[SPARK-1620] Handle uncaught exceptions in function run by Akka scheduler

If the intended behavior was that uncaught exceptions thrown in functions being run by the Akka scheduler would end up being handled by the default uncaught exception handler set in Executor, and if that behavior is, in fact, correct, then this is a way to accomplish that.  I'm not certain, though, that we shouldn't be doing something different to handle uncaught exceptions from some of these scheduled functions.

In any event, this PR covers all of the cases I comment on in [SPARK-1620](https://issues.apache.org/jira/browse/SPARK-1620).

Author: Mark Hamstra <[email protected]>

Closes apache#622 from markhamstra/SPARK-1620 and squashes the following commits:

071d193 [Mark Hamstra] refactored post-SPARK-1772
1a6a35e [Mark Hamstra] another style fix
d30eb94 [Mark Hamstra] scalastyle
3573ecd [Mark Hamstra] Use wrapped try/catch in Utils.tryOrExit
8fc0439 [Mark Hamstra] Make functions run by the Akka scheduler use Executor's UncaughtExceptionHandler

Author: markhamstra

core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala

@@ -30,7 +30,7 @@ import org.apache.spark.{Logging, SparkConf, SparkException}
 import org.apache.spark.deploy.{ApplicationDescription, ExecutorState}
 import org.apache.spark.deploy.DeployMessages._
 import org.apache.spark.deploy.master.Master
-import org.apache.spark.util.AkkaUtils
+import org.apache.spark.util.{Utils, AkkaUtils}
 
 /**
  * Interface allowing applications to speak with a Spark deploy cluster. Takes a master URL,
@@ -88,13 +88,15 @@ private[spark] class AppClient(
       var retries = 0
       registrationRetryTimer = Some {
         context.system.scheduler.schedule(REGISTRATION_TIMEOUT, REGISTRATION_TIMEOUT) {
-          retries += 1
-          if (registered) {
-            registrationRetryTimer.foreach(_.cancel())
-          } else if (retries >= REGISTRATION_RETRIES) {
-            markDead("All masters are unresponsive! Giving up.")
-          } else {
-            tryRegisterAllMasters()
+          Utils.tryOrExit {
+            retries += 1
+            if (registered) {
+              registrationRetryTimer.foreach(_.cancel())
+            } else if (retries >= REGISTRATION_RETRIES) {
+              markDead("All masters are unresponsive! Giving up.")
+            } else {
+              tryRegisterAllMasters()
+            }
           }
         }
       }

core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala

@@ -166,14 +166,16 @@ private[spark] class Worker(
     var retries = 0
     registrationRetryTimer = Some {
       context.system.scheduler.schedule(REGISTRATION_TIMEOUT, REGISTRATION_TIMEOUT) {
-        retries += 1
-        if (registered) {
-          registrationRetryTimer.foreach(_.cancel())
-        } else if (retries >= REGISTRATION_RETRIES) {
-          logError("All masters are unresponsive! Giving up.")
-          System.exit(1)
-        } else {
-          tryRegisterAllMasters()
+        Utils.tryOrExit {
+          retries += 1
+          if (registered) {
+            registrationRetryTimer.foreach(_.cancel())
+          } else if (retries >= REGISTRATION_RETRIES) {
+            logError("All masters are unresponsive! Giving up.")
+            System.exit(1)
+          } else {
+            tryRegisterAllMasters()
+          }
         }
       }
     }

core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala

@@ -31,6 +31,7 @@ import scala.util.Random
 import org.apache.spark._
 import org.apache.spark.TaskState.TaskState
 import org.apache.spark.scheduler.SchedulingMode.SchedulingMode
+import org.apache.spark.util.Utils
 
 /**
  * Schedules tasks for multiple types of clusters by acting through a SchedulerBackend.
@@ -139,7 +140,7 @@ private[spark] class TaskSchedulerImpl(
       import sc.env.actorSystem.dispatcher
       sc.env.actorSystem.scheduler.schedule(SPECULATION_INTERVAL milliseconds,
             SPECULATION_INTERVAL milliseconds) {
-        checkSpeculatableTasks()
+        Utils.tryOrExit { checkSpeculatableTasks() }
       }
     }
   }

core/src/main/scala/org/apache/spark/storage/BlockManager.scala

@@ -155,7 +155,7 @@ private[spark] class BlockManager(
     BlockManagerWorker.startBlockManagerWorker(this)
     if (!BlockManager.getDisableHeartBeatsForTesting(conf)) {
       heartBeatTask = actorSystem.scheduler.schedule(0.seconds, heartBeatFrequency.milliseconds) {
-        heartBeat()
+        Utils.tryOrExit { heartBeat() }
       }
     }
   }

core/src/main/scala/org/apache/spark/util/Utils.scala

@@ -40,6 +40,7 @@ import tachyon.client.{TachyonFile,TachyonFS}
 
 import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkException}
 import org.apache.spark.deploy.SparkHadoopUtil
+import org.apache.spark.executor.ExecutorUncaughtExceptionHandler
 import org.apache.spark.serializer.{DeserializationStream, SerializationStream, SerializerInstance}
 
 /**
@@ -780,6 +781,18 @@ private[spark] object Utils extends Logging {
     output.toString
   }
 
+  /**
+   * Execute a block of code that evaluates to Unit, forwarding any uncaught exceptions to the
+   * default UncaughtExceptionHandler
+   */
+  def tryOrExit(block: => Unit) {
+    try {
+      block
+    } catch {
+      case t: Throwable => ExecutorUncaughtExceptionHandler.uncaughtException(t)
+    }
+  }
+
   /**
    * A regular expression to match classes of the "core" Spark API that we want to skip when
    * finding the call site of a method.