DFExecutorPriorityStrategies

Value Members

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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4. def asInTheFlow: PartialFunction[actionQueue, actionQueue]

   

   Preserves the order in which actions are defined in the flow.

   Preserves the order in which actions are defined in the flow.

   returns

   same as input, no modifications

5. final def asInstanceOf[T0]: T0

   

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6. def clone(): AnyRef

   

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7. def defaultPriorityStrategy[C]: PartialFunction[actionQueue, actionQueue]

   

   Default strategy, at the moment it will not do anything.

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

   

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9. def equals(arg0: Any): Boolean

   

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10. def finalize(): Unit

    

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

    

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12. def hashCode(): Int

    

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13. final def isInstanceOf[T0]: Boolean

    

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14. final def ne(arg0: AnyRef): Boolean

    

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15. final def notify(): Unit

    

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16. final def notifyAll(): Unit

    

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17. def preferLoaders: PartialFunction[actionQueue, actionQueue]

    

    Preserves the order of the actions in which they are defined, but at first will give preference to loaders.

    Preserves the order of the actions in which they are defined, but at first will give preference to loaders. If there are no loaders, keeps the order.

18. def raceToOutputs[C]: PartialFunction[actionQueue, actionQueue]

    

    With Spark, waimak writers would usually force execution of the DAG and will produce outputs, while other waimak actions could be preparing the steps of the DAG.

    With Spark, waimak writers would usually force execution of the DAG and will produce outputs, while other waimak actions could be preparing the steps of the DAG. In order to load the Spark Executors with work this strategy: 1) will first choose only writers, as those are most likely to execute the DAG 2) if there were no writers, it will choose only actions with inputs, as those will to DAG execution faster 3) if there were no writers and actions with inputs leaves them as they are

19. def raceToOutputsAndThanSort(orderedLabels: Seq[String]): PartialFunction[actionQueue, actionQueue]

    

    In order to race to actions that execute Spark DAG faster, it is needed to schedule certain actions earlier, regardless in which order they are defined.

    In order to race to actions that execute Spark DAG faster, it is needed to schedule certain actions earlier, regardless in which order they are defined. This function will first apply rules from raceToOutputs and than will sort the selected actions in the order of labels defined by the 'orderedLabels' argument.

20. final def synchronized[T0](arg0: ⇒ T0): T0

    

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21. def toString(): String

    

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22. final def wait(): Unit

    

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

    

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

    

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