Aggregates and entities in Microservices - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Aggregate Boundaries

In a microservices system, which statement best describes the role of an aggregate?

A An aggregate is a cluster of domain objects that can be treated as a single unit for data changes and consistency.

B An aggregate is a single database table that stores all entities in the system.

C An aggregate is a microservice responsible for handling all user requests directly.

D An aggregate is a UI component that displays multiple entities on the screen.

💡 Hint

Think about how data consistency is maintained within a group of related objects.

An aggregate groups related entities and value objects to enforce consistency rules within its boundary. It acts as a single unit for updates.

❓ Architecture

intermediate

Entity Identity in Aggregates

Which option correctly explains how entities within an aggregate are identified?

A Entities inside an aggregate share the same global identifier as the aggregate root.

B Entities inside an aggregate are identified by the microservice name and the aggregate root's ID combined.

C Entities inside an aggregate do not have identifiers and are identified only by their attributes.

D Each entity inside an aggregate has its own unique identifier, but only the aggregate root's ID is exposed externally.

💡 Hint

Consider which entity is responsible for external references.

Entities within an aggregate have unique IDs internally, but only the aggregate root's ID is used outside to maintain encapsulation.

❓ scaling

advanced

Scaling Aggregates in Microservices

You have a microservice managing orders as aggregates. Each order aggregate contains multiple line item entities. What is the best approach to scale this microservice when order volume grows rapidly?

A Store line items in a separate database and query them on demand without transactional consistency.

B Split the order aggregate into separate microservices for order header and line items to scale independently.

C Keep the order aggregate as one unit and scale the microservice horizontally by adding more instances.

D Cache all order aggregates in memory to avoid database calls and improve performance.

💡 Hint

Think about aggregate boundaries and transactional consistency.

Aggregates should remain consistent units. Scaling by adding instances horizontally preserves aggregate boundaries and consistency.

❓ tradeoff

advanced

Tradeoffs in Aggregate Size

What is a key tradeoff when designing very large aggregates containing many entities?

A Large aggregates reduce complexity by grouping all entities but make it impossible to maintain consistency.

B Large aggregates improve performance by reducing the number of database calls but increase the risk of contention and slower updates.

C Large aggregates always improve scalability because they reduce the number of microservices needed.

D Large aggregates eliminate the need for aggregate roots and simplify entity management.

💡 Hint

Consider how aggregate size affects database transactions and concurrency.

Large aggregates reduce calls but can cause contention and slow updates due to locking and transaction size.

❓ estimation

expert

Estimating Aggregate Load Capacity

A microservice manages a 'Customer' aggregate with 5 entities inside. Each aggregate update takes 100ms on average. The system must handle 1000 updates per second. What is the minimum number of microservice instances needed to meet this load without queuing?

A At least 100 instances, because each instance can handle 10 updates per second (1000ms / 100ms).

B At least 10 instances, because 1000 updates * 100ms = 100 seconds total processing time per second.

C At least 5 instances, because there are 5 entities per aggregate and each takes 100ms.

D At least 20 instances, because 1000 updates / 50 updates per instance per second = 20.

💡 Hint

Calculate how many updates one instance can handle per second, then divide total load by that.

Each update takes 100ms, so one instance can handle 10 updates per second (1000ms / 100ms). To handle 1000 updates, 1000/10 = 100 instances are needed.