Class/Object

io.smartdatalake.workflow.dataobject

CustomDfDataObject

Related Docs: object CustomDfDataObject | package dataobject

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case class CustomDfDataObject(id: DataObjectId, creator: CustomDfCreatorConfig, schemaMin: Option[StructType] = None, metadata: Option[DataObjectMetadata] = None)(implicit instanceRegistry: InstanceRegistry) extends DataObject with CanCreateDataFrame with SchemaValidation with Product with Serializable

Generic DataObject containing a config object. E.g. used to implement a CustomAction that reads a Webservice.

Linear Supertypes
Serializable, Serializable, Product, Equals, SchemaValidation, CanCreateDataFrame, DataObject, SmartDataLakeLogger, ParsableFromConfig[DataObject], SdlConfigObject, AnyRef, Any
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Inherited
  1. CustomDfDataObject
  2. Serializable
  3. Serializable
  4. Product
  5. Equals
  6. SchemaValidation
  7. CanCreateDataFrame
  8. DataObject
  9. SmartDataLakeLogger
  10. ParsableFromConfig
  11. SdlConfigObject
  12. AnyRef
  13. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new CustomDfDataObject(id: DataObjectId, creator: CustomDfCreatorConfig, schemaMin: Option[StructType] = None, metadata: Option[DataObjectMetadata] = None)(implicit instanceRegistry: InstanceRegistry)

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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. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  5. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  6. def createReadSchema(writeSchema: StructType)(implicit session: SparkSession): StructType

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    Creates the read schema based on a given write schema.

    Creates the read schema based on a given write schema. Normally this is the same, but some DataObjects can remove & add columns on read (e.g. KafkaTopicDataObject, SparkFileDataObject) In this cases we have to break the DataFrame lineage und create a dummy DataFrame in init phase.

    Definition Classes
    CanCreateDataFrame
  7. val creator: CustomDfCreatorConfig

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  8. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  9. def factory: FromConfigFactory[DataObject]

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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
    CustomDfDataObject → ParsableFromConfig
  10. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  11. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  12. def getConnection[T <: Connection](connectionId: ConnectionId)(implicit registry: InstanceRegistry, ct: ClassTag[T], tt: scala.reflect.api.JavaUniverse.TypeTag[T]): T

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    Handle class cast exception when getting objects from instance registry

    Handle class cast exception when getting objects from instance registry

    Attributes
    protected
    Definition Classes
    DataObject
  13. def getConnectionReg[T <: Connection](connectionId: ConnectionId, registry: InstanceRegistry)(implicit ct: ClassTag[T], tt: scala.reflect.api.JavaUniverse.TypeTag[T]): T

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    Attributes
    protected
    Definition Classes
    DataObject
  14. def getDataFrame(partitionValues: Seq[PartitionValues] = Seq())(implicit session: SparkSession): DataFrame

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    Definition Classes
    CustomDfDataObject → CanCreateDataFrame
  15. val id: DataObjectId

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    A unique identifier for this instance.

    A unique identifier for this instance.

    Definition Classes
    CustomDfDataObject → DataObject → SdlConfigObject
  16. implicit val instanceRegistry: InstanceRegistry

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  17. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  18. lazy val logger: Logger

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    Attributes
    protected
    Definition Classes
    SmartDataLakeLogger
  19. val metadata: Option[DataObjectMetadata]

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    Additional metadata for the DataObject

    Additional metadata for the DataObject

    Definition Classes
    CustomDfDataObject → DataObject
  20. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  21. final def notify(): Unit

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

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    Definition Classes
    AnyRef
  23. def postRead(implicit session: SparkSession): Unit

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    Runs operations after reading from DataObject

    Runs operations after reading from DataObject

    Definition Classes
    DataObject
  24. def postWrite(implicit session: SparkSession): Unit

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    Runs operations after writing to DataObject

    Runs operations after writing to DataObject

    Definition Classes
    DataObject
  25. def preRead(implicit session: SparkSession): Unit

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    Runs operations before reading from DataObject

    Runs operations before reading from DataObject

    Definition Classes
    DataObject
  26. def preWrite(implicit session: SparkSession): Unit

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    Runs operations before writing to DataObject

    Runs operations before writing to DataObject

    Definition Classes
    DataObject
  27. def prepare(implicit session: SparkSession): Unit

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    Prepare & test DataObject's prerequisits

    Prepare & test DataObject's prerequisits

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

    Definition Classes
    DataObject
  28. val schemaMin: Option[StructType]

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    An optional, minimal schema that a DataObject schema must have to pass schema validation.

    An optional, minimal schema that a DataObject schema must have to pass schema validation.

    The schema validation semantics are: - Schema A is valid in respect to a minimal schema B when B is a subset of A. This means: the whole column set of B is contained in the column set of A.

    • A column of B is contained in A when A contains a column with equal name and data type.
    • Column order is ignored.
    • Column nullability is ignored.
    • Duplicate columns in terms of name and data type are eliminated (set semantics).

    Note: This is only used by the functionality defined in CanCreateDataFrame and CanWriteDataFrame, that is, when reading or writing Spark data frames from/to the underlying data container. io.smartdatalake.workflow.action.Actions that bypass Spark data frames ignore the schemaMin attribute if it is defined.

    Definition Classes
    CustomDfDataObject → SchemaValidation
  29. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  30. def toStringShort: String

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    Definition Classes
    DataObject
  31. def validateSchemaMin(df: DataFrame): Unit

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    Validate the schema of a given Spark Data Frame df against schemaMin.

    Validate the schema of a given Spark Data Frame df against schemaMin.

    df

    The data frame to validate.

    Definition Classes
    SchemaValidation
    Exceptions thrown

    SchemaViolationException is the schemaMin does not validate.

  32. final def wait(): Unit

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

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from SchemaValidation

Inherited from CanCreateDataFrame

Inherited from DataObject

Inherited from SmartDataLakeLogger

Inherited from ParsableFromConfig[DataObject]

Inherited from SdlConfigObject

Inherited from AnyRef

Inherited from Any

Ungrouped