Github user mccheah commented on a diff in the pull request:
https://github.com/apache/spark/pull/8007#discussion_r37811289
--- Diff:
core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala
---
@@ -91,6 +92,52 @@ private[spark] abstract class YarnSchedulerBackend(
}
/**
+ * Override the DriverEndpoint to add extra logic for the case when an
executor is disconnected.
+ * We should check the cluster manager and find if the loss of the
executor was caused by YARN
+ * force killing it due to preemption.
+ */
+ private class YarnDriverEndpoint(rpcEnv: RpcEnv, sparkProperties:
ArrayBuffer[(String, String)])
+ extends DriverEndpoint(rpcEnv, sparkProperties) {
+
+ private val handleDisconnectedExecutorThreadPool =
+
ThreadUtils.newDaemonCachedThreadPool("yarn-driver-handle-lost-executor-thread-pool")
+ implicit val askSchedulerExecutor =
ExecutionContext.fromExecutor(handleDisconnectedExecutorThreadPool)
+
+ /**
+ * When onDisconnected is received at the driver endpoint, the
superclass DriverEndpoint
+ * handles it by assuming the Executor was lost for a bad reason and
removes the executor
+ * immediately.
+ *
+ * In YARN's case however it is crucial to talk to the application
master and ask why the
+ * executor had exited. In particular, the executor may have exited
due to the executor
+ * having been preempted. If the executor "exited normally" according
to the application
+ * master then we pass that information down to the TaskSetManager to
inform the
+ * TaskSetManager that tasks on that lost executor should not count
towards a job failure.
+ */
+ override def onDisconnected(rpcAddress: RpcAddress): Unit = {
+ addressToExecutorId.get(rpcAddress).foreach({ executorId =>
+ val future =
yarnSchedulerEndpoint.ask[ExecutorLossReason](GetExecutorLossReason(executorId),
askTimeout)
+ future onSuccess {
+ case reason: ExecutorLossReason =>
+
driverEndpoint.askWithRetry[Boolean](RemoveExecutor(executorId, reason))
--- End diff --
Yeah, I found that cases when the ApplicationMaster was preempted didn't
end well in my testing of this PR. But it wasn't immediately clear if it was
because of the PR logic itself or if it's just a case that Spark on YARN
client-mode doesn't handle robustly at this time.
In any case, I agree that removing the hop is the right way to go.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
