io.smartdatalake.workflow.action.sparktransformer
RepartitionTransformer
Companion object RepartitionTransformer
case class RepartitionTransformer(name: String = "repartition", description: Option[String] = None, numberOfTasksPerPartition: Int, keyCols: Seq[String] = Seq()) extends ParsableDfTransformer with Product with Serializable
Repartition DataFrame For detailled description about repartitioning DataFrames see also SparkRepartitionDef
- name
name of the transformer
- description
Optional description of the transformer
- numberOfTasksPerPartition
Number of Spark tasks to create per partition value by repartitioning the DataFrame.
- keyCols
Optional key columns to distribute records over Spark tasks inside a partition value.
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Instance Constructors
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new
RepartitionTransformer(name: String = "repartition", description: Option[String] = None, numberOfTasksPerPartition: Int, keyCols: Seq[String] = Seq())
- name
name of the transformer
- description
Optional description of the transformer
- numberOfTasksPerPartition
Number of Spark tasks to create per partition value by repartitioning the DataFrame.
- keyCols
Optional key columns to distribute records over Spark tasks inside a partition value.
Value Members
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final
def
!=(arg0: Any): Boolean
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val
description: Option[String]
- Definition Classes
- RepartitionTransformer → 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.
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- RepartitionTransformer → ParsableFromConfig
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final
def
getClass(): Class[_]
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final
def
isInstanceOf[T0]: Boolean
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- val keyCols: Seq[String]
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val
name: String
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- RepartitionTransformer → DfTransformer
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final
def
ne(arg0: AnyRef): Boolean
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def
notify(): Unit
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def
notifyAll(): Unit
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- val numberOfTasksPerPartition: Int
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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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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
- RepartitionTransformer → 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
- PartitionValueTransformer
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