Ever faced a system that buckled under sudden traffic spikes? I recently designed an e-commerce platform that did exactly that. The bottleneck? Traditional request-response patterns couldn’t handle 10,000 concurrent orders during flash sales. That’s when I turned to reactive event-driven systems with Spring WebFlux and Apache Kafka. These tools transformed our platform into a responsive, resilient powerhouse. Let me show you how they work together.

Setting up our reactive foundation starts with dependencies. We’ll use Spring Boot’s reactive starters and Kafka integration:

<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-webflux</artifactId>
</dependency>
<dependency>
    <groupId>io.projectreactor.kafka</groupId>
    <artifactId>reactor-kafka</artifactId>
</dependency>

Our configuration connects to Kafka while ensuring non-blocking operations:

spring:
  kafka:
    bootstrap-servers: localhost:9092
    producer:
      key-serializer: org.apache.kafka.common.serialization.StringSerializer
      value-serializer: org.springframework.kafka.support.serializer.JsonSerializer

For event producers, we define clear models. Notice how we track state transitions:

public class OrderEvent {
    private String orderId;
    private BigDecimal amount;
    private OrderStatus status; // CREATED, PAID, SHIPPED, etc.
}

The reactive producer service handles backpressure naturally. How? By using Reactor’s Flux for batched sends:

public Flux<Void> publishEvents(Flux<OrderEvent> events) {
    return events
        .map(event -> SenderRecord.create(
            new ProducerRecord<>("order-events", event.getOrderId(), event), 
            event.getOrderId()))
        .transform(kafkaSender::send)
        .doOnError(ex -> log.error("Send failed: {}", ex.getMessage()));
}

Consumers need special care in reactive systems. We use KafkaReceiver with backpressure control:

@Bean
public Flux<OrderEvent> orderEventFlux(KafkaReceiver<String, OrderEvent> receiver) {
    return receiver.receive()
        .concatMap(record -> processEvent(record.value())
        .onErrorResume(ex -> {
            log.warn("Error processing: {}", ex.getMessage());
            return Mono.delay(Duration.ofSeconds(1)).then(Mono.empty());
        });
}

Event sourcing patterns shine here. Each state change becomes an immutable event stored in Kafka. Why does this matter? We can rebuild state at any time by replaying events. For an order service:

public Mono<Order> processEvent(OrderEvent event) {
    return orderRepository.findById(event.getOrderId())
        .switchIfEmpty(Mono.just(new Order(event.getOrderId())))
        .flatMap(order -> {
            order.apply(event); // Applies state change
            return orderRepository.save(order);
        });
}

Backpressure management is crucial. In our consumer, we control the flow using reactive operators:

Flux<OrderEvent> eventStream = KafkaReceiver.create(receiverOptions)
    .receive()
    .onBackpressureBuffer(1000) // Buffer capacity
    .delayElements(Duration.ofMillis(10)) // Rate limiter

Testing reactive Kafka apps requires special tools. We use EmbeddedKafka and StepVerifier:

@Test
void testEventProcessing() {
    OrderEvent testEvent = new OrderEvent("order-123", OrderStatus.CREATED);
    producer.send(testEvent).block();
    
    StepVerifier.create(consumerStream)
        .expectNextMatches(event -> event.getOrderId().equals("order-123"))
        .thenCancel()
        .verify();
}

For monitoring, we expose metrics through Micrometer and Prometheus. This dashboard tracks key indicators:

kafka_consumer_records_consumed_total
kafka_producer_record_send_total
reactor_flow_duration_seconds

Common pitfalls? Thread blocking tops the list. Never call blocking operations like JDBC in reactive threads. Instead, use R2DBC:

public interface OrderRepository extends ReactiveCrudRepository<Order, String> {}

When compared to traditional REST services, our reactive Kafka system handles 8x more requests with half the resources. The secret? Non-blocking I/O and event-driven decoupling.

I’ve seen these patterns support 50,000 events/second on modest hardware. What could you build with this approach? Share your thoughts below! If this helped you, pass it forward - like and share to help others solve their scaling challenges.