Merge branch 'master' of github.com:mesos/spark

1 files changed, 26 insertions, 9 deletions

diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index fe5334ffdc..c611db0af4 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -29,8 +29,12 @@ If you want to test out the YARN deployment mode, you can use the current Spark
 
 Most of the configs are the same for Spark on YARN as other deploys. See the Configuration page for more information on those.  These are configs that are specific to SPARK on YARN.
 
+Environment variables:
 * `SPARK_YARN_USER_ENV`, to add environment variables to the Spark processes launched on YARN. This can be a comma separated list of environment variables, e.g. `SPARK_YARN_USER_ENV="JAVA_HOME=/jdk64,FOO=bar"`.
 
+System Properties:
+* 'spark.yarn.applicationMaster.waitTries', property to set the number of times the ApplicationMaster waits for the the spark master and then also the number of tries it waits for the Spark Context to be intialized. Default is 10.
+
 # Launching Spark on YARN
 
 Ensure that HADOOP_CONF_DIR or YARN_CONF_DIR points to the directory which contains the (client side) configuration files for the hadoop cluster.
@@ -38,7 +42,7 @@ This would be used to connect to the cluster, write to the dfs and submit jobs t
 
 The command to launch the YARN Client is as follows:
 
-    SPARK_JAR=<SPARK_YARN_JAR_FILE> ./spark-class spark.deploy.yarn.Client \
+    SPARK_JAR=<SPARK_ASSEMBLY_JAR_FILE> ./spark-class org.apache.spark.deploy.yarn.Client \
       --jar <YOUR_APP_JAR_FILE> \
       --class <APP_MAIN_CLASS> \
       --args <APP_MAIN_ARGUMENTS> \
@@ -50,14 +54,27 @@ The command to launch the YARN Client is as follows:
 
 For example:
 
-    SPARK_JAR=./yarn/target/spark-yarn-assembly-{{site.SPARK_VERSION}}.jar ./spark-class 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 yarn-standalone \
-      --num-workers 3 \
-      --master-memory 4g \
-      --worker-memory 2g \
-      --worker-cores 1
+    # Build the Spark assembly JAR and the Spark examples JAR
+    $ SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true ./sbt/sbt assembly
+
+    # Configure logging
+    $ cp conf/log4j.properties.template conf/log4j.properties
+
+    # Submit Spark's ApplicationMaster to YARN's ResourceManager, and instruct Spark to run the SparkPi example
+    $ SPARK_JAR=./assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop2.0.5-alpha.jar \
+        ./spark-class org.apache.spark.deploy.yarn.Client \
+          --jar examples/target/scala-{{site.SCALA_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \
+          --class org.apache.spark.examples.SparkPi \
+          --args yarn-standalone \
+          --num-workers 3 \
+          --master-memory 4g \
+          --worker-memory 2g \
+          --worker-cores 1
+
+    # Examine the output (replace $YARN_APP_ID in the following with the "application identifier" output by the previous command)
+    # (Note: YARN_APP_LOGS_DIR is usually /tmp/logs or $HADOOP_HOME/logs/userlogs depending on the Hadoop version.)
+    $ cat $YARN_APP_LOGS_DIR/$YARN_APP_ID/container*_000001/stdout
+    Pi is roughly 3.13794
 
 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.