👀 Views: 124 💬 Answers: 1 📅 Created: 2025-10-17

fastapi asyncio sqlalchemy api Python

I'm writing unit tests and I recently switched to I'm integrating two systems and I've been researching this but Quick question that's been bugging me - During the migration of a legacy system to FastAPI, I ran into some challenges with managing sessions while implementing asynchronous API calls. The existing system relies on synchronous execution, and transitioning this to an asynchronous model has proven to be quite tricky. I've set up a basic FastAPI application and integrated SQLAlchemy for database interactions. Here's a snippet of how I'm currently structuring my session management: ```python from fastapi import FastAPI, Depends from sqlalchemy.orm import sessionmaker, Session from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine from sqlalchemy.ext.declarative import declarative_base DATABASE_URL = "postgresql+asyncpg://user:password@localhost/dbname" engine = create_async_engine(DATABASE_URL, echo=True) Base = declarative_base() async def get_session() -> AsyncSession: async with sessionmaker(bind=engine, class_=AsyncSession, expire_on_commit=False)() as session: yield session app = FastAPI() @app.get("/items/{item_id}") async def read_item(item_id: int, session: AsyncSession = Depends(get_session)): # Fetch item logic here pass ``` The problem arises when I try to make multiple API calls that depend on each other within an endpoint. For example, fetching related data in a single request often leads to session issues, like "Session is closed" or "Invalid request context" errors. I’ve tried using the `async with` pattern to ensure that the session is opened and closed correctly, but the errors still persist. Here’s another approach I attempted, trying to create a session for each call: ```python @app.get("/multi-fetch") async def multi_fetch(): async with get_session() as session: result1 = await session.execute(select(Item).where(Item.id == 1)) result2 = await session.execute(select(Item).where(Item.id == 2)) return {"result1": result1.scalar(), "result2": result2.scalar()} ``` Even with this setup, I still see issues when concurrent requests hit the same endpoint. Debugging shows that the context isn't maintained. Could there be a more efficient pattern I’m overlooking? Additionally, is there a recommended way to handle session lifecycle in FastAPI when making multiple asynchronous calls within a single request? Any insights or best practices would be greatly appreciated. I'm working on a service that needs to handle this. Any ideas what could be causing this? For context: I'm using Python on Ubuntu 22.04. I'd be grateful for any help. I'm using Python latest in this project. Thanks, I really appreciate it!