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Background Tasks Example - Fastapi Typing CST Test
Background Tasks Example — Fastapi Code
Run background tasks after returning a response.
from fastapi import FastAPI, BackgroundTasks
app = FastAPI()
def write_log(message: str):
with open('log.txt', 'a') as f:
f.write(message + '\n')
@app.post('/send/')
async def send_message(message: str, background_tasks: BackgroundTasks):
background_tasks.add_task(write_log, message)
return { 'message': message }Fastapi Language Guide
FastAPI is a modern, high-performance Python web framework for building APIs with automatic interactive documentation. It emphasizes speed, type hints, and ease of development, leveraging Python 3.7+ features.
Primary Use Cases
- ▸Building RESTful and JSON APIs
- ▸High-performance asynchronous backend
- ▸Microservices architecture
- ▸Data validation and processing APIs
- ▸Integrating with frontend frameworks or machine learning models
Notable Features
- ▸Automatic validation with Pydantic models
- ▸Asynchronous support with async/await
- ▸Interactive API documentation with Swagger UI/ReDoc
- ▸Dependency injection system for modular code
- ▸High performance close to Node.js frameworks
Origin & Creator
FastAPI was created by Sebastián Ramírez in 2018 to provide a fast and developer-friendly Python API framework.
Industrial Note
FastAPI is ideal for projects requiring high-performance async APIs, automatic data validation, and rapid development cycles in Python.
Quick Explain
- ▸FastAPI is designed for building RESTful APIs quickly and efficiently using Python.
- ▸Supports Python type hints for automatic validation and documentation generation.
- ▸Integrates with Pydantic for data validation and serialization.
- ▸Includes automatic interactive API docs using Swagger UI and ReDoc.
- ▸Well-suited for modern backend applications, microservices, and asynchronous APIs.
Core Features
- ▸Route handling with type-checked parameters
- ▸Data serialization/deserialization via Pydantic
- ▸Built-in support for OAuth2, JWT, and authentication
- ▸Dependency injection for reusable components
- ▸Asynchronous request handling for scalability
Learning Path
- ▸Learn Python 3.7+ basics
- ▸Understand FastAPI app and routing
- ▸Use Pydantic models for validation
- ▸Implement dependencies and middleware
- ▸Deploy FastAPI API to production
Practical Examples
- ▸Todo API with Pydantic validation
- ▸Blog backend with CRUD operations
- ▸Authentication API with JWT/OAuth2
- ▸E-commerce microservice backend
- ▸Machine learning model API serving predictions
Comparisons
- ▸FastAPI vs Flask: FastAPI is async-first with type hints; Flask is synchronous and lightweight
- ▸FastAPI vs Django REST Framework: FastAPI is async and high-performance; DRF is more opinionated and full-featured
- ▸FastAPI vs Tornado: FastAPI provides modern type hints and automatic docs; Tornado focuses on async I/O
- ▸FastAPI vs Falcon: Falcon is minimalist; FastAPI adds validation and docs
- ▸FastAPI vs Express: Python ecosystem vs Node.js ecosystem
Strengths
- ▸Very fast due to Starlette and Pydantic under the hood
- ▸Automatic docs reduce boilerplate
- ▸Strong Python type checking
- ▸Easy to integrate with async DB and external APIs
- ▸Extensible and modular via dependencies and routers
Limitations
- ▸Relatively new ecosystem compared to Django/Flask
- ▸Learning curve for async programming and dependencies
- ▸Less mature for server-side rendering
- ▸Requires understanding of Pydantic models
- ▸Complex projects may need careful organization to avoid spaghetti
When NOT to Use
- ▸Simple static websites without APIs
- ▸Projects heavily tied to synchronous libraries
- ▸Developers unfamiliar with async Python
- ▸Small scripts needing minimal setup
- ▸Projects requiring server-side rendering templates
Cheat Sheet
- ▸from fastapi import FastAPI -> create FastAPI instance
- ▸@app.get('/path') -> define GET route
- ▸@app.post('/path') -> define POST route
- ▸from pydantic import BaseModel -> define data model
- ▸uvicorn main:app --reload -> run server
FAQ
- ▸Is FastAPI free?
- ▸Yes - open-source under MIT license.
- ▸Does FastAPI support async?
- ▸Yes - async endpoints with async/await are supported.
- ▸Is FastAPI suitable for production?
- ▸Yes - high-performance and scalable.
- ▸Does FastAPI generate docs automatically?
- ▸Yes - interactive Swagger UI and ReDoc docs are generated.
- ▸How does FastAPI compare to Flask?
- ▸Faster, async-first, type-hint driven, automatic validation and docs.
30-Day Skill Plan
- ▸Week 1: Python async fundamentals
- ▸Week 2: FastAPI routes and Pydantic models
- ▸Week 3: Dependencies and middleware
- ▸Week 4: Authentication & async DB integration
- ▸Week 5: Deployment and performance optimization
Final Summary
- ▸FastAPI is a high-performance Python web framework for APIs.
- ▸Automatic data validation with Pydantic ensures reliability.
- ▸Async-first design supports high throughput and scalability.
- ▸Interactive API docs reduce boilerplate and improve usability.
- ▸Ideal for REST APIs, microservices, and modern backend applications.
Project Structure
- ▸main.py - entry point
- ▸routers/ - route modules
- ▸models/ - Pydantic models for data
- ▸dependencies/ - reusable dependencies
- ▸services/ - business logic and integrations
Monetization
- ▸Backend APIs for SaaS applications
- ▸Machine learning API services
- ▸Subscription-based platforms
- ▸E-commerce and content delivery backends
- ▸Enterprise-grade microservices
Productivity Tips
- ▸Use Pydantic models for consistent validation
- ▸Leverage dependency injection for reusable logic
- ▸Enable automatic docs to reduce manual work
- ▸Use async endpoints for high throughput
- ▸Automate testing and deployment pipelines
Basic Concepts
- ▸FastAPI app - core application instance
- ▸Routes - endpoints with automatic parameter validation
- ▸Pydantic models - data validation and serialization
- ▸Dependencies - reusable injected logic
- ▸Middleware - functions applied to requests/responses