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diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md new file mode 100644 index 0000000000..6fb81b6004 --- /dev/null +++ b/docs/running-on-yarn.md @@ -0,0 +1,51 @@ +--- +layout: global +title: Launching Spark on YARN +--- + +Experimental support for running over a [YARN (Hadoop +NextGen)](http://hadoop.apache.org/docs/r2.0.1-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. |