Microservices Design Patterns - Database per Service Pattern
The Database per Service pattern, also known as the Database per Microservice pattern, is a design approach in microservices architecture where each microservice has its own dedicated database.
The database is one of the most important components of microservices architecture, but it isn’t uncommon for developers to overlook the database per service pattern when building their services. Database organization will affect the efficiency and complexity of the application.
A database dedicated to one service can't be accessed by other services. This is one of the reasons that makes it much easier to scale and understand from a whole end-to-end business aspect.
Here's a breakdown of this pattern:
Key Characteristics:
Decentralized Data Management:
Each service owns its data, which means it also manages its database schema, data models, and data access patterns independently.
Autonomy:
Services can evolve independently without needing to coordinate schema changes with other services. This supports the principle of loose coupling.
Technology Diversity:
Different services can use different database technologies best suited for their specific needs. For example, one service might use a relational database like PostgreSQL for transactional integrity, while another might use a NoSQL database like MongoDB for flexible schema requirements.
Scalability:
Services can scale their databases independently based on their load and data growth, rather than scaling an entire monolithic database.
Data Consistency:
This pattern often leads to eventual consistency rather than immediate consistency across services, which can be managed through event sourcing, CQRS (Command Query Responsibility Segregation), or other patterns.
Advantages:
Isolation:
Changes in one service's database schema do not affect others, reducing the risk of widespread issues.
Performance:
Each service can optimize its database for its specific use case, potentially improving performance.
Security:
Data isolation can enhance security by limiting data exposure to only what's necessary for each service.
Challenges:
Data Consistency:
Ensuring data consistency across services can be complex. Techniques like saga patterns or compensating transactions might be needed.
Data Duplication:
There might be data duplication across services, which needs to be managed to keep data in sync.
Operational Overhead:
Managing multiple databases increases operational complexity, including backups, monitoring, and maintenance.
Implementation Considerations
Database Selection:
Choose the right database for each service based on its data model, query patterns, and scalability needs.
Data Integration:
Use APIs or events (like message queues) for services to communicate and keep data in sync.
Transaction Management:
Implement strategies for handling distributed transactions, like two-phase commit or saga patterns.
Monitoring and Management:
Tools and practices for overseeing multiple databases, possibly of different types, need to be in place.
Example Scenario:
Imagine an e-commerce platform:
Inventory Service:
Uses a NoSQL database for fast, flexible data access.
Order Service :
Uses a relational database for transactional integrity.
User Service:
Might use a Graph Database for social connections. Each of these services manages its own data, communicates through APIs or events, and scales independently.
Conclusion:
The Database per Service pattern is powerful for microservices architecture, offering significant benefits in terms of scalability, autonomy, and technology fit. However, it requires careful planning around data consistency, management, and integration to avoid pitfalls like data inconsistency or increased operational complexity.
