I’ve always been fascinated by how complex business systems evolve over time. Traditional approaches often lose the rich history of changes, making it hard to understand why something happened or to recover from errors. That’s why I started exploring event sourcing with Axon Framework and Spring Boot. This combination offers a powerful way to build resilient, scalable applications that remember every step of their journey.

Event sourcing means storing all changes to an application’s state as a sequence of immutable events. Instead of just keeping the current status, we save every action that led there. When combined with CQRS (Command Query Responsibility Segregation), we separate the write operations from reads. This allows each side to scale independently and optimize for its specific needs.

Have you ever wondered how financial systems maintain perfect audit trails or how e-commerce platforms handle complex order workflows? Event sourcing provides the answer. Let me show you how to implement this using Axon Framework.

First, we need to set up our Spring Boot project. Here’s a basic Maven configuration to get started:

<dependencies>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-web</artifactId>
    </dependency>
    <dependency>
        <groupId>org.axonframework</groupId>
        <artifactId>axon-spring-boot-starter</artifactId>
        <version>4.8.5</version>
    </dependency>
</dependencies>

The core of event sourcing lies in defining our events. These represent facts that have occurred in the system. For an order management system, we might have events like order creation, confirmation, or cancellation. Each event carries the data needed to understand what changed.

public class OrderCreatedEvent {
    private final String orderId;
    private final String customerId;
    private final BigDecimal amount;
    
    public OrderCreatedEvent(String orderId, String customerId, BigDecimal amount) {
        this.orderId = orderId;
        this.customerId = customerId;
        this.amount = amount;
    }
    // Getters omitted for brevity
}

Commands trigger these events. They represent intentions to change the system state. In Axon, we use command handlers to process these requests and emit events.

What happens when you need to handle complex business processes that span multiple aggregates? That’s where sagas come in. They coordinate long-running transactions across different parts of your system.

@Saga
public class OrderSaga {
    @StartSaga
    @SagaEventHandler(associationProperty = "orderId")
    public void handle(OrderCreatedEvent event) {
        // Start payment processing
        commandGateway.send(new ProcessPaymentCommand(event.getOrderId()));
    }
}

Building the query side involves creating projections that update read models based on events. This separation allows us to optimize queries without affecting the command side performance.

@Component
public class OrderProjection {
    @EventHandler
    public void on(OrderCreatedEvent event, @Timestamp Instant timestamp) {
        // Update read model in database
        orderRepository.save(new OrderView(event.getOrderId(), timestamp));
    }
}

Testing event-sourced systems requires a different approach. We need to verify that commands produce the correct events and that projections update properly. Axon provides excellent testing support for this.

@SpringBootTest
class OrderAggregateTest {
    @Autowired
    private CommandGateway commandGateway;
    
    @Test
    void shouldCreateOrder() {
        commandGateway.send(new CreateOrderCommand("order1", "customer1"));
        // Verify events were emitted
    }
}

One common challenge is event versioning. How do you handle changes to event structures over time? Axon supports upcasting to transform old events into new formats, ensuring backward compatibility.

Performance optimization becomes crucial as event streams grow long. Snapshotting allows us to periodically save the current state, reducing the number of events needed to rebuild an aggregate.

Have you considered how event sourcing affects database choices? While relational databases work well, event stores like Axon Server provide specialized features for handling large event streams efficiently.

Error handling in event-driven systems requires careful planning. Dead letter queues and retry mechanisms help ensure no events are lost during processing.

I’ve found that event sourcing particularly shines in domains requiring compliance, audit trails, or complex business logic. The ability to replay events and see exactly how state evolved has saved me countless debugging hours.

What if you need to integrate with external systems? Event sourcing makes this straightforward through event publishing and message brokers.

Remember that event sourcing isn’t a silver bullet. It adds complexity that might not be justified for simple CRUD applications. But for systems where history matters, it’s transformative.

As we wrap up, I encourage you to experiment with these concepts in your projects. The initial learning curve pays off in maintainability and insight into your system’s behavior.

If this guide helped you understand event sourcing better, please share it with colleagues who might benefit. I’d love to hear about your experiences in the comments below – what challenges have you faced with distributed systems? Your insights could help others on their journey.