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Kanzi

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

  • 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, see Wiki, Q&A and DeepWiki

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

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

There is Java implementation available here: https://github.com/flanglet/kanzi

Build Status Quality Gate Status Lines of Code Documentation License Ask DeepWiki

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 2.6.0

silesia.tar

Test machine: AMD Ryzen 9950X on Ubuntu 26.04

Go version 1.27.1

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 1 157 69 79,184,957
zstd 1.5.8 -T16 -2 6 11 69,443,247
kanzi -l 2 137 84 68,627,321
brotli 1.1.0 -2 880 333 68,040,160
gzip 1.13 -9 10328 704 67,651,076
kanzi -l 3 232 111 63,093,409
zstd 1.5.8 -T16 -5 138 123 62,867,556
kanzi -l 4 439 191 60,789,646
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 5 900 462 53,863,205
zstd 1.5.8 -T16 -19 11290 130 52,830,213
kanzi -l 6 1295 1056 49,472,110
xz 5.8.1 -9 43611 931 48,802,580
bsc 3.3.11 -T16 1201 698 47,900,848
kanzi -l 7 1628 1184 47,330,431
bzip3 1.5.1.r3-g428f422 -j 16 2348 2218 42,962,926
kanzi -l 8 7597 7791 42,962,913
kanzi -l 9 16397 16991 41,520,677

enwik8

Test machine: Apple M3 24 GB Sonoma 15.7.3

Go version 1.26.1

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

Compressor Encoding (ms) Decoding (ms) Size
Original 100,000,000
Kanzi -l 1 169 51 42,941,668
Kanzi -l 2 161 67 37,688,371
Kanzi -l 3 262 124 32,562,496
Kanzi -l 4 415 183 29,466,291
Kanzi -l 5 776 419 26,521,279
Kanzi -l 6 1269 985 24,076,777
Kanzi -l 7 2090 1670 22,817,366
Kanzi -l 8 7960 7835 21,181,998
Kanzi -l 9 10104 10888 20,035,687

Build

Using formal releases is recommended (see https://github.com/flanglet/kanzi-go/releases).

go install github.com/flanglet/kanzi-go/v2/app@v2.6.0

Otherwise, to build manually from the latest tag, follow the instructions below:

git clone https://github.com/flanglet/kanzi-go.git

cd kanzi-go/v2/app

go build Kanzi.go BlockCompressor.go BlockDecompressor.go InfoPrinter.go

The bistream is backward compatible, however, the guarantee only applies to releases. Users can expect incompatibilities or breakage due to bitstream changes in between releases.

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.

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