python-programming-guide.md

layout: global
title: Python Programming Guide

The Spark Python API (PySpark) exposes the Spark programming model 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 multiple types.
• PySpark does not yet support a few API calls, such as lookup and non-text input files, though these will be added in future releases.

In PySpark, RDDs support the same methods as their Scala counterparts but take Python functions and return Python collection types. Short functions can be passed to RDD methods using Python's lambda syntax:

{% highlight python %} logData = sc.textFile(logFile).cache() errors = logData.filter(lambda line: "ERROR" in line) {% endhighlight %}

You can also pass functions that are defined with the def keyword; this is useful for longer functions that can't be expressed using lambda:

{% highlight python %} def is_error(line): return "ERROR" in line errors = logData.filter(is_error) {% endhighlight %}

Functions can access objects in enclosing scopes, although modifications to those objects within RDD methods will not be propagated back:

{% highlight python %} error_keywords = ["Exception", "Error"] def is_error(line): return any(keyword in line for keyword in error_keywords) errors = logData.filter(is_error) {% endhighlight %}

PySpark will automatically ship these functions to workers, along with any objects that they reference. Instances of classes will be serialized and shipped to workers by PySpark, but classes themselves cannot be automatically distributed to workers. The Standalone Use section describes how to ship code dependencies to workers.

In addition, PySpark fully supports interactive use---simply run ./pyspark to launch an interactive shell.

Installing and Configuring PySpark

PySpark requires Python 2.6 or higher. PySpark applications 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 requires python to be available on the system PATH and use it to run programs; an alternate Python executable may be specified by setting the PYSPARK_PYTHON environment variable in conf/spark-env.sh (or .cmd on Windows).

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

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

Interactive Use

The pyspark script launches a Python interpreter that is configured to run PySpark applications. To use pyspark interactively, first build Spark, then launch it directly from the command line without any options: