Packages

class KGroupedStream[K, V] extends AnyRef

Wraps the Java class KGroupedStream and delegates method calls to the underlying Java object.

K

Type of keys

V

Type of values

See also

org.apache.kafka.streams.kstream.KGroupedStream

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Instance Constructors

  1. new KGroupedStream(inner: kstream.KGroupedStream[K, V])

    inner

    The underlying Java abstraction for KGroupedStream

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##: Int
    Definition Classes
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  3. final def ==(arg0: Any): Boolean
    Definition Classes
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  4. def aggregate[VR](initializer: => VR)(aggregator: (K, V, VR) => VR)(implicit materialized: Materialized[K, VR, ByteArrayKeyValueStore]): KTable[K, VR]

    Aggregate the values of records in this stream by the grouped key.

    Aggregate the values of records in this stream by the grouped key.

    initializer

    an Initializer that computes an initial intermediate aggregation result

    aggregator

    an Aggregator that computes a new aggregate result

    materialized

    an instance of Materialized used to materialize a state store.

    returns

    a KTable that contains "update" records with unmodified keys, and values that represent the latest (rolling) aggregate for each key

    See also

    org.apache.kafka.streams.kstream.KGroupedStream#aggregate

  5. final def asInstanceOf[T0]: T0
    Definition Classes
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  6. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
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    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @native()
  7. def cogroup[VR](aggregator: (K, V, VR) => VR): CogroupedKStream[K, VR]

    Create a new CogroupedKStream from this grouped KStream to allow cogrouping other KGroupedStream to it.

    Create a new CogroupedKStream from this grouped KStream to allow cogrouping other KGroupedStream to it.

    aggregator

    an Aggregator that computes a new aggregate result

    returns

    an instance of CogroupedKStream

    See also

    org.apache.kafka.streams.kstream.KGroupedStream#cogroup

  8. def count()(implicit materialized: Materialized[K, Long, ByteArrayKeyValueStore]): KTable[K, Long]

    Count the number of records in this stream by the grouped key.

    Count the number of records in this stream by the grouped key. The result is written into a local KeyValueStore (which is basically an ever-updating materialized view) provided by the given materialized.

    materialized

    an instance of Materialized used to materialize a state store.

    returns

    a KTable that contains "update" records with unmodified keys and Long values that represent the latest (rolling) count (i.e., number of records) for each key

    See also

    org.apache.kafka.streams.kstream.KGroupedStream#count

  9. final def eq(arg0: AnyRef): Boolean
    Definition Classes
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  10. def equals(arg0: AnyRef): Boolean
    Definition Classes
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  11. def finalize(): Unit
    Attributes
    protected[lang]
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    @throws(classOf[java.lang.Throwable])
  12. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
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    Annotations
    @native()
  13. def hashCode(): Int
    Definition Classes
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    Annotations
    @native()
  14. val inner: kstream.KGroupedStream[K, V]
  15. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  16. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  17. final def notify(): Unit
    Definition Classes
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    Annotations
    @native()
  18. final def notifyAll(): Unit
    Definition Classes
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    @native()
  19. def reduce(reducer: (V, V) => V)(implicit materialized: Materialized[K, V, ByteArrayKeyValueStore]): KTable[K, V]

    Combine the values of records in this stream by the grouped key.

    Combine the values of records in this stream by the grouped key.

    reducer

    a function (V, V) => V that computes a new aggregate result.

    materialized

    an instance of Materialized used to materialize a state store.

    returns

    a KTable that contains "update" records with unmodified keys, and values that represent the latest (rolling) aggregate for each key

    See also

    org.apache.kafka.streams.kstream.KGroupedStream#reduce

  20. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
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  21. def toString(): String
    Definition Classes
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  22. final def wait(): Unit
    Definition Classes
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    Annotations
    @throws(classOf[java.lang.InterruptedException])
  23. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
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    Annotations
    @throws(classOf[java.lang.InterruptedException])
  24. final def wait(arg0: Long): Unit
    Definition Classes
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    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  25. def windowedBy(windows: SessionWindows): SessionWindowedKStream[K, V]

    Create a new SessionWindowedKStream instance that can be used to perform session windowed aggregations.

    Create a new SessionWindowedKStream instance that can be used to perform session windowed aggregations.

    windows

    the specification of the aggregation SessionWindows

    returns

    an instance of SessionWindowedKStream

    See also

    org.apache.kafka.streams.kstream.KGroupedStream#windowedBy

  26. def windowedBy[W <: Window](windows: Windows[W]): TimeWindowedKStream[K, V]

    Create a new TimeWindowedKStream instance that can be used to perform windowed aggregations.

    Create a new TimeWindowedKStream instance that can be used to perform windowed aggregations.

    windows

    the specification of the aggregation Windows

    returns

    an instance of TimeWindowedKStream

    See also

    org.apache.kafka.streams.kstream.KGroupedStream#windowedBy

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