Ngone51 commented on code in PR #52792:
URL: https://github.com/apache/spark/pull/52792#discussion_r2536503480
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core/src/main/scala/org/apache/spark/executor/Executor.scala:
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@@ -381,7 +381,28 @@ private[spark] class Executor(
tr.kill(killMark._1, killMark._2)
killMarks.remove(taskId)
}
- threadPool.execute(tr)
+ try {
+ threadPool.execute(tr)
+ } catch {
+ case t: Throwable =>
+ try {
+ logError(log"Executor launch task ${MDC(TASK_NAME,
taskDescription.name)} failed," +
+ log" reason: ${MDC(REASON, t.getMessage)}")
+ context.statusUpdate(
+ taskDescription.taskId,
+ TaskState.FAILED,
+ env.closureSerializer.newInstance().serialize(new
ExceptionFailure(t, Seq.empty)))
+ } catch {
+ case oom: OutOfMemoryError =>
Review Comment:
> why OOM error is special here?
It's very likely to hit the OOM error again given that we're already in the
OOM situation. Therefore, in the case of OOM, we should not expect the
following operation could be always succesful. The special catch for the
`OutOfMemoryError` is just for logging an exact error code when the error
raises. This follow the behavior of
[SparkUncaughtExceptionHandler](https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/util/SparkUncaughtExceptionHandler.scala#L52).
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