I’ve spent countless hours building distributed systems, and one pattern that consistently delivers robust, scalable applications is event sourcing. When combined with Axon Framework and Spring Boot, it transforms how we handle data and business logic. Today, I want to guide you through creating high-performance event-sourced systems that stand the test of time. This approach has helped me build applications that are not only resilient but also provide a complete history of every change.

Event sourcing fundamentally changes how we think about state. Instead of storing only the current state of an entity, we capture every change as an immutable event. Think of it like a bank statement—you don’t just see your current balance; you see every transaction that led to it. This gives us a perfect audit trail and the ability to reconstruct state at any point in history. Have you ever needed to debug why a system reached a particular state? With event sourcing, you can replay events to see exactly what happened.

CQRS complements event sourcing by separating read and write operations. Commands change the system state, while queries retrieve data. This separation allows each side to scale independently and use optimized data models. For instance, your write model might use event stores, while your read model uses a relational database for fast queries.

Let’s start with a basic setup. Here’s how you can configure Axon with Spring Boot:

<dependency>
    <groupId>org.axonframework</groupId>
    <artifactId>axon-spring-boot-starter</artifactId>
    <version>4.8.4</version>
</dependency>

@SpringBootApplication
public class Application {
    public static void main(String[] args) {
        SpringApplication.run(Application.class, args);
    }
}

Aggregates are the heart of your domain model. They process commands and emit events. Here’s a simple bank account aggregate:

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

Commands represent intentions to change state. They should be simple and focused:

public class CreateAccountCommand {
    @TargetAggregateIdentifier
    private final String accountId;
    private final String accountHolder;
    private final BigDecimal initialBalance;
    
    // constructor and getters
}

Events are facts that have occurred. They’re immutable and describe what happened:

public class AccountCreatedEvent {
    private final String accountId;
    private final String accountHolder;
    private final BigDecimal initialBalance;
    
    // constructor and getters
}

How do we handle complex business workflows that span multiple aggregates? This is where sagas come in. Sagas manage long-running processes by reacting to events and sending commands. Imagine handling a money transfer between two accounts—a saga can ensure both debit and credit operations complete successfully.

Performance can become an issue as event streams grow long. Snapshots help by storing the current state at intervals, so we don’t need to replay all events every time. Here’s how you might configure snapshotting:

@Bean
public SnapshotTriggerDefinition snapshotTrigger(Snapshotter snapshotter) {
    return new EventCountSnapshotTriggerDefinition(snapshotter, 50);
}

Event schema evolution is another critical aspect. What happens when you need to change an event’s structure? Upcasting allows you to transform old event versions into new ones without losing data. This ensures your system can handle historical events even as your domain evolves.

Testing event-sourced systems requires a different approach. You need to verify that commands produce the correct events and that events are handled properly. Axon provides test fixtures that make this straightforward:

@SpringBootTest
class BankAccountTest {
    @Autowired
    private TestFixture<BankAccount> testFixture;
    
    @Test
    void testCreateAccount() {
        testFixture.givenNoPriorActivity()
                   .when(new CreateAccountCommand("acc1", "John", BigDecimal.valueOf(1000)))
                   .expectEvents(new AccountCreatedEvent("acc1", "John", BigDecimal.valueOf(1000)));
    }
}

In production, monitoring and deployment strategies are crucial. You’ll want to track event processing rates, command handling times, and saga completion rates. Distributed command handling can help scale your application across multiple nodes.

One common pitfall is overcomplicating event design. Keep events focused on what happened, not why. Another is not planning for schema changes from the start. Always version your events and plan for evolution.

I’ve found that starting simple and gradually introducing complexity works best. Begin with basic event sourcing and CQRS, then add sagas and other advanced patterns as needed. Remember, the goal is to build systems that are maintainable and scalable.

What challenges have you faced with traditional data storage approaches? Could event sourcing provide the solutions you need?

I hope this guide helps you build amazing event-sourced applications. If you found this useful, please like, share, and comment with your experiences. Let’s continue the conversation and learn from each other’s journeys in building robust software systems.