class SessionWindowedKStream[K, V] extends AnyRef
Wraps the Java class SessionWindowedKStream and delegates method calls to the underlying Java object.
- K
Type of keys
- V
Type of values
- See also
org.apache.kafka.streams.kstream.SessionWindowedKStream
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new
SessionWindowedKStream(inner: kstream.SessionWindowedKStream[K, V])
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The underlying Java abstraction for SessionWindowedKStream
Value Members
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def
aggregate[VR](initializer: ⇒ VR, named: Named)(aggregator: (K, V, VR) ⇒ VR, merger: (K, VR, VR) ⇒ VR)(implicit materialized: Materialized[K, VR, ByteArraySessionStore]): KTable[Windowed[K], VR]
Aggregate the values of records in this stream by the grouped key and defined
SessionWindows
.Aggregate the values of records in this stream by the grouped key and defined
SessionWindows
.- initializer
the initializer function
- named
a Named config used to name the processor in the topology
- aggregator
the aggregator function
- merger
the merger function
- materialized
an instance of
Materialized
used to materialize a state store.- returns
a windowed KTable that contains "update" records with unmodified keys, and values that represent the latest (rolling) aggregate for each key within a window
- See also
org.apache.kafka.streams.kstream.SessionWindowedKStream#aggregate
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def
aggregate[VR](initializer: ⇒ VR)(aggregator: (K, V, VR) ⇒ VR, merger: (K, VR, VR) ⇒ VR)(implicit materialized: Materialized[K, VR, ByteArraySessionStore]): KTable[Windowed[K], VR]
Aggregate the values of records in this stream by the grouped key and defined
SessionWindows
.Aggregate the values of records in this stream by the grouped key and defined
SessionWindows
.- initializer
the initializer function
- aggregator
the aggregator function
- merger
the merger function
- materialized
an instance of
Materialized
used to materialize a state store.- returns
a windowed KTable that contains "update" records with unmodified keys, and values that represent the latest (rolling) aggregate for each key within a window
- See also
org.apache.kafka.streams.kstream.SessionWindowedKStream#aggregate
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asInstanceOf[T0]: T0
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def
count(named: Named)(implicit materialized: Materialized[K, Long, ByteArraySessionStore]): KTable[Windowed[K], Long]
Count the number of records in this stream by the grouped key into
SessionWindows
.Count the number of records in this stream by the grouped key into
SessionWindows
.- named
a Named config used to name the processor in the topology
- materialized
an instance of
Materialized
used to materialize a state store.- returns
a windowed 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 within a window
- See also
org.apache.kafka.streams.kstream.SessionWindowedKStream#count
-
def
count()(implicit materialized: Materialized[K, Long, ByteArraySessionStore]): KTable[Windowed[K], Long]
Count the number of records in this stream by the grouped key into
SessionWindows
.Count the number of records in this stream by the grouped key into
SessionWindows
.- materialized
an instance of
Materialized
used to materialize a state store.- returns
a windowed 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 within a window
- See also
org.apache.kafka.streams.kstream.SessionWindowedKStream#count
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final
def
eq(arg0: AnyRef): Boolean
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equals(arg0: Any): Boolean
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finalize(): Unit
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getClass(): Class[_]
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hashCode(): Int
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- val inner: kstream.SessionWindowedKStream[K, V]
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def
isInstanceOf[T0]: Boolean
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ne(arg0: AnyRef): Boolean
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def
reduce(reducer: (V, V) ⇒ V, named: Named)(implicit materialized: Materialized[K, V, ByteArraySessionStore]): KTable[Windowed[K], V]
Combine values of this stream by the grouped key into
SessionWindows
.Combine values of this stream by the grouped key into
SessionWindows
.- reducer
a reducer function that computes a new aggregate result.
- materialized
an instance of
Materialized
used to materialize a state store.- returns
a windowed KTable that contains "update" records with unmodified keys, and values that represent the latest (rolling) aggregate for each key within a window
- See also
org.apache.kafka.streams.kstream.SessionWindowedKStream#reduce
-
def
reduce(reducer: (V, V) ⇒ V)(implicit materialized: Materialized[K, V, ByteArraySessionStore]): KTable[Windowed[K], V]
Combine values of this stream by the grouped key into
SessionWindows
.Combine values of this stream by the grouped key into
SessionWindows
.- reducer
a reducer function that computes a new aggregate result.
- materialized
an instance of
Materialized
used to materialize a state store.- returns
a windowed KTable that contains "update" records with unmodified keys, and values that represent the latest (rolling) aggregate for each key within a window
- See also
org.apache.kafka.streams.kstream.SessionWindowedKStream#reduce
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synchronized[T0](arg0: ⇒ T0): T0
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