Github user mccheah commented on a diff in the pull request:
https://github.com/apache/spark/pull/3638#discussion_r22486217
--- Diff:
core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala ---
@@ -256,15 +256,21 @@ private[spark] class TaskSchedulerImpl(
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
+ 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 => {
--- End diff --
My understanding was that if this task set fails, even if other task sets
can be executed, this task set would prevent being able to complete the whole
job. Again however this is new territory to me, so I'll follow this lead and
test it.
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