The central idea behind the "Database Per Microservice" design pattern is to assign each microservice its own dedicated database. This approach stands in contrast to the traditional monolithic architecture where a single, large database is shared by all the components of the application.
Why Use It?
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Isolation: Each microservice fully owns its data and schema changes. This guarantees that modifications within one microservice won't cause unexpected ripple effects on others, increasing reliability and resilience.
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Independent Scalability: You can independently scale databases for individual microservices based on their load and storage requirements. A high-traffic microservice won't be bottlenecked by a shared database.
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Technology Flexibility (Polyglot Persistence): Microservices can choose the most suitable database technology for their needs (e.g., SQL for transactional data, NoSQL for document storage, graph databases for complex relationships).
Clear Ownership: Promotes strict data encapsulation within a microservice boundary, improving maintainability.
Example
Imagine an e-commerce system. Here's how it might look with the "Database Per Microservice" pattern:
- Order Service: Manages orders and might use a relational database (e.g., MySQL, PostgreSQL) for its structured data.
- Product Catalog Service: Stores product information and could use a document database (e.g., MongoDB) to manage flexible product attributes.
- Recommendation Engine Service: Tracks user behavior and might opt for a graph database (e.g., Neo4j) to model complex relationships.
+-------------------+ +-------------------+ +-------------------+
| Order Service | | Product Catalog | | Recommendation |
| | | Service | | Engine Service |
+---------+---------+ +---------+---------+ +---------+---------+
| | |
| | |
+----+----+ +----+-------+ +----+-------+
| DB (SQL)| | DB (NoSQL) | | DB (Graph) |
+---------+ +------------+ +------------+
Challenges
- Data Consistency: Maintaining eventual consistency across distributed data stores can be complex, especially when transactions span multiple microservices (consider techniques like the Saga Pattern).
- Querying Across Services: Retrieving data that naturally spans multiple microservices might require API composition or event-driven data synchronization.
- Operational Overhead: Managing multiple databases increases operational complexity.
When to Consider?
The "Database Per Microservice" pattern is a great fit when:
- You're embracing a microservices architecture.
- Microservices have highly distinct data requirements regarding storage type or scaling.
For modern system design in general, microservices is the preferred architecture. Therefore, "A Database Per Microservice" is the default option. You can see this in most of our practice problem.