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https://issues.apache.org/jira/browse/SPARK-25678?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen resolved SPARK-25678.
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Resolution: Won't Fix
If there is more work to be done to make resource managers pluggable, I'd put
that under SPARK-19700. If it's about merging support for this cluster manager,
no I am pretty certain that would not happen in Spark.
> SPIP: Adding support in Spark for HPC cluster manager (PBS Professional)
> ------------------------------------------------------------------------
>
> Key: SPARK-25678
> URL: https://issues.apache.org/jira/browse/SPARK-25678
> Project: Spark
> Issue Type: New Feature
> Components: Scheduler
> Affects Versions: 3.0.0
> Reporter: Utkarsh Maheshwari
> Priority: Major
>
> I sent an email on the dev mailing list but got no response, hence filing a
> JIRA ticket.
>
> PBS (Portable Batch System) Professional is an open sourced workload
> management system for HPC clusters. Many organizations using PBS for managing
> their cluster also use Spark for Big Data but they are forced to divide the
> cluster into Spark cluster and PBS cluster either physically dividing the
> cluster nodes into two groups or starting Spark Standalone cluster manager's
> Master and Slaves as PBS jobs, leading to underutilization of resources.
>
> I am trying to add support in Spark to use PBS as a pluggable cluster
> manager. Going through the Spark codebase and looking at Mesos and Kubernetes
> integration, I found that we can get this working as follows:
>
> - Extend `ExternalClusterManager`.
> - Extend `CoarseGrainedSchedulerBackend`
> - This class can start `Executors` as PBS jobs.
> - The initial number of `Executors` are started `onStart`.
> - More `Executors` can be started as and when required using
> `doRequestTotalExecutors`.
> - `Executors` can be killed using `doKillExecutors`.
> - Extend `SparkApplication` to start `Driver` as a PBS job in cluster deploy
> mode.
> - This extended class can submit the Spark application again as a PBS job
> which with deploy mode = client, so that the application driver is started on
> a node in the cluster.
>
> I have a couple of questions:
> - Does this seem like a good idea to do this or should we look at other
> options?
> - What are the expectations from the initial prototype?
> - If this works, would Spark maintainers look forward to merging this or
> would they want it to be maintained as a fork?
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