========================== Announcing bcolz 1.0.0 RC2 ==========================
What's new ==========
Yeah, 1.0.0 is finally here. We are not introducing any exciting new feature (just some optimizations and bug fixes), but bcolz is already 6 years old and it implements most of the capabilities that it was designed for, so I decided to release a 1.0.0 meaning that the format is declared stable and that people can be assured that future bcolz releases will be able to read bcolz 1.0 data files (and probably much earlier ones too) for a long while. Such a format is fully described at:
Also, a 1.0.0 release means that bcolz 1.x series will be based on C-Blosc 1.x series (https://github.com/Blosc/c-blosc). After C-Blosc 2.x (https://github.com/Blosc/c-blosc2) would be out, a new bcolz 2.x is expected taking advantage of shiny new features of C-Blosc2 (more compressors, more filters, native variable length support and the concept of super-chunks), which should be very beneficial for next bcolz generation.
Important: this is a Release Candidate, so please test it as much as you can. If no issues would appear in a week or so, I will proceed to tag and release 1.0.0 final. Enjoy!
For a more detailed change log, see:
What it is ==========
*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:
Other users of bcolz are Visualfabriq (http://www.visualfabriq.com/) the Blaze project (http://blaze.pydata.org/), Quantopian (https://www.quantopian.com/) and Scikit-Allel (https://github.com/cggh/scikit-allel) which you can read more about by pointing your browser at the links below.
* *bquery*, A query and aggregation framework for Bcolz: * https://github.com/visualfabriq/bquery
* 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.ipy...
* Using compressed data containers for faster backtesting at scale: * https://quantopian.github.io/talks/NeedForSpeed/slides.html
* Provides an alternative backend to work with compressed arrays * https://scikit-allel.readthedocs.org/en/latest/model/bcolz.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
Home of Blosc compressor: http://blosc.org
User's mail list: email@example.com 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