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Github user mridulm commented on the pull request:
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I meant, I was expecting the default 3xx mb to be overhead : while this
ended up making it 2.5gig or so : causing master to fail due to inadequate
memory to run the job.
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--- Diff:
yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala ---
@@ -65,6 +65,18 @@ trait ClientBase extends Logging {
val
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yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala ---
@@ -65,6 +65,18 @@ trait ClientBase extends Logging {
val
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@mridulm
The following code in line with your thoughts?
https://github.com/witgo/spark/compare/SPARK-1930_different
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@@ -92,21 +92,22 @@ class ExecutorLauncher(args:
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Github user mridulm commented on the pull request:
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hey @sryza did we also add overhead for workers too ? iirc I had added it
only for master.
Do we need two different values for both ? Since the performance
Github user sryza commented on the pull request:
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Workers currently get 384 added as well (see
YarnAllocationHandler.MEMORY_OVERHEARD). At a first approximation, this seems
reasonable to me. The AM and executors are both
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--- Diff:
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@@ -90,6 +90,12 @@ private[yarn] class YarnAllocationHandler(
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I agree with @sryza .Spark automatically handle these better. Of course, we
can allow users to manually specify the special value.
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Github user mridulm commented on the pull request:
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The entire process tree is tracked ...
Note that yarn allocates in multiples of memory slots and kills only when
the container requirement is violated.
On
Github user sryza commented on the pull request:
https://github.com/apache/spark/pull/894#issuecomment-44366899
Agree with @tgravescs and @mridulm that a constant overhead makes more
sense.
@pwendell YARN includes the memory usage of subprocesses in its calculation.
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