Status

Current state: "Under DiscussionAdopted"

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. Since this seems to be a common requirement for users, this KIP aims to make this process simpler.

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 partitions()  method and construct a singleton list out of it. Here's how the interface would look like now:

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> {

    /**

import java.util.List;

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
     * @returnDetermine anthe integerpartition betweennumber 0for anda {@code numPartitions-1}, or {@code null} if the default partitioning logic should be usedrecord with the given key and value and the current number of partitions.
     */
    @Deprecated
 * @param topic Integer partition(Stringthe topic, Kname key,this Vrecord value,is int numPartitions);


 	/**sent to
     * @param Determinekey the Listkey of partitionthe numbersrecord
 to which a record, with the* given@param keyvalue andthe value andof the current numberrecord
     * @param ofnumPartitions partitions,the shouldtotal be multi-casted to. Note that returning a single valued list with value -1 is a shorthand
     * for broadcasting the record to all the partitions of the topic.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 * @param topic the topic name this record is sent topartition(String topic, K key, V value, int numPartitions);     


	/**
     * @paramDetermine key the keypartition ofnumbers theto record
which a record, with the *given @paramkey valueand the value ofand the current recordnumber
     * @paramof numPartitionspartitions, theshould totalbe number of partitionsmulti-casted to.
     * @return@param atopic Listthe oftopic integersname betweenthis 0record andis {@code numPartitions-1}, null would mean a broadcast to all partitions
	sent to
     * while@param ankey emptythe listkey wouldof mean the record
 would not be sent to* any@param partitions.
value the value of  * the partitions of the topic.record
     */

 @param numPartitions the total defaultnumber List<Integer>of partitions(String
 topic, K key, V value,* int@return numPartitions)an {
Optional of Set of integers between 0 and return Collections.singletonList(partition(topic, key, value, numPartitions));
    }
}      

Proposed Changes

{@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. 

  
  

  Code Block
  language java
  @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();

  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 list meaning we won't send the record to any partition, we would also be updating the droppedRecordsSensor. 

  Code Block
  language java
  @Override public <K, V> void send(final String topic, if (multicastPartitions.isEmpty()) { final K key, // If a record is not to be sent to any partition, mark finalit Vas value,dropped. log.info("Not sending the record finalto Headers headers,any partition"); droppedRecordsSensor.record(); final Long timestamp, } else { final Serializer<K> keySerializer, for (final int multicastPartition: multicastPartitions) { final Serializer<V> valueSerializer, send(topic, key, value, headers, multicastPartition, timestamp, keySerializer, valueSerializer, processorNodeId, context); final StreamPartitioner<? super K, ? super V> partitioner) {         List<Integer> multicastPartitions; } if (partitioner != null) { final List<PartitionInfo> partitions;} try { } } partitionselse = streamsProducer.partitionsFor(topic); { } catch (final TimeoutExceptionthrow timeoutException) { log.warn("Could not get partitions for topic {}, will retry", topic); new StreamsException("Could not get partition information for topic " + topic + " for task " + taskId + // re-throw to trigger `task.timeout.ms` ". This can happen if the topic does not exist."); throw timeoutException; }   } else { } catch send(finaltopic, KafkaException fatal) { key, value, headers, null, timestamp, keySerializer, valueSerializer, processorNodeId, context); // here} we  cannot  drop the message on the floor even if it is a transient timeout exception, // so we treat everything the same as a fatal exception throw new StreamsException("Could not determine the number of partitions for topic '" + topic + "' for task " + taskId + " due to " + fatal,   }

  
  

• 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

...

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

...

• 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.