com.alpine.plugin.core.spark.templates

InferredSparkDataFrameJob

abstract class InferredSparkDataFrameJob extends SparkDataFrameJob

A class for plugins which will use Schema inference

Linear Supertypes
SparkDataFrameJob, TemplatedSparkDataFrameJob[DataFrame, HdfsTabularDataset], SparkIOTypedPluginJob[HdfsTabularDataset, HdfsTabularDataset], AnyRef, Any
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  1. InferredSparkDataFrameJob
  2. SparkDataFrameJob
  3. TemplatedSparkDataFrameJob
  4. SparkIOTypedPluginJob
  5. AnyRef
  6. Any
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Instance Constructors

  1. new InferredSparkDataFrameJob()

Abstract Value Members

  1. abstract def transform(operatorParameters: OperatorParameters, dataFrame: DataFrame, 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.

    dataFrame

    - the input data

    listener

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

    returns

Concrete Value Members

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  3. final def ##(): Int

    Definition Classes
    AnyRef → Any

  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  6. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  7. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef

  9. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  10. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

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

    Definition Classes
    AnyRef → Any

  12. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  13. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

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

    Definition Classes
    AnyRef

  15. final def notify(): Unit

    Definition Classes
    AnyRef

  16. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  17. def onExecution(sparkContext: SparkContext, appConf: Map[String, String], 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.

    sparkContext

    Spark context created when the Spark job was submitted

    appConf

    a map containing system related parameters (rather than operator parameters) including all Spark parameters, workflow-level variables

    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.

    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

  18. def saveResults(transformedDataFrame: DataFrame, sparkUtils: SparkRuntimeUtils, storageFormat: String, outputPath: String, overwrite: Boolean, sourceOperatorInfo: Option[OperatorInfo], addendum: Map[String, AnyRef] = Map[String, AnyRef]()): 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.

    storageFormat

    - Parquet, Avro, and TSV

    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.

    returns

    Definition Classes
    SparkDataFrameJob → TemplatedSparkDataFrameJob

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

    Definition Classes
    AnyRef

  20. def toString(): String

    Definition Classes
    AnyRef → Any

  21. 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. 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

    Definition Classes
    InferredSparkDataFrameJob → SparkDataFrameJob

  22. 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

  23. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from SparkDataFrameJob

Inherited from TemplatedSparkDataFrameJob[DataFrame, HdfsTabularDataset]

Inherited from SparkIOTypedPluginJob[HdfsTabularDataset, HdfsTabularDataset]

Inherited from AnyRef

Inherited from Any

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