10 Microservices Anti-Patterns to Avoid for Scalable Applications

Microservices architecture promises scalability, agility, and resilience. Yet, in practice, many organizations struggle with pitfalls that can undermine these benefits. This article dives into common anti-patterns and provides pragmatic, experience-based solutions for building scalable microservices. 

Microservices Anti-Patterns to Avoid

1. Distributed Monolith

The Problem

When an existing monolith is broken into microservices architecture (or even when a set of microservices are created from scratch) but maintains high interdependence on each other, every deployment requires simultaneous updates across multiple microservices. This resembles a monolith with distributed components. It's not truly microservice architecture.

Real-World Scenario

Consider an eCommerce system composed of a payment service, an order service, and an inventory service. If this system has been designed so that these services all depend on each other to complete a user transaction, changes in one would require changes in or redeployment of other services. Although this design is distributed, it does not truly follow the microservice paradigm.

Practical Solution

Gradually decouple the services by identifying and isolating bounded contexts using Domain-Driven Design (DDD). These bounded contexts allow the isolation of individual microservice domains. For example, treat the "payment service" as an autonomous and standalone domain that only interacts with other services, such as the "order service" through API endpoints. Similarly, the "order service" can decouple from the "inventory service" by using an event-driven design (e.g., using Kafka).

Pro Tip

When dealing with migrations, use feature toggles to control new functionality while maintaining backward compatibility.

2. Shared Data Across Services

The Problem

Allowing multiple services to access a shared database (especially write access) violates the service independence principle required to truly reap the advantages of a microservice architecture. Changes to the schema can inadvertently break other services, leading to tight coupling.

Real-World Scenario

Consider an ERP system that has a sales service and an inventory service, among others. Both these services might access a common product table from a shared database. Any changes in the product schema followed by write operations by the product service can potentially break both the sales and inventory services and would require careful coordination across multiple teams.

Practical Solution

Introduce APIs for accessing shared data, encapsulating the database logic within a single service. Use read replicas or caching solutions (e.g., Redis) to improve performance while avoiding direct database access.

Practical Challenge

Moving towards independent databases for each of the microservices is a desired but disruptive idea to achieve in practice. If not possible right away, start by defining strict ownership rules focussed around Write operations. For example, you can assign only one service to have write access to a specific database while other services can only read from it or use API calls for reading data.

Pro Tip

If immediate separation is impractical, consider using schema-per-service isolation within the same database, which means that a given microservice will own the portion of the database schema that other microservices cannot directly access except via API calls.

Gotchas

In the case of schema-less databases like MongoDB, a rigid schema does not apply. However, there can still be hidden contracts between different microservices that read/write from the same collection. These services might store or modify data in ways that aren't fully compatible with the services reading them. Thus, even without an explicit schema, microservices using the same collection may develop hidden coupling. Therefore, the above considerations apply even in the case of schema-less databases.

3. Chatty Communication

The Problem

Services that frequently communicate with each other synchronously (e.g., via REST requests) introduce latency, increase failure risk, and reduce overall system performance.

Real-World Scenario

In a customer service system, every request to create a customer ticket must call customer service to fetch customer information, an account service to fetch account information, and possibly a notification service to send notifications. Synchronous calls to so many external services may overwhelm the network in high-traffic situations.

Practical Solution

If possible, consolidate API calls to a single call, which may be encapsulated behind a separate endpoint that performs these tasks with fewer network calls. Consider using asynchronous communication (RabbitMQ or Kafka) to decouple calls wherever they apply, such as in the case of the notifications service in this example.

Pro Tip

Use techniques like data caching and denormalization to minimize dependencies on real-time requests. Another way of achieving more efficiency here is by designing a read-optimized system that denormalizes data based on relationships in real-time as the data gets updated in the source instead of resolving all those relationships at the time when a user request is being executed. This can be achieved by building real-time streaming pipelines using Kafka (or other streaming technologies) that perform Change Data Capture (CDC) from the data source(s) to curate a read-optimized version of the information that can be directly queried when user requests are executed. This takes the heavy-lifting or resolving relationships and denormalizing away from the run time of the actual user requests.

4. Poorly Defined Service Boundaries

The Problem

Services with unclear or overlapping responsibilities lead to bloated, hard-to-maintain systems with complex relationships and unclear ownership.

Real-World Scenario

An e-commerce site with a "product" service that handles product creation, inventory updates, and customer reviews leads to over-complexity and entanglement of unrelated concerns.

Practical Solution

Use Domain-Driven Design. Break down microservices by functional responsibilities, not technical ones (e.g., separate services for inventory management, customer reviews, and products). This means that microservices should be based on clearly isolated functional domains identified using Domain-Driven Design.

Pro Tip

Organize cross-functional teams that are aligned with individual microservices to maintain strong domain ownership.

5. Inconsistent Communication Protocols

The Problem

Using a mix of communication protocols (e.g., REST, gRPC, WebSockets) without standardization leads to maintenance headaches and integration issues.

Real-World Scenario

A legacy microservice system uses REST for some internal calls, gRPC for others, and messaging for specific events. New developers face a steep learning curve and difficulty troubleshooting.

Practical Solution

Standardize communication protocols based on usage context (e.g., REST for external APIs, gRPC for low-latency internal calls). Document the standards and provide utilities to simplify common operations. Keep the usage honest with the philosophy behind the technology being used.

Pro Tip

Consider adopting a service mesh (e.g., Istio, Linkerd) to handle cross-service communication consistently.

6. Lack of Observability

The Problem

Limited insight into service health, interactions, and performance makes diagnosing issues difficult and reduces system resilience.

Real-World Scenario

A payment system failure cascades through multiple services, yet identifying the root cause requires manually checking logs across different instances.

Practical Solution

Invest in distributed tracing (e.g., OpenTelemetry), centralized logging (e.g., ELK stack), and metrics collection (e.g., Prometheus). Track transaction IDs across service boundaries for better traceability.

Pro Tip

Start small with logging and expand to distributed tracing as complexity increases.

7. Hardcoded Configuration

The Problem

Hardcoded database URLs, secrets, or service endpoints lead to inflexible deployments and security risks.

Real-World Scenario

A production database connection string is hardcoded in source code, making it prone to accidental exposure during version control updates. Sometimes, these configurations are externalized in the main source code and used directly in auxiliary helper scripts, such as one-time or less frequently used automation scripts.

Practical Solution

Externalize configurations using environment variables, configuration management systems (e.g., Consul), or tools like Kubernetes ConfigMaps and Secrets.

Pro Tip

Regularly audit configurations and secrets for security risks and enforce proper access controls. Automate this audit using special unit tests or shared scripts to be invoked during CI/CD build pipelines that check for any hardcoded connection strings in the source code before building it.

8. Ignoring Network Reliability and Latency

The Problem

Due to their distributed nature, microservices rely heavily on network communication, and network failures or latencies can disrupt system stability.

Real-World Scenario

An API call to an external payment provider times out, causing downstream services to hang or fail.

Practical Solution

Implement resilience patterns like retries with exponential backoff, circuit breakers (e.g., resilience4j), and timeout strategies. Use load balancers to distribute requests and reduce individual service load.

Pro Tip

Utilize service meshes for built-in resilience features like traffic control and fault injection.

9. Insufficient API Versioning

The Problem

Introducing breaking changes (like changing the structure of request/response or adding something in a non-backward compatible way) to service APIs without proper versioning leads to consumer-side failures and costly rollbacks.

Real-World Scenario

Updating a REST API’s payload structure without versioning causes multiple clients to break unexpectedly

Practical Solution

Use versioning strategies (e.g., URL path versioning /v1/resource, headers). Communicate changes clearly and offer deprecation periods for migration.

Pro Tip

Consider tools like OpenAPI to automatically document and track API changes.

10. Reinventing the Wheel for Infrastructure Concerns

The Problem

Custom-built solutions for service discovery, health checks, and load balancing can lead to increased maintenance and bugs. It takes time away from more important concerns of the application such as functional design, business features etc.

Real-World Scenario

Building a custom service discovery tool leads to operational overhead and compatibility issues with new microservices.

Practical Solution

Use battle-tested solutions like Kubernetes for service discovery, health checks, and load balancing. Leverage service meshes for consistent policies across services.

Pro Tip

Focus engineering efforts on business logic rather than reinventing infrastructure components.

Conclusion

Building scalable microservices has its share of rewards and challenges. By recognizing and addressing common anti-patterns — like distributed monoliths, shared data access, and excessive inter-service calls — we can maintain service independence, resilience, and scalability. Real-world constraints often require practical compromises, such as logical data separation or encapsulated APIs, especially with flexible databases like MongoDB. Success in microservices lies in balancing theory and practical realities, focusing on well-defined boundaries, consistent communication, and robust observability. With a pragmatic approach, we can fully unlock the potential of microservices while minimizing pitfalls.