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  1. Spark
  2. SPARK-9260

Standalone scheduling can overflow a worker with cores

    Details

    • Type: Bug
    • Status: Closed
    • Priority: Major
    • Resolution: Fixed
    • Affects Version/s: 1.4.0
    • Fix Version/s: 1.4.2, 1.5.0
    • Component/s: Deploy
    • Labels:
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      Description

      If the cluster is started with `spark.deploy.spreadOut = false`, then we may allocate more cores than is available on a worker. E.g. a worker has 8 cores, and an application sets `spark.cores.max = 10`, then we end up with the following screenshot:

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            • Assignee:
              nravi Nishkam Ravi
              Reporter:
              andrewor14 Andrew Or
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              • Created:
                Updated:
                Resolved: