content: /*
   * 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.processor;
  
  import org.apache.kafka.streams.Topology;
  
  import java.util.Optional;
  import java.util.Set;
  
  /**
   * Determine how records are distributed among the partitions in a Kafka topic. If not specified, the underlying producer's
   * default partitioning strategy will be used to determine the partition.
   * <p>
   * Kafka topics are divided into one or more <i>partitions</i>. Since each partition must fit on the servers that host it, so
   * using multiple partitions allows the topic to scale beyond a size that will fit on a single machine. Partitions also enable you
   * to use multiple instances of your topology to process in parallel all of the records on the topology's source topics.
   * <p>
   * When a topology is instantiated, each of its sources are assigned a subset of that topic's partitions. That means that only
   * those processors in that topology instance will consume the records from those partitions. In many cases, Kafka Streams will
   * automatically manage these instances, and adjust when new topology instances are added or removed.
   * <p>
   * Some topologies, though, need more control over which records appear in each partition. For example, some topologies that have
   * stateful processors may want all records within a range of keys to always be delivered to and handled by the same topology instance.
   * An upstream topology producing records to that topic can use a custom <i>stream partitioner</i> to precisely and consistently
   * determine to which partition each record should be written.
   * <p>
   * To do this, create a <code>StreamPartitioner</code> implementation, and when you build your topology specify that custom partitioner
   * when {@link Topology#addSink(String, String, org.apache.kafka.common.serialization.Serializer, org.apache.kafka.common.serialization.Serializer, StreamPartitioner, String...) adding a sink}
   * for that topic.
   * <p>
   * All StreamPartitioner implementations should be stateless and a pure function so they can be shared across topic and sink nodes.
   * 
   * @param <K> the type of keys
   * @param <V> the type of values
   * @see Topology#addSink(String, String, org.apache.kafka.common.serialization.Serializer,
   *      org.apache.kafka.common.serialization.Serializer, StreamPartitioner, String...)
   * @see Topology#addSink(String, String, StreamPartitioner, String...)
   */
  @FunctionalInterface
  public interface StreamPartitioner<K, V> {
  
      /**
       * Determine the number(s) of the partition(s) to which a record with the given key and value should be sent, 
       * for the given topic and current partition count
       * @param topic the topic name this record is sent to
       * @param key the key of the record
       * @param value the value of the record
       * @param numPartitions the total number of partitions
       * @return an Optional of Set of integers between 0 and {@code numPartitions-1},
       * Empty optional means using default partitioner
       * Optional of an empty set means the record won't be sent to any partitions i.e drop it.
       * Optional of Set of integers means the partitions to which the record should be sent to.
       * */
      Optional<Set<Integer>> partitions(String topic, K key, V value, int numPartitions);
  }
  