blosc 1.9.3.dev0

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

Author:

Francesc Alted

Author:

Valentin Haenel

Contact:

faltet@gmail.com

Contact:

valentin@haenel.co

Github:

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

URL:

http://python-blosc.blosc.org

Travis CI:

Appveyor:

PyPi:

Anaconda:

Gitter:

Code of Conduct:

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 3.6 or higher versions.

Installing

You can install binary packages with conda:

$ conda install -c conda-forge python-blosc

Or, install it as a typical Python source package (requires c-compiler and Python headers) from PyPi using pip:

$ pip install blosc

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.

Building

If you need more control, there are different ways to compile python-blosc, depending if you want to link with an already installed Blosc library or not.

$ python -m pip install -r requirements-dev.txt
$ python setup.py build_ext --inplace

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

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

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 whet your appetite, here are the results for an Intel Xeon E5-2695 v3 @ 2.30GHz, running Python 3.5, CentOS 7, but YMMV (and will vary!):

-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
python-blosc version: 1.5.1.dev0
Blosc version: 1.11.2 ($Date:: 2017-01-27 #$)
Compressors available: ['blosclz', 'lz4', 'lz4hc', 'snappy', 'zlib', 'zstd']
Compressor library versions:
  BloscLZ: 1.0.5
  LZ4: 1.7.5
  Snappy: 1.1.1
  Zlib: 1.2.7
  Zstd: 1.1.2
Python version: 3.5.2 |Continuum Analytics, Inc.| (default, Jul  2 2016, 17:53:06)
[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]
Platform: Linux-3.10.0-327.18.2.el7.x86_64-x86_64 (#1 SMP Thu May 12 11:03:55 UTC 2016)
Linux dist: CentOS Linux 7.2.1511
Processor: x86_64
Byte-ordering: little
Detected cores: 56
Number of threads to use by default: 4
  -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
Creating NumPy arrays with 10**8 int64/float64 elements:
  *** ctypes.memmove() *** Time for memcpy(): 0.276 s (2.70 GB/s)

Times for compressing/decompressing with clevel=5 and 24 threads

*** the arange linear distribution ***
  *** blosclz , noshuffle  ***  0.382 s (1.95 GB/s) / 0.300 s (2.48 GB/s)     Compr. ratio:   1.0x
  *** blosclz , shuffle    ***  0.042 s (17.77 GB/s) / 0.027 s (27.18 GB/s)   Compr. ratio:  57.1x
  *** blosclz , bitshuffle ***  0.094 s (7.94 GB/s) / 0.041 s (18.28 GB/s)    Compr. ratio:  74.0x
  *** lz4     , noshuffle  ***  0.156 s (4.79 GB/s) / 0.052 s (14.30 GB/s)    Compr. ratio:   2.0x
  *** lz4     , shuffle    ***  0.033 s (22.58 GB/s) / 0.034 s (22.03 GB/s)   Compr. ratio:  68.6x
  *** lz4     , bitshuffle ***  0.059 s (12.63 GB/s) / 0.053 s (14.18 GB/s)   Compr. ratio:  33.1x
  *** lz4hc   , noshuffle  ***  0.443 s (1.68 GB/s) / 0.070 s (10.62 GB/s)    Compr. ratio:   2.0x
  *** lz4hc   , shuffle    ***  0.102 s (7.31 GB/s) / 0.029 s (25.42 GB/s)    Compr. ratio:  97.5x
  *** lz4hc   , bitshuffle ***  0.206 s (3.62 GB/s) / 0.038 s (19.85 GB/s)    Compr. ratio: 180.5x
  *** snappy  , noshuffle  ***  0.154 s (4.84 GB/s) / 0.056 s (13.28 GB/s)    Compr. ratio:   2.0x
  *** snappy  , shuffle    ***  0.044 s (16.89 GB/s) / 0.047 s (15.95 GB/s)   Compr. ratio:  17.4x
  *** snappy  , bitshuffle ***  0.064 s (11.58 GB/s) / 0.061 s (12.26 GB/s)   Compr. ratio:  18.2x
  *** zlib    , noshuffle  ***  1.172 s (0.64 GB/s) / 0.135 s (5.50 GB/s)     Compr. ratio:   5.3x
  *** zlib    , shuffle    ***  0.260 s (2.86 GB/s) / 0.086 s (8.67 GB/s)     Compr. ratio: 120.8x
  *** zlib    , bitshuffle ***  0.262 s (2.84 GB/s) / 0.094 s (7.96 GB/s)     Compr. ratio: 260.1x
  *** zstd    , noshuffle  ***  0.973 s (0.77 GB/s) / 0.093 s (8.00 GB/s)     Compr. ratio:   7.8x
  *** zstd    , shuffle    ***  0.093 s (7.97 GB/s) / 0.023 s (32.71 GB/s)    Compr. ratio: 156.7x
  *** zstd    , bitshuffle ***  0.115 s (6.46 GB/s) / 0.029 s (25.60 GB/s)    Compr. ratio: 320.6x

*** the linspace linear distribution ***
  *** blosclz , noshuffle  ***  0.341 s (2.19 GB/s) / 0.291 s (2.56 GB/s)     Compr. ratio:   1.0x
  *** blosclz , shuffle    ***  0.132 s (5.65 GB/s) / 0.023 s (33.10 GB/s)    Compr. ratio:   2.0x
  *** blosclz , bitshuffle ***  0.166 s (4.50 GB/s) / 0.036 s (20.89 GB/s)    Compr. ratio:   2.8x
  *** lz4     , noshuffle  ***  0.142 s (5.26 GB/s) / 0.028 s (27.07 GB/s)    Compr. ratio:   1.0x
  *** lz4     , shuffle    ***  0.093 s (8.01 GB/s) / 0.030 s (24.87 GB/s)    Compr. ratio:   3.4x
  *** lz4     , bitshuffle ***  0.102 s (7.31 GB/s) / 0.039 s (19.13 GB/s)    Compr. ratio:   5.3x
  *** lz4hc   , noshuffle  ***  0.700 s (1.06 GB/s) / 0.044 s (16.77 GB/s)    Compr. ratio:   1.1x
  *** lz4hc   , shuffle    ***  0.203 s (3.67 GB/s) / 0.021 s (36.22 GB/s)    Compr. ratio:   8.6x
  *** lz4hc   , bitshuffle ***  0.342 s (2.18 GB/s) / 0.028 s (26.50 GB/s)    Compr. ratio:  14.2x
  *** snappy  , noshuffle  ***  0.271 s (2.75 GB/s) / 0.274 s (2.72 GB/s)     Compr. ratio:   1.0x
  *** snappy  , shuffle    ***  0.099 s (7.54 GB/s) / 0.042 s (17.55 GB/s)    Compr. ratio:   4.2x
  *** snappy  , bitshuffle ***  0.127 s (5.86 GB/s) / 0.043 s (17.20 GB/s)    Compr. ratio:   6.1x
  *** zlib    , noshuffle  ***  1.525 s (0.49 GB/s) / 0.158 s (4.70 GB/s)     Compr. ratio:   1.6x
  *** zlib    , shuffle    ***  0.346 s (2.15 GB/s) / 0.098 s (7.59 GB/s)     Compr. ratio:  10.7x
  *** zlib    , bitshuffle ***  0.420 s (1.78 GB/s) / 0.104 s (7.20 GB/s)     Compr. ratio:  18.0x
  *** zstd    , noshuffle  ***  1.061 s (0.70 GB/s) / 0.096 s (7.79 GB/s)     Compr. ratio:   1.9x
  *** zstd    , shuffle    ***  0.203 s (3.68 GB/s) / 0.052 s (14.21 GB/s)    Compr. ratio:  14.2x
  *** zstd    , bitshuffle ***  0.251 s (2.97 GB/s) / 0.047 s (15.84 GB/s)    Compr. ratio:  22.2x

*** the random distribution ***
  *** blosclz , noshuffle  ***  0.340 s (2.19 GB/s) / 0.285 s (2.61 GB/s)     Compr. ratio:   1.0x
  *** blosclz , shuffle    ***  0.091 s (8.21 GB/s) / 0.017 s (44.29 GB/s)    Compr. ratio:   3.9x
  *** blosclz , bitshuffle ***  0.080 s (9.27 GB/s) / 0.029 s (26.12 GB/s)    Compr. ratio:   6.1x
  *** lz4     , noshuffle  ***  0.150 s (4.95 GB/s) / 0.027 s (28.05 GB/s)    Compr. ratio:   2.4x
  *** lz4     , shuffle    ***  0.068 s (11.02 GB/s) / 0.029 s (26.03 GB/s)   Compr. ratio:   4.5x
  *** lz4     , bitshuffle ***  0.063 s (11.87 GB/s) / 0.054 s (13.70 GB/s)   Compr. ratio:   6.2x
  *** lz4hc   , noshuffle  ***  0.645 s (1.15 GB/s) / 0.019 s (39.22 GB/s)    Compr. ratio:   3.5x
  *** lz4hc   , shuffle    ***  0.257 s (2.90 GB/s) / 0.022 s (34.62 GB/s)    Compr. ratio:   5.1x
  *** lz4hc   , bitshuffle ***  0.128 s (5.80 GB/s) / 0.029 s (25.52 GB/s)    Compr. ratio:   6.2x
  *** snappy  , noshuffle  ***  0.164 s (4.54 GB/s) / 0.048 s (15.46 GB/s)    Compr. ratio:   2.2x
  *** snappy  , shuffle    ***  0.082 s (9.09 GB/s) / 0.043 s (17.39 GB/s)    Compr. ratio:   4.3x
  *** snappy  , bitshuffle ***  0.071 s (10.48 GB/s) / 0.046 s (16.08 GB/s)   Compr. ratio:   5.0x
  *** zlib    , noshuffle  ***  1.223 s (0.61 GB/s) / 0.093 s (7.97 GB/s)     Compr. ratio:   4.0x
  *** zlib    , shuffle    ***  0.636 s (1.17 GB/s) / 0.126 s (5.89 GB/s)     Compr. ratio:   5.5x
  *** zlib    , bitshuffle ***  0.327 s (2.28 GB/s) / 0.109 s (6.81 GB/s)     Compr. ratio:   6.2x
  *** zstd    , noshuffle  ***  1.432 s (0.52 GB/s) / 0.103 s (7.27 GB/s)     Compr. ratio:   4.2x
  *** zstd    , shuffle    ***  0.388 s (1.92 GB/s) / 0.031 s (23.71 GB/s)    Compr. ratio:   5.9x
  *** zstd    , bitshuffle ***  0.127 s (5.86 GB/s) / 0.033 s (22.77 GB/s)    Compr. ratio:   6.4x

Also, Blosc works quite well on ARM processors (even without NEON support yet):

-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
python-blosc version: 1.4.4
Blosc version: 1.11.2 ($Date:: 2017-01-27 #$)
Compressors available: ['blosclz', 'lz4', 'lz4hc', 'snappy', 'zlib', 'zstd']
Compressor library versions:
  BloscLZ: 1.0.5
  LZ4: 1.7.5
  Snappy: 1.1.1
  Zlib: 1.2.8
  Zstd: 1.1.2
Python version: 3.6.0 (default, Dec 31 2016, 21:20:16)
[GCC 4.9.2]
Platform: Linux-3.4.113-sun8i-armv7l (#50 SMP PREEMPT Mon Nov 14 08:41:55 CET 2016)
Linux dist: debian 9.0
Processor: not recognized
Byte-ordering: little
Detected cores: 4
Number of threads to use by default: 4
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
  *** ctypes.memmove() *** Time for memcpy():   0.015 s (93.57 MB/s)

Times for compressing/decompressing with clevel=5 and 4 threads

*** user input ***
  *** blosclz , noshuffle  ***  0.015 s (89.93 MB/s) / 0.010 s (138.32 MB/s)    Compr. ratio:   2.7x
  *** blosclz , shuffle    ***  0.023 s (60.25 MB/s) / 0.012 s (112.71 MB/s)    Compr. ratio:   2.3x
  *** blosclz , bitshuffle ***  0.018 s (77.63 MB/s) / 0.021 s (66.76 MB/s)     Compr. ratio:   7.3x
  *** lz4     , noshuffle  ***  0.008 s (177.14 MB/s) / 0.009 s (159.00 MB/s)   Compr. ratio:   3.6x
  *** lz4     , shuffle    ***  0.010 s (131.29 MB/s) / 0.012 s (117.69 MB/s)   Compr. ratio:   3.5x
  *** lz4     , bitshuffle ***  0.015 s (89.97 MB/s) / 0.022 s (63.62 MB/s)     Compr. ratio:   8.4x
  *** lz4hc   , noshuffle  ***  0.071 s (19.30 MB/s) / 0.007 s (186.64 MB/s)    Compr. ratio:   8.6x
  *** lz4hc   , shuffle    ***  0.079 s (17.30 MB/s) / 0.014 s (95.99 MB/s)     Compr. ratio:   6.2x
  *** lz4hc   , bitshuffle ***  0.062 s (22.23 MB/s) / 0.027 s (51.53 MB/s)     Compr. ratio:   9.7x
  *** snappy  , noshuffle  ***  0.008 s (173.87 MB/s) / 0.009 s (148.77 MB/s)   Compr. ratio:   4.4x
  *** snappy  , shuffle    ***  0.011 s (123.22 MB/s) / 0.016 s (85.16 MB/s)    Compr. ratio:   4.4x
  *** snappy  , bitshuffle ***  0.015 s (89.02 MB/s) / 0.021 s (64.87 MB/s)     Compr. ratio:   6.2x
  *** zlib    , noshuffle  ***  0.047 s (29.26 MB/s) / 0.011 s (121.83 MB/s)    Compr. ratio:  14.7x
  *** zlib    , shuffle    ***  0.080 s (17.20 MB/s) / 0.022 s (63.61 MB/s)     Compr. ratio:   9.4x
  *** zlib    , bitshuffle ***  0.059 s (23.50 MB/s) / 0.033 s (41.10 MB/s)     Compr. ratio:  10.5x
  *** zstd    , noshuffle  ***  0.113 s (12.21 MB/s) / 0.011 s (124.64 MB/s)    Compr. ratio:  15.6x
  *** zstd    , shuffle    ***  0.154 s (8.92 MB/s) / 0.026 s (52.56 MB/s)      Compr. ratio:   9.9x
  *** zstd    , bitshuffle ***  0.116 s (11.86 MB/s) / 0.036 s (38.40 MB/s)     Compr. ratio:  11.4x

For details on the ARM benchmark see: https://github.com/Blosc/python-blosc/issues/105

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

License

The software is licenses under a 3-Clause BSD licsense. A copy of the python-blosc license can be found in LICENSE.txt. A copy of all licenses can be found in LICENSES/.

Mailing list

Discussion about this module is welcome in the Blosc list:

blosc@googlegroups.com

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

Enjoy data!