Project Loom reduces the need for reactive programming in many cases by making blocking operations cheap via virtual threads, enabling simple, synchronous-style code to scale efficiently. 2. Reactive programming remains relevant for backpressure handling, high-volume or infinite data streams, and complex asynchronous composition. 3. Virtual threads excel in I/O-bound, blocking scenarios with readable code, while reactive patterns are still preferred for fine-grained control, stream processing, and environments without Loom. 4. The two can coexist, with reactive pipelines running on virtual threads or using virtual thread-backed schedulers, combining simplicity and control. 5. The future of Java concurrency favors simple blocking code by default, using reactive approaches only when their advanced capabilities are required, meaning reactive libraries like Reactor or WebFlux are not obsolete but should be used judiciously.

Reactive programming in Java has traditionally relied on libraries like Project Reactor or RxJava to handle asynchronous, non-blocking operations—especially in high-concurrency scenarios such as web servers or event-driven systems. However, with the arrival of Project Loom and virtual threads in Java 21 , the landscape of concurrent programming is shifting dramatically. This raises an important question: How does Project Loom impact reactive programming in Java?

The short answer: Virtual threads reduce the need for reactive programming in many common use cases, but reactive patterns still have their place—especially when dealing with backpressure, complex data streams, or functional composition.

Let’s break this down.

1. What Problem Did Reactive Programming Solve?

Before Loom, handling thousands of concurrent requests with traditional blocking I/O (e.g., JDBC, Thread.sleep(), or blocking HTTP calls) was expensive. Each thread consumed significant memory (1MB stack by default), and the OS had limits on thread count.

So reactive frameworks like Reactor and WebFlux emerged:

• Use non-blocking I/O and event loops (like Node.js).
• Share a small number of threads across many tasks.
• Process data as streams with operators like map, filter, flatMap.
• Support backpressure—consumers can control the rate of data flow.

This model worked well but came with complexity:

• Steep learning curve.
• Harder debugging (stack traces get fragmented).
• Limited interoperability with blocking APIs.

2. How Project Loom Changes the Game

Project Loom introduces virtual threads—lightweight threads managed by the JVM, not the OS.

Key benefits:

• Millions of virtual threads can run efficiently.
• Each virtual thread uses far less memory (~hundreds of bytes).
• They’re designed to block cheaply. Yes, blocking is now okay.

Example:

ExecutorService executor = Executors.newVirtualThreadPerTaskExecutor();

IntStream.range(0, 10_000).forEach(i -> {
    executor.submit(() -> {
        Thread.sleep(Duration.ofSeconds(1));
        System.out.println("Done: "   i);
        return null;
    });
});

This spawns 10,000 virtual threads that sleep (block) without killing your system. With platform threads, this would fail or crawl.

So now, instead of writing complex reactive chains to avoid blocking, you can write simple, synchronous-looking code that scales.

3. Does This Make Reactive Programming Obsolete?

Not entirely. Here’s where each approach shines:

? Use Virtual Threads When:

• You're doing I/O-bound work (e.g., HTTP calls, file operations).
• You want simple, readable code.
• You don’t need backpressure or stream processing.
• You’re integrating with blocking APIs (e.g., traditional JDBC).

void handleRequest() {
    var user = db.loadUser();     // blocking
    var config = service.getConfig(); // blocking HTTP
    emailService.send(user, config);
}

Run one of these per virtual thread—no need for Mono, Flux, or callback hell.

? Keep Reactive Programming When:

• You need backpressure (e.g., fast producer, slow consumer).
• You’re processing infinite or high-volume data streams.
• You want fine-grained control over scheduling and threading.
• You're in an environment where you can't use Loom (older Java versions).
• You rely on reactive ecosystems (e.g., Spring WebFlux, R2DBC).

Reactive still excels at composing asynchronous operations and managing resource usage under extreme load.

4. Can They Work Together?

Yes—and sometimes that’s the best approach.

You can run a reactive pipeline inside a virtual thread, or use virtual threads as the execution target for reactive tasks.

For example:

Mono.fromCallable(() -> veryExpensiveBlockingOperation())
    .subscribeOn(reactor.core.scheduler.Schedulers.fromExecutor(
        Executors.newVirtualThreadPerTaskExecutor()
    ));

Now your reactive stream benefits from cheap blocking via virtual threads.

Or, use Project Reactor with virtual threads in Spring Boot 6 :

@Bean
public Scheduler virtualThreadScheduler() {
    return Schedulers.fromExecutor(Executors.newVirtualThreadPerTaskExecutor());
}

Then:

Mono.just("hello")
    .publishOn(virtualThreadScheduler())
    .map(this::blockingTransform)
    .subscribe();

This hybrid model gives you the best of both worlds: reactive stream control with Loom’s concurrency efficiency.

Bottom Line

• Project Loom doesn’t kill reactive programming—it redefines its role.
• For most conventional server applications, virtual threads make simple blocking code scalable, reducing the need for reactive complexity.
• Reactive programming remains valuable for advanced stream processing, backpressure, and integration with reactive systems.
• The future may be "reactive when needed, simple by default."

So, should you stop using Reactor or WebFlux? No—but reconsider if you’re using them just to avoid thread exhaustion. With Loom, that problem is largely solved.

Basically: write simple code first. Reach for reactive only when you need its unique powers.

The above is the detailed content of Reactive Programming in Java with Project Loom and Virtual Threads. For more information, please follow other related articles on the PHP Chinese website!