I’ve been thinking a lot lately about how we build systems that truly remember. Not just what they are now, but how they got there. That’s what led me to explore event sourcing with Spring Boot and Kafka - a combination that creates applications with perfect memory.

When we implement event sourcing, we stop storing just the current state and start storing every change as an immutable event. This approach gives us audit trails, time travel capabilities, and system resilience that traditional CRUD systems can’t match.

Imagine building a banking application where every transaction is stored as a factual event. We don’t just update a balance - we record that a deposit happened. This changes how we think about data permanence and system design.

Why do you think financial systems and other critical applications benefit from this approach?

Let me show you how to set this up. First, we need our core dependencies. Here’s what your Maven configuration might look like:

<dependencies>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-web</artifactId>
    </dependency>
    <dependency>
        <groupId>org.springframework.kafka</groupId>
        <artifactId>spring-kafka</artifactId>
    </dependency>
</dependencies>

The heart of event sourcing lies in our event definitions. We create immutable events that represent facts about what happened in our system:

public class AccountCreatedEvent {
    private UUID accountId;
    private String owner;
    private BigDecimal initialBalance;
    private Instant timestamp;
    
    // Constructor, getters, and immutable setters
}

Each event becomes a building block of our application’s history. When we need to know the current state, we replay these events in sequence. This might sound inefficient, but there are smart ways to handle it.

How do you think we might optimize replaying thousands of events for a single entity?

We use Kafka as our event store because it provides durability, scalability, and built-in streaming capabilities. Here’s how we might produce events:

@Autowired
private KafkaTemplate<String, Object> kafkaTemplate;

public void publishEvent(BaseEvent event) {
    kafkaTemplate.send("account-events", event.getAggregateId().toString(), event);
}

The consumer side needs to handle these events and update read models or trigger other processes:

@KafkaListener(topics = "account-events")
public void handleEvent(BaseEvent event) {
    eventProcessor.process(event);
}

One challenge is ensuring we handle events in the correct order. Kafka’s partitioning helps here - we use the aggregate ID as the key to guarantee order within each entity.

What happens if we need to rebuild our read models from scratch? Event sourcing makes this straightforward - we simply replay all events. This is incredibly powerful for debugging or creating new projections.

Here’s how we might implement a simple event replay:

public void replayEvents(UUID aggregateId) {
    List<BaseEvent> events = eventStore.getEvents(aggregateId);
    events.forEach(event -> applyEvent(event, true));
}

We can also implement snapshots to avoid replaying every single event for frequently accessed entities:

public void createSnapshot(UUID aggregateId) {
    CurrentState state = buildStateFromEvents(aggregateId);
    snapshotStore.save(aggregateId, state);
}

Testing becomes more comprehensive with event sourcing. We can verify that commands produce the expected events and that events lead to the correct state changes:

@Test
void shouldProduceAccountCreatedEvent() {
    CreateAccountCommand command = new CreateAccountCommand("owner", 100.00);
    List<BaseEvent> events = commandHandler.handle(command);
    assertTrue(events.get(0) instanceof AccountCreatedEvent);
}

The beauty of this architecture is how it handles failure. If a service goes down, it can simply replay events to rebuild its state. No more worrying about database synchronization issues.

Have you considered how this might change your deployment strategies?

Performance considerations are important. We need to think about event serialization, database choices for event storage, and caching strategies for read models. Kafka’s partitioning and consumer groups help distribute the load effectively.

One thing I’ve learned: start simple. Don’t over-engineer your first event-sourced system. Begin with a bounded context where the benefits are clear, like order processing or financial transactions.

The debugging experience transforms with event sourcing. Instead of wondering “how did we get into this state?”, you can literally replay the events and see exactly what happened. This level of visibility is game-changing for complex systems.

What questions would you ask of a system that remembers everything?

I’d love to hear your thoughts on implementing event sourcing. Have you tried this pattern before? What challenges did you face? Share your experiences in the comments below, and if you found this useful, please like and share with others who might benefit from this approach.