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https://issues.apache.org/jira/browse/FLINK-3020?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15013266#comment-15013266
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ASF GitHub Bot commented on FLINK-3020:
---------------------------------------
Github user mxm commented on the pull request:
https://github.com/apache/flink/pull/1360#issuecomment-158009498
True. That's how it is handled on the batch side. Not sure about this
behavior though. If a user sets a default parallelism but uses operators with
`parallelism > defaultParallelism` this would fail, right? The rational behind
this is probably to maximize the parallelism for all operators and not have
operators with exceptional high parallelism.
> Local streaming execution: set number of task manager slots to the maximum
> parallelism
> --------------------------------------------------------------------------------------
>
> Key: FLINK-3020
> URL: https://issues.apache.org/jira/browse/FLINK-3020
> Project: Flink
> Issue Type: Bug
> Components: Local Runtime
> Affects Versions: 0.10.0
> Reporter: Maximilian Michels
> Assignee: Maximilian Michels
> Priority: Minor
> Fix For: 1.0.0, 0.10.1
>
>
> Quite an inconvenience is the local execution configuration behavior. It sets
> the number of task slots of the mini cluster to the default parallelism. This
> causes problem if you use {{setParallelism(parallelism)}} on an operator and
> set a parallelism larger than the default parallelism.
> {noformat}
> Caused by:
> org.apache.flink.runtime.jobmanager.scheduler.NoResourceAvailableException:
> Not enough free slots available to run the job. You can decrease the operator
> parallelism or increase the number of slots per TaskManager in the
> configuration. Task to schedule: < Attempt #0 (Flat Map (9/100)) @
> (unassigned) - [SCHEDULED] > with groupID < fa7240ee1fed08bd7e6278899db3e838
> > in sharing group < SlotSharingGroup [f3d578e9819be9c39ceee86cf5eb8c08,
> 8fa330746efa1d034558146e4604d0b4, fa7240ee1fed08bd7e6278899db3e838] >.
> Resources available to scheduler: Number of instances=1, total number of
> slots=8, available slots=0
> at
> org.apache.flink.runtime.jobmanager.scheduler.Scheduler.scheduleTask(Scheduler.java:256)
> at
> org.apache.flink.runtime.jobmanager.scheduler.Scheduler.scheduleImmediately(Scheduler.java:131)
> at
> org.apache.flink.runtime.executiongraph.Execution.scheduleForExecution(Execution.java:298)
> at
> org.apache.flink.runtime.executiongraph.ExecutionVertex.scheduleForExecution(ExecutionVertex.java:458)
> at
> org.apache.flink.runtime.executiongraph.ExecutionJobVertex.scheduleAll(ExecutionJobVertex.java:322)
> at
> org.apache.flink.runtime.executiongraph.ExecutionGraph.scheduleForExecution(ExecutionGraph.java:686)
> at
> org.apache.flink.runtime.jobmanager.JobManager$$anonfun$org$apache$flink$runtime$jobmanager$JobManager$$submitJob$1.apply$mcV$sp(JobManager.scala:982)
> at
> org.apache.flink.runtime.jobmanager.JobManager$$anonfun$org$apache$flink$runtime$jobmanager$JobManager$$submitJob$1.apply(JobManager.scala:962)
> at
> org.apache.flink.runtime.jobmanager.JobManager$$anonfun$org$apache$flink$runtime$jobmanager$JobManager$$submitJob$1.apply(JobManager.scala:962)
> at
> scala.concurrent.impl.Future$PromiseCompletingRunnable.liftedTree1$1(Future.scala:24)
> at
> scala.concurrent.impl.Future$PromiseCompletingRunnable.run(Future.scala:24)
> at akka.dispatch.TaskInvocation.run(AbstractDispatcher.scala:41)
> at
> akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:401)
> at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
> at
> scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
> ... 2 more
> {noformat}
> I propose to change this behavior to setting the number of task slots to the
> maximum parallelism present in the user program.
> What do you think?
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