case class DfTransformerFunctionWrapper(name: String, fn: (DataFrame) ⇒ DataFrame) extends DfTransformer with Product with Serializable
Legacy wrapper for pure DataFrame transformation function
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- new DfTransformerFunctionWrapper(name: String, fn: (DataFrame) ⇒ DataFrame)
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description: Option[String]
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- DfTransformerFunctionWrapper → DfTransformer
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- val fn: (DataFrame) ⇒ DataFrame
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def
prepare(actionId: ActionId)(implicit context: ActionPipelineContext): Unit
Optional function to implement validations in prepare phase.
Optional function to implement validations in prepare phase.
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- DfTransformer
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def
transform(actionId: ActionId, partitionValues: Seq[PartitionValues], df: DataFrame, dataObjectId: DataObjectId)(implicit context: ActionPipelineContext): 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)
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- DfTransformerFunctionWrapper → DfTransformer
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def
transformPartitionValues(actionId: ActionId, partitionValues: Seq[PartitionValues])(implicit context: ActionPipelineContext): Option[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.
- actionId
id of the action which executes this transformation. This is mainly used to prefix error messages.
- partitionValues
partition values to transform
- returns
Map of input to output partition values. This allows to map partition values forward and backward, which is needed in execution modes. Return None if mapping is 1:1.
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- PartitionValueTransformer
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