I’ve been thinking about high-performance microservices lately because modern applications demand more than just functionality—they need to be fast, resilient, and scalable. Traditional approaches often struggle under heavy loads, which is why I want to share how combining Spring WebFlux, Apache Kafka, and Redis can create systems that handle thousands of requests efficiently.

Let me walk you through building an event-driven order processing system. We’ll use reactive programming to handle concurrent connections without blocking threads. This approach lets us serve more users with fewer resources. Have you ever wondered how systems maintain responsiveness during traffic spikes?

Spring WebFlux forms our foundation. It uses Project Reactor to provide non-blocking I/O operations. Here’s a basic reactive controller:

@RestController
public class OrderController {
    
    @PostMapping("/orders")
    public Mono<Order> createOrder(@RequestBody Order order) {
        return orderService.createOrder(order);
    }
    
    @GetMapping("/orders/{id}")
    public Mono<Order> getOrder(@PathVariable String id) {
        return orderService.findById(id);
    }
}

Notice how we return Mono objects? These represent asynchronous operations that will complete later. This non-blocking approach allows our service to handle many concurrent requests efficiently.

Now, how do we make services communicate without creating tight coupling? Apache Kafka enables event-driven communication. Services publish events to topics, and other services consume them. This pattern improves scalability and fault tolerance.

Here’s how we produce events to Kafka:

@Service
public class OrderEventPublisher {
    
    private final KafkaTemplate<String, OrderEvent> kafkaTemplate;
    
    public Mono<Void> publishOrderCreated(Order order) {
        OrderEvent event = new OrderEvent(order.getId(), "CREATED");
        return Mono.fromFuture(
            kafkaTemplate.send("orders-topic", event)
        ).then();
    }
}

But what happens when the consumer service is down? Kafka retains messages, allowing consumers to process them when they come back online. This durability is crucial for reliable systems.

For data that needs quick access, we integrate Redis. It provides fast in-memory storage for frequently accessed data. Here’s how we cache order data:

@Service
public class OrderService {
    
    private final ReactiveRedisTemplate<String, Order> redisTemplate;
    
    public Mono<Order> findById(String id) {
        return redisTemplate.opsForValue().get(id)
            .switchIfEmpty(
                databaseRepository.findById(id)
                    .flatMap(order -> 
                        redisTemplate.opsForValue()
                            .set(id, order, Duration.ofMinutes(10))
                            .thenReturn(order)
                    )
            );
    }
}

This pattern checks Redis first, then queries the database if needed, and finally caches the result. How might this improve response times for popular items?

Error handling becomes critical in distributed systems. We implement retry mechanisms and circuit breakers:

public Mono<Order> processOrder(Order order) {
    return orderService.createOrder(order)
        .retryWhen(Retry.backoff(3, Duration.ofSeconds(1))
        .timeout(Duration.ofSeconds(30))
        .onErrorResume(TimeoutException.class, 
            error -> Mono.error(new ServiceUnavailableException()));
}

Testing reactive systems requires different approaches. We use StepVerifier to test reactive streams:

@Test
void testOrderCreation() {
    Mono<Order> result = orderService.createOrder(testOrder);
    
    StepVerifier.create(result)
        .expectNextMatches(order -> order.getId() != null)
        .verifyComplete();
}

Monitoring is essential for production systems. We expose metrics through Spring Actuator and use Micrometer to track performance:

management:
  endpoints:
    web:
      exposure:
        include: health,metrics,info
  metrics:
    tags:
      application: order-service

As we deploy to production, we consider scaling individual services based on their load patterns. The order service might need more instances during peak hours, while the notification service could scale based on event volume.

Building with these technologies requires understanding their strengths. Spring WebFlux handles web requests efficiently, Kafka manages event streaming reliably, and Redis provides fast data access. Together, they create systems that scale gracefully under pressure.

What challenges have you faced with microservices performance? I’d love to hear about your experiences and solutions. If this approach resonates with you, please share your thoughts in the comments below.