I’ve been thinking a lot lately about how we can build systems that not only handle massive scale but do so efficiently. The combination of Spring Boot, Apache Kafka, and Java’s new virtual threads creates a powerful foundation for high-performance event-driven microservices. Let me show you how these technologies work together to create systems that are both robust and incredibly efficient.

Why virtual threads? Traditional thread-per-request models can struggle under heavy load, but virtual threads give us the simplicity of synchronous code with the scalability of asynchronous processing. When you combine this with Kafka’s durable event streaming, you get something special.

Here’s how we can configure a Kafka consumer that leverages virtual threads:

@Configuration
@Slf4j
public class KafkaConsumerConfig {

    @Bean
    public ConcurrentKafkaListenerContainerFactory<String, String> 
    kafkaListenerContainerFactory() {
        ConcurrentKafkaListenerContainerFactory<String, String> factory = 
            new ConcurrentKafkaListenerContainerFactory<>();
        factory.setConsumerFactory(consumerFactory());
        factory.getContainerProperties().setConsumerTaskExecutor(
            VirtualThreadTaskExecutor.create("kafka-consumer-")
        );
        return factory;
    }
}

Notice how we’re using VirtualThreadTaskExecutor? This allows each message to be processed in its own virtual thread, dramatically increasing our ability to handle concurrent messages without the overhead of platform threads.

But what happens when things go wrong? Error handling becomes crucial in distributed systems. Here’s a pattern I’ve found effective:

@RetryableTopic(
    attempts = "3",
    backoff = @Backoff(delay = 1000, multiplier = 2),
    include = {DataAccessException.class, KafkaException.class}
)
@KafkaListener(topics = "order-events")
public void handleOrderEvent(OrderEvent event) {
    try {
        orderService.processOrder(event);
    } catch (BusinessException e) {
        log.error("Business rule violation: {}", e.getMessage());
        throw new NonRetryableException(e);
    }
}

This approach gives us automatic retries for transient errors while allowing us to handle business rule violations differently. The @RetryableTopic annotation configures exponential backoff, which helps prevent overwhelming downstream services during outages.

Performance tuning is where the real magic happens. Have you considered how different configurations affect your throughput? Here are some key producer settings:

@Bean
public ProducerFactory<String, OrderEvent> producerFactory() {
    Map<String, Object> config = new HashMap<>();
    config.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServers);
    config.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
    config.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, JsonSerializer.class);
    config.put(ProducerConfig.LINGER_MS_CONFIG, 20);
    config.put(ProducerConfig.BATCH_SIZE_CONFIG, 32 * 1024);
    config.put(ProducerConfig.COMPRESSION_TYPE_CONFIG, "lz4");
    return new DefaultKafkaProducerFactory<>(config);
}

The linger.ms and batch.size settings help us achieve better throughput by batching messages, while compression reduces network bandwidth. But remember, these settings involve trade-offs between latency and throughput.

Monitoring is non-negotiable in production. Spring Boot Actuator combined with Micrometer gives us excellent visibility:

management:
  endpoints:
    web:
      exposure:
        include: health, metrics, prometheus
  metrics:
    tags:
      application: order-service
    distribution:
      percentiles-histogram:
        http.server.requests: true

This configuration exposes metrics in a format that Prometheus can scrape, giving us insight into everything from message processing rates to error patterns.

What about testing? Testing event-driven systems requires a different approach. I recommend using embedded Kafka for integration tests:

@SpringBootTest
@EmbeddedKafka(partitions = 1, topics = {"order-events"})
class OrderServiceIntegrationTest {
    
    @Autowired
    private EmbeddedKafkaBroker embeddedKafka;
    
    @Test
    void shouldProcessOrderEvent() {
        OrderEvent event = createTestOrder();
        kafkaTemplate.send("order-events", event);
        
        await().atMost(5, SECONDS)
               .until(() -> orderRepository.count() == 1);
    }
}

This approach lets us test the entire flow from message production to processing without needing a full Kafka cluster.

The beauty of this architecture lies in its flexibility. Services can be developed, deployed, and scaled independently. New features can be added by simply subscribing to relevant events. The system becomes more resilient because services don’t need to know about each other’s implementation details.

I’d love to hear your thoughts on this approach. What challenges have you faced with event-driven architectures? Have you tried virtual threads in production yet? Share your experiences in the comments below, and if you found this useful, please like and share with others who might benefit from these patterns.

Remember, the goal isn’t just to make things work—it’s to build systems that can grow and adapt while maintaining performance and reliability. The patterns I’ve shared here have served me well in production environments handling millions of events daily.