Author

Francesc Alted

Contact

francesc@blosc.org

URL

http://www.blosc.org

Travis CI

Appveyor

Blosc¹ is a high performance compressor optimized for binary data. It has been designed to transmit data to the processor cache faster than the traditional, non-compressed, direct memory fetch approach via a memcpy() OS call. Blosc is the first compressor (that I'm aware of) that is meant not only to reduce the size of large datasets on-disk or in-memory, but also to accelerate memory-bound computations.

C-Blosc2 is the new major version of C-Blosc, with a revamped API and support for new filters (including filter pipelining), new compressors, but most importantly new data containers that are meant to overcome the 32-bit limitation of the original C-Blosc. These new data containers will be available in various forms, including in-memory and on-disk implementations.

C-Blosc2 is currently in alpha stage and its development will probably take several months. If you want to collaborate in the this development you are welcome. We are going to need help in supervising and refining the API, as well as in the design of the new containers. Testing for other platforms than Intel (specially ARM) will be appreciated as well.

Blosc is distributed using the MIT license, see LICENSES/BLOSC.txt for details.

C-Blosc is not like other compressors: it should rather be called a meta-compressor. This is so because it can use different compressors and filters (programs that generally improve compression ratio). At any rate, it can also be called a compressor because it happens that it already comes with several compressor and filters, so it can actually work like so.

Currently C-Blosc comes with support of BloscLZ, a compressor heavily based on FastLZ (http://fastlz.org/), LZ4 and LZ4HC (https://github.com/Cyan4973/lz4), Snappy (https://github.com/google/snappy) and Zlib (http://www.zlib.net/), as well as a highly optimized (it can use SSE2 or AVX2 instructions, if available) shuffle and bitshuffle filters (for info on how and why shuffling works, see slide 17 of http://www.slideshare.net/PyData/blosc-py-data-2014). However, different compressors or filters may be added in the future.

C-Blosc is in charge of coordinating the different compressor and filters so that they can leverage the blocking technique (described above) as well as multi-threaded execution (if several cores are available) automatically. That makes that every compressor and filter will work at very high speeds, even if it was not initially designed for doing blocking or multi-threading.

Other advantages of Blosc are:

• Meant for binary data: can take advantage of the type size meta-information for improved compression ratio (using the integrated shuffle and bitshuffle filters).
• Small overhead on non-compressible data: only a maximum of (16 + 4 * nthreads) additional bytes over the source buffer length are needed to compress any kind of input.
• Maximum destination length: contrarily to many other compressors, both compression and decompression routines have support for maximum size lengths for the destination buffer.

When taken together, all these features set Blosc apart from other similar solutions.

Blosc can also be built, tested and installed using CMake. Although this procedure might seem a bit more involved than the one described above, it is the most general because it allows to integrate other compressors than BloscLZ either from libraries or from internal sources. Hence, serious library developers are encouraged to use this way.

The following procedure describes the "out of source" build.

Create the build directory and move into it:

$ mkdir build
$ cd build

Now run CMake configuration and optionally specify the installation directory (e.g. '/usr' or '/usr/local'):

$ cmake -DCMAKE_INSTALL_PREFIX=your_install_prefix_directory ..

CMake allows to configure Blosc in many different ways, like prefering internal or external sources for compressors or enabling/disabling them. Please note that configuration can also be performed using UI tools provided by CMake (ccmake or cmake-gui):

$ ccmake ..      # run a curses-based interface
$ cmake-gui ..   # run a graphical interface

Build, test and install Blosc:

$ cmake --build .
$ ctest
$ cmake --build . --target install

The static and dynamic version of the Blosc library, together with header files, will be installed into the specified CMAKE_INSTALL_PREFIX.

Once you have compiled your Blosc library, you can easily link your apps with it as shown in the example/ directory.

The CMake files in Blosc are configured to automatically detect other compressors like LZ4, LZ4HC, Snappy or Zlib by default. So as long as the libraries and the header files for these libraries are accessible, these will be used by default. See an example here.

Note on Zlib: the library should be easily found on UNIX systems, although on Windows, you can help CMake to find it by setting the environment variable 'ZLIB_ROOT' to where zlib 'include' and 'lib' directories are. Also, make sure that Zlib DDL library is in your 'Windows' directory.

However, the full sources for LZ4, LZ4HC, Snappy and Zlib have been included in Blosc too. So, in general, you should not worry about not having (or CMake not finding) the libraries in your system because in this case, their sources will be automatically compiled for you. That effectively means that you can be confident in having a complete support for all the supported compression libraries in all supported platforms.

If you want to force Blosc to use external libraries instead of the included compression sources:

$ cmake -DPREFER_EXTERNAL_LZ4=ON ..

You can also disable support for some compression libraries:

$ cmake -DDEACTIVATE_SNAPPY=ON ..

If you run into compilation troubles when using Mac OSX, please make sure that you have installed the command line developer tools. You can always install them with:

$ xcode-select --install

Blosc has an official wrapper for Python. See:

https://github.com/Blosc/python-blosc

Blosc can be used from command line by using Bloscpack. See:

https://github.com/Blosc/bloscpack

For those who want to use Blosc as a filter in the HDF5 library, there is a sample implementation in the blosc/hdf5 project in:

https://github.com/Blosc/hdf5

There is an official mailing list for Blosc at:

blosc@googlegroups.com http://groups.google.es/group/blosc

See THANKS.rst.

Enjoy data!