I’ve been thinking a lot about building systems that not only handle complex business logic but also maintain a clear, auditable history of every change. That’s what led me to explore event sourcing with Axon Framework and Spring Boot. This approach fundamentally changes how we think about application state, and I want to show you how to implement it effectively.

Why does this matter? Because in today’s world, understanding not just what happened but why it happened can make all the difference. Have you ever needed to trace back exactly how a system arrived at its current state?

Let me walk you through setting this up. First, add Axon Framework to your Spring Boot project. The dependencies are straightforward – you’ll need the Axon Spring Boot starter and potentially Axon Server connector if you’re not using an embedded event store.

Here’s how your Maven configuration might look:

<dependencies>
    <dependency>
        <groupId>org.axonframework</groupId>
        <artifactId>axon-spring-boot-starter</artifactId>
        <version>4.9.0</version>
    </dependency>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-data-jpa</artifactId>
    </dependency>
</dependencies>

Now, let’s talk about the core building blocks. Commands represent your intent to change something in the system. Think of them as instructions rather than questions. Here’s a simple command implementation:

public class CreateOrderCommand {
    @TargetAggregateIdentifier
    private String orderId;
    private String productId;
    private int quantity;
    
    // Constructor and getters
}

But what happens when this command gets processed? That’s where events come in. Events are facts – things that have already happened and cannot be changed. They’re the foundation of your event-sourced system.

public class OrderCreatedEvent {
    private String orderId;
    private String productId;
    private int quantity;
    private Instant createdAt;
    
    // Constructor and getters
}

The aggregate is where the magic happens. It’s the guardian of your business rules and the source of your events. How does it maintain consistency while producing these events?

@Aggregate
public class OrderAggregate {
    @AggregateIdentifier
    private String orderId;
    
    @CommandHandler
    public OrderAggregate(CreateOrderCommand command) {
        apply(new OrderCreatedEvent(
            command.getOrderId(),
            command.getProductId(),
            command.getQuantity(),
            Instant.now()
        ));
    }
    
    @EventSourcingHandler
    public void on(OrderCreatedEvent event) {
        this.orderId = event.getOrderId();
        // Initialize other state
    }
}

Now, here’s something interesting: how do you make this data useful for queries? That’s where projections come in. They listen to events and build read-optimized views of your data.

@Component
public class OrderProjection {
    private final JdbcTemplate jdbcTemplate;
    
    @EventHandler
    public void on(OrderCreatedEvent event) {
        jdbcTemplate.update(
            "INSERT INTO orders (id, product_id, quantity) VALUES (?, ?, ?)",
            event.getOrderId(), event.getProductId(), event.getQuantity()
        );
    }
}

The beauty of this architecture is its flexibility. You can add new projections without touching your core domain logic. Need a different view of the same data? Just create another projection that listens to the same events.

But what about complex business processes that span multiple aggregates? That’s where sagas come into play. They coordinate long-running transactions by listening to events and sending commands.

Have you considered how this approach changes your testing strategy? Since events are your source of truth, you can test your system by replaying events and verifying the outcomes. This makes your tests more meaningful and your system more reliable.

The configuration is surprisingly simple with Spring Boot’s auto-configuration. Most of the wiring happens automatically, though you might want to customize certain aspects like event storage or serialization.

One thing I’ve learned through implementation: start simple. Don’t try to event source everything at once. Identify the parts of your system that truly benefit from this pattern – usually those requiring strong audit trails or temporal queries.

Remember that event sourcing isn’t a silver bullet. It adds complexity, and you need to consider things like event versioning and schema evolution from the start. But when applied to the right problems, it gives you capabilities that traditional CRUD systems simply can’t match.

What questions do you have about implementing this in your own projects? I’d love to hear about your experiences and challenges. If you found this useful, please share it with others who might benefit, and let me know in the comments what other aspects of event sourcing you’d like me to cover.