Web Reference: It's not necessary to disable dynamic allocation at all. Obviously the dynamic allocation contains some routines for "dynamic deallocation". In your case the executors kill earlier then it's needed. You can just increase the spark.dynamicAllocation.executorIdleTimeout config to fix this. So the executors won't being destroying so aggressively. CoarseGrainedSchedulerBackend is a SchedulerBackend and ExecutorAllocationClient. It is responsible for requesting resources from a cluster manager for executors to be able to launch tasks (on coarse-grained executors). What is the CoarseGrainedScheduler exception? The CoarseGrainedScheduler exception is a Spark error that occurs when the Spark cluster cannot find the CoarseGrainedScheduler. The CoarseGrainedScheduler is a scheduler that is responsible for scheduling tasks on the cluster.
YouTube Excerpt: By using resource managers, like Mesos, Spark can improve resource utilization by allowing multiple Spark frameworks to share ...
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