I’ve been thinking a lot about how modern applications handle complex data flows while maintaining reliability and auditability. That’s why I want to share my approach to building event sourcing systems using Spring Boot, Apache Kafka, and a custom event store. If you’ve ever struggled with tracking changes in your application or needed to reconstruct past states, this might change how you think about data persistence.

Event sourcing fundamentally changes how we store data. Instead of keeping only the current state, we record every change as an immutable event. Think about it like a detailed ledger where every transaction is permanently recorded. This approach gives you a complete history of what happened in your system, not just where things stand right now.

Have you ever wondered what your application’s data looked like last Tuesday at 3 PM? With traditional databases, that’s often impossible without complex auditing systems. Event sourcing makes this natural and straightforward.

Let me show you how to set this up. First, we need our infrastructure. Here’s a simple Docker Compose file to get PostgreSQL and Kafka running locally:

version: '3.8'
services:
  postgres:
    image: postgres:15
    environment:
      POSTGRES_DB: eventstore
    ports: ["5432:5432"]

  kafka:
    image: confluentinc/cp-kafka:7.4.0
    depends_on: [zookeeper]
    ports: ["9092:9092"]

Now, the core of our system is the event store. We’ll use Spring Data JPA to create an append-only log of events. Each event represents something meaningful that happened in your business domain.

What if you need to handle high-throughput scenarios? That’s where Kafka shines for distributing events to various consumers. Here’s how you might publish an event to Kafka:

@Autowired
private KafkaTemplate<String, Object> kafkaTemplate;

public void publishEvent(String topic, DomainEvent event) {
    kafkaTemplate.send(topic, event.getAggregateId(), event);
}

Aggregates are crucial in event sourcing. They ensure business rules are followed before events are created. Imagine an order system where you can’t add items after shipping. The aggregate enforces this.

Did you know that replaying events can rebuild your entire application state? This becomes incredibly powerful for debugging or creating new read models without touching the original data.

Building read models through projections lets you optimize for query performance. While your event store handles writes, specialized read databases can serve queries efficiently. This separation is what makes CQRS so effective with event sourcing.

Here’s a simple projection that updates a read model when events occur:

@EventListener
public void handleOrderCreated(OrderCreatedEvent event) {
    OrderSummary summary = new OrderSummary(event.getOrderId(), 
                                          event.getCustomerId(), 
                                          event.getTotalAmount());
    orderSummaryRepository.save(summary);
}

Schema evolution is inevitable. How do you handle events that change structure over time? I use versioning and careful migration strategies. Always design events to be backward compatible when possible.

Performance can suffer if you replay thousands of events for frequent aggregates. That’s why snapshots help. Periodically save the current state, then only replay events after the snapshot.

Testing event-sourced systems requires a different approach. You need to verify that commands produce correct events and that events rebuild proper states. Spring Boot’s test support makes this manageable.

What challenges have you faced with data consistency in distributed systems? Event sourcing combined with Kafka can provide strong eventual consistency guarantees.

Monitoring is non-negotiable. Use Spring Actuator and Kafka metrics to track event throughput, latency, and error rates. Proper alerting helps catch issues before they affect users.

In production, consider event retention policies and archival strategies. Not all events need to stay hot forever, but the complete history should remain accessible.

I’ve found that this architecture scales beautifully for complex domains. The initial learning curve pays off in maintainability and flexibility. Teams can work on different parts independently once the event contracts are established.

Remember that event sourcing isn’t for every use case. Simple CRUD applications might not benefit enough to justify the complexity. But for domains rich in business logic, it’s transformative.

What would your application look like if you could rewind and replay any point in its history? That perspective alone has helped me design more robust systems.

If this approach resonates with you, I’d love to hear about your experiences. Feel free to share your thoughts in the comments below. If you found this useful, please like and share it with others who might benefit from these ideas. Let’s keep the conversation going about building better software together.