FastAPI Explained: Modern Web Framework for High-Performance APIs

FastAPI vs Flask vs Django

FastAPI

• High Performance: Built on Starlette and Pydantic, supports async/await for non-blocking I/O.
• Automatic Documentation: Generates Swagger UI and ReDoc from type hints IT Engineer Salary in India .
• Type Hints & Validation: Strong typing with automatic request/response validation using Pydantic.
• Modern Pythonic Design: Leverages Python 3.7+ features fully.
• Great for APIs: Ideal for RESTful and asynchronous API development.
• Not Full-Stack: No built-in ORM, admin panel, or templating — must integrate third-party tools.
• Best Use Cases: High-performance APIs, microservices, and async-heavy applications.

Flask

• Lightweight and Flexible: Minimalistic core with freedom to structure your app.
• Simple to Learn: Great for beginners or small projects.
• Large Ecosystem: Many extensions (e.g., Flask-SQLAlchemy, Flask-Login).
• No Async Support: Lacks built-in support for async; needs frameworks like Quart.
• No Built-in Tools: Must add your own validation, ORM, auth, etc.
• Best Use Cases: Small to medium APIs, microservices, and when you want full control.

Django

• Full-Featured Framework: Comes with ORM, admin panel, user auth, templating, forms, and more.
• Rapid Development: Batteries-included approach accelerates app building.
• Scalable: Proven performance at scale (used by Instagram, Pinterest, etc.).
• Heavier for APIs: Overhead for simple APIs; often paired with Django REST Framework (DRF).
• Async Support Still Evolving: Basic async support introduced in recent versions, not as mature as FastAPI.
• Best Use Cases: Full-stack apps, CMS, e-commerce, dashboards, and admin-heavy systems.

Installing and Running FastAPI

Installing FastAPI is simple and requires Python 3.7 or above. Python Training Use pip to install FastAPI along with an ASGI server like Uvicorn:

• pip install fastapi
• pip install “uvicorn[standard]”

To run a FastAPI app:

• uvicorn main:app –reload

Here, main is the Python file name, and app is the FastAPI instance. The –reload flag enables auto-reloading during development.

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Creating First API Endpoint

A basic FastAPI application looks like this:

• from fastapi import FastAPI
• app = FastAPI()
• @app.get(“/”)
• def read_root():
• return {“message”: “Hello, FastAPI”}

This code defines a simple GET endpoint at the root URL. FastAPI automatically generates documentation for the endpoint at /docs (Swagger UI) and /redoc (ReDoc) Best Software Development Courses . You can also define POST, PUT, DELETE, and other HTTP methods just by changing the decorator.

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Handling Requests and Responses

FastAPI simplifies the process of handling incoming requests and sending structured responses. It supports query parameters, path parameters, Become an IT Engineer and body data with automatic parsing.

• @app.get(“/items/{item_id}”)
• def read_item(item_id: int, q: str = None):
• return {“item_id”: item_id, “query”: q}

Here, item_id is a path parameter, and q is an optional query parameter. FastAPI also lets you return custom response models and status codes with ease using Response and status_code.

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Data Validation with Pydantic

One of FastAPI’s core strengths is its use of Pydantic for data validation. Complete Guide on System Software You can define data models using Python classes:

• from pydantic import BaseModel
• class Item(BaseModel):
• name: str
• price: float
• in_stock: bool = True
• @app.post(“/items/”)
• def create_item(item: Item):
• return {“item”: item}

FastAPI automatically validates incoming JSON against the Item model and returns an error response if the data is invalid. This eliminates the need for manual validation logic and helps avoid bugs.

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Conclusion

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