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| <h1 class="title">Python Programming Guide</h1> |
| |
| <p>The Spark Python API (PySpark) exposes the Spark programming model to Python. |
| To learn the basics of Spark, we recommend reading through the |
| <a href="scala-programming-guide.html">Scala programming guide</a> 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.</p> |
| |
| <h1 id="key-differences-in-the-python-api">Key Differences in the Python API</h1> |
| |
| <p>There are a few key differences between the Python and Scala APIs:</p> |
| |
| <ul> |
| <li>Python is dynamically typed, so RDDs can hold objects of multiple types.</li> |
| <li>PySpark does not yet support a few API calls, such as <code>lookup</code> and non-text input files, though these will be added in future releases.</li> |
| </ul> |
| |
| <p>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 <a href="http://www.diveintopython.net/power_of_introspection/lambda_functions.html"><code>lambda</code></a> syntax:</p> |
| |
| <div class="highlight"><pre><code class="python"><span class="n">logData</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="p">(</span><span class="n">logFile</span><span class="p">)</span><span class="o">.</span><span class="n">cache</span><span class="p">()</span> |
| <span class="n">errors</span> <span class="o">=</span> <span class="n">logData</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">line</span><span class="p">:</span> <span class="s">"ERROR"</span> <span class="ow">in</span> <span class="n">line</span><span class="p">)</span> |
| </code></pre></div> |
| |
| <p>You can also pass functions that are defined with the <code>def</code> keyword; this is useful for longer functions that can’t be expressed using <code>lambda</code>:</p> |
| |
| <div class="highlight"><pre><code class="python"><span class="k">def</span> <span class="nf">is_error</span><span class="p">(</span><span class="n">line</span><span class="p">):</span> |
| <span class="k">return</span> <span class="s">"ERROR"</span> <span class="ow">in</span> <span class="n">line</span> |
| <span class="n">errors</span> <span class="o">=</span> <span class="n">logData</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">is_error</span><span class="p">)</span> |
| </code></pre></div> |
| |
| <p>Functions can access objects in enclosing scopes, although modifications to those objects within RDD methods will not be propagated back:</p> |
| |
| <div class="highlight"><pre><code class="python"><span class="n">error_keywords</span> <span class="o">=</span> <span class="p">[</span><span class="s">"Exception"</span><span class="p">,</span> <span class="s">"Error"</span><span class="p">]</span> |
| <span class="k">def</span> <span class="nf">is_error</span><span class="p">(</span><span class="n">line</span><span class="p">):</span> |
| <span class="k">return</span> <span class="nb">any</span><span class="p">(</span><span class="n">keyword</span> <span class="ow">in</span> <span class="n">line</span> <span class="k">for</span> <span class="n">keyword</span> <span class="ow">in</span> <span class="n">error_keywords</span><span class="p">)</span> |
| <span class="n">errors</span> <span class="o">=</span> <span class="n">logData</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">is_error</span><span class="p">)</span> |
| </code></pre></div> |
| |
| <p>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 <a href="#standalone-use">Standalone Use</a> section describes how to ship code dependencies to workers.</p> |
| |
| <p>In addition, PySpark fully supports interactive use—simply run <code>./bin/pyspark</code> to launch an interactive shell.</p> |
| |
| <h1 id="installing-and-configuring-pyspark">Installing and Configuring PySpark</h1> |
| |
| <p>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 <a href="http://pypy.org/">PyPy</a> or <a href="http://www.jython.org/">Jython</a>.</p> |
| |
| <p>By default, PySpark requires <code>python</code> to be available on the system <code>PATH</code> and use it to run programs; an alternate Python executable may be specified by setting the <code>PYSPARK_PYTHON</code> environment variable in <code>conf/spark-env.sh</code> (or <code>.cmd</code> on Windows).</p> |
| |
| <p>All of PySpark’s library dependencies, including <a href="http://py4j.sourceforge.net/">Py4J</a>, are bundled with PySpark and automatically imported.</p> |
| |
| <p>Standalone PySpark applications should be run using the <code>bin/pyspark</code> script, which automatically configures the Java and Python environment using the settings in <code>conf/spark-env.sh</code> or <code>.cmd</code>. |
| The script automatically adds the <code>bin/pyspark</code> package to the <code>PYTHONPATH</code>.</p> |
| |
| <h1 id="interactive-use">Interactive Use</h1> |
| |
| <p>The <code>bin/pyspark</code> script launches a Python interpreter that is configured to run PySpark applications. To use <code>pyspark</code> interactively, first build Spark, then launch it directly from the command line without any options:</p> |
| |
| <div class="highlight"><pre><code class="bash"><span class="nv">$ </span>sbt/sbt assembly |
| <span class="nv">$ </span>./bin/pyspark |
| </code></pre></div> |
| |
| <p>The Python shell can be used explore data interactively and is a simple way to learn the API:</p> |
| |
| <div class="highlight"><pre><code class="python"><span class="o">>>></span> <span class="n">words</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="p">(</span><span class="s">"/usr/share/dict/words"</span><span class="p">)</span> |
| <span class="o">>>></span> <span class="n">words</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">w</span><span class="p">:</span> <span class="n">w</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s">"spar"</span><span class="p">))</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span> |
| <span class="p">[</span><span class="s">u'spar'</span><span class="p">,</span> <span class="s">u'sparable'</span><span class="p">,</span> <span class="s">u'sparada'</span><span class="p">,</span> <span class="s">u'sparadrap'</span><span class="p">,</span> <span class="s">u'sparagrass'</span><span class="p">]</span> |
| <span class="o">>>></span> <span class="n">help</span><span class="p">(</span><span class="n">pyspark</span><span class="p">)</span> <span class="c"># Show all pyspark functions</span> |
| </code></pre></div> |
| |
| <p>By default, the <code>bin/pyspark</code> shell creates SparkContext that runs applications locally on a single core. |
| To connect to a non-local cluster, or use multiple cores, set the <code>MASTER</code> environment variable. |
| For example, to use the <code>bin/pyspark</code> shell with a <a href="spark-standalone.html">standalone Spark cluster</a>:</p> |
| |
| <div class="highlight"><pre><code class="bash"><span class="nv">$ MASTER</span><span class="o">=</span>spark://IP:PORT ./bin/pyspark |
| </code></pre></div> |
| |
| <p>Or, to use four cores on the local machine:</p> |
| |
| <div class="highlight"><pre><code class="bash"><span class="nv">$ MASTER</span><span class="o">=</span><span class="nb">local</span><span class="o">[</span>4<span class="o">]</span> ./bin/pyspark |
| </code></pre></div> |
| |
| <h2 id="ipython">IPython</h2> |
| |
| <p>It is also possible to launch PySpark in <a href="http://ipython.org">IPython</a>, the |
| enhanced Python interpreter. PySpark works with IPython 1.0.0 and later. To |
| use IPython, set the <code>IPYTHON</code> variable to <code>1</code> when running <code>bin/pyspark</code>:</p> |
| |
| <div class="highlight"><pre><code class="bash"><span class="nv">$ IPYTHON</span><span class="o">=</span>1 ./bin/pyspark |
| </code></pre></div> |
| |
| <p>Alternatively, you can customize the <code>ipython</code> command by setting <code>IPYTHON_OPTS</code>. For example, to launch |
| the <a href="http://ipython.org/notebook.html">IPython Notebook</a> with PyLab graphing support:</p> |
| |
| <div class="highlight"><pre><code class="bash"><span class="nv">$ IPYTHON_OPTS</span><span class="o">=</span><span class="s2">"notebook --pylab inline"</span> ./bin/pyspark |
| </code></pre></div> |
| |
| <p>IPython also works on a cluster or on multiple cores if you set the <code>MASTER</code> environment variable.</p> |
| |
| <h1 id="standalone-programs">Standalone Programs</h1> |
| |
| <p>PySpark can also be used from standalone Python scripts by creating a SparkContext in your script and running the script using <code>bin/pyspark</code>. |
| The Quick Start guide includes a <a href="quick-start.html#a-standalone-app-in-python">complete example</a> of a standalone Python application.</p> |
| |
| <p>Code dependencies can be deployed by listing them in the <code>pyFiles</code> option in the SparkContext constructor:</p> |
| |
| <div class="highlight"><pre><code class="python"><span class="kn">from</span> <span class="nn">pyspark</span> <span class="kn">import</span> <span class="n">SparkContext</span> |
| <span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="p">(</span><span class="s">"local"</span><span class="p">,</span> <span class="s">"App Name"</span><span class="p">,</span> <span class="n">pyFiles</span><span class="o">=</span><span class="p">[</span><span class="s">'MyFile.py'</span><span class="p">,</span> <span class="s">'lib.zip'</span><span class="p">,</span> <span class="s">'app.egg'</span><span class="p">])</span> |
| </code></pre></div> |
| |
| <p>Files listed here will be added to the <code>PYTHONPATH</code> and shipped to remote worker machines. |
| Code dependencies can be added to an existing SparkContext using its <code>addPyFile()</code> method.</p> |
| |
| <p>You can set <a href="configuration.html#spark-properties">configuration properties</a> by passing a |
| <a href="api/pyspark/pyspark.conf.SparkConf-class.html">SparkConf</a> object to SparkContext:</p> |
| |
| <div class="highlight"><pre><code class="python"><span class="kn">from</span> <span class="nn">pyspark</span> <span class="kn">import</span> <span class="n">SparkConf</span><span class="p">,</span> <span class="n">SparkContext</span> |
| <span class="n">conf</span> <span class="o">=</span> <span class="p">(</span><span class="n">SparkConf</span><span class="p">()</span> |
| <span class="o">.</span><span class="n">setMaster</span><span class="p">(</span><span class="s">"local"</span><span class="p">)</span> |
| <span class="o">.</span><span class="n">setAppName</span><span class="p">(</span><span class="s">"My app"</span><span class="p">)</span> |
| <span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="s">"spark.executor.memory"</span><span class="p">,</span> <span class="s">"1g"</span><span class="p">))</span> |
| <span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="p">(</span><span class="n">conf</span> <span class="o">=</span> <span class="n">conf</span><span class="p">)</span> |
| </code></pre></div> |
| |
| <h1 id="api-docs">API Docs</h1> |
| |
| <p><a href="api/pyspark/index.html">API documentation</a> for PySpark is available as Epydoc. |
| Many of the methods also contain <a href="http://docs.python.org/2/library/doctest.html">doctests</a> that provide additional usage examples.</p> |
| |
| <h1 id="libraries">Libraries</h1> |
| |
| <p><a href="mllib-guide.html">MLlib</a> is also available in PySpark. To use it, you’ll need |
| <a href="http://www.numpy.org">NumPy</a> version 1.7 or newer, and Python 2.7. The <a href="mllib-guide.html">MLlib guide</a> contains |
| some example applications.</p> |
| |
| <h1 id="where-to-go-from-here">Where to Go from Here</h1> |
| |
| <p>PySpark also includes several sample programs in the <a href="https://github.com/apache/incubator-spark/tree/master/python/examples"><code>python/examples</code> folder</a>. |
| You can run them by passing the files to <code>pyspark</code>; e.g.:</p> |
| |
| <pre><code>./bin/pyspark python/examples/wordcount.py |
| </code></pre> |
| |
| <p>Each program prints usage help when run without arguments.</p> |
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