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Third-Party Hadoop Distributions |
Spark can run against all versions of Cloudera's Distribution Including Apache Hadoop (CDH) and the Hortonworks Data Platform (HDP). There are a few things to keep in mind when using Spark with these distributions:
When compiling Spark, you'll need to specify the Hadoop version by defining the hadoop.version
property. For certain versions, you will need to specify additional profiles. For more detail,
see the guide on building with maven:
mvn -Dhadoop.version=1.0.4 -DskipTests clean package
mvn -Phadoop-2.2 -Dhadoop.version=2.2.0 -DskipTests clean package
The table below lists the corresponding hadoop.version code for each CDH/HDP release. Note that
some Hadoop releases are binary compatible across client versions. This means the pre-built Spark
distribution may "just work" without you needing to compile. That said, we recommend compiling with
the exact Hadoop version you are running to avoid any compatibility errors.
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In SBT, the equivalent can be achieved by setting the the hadoop.version property:
sbt/sbt -Dhadoop.version=1.0.4 assembly
In addition to compiling Spark itself against the right version, you need to add a Maven dependency on that
version of hadoop-client to any Spark applications you run, so they can also talk to the HDFS version
on the cluster. If you are using CDH, you also need to add the Cloudera Maven repository.
This looks as follows in SBT:
{% highlight scala %} libraryDependencies += "org.apache.hadoop" % "hadoop-client" % ""
// If using CDH, also add Cloudera repo resolvers += "Cloudera Repository" at "https://repository.cloudera.com/artifactory/cloudera-repos/" {% endhighlight %}
Or in Maven:
{% highlight xml %} ... org.apache.hadoop hadoop-client [version]
... Cloudera repository https://repository.cloudera.com/artifactory/cloudera-repos/{% endhighlight %}
As described in the Hardware Provisioning guide, Spark can run in a variety of deployment modes:
- Using dedicated set of Spark nodes in your cluster. These nodes should be co-located with your Hadoop installation.
- Running on the same nodes as an existing Hadoop installation, with a fixed amount memory and cores dedicated to Spark on each node.
- Run Spark alongside Hadoop using a cluster resource manager, such as YARN or Mesos.
These options are identical for those using CDH and HDP.
If you plan to read and write from HDFS using Spark, there are two Hadoop configuration files that should be included on Spark's classpath:
hdfs-site.xml, which provides default behaviors for the HDFS client.core-site.xml, which sets the default filesystem name.
The location of these configuration files varies across CDH and HDP versions, but
a common location is inside of /etc/hadoop/conf. Some tools, such as Cloudera Manager, create
configurations on-the-fly, but offer a mechanisms to download copies of them.
To make these files visible to Spark, set HADOOP_CONF_DIR in $SPARK_HOME/spark-env.sh
to a location containing the configuration files.