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             <h1 class="title">Spark SQL and DataFrame Guide</h1>


           <ul id="markdown-toc">
   <li><a href="#overview">Overview</a></li>
   <li><a href="#dataframes">DataFrames</a>    <ul>
       <li><a href="#starting-point-sqlcontext">Starting Point: <code>SQLContext</code></a></li>
       <li><a href="#creating-dataframes">Creating DataFrames</a></li>
       <li><a href="#dataframe-operations">DataFrame Operations</a></li>
       <li><a href="#running-sql-queries-programmatically">Running SQL Queries Programmatically</a></li>
       <li><a href="#interoperating-with-rdds">Interoperating with RDDs</a>        <ul>
           <li><a href="#inferring-the-schema-using-reflection">Inferring the Schema Using Reflection</a></li>
           <li><a href="#programmatically-specifying-the-schema">Programmatically Specifying the Schema</a></li>
         </ul>
       </li>
     </ul>
   </li>
   <li><a href="#data-sources">Data Sources</a>    <ul>
       <li><a href="#generic-loadsave-functions">Generic Load/Save Functions</a>        <ul>
           <li><a href="#manually-specifying-options">Manually Specifying Options</a></li>
           <li><a href="#save-modes">Save Modes</a></li>
           <li><a href="#saving-to-persistent-tables">Saving to Persistent Tables</a></li>
         </ul>
       </li>
       <li><a href="#parquet-files">Parquet Files</a>        <ul>
           <li><a href="#loading-data-programmatically">Loading Data Programmatically</a></li>
           <li><a href="#partition-discovery">Partition discovery</a></li>
           <li><a href="#schema-merging">Schema merging</a></li>
           <li><a href="#configuration">Configuration</a></li>
         </ul>
       </li>
       <li><a href="#json-datasets">JSON Datasets</a></li>
       <li><a href="#hive-tables">Hive Tables</a></li>
       <li><a href="#jdbc-to-other-databases">JDBC To Other Databases</a></li>
       <li><a href="#troubleshooting">Troubleshooting</a></li>
     </ul>
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   <li><a href="#performance-tuning">Performance Tuning</a>    <ul>
       <li><a href="#caching-data-in-memory">Caching Data In Memory</a></li>
       <li><a href="#other-configuration-options">Other Configuration Options</a></li>
     </ul>
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   <li><a href="#distributed-sql-engine">Distributed SQL Engine</a>    <ul>
       <li><a href="#running-the-thrift-jdbcodbc-server">Running the Thrift JDBC/ODBC server</a></li>
       <li><a href="#running-the-spark-sql-cli">Running the Spark SQL CLI</a></li>
     </ul>
   </li>
   <li><a href="#migration-guide">Migration Guide</a>    <ul>
       <li><a href="#upgrading-from-spark-sql-10-12-to-13">Upgrading from Spark SQL 1.0-1.2 to 1.3</a>        <ul>
           <li><a href="#rename-of-schemardd-to-dataframe">Rename of SchemaRDD to DataFrame</a></li>
           <li><a href="#unification-of-the-java-and-scala-apis">Unification of the Java and Scala APIs</a></li>
           <li><a href="#isolation-of-implicit-conversions-and-removal-of-dsl-package-scala-only">Isolation of Implicit Conversions and Removal of dsl Package (Scala-only)</a></li>
           <li><a href="#removal-of-the-type-aliases-in-orgapachesparksql-for-datatype-scala-only">Removal of the type aliases in org.apache.spark.sql for DataType (Scala-only)</a></li>
           <li><a href="#udf-registration-moved-to-sqlcontextudf-java--scala">UDF Registration Moved to <code>sqlContext.udf</code> (Java &amp; Scala)</a></li>
           <li><a href="#python-datatypes-no-longer-singletons">Python DataTypes No Longer Singletons</a></li>
         </ul>
       </li>
       <li><a href="#migration-guide-for-shark-user">Migration Guide for Shark User</a>        <ul>
           <li><a href="#scheduling">Scheduling</a></li>
           <li><a href="#reducer-number">Reducer number</a></li>
           <li><a href="#caching">Caching</a></li>
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       <li><a href="#compatibility-with-apache-hive">Compatibility with Apache Hive</a>        <ul>
           <li><a href="#deploying-in-existing-hive-warehouses">Deploying in Existing Hive Warehouses</a></li>
           <li><a href="#supported-hive-features">Supported Hive Features</a></li>
           <li><a href="#unsupported-hive-functionality">Unsupported Hive Functionality</a></li>
         </ul>
       </li>
     </ul>
   </li>
   <li><a href="#data-types">Data Types</a></li>
 </ul>

 <h1 id="overview">Overview</h1>

 <p>Spark SQL is a Spark module for structured data processing. It provides a programming abstraction called DataFrames and can also act as distributed SQL query engine.</p>

 <h1 id="dataframes">DataFrames</h1>

 <p>A DataFrame is a distributed collection of data organized into named columns. It is conceptually equivalent to a table in a relational database or a data frame in R/Python, but with richer optimizations under the hood. DataFrames can be constructed from a wide array of sources such as: structured data files, tables in Hive, external databases, or existing RDDs.</p>

 <p>The DataFrame API is available in <a href="api/scala/index.html#org.apache.spark.sql.DataFrame">Scala</a>, <a href="api/java/index.html?org/apache/spark/sql/DataFrame.html">Java</a>, and <a href="api/python/pyspark.sql.html#pyspark.sql.DataFrame">Python</a>.</p>

 <p>All of the examples on this page use sample data included in the Spark distribution and can be run in the <code>spark-shell</code> or the <code>pyspark</code> shell.</p>

 <h2 id="starting-point-sqlcontext">Starting Point: <code>SQLContext</code></h2>

 <div class="codetabs">
 <div data-lang="scala">

     <p>The entry point into all functionality in Spark SQL is the
 <a href="api/scala/index.html#org.apache.spark.sql.`SQLContext`"><code>SQLContext</code></a> class, or one of its
 descendants.  To create a basic <code>SQLContext</code>, all you need is a SparkContext.</p>

     <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">val</span> <span class="n">sc</span><span class="k">:</span> <span class="kt">SparkContext</span> <span class="c1">// An existing SparkContext.</span>
 <span class="k">val</span> <span class="n">sqlContext</span> <span class="k">=</span> <span class="k">new</span> <span class="n">org</span><span class="o">.</span><span class="n">apache</span><span class="o">.</span><span class="n">spark</span><span class="o">.</span><span class="n">sql</span><span class="o">.</span><span class="nc">SQLContext</span><span class="o">(</span><span class="n">sc</span><span class="o">)</span>

 <span class="c1">// this is used to implicitly convert an RDD to a DataFrame.</span>
 <span class="k">import</span> <span class="nn">sqlContext.implicits._</span></code></pre></div>

   </div>

 <div data-lang="java">

     <p>The entry point into all functionality in Spark SQL is the
 <a href="api/java/index.html#org.apache.spark.sql.SQLContext"><code>SQLContext</code></a> class, or one of its
 descendants.  To create a basic <code>SQLContext</code>, all you need is a SparkContext.</p>

     <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="n">JavaSparkContext</span> <span class="n">sc</span> <span class="o">=</span> <span class="o">...;</span> <span class="c1">// An existing JavaSparkContext.</span>
 <span class="n">SQLContext</span> <span class="n">sqlContext</span> <span class="o">=</span> <span class="k">new</span> <span class="n">org</span><span class="o">.</span><span class="na">apache</span><span class="o">.</span><span class="na">spark</span><span class="o">.</span><span class="na">sql</span><span class="o">.</span><span class="na">SQLContext</span><span class="o">(</span><span class="n">sc</span><span class="o">);</span></code></pre></div>

   </div>

 <div data-lang="python">

     <p>The entry point into all relational functionality in Spark is the
 <a href="api/python/pyspark.sql.SQLContext-class.html"><code>SQLContext</code></a> class, or one
 of its decedents.  To create a basic <code>SQLContext</code>, all you need is a SparkContext.</p>

     <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">SQLContext</span>
 <span class="n">sqlContext</span> <span class="o">=</span> <span class="n">SQLContext</span><span class="p">(</span><span class="n">sc</span><span class="p">)</span></code></pre></div>

   </div>
 </div>

 <p>In addition to the basic <code>SQLContext</code>, you can also create a <code>HiveContext</code>, which provides a
 superset of the functionality provided by the basic <code>SQLContext</code>. Additional features include
 the ability to write queries using the more complete HiveQL parser, access to Hive UDFs, and the
 ability to read data from Hive tables.  To use a <code>HiveContext</code>, you do not need to have an
 existing Hive setup, and all of the data sources available to a <code>SQLContext</code> are still available.
 <code>HiveContext</code> is only packaged separately to avoid including all of Hive&#8217;s dependencies in the default
 Spark build.  If these dependencies are not a problem for your application then using <code>HiveContext</code>
 is recommended for the 1.3 release of Spark.  Future releases will focus on bringing <code>SQLContext</code> up
 to feature parity with a <code>HiveContext</code>.</p>

 <p>The specific variant of SQL that is used to parse queries can also be selected using the
 <code>spark.sql.dialect</code> option.  This parameter can be changed using either the <code>setConf</code> method on
 a <code>SQLContext</code> or by using a <code>SET key=value</code> command in SQL.  For a <code>SQLContext</code>, the only dialect
 available is &#8220;sql&#8221; which uses a simple SQL parser provided by Spark SQL.  In a <code>HiveContext</code>, the
 default is &#8220;hiveql&#8221;, though &#8220;sql&#8221; is also available.  Since the HiveQL parser is much more complete,
 this is recommended for most use cases.</p>

 <h2 id="creating-dataframes">Creating DataFrames</h2>

 <p>With a <code>SQLContext</code>, applications can create <code>DataFrame</code>s from an <a href="#interoperating-with-rdds">existing <code>RDD</code></a>, from a Hive table, or from <a href="#data-sources">data sources</a>.</p>

 <p>As an example, the following creates a <code>DataFrame</code> based on the content of a JSON file:</p>

 <div class="codetabs">
 <div data-lang="scala">

     <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">val</span> <span class="n">sc</span><span class="k">:</span> <span class="kt">SparkContext</span> <span class="c1">// An existing SparkContext.</span>
 <span class="k">val</span> <span class="n">sqlContext</span> <span class="k">=</span> <span class="k">new</span> <span class="n">org</span><span class="o">.</span><span class="n">apache</span><span class="o">.</span><span class="n">spark</span><span class="o">.</span><span class="n">sql</span><span class="o">.</span><span class="nc">SQLContext</span><span class="o">(</span><span class="n">sc</span><span class="o">)</span>

 <span class="k">val</span> <span class="n">df</span> <span class="k">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">jsonFile</span><span class="o">(</span><span class="s">&quot;examples/src/main/resources/people.json&quot;</span><span class="o">)</span>

 <span class="c1">// Displays the content of the DataFrame to stdout</span>
 <span class="n">df</span><span class="o">.</span><span class="n">show</span><span class="o">()</span></code></pre></div>

   </div>

 <div data-lang="java">

     <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="n">JavaSparkContext</span> <span class="n">sc</span> <span class="o">=</span> <span class="o">...;</span> <span class="c1">// An existing JavaSparkContext.</span>
 <span class="n">SQLContext</span> <span class="n">sqlContext</span> <span class="o">=</span> <span class="k">new</span> <span class="n">org</span><span class="o">.</span><span class="na">apache</span><span class="o">.</span><span class="na">spark</span><span class="o">.</span><span class="na">sql</span><span class="o">.</span><span class="na">SQLContext</span><span class="o">(</span><span class="n">sc</span><span class="o">);</span>

 <span class="n">DataFrame</span> <span class="n">df</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="na">jsonFile</span><span class="o">(</span><span class="s">&quot;examples/src/main/resources/people.json&quot;</span><span class="o">);</span>

 <span class="c1">// Displays the content of the DataFrame to stdout</span>
 <span class="n">df</span><span class="o">.</span><span class="na">show</span><span class="o">();</span></code></pre></div>

   </div>

 <div data-lang="python">

     <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">SQLContext</span>
 <span class="n">sqlContext</span> <span class="o">=</span> <span class="n">SQLContext</span><span class="p">(</span><span class="n">sc</span><span class="p">)</span>

 <span class="n">df</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">jsonFile</span><span class="p">(</span><span class="s">&quot;examples/src/main/resources/people.json&quot;</span><span class="p">)</span>

 <span class="c"># Displays the content of the DataFrame to stdout</span>
 <span class="n">df</span><span class="o">.</span><span class="n">show</span><span class="p">()</span></code></pre></div>

   </div>
 </div>

 <h2 id="dataframe-operations">DataFrame Operations</h2>

 <p>DataFrames provide a domain-specific language for structured data manipulation in <a href="api/scala/index.html#org.apache.spark.sql.DataFrame">Scala</a>, <a href="api/java/index.html?org/apache/spark/sql/DataFrame.html">Java</a>, and <a href="api/python/pyspark.sql.html#pyspark.sql.DataFrame">Python</a>.</p>

 <p>Here we include some basic examples of structured data processing using DataFrames:</p>

 <div class="codetabs">
 <div data-lang="scala">

     <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">val</span> <span class="n">sc</span><span class="k">:</span> <span class="kt">SparkContext</span> <span class="c1">// An existing SparkContext.</span>
 <span class="k">val</span> <span class="n">sqlContext</span> <span class="k">=</span> <span class="k">new</span> <span class="n">org</span><span class="o">.</span><span class="n">apache</span><span class="o">.</span><span class="n">spark</span><span class="o">.</span><span class="n">sql</span><span class="o">.</span><span class="nc">SQLContext</span><span class="o">(</span><span class="n">sc</span><span class="o">)</span>

 <span class="c1">// Create the DataFrame</span>
 <span class="k">val</span> <span class="n">df</span> <span class="k">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">jsonFile</span><span class="o">(</span><span class="s">&quot;examples/src/main/resources/people.json&quot;</span><span class="o">)</span>

 <span class="c1">// Show the content of the DataFrame</span>
 <span class="n">df</span><span class="o">.</span><span class="n">show</span><span class="o">()</span>
 <span class="c1">// age  name</span>
 <span class="c1">// null Michael</span>
 <span class="c1">// 30   Andy</span>
 <span class="c1">// 19   Justin</span>

 <span class="c1">// Print the schema in a tree format</span>
 <span class="n">df</span><span class="o">.</span><span class="n">printSchema</span><span class="o">()</span>
 <span class="c1">// root</span>
 <span class="c1">// |-- age: long (nullable = true)</span>
 <span class="c1">// |-- name: string (nullable = true)</span>

 <span class="c1">// Select only the &quot;name&quot; column</span>
 <span class="n">df</span><span class="o">.</span><span class="n">select</span><span class="o">(</span><span class="s">&quot;name&quot;</span><span class="o">).</span><span class="n">show</span><span class="o">()</span>
 <span class="c1">// name</span>
 <span class="c1">// Michael</span>
 <span class="c1">// Andy</span>
 <span class="c1">// Justin</span>

 <span class="c1">// Select everybody, but increment the age by 1</span>
 <span class="n">df</span><span class="o">.</span><span class="n">select</span><span class="o">(</span><span class="s">&quot;name&quot;</span><span class="o">,</span> <span class="n">df</span><span class="o">(</span><span class="s">&quot;age&quot;</span><span class="o">)</span> <span class="o">+</span> <span class="mi">1</span><span class="o">).</span><span class="n">show</span><span class="o">()</span>
 <span class="c1">// name    (age + 1)</span>
 <span class="c1">// Michael null</span>
 <span class="c1">// Andy    31</span>
 <span class="c1">// Justin  20</span>

 <span class="c1">// Select people older than 21</span>
 <span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="o">(</span><span class="n">df</span><span class="o">(</span><span class="s">&quot;name&quot;</span><span class="o">)</span> <span class="o">&gt;</span> <span class="mi">21</span><span class="o">).</span><span class="n">show</span><span class="o">()</span>
 <span class="c1">// age name</span>
 <span class="c1">// 30  Andy</span>

 <span class="c1">// Count people by age</span>
 <span class="n">df</span><span class="o">.</span><span class="n">groupBy</span><span class="o">(</span><span class="s">&quot;age&quot;</span><span class="o">).</span><span class="n">count</span><span class="o">().</span><span class="n">show</span><span class="o">()</span>
 <span class="c1">// age  count</span>
 <span class="c1">// null 1</span>
 <span class="c1">// 19   1</span>
 <span class="c1">// 30   1</span></code></pre></div>

   </div>

 <div data-lang="java">

     <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="n">val</span> <span class="nl">sc:</span> <span class="n">JavaSparkContext</span> <span class="c1">// An existing SparkContext.</span>
 <span class="n">val</span> <span class="n">sqlContext</span> <span class="o">=</span> <span class="k">new</span> <span class="n">org</span><span class="o">.</span><span class="na">apache</span><span class="o">.</span><span class="na">spark</span><span class="o">.</span><span class="na">sql</span><span class="o">.</span><span class="na">SQLContext</span><span class="o">(</span><span class="n">sc</span><span class="o">)</span>

 <span class="c1">// Create the DataFrame</span>
 <span class="n">DataFrame</span> <span class="n">df</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="na">jsonFile</span><span class="o">(</span><span class="s">&quot;examples/src/main/resources/people.json&quot;</span><span class="o">);</span>

 <span class="c1">// Show the content of the DataFrame</span>
 <span class="n">df</span><span class="o">.</span><span class="na">show</span><span class="o">();</span>
 <span class="c1">// age  name</span>
 <span class="c1">// null Michael</span>
 <span class="c1">// 30   Andy</span>
 <span class="c1">// 19   Justin</span>

 <span class="c1">// Print the schema in a tree format</span>
 <span class="n">df</span><span class="o">.</span><span class="na">printSchema</span><span class="o">();</span>
 <span class="c1">// root</span>
 <span class="c1">// |-- age: long (nullable = true)</span>
 <span class="c1">// |-- name: string (nullable = true)</span>

 <span class="c1">// Select only the &quot;name&quot; column</span>
 <span class="n">df</span><span class="o">.</span><span class="na">select</span><span class="o">(</span><span class="s">&quot;name&quot;</span><span class="o">).</span><span class="na">show</span><span class="o">();</span>
 <span class="c1">// name</span>
 <span class="c1">// Michael</span>
 <span class="c1">// Andy</span>
 <span class="c1">// Justin</span>

 <span class="c1">// Select everybody, but increment the age by 1</span>
 <span class="n">df</span><span class="o">.</span><span class="na">select</span><span class="o">(</span><span class="s">&quot;name&quot;</span><span class="o">,</span> <span class="n">df</span><span class="o">.</span><span class="na">col</span><span class="o">(</span><span class="s">&quot;age&quot;</span><span class="o">).</span><span class="na">plus</span><span class="o">(</span><span class="mi">1</span><span class="o">)).</span><span class="na">show</span><span class="o">();</span>
 <span class="c1">// name    (age + 1)</span>
 <span class="c1">// Michael null</span>
 <span class="c1">// Andy    31</span>
 <span class="c1">// Justin  20</span>

 <span class="c1">// Select people older than 21</span>
 <span class="n">df</span><span class="o">.</span><span class="na">filter</span><span class="o">(</span><span class="n">df</span><span class="o">(</span><span class="s">&quot;name&quot;</span><span class="o">)</span> <span class="o">&gt;</span> <span class="mi">21</span><span class="o">).</span><span class="na">show</span><span class="o">();</span>
 <span class="c1">// age name</span>
 <span class="c1">// 30  Andy</span>

 <span class="c1">// Count people by age</span>
 <span class="n">df</span><span class="o">.</span><span class="na">groupBy</span><span class="o">(</span><span class="s">&quot;age&quot;</span><span class="o">).</span><span class="na">count</span><span class="o">().</span><span class="na">show</span><span class="o">();</span>
 <span class="c1">// age  count</span>
 <span class="c1">// null 1</span>
 <span class="c1">// 19   1</span>
 <span class="c1">// 30   1</span></code></pre></div>

   </div>

 <div data-lang="python">

     <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">SQLContext</span>
 <span class="n">sqlContext</span> <span class="o">=</span> <span class="n">SQLContext</span><span class="p">(</span><span class="n">sc</span><span class="p">)</span>

 <span class="c"># Create the DataFrame</span>
 <span class="n">df</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">jsonFile</span><span class="p">(</span><span class="s">&quot;examples/src/main/resources/people.json&quot;</span><span class="p">)</span>

 <span class="c"># Show the content of the DataFrame</span>
 <span class="n">df</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 <span class="c">## age  name</span>
 <span class="c">## null Michael</span>
 <span class="c">## 30   Andy</span>
 <span class="c">## 19   Justin</span>

 <span class="c"># Print the schema in a tree format</span>
 <span class="n">df</span><span class="o">.</span><span class="n">printSchema</span><span class="p">()</span>
 <span class="c">## root</span>
 <span class="c">## |-- age: long (nullable = true)</span>
 <span class="c">## |-- name: string (nullable = true)</span>

 <span class="c"># Select only the &quot;name&quot; column</span>
 <span class="n">df</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s">&quot;name&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 <span class="c">## name</span>
 <span class="c">## Michael</span>
 <span class="c">## Andy</span>
 <span class="c">## Justin</span>

 <span class="c"># Select everybody, but increment the age by 1</span>
 <span class="n">df</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s">&quot;name&quot;</span><span class="p">,</span> <span class="n">df</span><span class="o">.</span><span class="n">age</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 <span class="c">## name    (age + 1)</span>
 <span class="c">## Michael null</span>
 <span class="c">## Andy    31</span>
 <span class="c">## Justin  20</span>

 <span class="c"># Select people older than 21</span>
 <span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">name</span> <span class="o">&gt;</span> <span class="mi">21</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 <span class="c">## age name</span>
 <span class="c">## 30  Andy</span>

 <span class="c"># Count people by age</span>
 <span class="n">df</span><span class="o">.</span><span class="n">groupBy</span><span class="p">(</span><span class="s">&quot;age&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">count</span><span class="p">()</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 <span class="c">## age  count</span>
 <span class="c">## null 1</span>
 <span class="c">## 19   1</span>
 <span class="c">## 30   1</span></code></pre></div>

   </div>
 </div>

 <h2 id="running-sql-queries-programmatically">Running SQL Queries Programmatically</h2>

 <p>The <code>sql</code> function on a <code>SQLContext</code> enables applications to run SQL queries programmatically and returns the result as a <code>DataFrame</code>.</p>

 <div class="codetabs">
 <div data-lang="scala">

     <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">val</span> <span class="n">sqlContext</span> <span class="k">=</span> <span class="o">...</span>  <span class="c1">// An existing SQLContext</span>
 <span class="k">val</span> <span class="n">df</span> <span class="k">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">sql</span><span class="o">(</span><span class="s">&quot;SELECT * FROM table&quot;</span><span class="o">)</span></code></pre></div>

   </div>

 <div data-lang="java">

     <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="n">val</span> <span class="n">sqlContext</span> <span class="o">=</span> <span class="o">...</span>  <span class="c1">// An existing SQLContext</span>
 <span class="n">val</span> <span class="n">df</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="na">sql</span><span class="o">(</span><span class="s">&quot;SELECT * FROM table&quot;</span><span class="o">)</span></code></pre></div>

   </div>

 <div data-lang="python">

     <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">SQLContext</span>
 <span class="n">sqlContext</span> <span class="o">=</span> <span class="n">SQLContext</span><span class="p">(</span><span class="n">sc</span><span class="p">)</span>
 <span class="n">df</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">sql</span><span class="p">(</span><span class="s">&quot;SELECT * FROM table&quot;</span><span class="p">)</span></code></pre></div>

   </div>
 </div>

 <h2 id="interoperating-with-rdds">Interoperating with RDDs</h2>

 <p>Spark SQL supports two different methods for converting existing RDDs into DataFrames.  The first
 method uses reflection to infer the schema of an RDD that contains specific types of objects.  This
 reflection based approach leads to more concise code and works well when you already know the schema
 while writing your Spark application.</p>

 <p>The second method for creating DataFrames is through a programmatic interface that allows you to
 construct a schema and then apply it to an existing RDD.  While this method is more verbose, it allows
 you to construct DataFrames when the columns and their types are not known until runtime.</p>

 <h3 id="inferring-the-schema-using-reflection">Inferring the Schema Using Reflection</h3>
 <div class="codetabs">

 <div data-lang="scala">

     <p>The Scala interface for Spark SQL supports automatically converting an RDD containing case classes
 to a DataFrame.  The case class
 defines the schema of the table.  The names of the arguments to the case class are read using
 reflection and become the names of the columns. Case classes can also be nested or contain complex
 types such as Sequences or Arrays. This RDD can be implicitly converted to a DataFrame and then be
 registered as a table.  Tables can be used in subsequent SQL statements.</p>

     <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="c1">// sc is an existing SparkContext.</span>
 <span class="k">val</span> <span class="n">sqlContext</span> <span class="k">=</span> <span class="k">new</span> <span class="n">org</span><span class="o">.</span><span class="n">apache</span><span class="o">.</span><span class="n">spark</span><span class="o">.</span><span class="n">sql</span><span class="o">.</span><span class="nc">SQLContext</span><span class="o">(</span><span class="n">sc</span><span class="o">)</span>
 <span class="c1">// this is used to implicitly convert an RDD to a DataFrame.</span>
 <span class="k">import</span> <span class="nn">sqlContext.implicits._</span>

 <span class="c1">// Define the schema using a case class.</span>
 <span class="c1">// Note: Case classes in Scala 2.10 can support only up to 22 fields. To work around this limit,</span>
 <span class="c1">// you can use custom classes that implement the Product interface.</span>
 <span class="k">case</span> <span class="k">class</span> <span class="nc">Person</span><span class="o">(</span><span class="n">name</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span> <span class="n">age</span><span class="k">:</span> <span class="kt">Int</span><span class="o">)</span>

 <span class="c1">// Create an RDD of Person objects and register it as a table.</span>
 <span class="k">val</span> <span class="n">people</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="o">(</span><span class="s">&quot;examples/src/main/resources/people.txt&quot;</span><span class="o">).</span><span class="n">map</span><span class="o">(</span><span class="k">_</span><span class="o">.</span><span class="n">split</span><span class="o">(</span><span class="s">&quot;,&quot;</span><span class="o">)).</span><span class="n">map</span><span class="o">(</span><span class="n">p</span> <span class="k">=&gt;</span> <span class="nc">Person</span><span class="o">(</span><span class="n">p</span><span class="o">(</span><span class="mi">0</span><span class="o">),</span> <span class="n">p</span><span class="o">(</span><span class="mi">1</span><span class="o">).</span><span class="n">trim</span><span class="o">.</span><span class="n">toInt</span><span class="o">)).</span><span class="n">toDF</span><span class="o">()</span>
 <span class="n">people</span><span class="o">.</span><span class="n">registerTempTable</span><span class="o">(</span><span class="s">&quot;people&quot;</span><span class="o">)</span>

 <span class="c1">// SQL statements can be run by using the sql methods provided by sqlContext.</span>
 <span class="k">val</span> <span class="n">teenagers</span> <span class="k">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">sql</span><span class="o">(</span><span class="s">&quot;SELECT name FROM people WHERE age &gt;= 13 AND age &lt;= 19&quot;</span><span class="o">)</span>

 <span class="c1">// The results of SQL queries are DataFrames and support all the normal RDD operations.</span>
 <span class="c1">// The columns of a row in the result can be accessed by ordinal.</span>
 <span class="n">teenagers</span><span class="o">.</span><span class="n">map</span><span class="o">(</span><span class="n">t</span> <span class="k">=&gt;</span> <span class="s">&quot;Name: &quot;</span> <span class="o">+</span> <span class="n">t</span><span class="o">(</span><span class="mi">0</span><span class="o">)).</span><span class="n">collect</span><span class="o">().</span><span class="n">foreach</span><span class="o">(</span><span class="n">println</span><span class="o">)</span></code></pre></div>

   </div>

 <div data-lang="java">

     <p>Spark SQL supports automatically converting an RDD of <a href="http://stackoverflow.com/questions/3295496/what-is-a-javabean-exactly">JavaBeans</a>
 into a DataFrame.  The BeanInfo, obtained using reflection, defines the schema of the table.
 Currently, Spark SQL does not support JavaBeans that contain
 nested or contain complex types such as Lists or Arrays.  You can create a JavaBean by creating a
 class that implements Serializable and has getters and setters for all of its fields.</p>

     <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kd">public</span> <span class="kd">static</span> <span class="kd">class</span> <span class="nc">Person</span> <span class="kd">implements</span> <span class="n">Serializable</span> <span class="o">{</span>
   <span class="kd">private</span> <span class="n">String</span> <span class="n">name</span><span class="o">;</span>
   <span class="kd">private</span> <span class="kt">int</span> <span class="n">age</span><span class="o">;</span>

   <span class="kd">public</span> <span class="n">String</span> <span class="nf">getName</span><span class="o">()</span> <span class="o">{</span>
     <span class="k">return</span> <span class="n">name</span><span class="o">;</span>
   <span class="o">}</span>

   <span class="kd">public</span> <span class="kt">void</span> <span class="nf">setName</span><span class="o">(</span><span class="n">String</span> <span class="n">name</span><span class="o">)</span> <span class="o">{</span>
     <span class="k">this</span><span class="o">.</span><span class="na">name</span> <span class="o">=</span> <span class="n">name</span><span class="o">;</span>
   <span class="o">}</span>

   <span class="kd">public</span> <span class="kt">int</span> <span class="nf">getAge</span><span class="o">()</span> <span class="o">{</span>
     <span class="k">return</span> <span class="n">age</span><span class="o">;</span>
   <span class="o">}</span>

   <span class="kd">public</span> <span class="kt">void</span> <span class="nf">setAge</span><span class="o">(</span><span class="kt">int</span> <span class="n">age</span><span class="o">)</span> <span class="o">{</span>
     <span class="k">this</span><span class="o">.</span><span class="na">age</span> <span class="o">=</span> <span class="n">age</span><span class="o">;</span>
   <span class="o">}</span>
 <span class="o">}</span></code></pre></div>

     <p>A schema can be applied to an existing RDD by calling <code>createDataFrame</code> and providing the Class object
 for the JavaBean.</p>

     <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="c1">// sc is an existing JavaSparkContext.</span>
 <span class="n">SQLContext</span> <span class="n">sqlContext</span> <span class="o">=</span> <span class="k">new</span> <span class="n">org</span><span class="o">.</span><span class="na">apache</span><span class="o">.</span><span class="na">spark</span><span class="o">.</span><span class="na">sql</span><span class="o">.</span><span class="na">SQLContext</span><span class="o">(</span><span class="n">sc</span><span class="o">);</span>

 <span class="c1">// Load a text file and convert each line to a JavaBean.</span>
 <span class="n">JavaRDD</span><span class="o">&lt;</span><span class="n">Person</span><span class="o">&gt;</span> <span class="n">people</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="na">textFile</span><span class="o">(</span><span class="s">&quot;examples/src/main/resources/people.txt&quot;</span><span class="o">).</span><span class="na">map</span><span class="o">(</span>
   <span class="k">new</span> <span class="n">Function</span><span class="o">&lt;</span><span class="n">String</span><span class="o">,</span> <span class="n">Person</span><span class="o">&gt;()</span> <span class="o">{</span>
     <span class="kd">public</span> <span class="n">Person</span> <span class="nf">call</span><span class="o">(</span><span class="n">String</span> <span class="n">line</span><span class="o">)</span> <span class="kd">throws</span> <span class="n">Exception</span> <span class="o">{</span>
       <span class="n">String</span><span class="o">[]</span> <span class="n">parts</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="na">split</span><span class="o">(</span><span class="s">&quot;,&quot;</span><span class="o">);</span>

       <span class="n">Person</span> <span class="n">person</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">Person</span><span class="o">();</span>
       <span class="n">person</span><span class="o">.</span><span class="na">setName</span><span class="o">(</span><span class="n">parts</span><span class="o">[</span><span class="mi">0</span><span class="o">]);</span>
       <span class="n">person</span><span class="o">.</span><span class="na">setAge</span><span class="o">(</span><span class="n">Integer</span><span class="o">.</span><span class="na">parseInt</span><span class="o">(</span><span class="n">parts</span><span class="o">[</span><span class="mi">1</span><span class="o">].</span><span class="na">trim</span><span class="o">()));</span>

       <span class="k">return</span> <span class="n">person</span><span class="o">;</span>
     <span class="o">}</span>
   <span class="o">});</span>

 <span class="c1">// Apply a schema to an RDD of JavaBeans and register it as a table.</span>
 <span class="n">DataFrame</span> <span class="n">schemaPeople</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">people</span><span class="o">,</span> <span class="n">Person</span><span class="o">.</span><span class="na">class</span><span class="o">);</span>
 <span class="n">schemaPeople</span><span class="o">.</span><span class="na">registerTempTable</span><span class="o">(</span><span class="s">&quot;people&quot;</span><span class="o">);</span>

 <span class="c1">// SQL can be run over RDDs that have been registered as tables.</span>
 <span class="n">DataFrame</span> <span class="n">teenagers</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="na">sql</span><span class="o">(</span><span class="s">&quot;SELECT name FROM people WHERE age &gt;= 13 AND age &lt;= 19&quot;</span><span class="o">)</span>

 <span class="c1">// The results of SQL queries are DataFrames and support all the normal RDD operations.</span>
 <span class="c1">// The columns of a row in the result can be accessed by ordinal.</span>
 <span class="n">List</span><span class="o">&lt;</span><span class="n">String</span><span class="o">&gt;</span> <span class="n">teenagerNames</span> <span class="o">=</span> <span class="n">teenagers</span><span class="o">.</span><span class="na">map</span><span class="o">(</span><span class="k">new</span> <span class="n">Function</span><span class="o">&lt;</span><span class="n">Row</span><span class="o">,</span> <span class="n">String</span><span class="o">&gt;()</span> <span class="o">{</span>
   <span class="kd">public</span> <span class="n">String</span> <span class="nf">call</span><span class="o">(</span><span class="n">Row</span> <span class="n">row</span><span class="o">)</span> <span class="o">{</span>
     <span class="k">return</span> <span class="s">&quot;Name: &quot;</span> <span class="o">+</span> <span class="n">row</span><span class="o">.</span><span class="na">getString</span><span class="o">(</span><span class="mi">0</span><span class="o">);</span>
   <span class="o">}</span>
 <span class="o">}).</span><span class="na">collect</span><span class="o">();</span></code></pre></div>

   </div>

 <div data-lang="python">

     <p>Spark SQL can convert an RDD of Row objects to a DataFrame, inferring the datatypes.  Rows are constructed by passing a list of
 key/value pairs as kwargs to the Row class. The keys of this list define the column names of the table,
 and the types are inferred by looking at the first row.  Since we currently only look at the first
 row, it is important that there is no missing data in the first row of the RDD. In future versions we
 plan to more completely infer the schema by looking at more data, similar to the inference that is
 performed on JSON files.</p>

     <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="c"># sc is an existing SparkContext.</span>
 <span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">SQLContext</span><span class="p">,</span> <span class="n">Row</span>
 <span class="n">sqlContext</span> <span class="o">=</span> <span class="n">SQLContext</span><span class="p">(</span><span class="n">sc</span><span class="p">)</span>

 <span class="c"># Load a text file and convert each line to a Row.</span>
 <span class="n">lines</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">&quot;examples/src/main/resources/people.txt&quot;</span><span class="p">)</span>
 <span class="n">parts</span> <span class="o">=</span> <span class="n">lines</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">l</span><span class="p">:</span> <span class="n">l</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">&quot;,&quot;</span><span class="p">))</span>
 <span class="n">people</span> <span class="o">=</span> <span class="n">parts</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">p</span><span class="p">:</span> <span class="n">Row</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">age</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">])))</span>

 <span class="c"># Infer the schema, and register the DataFrame as a table.</span>
 <span class="n">schemaPeople</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">inferSchema</span><span class="p">(</span><span class="n">people</span><span class="p">)</span>
 <span class="n">schemaPeople</span><span class="o">.</span><span class="n">registerTempTable</span><span class="p">(</span><span class="s">&quot;people&quot;</span><span class="p">)</span>

 <span class="c"># SQL can be run over DataFrames that have been registered as a table.</span>
 <span class="n">teenagers</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">sql</span><span class="p">(</span><span class="s">&quot;SELECT name FROM people WHERE age &gt;= 13 AND age &lt;= 19&quot;</span><span class="p">)</span>

 <span class="c"># The results of SQL queries are RDDs and support all the normal RDD operations.</span>
 <span class="n">teenNames</span> <span class="o">=</span> <span class="n">teenagers</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">p</span><span class="p">:</span> <span class="s">&quot;Name: &quot;</span> <span class="o">+</span> <span class="n">p</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
 <span class="k">for</span> <span class="n">teenName</span> <span class="ow">in</span> <span class="n">teenNames</span><span class="o">.</span><span class="n">collect</span><span class="p">():</span>
   <span class="k">print</span> <span class="n">teenName</span></code></pre></div>

   </div>

 </div>

 <h3 id="programmatically-specifying-the-schema">Programmatically Specifying the Schema</h3>

 <div class="codetabs">

 <div data-lang="scala">

     <p>When case classes cannot be defined ahead of time (for example,
 the structure of records is encoded in a string, or a text dataset will be parsed
 and fields will be projected differently for different users),
 a <code>DataFrame</code> can be created programmatically with three steps.</p>

     <ol>
       <li>Create an RDD of <code>Row</code>s from the original RDD;</li>
       <li>Create the schema represented by a <code>StructType</code> matching the structure of
 <code>Row</code>s in the RDD created in Step 1.</li>
       <li>Apply the schema to the RDD of <code>Row</code>s via <code>createDataFrame</code> method provided
 by <code>SQLContext</code>.</li>
     </ol>

     <p>For example:</p>

     <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="c1">// sc is an existing SparkContext.</span>
 <span class="k">val</span> <span class="n">sqlContext</span> <span class="k">=</span> <span class="k">new</span> <span class="n">org</span><span class="o">.</span><span class="n">apache</span><span class="o">.</span><span class="n">spark</span><span class="o">.</span><span class="n">sql</span><span class="o">.</span><span class="nc">SQLContext</span><span class="o">(</span><span class="n">sc</span><span class="o">)</span>

 <span class="c1">// Create an RDD</span>
 <span class="k">val</span> <span class="n">people</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="o">(</span><span class="s">&quot;examples/src/main/resources/people.txt&quot;</span><span class="o">)</span>

 <span class="c1">// The schema is encoded in a string</span>
 <span class="k">val</span> <span class="n">schemaString</span> <span class="k">=</span> <span class="s">&quot;name age&quot;</span>

 <span class="c1">// Import Spark SQL data types and Row.</span>
 <span class="k">import</span> <span class="nn">org.apache.spark.sql._</span>

 <span class="c1">// Generate the schema based on the string of schema</span>
 <span class="k">val</span> <span class="n">schema</span> <span class="k">=</span>
   <span class="nc">StructType</span><span class="o">(</span>
     <span class="n">schemaString</span><span class="o">.</span><span class="n">split</span><span class="o">(</span><span class="s">&quot; &quot;</span><span class="o">).</span><span class="n">map</span><span class="o">(</span><span class="n">fieldName</span> <span class="k">=&gt;</span> <span class="nc">StructField</span><span class="o">(</span><span class="n">fieldName</span><span class="o">,</span> <span class="nc">StringType</span><span class="o">,</span> <span class="kc">true</span><span class="o">)))</span>

 <span class="c1">// Convert records of the RDD (people) to Rows.</span>
 <span class="k">val</span> <span class="n">rowRDD</span> <span class="k">=</span> <span class="n">people</span><span class="o">.</span><span class="n">map</span><span class="o">(</span><span class="k">_</span><span class="o">.</span><span class="n">split</span><span class="o">(</span><span class="s">&quot;,&quot;</span><span class="o">)).</span><span class="n">map</span><span class="o">(</span><span class="n">p</span> <span class="k">=&gt;</span> <span class="nc">Row</span><span class="o">(</span><span class="n">p</span><span class="o">(</span><span class="mi">0</span><span class="o">),</span> <span class="n">p</span><span class="o">(</span><span class="mi">1</span><span class="o">).</span><span class="n">trim</span><span class="o">))</span>

 <span class="c1">// Apply the schema to the RDD.</span>
 <span class="k">val</span> <span class="n">peopleDataFrame</span> <span class="k">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">createDataFrame</span><span class="o">(</span><span class="n">rowRDD</span><span class="o">,</span> <span class="n">schema</span><span class="o">)</span>

 <span class="c1">// Register the DataFrames as a table.</span>
 <span class="n">peopleDataFrame</span><span class="o">.</span><span class="n">registerTempTable</span><span class="o">(</span><span class="s">&quot;people&quot;</span><span class="o">)</span>

 <span class="c1">// SQL statements can be run by using the sql methods provided by sqlContext.</span>
 <span class="k">val</span> <span class="n">results</span> <span class="k">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">sql</span><span class="o">(</span><span class="s">&quot;SELECT name FROM people&quot;</span><span class="o">)</span>

 <span class="c1">// The results of SQL queries are DataFrames and support all the normal RDD operations.</span>
 <span class="c1">// The columns of a row in the result can be accessed by ordinal.</span>
 <span class="n">results</span><span class="o">.</span><span class="n">map</span><span class="o">(</span><span class="n">t</span> <span class="k">=&gt;</span> <span class="s">&quot;Name: &quot;</span> <span class="o">+</span> <span class="n">t</span><span class="o">(</span><span class="mi">0</span><span class="o">)).</span><span class="n">collect</span><span class="o">().</span><span class="n">foreach</span><span class="o">(</span><span class="n">println</span><span class="o">)</span></code></pre></div>

   </div>

 <div data-lang="java">

     <p>When JavaBean classes cannot be defined ahead of time (for example,
 the structure of records is encoded in a string, or a text dataset will be parsed and
 fields will be projected differently for different users),
 a <code>DataFrame</code> can be created programmatically with three steps.</p>

     <ol>
       <li>Create an RDD of <code>Row</code>s from the original RDD;</li>
       <li>Create the schema represented by a <code>StructType</code> matching the structure of
 <code>Row</code>s in the RDD created in Step 1.</li>
       <li>Apply the schema to the RDD of <code>Row</code>s via <code>createDataFrame</code> method provided
 by <code>SQLContext</code>.</li>
     </ol>

     <p>For example:</p>

     <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="c1">// Import factory methods provided by DataType.</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.sql.types.DataType</span><span class="o">;</span>
 <span class="c1">// Import StructType and StructField</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.sql.types.StructType</span><span class="o">;</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.sql.types.StructField</span><span class="o">;</span>
 <span class="c1">// Import Row.</span>
 <span class="kn">import</span> <span class="nn">org.apache.spark.sql.Row</span><span class="o">;</span>

 <span class="c1">// sc is an existing JavaSparkContext.</span>
 <span class="n">SQLContext</span> <span class="n">sqlContext</span> <span class="o">=</span> <span class="k">new</span> <span class="n">org</span><span class="o">.</span><span class="na">apache</span><span class="o">.</span><span class="na">spark</span><span class="o">.</span><span class="na">sql</span><span class="o">.</span><span class="na">SQLContext</span><span class="o">(</span><span class="n">sc</span><span class="o">);</span>

 <span class="c1">// Load a text file and convert each line to a JavaBean.</span>
 <span class="n">JavaRDD</span><span class="o">&lt;</span><span class="n">String</span><span class="o">&gt;</span> <span class="n">people</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="na">textFile</span><span class="o">(</span><span class="s">&quot;examples/src/main/resources/people.txt&quot;</span><span class="o">);</span>

 <span class="c1">// The schema is encoded in a string</span>
 <span class="n">String</span> <span class="n">schemaString</span> <span class="o">=</span> <span class="s">&quot;name age&quot;</span><span class="o">;</span>

 <span class="c1">// Generate the schema based on the string of schema</span>
 <span class="n">List</span><span class="o">&lt;</span><span class="n">StructField</span><span class="o">&gt;</span> <span class="n">fields</span> <span class="o">=</span> <span class="k">new</span> <span class="n">ArrayList</span><span class="o">&lt;</span><span class="n">StructField</span><span class="o">&gt;();</span>
 <span class="k">for</span> <span class="o">(</span><span class="n">String</span> <span class="nl">fieldName:</span> <span class="n">schemaString</span><span class="o">.</span><span class="na">split</span><span class="o">(</span><span class="s">&quot; &quot;</span><span class="o">))</span> <span class="o">{</span>
   <span class="n">fields</span><span class="o">.</span><span class="na">add</span><span class="o">(</span><span class="n">DataType</span><span class="o">.</span><span class="na">createStructField</span><span class="o">(</span><span class="n">fieldName</span><span class="o">,</span> <span class="n">DataType</span><span class="o">.</span><span class="na">StringType</span><span class="o">,</span> <span class="kc">true</span><span class="o">));</span>
 <span class="o">}</span>
 <span class="n">StructType</span> <span class="n">schema</span> <span class="o">=</span> <span class="n">DataType</span><span class="o">.</span><span class="na">createStructType</span><span class="o">(</span><span class="n">fields</span><span class="o">);</span>

 <span class="c1">// Convert records of the RDD (people) to Rows.</span>
 <span class="n">JavaRDD</span><span class="o">&lt;</span><span class="n">Row</span><span class="o">&gt;</span> <span class="n">rowRDD</span> <span class="o">=</span> <span class="n">people</span><span class="o">.</span><span class="na">map</span><span class="o">(</span>
   <span class="k">new</span> <span class="n">Function</span><span class="o">&lt;</span><span class="n">String</span><span class="o">,</span> <span class="n">Row</span><span class="o">&gt;()</span> <span class="o">{</span>
     <span class="kd">public</span> <span class="n">Row</span> <span class="nf">call</span><span class="o">(</span><span class="n">String</span> <span class="n">record</span><span class="o">)</span> <span class="kd">throws</span> <span class="n">Exception</span> <span class="o">{</span>
       <span class="n">String</span><span class="o">[]</span> <span class="n">fields</span> <span class="o">=</span> <span class="n">record</span><span class="o">.</span><span class="na">split</span><span class="o">(</span><span class="s">&quot;,&quot;</span><span class="o">);</span>
       <span class="k">return</span> <span class="n">Row</span><span class="o">.</span><span class="na">create</span><span class="o">(</span><span class="n">fields</span><span class="o">[</span><span class="mi">0</span><span class="o">],</span> <span class="n">fields</span><span class="o">[</span><span class="mi">1</span><span class="o">].</span><span class="na">trim</span><span class="o">());</span>
     <span class="o">}</span>
   <span class="o">});</span>

 <span class="c1">// Apply the schema to the RDD.</span>
 <span class="n">DataFrame</span> <span class="n">peopleDataFrame</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">rowRDD</span><span class="o">,</span> <span class="n">schema</span><span class="o">);</span>

 <span class="c1">// Register the DataFrame as a table.</span>
 <span class="n">peopleDataFrame</span><span class="o">.</span><span class="na">registerTempTable</span><span class="o">(</span><span class="s">&quot;people&quot;</span><span class="o">);</span>

 <span class="c1">// SQL can be run over RDDs that have been registered as tables.</span>
 <span class="n">DataFrame</span> <span class="n">results</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="na">sql</span><span class="o">(</span><span class="s">&quot;SELECT name FROM people&quot;</span><span class="o">);</span>

 <span class="c1">// The results of SQL queries are DataFrames and support all the normal RDD operations.</span>
 <span class="c1">// The columns of a row in the result can be accessed by ordinal.</span>
 <span class="n">List</span><span class="o">&lt;</span><span class="n">String</span><span class="o">&gt;</span> <span class="n">names</span> <span class="o">=</span> <span class="n">results</span><span class="o">.</span><span class="na">map</span><span class="o">(</span><span class="k">new</span> <span class="n">Function</span><span class="o">&lt;</span><span class="n">Row</span><span class="o">,</span> <span class="n">String</span><span class="o">&gt;()</span> <span class="o">{</span>
   <span class="kd">public</span> <span class="n">String</span> <span class="nf">call</span><span class="o">(</span><span class="n">Row</span> <span class="n">row</span><span class="o">)</span> <span class="o">{</span>
     <span class="k">return</span> <span class="s">&quot;Name: &quot;</span> <span class="o">+</span> <span class="n">row</span><span class="o">.</span><span class="na">getString</span><span class="o">(</span><span class="mi">0</span><span class="o">);</span>
   <span class="o">}</span>
 <span class="o">}).</span><span class="na">collect</span><span class="o">();</span></code></pre></div>

   </div>

 <div data-lang="python">

     <p>When a dictionary of kwargs cannot be defined ahead of time (for example,
 the structure of records is encoded in a string, or a text dataset will be parsed and
 fields will be projected differently for different users),
 a <code>DataFrame</code> can be created programmatically with three steps.</p>

     <ol>
       <li>Create an RDD of tuples or lists from the original RDD;</li>
       <li>Create the schema represented by a <code>StructType</code> matching the structure of
 tuples or lists in the RDD created in the step 1.</li>
       <li>Apply the schema to the RDD via <code>createDataFrame</code> method provided by <code>SQLContext</code>.</li>
     </ol>

     <p>For example:</p>

     <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="c"># Import SQLContext and data types</span>
 <span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="o">*</span>

 <span class="c"># sc is an existing SparkContext.</span>
 <span class="n">sqlContext</span> <span class="o">=</span> <span class="n">SQLContext</span><span class="p">(</span><span class="n">sc</span><span class="p">)</span>

 <span class="c"># Load a text file and convert each line to a tuple.</span>
 <span class="n">lines</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">&quot;examples/src/main/resources/people.txt&quot;</span><span class="p">)</span>
 <span class="n">parts</span> <span class="o">=</span> <span class="n">lines</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">l</span><span class="p">:</span> <span class="n">l</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">&quot;,&quot;</span><span class="p">))</span>
 <span class="n">people</span> <span class="o">=</span> <span class="n">parts</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">p</span><span class="p">:</span> <span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">strip</span><span class="p">()))</span>

 <span class="c"># The schema is encoded in a string.</span>
 <span class="n">schemaString</span> <span class="o">=</span> <span class="s">&quot;name age&quot;</span>

 <span class="n">fields</span> <span class="o">=</span> <span class="p">[</span><span class="n">StructField</span><span class="p">(</span><span class="n">field_name</span><span class="p">,</span> <span class="n">StringType</span><span class="p">(),</span> <span class="bp">True</span><span class="p">)</span> <span class="k">for</span> <span class="n">field_name</span> <span class="ow">in</span> <span class="n">schemaString</span><span class="o">.</span><span class="n">split</span><span class="p">()]</span>
 <span class="n">schema</span> <span class="o">=</span> <span class="n">StructType</span><span class="p">(</span><span class="n">fields</span><span class="p">)</span>

 <span class="c"># Apply the schema to the RDD.</span>
 <span class="n">schemaPeople</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span><span class="n">people</span><span class="p">,</span> <span class="n">schema</span><span class="p">)</span>

 <span class="c"># Register the DataFrame as a table.</span>
 <span class="n">schemaPeople</span><span class="o">.</span><span class="n">registerTempTable</span><span class="p">(</span><span class="s">&quot;people&quot;</span><span class="p">)</span>

 <span class="c"># SQL can be run over DataFrames that have been registered as a table.</span>
 <span class="n">results</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">sql</span><span class="p">(</span><span class="s">&quot;SELECT name FROM people&quot;</span><span class="p">)</span>

 <span class="c"># The results of SQL queries are RDDs and support all the normal RDD operations.</span>
 <span class="n">names</span> <span class="o">=</span> <span class="n">results</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">p</span><span class="p">:</span> <span class="s">&quot;Name: &quot;</span> <span class="o">+</span> <span class="n">p</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
 <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">names</span><span class="o">.</span><span class="n">collect</span><span class="p">():</span>
   <span class="k">print</span> <span class="n">name</span></code></pre></div>

   </div>

 </div>

 <h1 id="data-sources">Data Sources</h1>

 <p>Spark SQL supports operating on a variety of data sources through the <code>DataFrame</code> interface.
 A DataFrame can be operated on as normal RDDs and can also be registered as a temporary table.
 Registering a DataFrame as a table allows you to run SQL queries over its data.  This section
 describes the general methods for loading and saving data using the Spark Data Sources and then
 goes into specific options that are available for the built-in data sources.</p>

 <h2 id="generic-loadsave-functions">Generic Load/Save Functions</h2>

 <p>In the simplest form, the default data source (<code>parquet</code> unless otherwise configured by
 <code>spark.sql.sources.default</code>) will be used for all operations.</p>

 <div class="codetabs">
 <div data-lang="scala">

     <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">val</span> <span class="n">df</span> <span class="k">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">load</span><span class="o">(</span><span class="s">&quot;people.parquet&quot;</span><span class="o">)</span>
 <span class="n">df</span><span class="o">.</span><span class="n">select</span><span class="o">(</span><span class="s">&quot;name&quot;</span><span class="o">,</span> <span class="s">&quot;age&quot;</span><span class="o">).</span><span class="n">save</span><span class="o">(</span><span class="s">&quot;namesAndAges.parquet&quot;</span><span class="o">)</span></code></pre></div>

   </div>

 <div data-lang="java">

     <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="n">DataFrame</span> <span class="n">df</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="na">load</span><span class="o">(</span><span class="s">&quot;people.parquet&quot;</span><span class="o">);</span>
 <span class="n">df</span><span class="o">.</span><span class="na">select</span><span class="o">(</span><span class="s">&quot;name&quot;</span><span class="o">,</span> <span class="s">&quot;age&quot;</span><span class="o">).</span><span class="na">save</span><span class="o">(</span><span class="s">&quot;namesAndAges.parquet&quot;</span><span class="o">);</span></code></pre></div>

   </div>

 <div data-lang="python">

     <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="n">df</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s">&quot;people.parquet&quot;</span><span class="p">)</span>
 <span class="n">df</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s">&quot;name&quot;</span><span class="p">,</span> <span class="s">&quot;age&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s">&quot;namesAndAges.parquet&quot;</span><span class="p">)</span></code></pre></div>

   </div>
 </div>

 <h3 id="manually-specifying-options">Manually Specifying Options</h3>

 <p>You can also manually specify the data source that will be used along with any extra options
 that you would like to pass to the data source.  Data sources are specified by their fully qualified
 name (i.e., <code>org.apache.spark.sql.parquet</code>), but for built-in sources you can also use the shorted
 name (<code>json</code>, <code>parquet</code>, <code>jdbc</code>).  DataFrames of any type can be converted into other types
 using this syntax.</p>

 <div class="codetabs">
 <div data-lang="scala">

     <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">val</span> <span class="n">df</span> <span class="k">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">load</span><span class="o">(</span><span class="s">&quot;people.json&quot;</span><span class="o">,</span> <span class="s">&quot;json&quot;</span><span class="o">)</span>
 <span class="n">df</span><span class="o">.</span><span class="n">select</span><span class="o">(</span><span class="s">&quot;name&quot;</span><span class="o">,</span> <span class="s">&quot;age&quot;</span><span class="o">).</span><span class="n">save</span><span class="o">(</span><span class="s">&quot;namesAndAges.parquet&quot;</span><span class="o">,</span> <span class="s">&quot;parquet&quot;</span><span class="o">)</span></code></pre></div>

   </div>

 <div data-lang="java">

     <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="n">DataFrame</span> <span class="n">df</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="na">load</span><span class="o">(</span><span class="s">&quot;people.json&quot;</span><span class="o">,</span> <span class="s">&quot;json&quot;</span><span class="o">);</span>
 <span class="n">df</span><span class="o">.</span><span class="na">select</span><span class="o">(</span><span class="s">&quot;name&quot;</span><span class="o">,</span> <span class="s">&quot;age&quot;</span><span class="o">).</span><span class="na">save</span><span class="o">(</span><span class="s">&quot;namesAndAges.parquet&quot;</span><span class="o">,</span> <span class="s">&quot;parquet&quot;</span><span class="o">);</span></code></pre></div>

   </div>

 <div data-lang="python">

     <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="n">df</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s">&quot;people.json&quot;</span><span class="p">,</span> <span class="s">&quot;json&quot;</span><span class="p">)</span>
 <span class="n">df</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s">&quot;name&quot;</span><span class="p">,</span> <span class="s">&quot;age&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s">&quot;namesAndAges.parquet&quot;</span><span class="p">,</span> <span class="s">&quot;parquet&quot;</span><span class="p">)</span></code></pre></div>

   </div>
 </div>

 <h3 id="save-modes">Save Modes</h3>

 <p>Save operations can optionally take a <code>SaveMode</code>, that specifies how to handle existing data if
 present.  It is important to realize that these save modes do not utilize any locking and are not
 atomic.  Thus, it is not safe to have multiple writers attempting to write to the same location.
 Additionally, when performing a <code>Overwrite</code>, the data will be deleted before writing out the
 new data.</p>

 <table class="table">
 <tr><th>Scala/Java</th><th>Python</th><th>Meaning</th></tr>
 <tr>
   <td><code>SaveMode.ErrorIfExists</code> (default)</td>
   <td><code>"error"</code> (default)</td>
   <td>
     When saving a DataFrame to a data source, if data already exists,
     an exception is expected to be thrown.
   </td>
 </tr>
 <tr>
   <td><code>SaveMode.Append</code></td>
   <td><code>"append"</code></td>
   <td>
     When saving a DataFrame to a data source, if data/table already exists,
     contents of the DataFrame are expected to be appended to existing data.
   </td>
 </tr>
 <tr>
   <td><code>SaveMode.Overwrite</code></td>
   <td><code>"overwrite"</code></td>
   <td>
     Overwrite mode means that when saving a DataFrame to a data source,
     if data/table already exists, existing data is expected to be overwritten by the contents of
     the DataFrame.
   </td>
 </tr>
 <tr>
   <td><code>SaveMode.Ignore</code></td>
   <td><code>"ignore"</code></td>
   <td>
     Ignore mode means that when saving a DataFrame to a data source, if data already exists,
     the save operation is expected to not save the contents of the DataFrame and to not
     change the existing data.  This is similar to a `CREATE TABLE IF NOT EXISTS` in SQL.
   </td>
 </tr>
 </table>

 <h3 id="saving-to-persistent-tables">Saving to Persistent Tables</h3>

 <p>When working with a <code>HiveContext</code>, <code>DataFrames</code> can also be saved as persistent tables using the
 <code>saveAsTable</code> command.  Unlike the <code>registerTempTable</code> command, <code>saveAsTable</code> will materialize the
 contents of the dataframe and create a pointer to the data in the HiveMetastore.  Persistent tables
 will still exist even after your Spark program has restarted, as long as you maintain your connection
 to the same metastore.  A DataFrame for a persistent table can be created by calling the <code>table</code>
 method on a <code>SQLContext</code> with the name of the table.</p>

 <p>By default <code>saveAsTable</code> will create a &#8220;managed table&#8221;, meaning that the location of the data will
 be controlled by the metastore.  Managed tables will also have their data deleted automatically
 when a table is dropped.</p>

 <h2 id="parquet-files">Parquet Files</h2>

 <p><a href="http://parquet.io">Parquet</a> is a columnar format that is supported by many other data processing systems.
 Spark SQL provides support for both reading and writing Parquet files that automatically preserves the schema
 of the original data.</p>

 <h3 id="loading-data-programmatically">Loading Data Programmatically</h3>

 <p>Using the data from the above example:</p>

 <div class="codetabs">

 <div data-lang="scala">

     <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="c1">// sqlContext from the previous example is used in this example.</span>
 <span class="c1">// This is used to implicitly convert an RDD to a DataFrame.</span>
 <span class="k">import</span> <span class="nn">sqlContext.implicits._</span>

 <span class="k">val</span> <span class="n">people</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span class="kt">Person</span><span class="o">]</span> <span class="k">=</span> <span class="o">...</span> <span class="c1">// An RDD of case class objects, from the previous example.</span>

 <span class="c1">// The RDD is implicitly converted to a DataFrame by implicits, allowing it to be stored using Parquet.</span>
 <span class="n">people</span><span class="o">.</span><span class="n">saveAsParquetFile</span><span class="o">(</span><span class="s">&quot;people.parquet&quot;</span><span class="o">)</span>

 <span class="c1">// Read in the parquet file created above.  Parquet files are self-describing so the schema is preserved.</span>
 <span class="c1">// The result of loading a Parquet file is also a DataFrame.</span>
 <span class="k">val</span> <span class="n">parquetFile</span> <span class="k">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">parquetFile</span><span class="o">(</span><span class="s">&quot;people.parquet&quot;</span><span class="o">)</span>

 <span class="c1">//Parquet files can also be registered as tables and then used in SQL statements.</span>
 <span class="n">parquetFile</span><span class="o">.</span><span class="n">registerTempTable</span><span class="o">(</span><span class="s">&quot;parquetFile&quot;</span><span class="o">)</span>
 <span class="k">val</span> <span class="n">teenagers</span> <span class="k">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">sql</span><span class="o">(</span><span class="s">&quot;SELECT name FROM parquetFile WHERE age &gt;= 13 AND age &lt;= 19&quot;</span><span class="o">)</span>
 <span class="n">teenagers</span><span class="o">.</span><span class="n">map</span><span class="o">(</span><span class="n">t</span> <span class="k">=&gt;</span> <span class="s">&quot;Name: &quot;</span> <span class="o">+</span> <span class="n">t</span><span class="o">(</span><span class="mi">0</span><span class="o">)).</span><span class="n">collect</span><span class="o">().</span><span class="n">foreach</span><span class="o">(</span><span class="n">println</span><span class="o">)</span></code></pre></div>

   </div>

 <div data-lang="java">

     <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="c1">// sqlContext from the previous example is used in this example.</span>

 <span class="n">DataFrame</span> <span class="n">schemaPeople</span> <span class="o">=</span> <span class="o">...</span> <span class="c1">// The DataFrame from the previous example.</span>

 <span class="c1">// DataFrames can be saved as Parquet files, maintaining the schema information.</span>
 <span class="n">schemaPeople</span><span class="o">.</span><span class="na">saveAsParquetFile</span><span class="o">(</span><span class="s">&quot;people.parquet&quot;</span><span class="o">);</span>

 <span class="c1">// Read in the Parquet file created above.  Parquet files are self-describing so the schema is preserved.</span>
 <span class="c1">// The result of loading a parquet file is also a DataFrame.</span>
 <span class="n">DataFrame</span> <span class="n">parquetFile</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="na">parquetFile</span><span class="o">(</span><span class="s">&quot;people.parquet&quot;</span><span class="o">);</span>

 <span class="c1">//Parquet files can also be registered as tables and then used in SQL statements.</span>
 <span class="n">parquetFile</span><span class="o">.</span><span class="na">registerTempTable</span><span class="o">(</span><span class="s">&quot;parquetFile&quot;</span><span class="o">);</span>
 <span class="n">DataFrame</span> <span class="n">teenagers</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="na">sql</span><span class="o">(</span><span class="s">&quot;SELECT name FROM parquetFile WHERE age &gt;= 13 AND age &lt;= 19&quot;</span><span class="o">);</span>
 <span class="n">List</span><span class="o">&lt;</span><span class="n">String</span><span class="o">&gt;</span> <span class="n">teenagerNames</span> <span class="o">=</span> <span class="n">teenagers</span><span class="o">.</span><span class="na">map</span><span class="o">(</span><span class="k">new</span> <span class="n">Function</span><span class="o">&lt;</span><span class="n">Row</span><span class="o">,</span> <span class="n">String</span><span class="o">&gt;()</span> <span class="o">{</span>
   <span class="kd">public</span> <span class="n">String</span> <span class="nf">call</span><span class="o">(</span><span class="n">Row</span> <span class="n">row</span><span class="o">)</span> <span class="o">{</span>
     <span class="k">return</span> <span class="s">&quot;Name: &quot;</span> <span class="o">+</span> <span class="n">row</span><span class="o">.</span><span class="na">getString</span><span class="o">(</span><span class="mi">0</span><span class="o">);</span>
   <span class="o">}</span>
 <span class="o">}).</span><span class="na">collect</span><span class="o">();</span></code></pre></div>

   </div>

 <div data-lang="python">

     <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="c"># sqlContext from the previous example is used in this example.</span>

 <span class="n">schemaPeople</span> <span class="c"># The DataFrame from the previous example.</span>

 <span class="c"># DataFrames can be saved as Parquet files, maintaining the schema information.</span>
 <span class="n">schemaPeople</span><span class="o">.</span><span class="n">saveAsParquetFile</span><span class="p">(</span><span class="s">&quot;people.parquet&quot;</span><span class="p">)</span>

 <span class="c"># Read in the Parquet file created above.  Parquet files are self-describing so the schema is preserved.</span>
 <span class="c"># The result of loading a parquet file is also a DataFrame.</span>
 <span class="n">parquetFile</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">parquetFile</span><span class="p">(</span><span class="s">&quot;people.parquet&quot;</span><span class="p">)</span>

 <span class="c"># Parquet files can also be registered as tables and then used in SQL statements.</span>
 <span class="n">parquetFile</span><span class="o">.</span><span class="n">registerTempTable</span><span class="p">(</span><span class="s">&quot;parquetFile&quot;</span><span class="p">);</span>
 <span class="n">teenagers</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">sql</span><span class="p">(</span><span class="s">&quot;SELECT name FROM parquetFile WHERE age &gt;= 13 AND age &lt;= 19&quot;</span><span class="p">)</span>
 <span class="n">teenNames</span> <span class="o">=</span> <span class="n">teenagers</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">p</span><span class="p">:</span> <span class="s">&quot;Name: &quot;</span> <span class="o">+</span> <span class="n">p</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
 <span class="k">for</span> <span class="n">teenName</span> <span class="ow">in</span> <span class="n">teenNames</span><span class="o">.</span><span class="n">collect</span><span class="p">():</span>
   <span class="k">print</span> <span class="n">teenName</span></code></pre></div>

   </div>

 <div data-lang="sql">

     <div class="highlight"><pre><code class="language-sql" data-lang="sql"><span class="k">CREATE</span> <span class="k">TEMPORARY</span> <span class="k">TABLE</span> <span class="n">parquetTable</span>
 <span class="k">USING</span> <span class="n">org</span><span class="p">.</span><span class="n">apache</span><span class="p">.</span><span class="n">spark</span><span class="p">.</span><span class="k">sql</span><span class="p">.</span><span class="n">parquet</span>
 <span class="k">OPTIONS</span> <span class="p">(</span>
   <span class="n">path</span> <span class="ss">&quot;examples/src/main/resources/people.parquet&quot;</span>
 <span class="p">)</span>

 <span class="k">SELECT</span> <span class="o">*</span> <span class="k">FROM</span> <span class="n">parquetTable</span></code></pre></div>

   </div>

 </div>

 <h3 id="partition-discovery">Partition discovery</h3>

 <p>Table partitioning is a common optimization approach used in systems like Hive.  In a partitioned
 table, data are usually stored in different directories, with partitioning column values encoded in
 the path of each partition directory.  The Parquet data source is now able to discover and infer
 partitioning information automatically.  For exmaple, we can store all our previously used
 population data into a partitioned table using the following directory structure, with two extra
 columns, <code>gender</code> and <code>country</code> as partitioning columns:</p>

 <div class="highlight"><pre><code class="language-text" data-lang="text">path
 └── to
     └── table
         ├── gender=male
         │   ├── ...
         │   │
         │   ├── country=US
         │   │   └── data.parquet
         │   ├── country=CN
         │   │   └── data.parquet
         │   └── ...
         └── gender=female
             ├── ...
             │
             ├── country=US
             │   └── data.parquet
             ├── country=CN
             │   └── data.parquet
             └── ...</code></pre></div>

 <p>By passing <code>path/to/table</code> to either <code>SQLContext.parquetFile</code> or <code>SQLContext.load</code>, Spark SQL will
 automatically extract the partitioning information from the paths.  Now the schema of the returned
 DataFrame becomes:</p>

 <div class="highlight"><pre><code class="language-text" data-lang="text">root
 |-- name: string (nullable = true)
 |-- age: long (nullable = true)
 |-- gender: string (nullable = true)
 |-- country: string (nullable = true)</code></pre></div>

 <p>Notice that the data types of the partitioning columns are automatically inferred.  Currently,
 numeric data types and string type are supported.</p>

 <h3 id="schema-merging">Schema merging</h3>

 <p>Like ProtocolBuffer, Avro, and Thrift, Parquet also supports schema evolution.  Users can start with
 a simple schema, and gradually add more columns to the schema as needed.  In this way, users may end
 up with multiple Parquet files with different but mutually compatible schemas.  The Parquet data
 source is now able to automatically detect this case and merge schemas of all these files.</p>

 <div class="codetabs">

 <div data-lang="scala">

     <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="c1">// sqlContext from the previous example is used in this example.</span>
 <span class="c1">// This is used to implicitly convert an RDD to a DataFrame.</span>
 <span class="k">import</span> <span class="nn">sqlContext.implicits._</span>

 <span class="c1">// Create a simple DataFrame, stored into a partition directory</span>
 <span class="k">val</span> <span class="n">df1</span> <span class="k">=</span> <span class="n">sparkContext</span><span class="o">.</span><span class="n">makeRDD</span><span class="o">(</span><span class="mi">1</span> <span class="n">to</span> <span class="mi">5</span><span class="o">).</span><span class="n">map</span><span class="o">(</span><span class="n">i</span> <span class="k">=&gt;</span> <span class="o">(</span><span class="n">i</span><span class="o">,</span> <span class="n">i</span> <span class="o">*</span> <span class="mi">2</span><span class="o">)).</span><span class="n">toDF</span><span class="o">(</span><span class="s">&quot;single&quot;</span><span class="o">,</span> <span class="s">&quot;double&quot;</span><span class="o">)</span>
 <span class="n">df1</span><span class="o">.</span><span class="n">saveAsParquetFile</span><span class="o">(</span><span class="s">&quot;data/test_table/key=1&quot;</span><span class="o">)</span>

 <span class="c1">// Create another DataFrame in a new partition directory,</span>
 <span class="c1">// adding a new column and dropping an existing column</span>
 <span class="k">val</span> <span class="n">df2</span> <span class="k">=</span> <span class="n">sparkContext</span><span class="o">.</span><span class="n">makeRDD</span><span class="o">(</span><span class="mi">6</span> <span class="n">to</span> <span class="mi">10</span><span class="o">).</span><span class="n">map</span><span class="o">(</span><span class="n">i</span> <span class="k">=&gt;</span> <span class="o">(</span><span class="n">i</span><span class="o">,</span> <span class="n">i</span> <span class="o">*</span> <span class="mi">3</span><span class="o">)).</span><span class="n">toDF</span><span class="o">(</span><span class="s">&quot;single&quot;</span><span class="o">,</span> <span class="s">&quot;triple&quot;</span><span class="o">)</span>
 <span class="n">df2</span><span class="o">.</span><span class="n">saveAsParquetFile</span><span class="o">(</span><span class="s">&quot;data/test_table/key=2&quot;</span><span class="o">)</span>

 <span class="c1">// Read the partitioned table</span>
 <span class="k">val</span> <span class="n">df3</span> <span class="k">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">parquetFile</span><span class="o">(</span><span class="s">&quot;data/test_table&quot;</span><span class="o">)</span>
 <span class="n">df3</span><span class="o">.</span><span class="n">printSchema</span><span class="o">()</span>

 <span class="c1">// The final schema consists of all 3 columns in the Parquet files together</span>
 <span class="c1">// with the partiioning column appeared in the partition directory paths.</span>
 <span class="c1">// root</span>
 <span class="c1">// |-- single: int (nullable = true)</span>
 <span class="c1">// |-- double: int (nullable = true)</span>
 <span class="c1">// |-- triple: int (nullable = true)</span>
 <span class="c1">// |-- key : int (nullable = true)</span></code></pre></div>

   </div>

 <div data-lang="python">

     <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="c"># sqlContext from the previous example is used in this example.</span>

 <span class="c"># Create a simple DataFrame, stored into a partition directory</span>
 <span class="n">df1</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>\
                                    <span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">i</span><span class="p">:</span> <span class="n">Row</span><span class="p">(</span><span class="n">single</span><span class="o">=</span><span class="n">i</span><span class="p">,</span> <span class="n">double</span><span class="o">=</span><span class="n">i</span> <span class="o">*</span> <span class="mi">2</span><span class="p">)))</span>
 <span class="n">df1</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s">&quot;data/test_table/key=1&quot;</span><span class="p">,</span> <span class="s">&quot;parquet&quot;</span><span class="p">)</span>

 <span class="c"># Create another DataFrame in a new partition directory,</span>
 <span class="c"># adding a new column and dropping an existing column</span>
 <span class="n">df2</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">11</span><span class="p">))</span>
                                    <span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">i</span><span class="p">:</span> <span class="n">Row</span><span class="p">(</span><span class="n">single</span><span class="o">=</span><span class="n">i</span><span class="p">,</span> <span class="n">triple</span><span class="o">=</span><span class="n">i</span> <span class="o">*</span> <span class="mi">3</span><span class="p">)))</span>
 <span class="n">df2</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s">&quot;data/test_table/key=2&quot;</span><span class="p">,</span> <span class="s">&quot;parquet&quot;</span><span class="p">)</span>

 <span class="c"># Read the partitioned table</span>
 <span class="n">df3</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">parquetFile</span><span class="p">(</span><span class="s">&quot;data/test_table&quot;</span><span class="p">)</span>
 <span class="n">df3</span><span class="o">.</span><span class="n">printSchema</span><span class="p">()</span>

 <span class="c"># The final schema consists of all 3 columns in the Parquet files together</span>
 <span class="c"># with the partiioning column appeared in the partition directory paths.</span>
 <span class="c"># root</span>
 <span class="c"># |-- single: int (nullable = true)</span>
 <span class="c"># |-- double: int (nullable = true)</span>
 <span class="c"># |-- triple: int (nullable = true)</span>
 <span class="c"># |-- key : int (nullable = true)</span></code></pre></div>

   </div>

 </div>

 <h3 id="configuration">Configuration</h3>

 <p>Configuration of Parquet can be done using the <code>setConf</code> method on <code>SQLContext</code> or by running
 <code>SET key=value</code> commands using SQL.</p>

 <table class="table">
 <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
 <tr>
   <td><code>spark.sql.parquet.binaryAsString</code></td>
   <td>false</td>
   <td>
     Some other Parquet-producing systems, in particular Impala and older versions of Spark SQL, do
     not differentiate between binary data and strings when writing out the Parquet schema.  This
     flag tells Spark SQL to interpret binary data as a string to provide compatibility with these systems.
   </td>
 </tr>
 <tr>
   <td><code>spark.sql.parquet.int96AsTimestamp</code></td>
   <td>true</td>
   <td>
     Some Parquet-producing systems, in particular Impala, store Timestamp into INT96. Spark would also
     store Timestamp as INT96 because we need to avoid precision lost of the nanoseconds field. This
     flag tells Spark SQL to interpret INT96 data as a timestamp to provide compatibility with these systems.
   </td>
 </tr>
 <tr>
   <td><code>spark.sql.parquet.cacheMetadata</code></td>
   <td>true</td>
   <td>
     Turns on caching of Parquet schema metadata.  Can speed up querying of static data.
   </td>
 </tr>
 <tr>
   <td><code>spark.sql.parquet.compression.codec</code></td>
   <td>gzip</td>
   <td>
     Sets the compression codec use when writing Parquet files. Acceptable values include:
     uncompressed, snappy, gzip, lzo.
   </td>
 </tr>
 <tr>
   <td><code>spark.sql.parquet.filterPushdown</code></td>
   <td>false</td>
   <td>
     Turn on Parquet filter pushdown optimization. This feature is turned off by default because of a known
     bug in Paruet 1.6.0rc3 (<a href="https://issues.apache.org/jira/browse/PARQUET-136">PARQUET-136</a>).
     However, if your table doesn't contain any nullable string or binary columns, it's still safe to turn
     this feature on.
   </td>
 </tr>
 <tr>
   <td><code>spark.sql.hive.convertMetastoreParquet</code></td>
   <td>true</td>
   <td>
     When set to false, Spark SQL will use the Hive SerDe for parquet tables instead of the built in
     support.
   </td>
 </tr>
 </table>

 <h2 id="json-datasets">JSON Datasets</h2>
 <div class="codetabs">

 <div data-lang="scala">
     <p>Spark SQL can automatically infer the schema of a JSON dataset and load it as a DataFrame.
 This conversion can be done using one of two methods in a <code>SQLContext</code>:</p>

     <ul>
       <li><code>jsonFile</code> - loads data from a directory of JSON files where each line of the files is a JSON object.</li>
       <li><code>jsonRDD</code> - loads data from an existing RDD where each element of the RDD is a string containing a JSON object.</li>
     </ul>

     <p>Note that the file that is offered as <em>jsonFile</em> is not a typical JSON file. Each
 line must contain a separate, self-contained valid JSON object. As a consequence,
 a regular multi-line JSON file will most often fail.</p>

     <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="c1">// sc is an existing SparkContext.</span>
 <span class="k">val</span> <span class="n">sqlContext</span> <span class="k">=</span> <span class="k">new</span> <span class="n">org</span><span class="o">.</span><span class="n">apache</span><span class="o">.</span><span class="n">spark</span><span class="o">.</span><span class="n">sql</span><span class="o">.</span><span class="nc">SQLContext</span><span class="o">(</span><span class="n">sc</span><span class="o">)</span>

 <span class="c1">// A JSON dataset is pointed to by path.</span>
 <span class="c1">// The path can be either a single text file or a directory storing text files.</span>
 <span class="k">val</span> <span class="n">path</span> <span class="k">=</span> <span class="s">&quot;examples/src/main/resources/people.json&quot;</span>
 <span class="c1">// Create a DataFrame from the file(s) pointed to by path</span>
 <span class="k">val</span> <span class="n">people</span> <span class="k">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">jsonFile</span><span class="o">(</span><span class="n">path</span><span class="o">)</span>

 <span class="c1">// The inferred schema can be visualized using the printSchema() method.</span>
 <span class="n">people</span><span class="o">.</span><span class="n">printSchema</span><span class="o">()</span>
 <span class="c1">// root</span>
 <span class="c1">//  |-- age: integer (nullable = true)</span>
 <span class="c1">//  |-- name: string (nullable = true)</span>

 <span class="c1">// Register this DataFrame as a table.</span>
 <span class="n">people</span><span class="o">.</span><span class="n">registerTempTable</span><span class="o">(</span><span class="s">&quot;people&quot;</span><span class="o">)</span>

 <span class="c1">// SQL statements can be run by using the sql methods provided by sqlContext.</span>
 <span class="k">val</span> <span class="n">teenagers</span> <span class="k">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">sql</span><span class="o">(</span><span class="s">&quot;SELECT name FROM people WHERE age &gt;= 13 AND age &lt;= 19&quot;</span><span class="o">)</span>

 <span class="c1">// Alternatively, a DataFrame can be created for a JSON dataset represented by</span>
 <span class="c1">// an RDD[String] storing one JSON object per string.</span>
 <span class="k">val</span> <span class="n">anotherPeopleRDD</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="o">(</span>
   <span class="s">&quot;&quot;&quot;{&quot;name&quot;:&quot;Yin&quot;,&quot;address&quot;:{&quot;city&quot;:&quot;Columbus&quot;,&quot;state&quot;:&quot;Ohio&quot;}}&quot;&quot;&quot;</span> <span class="o">::</span> <span class="nc">Nil</span><span class="o">)</span>
 <span class="k">val</span> <span class="n">anotherPeople</span> <span class="k">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">jsonRDD</span><span class="o">(</span><span class="n">anotherPeopleRDD</span><span class="o">)</span></code></pre></div>

   </div>

 <div data-lang="java">
     <p>Spark SQL can automatically infer the schema of a JSON dataset and load it as a DataFrame.
 This conversion can be done using one of two methods in a <code>SQLContext</code> :</p>

     <ul>
       <li><code>jsonFile</code> - loads data from a directory of JSON files where each line of the files is a JSON object.</li>
       <li><code>jsonRDD</code> - loads data from an existing RDD where each element of the RDD is a string containing a JSON object.</li>
     </ul>

     <p>Note that the file that is offered as <em>jsonFile</em> is not a typical JSON file. Each
 line must contain a separate, self-contained valid JSON object. As a consequence,
 a regular multi-line JSON file will most often fail.</p>

     <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="c1">// sc is an existing JavaSparkContext.</span>
 <span class="n">SQLContext</span> <span class="n">sqlContext</span> <span class="o">=</span> <span class="k">new</span> <span class="n">org</span><span class="o">.</span><span class="na">apache</span><span class="o">.</span><span class="na">spark</span><span class="o">.</span><span class="na">sql</span><span class="o">.</span><span class="na">SQLContext</span><span class="o">(</span><span class="n">sc</span><span class="o">);</span>

 <span class="c1">// A JSON dataset is pointed to by path.</span>
 <span class="c1">// The path can be either a single text file or a directory storing text files.</span>
 <span class="n">String</span> <span class="n">path</span> <span class="o">=</span> <span class="s">&quot;examples/src/main/resources/people.json&quot;</span><span class="o">;</span>
 <span class="c1">// Create a DataFrame from the file(s) pointed to by path</span>
 <span class="n">DataFrame</span> <span class="n">people</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="na">jsonFile</span><span class="o">(</span><span class="n">path</span><span class="o">);</span>

 <span class="c1">// The inferred schema can be visualized using the printSchema() method.</span>
 <span class="n">people</span><span class="o">.</span><span class="na">printSchema</span><span class="o">();</span>
 <span class="c1">// root</span>
 <span class="c1">//  |-- age: integer (nullable = true)</span>
 <span class="c1">//  |-- name: string (nullable = true)</span>

 <span class="c1">// Register this DataFrame as a table.</span>
 <span class="n">people</span><span class="o">.</span><span class="na">registerTempTable</span><span class="o">(</span><span class="s">&quot;people&quot;</span><span class="o">);</span>

 <span class="c1">// SQL statements can be run by using the sql methods provided by sqlContext.</span>
 <span class="n">DataFrame</span> <span class="n">teenagers</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="na">sql</span><span class="o">(</span><span class="s">&quot;SELECT name FROM people WHERE age &gt;= 13 AND age &lt;= 19&quot;</span><span class="o">);</span>

 <span class="c1">// Alternatively, a DataFrame can be created for a JSON dataset represented by</span>
 <span class="c1">// an RDD[String] storing one JSON object per string.</span>
 <span class="n">List</span><span class="o">&lt;</span><span class="n">String</span><span class="o">&gt;</span> <span class="n">jsonData</span> <span class="o">=</span> <span class="n">Arrays</span><span class="o">.</span><span class="na">asList</span><span class="o">(</span>
   <span class="s">&quot;{\&quot;name\&quot;:\&quot;Yin\&quot;,\&quot;address\&quot;:{\&quot;city\&quot;:\&quot;Columbus\&quot;,\&quot;state\&quot;:\&quot;Ohio\&quot;}}&quot;</span><span class="o">);</span>
 <span class="n">JavaRDD</span><span class="o">&lt;</span><span class="n">String</span><span class="o">&gt;</span> <span class="n">anotherPeopleRDD</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="na">parallelize</span><span class="o">(</span><span class="n">jsonData</span><span class="o">);</span>
 <span class="n">DataFrame</span> <span class="n">anotherPeople</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="na">jsonRDD</span><span class="o">(</span><span class="n">anotherPeopleRDD</span><span class="o">);</span></code></pre></div>

   </div>

 <div data-lang="python">
     <p>Spark SQL can automatically infer the schema of a JSON dataset and load it as a DataFrame.
 This conversion can be done using one of two methods in a <code>SQLContext</code>:</p>

     <ul>
       <li><code>jsonFile</code> - loads data from a directory of JSON files where each line of the files is a JSON object.</li>
       <li><code>jsonRDD</code> - loads data from an existing RDD where each element of the RDD is a string containing a JSON object.</li>
     </ul>

     <p>Note that the file that is offered as <em>jsonFile</em> is not a typical JSON file. Each
 line must contain a separate, self-contained valid JSON object. As a consequence,
 a regular multi-line JSON file will most often fail.</p>

     <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="c"># sc is an existing SparkContext.</span>
 <span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">SQLContext</span>
 <span class="n">sqlContext</span> <span class="o">=</span> <span class="n">SQLContext</span><span class="p">(</span><span class="n">sc</span><span class="p">)</span>

 <span class="c"># A JSON dataset is pointed to by path.</span>
 <span class="c"># The path can be either a single text file or a directory storing text files.</span>
 <span class="n">path</span> <span class="o">=</span> <span class="s">&quot;examples/src/main/resources/people.json&quot;</span>
 <span class="c"># Create a DataFrame from the file(s) pointed to by path</span>
 <span class="n">people</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">jsonFile</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>

 <span class="c"># The inferred schema can be visualized using the printSchema() method.</span>
 <span class="n">people</span><span class="o">.</span><span class="n">printSchema</span><span class="p">()</span>
 <span class="c"># root</span>
 <span class="c">#  |-- age: integer (nullable = true)</span>
 <span class="c">#  |-- name: string (nullable = true)</span>

 <span class="c"># Register this DataFrame as a table.</span>
 <span class="n">people</span><span class="o">.</span><span class="n">registerTempTable</span><span class="p">(</span><span class="s">&quot;people&quot;</span><span class="p">)</span>

 <span class="c"># SQL statements can be run by using the sql methods provided by `sqlContext`.</span>
 <span class="n">teenagers</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">sql</span><span class="p">(</span><span class="s">&quot;SELECT name FROM people WHERE age &gt;= 13 AND age &lt;= 19&quot;</span><span class="p">)</span>

 <span class="c"># Alternatively, a DataFrame can be created for a JSON dataset represented by</span>
 <span class="c"># an RDD[String] storing one JSON object per string.</span>
 <span class="n">anotherPeopleRDD</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">([</span>
   <span class="s">&#39;{&quot;name&quot;:&quot;Yin&quot;,&quot;address&quot;:{&quot;city&quot;:&quot;Columbus&quot;,&quot;state&quot;:&quot;Ohio&quot;}}&#39;</span><span class="p">])</span>
 <span class="n">anotherPeople</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">jsonRDD</span><span class="p">(</span><span class="n">anotherPeopleRDD</span><span class="p">)</span></code></pre></div>

   </div>

 <div data-lang="sql">

     <div class="highlight"><pre><code class="language-sql" data-lang="sql"><span class="k">CREATE</span> <span class="k">TEMPORARY</span> <span class="k">TABLE</span> <span class="n">jsonTable</span>
 <span class="k">USING</span> <span class="n">org</span><span class="p">.</span><span class="n">apache</span><span class="p">.</span><span class="n">spark</span><span class="p">.</span><span class="k">sql</span><span class="p">.</span><span class="n">json</span>
 <span class="k">OPTIONS</span> <span class="p">(</span>
   <span class="n">path</span> <span class="ss">&quot;examples/src/main/resources/people.json&quot;</span>
 <span class="p">)</span>

 <span class="k">SELECT</span> <span class="o">*</span> <span class="k">FROM</span> <span class="n">jsonTable</span></code></pre></div>

   </div>

 </div>

 <h2 id="hive-tables">Hive Tables</h2>

 <p>Spark SQL also supports reading and writing data stored in <a href="http://hive.apache.org/">Apache Hive</a>.
 However, since Hive has a large number of dependencies, it is not included in the default Spark assembly.
 Hive support is enabled by adding the <code>-Phive</code> and <code>-Phive-thriftserver</code> flags to Spark&#8217;s build.
 This command builds a new assembly jar that includes Hive. Note that this Hive assembly jar must also be present
 on all of the worker nodes, as they will need access to the Hive serialization and deserialization libraries
 (SerDes) in order to access data stored in Hive.</p>

 <p>Configuration of Hive is done by placing your <code>hive-site.xml</code> file in <code>conf/</code>.</p>

 <div class="codetabs">

 <div data-lang="scala">

     <p>When working with Hive one must construct a <code>HiveContext</code>, which inherits from <code>SQLContext</code>, and
 adds support for finding tables in the MetaStore and writing queries using HiveQL. Users who do
 not have an existing Hive deployment can still create a <code>HiveContext</code>.  When not configured by the
 hive-site.xml, the context automatically creates <code>metastore_db</code> and <code>warehouse</code> in the current
 directory.</p>

     <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="c1">// sc is an existing SparkContext.</span>
 <span class="k">val</span> <span class="n">sqlContext</span> <span class="k">=</span> <span class="k">new</span> <span class="n">org</span><span class="o">.</span><span class="n">apache</span><span class="o">.</span><span class="n">spark</span><span class="o">.</span><span class="n">sql</span><span class="o">.</span><span class="n">hive</span><span class="o">.</span><span class="nc">HiveContext</span><span class="o">(</span><span class="n">sc</span><span class="o">)</span>

 <span class="n">sqlContext</span><span class="o">.</span><span class="n">sql</span><span class="o">(</span><span class="s">&quot;CREATE TABLE IF NOT EXISTS src (key INT, value STRING)&quot;</span><span class="o">)</span>
 <span class="n">sqlContext</span><span class="o">.</span><span class="n">sql</span><span class="o">(</span><span class="s">&quot;LOAD DATA LOCAL INPATH &#39;examples/src/main/resources/kv1.txt&#39; INTO TABLE src&quot;</span><span class="o">)</span>

 <span class="c1">// Queries are expressed in HiveQL</span>
 <span class="n">sqlContext</span><span class="o">.</span><span class="n">sql</span><span class="o">(</span><span class="s">&quot;FROM src SELECT key, value&quot;</span><span class="o">).</span><span class="n">collect</span><span class="o">().</span><span class="n">foreach</span><span class="o">(</span><span class="n">println</span><span class="o">)</span></code></pre></div>

   </div>

 <div data-lang="java">

     <p>When working with Hive one must construct a <code>HiveContext</code>, which inherits from <code>SQLContext</code>, and
 adds support for finding tables in the MetaStore and writing queries using HiveQL. In addition to
 the <code>sql</code> method a <code>HiveContext</code> also provides an <code>hql</code> methods, which allows queries to be
 expressed in HiveQL.</p>

     <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="c1">// sc is an existing JavaSparkContext.</span>
 <span class="n">HiveContext</span> <span class="n">sqlContext</span> <span class="o">=</span> <span class="k">new</span> <span class="n">org</span><span class="o">.</span><span class="na">apache</span><span class="o">.</span><span class="na">spark</span><span class="o">.</span><span class="na">sql</span><span class="o">.</span><span class="na">hive</span><span class="o">.</span><span class="na">HiveContext</span><span class="o">(</span><span class="n">sc</span><span class="o">);</span>

 <span class="n">sqlContext</span><span class="o">.</span><span class="na">sql</span><span class="o">(</span><span class="s">&quot;CREATE TABLE IF NOT EXISTS src (key INT, value STRING)&quot;</span><span class="o">);</span>
 <span class="n">sqlContext</span><span class="o">.</span><span class="na">sql</span><span class="o">(</span><span class="s">&quot;LOAD DATA LOCAL INPATH &#39;examples/src/main/resources/kv1.txt&#39; INTO TABLE src&quot;</span><span class="o">);</span>

 <span class="c1">// Queries are expressed in HiveQL.</span>
 <span class="n">Row</span><span class="o">[]</span> <span class="n">results</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="na">sql</span><span class="o">(</span><span class="s">&quot;FROM src SELECT key, value&quot;</span><span class="o">).</span><span class="na">collect</span><span class="o">();</span></code></pre></div>

   </div>

 <div data-lang="python">

     <p>When working with Hive one must construct a <code>HiveContext</code>, which inherits from <code>SQLContext</code>, and
 adds support for finding tables in the MetaStore and writing queries using HiveQL. In addition to
 the <code>sql</code> method a <code>HiveContext</code> also provides an <code>hql</code> methods, which allows queries to be
 expressed in HiveQL.</p>

     <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="c"># sc is an existing SparkContext.</span>
 <span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">HiveContext</span>
 <span class="n">sqlContext</span> <span class="o">=</span> <span class="n">HiveContext</span><span class="p">(</span><span class="n">sc</span><span class="p">)</span>

 <span class="n">sqlContext</span><span class="o">.</span><span class="n">sql</span><span class="p">(</span><span class="s">&quot;CREATE TABLE IF NOT EXISTS src (key INT, value STRING)&quot;</span><span class="p">)</span>
 <span class="n">sqlContext</span><span class="o">.</span><span class="n">sql</span><span class="p">(</span><span class="s">&quot;LOAD DATA LOCAL INPATH &#39;examples/src/main/resources/kv1.txt&#39; INTO TABLE src&quot;</span><span class="p">)</span>

 <span class="c"># Queries can be expressed in HiveQL.</span>
 <span class="n">results</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">sql</span><span class="p">(</span><span class="s">&quot;FROM src SELECT key, value&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span></code></pre></div>

   </div>
 </div>

 <h2 id="jdbc-to-other-databases">JDBC To Other Databases</h2>

 <p>Spark SQL also includes a data source that can read data from other databases using JDBC.  This
 functionality should be preferred over using <a href="api/scala/index.html#org.apache.spark.rdd.JdbcRDD">JdbcRDD</a>.
 This is because the results are returned
 as a DataFrame and they can easily be processed in Spark SQL or joined with other data sources.
 The JDBC data source is also easier to use from Java or Python as it does not require the user to
 provide a ClassTag.
 (Note that this is different than the Spark SQL JDBC server, which allows other applications to
 run queries using Spark SQL).</p>

 <p>To get started you will need to include the JDBC driver for you particular database on the
 spark classpath.  For example, to connect to postgres from the Spark Shell you would run the
 following command:</p>

 <div class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">SPARK_CLASSPATH</span><span class="o">=</span>postgresql-9.3-1102-jdbc41.jar bin/spark-shell</code></pre></div>

 <p>Tables from the remote database can be loaded as a DataFrame or Spark SQL Temporary table using
 the Data Sources API.  The following options are supported:</p>

 <table class="table">
   <tr><th>Property Name</th><th>Meaning</th></tr>
   <tr>
     <td><code>url</code></td>
     <td>
       The JDBC URL to connect to.
     </td>
   </tr>
   <tr>
     <td><code>dbtable</code></td>
     <td>
       The JDBC table that should be read.  Note that anything that is valid in a `FROM` clause of
       a SQL query can be used.  For example, instead of a full table you could also use a
       subquery in parentheses.
     </td>
   </tr>

   <tr>
     <td><code>driver</code></td>
     <td>
       The class name of the JDBC driver needed to connect to this URL.  This class with be loaded
       on the master and workers before running an JDBC commands to allow the driver to
       register itself with the JDBC subsystem.
     </td>
   </tr>
   <tr>
     <td><code>partitionColumn, lowerBound, upperBound, numPartitions</code></td>
     <td>
       These options must all be specified if any of them is specified.  They describe how to
       partition the table when reading in parallel from multiple workers.
       <code>partitionColumn</code> must be a numeric column from the table in question.
     </td>
   </tr>
 </table>

 <div class="codetabs">

 <div data-lang="scala">

     <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">val</span> <span class="n">jdbcDF</span> <span class="k">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">load</span><span class="o">(</span><span class="s">&quot;jdbc&quot;</span><span class="o">,</span> <span class="nc">Map</span><span class="o">(</span>
   <span class="s">&quot;url&quot;</span> <span class="o">-&gt;</span> <span class="s">&quot;jdbc:postgresql:dbserver&quot;</span><span class="o">,</span>
   <span class="s">&quot;dbtable&quot;</span> <span class="o">-&gt;</span> <span class="s">&quot;schema.tablename&quot;</span><span class="o">))</span></code></pre></div>

   </div>

 <div data-lang="java">

     <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="n">Map</span><span class="o">&lt;</span><span class="n">String</span><span class="o">,</span> <span class="n">String</span><span class="o">&gt;</span> <span class="n">options</span> <span class="o">=</span> <span class="k">new</span> <span class="n">HashMap</span><span class="o">&lt;</span><span class="n">String</span><span class="o">,</span> <span class="n">String</span><span class="o">&gt;();</span>
 <span class="n">options</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="s">&quot;url&quot;</span><span class="o">,</span> <span class="s">&quot;jdbc:postgresql:dbserver&quot;</span><span class="o">);</span>
 <span class="n">options</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="s">&quot;dbtable&quot;</span><span class="o">,</span> <span class="s">&quot;schema.tablename&quot;</span><span class="o">);</span>

 <span class="n">DataFrame</span> <span class="n">jdbcDF</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="na">load</span><span class="o">(</span><span class="s">&quot;jdbc&quot;</span><span class="o">,</span> <span class="n">options</span><span class="o">)</span></code></pre></div>

   </div>

 <div data-lang="python">

     <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="n">df</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s">&quot;jdbc&quot;</span><span class="p">,</span> <span class="n">url</span><span class="o">=</span><span class="s">&quot;jdbc:postgresql:dbserver&quot;</span><span class="p">,</span> <span class="n">dbtable</span><span class="o">=</span><span class="s">&quot;schema.tablename&quot;</span><span class="p">)</span></code></pre></div>

   </div>

 <div data-lang="sql">

     <div class="highlight"><pre><code class="language-sql" data-lang="sql"><span class="k">CREATE</span> <span class="k">TEMPORARY</span> <span class="k">TABLE</span> <span class="n">jdbcTable</span>
 <span class="k">USING</span> <span class="n">org</span><span class="p">.</span><span class="n">apache</span><span class="p">.</span><span class="n">spark</span><span class="p">.</span><span class="k">sql</span><span class="p">.</span><span class="n">jdbc</span>
 <span class="k">OPTIONS</span> <span class="p">(</span>
   <span class="n">url</span> <span class="ss">&quot;jdbc:postgresql:dbserver&quot;</span><span class="p">,</span>
   <span class="n">dbtable</span> <span class="ss">&quot;schema.tablename&quot;</span>
 <span class="p">)</span></code></pre></div>

   </div>
 </div>

 <h2 id="troubleshooting">Troubleshooting</h2>

 <ul>
   <li>The JDBC driver class must be visible to the primordial class loader on the client session and on all executors. This is because Java&#8217;s DriverManager class does a security check that results in it ignoring all drivers not visible to the primordial class loader when one goes to open a connection. One convenient way to do this is to modify compute_classpath.sh on all worker nodes to include your driver JARs.</li>
   <li>Some databases, such as H2, convert all names to upper case. You&#8217;ll need to use upper case to refer to those names in Spark SQL.</li>
 </ul>

 <h1 id="performance-tuning">Performance Tuning</h1>

 <p>For some workloads it is possible to improve performance by either caching data in memory, or by
 turning on some experimental options.</p>

 <h2 id="caching-data-in-memory">Caching Data In Memory</h2>

 <p>Spark SQL can cache tables using an in-memory columnar format by calling <code>sqlContext.cacheTable("tableName")</code> or <code>dataFrame.cache()</code>.
 Then Spark SQL will scan only required columns and will automatically tune compression to minimize
 memory usage and GC pressure. You can call <code>sqlContext.uncacheTable("tableName")</code> to remove the table from memory.</p>

 <p>Configuration of in-memory caching can be done using the <code>setConf</code> method on <code>SQLContext</code> or by running
 <code>SET key=value</code> commands using SQL.</p>

 <table class="table">
 <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
 <tr>
   <td><code>spark.sql.inMemoryColumnarStorage.compressed</code></td>
   <td>true</td>
   <td>
     When set to true Spark SQL will automatically select a compression codec for each column based
     on statistics of the data.
   </td>
 </tr>
 <tr>
   <td><code>spark.sql.inMemoryColumnarStorage.batchSize</code></td>
   <td>10000</td>
   <td>
     Controls the size of batches for columnar caching.  Larger batch sizes can improve memory utilization
     and compression, but risk OOMs when caching data.
   </td>
 </tr>

 </table>

 <h2 id="other-configuration-options">Other Configuration Options</h2>

 <p>The following options can also be used to tune the performance of query execution.  It is possible
 that these options will be deprecated in future release as more optimizations are performed automatically.</p>

 <table class="table">
   <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
   <tr>
     <td><code>spark.sql.autoBroadcastJoinThreshold</code></td>
     <td>10485760 (10 MB)</td>
     <td>
       Configures the maximum size in bytes for a table that will be broadcast to all worker nodes when
       performing a join.  By setting this value to -1 broadcasting can be disabled.  Note that currently
       statistics are only supported for Hive Metastore tables where the command
       `ANALYZE TABLE &lt;tableName&gt; COMPUTE STATISTICS noscan` has been run.
     </td>
   </tr>
   <tr>
     <td><code>spark.sql.codegen</code></td>
     <td>false</td>
     <td>
       When true, code will be dynamically generated at runtime for expression evaluation in a specific
       query.  For some queries with complicated expression this option can lead to significant speed-ups.
       However, for simple queries this can actually slow down query execution.
     </td>
   </tr>
   <tr>
     <td><code>spark.sql.shuffle.partitions</code></td>
     <td>200</td>
     <td>
       Configures the number of partitions to use when shuffling data for joins or aggregations.
     </td>
   </tr>
 </table>

 <h1 id="distributed-sql-engine">Distributed SQL Engine</h1>

 <p>Spark SQL can also act as a distributed query engine using its JDBC/ODBC or command-line interface. In this mode, end-users or applications can interact with Spark SQL directly to run SQL queries, without the need to write any code.</p>

 <h2 id="running-the-thrift-jdbcodbc-server">Running the Thrift JDBC/ODBC server</h2>

 <p>The Thrift JDBC/ODBC server implemented here corresponds to the <a href="https://cwiki.apache.org/confluence/display/Hive/Setting+Up+HiveServer2"><code>HiveServer2</code></a>
 in Hive 0.13. You can test the JDBC server with the beeline script that comes with either Spark or Hive 0.13.</p>

 <p>To start the JDBC/ODBC server, run the following in the Spark directory:</p>

 <pre><code>./sbin/start-thriftserver.sh
 </code></pre>

 <p>This script accepts all <code>bin/spark-submit</code> command line options, plus a <code>--hiveconf</code> option to
 specify Hive properties.  You may run <code>./sbin/start-thriftserver.sh --help</code> for a complete list of
 all available options.  By default, the server listens on localhost:10000. You may override this
 bahaviour via either environment variables, i.e.:</p>

 <div class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nb">export </span><span class="nv">HIVE_SERVER2_THRIFT_PORT</span><span class="o">=</span>&lt;listening-port&gt;
 <span class="nb">export </span><span class="nv">HIVE_SERVER2_THRIFT_BIND_HOST</span><span class="o">=</span>&lt;listening-host&gt;
 ./sbin/start-thriftserver.sh <span class="se">\</span>
   --master &lt;master-uri&gt; <span class="se">\</span>
   ...</code></pre></div>

 <p>or system properties:</p>

 <div class="highlight"><pre><code class="language-bash" data-lang="bash">./sbin/start-thriftserver.sh <span class="se">\</span>
   --hiveconf hive.server2.thrift.port<span class="o">=</span>&lt;listening-port&gt; <span class="se">\</span>
   --hiveconf hive.server2.thrift.bind.host<span class="o">=</span>&lt;listening-host&gt; <span class="se">\</span>
   --master &lt;master-uri&gt;
   ...</code></pre></div>

 <p>Now you can use beeline to test the Thrift JDBC/ODBC server:</p>

 <pre><code>./bin/beeline
 </code></pre>

 <p>Connect to the JDBC/ODBC server in beeline with:</p>

 <pre><code>beeline&gt; !connect jdbc:hive2://localhost:10000
 </code></pre>

 <p>Beeline will ask you for a username and password. In non-secure mode, simply enter the username on
 your machine and a blank password. For secure mode, please follow the instructions given in the
 <a href="https://cwiki.apache.org/confluence/display/Hive/HiveServer2+Clients">beeline documentation</a>.</p>

 <p>Configuration of Hive is done by placing your <code>hive-site.xml</code> file in <code>conf/</code>.</p>

 <p>You may also use the beeline script that comes with Hive.</p>

 <p>Thrift JDBC server also supports sending thrift RPC messages over HTTP transport.
 Use the following setting to enable HTTP mode as system property or in <code>hive-site.xml</code> file in <code>conf/</code>:</p>

 <pre><code>hive.server2.transport.mode - Set this to value: http
 hive.server2.thrift.http.port - HTTP port number fo listen on; default is 10001
 hive.server2.http.endpoint - HTTP endpoint; default is cliservice
 </code></pre>

 <p>To test, use beeline to connect to the JDBC/ODBC server in http mode with:</p>

 <pre><code>beeline&gt; !connect jdbc:hive2://&lt;host&gt;:&lt;port&gt;/&lt;database&gt;?hive.server2.transport.mode=http;hive.server2.thrift.http.path=&lt;http_endpoint&gt;
 </code></pre>

 <h2 id="running-the-spark-sql-cli">Running the Spark SQL CLI</h2>

 <p>The Spark SQL CLI is a convenient tool to run the Hive metastore service in local mode and execute
 queries input from the command line. Note that the Spark SQL CLI cannot talk to the Thrift JDBC server.</p>

 <p>To start the Spark SQL CLI, run the following in the Spark directory:</p>

 <pre><code>./bin/spark-sql
 </code></pre>

 <p>Configuration of Hive is done by placing your <code>hive-site.xml</code> file in <code>conf/</code>.
 You may run <code>./bin/spark-sql --help</code> for a complete list of all available
 options.</p>

 <h1 id="migration-guide">Migration Guide</h1>

 <h2 id="upgrading-from-spark-sql-10-12-to-13">Upgrading from Spark SQL 1.0-1.2 to 1.3</h2>

 <p>In Spark 1.3 we removed the &#8220;Alpha&#8221; label from Spark SQL and as part of this did a cleanup of the
 available APIs.  From Spark 1.3 onwards, Spark SQL will provide binary compatibility with other
 releases in the 1.X series.  This compatibility guarantee excludes APIs that are explicitly marked
 as unstable (i.e., DeveloperAPI or Experimental).</p>

 <h4 id="rename-of-schemardd-to-dataframe">Rename of SchemaRDD to DataFrame</h4>

 <p>The largest change that users will notice when upgrading to Spark SQL 1.3 is that <code>SchemaRDD</code> has
 been renamed to <code>DataFrame</code>.  This is primarily because DataFrames no longer inherit from RDD
 directly, but instead provide most of the functionality that RDDs provide though their own
 implementation.  DataFrames can still be converted to RDDs by calling the <code>.rdd</code> method.</p>

 <p>In Scala there is a type alias from <code>SchemaRDD</code> to <code>DataFrame</code> to provide source compatibility for
 some use cases.  It is still recommended that users update their code to use <code>DataFrame</code> instead.
 Java and Python users will need to update their code.</p>

 <h4 id="unification-of-the-java-and-scala-apis">Unification of the Java and Scala APIs</h4>

 <p>Prior to Spark 1.3 there were separate Java compatible classes (<code>JavaSQLContext</code> and <code>JavaSchemaRDD</code>)
 that mirrored the Scala API.  In Spark 1.3 the Java API and Scala API have been unified.  Users
 of either language should use <code>SQLContext</code> and <code>DataFrame</code>.  In general theses classes try to
 use types that are usable from both languages (i.e. <code>Array</code> instead of language specific collections).
 In some cases where no common type exists (e.g., for passing in closures or Maps) function overloading
 is used instead.</p>

 <p>Additionally the Java specific types API has been removed.  Users of both Scala and Java should
 use the classes present in <code>org.apache.spark.sql.types</code> to describe schema programmatically.</p>

 <h4 id="isolation-of-implicit-conversions-and-removal-of-dsl-package-scala-only">Isolation of Implicit Conversions and Removal of dsl Package (Scala-only)</h4>

 <p>Many of the code examples prior to Spark 1.3 started with <code>import sqlContext._</code>, which brought
 all of the functions from sqlContext into scope.  In Spark 1.3 we have isolated the implicit
 conversions for converting <code>RDD</code>s into <code>DataFrame</code>s into an object inside of the <code>SQLContext</code>.
 Users should now write <code>import sqlContext.implicits._</code>.</p>

 <p>Additionally, the implicit conversions now only augment RDDs that are composed of <code>Product</code>s (i.e.,
 case classes or tuples) with a method <code>toDF</code>, instead of applying automatically.</p>

 <p>When using function inside of the DSL (now replaced with the <code>DataFrame</code> API) users used to import
 <code>org.apache.spark.sql.catalyst.dsl</code>.  Instead the public dataframe functions API should be used:
 <code>import org.apache.spark.sql.functions._</code>.</p>

 <h4 id="removal-of-the-type-aliases-in-orgapachesparksql-for-datatype-scala-only">Removal of the type aliases in org.apache.spark.sql for DataType (Scala-only)</h4>

 <p>Spark 1.3 removes the type aliases that were present in the base sql package for <code>DataType</code>. Users
 should instead import the classes in <code>org.apache.spark.sql.types</code></p>

 <h4 id="udf-registration-moved-to-sqlcontextudf-java--scala">UDF Registration Moved to <code>sqlContext.udf</code> (Java &amp; Scala)</h4>

 <p>Functions that are used to register UDFs, either for use in the DataFrame DSL or SQL, have been
 moved into the udf object in <code>SQLContext</code>.</p>

 <div class="codetabs">
 <div data-lang="scala">

     <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="n">sqlCtx</span><span class="o">.</span><span class="na">udf</span><span class="o">.</span><span class="na">register</span><span class="o">(</span><span class="s">&quot;strLen&quot;</span><span class="o">,</span> <span class="o">(</span><span class="nl">s:</span> <span class="n">String</span><span class="o">)</span> <span class="o">=&gt;</span> <span class="n">s</span><span class="o">.</span><span class="na">length</span><span class="o">())</span></code></pre></div>

   </div>

 <div data-lang="java">

     <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="n">sqlCtx</span><span class="o">.</span><span class="na">udf</span><span class="o">().</span><span class="na">register</span><span class="o">(</span><span class="s">&quot;strLen&quot;</span><span class="o">,</span> <span class="o">(</span><span class="n">String</span> <span class="n">s</span><span class="o">)</span> <span class="o">-&gt;</span> <span class="o">{</span> <span class="n">s</span><span class="o">.</span><span class="na">length</span><span class="o">();</span> <span class="o">});</span></code></pre></div>

   </div>

 </div>

 <p>Python UDF registration is unchanged.</p>

 <h4 id="python-datatypes-no-longer-singletons">Python DataTypes No Longer Singletons</h4>

 <p>When using DataTypes in Python you will need to construct them (i.e. <code>StringType()</code>) instead of
 referencing a singleton.</p>

 <h2 id="migration-guide-for-shark-user">Migration Guide for Shark User</h2>

 <h3 id="scheduling">Scheduling</h3>
 <p>To set a <a href="job-scheduling.html#fair-scheduler-pools">Fair Scheduler</a> pool for a JDBC client session,
 users can set the <code>spark.sql.thriftserver.scheduler.pool</code> variable:</p>

 <pre><code>SET spark.sql.thriftserver.scheduler.pool=accounting;
 </code></pre>

 <h3 id="reducer-number">Reducer number</h3>

 <p>In Shark, default reducer number is 1 and is controlled by the property <code>mapred.reduce.tasks</code>. Spark
 SQL deprecates this property in favor of <code>spark.sql.shuffle.partitions</code>, whose default value
 is 200. Users may customize this property via <code>SET</code>:</p>

 <pre><code>SET spark.sql.shuffle.partitions=10;
 SELECT page, count(*) c
 FROM logs_last_month_cached
 GROUP BY page ORDER BY c DESC LIMIT 10;
 </code></pre>

 <p>You may also put this property in <code>hive-site.xml</code> to override the default value.</p>

 <p>For now, the <code>mapred.reduce.tasks</code> property is still recognized, and is converted to
 <code>spark.sql.shuffle.partitions</code> automatically.</p>

 <h3 id="caching">Caching</h3>

 <p>The <code>shark.cache</code> table property no longer exists, and tables whose name end with <code>_cached</code> are no
 longer automatically cached. Instead, we provide <code>CACHE TABLE</code> and <code>UNCACHE TABLE</code> statements to
 let user control table caching explicitly:</p>

 <pre><code>CACHE TABLE logs_last_month;
 UNCACHE TABLE logs_last_month;
 </code></pre>

 <p><strong>NOTE:</strong> <code>CACHE TABLE tbl</code> is now <strong>eager</strong> by default not <strong>lazy</strong>. Don’t need to trigger cache materialization manually anymore.</p>

 <p>Spark SQL newly introduced a statement to let user control table caching whether or not lazy since Spark 1.2.0:</p>

 <pre><code>CACHE [LAZY] TABLE [AS SELECT] ...
 </code></pre>

 <p>Several caching related features are not supported yet:</p>

 <ul>
   <li>User defined partition level cache eviction policy</li>
   <li>RDD reloading</li>
   <li>In-memory cache write through policy</li>
 </ul>

 <h2 id="compatibility-with-apache-hive">Compatibility with Apache Hive</h2>

 <p>Spark SQL is designed to be compatible with the Hive Metastore, SerDes and UDFs.  Currently Spark
 SQL is based on Hive 0.12.0 and 0.13.1.</p>

 <h4 id="deploying-in-existing-hive-warehouses">Deploying in Existing Hive Warehouses</h4>

 <p>The Spark SQL Thrift JDBC server is designed to be &#8220;out of the box&#8221; compatible with existing Hive
 installations. You do not need to modify your existing Hive Metastore or change the data placement
 or partitioning of your tables.</p>

 <h3 id="supported-hive-features">Supported Hive Features</h3>

 <p>Spark SQL supports the vast majority of Hive features, such as:</p>

 <ul>
   <li>Hive query statements, including:
     <ul>
       <li><code>SELECT</code></li>
       <li><code>GROUP BY</code></li>
       <li><code>ORDER BY</code></li>
       <li><code>CLUSTER BY</code></li>
       <li><code>SORT BY</code></li>
     </ul>
   </li>
   <li>All Hive operators, including:
     <ul>
       <li>Relational operators (<code>=</code>, <code>⇔</code>, <code>==</code>, <code>&lt;&gt;</code>, <code>&lt;</code>, <code>&gt;</code>, <code>&gt;=</code>, <code>&lt;=</code>, etc)</li>
       <li>Arithmetic operators (<code>+</code>, <code>-</code>, <code>*</code>, <code>/</code>, <code>%</code>, etc)</li>
       <li>Logical operators (<code>AND</code>, <code>&amp;&amp;</code>, <code>OR</code>, <code>||</code>, etc)</li>
       <li>Complex type constructors</li>
       <li>Mathematical functions (<code>sign</code>, <code>ln</code>, <code>cos</code>, etc)</li>
       <li>String functions (<code>instr</code>, <code>length</code>, <code>printf</code>, etc)</li>
     </ul>
   </li>
   <li>User defined functions (UDF)</li>
   <li>User defined aggregation functions (UDAF)</li>
   <li>User defined serialization formats (SerDes)</li>
   <li>Joins
     <ul>
       <li><code>JOIN</code></li>
       <li><code>{LEFT|RIGHT|FULL} OUTER JOIN</code></li>
       <li><code>LEFT SEMI JOIN</code></li>
       <li><code>CROSS JOIN</code></li>
     </ul>
   </li>
   <li>Unions</li>
   <li>Sub-queries
     <ul>
       <li><code>SELECT col FROM ( SELECT a + b AS col from t1) t2</code></li>
     </ul>
   </li>
   <li>Sampling</li>
   <li>Explain</li>
   <li>Partitioned tables</li>
   <li>View</li>
   <li>All Hive DDL Functions, including:
     <ul>
       <li><code>CREATE TABLE</code></li>
       <li><code>CREATE TABLE AS SELECT</code></li>
       <li><code>ALTER TABLE</code></li>
     </ul>
   </li>
   <li>Most Hive Data types, including:
     <ul>
       <li><code>TINYINT</code></li>
       <li><code>SMALLINT</code></li>
       <li><code>INT</code></li>
       <li><code>BIGINT</code></li>
       <li><code>BOOLEAN</code></li>
       <li><code>FLOAT</code></li>
       <li><code>DOUBLE</code></li>
       <li><code>STRING</code></li>
       <li><code>BINARY</code></li>
       <li><code>TIMESTAMP</code></li>
       <li><code>DATE</code></li>
       <li><code>ARRAY&lt;&gt;</code></li>
       <li><code>MAP&lt;&gt;</code></li>
       <li><code>STRUCT&lt;&gt;</code></li>
     </ul>
   </li>
 </ul>

 <h3 id="unsupported-hive-functionality">Unsupported Hive Functionality</h3>

 <p>Below is a list of Hive features that we don&#8217;t support yet. Most of these features are rarely used
 in Hive deployments.</p>

 <p><strong>Major Hive Features</strong></p>

 <ul>
   <li>Tables with buckets: bucket is the hash partitioning within a Hive table partition. Spark SQL
 doesn&#8217;t support buckets yet.</li>
 </ul>

 <p><strong>Esoteric Hive Features</strong>
 * <code>UNION</code> type
 * Unique join
 * Column statistics collecting: Spark SQL does not piggyback scans to collect column statistics at
   the moment and only supports populating the sizeInBytes field of the hive metastore.</p>

 <p><strong>Hive Input/Output Formats</strong></p>

 <ul>
   <li>File format for CLI: For results showing back to the CLI, Spark SQL only supports TextOutputFormat.</li>
   <li>Hadoop archive</li>
 </ul>

 <p><strong>Hive Optimizations</strong></p>

 <p>A handful of Hive optimizations are not yet included in Spark. Some of these (such as indexes) are
 less important due to Spark SQL&#8217;s in-memory computational model. Others are slotted for future
 releases of Spark SQL.</p>

 <ul>
   <li>Block level bitmap indexes and virtual columns (used to build indexes)</li>
   <li>Automatically determine the number of reducers for joins and groupbys: Currently in Spark SQL, you
 need to control the degree of parallelism post-shuffle using &#8220;<code>SET spark.sql.shuffle.partitions=[num_tasks];</code>&#8221;.</li>
   <li>Meta-data only query: For queries that can be answered by using only meta data, Spark SQL still
 launches tasks to compute the result.</li>
   <li>Skew data flag: Spark SQL does not follow the skew data flags in Hive.</li>
   <li><code>STREAMTABLE</code> hint in join: Spark SQL does not follow the <code>STREAMTABLE</code> hint.</li>
   <li>Merge multiple small files for query results: if the result output contains multiple small files,
 Hive can optionally merge the small files into fewer large files to avoid overflowing the HDFS
 metadata. Spark SQL does not support that.</li>
 </ul>

 <h1 id="data-types">Data Types</h1>

 <p>Spark SQL and DataFrames support the following data types:</p>

 <ul>
   <li>Numeric types
     <ul>
       <li><code>ByteType</code>: Represents 1-byte signed integer numbers.
   The range of numbers is from <code>-128</code> to <code>127</code>.</li>
       <li><code>ShortType</code>: Represents 2-byte signed integer numbers.
   The range of numbers is from <code>-32768</code> to <code>32767</code>.</li>
       <li><code>IntegerType</code>: Represents 4-byte signed integer numbers.
   The range of numbers is from <code>-2147483648</code> to <code>2147483647</code>.</li>
       <li><code>LongType</code>: Represents 8-byte signed integer numbers.
   The range of numbers is from <code>-9223372036854775808</code> to <code>9223372036854775807</code>.</li>
       <li><code>FloatType</code>: Represents 4-byte single-precision floating point numbers.</li>
       <li><code>DoubleType</code>: Represents 8-byte double-precision floating point numbers.</li>
       <li><code>DecimalType</code>: Represents arbitrary-precision signed decimal numbers. Backed internally by <code>java.math.BigDecimal</code>. A <code>BigDecimal</code> consists of an arbitrary precision integer unscaled value and a 32-bit integer scale.</li>
     </ul>
   </li>
   <li>String type
     <ul>
       <li><code>StringType</code>: Represents character string values.</li>
     </ul>
   </li>
   <li>Binary type
     <ul>
       <li><code>BinaryType</code>: Represents byte sequence values.</li>
     </ul>
   </li>
   <li>Boolean type
     <ul>
       <li><code>BooleanType</code>: Represents boolean values.</li>
     </ul>
   </li>
   <li>Datetime type
     <ul>
       <li><code>TimestampType</code>: Represents values comprising values of fields year, month, day,
   hour, minute, and second.</li>
       <li><code>DateType</code>: Represents values comprising values of fields year, month, day.</li>
     </ul>
   </li>
   <li>Complex types
     <ul>
       <li><code>ArrayType(elementType, containsNull)</code>: Represents values comprising a sequence of
   elements with the type of <code>elementType</code>. <code>containsNull</code> is used to indicate if
   elements in a <code>ArrayType</code> value can have <code>null</code> values.</li>
       <li><code>MapType(keyType, valueType, valueContainsNull)</code>:
   Represents values comprising a set of key-value pairs. The data type of keys are
   described by <code>keyType</code> and the data type of values are described by <code>valueType</code>.
   For a <code>MapType</code> value, keys are not allowed to have <code>null</code> values. <code>valueContainsNull</code>
   is used to indicate if values of a <code>MapType</code> value can have <code>null</code> values.</li>
       <li><code>StructType(fields)</code>: Represents values with the structure described by
   a sequence of <code>StructField</code>s (<code>fields</code>).
         <ul>
           <li><code>StructField(name, dataType, nullable)</code>: Represents a field in a <code>StructType</code>.
   The name of a field is indicated by <code>name</code>. The data type of a field is indicated
   by <code>dataType</code>. <code>nullable</code> is used to indicate if values of this fields can have
   <code>null</code> values.</li>
         </ul>
       </li>
     </ul>
   </li>
 </ul>

 <div class="codetabs">
 <div data-lang="scala">

     <p>All data types of Spark SQL are located in the package <code>org.apache.spark.sql.types</code>.
 You can access them by doing</p>

     <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span>  <span class="nn">org.apache.spark.sql.types._</span></code></pre></div>

     <table class="table">
 <tr>
   <th style="width:20%">Data type</th>
   <th style="width:40%">Value type in Scala</th>
   <th>API to access or create a data type</th></tr>
 <tr>
   <td> <b>ByteType</b> </td>
   <td> Byte </td>
   <td>
   ByteType
   </td>
 </tr>
 <tr>
   <td> <b>ShortType</b> </td>
   <td> Short </td>
   <td>
   ShortType
   </td>
 </tr>
 <tr>
   <td> <b>IntegerType</b> </td>
   <td> Int </td>
   <td>
   IntegerType
   </td>
 </tr>
 <tr>
   <td> <b>LongType</b> </td>
   <td> Long </td>
   <td>
   LongType
   </td>
 </tr>
 <tr>
   <td> <b>FloatType</b> </td>
   <td> Float </td>
   <td>
   FloatType
   </td>
 </tr>
 <tr>
   <td> <b>DoubleType</b> </td>
   <td> Double </td>
   <td>
   DoubleType
   </td>
 </tr>
 <tr>
   <td> <b>DecimalType</b> </td>
   <td> java.math.BigDecimal </td>
   <td>
   DecimalType
   </td>
 </tr>
 <tr>
   <td> <b>StringType</b> </td>
   <td> String </td>
   <td>
   StringType
   </td>
 </tr>
 <tr>
   <td> <b>BinaryType</b> </td>
   <td> Array[Byte] </td>
   <td>
   BinaryType
   </td>
 </tr>
 <tr>
   <td> <b>BooleanType</b> </td>
   <td> Boolean </td>
   <td>
   BooleanType
   </td>
 </tr>
 <tr>
   <td> <b>TimestampType</b> </td>
   <td> java.sql.Timestamp </td>
   <td>
   TimestampType
   </td>
 </tr>
 <tr>
   <td> <b>DateType</b> </td>
   <td> java.sql.Date </td>
   <td>
   DateType
   </td>
 </tr>
 <tr>
   <td> <b>ArrayType</b> </td>
   <td> scala.collection.Seq </td>
   <td>
   ArrayType(<i>elementType</i>, [<i>containsNull</i>])<br />
   <b>Note:</b> The default value of <i>containsNull</i> is <i>true</i>.
   </td>
 </tr>
 <tr>
   <td> <b>MapType</b> </td>
   <td> scala.collection.Map </td>
   <td>
   MapType(<i>keyType</i>, <i>valueType</i>, [<i>valueContainsNull</i>])<br />
   <b>Note:</b> The default value of <i>valueContainsNull</i> is <i>true</i>.
   </td>
 </tr>
 <tr>
   <td> <b>StructType</b> </td>
   <td> org.apache.spark.sql.Row </td>
   <td>
   StructType(<i>fields</i>)<br />
   <b>Note:</b> <i>fields</i> is a Seq of StructFields. Also, two fields with the same
   name are not allowed.
   </td>
 </tr>
 <tr>
   <td> <b>StructField</b> </td>
   <td> The value type in Scala of the data type of this field
   (For example, Int for a StructField with the data type IntegerType) </td>
   <td>
   StructField(<i>name</i>, <i>dataType</i>, <i>nullable</i>)
   </td>
 </tr>
 </table>

   </div>

 <div data-lang="java">

     <p>All data types of Spark SQL are located in the package of
 <code>org.apache.spark.sql.types</code>. To access or create a data type,
 please use factory methods provided in
 <code>org.apache.spark.sql.types.DataTypes</code>.</p>

     <table class="table">
 <tr>
   <th style="width:20%">Data type</th>
   <th style="width:40%">Value type in Java</th>
   <th>API to access or create a data type</th></tr>
 <tr>
   <td> <b>ByteType</b> </td>
   <td> byte or Byte </td>
   <td>
   DataTypes.ByteType
   </td>
 </tr>
 <tr>
   <td> <b>ShortType</b> </td>
   <td> short or Short </td>
   <td>
   DataTypes.ShortType
   </td>
 </tr>
 <tr>
   <td> <b>IntegerType</b> </td>
   <td> int or Integer </td>
   <td>
   DataTypes.IntegerType
   </td>
 </tr>
 <tr>
   <td> <b>LongType</b> </td>
   <td> long or Long </td>
   <td>
   DataTypes.LongType
   </td>
 </tr>
 <tr>
   <td> <b>FloatType</b> </td>
   <td> float or Float </td>
   <td>
   DataTypes.FloatType
   </td>
 </tr>
 <tr>
   <td> <b>DoubleType</b> </td>
   <td> double or Double </td>
   <td>
   DataTypes.DoubleType
   </td>
 </tr>
 <tr>
   <td> <b>DecimalType</b> </td>
   <td> java.math.BigDecimal </td>
   <td>
   DataTypes.createDecimalType()<br />
   DataTypes.createDecimalType(<i>precision</i>, <i>scale</i>).
   </td>
 </tr>
 <tr>
   <td> <b>StringType</b> </td>
   <td> String </td>
   <td>
   DataTypes.StringType
   </td>
 </tr>
 <tr>
   <td> <b>BinaryType</b> </td>
   <td> byte[] </td>
   <td>
   DataTypes.BinaryType
   </td>
 </tr>
 <tr>
   <td> <b>BooleanType</b> </td>
   <td> boolean or Boolean </td>
   <td>
   DataTypes.BooleanType
   </td>
 </tr>
 <tr>
   <td> <b>TimestampType</b> </td>
   <td> java.sql.Timestamp </td>
   <td>
   DataTypes.TimestampType
   </td>
 </tr>
 <tr>
   <td> <b>DateType</b> </td>
   <td> java.sql.Date </td>
   <td>
   DataTypes.DateType
   </td>
 </tr>
 <tr>
   <td> <b>ArrayType</b> </td>
   <td> java.util.List </td>
   <td>
   DataTypes.createArrayType(<i>elementType</i>)<br />
   <b>Note:</b> The value of <i>containsNull</i> will be <i>true</i><br />
   DataTypes.createArrayType(<i>elementType</i>, <i>containsNull</i>).
   </td>
 </tr>
 <tr>
   <td> <b>MapType</b> </td>
   <td> java.util.Map </td>
   <td>
   DataTypes.createMapType(<i>keyType</i>, <i>valueType</i>)<br />
   <b>Note:</b> The value of <i>valueContainsNull</i> will be <i>true</i>.<br />
   DataTypes.createMapType(<i>keyType</i>, <i>valueType</i>, <i>valueContainsNull</i>)<br />
   </td>
 </tr>
 <tr>
   <td> <b>StructType</b> </td>
   <td> org.apache.spark.sql.Row </td>
   <td>
   DataTypes.createStructType(<i>fields</i>)<br />
   <b>Note:</b> <i>fields</i> is a List or an array of StructFields.
   Also, two fields with the same name are not allowed.
   </td>
 </tr>
 <tr>
   <td> <b>StructField</b> </td>
   <td> The value type in Java of the data type of this field
   (For example, int for a StructField with the data type IntegerType) </td>
   <td>
   DataTypes.createStructField(<i>name</i>, <i>dataType</i>, <i>nullable</i>)
   </td>
 </tr>
 </table>

   </div>

 <div data-lang="python">

     <p>All data types of Spark SQL are located in the package of <code>pyspark.sql.types</code>.
 You can access them by doing</p>

     <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">pyspark.sql.types</span> <span class="kn">import</span> <span class="o">*</span></code></pre></div>

     <table class="table">
 <tr>
   <th style="width:20%">Data type</th>
   <th style="width:40%">Value type in Python</th>
   <th>API to access or create a data type</th></tr>
 <tr>
   <td> <b>ByteType</b> </td>
   <td>
   int or long <br />
   <b>Note:</b> Numbers will be converted to 1-byte signed integer numbers at runtime.
   Please make sure that numbers are within the range of -128 to 127.
   </td>
   <td>
   ByteType()
   </td>
 </tr>
 <tr>
   <td> <b>ShortType</b> </td>
   <td>
   int or long <br />
   <b>Note:</b> Numbers will be converted to 2-byte signed integer numbers at runtime.
   Please make sure that numbers are within the range of -32768 to 32767.
   </td>
   <td>
   ShortType()
   </td>
 </tr>
 <tr>
   <td> <b>IntegerType</b> </td>
   <td> int or long </td>
   <td>
   IntegerType()
   </td>
 </tr>
 <tr>
   <td> <b>LongType</b> </td>
   <td>
   long <br />
   <b>Note:</b> Numbers will be converted to 8-byte signed integer numbers at runtime.
   Please make sure that numbers are within the range of
   -9223372036854775808 to 9223372036854775807.
   Otherwise, please convert data to decimal.Decimal and use DecimalType.
   </td>
   <td>
   LongType()
   </td>
 </tr>
 <tr>
   <td> <b>FloatType</b> </td>
   <td>
   float <br />
   <b>Note:</b> Numbers will be converted to 4-byte single-precision floating
   point numbers at runtime.
   </td>
   <td>
   FloatType()
   </td>
 </tr>
 <tr>
   <td> <b>DoubleType</b> </td>
   <td> float </td>
   <td>
   DoubleType()
   </td>
 </tr>
 <tr>
   <td> <b>DecimalType</b> </td>
   <td> decimal.Decimal </td>
   <td>
   DecimalType()
   </td>
 </tr>
 <tr>
   <td> <b>StringType</b> </td>
   <td> string </td>
   <td>
   StringType()
   </td>
 </tr>
 <tr>
   <td> <b>BinaryType</b> </td>
   <td> bytearray </td>
   <td>
   BinaryType()
   </td>
 </tr>
 <tr>
   <td> <b>BooleanType</b> </td>
   <td> bool </td>
   <td>
   BooleanType()
   </td>
 </tr>
 <tr>
   <td> <b>TimestampType</b> </td>
   <td> datetime.datetime </td>
   <td>
   TimestampType()
   </td>
 </tr>
 <tr>
   <td> <b>DateType</b> </td>
   <td> datetime.date </td>
   <td>
   DateType()
   </td>
 </tr>
 <tr>
   <td> <b>ArrayType</b> </td>
   <td> list, tuple, or array </td>
   <td>
   ArrayType(<i>elementType</i>, [<i>containsNull</i>])<br />
   <b>Note:</b> The default value of <i>containsNull</i> is <i>True</i>.
   </td>
 </tr>
 <tr>
   <td> <b>MapType</b> </td>
   <td> dict </td>
   <td>
   MapType(<i>keyType</i>, <i>valueType</i>, [<i>valueContainsNull</i>])<br />
   <b>Note:</b> The default value of <i>valueContainsNull</i> is <i>True</i>.
   </td>
 </tr>
 <tr>
   <td> <b>StructType</b> </td>
   <td> list or tuple </td>
   <td>
   StructType(<i>fields</i>)<br />
   <b>Note:</b> <i>fields</i> is a Seq of StructFields. Also, two fields with the same
   name are not allowed.
   </td>
 </tr>
 <tr>
   <td> <b>StructField</b> </td>
   <td> The value type in Python of the data type of this field
   (For example, Int for a StructField with the data type IntegerType) </td>
   <td>
   StructField(<i>name</i>, <i>dataType</i>, <i>nullable</i>)
   </td>
 </tr>
 </table>

   </div>

 </div>


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