Refactoring to Spring Boot 4: Why I Finally Deleted My Reactive Code

I spent the last three days staring at a stack trace that just wouldn’t quit. You know the one—where the error is buried somewhere in a reactive chain, wrapped in seventeen layers of Mono and Flux, and the actual line number points to a framework class you’ve never heard of. It was 2 AM on a Tuesday, and I was ready to rewrite the entire inventory service in Go.

But I didn’t. Instead, I bit the bullet and pushed the upgrade button. We moved the core services to Spring Boot 4 and Java 25.

Look, I’m usually the first person to roll my eyes at major version bumps. They usually mean breaking changes, updated dependencies that don’t talk to each other, and a week of fixing tests. But this time? It actually solved the biggest headache I’ve had for the last five years: the complexity of asynchronous code.

The Death of “Reactive” Boilerplate

For years, we were told that to get performance in our microservices, we had to go non-blocking. We had to embrace the functional-reactive style. And sure, it scaled. But debugging it was a nightmare.

With Java 25 running on Spring Boot 4, Virtual Threads aren’t just an “experimental feature” anymore—they are the standard. The best part? I got to delete about 40% of my codebase. All those messy .flatMap() and .subscribe() calls? Gone. Replaced by boring, imperative, blocking code that runs just as fast.

Here’s what our order processing logic looked like before. It was a mess of callbacks:

// The Old Way (Reactive Hell)
public Mono<OrderConfirmation> processOrder(String orderId) {
    return repository.findById(orderId)
        .flatMap(order -> inventoryClient.checkStock(order.getProductId())
            .flatMap(hasStock -> {
                if (hasStock) {
                    return paymentClient.charge(order)
                        .map(payment -> new OrderConfirmation(order, "CONFIRMED"));
                }
                return Mono.error(new OutOfStockException());
            }));
}

If that throws an exception, good luck figuring out where it happened. Now, with Spring Boot 4 fully embracing the mature Virtual Thread model in Java 25, I rewrote it like this:

// The New Way (Java 25 + Spring Boot 4)
@Transactional
public OrderConfirmation processOrder(String orderId) {
    var order = repository.findById(orderId)
        .orElseThrow(() -> new OrderNotFoundException(orderId));

    // This looks blocking, but on a Virtual Thread, it yields efficiently
    if (!inventoryClient.checkStock(order.productId())) {
        throw new OutOfStockException();
    }

    var payment = paymentClient.charge(order);
    return new OrderConfirmation(order, "CONFIRMED");
}

It reads like code I wrote in 2015, but it handles thousands of concurrent requests without sweating. The JVM handles the suspension points. I don’t have to think about it. I just write logic.

Data-Oriented Programming is Finally Usable

The other thing that hit me during this refactor was how much cleaner the domain logic got. Java 25 has really polished the Pattern Matching for switch statements. We use a lot of event-driven patterns in our microservices (Kafka consumers, mostly), and handling different event types used to require a visitor pattern or a nasty chain of if-else checks.

We use Records for all our DTOs now. No Lombok, no generated getters. Just data. Combined with the new switch syntax, our event listeners look incredibly sharp. We’re using the deconstruction patterns that finally stabilized late last year.

public void handlePaymentEvent(PaymentEvent event) {
    switch (event) {
        case PaymentAuthorized(var id, var amount, var timestamp) 
            when amount.compareTo(BigDecimal.valueOf(10000)) > 0 -> 
                fraudDetectionService.flagHighValue(id, amount);
                
        case PaymentAuthorized(var id, _, _) -> 
            orderService.initiateShipping(id);
            
        case PaymentDeclined(var id, var reason) -> 
            notificationService.sendFailureEmail(id, reason);
            
        case null -> throw new IllegalArgumentException("Event cannot be null");
    }
}

Notice the underscore _ for unused variables? That little addition in Java makes it so clear what data actually matters in that specific context. It’s a small thing, but when you’re reading code at 4 PM on a Friday, clarity is everything.

Structured Concurrency: The Hidden Gem

One specific problem I ran into was aggregating data for our dashboard. We need to fetch user details, recent orders, and loyalty points simultaneously. Before, I’d use CompletableFuture.allOf(), which always felt clunky and error-prone. If one failed, handling the others was a pain.

Spring Boot 4 exposes the Java 25 Structured Concurrency API beautifully. It treats a group of tasks as a single unit of work. If the main task is cancelled, the sub-tasks get cancelled automatically. No more orphaned threads.

public UserDashboard getDashboard(String userId) {
    try (var scope = new StructuredTaskScope.ShutdownOnFailure()) {
        
        // Fork these tasks in parallel
        var userTask = scope.fork(() -> userService.getUser(userId));
        var ordersTask = scope.fork(() -> orderService.getRecentOrders(userId));
        var pointsTask = scope.fork(() -> loyaltyService.getPoints(userId));

        // Wait for all to finish, or fail if one fails
        scope.join();
        scope.throwIfFailed();

        // Construct the result
        return new UserDashboard(
            userTask.get(), 
            ordersTask.get(), 
            pointsTask.get()
        );
    } catch (ExecutionException | InterruptedException e) {
        throw new RuntimeException("Dashboard unavailable", e);
    }
}

This is safer than the old executor services because the scope ensures clean-up. If getUser fails, the other two are cancelled immediately. It saves resources and prevents those weird bugs where a background thread tries to update a UI or database transaction that’s already dead.

Spring Cloud Config is Less Annoying Now

I have to mention the infrastructure side. Spring Cloud has stripped back a lot of the magic. In the previous versions, I felt like I was fighting the auto-configuration half the time.

With the latest release, they’ve leaned heavily into the “Kubernetes-native” approach. We stopped using a dedicated Config Server for our smaller clusters and just mapped ConfigMap directly to properties with the new cleaner binders. It feels less like “Spring magic” and more like standard cloud architecture.

The RestClient interface that replaced RestTemplate (and the declarative HTTP clients) is also much more intuitive for service-to-service calls. It feels very similar to writing a controller interface.

@HttpExchange("/orders")
public interface OrderClient {

    @PostExchange
    Order createOrder(@RequestBody OrderRequest request);

    @GetExchange("/{id}")
    Order getOrder(@PathVariable("id") String id);
}

We just define the interface, point Spring at the base URL in the application.yaml, and inject it. No implementation needed. It’s not brand new technology, but in Spring Boot 4, the integration with observation (metrics/tracing) works out of the box without adding five different dependencies.

It’s Not All Sunshine

I won’t lie—the upgrade wasn’t entirely free. We had some older libraries that relied on synchronized blocks which still cause pinning on virtual threads. We had to hunt those down and replace them with ReentrantLock or just update the library versions.

Also, the new strictness in Java 25 regarding encapsulation meant we had to open up a few modules explicitly that we used to access via reflection hacks. It’s better for security, but annoying when your build fails because of a library you haven’t touched in three years.

But honestly? Being able to write simple, blocking code that scales like crazy is worth the weekend of configuration tweaking. Java microservices finally feel like they aren’t fighting against the language.