[SPARK-10248][CORE] track exceptions in dagscheduler event loop in tests

`DAGSchedulerEventLoop` normally only logs errors (so it can continue to process more events, from other jobs).  However, this is not desirable in the tests -- the tests should be able to easily detect any exception, and also shouldn't silently succeed if there is an exception.

This was suggested by mateiz on https://github.com/apache/spark/pull/7699.  It may have already turned up an issue in "zero split job".

Author: Imran Rashid <irashid@cloudera.com>

Closes #8466 from squito/SPARK-10248.

1 files changed, 3 insertions, 2 deletions

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index 8d0e0c8624..b128ed50ca 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -805,7 +805,8 @@ class DAGScheduler(
 
   private[scheduler] def cleanUpAfterSchedulerStop() {
     for (job <- activeJobs) {
-      val error = new SparkException("Job cancelled because SparkContext was shut down")
+      val error =
+        new SparkException(s"Job ${job.jobId} cancelled because SparkContext was shut down")
       job.listener.jobFailed(error)
       // Tell the listeners that all of the running stages have ended.  Don't bother
       // cancelling the stages because if the DAG scheduler is stopped, the entire application
@@ -1295,7 +1296,7 @@ class DAGScheduler(
       case TaskResultLost =>
         // Do nothing here; the TaskScheduler handles these failures and resubmits the task.
 
-      case other =>
+      case _: ExecutorLostFailure | TaskKilled | UnknownReason =>
         // Unrecognized failure - also do nothing. If the task fails repeatedly, the TaskScheduler
         // will abort the job.
     }