Class

org.apache.flink.streaming.api.scala.extensions.impl.acceptPartialFunctions

OnWindowedStream

Related Doc: package acceptPartialFunctions

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class OnWindowedStream[T, K, W <: Window] extends AnyRef

Wraps a joined data stream, allowing to use anonymous partial functions to perform extraction of items in a tuple, case class instance or collection

T

The type of the data stream items from the right input of the join

K

The type of key

W

The type of the window

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Instance Constructors

  1. new OnWindowedStream(stream: WindowedStream[T, K, W])

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    stream

    The wrapped data stream

Value Members

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

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  2. final def ##(): Int

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

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  4. def applyWith[ACC, R](initialValue: ACC)(foldFunction: (ACC, T) ⇒ ACC, windowFunction: (K, W, Stream[ACC]) ⇒ TraversableOnce[R])(implicit arg0: TypeInformation[ACC], arg1: TypeInformation[R]): DataStream[R]

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    Applies the given window function to each window.

    Applies the given window function to each window. The window function is called for each evaluation of the window for each key individually. The output of the window function is interpreted as a regular non-windowed stream.

    Arriving data is incrementally aggregated using the given fold function.

    initialValue

    The initial value of the fold

    foldFunction

    The fold function that is used for incremental aggregation

    windowFunction

    The window function.

    returns

    The data stream that is the result of applying the window function to the window.

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    @PublicEvolving()

  5. final def asInstanceOf[T0]: T0

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  6. def clone(): AnyRef

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

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  8. def equals(arg0: Any): Boolean

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  9. def finalize(): Unit

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  10. def foldWith[R](initialValue: R)(function: (R, T) ⇒ R)(implicit arg0: TypeInformation[R]): DataStream[R]

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    Applies the given fold function to each window.

    Applies the given fold function to each window. The window function is called for each evaluation of the window for each key individually. The output of the reduce function is interpreted as a regular non-windowed stream.

    function

    The fold function.

    returns

    The data stream that is the result of applying the fold function to the window.

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

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  12. def hashCode(): Int

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  13. final def isInstanceOf[T0]: Boolean

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

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  15. final def notify(): Unit

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  16. final def notifyAll(): Unit

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  17. def reduceWith(function: (T, T) ⇒ T): DataStream[T]

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    Applies a reduce function to the window.

    Applies a reduce function to the window. The window function is called for each evaluation of the window for each key individually. The output of the reduce function is interpreted as a regular non-windowed stream.

    This window will try and pre-aggregate data as much as the window policies permit. For example,tumbling time windows can perfectly pre-aggregate the data, meaning that only one element per key is stored. Sliding time windows will pre-aggregate on the granularity of the slide interval, so a few elements are stored per key (one per slide interval). Custom windows may not be able to pre-aggregate, or may need to store extra values in an aggregation tree.

    function

    The reduce function.

    returns

    The data stream that is the result of applying the reduce function to the window.

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

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  19. def toString(): String

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  20. final def wait(): Unit

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

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

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