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  1. Spark
  2. SPARK-19941

Spark should not schedule tasks on executors on decommissioning YARN nodes

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Details

    • Improvement
    • Status: Resolved
    • Major
    • Resolution: Duplicate
    • 2.1.0
    • None
    • Scheduler, Spark Core, YARN
    • None
    • Hadoop 2.8.0-rc1

    Description

      Hadoop 2.8 added a mechanism to gracefully decommission Node Managers in YARN: https://issues.apache.org/jira/browse/YARN-914

      Essentially you can mark nodes to be decommissioned, and let them a) finish work in progress and b) finish serving shuffle data. But no new work will be scheduled on the node.

      Spark should respect when NMs are set to decommissioned, and similarly decommission executors on those nodes by not scheduling any more tasks on them.

      It looks like in the future YARN may inform the app master when containers will be killed: https://issues.apache.org/jira/browse/YARN-3784. However, I don't think Spark should schedule based on a timeout. We should gracefully decommission the executor as fast as possible (which is the spirit of YARN-914). The app master can query the RM for NM statuses (if it doesn't already have them) and stop scheduling on executors on NMs that are decommissioning.

      Stretch feature: The timeout may be useful in determining whether running further tasks on the executor is even helpful. Spark may be able to tell that shuffle data will not be consumed by the time the node is decommissioned, so it is not worth computing. The executor can be killed immediately.

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              Unassigned Unassigned
              Karthik Palaniappan Karthik Palaniappan
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                Updated:
                Resolved: