[ https://issues.apache.org/jira/browse/SPARK-1882?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen resolved SPARK-1882. ------------------------------ Resolution: Won't Fix I think this is mostly subsumed by the idea of dynamic allocation now > Support dynamic memory sharing in Mesos > --------------------------------------- > > Key: SPARK-1882 > URL: https://issues.apache.org/jira/browse/SPARK-1882 > Project: Spark > Issue Type: Improvement > Components: Mesos > Affects Versions: 1.0.0 > Reporter: Andrew Ash > > Fine grained mode Mesos currently supports sharing CPUs very well, but > requires that memory be pre-partitioned according to the executor memory > parameter. Mesos supports dynamic memory allocation in addition to dynamic > CPU allocation, so we should utilize this feature in Spark. > See below where when the Mesos backend accepts a resource offer it only > checks that there's enough memory to cover sc.executorMemory, and doesn't > ever take a fraction of the memory available. The memory offer is accepted > all or nothing from a pre-defined parameter. > Coarse mode: > https://github.com/apache/spark/blob/3ce526b168050c572a1feee8e0121e1426f7d9ee/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala#L208 > Fine mode: > https://github.com/apache/spark/blob/a5150d199ca97ab2992bc2bb221a3ebf3d3450ba/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala#L114 -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org