Announcing PyTables 0.7.2 -------------------------
PyTables is a hierarchical database package designed to efficently 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 save and retrieve large amounts of data. Besides, it provides flexible indexed access on disk to anywhere in the data you want to go.
On this release you will not find any exciting new features. It is mainly a maintenance release where the next issues has been addressed: - a memory leak was fixed - memory consumption is being addressed and lowered - much faster opening of files - Some important index patterns cases in table reads has been optimized
More in detail:
What's new -----------
- Fixed a nasty memory leak located on the C libraries (it was happening during HDF5 attribute writes). After that, the memory consumption when using large object trees has dropped quite a bit. However, there remains some small leaks that has been tracked down to the underlying numarray library. These leaks has been reported, and hopefully they should be fixed more sooner than later.
- Table buffers are built dinamically now, so if Tables are not accessed for reading or writing this memory will not be booked. This will help to reduce the memory consumption.
- The opening of files with lots of nodes has been accelerated between a factor 2 or 3. For example, a file with 10 groups and 3000 tables that takes 9.3 seconds to open in 0.7.1, now takes only 2.8 seconds.
- The Table.read() method has been refactored and optimized and some parts of its code has been moved to Pyrex. In particular, in the special case of step=1, up to a factor 5 of speedup (reaching 160 MB/s on a Pentium4 @ 2 GHz) when reading table contents can be achieved now.
- Done some cosmetic changes in the user manual, but, as no new features has been added, you won't need to read the manual again :-)
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" seems to be quite a strange requirement for an 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 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.
I'm using Linux as the main development platform, but PyTables should be easy to compile/install on 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 and running IRIX 6.5.
Regarding Windows platforms, PyTables has been tested with Windows 2000 and Windows XP, but it should also work with other flavors.
An example? -----------
For online code examples, have a look at
Web site --------
Go to the PyTables web site for more details:
Share your experience ---------------------
Let me know of any bugs, suggestions, gripes, kudos, etc. you may have.
-- Francesc Alted