Database per service pattern in Microservices - System Design Guide

Problem Statement

When multiple microservices share a single database, changes by one service can break others, causing data corruption and deployment delays. This tight coupling reduces service autonomy and makes scaling or evolving services independently very difficult.

Solution

Each microservice owns and manages its own database, isolating its data from others. Services communicate through APIs or messaging to share data, ensuring loose coupling and independent evolution. This separation allows teams to choose the best database type per service and deploy changes without impacting others.

Architecture

Microservice A

┌───────────┐

↓

→API / Messaging Bus

This diagram shows three microservices each with its own database. They communicate through an API or messaging bus, avoiding direct database sharing.

Trade-offs

✓ Pros

→

Enables independent deployment and scaling of each microservice.

→

Prevents data corruption caused by shared database schema changes.

→

Allows use of different database technologies optimized per service.

→

Improves fault isolation; one database failure doesn't affect others.

✗ Cons

→

Data consistency across services becomes more complex and often eventual.

→

Requires additional effort to implement inter-service communication.

→

Increases operational overhead managing multiple databases.

When building a microservices architecture with multiple teams needing autonomy, or when services have distinct data models and scaling needs beyond 1000 requests per second.

For small applications with fewer than 5 services or low data volume where the complexity of multiple databases outweighs benefits.

Real World Examples

Amazon

Each microservice owns its own database to allow independent scaling and deployment, avoiding cross-service data conflicts.

Netflix

Uses database per service to isolate failures and optimize data storage per service type, improving resilience and performance.

Uber

Separates databases per service to handle diverse data models like trips, payments, and user profiles independently.

Alternatives

Shared database pattern

Multiple services share the same database schema and instance.

Use when: When services are tightly coupled or the system is small and simple, requiring less operational overhead.

Command Query Responsibility Segregation (CQRS)

Separates read and write models, often with different databases, but still can be per service or shared.

Use when: When read and write workloads differ significantly and require optimization.

Summary

Database per service pattern isolates data ownership to each microservice, preventing tight coupling.

It enables independent scaling, deployment, and technology choices per service.

This pattern requires careful design of inter-service communication and data consistency.

Practice

1. What is the main advantage of the Database per service pattern in microservices architecture?

easy

A. It reduces the number of databases needed in the system.

B. All services share the same database for easier data management.

C. Each service can be developed, deployed, and scaled independently.

D. It allows direct database access between services.

Solution

1. Step 1: Understand the pattern's goal

   The Database per service pattern means each microservice owns its own database to avoid tight coupling.

2. Step 2: Analyze the benefits

   This independence allows each service to be developed, deployed, and scaled without affecting others.

3. Final Answer:

   Each service can be developed, deployed, and scaled independently. -> Option C

4. Quick Check:

   Service independence [OK]

Hint: Database per service means independent databases per microservice [OK]

Common Mistakes:

• Thinking all services share one database
• Assuming database sharing improves independence
• Believing it reduces total databases

2. Which of the following is the correct way for microservices to access data in the Database per service pattern?

easy

A. Directly query another service's database.

B. Use database triggers to sync data between services.

C. Share a common database connection pool.

D. Use APIs to communicate and request data from other services.

Solution

1. Step 1: Recall communication rules in this pattern

   Services do not share databases; they communicate via APIs to maintain independence.

2. Step 2: Identify correct data access method

   Using APIs ensures loose coupling and clear service boundaries.

3. Final Answer:

   Use APIs to communicate and request data from other services. -> Option D

4. Quick Check:

   API communication [OK]

Hint: Microservices talk via APIs, not direct DB access [OK]

Common Mistakes:

• Trying to query other service databases directly
• Assuming shared connection pools exist
• Using database triggers for cross-service sync

3. Consider two microservices: OrderService and InventoryService, each with its own database. If OrderService needs to check stock before placing an order, what is the correct flow?

medium

A. OrderService sends an API request to InventoryService to get stock information.

B. InventoryService pushes stock updates to OrderService's database.

C. OrderService writes stock info to its own database and reads from there.

D. OrderService queries InventoryService's database directly to check stock.

Solution

1. Step 1: Identify data ownership

   InventoryService owns stock data in its own database; OrderService cannot access it directly.

2. Step 2: Determine communication method

   OrderService must call InventoryService's API to get current stock info.

3. Final Answer:

   OrderService sends an API request to InventoryService to get stock information. -> Option A

4. Quick Check:

   API call for data [OK]

Hint: Always use API calls to get data from other services [OK]

Common Mistakes:

• Direct DB queries between services
• Duplicating data in multiple databases
• Relying on push updates to other service DBs

4. A developer tries to implement the Database per service pattern but notices data inconsistency between services. What is the most likely cause?

medium

A. Services are sharing the same database schema.

B. Services are directly querying each other's databases.

C. Services communicate asynchronously via APIs.

D. Each service has its own database and communicates via APIs.

Solution

1. Step 1: Identify incorrect practice

   Directly querying another service's database breaks independence and can cause stale or inconsistent data.

2. Step 2: Understand correct communication

   Services should communicate via APIs to keep data consistent and boundaries clear.

3. Final Answer:

   Services are directly querying each other's databases. -> Option B

4. Quick Check:

   Direct DB queries cause inconsistency [OK]

Hint: Avoid direct DB queries between services to prevent inconsistency [OK]

Common Mistakes:

• Assuming shared schema is the problem
• Thinking async API calls cause inconsistency
• Believing separate DBs cause inconsistency

5. You are designing a microservices system with the Database per service pattern. How can you ensure data consistency across services when a transaction involves multiple services?

hard

A. Implement eventual consistency using event-driven communication and compensating actions.

B. Use distributed transactions with two-phase commit across all databases.

C. Allow services to share a single database to simplify transactions.

D. Synchronize databases by copying data between services periodically.

Solution

1. Step 1: Understand distributed transaction challenges

   Two-phase commit is complex and reduces service independence, so it's rarely used in microservices.

2. Step 2: Identify best practice for consistency

   Event-driven communication with eventual consistency and compensating actions allows services to stay independent and handle failures gracefully.

3. Final Answer:

   Implement eventual consistency using event-driven communication and compensating actions. -> Option A

4. Quick Check:

   Event-driven eventual consistency [OK]

Hint: Use events and compensations for cross-service consistency [OK]

Common Mistakes:

• Trying distributed two-phase commit in microservices
• Sharing a single database defeats independence
• Periodic data copying causes stale data