Github user sryza commented on a diff in the pull request:
https://github.com/apache/spark/pull/120#discussion_r10554025
--- Diff:
yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
---
@@ -133,11 +148,11 @@ class ClientArguments(val args: Array[String], val
sparkConf: SparkConf) {
" --class CLASS_NAME Name of your application's main class
(required)\n" +
" --args ARGS Arguments to be passed to your
application's main class.\n" +
" Mutliple invocations are possible,
each will be passed in order.\n" +
- " --num-workers NUM Number of workers to start (Default:
2)\n" +
- " --worker-cores NUM Number of cores for the workers
(Default: 1).\n" +
- " --master-class CLASS_NAME Class Name for Master (Default:
spark.deploy.yarn.ApplicationMaster)\n" +
- " --master-memory MEM Memory for Master (e.g. 1000M, 2G)
(Default: 512 Mb)\n" +
- " --worker-memory MEM Memory per Worker (e.g. 1000M, 2G)
(Default: 1G)\n" +
+ " --num-executors NUM Number of executors to start (Default:
2)\n" +
+ " --executor-cores NUM Number of cores for the executors
(Default: 1).\n" +
+ " --am-class CLASS_NAME Class Name for application master
(Default: spark.deploy.yarn.ApplicationMaster)\n" +
--- End diff --
The application master used for yarn-client mode is different from the
application master used for standalone mode (the latter runs the driver and the
former is only used to request resources). They both use
org.apache.spark.deploy.yarn.Client to submit the app to YARN, but each uses a
different AM class.
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