Common Coupling

What is Common Coupling?#

Common coupling occurs when multiple services or components in a distributed system share responsibility for managing the same data. This often involves services sharing a common database or file store. While seemingly convenient, this approach can introduce significant challenges in system maintenance, scalability, and overall architecture integrity.

Example#

Consider an e-commerce platform with an order management system:

• The Order service can set the order state to CREATED or CONFIRMED.
• The Delivery service can update the order state to DELIVERED.
• Both services directly access and modify the same database table to update the order state.

This scenario exemplifies common coupling, as multiple services share and modify common data.

Challenges of Common Coupling#

1. Tight Coupling: Services become interdependent due to their shared data store, making system changes more complex.

2. Data Integrity Risks: Multiple services modifying shared data increase the likelihood of inconsistencies and race conditions.

3. Reduced Service Autonomy: Services lose the ability to evolve independently, as changes to shared data schemas require cross-service coordination.

4. Increased Complexity: As the system grows, managing access patterns and ensuring data consistency becomes increasingly challenging.

5. Compromised Fault Isolation: Issues in one service can more easily propagate to others due to the shared data store.

6. Scalability Limitations: Common data stores can become bottlenecks, hindering system scalability.

Strategies to Handle Common Coupling#

To address the challenges associated with common coupling, consider implementing these approaches:

1. Establish Service Ownership: Assign clear ownership of data to specific services. In our example, the Order service could be the sole owner of order data.

2. Implement API-Driven Communication: Instead of direct database access, services should communicate through well-defined APIs. For instance, the Delivery service could call an API on the Order service to update the order state.

3. Use Event-Driven Architecture: Implement an event-driven system where services publish events when data changes, allowing other services to react accordingly.

Example Implementation#

Let’s enhance our e-commerce platform example to demonstrate improved architecture:

In this improved design:

• The Order service exclusively owns and manages order data.
• The Delivery service updates order status by calling an API on the Order service.

Key Takeaways#

• Common coupling in distributed systems can lead to tight interdependencies and data integrity issues.
• Assigning clear data ownership to specific services helps maintain system boundaries.
• API-driven communication and event-driven architectures can significantly reduce common coupling.
• Asynchronous communication helps in decoupling services and improving system resilience.

FAQs#