Spring is a Java framework for wiring application components through inversion of control and dependency injection. In Kafka applications, it reduces infrastructure code for producers, consumers, and stream processing. section 12.0 section 12.3
Instead of constructing collaborators directly, an application depends on interfaces while the container supplies implementations through constructors, setters, or fields. This separation supports implementation changes and straightforward mock-based testing. section 12.1
A practical Spring Kafka application typically uses these pieces:
- Spring Kafka supplies abstractions around Kafka producers and consumers, including KafkaTemplate and KafkaListener support. section 12.4
- Spring Boot applies convention over configuration, reducing setup through annotations such as @SpringBootApplication and @EnableKafka. section 12.4
- Spring Kafka Streams activates stream processing with @EnableKafkaStreams and can manage application lifecycle infrastructure. section 12.3
- Using only dependency injection preserves more control, while Spring Boot simplifies development but adds framework-managed behavior. section 12.3
Listing 12.1 PaymentProcessor class
public class PaymentProcessor implements Processor { #1
private NetworkClient networkClient; #2
public PaymentProcessor(NetworkClient networkClient) {
this.networkClient = networkClient; #3
}
public void handlePayment() {
payment = networkClient.receive(); #4
// do some work
networkClient.send(processedPayment);
}
}
A payment processor receives and sends payments through a NetworkClient interface, demonstrating constructor injection and separation from network implementation details. section 12.1
Loan approval with Kafka Streams
A loan application topology reads applications, calculates monthly income, debt, and loan-payment ratios, and evaluates approval logic in a Kafka Streams pipeline. A Spring Boot configuration can expose web infrastructure for interactive queries over stateful results. A manual Spring setup can instead construct the topology, create KafkaStreams, start it, observe its state, and close it during shutdown. section 12.3
