Merge remote-tracking branch 'origin/master' into scala-2.9

3 files changed, 13 insertions, 6 deletions

diff --git a/core/src/main/scala/spark/Executor.scala b/core/src/main/scala/spark/Executor.scala
index 7764acad1a..54e169a1a1 100644
--- a/core/src/main/scala/spark/Executor.scala
+++ b/core/src/main/scala/spark/Executor.scala
@@ -69,9 +69,9 @@ class Executor extends mesos.Executor with Logging {
           d.sendStatusUpdate(new TaskStatus(
             taskId, TaskState.TASK_FAILED, Utils.serialize(reason)))
         }
-        case e: Exception => {
+        case t: Throwable => {
           // TODO: Handle errors in tasks less dramatically
-          logError("Exception in task ID " + taskId, e)
+          logError("Exception in task ID " + taskId, t)
           System.exit(1)
         }
       }
diff --git a/core/src/main/scala/spark/LocalScheduler.scala b/core/src/main/scala/spark/LocalScheduler.scala
index 88da0c1af1..7d3ab661aa 100644
--- a/core/src/main/scala/spark/LocalScheduler.scala
+++ b/core/src/main/scala/spark/LocalScheduler.scala
@@ -36,9 +36,9 @@ private class LocalScheduler(threads: Int) extends DAGScheduler with Logging {
             logInfo("Finished task " + i)
             taskEnded(tasks(i), Success, result, accumUpdates)
           } catch {
-            case e: Exception => {
+            case t: Throwable => {
               // TODO: Do something nicer here
-              logError("Exception in task " + i, e)
+              logError("Exception in task " + i, t)
               System.exit(1)
               null
             }
diff --git a/core/src/main/scala/spark/SparkContext.scala b/core/src/main/scala/spark/SparkContext.scala
index 2f2ff0e801..5d27a3d46b 100644
--- a/core/src/main/scala/spark/SparkContext.scala
+++ b/core/src/main/scala/spark/SparkContext.scala
@@ -148,7 +148,11 @@ extends Logging {
       None
   }
 
-  private[spark] def runJob[T, U](rdd: RDD[T], func: Iterator[T] => U, partitions: Seq[Int])
+  /**
+   * Run a function on a given set of partitions in an RDD and return the results.
+   * This is the main entry point to the scheduler, by which all actions get launched.
+   */
+  def runJob[T, U](rdd: RDD[T], func: Iterator[T] => U, partitions: Seq[Int])
                                  (implicit m: ClassManifest[U])
       : Array[U] = {
     logInfo("Starting job...")
@@ -158,7 +162,10 @@ extends Logging {
     result
   }
 
-  private[spark] def runJob[T, U](rdd: RDD[T], func: Iterator[T] => U)
+  /**
+   * Run a job on all partitions in an RDD and return the results in an array.
+   */
+  def runJob[T, U](rdd: RDD[T], func: Iterator[T] => U)
                                  (implicit m: ClassManifest[U])
       : Array[U] = {
     runJob(rdd, func, 0 until rdd.splits.size)