ANN: PyTables 1.1 released
========================= Announcing PyTables 1.1 ========================= The PyTables development team is happy to announce the availability of a new version of PyTables package. On this version you will find support for a nice set of new features, like nested datatypes, enumerated datatypes, nested iterators (for reading only), support for native HDF5 multidimensional attributes, a new object for dealing with compressed, non-enlargeable arrays (CArray), bzip2 compression support and more. Many bugs has been addressed as well. 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. Changes more in depth ===================== Improvements: - Support for nested datatypes is in place. You can now made columns of tables that host another columns for an unlimited depth (well, theoretically, in practice until the python recursive limit would be reached). Convenient NestedRecArray objects has been implemented as data containers. Cols and Description accessors has been improved so you can navigate on the type hierarchy very easily (natural naming is has been implemented for the task). - ``Table``, ``EArray`` and ``VLArray`` objects now support enumerated types. ``Array`` objects support opening existing HDF5 enumerated arrays. Enumerated types are restricted sets of ``(name, value)`` pairs. Use the ``Enum`` class to easily define new enumerations that will be saved along with your data. - Now, the HDF5 library is responsible to do data conversions when the datasets are written in a machine with different byte-ordering than the machine that reads the dataset. With this, all the data is converted on-the-fly and you always get native datatypes in memory. I think this approach to be more convenient in terms of CPU consumption when using these datasets. Right now, this only works for tables, though. - Added support for native HDF5 multidimensional attributes. Now, you can load native HDF5 files that contains fully multidimensional attributes; these attributes will be mapped to NumArray objects. Also, when you save NumArray objects as attributes, they get saved as native HDF5 attributes (before, NumArray attributes where pickled). - A brand-new class, called CArray, has been introduced. It's mainly like an Array class (i.e. non-enlargeable), but with compression capabilities enabled. The existence of CArray also allows PyTables to read native HDF5 chunked, non-enlargeable datasets. - Bzip2 compressor is supported. Such a support was already in PyTables 1.0, but forgot to announce it. - New LZO2 (http://www.oberhumer.com/opensource/lzo/lzonews.php) compressor is supported. The installer now recognizes whether LZO1 or LZO2 is installed, and adapts automatically to it. If both are installed in your system, then LZO2 is chosen. LZO2 claims to be fully compatible (both backward and forward) with LZO1, so you should not experience any problem during this transition. - The old limit of 256 columns in a table has been released. Now, you can have tables with any number of columns, although if you try to use a too high number (i.e. > 1024), you will start to consume a lot of system resources. You have been warned!. - The limit in the length of column names has been released also. - Nested iterators for reading in tables are supported now. - A new section in tutorial about how to modify values in tables and arrays has been added to the User's Manual. Backward-incompatible changes: - None. Bug fixes: - VLArray now correctly updates the number of rows internal counter when opening an existing VLArray object. Now you can add new rows to existing VLA's without problems. - Tuple flavor for VLArrays now works as intended, i.e. reading VLArray objects will always return tuples even in the case of multidimensional Atoms. Before, this operations returned a mix of tuples and lists. - If a column was not able to be indexed because it has too few entries, then _whereInRange is called instead of _whereIndexed. Fixes #1203202. - You can call now Row.append() in the middle of Table iterators without resetting loop counters. Fixes #1205588. - PyTables used to give a segmentation fault when removing the last row out of a table with the table.removeRows() method. This is due to a limitation in the HDF5 library. Until this get fixed in HDF5, a NotImplemented error is raised when trying to do that. Address #1201023. - You can safely break a loop over an iterator returned by Table.where(). Fixes #1234637. - When removing a Group with hidden child groups, those are effectively closed now. - Now, there is a distinction between shapes 1 and (1,) in tables. The former represents a scalar, and the later a 1-D array with just one element. That follows the numarray convention for records, and makes more sense as well. Before 1.1, shapes 1 and (1,) were represented by an scalar on disk. Known bugs: - Classes inheriting from IsDescription subclasses do not inherit columns defined in the super-class. See SF bug #1207732 for more info. - Time datatypes are non-portable between big-endian and little-endian architectures. This is ultimately a consequence of a HDF5 limitation. See SF bug #1234709 for more info. Known issues: - UCL compressor seems to work badly on MacOSX platforms. Until the problem would be isolated and eventually solved, UCL will not be compiled by default on MacOSX platforms, even if the installer finds it in the system. However, if you still want to get UCL support on MacOSX, you can use the --force-ucl flag in setup.py. Important note for Python 2.4 and 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/5-164-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/5-164-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. Perhaps its more interesting feature is that it optimizes memory and disk resources so that data take much less space (between a factor 3 to 5, and more if the data is compressible) than other solutions, like for example, relational or object oriented databases. 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. Where can PyTables be applied? ============================== PyTables is not designed to work as a relational database competitor, but rather as a teammate. If you want to work with large datasets of multidimensional data (for example, for multidimensional analysis), or just provide a categorized structure for some portions of your cluttered RDBS, then give PyTables a try. It works well for storing data from data acquisition systems (DAS), simulation software, network data monitoring systems (for example, traffic measurements of IP packets on routers), very large XML files, or for creating a centralized repository for system logs, to name only a few possible uses. What is a table? ================ A table is defined as a collection of records whose values are stored in fixed-length fields. All records have the same structure and all values in each field have the same data type. The terms "fixed-length" and "strict data types" seem to be quite a strange requirement for a language like Python that supports dynamic data types, but they serve a useful function if the goal is to save very large quantities of data (such as is generated by many scientific applications, for example) in an efficient manner that reduces demand on CPU time and I/O resources. What is HDF5? ============= For those people who know nothing about HDF5, it is a general purpose library and file format for storing scientific data made at NCSA. HDF5 can store two primary objects: datasets and groups. A dataset is essentially a multidimensional array of data elements, and a group is a structure for organizing objects in an HDF5 file. Using these two basic constructs, one can create and store almost any kind of scientific data structure, such as images, arrays of vectors, and structured and unstructured grids. You can also mix and match them in HDF5 files according to your needs. Platforms ========= We are using Linux on top of Intel32 as the main development platform, but PyTables should be easy to compile/install on other UNIX machines. This package has also been successfully compiled and tested on a FreeBSD 5.4 with Opteron64 processors, a UltraSparc platform with Solaris 7 and Solaris 8, a SGI Origin3000 with Itanium processors running IRIX 6.5 (using the gcc compiler), Microsoft Windows and MacOSX (10.2 although 10.3 should work fine as well). In particular, it has been thoroughly tested on 64-bit platforms, like Linux-64 on top of an Intel Itanium, AMD Opteron (in 64-bit mode) or PowerPC G5 (in 64-bit mode) where all the tests pass successfully. Regarding Windows platforms, PyTables has been tested with Windows 2000 and Windows XP (using the Microsoft Visual C compiler), but it should also work with other flavors as well. Web site ======== Go to the PyTables web site for more details: http://pytables.sourceforge.net/ To know more about the company behind the PyTables development, see: http://www.carabos.com/ Share your experience ===================== Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. ---- **Enjoy data!** -- The PyTables Team
participants (1)
-
Francesc Altet