Github user twinkle-sachdeva commented on a diff in the pull request:
https://github.com/apache/spark/pull/5449#discussion_r28215620
--- Diff:
yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala ---
@@ -94,6 +98,14 @@ private[yarn] class YarnAllocator(
// Additional memory overhead.
protected val memoryOverhead: Int =
sparkConf.getInt("spark.yarn.executor.memoryOverhead",
math.max((MEMORY_OVERHEAD_FACTOR * executorMemory).toInt,
MEMORY_OVERHEAD_MIN))
+
+ // Make the maximum executor failure check to be relative with respect
to duration
+ private val relativeMaxExecutorFailureCheck =
--- End diff --
Hi,
I am not sure if just batch window will do, as the D stream window needs to
be some multiple of it. Also, in our use case a long running spark application,
there will be no concept of batch window as such.
Thanks,
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