SparkDataFrameJob

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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. final def getClass(): Class[_]

   

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11. final def isInstanceOf[T0]: Boolean

    

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

    

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13. final def notify(): Unit

    

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14. final def notifyAll(): Unit

    

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15. def onExecution(alpineSparkEnvironment: AlpineSparkEnvironment, input: HdfsTabularDataset, operatorParameters: OperatorParameters, listener: OperatorListener): HdfsTabularDataset

    

    The driver function for the Spark job.

    The driver function for the Spark job. Unlike the corresponding function in the parent class, this function allows you to work with IOBase types directly. YOU MUST Override one of the two 'onExecution' methods.

    alpineSparkEnvironment

    Information about the spark job including the Spark session (unified spark context) created when job was submitted

    input

    the ioBase object which you have defined as the input to your plugin. For example, if the GUI node of the plugin takes an HDFSTabularDataset, this input parameter will be that dataset.

    operatorParameters

    -the parameter values set in the GUI node. Their value can be accessed via the "key" defined for each parameter added to the OperatorDialog in the GUI node.

    listener

    a listener object which allows you to send messages to the Alpine GUI during the Spark job

    returns

    the output of your plugin

    Definition Classes

    TemplatedSparkDataFrameJob → SparkIOTypedPluginJob

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    @throws( ... )

16. def saveResults(transformedDataFrame: DataFrame, sparkUtils: SparkRuntimeUtils, storageFormat: HdfsStorageFormatType, compressionType: HdfsCompressionType, outputPath: String, overwrite: Boolean, addendum: Map[String, AnyRef] = Map[String, AnyRef](), tSVAttributes: TSVAttributes = TSVAttributes.defaultCSV): HdfsTabularDataset

    

    Writes the dataFrame to HDFS as either a Parquet dataset, Avro dataset, or tabular delimited dataset.

    Writes the dataFrame to HDFS as either a Parquet dataset, Avro dataset, or tabular delimited dataset.

    transformedDataFrame

    The data frame that is to be stored to HDFS.

    sparkUtils

    - contains utility methods to write data and to convert between Alpine header types and Spark SQL schemas

    storageFormat

    - Parquet, Avro, and CSV

    compressionType

    - HdfsCompressionType

    outputPath

    The location in HDFS to store the data frame.

    overwrite

    - If false will throw a "File Already Exists" exception if the output path already exists. If true will delete the existing results before trying to write the new ones.

    Definition Classes

    SparkDataFrameJob → TemplatedSparkDataFrameJob

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

    

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18. def toString(): String

    

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19. def transform(operatorParameters: OperatorParameters, dataFrame: DataFrame, sparkUtils: SparkRuntimeUtils, listener: OperatorListener): DataFrame

    

    Define the transformation from the input dataset, expressed as a dataFrame, where the schema corresponds to the Alpine column header to the output dataset, also as a dataFrame.

    Define the transformation from the input dataset, expressed as a dataFrame, where the schema corresponds to the Alpine column header to the output dataset, also as a dataFrame.

    Override this method to define a DataFrame transformation, if you do not want to save any additional output (the default is to output the data frame and show a preiew of the data frame as a visualization). dataset). To define an addendum to create additional output use the 'TransformWithAddendum' method. If you use this version schema inference will not work.

    dataFrame

    - the input data

    sparkUtils

    - a sparkUtils object including the Spark context

    listener

    - the operator listener object which can be used to print messages to the GUI.

    returns

    your transformed DataFrame (Default implementation returns the input DataFrame)

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    @throws( ... )

20. def transformWithAddendum(operatorParameters: OperatorParameters, dataFrame: DataFrame, sparkUtils: SparkRuntimeUtils, listener: OperatorListener): (DataFrame, Map[String, AnyRef])

    

    Define the transformation from the input dataset, expressed as a dataFrame, where the schema corresponds to the Alpine column header to the output dataset, as a dataData '.

    Define the transformation from the input dataset, expressed as a dataFrame, where the schema corresponds to the Alpine column header to the output dataset, as a dataData '. In addition return a map of type String -> AnyRef (Object in java) which will be added to the output.

    dataFrame

    - the input data

    sparkUtils

    - a sparkUtils object including the Spark context

    listener

    - the operator listener object which can be used to print messages to the GUI.

    returns

    the output DataFrame and a map containing the keys and values to add to the output. (Default implementation returns the input DataFrame with no Addendum information)

    Definition Classes

    SparkDataFrameJob → TemplatedSparkDataFrameJob

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21. final def wait(): Unit

    

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

    

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

    

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