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. def aggregateStageMetrics(nameTempView: String = "PerfStageMetrics"): DataFrame

   

5. final def asInstanceOf[T0]: T0

   

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6. def begin(): Long

   

7. var beginSnapshot: Long

   

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

8. def clone(): AnyRef

   

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

   

10. 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

11. def end(): Long

    

12. var endSnapshot: Long

    

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

    

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14. def finalize(): Unit

    

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

    

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16. final def isInstanceOf[T0]: Boolean

    

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17. val listenerStage: StageInfoRecorderListener

    

    This inserts the custom Spark Listener into the live Spark Context

18. lazy val logger: Logger

    

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

    

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20. final def notify(): Unit

    

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21. final def notifyAll(): Unit

    

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22. def printAccumulables(): Unit

    

23. def printReport(): Unit

    

24. def report(): String

    

    Custom aggregations and post-processing of metrics data

25. def reportAccumulables(): String

    

    for internal metrics sum all the values, for the accumulables compute max value for each accId and name

26. def reportMap(): Map[String, String]

    

    Custom aggregations and post-processing of metrics data

27. def runAndMeasure[T](f: ⇒ T): T

    

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

28. 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

29. def sendReportPrometheus(serverIPnPort: String, metricsJob: String, labelName: String = sparkSession.sparkContext.appName, labelValue: String = ...): Unit

    

    Send the metrics to Prometheus.

    Send the metrics to Prometheus. serverIPnPort: String with prometheus pushgateway address, format is hostIP:Port, metricsJob: job name, labelName: metrics label name, default is sparkSession.sparkContext.appName, labelValue: metrics label value, default is sparkSession.sparkContext.applicationId

30. val sparkSession: SparkSession

    

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

    

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32. final def wait(): Unit

    

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

    

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

    

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