What’s new in PyTables 0.8¶
On this release, many enhancements has been added and some bugs has been fixed. Here is the (non-exhaustive) list:
The new VLArray class enables you to store large lists of rows containing variable numbers of elements. The elements can be scalars or fully multimensional objects, in the PyTables tradition. This class supports two special objects as rows: Unicode strings (UTF-8 codification is used internally) and generic Python objects (through the use of cPickle).
The new EArray class allows you to enlarge already existing multidimensional homogeneous data objects. Consider it an extension of the already existing Array class, but with more functionality. Online compression or other filters can be applied to EArray instances, for example.
Another nice feature of EA’s is their support for fully multidimensional data selection with extended slices. You can write “earray[1,2:3,…,4:200]”, for example, to get the desired dataset slice from the disk. This is implemented using the powerful selection capabilities of the HDF5 library, which results in very highly efficient I/O operations. The same functionality has been added to Array objects as well.
New UnImplemented class. If a dataset contains unsupported datatypes, it will be associated with an UnImplemented instance, then inserted into to the object tree as usual. This allows you to continue to work with supported objects while retaining access to attributes of unsupported datasets. This has changed from previous versions, where a RuntimeError occurred when an unsupported object was encountered.
The combination of the new UnImplemented class with the support for new datatypes will enable PyTables to greatly increase the number of types of native HDF5 files that can be read and modified.
Boolean support has been added for all the Leaf objects.
The Table class has now an append() method that allows you to save large buffers of data in one go (i.e. bypassing the Row accessor). This can greatly improve data gathering speed.
- The standard HDF5 shuffle filter (to further enhance the
compression level) is supported.
The standard HDF5 fletcher32 checksum filter is supported.
As the supported number of filters is growing (and may be further increased in the future), a Filters() class has been introduced to handle filters more easily. In order to add support for this class, it was necessary to make a change in the createTable() method that is not backwards compatible: the “compress” and “complib” parameters are deprecated now and the “filters” parameter should be used in their place. You will be able to continue using the old parameters (only a Deprecation warning will be issued) for the next few releases, but you should migrate to the new version as soon as possible. In general, you can easily migrate old code by substituting code in its place:
table = fileh.createTable(group, 'table', Test, '', complevel, complib)
should be replaced by:
table = fileh.createTable(group, 'table', Test, '', Filters(complevel, complib))
A copy() method that supports slicing and modification of filtering capabilities has been added for all the Leaf objects. See the User’s Manual for more information.
A couple of new methods, namely copyFile() and copyChilds(), have been added to File class, to permit easy replication of complete hierarchies or sub-hierarchies, even to other files. You can change filters during the copy process as well.
Two new utilities has been added: ptdump and ptrepack. The utility ptdump allows the user to examine the contents of PyTables files (both metadata and actual data). The powerful ptrepack utility lets you selectively copy (portions of) hierarchies to specific locations in other files. It can be also used as an importer for generic HDF5 files.
The meaning of the stop parameter in read() methods has changed. Now a value of ‘None’ means the last row, and a value of 0 (zero) means the first row. This is more consistent with the range() function in python and the __getitem__() special method in numarray.
The method Table.removeRows() is no longer limited by table size. You can now delete rows regardless of the size of the table.
The “numarray” value has been added to the flavor parameter in the Table.read() method for completeness.
The attributes (.attr instance variable) are Python properties now. Access to their values is no longer lazy, i.e. you will be able to see both system or user attributes from the command line using the tab-completion capability of your python console (if enabled).
Documentation has been greatly improved to explain all the new functionality. In particular, the internal format of PyTables is now fully described. You can now build “native” PyTables files using any generic HDF5 software by just duplicating their format.
Many new tests have been added, not only to check new functionality but also to more stringently check existing functionality. There are more than 800 different tests now (and the number is increasing :).
PyTables has a new record in the data size that fits in one single file: more than 5 TB (yeah, more than 5000 GB), that accounts for 11 GB compressed, has been created on an AMD Opteron machine running Linux-64 (the 64 bits version of the Linux kernel). See the gory details in: http://pytables.sf.net/html/HowFast.html.
New platforms supported: PyTables has been compiled and tested under Linux32 (Intel), Linux64 (AMD Opteron and Alpha), Win32 (Intel), MacOSX (PowerPC), FreeBSD (Intel), Solaris (6, 7, 8 and 9 with UltraSparc), IRIX64 (IRIX 6.5 with R12000) and it probably works in many more architectures. In particular, release 0.8 is the first one that provides a relatively clean porting to 64-bit platforms.
As always, some bugs have been solved (especially bugs that occur when deleting and/or overwriting attributes).
And last, but definitely not least, a new donations section has been added to the PyTables web site (http://sourceforge.net/projects/pytables, then follow the “Donations” tag). If you like PyTables and want this effort to continue, please, donate!
– Francesc Alted email@example.com