PyTables: hierarchical datasets in Python
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.
PyTables comes with out-of-box support for the Blosc compressor. This allows for extremely high compression speed, while keeping decent compression ratios. By doing so, I/O can be accelerated by a large extent, and you may end achieving higher performance than the bandwidth provided by your I/O subsystem. See the Tuning The Chunksize section of the Optimization Tips chapter of user documentation for some benchmarks.
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.
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.
There are other useful objects like arrays, enlargeable arrays or variable length arrays that can cope with different missions on your project.
Easy to use
One of the principal objectives of PyTables is to be user-friendly. In addition, many different iterators have been implemented so as to enable the interactive work to be as productive as possible.
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.
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, it comes with support for the Blosc, LZO and bzip2 compressor libraries. Blosc is mandatory, but PyTables comes with Blosc sources so, although it is recommended to have Blosc installed in your system, you don't absolutely need to install it separately. LZO and bzip2 compression libraries are, however, optional.
Make sure you have HDF5 version 1.8.4 or above.
On OSX you can install HDF5 using Homebrew:
$ brew install hdf5
On debian bases distributions:
$ sudo apt-get install libhdf5-serial-dev
If you have the HDF5 library in some non-standard location (that is, where the compiler and the linker can't find it) you can use the environment variable HDF5_DIR to specify its location. See the manual for more details.
- For stability (and performance too) reasons, it is strongly recommended that you install the C-Blosc library separately, although you might want PyTables to use its internal C-Blosc sources.
Optionally, consider to install the LZO compression library and/or the bzip2 compression library.
$ python3 -m pip install tables
To run the test suite run:
$ python3 -m tables.tests.test_all
If there is some test that does not pass, please send the complete output for tests back to us.
Enjoy data! -- The PyTables Team