PyTables is a package for managing hierarchical datasets, 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 saving and retrieving 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 3 to 5 times and more if the data is compressible) than other solutions, like for example, relational or object-oriented databases.

PyTables supports the Blosc compressor out of the box. 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 up achieving higher performance than the bandwidth provided by your I/O subsystem. See the Tuning The Chunksize section of the Optimization Tips chapter of the user documentation for some benchmarks.

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, 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 use cases.

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 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 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 use cases on your project.

One of the principal objectives of PyTables is to be user-friendly. In addition, many different iterators have been implemented to make interactive work as productive as possible.

We use Linux on top of Intel32 and Intel64 boxes as the main development platforms, but PyTables should be easy to compile/install on other UNIX (including macOS) or Windows machines.

To compile PyTables, you will need a recent version of the HDF5 (C flavor) library, the Zlib compression library, and the NumPy and Numexpr packages. Besides, PyTables 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 1.10.5 or above. On Debian-based Linux distributions, you can install it with:

$ sudo apt install libhdf5-serial-dev

1. Install with pip <https://pip.pypa.io/en/stable/>:

   $ python3 -m pip install tables
   

2. To run the test suite:

   $ python3 -m tables.tests.test_all
   

   If there is some test that does not pass, please send us the complete output using the [GitHub Issue Tracker](https://github.com/PyTables/PyTables/issues/new).

Enjoy data! -- The PyTables Team