multitables 2.0.1
High performance parallel reading of HDF5 files using PyTables, multiprocessing, and shared memory.
License: MIT
Keywords: tables hdf5 parallel concurrent

`multitables <https://github.com/ghcollin/multitables>`_ is a python library designed for high speed access to HDF5 files.
Access to HDF5 is provided by the PyTables library (``tables``).
Multiple processes are launched to read a HDF5 in parallel, allowing concurrent decompression.
Data is streamed back to the invoker by use of shared memory space, removing the usual multiprocessing
communication overhead.

The data is organised by rows of an array (elements of the outer-most dimension), and groups of these rows form blocks.
With the ``Streamer`` interface, there is **no guarantee** on the ordering of the rows and/or blocks returned to the user, due to the
concurrent nature of the library. They are returned as they become available. On-disk ordering can be forced using
the ``ordered`` option, which may result in a performance penalty.

The ``Reader`` interface allows random access, which gives fine grained control over the ordering of requests.

`Performance gains <http://multitables.readthedocs.io/en/latest/benchmark.html>`_ of at
least 2x can be achieved when reading from an SSD.

New with version 2
==================

Random access reads are now possible through asynchronous requests.
The results of these requests are placed in shared memory.
See the how-to and unit tests for examples of the new interface.

Licence
=======

This software is distributed under the MIT licence.
See the `LICENSE.txt <https://github.com/ghcollin/multitables/blob/master/LICENSE.txt>`_ file for details.

Installation
============

::

    pip install multitables

``multitables`` depends on ``tables`` (the pytables package), ``numpy``, ``msgpack``, and ``wrapt``.
The package is compatible with the latest versions of Python 3, as pytables no longer supports Python 2.

Quick start: Streaming
======================

.. code:: python

    import multitables
    stream = multitables.Streamer(filename='/path/to/h5/file')
    for row in stream.get_generator(path='/internal/h5/path'):
        do_something(row)

Quick start: Random access
==========================

.. code:: python

    import multitables
    reader = multitables.Reader(filename='/path/to/h5/file')

    dataset = reader.get_dataset(path='/internal/h5/path')
    stage = dataset.create_stage(10) # Size of the shared
                                        # memory stage in rows

    req = dataset['col_A'][30:35] # Create a request as you
                                     # would index normally.

    future = reader.request(req, stage) # Schedule the request
    with future.get_unsafe() as data:
        do_something(data)
    data = None # Always set data to None after get_unsafe to
                # prevent a dangling reference

    # ... or use a safer proxy method

    req = dataset.col('col_A')[30:35,...,:100]

    future = reader.request(req, stage)
    with future.get_proxy() as data:
        do_something(data)

    # ... or get a copy of the data

    req = dataset['col_A'][30:35,np.arange(500) > 45]

    future = reader.request(req, stage)
    do_something(future.get())

Examples
========

See the `how-to <http://multitables.readthedocs.io/en/latest/howto.html>`_ for more in-depth documentation, and the
`unit tests <https://github.com/ghcollin/multitables/blob/master/multitables_test_v2.py>`_ for complete examples.

Documentation
=============

`Online documentation <http://multitables.readthedocs.io/en/latest/>`_ is available.
A `how to <http://multitables.readthedocs.io/en/latest/howto.html>`_ gives a basic overview of the library.
A `benchmark <http://multitables.readthedocs.io/en/latest/benchmark.html>`_ tests the speed of the library using various
compression algorithms and hardware configurations.

Offline documentation can be built from the ``docs`` folder using ``sphinx``.