kafka KGroupedTable 源码

kafka KGroupedTable 代码

文件路径：/streams/streams-scala/src/main/scala/org/apache/kafka/streams/scala/kstream/KGroupedTable.scala

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements. See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License. You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package org.apache.kafka.streams.scala
package kstream

import org.apache.kafka.streams.kstream.{KGroupedTable => KGroupedTableJ}
import org.apache.kafka.streams.scala.FunctionsCompatConversions.{
  AggregatorFromFunction,
  InitializerFromFunction,
  ReducerFromFunction
}

/**
 * Wraps the Java class KGroupedTable and delegates method calls to the underlying Java object.
 *
 * @tparam K Type of keys
 * @tparam V Type of values
 * @param inner The underlying Java abstraction for KGroupedTable
 * @see `org.apache.kafka.streams.kstream.KGroupedTable`
 */
class KGroupedTable[K, V](inner: KGroupedTableJ[K, V]) {

  /**
   * Count number of records of the original [[KTable]] that got [[KTable#groupBy]] to
   * the same key into a new instance of [[KTable]].
   *
   * @param materialized an instance of `Materialized` used to materialize a state store.
   * @return 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 `org.apache.kafka.streams.kstream.KGroupedTable#count`
   */
  def count()(implicit materialized: Materialized[K, Long, ByteArrayKeyValueStore]): KTable[K, Long] = {
    val c: KTable[K, java.lang.Long] =
      new KTable(inner.count(materialized.asInstanceOf[Materialized[K, java.lang.Long, ByteArrayKeyValueStore]]))
    c.mapValues[Long](Long2long _)
  }

  /**
   * Count number of records of the original [[KTable]] that got [[KTable#groupBy]] to
   * the same key into a new instance of [[KTable]].
   *
   * @param named        a [[Named]] config used to name the processor in the topology
   * @param materialized an instance of `Materialized` used to materialize a state store.
   * @return 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 `org.apache.kafka.streams.kstream.KGroupedTable#count`
   */
  def count(named: Named)(implicit materialized: Materialized[K, Long, ByteArrayKeyValueStore]): KTable[K, Long] = {
    val c: KTable[K, java.lang.Long] =
      new KTable(inner.count(named, materialized.asInstanceOf[Materialized[K, java.lang.Long, ByteArrayKeyValueStore]]))
    c.mapValues[Long](Long2long _)
  }

  /**
   * Combine the value of records of the original [[KTable]] that got [[KTable#groupBy]]
   * to the same key into a new instance of [[KTable]].
   *
   * @param adder        a function that adds a new value to the aggregate result
   * @param subtractor   a function that removed an old value from the aggregate result
   * @param materialized an instance of `Materialized` used to materialize a state store.
   * @return a [[KTable]] that contains "update" records with unmodified keys, and values that represent the
   *         latest (rolling) aggregate for each key
   * @see `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] =
    new KTable(inner.reduce(adder.asReducer, subtractor.asReducer, materialized))

  /**
   * Combine the value of records of the original [[KTable]] that got [[KTable#groupBy]]
   * to the same key into a new instance of [[KTable]].
   *
   * @param adder        a function that adds a new value to the aggregate result
   * @param subtractor   a function that removed an old value from the aggregate result
   * @param named        a [[Named]] config used to name the processor in the topology
   * @param materialized an instance of `Materialized` used to materialize a state store.
   * @return a [[KTable]] that contains "update" records with unmodified keys, and values that represent the
   *         latest (rolling) aggregate for each key
   * @see `org.apache.kafka.streams.kstream.KGroupedTable#reduce`
   */
  def reduce(adder: (V, V) => V, subtractor: (V, V) => V, named: Named)(implicit
    materialized: Materialized[K, V, ByteArrayKeyValueStore]
  ): KTable[K, V] =
    new KTable(inner.reduce(adder.asReducer, subtractor.asReducer, named, materialized))

  /**
   * 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.
   *
   * @param initializer  a function that provides an initial aggregate result value
   * @param adder        a function that adds a new record to the aggregate result
   * @param subtractor   an aggregator function that removed an old record from the aggregate result
   * @param materialized an instance of `Materialized` used to materialize a state store.
   * @return a [[KTable]] that contains "update" records with unmodified keys, and values that represent the
   *         latest (rolling) aggregate for each key
   * @see `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] =
    new KTable(
      inner.aggregate((() => initializer).asInitializer, adder.asAggregator, subtractor.asAggregator, materialized)
    )

  /**
   * 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.
   *
   * @param initializer  a function that provides an initial aggregate result value
   * @param named        a [[Named]] config used to name the processor in the topology
   * @param adder        a function that adds a new record to the aggregate result
   * @param subtractor   an aggregator function that removed an old record from the aggregate result
   * @param materialized an instance of `Materialized` used to materialize a state store.
   * @return a [[KTable]] that contains "update" records with unmodified keys, and values that represent the
   *         latest (rolling) aggregate for each key
   * @see `org.apache.kafka.streams.kstream.KGroupedTable#aggregate`
   */
  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] =
    new KTable(
      inner.aggregate(
        (() => initializer).asInitializer,
        adder.asAggregator,
        subtractor.asAggregator,
        named,
        materialized
      )
    )
}

相关信息

kafka 源码目录

相关文章

kafka Branched 源码

kafka BranchedKStream 源码

kafka CogroupedKStream 源码

kafka Consumed 源码

kafka Grouped 源码

kafka Joined 源码

kafka KGroupedStream 源码

kafka KStream 源码

kafka KTable 源码

kafka Materialized 源码