How to distinguish between business logic and storage logic in back-end development?

Back-end three-layer architecture: the boundary between business logic and data access logic

In back-end development, the common three-layer architectures of controller, service and dao are relatively clear in the controller and service layers. They are mainly implemented by separating business logic and presentation logic, such as decoupling message queue (MQ), HTTP, RPC, etc. from business logic. However, the boundary between the service layer and the dao layer, especially after the introduction of the manager layer, often confuses developers.

In Python back-end development, business logic is sometimes mixed in the model layer, such as business query methods such as usermodel.is_super() , or native database operations such as usermodel.objects.all() , and even cross-table operations such as usermodel.** .

Analysis of business logic and non-business logic

The key to business logic and non-business logic lies in whether it directly relates to customer needs. Logic that customers cannot perceive is often considered non-business logic, including:

1. Database structure and association relationship: For example, usermanager.delete() and departmentmanager.delete() methods can handle the deletion of the association table (such as userdeptmodel ) at the same time, without calling the dao layer method twice at the service layer. Even without a manager layer, the dao layer can perform such association or cross-table operations as long as these operations are independent of business logic.

    class UserManager:
       def delete(self):
           userdao.delete()
           userdeptdao.delete()
   class DepartmentManager:
       def delete(self):
           departmentdao.delete()
           userdeptdao.delete()

2. Password salt: Users only need to know that the password is not stored in plain text, and the salt addition operation can be processed in the dao or manager layer.

    class UserDao:
       def make_password(self, passwd):
           return salt(passwd) # add salt function def save(self):
           self.passwd = self.make_password(self.passwd)
           super().save()

3. Naming and definition of dao layer method: Naming dao layer method, for example, whether semantic names such as get_super_user are suitable depends on whether they are related to business logic. If super is not business-related, it is acceptable to use get_super_user .

4. HTTP request encapsulation: Backend dependencies (such as services provided by other teams) can be encapsulated into dao-layer methods, rather than service-layer methods.

Implement functions similar to Django filter in Django/Flask

When implementing Django filter-like functions in Django and Flask, you often encounter layer-by-layer penetration problems because the dao layer needs to pass in request parameters. In the absence of a dependency injection framework like Spring, you can consider:

• In Java, frameworks such as MyBatis or JPA are usually used to manage data access and filtering logic through annotations and configuration files.

The relationship between data entities and three-layer architecture

Data entities represent data objects in the system. In a three-layer architecture, controller, service and dao layers do not strictly correspond to one by one:

• The dao layer may contain multiple methods to process different data entities, such as userdao and departmentdao .
• The service layer may need to combine multiple dao layer methods to implement complete business logic.

In short, the dao layer is only responsible for data storage interaction and does not include business logic; the service layer is responsible for executing business logic. For example, when creating a user, the service layer checks whether the user name is duplicated, and then calls the dao layer method to save the user. This architecture is designed to divide the system by responsibility and improve the maintainability and scalability of the code.

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