io.smartdatalake.workflow.action.sparktransformer
ScalaCodeDfsTransformer
Companion object ScalaCodeDfsTransformer
case class ScalaCodeDfsTransformer(name: String = "scalaTransform", description: Option[String] = None, code: Option[String] = None, file: Option[String] = None, options: Map[String, String] = Map(), runtimeOptions: Map[String, String] = Map()) extends OptionsDfsTransformer with Product with Serializable
Configuration of a custom Spark-DataFrame transformation between many inputs and many outputs (n:m) as Scala code which is compiled at runtime. Define a transform function which receives a map of input DataObjectIds with DataFrames and a map of options and has to return a map of output DataObjectIds with DataFrames. The scala code has to implement a function of type fnTransformType.
- name
name of the transformer
- description
Optional description of the transformer
- code
Scala code for transformation. The scala code needs to be a function of type fnTransformType.
- file
File where scala code for transformation is loaded from. The scala code in the file needs to be a function of type fnTransformType.
- options
Options to pass to the transformation
- runtimeOptions
optional tuples of [key, spark sql expression] to be added as additional options when executing transformation. The spark sql expressions are evaluated against an instance of DefaultExpressionData.
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- PartitionValueTransformer
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Instance Constructors
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new
ScalaCodeDfsTransformer(name: String = "scalaTransform", description: Option[String] = None, code: Option[String] = None, file: Option[String] = None, options: Map[String, String] = Map(), runtimeOptions: Map[String, String] = Map())
- name
name of the transformer
- description
Optional description of the transformer
- code
Scala code for transformation. The scala code needs to be a function of type fnTransformType.
- file
File where scala code for transformation is loaded from. The scala code in the file needs to be a function of type fnTransformType.
- options
Options to pass to the transformation
- runtimeOptions
optional tuples of [key, spark sql expression] to be added as additional options when executing transformation. The spark sql expressions are evaluated against an instance of DefaultExpressionData.
Value Members
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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
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- @throws( ... ) @native() @HotSpotIntrinsicCandidate()
- val code: Option[String]
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val
description: Option[String]
- Definition Classes
- ScalaCodeDfsTransformer → DfsTransformer
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final
def
eq(arg0: AnyRef): Boolean
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def
factory: FromConfigFactory[ParsableDfsTransformer]
Returns the factory that can parse this type (that is, type
CO).Returns the factory that can parse this type (that is, type
CO).Typically, implementations of this method should return the companion object of the implementing class. The companion object in turn should implement FromConfigFactory.
- returns
the factory (object) for this class.
- Definition Classes
- ScalaCodeDfsTransformer → ParsableFromConfig
- val file: Option[String]
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final
def
getClass(): Class[_]
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final
def
isInstanceOf[T0]: Boolean
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val
name: String
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- ScalaCodeDfsTransformer → DfsTransformer
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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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val
options: Map[String, String]
- Definition Classes
- ScalaCodeDfsTransformer → OptionsDfsTransformer
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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.
- Definition Classes
- DfsTransformer
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val
runtimeOptions: Map[String, String]
- Definition Classes
- ScalaCodeDfsTransformer → OptionsDfsTransformer
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
transform(actionId: ActionId, partitionValues: Seq[PartitionValues], dfs: Map[String, DataFrame])(implicit context: ActionPipelineContext): Map[String, DataFrame]
Function to be implemented to define the transformation between many inputs and many outputs (n:m)
Function to be implemented to define the transformation between many inputs and many outputs (n:m)
- actionId
id of the action which executes this transformation. This is mainly used to prefix error messages.
- partitionValues
partition values to transform
- dfs
Map of (dataObjectId, DataFrame) tuples available as input
- returns
Map of transformed (dataObjectId, DataFrame) tuples
- Definition Classes
- OptionsDfsTransformer → DfsTransformer
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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.
- Definition Classes
- OptionsDfsTransformer → PartitionValueTransformer
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def
transformPartitionValuesWithOptions(actionId: ActionId, partitionValues: Seq[PartitionValues], options: Map[String, String])(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. see also DfsTransformer.transformPartitionValues()
- options
Options specified in the configuration for this transformation, including evaluated runtimeOptions
- Definition Classes
- OptionsDfsTransformer
- Annotations
- @Scaladoc()
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def
transformWithOptions(actionId: ActionId, partitionValues: Seq[PartitionValues], dfs: Map[String, DataFrame], options: Map[String, String])(implicit context: ActionPipelineContext): Map[String, DataFrame]
Function to be implemented to define the transformation between many inputs and many outputs (n:m) see also DfsTransformer.transform()
Function to be implemented to define the transformation between many inputs and many outputs (n:m) see also DfsTransformer.transform()
- options
Options specified in the configuration for this transformation, including evaluated runtimeOptions
- Definition Classes
- ScalaCodeDfsTransformer → OptionsDfsTransformer
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final
def
wait(arg0: Long, arg1: Int): Unit
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wait(arg0: Long): Unit
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def
wait(): Unit
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