Github user JoshRosen commented on a diff in the pull request:
https://github.com/apache/spark/pull/3638#discussion_r22491893
--- 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 --
What about scenarios where you have multiple concurrent jobs (e.g. in an
environment like Databricks Cloud, Spark Jobserver, etc)? I agree that the job
associated with this task set is doomed, but other jobs should still be able to
make progress and those jobs' task sets might still be schedulable.
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