FastAPI Async Performance Tuning

FastAPI leverages Starlette and Pydantic for high speed. However, unoptimized DB connections or blocking I/O will starve event loop workers.

1. Async PostgreSQL Connection Pooling with AsyncPG

from fastapi import FastAPI
from asyncpg import create_pool

app = FastAPI()

@app.on_event("startup")
async def startup():
    app.state.pool = await create_pool(
        dsn="postgresql://user:pass@localhost/db",
        min_size=10,
        max_size=50,
        timeout=10.0
    )

@app.get("/api/v1/users/{user_id}")
async def get_user(user_id: int):
    async with app.state.pool.acquire() as conn:
        user = await conn.fetchrow("SELECT id, email FROM users WHERE id = $1", user_id)
        return dict(user)

2. Production Uvicorn Run Configuration

gunicorn main:app \
  --workers 4 \
  --worker-class uvicorn.workers.UvicornWorker \
  --bind 0.0.0.0:8000 \
  --timeout 30 \
  --keep-alive 5