Database per service pattern in Microservices - Deep Dive

Overview - Database per service pattern

What is it?

The Database per service pattern means each microservice has its own separate database. This way, services do not share a single database but manage their own data independently. It helps keep services isolated and reduces dependencies between them. This pattern supports microservices' goal of being independent and scalable.

Why it matters

Without this pattern, microservices would share one database, causing tight coupling and making it hard to change or scale services independently. If one service changes the database schema, it could break others. This pattern solves that by giving each service control over its own data, improving reliability and flexibility in large systems.

Where it fits

Before learning this, you should understand what microservices are and the basics of databases. After this, you can learn about data consistency, event-driven communication, and patterns like Saga for managing transactions across services.

Mental Model

Core Idea

Each microservice owns and manages its own database to stay independent and avoid conflicts.

Think of it like...

Imagine a group project where each team member keeps their own notebook instead of sharing one. This way, they can write and organize their notes freely without worrying about others changing them.

┌───────────────┐   ┌───────────────┐   ┌───────────────┐
│ Microservice A│   │ Microservice B│   │ Microservice C│
│ ┌───────────┐ │   │ ┌───────────┐ │   │ ┌───────────┐ │
│ │ Database A│ │   │ │ Database B│ │   │ │ Database C│ │
│ └───────────┘ │   │ └───────────┘ │   │ └───────────┘ │
└───────────────┘   └───────────────┘   └───────────────┘

Build-Up - 7 Steps

1

Foundation

What is a Microservice?

Concept: Understand the basic idea of microservices as small, independent services.

Microservices are small programs that do one job well. Each microservice runs separately and can be changed without affecting others. They communicate over the network to work together as a system.

Result

You know that microservices are independent units that form a bigger application.

Understanding microservices is key because the database per service pattern depends on each service being independent.

2

Foundation

Basics of Databases

Concept: Learn what a database is and why applications use them.

A database stores information so programs can save and find data quickly. It can be a simple list or a complex system with tables and rules. Applications use databases to keep user info, settings, and more.

Result

You understand why services need databases to keep their data safe and organized.

Knowing databases helps you see why separating them per service matters for data control.

3

Intermediate

Why Share Databases Causes Problems

🤔Before reading on: do you think sharing one database among services makes coordination easier or causes hidden risks? Commit to your answer.

Concept: Explore the issues when multiple services use the same database.

If many services use one database, they depend on each other's data structure. Changing one service's data can break others. It also makes scaling harder because the database becomes a bottleneck.

Result

You see that shared databases create tight links between services, reducing independence.

Knowing these problems explains why the database per service pattern is needed for true service independence.

4

Intermediate

How Database per Service Works

🤔Before reading on: do you think giving each service its own database increases complexity or simplifies service management? Commit to your answer.

Concept: Learn the core idea of assigning a separate database to each microservice.

Each microservice has its own database it controls fully. No other service accesses it directly. Services communicate by sending messages, not by sharing data stores. This keeps services isolated and flexible.

Result

You understand the pattern that ensures services do not interfere with each other's data.

Understanding this pattern is crucial because it enables independent development and scaling of services.

5

Intermediate

Data Consistency Challenges

🤔Before reading on: do you think having separate databases makes keeping data consistent easier or harder? Commit to your answer.

Concept: Recognize the difficulty of keeping data in sync across multiple databases.

When services have separate databases, making sure data matches everywhere is tricky. For example, if one service updates data, others need to know. This requires special communication patterns like events or transactions.

Result

You realize that database per service adds complexity in data coordination.

Knowing this challenge prepares you to learn advanced patterns for data consistency in microservices.

6

Advanced

Handling Transactions Across Services

🤔Before reading on: do you think traditional database transactions work across multiple service databases? Commit to your answer.

Concept: Learn why classic transactions don't work well with database per service and what alternatives exist.

Traditional transactions lock data to keep it consistent, but they don't work across separate databases owned by different services. Instead, patterns like Saga use a series of steps with compensations to keep data consistent without locks.

Result

You understand why new transaction patterns are needed in microservices with separate databases.

Knowing this helps you design reliable systems that keep data correct without blocking services.

7

Expert

Surprising Impact on Performance and Scaling

🤔Before reading on: do you think having multiple databases improves or worsens system performance? Commit to your answer.

Concept: Discover how database per service can both improve and complicate performance and scaling.

Having separate databases lets each service scale its data storage independently, improving performance under load. But it also means more databases to manage and monitor, increasing operational complexity. Balancing these trade-offs is key in production.

Result

You see the nuanced effects of this pattern on system behavior and maintenance.

Understanding these trade-offs helps experts design systems that scale well without becoming unmanageable.

Under the Hood

Each microservice runs with its own database instance or schema, fully controlling its data. Services do not share tables or schemas. Communication between services happens via APIs or messaging systems, not direct database queries. This separation enforces data ownership and isolation, preventing accidental data corruption or schema conflicts.

Why designed this way?

This pattern was created to solve the problem of tight coupling caused by shared databases in monolithic or early microservice systems. It allows teams to work independently, choose the best database technology per service, and deploy changes without coordination delays. Alternatives like shared databases were simpler but caused scaling and reliability issues.

┌───────────────┐       ┌───────────────┐       ┌───────────────┐
│ Microservice A│──────▶│ API / Message │◀──────│ Microservice B│
│ ┌───────────┐ │       └───────────────┘       │ ┌───────────┐ │
│ │ Database A│ │                               │ │ Database B│ │
│ └───────────┘ │                               │ └───────────┘ │
└───────────────┘                               └───────────────┘

Myth Busters - 4 Common Misconceptions

Quick: Does database per service mean all services must use the same database technology? Commit yes or no.

Common Belief: All microservices must use the same type of database to keep things simple.

Reality: Each service can choose the database technology that fits its needs best, like SQL, NoSQL, or graph databases.

Why it matters: Believing all services must use the same database limits flexibility and can lead to inefficient data handling.

Quick: Do you think database per service eliminates all data consistency problems? Commit yes or no.

Common Belief: Having separate databases solves all data consistency issues automatically.

Reality: Separate databases make consistency harder because data is spread out and must be synchronized through communication patterns.

Why it matters: Ignoring this leads to data mismatches and bugs in distributed systems.

Quick: Is it true that database per service always improves performance? Commit yes or no.

Common Belief: Using separate databases always makes the system faster.

Reality: While it can improve scaling, managing multiple databases adds overhead and complexity that can hurt performance if not handled well.

Why it matters: Overestimating performance gains can cause poor design choices and operational difficulties.

Quick: Can microservices directly query each other's databases in this pattern? Commit yes or no.

Common Belief: Microservices can access other services' databases if needed for data.

Reality: Direct database access between services breaks isolation and is against the pattern's principles; services should communicate via APIs or messages.

Why it matters: Violating this causes tight coupling and defeats the purpose of independent services.

Expert Zone

1

Some services may use multiple databases internally but still expose a single data interface to others, maintaining the pattern's isolation.

2

Choosing the right database technology per service can optimize performance but requires careful evaluation of trade-offs like consistency and complexity.

3

Eventual consistency is often accepted in this pattern, requiring developers to design for temporary data mismatches.

When NOT to use

Avoid this pattern when your system requires strong, immediate consistency across all data or when the overhead of managing multiple databases outweighs benefits. In such cases, a shared database or a monolithic design might be better.

Production Patterns

In real systems, database per service is combined with event-driven architectures, API gateways, and Saga patterns to handle data consistency and service communication reliably.

Connections

Event-Driven Architecture

Builds-on

Understanding database per service helps grasp why event-driven communication is needed to keep data consistent across independent services.

Domain-Driven Design

Same pattern

Both emphasize clear boundaries and ownership, making database per service a natural fit for implementing bounded contexts.

Organizational Team Structure

Analogy to

Just like teams work best when they own their tasks and tools, microservices work best when they own their data and databases.

Common Pitfalls

#1 Trying to share a database schema between services to avoid duplication.

Wrong approach:

Service A and Service B both directly read and write to the same 'users' table in one database.

Correct approach:

Service A has its own 'users' database; Service B accesses user data via Service A's API, not the database.

Root cause:

Misunderstanding that database sharing breaks service independence and causes tight coupling.

#2 Ignoring data synchronization needs between services with separate databases.

Wrong approach:

Services update their own databases without notifying others, expecting data to stay consistent automatically.

Correct approach:

Services publish events or use messaging to inform others about data changes for synchronization.

Root cause:

Underestimating the complexity of distributed data consistency.

#3 Allowing direct database queries across service boundaries for convenience.

Wrong approach:

Service B runs SQL queries directly on Service A's database to get data.

Correct approach:

Service B calls Service A's API to get needed data, respecting service boundaries.

Root cause:

Ignoring the principle of service autonomy and encapsulation.

Key Takeaways

Database per service pattern ensures each microservice controls its own data, promoting independence and flexibility.

Sharing databases among services creates tight coupling and risks breaking changes, which this pattern avoids.

Separate databases require new ways to keep data consistent, like event-driven communication and Saga patterns.

This pattern improves scalability but adds operational complexity that must be managed carefully.

Understanding this pattern is essential for designing robust, scalable microservice architectures.