python-programming-guide.md

layout: global
title: Python Programming Guide

The Spark Python API (PySpark) exposes most of the Spark features available in the Scala version to Python. To learn the basics of Spark, we recommend reading through the Scala programming guide first; it should be easy to follow even if you don't know Scala. This guide will show how to use the Spark features described there in Python.

Key Differences in the Python API

There are a few key differences between the Python and Scala APIs:

• Python is dynamically typed, so RDDs can hold objects of different types.
• PySpark does not currently support the following Spark features:
  • Accumulators
  • Special functions on RRDs of doubles, such as mean and stdev
  • Approximate jobs / functions, such as countApprox and sumApprox.
  • lookup
  • mapPartitionsWithSplit
  • persist at storage levels other than MEMORY_ONLY
  • sample
  • sort

Installing and Configuring PySpark

PySpark requires Python 2.6 or higher. PySpark jobs are executed using a standard cPython interpreter in order to support Python modules that use C extensions. We have not tested PySpark with Python 3 or with alternative Python interpreters, such as PyPy or Jython. By default, PySpark's scripts will run programs using python; an alternate Python executable may be specified by setting the PYSPARK_PYTHON environment variable in conf/spark-env.sh.

All of PySpark's library dependencies, including Py4J, are bundled with PySpark and automatically imported.

Standalone PySpark jobs should be run using the run-pyspark script, which automatically configures the Java and Python environmnt using the settings in conf/spark-env.sh. The script automatically adds the pyspark package to the PYTHONPATH.

Interactive Use

PySpark's pyspark-shell script provides a simple way to learn the API:

{% highlight python %}

words = sc.textFile("/usr/share/dict/words") words.filter(lambda w: w.startswith("spar")).take(5) [u'spar', u'sparable', u'sparada', u'sparadrap', u'sparagrass'] {% endhighlight %}

Standalone Use

PySpark can also be used from standalone Python scripts by creating a SparkContext in the script and running the script using the run-pyspark script in the pyspark directory. The Quick Start guide includes a complete example of a standalone Python job.

Code dependencies can be deployed by listing them in the pyFiles option in the SparkContext constructor:

{% highlight python %} from pyspark import SparkContext sc = SparkContext("local", "Job Name", pyFiles=['MyFile.py', 'lib.zip', 'app.egg']) {% endhighlight %}

Files listed here will be added to the PYTHONPATH and shipped to remote worker machines. Code dependencies can be added to an existing SparkContext using its addPyFile() method.

Where to Go from Here

PySpark includes several sample programs using the Python API in pyspark/examples. You can run them by passing the files to the pyspark-run script included in PySpark -- for example ./pyspark-run examples/wordcount.py. Each example program prints usage help when run without any arguments.

We currently provide API documentation for the Python API as Epydoc. Many of the RDD method descriptions contain doctests that provide additional usage examples.