class TimeWindowedKStream[K, V] extends AnyRef
Wraps the Java class TimeWindowedKStream and delegates method calls to the underlying Java object.
- K
Type of keys
- V
Type of values
- See also
org.apache.kafka.streams.kstream.TimeWindowedKStream
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new
TimeWindowedKStream(inner: kstream.TimeWindowedKStream[K, V])
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The underlying Java abstraction for TimeWindowedKStream
Value Members
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def
aggregate[VR](initializer: ⇒ VR, named: Named)(aggregator: (K, V, VR) ⇒ VR)(implicit materialized: Materialized[K, VR, ByteArrayWindowStore]): KTable[Windowed[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 function that computes an initial intermediate aggregation result
- named
a Named config used to name the processor in the topology
- aggregator
an aggregator function 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.TimeWindowedKStream#aggregate
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def
aggregate[VR](initializer: ⇒ VR)(aggregator: (K, V, VR) ⇒ VR)(implicit materialized: Materialized[K, VR, ByteArrayWindowStore]): KTable[Windowed[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 function that computes an initial intermediate aggregation result
- aggregator
an aggregator function 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.TimeWindowedKStream#aggregate
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def
count(named: Named)(implicit materialized: Materialized[K, Long, ByteArrayWindowStore]): KTable[Windowed[K], Long]
Count the number of records in this stream by the grouped key and the defined windows.
Count the number of records in this stream by the grouped key and the defined windows.
- 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 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.TimeWindowedKStream#count
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def
count()(implicit materialized: Materialized[K, Long, ByteArrayWindowStore]): KTable[Windowed[K], Long]
Count the number of records in this stream by the grouped key and the defined windows.
Count the number of records in this stream by the grouped key and the defined windows.
- 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.TimeWindowedKStream#count
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eq(arg0: AnyRef): Boolean
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getClass(): Class[_]
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hashCode(): Int
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- val inner: kstream.TimeWindowedKStream[K, V]
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isInstanceOf[T0]: Boolean
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def
reduce(reducer: (V, V) ⇒ V, named: Named)(implicit materialized: Materialized[K, V, ByteArrayWindowStore]): KTable[Windowed[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 that computes a new aggregate result
- 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 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.TimeWindowedKStream#reduce
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def
reduce(reducer: (V, V) ⇒ V)(implicit materialized: Materialized[K, V, ByteArrayWindowStore]): KTable[Windowed[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 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.TimeWindowedKStream#reduce
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