A Python wrapper for the extremely fast Blosc compression library
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README.rst

python-blosc: a Python wrapper for the extremely fast Blosc compression library

Author:Francesc Alted
Author:Valentin Hänel
Contact:faltet@gmail.com
Contact:valentin@haenel.co
URL:https://github.com/Blosc/python-blosc
URL:http://python-blosc.blosc.org
Travis CI:travis
Appveyor:appveyor
PyPi:version pypi

What it is

Blosc (http://blosc.org) 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 works well for compressing numerical arrays that contains data with relatively low entropy, like sparse data, time series, grids with regular-spaced values, etc.

python-blosc a Python package that wraps Blosc. python-blosc supports Python 2.6, 2.7 and 3.1, 3.2, 3.3 or higher versions.

Building

There are different ways to compile python-blosc, depending if you want to link with an already installed Blosc library or not.

Compiling with an installed Blosc library (recommended)

Python and Blosc-powered extensions have a difficult relationship when compiled using GCC, so this is why using an external C-Blosc library is recommended for maximum performance (for details, see https://github.com/Blosc/python-blosc/issues/110).

Go to https://github.com/Blosc/c-blosc/releases and download and install the C-Blosc library. Then, you can tell python-blosc where is the C-Blosc library in a couple of ways:

Using an environment variable:

$ BLOSC_DIR=/usr/local     (or "set BLOSC_DIR=\blosc" on Win)
$ export BLOSC_DIR         (not needed on Win)
$ python setup.py build_ext --inplace

Using a flag:

$ python setup.py build_ext --inplace --blosc=/usr/local

Compiling without an installed Blosc library

Warning: This way of compiling is discouraged for performance reasons. See the previous section.

python-blosc also comes with the Blosc sources with it so, assuming that you have a C++ compiler installed, do:

$ python setup.py build_ext --inplace

That's all. You can proceed with testing section now.

Note: The requirement for the C++ compiler is just for the Snappy dependency. The rest of the other components of Blosc are pure C (including the LZ4 and Zlib libraries).

Testing

After compiling, you can quickly check that the package is sane by running the doctests in blosc/test.py:

$ PYTHONPATH=.   (or "set PYTHONPATH=." on Win)
$ export PYTHONPATH=.  (not needed on Win)
$ python blosc/test.py  (add -v for verbose mode)

Or alternatively, you can use the third-party nosetests script:

$ nosetests --with-doctest (add -v for verbose mode)

Once installed, you can re-run the tests at any time with:

$ python -c "import blosc; blosc.test()"

Benchmarking

If curious, you may want to run a small benchmark that compares a plain NumPy array copy against compression through different compressors in your Blosc build:

$ PYTHONPATH=. python bench/compress_ptr.py

Just to wet you appetite, here are the results for an Intel E3-1240 v3 @ 3.40GHz, running Python 2.7 and Gentoo Base System release 2.2, but YMMV (and will vary!):

Creating NumPy arrays with 10**8 int64/float64 elements:
  *** ctypes.memmove() *** Time for memcpy():   0.295 s (2.53 GB/s)

Times for compressing/decompressing with clevel=5 and 8 threads

*** the arange linear distribution ***
  *** blosclz , noshuffle  ***  0.455 s (1.64 GB/s) / 0.087 s (8.58 GB/s)       Compr. ratio:   1.0x
  *** blosclz , shuffle    ***  0.108 s (6.93 GB/s) / 0.075 s (10.00 GB/s)      Compr. ratio:  57.1x
  *** blosclz , bitshuffle ***  0.120 s (6.19 GB/s) / 0.107 s (6.97 GB/s)       Compr. ratio:  74.0x
  *** lz4     , noshuffle  ***  0.342 s (2.18 GB/s) / 0.212 s (3.52 GB/s)       Compr. ratio:   2.0x
  *** lz4     , shuffle    ***  0.078 s (9.54 GB/s) / 0.093 s (8.02 GB/s)       Compr. ratio:  58.6x
  *** lz4     , bitshuffle ***  0.116 s (6.41 GB/s) / 0.135 s (5.53 GB/s)       Compr. ratio:  52.5x
  *** lz4hc   , noshuffle  ***  8.142 s (0.09 GB/s) / 0.212 s (3.52 GB/s)       Compr. ratio:   2.0x
  *** lz4hc   , shuffle    ***  0.140 s (5.33 GB/s) / 0.092 s (8.06 GB/s)       Compr. ratio: 137.2x
  *** lz4hc   , bitshuffle ***  1.572 s (0.47 GB/s) / 0.142 s (5.25 GB/s)       Compr. ratio: 208.9x
  *** snappy  , noshuffle  ***  0.381 s (1.95 GB/s) / 0.244 s (3.06 GB/s)       Compr. ratio:   2.0x
  *** snappy  , shuffle    ***  0.073 s (10.25 GB/s) / 0.136 s (5.48 GB/s)      Compr. ratio:  17.4x
  *** snappy  , bitshuffle ***  0.126 s (5.92 GB/s) / 0.177 s (4.22 GB/s)       Compr. ratio:  18.2x
  *** zlib    , noshuffle  ***  5.298 s (0.14 GB/s) / 0.401 s (1.86 GB/s)       Compr. ratio:   5.3x
  *** zlib    , shuffle    ***  0.974 s (0.76 GB/s) / 0.393 s (1.90 GB/s)       Compr. ratio: 237.3x
  *** zlib    , bitshuffle ***  1.026 s (0.73 GB/s) / 0.444 s (1.68 GB/s)       Compr. ratio: 305.4x

*** the linspace linear distribution ***
  *** blosclz , noshuffle  ***  0.434 s (1.72 GB/s) / 0.088 s (8.45 GB/s)       Compr. ratio:   1.0x
  *** blosclz , shuffle    ***  0.298 s (2.50 GB/s) / 0.090 s (8.32 GB/s)       Compr. ratio:   2.0x
  *** blosclz , bitshuffle ***  0.476 s (1.56 GB/s) / 0.166 s (4.50 GB/s)       Compr. ratio:   2.8x
  *** lz4     , noshuffle  ***  0.219 s (3.41 GB/s) / 0.088 s (8.45 GB/s)       Compr. ratio:   1.0x
  *** lz4     , shuffle    ***  0.190 s (3.92 GB/s) / 0.112 s (6.63 GB/s)       Compr. ratio:   3.2x
  *** lz4     , bitshuffle ***  0.248 s (3.00 GB/s) / 0.149 s (5.00 GB/s)       Compr. ratio:   4.9x
  *** lz4hc   , noshuffle  ***  2.797 s (0.27 GB/s) / 0.211 s (3.53 GB/s)       Compr. ratio:   1.2x
  *** lz4hc   , shuffle    ***  0.528 s (1.41 GB/s) / 0.085 s (8.78 GB/s)       Compr. ratio:  24.1x
  *** lz4hc   , bitshuffle ***  2.918 s (0.26 GB/s) / 0.131 s (5.71 GB/s)       Compr. ratio:  35.0x
  *** snappy  , noshuffle  ***  0.088 s (8.49 GB/s) / 0.087 s (8.61 GB/s)       Compr. ratio:   1.0x
  *** snappy  , shuffle    ***  0.235 s (3.16 GB/s) / 0.176 s (4.24 GB/s)       Compr. ratio:   4.2x
  *** snappy  , bitshuffle ***  0.317 s (2.35 GB/s) / 0.198 s (3.76 GB/s)       Compr. ratio:   6.1x
  *** zlib    , noshuffle  ***  6.569 s (0.11 GB/s) / 0.718 s (1.04 GB/s)       Compr. ratio:   1.6x
  *** zlib    , shuffle    ***  1.313 s (0.57 GB/s) / 0.339 s (2.20 GB/s)       Compr. ratio:  27.0x
  *** zlib    , bitshuffle ***  1.348 s (0.55 GB/s) / 0.380 s (1.96 GB/s)       Compr. ratio:  35.2x

*** the random distribution ***
  *** blosclz , noshuffle  ***  0.517 s (1.44 GB/s) / 0.087 s (8.60 GB/s)       Compr. ratio:   1.0x
  *** blosclz , shuffle    ***  0.212 s (3.52 GB/s) / 0.070 s (10.62 GB/s)      Compr. ratio:   3.9x
  *** blosclz , bitshuffle ***  0.181 s (4.13 GB/s) / 0.104 s (7.16 GB/s)       Compr. ratio:   6.1x
  *** lz4     , noshuffle  ***  0.373 s (2.00 GB/s) / 0.149 s (5.00 GB/s)       Compr. ratio:   2.1x
  *** lz4     , shuffle    ***  0.135 s (5.52 GB/s) / 0.101 s (7.36 GB/s)       Compr. ratio:   4.5x
  *** lz4     , bitshuffle ***  0.129 s (5.77 GB/s) / 0.138 s (5.39 GB/s)       Compr. ratio:   6.1x
  *** lz4hc   , noshuffle  ***  4.684 s (0.16 GB/s) / 0.101 s (7.36 GB/s)       Compr. ratio:   3.2x
  *** lz4hc   , shuffle    ***  3.223 s (0.23 GB/s) / 0.101 s (7.37 GB/s)       Compr. ratio:   5.4x
  *** lz4hc   , bitshuffle ***  0.429 s (1.74 GB/s) / 0.139 s (5.36 GB/s)       Compr. ratio:   6.2x
  *** snappy  , noshuffle  ***  0.461 s (1.62 GB/s) / 0.257 s (2.90 GB/s)       Compr. ratio:   2.2x
  *** snappy  , shuffle    ***  0.166 s (4.49 GB/s) / 0.160 s (4.66 GB/s)       Compr. ratio:   4.3x
  *** snappy  , bitshuffle ***  0.136 s (5.48 GB/s) / 0.167 s (4.45 GB/s)       Compr. ratio:   5.0x
  *** zlib    , noshuffle  ***  5.383 s (0.14 GB/s) / 0.499 s (1.49 GB/s)       Compr. ratio:   3.9x
  *** zlib    , shuffle    ***  2.903 s (0.26 GB/s) / 0.408 s (1.83 GB/s)       Compr. ratio:   6.1x
  *** zlib    , bitshuffle ***  1.403 s (0.53 GB/s) / 0.433 s (1.72 GB/s)

In case you find your own results interesting, please report them back to the authors!

Installing

Install it as a typical Python package:

$ python setup.py install

Documentation

The Sphinx based documentation is here:

http://python-blosc.blosc.org

Also, some examples are available on python-blosc wiki page:

http://github.com/blosc/python-blosc/wiki

Lastly, here is the recording and the slides from the talk "Compress me stupid" at the EuroPython 2014.

Mailing list

Discussion about this module is welcome in the Blosc list:

blosc@googlegroups.com

http://groups.google.es/group/blosc


Enjoy data!