mridulm commented on a change in pull request #30650:
URL: https://github.com/apache/spark/pull/30650#discussion_r548145864
##########
File path: core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
##########
@@ -439,77 +446,109 @@ private[spark] class TaskSetManager(
}
}
+ var dequeuedTaskIndex: Option[Int] = None
val taskDescription =
dequeueTask(execId, host, allowedLocality)
.map { case (index, taskLocality, speculative) =>
- // Found a task; do some bookkeeping and return a task description
- val task = tasks(index)
- val taskId = sched.newTaskId()
- // Do various bookkeeping
- copiesRunning(index) += 1
- val attemptNum = taskAttempts(index).size
- val info = new TaskInfo(taskId, index, attemptNum, curTime,
- execId, host, taskLocality, speculative)
- taskInfos(taskId) = info
- taskAttempts(index) = info :: taskAttempts(index)
- if (legacyLocalityWaitReset && maxLocality != TaskLocality.NO_PREF) {
- resetDelayScheduleTimer(Some(taskLocality))
- }
- // Serialize and return the task
- val serializedTask: ByteBuffer = try {
- ser.serialize(task)
- } 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_KIB *
1024 &&
- !emittedTaskSizeWarning) {
- emittedTaskSizeWarning = true
- logWarning(s"Stage ${task.stageId} contains a task of very large
size " +
- s"(${serializedTask.limit() / 1024} KiB). The maximum recommended
task size is " +
- s"${TaskSetManager.TASK_SIZE_TO_WARN_KIB} KiB.")
- }
- addRunningTask(taskId)
-
- // We used to log the time it takes to serialize the task, but task
size is already
- // a good proxy to task serialization time.
- // val timeTaken = clock.getTime() - startTime
- val tName = taskName(taskId)
- logInfo(s"Starting $tName ($host, executor ${info.executorId}, " +
- s"partition ${task.partitionId}, $taskLocality,
${serializedTask.limit()} bytes) " +
- s"taskResourceAssignments ${taskResourceAssignments}")
-
- sched.dagScheduler.taskStarted(task, info)
- new TaskDescription(
- taskId,
- attemptNum,
- execId,
- tName,
- index,
- task.partitionId,
- addedFiles,
- addedJars,
- addedArchives,
- task.localProperties,
- taskResourceAssignments,
- serializedTask)
- }
+ dequeuedTaskIndex = Some(index)
+ if (legacyLocalityWaitReset && maxLocality !=
TaskLocality.NO_PREF) {
+ resetDelayScheduleTimer(Some(taskLocality))
Review comment:
Do this for barrier tasks only if schedule goes through ?
##########
File path:
core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
##########
@@ -661,35 +667,51 @@ private[spark] class TaskSchedulerImpl(
}
if (launchedAnyTask && taskSet.isBarrier) {
+ val barrierPendingLaunchTasks =
taskSet.barrierPendingLaunchTasks.values.toArray
// Check whether the barrier tasks are partially launched.
- // TODO SPARK-24818 handle the assert failure case (that can happen
when some locality
- // requirements are not fulfilled, and we should revert the launched
tasks).
- if (addressesWithDescs.size != taskSet.numTasks) {
- val errorMsg =
- s"Fail resource offers for barrier stage ${taskSet.stageId}
because only " +
- s"${addressesWithDescs.size} out of a total number of
${taskSet.numTasks}" +
- s" tasks got resource offers. This happens because barrier
execution currently " +
- s"does not work gracefully with delay scheduling. We highly
recommend you to " +
- s"disable delay scheduling by setting spark.locality.wait=0 as
a workaround if " +
- s"you see this error frequently."
- logWarning(errorMsg)
- taskSet.abort(errorMsg)
- throw new SparkException(errorMsg)
- }
+ if (barrierPendingLaunchTasks.size != taskSet.numTasks) {
+ barrierPendingLaunchTasks.foreach { task =>
Review comment:
nit: `barrierPendingLaunchTasks.reverse`
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