Before we start, let’s briefly understand what an “API framework” is. Think of an API framework as a set of pre - made tools for “building blocks” when you’re playing with building blocks. Instead of writing all the code from scratch, you can directly use the components provided by the framework to quickly assemble interfaces that allow programs to communicate with each other (such as an interface where you request a page and the backend returns data).
Traditional API frameworks (such as Flask, Django REST Framework, etc.) have existed for many years, while FastAPI is a new framework that has risen in recent years. Many novices may ask: “Since there are already established frameworks, why should we learn FastAPI? What are the essential differences between them?”
1. Learning Curve: A Leap from “Writing Interfaces” to “Writing Good Interfaces”¶
Characteristics of Traditional Frameworks (such as Flask):
Flask is a typical “lightweight” framework with a low learning threshold. Writing a simple interface may only require a few lines of code. For example:
# Flask example: a simple GET interface
from flask import Flask, jsonify
app = Flask(__name__)
@app.route('/hello/<name>')
def hello(name):
return jsonify({"message": f"Hello, {name}!"})
However, if you need to do more complex things (such as parameter validation and data format processing), you may need to manually write a lot of additional code, such as checking if name is empty and whether the data type is correct.
Characteristics of FastAPI:
FastAPI simplifies development by leveraging Python’s Type Hints. For example, writing the same /hello interface:
# FastAPI example: the same simple GET interface
from fastapi import FastAPI
app = FastAPI()
@app.get("/hello/{name}")
def hello(name: str): # name: str is the type hint
return {"message": f"Hello, {name}!"}
Here, name: str is not just a “comment”; it tells FastAPI: “This parameter must be of type string”. FastAPI will automatically perform parameter type validation and error prompts. For instance, if a user passes a number for name, FastAPI will directly report an error “Incorrect parameter type” without you having to manually write if not isinstance(name, str): return 400.
2. Auto - Generated “Interactive Documentation”: Saves Time on Documentation Development¶
Traditional Frameworks: Manual Integration of Documentation Tools
Most traditional frameworks (such as Flask) require installing additional plugins to generate API documentation. For example, when using Flask, you need to use libraries like flask - restx and manually configure Swagger UI. Moreover, the generated documentation may not be as intuitive as expected.
FastAPI: Built - in “Out - of - the - Box” Documentation
FastAPI automatically generates interactive documentation based on the OpenAPI specification (Swagger). Once the project is run, you can access /docs or /redoc to see detailed information about all interfaces:
- The path, parameters, and return values of each interface
- Directly test the interface on the page (e.g., input parameters, click “Try it out” to send a request)
- Automatically generate JSON - formatted interface descriptions, which is convenient for front - end or other teams to call
This is a real “hand - free” experience for novices - you only need to write code, and the documentation is automatically generated, avoiding the repetitive work of “writing interfaces + writing documentation” in traditional frameworks.
3. Data Validation: Automatically “Filters Wrong Inputs” for You¶
Traditional Frameworks: Manual Validation Logic
Suppose you want to develop a “user registration interface” and need to check if the username is empty, whether the age is a number, and whether the email is valid. A traditional framework may require you to manually write:
# Flask example: manual parameter validation
@app.post("/register")
def register(name, age, email):
if not name:
return {"error": "Username cannot be empty"}
if not isinstance(age, int) or age < 0:
return {"error": "Age must be a positive integer"}
# Other validation logic...
return {"success": True}
Once the parameter logic becomes complex, the validation code becomes cumbersome and error - prone.
FastAPI: One Line of Code for Automatic Validation
FastAPI uses the Pydantic library (built into FastAPI) to implement data models and automatically validate through type hints:
from pydantic import BaseModel # Library for defining data models
class User(BaseModel): # Define the user data structure
name: str
age: int
email: str
@app.post("/register")
def register(user: User): # FastAPI automatically parses and validates request data
return {"success": True, "user": user.dict()} # Return the validated data
Here, user: User will automatically check:
- Whether name is a non - empty string (Pydantic requires non - optional fields by default)
- Whether age is an integer and greater than or equal to 0
- Whether email conforms to the email format (if defined as email: EmailStr)
If the user passes incorrect data (such as age being the string “abc”), FastAPI will directly return a 422 Unprocessable Entity error and provide specific error reasons (such as “The entered age must be an integer”).
4. Asynchronous Support: “No Stuttering” in High - Concurrency Scenarios¶
Traditional Frameworks: Synchronous Blocking, Weak Concurrency
Most traditional frameworks (such as Flask + Gunicorn) are synchronous servers by default. When a time - consuming operation (such as reading a database or calling a third - party interface) is encountered, the entire thread will “freeze”, making it unable to handle other requests. For example:
# Flask synchronous interface (assuming a time - consuming operation)
import time
@app.get("/slow")
def slow():
time.sleep(5) # Simulate a time - consuming operation (e.g., database query)
return {"data": "Done"}
At this time, if 100 users access /slow at the same time, only one user can return data in the first 5 seconds, and other users will have to wait, resulting in slow system response.
FastAPI: Asynchronous Non - Blocking, Higher Efficiency
FastAPI is based on ASGI servers (such as Uvicorn) and Python 3.7+’s asynchronous syntax, supporting “asynchronous interfaces”. When an interface encounters a time - consuming operation, it will not block the entire thread but switch to processing other requests:
# FastAPI asynchronous interface (assuming a time - consuming operation)
import asyncio
@app.get("/slow")
async def slow(): # Define an asynchronous interface with async def
await asyncio.sleep(5) # Asynchronous waiting (will not block the thread)
return {"data": "Done"}
In this way, even if 100 users access /slow at the same time, FastAPI can process all requests within 5 seconds, avoiding the “stuttering” problem of traditional frameworks.
5. Code Conciseness: “Write Less Code, Do More Things”¶
For interfaces with the same functionality, FastAPI often requires fewer lines of code. Take a “query user” interface as an example:
Traditional Framework (Flask):
from flask import Flask, jsonify
from flask_sqlalchemy import SQLAlchemy # Assume using SQLAlchemy to operate the database
app = Flask(__name__)
db = SQLAlchemy(app)
class User(db.Model):
id = db.Column(db.Integer, primary_key=True)
name = db.Column(db.String(50))
@app.get("/user/<int:user_id>")
def get_user(user_id):
user = User.query.get(user_id)
if not user:
return jsonify({"error": "User not found"}), 404
return jsonify({
"id": user.id,
"name": user.name
})
It is necessary to define the model, manually query, handle empty values, and return JSON, among other things.
FastAPI:
from fastapi import FastAPI
from pydantic import BaseModel
from sqlalchemy.orm import Session # FastAPI supports dependency injection to manage database connections
app = FastAPI()
class User(BaseModel):
id: int
name: str
@app.get("/user/{user_id}", response_model=User) # Automatically return JSON format
def get_user(user_id: int, db: Session = Depends(get_db)): # Dependency injection automatically obtains database connection
user = db.query(UserModel).filter(UserModel.id == user_id).first() # Assume UserModel is the database model
if not user:
raise HTTPException(status_code=404, detail="User not found")
return user
Although the number of lines of code is similar, FastAPI makes the code more focused on business logic and reduces template - style repetitive code through features such as dependency injection (automatically obtaining database connections), response_model (automatically returning the JSON structure defined by the model), and HTTPException (automatically returning errors).
Summary: Should You Choose FastAPI or Traditional Frameworks?¶
-
Scenarios for choosing FastAPI:
If you need to develop APIs quickly, value automatic documentation, want more concise and robust code, or need to handle high - concurrency requests (such as background services and microservices), FastAPI is a better choice. It is especially suitable for novices to learn because “type hints + automatic validation + built - in documentation” allows you to quickly write “bug - free” interfaces. -
Advantages of traditional frameworks:
Traditional frameworks (such as Flask) are more lightweight and suitable for simple projects (such as personal blog interfaces) or scenarios with low performance requirements. Learning traditional frameworks can also help you understand the underlying logic of API development.
Novice suggestion: It is more efficient to start with FastAPI. Its design philosophy is “to implement the clearest logic with the least code”. At the same time, you can learn practical skills such as Python type hints and asynchronous programming, which can greatly improve your API development ability in the long run.
Finally, end with a metaphor: Traditional frameworks are like “manual tools”, suitable for fine - tuning but inefficient; FastAPI is like “power tools”, easier to use and suitable for quickly assembling complex projects.