Status

Current state: "Adopted"

Discussion thread: 

JIRA: KAFKA-13602

Please keep the discussion on the mailing list rather than commenting on the wiki (wiki discussions get unwieldy fast).

Motivation

Lot of times, in Kafka Streams users want to send a record to more than one partition on the sink topic. Currently, if a user wants to replicate a message into N partitions, the only way of doing that is to replicate the message N times and then plug-in a new custom partitioner to write the message N times into N different partitions. To give a more concrete example, let's say there's a sink topic with 10 partitions and a user wants to send records only to even numbered partitions.


In today's world, this is how the user can do the same:

final int numPartitions = 10;

final KStream<String, String> inputStream = builder.stream("input-topic", Consumed.with(Serdes.String(), Serdes.String()));

for (int i = 0; i < numPartitions; i += 2) {
	final int evenNumberedPartition = i;
    inputStream.to("output-topic", Produced.with(Serdes.String(), Serdes.String(), (topic, key, val, numPartitions) -> evenNumberedPartition));
}

As can be seen, there's no implicit way of sending the records to multiple partitions. This KIP aims to make this process simpler in Kafka Streams. As a side note, this KIP also adds a provision to drop records using a custom partitioner.

Public Interfaces

The StreamPartitioner method would have a new method added called partitions()  and the current partition()  method would be marked as deprecated. The partitions()  method would be marked as  default within which it would invoke partition()  method and construct a singleton set out of it. Here's how the interface would look like now:

import java.util.Set;
import java.util.Collections;

  public interface StreamPartitioner<K, V> {

    /**
     * Determine the partition number for a record with the given key and value and the current number of partitions.
     *
     * @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 integer between 0 and {@code numPartitions-1}, or {@code null} if the default partitioning logic should be used
     */
    @Deprecated
    Integer partition(String topic, K key, V value, int numPartitions);     


	/**
     * Determine the partition numbers to which a record, with the given key and value and the current number
     * of partitions, should be multi-casted to.
     * @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 use default partitioner
     * Optional of an empty set means the record won't be sent to any partitions i.e dropped.
     * Optional of Set of integers means the partitions to which the record should be sent to.
     * */
    default Optional<Set<Integer>> partitions(String topic, K key, V value, int numPartitions) {
        Integer partition = partition(topic, key, value, numPartitions);
        return partition == null ? Optional.empty() : Optional.of(Collections.singleton(partition(topic, key, value, numPartitions)));
    }  

}      


The return type is a Set so that for cases of a faulty implementation of partitions  method which might return same partition number multiple times, we would still record it only once.

Proposed Changes

  • RecordCollector and it's implementation RecordCollectorImpl has the method which accepts a StreamPartitioner object and pushes it to the partitions returned by the partition method. This is the core piece of logic which would allow multi/broadcasting records. Here are the high level changes. Note that when the partitions()  method return an empty set meaning we won't send the record to any partition, we would also be updating the droppedRecordsSensor. 
    @Override
    public <K, V> void send(final String topic,
                            final K key,
                            final V value,
                            final Headers headers,
                            final Long timestamp,
                            final Serializer<K> keySerializer,
                            final Serializer<V> valueSerializer,
                            final StreamPartitioner<? super K, ? super V> partitioner) {         

        if (partitioner != null) {

            // Existing logic to fetch partitions

			// Changes to be made due to the KIP             
			if (partitions.size() > 0) {
                final Optional<Set<Integer>> maybeMulticastPartitions = partitioner.partitions(topic, key, value, partitions.size());
                if (!maybeMulticastPartitions.isPresent()) {
                    // New change. Use default partitioner
                    send(topic, key, value, headers, null, timestamp, keySerializer, valueSerializer, processorNodeId, context);
                } else {
                    Set<Integer> multicastPartitions = maybeMulticastPartitions.get();
                    if (multicastPartitions.isEmpty()) {
                        // If a record is not to be sent to any partition, mark it as dropped.
                        log.info("Not sending the record to any partition");
                        droppedRecordsSensor.record();
                    } else {
                        for (final int multicastPartition: multicastPartitions) {
                            send(topic, key, value, headers, multicastPartition, timestamp, keySerializer, valueSerializer, processorNodeId, context);
                        }
                    }
                }
            } else {
                throw new StreamsException("Could not get partition information for topic " + topic + " for task " + taskId +
                        ". This can happen if the topic does not exist.");
            }
  		} else {
            send(topic, key, value, headers, null, timestamp, keySerializer, valueSerializer, processorNodeId, context);
        }       
	}
  • StreamPartitioner is also used in FK-join and Interactive queries. For FK-join and IQ, the invocation of partition()  would be replaced by partitions()  and a runtime check would be added to ensure that the returned set is singleton. 


Example Usage

Continuing the example from the motivation section, with the new interface, here's how users can send to even number partitions:

// Define a partitioner class which sends to even numbered partitions

public static class EvenPartitioner<K, V> implements StreamPartitioner<K, V> {

        @Override
        public Integer partition(String topic, K key, V value, int numPartitions) {
            return null;
        }

        @Override
        public Set<Integer> partitions(String topic, K key, V value, int numPartitions) {
            final Set<Integer> partitions = new HashSet<>();
            for (int i = 0; i < numPartitions; i += 2) {
                partitions.add(i);
            }
            return partitions;
        }
    }

// Broadcasting 
public static class BroadcastingPartitioner<K, V> implements StreamPartitioner<K, V> {

        @Override
        public Integer partition(String topic, K key, V value, int numPartitions) {
            return null;
        }

        @Override
        public Set<Integer> partitions(String topic, K key, V value, int numPartitions) {                     
				return IntStream.range(0, numPartitions).boxed().collect(Collectors.toSet());
         }
    }

// Don't send to any partitions
public static class DroppingPartitioner<K, V> implements StreamPartitioner<K, V> {

        @Override
        public Integer partition(String topic, K key, V value, int numPartitions) {
            return null;
        }

        @Override
        public Set<Integer> partitions(String topic, K key, V value, int numPartitions) {
            return Collections.emptySet();
        }
    }


// Build Stream.
final KStream<String, String> inputStream = builder.stream("input-topic", Consumed.with(Serdes.String(), Serdes.String()));

// Send to even numbered partitions
inputStream.to("output-topic", Produced.with(Serdes.String(), Serdes.String(), (topic, key, val, numPartitions) -> new EvenPartitioner()));
// Broadcast
inputStream.to("output-topic", Produced.with(Serdes.String(), Serdes.String(), (topic, key, val, numPartitions) -> new BroadcastingPartitioner()));
// Send to even numbered partitions
inputStream.to("output-topic", Produced.with(Serdes.String(), Serdes.String(), (topic, key, val, numPartitions) -> new DroppingPartitioner()));
}

This way users just need to define the partitioner and the internal routing mechanism would take care of sending the records to relevant or no partitions.

Compatibility, Deprecation, and Migration Plan

This KIP deprecates partition()  method in StreamPartitioner . Here is what we would be doing as part of the same:

  • StreamPartitioner is also used in FK-join and IQ. The invocation of partition()  would be replaced by partitions()  and a runtime check would be added to ensure that the returned set is singleton. 
  • Adding sufficient logging

Rejected Alternatives

The first 3 alternatives were rejected as they focussed only broadcasting to all partitions which seemed restrictive.

  • extend to() / addSink() with a "broadcast" option/config in KafkaStreams.
  • add toAllPartitions() / addBroadcastSink() methods in KafkaStreams.
  • allow StreamPartitioner to return `-1` for "all partitions".
  • Adding a separate class to handle multi casting. This was rejected in favour of enhancing the current StreamPartitioner  interface.

Test Plan

  • Add new tests(unit/integration) similar to the partition  tests for the newly introduced partitions  method.
  • Add tests to fail when a non singleton set is returned for FK joins and IQ when querying.



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