CustomDfTransformer

Abstract Value Members

abstract def transform(session: SparkSession, options: Map[String, String], df: DataFrame, dataObjectId: String): DataFrame

Function to be implemented to define the transformation between an input and output DataFrame (1:1)
Function to be implemented to define the transformation between an input and output DataFrame (1:1)
session
Spark Session
options
Options specified in the configuration for this transformation
df
DataFrames to be transformed
dataObjectId
Id of DataObject of SubFeed
returns
Transformed DataFrame

Concrete Value Members

final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: Any): Boolean

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final def asInstanceOf[T0]: T0

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def clone(): AnyRef

Attributes
protected[java.lang]
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@throws( ... )
final def eq(arg0: AnyRef): Boolean

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def equals(arg0: Any): Boolean

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def finalize(): Unit

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protected[java.lang]
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@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

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def hashCode(): Int

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final def isInstanceOf[T0]: Boolean

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final def ne(arg0: AnyRef): Boolean

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final def notify(): Unit

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final def notifyAll(): Unit

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

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def toString(): String

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def transformPartitionValues(options: Map[String, String], partitionValues: Seq[PartitionValues]): Map[PartitionValues, PartitionValues]

Optional function to define the transformation of input to output partition values.
Optional function to define the transformation of input to output partition values. For example this enables to implement aggregations where multiple input partitions are combined into one output partition. Note that the default value is input = output partition values, which should be correct for most use cases.
options
Options specified in the configuration for this transformation
partitionValues
partition values to be transformed
final def wait(): Unit

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

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final def wait(arg0: Long): Unit

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@throws( ... )

Related Doc: package customlogic

trait CustomDfTransformer extends Serializable

Abstract Value Members

abstract def transform(session: SparkSession, options: Map[String, String], df: DataFrame, dataObjectId: String): DataFrame

Concrete Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

def toString(): String

def transformPartitionValues(options: Map[String, String], partitionValues: Seq[PartitionValues]): Map[PartitionValues, PartitionValues]

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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