I was working on a complex e-commerce system recently when I hit a wall. Our application kept struggling with audit trails and scaling read operations. Every time we needed to track why an order changed or handle high traffic, things got messy. That’s when I discovered Event Sourcing and CQRS. These patterns transformed how we handle data, and I want to show you how to implement them with Spring Boot, Axon Framework, and MongoDB. Stick with me—this could solve your persistent data management headaches too.

Have you ever wondered what happens if you lose your database and only have the current state saved? Event Sourcing changes that by storing every state change as an event. Instead of just keeping the final order total, we save each item addition, price update, and status change. This gives us a complete history. CQRS takes it further by separating read and write operations. Why handle complex queries with the same logic that processes commands? They serve different purposes.

Let me show you how to set this up. First, create a Spring Boot project and add Axon and MongoDB dependencies. Here’s a snippet from my Maven configuration:

<dependency>
    <groupId>org.axonframework</groupId>
    <artifactId>axon-spring-boot-starter</artifactId>
    <version>4.9.1</version>
</dependency>
<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-data-mongodb</artifactId>
</dependency>

Configure your application to use MongoDB as the event store. I use this in my application.yml:

spring:
  data:
    mongodb:
      uri: mongodb://localhost:27017/eventsourcing
axon:
  eventhandling:
    processors:
      order-projection:
        mode: tracking

Now, think about your domain. For an order system, the aggregate is key. It’s the core entity that handles commands and produces events. Here’s how I define an OrderAggregate:

@Aggregate
public class OrderAggregate {
    @AggregateIdentifier
    private String orderId;
    private OrderStatus status;

    @CommandHandler
    public OrderAggregate(CreateOrderCommand command) {
        apply(new OrderCreatedEvent(command.getOrderId(), command.getItems()));
    }

    @EventSourcingHandler
    public void on(OrderCreatedEvent event) {
        this.orderId = event.getOrderId();
        this.status = OrderStatus.CREATED;
    }
}

Commands trigger actions. For instance, when a user places an order, we send a CreateOrderCommand. Events are the results—OrderCreatedEvent gets stored. Notice how the event handler updates the aggregate state. This might seem extra work, but what if you need to replay events to debug an issue? You have the full timeline.

Storing events in MongoDB is straightforward with Axon. It handles the serialization and storage for you. The beauty is that your event store becomes the source of truth. Queries, however, are handled separately. I create a denormalized view in another MongoDB collection for fast reads. This is where CQRS shines. Write operations don’t slow down read operations.

Here’s a simple event handler that updates the query side:

@Component
public class OrderProjection {
    @EventHandler
    public void on(OrderCreatedEvent event, MongoTemplate mongoTemplate) {
        OrderView view = new OrderView(event.getOrderId(), "CREATED");
        mongoTemplate.save(view, "order_views");
    }
}

When testing, I focus on the command and query sides separately. For commands, I use Axon’s test fixtures to ensure events are correctly applied. For queries, I verify the projection updates. Have you considered how you’d test eventual consistency? It’s crucial in distributed systems.

Performance can be tuned by adjusting batch sizes and using snapshots for aggregates with many events. I set batch size to 100 in configuration to balance load and latency.

One common pitfall is overcomplicating the event model. Start simple—add only necessary fields to events. Another issue is handling failures in event handlers. I use retry mechanisms and dead-letter queues.

In my experience, this setup scales well. We handle thousands of orders daily with minimal issues. The audit capability alone saved us during compliance checks.

What challenges are you facing with your current architecture? Could separating reads and writes help?

I encourage you to try this approach. Start small with a single aggregate. The initial learning curve pays off in maintainability and scalability.

If you found this helpful, please like and share this article. Your comments and experiences would be valuable—let’s discuss how Event Sourcing and CQRS can fit your projects.