👀 Views: 0 💬 Answers: 1 📅 Created: 2025-09-21

fastapi sqlalchemy asyncio performance Python

Integrating async capabilities in my FastAPI application has been a challenge, particularly regarding database interactions with SQLAlchemy. During development, I've set up my routes using async functions, which has helped with concurrency, but I'm noticing that database queries still seem to block the event loop, leading to performance bottlenecks. Currently, I’m using SQLAlchemy 1.4 with the `asyncpg` driver. Here's a simplified version of my code: ```python from fastapi import FastAPI from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine from sqlalchemy.orm import sessionmaker DATABASE_URL = "postgresql+asyncpg://user:password@localhost/db" engine = create_async_engine(DATABASE_URL, echo=True) session_local = sessionmaker(bind=engine, class_=AsyncSession, expire_on_commit=False) app = FastAPI() @app.get("/items/{item_id}") async def read_item(item_id: int): async with session_local() as session: result = await session.execute(select(Item).filter(Item.id == item_id)) return result.scalars().first() ``` I've tried utilizing `await` on all the database calls, ensuring that I’m not blocking the event loop. However, when I profile using `asyncio` and `memory_profiler`, I still see significant delays during high load tests. I've also experimented with connection pooling settings in `create_async_engine`, but the performance gain has been marginal. Additionally, I read that switching to `aiomysql` could improve performance slightly for MySQL databases, but since I’m already committed to PostgreSQL, I’m hesitant to switch. Has anyone successfully optimized their async database interactions in FastAPI using SQLAlchemy? Are there specific techniques or configurations that I might have overlooked? Any insights would be greatly appreciated.