I’ve been thinking a lot about how we handle concurrency in Java applications lately. With modern systems demanding higher throughput and better resource utilization, traditional thread management often feels like trying to fit a square peg in a round hole. That’s why Project Loom’s virtual threads caught my attention—they promise to revolutionize how we write concurrent code without the complexity we’ve grown accustomed to. If you’re building Spring Boot applications, this is something you’ll want to understand thoroughly.

Virtual threads change the game by making thread creation almost free. Unlike platform threads that tie up expensive OS resources, virtual threads are managed entirely within the JVM. This means you can have millions of threads running concurrently without bringing your system to its knees. Why do we care? Because most web applications spend most of their time waiting—waiting for database queries, external API calls, or file operations. Virtual threads excel exactly in these scenarios.

Let me show you the difference with a simple example. Traditional thread pools can struggle under load:

// Limited by thread pool size
ExecutorService executor = Executors.newFixedThreadPool(200);
for (int i = 0; i < 1000; i++) {
    executor.submit(() -> {
        Thread.sleep(1000); // Simulating I/O
        return "Done";
    });
}

Now compare that to virtual threads:

// Handles massive concurrency effortlessly
try (ExecutorService executor = Executors.newVirtualThreadPerTaskExecutor()) {
    for (int i = 0; i < 100_000; i++) {
        executor.submit(() -> {
            Thread.sleep(1000);
            return "Done";
        });
    }
}

Notice how we jumped from 1,000 tasks to 100,000? That’s the power shift we’re talking about. But have you ever wondered what happens behind the scenes when your application suddenly needs to handle ten times more concurrent users?

Setting up virtual threads in Spring Boot is surprisingly straightforward. Since Spring Boot 3.2, you can enable them with a simple configuration property in your application.properties file:

spring.threads.virtual.enabled=true

This single line tells Spring to use virtual threads for handling web requests and asynchronous tasks. The framework automatically configures the underlying Tomcat server to use a virtual thread per task executor. Isn’t it remarkable how such a small change can have such a big impact?

Let me share a personal experience. I recently migrated a payment processing service to use virtual threads. The service handles thousands of concurrent payment validations and external API calls. Before the migration, we were carefully tuning thread pool sizes and dealing with occasional thread starvation. After switching to virtual threads, the code became simpler and the system handled peak loads much more gracefully.

Here’s how you might implement a service method using virtual threads for asynchronous processing:

@Service
public class PaymentService {
    @Async
    public CompletableFuture<PaymentResult> processPaymentAsync(PaymentRequest request) {
        // This runs on a virtual thread
        log.info("Processing payment on thread: {}", Thread.currentThread());
        PaymentResult result = externalGateway.charge(request);
        return CompletableFuture.completedFuture(result);
    }
}

The @Async annotation combined with virtual threads means each call gets its own lightweight thread. No more worrying about thread pool exhaustion. What would happen if you applied this pattern to your most I/O-intensive operations?

Monitoring virtual threads requires some adjustment though. Traditional thread dumps won’t show you the full picture since virtual threads are much more dynamic. Spring Boot Actuator’s thread dump endpoint now includes virtual thread information, but you might want to add custom logging:

@EventListener
public void logVirtualThreadUsage(ContextRefreshedEvent event) {
    Thread virtualThread = Thread.ofVirtual().unstarted(() -> {});
    log.info("Virtual thread support active: {}", virtualThread.isVirtual());
}

This helps verify that virtual threads are actually being used in your application. Have you checked your current monitoring setup to see if it’s ready for virtual threads?

One important consideration is that virtual threads work best with blocking I/O operations. If you’re using reactive programming with Project Reactor, you might not see immediate benefits since reactive stacks are already non-blocking. However, for traditional servlet-based applications with blocking I/O, the improvement can be dramatic.

I remember working with a team that was considering rewriting their entire application to use reactive programming. After experimenting with virtual threads, they achieved similar scalability with far less code changes. The mental model of writing blocking code with virtual threads is much simpler than mastering reactive streams.

Here’s a practical example of handling database operations with virtual threads:

@Repository
public class OrderRepository {
    public List<Order> findRecentOrders(LocalDateTime since) {
        // This blocking call no longer ties up precious OS threads
        return entityManager.createQuery(
            "SELECT o FROM Order o WHERE o.createdAt >= :since", Order.class)
            .setParameter("since", since)
            .getResultList();
    }
}

The beauty is that your existing blocking code continues to work, but now it scales much better. How many of your current database calls could benefit from this approach?

When you start using virtual threads, you’ll notice that thread-local storage still works, but there are some caveats. Since virtual threads can be much more numerous, you need to be mindful of memory usage with thread locals. Also, certain libraries might not be fully compatible yet, so thorough testing is essential.

In my projects, I’ve found that the combination of virtual threads with structured concurrency (another Loom feature) makes error handling and resource management much cleaner. But that’s a topic for another discussion.

The performance implications are significant. Virtual threads reduce context switching overhead and make better use of available CPU cores. For applications dominated by I/O wait times, you can expect to handle many more concurrent requests with the same hardware. Have you measured how much time your application spends waiting versus actually computing?

As we wrap up, I encourage you to experiment with virtual threads in your Spring Boot applications. Start with a development environment and gradually introduce them to non-critical paths. The learning curve is surprisingly gentle, and the potential rewards are substantial.

I’d love to hear about your experiences with virtual threads. Have you tried them in production yet? What challenges did you face? Share your thoughts in the comments below, and if you found this helpful, please like and share this article with your colleagues who might benefit from it.