Lately, I’ve been thinking about how modern applications must handle thousands of simultaneous requests without breaking a sweat. This led me to explore reactive, event-driven systems—a powerful combination for building scalable, resilient microservices. Today, I want to share how you can construct such systems using Spring WebFlux, Apache Kafka, and MongoDB. Whether you’re building a high-traffic e-commerce platform or a real-time data processing service, this approach can help you meet demanding performance requirements.

Reactive programming changes how we handle data flow. Instead of blocking threads while waiting for I/O operations, it uses non-blocking calls and processes events asynchronously. This means your application can serve more users with fewer resources. Have you ever wondered how some systems manage to stay responsive under heavy load? Reactive principles are often the answer.

Let’s start with Spring WebFlux. It provides a reactive foundation for building web applications. Here’s a simple reactive endpoint:

@RestController
@RequestMapping("/orders")
public class OrderController {

    @PostMapping
    public Mono<Order> createOrder(@RequestBody Order order) {
        return orderService.createOrder(order);
    }
}

Notice the use of Mono—a reactive type that represents a single value or empty result. It allows the thread to be freed up while waiting for the database or other services to respond.

But what about managing state across services? That’s where event-driven architecture shines. Services communicate through events, ensuring loose coupling. For instance, when an order is created, it can publish an event that other services react to. This way, the order service doesn’t need to know about inventory or payment processing—it just emits events.

Apache Kafka excels here as a durable event log. Here’s how you can produce an event reactively:

@Service
public class OrderEventPublisher {

    private final SenderOptions<String, OrderEvent> senderOptions;
    
    public OrderEventPublisher(KafkaProperties properties) {
        this.senderOptions = SenderOptions.create(properties.buildProducerProperties());
    }

    public Mono<SenderResult<Void>> publishOrderCreated(OrderEvent event) {
        return KafkaSender.create(senderOptions)
                .send(Mono.just(SenderRecord.create("orders-topic", event.getOrderId(), event, null)))
                .next();
    }
}

Now, how do we persist data reactively? Spring Data MongoDB Reactive offers non-blocking database operations. Instead of traditional blocking repositories, you use reactive ones:

public interface OrderRepository extends ReactiveMongoRepository<Order, String> {
    Flux<Order> findByCustomerId(String customerId);
}

This returns a Flux<Order>, which is a stream of multiple orders. It integrates seamlessly with WebFlux, allowing end-to-end non-blocking processing.

But what happens when something goes wrong? Error handling in reactive streams requires a different mindset. You can use operators like onErrorResume or onErrorReturn to gracefully handle failures:

public Mono<Order> findOrderById(String id) {
    return orderRepository.findById(id)
            .onErrorResume(throwable -> {
                log.error("Error fetching order", throwable);
                return Mono.empty();
            });
}

Building such systems also demands attention to backpressure—the mechanism that lets consumers signal how much data they can handle. Reactive streams naturally support this, preventing fast producers from overwhelming slow consumers.

Have you considered how to test these reactive flows? Tools like StepVerifier help validate reactive sequences:

@Test
void testOrderCreation() {
    Order order = new Order("customer123", List.of(new OrderItem("item1", 2)));
    
    StepVerifier.create(orderService.createOrder(order))
            .expectNextMatches(createdOrder -> createdOrder.getId() != null)
            .verifyComplete();
}

Monitoring is another critical aspect. Integrating Micrometer and Prometheus allows you to track metrics like request rates, latency, and error counts in real-time.

As we wrap up, I encourage you to experiment with these tools. Start small—perhaps with a single reactive endpoint—and gradually incorporate event-driven patterns. The benefits in scalability and resilience are well worth the learning curve.

If you found this helpful, feel free to like, share, or comment with your experiences. I’d love to hear how you’re using reactive and event-driven architectures in your projects!