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

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

If gzip is beginning to show its age, modern alternatives like zstd and brotli offer compelling replacements. Both are open-source, standardized, and used daily by millions. Zstd is especially notable for its exceptional speed and is often the best choice in general-purpose compression.

However, there are scenarios where Kanzi may offer superior performance:

While gzip, LZMA, brotli, and zstd are all based on LZ (Lempel-Ziv) compression, they are inherently limited in the compression ratios they can achieve. Kanzi goes further by incorporating BWT (Burrows-Wheeler Transform) and CM (Context Modeling), which can outperform traditional LZ-based methods in certain cases.

LZ-based compressors are ideal for software distribution, where data is compressed once and decompressed many times, thanks to their fast decompression speeds—though they tend to be slower when compressing at higher ratios. But in other scenarios—such as real-time data generation, one-off data transfers, or backups—compression speed becomes critical. Here, Kanzi can shine.

Kanzi also features a suite of built-in, customizable data transforms tailored for specific data types (e.g., multimedia, UTF, text, DNA, etc.), which can be selectively applied during compression for better efficiency.

Furthermore, Kanzi is designed to leverage modern multi-core CPUs to boost performance.

Finally, extensibility is a key strength: implementing new transforms or entropy codecs—whether for experimentation or to improve performance on niche data types—is straightforward and developer-friendly.

Test machine:

Test machine:

Apple M3 24 GB Sonoma 14.6.1

Kanzi version 2.4.0 Java implementation

JDK 23.0.1+11-39

On this machine, Kanzi uses 4 threads (half of CPUs by default).

bzip3 runs with 4 threads.

zstd and lz4 use 4 threads for compression and 1 for decompression, other compressors are single threaded.

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.

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

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

First option (ant):

ant

Second option (maven):

mvn -Dmaven.test.skip=true

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.