👀 Views: 3 💬 Answers: 1 📅 Created: 2025-06-07

FastAPI PostgreSQL AsyncIO Python

I'm working on a personal project and I'm running into issues with scaling my FastAPI application that uses PostgreSQL as the database backend. Currently, my application works well locally, but when I deploy it on AWS with increased traffic, I encounter the behavior `psycopg2.OperationalError: FATAL: remaining connection slots are reserved for non-replication superuser connections`. I suspect this is due to the default connection limits of PostgreSQL being exceeded when many users access the app simultaneously. I've implemented asynchronous database calls using `asyncpg`, but I still feel like I'm hitting a wall with connections. Here’s a snippet of how I’m currently creating the database connection pool: ```python import asyncpg from fastapi import FastAPI, Depends app = FastAPI() async def get_db(): pool = await asyncpg.create_pool( user='user', password='password', database='database', host='localhost', max_size=20 ) async with pool.acquire() as connection: yield connection ``` I thought setting `max_size=20` was sufficient, but it seems like it isn’t handling the load. I also changed the configuration of PostgreSQL to allow for more connections by modifying the `postgresql.conf` file to increase `max_connections`, but it didn't seem to resolve the scenario. In addition to modifying connection pool settings, I've tried implementing a retry mechanism for queries, but I'm concerned that this might just mask the underlying scenario rather than fixing it. Also, how can I better manage database connections in a high-concurrency environment while still using FastAPI and async features? Any advice on optimally configuring PostgreSQL for async applications and best practices for connection pooling would be immensely helpful. For context: I'm using Python on macOS.