Introduction

FastAPI is a modern, fast Python framework for building APIs. However, by default, the FastAPI development server (uvicorn) only runs a single worker process, which means it can only handle one request at a time. For production and handling concurrent requests, we need Gunicorn as a process manager.

Gunicorn (Green Unicorn) is an HTTP server for Python that can run multiple worker processes, allowing our FastAPI application to handle many requests simultaneously.

Why Gunicorn?

• Multiple worker processes for concurrent request handling
• Automatic load balancing between workers
• Graceful restart without downtime
• Production-ready and battle-tested
• Compatible with uvicorn workers for async support

Installation

First, install the required dependencies:

pip install fastapi uvicorn gunicorn

Or add to requirements.txt:

fastapi==0.109.0
uvicorn[standard]==0.27.0
gunicorn==21.2.0

Then install:

pip install -r requirements.txt

Creating a Simple FastAPI Application

Create a main.py file:

from fastapi import FastAPI
import time
import os

app = FastAPI()

@app.get("/")
async def root():
    return {
        "message": "Hello World",
        "workerpid": os.getpid()

    }

@app.get("/slow")
async def slowendpoint():
    # Simulate time-consuming operation
    time.sleep(5)
    return {
        "message": "This took 5 seconds",
        "workerpid": os.getpid()

    }

@app.get("/health")
async def healthcheck():
    return {"status": "healthy"}

Setting Up Gunicorn with Uvicorn Workers

Gunicorn itself is a synchronous server. To get async benefits from FastAPI, we use Uvicorn workers.

Running from Command Line

gunicorn main:app --workers 4 --worker-class uvicorn.workers.UvicornWorker --bind 0.0.0.0:8000

• main:app - module:application (main.py with app variable)
• --workers 4 - number of worker processes
• --worker-class uvicorn.workers.UvicornWorker - use async uvicorn workers
• --bind 0.0.0.0:8000 - host and port

Creating a Configuration File

Create a gunicornconf.py file for more structured configuration:

import multiprocessing
import os

Server Socket
bind = "0.0.0.0:8000"
backlog = 2048

Worker Processes
workers = int(os.getenv("WORKERS", multiprocessing.cpucount()  2 + 1))

workerclass = "uvicorn.workers.UvicornWorker"

workerconnections = 1000
maxrequests = 10000

maxrequestsjitter = 1000

timeout = 120
keepalive = 5

Logging
accesslog = "-"
errorlog = "-"
loglevel = "info"
accesslogformat = '%(h)s %(l)s %(u)s %(t)s "%(r)s" %(s)s %(b)s "%(f)s" "%(a)s" %(D)s'


Process Naming
procname = "fastapiapp"


Server Mechanics
daemon = False
pidfile = None
user = None
group = None
tmpuploaddir = None


Graceful Timeout
gracefultimeout = 30

gunicorn main:app -c gunicornconf.py

workers = (2  CPUCORES) + 1

• 2 CPU cores = 5 workers
• 4 CPU cores = 9 workers
• 8 CPU cores = 17 workers

• For CPU-bound tasks: use the formula above
• For I/O-bound tasks: can use more workers
• Monitor memory usage, don't run out of RAM
• Start with the standard formula, then adjust based on monitoring

Testing Concurrent Requests

Test whether concurrent invocation works correctly.

Using Python

Create a testconcurrent.py file:

import asyncio
import aiohttp
import time