Github user vanzin commented on a diff in the pull request:
https://github.com/apache/spark/pull/8007#discussion_r37800469
--- 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 --
If you pull `amEndpoint` out of `YarnSchedulerEndpoint` you can avoid this
extra hop through the RPC layer, since we know both `YarnDriverEndpoint` and
`YarnSchedulerEndpoint` are running in the same VM.
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