GitHub user brndnmtthws opened a pull request:
https://github.com/apache/spark/pull/3024
[SPARK-4158] Fix for missing resources.
Mesos offers may not contain all resources, and Spark needs to check to
ensure they are present and sufficient. Spark may throw an erroneous
Github user brndnmtthws commented on the pull request:
https://github.com/apache/spark/pull/2401#issuecomment-57833282
Done as per @andrewor14's suggestions.
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Github user brndnmtthws commented on the pull request:
https://github.com/apache/spark/pull/2401#issuecomment-57814884
That test failure appears to be unrelated.
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Github user brndnmtthws commented on the pull request:
https://github.com/apache/spark/pull/2401#issuecomment-57806924
Fixed typo's (also switch from `Math.max` to `math.max` because that seems
to be the Scala way).
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Github user brndnmtthws commented on the pull request:
https://github.com/apache/spark/pull/2401#issuecomment-57703146
Updated to match YARN.
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Github user brndnmtthws commented on the pull request:
https://github.com/apache/spark/pull/2401#issuecomment-57700183
It's counterintuitive to the policy used elsewhere, but if it will appease
the Spark folks, I will make the change.
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Github user brndnmtthws commented on the pull request:
https://github.com/apache/spark/pull/2401#issuecomment-57698498
The definition is indeed the same, but I don't see how the YARN patch
solves that better than this one.
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Github user brndnmtthws commented on the pull request:
https://github.com/apache/spark/pull/2401#issuecomment-57697287
Mesos will indeed kill your contains as well (provided cgroup limits are
enabled). I also don't see how this would necessarily apply differently for
Python,
Github user brndnmtthws commented on the pull request:
https://github.com/apache/spark/pull/2401#issuecomment-57696587
As expressed elsewhere, I think the model that the #2485 has doesn't make
sense. The most important knob is the overhead fraction, rather than the
minimum numb
Github user brndnmtthws commented on the pull request:
https://github.com/apache/spark/pull/2453#issuecomment-57676255
This code has been tested & verified on a cluster of this size:
. I'm not familiar with Spark's executio
Github user brndnmtthws commented on the pull request:
https://github.com/apache/spark/pull/2401#issuecomment-55821735
YARN has a similar strategy, from what I can tell. There's a separate
config value, `spark.yarn.executor.memoryOverhead`.
We could go the other way,
Github user brndnmtthws commented on the pull request:
https://github.com/apache/spark/pull/2401#issuecomment-55820092
Updated diff based on coding.
I've also added the same thing to the coarse scheduler, and updated the
documentation accordingly.
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GitHub user brndnmtthws opened a pull request:
https://github.com/apache/spark/pull/2401
[SPARK-3535][Mesos] Add 15% task memory overhead.
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Github user brndnmtthws commented on the pull request:
https://github.com/apache/spark/pull/1358#issuecomment-55659794
It seems that this is a symptom of the following issue:
https://issues.apache.org/jira/browse/SPARK-3535
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Github user brndnmtthws commented on the pull request:
https://github.com/apache/spark/pull/1358#issuecomment-55310679
Yep, also hitting this same problem. We're running Spark 1.0.2 and Mesos
0.20.0.
From a quick analysis, it looks like a bug in Spark.
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