Apache Kafka supports topic partitioning natively.
Sometimes it is advantageous to send data to specific partitions — for example, when you want to strictly order message processing (all messages for a particular customer should go to the same partition).
The following example shows how to configure the producer and consumer side:
@SpringBootApplication @EnableBinding(Source.class) public class KafkaPartitionProducerApplication { private static final Random RANDOM = new Random(System.currentTimeMillis()); private static final String[] data = new String[] { "foo1", "bar1", "qux1", "foo2", "bar2", "qux2", "foo3", "bar3", "qux3", "foo4", "bar4", "qux4", }; public static void main(String[] args) { new SpringApplicationBuilder(KafkaPartitionProducerApplication.class) .web(false) .run(args); } @InboundChannelAdapter(channel = Source.OUTPUT, poller = @Poller(fixedRate = "5000")) public Message<?> generate() { String value = data[RANDOM.nextInt(data.length)]; System.out.println("Sending: " + value); return MessageBuilder.withPayload(value) .setHeader("partitionKey", value) .build(); } }
application.yml.
spring: cloud: stream: bindings: output: destination: partitioned.topic producer: partitioned: true partition-key-expression: headers['partitionKey'] partition-count: 12
Important | |
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The topic must be provisioned to have enough partitions to achieve the desired concurrency for all consumer groups.
The above configuration supports up to 12 consumer instances (6 if their |
Note | |
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The preceding configuration uses the default partitioning ( |
Since partitions are natively handled by Kafka, no special configuration is needed on the consumer side. Kafka allocates partitions across the instances.
The following Spring Boot application listens to a Kafka stream and prints (to the console) the partition ID to which each message goes:
@SpringBootApplication @EnableBinding(Sink.class) public class KafkaPartitionConsumerApplication { public static void main(String[] args) { new SpringApplicationBuilder(KafkaPartitionConsumerApplication.class) .web(false) .run(args); } @StreamListener(Sink.INPUT) public void listen(@Payload String in, @Header(KafkaHeaders.RECEIVED_PARTITION_ID) int partition) { System.out.println(in + " received from partition " + partition); } }
application.yml.
spring: cloud: stream: bindings: input: destination: partitioned.topic group: myGroup
You can add instances as needed.
Kafka rebalances the partition allocations.
If the instance count (or instance count * concurrency
) exceeds the number of partitions, some consumers are idle.