[Numpy-discussion] [ANN] PyTables (A Hierarchical Database) 1.2.2 is out

Francesc Altet faltet at carabos.com
Thu Feb 16 02:57:07 EST 2006

 Announcing PyTables 1.2.2

This is a maintenance version. Some important improvements and bug fixes
has been addressed in it.

Go to the PyTables web site for downloading the beast:

or keep reading for more info about the new features and bugs fixed in
this version.

Changes more in depth


- Multidimensional arrays of strings are now supported as node
  attributes.  They just need to be wrapped into ``CharArray`` objects
  (see the ``numarray.strings`` module).

- The limit of 512 KB for row sizes in tables has been removed. Now,
  there is no limit in the row size.

- When using table row iterators in non-iterator contexts, a warning is
  issued recommending the users to use them in iterator
  contexts. Before, when these iterators were used, it was printed a
  regular record get from an arbitrary place of the memory, giving a
  non-sense record as a result.

- Compression libraries are now dynamically loaded as different
  extension modules, so there is no longer need for producing several
  binary packages supporting different sets of compressors.

Bug fixes:

- Solved a leak that exposed when reading VLArray data. The problem was
  due to the usage of different heaps (C and Python) of memory. Thanks
  to Russel Howe to report this and to provide an initial patch.

Known issues:

- Time datatypes are non-portable between big-endian and little-endian
  architectures. This is ultimately a consequence of an HDF5
  limitation. See SF bug #1234709 for more info.

Backward-incompatible changes:

- Please, see RELEASE-NOTES.txt file.

Important notes for Windows users

If you are willing to use PyTables with Python 2.4 in Windows
platforms, you will need to get the HDF5 library compiled for MSVC
7.1, aka .NET 2003.  It can be found at:

Users of Python 2.3 on Windows will have to download the version of
HDF5 compiled with MSVC 6.0 available in:

Also, note that support for the UCL compressor has not been added in the
binary build of PyTables for Windows because of memory problems (perhaps
some bad interaction between UCL and something else). Eventually, UCL
support might be dropped in the future, so, please, refrain to create
datasets compressed with it.

What it is

**PyTables** is a package for managing hierarchical datasets and
designed to efficiently cope with extremely large amounts of data
(with support for full 64-bit file addressing).  It features an
object-oriented interface that, combined with C extensions for the
performance-critical parts of the code, makes it a very easy-to-use
tool for high performance data storage and retrieval.

PyTables runs on top of the HDF5 library and numarray (Numeric is also
supported and NumPy support is coming along) package for achieving
maximum throughput and convenient use.

Besides, PyTables I/O for table objects is buffered, implemented in C
and carefully tuned so that you can reach much better performance with
PyTables than with your own home-grown wrappings to the HDF5
library. PyTables sports indexing capabilities as well, allowing doing
selections in tables exceeding one billion of rows in just seconds.


This version has been extensively checked on quite a few platforms, like
Linux on Intel32 (Pentium), Win on Intel32 (Pentium), Linux on Intel64
(Itanium2), FreeBSD on AMD64 (Opteron), Linux on PowerPC and MacOSX on
PowerPC. For other platforms, chances are that the code can be easily
compiled and run without further issues. Please, contact us in case
you are experiencing problems.


Go to the PyTables web site for more details:


About the HDF5 library:


About numarray:


To know more about the company behind the PyTables development, see:



Thanks to various the users who provided feature improvements,
patches, bug reports, support and suggestions. See THANKS file in
distribution package for a (incomplete) list of contributors. Many
thanks also to SourceForge who have helped to make and distribute this
package! And last but not least, a big thank you to THG
(http://www.hdfgroup.org/) for sponsoring many of the new features
recently introduced in PyTables.

Share your experience

Let us know of any bugs, suggestions, gripes, kudos, etc. you may


  **Enjoy data!**

  -- The PyTables Team

>0,0<   Francesc Altet     http://www.carabos.com/
V   V   Cárabos Coop. V.   Enjoy Data

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