StageMetrics

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. final def asInstanceOf[T0]: T0

   

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5. def begin(): Long

   

6. var beginSnapshot: Long

   

   Variables used to store the start and end time of the period of interest for the metrics report

7. def clone(): AnyRef

   

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8. def createAccumulablesDF(nameTempView: String = "AccumulablesStageMetrics"): DataFrame

   

9. def createStageMetricsDF(nameTempView: String = "PerfStageMetrics"): DataFrame

   

   Move data recorded from the custom listener into a DataFrame and register it as a view for easier processing

10. def end(): Long

    

11. var endSnapshot: Long

    

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

    

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13. def finalize(): Unit

    

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

    

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15. final def isInstanceOf[T0]: Boolean

    

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16. val listenerStage: StageInfoRecorderListener

    

    This inserts the custom Spark Listener into the live Spark Context

17. lazy val logger: Logger

    

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

    

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19. final def notify(): Unit

    

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20. final def notifyAll(): Unit

    

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21. def printAccumulables(): Unit

    

    for internal metrics summ all the values, for the accumulables compute max value for eax accId and name

22. def printReport(): Unit

    

    Custom aggreagations and post-processing of the metrics data

23. def runAndMeasure[T](f: ⇒ T): T

    

    Shortcut to run and measure the metrics for Spark execution, built after spark.time()

24. def saveData(df: DataFrame, fileName: String, fileFormat: String = "json"): Unit

    

    Helper method to save data, we expect to have small amounts of data so collapsing to 1 partition seems OK

25. val sparkSession: SparkSession

    

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

    

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27. final def wait(): Unit

    

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

    

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

    

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