[Numpy-discussion] ANN: bcolz 0.7.0 released

[Anthony Scopatz]

Congrats Francesc!


On Tue, Jul 22, 2014 at 9:53 AM, Francesc Alted <faltet at gmail.com> wrote:

> ======================
> Announcing bcolz 0.7.0
> ======================
>
> What's new
> ==========
>
> In this release, support for Python 3 has been added, Pandas and
> HDF5/PyTables conversion, support for different compressors via latest
> release of Blosc, and a new `iterblocks()` iterator.
>
> Also, intensive benchmarking has lead to an important tuning of buffer
> sizes parameters so that compression and evaluation goes faster than
> ever.  Together, bcolz and the Blosc compressor, are finally fullfilling
> 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
>
> ``bcolz`` is a renaming of the ``carray`` project.  The new goals for
> the project are to create simple, yet flexible compressed containers,
> that can live either on-disk or in-memory, and with some
> high-performance iterators (like `iter()`, `where()`) for querying them.
>
> For more detailed info, see the release notes in:
> https://github.com/Blosc/bcolz/wiki/Release-Notes
>
>
> What it is
> ==========
>
> bcolz provides columnar and compressed data containers.  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, a
> high-performance compressor that is optimized for binary data.
>
> 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, 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.
>
>
> Installing
> ==========
>
> bcolz is in the PyPI repository, so installing it is easy:
>
> $ pip install -U bcolz
>
>
> Resources
> =========
>
> 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:
> bcolz at googlegroups.com
> http://groups.google.com/group/bcolz
>
> License is the new BSD:
> https://github.com/Blosc/bcolz/blob/master/LICENSES/BCOLZ.txt
>
>
> ----
>
>    **Enjoy data!**
>
> -- Francesc Alted
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>
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