#XXX version-specific blurb XXX#
bcolz provides columnar and compressed data containers that can live
either on-disk or in-memory. Column storage allows for efficiently
querying tables with a large number of columns. It also allows for
cheap addition and removal of column. In addition, bcolz objects are
compressed by default for reducing memory/disk I/O needs. The
compression process is carried out internally by Blosc, an
extremely fast meta-compressor that is optimized for binary data. Lastly,
high-performance iterators (like iter(), where()) for querying
the objects are provided.
bcolz can use numexpr internally so as to accelerate many vector and query operations (although it can use pure NumPy for doing so too). numexpr optimizes the memory usage and use several cores for doing the computations, so it is blazing fast. Moreover, since the carray/ctable containers can be disk-based, and it is possible to use them for seamlessly performing out-of-memory computations.
bcolz has minimal dependencies (NumPy), comes with an exhaustive test suite and fully supports both 32-bit and 64-bit platforms. Also, it is typically tested on both UNIX and Windows operating systems.
Together, bcolz and the Blosc compressor, are finally fulfilling the promise of accelerating memory I/O, at least for some real scenarios:
http://nbviewer.ipython.org/github/Blosc/movielens-bench/blob/master/querying-ep14.ipynb#Plots
Other users of bcolz are Visualfabriq (http://www.visualfabriq.com/) the Blaze project (http://blaze.pydata.org/) and Quantopian (https://www.quantopian.com/) which you can read more about by pointing your browser at the links below.
- Visualfabriq:
- bquery, A query and aggregation framework for Bcolz:
- https://github.com/visualfabriq/bquery
- Blaze:
- Notebooks showing Blaze + Pandas + BColz interaction:
- http://nbviewer.ipython.org/url/blaze.pydata.org/notebooks/timings-csv.ipynb
- http://nbviewer.ipython.org/url/blaze.pydata.org/notebooks/timings-bcolz.ipynb
- Quantopian:
- Using compressed data containers for faster backtesting at scale:
- https://quantopian.github.io/talks/NeedForSpeed/slides.html
bcolz is in the PyPI repository, so installing it is easy:
$ pip install -U bcolz
Visit the main bcolz site repository at: http://github.com/Blosc/bcolz
Manual: http://bcolz.blosc.org
Home of Blosc compressor: http://blosc.org
User's mail list: [email protected] http://groups.google.com/group/bcolz
License is the new BSD: https://github.com/Blosc/bcolz/blob/master/LICENSES/BCOLZ.txt
Release notes can be found in the Git repository: https://github.com/Blosc/bcolz/blob/master/RELEASE_NOTES.rst
Enjoy data!