Recently, I found myself redesigning a financial transaction system that needed absolute accuracy, complete audit trails, and the ability to scale under heavy load. Traditional CRUD approaches felt limiting—how could I guarantee data consistency while enabling real-time analytics and seamless integration? This led me to explore event sourcing, a pattern where every state change is stored as an immutable event. Combining Spring Boot, Axon Framework, and Apache Kafka provided the robust foundation I needed. Let me walk you through how you can build such systems too.

Event sourcing captures every change to your application state as a sequence of events. Instead of overwriting data, you accumulate history. Think of it like a bank statement: rather than just knowing your current balance, you see every deposit and withdrawal. This approach brings transparency, replayability, and resilience to your systems. Have you ever wondered what really happened in your application six months ago? With event sourcing, you can know precisely.

Axon Framework simplifies implementing event sourcing and CQRS in Java. It handles the complexity of managing aggregates, events, and commands. Here’s a basic example of an aggregate in Axon:

@Aggregate
public class BankAccount {
    @AggregateIdentifier
    private String accountId;
    private BigDecimal balance;

    @CommandHandler
    public BankAccount(CreateAccountCommand command) {
        apply(new AccountCreatedEvent(command.getAccountId(), command.getInitialBalance()));
    }

    @EventSourcingHandler
    public void on(AccountCreatedEvent event) {
        this.accountId = event.getAccountId();
        this.balance = event.getInitialBalance();
    }

    protected BankAccount() {}
}

Notice how the aggregate uses events to capture state changes. The @EventSourcingHandler rebuilds the current state by replaying events. This might seem counterintuitive at first—why store every change? But consider the benefits: complete audit trails, temporal querying, and the ability to rebuild state from scratch.

Apache Kafka acts as the event store and message broker in this architecture. It ensures events are persisted and distributed reliably. Configuring Kafka with Spring Boot is straightforward:

spring:
  kafka:
    bootstrap-servers: localhost:9092
    producer:
      key-serializer: org.apache.kafka.common.serialization.StringSerializer
      value-serializer: org.springframework.kafka.support.serializer.JsonSerializer

Kafka’s durability and scalability make it ideal for event-driven systems. How would your system behave if one component failed? With Kafka, events are stored and can be replayed, ensuring no data loss.

CQRS separates read and write operations, optimizing each for its purpose. Commands modify state, while queries retrieve data. This separation allows you to scale each side independently and use different data models. For instance, your write model might use event sourcing, while your read model uses a SQL database optimized for queries:

@Entity
@Table(name = "account_summary")
public class AccountSummary {
    @Id
    private String accountId;
    private BigDecimal currentBalance;
    private LocalDateTime lastUpdated;

    // Updated by event handlers
    public void updateBalance(BigDecimal newBalance) {
        this.currentBalance = newBalance;
        this.lastUpdated = LocalDateTime.now();
    }
}

Projections update the read model as events occur. This eventual consistency model might feel unfamiliar, but it enables high performance and scalability. How do you handle complex business processes that span multiple aggregates? Axon’s sagas come to the rescue:

@Saga
public class TransferSaga {
    @StartSaga
    @SagaEventHandler(associationProperty = "transferId")
    public void handle(TransferStartedEvent event) {
        // Initiate the transfer process
    }

    @EndSaga
    @SagaEventHandler(associationProperty = "transferId")
    public void handle(TransferCompletedEvent event) {
        // Mark the saga as complete
    }
}

Testing event-sourced systems requires a different approach. Axon provides test fixtures that simplify testing aggregates and sagas:

@Test
void testMoneyTransfer() {
    fixture.given(new AccountCreatedEvent("acc1", new BigDecimal("1000")))
           .when(new TransferMoneyCommand("acc1", "acc2", new BigDecimal("100")))
           .expectSuccessfulHandlerExecution()
           .expectEvents(new MoneyTransferredEvent("acc1", "acc2", new BigDecimal("100")));
}

Performance optimization often involves tuning event handlers, using snapshots for aggregates with long event histories, and partitioning Kafka topics. Monitoring event processing metrics and consumer lag becomes critical in production.

Building with event sourcing requires a shift in mindset. You’re designing for change and transparency. The initial complexity pays dividends in maintainability and scalability. What challenges have you faced with traditional data persistence that event sourcing might solve?

I encourage you to experiment with these patterns in your next project. The combination of Spring Boot, Axon, and Kafka provides a powerful toolkit for building resilient, scalable systems. If you found this helpful, please like, share, or comment with your experiences—I’d love to hear how you approach event-driven architecture.