Spring Kafka¶

Spring Kafka provides declarative, annotation-based Kafka integration with KafkaTemplate for producing and @KafkaListener for consuming, plus Spring Cloud Stream for middleware-agnostic event-driven microservices.

Key Facts¶

KafkaTemplate wraps KafkaProducer with Spring conventions
@KafkaListener auto-manages consumer lifecycle, deserialization, offset commits
@Component annotation enables automatic bean discovery and dependency injection
Spring Boot auto-configures Kafka with application.properties or application.yml
Manual acknowledge via Acknowledgment.acknowledge() for at-least-once processing
Spring Cloud Stream: middleware-agnostic abstraction with binders for Kafka, RabbitMQ, Kinesis, etc.
Spring Cloud Data Flow: orchestration platform for composing streaming/batch pipelines

Patterns¶

Producer¶

@Component
public class OrderProducer {
    @Autowired
    private KafkaTemplate<String, String> kafkaTemplate;

    public void send(String orderId, String orderJson) {
        kafkaTemplate.send("orders", orderId, orderJson);
    }
}

Consumer¶

@Component
public class OrderConsumer {
    @KafkaListener(topics = "orders", groupId = "order-service")
    public void consume(String message, Acknowledgment ack) {
        processOrder(message);
        ack.acknowledge();  // Manual commit
    }
}

Configuration¶

spring:
  kafka:
    bootstrap-servers: localhost:9092
    producer:
      key-serializer: org.apache.kafka.common.serialization.StringSerializer
      value-serializer: org.apache.kafka.common.serialization.StringSerializer
    consumer:
      group-id: my-group
      auto-offset-reset: earliest
      enable-auto-commit: false
      key-deserializer: org.apache.kafka.common.serialization.StringDeserializer
      value-deserializer: org.apache.kafka.common.serialization.StringDeserializer

Spring Cloud Stream (Functional)¶

@Bean
public Function<String, String> uppercase() {
    return value -> value.toUpperCase();
}
// Auto-binds to input/output channels; no explicit Kafka API calls

Gotchas¶

Default settings are adequate for many use cases - don't over-configure
Spring approach is more declarative than Alpakka - no manual stream management, Spring handles lifecycle
Less control over topology - for complex stream processing, use kafka streams instead