Merge branch 'master' into SPARK-4924

Conflicts:
	bin/spark-submit
	bin/spark-submit2.cmd

Author: Marcelo Vanzin

core/src/main/scala/org/apache/spark/TaskNotSerializableException.scala

@@ -0,0 +1,25 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark
+
+import org.apache.spark.annotation.DeveloperApi
+
+/**
+ * Exception thrown when a task cannot be serialized.
+ */
+private[spark] class TaskNotSerializableException(error: Throwable) extends Exception(error)

core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala

@@ -392,7 +392,8 @@ private[spark] class SparkSubmitArguments(args: Seq[String], env: Map[String, St
         |  --queue QUEUE_NAME          The YARN queue to submit to (Default: "default").
         |  --num-executors NUM         Number of executors to launch (Default: 2).
         |  --archives ARCHIVES         Comma separated list of archives to be extracted into the
-        |                              working directory of each executor.""".stripMargin
+        |                              working directory of each executor.
+      """.stripMargin
     )
     SparkSubmit.exitFn()
   }

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

@@ -866,26 +866,6 @@ class DAGScheduler(
     }
 
     if (tasks.size > 0) {
-      // Preemptively serialize a task to make sure it can be serialized. We are catching this
-      // exception here because it would be fairly hard to catch the non-serializable exception
-      // down the road, where we have several different implementations for local scheduler and
-      // cluster schedulers.
-      //
-      // We've already serialized RDDs and closures in taskBinary, but here we check for all other
-      // objects such as Partition.
-      try {
-        closureSerializer.serialize(tasks.head)
-      } catch {
-        case e: NotSerializableException =>
-          abortStage(stage, "Task not serializable: " + e.toString)
-          runningStages -= stage
-          return
-        case NonFatal(e) => // Other exceptions, such as IllegalArgumentException from Kryo.
-          abortStage(stage, s"Task serialization failed: $e\n${e.getStackTraceString}")
-          runningStages -= stage
-          return
-      }
-
       logInfo("Submitting " + tasks.size + " missing tasks from " + stage + " (" + stage.rdd + ")")
       stage.pendingTasks ++= tasks
       logDebug("New pending tasks: " + stage.pendingTasks)

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.scheduler.TaskLocality.TaskLocality
 import org.apache.spark.util.Utils
 import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.storage.BlockManagerId
@@ -209,6 +210,40 @@ private[spark] class TaskSchedulerImpl(
       .format(manager.taskSet.id, manager.parent.name))
   }
 
+  private def resourceOfferSingleTaskSet(
+      taskSet: TaskSetManager,
+      maxLocality: TaskLocality,
+      shuffledOffers: Seq[WorkerOffer],
+      availableCpus: Array[Int],
+      tasks: Seq[ArrayBuffer[TaskDescription]]) : Boolean = {
+    var launchedTask = false
+    for (i <- 0 until shuffledOffers.size) {
+      val execId = shuffledOffers(i).executorId
+      val host = shuffledOffers(i).host
+      if (availableCpus(i) >= CPUS_PER_TASK) {
+        try {
+          for (task <- taskSet.resourceOffer(execId, host, maxLocality)) {
+            tasks(i) += task
+            val tid = task.taskId
+            taskIdToTaskSetId(tid) = taskSet.taskSet.id
+            taskIdToExecutorId(tid) = execId
+            executorsByHost(host) += execId
+            availableCpus(i) -= CPUS_PER_TASK
+            assert(availableCpus(i) >= 0)
+            launchedTask = true
+          }
+        } catch {
+          case e: TaskNotSerializableException =>
+            logError(s"Resource offer failed, task set ${taskSet.name} was not serializable")
+            // Do not offer resources for this task, but don't throw an error to allow other
+            // task sets to be submitted.
+            return launchedTask
+        }
+      }
+    }
+    return launchedTask
+  }
+
   /**
    * Called by cluster manager to offer resources on slaves. We respond by asking our active task
    * sets for tasks in order of priority. We fill each node with tasks in a round-robin manner so
@@ -251,23 +286,8 @@ private[spark] class TaskSchedulerImpl(
     var launchedTask = false
     for (taskSet <- sortedTaskSets; maxLocality <- taskSet.myLocalityLevels) {
       do {
-        launchedTask = false
-        for (i <- 0 until shuffledOffers.size) {
-          val execId = shuffledOffers(i).executorId
-          val host = shuffledOffers(i).host
-          if (availableCpus(i) >= CPUS_PER_TASK) {
-            for (task <- taskSet.resourceOffer(execId, host, maxLocality)) {
-              tasks(i) += task
-              val tid = task.taskId
-              taskIdToTaskSetId(tid) = taskSet.taskSet.id
-              taskIdToExecutorId(tid) = execId
-              executorsByHost(host) += execId
-              availableCpus(i) -= CPUS_PER_TASK
-              assert(availableCpus(i) >= 0)
-              launchedTask = true
-            }
-          }
-        }
+        launchedTask = resourceOfferSingleTaskSet(
+            taskSet, maxLocality, shuffledOffers, availableCpus, tasks)
       } while (launchedTask)
     }
 

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

@@ -18,12 +18,14 @@
 package org.apache.spark.scheduler
 
 import java.io.NotSerializableException
+import java.nio.ByteBuffer
 import java.util.Arrays
 
 import scala.collection.mutable.ArrayBuffer
 import scala.collection.mutable.HashMap
 import scala.collection.mutable.HashSet
 import scala.math.{min, max}
+import scala.util.control.NonFatal
 
 import org.apache.spark._
 import org.apache.spark.executor.TaskMetrics
@@ -417,6 +419,7 @@ private[spark] class TaskSetManager(
    * @param host  the host Id of the offered resource
    * @param maxLocality the maximum locality we want to schedule the tasks at
    */
+  @throws[TaskNotSerializableException]
   def resourceOffer(
       execId: String,
       host: String,
@@ -456,10 +459,17 @@ private[spark] class TaskSetManager(
           }
           // Serialize and return the task
           val startTime = clock.getTime()
-          // We rely on the DAGScheduler to catch non-serializable closures and RDDs, so in here
-          // we assume the task can be serialized without exceptions.
-          val serializedTask = Task.serializeWithDependencies(
-            task, sched.sc.addedFiles, sched.sc.addedJars, ser)
+          val serializedTask: ByteBuffer = try {
+            Task.serializeWithDependencies(task, sched.sc.addedFiles, sched.sc.addedJars, ser)
+          } catch {
+            // If the task cannot be serialized, then there's no point to re-attempt the task,
+            // as it will always fail. So just abort the whole task-set.
+            case NonFatal(e) =>
+              val msg = s"Failed to serialize task $taskId, not attempting to retry it."
+              logError(msg, e)
+              abort(s"$msg Exception during serialization: $e")
+              throw new TaskNotSerializableException(e)
+          }
           if (serializedTask.limit > TaskSetManager.TASK_SIZE_TO_WARN_KB * 1024 &&
               !emittedTaskSizeWarning) {
             emittedTaskSizeWarning = true

core/src/test/scala/org/apache/spark/SharedSparkContext.scala

@@ -30,7 +30,7 @@ trait SharedSparkContext extends BeforeAndAfterAll { self: Suite =>
   var conf = new SparkConf(false)
 
   override def beforeAll() {
-    _sc = new SparkContext("local", "test", conf)
+    _sc = new SparkContext("local[4]", "test", conf)
     super.beforeAll()
   }
 

core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala

@@ -17,6 +17,10 @@
 
 package org.apache.spark.rdd
 
+import java.io.{ObjectInputStream, ObjectOutputStream, IOException}
+
+import com.esotericsoftware.kryo.KryoException
+
 import scala.collection.mutable.{ArrayBuffer, HashMap}
 import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
@@ -887,6 +891,23 @@ class RDDSuite extends FunSuite with SharedSparkContext {
     assert(ancestors6.count(_.isInstanceOf[CyclicalDependencyRDD[_]]) === 3)
   }
 
+  test("task serialization exception should not hang scheduler") {
+    class BadSerializable extends Serializable {
+      @throws(classOf[IOException])
+      private def writeObject(out: ObjectOutputStream): Unit = throw new KryoException("Bad serialization")
+
+      @throws(classOf[IOException])
+      private def readObject(in: ObjectInputStream): Unit = {}
+    }
+    // Note that in the original bug, SPARK-4349, that this verifies, the job would only hang if there were
+    // more threads in the Spark Context than there were number of objects in this sequence.
+    intercept[Throwable] {
+      sc.parallelize(Seq(new BadSerializable, new BadSerializable)).collect
+    }
+    // Check that the context has not crashed
+    sc.parallelize(1 to 100).map(x => x*2).collect
+  }
+
   /** A contrived RDD that allows the manual addition of dependencies after creation. */
   private class CyclicalDependencyRDD[T: ClassTag] extends RDD[T](sc, Nil) {
     private val mutableDependencies: ArrayBuffer[Dependency[_]] = ArrayBuffer.empty

core/src/test/scala/org/apache/spark/scheduler/NotSerializableFakeTask.scala

@@ -0,0 +1,40 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.scheduler
+
+import java.io.{ObjectInputStream, ObjectOutputStream, IOException}
+
+import org.apache.spark.TaskContext
+
+/**
+ * A Task implementation that fails to serialize.
+ */
+private[spark] class NotSerializableFakeTask(myId: Int, stageId: Int) extends Task[Array[Byte]](stageId, 0) {
+  override def runTask(context: TaskContext): Array[Byte] = Array.empty[Byte]
+  override def preferredLocations: Seq[TaskLocation] = Seq[TaskLocation]()
+
+  @throws(classOf[IOException])
+  private def writeObject(out: ObjectOutputStream): Unit = {
+    if (stageId == 0) {
+      throw new IllegalStateException("Cannot serialize")
+    }
+  }
+
+  @throws(classOf[IOException])
+  private def readObject(in: ObjectInputStream): Unit = {}
+}

core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala

@@ -100,4 +100,34 @@ class TaskSchedulerImplSuite extends FunSuite with LocalSparkContext with Loggin
     assert(1 === taskDescriptions.length)
     assert("executor0" === taskDescriptions(0).executorId)
   }
+
+  test("Scheduler does not crash when tasks are not serializable") {
+    sc = new SparkContext("local", "TaskSchedulerImplSuite")
+    val taskCpus = 2
+
+    sc.conf.set("spark.task.cpus", taskCpus.toString)
+    val taskScheduler = new TaskSchedulerImpl(sc)
+    taskScheduler.initialize(new FakeSchedulerBackend)
+    // Need to initialize a DAGScheduler for the taskScheduler to use for callbacks.
+    val dagScheduler = new DAGScheduler(sc, taskScheduler) {
+      override def taskStarted(task: Task[_], taskInfo: TaskInfo) {}
+      override def executorAdded(execId: String, host: String) {}
+    }
+    val numFreeCores = 1
+    taskScheduler.setDAGScheduler(dagScheduler)
+    var taskSet = new TaskSet(Array(new NotSerializableFakeTask(1, 0), new NotSerializableFakeTask(0, 1)), 0, 0, 0, null)
+    val multiCoreWorkerOffers = Seq(new WorkerOffer("executor0", "host0", taskCpus),
+      new WorkerOffer("executor1", "host1", numFreeCores))
+    taskScheduler.submitTasks(taskSet)
+    var taskDescriptions = taskScheduler.resourceOffers(multiCoreWorkerOffers).flatten
+    assert(0 === taskDescriptions.length)
+
+    // Now check that we can still submit tasks
+    // Even if one of the tasks has not-serializable tasks, the other task set should still be processed without error
+    taskScheduler.submitTasks(taskSet)
+    taskScheduler.submitTasks(FakeTask.createTaskSet(1))
+    taskDescriptions = taskScheduler.resourceOffers(multiCoreWorkerOffers).flatten
+    assert(taskDescriptions.map(_.executorId) === Seq("executor0"))
+  }
+
 }

core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala

@@ -17,6 +17,7 @@
 
 package org.apache.spark.scheduler
 
+import java.io.{ObjectInputStream, ObjectOutputStream, IOException}
 import java.util.Random
 
 import scala.collection.mutable.ArrayBuffer
@@ -563,6 +564,19 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging {
     assert(manager.emittedTaskSizeWarning)
   }
 
+  test("Not serializable exception thrown if the task cannot be serialized") {
+    sc = new SparkContext("local", "test")
+    val sched = new FakeTaskScheduler(sc, ("exec1", "host1"))
+
+    val taskSet = new TaskSet(Array(new NotSerializableFakeTask(1, 0), new NotSerializableFakeTask(0, 1)), 0, 0, 0, null)
+    val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES)
+
+    intercept[TaskNotSerializableException] {
+      manager.resourceOffer("exec1", "host1", ANY)
+    }
+    assert(manager.isZombie)
+  }
+
   test("abort the job if total size of results is too large") {
     val conf = new SparkConf().set("spark.driver.maxResultSize", "2m")
     sc = new SparkContext("local", "test", conf)