FastAPI Query Parameters: How to Implement Parameter Filtering with Query and Path
In FastAPI, parameter handling is a core aspect. Query parameters (after the question mark in the URL) and path parameters (within the URL path) need to be processed using the `Query` and `Path` utilities, respectively. Query parameters can have default values (e.g., setting `age` to default to 18), be marked as required (using `...`), and apply validation rules such as `min_length` and `gt` (greater than), which restrict string lengths or numeric ranges. For example, `Query(..., min_length=5, max_length=50)` ensures a string parameter meets length constraints. Path parameters are handled with `Path` for validation, such as ensuring `user_id` is a positive integer (`gt=0`). Both utilities support type conversion, range filtering, and automatically generate Swagger documentation. `Query` is primarily for optional parameters (e.g., setting `name` to default to `None`) and required validation, while `Path` focuses on type validation for path parameters (e.g., integers). Proper use of these utilities enhances interface robustness, reduces invalid data, and simplifies parameter processing logic.
Read MorePractical Guide to FastAPI Middleware: Implementing Request Logging and Response Time Statistics
This article introduces the use of FastAPI middleware for uniformly handling request/response logic (such as logging and authentication) to avoid repetitive code. First, FastAPI and Uvicorn need to be installed. The core is to inherit Starlette's BaseHTTPMiddleware and implement the dispatch method to handle middleware logic: record the request start time, call call_next to obtain the response, calculate the response time, construct and output logs containing method, path, IP, response time, and status code. To add the middleware to the application, app.add_middleware must be called. During testing, define a simple route, access it, and the log will be output to the console. Extensions and optimizations can include using the logging module, recording request bodies, and exception handling. Middleware simplifies development, improves code maintainability, and is suitable for common logic such as authentication and CORS.
Read MoreDetailed Explanation of FastAPI Dependency Injection: Basic and Advanced Usage of Depends
Dependency Injection (DI) core is to inject dependencies (such as database connections) into functions automatically by the system, rather than having the functions acquire them themselves, thereby enhancing code reusability and decoupling. FastAPI implements this through `Depends`, which involves two steps: defining a dependency function (to produce a dependency object, e.g., simulating a database connection), and declaring the dependency in the path function using `Depends(dependency_function)`, where FastAPI automatically calls and injects the result. Dependency functions can accept path/query parameters, such as querying a user based on a `user_id`. Advanced usages include: nested dependencies (dependencies on other dependencies), caching dependencies with `lru_cache` (singleton), asynchronous dependencies (adapting to asynchronous path functions), and combining with Pydantic for parameter validation. Core advantages: code reusability, decoupling (path functions only focus on business logic), testability (dependencies can be replaced with mocks), and extensibility (adding new dependencies only requires modifying the dependency function). Mastering `Depends` enables a clearer and more robust API structure.
Read MoreDetailed Explanation of FastAPI Status Codes: Usage Scenarios for 200, 404, 500, etc.
HTTP status codes are numeric codes returned by servers to indicate the result of request processing. Proper configuration in FastAPI helps clients understand the outcome of requests. There are two ways to set status codes in FastAPI: directly returning a tuple (data + status code), or using the `HTTPException` exception (recommended for error scenarios). Common core status codes and scenarios: 200 (Request successful, used for returning data in GET/PUT, etc.); 404 (Resource not found, when GET/DELETE requests cannot locate the target); 500 (Internal server error, requires exception handling to avoid exposure); 201 (POST request successfully created a resource, returns the new resource); 204 (No content, successful DELETE/PUT with no returned data); 400 (Bad request, e.g., format or required field issues); 401 (Unauthorized, user not logged in); 403 (Forbidden, authenticated but with insufficient permissions). Best practices: Map status codes to corresponding HTTP methods, e.g., 200/404 for GET, 201 for POST, 204 for DELETE. Correctly using status codes prevents client-side errors, and FastAPI's Swagger documentation aids in debugging.
Read MoreFastAPI + Uvicorn: Basic Configuration for Local Development and Deployment
This article introduces the web development and deployment process using FastAPI with Uvicorn. FastAPI is a high-performance Python framework supporting asynchronous operations and automatic API documentation. Uvicorn, as an ASGI server, is the recommended deployment tool for FastAPI, and their combination enables efficient development. **Environment Installation**: First, create a virtual environment (e.g., `python -m venv venv`), activate it, and then install dependencies with `pip install fastapi uvicorn`. **Development Configuration**: Write `main.py`, define routes (e.g., root route `/` and parameterized route `/items/{item_id}`), and start the server with `uvicorn main:app --reload` for auto-reloading in development mode. Verify the interface by accessing `http://127.0.0.1:8000`. **Production Deployment**: Use the basic command `uvicorn main:app --host 0.0.0.0 --port 8000`. Specify the number of worker processes with `--workers` for multi-processing. Ensure the deployment server opens the port and manage processes via `nohup` or `systemd`. **Common Issues**: For port conflicts, change the port. If the service is unreachable, confirm `--host 0.0.0.0` and firewall settings. If installation fails, update pip or check Python version compatibility.
Read MoreFastAPI Error Handling: Practical Guide to HTTP Status Codes and Exception Catching
API error handling is crucial for system robustness and user experience, and FastAPI provides a concise and efficient error handling mechanism. The core of the article includes: ### 1. Necessity of Error Handling It is necessary to clarify the error cause and use standard HTTP status codes (e.g., 404 for resource not found, 400 for parameter errors) to avoid system crashes and ensure service stability. ### 2. HTTP Status Codes and FastAPI Applications FastAPI supports all standard status codes. Route functions can directly specify them via `return` or `raise`. For example: `return {"detail": "Item not found"}` returns 404, or `HTTPException` can be used to explicitly throw errors. ### 3. FastAPI Exception Catching Mechanism It is recommended to actively throw errors using `HTTPException` with specified status codes and messages, such as returning 404 when a user does not exist. FastAPI automatically handles parameter type errors and returns a 422 status code. ### 4. Custom Exceptions and Global Fallback Business logic exceptions (e.g., insufficient balance) can be defined and uniformly handled using `@app.exception_handler` to return standard errors. The global exception handler falls back to uncaught exceptions to prevent server crashes. ### Best Practices Use `HTTPException` for standard errors, custom exceptions for business logic, and leverage automatic parameter
Read MoreWhy is FastAPI Lighter Than Django? A Comparative Analysis for Beginners
This article focuses on "lightweight" frameworks, comparing the core differences between FastAPI and Django. "Lightweight" refers to simple configuration, minimal code, a gentle learning curve, and focused functionality. FastAPI is a typical representative, while Django is a full-stack framework with comprehensive features but is not considered lightweight. FastAPI's lightness is evident in: minimal dependencies (only `fastapi` and `uvicorn` are required), concise code (API can be written in just a few lines), functionality focused on API development (no redundant features like an Admin backend), automatic generation of interactive API documentation (Swagger UI and ReDoc), and native support for asynchronous programming. As a full-stack framework, Django offers comprehensive features (ORM, Admin, forms, etc.), but it has a complex structure for beginners (requiring multiple steps such as project creation, app setup, and route configuration), a steep learning curve, scattered files, and is prone to distracting users with irrelevant functionalities. Beginners should choose based on their needs: FastAPI is suitable for rapid API development (e.g., backend interfaces, microservices), while Django is ideal for full-stack projects (e.g., e-commerce, CMS). Lightweight does not mean having fewer features; rather, it emphasizes focus and simplicity. FastAPI is the best entry tool for API beginners.
Read MoreBeginners Guide: How to Use Pydantic for Data Validation in FastAPI
This article introduces the core content of using Pydantic for data validation in FastAPI. Data validation is crucial in web development, and FastAPI leverages the built-in Pydantic library to achieve efficient validation. Pydantic enables automatic checking of input validity by defining data models based on type hints (inheriting from BaseModel), supporting both basic and complex types (e.g., list, dict), and distinguishing between required fields (without default values) and optional fields (with default values). In FastAPI, Pydantic models are mainly used to handle request data (such as POST request bodies). FastAPI automatically parses and validates the data, returning a 422 error with detailed information if validation fails. Response data can also be validated using a Pydantic model via the response_model parameter to ensure the correct return format. Additionally, Pydantic supports custom validation, such as setting field constraints (e.g., length, range) through Field or using custom functions (e.g., email format verification). The advantages of Pydantic lie in automatic validation, user-friendly error messages, type safety, and flexible extension. It prevents program crashes or security vulnerabilities caused by invalid data, making it a core tool for building secure and robust APIs with FastAPI.
Read MoreFastAPI Basic Tutorial: Basic Usage of Routes, Requests, and Responses
FastAPI is a high-performance Python-based web framework with performance comparable to Node.js and Go. It features auto-generated Swagger UI and ReDoc documentation, type hint-based data validation, and concise, easy-to-learn code. Installation requires `pip install fastapi uvicorn` to obtain the framework and ASGI server. Basic routes are defined using the `@app` decorator, supporting path parameters (e.g., `/items/{item_id}`) and query parameters (e.g., `/search?q=test`), with automatic parameter type validation and conversion. Request handling relies on Pydantic models to define JSON request bodies, such as the `Item` class for receiving POST data. Response handling can specify `response_model` to return Pydantic models or use `status_code` to set status codes (e.g., 201). A complete example includes multiple routes and request/response handling. To run it, use `uvicorn main:app --reload`, and access `/docs` or `/redoc` to view the auto-generated API documentation. FastAPI enhances API development efficiency with its simplicity and automatic documentation features, making it suitable for quickly developing high-performance web services.
Read MoreLearning FastAPI from Scratch: Quickly Understanding the Core Concepts of API Development
API serves as a bridge for communication between different software systems. As a Python web framework, FastAPI has gained popularity due to its advantages such as simplicity, high performance, automatic API documentation generation, type hint support, and async-friendliness. Quick Start: After installing FastAPI and Uvicorn, write a main.py to define routes (e.g., @app.get("/")), then run Uvicorn to access the interface and return JSON data. Core concepts include: routes (URLs mapped to handler functions), HTTP request methods (GET for data retrieval, POST for data submission), three data handling methods (path parameters, query parameters, request bodies), and data validation (auto-validation via Pydantic models). Interactive documentation is automatically generated via Swagger UI (/docs) and ReDoc (/redoc). After mastering the basics, advanced learning can focus on asynchronous development, middleware, and database integration.
Read MoreRESTful API Getting Started: Developing a Simple GET Data Interface with Flask
This article introduces the concept of RESTful APIs and implementing a GET interface with Flask. RESTful APIs are HTTP-based front-end and back-end interaction architectures centered on resources, manipulating resources through GET/POST/PUT/DELETE operations, stateless, and returning JSON data. Flask is chosen for its lightweight and flexibility, making it suitable for beginner development. Flask can be installed via `pip install flask` (virtual environment is optional). Implementation involves two steps: first, define the root path `/` to return "Hello, Flask!", with the core code being the `@app.route('/')` decorator and the `return` statement with a string; second, implement the `/users` interface to return user list JSON data, requiring the import of `jsonify` and returning the converted list. After starting the application, access `http://localhost:5000/users` through a browser, curl, or Postman for testing. Core steps include: importing Flask, initializing the application, defining route functions, returning data, and starting the service. Future extensions can include more routes or database integration.
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