The Hybrid Mess of a Shared Database for Multiple Products
Why Initial Simplicity Can Lead to Long-Term Complexity and How to Avoid It
In the quest for an enterprise integration pattern, the pattern of a shared database often seems appealing. On the surface, it promises streamlined data synchronization and simplified data access. However, for organizations with multiple products, this approach can quickly devolve into what I term a hybrid mess. Let’s explore why a shared database for multiple products can be a detrimental choice, highlighting the complex issues that arise from this setup while also considering scenarios where it might still be appropriate.
Initial Appeal 🚀 vs. Long-Term Issues ⚠️
At first glance, a shared database seems like a quick win. It facilitates simple data synchronization, offers a single source of truth, and can accelerate initial development. However, this honeymoon period is short-lived. Over time, this setup leads to performance bottlenecks, tight coupling, and a single point of failure. The simplicity that seemed advantageous initially becomes a significant liability as the system grows.
The Distributed Monolith 🗿
A shared database often transforms a supposedly microservice-based architecture into what’s known as a distributed monolith. Despite designing the applications as separate services, the shared database enables tight coupling between them. This becomes even worse when there is API communication between the products.
Tight Coupling: Any change in the database schema necessitates coordination across the services that share the schema, hindering independent development and deployment.
Shared Failure Points: The centralized database becomes a single point of failure. All dependent services experience widespread downtime if the centralized database fails. Moreover, if one service consumes a lot of resources in the database, it affects the other services that coexist in the same database.
Complex Deployments: Synchronized deployments become necessary to avoid breaking changes, complicating the CI/CD pipeline, and reducing agility.
Lack of Clear Boundaries: Shared databases blur the lines between different applications, leading to a convoluted design.
High Maintenance Overhead: Maintaining such a system becomes increasingly difficult. Changes in one area can have far-reaching effects, increasing the risk of bugs.
Monolithic Data Layer 🗿
Despite the applications' microservice structure, the shared database leads to a monolithic data layer. This compromises numerous advantages of microservice architecture, including:
Independent Scaling: Different services may have conflicting requirements, making it difficult to optimize and scale the database to meet a variety of needs.
Independent Development and Deployment: If done properly, microservices allow teams to develop and deploy their components independently.
Alternative Approach: Microservices with Independent Databases Using Messaging 📬
To avoid the pitfalls of a shared database, consider microservices with independent databases and a messaging system for communication. This approach offers several benefits:
Isolation and Independent Scaling: Each microservice has its own database, allowing for better isolation and independent scaling.
Event-Driven Communication: Use messaging systems (e.g., Kafka, RabbitMQ) for decoupled communication between services.
Asynchronous Processing: Messaging allows for asynchronous processing, improving system performance and reliability.
Flexibility and Innovation: Teams can choose the best technologies for their needs, promoting innovation.
Parallel Development and Isolation: Multiple teams can work concurrently without stepping on each other's toes.
Pitfalls ⚠️
This approach also has challenges:
Increased Complexity: Implementing and managing a messaging system introduces additional complexity.
Data Consistency Challenges: Ensuring eventual consistency across distributed services can be difficult.
Operational Overhead: Operating and maintaining a messaging infrastructure adds to the operational burden.
Latency and Performance Considerations: Asynchronous communication has the potential to introduce latency.
Practical Guidance for Managing Microservices Complexities 🧩
Managing the complexities of microservices with independent databases and messaging systems is crucial.
Invest in Monitoring and Logging: Ensure that robust monitoring and logging are implemented.
Design for Failure: Assume that services will fail and plan for resilience.
Ensure Data Consistency: Use patterns like event sourcing and CQRS to manage data consistency.
When Might a Shared Database Be Appropriate 🗃️?
While the shared database approach has significant downsides, there are scenarios where it might still be appropriate:
Small Teams and Simple Applications: For small teams working on relatively simple applications with low interdependence, a shared database might suffice and simplify the initial setup.
Tightly Coupled Features: When features are inherently tightly coupled and need to share a significant amount of data frequently, a shared database can reduce the overhead of constant data synchronization.
Legacy Systems: Integrating with legacy systems that are not designed for a microservices architecture might necessitate a shared database, at least temporarily during a transition phase.
Conclusion 🔚
A shared database might offer initial simplicity, but the long-term challenges can outweigh the benefits. Instead, consider a microservices architecture with independent databases and messaging systems. This approach provides better isolation, scalability, and flexibility, allowing for a maintainable, scalable, and resilient system. By carefully weighing the benefits and pitfalls and incorporating practical strategies, you can create a robust infrastructure that supports your organization’s dynamic needs.
Engage with Me! 📣
I’d love to hear your thoughts and experiences on this topic. Have you faced challenges with a shared database in your projects? How did you address them? What strategies have you found effective for managing microservices with independent databases?