Lately, I’ve been grappling with how to build systems that maintain an accurate historical record while scaling efficiently. Traditional CRUD approaches often fall short, especially when we need to track every state change or reconstruct past states. This challenge led me to explore Event Sourcing with Spring Boot and Apache Kafka – a powerful combination that fundamentally changes how we handle data.

Why Event Sourcing?

Instead of overwriting data, we capture every change as an immutable event. Think of it like a financial ledger: you don’t edit past transactions; you add new ones. This gives us a complete audit trail and the ability to rebuild state at any point. Have you ever needed to know exactly what changed in your system last Tuesday at 3 PM? That’s where this shines.

Getting Started

First, we set up our environment. Here’s the Maven configuration I used:

<!-- pom.xml -->
<dependencies>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-web</artifactId>
    </dependency>
    <dependency>
        <groupId>org.springframework.kafka</groupId>
        <artifactId>spring-kafka</artifactId>
    </dependency>
    <dependency>
        <groupId>com.h2database</groupId>
        <artifactId>h2</artifactId>
        <scope>runtime</scope>
    </dependency>
</dependencies>

And the Kafka configuration:

# application.yml
spring:
  kafka:
    bootstrap-servers: localhost:9092
    producer:
      value-serializer: org.springframework.kafka.support.serializer.JsonSerializer
    consumer:
      group-id: event-group
      value-deserializer: org.springframework.kafka.support.serializer.JsonDeserializer

Core Components

Events are the foundation. Let’s define a base interface:

public interface DomainEvent {
    UUID eventId();
    String aggregateId();
    Instant timestamp();
}

Concrete events extend this. For a banking app:

public class FundsDepositedEvent implements DomainEvent {
    private UUID eventId;
    private String accountId;
    private BigDecimal amount;
    private Instant timestamp;
    
    // Constructor, getters, and interface implementation
}

Event Storage and Retrieval

We store events in Kafka topics. Spring’s KafkaTemplate simplifies publishing:

@Service
public class EventPublisher {
    @Autowired
    private KafkaTemplate<String, DomainEvent> kafkaTemplate;
    
    public void publish(String topic, DomainEvent event) {
        kafkaTemplate.send(topic, event.aggregateId(), event);
    }
}

For consumption, we use @KafkaListener:

@Service
public class EventConsumer {
    @KafkaListener(topics = "account-events")
    public void handleEvent(DomainEvent event) {
        // Rebuild state or update read models
    }
}

Handling Evolution

Events change over time. How do we maintain compatibility? I use versioned schemas:

public class FundsDepositedEventV2 implements DomainEvent {
    private UUID eventId;
    private String accountId;
    private BigDecimal amount;
    private String currency;  // New field
    private Instant timestamp;
}

When replaying events, I convert old versions to new ones using a transformation layer. This ensures backward compatibility without breaking consumers.

Performance Boosters

For aggregates with thousands of events, snapshots prevent replay bottlenecks. Here’s a snapshot strategy:

public class AccountSnapshot {
    private String accountId;
    private BigDecimal balance;
    private Long lastEventSequence;
}

We save this state periodically and load it before applying recent events.

Testing and Resilience

Testing event-driven systems requires special care. I use Testcontainers for integration tests:

@Testcontainers
class EventSourcingTest {
    @Container
    static KafkaContainer kafka = new KafkaContainer(DockerImageName.parse("confluentinc/cp-kafka:7.0.0"));
    
    // Test methods validate event flow
}

For production, I implement dead-letter queues and idempotent consumers. This snippet handles duplicate events:

@KafkaListener(topics = "account-events")
public void processEvent(@Header(KafkaHeaders.RECEIVED_KEY) String key, 
                         DomainEvent event) {
    if (eventStore.exists(event.eventId())) return; // Deduplication
    // Process event
}

Common Pitfalls

Watch for these:

1. Event ordering: Use Kafka partitioning keys to guarantee order per aggregate
2. Schema drift: Validate events with JSON Schema registries
3. Performance: Snapshot frequently for large aggregates
4. Idempotency: Always check for duplicate events

Have you considered how you’d rebuild state if your database vanished tomorrow? With event sourcing, you’d simply replay the event log – a game-changer for resilience.

Final Thoughts

This approach has transformed how I design systems requiring auditability and temporal queries. The initial complexity pays off in traceability and flexibility. Give it a try in your next Spring Boot project!

If you found this useful, please share it with your network. I’d love to hear about your experiences with event sourcing – leave a comment below!