KafkaJob

Value Members

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

   

   Definition Classes

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2. final def ##(): Int

   

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

   

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4. def analyze(fullTableName: String): Any

   

   Attributes

   protected

   Definition Classes

   SparkJob

5. def appendToFile(storageHandler: StorageHandler, 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

   Attributes

   protected

   Definition Classes

   SparkJob

6. final def asInstanceOf[T0]: T0

   

   Definition Classes

   Any

7. def clone(): AnyRef

   

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   protected[java.lang]

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   @throws( ... )

8. def createViews(views: Views, sqlParameters: Map[String, String]): Unit

   

   Attributes

   protected

   Definition Classes

   SparkJob

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

   

   Definition Classes

   AnyRef

10. def equals(arg0: Any): Boolean

    

    Definition Classes

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11. def finalize(): Unit

    

    Attributes

    protected[java.lang]

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    @throws( classOf[java.lang.Throwable] )

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

    

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13. def hashCode(): Int

    

    Definition Classes

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14. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

15. val kafkaJobConfig: KafkaJobConfig

    

16. def load(): Try[SparkJobResult]

    

17. val logger: Logger

    

    Attributes

    protected

    Definition Classes

    StrictLogging

18. def name: String

    

    Definition Classes

    KafkaJob → JobBase

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

    

    Definition Classes

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20. final def notify(): Unit

    

    Definition Classes

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21. final def notifyAll(): Unit

    

    Definition Classes

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22. def offload(): Try[SparkJobResult]

    

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

    

    Attributes

    protected

    Definition Classes

    SparkJob

24. 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 ingestion time, use the reserved column names :

    • comet_date
    • comet_year
    • comet_month
    • comet_day
    • comet_hour
    • comet_minute These columns are renamed to "date", "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

    Attributes

    protected

    Definition Classes

    SparkJob

25. def registerUdf(udf: String): Unit

    

    Attributes

    protected

    Definition Classes

    SparkJob

26. def run(): Try[JobResult]

    

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

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

    returns

    : Spark Dataframe for Spark Jobs None otherwise

    Definition Classes

    KafkaJob → JobBase

27. lazy val session: SparkSession

    

    Definition Classes

    SparkJob

28. implicit val settings: Settings

    

    Definition Classes

    KafkaJob → JobBase

29. lazy val sparkEnv: SparkEnv

    

    Definition Classes

    SparkJob

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

    

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    AnyRef

31. def toString(): String

    

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32. final def wait(): Unit

    

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    @throws( ... )

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

    

    Definition Classes

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

    

    Definition Classes

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    @throws( ... )