I’ve been thinking about how we manage data in modern applications. Traditional approaches often fall short when we need full audit trails, temporal querying, or resilience against failures. That’s why I decided to explore event sourcing - a method that captures every state change as immutable events. This approach provides complete history tracking and enables powerful capabilities like time travel debugging. Let me show you how to implement this properly using Spring Boot, Axon Framework, and MongoDB.

Why choose this stack? Spring Boot simplifies development, Axon provides robust event sourcing tools, and MongoDB offers flexible document storage. Together, they create a production-ready foundation. Have you considered how your applications could benefit from an immutable event log?

Let’s start with dependencies. Our pom.xml includes Axon’s Spring Boot starter and MongoDB extensions:

<dependencies>
    <dependency>
        <groupId>org.axonframework</groupId>
        <artifactId>axon-spring-boot-starter</artifactId>
        <version>4.8.0</version>
    </dependency>
    <dependency>
        <groupId>org.axonframework.extensions.mongo</groupId>
        <artifactId>axon-mongo</artifactId>
        <version>4.6.0</version>
    </dependency>
    <!-- Other Spring Boot dependencies -->
</dependencies>

Commands represent actions in our system, while events record what happened. For our order system:

public class CreateOrderCommand {
    @TargetAggregateIdentifier
    private final String orderId;
    private final String customerId;
    // Additional fields and validation
}

public class OrderCreatedEvent {
    private final String orderId;
    private final LocalDateTime createdDate;
    // Other relevant data
}

The aggregate root processes commands and emits events. Notice how we validate before applying state changes:

@Aggregate
public class OrderAggregate {
    @AggregateIdentifier
    private String orderId;
    
    @CommandHandler
    public OrderAggregate(CreateOrderCommand command) {
        if (command.getItems().isEmpty()) {
            throw new IllegalArgumentException("Order requires items");
        }
        apply(new OrderCreatedEvent(command.getOrderId(), ...));
    }
    
    @EventSourcingHandler
    public void on(OrderCreatedEvent event) {
        this.orderId = event.getOrderId();
        // Set other state fields
    }
}

What happens when business rules change? We handle schema evolution carefully. Axon’s @Revision annotation helps:

@Revision("2.0")
public class OrderCreatedEventV2 extends OrderCreatedEvent {
    private final String promotionCode;
    // Additional fields
}

For MongoDB storage, we configure Axon’s event store:

@Configuration
public class AxonConfig {
    @Bean
    public EventStorageEngine storageEngine(MongoClient client) {
        return MongoEventStorageEngine.builder()
            .mongoTemplate(DefaultMongoTemplate.builder()
                .mongoDatabase(client)
                .build())
            .build();
    }
}

Testing is critical. Axon’s test support makes this straightforward:

@SpringBootTest
public class OrderTest {
    @Autowired
    private CommandGateway commandGateway;
    
    @Test
    public void testOrderCreation() {
        String orderId = "order-123";
        commandGateway.send(new CreateOrderCommand(orderId, ...));
        // Verify events were emitted
    }
}

Performance matters in production. We optimize by:

1. Configuring snapshotting to reduce event replay

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

2. Using separate databases for reads and writes

3. Implementing caching in query handlers

When errors occur, Axon’s replay mechanisms let us rebuild state. How would you handle a corrupted aggregate? We initiate replays through the command bus:

eventProcessingConfiguration
    .eventProcessorByProcessingGroup("orders")
    .ifPresent(ep -> ep.shutDown());
eventProcessingConfiguration
    .eventProcessorByProcessingGroup("orders")
    .ifPresent(StartingEventProcessor::start);

This approach gives us resilience. Failed commands can be retried, and events reprocessed without data loss. What reliability improvements could this bring to your systems?

I’ve found event sourcing transforms how we think about data. It provides audit capabilities out of the box, enables temporal querying, and simplifies debugging. The initial learning curve pays off in long-term maintainability. For our order system, we now have a complete history of every state change.

Try implementing this pattern in your next Spring Boot project. Start with a bounded context where auditability matters. You’ll gain powerful insights into your system’s behavior. What challenges have you faced with traditional data storage that event sourcing could solve?

If you found this guide helpful, please like and share it. Have questions or experiences with event sourcing? Let’s discuss in the comments - I’d love to hear your thoughts and learn from your implementations.