2013-09-06 15:15:49 -04:00
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---
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layout: global
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2013-09-08 03:44:41 -04:00
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title: Running with Cloudera and HortonWorks
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---
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2013-09-08 03:44:41 -04:00
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Spark can run against all versions of Cloudera's Distribution Including Apache Hadoop (CDH) and
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the Hortonworks Data Platform (HDP). There are a few things to keep in mind when using Spark
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with these distributions:
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# Compile-time Hadoop Version
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2014-05-12 22:44:14 -04:00
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When compiling Spark, you'll need to specify the Hadoop version by defining the `hadoop.version`
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property. For certain versions, you will need to specify additional profiles. For more detail,
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see the guide on [building with maven](building-with-maven.html#specifying-the-hadoop-version):
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mvn -Dhadoop.version=1.0.4 -DskipTests clean package
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mvn -Phadoop-2.2 -Dhadoop.version=2.2.0 -DskipTests clean package
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2014-05-12 22:44:14 -04:00
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The table below lists the corresponding `hadoop.version` code for each CDH/HDP release. Note that
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some Hadoop releases are binary compatible across client versions. This means the pre-built Spark
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distribution may "just work" without you needing to compile. That said, we recommend compiling with
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the _exact_ Hadoop version you are running to avoid any compatibility errors.
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<table>
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<tr valign="top">
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<td>
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<h3>CDH Releases</h3>
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<table class="table" style="width:350px; margin-right: 20px;">
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<tr><th>Release</th><th>Version code</th></tr>
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<tr><td>CDH 4.X.X (YARN mode)</td><td>2.0.0-cdh4.X.X</td></tr>
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<tr><td>CDH 4.X.X</td><td>2.0.0-mr1-cdh4.X.X</td></tr>
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<tr><td>CDH 3u6</td><td>0.20.2-cdh3u6</td></tr>
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<tr><td>CDH 3u5</td><td>0.20.2-cdh3u5</td></tr>
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<tr><td>CDH 3u4</td><td>0.20.2-cdh3u4</td></tr>
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</table>
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</td>
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<td>
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<h3>HDP Releases</h3>
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<table class="table" style="width:350px;">
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<tr><th>Release</th><th>Version code</th></tr>
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<tr><td>HDP 1.3</td><td>1.2.0</td></tr>
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<tr><td>HDP 1.2</td><td>1.1.2</td></tr>
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<tr><td>HDP 1.1</td><td>1.0.3</td></tr>
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<tr><td>HDP 1.0</td><td>1.0.3</td></tr>
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<tr><td>HDP 2.0</td><td>2.2.0</td></tr>
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</table>
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</td>
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</tr>
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</table>
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2014-05-12 22:44:14 -04:00
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In SBT, the equivalent can be achieved by setting the SPARK_HADOOP_VERSION flag:
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SPARK_HADOOP_VERSION=1.0.4 sbt/sbt assembly
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# Linking Applications to the Hadoop Version
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In addition to compiling Spark itself against the right version, you need to add a Maven dependency on that
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version of `hadoop-client` to any Spark applications you run, so they can also talk to the HDFS version
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on the cluster. If you are using CDH, you also need to add the Cloudera Maven repository.
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This looks as follows in SBT:
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{% highlight scala %}
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libraryDependencies += "org.apache.hadoop" % "hadoop-client" % "<version>"
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// If using CDH, also add Cloudera repo
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resolvers += "Cloudera Repository" at "https://repository.cloudera.com/artifactory/cloudera-repos/"
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{% endhighlight %}
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Or in Maven:
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{% highlight xml %}
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<project>
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<dependencies>
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...
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<dependency>
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<groupId>org.apache.hadoop</groupId>
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<artifactId>hadoop-client</artifactId>
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<version>[version]</version>
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</dependency>
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</dependencies>
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<!-- If using CDH, also add Cloudera repo -->
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<repositories>
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...
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<repository>
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<id>Cloudera repository</id>
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<url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>
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</repository>
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</repositories>
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</project>
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{% endhighlight %}
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# Where to Run Spark
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As described in the [Hardware Provisioning](hardware-provisioning.html#storage-systems) guide,
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Spark can run in a variety of deployment modes:
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* Using dedicated set of Spark nodes in your cluster. These nodes should be co-located with your
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Hadoop installation.
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* Running on the same nodes as an existing Hadoop installation, with a fixed amount memory and
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cores dedicated to Spark on each node.
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* Run Spark alongside Hadoop using a cluster resource manager, such as YARN or Mesos.
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2013-09-07 17:37:54 -04:00
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These options are identical for those using CDH and HDP.
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# Inheriting Cluster Configuration
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If you plan to read and write from HDFS using Spark, there are two Hadoop configuration files that
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should be included on Spark's classpath:
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* `hdfs-site.xml`, which provides default behaviors for the HDFS client.
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* `core-site.xml`, which sets the default filesystem name.
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The location of these configuration files varies across CDH and HDP versions, but
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a common location is inside of `/etc/hadoop/conf`. Some tools, such as Cloudera Manager, create
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configurations on-the-fly, but offer a mechanisms to download copies of them.
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2014-04-22 22:22:06 -04:00
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To make these files visible to Spark, set `HADOOP_CONF_DIR` in `$SPARK_HOME/spark-env.sh`
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to a location containing the configuration files.
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