Lately, I’ve been thinking about how we can build systems that don’t just work, but truly perform under pressure. The shift toward event-driven microservices is more than a trend—it’s a response to the demand for scalable, resilient applications. Combining Spring Boot, Apache Kafka, and Java’s new Virtual Threads offers a powerful way to meet that demand. I want to share what I’ve learned, with practical examples you can apply right away.

Why are Virtual Threads such a game-changer? Traditional thread-per-request models often struggle with high concurrency, consuming substantial memory and CPU. Virtual Threads, introduced in Java 21, change that. They allow us to handle thousands—even millions—of concurrent tasks with minimal overhead, making them perfect for event-driven workloads.

Let’s look at a basic setup. First, enable Virtual Threads in your Spring Boot application by adding this to your application.properties:

spring.threads.virtual.enabled=true

With that single line, your application starts using lightweight threads for all asynchronous operations. Now, imagine you’re building an order service. Here’s how you might define a simple event producer using Spring Kafka:

@Service
public class OrderEventProducer {

    @Autowired
    private KafkaTemplate<String, OrderEvent> kafkaTemplate;

    public void publishOrderCreated(Order order) {
        OrderEvent event = new OrderEvent(order.getId(), "ORDER_CREATED", order.getItems());
        kafkaTemplate.send("order-events", event);
    }
}

Notice how straightforward it is. But what happens when things go wrong? How do we ensure events aren’t lost if the Kafka broker is temporarily unavailable? Spring Kafka provides built-in retry mechanisms and dead-letter queues to handle these scenarios gracefully.

On the consumer side, Virtual Threads really shine. Instead of blocking platform threads, each message can be processed on a virtual thread, allowing massive concurrency without resource exhaustion. Here’s a sample consumer:

@KafkaListener(topics = "order-events", groupId = "inventory-group")
public void handleOrderEvent(OrderEvent event) {
    log.info("Processing event: {}", event.getType());
    inventoryService.updateStock(event);
}

With Virtual Threads, this listener can handle thousands of events concurrently, each on its own virtual thread, without the overhead of traditional threading.

But how do you monitor such a system? Observability is key. Integrating Micrometer and Kafka metrics gives you real-time insights into event flow, latency, and error rates. You can track everything from consumer lag to thread utilization, ensuring your system remains healthy under load.

Testing is another critical area. How do you verify that events are produced and consumed correctly? Using Testcontainers, you can spin up a real Kafka instance in your tests, providing an environment that mirrors production.

@Testcontainers
public class OrderEventTest {

    @Container
    static KafkaContainer kafka = new KafkaContainer(DockerImageName.parse("confluentinc/cp-kafka:7.5.0"));

    @Test
    public void testOrderEventProduction() {
        // Test logic here
    }
}

This approach ensures your tests are both reliable and representative of real-world conditions.

In my experience, the combination of Spring Boot, Kafka, and Virtual Threads isn’t just about performance—it’s about building systems that are both robust and adaptable. You can scale individual components independently, handle failures gracefully, and meet user demands without constant re-architecture.

What challenges have you faced with event-driven systems? Have you tried Virtual Threads in production yet? I’d love to hear your thoughts and experiences. If this article helped you, please like, share, or comment below—let’s keep the conversation going.