Github user pwendell commented on a diff in the pull request:
https://github.com/apache/spark/pull/120#discussion_r10553691
--- 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 --
On this one I'm still a bit confused. @tgravescs what was the reason for
making this configurable? Are there other ApplicationMaster implementations
that could be used here?
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