implementing Asynchronous Database Calls in FastAPI: Handling Connection Timeouts
I've been working on this all day and I've searched everywhere and can't find a clear answer. I'm stuck trying to I'm a bit lost with Quick question that's been bugging me - I tried several approaches but none seem to work. I'm experiencing connection timeouts when performing database operations asynchronously in my FastAPI application using SQLAlchemy. My setup uses SQLAlchemy 1.4.22 and FastAPI 0.68.1 with a PostgreSQL database. The scenario arises when I try to query a large table with around 1 million records, and I consistently hit the following behavior: ``` sqlalchemy.exc.OperationalError: (psycopg2.OperationalError) timeout expired ``` I have set up the database connection using the following code: ```python from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine from sqlalchemy.orm import sessionmaker DATABASE_URL = "postgresql+asyncpg://user:password@localhost/dbname" engine = create_async_engine(DATABASE_URL, echo=True) AsyncSessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine, class_=AsyncSession) ``` In my route handler, I utilize the session as follows: ```python from fastapi import FastAPI, Depends from sqlalchemy.ext.asyncio import AsyncSession app = FastAPI() async def get_db() -> AsyncSession: async with AsyncSessionLocal() as session: yield session @app.get("/items/") async def read_items(db: AsyncSession = Depends(get_db)): result = await db.execute("SELECT * FROM large_table") items = result.fetchall() return items ``` I have tried increasing the connection timeout in the database settings and also adjusted the pool size and max overflow parameters in the `create_async_engine` call, but the scenario continues. Any insights on how to handle this situation or optimize the queries would be greatly appreciated! For context: I'm using Python on macOS. What's the best practice here? For context: I'm using Python on Windows. Any ideas what could be causing this? This is happening in both development and production on Linux. What are your experiences with this? I recently upgraded to Python 3.11. The stack includes Python and several other technologies. How would you solve this? I recently upgraded to Python latest.