I’ve been building distributed systems for over a decade, and I keep returning to event sourcing as one of the most powerful patterns for creating resilient, scalable applications. Today, I want to share my practical experience implementing event sourcing with Axon Framework and Spring Boot. This approach has fundamentally changed how I think about data persistence and system design.

Have you ever considered what happens when you need to understand why your application reached a particular state? Traditional CRUD systems often leave us guessing about the journey, not just the destination. Event sourcing solves this by storing every state change as an immutable event. These events become the single source of truth for your system.

Let me show you how to get started. First, we’ll set up a Spring Boot project with Axon dependencies. Here’s the essential configuration:

<dependencies>
    <dependency>
        <groupId>org.axonframework</groupId>
        <artifactId>axon-spring-boot-starter</artifactId>
        <version>4.8.0</version>
    </dependency>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-data-jpa</artifactId>
    </dependency>
</dependencies>

The core of event sourcing lies in separating commands from queries. Commands represent intentions to change state, while events represent facts that have already occurred. This separation, known as CQRS, allows your system to scale reads and writes independently.

How do we model business entities in this paradigm? We use aggregates. An aggregate is a cluster of domain objects treated as a single unit. Here’s a bank account aggregate example:

@Aggregate
public class BankAccount {
    @AggregateIdentifier
    private String accountId;
    private BigDecimal balance;
    private String accountHolder;
    
    @CommandHandler
    public BankAccount(CreateBankAccountCommand command) {
        apply(new BankAccountCreatedEvent(
            command.getAccountId(),
            command.getAccountHolder(),
            command.getInitialBalance()
        ));
    }
    
    @EventSourcingHandler
    public void on(BankAccountCreatedEvent event) {
        this.accountId = event.getAccountId();
        this.balance = event.getInitialBalance();
        this.accountHolder = event.getAccountHolder();
    }
}

Notice how the command handler validates business rules and emits events, while event sourcing handlers update the aggregate state. This separation ensures that your business logic remains clean and testable.

What about handling complex workflows that span multiple aggregates? That’s where sagas come in. Sagas manage long-running business processes by listening to events and issuing new commands. They help maintain consistency across different parts of your system.

Here’s a simple saga that handles money transfers:

@Saga
public class MoneyTransferSaga {
    @StartSaga
    @SagaEventHandler(associationProperty = "transferId")
    public void handle(TransferInitiatedEvent event) {
        // Issue commands to debit and credit accounts
    }
    
    @EndSaga
    @SagaEventHandler(associationProperty = "transferId")
    public void handle(TransferCompletedEvent event) {
        // Clean up saga state
    }
}

Testing event-sourced systems requires a different approach. Axon provides excellent testing support. You can verify that commands produce the expected events and that your aggregates behave correctly under various scenarios.

Have you thought about how your event schema might evolve over time? Event upcasting allows you to handle changes in event structure without breaking existing event streams. This is crucial for maintaining system integrity as your domain evolves.

Performance optimization becomes interesting with event sourcing. Since events are immutable, you can cache projections aggressively. The write model focuses on consistency, while read models can be optimized for query performance.

In production systems, I’ve found that event sourcing provides invaluable audit trails and the ability to reconstruct state at any point in time. It does require careful planning around event versioning and storage strategies.

One common question I hear is whether event sourcing adds unnecessary complexity. In my experience, the initial investment pays dividends in maintainability and business insight. The complete history of changes becomes a first-class citizen in your system.

As we implement these patterns, remember that event sourcing works best when combined with domain-driven design. Understanding your business domain deeply helps identify the right events and aggregates.

I hope this guide helps you start your event sourcing journey. What challenges have you faced with traditional data persistence? Share your experiences in the comments below. If you found this useful, please like and share this article with others who might benefit from it. Let’s continue the conversation about building better software systems together.