Skip to content
A Python package to manage extremely large amounts of data
Python C C++ CMake Shell Makefile
Latest commit 4f07fea Jun 28, 2016 @FrancescAlted FrancescAlted committed on GitHub Merge pull request #559 from FrancescAlted/develop
Internal c-blosc bumped to 1.9.2
Failed to load latest commit information.
LICENSES Update external lib licenses Aug 14, 2015
bench pytables_backend had hard-coded paths that are used as workspace by b… Apr 29, 2016
c-blosc Internal c-blosc bumped to 1.9.2 Jun 27, 2016
contrib Formatting Sep 1, 2012
doc Fix numexpr link Jun 23, 2016
examples Work around Array limitations in objecttree.py example Jul 16, 2015
hdf5-blosc More comments on the infamous lock issue for Blosc Apr 23, 2016
src Do not shuffle when complevel == 0 Apr 22, 2016
tables Explanation on why MAX_BLOSC_THREADS has been set to 1 Apr 23, 2016
utils initial working version of ptree, walk_nodes is a bit slow Nov 8, 2014
.gitignore Update gitignore Oct 4, 2014
.travis.yml Enable Travis testing for Python 3.5 Sep 19, 2015
ANNOUNCE.txt.in Post 3.2.0 release actions done May 6, 2015
LICENSE.txt Changed the baseline for 3.2.0rc1 Apr 21, 2015
MANIFEST.in Internal C-Blosc bumped to 1.8.1 Apr 17, 2016
Makefile Fix the manifest template to include the rename README.rst Aug 15, 2014
README.rst Updated README and installation procedures, specially with required v… Apr 20, 2016
RELEASE_NOTES.txt Explanation on why MAX_BLOSC_THREADS has been set to 1 Apr 23, 2016
THANKS fix typo Apr 19, 2013
VERSION Bump version after 3.2.1 release Aug 12, 2015
cpuinfo.py Internal C-Blosc bumped to 1.8.1 Apr 17, 2016
requirements.txt Updated README and installation procedures, specially with required v… Apr 20, 2016
setup.cfg Merged in r4147 and r4148 (put in sync setup.cfg and setup.py). Jun 9, 2009
setup.py BitShuffle support is complete (bar oversights) Apr 20, 2016
subtree-merge-blosc.sh Updated to the new version (0.12) of the subtree merge script for Blosc Apr 15, 2015

README.rst

PyTables: hierarchical datasets in Python

Join the chat at https://gitter.im/PyTables/PyTables https://travis-ci.org/PyTables/PyTables.svg?branch=develop Code Climate
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.

State-of-the-art compression

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.

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.

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.

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.

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, it comes with support for the Blosc, LZO and bzip2 compressor libraries. Blosc is mandatory, but PyTables comes with Blosc sources so, although it si 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.

Installation

  1. Make sure you have HDF5 version 1.8.4 or above.

    On OSX you can install HDF5 using Homebrew:

    $ brew tap homebrew/science
    $ brew install hdf5
    

    On ubuntu:

    $ 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.

  1. 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.
  1. Optionally, consider to install the LZO compression library and/or the bzip2 compression library.

  2. Install!:

    $ pip install tables
    
  3. To run the test suite run:

    $ python -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

Something went wrong with that request. Please try again.