Kanzi

Kanzi is a modern, modular, expandable and efficient lossless data compressor implemented in Java.

• modern: state-of-the-art algorithms are implemented and multi-core CPUs can take advantage of the built-in multi-threading.
• modular: entropy codec and a combination of transforms can be provided at runtime to best match the kind of data to compress.
• expandable: clean design with heavy use of interfaces as contracts makes integrating and expanding the code easy. No dependencies.
• efficient: the code is optimized for efficiency (trade-off between compression ratio and speed).

Unlike the most common lossless data compressors, Kanzi uses a variety of different compression algorithms and supports a wider range of compression ratios as a result. Most usual compressors do not take advantage of the many cores and threads available on modern CPUs (what a waste!). Kanzi is multithreaded by design and uses several threads by default to compress blocks concurrently. It is not compatible with standard compression formats. Kanzi is a lossless data compressor, not an archiver. It uses checksums (optional but recommended) to validate data integrity but does not have a mechanism for data recovery. It also lacks data deduplication across files.

For more details, check https://github.com/flanglet/kanzi/wiki.

See how to reuse the code here: https://github.com/flanglet/kanzi/wiki/Using-and-extending-the-code

There is a C++ implementation available here: https://github.com/flanglet/kanzi-cpp

There is Go implementation available here: https://github.com/flanglet/kanzi-go

Why Kanzi

There are many excellent, open-source lossless data compressors available already.

If gzip is starting to show its age, zstd and brotli are open-source, standardized and used daily by millions of people. Zstd is incredibly fast and probably the best choice in many cases. There are a few scenarios where Kanzi could be a better choice:

• gzip, lzma, brotli, zstd are all LZ based. It means that they can reach certain compression ratios only. Kanzi also makes use of BWT and CM which can compress beyond what LZ can do.

• These LZ based compressors are well suited for software distribution (one compression / many decompressions) due to their fast decompression (but low compression speed at high compression ratios). There are other scenarios where compression speed is critical: when data is generated before being compressed and consumed (one compression / one decompression) or during backups (many compressions / one decompression).

• Kanzi has built-in customized data transforms (multimedia, utf, text, dna, ...) that can be chosen and combined at compression time to better compress specific kinds of data.

• Kanzi can take advantage of the multiple cores of a modern CPU to improve performance

• It is easy to implement a new transform or entropy codec to either test an idea or improve compression ratio on specific kinds of data.

Benchmarks

Test machine:

AWS c5a8xlarge: AMD EPYC 7R32 (32 vCPUs), 64 GB RAM

openjdk 21.0.1+12-29

Ubuntu 22.04.3 LTS

Kanzi version 2.2 Java implementation.

On this machine kanzi can use up to 16 threads (depending on compression level). bzip3 uses 16 threads. zstd can use 2 for compression, other compressors are single threaded.

silesia.tar

Download at http://sun.aei.polsl.pl/~sdeor/corpus/silesia.zip

Compressor	Encoding (sec)	Decoding (sec)	Size
Original			211,957,760
Kanzi -l 1	1.337	1.186	80,284,705
lz4 1.9.5 -4	3.397	0.987	79,914,864
Zstd 1.5.5 -2	0.761	0.286	69,590,245
Kanzi -l 2	1.343	1.343	68,231,498
Brotli 1.1.0 -2	1.749	2.459	68,044,145
Gzip 1.10 -9	20.15	1.316	67,652,229
Kanzi -l 3	1.906	1.692	64,916,444
Zstd 1.5.5 -5	2.003	0.324	63,103,408
Kanzi -l 4	2.458	2.521	60,770,201
Zstd 1.5.5 -9	4.166	0.282	59,444,065
Brotli 1.1.0 -6	14.53	4.263	58,552,177
Zstd 1.5.5 -13	19.15	0.276	58,061,115
Brotli 1.1.0 -9	70.07	7.149	56,408,353
Bzip2 1.0.8 -9	16.94	6.734	54,572,500
Kanzi -l 5	3.228	2.268	54,051,139
Zstd 1.5.5 -19	92.82	0.302	52,989,654
Kanzi -l 6	4.950	2.522	49,517,823
Lzma 5.2.5 -9	92.6	3.075	48,744,632
Kanzi -l 7	4.478	3.181	47,308,484
bzip3 1.3.2.r4-gb2d61e8 -j 16	2.682	3.221	47,237,088
Kanzi -l 8	10.67	11.13	43,247,248
Kanzi -l 9	24.78	26.73	41,807,179
zpaq 7.15 -m5 -t16	213.8	213.8	40,050,429

enwik8

Download at https://mattmahoney.net/dc/enwik8.zip

Compressor	Encoding (sec)	Decoding (sec)	Size
Original			100,000,000
Kanzi -l 1	1.221	0.684	43,747,730
Kanzi -l 2	1.254	0.907	37,745,093
Kanzi -l 3	1.093	0.989	33,839,184
Kanzi -l 4	1.800	1.648	29,598,635
Kanzi -l 5	2.066	1.740	26,527,955
Kanzi -l 6	2.648	1.743	24,076,669
Kanzi -l 7	3.742	1.741	22,817,376
Kanzi -l 8	6.619	6.633	21,181,978
Kanzi -l 9	17.81	18.23	20,035,133

Credits

Matt Mahoney, Yann Collet, Jan Ondrus, Yuta Mori, Ilya Muravyov, Neal Burns, Fabian Giesen, Jarek Duda, Ilya Grebnov

Disclaimer

Use at your own risk. Always keep a copy of your original files.