Database decomposition strategy in Microservices - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding database decomposition in microservices

Which of the following best describes the main goal of database decomposition in a microservices architecture?

A To replicate the entire database for each microservice to ensure data consistency.

B To merge multiple small databases into one large database to simplify data management.

C To split a large monolithic database into smaller, service-specific databases to reduce coupling and improve scalability.

D To use a single shared database schema for all microservices to avoid data duplication.

💡 Hint

Think about how microservices aim to be independent and scalable.

Database decomposition means breaking a big database into smaller parts owned by each microservice. This reduces dependencies and helps each service scale independently.

❓ Architecture

intermediate

Choosing a decomposition strategy

You have a monolithic e-commerce database with tables for users, orders, products, and payments. Which decomposition strategy best fits splitting this database for microservices?

A Decompose by business capability: create separate databases for user management, order processing, product catalog, and payment services.

B Decompose by data type: separate databases for numeric data, text data, and date data.

C Decompose by database size: split the database into equal-sized chunks regardless of data type.

D Decompose by access frequency: put frequently accessed tables in one database and rarely accessed tables in another.

💡 Hint

Think about how microservices align with business functions.

Decomposing by business capability aligns each microservice with a specific domain, making services independent and easier to maintain.

❓ scaling

advanced

Scaling challenges with database decomposition

After decomposing a monolithic database into multiple microservice databases, which challenge is most likely to arise when services need to perform transactions involving multiple databases?

A Avoiding data duplication by sharing the same database schema.

B Increasing the size of a single database to handle all transactions.

C Reducing network latency by merging all databases back into one.

D Ensuring data consistency across distributed databases without using distributed transactions.

💡 Hint

Consider the difficulty of keeping data consistent when it is spread across services.

Distributed transactions are hard to manage in microservices. Ensuring consistency without them is a key challenge after decomposition.

❓ tradeoff

advanced

Tradeoffs in database decomposition strategies

What is a common tradeoff when choosing to decompose databases by business capability in microservices?

A Improved service independence but increased complexity in data consistency and cross-service queries.

B Faster development cycles but higher risk of data loss.

C Simplified data management but reduced service scalability.

D Lower operational costs but slower response times.

💡 Hint

Think about what happens when data is split but services still need to work together.

Decomposing by business capability improves independence but makes data consistency and queries across services more complex.

❓ estimation

expert

Estimating capacity for decomposed microservice databases

You have decomposed a monolithic database into three microservice databases: UserDB, OrderDB, and ProductDB. UserDB handles 1000 requests/sec, OrderDB 500 requests/sec, and ProductDB 2000 requests/sec. If each request reads 5 KB on average, what is the total read throughput in MB/sec across all databases?

A Approximately 8.5 MB/sec

B Approximately 17.5 MB/sec

C Approximately 12.5 MB/sec

D Approximately 25 MB/sec

💡 Hint

Calculate total requests per second multiplied by average data size, then convert KB to MB.

Total requests = 1000 + 500 + 2000 = 3500 req/sec. Each request reads 5 KB, so total KB/sec = 3500 * 5 = 17500 KB/sec. Convert to MB/sec: 17500 / 1024 ≈ 17.1 MB/sec, rounded to 17.5 MB/sec.