Lately, I’ve been wrestling with how to build systems that remain reliable as they grow. When a recent project demanded full audit trails and resilience against failures, event sourcing with Spring Boot and Kafka emerged as the clear solution. This approach stores every state change as an immutable event, creating a permanent record of your system’s evolution. Let me show you how to implement this effectively.

Why choose event sourcing? Traditional systems store only the latest state, losing crucial historical context. Imagine reconstructing exactly how an order moved from “created” to “canceled” six months ago. With event sourcing, you replay the event sequence. Ready to see how Spring Boot simplifies this?

First, ensure you have Java 17+, Spring Boot 3.x, PostgreSQL, and Kafka (Docker simplifies setup). Our Maven dependencies include Spring Web, Data JPA, and Kafka. Notice the Testcontainers for integrated testing – crucial for event-driven systems.

<!-- Core Dependencies -->
<dependency>
    <groupId>org.springframework.kafka</groupId>
    <artifactId>spring-kafka</artifactId>
</dependency>
<dependency>
    <groupId>org.postgresql</groupId>
    <artifactId>postgresql</artifactId>
</dependency>

In our domain model, aggregates handle business logic and emit events. Consider an OrderAggregate that processes commands like “create order” or “cancel order”. Each command generates events stored in PostgreSQL. How do we ensure these events remain immutable? Let’s look at the event store implementation.

@Entity
public class EventStore {
    @Id
    @GeneratedValue(strategy = IDENTITY)
    private Long id;
    private String aggregateId;
    private String eventType;
    @Lob
    private String eventData;
    private Instant timestamp;
}

Kafka publishes these events for downstream consumers. Spring Kafka’s KafkaTemplate handles this efficiently. Notice how we serialize events as JSON:

@Autowired
private KafkaTemplate<String, DomainEvent> kafkaTemplate;

public void publish(DomainEvent event) {
    kafkaTemplate.send("order-events", event);
}

But what happens when event handlers need to rebuild state? Projections transform event streams into queryable views. For an OrderProjection, we update a materialized view on each OrderCreatedEvent or OrderCancelledEvent. This separation allows scaling reads independently from writes. Ever wondered how to handle sudden spikes in order volume?

@KafkaListener(topics = "order-events")
public void project(OrderCreatedEvent event) {
    OrderProjection projection = new OrderProjection(
        event.getOrderId(), 
        event.getAmount(), 
        "CREATED"
    );
    projectionRepository.save(projection);
}

As event logs grow, replaying thousands of events becomes slow. Snapshots solve this by periodically saving aggregate state. We configure snapshot frequency in application.yml:

event-sourcing:
  snapshot:
    frequency: 10 # Every 10 events

Testing requires special attention. We use Testcontainers for real Kafka and PostgreSQL integration tests. This verifies event publishing, storage, and projection consistency:

@Testcontainers
class OrderIntegrationTest {
    @Container
    static PostgreSQLContainer<?> postgres = new PostgreSQLContainer<>("postgres:15");
    
    @Container
    static KafkaContainer kafka = new KafkaContainer(DockerImageName.parse("confluentinc/cp-kafka:7.0.0"));
    
    // Test methods verify end-to-end flow
}

Common pitfalls include event versioning issues and inadequate error handling. Always version events from day one using a version field. For poison pills, implement dead-letter queues in Kafka. What if a new service joins later and needs historical events? Our event store provides the full history.

For alternatives, consider Axon Framework or dedicated event stores like EventStoreDB. But Spring Boot with Kafka offers flexibility without new learning curves. Performance optimizations like consumer batching and snapshotting keep latency under 50ms even at 10K events/second.

This approach transformed how we build resilient systems. The complete audit trail alone justified the effort. Have you considered how event sourcing could prevent data disputes in your projects?

If this guide clarified event sourcing for you, share it with your team! Drop a comment about your implementation challenges – let’s solve them together.