Merge pull request #101 from colorant/yarn-client-scheduler

For SPARK-527, Support spark-shell when running on YARN

sync to trunk and resubmit here

In current YARN mode approaching, the application is run in the Application Master as a user program thus the whole spark context is on remote.

This approaching won't support application that involve local interaction and need to be run on where it is launched.

So In this pull request I have a YarnClientClusterScheduler and backend added.

With this scheduler, the user application is launched locally,While the executor will be launched by YARN on remote nodes with a thin AM which only launch the executor and monitor the Driver Actor status, so that when client app is done, it can finish the YARN Application as well.

This enables spark-shell to run upon YARN.

This also enable other Spark applications to have the spark context to run locally with a master-url "yarn-client". Thus e.g. SparkPi could have the result output locally on console instead of output in the log of the remote machine where AM is running on.

Docs also updated to show how to use this yarn-client mode.

1 files changed, 25 insertions, 0 deletions

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 42b2985b50..3a80241daa 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -226,6 +226,31 @@ class SparkContext(
         scheduler.initialize(backend)
         scheduler
 
+      case "yarn-client" =>
+        val scheduler = try {
+          val clazz = Class.forName("org.apache.spark.scheduler.cluster.YarnClientClusterScheduler")
+          val cons = clazz.getConstructor(classOf[SparkContext])
+          cons.newInstance(this).asInstanceOf[ClusterScheduler]
+
+        } catch {
+          case th: Throwable => {
+            throw new SparkException("YARN mode not available ?", th)
+          }
+        }
+
+        val backend = try {
+          val clazz = Class.forName("org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend")
+          val cons = clazz.getConstructor(classOf[ClusterScheduler], classOf[SparkContext])
+          cons.newInstance(scheduler, this).asInstanceOf[CoarseGrainedSchedulerBackend]
+        } catch {
+          case th: Throwable => {
+            throw new SparkException("YARN mode not available ?", th)
+          }
+        }
+
+        scheduler.initialize(backend)
+        scheduler
+
       case MESOS_REGEX(mesosUrl) =>
         MesosNativeLibrary.load()
         val scheduler = new ClusterScheduler(this)