Github user pwendell commented on a diff in the pull request:
https://github.com/apache/spark/pull/95#discussion_r10360526
--- Diff: docs/running-on-yarn.md ---
@@ -82,35 +84,30 @@ For example:
./bin/spark-class org.apache.spark.deploy.yarn.Client \
--jar
examples/target/scala-{{site.SCALA_BINARY_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar
\
--class org.apache.spark.examples.SparkPi \
- --args yarn-standalone \
+ --args yarn-cluster \
--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 start the default
Application Master. Then SparkPi will be run as a child thread of Application
Master, YARN Client will periodically polls the Application Master for status
updates and displays them in the console. The client will exit once your
application has finished running.
+The above starts a YARN client program which starts the default
Application Master. Then SparkPi will be run as a child thread of Application
Master. The client will periodically poll the Application Master for status
updates and display them in the console. The client will exit once your
application has finished running.
-With this mode, your application is actually run on the remote machine
where the Application Master is run upon. Thus application that involve local
interaction will not work well, e.g. spark-shell.
+Because the application is run on a remote machine where the Application
Master is running, applications that involve local interaction, such as
spark-shell, will not work well.
--- End diff --
It might make sense to just say "will not work"
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