Class/Object

io.smartdatalake.workflow.action

DeduplicateAction

Related Docs: object DeduplicateAction | package action

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case class DeduplicateAction(id: ActionObjectId, inputId: DataObjectId, outputId: DataObjectId, transformer: Option[CustomDfTransformerConfig] = None, columnBlacklist: Option[Seq[String]] = None, columnWhitelist: Option[Seq[String]] = None, filterClause: Option[String] = None, standardizeDatatypes: Boolean = false, ignoreOldDeletedColumns: Boolean = false, ignoreOldDeletedNestedColumns: Boolean = true, breakDataFrameLineage: Boolean = false, persist: Boolean = false, executionMode: Option[ExecutionMode] = None, metricsFailCondition: Option[String] = None, metadata: Option[ActionMetadata] = None)(implicit instanceRegistry: InstanceRegistry) extends SparkSubFeedAction with Product with Serializable

Action to deduplicate a subfeed. Deduplication keeps the last record for every key, also after it has been deleted in the source. It needs a transactional table as output with defined primary keys.

inputId

inputs DataObject

outputId

output DataObject

ignoreOldDeletedColumns

if true, remove no longer existing columns in Schema Evolution

ignoreOldDeletedNestedColumns

if true, remove no longer existing columns from nested data types in Schema Evolution. Keeping deleted columns in complex data types has performance impact as all new data in the future has to be converted by a complex function.

executionMode

optional execution mode for this Action

metricsFailCondition

optional spark sql expression evaluated as where-clause against dataframe of metrics. Available columns are dataObjectId, key, value. If there are any rows passing the where clause, a MetricCheckFailed exception is thrown.

Linear Supertypes
Serializable, Serializable, Product, Equals, SparkSubFeedAction, SparkAction, Action, SmartDataLakeLogger, DAGNode, ParsableFromConfig[Action], SdlConfigObject, AnyRef, Any
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Inherited
  1. DeduplicateAction
  2. Serializable
  3. Serializable
  4. Product
  5. Equals
  6. SparkSubFeedAction
  7. SparkAction
  8. Action
  9. SmartDataLakeLogger
  10. DAGNode
  11. ParsableFromConfig
  12. SdlConfigObject
  13. AnyRef
  14. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new DeduplicateAction(id: ActionObjectId, inputId: DataObjectId, outputId: DataObjectId, transformer: Option[CustomDfTransformerConfig] = None, columnBlacklist: Option[Seq[String]] = None, columnWhitelist: Option[Seq[String]] = None, filterClause: Option[String] = None, standardizeDatatypes: Boolean = false, ignoreOldDeletedColumns: Boolean = false, ignoreOldDeletedNestedColumns: Boolean = true, breakDataFrameLineage: Boolean = false, persist: Boolean = false, executionMode: Option[ExecutionMode] = None, metricsFailCondition: Option[String] = None, metadata: Option[ActionMetadata] = None)(implicit instanceRegistry: InstanceRegistry)

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    inputId

    inputs DataObject

    outputId

    output DataObject

    ignoreOldDeletedColumns

    if true, remove no longer existing columns in Schema Evolution

    ignoreOldDeletedNestedColumns

    if true, remove no longer existing columns from nested data types in Schema Evolution. Keeping deleted columns in complex data types has performance impact as all new data in the future has to be converted by a complex function.

    executionMode

    optional execution mode for this Action

    metricsFailCondition

    optional spark sql expression evaluated as where-clause against dataframe of metrics. Available columns are dataObjectId, key, value. If there are any rows passing the where clause, a MetricCheckFailed exception is thrown.

Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  4. def addRuntimeEvent(phase: ExecutionPhase, state: RuntimeEventState, msg: Option[String] = None, results: Seq[SubFeed] = Seq()): Unit

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    Adds an action event

    Adds an action event

    Definition Classes
    Action
  5. def applyAdditional(subFeed: SparkSubFeed, additional: (SparkSubFeed, Option[DataFrame], Seq[String], LocalDateTime) ⇒ SparkSubFeed, output: TableDataObject)(implicit session: SparkSession, context: ActionPipelineContext): SparkSubFeed

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    applies an optional additional transformation

    applies an optional additional transformation

    Definition Classes
    SparkAction
  6. def applyBlackWhitelists(subFeed: SparkSubFeed, columnBlacklist: Option[Seq[String]], columnWhitelist: Option[Seq[String]]): SparkSubFeed

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    applies columnBlackList and columnWhitelist

    applies columnBlackList and columnWhitelist

    Definition Classes
    SparkAction
  7. def applyCastDecimal2IntegralFloat(subFeed: SparkSubFeed): SparkSubFeed

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    applies type casting decimal -> integral/float

    applies type casting decimal -> integral/float

    Definition Classes
    SparkAction
  8. def applyCustomTransformation(inputSubFeed: SparkSubFeed, transformer: Option[CustomDfTransformerConfig])(implicit session: SparkSession): SparkSubFeed

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    applies the transformers

    applies the transformers

    Definition Classes
    SparkAction
  9. def applyFilter(subFeed: SparkSubFeed, filterClauseExpr: Option[Column]): SparkSubFeed

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    applies filterClauseExpr

    applies filterClauseExpr

    Definition Classes
    SparkAction
  10. def applyTransformations(inputSubFeed: SparkSubFeed, transformer: Option[CustomDfTransformerConfig], columnBlacklist: Option[Seq[String]], columnWhitelist: Option[Seq[String]], standardizeDatatypes: Boolean, output: DataObject, additional: Option[(SparkSubFeed, Option[DataFrame], Seq[String], LocalDateTime) ⇒ SparkSubFeed], filterClauseExpr: Option[Column] = None)(implicit session: SparkSession, context: ActionPipelineContext): SparkSubFeed

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    applies all the transformations above

    applies all the transformations above

    Definition Classes
    SparkAction
  11. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  12. val breakDataFrameLineage: Boolean

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    Stop propagating input DataFrame through action and instead get a new DataFrame from DataObject.

    Stop propagating input DataFrame through action and instead get a new DataFrame from DataObject. This can help to save memory and performance if the input DataFrame includes many transformations from previous Actions. The new DataFrame will be initialized according to the SubFeed's partitionValues.

    Definition Classes
    DeduplicateAction → SparkAction
  13. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  14. val columnBlacklist: Option[Seq[String]]

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  15. val columnWhitelist: Option[Seq[String]]

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  16. def deduplicate(baseDf: DataFrame, newDf: DataFrame, keyColumns: Seq[String])(implicit session: SparkSession): DataFrame

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    deduplicate -> keep latest record per key

    deduplicate -> keep latest record per key

    baseDf

    existing data

    newDf

    new data

    returns

    deduplicated data

  17. def enableRuntimeMetrics(): Unit

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    Runtime metrics

    Runtime metrics

    Note: runtime metrics are disabled by default, because they are only collected when running Actions from an ActionDAG. This is not the case for Tests or other use cases. If enabled exceptions are thrown if metrics are not found.

    Definition Classes
    Action
  18. def enrichSubFeedDataFrame(input: DataObject with CanCreateDataFrame, subFeed: SparkSubFeed, phase: ExecutionPhase)(implicit session: SparkSession, context: ActionPipelineContext): SparkSubFeed

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    Enriches SparkSubFeed with DataFrame if not existing

    Enriches SparkSubFeed with DataFrame if not existing

    input

    input data object.

    subFeed

    input SubFeed.

    Definition Classes
    SparkAction
  19. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  20. final def exec(subFeeds: Seq[SubFeed])(implicit session: SparkSession, context: ActionPipelineContext): Seq[SubFeed]

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    Action.exec implementation

    Action.exec implementation

    subFeeds

    SparkSubFeed's to be processed

    returns

    processed SparkSubFeed's

    Definition Classes
    SparkSubFeedAction → Action
  21. val executionMode: Option[ExecutionMode]

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    optional execution mode for this Action

    optional execution mode for this Action

    Definition Classes
    DeduplicateAction → SparkAction
  22. def factory: FromConfigFactory[Action]

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    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
    DeduplicateAction → ParsableFromConfig
  23. val filterClause: Option[String]

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  24. def filterDataFrame(df: DataFrame, partitionValues: Seq[PartitionValues], genericFilter: Option[Column]): DataFrame

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    Filter DataFrame with given partition values

    Filter DataFrame with given partition values

    df

    DataFrame to filter

    partitionValues

    partition values to use as filter condition

    genericFilter

    filter expression to apply

    returns

    filtered DataFrame

    Definition Classes
    SparkAction
  25. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  26. def getAllLatestMetrics: Map[DataObjectId, Option[ActionMetrics]]

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    Definition Classes
    Action
  27. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  28. def getFinalMetrics(dataObjectId: DataObjectId): Option[ActionMetrics]

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    Definition Classes
    Action
  29. def getInputDataObject[T <: DataObject](id: DataObjectId)(implicit arg0: ClassTag[T], arg1: scala.reflect.api.JavaUniverse.TypeTag[T], registry: InstanceRegistry): T

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    Attributes
    protected
    Definition Classes
    Action
  30. def getLatestMetrics(dataObjectId: DataObjectId): Option[ActionMetrics]

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    Definition Classes
    Action
  31. def getOutputDataObject[T <: DataObject](id: DataObjectId)(implicit arg0: ClassTag[T], arg1: scala.reflect.api.JavaUniverse.TypeTag[T], registry: InstanceRegistry): T

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    Attributes
    protected
    Definition Classes
    Action
  32. def getRuntimeInfo: Option[RuntimeInfo]

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    get latest runtime information for this action

    get latest runtime information for this action

    Definition Classes
    Action
  33. val id: ActionObjectId

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    A unique identifier for this instance.

    A unique identifier for this instance.

    Definition Classes
    DeduplicateAction → Action → SdlConfigObject
  34. val ignoreOldDeletedColumns: Boolean

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    if true, remove no longer existing columns in Schema Evolution

  35. val ignoreOldDeletedNestedColumns: Boolean

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    if true, remove no longer existing columns from nested data types in Schema Evolution.

    if true, remove no longer existing columns from nested data types in Schema Evolution. Keeping deleted columns in complex data types has performance impact as all new data in the future has to be converted by a complex function.

  36. final def init(subFeeds: Seq[SubFeed])(implicit session: SparkSession, context: ActionPipelineContext): Seq[SubFeed]

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    Action.init implementation

    Action.init implementation

    subFeeds

    SparkSubFeed's to be processed

    returns

    processed SparkSubFeed's

    Definition Classes
    SparkSubFeedAction → Action
  37. val input: DataObject with CanCreateDataFrame

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    Input DataObject which can CanCreateDataFrame

    Input DataObject which can CanCreateDataFrame

    Definition Classes
    DeduplicateActionSparkSubFeedAction
  38. val inputId: DataObjectId

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    inputs DataObject

  39. val inputs: Seq[DataObject with CanCreateDataFrame]

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    Input DataObjects To be implemented by subclasses

    Input DataObjects To be implemented by subclasses

    Definition Classes
    DeduplicateAction → Action
  40. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  41. lazy val logger: Logger

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    Attributes
    protected
    Definition Classes
    SmartDataLakeLogger
  42. val metadata: Option[ActionMetadata]

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    Additional metadata for the Action

    Additional metadata for the Action

    Definition Classes
    DeduplicateAction → Action
  43. val metricsFailCondition: Option[String]

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    optional spark sql expression evaluated as where-clause against dataframe of metrics.

    optional spark sql expression evaluated as where-clause against dataframe of metrics. Available columns are dataObjectId, key, value. If there are any rows passing the where clause, a MetricCheckFailed exception is thrown.

    Definition Classes
    DeduplicateAction → Action
  44. def multiTransformSubfeed(subFeed: SparkSubFeed, transformers: Seq[(DataFrame) ⇒ DataFrame]): SparkSubFeed

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    applies multiple transformations to a sequence of subfeeds

    applies multiple transformations to a sequence of subfeeds

    Definition Classes
    SparkAction
  45. def multiTransformSubfeeds(subFeeds: Seq[SparkSubFeed], transformers: Seq[(DataFrame) ⇒ DataFrame]): Seq[SparkSubFeed]

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    applies multiple transformations to a sequence of subfeeds

    applies multiple transformations to a sequence of subfeeds

    Definition Classes
    SparkAction
  46. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  47. def nodeId: String

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    provide an implementation of the DAG node id

    provide an implementation of the DAG node id

    Definition Classes
    Action → DAGNode
  48. final def notify(): Unit

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    Definition Classes
    AnyRef
  49. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  50. def onRuntimeMetrics(dataObjectId: Option[DataObjectId], metrics: ActionMetrics): Unit

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    Definition Classes
    Action
  51. val output: TransactionalSparkTableDataObject

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    Output DataObject which can CanWriteDataFrame

    Output DataObject which can CanWriteDataFrame

    Definition Classes
    DeduplicateActionSparkSubFeedAction
  52. val outputId: DataObjectId

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    output DataObject

  53. val outputs: Seq[TransactionalSparkTableDataObject]

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    Output DataObjects To be implemented by subclasses

    Output DataObjects To be implemented by subclasses

    Definition Classes
    DeduplicateAction → Action
  54. val persist: Boolean

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    Force persisting DataFrame on Disk.

    Force persisting DataFrame on Disk. This helps to reduce memory needed for caching the DataFrame content and can serve as a recovery point in case an task get's lost.

    Definition Classes
    DeduplicateAction → SparkAction
  55. final def postExec(inputSubFeeds: Seq[SubFeed], outputSubFeeds: Seq[SubFeed])(implicit session: SparkSession, context: ActionPipelineContext): Unit

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    Executes operations needed after executing an action.

    Executes operations needed after executing an action. In this step any phase on Input- or Output-DataObjects needed after the main task is executed, e.g. JdbcTableDataObjects postWriteSql or CopyActions deleteInputData.

    Definition Classes
    SparkSubFeedAction → Action
  56. def postExecSubFeed(inputSubFeed: SubFeed, outputSubFeed: SubFeed)(implicit session: SparkSession, context: ActionPipelineContext): Unit

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    Definition Classes
    SparkSubFeedAction
  57. def preExec(subFeeds: Seq[SubFeed])(implicit session: SparkSession, context: ActionPipelineContext): Unit

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    Executes operations needed before executing an action.

    Executes operations needed before executing an action. In this step any phase on Input- or Output-DataObjects needed before the main task is executed, e.g. JdbcTableDataObjects preWriteSql

    Definition Classes
    Action
  58. def prepare(implicit session: SparkSession, context: ActionPipelineContext): Unit

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    Prepare DataObjects prerequisites.

    Prepare DataObjects prerequisites. In this step preconditions are prepared & tested: - connections can be created - needed structures exist, e.g Kafka topic or Jdbc table

    This runs during the "prepare" phase of the DAG.

    Definition Classes
    SparkAction → Action
  59. def prepareInputSubFeed(subFeed: SparkSubFeed, input: DataObject with CanCreateDataFrame)(implicit session: SparkSession, context: ActionPipelineContext): SparkSubFeed

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    Applies changes to a SubFeed from a previous action in order to be used as input for this actions transformation.

    Applies changes to a SubFeed from a previous action in order to be used as input for this actions transformation.

    Definition Classes
    SparkAction
  60. def recursiveInputs: Seq[DataObject with CanCreateDataFrame]

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    Recursive Inputs are not supported on SparkSubFeedAction (only on SparkSubFeedsAction) so set to empty Seq

    Recursive Inputs are not supported on SparkSubFeedAction (only on SparkSubFeedsAction) so set to empty Seq

    Definition Classes
    SparkSubFeedAction → Action
  61. def reset(): Unit

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    Resets the runtime state of this Action This is mainly used for testing

    Resets the runtime state of this Action This is mainly used for testing

    Definition Classes
    Action
  62. def setSparkJobMetadata(operation: Option[String] = None)(implicit session: SparkSession): Unit

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    Sets the util job description for better traceability in the Spark UI

    Sets the util job description for better traceability in the Spark UI

    Note: This sets Spark local properties, which are propagated to the respective executor tasks. We rely on this to match metrics back to Actions and DataObjects. As writing to a DataObject on the Driver happens uninterrupted in the same exclusive thread, this is suitable.

    operation

    phase description (be short...)

    Definition Classes
    Action
  63. val standardizeDatatypes: Boolean

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

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    Definition Classes
    AnyRef
  65. final def toString(): String

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    This is displayed in ascii graph visualization

    This is displayed in ascii graph visualization

    Definition Classes
    Action → AnyRef → Any
  66. def toStringMedium: String

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    Definition Classes
    Action
  67. def toStringShort: String

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    Definition Classes
    Action
  68. def transform(subFeed: SparkSubFeed)(implicit session: SparkSession, context: ActionPipelineContext): SparkSubFeed

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    Transform a SparkSubFeed.

    Transform a SparkSubFeed. To be implemented by subclasses.

    subFeed

    SparkSubFeed to be transformed

    returns

    transformed SparkSubFeed

    Definition Classes
    DeduplicateActionSparkSubFeedAction
  69. def transformSubfeeds(subFeeds: Seq[SparkSubFeed], transformer: (DataFrame) ⇒ DataFrame): Seq[SparkSubFeed]

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    transform sequence of subfeeds

    transform sequence of subfeeds

    Definition Classes
    SparkAction
  70. val transformer: Option[CustomDfTransformerConfig]

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  71. object udfs extends Serializable

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  72. def updateSubFeedAfterWrite(subFeed: SparkSubFeed)(implicit session: SparkSession, context: ActionPipelineContext): SparkSubFeed

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    Definition Classes
    SparkAction
  73. def validateAndUpdateSubFeedPartitionValues(output: DataObject, subFeed: SparkSubFeed)(implicit session: SparkSession): SparkSubFeed

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    Updates the partition values of a SubFeed to the partition columns of an output, removing not existing columns from the partition values.

    Updates the partition values of a SubFeed to the partition columns of an output, removing not existing columns from the partition values. Further the transformed DataFrame is validated to have the output's partition columns included.

    output

    output DataObject

    subFeed

    SubFeed with transformed DataFrame

    returns

    SubFeed with updated partition values.

    Definition Classes
    SparkAction
  74. def validateDataFrameContainsCols(df: DataFrame, columns: Seq[String], debugName: String): Unit

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    Validate that DataFrame contains a given list of columns, throwing an exception otherwise.

    Validate that DataFrame contains a given list of columns, throwing an exception otherwise.

    df

    DataFrame to validate

    columns

    Columns that must exist in DataFrame

    debugName

    name to mention in exception

    Definition Classes
    SparkAction
  75. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  76. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  77. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  78. def writeSubFeed(subFeed: SparkSubFeed, output: DataObject with CanWriteDataFrame)(implicit session: SparkSession): Boolean

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    writes subfeed to output respecting given execution mode

    writes subfeed to output respecting given execution mode

    returns

    true if no data was transfered, otherwise false

    Definition Classes
    SparkAction

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from SparkSubFeedAction

Inherited from SparkAction

Inherited from Action

Inherited from SmartDataLakeLogger

Inherited from DAGNode

Inherited from ParsableFromConfig[Action]

Inherited from SdlConfigObject

Inherited from AnyRef

Inherited from Any

Ungrouped