Github user witgo commented on a diff in the pull request:
https://github.com/apache/spark/pull/15505#discussion_r96108161
--- Diff:
core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
---
@@ -244,32 +245,45 @@ class CoarseGrainedSchedulerBackend(scheduler:
TaskSchedulerImpl, val rpcEnv: Rp
// Launch tasks returned by a set of resource offers
private def launchTasks(tasks: Seq[Seq[TaskDescription]]) {
for (task <- tasks.flatten) {
- val serializedTask = TaskDescription.encode(task)
- if (serializedTask.limit >= maxRpcMessageSize) {
- scheduler.taskIdToTaskSetManager.get(task.taskId).foreach {
taskSetMgr =>
- try {
- var msg = "Serialized task %s:%d was %d bytes, which exceeds
max allowed: " +
- "spark.rpc.message.maxSize (%d bytes). Consider increasing
" +
- "spark.rpc.message.maxSize or using broadcast variables
for large values."
- msg = msg.format(task.taskId, task.index,
serializedTask.limit, maxRpcMessageSize)
- taskSetMgr.abort(msg)
- } catch {
- case e: Exception => logError("Exception in error callback",
e)
- }
- }
+ val serializedTask = try {
+ TaskDescription.encode(task)
+ } catch {
+ case NonFatal(e) =>
+ abortTaskSetManager(scheduler, task.taskId,
--- End diff --
Yes, it's a big issue.
We can first verify how much performance is lost by we first have to create
all the serialized tasks to make sure they all work. Maybe no performance
degradation.
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