Building upon the foundational era discussed in Part 1, we continue our exploration of backend architecture evolution through the insights of Zhang Lei and Zhang Huan. This article focuses on the transformative shift to distributed systems and microservices architecture.

The Internet Revolution

“The emergence of numerous software aimed at individuals has had a significant impact on backend architecture,” explains Zhang Lei. “Many systems have evolved from single-machine to distributed systems, fundamentally changing how we approach backend development.”

Scale and Complexity

The scale of modern systems is unprecedented:

  • TikTok operates over 100,000 microservices
  • Each engineer maintains approximately 10 services
  • Daily changes number in the thousands
// Modern distributed service example
@Service
public class DistributedOrderService {
    private final ServiceRegistry registry;
    private final LoadBalancer loadBalancer;
    
    public Order processOrder(OrderRequest request) {
        // Distributed processing with service discovery
        InventoryService inventory = registry.getService("inventory");
        PaymentService payment = registry.getService("payment");
        
        return loadBalancer.execute(() -> {
            inventory.reserve(request.getItems());
            payment.process(request.getPayment());
            return createOrder(request);
        });
    }
}

The Rise of Microservices

Zhang Huan reflects on the transition: “The emergence of microservices is inevitable. When modifying even a simple function in a monolithic application, the entire application needs to be released. This approach cannot support modern architectural evolution.”

Key Benefits of Microservices

  1. Independent Deployment
  2. Technology Stack Freedom
  3. Scalability per Service
  4. Improved Fault Isolation
// Microservice architecture example
@SpringBootApplication
public class UserServiceApplication {
    @Bean
    public RestTemplate restTemplate() {
        return new RestTemplate();
    }
    
    @Bean
    public CircuitBreaker circuitBreaker() {
        return CircuitBreaker.builder()
            .failureThreshold(3)
            .resetTimeout(Duration.ofSeconds(30))
            .build();
    }
}

Distributed System Challenges

Zhang Lei emphasizes the complexity: “Distributed systems bring new challenges in data consistency, monitoring, and debugging. We spent extensive time researching and solving these fundamental problems.”

Critical Considerations

  1. Data Consistency
// Distributed transaction handling
@Transactional
public class DistributedTransactionManager {
    private final TransactionCoordinator coordinator;
    
    public void executeTransaction(List<Operation> operations) {
        // Two-phase commit protocol
        String txId = coordinator.begin();
        try {
            for (Operation op : operations) {
                coordinator.prepare(txId, op);
            }
            coordinator.commit(txId);
        } catch (Exception e) {
            coordinator.rollback(txId);
            throw e;
        }
    }
}
  1. Service Discovery
// Service discovery implementation
public class ServiceDiscovery {
    private final ZooKeeper zk;
    
    public List<ServiceInstance> getInstances(String serviceName) {
        return zk.getChildren("/services/" + serviceName, true)
            .stream()
            .map(this::parseServiceData)
            .collect(Collectors.toList());
    }
}
  1. Monitoring and Debugging
// Distributed tracing
@Aspect
public class DistributedTracing {
    @Around("execution(* com.service.*.*(..))")
    public Object trace(ProceedingJoinPoint joinPoint) {
        String traceId = TraceContext.getCurrentTrace();
        MDC.put("traceId", traceId);
        try {
            return joinPoint.proceed();
        } finally {
            MDC.remove("traceId");
        }
    }
}

Cloud Integration

“Cloud computing utilizes virtualization technology, and computation should be integrated more closely with it,” states Zhang Lei. This integration has led to significant changes in how backend systems are deployed and managed.

Cloud-Native Architecture

// Kubernetes deployment configuration
@Configuration
public class KubernetesConfig {
    @Bean
    public KubernetesClient kubernetesClient() {
        return new DefaultKubernetesClient();
    }
    
    @Bean
    public ServiceDeployment serviceDeployment() {
        return Deployment.builder()
            .withReplicas(3)
            .withStrategy(new RollingUpdateStrategy())
            .build();
    }
}

Performance and Scalability

Zhang Huan notes: “High capacity, stability, and heavyweight services such as caching, asynchronous processing, and rate limiting are indispensable parts of our service architecture.”

Performance Optimization Techniques

// Rate limiting implementation
public class RateLimiter {
    private final Redis redis;
    private final String key;
    private final int limit;
    
    public boolean tryAcquire() {
        return redis.eval("""
            local current = redis.call('incr', KEYS[1])
            if current > tonumber(ARGV[1]) then
                return 0
            end
            redis.call('expire', KEYS[1], 60)
            return 1
            """, Arrays.asList(key), Arrays.asList(String.valueOf(limit)));
    }
}

Looking Forward

As we move into the final article of this series, we’ll explore how artificial intelligence and modern development practices are reshaping backend architecture. The distributed systems and microservices era has established new paradigms for scalability and reliability, setting the stage for even more exciting developments in backend technology.

Understanding these architectural patterns is crucial for developers working on modern distributed systems. The principles established during this era continue to evolve, driving innovation in backend development.