I’ve been thinking a lot about how modern applications handle complex state changes while maintaining auditability and scalability. Recently, I worked on a financial system where tracking every transaction’s history wasn’t just a feature—it was a regulatory requirement. That’s when event sourcing transformed from an architectural pattern into a practical necessity for me. What if I told you there’s a way to build systems where every state change is permanently recorded, and you can reconstruct any past state with perfect accuracy?

Event sourcing stores state changes as immutable events rather than updating a current state snapshot. Imagine your application’s history preserved like chapters in a book, where you can flip back to any page and see exactly what happened. Have you ever faced a situation where you needed to understand why a particular decision was made months ago?

Let me show you how to implement this using Spring Boot, Apache Kafka, and a custom event store. We’ll start with the foundation—defining our events. Here’s a basic event structure:

public abstract class DomainEvent {
    private final UUID eventId;
    private final Instant occurredOn;
    private final String aggregateId;
    
    protected DomainEvent(String aggregateId) {
        this.eventId = UUID.randomUUID();
        this.occurredOn = Instant.now();
        this.aggregateId = aggregateId;
    }
}

Now, consider an e-commerce system. When a user places an order, we might have events like OrderCreated, OrderCancelled, or PaymentProcessed. Each event captures what happened, not what changed. How would you handle events that need to evolve over time without breaking existing systems?

Here’s a practical event implementation:

public class OrderCreated extends DomainEvent {
    private final String orderId;
    private final BigDecimal amount;
    private final String customerId;
    
    public OrderCreated(String orderId, BigDecimal amount, String customerId) {
        super(orderId);
        this.orderId = orderId;
        this.amount = amount;
        this.customerId = customerId;
    }
}

To store these events, we use PostgreSQL with a simple table structure. Why PostgreSQL? Its transactional consistency and JSON support make it ideal for event storage.

CREATE TABLE event_store (
    id BIGSERIAL PRIMARY KEY,
    aggregate_id VARCHAR(255) NOT NULL,
    event_type VARCHAR(255) NOT NULL,
    event_data JSONB NOT NULL,
    version INTEGER NOT NULL,
    occurred_on TIMESTAMP NOT NULL
);

Spring Boot makes event publishing straightforward. But what happens when you need to scale across multiple services? That’s where Apache Kafka shines. It ensures events are distributed reliably. Here’s how to configure a Kafka producer:

@Configuration
public class KafkaConfig {
    @Bean
    public ProducerFactory<String, Object> producerFactory() {
        Map<String, Object> config = new HashMap<>();
        config.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
        config.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
        config.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, JsonSerializer.class);
        return new DefaultKafkaProducerFactory<>(config);
    }
}

When building aggregates—the core entities that handle commands and produce events—we need to ensure they reconstruct state correctly. Have you considered how to rebuild an object’s state from hundreds of historical events efficiently?

public class OrderAggregate {
    private String orderId;
    private OrderStatus status;
    private List<DomainEvent> changes = new ArrayList<>();
    
    public OrderAggregate(String orderId, List<DomainEvent> history) {
        this.orderId = orderId;
        for (DomainEvent event : history) {
            apply(event);
        }
    }
    
    public void createOrder(BigDecimal amount, String customerId) {
        if (this.status != null) {
            throw new IllegalStateException("Order already exists");
        }
        apply(new OrderCreated(orderId, amount, customerId));
    }
    
    private void apply(DomainEvent event) {
        this.changes.add(event);
        // Update internal state based on event type
    }
}

CQRS separates read and write models, allowing optimized queries without affecting command performance. For instance, while writes go through the event-sourced aggregate, reads can use materialized views in a separate database. How would you handle eventual consistency between write and read models?

Snapshotting improves performance by periodically saving the current state. Instead of replaying thousands of events, we load the latest snapshot and apply only recent events. Here’s a snapshot entity:

@Entity
public class OrderSnapshot {
    @Id
    private String orderId;
    private String stateJson;
    private Long version;
    private Instant createdOn;
}

Monitoring is crucial. We use Spring Boot Actuator with Micrometer to track event processing metrics. What metrics would you prioritize in a production event-sourcing system?

management:
  endpoints:
    web:
      exposure:
        include: health,metrics,events
  metrics:
    distribution:
      percentiles-histogram:
        http.server.requests: true

Event versioning requires careful planning. We use schema evolution techniques, like adding optional fields, to maintain backward compatibility. When an old event is read, we upgrade it to the latest version during deserialization.

Testing event-sourced systems involves verifying event sequences and state reconstruction. Spring Boot’s test slices and Testcontainers help create isolated test environments with real databases and Kafka.

Throughout my journey with event sourcing, I’ve found that the initial complexity pays off in maintainability and insight. The ability to replay events for new features or debug production issues has saved countless hours. What challenges do you anticipate when adopting event sourcing in your projects?

If you found this exploration helpful, please like and share this article with your team. I’d love to hear about your experiences in the comments—what patterns have worked well for you, or what obstacles have you encountered?