Why Database decomposition strategy in Microservices? - Purpose & Use Cases

The Big Idea

What if your entire app's data was tangled in one place, making every change risky and slow?

The Scenario

Imagine a small team trying to manage a huge spreadsheet with all company data in one place. Every time someone updates a row, others have to wait, and mistakes happen often because it's hard to keep track of everything.

The Problem

Using one big database for all services slows down the system, causes conflicts when multiple teams work together, and makes fixing problems a nightmare. It's like everyone trying to write on the same paper at once--messy and frustrating.

The Solution

Database decomposition splits the big database into smaller, focused parts. Each microservice owns its own data, so teams can work independently without stepping on each other's toes. This makes the system faster, easier to fix, and ready to grow.

Before vs After

✗ Before

SELECT * FROM all_data WHERE service='orders';

✓ After

SELECT * FROM orders.orders WHERE order_id=123;

What It Enables

It enables building scalable, independent services that can evolve and deploy without waiting on others.

Real Life Example

Think of an online store where the payment system, product catalog, and user reviews each have their own database. If the reviews database slows down, it won't stop payments or product browsing.

Key Takeaways

One big database creates bottlenecks and risks.

Decomposing databases aligns data with microservices for independence.

This leads to faster, more reliable, and scalable systems.

Practice

1. Which of the following best describes vertical decomposition in database design for microservices?

easy

A. Dividing a database by rows to distribute data across multiple databases

B. Combining multiple databases into one large database

C. Separating databases based on geographic location

D. Splitting a database by grouping related tables or columns into separate databases

Solution

1. Step 1: Understand vertical decomposition

   Vertical decomposition means splitting a database by grouping related tables or columns, often by business capability or domain.

2. Step 2: Compare with other options

   Horizontal decomposition splits by rows, geographic is location-based, and combining is the opposite of decomposition.

3. Final Answer:

   Splitting a database by grouping related tables or columns into separate databases -> Option D

4. Quick Check:

   Vertical decomposition = splitting by columns/tables [OK]

Hint: Vertical = split by columns or tables, horizontal = split by rows [OK]

Common Mistakes:

• Confusing vertical with horizontal decomposition
• Thinking vertical means geographic split
• Assuming decomposition means combining databases

2. Which of the following is the correct description of horizontal decomposition in microservices database design?

easy

A. Dividing data by rows, such as by customer or region

B. Splitting data by columns or tables based on functionality

C. Merging multiple databases into one for simplicity

D. Separating databases by different database engines

Solution

1. Step 1: Define horizontal decomposition

   Horizontal decomposition splits data by rows, for example, dividing customers by region or user ID ranges.

2. Step 2: Eliminate incorrect options

   Splitting data by columns or tables based on functionality describes vertical decomposition, C is merging (not decomposition), and D is about engines, not decomposition strategy.

3. Final Answer:

   Dividing data by rows, such as by customer or region -> Option A

4. Quick Check:

   Horizontal decomposition = split by rows [OK]

Hint: Horizontal = split by rows, vertical = split by columns [OK]

Common Mistakes:

• Mixing horizontal with vertical decomposition
• Thinking horizontal means merging databases
• Confusing database engine separation with decomposition

3. Consider a microservices system where the user database is split by region using horizontal decomposition. If a query requests all users from Europe, which database(s) will be queried?

medium

A. Only the database shard containing European users

B. All database shards regardless of region

C. Only the database shard containing North American users

D. A combined database with all users merged

Solution

1. Step 1: Understand horizontal decomposition by region

   Horizontal decomposition splits data by rows, so each shard holds users from a specific region.

2. Step 2: Identify which shard to query

   Querying European users targets only the shard holding European data, not others.

3. Final Answer:

   Only the database shard containing European users -> Option A

4. Quick Check:

   Query targets relevant shard only [OK]

Hint: Query only the shard holding requested data region [OK]

Common Mistakes:

• Querying all shards unnecessarily
• Querying wrong region shard
• Assuming data is merged in one database

4. A microservices team decomposed their database vertically but notices frequent cross-service joins causing latency. What is the likely cause and fix?

medium

A. Cause: Using NoSQL instead of SQL; Fix: Switch to SQL databases

B. Cause: Horizontal decomposition; Fix: Merge databases into one

C. Cause: Poor vertical decomposition causing cross-service joins; Fix: Redesign to reduce cross-service dependencies

D. Cause: Too many database shards; Fix: Increase shards further

Solution

1. Step 1: Identify problem with vertical decomposition

   Vertical decomposition splits by tables/domains, but if services need to join data often, it causes latency.

2. Step 2: Recommend fix

   Redesign to reduce cross-service joins by better domain boundaries or data duplication to avoid latency.

3. Final Answer:

   Poor vertical decomposition causing cross-service joins; Fix: Redesign to reduce cross-service dependencies -> Option C

4. Quick Check:

   Cross-service joins cause latency; fix by better decomposition [OK]

Hint: Cross-service joins mean bad vertical split; redesign domains [OK]

Common Mistakes:

• Confusing horizontal with vertical decomposition issues
• Thinking merging databases fixes latency
• Blaming database type instead of design

5. A company wants to scale their microservices database by splitting user data by country (horizontal) and splitting user profile and orders into separate databases (vertical). What is the best approach to handle queries that need both profile and order data for users in a specific country?

hard

A. Perform cross-database joins directly on all shards for each country

B. Use API composition to aggregate data from profile and order services after querying country-specific shards

C. Merge profile and order data into a single database shard per country

D. Store all user data in one large database to avoid complexity

Solution

1. Step 1: Understand combined vertical and horizontal decomposition

   Data is split horizontally by country and vertically by data type (profile, orders), so data is in different shards and databases.

2. Step 2: Choose best query approach

   Cross-database joins are expensive and complex; merging data loses benefits. API composition aggregates data from services after querying relevant shards efficiently.

3. Final Answer:

   Use API composition to aggregate data from profile and order services after querying country-specific shards -> Option B

4. Quick Check:

   API composition handles multi-db queries efficiently [OK]

Hint: Use API composition to combine data from vertical and horizontal splits [OK]

Common Mistakes:

• Trying cross-database joins causing latency
• Merging databases losing scalability
• Ignoring decomposition benefits for simplicity