I’ve been thinking about concurrency in Java applications a lot lately. Traditional thread pools just don’t cut it when you need to handle thousands of simultaneous connections efficiently. The memory overhead alone can be staggering. That’s why virtual threads caught my attention—they offer a fundamentally different approach to concurrency that could change how we build scalable applications.

What if I told you there’s a way to handle millions of concurrent operations without the memory bloat of traditional threads?

Virtual threads are lightweight threads managed by the Java Virtual Machine rather than the operating system. They’re not tied to OS threads, which means you can create thousands—even millions—of them without overwhelming your system. Here’s how simple they are to create:

Thread virtualThread = Thread.ofVirtual()
    .name("data-processor")
    .start(() -> processData());

Spring Boot 3.2 makes integrating virtual threads straightforward. You can configure your entire application to use virtual threads instead of platform threads with just a few properties:

spring:
  threads:
    virtual:
      enabled: true

When working with database operations, virtual threads shine. Traditional blocking I/O operations become much more efficient because the JVM can park the virtual thread while waiting for the database response, freeing up the underlying carrier thread to handle other work. This means your database calls won’t tie up expensive OS threads anymore.

Have you ever wondered how this compares to reactive programming?

While reactive patterns are great for non-blocking operations, they require a different mental model and coding style. Virtual threads let you write straightforward blocking code while achieving similar scalability benefits. The choice between them often comes down to your team’s expertise and specific use case requirements.

For HTTP client operations, virtual threads work beautifully with Spring’s WebClient. You can make multiple external API calls concurrently without the complexity of reactive chains:

@RestController
public class DataController {
    
    private final WebClient webClient;
    
    public DataController(WebClient.Builder webClientBuilder) {
        this.webClient = webClientBuilder.build();
    }
    
    @GetMapping("/user-data")
    public UserData getUserData(@PathVariable String userId) {
        try (var executor = Executors.newVirtualThreadPerTaskExecutor()) {
            var userFuture = executor.submit(() -> 
                webClient.get()
                    .uri("/users/{id}", userId)
                    .retrieve()
                    .bodyToMono(User.class)
                    .block()
            );
            
            var ordersFuture = executor.submit(() ->
                webClient.get()
                    .uri("/users/{id}/orders", userId)
                    .retrieve()
                    .bodyToMono(List.class)
                    .block()
            );
            
            return new UserData(userFuture.get(), ordersFuture.get());
        }
    }
}

Performance testing shows remarkable results. Applications using virtual threads can handle significantly more concurrent requests with lower memory usage compared to traditional thread pool configurations. But how do you know when you’re hitting the limits?

Monitoring is crucial. Spring Boot Actuator provides excellent support for observing virtual thread behavior. You can track metrics like virtual thread creation rates, carrier thread utilization, and blocking operations:

management:
  endpoints:
    web:
      exposure:
        include: metrics,health,threaddump
  metrics:
    tags:
      application: ${spring.application.name}

Some best practices I’ve learned: avoid using synchronized blocks with virtual threads, as they can pin the virtual thread to its carrier. Instead, use ReentrantLock or other concurrent utilities. Also, be mindful of thread-local variables—they work with virtual threads but can increase memory usage if overused.

What about error handling and debugging?

Virtual threads maintain full stack traces, making debugging much easier compared to reactive programming. Exception handling follows the familiar try-catch patterns we’ve used for years. The learning curve is significantly gentler than adopting full reactive programming.

As we move forward with these patterns, I’m excited to see how virtual threads will evolve our approach to building highly concurrent applications. They offer a compelling middle ground between traditional threading and reactive programming, combining simplicity with scalability.

If you found this helpful or have experiences with virtual threads to share, I’d love to hear your thoughts. Please like, share, or comment below—let’s continue this conversation about building better, more scalable applications together.