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GitHub - flanglet/kanzi-cpp: Fast lossless data compression in C++
Fast lossless data compression in C++. Contribute to flanglet/kanzi-cpp development by creating an account on GitHub.
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GitHub - flanglet/kanzi-cpp: Fast lossless data compression in C++

GitHub - flanglet/kanzi-cpp: Fast lossless data compression in C++

Kanzi

Kanzi is a modern, modular, portable and efficient lossless data compressor implemented in C++.

  • 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.
  • portable: many OSes, compilers and C++ versions are supported (see below).
  • 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 concurrent by design and uses threads to compress several blocks in parallel. 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. However, Kanzi generates a bitstream that is seekable (one or several consecutive blocks can be decompressed without the need for the whole bitstream to be decompressed).

For more details, see Wiki and Q&A

See how to reuse the C and C++ APIs: here

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

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

Build Status Quality Gate Status Lines of Code License

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 can 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

  • Implementing a new transform or entropy codec (to either test an idea or improve compression ratio on specific kinds of data) is simple.

Benchmarks

Test machine:

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

Ubuntu clang++ version 15.0.7 + tcmalloc

Ubuntu 24.04 LTS

Kanzi version 2.3.0 C++ implementation

On this machine, Kanzi uses up to 16 threads (half of CPUs by default).

bzip3 and zpaq use 16 threads. zstd uses 16 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.

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 0.263 0.231 80,277,212
Lz4 1.9.5 -4 0.321 0.330 79,912,419
Zstd 1.5.6 -2 -T16 0.151 0.271 69,556,157
Kanzi -l 2 0.267 0.253 68,195,845
Brotli 1.1.0 -2 1.749 0.761 68,041,629
Gzip 1.12 -9 20.09 1.403 67,652,449
Kanzi -l 3 0.446 0.287 65,613,695
Zstd 1.5.6 -5 -T16 0.356 0.289 63,131,656
Kanzi -l 4 0.543 0.373 61,249,959
Zstd 1.5.5 -9 -T16 0.690 0.278 59,429,335
Brotli 1.1.0 -6 8.388 0.677 58,571,909
Zstd 1.5.6 -13 -T16 3.244 0.272 58,041,112
Brotli 1.1.0 -9 70.07 0.761 56,376,419
Bzip2 1.0.8 -9 16.94 6.734 54,572,500
Kanzi -l 5 1.627 0.883 54,039,773
Zstd 1.5.6 -19 -T16 20.87 0.303 52,889,925
Kanzi -l 6 2.312 1.227 49,567,817
Lzma 5.4.5 -9 95.97 3.172 48,745,354
Kanzi -l 7 2.686 2.553 47,520,629
bzip3 1.3.2.r4-gb2d61e8 -j 16 2.682 3.221 47,237,088
Kanzi -l 8 7.260 8.021 43,167,429
Kanzi -l 9 18.99 21.07 41,497,835
zpaq 7.15 -m5 -t16 213.8 213.8 40,050,429

enwik8

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

Tested on Ubuntu 22.04.4 LTS, i7-7700K CPU @ 4.20GHz, 32 GB RAM, clang-15, 4 threads (default)

Compressor Encoding (ms) Decoding (ms) Size
Original 100,000,000
Kanzi -l 1 251 87 43,746,017
Kanzi -l 2 268 114 37,816,913
Kanzi -l 3 512 175 33,865,383
Kanzi -l 4 546 249 29,597,577
Kanzi -l 5 1030 500 26,528,023
Kanzi -l 6 1537 799 24,076,674
Kanzi -l 7 2695 2045 22,817,373
Kanzi -l 8 7217 7314 21,181,983
Kanzi -l 9 11336 11574 20,035,138

Round-trip scores for LZ

Below is a table showing silesia.tar compressed using different LZ compressors (no entropy) in single-threaded mode.

The efficiency score is computed as such: score(lambda) = compTime + 2 x decompTime + 10^-lambda x compSize

A lower score is better. Best scores are in bold.

Tested on Ubuntu 22.04.4 LTS, i7-7700K CPU @ 4.20GHz, 32 GB RAM, clang-15

Compressor Encoding (sec) Decoding (sec) Size Score(5) Score(6) Score(7)
FastLZ -2 1.85 0.84 101114153 1014.66 104.63 13.63
Lizard 1.1.0 -11 0.76 0.24 93967850 940.91 95.20 10.63
Lz4 1.9.5 -2 -T1 0.81 0.21 89208908 893.32 90.44 10.15
Lzturbo 1.2 -11 -p0 1.09 0.34 88657053 888.35 90.43 10.64
lzav (1) 0.52 0.19 88221200 883.12 89.13 9.73
s2 -cpu 1 0.81 0.40 86646819 868.08 88.25 10.27
LZ4x 1.60 -2 1.13 0.22 87883674 880.40 89.44 10.35
lzav (2) 0.67 0.40 86505609 866.53 87.98 10.12
Lizard 1.1.0 -12 1.48 0.23 86340434 865.35 88.29 10.58
LZ4x 1.60 -3 1.36 0.24 85483806 856.67 87.32 10.38
Kanzi 2.3 -t lz -j 1 (1) 0.83 0.24 83355862 834.87 84.67 9.65
Lzturbo 1.2 -12 -p0 2.40 0.22 83179291 834.63 86.02 11.16
Kanzi 2.3 -t lz -j 1 (2) 0.99 0.35 82652955 828.22 84.34 9.96
Kanzi 2.3 -t lzx -j 1 (1) 1.09 0.22 81485228 816.39 83.02 9.68
Lz4 1.9.5 -3 -T1 2.33 0.21 81441623 817.17 84.19 10.90
Kanzi 2.3 -t lzx -j 1 (2) 1.52 0.35 79014650 792.37 81.23 10.12

References:

FastLZ Lizard LZ4 S2 LZAV LZ4x LZTurbo

lz4@97291fc50

kanzi@af12d07f2

lzav@10f7e2ac

(1) processing 4MB blocks

(2) processing whole file at once

More benchmarks

Comprehensive lzbench benchmarks

More round trip scores

Build Kanzi

The C++ code can be built on Windows with Visual Studio, Linux, macOS and Android with g++ and/or clang++. There are no dependencies. Porting to other operating systems should be straightforward.

Visual Studio 2008

Unzip the file "Kanzi_VS2008.zip" in place. The solution generates a Windows 32 binary. Multithreading is not supported with this version.

Visual Studio 2022

Unzip the file "Kanzi_VS2022.zip" in place. The solution generates a Windows 64 binary and library. Multithreading is supported with this version.

mingw-w64

Go to the source directory and run 'make clean && mingw32-make.exe kanzi'. The Makefile contains all the necessary targets. Tested successfully on Win64 with mingw-w64 g++ 8.1.0. Multithreading is supportedwith g++ version 5.0.0 or newer. Builds successfully with C++11, C++14, C++17.

Linux

Go to the source directory and run 'make clean && make kanzi'. The Makefile contains all the necessary targets. Build successfully on Ubuntu with many versions of g++ and clang++. Multithreading is supported with g++ version 5.0.0 or newer. Builds successfully with C++98, C++11, C++14, C++17, C++20.

MacOS

Go to the source directory and run 'make clean && make kanzi'. The Makefile contains all the necessary targets. Build successfully on MacOs with several versions of clang++. Multithreading is supported.

BSD

The makefile uses the gnu-make syntax. First, make sure gmake is present (or install it: 'pkg_add gmake'). Go to the source directory and run 'gmake clean && gmake kanzi'. The Makefile contains all the necessary targets. Multithreading is supported.

Makefile targets

clean:     removes objects, libraries and binaries
kanzi:     builds the kanzi executable
lib:       builds static and dynamic libraries
test:      builds test binaries
all:       kanzi + lib + test
install:   installs libraries, headers and executable
uninstall: removes installed libraries, headers and executable

For those who prefer cmake, run the following commands:

mkdir build
cd build
cmake ..
make

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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