ClusterUtil

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1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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6. final def eq(arg0: AnyRef): Boolean

   

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9. final def getClass(): Class[_]

   

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10. def getDefaultNumExecutorCores(spark: SparkSession, log: Logger, master: Option[String] = None): Int

    

    Get number of default cores from sparkSession(required) or master(optional) for 1 executor.

    Get number of default cores from sparkSession(required) or master(optional) for 1 executor.

    spark

    The current spark session. If master parameter is not set, the master in the spark session is used.

    master

    This param is needed for unittest. If set, the function return the value for it. if not set, basically, master in spark (SparkSession) is used.

    returns

    The number of default cores per executor based on master.

11. def getDriverHost(dataset: Dataset[_]): String

    

12. def getExecutors(dataset: Dataset[_]): Array[(Int, String)]

    

    Returns a list of executor id and host.

    Returns a list of executor id and host.

    dataset

    The dataset containing the current spark session.

    returns

    List of executors as an array of (id,host).

13. def getHostToIP(hostname: String): String

    

14. def getJVMCPUs(spark: SparkSession): Int

    

15. def getNumExecutorTasks(dataset: Dataset[_], numTasksPerExec: Int, log: Logger): Int

    

    Returns the number of executors * number of tasks.

    Returns the number of executors * number of tasks.

    dataset

    The dataset containing the current spark session.

    numTasksPerExec

    The number of tasks per executor.

    returns

    The number of executors * number of tasks.

16. def getNumTasksPerExecutor(dataset: Dataset[_], log: Logger): Int

    

    Get number of tasks from dummy dataset for 1 executor.

    Get number of tasks from dummy dataset for 1 executor. Note: all executors have same number of cores, and this is more reliable than getting value from conf.

    dataset

    The dataset containing the current spark session.

    returns

    The number of tasks per executor.

17. def getTaskCpus(dataset: Dataset[_], log: Logger): Int

    

18. def hashCode(): Int

    

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21. final def notify(): Unit

    

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25. final def wait(): Unit

    

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26. final def wait(arg0: Long, arg1: Int): Unit

    

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27. final def wait(arg0: Long): Unit

    

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