Microservices Architecture (Part 3): Database Per Service

In the world of microservices, one of the core principles that helps ensure true independence and scalability is the Database per Service pattern. Unlike monolithic architectures, where all services share a single database, each microservice in a microservices architecture manages its own database. But why is this so important, and how does it benefit your system?

Let’s dive into why the Database per Service approach matters and how to handle the complexities it introduces.

Why Database per Service?

1. Decoupling for True Independence

One of the key goals of microservices is decoupling, ensuring that services are as independent as possible. By giving each service its own database, you eliminate one of the biggest sources of coupling: shared data. Each service can evolve, change its data schema, and scale independently without impacting others.

2. Autonomy in Data Management

Each microservice can choose the database technology that best suits its needs. For example, a service handling user data might use a relational database like PostgreSQL, while a service responsible for real-time analytics could opt for a NoSQL solution like MongoDB or a time-series database like InfluxDB. This technology-agnostic approach gives you the flexibility to optimize each service’s performance.

3. Improved Fault Isolation

In a monolithic system with a shared database, a failure in one part of the system, such as a schema change or a database issue, can affect the entire application. With a database per service, failures are isolated to the affected service. This containment helps prevent cascading failures across the system.

4. Scaling Services Independently

Having independent databases for each microservice enables fine-grained scaling. You can scale services independently based on demand, without worrying about scaling a shared database or facing performance bottlenecks. This granular scalability is one of the biggest advantages in high-traffic applications.

Challenges of Database per Service

While the Database per Service pattern offers many advantages, it does come with its own set of challenges. Let’s address some of the most common ones and how to overcome them:

Data Consistency

In a monolithic architecture, maintaining data consistency is easier because all services share a single database. With microservices, maintaining consistency across services that have separate databases can be tricky.

Data Duplication

With each service having its own database, there’s often a need to duplicate data between services for different business operations. While this might seem inefficient, it’s a necessary trade-off for decoupling.

Querying Across Services

In a monolithic system, querying across multiple entities is simple because they share the same database. In a microservices architecture, where each service has its own database, querying across services becomes more complicated.

How to Implement Database Per Service ?

Keep Databases Separate

Each service should only access its own database. If another service needs information, it should ask through an API, not by directly connecting to the other service’s database. This keeps services separate and reduces the risk of one service affecting another.

Use Events to Share Updates

When one service updates its data and other services need to know, have it send out a notification (an event). Other services can listen for these events and update their own data accordingly. This keeps everything in sync without making services tightly connected.

Pick the Right Database for the Job

One great thing about this pattern is that each service can use the best database for its job. For example, one service might need a traditional database for complex queries, while another might need a fast, flexible database to handle large amounts of data. Choose what works best for each service.

Handle Data Copies Smartly

Sometimes, services may need the same data (like customer information). Instead of sharing a single database, each service keeps its own copy of the data. Use events to keep these copies up-to-date. It’s normal for data to be duplicated, but you need to make sure updates are properly shared.

Case Study

Consider an e-commerce application using microservices architecture. You might have separate services for product catalog, payments and orders, Each with its own database. Here’s how Database Per Service enhances this system:

• Product Catalog: Uses a NoSQL database to store large amounts of data about products.
• Payments: Uses a relational database for transactional integrity.
• Orders: Uses a database designed for reliable performance and minimal downtime.

If one service's database encounters issues (say, the product catalog goes down for maintenance), the other services continue to operate without disruption. Customers can still place orders, process payments, and access their accounts.

Final Thoughts

The Database per Service pattern is a fundamental principle of microservices architecture that enables services to be fully autonomous, scalable, and decoupled. While it introduces some complexity, particularly around data consistency and querying, those challenges can be overcome with careful design and by embracing principles like eventual consistency and event-driven communication.

🔥 P.S. In the next post of our Microservices Blog Series, we’ll take a closer look at Service-to-Service Communication and explore how microservices interact using REST, gRPC, and message brokers. Stay tuned, and feel free to share your thoughts in the comments! 😊