Mastering CQRS: When and How to Implement Command Query Responsibility Segregation

Introduction?

This article explains the CQRS (Command Query Responsibility Segregation) pattern, its benefits, drawbacks, and scenarios for its use.?

Before diving into the CQRS pattern, we will explore situations where the CQRS pattern is not applied and discuss the issues that lead us to use it.

Case Studies Without CQRS

Case 1: Microservice Application

In a microservice architecture, a client application (e.g., a web app) needs to read data from multiple services. For instance, if the client app requires order history, it may access the order service, accounting service, and delivery service.?

One common solution is to use the API Gateway pattern. This allows the client to make a single request to an endpoint that aggregates data from multiple services. However, this approach can negatively impact performance due to the large amount of data being aggregated in memory. (For more on this, see my previous article, Mastering Microservice Patterns: Unlocking the Power of the API Gateway.)

Case 2: Write-Heavy Applications

Consider an application with a significant number of write operations or complex read queries. To enhance the performance of read queries, developers often create multiple indexes. However, this can adversely affect the performance of write queries.

To address these issues, we can split the data handling into two separate databases—one for write operations and another for read queries. This separation can be effectively managed using the CQRS pattern.

What is the CQRS Pattern?

CQRS stands for Command Query Responsibility Segregation. It is an architectural pattern that separates the processes of updating data (commands) from those of retrieving data (queries).

How to Implement the CQRS Pattern?

1. Database Separation: Create one database for write operations and another for read operations. The structure of the command (write) database may differ from the structure of the query (read) database, with the query schema often denormalized to enhance read performance.

2. Technology Choices: Use different types of databases for each operation (e.g., SQL for write and NoSQL for read).

3. Data Synchronization: Sync data between the read and write databases. After a write operation, publish an event through a message broker to update the read database.

4. Event Sourcing: Consider using the event sourcing pattern alongside CQRS, where instead of saving the current state in the write database, a sequence of events is saved in an event store.

Benefits of Using the CQRS Pattern

1. Scalability: Read and write databases can be scaled separately based on their load.
2. Technology Flexibility: Different database technologies can be used for each operation (e.g., SQL for writes and NoSQL for reads).
3. Enhanced Performance: Separating read and write queries can improve overall performance.

Drawbacks of Using the CQRS Pattern

1. Complexity: The separation of write and read queries adds complexity to the system.
2. Eventual Consistency: Clients may read outdated data due to the synchronization process between the two databases.

Conclusion?

The decision to use the CQRS pattern depends on the specific needs of your application. If your application does not have heavy write queries, the read queries are simple, or the client app reads small amounts of data from multiple services, you should avoid this pattern due to the added complexity. However, if the complexity of the CQRS pattern is less than the issues it resolves—such as performance problems, heavy write or read queries, or aggregating large data from multiple services in a microservice application—then consider implementing the CQRS pattern.