I'm happy to announce the first candidate release for PyTables 2.2 series.
Among the most exciting improvements of this release is the support of threads
in several parts of PyTables, namely Blosc and, optionally, Numexpr (which is
out of the main distribution now and becomes a requisite).
In particular, I'm quite happy of how performs the recent multi-threaded
implementation that undergone Blosc in 0.9. It uses a pool of threads
technique in order to reduce thread management to a bare minimum. When all
the tests would be finished, I expect to release Blosc 1.0 very soon now
(hopefully before PyTables 2.2 final).
These additions will allow you to make full use of the raw speed of nowadays
multi-core processors in the parts of the code that can use parallelism, and
are only the beginning of a series of future multi-core improvements inside
Here it is the official announcement:
Announcing PyTables 2.2rc1
PyTables is a library for managing hierarchical datasets and designed to
efficiently cope with extremely large amounts of data with support for
full 64-bit file addressing. PyTables runs on top of the HDF5 library
and NumPy package for achieving maximum throughput and convenient use.
This is the first release candidate for PyTables 2.2. On it, Numexpr is
not included anymore and is now a requisite and the Blosc compressor has
been updated to 0.9, which comes with integrated support for threads.
Also, Cython is used per default now to build Pyrex extensions.
Finally, a handful of bugs have been addressed and squashed.
In case you want to know more in detail what has changed in this
version, have a look at:
You can download a source package with generated PDF and HTML docs, as
well as binaries for Windows, from:
For an on-line version of the manual, visit:
About the HDF5 library:
Thanks to many users who provided feature improvements, patches, bug
reports, support and suggestions. See the ``THANKS`` file in the
distribution package for a (incomplete) list of contributors. Most
specially, a lot of kudos go to the HDF5 and NumPy (and numarray!)
makers. Without them, PyTables simply would not exist.
Share your experience
Let us know of any bugs, suggestions, gripes, kudos, etc. you may