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PyTables is a package for managing hierarchical datasets and designed to efficiently and easily cope with extremely large amounts of data.

README.rst

PyTables: hierarchical datasets in Python

URL: http://www.pytables.org/

PyTables is a package for managing hierarchical datasets and designed to efficiently cope with extremely large amounts of data.

It is built on top of the HDF5 library and the NumPy package. It features an object-oriented interface that, combined with C extensions for the performance-critical parts of the code (generated using Cython), makes it a fast, yet extremely easy to use tool for interactively save and retrieve very large amounts of data. One important feature of PyTables is that it optimizes memory and disk resources so that they 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.

Not a RDBMS replacement

PyTables is not designed to work as a relational database replacement, 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), or as a centralized repository for system logs, to name only a few possible uses.

Tables

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 interpreted language like Python, 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.

Arrays

There are other useful objects like arrays, enlargeable arrays or variable length arrays that can cope with different missions on your project. Also, quite a bit of effort has been invested to make browsing the hierarchical data structure a pleasant experience. PyTables implements a few easy-to-use methods for browsing. See the documentation (located in the doc/ directory) for more details.

Easy to use

One of the principal objectives of PyTables is to be user-friendly. To that end, special Python features like generators, slots and metaclasses in new-brand classes have been used. In addition, iterators has been implemented were context was appropriate so as to enable the interactive work to be as productive as possible. For these reasons, you will need to use Python 2.6 or higher to take advantage of PyTables.

Platforms

We are using Linux on top of Intel32 and Intel64 boxes as the main development platforms, but PyTables should be easy to compile/install on other UNIX or Windows machines. Nonetheless, caveat emptor: more testing is needed to achieve complete portability, we'd appreciate input on how it compiles and installs on your platform.

Compiling

To compile PyTables you will need, at least, a recent version of HDF5 (C flavor) library, the Zlib compression library and the NumPy and Numexpr packages. Besides, if you want to take advantage of the LZO and bzip2 compression libraries support you will also need recent versions of them. LZO and bzip2 compression libraries are, however, optional.

We've tested this PyTables version with HDF5 1.8.11/1.8.12, NumPy 1.7.1/1.8.0 and Numexpr 2.2.2, and you need to use these versions, or higher, to make use of PyTables.

Installation

The Python Distutils are used to build and install PyTables, so it is fairly simple to get things ready to go. Following are very simple instructions on how to proceed. However, more detailed instructions, including a section on binary installation for Windows users, is available in Chapter 2 of the User's Manual (doc/usersguide.pdf or http://www.pytables.org/moin/HowToUse).

  1. First, make sure that you have HDF5, NumPy and Numexpr installed (you will need at least HDF5 1.8.4, HDF5 >= 1.8.7 is strongly recommended, NumPy 1.4.1 and Numexpr 2.0). If don't, get them from http://www.hdfgroup.org/HDF5/, http://www.numpy.org and http://code.google.com/p/numexpr. Compile/install them.

    Optionally, consider to install the excellent LZO compression library from http://www.oberhumer.com/opensource/. You can also install the high-performance bzip2 compression library, available at http://www.bzip.org/.

  2. From the main PyTables distribution directory run this command, (plus any extra flags needed as discussed above):

    $ python setup.py build_ext --inplace
    
  3. To run the test suite, set the PYTHONPATH environment variable to include the . directory, enter the Python interpreter and issue the commands:

    >>> import tables
    >>> tables.test()
    

    If there is some test that does not pass, please send the complete output for tests back to us.

  4. To install the entire PyTables Python package, run this command as the root user (remember to add any extra flags needed):

    $ python setup.py install
    

That's it! Good luck, and let us know of any bugs, suggestions, gripes, kudos, etc. you may have.


Enjoy data!

—The PyTables Team

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