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+---
+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.