Top 10 Microservices Mistakes and How to Avoid Them

Microservices architecture has transformed modern application development by providing scalability, flexibility, and resilience. However, designing and implementing microservices comes with challenges and pitfalls that can lead to performance issues, security risks, and maintainability problems.

In this article, we will cover the top 10 mistakes developers make when building microservices and how to avoid them with best practices.

1️⃣ Poorly Defined Service Boundaries 🔍

❌ Mistake: Creating Too Many or Too Few Services

A common mistake is splitting services too aggressively or keeping monolithic behavior in microservices.

Bad Example: ❌

• Splitting User Management into separate UserService, ProfileService, AddressService when they should be one.
• Keeping all user-related logic in a single microservice, leading to a monolith in disguise.

✅ Solution: Follow Domain-Driven Design (DDD)

• Use Bounded Contexts to define clear service boundaries.
• Group related functionalities together to avoid excessive API calls.

✔ Good Example: ✅
A UserService that manages user data, profile, and addresses while keeping authentication separate in an AuthService.

2️⃣ Synchronous Communication Between Microservices ⏳

❌ Mistake: Using REST APIs for Internal Microservices Communication

Many developers make all microservices communicate synchronously using HTTP REST, creating latency issues and service coupling.

Bad Example: ❌

@RestController
@RequestMapping("/order")
public class OrderController {
    @Autowired
    private PaymentService paymentService;

    @PostMapping
    public ResponseEntity<String> createOrder(@RequestBody Order order) {
        boolean paymentProcessed = paymentService.processPayment(order.getId());
        return ResponseEntity.ok("Order Created");
    }
}

✔ Issue: If PaymentService is down, OrderService fails!

✅ Solution: Use Asynchronous Messaging (Event-Driven Architecture)

• Use Kafka, RabbitMQ, or AWS SNS/SQS for event-based communication.
• Implement Circuit Breakers to handle failures.

✔ Good Example (Using Kafka Event Publishing) ✅

public void createOrder(Order order) {
    kafkaTemplate.send("order-created", order.getId());
}

Now, even if PaymentService is down, the order will be processed when it’s back online.

3️⃣ Not Implementing API Gateway 🚪

❌ Mistake: Exposing Each Microservice Directly

• Making frontend call multiple microservices directly can lead to security risks and performance issues.

✅ Solution: Use an API Gateway (e.g., Kong, Zuul, API Gateway)

✔ Benefits of API Gateway:

• Single Entry Point: Routes requests to the correct microservice.
• Security: Handles authentication, authorization, rate-limiting.
• Load Balancing: Distributes traffic efficiently.

✔ Good Example (Spring Cloud Gateway) ✅

@Bean
public RouteLocator customRouteLocator(RouteLocatorBuilder builder) {
    return builder.routes()
        .route("order-service", r -> r.path("/orders/**")
            .uri("lb://ORDER-SERVICE"))
        .build();
}

Now, all traffic goes through the API Gateway instead of direct microservice calls.

4️⃣ Not Handling Distributed Transactions Properly ❌

❌ Mistake: Using Local Transactions in Distributed Systems

• Microservices have their own databases, so traditional ACID transactions (2PC) don’t work.

✅ Solution: Use Saga Pattern

✔ Best Approach: Compensating transactions using Event Choreography or Orchestration.

✔ Good Example (Saga Pattern Using Orchestration) ✅

@Transactional
public void createOrder(Order order) {
    orderRepository.save(order);
    paymentService.processPayment(order.getId()); // Payment fails? Rollback order!
}

This ensures that if one step fails, the previous steps roll back.

5️⃣ Not Securing Microservices Properly 🔒

❌ Mistake: No Authentication or Authorization

A public API without security allows unauthorized access.

Bad Example (No Authentication) ❌

@GetMapping("/users")
public List<User> getUsers() {
    return userService.getAllUsers();
}

✔ Issue: Anyone can access user data!

✅ Solution: Secure APIs with OAuth2 and JWT

✔ Good Example: ✅

@EnableWebSecurity
public class SecurityConfig {
    @Bean
    public SecurityFilterChain securityFilterChain(HttpSecurity http) throws Exception {
        http.authorizeHttpRequests(auth -> auth.anyRequest().authenticated())
            .oauth2ResourceServer(OAuth2ResourceServerConfigurer::jwt);
        return http.build();
    }
}

✔ Best Practices:

• Use OAuth2 with JWT for authentication.
• Use Role-Based Access Control (RBAC) for authorization.

6️⃣ Ignoring Service Discovery & Load Balancing 🔄

❌ Mistake: Hardcoding Service URLs

If service instances change dynamically, hardcoded URLs fail.

Bad Example: ❌

String paymentServiceUrl = "http://localhost:8082/pay";

✅ Solution: Use Service Discovery (Eureka, Consul)

✔ Good Example (Eureka Client) ✅

@LoadBalanced
@Bean
public RestTemplate restTemplate() {
    return new RestTemplate();
}

✔ Benefits:

• Dynamically discover services.
• Automatically load balance requests.

7️⃣ Not Implementing Circuit Breakers & Fault Tolerance ⚡

❌ Mistake: No Failure Handling

If a service goes down, dependent services also fail.

✅ Solution: Use Resilience4j Circuit Breaker

✔ Good Example: ✅

@CircuitBreaker(name = "orderService", fallbackMethod = "fallback")
public OrderResponse createOrder(Order order) {
    return orderService.placeOrder(order);
}

public OrderResponse fallback(Order order, Throwable t) {
    return new OrderResponse("Service temporarily unavailable");
}

✔ Best Practices:

• Use timeouts, retries, and circuit breakers to prevent cascading failures.

8️⃣ Poor Logging & Monitoring 📊

❌ Mistake: No Centralized Logging

If logs aren’t centralized, debugging distributed services is a nightmare.

✅ Solution: Use ELK Stack (Elasticsearch, Logstash, Kibana)

✔ Good Example (Spring Boot with Logback) ✅

@Slf4j
@RestController
@RequestMapping("/users")
public class UserController {
    @GetMapping("/{id}")
    public ResponseEntity<UserDTO> getUser(@PathVariable Long id) {
        log.info("Fetching user with id: {}", id);
        return ResponseEntity.ok(userService.getUserById(id));
    }
}

✔ Best Practices:

• Use centralized logging (ELK, Prometheus, Grafana).
• Log meaningful information without exposing sensitive data.

9️⃣ Not Versioning APIs Properly 🔄

❌ Mistake: No API Versioning

Without API versioning, changes break clients.

✅ Solution: Use Versioning in API URLs

✔ Good Example: ✅

@RequestMapping("/api/v1/users")

✔ Best Practices:

• Use v1, v2 in URLs.
• Deprecate old versions gradually.

🔟 Deploying Without Containerization 🐳

❌ Mistake: Running Services Without Containers

Deploying microservices without Docker/Kubernetes makes scalability difficult.

✅ Solution: Use Docker & Kubernetes

✔ Best Practices:

• Use Docker for containerized deployments.
• Use Kubernetes for service orchestration.

✔ Example Dockerfile:

FROM openjdk:17
COPY target/app.jar app.jar
CMD ["java", "-jar", "app.jar"]

🎯 Conclusion

Microservices offer scalability and flexibility, but poor implementation leads to performance, security, and maintenance issues.

Quick Recap

✔ Define proper service boundaries
✔ Use async messaging instead of synchronous REST
✔ Secure APIs with OAuth2 & JWT
✔ Implement circuit breakers & service discovery
✔ Use centralized logging & monitoring

🔑 Keywords:

Microservices best practices, Microservices security, REST API security, OAuth2 authentication, Spring Boot microservices, API Gateway, Circuit Breaker, Kubernetes, Docker