Kanzi

Kanzi is a modern, modular, portable, and efficient lossless data compressor written in Java.

• Modern: Kanzi implements state-of-the-art compression algorithms and is built to fully utilize multi-core CPUs via built-in multi-threading.
• Modular: Entropy codecs and data transforms can be selected and combined at runtime to best suit the specific data being compressed.
• Expandable: A clean, interface-driven design—with no external dependencies—makes Kanzi easy to integrate, extend, and customize.
• Efficient: Carefully optimized to balance compression ratio and speed for practical, high-performance usage.

Unlike most mainstream lossless compressors, Kanzi is not limited to a single compression paradigm. By combining multiple algorithms and techniques, it supports a broader range of compression ratios and adapts better to diverse data types.

Most traditional compressors underutilize modern hardware by running single-threaded—even on machines with many cores. Kanzi, in contrast, is concurrent by design, compressing multiple blocks in parallel across threads for significant performance gains. However, it is not compatible with standard compression formats.

It’s important to note that Kanzi is a data compressor, not an archiver. It includes optional checksums for verifying data integrity, but does not provide features like cross-file deduplication or data recovery mechanisms. That said, it produces a seekable bitstream—meaning one or more consecutive blocks can be decompressed independently, without needing to process the entire stream.

For more details, check Wiki, QA and DeepWiki

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

While excellent open-source compressors like zstd and brotli exist, they are primarily based on Lempel-Ziv (LZ) algorithms. Zstd, in particular, is a fantastic general-purpose choice known for its speed. However, LZ-based tools have inherent limits regarding compression ratios.

Kanzi offers a compelling alternative for specific high-performance scenarios:

• Beyond LZ: By incorporating Burrows-Wheeler Transform (BWT) and Context Modeling (CM), Kanzi can achieve compression ratios that traditional LZ methods cannot.

• Speed where it counts: While LZ is ideal for "compress once, decompress often" (like software distribution), it often slows down significantly at high compression settings. Kanzi leverages multi-core CPUs to maintain performance, making it highly effective for backups, real-time data generation, and one-off transfers.

• Content-Aware: Kanzi features built-in, customizable transforms for specific data types (e.g., multimedia, DNA, UTF text), improving efficiency where generic compressors fail.

• Extensible: The architecture is developer-friendly, making it straightforward to implement new transforms or entropy codecs for experimentation or niche data types.

Benchmarks

Kanzi version 2.5.0

java 25 2025-09-16 LTS

Note: The default block size at level 9 is 32MB, severely limiting the number of threads
in use, especially with enwik8, but all tests are performed with default values.

silesia.tar

Test machine: AMD Ryzen 9950X on Ubuntu 25.10

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

Compressor	Encoding (ms)	Decoding (ms)	Size
Original			211,957,760
lz4 1.1.10 -T16 -4	18	13	79,910,851
kanzi -l 1	510	183	79,331,051
zstd 1.5.8 -T16 -2	6	11	69,443,247
kanzi -l 2	702	317	68,616,621
brotli 1.1.0 -2	880	333	68,040,160
gzip 1.13 -9	10328	704	67,651,076
kanzi -l 3	896	470	63,966,794
zstd 1.5.8 -T16 -5	138	123	62,867,556
kanzi -l 4	1283	743	61,183,757
zstd 1.5.8 -T16 -9	320	114	59,233,481
brotli 1.1.0 -6	4039	299	58,511,709
zstd 1.5.8 -T16 -13	1820	112	57,843,283
brotli 1.1.0 -9	23030	293	56,407,229
bzip2 1.0.8 -9	8223	3453	54,588,597
kanzi -l 5	1717	752	53,853,702
zstd 1.5.8 -T16 -19	11290	130	52,830,213
kanzi -l 6	1913	788	49,472,084
xz 5.8.1 -9	43611	931	48,802,580
bsc 3.3.11 -T16	1201	698	47,900,848
kanzi -l 7	1684	1046	47,330,422
bzip3 1.5.1.r3-g428f422 -j 16	2348	2218	47,260,281
kanzi -l 8	5842	6025	42,962,913
kanzi -l 9	15069	14985	41,520,670

enwik8

Test machine: Apple M3 24 GB Sonoma 15.7.3

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

Compressor	Encoding (ms)	Decoding (ms)	Size
Original			100,000,000
Kanzi -l 1	558	185	42,870,183
Kanzi -l 2	534	241	37,544,247
Kanzi -l 3	998	519	32,551,405
Kanzi -l 4	1073	694	29,536,581
Kanzi -l 5	1485	808	26,528,254
Kanzi -l 6	1974	1165	24,076,765
Kanzi -l 7	2665	1743	22,817,360
Kanzi -l 8	7270	7341	21,181,992
Kanzi -l 9	10521	10365	20,035,144

Build

First option (ant):

ant

Second option (maven):

mvn -Dmaven.test.skip=true

Third option (gradle):

./gradlew clean build

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.