I’ve been building software systems for years, and recently, I found myself struggling with a common problem: how to maintain a complete, reliable history of changes in a complex application. Traditional databases only store the current state, which makes it hard to track what happened, when, and why. This frustration led me to explore event sourcing, and combining it with Spring Boot and Apache Kafka transformed how I design resilient systems. In this guide, I’ll walk you through implementing event sourcing step by step, using practical examples from a banking application I developed.

Event sourcing is about storing every change as an immutable event, rather than just the latest data. Think of it like a bank statement that lists every transaction instead of just showing the current balance. This approach makes your system audit-friendly and capable of reconstructing any past state. Why settle for partial history when you can have it all?

Let’s start by setting up the project. I use Spring Boot for its simplicity and Apache Kafka for robust event streaming. Here’s a basic Maven configuration to get you going:

<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>
    </dependency>
</dependencies>

With the setup ready, the core of event sourcing lies in events and aggregates. Events are facts that can’t be changed, like “AccountOpened” or “FundsDeposited”. I define them as simple Java classes. For instance, in my banking app, an event might look like this:

public class AccountOpenedEvent extends DomainEvent {
    private String accountHolder;
    private BigDecimal initialBalance;

    public AccountOpenedEvent(UUID accountId, String accountHolder, BigDecimal initialBalance) {
        super(accountId, 1L);
        this.accountHolder = accountHolder;
        this.initialBalance = initialBalance;
    }
    // Getters and other methods
}

Aggregates, like an Account, are built by replaying these events. Have you ever considered how replaying past actions can rebuild an object’s state? It’s like rewinding a movie to see how a scene unfolded. Here’s a snippet from my Account aggregate:

public class Account extends AggregateRoot {
    private BigDecimal balance;
    private String holder;

    public void apply(AccountOpenedEvent event) {
        this.id = event.getAggregateId();
        this.holder = event.getAccountHolder();
        this.balance = event.getInitialBalance();
    }

    public void deposit(BigDecimal amount) {
        if (amount.compareTo(BigDecimal.ZERO) <= 0) {
            throw new IllegalArgumentException("Deposit amount must be positive");
        }
        applyEvent(new FundsDepositedEvent(this.id, amount, this.version + 1));
    }
}

Next, we need an event store to persist these events. I use a database table to store them in order. This ensures that every change is recorded and can be retrieved later. How do you handle data that grows over time without losing history?

Integrating Apache Kafka allows events to be streamed across services. I set up Kafka producers to publish events and consumers to process them. This decouples components and makes the system scalable. Here’s a simple Kafka configuration in Spring:

spring:
  kafka:
    bootstrap-servers: localhost:9092
    producer:
      key-serializer: org.apache.kafka.common.serialization.StringSerializer
      value-serializer: org.springframework.kafka.support.serializer.JsonSerializer

Command Query Responsibility Segregation (CQRS) pairs well with event sourcing by separating read and write operations. In my app, commands like “OpenAccount” modify state, while queries fetch data from optimized views. This improves performance and clarity. What if your reads and writes had different data models?

Handling event versioning is crucial as your system evolves. I use version numbers in events to manage changes without breaking existing code. For example, if I add a new field to an event, I ensure backward compatibility by handling old versions gracefully.

Testing event-sourced systems involves replaying events to verify state changes. I write unit tests for aggregates and integration tests with Kafka to ensure events are processed correctly. How do you test systems where history matters?

Performance can be optimized by snapshotting aggregates to avoid replaying all events every time. I implement snapshots that save the current state periodically, reducing load.

Common pitfalls include not planning for schema changes or overcomplicating event design. I learned to keep events simple and focus on the essential facts. Have you faced issues with data consistency in distributed systems?

Alternative approaches like using dedicated event stores exist, but Spring Boot and Kafka offer a balanced mix of ease and power. In my projects, this combination has proven reliable for high-throughput scenarios.

I hope this guide helps you build robust applications with event sourcing. If you found it useful, please like, share, and comment with your experiences—I’d love to hear how you’re applying these ideas!