Ngone51 commented on code in PR #37924:
URL: https://github.com/apache/spark/pull/37924#discussion_r990925620
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core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala:
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@@ -2159,6 +2176,26 @@ private[spark] class DAGScheduler(
}
}
+ /**
+ * Whether executor is decommissioning or decommissioned.
+ * Return true when:
+ * 1. Waiting for decommission start
+ * 2. Under decommission process
+ * Return false when:
+ * 1. Stopped or terminated after finishing decommission
+ * 2. Under decommission process, then removed by driver with other reasons
+ */
+ private[scheduler] def isExecutorDecommissioningOrDecommissioned(
+ taskScheduler: TaskScheduler, bmAddress: BlockManagerId): Boolean = {
+ if (bmAddress != null) {
+ taskScheduler
+ .getExecutorDecommissionState(bmAddress.executorId)
Review Comment:
Hi @warrenzhu25 The general idea looks good to me. Just have one concern
here: I think `getExecutorDecommissionState` could return empty (which means
the executor decommission has finished) in most cases if we hit shuffle fetch
failures. The shuffle data is not cleaned up during the executor decommission
but just be duplicated in another executor. So, normally, the reduce stage
should still fetch shuffle data from the decommissioning executor successfully.
Fetch failures should only happen when the executor exits itself due to network
connection interruption. In that case, the executor could be already removed
from the `TaskSchedulerImpl.executorsPendingDecommission` on the executor lost
event, which would result in `getExecutorDecommissionState` returns empty.
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