Design Microservices Architecture with Patterns and Principles - Eventual Consistency Principle

The video tutorial covers the concept of eventual consistency, particularly in the context of using the CQRS pattern with event sourcing. It explains how eventual consistency is suitable for systems prioritizing high availability over strong consistency, and discusses the CAP theorem's consistency levels. The tutorial compares strong and eventual consistency, providing examples for each. It also details the implementation of CQRS and event sourcing, highlighting the use of message brokers and denormalization into materialized views. Finally, it reviews several microservice query patterns, including materialized view, CQRS, event sourcing, and eventual consistency.

What is the main advantage of eventual consistency in distributed systems?

• It ensures immediate data consistency.

• It eliminates data latency.

• It prioritizes high availability over strong consistency.

• It guarantees data accuracy at all times.

According to the CAP theorem, what must be considered when designing microservice databases?

• The number of microservices

• The type of database used

• The speed of data processing

• The consistency level required

Which of the following is an example of strong consistency?

• E-commerce product reviews

• YouTube video view counters

• Social media likes

• Bank account withdrawals

What happens to data in a system with eventual consistency?

• Data consistency is guaranteed at all times.

• Data is never consistent.

• Data is immediately consistent for all clients.

• Data becomes consistent after a certain period.

How does the CQRS pattern contribute to eventual consistency?

• By using a single database for all operations

• By eliminating the need for message brokers

• By separating read and write operations

• By ensuring immediate data updates