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

Spark allows you to launch jobs on an existing [YARN](http://hadoop.apache.org/common/docs/r0.23.1/hadoop-yarn/hadoop-yarn-site/YARN.html) cluster. 

## 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_2.9.1-0.6.0-SNAPSHOT.jar` file can be generated by running `sbt/sbt package`.

## 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-0.6.0-SNAPSHOT.jar ./run spark.deploy.yarn.Client 
    --jar examples/target/scala-2.9.1/spark-examples_2.9.1-0.6.0-SNAPSHOT.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.