Add documentation for Python API.

docs/_layouts/global.html

@@ -47,6 +47,7 @@
                                 <li><a href="quick-start.html">Quick Start</a></li>
                                 <li><a href="scala-programming-guide.html">Scala</a></li>
                                 <li><a href="java-programming-guide.html">Java</a></li>
+                                <li><a href="python-programming-guide.html">Python</a></li>
                             </ul>
                         </li>
 

docs/api.md

@@ -8,3 +8,4 @@ Here you can find links to the Scaladoc generated for the Spark sbt subprojects.
 - [Core](api/core/index.html)
 - [Examples](api/examples/index.html)
 - [Bagel](api/bagel/index.html)
+- [PySpark](api/pyspark/index.html)

docs/index.md

@@ -7,11 +7,11 @@ title: Spark Overview
 TODO(andyk): Rewrite to make the Java API a first class part of the story.
 {% endcomment %}
 
-Spark is a MapReduce-like cluster computing framework designed for low-latency iterative jobs and interactive use from an 
-interpreter. It provides clean, language-integrated APIs in Scala and Java, with a rich array of parallel operators. Spark can 
-run on top of the [Apache Mesos](http://incubator.apache.org/mesos/) cluster manager, 
+Spark is a MapReduce-like cluster computing framework designed for low-latency iterative jobs and interactive use from an interpreter.
+It provides clean, language-integrated APIs in Scala, Java, and Python, with a rich array of parallel operators.
+Spark can run on top of the [Apache Mesos](http://incubator.apache.org/mesos/) cluster manager,
 [Hadoop YARN](http://hadoop.apache.org/docs/r2.0.1-alpha/hadoop-yarn/hadoop-yarn-site/YARN.html),
-Amazon EC2, or without an independent resource manager ("standalone mode"). 
+Amazon EC2, or without an independent resource manager ("standalone mode").
 
 # Downloading
 
@@ -59,6 +59,7 @@ of `project/SparkBuild.scala`, then rebuilding Spark (`sbt/sbt clean compile`).
 * [Quick Start](quick-start.html): a quick introduction to the Spark API; start here!
 * [Spark Programming Guide](scala-programming-guide.html): an overview of Spark concepts, and details on the Scala API
 * [Java Programming Guide](java-programming-guide.html): using Spark from Java
+* [Python Programming Guide](python-programming-guide.html): using Spark from Python
 
 **Deployment guides:**
 
@@ -72,7 +73,7 @@ of `project/SparkBuild.scala`, then rebuilding Spark (`sbt/sbt clean compile`).
 
 * [Configuration](configuration.html): customize Spark via its configuration system
 * [Tuning Guide](tuning.html): best practices to optimize performance and memory use
-* [API Docs (Scaladoc)](api/core/index.html)
+* API Docs: [Java/Scala (Scaladoc)](api/core/index.html) and [Python (Epydoc)](api/pyspark/index.html)
 * [Bagel](bagel-programming-guide.html): an implementation of Google's Pregel on Spark
 * [Contributing to Spark](contributing-to-spark.html)
 

docs/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](scala-programming-guide.html) 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](http://pypy.org/) or [Jython](http://www.jython.org/).
+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](http://py4j.sourceforge.net/), 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](quick-start.html#a-standalone-job-in-python) 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](api/pyspark/index.html) for the Python API as Epydoc.
+Many of the RDD method descriptions contain [doctests](http://docs.python.org/2/library/doctest.html) that provide additional usage examples.

docs/quick-start.md

@@ -6,7 +6,8 @@ title: Quick Start
 * This will become a table of contents (this text will be scraped).
 {:toc}
 
-This tutorial provides a quick introduction to using Spark. We will first introduce the API through Spark's interactive Scala shell (don't worry if you don't know Scala -- you will need much for this), then show how to write standalone jobs in Scala and Java. See the [programming guide](scala-programming-guide.html) for a fuller reference.
+This tutorial provides a quick introduction to using Spark. We will first introduce the API through Spark's interactive Scala shell (don't worry if you don't know Scala -- you will need much for this), then show how to write standalone jobs in Scala, Java, and Python.
+See the [programming guide](scala-programming-guide.html) for a more complete reference.
 
 To follow along with this guide, you only need to have successfully built Spark on one machine. Simply go into your Spark directory and run:
 
@@ -230,3 +231,40 @@ Lines with a: 8422, Lines with b: 1836
 {% endhighlight %}
 
 This example only runs the job locally; for a tutorial on running jobs across several machines, see the [Standalone Mode](spark-standalone.html) documentation, and consider using a distributed input source, such as HDFS.
+
+# A Standalone Job In Python
+Now we will show how to write a standalone job using the Python API (PySpark).
+
+As an example, we'll create a simple Spark job, `SimpleJob.py`:
+
+{% highlight python %}
+"""SimpleJob.py"""
+from pyspark import SparkContext
+
+logFile = "/var/log/syslog"  # Should be some file on your system
+sc = SparkContext("local", "Simple job")
+logData = sc.textFile(logFile).cache()
+
+numAs = logData.filter(lambda s: 'a' in s).count()
+numBs = logData.filter(lambda s: 'b' in s).count()
+
+print "Lines with a: %i, lines with b: %i" % (numAs, numBs)
+{% endhighlight %}
+
+
+This job simply counts the number of lines containing 'a' and the number containing 'b' in a system log file.
+Like in the Scala and Java examples, we use a SparkContext to create RDDs.
+We can pass Python functions to Spark, which are automatically serialized along with any variables that they reference.
+For jobs that use custom classes or third-party libraries, we can add those code dependencies to SparkContext to ensure that they will be available on remote machines; this is described in more detail in the [Python programming guide](python-programming-guide).
+`SimpleJob` is simple enough that we do not need to specify any code dependencies.
+
+We can run this job using the `run-pyspark` script in `$SPARK_HOME/pyspark`:
+
+{% highlight python %}
+$ cd $SPARK_HOME
+$ ./pyspark/run-pyspark SimpleJob.py
+...
+Lines with a: 8422, Lines with b: 1836
+{% endhighlight python %}
+
+This example only runs the job locally; for a tutorial on running jobs across several machines, see the [Standalone Mode](spark-standalone.html) documentation, and consider using a distributed input source, such as HDFS.

pyspark/README

-# PySpark
-
-PySpark is a Python API for Spark.
-
-PySpark jobs are writen in Python and executed using a standard Python
-interpreter; this supports modules that use Python C extensions.  The
-API is based on the Spark Scala API and uses regular Python functions
-and lambdas to support user-defined functions.  PySpark supports
-interactive use through a standard Python interpreter; it can
-automatically serialize closures and ship them to worker processes.
-
-PySpark is built on top of the Spark Java API.  Data is uniformly
-represented as serialized Python objects and stored in Spark Java
-processes, which communicate with PySpark worker processes over pipes.
-
-## Features
-
-PySpark supports most of the Spark API, including broadcast variables.
-RDDs are dynamically typed and can hold any Python object.
-
-PySpark does not support:
-
-- Special functions on RDDs of doubles
-- Accumulators
-
-## Examples and Documentation
-
-The PySpark source contains docstrings and doctests that document its
-API.  The public classes are in `context.py` and `rdd.py`.
-
-The `pyspark/pyspark/examples` directory contains a few complete
-examples.
-
-## Installing PySpark
-#
-To use PySpark, `SPARK_HOME` should be set to the location of the Spark
-package.
-
-## Running PySpark
-
-The easiest way to run PySpark is to use the `run-pyspark` and
-`pyspark-shell` scripts, which are included in the `pyspark` directory.

pyspark/pyspark/__init__.py

 import sys
 import os
 sys.path.insert(0, os.path.join(os.environ["SPARK_HOME"], "pyspark/lib/py4j0.7.egg"))
+
+
+from pyspark.context import SparkContext
+
+
+__all__ = ["SparkContext"]