MetricsJob

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 clone(): AnyRef

   

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

   

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7. def equals(arg0: Any): Boolean

   

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8. def finalize(): Unit

   

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9. def generateFullMetric(dataMetric: DataFrame, listAttibutes: List[String], colName: List[Column]): DataFrame

   

   Function that retrieves full metrics dataframe with both set discrete and continuous metrics

   Function that retrieves full metrics dataframe with both set discrete and continuous metrics

   dataMetric

   : dataframe obtain from computeDiscretMetric( ) or computeContinuiousMetric( )

   listAttibutes

   : list of all variables

   colName

   : list of column

   returns

   Dataframe : that contain the full metrics with all variables and all metrics

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

    

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11. def hashCode(): Int

    

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12. final def isInstanceOf[T0]: Boolean

    

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13. def lockPath(path: String): Path

    

14. val logger: Logger

    

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    protected

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    StrictLogging

15. def metricsPath(path: String): Path

    

    Function to build the metrics save path

    Function to build the metrics save path

    path

    : path where metrics are stored

    returns

    : path where the metrics for the specified schema are stored

16. def name: String

    

    Definition Classes

    MetricsJob → SparkJob

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

    

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18. final def notify(): Unit

    

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19. final def notifyAll(): Unit

    

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20. def partitionDataset(dataset: DataFrame, partition: List[String]): DataFrame

    

    Definition Classes

    SparkJob

21. def partitionedDatasetWriter(dataset: DataFrame, partition: List[String]): DataFrameWriter[Row]

    

    Partition a dataset using dataset columns.

    Partition a dataset using dataset columns. To partition the dataset using the igestion time, use the reserved column names :

    • comet_year
    • comet_month
    • comet_day
    • comet_hour
    • comet_minute These columsn are renamed to "year", "month", "day", "hour", "minute" in the dataset and their values is set to the current date/time.

    dataset

    : Input dataset

    partition

    : list of columns to use for partitioning.

    returns

    The Spark session used to run this job

    Definition Classes

    SparkJob

22. def run(dataUse: DataFrame, timestamp: Timestamp): Try[SparkSession]

    

23. def run(): Try[SparkSession]

    

    Just to force any spark job to implement its entry point using within the "run" method

    Just to force any spark job to implement its entry point using within the "run" method

    returns

    : Spark Session used for the job

    Definition Classes

    MetricsJob → SparkJob

24. def save(dataToSave: DataFrame, path: Path): Unit

    

    Saves a dataset.

    Saves a dataset. If the path is empty (the first time we call metrics on the schema) then we can write.

    If there's already parquet files stored in it, then create a temporary directory to compute on, and flush the path to move updated metrics in it

    dataToSave

    : dataset to be saved

    path

    : Path to save the file at

25. lazy val session: SparkSession

    

    Definition Classes

    SparkJob

26. implicit val settings: Settings

    

    Definition Classes

    MetricsJob → SparkJob

27. lazy val sparkEnv: SparkEnv

    

    Definition Classes

    SparkJob

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

    

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29. def toString(): String

    

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30. def unionDisContMetric(discreteDataset: Option[DataFrame], continuousDataset: Option[DataFrame], domain: Domain, schema: Schema, count: Long, ingestionTime: Timestamp, stageState: Stage): Option[DataFrame]

    

    Function Function that unifies discrete and continuous metrics dataframe, then write save the result to parquet

    Function Function that unifies discrete and continuous metrics dataframe, then write save the result to parquet

    discreteDataset

    : dataframe that contains all the discrete metrics

    continuousDataset

    : dataframe that contains all the continuous metrics

    domain

    : name of the domain

    schema

    : schema of the initial data

    ingestionTime

    : time which correspond to the ingestion

    stageState

    : stage (unit / global)

31. final def wait(): Unit

    

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

    

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

    

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