Fast Api Repository Pattern And Service Layer Medium

This document explains the Repository Pattern implementation in the fastapi-clean-architecture project. The Repository Pattern creates an abstraction layer between data access logic and business logic, encapsulating database operations and providing a collection-like interface for accessing domain objects. This page covers the base repository implementation, specialized repositories, query building, and error handling within the repository layer. For details on how repositories are consumed, see Service Layer. The Repository Pattern isolates the data layer from the rest of the application, providing a clean separation between domain objects and their data source. In this application, repositories abstract interactions with the SQLAlchemy ORM, making data access components interchangeable and testable.

Sources: app/repository/base_repository.py15-103 app/repository/post_repository.py11-38 The repository architecture follows a hierarchical design with a generic BaseRepository that implements common CRUD operations, and specialized repositories for each domain entity that extend this base class. There was an error while loading. Please reload this page. There was an error while loading. Please reload this page.

There was an error while loading. Please reload this page. There was an error while loading. Please reload this page. First of all, thanks for providing such a good content for us all. Second, I have been struggling to setup a good backend API, as you know, many moving parts and workflows to test.

While trying to do so, I stumbled on patterns Repository and Service, which are good practices described, among others, on [1]. IT would be very helpful to have such, and extend the current backend counterpart. If we look at what the api with the repository endpoints are doing, we'll agree that are very simple. But what usually happens is that business applications typically require different kinds of interfaces (API, user interface, command line client, etc) to the data and the logic they implement (3rd party integrations, repositories,...). The same concept used in the repository pattern to create a common interface for the data, can be used to define a common interaction with the application to invoke the business logic: fetching data... And most of this things don't have anything to do with having a web aPI endpoint.

This is what we might call orchestration layer , use-case layer or service layer. To continue with our simple examples, we'll create a service to paginate results. Let's start creating the test file, as usual: Building a FastAPI application that scales? It's not just about async endpoints and Pydantic models. In this post, I'll share how to structure FastAPI applications for long-term maintainability using layered architecture and dependency injection.

You'll learn how to keep your code organized, write testable code, and build a flexible system that can evolve with your needs. Layered architecture (also known as n-tier architecture) is a pattern that organizes code into distinct layers, each with a specific responsibility. This separation of concerns makes the codebase more maintainable, testable, and flexible. Think of it like a well-organized kitchen in a restaurant: each station has its specific role, and the flow of work moves in one direction, from preparation to plating. The presentation layer is your application's front door. It's where FastAPI lives, along with other entry points like Celery tasks and Typer commands.

This layer is responsible for handling all external communication, whether it's HTTP requests, background jobs, or CLI commands. Here's a typical example of how this layer looks in practice: Minimal code focusing on input/output mapping: The layer acts as a translator between external formats (HTTP, Celery, CLI) and your service layer's standardized format, handling both request transformation and response formatting. FastAPI has a wonderful Dependency Injection (DI) system that works reasonably well at the controller level. However, to build a robust and testable application, it is helpful to make use of a dependency injection chain that spans multiple layers of the application. This approach not only promotes a clean architecture but also significantly improves the test situation.

Typically, a FastAPI application consists of a controller layer (the FastAPI endpoints), a service layer and often a repository layer. While FastAPI provides a way to inject dependencies into the controller layer, it does not provide a way to inject dependencies into the service or repository layers. But this does not mean that we cannot use dependency injection across multiple layers. By building a dependency injection chain that spans multiple layers, we can inject dependencies into the service and repository layers as well. Let's see how we can do this and get our hands dirty with some code. A typical application might look like this:

This document explains the repository pattern implementation in the FastAPI starter project. The repository pattern provides a clean abstraction for data access operations, separating database interaction logic from business logic. This page covers the repository classes, their methods, how they interact with the database layer, and how they are used by service classes. For information about the overall database configuration and migrations, see Database Configuration. The repository pattern serves as an intermediary between the data source (database) and the business logic layer. It provides a collection-like interface for accessing domain objects while encapsulating the database access logic.

Sources: src/repositories/item.py1-78 src/repositories/user.py1-78 The codebase implements two main repository classes: Both repositories follow a similar pattern, providing CRUD operations for their respective entities.

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