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 <table width="100%" summary="page for sparkR.session {SparkR}"><tr><td>sparkR.session {SparkR}</td><td style="text-align: right;">R Documentation</td></tr></table>

 <h2>Get the existing SparkSession or initialize a new SparkSession.</h2>

 <h3>Description</h3>

 <p>SparkSession is the entry point into SparkR. <code>sparkR.session</code> gets the existing
 SparkSession or initializes a new SparkSession.
 Additional Spark properties can be set in <code>...</code>, and these named parameters take priority
 over values in <code>master</code>, <code>appName</code>, named lists of <code>sparkConfig</code>.
 </p>


 <h3>Usage</h3>

 <pre>
 sparkR.session(master = "", appName = "SparkR",
   sparkHome = Sys.getenv("SPARK_HOME"), sparkConfig = list(),
   sparkJars = "", sparkPackages = "", enableHiveSupport = TRUE, ...)
 </pre>


 <h3>Arguments</h3>

 <table summary="R argblock">
 <tr valign="top"><td><code>master</code></td>
 <td>
 <p>the Spark master URL.</p>
 </td></tr>
 <tr valign="top"><td><code>appName</code></td>
 <td>
 <p>application name to register with cluster manager.</p>
 </td></tr>
 <tr valign="top"><td><code>sparkHome</code></td>
 <td>
 <p>Spark Home directory.</p>
 </td></tr>
 <tr valign="top"><td><code>sparkConfig</code></td>
 <td>
 <p>named list of Spark configuration to set on worker nodes.</p>
 </td></tr>
 <tr valign="top"><td><code>sparkJars</code></td>
 <td>
 <p>character vector of jar files to pass to the worker nodes.</p>
 </td></tr>
 <tr valign="top"><td><code>sparkPackages</code></td>
 <td>
 <p>character vector of package coordinates</p>
 </td></tr>
 <tr valign="top"><td><code>enableHiveSupport</code></td>
 <td>
 <p>enable support for Hive, fallback if not built with Hive support; once
 set, this cannot be turned off on an existing session</p>
 </td></tr>
 <tr valign="top"><td><code>...</code></td>
 <td>
 <p>named Spark properties passed to the method.</p>
 </td></tr>
 </table>


 <h3>Details</h3>

 <p>When called in an interactive session, this method checks for the Spark installation, and, if not
 found, it will be downloaded and cached automatically. Alternatively, <code>install.spark</code> can
 be called manually.
 </p>
 <p>A default warehouse is created automatically in the current directory when a managed table is
 created via <code>sql</code> statement <code>CREATE TABLE</code>, for example. To change the location of the
 warehouse, set the named parameter <code>spark.sql.warehouse.dir</code> to the SparkSession. Along with
 the warehouse, an accompanied metastore may also be automatically created in the current
 directory when a new SparkSession is initialized with <code>enableHiveSupport</code> set to
 <code>TRUE</code>, which is the default. For more details, refer to Hive configuration at
 <a href="http://spark.apache.org/docs/latest/sql-programming-guide.html#hive-tables">http://spark.apache.org/docs/latest/sql-programming-guide.html#hive-tables</a>.
 </p>
 <p>For details on how to initialize and use SparkR, refer to SparkR programming guide at
 <a href="http://spark.apache.org/docs/latest/sparkr.html#starting-up-sparksession">http://spark.apache.org/docs/latest/sparkr.html#starting-up-sparksession</a>.
 </p>


 <h3>Note</h3>

 <p>sparkR.session since 2.0.0
 </p>


 <h3>Examples</h3>

 <pre><code class="r">## Not run:
 ##D sparkR.session()
 ##D df &lt;- read.json(path)
 ##D
 ##D sparkR.session(&quot;local[2]&quot;, &quot;SparkR&quot;, &quot;/home/spark&quot;)
 ##D sparkR.session(&quot;yarn-client&quot;, &quot;SparkR&quot;, &quot;/home/spark&quot;,
 ##D                list(spark.executor.memory=&quot;4g&quot;),
 ##D                c(&quot;one.jar&quot;, &quot;two.jar&quot;, &quot;three.jar&quot;),
 ##D                c(&quot;com.databricks:spark-avro_2.11:2.0.1&quot;))
 ##D sparkR.session(spark.master = &quot;yarn-client&quot;, spark.executor.memory = &quot;4g&quot;)
 ## End(Not run)
 </code></pre>


 <hr /><div style="text-align: center;">[Package <em>SparkR</em> version 2.3.1 <a href="00Index.html">Index</a>]</div>
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	<table width="100%" summary="page for sparkR.session {SparkR}"><tr><td>sparkR.session {SparkR}</td><td style="text-align: right;">R Documentation</td></tr></table>

	<h2>Get the existing SparkSession or initialize a new SparkSession.</h2>

	<h3>Description</h3>

	<p>SparkSession is the entry point into SparkR. <code>sparkR.session</code> gets the existing
	SparkSession or initializes a new SparkSession.
	Additional Spark properties can be set in <code>...</code>, and these named parameters take priority
	over values in <code>master</code>, <code>appName</code>, named lists of <code>sparkConfig</code>.
	</p>


	<h3>Usage</h3>

	<pre>
	sparkR.session(master = "", appName = "SparkR",
	sparkHome = Sys.getenv("SPARK_HOME"), sparkConfig = list(),
	sparkJars = "", sparkPackages = "", enableHiveSupport = TRUE, ...)
	</pre>


	<h3>Arguments</h3>

	<table summary="R argblock">
	<tr valign="top"><td><code>master</code></td>
	<td>
	<p>the Spark master URL.</p>
	</td></tr>
	<tr valign="top"><td><code>appName</code></td>
	<td>
	<p>application name to register with cluster manager.</p>
	</td></tr>
	<tr valign="top"><td><code>sparkHome</code></td>
	<td>
	<p>Spark Home directory.</p>
	</td></tr>
	<tr valign="top"><td><code>sparkConfig</code></td>
	<td>
	<p>named list of Spark configuration to set on worker nodes.</p>
	</td></tr>
	<tr valign="top"><td><code>sparkJars</code></td>
	<td>
	<p>character vector of jar files to pass to the worker nodes.</p>
	</td></tr>
	<tr valign="top"><td><code>sparkPackages</code></td>
	<td>
	<p>character vector of package coordinates</p>
	</td></tr>
	<tr valign="top"><td><code>enableHiveSupport</code></td>
	<td>
	<p>enable support for Hive, fallback if not built with Hive support; once
	set, this cannot be turned off on an existing session</p>
	</td></tr>
	<tr valign="top"><td><code>...</code></td>
	<td>
	<p>named Spark properties passed to the method.</p>
	</td></tr>
	</table>


	<h3>Details</h3>

	<p>When called in an interactive session, this method checks for the Spark installation, and, if not
	found, it will be downloaded and cached automatically. Alternatively, <code>install.spark</code> can
	be called manually.
	</p>
	<p>A default warehouse is created automatically in the current directory when a managed table is
	created via <code>sql</code> statement <code>CREATE TABLE</code>, for example. To change the location of the
	warehouse, set the named parameter <code>spark.sql.warehouse.dir</code> to the SparkSession. Along with
	the warehouse, an accompanied metastore may also be automatically created in the current
	directory when a new SparkSession is initialized with <code>enableHiveSupport</code> set to
	<code>TRUE</code>, which is the default. For more details, refer to Hive configuration at
	<a href="http://spark.apache.org/docs/latest/sql-programming-guide.html#hive-tables">http://spark.apache.org/docs/latest/sql-programming-guide.html#hive-tables</a>.
	</p>
	<p>For details on how to initialize and use SparkR, refer to SparkR programming guide at
	<a href="http://spark.apache.org/docs/latest/sparkr.html#starting-up-sparksession">http://spark.apache.org/docs/latest/sparkr.html#starting-up-sparksession</a>.
	</p>


	<h3>Note</h3>

	<p>sparkR.session since 2.0.0
	</p>


	<h3>Examples</h3>

	<pre><code class="r">## Not run:
	##D sparkR.session()
	##D df <- read.json(path)
	##D
	##D sparkR.session("local[2]", "SparkR", "/home/spark")
	##D sparkR.session("yarn-client", "SparkR", "/home/spark",
	##D list(spark.executor.memory="4g"),
	##D c("one.jar", "two.jar", "three.jar"),
	##D c("com.databricks:spark-avro_2.11:2.0.1"))
	##D sparkR.session(spark.master = "yarn-client", spark.executor.memory = "4g")
	## End(Not run)
	</code></pre>


	<hr /><div style="text-align: center;">[Package <em>SparkR</em> version 2.3.1 <a href="00Index.html">Index</a>]</div>
	</body></html>