Hi I have a 1.0.0 cluster with multiple worker nodes that deploy a number of external tasks, through getRuntime().exec. Currently I have no control on how many nodes get deployed for a given task. At times scheduler evenly distributes the executors among all nodes and at other times it only uses 1 node. (The difficulty with the latter is that the deployed tasks run out of memory, at which point kernel intervenes and kills them.) I've tried setting spark.cores.max to available number of cores, spark.deploy.spreadOut to true, spark.scheduler.mode to FAIR, etc., to no avail. Is there a non-documented parameter or a priming procedure to do this? Cheers,
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