KTable

class KTable[K, V] extends AnyRef

Wraps the Java class org.apache.kafka.streams.kstream.KTable and delegates method calls to the underlying Java object.

K: Type of keys
V: Type of values

See also: org.apache.kafka.streams.kstream.KTable

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new KTable(inner: kstream.KTable[K, V])
inner
The underlying Java abstraction for KTable

Value Members

final def !=(arg0: Any): Boolean
Definition Classes
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final def ##: Int
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final def ==(arg0: Any): Boolean
Definition Classes
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final def asInstanceOf[T0]: T0
Definition Classes
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def clone(): AnyRef
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protected[lang]
Definition Classes
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@throws(classOf[java.lang.CloneNotSupportedException]) @native() @HotSpotIntrinsicCandidate()
final def eq(arg0: AnyRef): Boolean
Definition Classes
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def equals(arg0: AnyRef): Boolean
Definition Classes
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def filter(predicate: (K, V) => Boolean, named: Named, materialized: Materialized[K, V, ByteArrayKeyValueStore]): KTable[K, V]
Create a new KTable that consists all records of this KTable which satisfies the given predicate
Create a new KTable that consists all records of this KTable which satisfies the given predicate
predicate
a filter that is applied to each record
named
a Named config used to name the processor in the topology
materialized
a Materialized that describes how the StateStore for the resulting KTable should be materialized.
returns
a KTable that contains only those records that satisfy the given predicate
See also
org.apache.kafka.streams.kstream.KTable#filter
def filter(predicate: (K, V) => Boolean, materialized: Materialized[K, V, ByteArrayKeyValueStore]): KTable[K, V]
Create a new KTable that consists all records of this KTable which satisfies the given predicate
Create a new KTable that consists all records of this KTable which satisfies the given predicate
predicate
a filter that is applied to each record
materialized
a Materialized that describes how the StateStore for the resulting KTable should be materialized.
returns
a KTable that contains only those records that satisfy the given predicate
See also
org.apache.kafka.streams.kstream.KTable#filter
def filter(predicate: (K, V) => Boolean, named: Named): KTable[K, V]
Create a new KTable that consists all records of this KTable which satisfies the given predicate
Create a new KTable that consists all records of this KTable which satisfies the given predicate
predicate
a filter that is applied to each record
named
a Named config used to name the processor in the topology
returns
a KTable that contains only those records that satisfy the given predicate
See also
org.apache.kafka.streams.kstream.KTable#filter
def filter(predicate: (K, V) => Boolean): KTable[K, V]
Create a new KTable that consists all records of this KTable which satisfies the given predicate
Create a new KTable that consists all records of this KTable which satisfies the given predicate
predicate
a filter that is applied to each record
returns
a KTable that contains only those records that satisfy the given predicate
See also
org.apache.kafka.streams.kstream.KTable#filter
def filterNot(predicate: (K, V) => Boolean, named: Named, materialized: Materialized[K, V, ByteArrayKeyValueStore]): KTable[K, V]
Create a new KTable that consists all records of this KTable which do not satisfy the given predicate
Create a new KTable that consists all records of this KTable which do not satisfy the given predicate
predicate
a filter that is applied to each record
named
a Named config used to name the processor in the topology
materialized
a Materialized that describes how the StateStore for the resulting KTable should be materialized.
returns
a KTable that contains only those records that do not satisfy the given predicate
See also
org.apache.kafka.streams.kstream.KTable#filterNot
def filterNot(predicate: (K, V) => Boolean, materialized: Materialized[K, V, ByteArrayKeyValueStore]): KTable[K, V]
Create a new KTable that consists all records of this KTable which do not satisfy the given predicate
Create a new KTable that consists all records of this KTable which do not satisfy the given predicate
predicate
a filter that is applied to each record
materialized
a Materialized that describes how the StateStore for the resulting KTable should be materialized.
returns
a KTable that contains only those records that do not satisfy the given predicate
See also
org.apache.kafka.streams.kstream.KTable#filterNot
def filterNot(predicate: (K, V) => Boolean, named: Named): KTable[K, V]
Create a new KTable that consists all records of this KTable which do not satisfy the given predicate
Create a new KTable that consists all records of this KTable which do not satisfy the given predicate
predicate
a filter that is applied to each record
named
a Named config used to name the processor in the topology
returns
a KTable that contains only those records that do not satisfy the given predicate
See also
org.apache.kafka.streams.kstream.KTable#filterNot
def filterNot(predicate: (K, V) => Boolean): KTable[K, V]
Create a new KTable that consists all records of this KTable which do not satisfy the given predicate
Create a new KTable that consists all records of this KTable which do not satisfy the given predicate
predicate
a filter that is applied to each record
returns
a KTable that contains only those records that do not satisfy the given predicate
See also
org.apache.kafka.streams.kstream.KTable#filterNot
final def getClass(): Class[_ <: AnyRef]
Definition Classes
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Annotations
@native() @HotSpotIntrinsicCandidate()
def groupBy[KR, VR](selector: (K, V) => (KR, VR))(implicit grouped: Grouped[KR, VR]): KGroupedTable[KR, VR]
Re-groups the records of this KTable using the provided key/value mapper and Serdes as specified by Grouped.
Re-groups the records of this KTable using the provided key/value mapper and Serdes as specified by Grouped.
selector
a function that computes a new grouping key and value to be aggregated
grouped
the Grouped instance used to specify Serdes
returns
a KGroupedTable that contains the re-grouped records of the original KTable
See also
org.apache.kafka.streams.kstream.KTable#groupBy
def hashCode(): Int
Definition Classes
AnyRef → Any
Annotations
@native() @HotSpotIntrinsicCandidate()
val inner: kstream.KTable[K, V]
final def isInstanceOf[T0]: Boolean
Definition Classes
Any
def join[VR, KO, VO](other: KTable[KO, VO], keyExtractor: Function[V, KO], joiner: ValueJoiner[V, VO, VR], tableJoined: TableJoined[K, KO], materialized: Materialized[K, VR, KeyValueStore[Bytes, Array[Byte]]]): KTable[K, VR]
Join records of this KTable with another KTable's records using non-windowed inner join.
Join records of this KTable with another KTable's records using non-windowed inner join. Records from this table are joined according to the result of keyExtractor on the other KTable.
other
the other KTable to be joined with this KTable, keyed on the value obtained from keyExtractor
keyExtractor
a function that extracts the foreign key from this table's value
joiner
a function that computes the join result for a pair of matching records
tableJoined
a org.apache.kafka.streams.kstream.TableJoined used to configure partitioners and names of internal topics and stores
materialized
a Materialized that describes how the StateStore for the resulting KTable should be materialized.
returns
a KTable that contains join-records for each key and values computed by the given joiner, one for each matched record-pair with the same key
def join[VR, KO, VO](other: KTable[KO, VO], keyExtractor: Function[V, KO], joiner: ValueJoiner[V, VO, VR], materialized: Materialized[K, VR, KeyValueStore[Bytes, Array[Byte]]]): KTable[K, VR]
Join records of this KTable with another KTable's records using non-windowed inner join.
Join records of this KTable with another KTable's records using non-windowed inner join. Records from this table are joined according to the result of keyExtractor on the other KTable.
other
the other KTable to be joined with this KTable, keyed on the value obtained from keyExtractor
keyExtractor
a function that extracts the foreign key from this table's value
joiner
a function that computes the join result for a pair of matching records
materialized
a Materialized that describes how the StateStore for the resulting KTable should be materialized.
returns
a KTable that contains join-records for each key and values computed by the given joiner, one for each matched record-pair with the same key
def join[VO, VR](other: KTable[K, VO], named: Named, materialized: Materialized[K, VR, ByteArrayKeyValueStore])(joiner: (V, VO) => VR): KTable[K, VR]
Join records of this KTable with another KTable's records using non-windowed inner equi join.
Join records of this KTable with another KTable's records using non-windowed inner equi join.
other
the other KTable to be joined with this KTable
named
a Named config used to name the processor in the topology
materialized
a Materialized that describes how the StateStore for the resulting KTable should be materialized.
joiner
a function that computes the join result for a pair of matching records
returns
a KTable that contains join-records for each key and values computed by the given joiner, one for each matched record-pair with the same key
See also
org.apache.kafka.streams.kstream.KTable#join
def join[VO, VR](other: KTable[K, VO], materialized: Materialized[K, VR, ByteArrayKeyValueStore])(joiner: (V, VO) => VR): KTable[K, VR]
Join records of this KTable with another KTable's records using non-windowed inner equi join.
Join records of this KTable with another KTable's records using non-windowed inner equi join.
other
the other KTable to be joined with this KTable
materialized
a Materialized that describes how the StateStore for the resulting KTable should be materialized.
joiner
a function that computes the join result for a pair of matching records
returns
a KTable that contains join-records for each key and values computed by the given joiner, one for each matched record-pair with the same key
See also
org.apache.kafka.streams.kstream.KTable#join
def join[VO, VR](other: KTable[K, VO], named: Named)(joiner: (V, VO) => VR): KTable[K, VR]
Join records of this KTable with another KTable's records using non-windowed inner equi join.
Join records of this KTable with another KTable's records using non-windowed inner equi join.
other
the other KTable to be joined with this KTable
named
a Named config used to name the processor in the topology
joiner
a function that computes the join result for a pair of matching records
returns
a KTable that contains join-records for each key and values computed by the given joiner, one for each matched record-pair with the same key
See also
org.apache.kafka.streams.kstream.KTable#join
def join[VO, VR](other: KTable[K, VO])(joiner: (V, VO) => VR): KTable[K, VR]
Join records of this KTable with another KTable's records using non-windowed inner equi join.
Join records of this KTable with another KTable's records using non-windowed inner equi join.
other
the other KTable to be joined with this KTable
joiner
a function that computes the join result for a pair of matching records
returns
a KTable that contains join-records for each key and values computed by the given joiner, one for each matched record-pair with the same key
See also
org.apache.kafka.streams.kstream.KTable#join
def leftJoin[VR, KO, VO](other: KTable[KO, VO], keyExtractor: Function[V, KO], joiner: ValueJoiner[V, VO, VR], tableJoined: TableJoined[K, KO], materialized: Materialized[K, VR, KeyValueStore[Bytes, Array[Byte]]]): KTable[K, VR]
Join records of this KTable with another KTable's records using non-windowed left join.
Join records of this KTable with another KTable's records using non-windowed left join. Records from this table are joined according to the result of keyExtractor on the other KTable.
other
the other KTable to be joined with this KTable, keyed on the value obtained from keyExtractor
keyExtractor
a function that extracts the foreign key from this table's value
joiner
a function that computes the join result for a pair of matching records
tableJoined
a org.apache.kafka.streams.kstream.TableJoined used to configure partitioners and names of internal topics and stores
materialized
a Materialized that describes how the StateStore for the resulting KTable should be materialized.
returns
a KTable that contains join-records for each key and values computed by the given joiner, one for each matched record-pair with the same key
def leftJoin[VR, KO, VO](other: KTable[KO, VO], keyExtractor: Function[V, KO], joiner: ValueJoiner[V, VO, VR], materialized: Materialized[K, VR, KeyValueStore[Bytes, Array[Byte]]]): KTable[K, VR]
Join records of this KTable with another KTable's records using non-windowed left join.
Join records of this KTable with another KTable's records using non-windowed left join. Records from this table are joined according to the result of keyExtractor on the other KTable.
other
the other KTable to be joined with this KTable, keyed on the value obtained from keyExtractor
keyExtractor
a function that extracts the foreign key from this table's value
joiner
a function that computes the join result for a pair of matching records
materialized
a Materialized that describes how the StateStore for the resulting KTable should be materialized.
returns
a KTable that contains join-records for each key and values computed by the given joiner, one for each matched record-pair with the same key
def leftJoin[VO, VR](other: KTable[K, VO], named: Named, materialized: Materialized[K, VR, ByteArrayKeyValueStore])(joiner: (V, VO) => VR): KTable[K, VR]
Join records of this KTable with another KTable's records using non-windowed left equi join.
Join records of this KTable with another KTable's records using non-windowed left equi join.
other
the other KTable to be joined with this KTable
named
a Named config used to name the processor in the topology
materialized
a Materialized that describes how the StateStore for the resulting KTable should be materialized.
joiner
a function that computes the join result for a pair of matching records
returns
a KTable that contains join-records for each key and values computed by the given joiner, one for each matched record-pair with the same key
See also
org.apache.kafka.streams.kstream.KTable#leftJoin
def leftJoin[VO, VR](other: KTable[K, VO], materialized: Materialized[K, VR, ByteArrayKeyValueStore])(joiner: (V, VO) => VR): KTable[K, VR]
Join records of this KTable with another KTable's records using non-windowed left equi join.
Join records of this KTable with another KTable's records using non-windowed left equi join.
other
the other KTable to be joined with this KTable
materialized
a Materialized that describes how the StateStore for the resulting KTable should be materialized.
joiner
a function that computes the join result for a pair of matching records
returns
a KTable that contains join-records for each key and values computed by the given joiner, one for each matched record-pair with the same key
See also
org.apache.kafka.streams.kstream.KTable#leftJoin
def leftJoin[VO, VR](other: KTable[K, VO], named: Named)(joiner: (V, VO) => VR): KTable[K, VR]
Join records of this KTable with another KTable's records using non-windowed left equi join.
Join records of this KTable with another KTable's records using non-windowed left equi join.
other
the other KTable to be joined with this KTable
named
a Named config used to name the processor in the topology
joiner
a function that computes the join result for a pair of matching records
returns
a KTable that contains join-records for each key and values computed by the given joiner, one for each matched record-pair with the same key
See also
org.apache.kafka.streams.kstream.KTable#leftJoin
def leftJoin[VO, VR](other: KTable[K, VO])(joiner: (V, VO) => VR): KTable[K, VR]
Join records of this KTable with another KTable's records using non-windowed left equi join.
Join records of this KTable with another KTable's records using non-windowed left equi join.
other
the other KTable to be joined with this KTable
joiner
a function that computes the join result for a pair of matching records
returns
a KTable that contains join-records for each key and values computed by the given joiner, one for each matched record-pair with the same key
See also
org.apache.kafka.streams.kstream.KTable#leftJoin
def mapValues[VR](mapper: (K, V) => VR, named: Named, materialized: Materialized[K, VR, ByteArrayKeyValueStore]): KTable[K, VR]
Create a new KTable by transforming the value of each record in this KTable into a new value (with possible new type) in the new KTable.
Create a new KTable by transforming the value of each record in this KTable into a new value (with possible new type) in the new KTable.
The provided mapper, a function (K, V) => VR is applied to each input record value and computes a new value for it
mapper
, a function (K, V) => VR that computes a new output value
named
a Named config used to name the processor in the topology
materialized
a Materialized that describes how the StateStore for the resulting KTable should be materialized.
returns
a KTable that contains records with unmodified key and new values (possibly of different type)
See also
org.apache.kafka.streams.kstream.KTable#mapValues
def mapValues[VR](mapper: (K, V) => VR, materialized: Materialized[K, VR, ByteArrayKeyValueStore]): KTable[K, VR]
Create a new KTable by transforming the value of each record in this KTable into a new value (with possible new type) in the new KTable.
Create a new KTable by transforming the value of each record in this KTable into a new value (with possible new type) in the new KTable.
The provided mapper, a function (K, V) => VR is applied to each input record value and computes a new value for it
mapper
, a function (K, V) => VR that computes a new output value
materialized
a Materialized that describes how the StateStore for the resulting KTable should be materialized.
returns
a KTable that contains records with unmodified key and new values (possibly of different type)
See also
org.apache.kafka.streams.kstream.KTable#mapValues
def mapValues[VR](mapper: (K, V) => VR, named: Named): KTable[K, VR]
Create a new KTable by transforming the value of each record in this KTable into a new value (with possible new type) in the new KTable.
Create a new KTable by transforming the value of each record in this KTable into a new value (with possible new type) in the new KTable.
The provided mapper, a function (K, V) => VR is applied to each input record value and computes a new value for it
mapper
, a function (K, V) => VR that computes a new output value
named
a Named config used to name the processor in the topology
returns
a KTable that contains records with unmodified key and new values (possibly of different type)
See also
org.apache.kafka.streams.kstream.KTable#mapValues
def mapValues[VR](mapper: (K, V) => VR): KTable[K, VR]
Create a new KTable by transforming the value of each record in this KTable into a new value (with possible new type) in the new KTable.
Create a new KTable by transforming the value of each record in this KTable into a new value (with possible new type) in the new KTable.
The provided mapper, a function (K, V) => VR is applied to each input record value and computes a new value for it
mapper
, a function (K, V) => VR that computes a new output value
returns
a KTable that contains records with unmodified key and new values (possibly of different type)
See also
org.apache.kafka.streams.kstream.KTable#mapValues
def mapValues[VR](mapper: (V) => VR, named: Named, materialized: Materialized[K, VR, ByteArrayKeyValueStore]): KTable[K, VR]
Create a new KTable by transforming the value of each record in this KTable into a new value (with possible new type) in the new KTable.
Create a new KTable by transforming the value of each record in this KTable into a new value (with possible new type) in the new KTable.
The provided mapper, a function V => VR is applied to each input record value and computes a new value for it
mapper
, a function V => VR that computes a new output value
named
a Named config used to name the processor in the topology
materialized
a Materialized that describes how the StateStore for the resulting KTable should be materialized.
returns
a KTable that contains records with unmodified key and new values (possibly of different type)
See also
org.apache.kafka.streams.kstream.KTable#mapValues
def mapValues[VR](mapper: (V) => VR, materialized: Materialized[K, VR, ByteArrayKeyValueStore]): KTable[K, VR]
Create a new KTable by transforming the value of each record in this KTable into a new value (with possible new type) in the new KTable.
Create a new KTable by transforming the value of each record in this KTable into a new value (with possible new type) in the new KTable.
The provided mapper, a function V => VR is applied to each input record value and computes a new value for it
mapper
, a function V => VR that computes a new output value
materialized
a Materialized that describes how the StateStore for the resulting KTable should be materialized.
returns
a KTable that contains records with unmodified key and new values (possibly of different type)
See also
org.apache.kafka.streams.kstream.KTable#mapValues
def mapValues[VR](mapper: (V) => VR, named: Named): KTable[K, VR]
Create a new KTable by transforming the value of each record in this KTable into a new value (with possible new type) in the new KTable.
Create a new KTable by transforming the value of each record in this KTable into a new value (with possible new type) in the new KTable.
The provided mapper, a function V => VR is applied to each input record value and computes a new value for it
mapper
, a function V => VR that computes a new output value
named
a Named config used to name the processor in the topology
returns
a KTable that contains records with unmodified key and new values (possibly of different type)
See also
org.apache.kafka.streams.kstream.KTable#mapValues
def mapValues[VR](mapper: (V) => VR): KTable[K, VR]
Create a new KTable by transforming the value of each record in this KTable into a new value (with possible new type) in the new KTable.
Create a new KTable by transforming the value of each record in this KTable into a new value (with possible new type) in the new KTable.
The provided mapper, a function V => VR is applied to each input record value and computes a new value for it
mapper
, a function V => VR that computes a new output value
returns
a KTable that contains records with unmodified key and new values (possibly of different type)
See also
org.apache.kafka.streams.kstream.KTable#mapValues
final def ne(arg0: AnyRef): Boolean
Definition Classes
AnyRef
final def notify(): Unit
Definition Classes
AnyRef
Annotations
@native() @HotSpotIntrinsicCandidate()
final def notifyAll(): Unit
Definition Classes
AnyRef
Annotations
@native() @HotSpotIntrinsicCandidate()
def outerJoin[VO, VR](other: KTable[K, VO], named: Named, materialized: Materialized[K, VR, ByteArrayKeyValueStore])(joiner: (V, VO) => VR): KTable[K, VR]
Join records of this KTable with another KTable's records using non-windowed outer equi join.
Join records of this KTable with another KTable's records using non-windowed outer equi join.
other
the other KTable to be joined with this KTable
named
a Named config used to name the processor in the topology
materialized
a Materialized that describes how the StateStore for the resulting KTable should be materialized.
joiner
a function that computes the join result for a pair of matching records
returns
a KTable that contains join-records for each key and values computed by the given joiner, one for each matched record-pair with the same key
See also
org.apache.kafka.streams.kstream.KTable#leftJoin
def outerJoin[VO, VR](other: KTable[K, VO], materialized: Materialized[K, VR, ByteArrayKeyValueStore])(joiner: (V, VO) => VR): KTable[K, VR]
Join records of this KTable with another KTable's records using non-windowed outer equi join.
Join records of this KTable with another KTable's records using non-windowed outer equi join.
other
the other KTable to be joined with this KTable
materialized
a Materialized that describes how the StateStore for the resulting KTable should be materialized.
joiner
a function that computes the join result for a pair of matching records
returns
a KTable that contains join-records for each key and values computed by the given joiner, one for each matched record-pair with the same key
See also
org.apache.kafka.streams.kstream.KTable#leftJoin
def outerJoin[VO, VR](other: KTable[K, VO], named: Named)(joiner: (V, VO) => VR): KTable[K, VR]
Join records of this KTable with another KTable's records using non-windowed outer equi join.
Join records of this KTable with another KTable's records using non-windowed outer equi join.
other
the other KTable to be joined with this KTable
named
a Named config used to name the processor in the topology
joiner
a function that computes the join result for a pair of matching records
returns
a KTable that contains join-records for each key and values computed by the given joiner, one for each matched record-pair with the same key
See also
org.apache.kafka.streams.kstream.KTable#leftJoin
def outerJoin[VO, VR](other: KTable[K, VO])(joiner: (V, VO) => VR): KTable[K, VR]
Join records of this KTable with another KTable's records using non-windowed outer equi join.
Join records of this KTable with another KTable's records using non-windowed outer equi join.
other
the other KTable to be joined with this KTable
joiner
a function that computes the join result for a pair of matching records
returns
a KTable that contains join-records for each key and values computed by the given joiner, one for each matched record-pair with the same key
See also
org.apache.kafka.streams.kstream.KTable#leftJoin
def queryableStoreName: String
Get the name of the local state store used that can be used to query this KTable.
Get the name of the local state store used that can be used to query this KTable.
returns
the underlying state store name, or null if this KTable cannot be queried.
def suppress(suppressed: Suppressed[_ >: K]): KTable[K, V]
Suppress some updates from this changelog stream, determined by the supplied org.apache.kafka.streams.kstream.Suppressed configuration.
Suppress some updates from this changelog stream, determined by the supplied org.apache.kafka.streams.kstream.Suppressed configuration.
This controls what updates downstream table and stream operations will receive.
suppressed
Configuration object determining what, if any, updates to suppress.
returns
A new KTable with the desired suppression characteristics.
See also
org.apache.kafka.streams.kstream.KTable#suppress
final def synchronized[T0](arg0: => T0): T0
Definition Classes
AnyRef
def toStream[KR](mapper: (K, V) => KR, named: Named): KStream[KR, V]
Convert this changelog stream to a KStream using the given key/value mapper to select the new key
Convert this changelog stream to a KStream using the given key/value mapper to select the new key
mapper
a function that computes a new key for each record
named
a Named config used to name the processor in the topology
returns
a KStream that contains the same records as this KTable
See also
org.apache.kafka.streams.kstream.KTable#toStream
def toStream[KR](mapper: (K, V) => KR): KStream[KR, V]
Convert this changelog stream to a KStream using the given key/value mapper to select the new key
Convert this changelog stream to a KStream using the given key/value mapper to select the new key
mapper
a function that computes a new key for each record
returns
a KStream that contains the same records as this KTable
See also
org.apache.kafka.streams.kstream.KTable#toStream
def toStream(named: Named): KStream[K, V]
Convert this changelog stream to a KStream.
Convert this changelog stream to a KStream.
named
a Named config used to name the processor in the topology
returns
a KStream that contains the same records as this KTable
See also
org.apache.kafka.streams.kstream.KTable#toStream
def toStream: KStream[K, V]
Convert this changelog stream to a KStream.
Convert this changelog stream to a KStream.
returns
a KStream that contains the same records as this KTable
See also
org.apache.kafka.streams.kstream.KTable#toStream
def toString(): String
Definition Classes
AnyRef → Any
def transformValues[VR](valueTransformerWithKeySupplier: ValueTransformerWithKeySupplier[K, V, VR], materialized: Materialized[K, VR, KeyValueStore[Bytes, Array[Byte]]], named: Named, stateStoreNames: String*): KTable[K, VR]
Create a new KTable by transforming the value of each record in this KTable into a new value, (with possibly new type).
Create a new KTable by transforming the value of each record in this KTable into a new value, (with possibly new type). A ValueTransformer (provided by the given ValueTransformerSupplier) is applied to each input record value and computes a new value for it. This is similar to #mapValues(ValueMapperWithKey), but more flexible, allowing stateful, rather than stateless, record-by-record operation, access to additional state-stores, and access to the ProcessorContext. In order to assign a state, the state must be created and registered beforehand via stores added via addStateStore or addGlobalStore before they can be connected to the Transformer The resulting KTable is materialized into another state store (additional to the provided state store names) as specified by the user via Materialized parameter, and is queryable through its given name.
valueTransformerWithKeySupplier
a instance of ValueTransformerWithKeySupplier that generates a ValueTransformerWithKey At least one transformer instance will be created per streaming task. Transformer implementations doe not need to be thread-safe.
materialized
an instance of Materialized used to describe how the state store of the resulting table should be materialized.
named
a Named config used to name the processor in the topology
stateStoreNames
the names of the state stores used by the processor
returns
a KStream that contains records with unmodified key and new values (possibly of different type)
See also
org.apache.kafka.streams.kstream.KStream#transformValues
def transformValues[VR](valueTransformerWithKeySupplier: ValueTransformerWithKeySupplier[K, V, VR], materialized: Materialized[K, VR, KeyValueStore[Bytes, Array[Byte]]], stateStoreNames: String*): KTable[K, VR]
Create a new KTable by transforming the value of each record in this KTable into a new value, (with possibly new type).
Create a new KTable by transforming the value of each record in this KTable into a new value, (with possibly new type). A ValueTransformer (provided by the given ValueTransformerSupplier) is applied to each input record value and computes a new value for it. This is similar to #mapValues(ValueMapperWithKey), but more flexible, allowing stateful, rather than stateless, record-by-record operation, access to additional state-stores, and access to the ProcessorContext. In order to assign a state, the state must be created and registered beforehand via stores added via addStateStore or addGlobalStore before they can be connected to the Transformer The resulting KTable is materialized into another state store (additional to the provided state store names) as specified by the user via Materialized parameter, and is queryable through its given name.
valueTransformerWithKeySupplier
a instance of ValueTransformerWithKeySupplier that generates a ValueTransformerWithKey At least one transformer instance will be created per streaming task. Transformer implementations doe not need to be thread-safe.
materialized
an instance of Materialized used to describe how the state store of the resulting table should be materialized.
stateStoreNames
the names of the state stores used by the processor
returns
a KStream that contains records with unmodified key and new values (possibly of different type)
See also
org.apache.kafka.streams.kstream.KStream#transformValues
def transformValues[VR](valueTransformerWithKeySupplier: ValueTransformerWithKeySupplier[K, V, VR], named: Named, stateStoreNames: String*): KTable[K, VR]
Create a new KTable by transforming the value of each record in this KTable into a new value, (with possibly new type).
Create a new KTable by transforming the value of each record in this KTable into a new value, (with possibly new type). Transform the value of each input record into a new value (with possible new type) of the output record. A ValueTransformerWithKey (provided by the given ValueTransformerWithKeySupplier) is applied to each input record value and computes a new value for it. This is similar to #mapValues(ValueMapperWithKey), but more flexible, allowing access to additional state-stores, and to the ProcessorContext. If the downstream topology uses aggregation functions, (e.g. KGroupedTable#reduce, KGroupedTable#aggregate, etc), care must be taken when dealing with state, (either held in state-stores or transformer instances), to ensure correct aggregate results. In contrast, if the resulting KTable is materialized, (cf. #transformValues(ValueTransformerWithKeySupplier, Materialized, String...)), such concerns are handled for you. In order to assign a state, the state must be created and registered beforehand via stores added via addStateStore or addGlobalStore before they can be connected to the Transformer
valueTransformerWithKeySupplier
a instance of ValueTransformerWithKeySupplier that generates a ValueTransformerWithKey. At least one transformer instance will be created per streaming task. Transformer implementations doe not need to be thread-safe.
named
a Named config used to name the processor in the topology
stateStoreNames
the names of the state stores used by the processor
returns
a KStream that contains records with unmodified key and new values (possibly of different type)
See also
org.apache.kafka.streams.kstream.KStream#transformValues
def transformValues[VR](valueTransformerWithKeySupplier: ValueTransformerWithKeySupplier[K, V, VR], stateStoreNames: String*): KTable[K, VR]
Create a new KTable by transforming the value of each record in this KTable into a new value, (with possibly new type).
Create a new KTable by transforming the value of each record in this KTable into a new value, (with possibly new type). Transform the value of each input record into a new value (with possible new type) of the output record. A ValueTransformerWithKey (provided by the given ValueTransformerWithKeySupplier) is applied to each input record value and computes a new value for it. This is similar to #mapValues(ValueMapperWithKey), but more flexible, allowing access to additional state-stores, and to the ProcessorContext. If the downstream topology uses aggregation functions, (e.g. KGroupedTable#reduce, KGroupedTable#aggregate, etc), care must be taken when dealing with state, (either held in state-stores or transformer instances), to ensure correct aggregate results. In contrast, if the resulting KTable is materialized, (cf. #transformValues(ValueTransformerWithKeySupplier, Materialized, String...)), such concerns are handled for you. In order to assign a state, the state must be created and registered beforehand via stores added via addStateStore or addGlobalStore before they can be connected to the Transformer
valueTransformerWithKeySupplier
a instance of ValueTransformerWithKeySupplier that generates a ValueTransformerWithKey. At least one transformer instance will be created per streaming task. Transformer implementations doe not need to be thread-safe.
stateStoreNames
the names of the state stores used by the processor
returns
a KStream that contains records with unmodified key and new values (possibly of different type)
See also
org.apache.kafka.streams.kstream.KStream#transformValues
final def wait(arg0: Long, arg1: Int): Unit
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@throws(classOf[java.lang.InterruptedException])
final def wait(arg0: Long): Unit
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@throws(classOf[java.lang.InterruptedException]) @native()
final def wait(): Unit
Definition Classes
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@throws(classOf[java.lang.InterruptedException])

Deprecated Value Members

def finalize(): Unit
Attributes
protected[lang]
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@throws(classOf[java.lang.Throwable]) @Deprecated @Deprecated
Deprecated
def join[VR, KO, VO](other: KTable[KO, VO], keyExtractor: Function[V, KO], joiner: ValueJoiner[V, VO, VR], named: Named, materialized: Materialized[K, VR, KeyValueStore[Bytes, Array[Byte]]]): KTable[K, VR]
Join records of this KTable with another KTable's records using non-windowed inner join.
Join records of this KTable with another KTable's records using non-windowed inner join. Records from this table are joined according to the result of keyExtractor on the other KTable.
other
the other KTable to be joined with this KTable, keyed on the value obtained from keyExtractor
keyExtractor
a function that extracts the foreign key from this table's value
joiner
a function that computes the join result for a pair of matching records
named
a Named config used to name the processor in the topology
materialized
a Materialized that describes how the StateStore for the resulting KTable should be materialized.
returns
a KTable that contains join-records for each key and values computed by the given joiner, one for each matched record-pair with the same key
Annotations
@deprecated
Deprecated
(Since version 3.1) Use join(KTable, Function, ValueJoiner, TableJoined, Materialized) instead
def leftJoin[VR, KO, VO](other: KTable[KO, VO], keyExtractor: Function[V, KO], joiner: ValueJoiner[V, VO, VR], named: Named, materialized: Materialized[K, VR, KeyValueStore[Bytes, Array[Byte]]]): KTable[K, VR]
Join records of this KTable with another KTable's records using non-windowed left join.
Join records of this KTable with another KTable's records using non-windowed left join. Records from this table are joined according to the result of keyExtractor on the other KTable.
other
the other KTable to be joined with this KTable, keyed on the value obtained from keyExtractor
keyExtractor
a function that extracts the foreign key from this table's value
joiner
a function that computes the join result for a pair of matching records
named
a Named config used to name the processor in the topology
materialized
a Materialized that describes how the StateStore for the resulting KTable should be materialized.
returns
a KTable that contains join-records for each key and values computed by the given joiner, one for each matched record-pair with the same key
Annotations
@deprecated
Deprecated
(Since version 3.1) Use leftJoin(KTable, Function, ValueJoiner, TableJoined, Materialized) instead

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class KTable[K, V] extends AnyRef

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KTable

class KTable[K, V] extends AnyRef

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KTable