Why Service decomposition strategies in Microservices? - Purpose & Use Cases

The Big Idea

What if your giant app could become a team of experts working perfectly together?

The Scenario

Imagine building a huge app all in one place, like a giant messy kitchen where every chef tries to cook every dish at once.

The Problem

When everything is tangled together, making changes is slow and risky. One small mistake can spoil the whole meal, and the kitchen gets overcrowded and chaotic.

The Solution

Service decomposition breaks the big app into smaller, focused parts, like having separate chefs each specializing in one dish. This keeps things clean, faster, and easier to manage.

Before vs After

✗ Before

function processOrder(order) { validate(order); calculatePrice(order); updateInventory(order); sendNotification(order); }

✓ After

OrderService.process(order); InventoryService.update(order); NotificationService.send(order);

What It Enables

It lets teams work independently, scale parts separately, and quickly adapt to changes without breaking everything.

Real Life Example

Think of an online store where the payment system, product catalog, and user reviews are all separate services, each running smoothly on its own.

Key Takeaways

Monolithic apps get messy and hard to change.

Breaking apps into services makes development faster and safer.

Service decomposition helps build scalable, maintainable systems.

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