class XmlSimplePrivacyJob extends IngestionJob

Used only to apply data masking rules (privacy) on one or more simple elements in XML data. The input XML file is read as a text file. Privacy rules are applied on the resulting DataFrame and the result is saved accepted area. In the definition of the XML Schema: - schema.metadata.format should be set to TEXT_XML - schema.attributes should only contain the attributes on which privacy should be applied Comet.defaultWriteFormat should be set text in order to have an XML formatted output file Comet.privacyOnly should be set to true to save the result in one file (coalesce 1)

Linear Supertypes
IngestionJob, SparkJob, JobBase, StrictLogging, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. XmlSimplePrivacyJob
  2. IngestionJob
  3. SparkJob
  4. JobBase
  5. StrictLogging
  6. AnyRef
  7. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new XmlSimplePrivacyJob(domain: Domain, schema: Schema, types: List[Type], path: List[Path], storageHandler: StorageHandler, schemaHandler: SchemaHandler, options: Map[String, String])(implicit settings: Settings)

Type Members

  1. type JdbcConfigName = String
    Definition Classes
    JobBase

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. def analyze(fullTableName: String): Any
    Attributes
    protected
    Definition Classes
    SparkJob
  5. def applyIgnore(dfIn: DataFrame): Dataset[Row]
    Attributes
    protected
    Definition Classes
    IngestionJob
  6. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  7. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  8. def createSparkViews(views: Views, sqlParameters: Map[String, String]): Unit
    Attributes
    protected
    Definition Classes
    SparkJob
  9. val domain: Domain
    Definition Classes
    XmlSimplePrivacyJob → IngestionJob
  10. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  11. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  12. lazy val extension: String
    Definition Classes
    IngestionJob
  13. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  14. val flatRowValidator: GenericRowValidator
    Attributes
    protected
    Definition Classes
    IngestionJob
  15. lazy val format: String
    Definition Classes
    IngestionJob
  16. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  17. def getWriteMode(): WriteMode
    Definition Classes
    IngestionJob
  18. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  19. def ingest(dataset: DataFrame): (RDD[_], RDD[_])

    ingestion algorithm

    ingestion algorithm

    Attributes
    protected
    Definition Classes
    XmlSimplePrivacyJob → IngestionJob
  20. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  21. def loadDataSet(): Try[DataFrame]

    Dataset loading strategy (JSON / CSV / ...)

    Dataset loading strategy (JSON / CSV / ...)

    returns

    Spark Dataframe loaded using metadata options

    Attributes
    protected
    Definition Classes
    XmlSimplePrivacyJob → IngestionJob
  22. val logger: Logger
    Attributes
    protected
    Definition Classes
    StrictLogging
  23. lazy val metadata: Metadata

    Merged metadata

    Merged metadata

    Definition Classes
    IngestionJob
  24. def name: String
    Definition Classes
    XmlSimplePrivacyJob → JobBase
  25. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  26. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  27. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  28. val now: Timestamp
    Definition Classes
    IngestionJob
  29. val options: Map[String, String]
    Definition Classes
    XmlSimplePrivacyJob → IngestionJob
  30. def parseViewDefinition(valueWithEnv: String): (SinkType, Option[JdbcConfigName], String)

    valueWithEnv

    in the form [SinkType:[configName:]]viewName

    returns

    (SinkType, configName, viewName)

    Attributes
    protected
    Definition Classes
    JobBase
  31. def partitionDataset(dataset: DataFrame, partition: List[String]): DataFrame
    Attributes
    protected
    Definition Classes
    SparkJob
  32. def partitionedDatasetWriter(dataset: DataFrame, partition: List[String]): DataFrameWriter[Row]

    Partition a dataset using dataset columns.

    Partition a dataset using dataset columns. To partition the dataset using the ingestion time, use the reserved column names :

    • comet_date
    • comet_year
    • comet_month
    • comet_day
    • comet_hour
    • comet_minute These columns are renamed to "date", "year", "month", "day", "hour", "minute" in the dataset and their values is set to the current date/time.
    dataset

    : Input dataset

    partition

    : list of columns to use for partitioning.

    returns

    The Spark session used to run this job

    Attributes
    protected
    Definition Classes
    SparkJob
  33. val path: List[Path]
    Definition Classes
    XmlSimplePrivacyJob → IngestionJob
  34. def registerUdf(udf: String): Unit
    Attributes
    protected
    Definition Classes
    SparkJob
  35. def reorderAttributes(dataFrame: DataFrame): List[Attribute]
    Definition Classes
    IngestionJob
  36. def run(): Try[JobResult]

    Main entry point as required by the Spark Job interface

    Main entry point as required by the Spark Job interface

    returns

    : Spark Session used for the job

    Definition Classes
    IngestionJob → JobBase
  37. def saveAccepted(dataframe: DataFrame): (DataFrame, Path)

    Merge new and existing dataset if required Save using overwrite / Append mode

    Merge new and existing dataset if required Save using overwrite / Append mode

    Attributes
    protected
    Definition Classes
    IngestionJob
  38. def saveRejected(rejectedRDD: RDD[String]): Try[Path]
    Attributes
    protected
    Definition Classes
    IngestionJob
  39. val schema: Schema
    Definition Classes
    XmlSimplePrivacyJob → IngestionJob
  40. val schemaHandler: SchemaHandler
    Definition Classes
    XmlSimplePrivacyJob → IngestionJob
  41. lazy val session: SparkSession
    Definition Classes
    SparkJob
  42. implicit val settings: Settings
    Definition Classes
    XmlSimplePrivacyJob → JobBase
  43. lazy val sparkEnv: SparkEnv
    Definition Classes
    SparkJob
  44. val storageHandler: StorageHandler
    Definition Classes
    XmlSimplePrivacyJob → IngestionJob
  45. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  46. def toString(): String
    Definition Classes
    AnyRef → Any
  47. val treeRowValidator: GenericRowValidator
    Attributes
    protected
    Definition Classes
    IngestionJob
  48. val types: List[Type]
    Definition Classes
    XmlSimplePrivacyJob → IngestionJob
  49. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  50. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  51. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()

Inherited from IngestionJob

Inherited from SparkJob

Inherited from JobBase

Inherited from StrictLogging

Inherited from AnyRef

Inherited from Any

Ungrouped