Service decomposition strategies in Microservices - Architecture Diagram

System Overview - Service decomposition strategies

This system explains how a large application is broken down into smaller, independent services called microservices. Each service handles a specific business function. The goal is to improve scalability, maintainability, and team autonomy.

Architecture Diagram

User
  |
  v
Load Balancer
  |
  v
API Gateway
  |
  +---------------------------+---------------------------+---------------------------+
  |                           |                           |
  v                           v                           v
User Service             Order Service               Payment Service
  |                           |                           |
  v                           v                           v
User Database           Order Database             Payment Database
  \
   \-> Cache Layer (shared or per service)

Components

User

actor

End user who interacts with the system

Load Balancer

load_balancer

Distributes incoming requests evenly to API Gateway instances

API Gateway

api_gateway

Single entry point that routes requests to appropriate microservices

User Service

service

Handles user-related operations like registration and profile management

Order Service

service

Manages order creation, updates, and tracking

Payment Service

service

Processes payments and manages billing

User Database

database

Stores user data for User Service

Order Database

database

Stores order data for Order Service

Payment Database

database

Stores payment data for Payment Service

Cache Layer

cache

Speeds up data access by storing frequently used data temporarily

Request Flow - 11 Hops

User → Load Balancer

Load Balancer → API Gateway

API Gateway → User Service

User Service → Cache Layer

Cache Layer → User Service

User Service → User Database

User Database → User Service

User Service → Cache Layer

User Service → API Gateway

API Gateway → Load Balancer

Load Balancer → User

Failure Scenario

Component Fails:User Database

Impact:User Service cannot retrieve or store user data, causing failures in user operations. Cache may still serve stale data for reads.

Mitigation:Use database replication for failover. Cache serves stale data temporarily. Alert system triggers for manual intervention.

Architecture Quiz - 3 Questions

Which component directs user requests to the correct microservice?

A Load Balancer

B Cache Layer

C API Gateway

D User Database

Design Principle

This architecture shows how breaking a large system into focused microservices improves scalability and team independence. Using an API Gateway centralizes routing, while caches reduce database load and improve response times.

Practice

1. Which of the following best describes the main goal of service decomposition in microservices?

easy

A. Combining multiple services into one large service

B. Creating a single database for all services

C. Removing all dependencies between services

D. Breaking a large system into smaller, manageable services

Solution

1. Step 1: Understand the purpose of decomposition

   Service decomposition aims to split a big system into smaller parts for easier management.

2. Step 2: Evaluate options against this goal

   Only Breaking a large system into smaller, manageable services describes breaking down a system into smaller services, which matches the goal.

3. Final Answer:

   Breaking a large system into smaller, manageable services -> Option D

4. Quick Check:

   Service decomposition = smaller services [OK]

Hint: Decomposition means splitting big into small [OK]

Common Mistakes:

• Thinking decomposition means merging services
• Assuming it removes all dependencies
• Confusing decomposition with database design

2. Which of the following is a common strategy to decompose microservices?

easy

A. By server hardware

B. By business capability

C. By programming language

D. By network protocol

Solution

1. Step 1: Recall common decomposition strategies

   Common strategies include decomposing by business capability, subdomain, or data entity.

2. Step 2: Match options to known strategies

   Only By business capability matches a recognized strategy; others are unrelated to service design.

3. Final Answer:

   By business capability -> Option B

4. Quick Check:

   Decompose by business function = C [OK]

Hint: Decompose by what the business does [OK]

Common Mistakes:

• Choosing technical infrastructure as decomposition criteria
• Confusing programming language with service boundaries
• Thinking network protocols define services

3. Given a system with services decomposed by subdomain, which of the following is a likely benefit?

medium

A. Single point of failure for all features

B. Reduced number of services to manage

C. Improved team autonomy and focused development

D. Elimination of all data duplication

Solution

1. Step 1: Understand subdomain decomposition

   Decomposing by subdomain groups services by business areas, enabling teams to work independently.

2. Step 2: Analyze benefits

   This approach improves team autonomy and focus, but does not reduce services or eliminate data duplication fully.

3. Final Answer:

   Improved team autonomy and focused development -> Option C

4. Quick Check:

   Subdomain decomposition = team autonomy [OK]

Hint: Subdomain splits by business area, helps teams [OK]

Common Mistakes:

• Assuming fewer services means better decomposition
• Expecting zero data duplication always
• Thinking it creates single failure points

4. A team decomposed services by data entity but faces tight coupling between services. What is the likely cause?

medium

A. Services share too much data and depend on each other

B. Services are deployed on different servers

C. Services use different programming languages

D. Services have separate databases

Solution

1. Step 1: Identify cause of tight coupling

   Tight coupling often happens when services share data heavily and depend on each other.

2. Step 2: Evaluate options

   Only Services share too much data and depend on each other explains tight coupling due to shared data and dependencies; others are unrelated.

3. Final Answer:

   Services share too much data and depend on each other -> Option A

4. Quick Check:

   Tight coupling = shared data dependency [OK]

Hint: Tight coupling means services depend on shared data [OK]

Common Mistakes:

• Blaming deployment location for coupling
• Thinking different languages cause tight coupling
• Assuming separate databases cause coupling

5. You are designing a microservices system for an online store. Which decomposition strategy best supports independent team ownership and scalability?

hard

A. Decompose by business capability like order management, payment, and inventory

B. Decompose by database tables to minimize data duplication

C. Decompose by programming language to use best tools per service

D. Decompose by server location to reduce network latency

Solution

1. Step 1: Identify goals for decomposition

   Independent team ownership and scalability require clear service boundaries aligned with business functions.

2. Step 2: Match strategies to goals

   Decomposing by business capability groups related functions, enabling teams to own services and scale independently.

3. Step 3: Evaluate other options

   Decomposing by tables or languages does not align with team ownership; server location affects latency, not ownership.

4. Final Answer:

   Decompose by business capability like order management, payment, and inventory -> Option A

5. Quick Check:

   Business capability decomposition = team ownership + scalability [OK]

Hint: Group by business functions for team and scale benefits [OK]

Common Mistakes:

• Choosing database tables over business functions
• Thinking programming language defines service boundaries
• Focusing on server location instead of service design