Why Database per service pattern in Microservices? - Purpose & Use Cases

The Big Idea

What if one small change could stop your whole app from breaking? Discover how to avoid that with a simple pattern.

The Scenario

Imagine a team building a big app where many parts share one giant database. Every time someone changes the database, it risks breaking others. It feels like everyone is trying to use the same notebook at once, causing confusion and mistakes.

The Problem

Using one shared database slows down development because teams must coordinate every change. Bugs spread easily since one service's error can corrupt shared data. It's hard to scale or update parts independently, making the whole system fragile and slow.

The Solution

The database per service pattern gives each service its own private database. This way, teams work independently without stepping on each other's toes. Services control their data fully, making the system more reliable, easier to scale, and faster to develop.

Before vs After

✗ Before

ServiceA and ServiceB both read/write to the same 'users' table in one big database.

✓ After

ServiceA uses its own 'users' database; ServiceB uses a separate 'users' database.

What It Enables

This pattern unlocks true independence for each service, allowing faster updates, better fault isolation, and easier scaling.

Real Life Example

Think of an online store where the payment service and product catalog service each have their own databases. If the payment database needs an update, it won't affect the product catalog, so the store keeps running smoothly.

Key Takeaways

Sharing one database creates tight coupling and risks.

Database per service isolates data, reducing errors and conflicts.

It enables independent development, deployment, and scaling.

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