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

Experimental support for running over a [YARN (Hadoop
NextGen)](http://hadoop.apache.org/docs/r2.0.2-alpha/hadoop-yarn/hadoop-yarn-site/YARN.html)
cluster was added to Spark in version 0.6.0.  Because YARN depends on version
2.0 of the Hadoop libraries, this currently requires checking out a separate
branch of Spark, called `yarn`, which you can do as follows:

    git clone git://github.com/mesos/spark
    cd spark
    git checkout -b yarn --track origin/yarn


# Preparations

- In order to distribute Spark within the cluster, it must be packaged into a single JAR file. This can be done by running `sbt/sbt assembly`
- 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 package`. 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.

# Launching Spark on YARN

The command to launch the YARN Client is as follows:

    SPARK_JAR=<SPARK_YAR_FILE> ./run spark.deploy.yarn.Client \
      --jar <YOUR_APP_JAR_FILE> \
      --class <APP_MAIN_CLASS> \
      --args <APP_MAIN_ARGUMENTS> \
      --num-workers <NUMBER_OF_WORKER_MACHINES> \
      --worker-memory <MEMORY_PER_WORKER> \
      --worker-cores <CORES_PER_WORKER>

For example:

    SPARK_JAR=./core/target/spark-core-assembly-{{site.SPARK_VERSION}}.jar ./run 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 standalone \
      --num-workers 3 \
      --worker-memory 2g \
      --worker-cores 2

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 "standalone" master url. This starts the scheduler without forcing it to connect to a cluster. A good way to handle this is to pass "standalone" as an argument to your program, as shown in the example above.
- YARN does not support requesting container resources based on the number of cores. Thus the numbers of cores given via command line arguments cannot be guaranteed.