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https://issues.apache.org/jira/browse/SPARK-17522?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15500078#comment-15500078
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Sun Rui commented on SPARK-17522:
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yes. It can be tuned by a config option according to the wordload. that's why
the experimental code reads the conf "spark.deploy.spreadOut"
> [MESOS] More even distribution of executors on Mesos cluster
> ------------------------------------------------------------
>
> Key: SPARK-17522
> URL: https://issues.apache.org/jira/browse/SPARK-17522
> Project: Spark
> Issue Type: Improvement
> Components: Mesos
> Affects Versions: 2.0.0
> Reporter: Sun Rui
>
> The MesosCoarseGrainedSchedulerBackend launch executors in a round-robin way
> among accepted offers that are received at once, but it is observed that
> typically executors are launched on a small number of slaves.
> It is found that MesosCoarseGrainedSchedulerBackend mostly is receiving only
> one offer once on a cluster composed of many nodes, so that the round-robin
> assignment of executors among offers do not have expected result, which leads
> to the fact that executors are located on a smaller number of slave nodes
> than expected, which suffers bad data locality.
> An experimental slight change to
> MesosCoarseGrainedSchedulerBackend::buildMesosTasks() shows better executor
> distribution among nodes:
> {code}
> while (launchTasks) {
> launchTasks = false
> for (offer <- offers) {
> ...
> }
> + if (conf.getBoolean("spark.deploy.spreadOut", true)) {
> + launchTasks = false
> + }
> }
> tasks.toMap
> {code}
> One of my spark programs can run 30% faster due to this change because of
> better data locality.
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