org.apache.spark.streaming.scheduler

StreamInputInfo

case class StreamInputInfo(inputStreamId: Int, numRecords: Long, metadata: Map[String, Any] = ...) extends Product with Serializable

:: DeveloperApi :: Track the information of input stream at specified batch time.

inputStreamId

the input stream id

numRecords

the number of records in a batch

metadata

metadata for this batch. It should contain at least one standard field named "Description" which maps to the content that will be shown in the UI.

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@DeveloperApi()
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Instance Constructors

  1. new StreamInputInfo(inputStreamId: Int, numRecords: Long, metadata: Map[String, Any] = ...)

    inputStreamId

    the input stream id

    numRecords

    the number of records in a batch

    metadata

    metadata for this batch. It should contain at least one standard field named "Description" which maps to the content that will be shown in the UI.

Value Members

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

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

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  3. final def ##(): Int

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

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

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  9. def finalize(): Unit

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

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  11. val inputStreamId: Int

    the input stream id

  12. final def isInstanceOf[T0]: Boolean

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  13. val metadata: Map[String, Any]

    metadata for this batch.

    metadata for this batch. It should contain at least one standard field named "Description" which maps to the content that will be shown in the UI.

  14. def metadataDescription: Option[String]

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

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  16. final def notify(): Unit

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  17. final def notifyAll(): Unit

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  18. val numRecords: Long

    the number of records in a batch

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

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  20. final def wait(): Unit

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    @throws( ... )
  21. final def wait(arg0: Long, arg1: Int): Unit

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    @throws( ... )
  22. final def wait(arg0: Long): Unit

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