[SPARK-46383] Reduce Driver Heap Usage by Reducing the Lifespan of TaskInfo.accumulables()

core/src/main/scala/org/apache/spark/internal/config/package.scala

@@ -2620,4 +2620,14 @@ package object config {
       .stringConf
       .toSequence
       .createWithDefault("org.apache.spark.sql.connect.client" :: Nil)
+
+  private[spark] val DROP_TASK_INFO_ACCUMULABLES_ON_TASK_COMPLETION =
+    ConfigBuilder("spark.scheduler.dropTaskInfoAccumulablesOnTaskCompletion.enabled")
+    .internal()
+    .doc("If true, the task info accumulables will be cleared upon task completion in " +
+      "TaskSetManager. This reduces the heap usage of the driver by only referencing the " +
+      "task info accumulables for the active tasks and not for completed tasks.")
+    .version("4.0.0")
+    .booleanConf
+    .createWithDefault(false)
 }

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

@@ -45,7 +45,7 @@ class TaskInfo(
     val executorId: String,
     val host: String,
     val taskLocality: TaskLocality.TaskLocality,
-    val speculative: Boolean) {
+    val speculative: Boolean) extends Cloneable {
 
   /**
    * This api doesn't contains partitionId, please use the new api.
@@ -83,6 +83,14 @@ class TaskInfo(
     _accumulables = newAccumulables
   }
 
+  override def clone(): TaskInfo = super.clone().asInstanceOf[TaskInfo]
+
+  private[scheduler] def cloneWithEmptyAccumulables(): TaskInfo = {
+    val cloned = clone()
+    cloned.setAccumulables(Nil)
+    cloned
+  }
+
   /**
    * The time when the task has completed successfully (including the time to remotely fetch
    * results, if necessary).

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

@@ -258,6 +258,9 @@ private[spark] class TaskSetManager(
 
   private[scheduler] var emittedTaskSizeWarning = false
 
+  private[scheduler] val dropTaskInfoAccumulablesOnTaskCompletion =
+    conf.get(DROP_TASK_INFO_ACCUMULABLES_ON_TASK_COMPLETION)
+
   /** Add a task to all the pending-task lists that it should be on. */
   private[spark] def addPendingTask(
       index: Int,
@@ -787,6 +790,11 @@ private[spark] class TaskSetManager(
     // SPARK-37300: when the task was already finished state, just ignore it,
     // so that there won't cause successful and tasksSuccessful wrong result.
     if(info.finished) {
+      if (dropTaskInfoAccumulablesOnTaskCompletion) {
+        // SPARK-46383: Clear out the accumulables for a completed task to reduce accumulable
+        // lifetime.
+        info.setAccumulables(Nil)
+      }
       return
     }
     val index = info.index
@@ -804,6 +812,8 @@ private[spark] class TaskSetManager(
       // Handle this task as a killed task
       handleFailedTask(tid, TaskState.KILLED,
         TaskKilled("Finish but did not commit due to another attempt succeeded"))
+      // SPARK-46383: Not clearing the accumulables here because they are already cleared in
+      // handleFailedTask.
       return
     }
 
@@ -846,11 +856,50 @@ private[spark] class TaskSetManager(
     // "result.value()" in "TaskResultGetter.enqueueSuccessfulTask" before reaching here.
     // Note: "result.value()" only deserializes the value when it's called at the first time, so
     // here "result.value()" just returns the value and won't block other threads.
-    sched.dagScheduler.taskEnded(tasks(index), Success, result.value(), result.accumUpdates,
-      result.metricPeaks, info)
+
+    emptyTaskInfoAccumulablesAndNotifyDagScheduler(tid, tasks(index), Success, result.value(),
+      result.accumUpdates, result.metricPeaks)
     maybeFinishTaskSet()
   }
 
+  /**
+   * A wrapper around [[DAGScheduler.taskEnded()]] that empties out the accumulables for the
+   * TaskInfo object, corresponding to the completed task, referenced by this class.
+   *
+   * SPARK-46383: For the completed task, we ship the original TaskInfo to the DAGScheduler and only
+   * retain a cloned TaskInfo in this class. We then set the accumulables to Nil for the TaskInfo
+   * object that corresponds to the completed task.
+   * We do this to release references to `TaskInfo.accumulables()` as the TaskInfo
+   * objects held by this class are long-lived and have a heavy memory footprint on the driver.
+   *
+   * This is safe as the TaskInfo accumulables are not needed once they are shipped to the
+   * DAGScheduler where they are aggregated. Additionally, the original TaskInfo, and not a
+   * clone, must be sent to the DAGScheduler as this TaskInfo object is sent to the
+   * DAGScheduler on multiple events during the task's lifetime. Users can install
+   * SparkListeners that compare the TaskInfo objects across these SparkListener events and
+   * thus the TaskInfo object sent to the DAGScheduler must always reference the same TaskInfo
+   * object.
+   */
+  private def emptyTaskInfoAccumulablesAndNotifyDagScheduler(
+      taskId: Long,
+      task: Task[_],
+      reason: TaskEndReason,
+      result: Any,
+      accumUpdates: Seq[AccumulatorV2[_, _]],
+      metricPeaks: Array[Long]): Unit = {
+    val taskInfoWithAccumulables = taskInfos(taskId);
+    if (dropTaskInfoAccumulablesOnTaskCompletion) {
+      val index = taskInfoWithAccumulables.index
+      val clonedTaskInfo = taskInfoWithAccumulables.cloneWithEmptyAccumulables()
+      // Update this task's taskInfo while preserving its position in the list
+      taskAttempts(index) =
+        taskAttempts(index).map { i => if (i eq taskInfoWithAccumulables) clonedTaskInfo else i }
+      taskInfos(taskId) = clonedTaskInfo
+    }
+    sched.dagScheduler.taskEnded(task, reason, result, accumUpdates, metricPeaks,
+      taskInfoWithAccumulables)
+  }
+
   private[scheduler] def markPartitionCompleted(partitionId: Int): Unit = {
     partitionToIndex.get(partitionId).foreach { index =>
       if (!successful(index)) {
@@ -874,6 +923,11 @@ private[spark] class TaskSetManager(
     // SPARK-37300: when the task was already finished state, just ignore it,
     // so that there won't cause copiesRunning wrong result.
     if (info.finished) {
+      if (dropTaskInfoAccumulablesOnTaskCompletion) {
+        // SPARK-46383: Clear out the accumulables for a completed task to reduce accumulable
+        // lifetime.
+        info.setAccumulables(Nil)
+      }
       return
     }
     removeRunningTask(tid)
@@ -908,7 +962,8 @@ private[spark] class TaskSetManager(
         if (ef.className == classOf[NotSerializableException].getName) {
           // If the task result wasn't serializable, there's no point in trying to re-execute it.
           logError(s"$task had a not serializable result: ${ef.description}; not retrying")
-          sched.dagScheduler.taskEnded(tasks(index), reason, null, accumUpdates, metricPeaks, info)
+          emptyTaskInfoAccumulablesAndNotifyDagScheduler(tid, tasks(index), reason, null,
+            accumUpdates, metricPeaks)
           abort(s"$task had a not serializable result: ${ef.description}")
           return
         }
@@ -917,7 +972,8 @@ private[spark] class TaskSetManager(
           // re-execute it.
           logError("Task %s in stage %s (TID %d) can not write to output file: %s; not retrying"
             .format(info.id, taskSet.id, tid, ef.description))
-          sched.dagScheduler.taskEnded(tasks(index), reason, null, accumUpdates, metricPeaks, info)
+          emptyTaskInfoAccumulablesAndNotifyDagScheduler(tid, tasks(index), reason, null,
+            accumUpdates, metricPeaks)
           abort("Task %s in stage %s (TID %d) can not write to output file: %s".format(
             info.id, taskSet.id, tid, ef.description))
           return
@@ -970,7 +1026,8 @@ private[spark] class TaskSetManager(
       isZombie = true
     }
 
-    sched.dagScheduler.taskEnded(tasks(index), reason, null, accumUpdates, metricPeaks, info)
+    emptyTaskInfoAccumulablesAndNotifyDagScheduler(tid, tasks(index), reason, null,
+      accumUpdates, metricPeaks)
 
     if (!isZombie && reason.countTowardsTaskFailures) {
       assert (null != failureReason)
@@ -1086,8 +1143,8 @@ private[spark] class TaskSetManager(
           addPendingTask(index)
           // Tell the DAGScheduler that this task was resubmitted so that it doesn't think our
           // stage finishes when a total of tasks.size tasks finish.
-          sched.dagScheduler.taskEnded(
-            tasks(index), Resubmitted, null, Seq.empty, Array.empty, info)
+          emptyTaskInfoAccumulablesAndNotifyDagScheduler(tid,
+            tasks(index), Resubmitted, null, Seq.empty, Array.empty)
         }
       }
     }

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

@@ -18,6 +18,7 @@
 package org.apache.spark.scheduler
 
 import java.io.{Externalizable, ObjectInput, ObjectOutput}
+import java.util.{Collections, IdentityHashMap}
 import java.util.concurrent.Semaphore
 
 import scala.collection.mutable
@@ -289,6 +290,19 @@ class SparkListenerSuite extends SparkFunSuite with LocalSparkContext with Match
     stageInfo.rddInfos.forall(_.numPartitions == 4) should be {true}
   }
 
+  test("SPARK-46383: Track TaskInfo objects") {
+    // Test that the same TaskInfo object is sent to the `DAGScheduler` in the `onTaskStart` and
+    // `onTaskEnd` events.
+    val conf = new SparkConf().set(DROP_TASK_INFO_ACCUMULABLES_ON_TASK_COMPLETION, true)
+    sc = new SparkContext("local", "SparkListenerSuite", conf)
+    val listener = new SaveActiveTaskInfos
+    sc.addSparkListener(listener)
+    val rdd1 = sc.parallelize(1 to 100, 4)
+    sc.runJob(rdd1, (items: Iterator[Int]) => items.size, Seq(0, 1))
+    sc.listenerBus.waitUntilEmpty()
+    listener.taskInfos.size should be { 0 }
+  }
+
   test("local metrics") {
     sc = new SparkContext("local", "SparkListenerSuite")
     val listener = new SaveStageAndTaskInfo
@@ -643,6 +657,27 @@ class SparkListenerSuite extends SparkFunSuite with LocalSparkContext with Match
     }
   }
 
+  /**
+   * A simple listener that tracks task infos for all active tasks.
+   */
+  private class SaveActiveTaskInfos extends SparkListener {
+    // Use a set based on IdentityHashMap instead of a HashSet to track unique references of
+    // TaskInfo objects.
+    val taskInfos = Collections.newSetFromMap[TaskInfo](new IdentityHashMap)
+
+    override def onTaskStart(taskStart: SparkListenerTaskStart): Unit = {
+      val info = taskStart.taskInfo
+      if (info != null) {
+        taskInfos.add(info)
+      }
+    }
+
+    override def onTaskEnd(task: SparkListenerTaskEnd): Unit = {
+      val info = task.taskInfo
+      taskInfos.remove(info)
+    }
+  }
+
   /**
    * A simple listener that saves the task indices for all task events.
    */

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

@@ -61,6 +61,12 @@ class FakeDAGScheduler(sc: SparkContext, taskScheduler: FakeTaskScheduler)
       accumUpdates: Seq[AccumulatorV2[_, _]],
       metricPeaks: Array[Long],
       taskInfo: TaskInfo): Unit = {
+    // Set task accumulables emulating DAGScheduler behavior to enable tests related to
+    // `TaskInfo.accumulables`.
+    accumUpdates.foreach(acc =>
+      taskInfo.setAccumulables(
+        acc.toInfo(Some(acc.value), Some(acc.value)) +: taskInfo.accumulables)
+    )
     taskScheduler.endedTasks(taskInfo.index) = reason
   }
 
@@ -229,6 +235,51 @@ class TaskSetManagerSuite
     super.afterEach()
   }
 
+  test("SPARK-46383: TaskInfo accumulables are cleared upon task completion") {
+    val conf = new SparkConf().
+      set(config.DROP_TASK_INFO_ACCUMULABLES_ON_TASK_COMPLETION, true)
+    sc = new SparkContext("local", "test", conf)
+    sched = new FakeTaskScheduler(sc, ("exec1", "host1"))
+    val taskSet = FakeTask.createTaskSet(2)
+    val clock = new ManualClock
+    val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES, clock = clock)
+    val accumUpdates = taskSet.tasks.head.metrics.internalAccums
+
+    // Offer a host. This will launch the first task.
+    val taskOption = manager.resourceOffer("exec1", "host1", NO_PREF)._1
+    assert(taskOption.isDefined)
+
+    clock.advance(1)
+    // Tell it the first task has finished successfully
+    manager.handleSuccessfulTask(0, createTaskResult(0, accumUpdates))
+    assert(sched.endedTasks(0) === Success)
+
+    // Only one task was launched and it completed successfully, thus the TaskInfo accumulables
+    // should be empty.
+    assert(!manager.taskInfos.exists(t => !t._2.accumulables.isEmpty))
+    assert(manager.taskAttempts.flatMap(t => t.filter(!_.accumulables.isEmpty)).isEmpty)
+
+    // Fail the second task (MAX_TASK_FAILURES - 1) times.
+    (1 to manager.maxTaskFailures - 1).foreach { index =>
+      val offerResult = manager.resourceOffer("exec1", "host1", ANY)._1
+      assert(offerResult.isDefined,
+        "Expect resource offer on iteration %s to return a task".format(index))
+      assert(offerResult.get.index === 1)
+      manager.handleFailedTask(offerResult.get.taskId, TaskState.FINISHED, TaskResultLost)
+    }
+
+    clock.advance(1)
+    // Successfully finish the second task.
+    val taskOption1 = manager.resourceOffer("exec1", "host1", ANY)._1
+    manager.handleSuccessfulTask(taskOption1.get.taskId, createTaskResult(1, accumUpdates))
+    assert(sched.endedTasks(1) === Success)
+    // The TaskInfo accumulables should be empty as the second task has now completed successfully.
+    assert(!manager.taskInfos.exists(t => !t._2.accumulables.isEmpty))
+    assert(manager.taskAttempts.flatMap(t => t.filter(!_.accumulables.isEmpty)).isEmpty)
+
+    assert(sched.finishedManagers.contains(manager))
+  }
+
   test("TaskSet with no preferences") {
     sc = new SparkContext("local", "test")
     sched = new FakeTaskScheduler(sc, ("exec1", "host1"))