I’ve been thinking a lot about how modern applications handle complexity while remaining scalable and resilient. The shift from traditional request-response models to event-driven architectures isn’t just a trend—it’s becoming essential for systems that need to handle high throughput, maintain data consistency across services, and provide real-time capabilities. This is why I want to share my experience with building event-driven microservices using Spring Cloud Stream, Apache Kafka, and distributed tracing.

When you start building with events, you quickly realize how much cleaner service boundaries become. Instead of services calling each other directly, they publish events that other services can react to. This loose coupling means you can update or scale services independently. But how do you ensure these events are processed reliably and in order?

Let me show you a basic setup using Spring Cloud Stream with Kafka. First, you’ll need to define your binding interfaces:

public interface OrderStreams {
    String OUTPUT = "orders-out";
    String INPUT = "orders-in";
    
    @Output(OUTPUT)
    MessageChannel outboundOrders();
    
    @Input(INPUT)
    SubscribableChannel inboundOrders();
}

Then in your application configuration:

spring:
  cloud:
    stream:
      bindings:
        orders-out:
          destination: orders
          content-type: application/json
        orders-in:
          destination: orders
          group: order-processor
          content-type: application/json
      kafka:
        binder:
          brokers: localhost:9092

Now, what happens when things go wrong? Error handling becomes crucial in distributed systems. Have you considered how you’d handle failed message processing?

Dead letter queues are your friend here. When a message fails processing after retries, it goes to a special topic where you can inspect and handle it separately:

spring:
  cloud:
    stream:
      kafka:
        bindings:
          orders-in:
            consumer:
              enableDlq: true
              dlqName: orders-dlq
              autoCommitOnError: true

Distributed tracing gives you visibility across service boundaries. With Spring Cloud Sleuth and Zipkin, you can trace a request as it moves through multiple services and message brokers:

@Slf4j
@Component
public class OrderService {
    private final Tracer tracer;
    
    public Order processOrder(Order order) {
        Span span = tracer.nextSpan().name("process-order").start();
        try (Tracer.SpanInScope ws = tracer.withSpan(span)) {
            log.info("Processing order {}", order.getId());
            // Business logic here
            return order;
        } finally {
            span.end();
        }
    }
}

Schema evolution is another critical aspect. How do you ensure that changes to your event schemas don’t break existing consumers? The Schema Registry helps manage compatible evolution:

@Bean
public SchemaRegistryClient schemaRegistryClient() {
    ConfluentSchemaRegistryClient client = new ConfluentSchemaRegistryClient();
    client.setEndpoint("http://localhost:8081");
    return client;
}

When deploying to production, containerization and orchestration become essential. Docker Compose helps during development, but Kubernetes provides the production-grade orchestration you need:

# k8s-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: order-service
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: order-service
        image: order-service:latest
        env:
        - name: SPRING_PROFILES_ACTIVE
          value: "kubernetes"
        - name: KAFKA_BROKERS
          value: "kafka-cluster:9092"

Monitoring and observability complete the picture. Prometheus metrics and Grafana dashboards help you understand system behavior under load:

management:
  endpoints:
    web:
      exposure:
        include: health, info, metrics, prometheus
  metrics:
    export:
      prometheus:
        enabled: true

Building event-driven systems requires careful consideration of many moving parts. From ensuring message ordering and handling failures to maintaining schema compatibility and monitoring performance, each aspect contributes to a robust architecture. The payoff is worth it—systems that scale gracefully, handle failures elegantly, and provide real-time capabilities.

What challenges have you faced when building distributed systems? I’d love to hear your experiences and solutions. If you found this helpful, please share it with others who might benefit, and feel free to leave your thoughts in the comments below.