Class

com.coxautodata.waimak.dataflow.spark.dataquality

DatasetChecks

Related Doc: package dataquality

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case class DatasetChecks(checks: Seq[DatasetCheck[_]]) extends DataQualityCheck[DatasetChecks] with Product with Serializable

A simple implementation of DataQualityCheck which doesn't perform any optimisation on check concatenation. Only use this if it's really necessary, otherwise use the Deequ implementation in the waimak-deequ module.

Linear Supertypes
Serializable, Serializable, Product, Equals, DataQualityCheck[DatasetChecks], AnyRef, Any
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Inherited
  1. DatasetChecks
  2. Serializable
  3. Serializable
  4. Product
  5. Equals
  6. DataQualityCheck
  7. AnyRef
  8. Any
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Visibility
  1. Public
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Instance Constructors

  1. new DatasetChecks(checks: Seq[DatasetCheck[_]])

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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. def ++(other: DatasetChecks): DatasetChecks

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    Combine this check with another check to take advantage of potential optimisations

    Combine this check with another check to take advantage of potential optimisations

    other

    the check to be combined with this one

    returns

    the two checks combined into a new check

    Definition Classes
    DatasetChecks → DataQualityCheck

  4. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  5. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  6. val checks: Seq[DatasetCheck[_]]

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  7. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef

  9. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  10. def getAlerts(label: String, data: Dataset[_]): Seq[DataQualityAlert]

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    Get any alerts for this check for a given label and Dataset.

    Get any alerts for this check for a given label and Dataset. Normally, no alerts should be returned if the check passes, although alerting on a successful check could also be a valid use case.

    label

    the label which the check is being performed on

    data

    the Dataset on which to perform the check

    returns

    the data quality alerts

    Definition Classes
    DatasetChecks → DataQualityCheck

  11. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any

  12. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  13. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef

  14. final def notify(): Unit

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    Definition Classes
    AnyRef

  15. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

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

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    Definition Classes
    AnyRef

  17. def validateCheck: Try[Unit]

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    Validates the check

    Validates the check

    returns

    true if this is a valid check, false otherwise

    Definition Classes
    DataQualityCheck

  18. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  19. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  20. 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 DataQualityCheck[DatasetChecks]

Inherited from AnyRef

Inherited from Any

Ungrouped