Github user CodingCat commented on a diff in the pull request:
https://github.com/apache/spark/pull/214#discussion_r10919090
--- Diff:
core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala ---
@@ -198,6 +201,13 @@ private[spark] class TaskSchedulerImpl(
*/
def resourceOffers(offers: Seq[WorkerOffer]): Seq[Seq[TaskDescription]]
= synchronized {
SparkEnv.set(sc.env)
+ // Make thread pool local for shutdown before the function returns
+ // This is for driver can exit normally which not call sc.stop or
sys.exit
+ val serializeWorkerPool = new ThreadPoolExecutor(
--- End diff --
I propose using message just because I saw DAGScheduler uses message to
handle everything, and CSB has the similar architecture, an inner actor to
handle everything...
I was thinking that ReviveOffer is just to get some workerOffers, a new
LaunchTask message is to launch new tasks to the executor, or you can
encapsulate the message sending process in a function of CSB....en...the later
one seems more consistent with the current architecture....
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