FastAPI Backend Development Bootcamp · 课时

异步数据库访问

探索 `asyncpg` 和 `SQLModel` 等异步数据库驱动程序和 ORM,以执行非阻塞数据库操作。

第 2 / 4 课11 个步骤

异步数据库访问 是 CoddyKit 上的免费 FastAPI Backend Development Bootcamp 课时。 这是第 2 节课,共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 FastAPI Backend Development Bootcamp 学习路径的一部分,你的进度在网页和 CoddyKit 应用中同步。 FastAPI Backend Development Bootcamp 课程共包含 4 节课。

本课时的部分内容尚未翻译,以英文显示。

Why Async DB Access?

When building high-performance web applications, especially with frameworks like FastAPI, database operations can often become a bottleneck. Traditional database calls are "blocking", meaning your application waits for the database to respond before moving on.

Asynchronous database access allows your application to perform other tasks while waiting for the database, preventing your API from becoming unresponsive under heavy load. This is crucial for scalability!

Sync vs. Async DB Calls

Imagine a restaurant where one chef handles everything. If a customer orders a complex dish (a database query), the chef stops all other work until that dish is complete. This is synchronous I/O.

In an asynchronous setup, the chef starts the complex dish, then immediately moves to prepare simpler dishes or take new orders while the complex dish cooks in the background. When the complex dish is ready, the chef picks it up. This non-blocking approach boosts efficiency!

Meet asyncpg: The Async Driver

For PostgreSQL, the go-to asynchronous driver in Python is asyncpg. It's a high-performance library specifically designed to work with Python's asyncio framework.

  • Fast: Written partly in C for speed.
  • Asynchronous: Fully non-blocking.
  • Direct: Provides a direct interface to PostgreSQL.

It's often used as the underlying driver for async ORMs or when you need fine-grained control.

asyncpg in Action

Let's see a basic example of connecting to a PostgreSQL database and running a simple query using asyncpg. Remember to replace placeholder credentials with your own!

import asyncio
import asyncpg

async def main():
    conn = None
    try:
        conn = await asyncpg.connect(user='postgres', password='mysecretpassword',
                                     database='testdb', host='localhost')
        print("Connected to PostgreSQL!")
        
        # Execute a query
        result = await conn.fetchval('SELECT 1 + 1')
        print(f"Query result: {result}") # Should be 2
        
    except Exception as e:
        print(f"Error: {e}")
    finally:
        if conn:
            await conn.close()
            print("Connection closed.")

if __name__ == "__main__":
    asyncio.run(main())

Awaiting Database Calls

In the previous example, you saw the await keyword before asyncpg.connect() and conn.fetchval(). This is crucial for asynchronous operations.

  • await tells Python: "This operation might take time, so pause here and let other tasks run in the meantime."
  • When the database operation completes, the task resumes from where it left off.
  • This non-blocking wait is what makes your FastAPI application scalable.

SQLModel: Async ORM Power

While asyncpg gives you low-level control, an Object Relational Mapper (ORM) simplifies database interactions by mapping database tables to Python objects. SQLModel is a modern, async-first ORM built on:

  • Pydantic: For data validation and serialization.
  • SQLAlchemy: The powerful and mature Python SQL toolkit.

It lets you define models once and use them for both your API request/response and database schema!

Setting up SQLModel for Async

To use SQLModel asynchronously, you need an asynchronous database engine. This typically involves using an async driver like asyncpg (which SQLAlchemy can use via asyncio). Here's how you'd set up the engine:

from sqlmodel import create_engine, SQLModel
import asyncio

# Replace with your actual async PostgreSQL connection string
# The 'postgresql+asyncpg' part tells SQLAlchemy to use asyncpg
DATABASE_URL = "postgresql+asyncpg://postgres:mysecretpassword@localhost/testdb"

async def main():
    engine = create_engine(DATABASE_URL, echo=True)
    print("Async SQLModel engine created.")
    
    # In a real app, you'd usually create tables here
    # async with engine.begin() as conn:
    #     await conn.run_sync(SQLModel.metadata.create_all)
    
    # Just demonstrating engine creation for this example
    await engine.dispose()
    print("Engine disposed.")

if __name__ == "__main__":
    asyncio.run(main())

Your First SQLModel

Defining a model in SQLModel is super intuitive. You inherit from SQLModel and use Pydantic-like field declarations. This single definition creates both your database table schema and your API's data validation schema!

from typing import Optional
from sqlmodel import Field, SQLModel

class Hero(SQLModel, table=True):
    id: Optional[int] = Field(default=None, primary_key=True)
    name: str = Field(index=True)
    secret_name: str
    age: Optional[int] = Field(default=None, index=True)

# This model can now be used with FastAPI for request bodies
# and with SQLAlchemy for database interactions.
print("Hero model defined successfully!")

Async CRUD with SQLModel

Now let's perform a simple Create and Read operation using our Hero model and the async engine. We'll use AsyncSession from SQLAlchemy's ORM for database interactions.

from typing import Optional
from sqlmodel import Field, SQLModel, create_engine, Session, select
from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine
from sqlalchemy.orm import sessionmaker
import asyncio

DATABASE_URL = "postgresql+asyncpg://postgres:mysecretpassword@localhost/testdb"
async_engine = create_async_engine(DATABASE_URL, echo=False)
AsyncSessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=async_engine, class_=AsyncSession)

class Hero(SQLModel, table=True):
    id: Optional[int] = Field(default=None, primary_key=True)
    name: str = Field(index=True)
    secret_name: str
    age: Optional[int] = Field(default=None, index=True)

async def create_db_and_tables():
    async with async_engine.begin() as conn:
        await conn.run_sync(SQLModel.metadata.create_all)
    print("Database tables created/updated.")

async def create_hero(hero: Hero):
    async with AsyncSessionLocal() as session:
        session.add(hero)
        await session.commit()
        await session.refresh(hero)
        print(f"Created hero: {hero.name} (ID: {hero.id})")
        return hero

async def get_heroes():
    async with AsyncSessionLocal() as session:
        statement = select(Hero)
        results = await session.execute(statement)
        heroes = results.scalars().all()
        print("\nAll Heroes:")
        for hero in heroes:
            print(f"- {hero.name} (Age: {hero.age})")
        return heroes

async def main():
    await create_db_and_tables()

    hero_1 = Hero(name="Deadpond", secret_name="Dive Wilson", age=28)
    hero_2 = Hero(name="Spider-Boy", secret_name="Pedro Parqueador")
    hero_3 = Hero(name="Rusty-Man", secret_name="Tommy Sharp", age=48)

    await create_hero(hero_1)
    await create_hero(hero_2)
    await create_hero(hero_3)

    await get_heroes()
    await async_engine.dispose()

if __name__ == "__main__":
    asyncio.run(main())

Async DB Check

You've learned about the importance of asynchronous database access and explored tools like asyncpg and SQLModel. Let's test your understanding!

Recap: Async DB for Scale

Great job! You've successfully explored asynchronous database access.

  • We understood why non-blocking I/O is vital for high-performance FastAPI apps.
  • We introduced asyncpg as a low-level async PostgreSQL driver.
  • We learned about SQLModel, an async-first ORM combining Pydantic and SQLAlchemy.
  • We saw practical examples of setting up and performing CRUD operations with these tools.

Mastering async database interactions is a key step towards building truly scalable and responsive backend services!

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常见问题解答

「异步数据库访问」课时是免费的吗?

是的 — 「异步数据库访问」的完整文本可在网页上免费阅读。要进行交互式练习(内置代码编辑器和全天候 AI 导师)并解锁 FastAPI Backend Development Bootcamp 课程的其余内容,请升级到 CoddyKit PRO。 FastAPI Backend Development Bootcamp 课程共包含 4 节课。

「异步数据库访问」这节课中我会学到什么?

探索 `asyncpg` 和 `SQLModel` 等异步数据库驱动程序和 ORM,以执行非阻塞数据库操作。 你通过在浏览器中直接运行的动手代码来练习 FastAPI Backend Development Bootcamp,全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 FastAPI Backend Development Bootcamp 需要有经验吗?

无需任何先前经验。CoddyKit 上的 FastAPI Backend Development Bootcamp 课程适合初学者到高级学习者,你可以从这里开始或从头开始,按照自己的节奏学习。 这是第 2 节课,共 4 节。

「异步数据库访问」课时需要多长时间?

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动,所以你能稳步进步,并在网页和应用中从离开的地方继续。

我能在这节 FastAPI Backend Development Bootcamp 课中编写并运行代码吗?

能。每节 FastAPI Backend Development Bootcamp 课都包含内置代码编辑器,你可以在浏览器中直接编写并运行真实代码,并获得即时 AI 反馈 — 无需本地设置。

此课程中的所有课时

  1. 使用 Redis 的缓存策略
  2. 异步数据库访问
  3. 负载均衡与监控
  4. 后台任务与作业队列
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