Launching Spark on YARN

Experimental support for running over a YARN (Hadoop NextGen) 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://
cd spark
git checkout -b yarn --track origin/yarn


If you want to test out the YARN deployment mode, you can use the current Spark examples. A spark-examples_2.9.2-0.6.0 file can be generated by running sbt/sbt package. NOTE: since the documentation you’re reading is for Spark version 0.6.0, we are assuming here that you have downloaded Spark 0.6.0 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> \
  --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-0.6.0.jar ./run spark.deploy.yarn.Client \
  --jar examples/target/scala-2.9.2/spark-examples_2.9.2-0.6.0.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