean it's the best for everything.
Until now the go-to has been DEFLATE (gzip and zip) but zstd is definitely competing against it because it is better in almost every way.
If your JSON file has many of the same object, you could see ratios in the single digits.
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
(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.
Yep, i found it in the source here:
- https://github.com/facebook/zstd/blob/d9c0c7e2cf8a8bf9fb98d3...
- https://github.com/facebook/zstd/blob/d9c0c7e2cf8a8bf9fb98d3...
Also, zstd docs say:
> Note: If windowLog is set to larger than 27, --long=windowLog or --memory=windowSize needs to be passed to the decompressor.
That always seemed annoying to me. They couldn't allocate 5 more bits somewhere to let the decompressor autodetect longer window sizes?
I believe this is just to prevent the decompressor from arbitrarily blowing up memory usage based on the input; I think if you want to accept long windows you can just always decompress with --long=63 regardless of whether the input needs it? (you will run out of RAM decompressing a long=63 file though of course)
It's actually 8 bits: https://www.rfc-editor.org/rfc/rfc8878.html#name-window-desc...
These command line parameters change the maximum the decompressor will allow. It's 128 MiB by default in the command line decompressor; other uses (like the "zstd" content coding for HTTP in web browsers) use a lower limit of 8 MiB (see https://www.rfc-editor.org/rfc/rfc9659.html).
The benchmarks report 687M for their run of zstd, and 12178M and 18301M for the two runs of BZip3. Which itself is a bit eyebrow raising
it has been a long time since: "lies, damn lies, benchmarks" failed to hold true. Sometimes I wonder why gaming benchmarks has become so common.
Sorry! That was a typo, it should have been 512MB (now fixed). Still huge.
I got some really good results with bzip3 compression Wikipedia XML dumps, and I would like to check if it's actually better or if I was just calling zstd wrong.
Sounds like perhaps a nice testcase for formalization + AI?
For something like these compression algos, though, I imagine it would be much easier since they already have actual proofs out there.
So why would anybody, hobbyist or enterprise, use this? Or does something older like 7zip also have this caveat that I've never experienced.
> THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE....
Also practically, this isn't MIT. It is LGPL 3, which I believe includes the warranty terms of GPL 3, i.e., no warranty. So we're in the same place anyway.
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.
Oh I didn’t know that.
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.
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
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.