Using Repository Pattern for CQRS in Go with In-Memory and MSSQL Examples

In this article, we will explore how to implement the repository pattern for CQRS (Command Query Responsibility Segregation) in Go. The goal is to separate the logic for reading and writing data, making the code easier to test, maintain, and swap implementations. We will use an in-memory repository for testing purposes and an MSSQL repository for production. We’ll also implement a PersonService that encapsulates the business logic.

The Structure: Person

We'll use a simple Person struct to demonstrate the repository pattern.

Repository Interfaces

The repository will have two main interfaces: Reader and Writer.

• Reader: Handles the read operations (List and Get).
• Writer: Handles the write operations (Add).

We will also create a Repository interface that combines both.

PersonService and UseCase Interface

To implement business logic, we will introduce a PersonService that implements the UseCase interface. This service will interact with the repository.

The PersonService handles business logic like adding new people and fetching data from the repository.

In-Memory Repository (for Testing)

We’ll first create an in-memory repository that we can easily use for testing purposes.

??This struct must implement all methods of the Repository Interface??

MSSQL Repository (for Production)

Next, we’ll create an MSSQL repository that interacts with a real database.

??This struct must implement all methods of the Repository Interface??

Demonstration using In-Memory Repository

To show how easy it is to test, here’s an example using the in-memory repository.

This demonstrates how simple it is to test the PersonService using an in-memory repository, with the ability to swap out the implementation for MSSQL in production.

Conclusion

Using the repository pattern for CQRS in Go allows for clear separation of read and write concerns and makes it easy to swap out implementations like in-memory or MSSQL. With the PersonService handling business logic, the code remains clean and easy to test.

Whether you’re dealing with a large production system or simply writing unit tests, this approach offers flexibility and scalability.