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XFrame data manipulation for Spark.

Project description

xFrames 0.2 Library (BETA)

The xFrames Library provides a consistent and scaleable data science
library that is built on top of industry-standard open source
technologies. xFrames provides the following advantages compared to other
DataFrame implementations:

- A simple and well-tested Data Science library and Python based
- Powerful abstraction over underlying scaleable big data and machine
learning frameworks: Apache Spark, Spark DataFrames and ML libraries.
- Dockerized container that bundles IPython notebooks, scientific
libraries, Apache Spark and other dependencies for painless setup.
- The library is extensible, allowing developers to add their own
useful features and functionality.

How xFrames Benefits You

If you're a data scientist, xFrames will isolate framework dependencies
and their configuration within a single disposable, containerized
environment, without compromising on any of the tools you're used to
working with (notebooks, dataframes, machine learning and big data
frameworks, etc.). Once you or someone else creates a single xFrames
container, you just need to run the container and everything is
installed and configured for you to work. Other members of your team
create their development environments from the same configuration, so
whether you're working on Linux, Mac OS X, or Windows, all your team
members are running data experiments in the same environment, against
the same dependencies, all configured the same way. Say goodbye to
painful setup times and "works on my machine" bugs.

Minimum Requirements


- Ubuntu 12.04 and above
- Docker >= 1.5 installation


- Docker >= 1.5 installation


- Run in VM

Download Library


git clone xframes

Build docker container

Go to the docker directory and follow the build instructions in

Review introductory presentation

After starting docker container, browse to http://localhost:7777/tree.
Then open info/Presentation.ipynb.


You can view local documentation in localhost:8000


This SDK is provided under the 3-clause BSD `license <LICENSE>`__.

Install Library On Existing Spark Installation

If you have an existing Spark installation that you already use with
pySpark, then you can simply install the library to work with that.

From the source distribution, you can either:

1. Include the xpatterns directory in PYTHONPATH, or
2. Build an archive (see below) and install it on a different machine.

Building the Library

In the source distribution, run


python sdist --formats=zip

This will create a file dist/xframes-<version>.zip This file can be copied to
the server where you want to install xframes.

Install by:


unzip xframes.<version>.zip
cd xframes.<version>
python install

Install With Pip

You can also install with pip:


pip install xframes

Running xFrames
xFrames uses pySpark, so you have to have Spark set up.

You might have an existing Spark installation running in Cluster Mode,
managed by the the Standalone, YARN, or Mesos cluster manager.
In this case, you need to set "master" to point to the cluster, using one
of the configuration methods described below.

If not, it is easy to set up spark in local mode.
Download spark from
Get the tar.gz, uncompress it, and put it in some convenient directory.
Then set:


export SPARK_HOME=<spark distribution>
export PYTHONPATH=${SPARK_HOME}/python:${SPARK_HOME}/python/lib/

You can test by running this program:

from xframes import XFrame
print XFrame({'id': [1, 2, 3], 'val': ['a', 'b', 'c']})

$ python

This should print:


| id | val |
| 1 | a |
| 2 | b |
| 3 | c |
[? rows x 2 columns]

You may notice that a great deal of debug output appears on stdout.
This is because, by default, Spark displays log output on stdout.
You can change this by supplying a file and setting
SPARK_CONF_DIR to the directory containing it. There is a sample
config dir "conf" under the xframes install directory. You can copy this
to your current directory and set:


export SPARK_CONF_DIR=`pwd`/conf

Then when you run, you will see only the output that your program prints.

Running in a IPython Notebook

XFrames works especially well in an IPython notebook.
If you set up spark as outline above, by setting PYTHONPATH, SPARK_HOME
and SPARK_CONF_DIR before you launch the notebook server, then
you can run the same test program and get the expected results.

Running in a Virtual Environment

XFrames alwo works well in a virtual environment.

Create a virtual environment:


virtualenv venv

And then install into it:


source venv/bin/activate
pip install xframes

XFrames depends on numpy, which it installs into the virtual environment.
XFrames includes support for pandas and matplotlib, which you can
install if you want to use them.


pip install pandas
pip install matplotlib

If running in a notebook, you would then run the notebook server:


ipython notebook

Configurating Spark

Spark has a large number of configuration parameters, described at:

There are a number of ways to supply these configuration parameters.
One of these is to supply a file spark-defaults.conf, in the directory pointed
to by SPARK_CONF_DIR described above. There is a template to guide you.
This works when you start a local spark instance.

To affect only the spark context used by a single xFrames program, you can
either provide xFrames-specific defaults, application-speficic configuration,
or you can supply configurations at run time.

For xFrames-specific defaults, edit the file "defaults.ini" found in the xframes
directory in the xframe installation.

For application-specific defaults, use a file "config.ini" in the current directory where you run
your xFrames application. It is structured similarly to "defaults.ini".

To provide run-time configuration, use SparkInitContext.set() to set configuration parameters before
running any Spark operations.

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