class KGroupedTable[K, V] extends AnyRef
Wraps the Java class KGroupedTable and delegates method calls to the underlying Java object.
- K
Type of keys
- V
Type of values
- See also
org.apache.kafka.streams.kstream.KGroupedTable
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- new KGroupedTable(inner: kstream.KGroupedTable[K, V])
- inner
The underlying Java abstraction for KGroupedTable
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- def aggregate[VR](initializer: => VR, named: Named)(adder: (K, V, VR) => VR, subtractor: (K, V, VR) => VR)(implicit materialized: Materialized[K, VR, ByteArrayKeyValueStore]): KTable[K, VR]
Aggregate the value of records of the original KTable that got KTable#groupBy to the same key into a new instance of KTable using default serializers and deserializers.
Aggregate the value of records of the original KTable that got KTable#groupBy to the same key into a new instance of KTable using default serializers and deserializers.
- initializer
a function that provides an initial aggregate result value
- named
a Named config used to name the processor in the topology
- adder
a function that adds a new record to the aggregate result
- subtractor
an aggregator function that removed an old record from the 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.KGroupedTable#aggregate
- def aggregate[VR](initializer: => VR)(adder: (K, V, VR) => VR, subtractor: (K, V, VR) => VR)(implicit materialized: Materialized[K, VR, ByteArrayKeyValueStore]): KTable[K, VR]
Aggregate the value of records of the original KTable that got KTable#groupBy to the same key into a new instance of KTable using default serializers and deserializers.
Aggregate the value of records of the original KTable that got KTable#groupBy to the same key into a new instance of KTable using default serializers and deserializers.
- initializer
a function that provides an initial aggregate result value
- adder
a function that adds a new record to the aggregate result
- subtractor
an aggregator function that removed an old record from the 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.KGroupedTable#aggregate
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- def count(named: Named)(implicit materialized: Materialized[K, Long, ByteArrayKeyValueStore]): KTable[K, Long]
Count number of records of the original KTable that got KTable#groupBy to the same key into a new instance of KTable.
Count number of records of the original KTable that got KTable#groupBy to the same key into a new instance of KTable.
- 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.KGroupedTable#count
- def count()(implicit materialized: Materialized[K, Long, ByteArrayKeyValueStore]): KTable[K, Long]
Count number of records of the original KTable that got KTable#groupBy to the same key into a new instance of KTable.
Count number of records of the original KTable that got KTable#groupBy to the same key into a new instance of KTable.
- 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.KGroupedTable#count
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- def reduce(adder: (V, V) => V, subtractor: (V, V) => V, named: Named)(implicit materialized: Materialized[K, V, ByteArrayKeyValueStore]): KTable[K, V]
Combine the value of records of the original KTable that got KTable#groupBy to the same key into a new instance of KTable.
Combine the value of records of the original KTable that got KTable#groupBy to the same key into a new instance of KTable.
- adder
a function that adds a new value to the aggregate result
- subtractor
a function that removed an old value from the 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.KGroupedTable#reduce
- def reduce(adder: (V, V) => V, subtractor: (V, V) => V)(implicit materialized: Materialized[K, V, ByteArrayKeyValueStore]): KTable[K, V]
Combine the value of records of the original KTable that got KTable#groupBy to the same key into a new instance of KTable.
Combine the value of records of the original KTable that got KTable#groupBy to the same key into a new instance of KTable.
- adder
a function that adds a new value to the aggregate result
- subtractor
a function that removed an old value from the 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.KGroupedTable#reduce
- final def synchronized[T0](arg0: => T0): T0
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