I’ve been reflecting on how modern applications handle data changes, especially in systems where every transaction matters. This led me to explore event sourcing, a method that captures every state change as an immutable event. Today, I want to guide you through implementing this powerful pattern using Spring Boot and Apache Kafka. We’ll build a banking application where every deposit, withdrawal, and account creation is permanently recorded. Why settle for just the current balance when you can reconstruct any moment in time?

Event sourcing stores all changes as a sequence of events rather than just the final state. Imagine your bank account: instead of only knowing your current balance, you could see every transaction that led to it. This approach provides a complete history, making it ideal for audit trails and complex business logic. Have you ever faced a situation where you needed to trace back exactly what went wrong in a system? Event sourcing makes that possible.

Let’s start by setting up our project. We’ll use Spring Boot for its ease of use and Apache Kafka for handling event streams. First, add these dependencies to your Maven configuration. This includes Spring Boot starters for web, data JPA, and Kafka, along with PostgreSQL for storage.

<dependencies>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-web</artifactId>
    </dependency>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-data-jpa</artifactId>
    </dependency>
    <dependency>
        <groupId>org.springframework.kafka</groupId>
        <artifactId>spring-kafka</artifactId>
    </dependency>
    <dependency>
        <groupId>org.postgresql</groupId>
        <artifactId>postgresql</artifactId>
        <scope>runtime</scope>
    </dependency>
</dependencies>

Next, configure your application to connect to PostgreSQL and Kafka. Here’s a snippet from the application.yml file. Notice how we specify the database URL and Kafka bootstrap servers. This ensures our app can persist events and stream them efficiently.

spring:
  datasource:
    url: jdbc:postgresql://localhost:5432/eventstore
    username: postgres
    password: postgres
  kafka:
    bootstrap-servers: localhost:9092
    producer:
      key-serializer: org.apache.kafka.common.serialization.StringSerializer
      value-serializer: org.springframework.kafka.support.serializer.JsonSerializer

Now, let’s define our domain events. Events are immutable facts—like “AccountCreated” or “MoneyDeposited.” Each event should include essential data, such as the account ID and amount. Here’s a simple Java class for a deposit event. Notice how it extends a base event class to maintain consistency.

public class MoneyDepositedEvent extends BaseEvent {
    private final BigDecimal amount;
    
    public MoneyDepositedEvent(String accountId, BigDecimal amount) {
        super(accountId);
        this.amount = amount;
    }
    
    public BigDecimal getAmount() {
        return amount;
    }
}

Aggregates are central to event sourcing—they manage state by applying events. For our banking app, the Account aggregate reconstructs its balance from past events. When a new event occurs, like a deposit, the aggregate updates its state. How do you think we handle withdrawals without risking negative balances? We validate commands before generating events.

Kafka plays a key role in distributing events. After saving an event to the database, we publish it to a Kafka topic. This allows other services to react, like updating a read model for fast queries. Here’s how you can configure a Kafka producer in Spring.

@Configuration
public class KafkaProducerConfig {
    @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);
    }
    
    @Bean
    public KafkaTemplate<String, Object> kafkaTemplate() {
        return new KafkaTemplate<>(producerFactory());
    }
}

Read models are optimized views built from events. For instance, we might have a table showing current account balances, updated as events are processed. This separation of commands and queries improves performance. What happens if an event fails to process? We use idempotent handlers to avoid duplicates.

Testing is crucial. I always write tests that replay events to verify state reconstruction. With Spring Boot, you can use @SpringBootTest to integrate Kafka and database testing. This ensures your event flow works as expected under various scenarios.

Performance can be a concern with large event streams. We use snapshots—periodic saves of the current state—to speed up aggregate reconstruction. For example, instead of replaying thousands of events, we load the latest snapshot and apply only recent events.

In my experience, event sourcing adds clarity to complex domains. It forces you to think in terms of what happened, not just what is. This mindset shift can prevent many common bugs. Have you considered how event sourcing could simplify debugging in your projects?

As we wrap up, I encourage you to experiment with this setup. Start small, perhaps with a single aggregate, and gradually add more events. The investment in learning event sourcing pays off in robust, maintainable systems.

I hope this guide inspires you to implement event sourcing in your next project. If you found it helpful, please like, share, and comment with your experiences or questions. Your feedback helps me create better content for everyone!