[Numpy-discussion] ANN: PyTables (a hierarchical database) 1.3.2 released (original) (raw)
Francesc Altet faltet at carabos.com
Wed Jun 21 05:14:58 EDT 2006
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=========================== Announcing PyTables 1.3.2
This is a new minor release of PyTables. There you will find, among other things, improved support for NumPy strings and the ability to create indexes of NumPy-flavored tables (this capability was broken in earlier versions).
Important note: one of the fixes addresses an important bug that shows when browsing files with lots of nodes, making PyTables to crash. Because of this, an upgrade is encouraged.
Go to the PyTables web site for downloading the beast: http://www.pytables.org/
or keep reading for more info about the new features and bugs fixed.
Changes more in depth
Bug fixes:
Changed the nodes in the lru cache heap from Pyrex to pure Python ones. This fixes a problem that can appear in certain situations (mainly, when navigating back and forth along lots of Node objects). While this fix is sub-optimal, at least it leads to well behaviour until the faster approach will eventually get back.
Due to different conventions in padding chars, it has been added a special case when converting from numarray strings into numpy ones so that these different conventions are handled correctly. Fixes ticket #13 and other strange numpy string quirks (thanks to Pepe Barbe).
Solved an issue that appeared when indexing Table columns with flavor 'numpy'. Now, tables that are 'numpy' flavored can be indexed as well.
Solved an issue when saving string atoms with
VLArraywith a flavor different from "python". The problem was that the item sizes of the original strings were not checked, so rubish was put on-disk. Now, if an item size of the input is different from the item size of the atom, a conversion is forced. Added tests to check for these situations.Fixed a problem with removing a table with indexed columns under certain situations. Thanks to Andrew Straw for reporting it.
Fixed a small glitch in the
ptdumputility that prevented dumpingEArraydata with an enlargeable dimension different from the first one.Make parent node unreference child node when creation fails. Fixes ticket #12 (thanks to Eilif).
Saving zero-length strings in Array objects used to raise a ZeroDivisionError. Now, it returns a more sensible NotImplementedError until this is supported.
Backward-incompatible changes:
- Please, see
RELEASE-NOTES.txtfile.
Deprecated features:
- None
Important note 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
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 (but NumPy and Numeric are also supported) 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.
Platforms
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 PowerPC64) 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.
Resources
Go to the PyTables web site for more details:
About the HDF5 library:
http://hdf.ncsa.uiuc.edu/HDF5/
About numarray:
http://www.stsci.edu/resources/software_hardware/numarray
To know more about the company behind the PyTables development, see:
Acknowledgments
Thanks to various the 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. 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.
Enjoy data!
-- The PyTables Team
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