[
https://issues.apache.org/jira/browse/SPARK-8881?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14617732#comment-14617732
]
Nishkam Ravi commented on SPARK-8881:
-------------------------------------
No that's not the problem.
You have 4 workers with 16 cores each. You request 3 executors (spark.cores.max
= 48, spark.executor.cores = 16). App hangs. Because the following condition is
never satisfied: while (coresLeft >= coresPerExecutor && worker.memoryFree >=
memoryPerExecutor). You will have to stare at the scheduling algorithm for a
good 5 minutes to understand what's happening. Try to simulate the case stated
above.
> Scheduling fails if num_executors < num_workers
> -----------------------------------------------
>
> Key: SPARK-8881
> URL: https://issues.apache.org/jira/browse/SPARK-8881
> Project: Spark
> Issue Type: Bug
> Components: Deploy
> Affects Versions: 1.4.0, 1.5.0
> Reporter: Nishkam Ravi
>
> Current scheduling algorithm (in Master.scala) has two issues:
> 1. cores are allocated one at a time instead of spark.executor.cores at a time
> 2. when spark.cores.max/spark.executor.cores < num_workers, executors are not
> launched and the app hangs (due to 1)
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
