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

Linear Supertypes
AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. KTable
  2. AnyRef
  3. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. Protected

Instance Constructors

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

    inner

    The underlying Java abstraction for KTable

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##: Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @native()
  6. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  7. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable])
  17. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  18. 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

  19. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  20. val inner: kstream.KTable[K, V]
  21. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  22. 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

  23. 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

  24. 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

  25. 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

  26. 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

  27. 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

  28. 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

  29. 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

  30. 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

  31. 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

  32. 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

  33. 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

  34. 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

  35. 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

  36. 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

  37. 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

  38. 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

  39. 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

  40. 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

  41. 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

  42. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  43. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  44. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  45. 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

  46. 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

  47. 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

  48. 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

  49. 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.

  50. 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

  51. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  52. 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

  53. 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

  54. 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

  55. 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

  56. def toString(): String
    Definition Classes
    AnyRef → Any
  57. 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

  58. 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

  59. 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

  60. 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

  61. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  62. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  63. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()

Inherited from AnyRef

Inherited from Any

Ungrouped