Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/8007#discussion_r39097379
--- Diff:
core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala
---
@@ -90,6 +93,41 @@ private[spark] abstract class YarnSchedulerBackend(
}
}
+ override def createDriverEndpoint(properties: Seq[(String, String)]):
DriverEndpoint = {
+ new YarnDriverEndpoint(rpcEnv, properties)
+ }
+
+ /**
+ * 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.
+ *
+ * Specifically, this endpoint is the endpoint used by
CoarseGrainedSchedulerBackend. Unlike
+ * YarnSchedulerEndpoint, it handles messages from executors the driver
is connected to. It
+ * does query the application master when the onDisconnected event is
received, however.
+ *
+ */
+ private class YarnDriverEndpoint(rpcEnv: RpcEnv, sparkProperties:
Seq[(String, String)])
+ extends DriverEndpoint(rpcEnv, sparkProperties) {
+
+ /**
+ * 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.
+ */
--- End diff --
can you add a TODO here that succinctly describes the race condition?
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