Hi!, I'm pleased to announce the availability of PyTables 0.8.1. PyTables is a hierarchical database package designed to efficiently manage very large amounts of data. PyTables is built on top of the HDF5 library and the numarray package. It features an object-oriented interface that, combined with natural naming and C-code generated from Pyrex sources, makes it a fast, yet extremely easy-to-use tool for interactively saving and retrieving different kinds of datasets. It also provides flexible indexed access on disk to anywhere in the data. The primary purpose of this release is to incorporate updates to related to the newly released numarray 1.0. I've taken the opportunity to backport some improvements added in PyTables 0.9 (in alpha stage) as well as to fix the known problems. Improvements: - The logic for computing the buffer sizes has been revamped. As a consequence, the performance of writing/reading tables with large row sizes has improved by a factor of ten or more, now exceeding 70 MB/s for writing and 130 MB/s for reading (using compression). See http://sf.net/mailarchive/forum.php?thread_id=4963045&forum_id=13760 for more info. - The maximum row size for tables has been raised to 512 KB (before it was 8 KB, due to some internal limitations) - Documentation has been improved in minor details. As a result of a fix in the underlying documentation system (tbook), chapters start now at odd pages, instead of even. So those of you who want to print to double side probably will have better luck now when aligning pages ;). Another one is that HTML documentation has improved its look as well. Bug Fixes: - Indexing of Arrays with list or tuple flavors (#968131) When retrieving single elements from an array with 'List' or 'Tuple' flavors, an error occurred. This has been corrected and now you can retrieve fileh.root.array[2] without problems for 'List' or 'Tuple' flavored (E, VL)Arrays. - Iterators on Arrays with list or tuple flavors fail (#968132) When using iterators with Array objects with 'List' or 'Tuple' flavors, an error occurred. This has been corrected. - Last Index (-1) of Arrays doesn't work (#968149) When accessing to the last element in an Array using the notation -1, an empty list (or tuple or array) is returned instead of the proper value. This happened in general with all negative indices. Fixed. - Table.read(flavor="List") should return pure lists (#972534) However, it used to return a pointer to numarray.records.Record instances, as in:
fileh.root.table.read(1,2,flavor="List") [<numarray.records.Record instance at 0x4128352c>] fileh.root.table.read(1,3,flavor="List") [<numarray.records.Record instance at 0x4128396c>, <numarray.records.Record instance at 0x41283a8c>]
Now the next records are returned:
fileh.root.table.read(1,2, flavor=List) [(' ', 1, 1.0)] fileh.root.table.read(1,3, flavor=List) [(' ', 1, 1.0), (' ', 2, 2.0)]
In addition, when reading a single row of a table, a numarray.records.Record pointer was returned:
fileh.root.table[1] <numarray.records.Record instance at 0x4128398c>
Now, it returns a tuple:
fileh.root.table[1] (' ', 1, 1.0)
Which I think is more consistent, and more Pythonic. - Copy of leaves fails... (#973370) Attempting to copy leaves (Table or Array with different flavors) on top of themselves caused an internal error in PyTables. This has been corrected by silently avoiding the copy and returning the original Leaf as a result. Minor changes: - When assigning a value to a non-existing field in a table row, now a KeyError is raised, instead of the AttributeError that was issued before. I think this is more consistent with the type of error. - Tests have been improved so as to pass the whole suite when compiled in 64 bit mode on a Linux/PowerPC machine (namely a dual-G5 Powermac running a 64-bit, 2.6.4 Linux kernel and the preview YDL distribution for G5, with 64-bit GCC toolchain). Thanks to Ciro Cattuto for testing and reporting the modifications that were needed. Where PyTables can 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 --------- I'm using Linux (Intel 32-bit) as the main development platform, but PyTables should be easy to compile/install on many other UNIX machines. This package has also passed all the tests on a UltraSparc platform with Solaris 7 and Solaris 8. It also compiles and passes all the tests on a SGI Origin2000 with MIPS R12000 processors, with the MIPSPro compiler and running IRIX 6.5. It also runs fine on Linux 64-bit platforms, like an AMD Opteron running SuSe Linux Enterprise Server or PowerPC G5 with Linux 2.6.x in 64bit mode. It has also been tested in MacOSX platforms (10.2 but should also work on newer versions). 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. An example? ----------- For online code examples, have a look at http://pytables.sourceforge.net/html/tut/tutorial1-1.html and, for newly introduced Variable Length Arrays: http://pytables.sourceforge.net/html/tut/vlarray2.html Web site -------- Go to the PyTables web site for more details: http://pytables.sourceforge.net/ Share your experience --------------------- Let me know of any bugs, suggestions, gripes, kudos, etc. you may have. Cheers, -- Francesc Alted
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Francesc Alted