Github user tgravescs commented on a diff in the pull request:
https://github.com/apache/spark/pull/1180#discussion_r15351419
--- Diff:
yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
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@@ -282,15 +298,22 @@ class ExecutorLauncher(args:
Github user tgravescs commented on a diff in the pull request:
https://github.com/apache/spark/pull/1180#discussion_r15351507
--- Diff:
yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
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@@ -115,7 +117,33 @@ private[spark] class
Github user witgo commented on a diff in the pull request:
https://github.com/apache/spark/pull/1180#discussion_r15352693
--- Diff:
yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
---
@@ -115,7 +117,33 @@ private[spark] class
Github user witgo commented on a diff in the pull request:
https://github.com/apache/spark/pull/1180#discussion_r15352756
--- Diff:
yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
---
@@ -282,15 +298,22 @@ class ExecutorLauncher(args:
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/1180#issuecomment-50033847
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Github user SparkQA commented on the pull request:
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QA results for PR 1180:br- This patch PASSES unit tests.br- This patch
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Github user SparkQA commented on the pull request:
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Github user tgravescs commented on the pull request:
https://github.com/apache/spark/pull/1180#issuecomment-50043360
The stopping flag isn't synchronized in anyway so its still possible for it
to stop twice, does that cause any issues?
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Github user witgo commented on the pull request:
https://github.com/apache/spark/pull/1180#issuecomment-50044511
This situation exists in theory.
[SparkContext.scala#L992](https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/SparkContext.scala#L992)
deal
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QA results for PR 1180:br- This patch PASSES unit tests.br- This patch
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Github user SparkQA commented on the pull request:
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QA results for PR 1180:br- This patch PASSES unit tests.br- This patch
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Github user tgravescs commented on the pull request:
https://github.com/apache/spark/pull/1180#issuecomment-50068167
+1, thanks @witgo
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Github user asfgit closed the pull request at:
https://github.com/apache/spark/pull/1180
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Github user tgravescs commented on a diff in the pull request:
https://github.com/apache/spark/pull/1180#discussion_r15289308
--- Diff:
yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
---
@@ -115,7 +117,30 @@ private[spark] class
Github user tgravescs commented on a diff in the pull request:
https://github.com/apache/spark/pull/1180#discussion_r15289735
--- Diff:
yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
---
@@ -115,7 +117,30 @@ private[spark] class
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Github user witgo commented on the pull request:
https://github.com/apache/spark/pull/1180#issuecomment-49881861
A little error repair at once.
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Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/1180#issuecomment-49882258
QA results for PR 1180:br- This patch FAILED unit tests.br- This patch
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Github user lianhuiwang commented on the pull request:
https://github.com/apache/spark/pull/1180#issuecomment-49883664
i think a long-running application,sometimes there has
maxNumExecutorFailures because yarn's reason, but yarn quickly provide spark
to new container.although
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Github user tgravescs commented on the pull request:
https://github.com/apache/spark/pull/1180#issuecomment-49884681
@lianhuiwang I'm not sure I follow. Why would we want to kill it if you
have minNumExecutors. Yarn will just give you more. I understand that long
running
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Github user witgo commented on the pull request:
https://github.com/apache/spark/pull/1180#issuecomment-49886617
Done
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Github user lianhuiwang commented on the pull request:
https://github.com/apache/spark/pull/1180#issuecomment-49886521
@tgravescs i think if yarn will give application more executors,
application will continue work and it donot need maxNumExecutorFailures. i
think
Github user tgravescs commented on the pull request:
https://github.com/apache/spark/pull/1180#issuecomment-49887688
The reason for maxNumExecutorsFailure isn't because yarn can't give it
more, its because something has happened to enough of your executors that you
think there is a
Github user lianhuiwang commented on the pull request:
https://github.com/apache/spark/pull/1180#issuecomment-49890478
thank you.but i think some errors like disk failures,machines go down
cannot include it.so maybe we need to exclude some errors that yarn report.how
about your idea?
Github user tgravescs commented on the pull request:
https://github.com/apache/spark/pull/1180#issuecomment-49891554
Yarn doesn't necessarily tell you the type of error, or know for that
matter. Your process can crash for many different reasons. Its just a much
harder to do. This
Github user lianhuiwang commented on the pull request:
https://github.com/apache/spark/pull/1180#issuecomment-49892019
i know. i am ok for this PR.thanks
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Github user SparkQA commented on the pull request:
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QA results for PR 1180:br- This patch PASSES unit tests.brbrFor more
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Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/1180#issuecomment-49899671
QA results for PR 1180:br- This patch FAILED unit tests.br- This patch
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Github user SparkQA commented on the pull request:
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QA results for PR 1180:br- This patch PASSES unit tests.br- This patch
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