fastapi tag description
View All Tags
FastAPI's Dependency Injection (DI) system, powered by the Depends function, is a powerful concept built on simple Python functions. For beginners, the best use cases are those that demonstrate code reuse, logic separation, and early request validation without requiring complex external libraries.
By focusing on these three patterns, you learn to keep your route functions clean and focus only on the core business logic.
The Singleton pattern ensures that a class has only one instance and provides a global point of access to it. In web development, this is essential for managing resources like database pools, configuration objects, logging handlers, or complex machine learning models that are costly to initialize.
In FastAPI, while dependencies are generally executed once per request and the result is cached, this does not guarantee that the underlying class is instantiated only once across the entire application's lifecycle. To enforce a true global Singleton, we must use Python's built-in methods, typically overriding __new__.
Dependency Injection (DI) is a core principle in FastAPI, allowing you to seamlessly integrate reusable logic, external resources (like database connections), configuration settings, and security checks into your API routes. FastAPI's DI system is built on standard Python features: type hinting and function calls, using the special Depends callable.
msgspec is an extremely fast serialization and validation library that consistently outperforms Pydantic v2 in benchmarks. This performance advantage comes from its design as a lean, compiled-code-based library focused on a narrow set of data handling tasks, whereas Pydantic v2 is a feature-rich framework.
The performance differences are most pronounced in two key areas: parsing/decoding (converting data like JSON into Python objects) and serialization/encoding (converting Python objects into data like JSON).
msgspec is a Python library designed for high-performance serialization and validation, which makes it a powerful partner for FastAPI. It can be used as a drop-in replacement for FastAPI's default Pydantic models to handle data validation and schema generation, often leading to a significant speedup in API performance.
The best and most precise way to measure the execution time of a function or endpoint in FastAPI is by using a custom decorator or a middleware. This approach allows you to wrap your functions with timing logic without modifying the function's code itself, promoting clean, reusable, and maintainable code.
Here's a guide to integrating Supabase with a FastAPI application. We'll cover environment setup, connecting to Supabase, and performing basic CRUD operations.