==========
Bitshuffle
==========
Filter for improving compression of typed binary data.
Bitshuffle is an algorithm that rearranges typed, binary data for improving
compression, as well as a python/C package that implements this algorithm
within the Numpy framework.
The library can be used along side HDF5 to compress and decompress datasets and
is integrated through the `dynamically loaded filters`_ framework. Bitshuffle
is HDF5 filter number ``32008``.
Algorithmically, Bitshuffle is closely related to HDF5's `Shuffle filter`_
except it operates at the bit level instead of the byte level. Arranging a
typed data array in to a matrix with the elements as the rows and the bits
within the elements as the columns, Bitshuffle "transposes" the matrix,
such that all the least-significant-bits are in a row, etc. This transpose
is performed within blocks of data roughly 8 kB long [1]_.
This does not in itself compress data, only rearranges it for more efficient
compression. To perform the actual compression you will need a compression
library. Bitshuffle has been designed to be well matched to Marc Lehmann's
LZF_ as well as LZ4_ and ZSTD_. Note that because Bitshuffle modifies the data at the bit
level, sophisticated entropy reducing compression libraries such as GZIP and
BZIP are unlikely to achieve significantly better compression than simpler and
faster duplicate-string-elimination algorithms such as LZF, LZ4 and ZSTD. Bitshuffle
thus includes routines (and HDF5 filter options) to apply LZ4 and ZSTD compression to
each block after shuffling [2]_.
The Bitshuffle algorithm relies on neighbouring elements of a dataset being
highly correlated to improve data compression. Any correlations that span at
least 24 elements of the dataset may be exploited to improve compression.
Bitshuffle was designed with performance in mind. On most machines the
time required for Bitshuffle+LZ4 is insignificant compared to the time required
to read or write the compressed data to disk. Because it is able to exploit the
SSE and AVX instruction sets present on modern Intel and AMD processors, on
these machines compression is only marginally slower than an out-of-cache
memory copy. On modern x86 processors you can expect Bitshuffle to have a
throughput of roughly 1 byte per clock cycle, and on the Haswell generation of
Intel processors (2013) and later, you can expect up to 2 bytes per clock
cycle. In addition, Bitshuffle is parallelized using OpenMP.
As a bonus, Bitshuffle ships with a dynamically loaded version of
`h5py`'s LZF compression filter, such that the filter can be transparently
used outside of python and in command line utilities such as ``h5dump``.
.. [1] Chosen to fit comfortably within L1 cache as well as be well matched
window of the LZF compression library.
.. [2] Over applying bitshuffle to the full dataset then applying LZ4/ZSTD
compression, this has the tremendous advantage that the block is
already in the L1 cache.
.. _`dynamically loaded filters`: http://www.hdfgroup.org/HDF5/doc/Advanced/DynamicallyLoadedFilters/HDF5DynamicallyLoadedFilters.pdf
.. _`Shuffle filter`: http://www.hdfgroup.org/HDF5/doc_resource/H5Shuffle_Perf.pdf
.. _LZF: http://oldhome.schmorp.de/marc/liblzf.html
.. _LZ4: https://code.google.com/p/lz4/
.. _ZSTD: https://github.com/facebook/zstd
Applications
------------
Bitshuffle might be right for your application if:
- You need to compress typed binary data.
- Your data is arranged such that adjacent elements over the fastest varying
index of your dataset are similar (highly correlated).
- A special case of the previous point is if you are only exercising a subset
of the bits in your data-type, as is often true of integer data.
- You need both high compression ratios and high performance.
Comparing Bitshuffle to other compression algorithms and HDF5 filters:
- Bitshuffle is less general than many other compression algorithms.
To achieve good compression ratios, consecutive elements of your data must
be highly correlated.
- For the right datasets, Bitshuffle is one of the few compression
algorithms that promises both high throughput and high compression ratios.
- Bitshuffle should have roughly the same throughput as Shuffle, but
may obtain higher compression ratios.
- The MAFISC_ filter actually includes something similar to Bitshuffle as one of
its prefilters, However, MAFICS's emphasis is on obtaining high compression
ratios at all costs, sacrificing throughput.
.. _MAFISC: http://wr.informatik.uni-hamburg.de/research/projects/icomex/mafisc
Installation for Python
-----------------------
In most cases bitshuffle can be installed by `pip`::
pip install bitshuffle
On Linux and macOS x86_64 platforms binary wheels are available, on other platforms a
source build will be performed. The binary wheels are built with AVX2 support and will
only run processors that support these instructions (most processors from 2015 onwards,
i.e. Intel Haswell, AMD Excavator and later). On an unsupported processor these builds
of bitshuffle will crash with `SIGILL`. To run on unsupported x86_64 processors, or
target newer instructions such as AVX512, you should perform a build from source.
This can be forced by giving pip the `--no-binary=bitshuffle` option.
Source installation requires python 2.7+ or 3.3+, HDF5 1.8.4 or later, HDF5 for python
(h5py), Numpy and Cython. Bitshuffle is linked against HDF5. To use the dynamically
loaded HDF5 filter requires HDF5 1.8.11 or later.
For total control, bitshuffle can be built using `python setup.py`. If ZSTD support is
to be enabled the ZSTD repo needs to pulled into bitshuffle before installation with::
git submodule update --init
To build and install bitshuffle::
python setup.py install [--h5plugin [--h5plugin-dir=spam] --zstd]
To get finer control of installation options, including whether to compile with OpenMP
multi-threading and the target microarchitecture copy the ``setup.cfg.example`` to
``setup.cfg`` and edit the values therein.
If using the dynamically loaded HDF5 filter (which gives you access to the
Bitshuffle and LZF filters outside of python), set the environment variable
``HDF5_PLUGIN_PATH`` to the value of ``--h5plugin-dir`` or use HDF5's default
search location of ``/usr/local/hdf5/lib/plugin``.
ZSTD support is enabled with ``--zstd``.
If you get an error about missing source files when building the extensions,
try upgrading setuptools. There is a weird bug where setuptools prior to 0.7
doesn't work properly with Cython in some cases.
.. _source: http://docs.h5py.org/en/latest/build.html#source-installation
Usage from Python
-----------------
The `bitshuffle` module contains routines for shuffling and unshuffling
Numpy arrays.
If installed with the dynamically loaded filter plugins, Bitshuffle can be used
in conjunction with HDF5 both inside and outside of python, in the same way as
any other filter; simply by specifying the filter number ``32008``. Otherwise
the filter will be available only within python and only after importing
`bitshuffle.h5`. Reading Bitshuffle encoded datasets will be transparent.
The filter can be added to new datasets either through the `h5py` low level
interface or through the convenience functions provided in
`bitshuffle.h5`. See the docstrings and unit tests for examples. For `h5py`
version 2.5.0 and later Bitshuffle can be added to new datasets through the
high level interface, as in the example below.
The compression algorithm can be configured using the `filter_opts` in
`bitshuffle.h5.create_dataset()`. LZ4 is chosen with:
`(BLOCK_SIZE, h5.H5_COMPRESS_LZ4)` and ZSTD with:
`(BLOCK_SIZE, h5.H5_COMPRESS_ZSTD, COMP_LVL)`. See `test_h5filter.py` for an example.
Example h5py
------------
::
import h5py
import numpy
import bitshuffle.h5
print(h5py.__version__) # >= '2.5.0'
f = h5py.File(filename, "w")
# block_size = 0 let Bitshuffle choose its value
block_size = 0
dataset = f.create_dataset(
"data",
(100, 100, 100),
compression=bitshuffle.h5.H5FILTER,
compression_opts=(block_size, bitshuffle.h5.H5_COMPRESS_LZ4),
dtype='float32',
)
# create some random data
array = numpy.random.rand(100, 100, 100)
array = array.astype('float32')
dataset[:] = array
f.close()
Usage from C
------------
If you wish to use Bitshuffle in your C program and would prefer not to use the
HDF5 dynamically loaded filter, the C library in the ``src/`` directory is
self-contained and complete.
Usage from Java
---------------
You can use Bitshuffle even in Java and the routines for shuffling and unshuffling
are ported into `snappy-java`_. To use the routines, you need to add the following
dependency to your pom.xml::
<dependency>
<groupId>org.xerial.snappy</groupId>
<artifactId>snappy-java</artifactId>
<version>1.1.3-M1</version>
</dependency>
First, import org.xerial.snapy.BitShuffle in your Java code::
import org.xerial.snappy.BitShuffle;
Then, you use them like this::
int[] data = new int[] {1, 3, 34, 43, 34};
byte[] shuffledData = BitShuffle.bitShuffle(data);
int[] result = BitShuffle.bitUnShuffleIntArray(shuffledData);
.. _`snappy-java`: https://github.com/xerial/snappy-java
Rust HDF5 plugin
----------------
If you wish to open HDF5 files compressed with bitshuffle in your Rust program, there is a `Rust binding`_ for it.
In your Cargo.toml::
[dependencies]
...
hdf5-bitshuffle = "0.9"
...
To register the plugin in your code::
use hdf5_bitshuffle::register_bitshuffle_plugin;
fn main() {
register_bitshuffle_plugin();
}
.. _`Rust binding`: https://docs.rs/hdf5-bitshuffle/latest/hdf5_bitshuffle/
Anaconda
--------
The conda package can be build via::
conda build conda-recipe
For Best Results
----------------
Here are a few tips to help you get the most out of Bitshuffle:
- For multi-dimensional datasets, order your data such that the fastest varying
dimension is the one over which your data is most correlated (have
values that change the least), or fake this using chunks.
- To achieve the highest throughput, use a data type that is 64 *bytes* or
smaller. If you have a very large compound data type, consider adding a
dimension to your datasets instead.
- To make full use of the SSE2 instruction set, use a data type whose size
is a multiple of 2 bytes. For the AVX2 instruction set, use a data type whose
size is a multiple of 4 bytes.
Citing Bitshuffle
-----------------
Bitshuffle was initially described in
http://dx.doi.org/10.1016/j.ascom.2015.07.002, pre-print available at
http://arxiv.org/abs/1503.00638.
Raw data
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"description": "==========\nBitshuffle\n==========\n\nFilter for improving compression of typed binary data.\n\nBitshuffle is an algorithm that rearranges typed, binary data for improving\ncompression, as well as a python/C package that implements this algorithm\nwithin the Numpy framework.\n\nThe library can be used along side HDF5 to compress and decompress datasets and\nis integrated through the `dynamically loaded filters`_ framework. Bitshuffle\nis HDF5 filter number ``32008``.\n\nAlgorithmically, Bitshuffle is closely related to HDF5's `Shuffle filter`_\nexcept it operates at the bit level instead of the byte level. Arranging a\ntyped data array in to a matrix with the elements as the rows and the bits\nwithin the elements as the columns, Bitshuffle \"transposes\" the matrix,\nsuch that all the least-significant-bits are in a row, etc. This transpose\nis performed within blocks of data roughly 8 kB long [1]_.\n\nThis does not in itself compress data, only rearranges it for more efficient\ncompression. To perform the actual compression you will need a compression\nlibrary. Bitshuffle has been designed to be well matched to Marc Lehmann's\nLZF_ as well as LZ4_ and ZSTD_. Note that because Bitshuffle modifies the data at the bit\nlevel, sophisticated entropy reducing compression libraries such as GZIP and\nBZIP are unlikely to achieve significantly better compression than simpler and\nfaster duplicate-string-elimination algorithms such as LZF, LZ4 and ZSTD. Bitshuffle\nthus includes routines (and HDF5 filter options) to apply LZ4 and ZSTD compression to\neach block after shuffling [2]_.\n\nThe Bitshuffle algorithm relies on neighbouring elements of a dataset being\nhighly correlated to improve data compression. Any correlations that span at\nleast 24 elements of the dataset may be exploited to improve compression.\n\nBitshuffle was designed with performance in mind. On most machines the\ntime required for Bitshuffle+LZ4 is insignificant compared to the time required\nto read or write the compressed data to disk. Because it is able to exploit the\nSSE and AVX instruction sets present on modern Intel and AMD processors, on\nthese machines compression is only marginally slower than an out-of-cache\nmemory copy. On modern x86 processors you can expect Bitshuffle to have a\nthroughput of roughly 1 byte per clock cycle, and on the Haswell generation of\nIntel processors (2013) and later, you can expect up to 2 bytes per clock\ncycle. In addition, Bitshuffle is parallelized using OpenMP.\n\nAs a bonus, Bitshuffle ships with a dynamically loaded version of\n`h5py`'s LZF compression filter, such that the filter can be transparently\nused outside of python and in command line utilities such as ``h5dump``.\n\n.. [1] Chosen to fit comfortably within L1 cache as well as be well matched\n window of the LZF compression library.\n\n.. [2] Over applying bitshuffle to the full dataset then applying LZ4/ZSTD\n compression, this has the tremendous advantage that the block is\n already in the L1 cache.\n\n.. _`dynamically loaded filters`: http://www.hdfgroup.org/HDF5/doc/Advanced/DynamicallyLoadedFilters/HDF5DynamicallyLoadedFilters.pdf\n\n.. _`Shuffle filter`: http://www.hdfgroup.org/HDF5/doc_resource/H5Shuffle_Perf.pdf\n\n.. _LZF: http://oldhome.schmorp.de/marc/liblzf.html\n\n.. _LZ4: https://code.google.com/p/lz4/\n\n.. _ZSTD: https://github.com/facebook/zstd\n\n\nApplications\n------------\n\nBitshuffle might be right for your application if:\n\n- You need to compress typed binary data.\n- Your data is arranged such that adjacent elements over the fastest varying\n index of your dataset are similar (highly correlated).\n- A special case of the previous point is if you are only exercising a subset\n of the bits in your data-type, as is often true of integer data.\n- You need both high compression ratios and high performance.\n\n\nComparing Bitshuffle to other compression algorithms and HDF5 filters:\n\n- Bitshuffle is less general than many other compression algorithms.\n To achieve good compression ratios, consecutive elements of your data must\n be highly correlated.\n- For the right datasets, Bitshuffle is one of the few compression\n algorithms that promises both high throughput and high compression ratios.\n- Bitshuffle should have roughly the same throughput as Shuffle, but\n may obtain higher compression ratios.\n- The MAFISC_ filter actually includes something similar to Bitshuffle as one of\n its prefilters, However, MAFICS's emphasis is on obtaining high compression\n ratios at all costs, sacrificing throughput.\n\n.. _MAFISC: http://wr.informatik.uni-hamburg.de/research/projects/icomex/mafisc\n\n\nInstallation for Python\n-----------------------\n\n\nIn most cases bitshuffle can be installed by `pip`::\n\n pip install bitshuffle\n\nOn Linux and macOS x86_64 platforms binary wheels are available, on other platforms a\nsource build will be performed. The binary wheels are built with AVX2 support and will\nonly run processors that support these instructions (most processors from 2015 onwards,\ni.e. Intel Haswell, AMD Excavator and later). On an unsupported processor these builds\nof bitshuffle will crash with `SIGILL`. To run on unsupported x86_64 processors, or\ntarget newer instructions such as AVX512, you should perform a build from source.\nThis can be forced by giving pip the `--no-binary=bitshuffle` option.\n\nSource installation requires python 2.7+ or 3.3+, HDF5 1.8.4 or later, HDF5 for python\n(h5py), Numpy and Cython. Bitshuffle is linked against HDF5. To use the dynamically\nloaded HDF5 filter requires HDF5 1.8.11 or later.\n\nFor total control, bitshuffle can be built using `python setup.py`. If ZSTD support is\nto be enabled the ZSTD repo needs to pulled into bitshuffle before installation with::\n\n git submodule update --init\n\nTo build and install bitshuffle::\n\n python setup.py install [--h5plugin [--h5plugin-dir=spam] --zstd]\n\nTo get finer control of installation options, including whether to compile with OpenMP\nmulti-threading and the target microarchitecture copy the ``setup.cfg.example`` to\n``setup.cfg`` and edit the values therein.\n\nIf using the dynamically loaded HDF5 filter (which gives you access to the\nBitshuffle and LZF filters outside of python), set the environment variable\n``HDF5_PLUGIN_PATH`` to the value of ``--h5plugin-dir`` or use HDF5's default\nsearch location of ``/usr/local/hdf5/lib/plugin``.\n\nZSTD support is enabled with ``--zstd``.\n\nIf you get an error about missing source files when building the extensions,\ntry upgrading setuptools. There is a weird bug where setuptools prior to 0.7\ndoesn't work properly with Cython in some cases.\n\n.. _source: http://docs.h5py.org/en/latest/build.html#source-installation\n\n\nUsage from Python\n-----------------\n\nThe `bitshuffle` module contains routines for shuffling and unshuffling\nNumpy arrays.\n\nIf installed with the dynamically loaded filter plugins, Bitshuffle can be used\nin conjunction with HDF5 both inside and outside of python, in the same way as\nany other filter; simply by specifying the filter number ``32008``. Otherwise\nthe filter will be available only within python and only after importing\n`bitshuffle.h5`. Reading Bitshuffle encoded datasets will be transparent.\nThe filter can be added to new datasets either through the `h5py` low level\ninterface or through the convenience functions provided in\n`bitshuffle.h5`. See the docstrings and unit tests for examples. For `h5py`\nversion 2.5.0 and later Bitshuffle can be added to new datasets through the\nhigh level interface, as in the example below.\n\nThe compression algorithm can be configured using the `filter_opts` in\n`bitshuffle.h5.create_dataset()`. LZ4 is chosen with:\n`(BLOCK_SIZE, h5.H5_COMPRESS_LZ4)` and ZSTD with:\n`(BLOCK_SIZE, h5.H5_COMPRESS_ZSTD, COMP_LVL)`. See `test_h5filter.py` for an example.\n\nExample h5py\n------------\n::\n\n import h5py\n import numpy\n import bitshuffle.h5\n\n print(h5py.__version__) # >= '2.5.0'\n\n f = h5py.File(filename, \"w\")\n\n # block_size = 0 let Bitshuffle choose its value\n block_size = 0\n\n dataset = f.create_dataset(\n \"data\",\n (100, 100, 100),\n compression=bitshuffle.h5.H5FILTER,\n compression_opts=(block_size, bitshuffle.h5.H5_COMPRESS_LZ4),\n dtype='float32',\n )\n\n # create some random data\n array = numpy.random.rand(100, 100, 100)\n array = array.astype('float32')\n\n dataset[:] = array\n\n f.close()\n\n\nUsage from C\n------------\n\nIf you wish to use Bitshuffle in your C program and would prefer not to use the\nHDF5 dynamically loaded filter, the C library in the ``src/`` directory is\nself-contained and complete.\n\n\nUsage from Java\n---------------\n\nYou can use Bitshuffle even in Java and the routines for shuffling and unshuffling\nare ported into `snappy-java`_. To use the routines, you need to add the following\ndependency to your pom.xml::\n\n <dependency>\n <groupId>org.xerial.snappy</groupId>\n <artifactId>snappy-java</artifactId>\n <version>1.1.3-M1</version>\n </dependency>\n\nFirst, import org.xerial.snapy.BitShuffle in your Java code::\n\n import org.xerial.snappy.BitShuffle;\n\nThen, you use them like this::\n\n int[] data = new int[] {1, 3, 34, 43, 34};\n byte[] shuffledData = BitShuffle.bitShuffle(data);\n int[] result = BitShuffle.bitUnShuffleIntArray(shuffledData);\n\n.. _`snappy-java`: https://github.com/xerial/snappy-java\n\n\nRust HDF5 plugin\n----------------\n\nIf you wish to open HDF5 files compressed with bitshuffle in your Rust program, there is a `Rust binding`_ for it.\nIn your Cargo.toml::\n\n [dependencies]\n ...\n hdf5-bitshuffle = \"0.9\"\n ...\n\nTo register the plugin in your code::\n\n use hdf5_bitshuffle::register_bitshuffle_plugin;\n\n fn main() {\n register_bitshuffle_plugin();\n }\n\n.. _`Rust binding`: https://docs.rs/hdf5-bitshuffle/latest/hdf5_bitshuffle/\n\nAnaconda\n--------\n\nThe conda package can be build via::\n\n conda build conda-recipe\n\n\nFor Best Results\n----------------\n\nHere are a few tips to help you get the most out of Bitshuffle:\n\n- For multi-dimensional datasets, order your data such that the fastest varying\n dimension is the one over which your data is most correlated (have\n values that change the least), or fake this using chunks.\n- To achieve the highest throughput, use a data type that is 64 *bytes* or\n smaller. If you have a very large compound data type, consider adding a\n dimension to your datasets instead.\n- To make full use of the SSE2 instruction set, use a data type whose size\n is a multiple of 2 bytes. For the AVX2 instruction set, use a data type whose\n size is a multiple of 4 bytes.\n\n\nCiting Bitshuffle\n-----------------\n\nBitshuffle was initially described in\nhttp://dx.doi.org/10.1016/j.ascom.2015.07.002, pre-print available at\nhttp://arxiv.org/abs/1503.00638.\n",
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