C-Blosc2#

A fast, compressed and persistent data store library for C#

Author

The Blosc Development Team

Contact

blosc@blosc.org

URL

https://www.blosc.org

Gitter

Join the chat at https://gitter.im/Blosc/c-blosc

Actions

actions

NumFOCUS

numfocus

Code of Conduct

Contributor Covenant

What is it?#

Blosc is a high performance compressor optimized for binary data (i.e. floating point numbers, integers and booleans, although it can handle string data too). 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 main goal is not just 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, and is backward compatible with both the C-Blosc1 API and its in-memory format. However, the reverse thing is generally not true for the format; buffers generated with C-Blosc2 are not format-compatible with C-Blosc1 (i.e. forward compatibility is not supported). In case you want to ensure full API compatibility with C-Blosc1 API, define the BLOSC1_COMPAT symbol.

See a 3 minutes introductory video to Blosc2.

Blosc2 NDim: an N-Dimensional store#

One of the latest and more exciting additions in C-Blosc2 is the Blosc2 NDim layer (or b2nd for short), allowing to create and read n-dimensional datasets in an extremely efficient way thanks to a n-dim 2-level partitioning, that allows to slice and dice arbitrary large and compressed data in a more fine-grained way:

https://github.com/Blosc/c-blosc2/blob/main/images/b2nd-2level-parts.png?raw=true

To wet you appetite, here it is how the NDArray object in the Python wrapper performs on getting slices orthogonal to the different axis of a 4-dim dataset:

https://github.com/Blosc/c-blosc2/blob/main/images/Read-Partial-Slices-B2ND.png?raw=true

We have blogged about this: https://www.blosc.org/posts/blosc2-ndim-intro

We also have a ~2 min explanatory video on why slicing in a pineapple-style (aka double partition) is useful:

Slicing a dataset in pineapple-style

New features in C-Blosc2#

  • 64-bit containers: the first-class container in C-Blosc2 is the super-chunk or, for brevity, schunk, that is made by smaller chunks which are essentially C-Blosc1 32-bit containers. The super-chunk can be backed or not by another container which is called a frame (see later).

  • NDim containers (b2nd): allow to store n-dimensional data that can efficiently read datasets in slices that can be n-dimensional too. To achieve this, a n-dimensional 2-level partitioning has been implemented. This capabilities were formerly part of Caterva, and now it is included in C-Blosc2 for convenience. Caterva is now deprecated.

  • More filters: besides shuffle and bitshuffle already present in C-Blosc1, C-Blosc2 already implements:

    • bytedelta: calculates the difference between bytes in a block that has been shuffled already. We have blogged about bytedelta.

    • delta: the stored blocks inside a chunk are diff’ed with respect to first block in the chunk. The idea is that, in some situations, the diff will have more zeros than the original data, leading to better compression.

    • trunc_prec: it zeroes the least significant bits of the mantissa of float32 and float64 types. When combined with the shuffle or bitshuffle filter, this leads to more contiguous zeros, which are compressed better.

  • A filter pipeline: the different filters can be pipelined so that the output of one can the input for the other. A possible example is a delta followed by shuffle, or as described above, trunc_prec followed by bitshuffle.

  • Prefilters: allow to apply user-defined C callbacks prior the filter pipeline during compression. See test_prefilter.c for an example of use.

  • Postfilters: allow to apply user-defined C callbacks after the filter pipeline during decompression. The combination of prefilters and postfilters could be interesting for supporting e.g. encryption (via prefilters) and decryption (via postfilters). Also, a postfilter alone can be used to produce on-the-flight computation based on existing data (or other metadata, like e.g. coordinates). See test_postfilter.c for an example of use.

  • SIMD support for ARM (NEON): this allows for faster operation on ARM architectures. Only shuffle is supported right now, but the idea is to implement bitshuffle for NEON too. Thanks to Lucian Marc.

  • SIMD support for PowerPC (ALTIVEC): this allows for faster operation on PowerPC architectures. Both shuffle and bitshuffle are supported; however, this has been done via a transparent mapping from SSE2 into ALTIVEC emulation in GCC 8, so performance could be better (but still, it is already a nice improvement over native C code; see PR Blosc/c-blosc2#59 for details). Thanks to Jerome Kieffer and ESRF for sponsoring the Blosc team in helping him in this task.

  • Dictionaries: when a block is going to be compressed, C-Blosc2 can use a previously made dictionary (stored in the header of the super-chunk) for compressing all the blocks that are part of the chunks. This usually improves the compression ratio, as well as the decompression speed, at the expense of a (small) overhead in compression speed. Currently, it is only supported in the zstd codec, but would be nice to extend it to lz4 and blosclz at least.

  • Contiguous frames: allow to store super-chunks contiguously, either on-disk or in-memory. When a super-chunk is backed by a frame, instead of storing all the chunks sparsely in-memory, they are serialized inside the frame container. The frame can be stored on-disk too, meaning that persistence of super-chunks is supported.

  • Sparse frames: each chunk in a super-chunk is stored in a separate file or different memory area, as well as the metadata. This is allows for more efficient updates/deletes than in contiguous frames (i.e. avoiding ‘holes’ in monolithic files). The drawback is that it consumes more inodes when on-disk. Thanks to Marta Iborra for this contribution.

  • Partial chunk reads: there is support for reading just part of chunks, so avoiding to read the whole thing and then discard the unnecessary data.

  • Parallel chunk reads: when several blocks of a chunk are to be read, this is done in parallel by the decompressing machinery. That means that every thread is responsible to read, post-filter and decompress a block by itself, leading to an efficient overlap of I/O and CPU usage that optimizes reads to a maximum.

  • Meta-layers: optionally, the user can add meta-data for different uses and in different layers. For example, one may think on providing a meta-layer for NumPy so that most of the meta-data for it is stored in a meta-layer; then, one can place another meta-layer on top of the latter for adding more high-level info if desired (e.g. geo-spatial, meteorological…).

  • Variable length meta-layers: the user may want to add variable-length meta information that can be potentially very large (up to 2 GB). The regular meta-layer described above is very quick to read, but meant to store fixed-length and relatively small meta information. Variable length metalayers are stored in the trailer of a frame, whereas regular meta-layers are in the header.

  • Efficient support for special values: large sequences of repeated values can be represented with an efficient, simple and fast run-length representation, without the need to use regular codecs. With that, chunks or super-chunks with values that are the same (zeros, NaNs or any value in general) can be built in constant time, regardless of the size. This can be useful in situations where a lot of zeros (or NaNs) need to be stored (e.g. sparse matrices).

  • Nice markup for documentation: we are currently using a combination of Sphinx + Doxygen + Breathe for documenting the C-API. See https://www.blosc.org/c-blosc2/c-blosc2.html. Thanks to Alberto Sabater and Aleix Alcacer for contributing the support for this.

  • Plugin capabilities for filters and codecs: we have a plugin register capability inplace so that the info about the new filters and codecs can be persisted and transmitted to different machines. See Blosc/c-blosc2 for a self-contained example. Thanks to the NumFOCUS foundation for providing a grant for doing this, and Oscar Griñón and Aleix Alcacer for the implementation.

  • Pluggable tuning capabilities: this will allow users with different needs to define an interface so as to better tune different parameters like the codec, the compression level, the filters to use, the blocksize or the shuffle size. Thanks to ironArray for sponsoring us in doing this.

  • Support for I/O plugins: so that users can extend the I/O capabilities beyond the current filesystem support. Things like the use of databases or S3 interfaces should be possible by implementing these interfaces. Thanks to ironArray for sponsoring us in doing this.

  • Python wrapper: we have a preliminary wrapper in the works. You can have a look at our ongoing efforts in the python-blosc2 repo. Thanks to the Python Software Foundation for providing a grant for doing this.

  • Security: we are actively using using the OSS-Fuzz and ClusterFuzz for uncovering programming errors in C-Blosc2. Thanks to Google for sponsoring us in doing this, and to Nathan Moinvaziri for most of the work here.

More info about the improved capabilities of C-Blosc2 can be found in this talk.

C-Blosc2 API and format have been frozen, and that means that there is guarantee that your programs will continue to work with future versions of the library, and that next releases will be able to read from persistent storage generated from previous releases (as of 2.0.0).

Python wrapper#

We are officially supporting (thanks to the Python Software Foundation) a Python wrapper for Blosc2. It supports all the features of the predecessor python-blosc package plus most of the bells and whistles of C-Blosc2, like 64-bit and multidimensional containers. As a bonus, the python-blosc2 package comes with wheels and binary versions of the C-Blosc2 libraries, so anyone, even non-Python users can install C-Blosc2 binaries easily with:

pip install blosc2

Compiling the C-Blosc2 library with CMake#

Blosc can be built, tested and installed using CMake. The following procedure describes a typical CMake build.

Create the build directory inside the sources and move into it:

git clone https://github.com/Blosc/c-blosc2
cd c-blosc2
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 preferring 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 examples/ directory.

Handling support for codecs (LZ4, LZ4HC, Zstd, Zlib)#

C-Blosc2 comes with full sources for LZ4, LZ4HC, Zstd, and Zlib and in general, you should not worry about not having (or CMake not finding) the libraries in your system because by default the included sources will be automatically compiled and included in the C-Blosc2 library. This means that you can be confident in having a complete support for all these codecs in all the official Blosc deployments. Of course, if you consider this is too bloated, you can exclude support for some of them.

For example, let’s suppose that you want to disable support for Zstd:

cmake -DDEACTIVATE_ZSTD=ON ..

Or, you may want to use a codec in an external library already in the system:

cmake -DPREFER_EXTERNAL_LZ4=ON ..

Supported platforms#

C-Blosc2 is meant to support all platforms where a C99 compliant C compiler can be found. The ones that are mostly tested are Intel (Linux, Mac OSX and Windows), ARM (Linux, Mac), and PowerPC (Linux). More on ARM support in README_ARM.rst.

For Windows, you will need at least VS2015 or higher on x86 and x64 targets (i.e. ARM is not supported on Windows).

For Mac OSX, make sure that you have the command line developer tools available. You can always install them with:

xcode-select --install

For Mac OSX on arm64 architecture, you may want to compile it like this:

CC="clang -arch arm64" cmake ..

Display error messages#

By default error messages are disabled. To display them, you just need to activate the Blosc tracing machinery by setting the BLOSC_TRACE environment variable.

Contributing#

If you want to collaborate in this development you are welcome. We need help in the different areas listed at the ROADMAP; also, be sure to read our DEVELOPING-GUIDE and our Code of Conduct. Blosc is distributed using the BSD license.

Tweeter feed#

Follow @Blosc2 so as to get informed about the latest developments.

Citing Blosc#

You can cite our work on the different libraries under the Blosc umbrella as:

@ONLINE{blosc,
  author = {{Blosc Development Team}},
  title = "{A fast, compressed and persistent data store library}",
  year = {2009-2023},
  note = {https://blosc.org}
}

Acknowledgments#

See THANKS document.


– The Blosc Development Team. We make compression better.

Contents#