---
layout: global
title: Launching Spark on YARN
---

Support for running on [YARN (Hadoop
NextGen)](http://hadoop.apache.org/docs/r2.0.2-alpha/hadoop-yarn/hadoop-yarn-site/YARN.html)
was added to Spark in version 0.6.0, and improved in 0.7.0 and 0.8.0.

# Building a YARN-Enabled Assembly JAR

We need a consolidated Spark JAR (which bundles all the required dependencies) to run Spark jobs on a YARN cluster.
This can be built by setting the Hadoop version and `SPARK_YARN` environment variable, as follows:

    SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true ./sbt/sbt assembly

The assembled JAR will be something like this:
`./assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly_{{site.SPARK_VERSION}}-hadoop2.0.5.jar`.


# Preparations

- Building a YARN-enabled assembly (see above).
- Your application code must be packaged into a separate JAR file.

If you want to test out the YARN deployment mode, you can use the current Spark examples. A `spark-examples_{{site.SCALA_VERSION}}-{{site.SPARK_VERSION}}` file can be generated by running `sbt/sbt assembly`. NOTE: since the documentation you're reading is for Spark version {{site.SPARK_VERSION}}, we are assuming here that you have downloaded Spark {{site.SPARK_VERSION}} or checked it out of source control. If you are using a different version of Spark, the version numbers in the jar generated by the sbt package command will obviously be different.

# Configuration

Most of the configs are the same for Spark on YARN as other deploys. See the Configuration page for more information on those.  These are configs that are specific to SPARK on YARN.

* `SPARK_YARN_USER_ENV`, to add environment variables to the Spark processes launched on YARN. This can be a comma separated list of environment variables, e.g. `SPARK_YARN_USER_ENV="JAVA_HOME=/jdk64,FOO=bar"`.

# Launching Spark on YARN

Ensure that HADOOP_CONF_DIR or YARN_CONF_DIR points to the directory which contains the (client side) configuration files for the hadoop cluster.
This would be used to connect to the cluster, write to the dfs and submit jobs to the resource manager.

The command to launch the YARN Client is as follows:

    SPARK_JAR=<SPARK_YARN_JAR_FILE> ./spark-class spark.deploy.yarn.Client \
      --jar <YOUR_APP_JAR_FILE> \
      --class <APP_MAIN_CLASS> \
      --args <APP_MAIN_ARGUMENTS> \
      --num-workers <NUMBER_OF_WORKER_MACHINES> \
      --master-memory <MEMORY_FOR_MASTER> \
      --worker-memory <MEMORY_PER_WORKER> \
      --worker-cores <CORES_PER_WORKER> \
      --queue <queue_name>

For example:

    SPARK_JAR=./yarn/target/spark-yarn-assembly-{{site.SPARK_VERSION}}.jar ./spark-class spark.deploy.yarn.Client \
      --jar examples/target/scala-{{site.SCALA_VERSION}}/spark-examples_{{site.SCALA_VERSION}}-{{site.SPARK_VERSION}}.jar \
      --class spark.examples.SparkPi \
      --args yarn-standalone \
      --num-workers 3 \
      --master-memory 4g \
      --worker-memory 2g \
      --worker-cores 1

The above starts a YARN Client programs which periodically polls the Application Master for status updates and displays them in the console. The client will exit once your application has finished running.

# Important Notes

- When your application instantiates a Spark context it must use a special "yarn-standalone" master url. This starts the scheduler without forcing it to connect to a cluster. A good way to handle this is to pass "yarn-standalone" as an argument to your program, as shown in the example above.
- We do not requesting container resources based on the number of cores. Thus the numbers of cores given via command line arguments cannot be guaranteed.
- The local directories used for spark will be the local directories configured for YARN (Hadoop Yarn config yarn.nodemanager.local-dirs). If the user specifies spark.local.dir, it will be ignored.