[ANN] PyTables (A Hierarchical Database) 1.2.2 is out
=========================== 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: http://pytables.sourceforge.net/
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.
- 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.
- 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.
- 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: ftp://ftp.ncsa.uiuc.edu/HDF/HDF5/current/bin/windows/5-165-win-net.ZIP
Users of Python 2.3 on Windows will have to download the version of HDF5 compiled with MSVC 6.0 available in: ftp://ftp.ncsa.uiuc.edu/HDF/HDF5/current/bin/windows/5-165-win.ZIP
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:
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 have.
-- The PyTables Team