I’ve been thinking a lot about how modern applications need to handle massive traffic spikes while remaining responsive. Last month, I watched our team struggle with a traditional REST service that collapsed under just 100 concurrent users. That experience pushed me to explore reactive architectures that can scale efficiently. Today, I want to share how you can build microservices that handle thousands of requests per second while maintaining stability.

Reactive programming changes how we think about data flow. Instead of blocking threads while waiting for responses, we work with streams of data that process asynchronously. Have you ever wondered what happens when your service receives more requests than it can handle? Spring WebFlux provides the foundation for non-blocking operations. Let me show you a basic reactive endpoint.

@RestController
public class OrderController {
    @PostMapping("/orders")
    public Mono<Order> createOrder(@RequestBody OrderRequest request) {
        return orderService.processOrder(request)
            .doOnNext(order -> log.info("Order processed: {}", order.id()));
    }
}

This code returns a Mono, which represents a single value that might not be available immediately. The thread doesn’t block while processing the order, freeing it to handle other requests. But what about when you need to process multiple events in sequence?

Redis Streams offer persistent, ordered event logs that multiple services can consume. I’ve used this pattern to build resilient order processing systems where events never get lost, even during failures. Here’s how you might produce events to a Redis stream.

@Service
public class OrderEventService {
    private final ReactiveRedisTemplate<String, Object> redisTemplate;
    
    public Mono<String> publishOrderEvent(OrderEvent event) {
        ObjectRecord<String, OrderEvent> record = StreamRecords
            .objectBacked(event)
            .withStreamKey("orders-stream");
            
        return redisTemplate.opsForStream()
            .add(record)
            .map(RecordId::getValue);
    }
}

Consuming these events requires careful consideration of consumer groups and acknowledgment. Did you know Redis Streams can maintain multiple consumer groups reading the same stream at different positions? This enables powerful patterns like event sourcing and CQRS.

Now, imagine a downstream service starts responding slowly. Without proper safeguards, this could cascade failures throughout your system. Resilience4j’s circuit breaker pattern prevents this by monitoring failure rates and temporarily stopping requests to troubled services.

@Service
public class PaymentService {
    private final CircuitBreaker circuitBreaker;
    
    public PaymentService(CircuitBreakerRegistry registry) {
        this.circuitBreaker = registry.circuitBreaker("payment-service");
    }
    
    public Mono<PaymentResult> processPayment(Order order) {
        return Mono.fromCallable(() -> externalPaymentGateway.charge(order))
            .transformDeferred(CircuitBreakerOperator.of(circuitBreaker))
            .onErrorResume(Exception.class, ex -> 
                Mono.just(PaymentResult.failed("Payment service unavailable")));
    }
}

When failures exceed a threshold, the circuit opens and subsequent calls fail fast without overwhelming the external service. This gives the system time to recover. How would you handle the user experience during these temporary outages?

Combining these technologies creates a robust architecture. WebFlux handles the reactive foundation, Redis Streams manage event flow, and Resilience4j adds fault tolerance. I often configure backpressure to prevent memory issues when producers outpace consumers.

Testing reactive code requires a different approach. Reactor’s StepVerifier helps validate asynchronous streams.

@Test
void testOrderProcessing() {
    StepVerifier.create(orderService.processOrder(testRequest))
        .expectNextMatches(order -> order.status() == OrderStatus.PENDING)
        .verifyComplete();
}

Monitoring becomes crucial in production. I integrate Micrometer metrics to track event processing rates, circuit breaker states, and response times. Proper observability lets you spot issues before they affect users.

Building these systems taught me valuable lessons. Always design for failure, use reactive patterns consistently, and test under realistic load conditions. Small optimizations, like batching Redis operations, can significantly improve throughput.

What challenges have you faced with microservice communication? I’d love to hear about your experiences in the comments.

This approach has transformed how I build scalable systems. The combination of reactive programming, event streaming, and circuit breakers creates services that handle real-world demands gracefully. If you found this helpful, please share it with others who might benefit. Your feedback and questions in the comments help me improve future content. Let’s keep the conversation going!