io.smartdatalake.workflow.action.sparktransformer
ScalaNotebookDfTransformer
Companion object ScalaNotebookDfTransformer
case class ScalaNotebookDfTransformer(name: String = "scalaTransform", description: Option[String] = None, url: String, functionName: String, authMode: Option[AuthMode] = None, options: Map[String, String] = Map(), runtimeOptions: Map[String, String] = Map()) extends OptionsDfTransformer with Product with Serializable
Configuration of a custom Spark-DataFrame transformation between one input and one output (1:1) as Scala code which is compiled at runtime. The code is loaded from a Notebook. It should define a transform function with a configurable name, which receives a DataObjectId, a DataFrame and a map of options and has to return a DataFrame, see also (fnTransformType). Notebook-cells starting with "//!IGNORE" will be ignored.
- name
name of the transformer
- description
Optional description of the transformer
- url
Url to download notebook in IPYNB-format, which defines transformation.
- functionName
The notebook needs to contain a Scala-function with this name and type fnTransformType.
- authMode
optional authentication information for webservice, e.g. BasicAuthMode for user/pw authentication
- 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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- @Scaladoc()
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- OptionsDfTransformer
- ParsableDfTransformer
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- DfTransformer
- PartitionValueTransformer
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Instance Constructors
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new
ScalaNotebookDfTransformer(name: String = "scalaTransform", description: Option[String] = None, url: String, functionName: String, authMode: Option[AuthMode] = None, options: Map[String, String] = Map(), runtimeOptions: Map[String, String] = Map())
- name
name of the transformer
- description
Optional description of the transformer
- url
Url to download notebook in IPYNB-format, which defines transformation.
- functionName
The notebook needs to contain a Scala-function with this name and type fnTransformType.
- authMode
optional authentication information for webservice, e.g. BasicAuthMode for user/pw authentication
- 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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- val authMode: Option[AuthMode]
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def
clone(): AnyRef
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- @throws( ... ) @native() @HotSpotIntrinsicCandidate()
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val
description: Option[String]
- Definition Classes
- ScalaNotebookDfTransformer → DfTransformer
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final
def
eq(arg0: AnyRef): Boolean
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def
factory: FromConfigFactory[ParsableDfTransformer]
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
- ScalaNotebookDfTransformer → ParsableFromConfig
- val functionName: 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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- ScalaNotebookDfTransformer → DfTransformer
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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
- ScalaNotebookDfTransformer → OptionsDfTransformer
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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
- ScalaNotebookDfTransformer → DfTransformer
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val
runtimeOptions: Map[String, String]
- Definition Classes
- ScalaNotebookDfTransformer → OptionsDfTransformer
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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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)
- Definition Classes
- OptionsDfTransformer → 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.
- Definition Classes
- OptionsDfTransformer → 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.
- options
Options specified in the configuration for this transformation, including evaluated runtimeOptions
- Definition Classes
- OptionsDfTransformer
- Annotations
- @Scaladoc()
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def
transformWithOptions(actionId: ActionId, partitionValues: Seq[PartitionValues], df: DataFrame, dataObjectId: DataObjectId, options: Map[String, String])(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)
- options
Options specified in the configuration for this transformation, including evaluated runtimeOptions
- Definition Classes
- ScalaNotebookDfTransformer → OptionsDfTransformer
- val url: String
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final
def
wait(arg0: Long, arg1: Int): Unit
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wait(): Unit
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