Microservices Anti-Patterns: Common Mistakes and How to Avoid Them

Microservices architecture promises scalability, resilience, and independent deployability. However, many teams fall into common traps that undermine these benefits. This article explores the most prevalent anti-patterns and provides practical solutions.

1. The Distributed Monolith

This occurs when microservices are tightly coupled despite being distributed across different processes. Symptoms include:

• Services must be deployed together
• Database-level coupling through shared schemas
• Synchronous communication chains
• Common release cycles across teams

Solution: Implement Proper Bounded Contexts

// BAD: Tight coupling
@Service
public class OrderService {
  // Directly accesses Customer database
  @Autowired
  private CustomerRepository customerRepository;
}

// GOOD: Independent contexts
@Service
public class OrderService {
  // Uses event-driven communication
  @EventListener
  public void handleCustomerCreated(CustomerCreatedEvent event) {
    // Maintain local read model
    customerCache.save(event.getCustomerId(), event.getCustomerData());
  }
}

2. Chatty Services Anti-Pattern

Excessive inter-service communication creates latency and reduces resilience:

// BAD: Multiple synchronous calls
public OrderResponse createOrder(OrderRequest request) {
  // Validate customer (HTTP call)
  Customer customer = customerService.validate(request.getCustomerId());
  
  // Check inventory (HTTP call)
  InventoryStatus inventory = inventoryService.check(request.getItems());
  
  // Process payment (HTTP call)
  PaymentResult payment = paymentService.process(request.getPayment());
  
  // Multiple points of failure!
}

Solution: Implement API Composition or Event-Driven Approach

// GOOD: Event-driven with saga pattern
@Service
public class OrderOrchestrator {
  
  @Transactional
  public void createOrder(OrderRequest request) {
    // 1. Start saga
    OrderCreatedEvent event = new OrderCreatedEvent(request);
    kafkaTemplate.send("order-created", event);
    
    // 2. Services react asynchronously
    // CustomerService validates
    // InventoryService reserves items
    // PaymentService processes payment
  }
}

3. Shared Database Anti-Pattern

The most dangerous anti-pattern where multiple services share the same database:

• Schema changes affect multiple services
• No clear ownership of data
• Transactional integrity across services becomes complex
• Scaling becomes difficult

Solution: Database per Service Pattern

# docker-compose.yml - Each service has its own database
version: '3.8'
services:
  order-service:
    image: order-service:latest
    environment:
      - SPRING_DATASOURCE_URL=jdbc:postgresql://order-db:5432/orderdb
    
  order-db:
    image: postgres:14
    environment:
      - POSTGRES_DB=orderdb
  
  customer-service:
    image: customer-service:latest
    environment:
      - SPRING_DATASOURCE_URL=jdbc:postgresql://customer-db:5432/customerdb
  
  customer-db:
    image: postgres:14
    environment:
      - POSTGRES_DB=customerdb

4. Lack of Observability

Without proper monitoring, debugging distributed systems becomes impossible:

Solution: Implement Comprehensive Observability

# application.yml - Spring Boot observability config
management:
  tracing:
    sampling:
      probability: 1.0
  endpoints:
    web:
      exposure:
        include: health,metrics,prometheus
  metrics:
    export:
      prometheus:
        enabled: true
  
logging:
  pattern:
    level: "%5p [${spring.application.name},%X{traceId},%X{spanId}]"

Best Practices Summary

1. Design around business capabilities, not technical layers
2. Implement event-driven communication for loose coupling
3. Maintain separate databases for each service
4. Implement circuit breakers and retries
5. Establish clear API contracts and versioning
6. Invest in comprehensive observability from day one
7. Implement consumer-driven contract testing

Avoiding these anti-patterns requires discipline and continuous refactoring. Remember that microservices are not a silver bullet but a trade-off between complexity and scalability.