gunicorn-26-0-0

name

gunicorn-26-0-0

description

Complete toolkit for Gunicorn 26.0.0 — Python WSGI/ASGI HTTP server with pre-fork worker model. Covers installation, configuration, worker selection (sync, gthread, gevent, ASGI), deployment behind Nginx/systemd/Docker, SSL/TLS, HTTP/2, Dirty Arbiters for heavy workloads, uWSGI protocol, and runtime control via gunicornc. Use when deploying Python web applications (Django, Flask, FastAPI, Starlette) to production, tuning Gunicorn worker counts and timeouts, configuring reverse proxy integration, or setting up async/ASGI workloads with streaming and Dirty Arbiters.

Gunicorn 26.0.0

Overview

Gunicorn (Green Unicorn) is a Python WSGI HTTP server for UNIX, using a pre-fork worker model ported from Ruby's Unicorn. It is broadly compatible with web frameworks, simply implemented, light on resources, and production-proven since 2010.

Version 26.0.0 builds on v25 features including Dirty Arbiters (separate process pools for heavy workloads), HTTP/2 support, the native ASGI worker, and the gunicornc control interface. Requires Python 3.12+.

When to Use

• Deploying a Python web application (Django, Flask, FastAPI, Starlette) to production
• Tuning Gunicorn worker counts, timeouts, or thread pools
• Configuring reverse proxy integration (Nginx, HAProxy) with Gunicorn
• Setting up ASGI workloads with WebSockets, streaming, or Dirty Arbiters
• Managing Gunicorn at runtime via signals or the gunicornc control interface
• Choosing between worker types (sync, gthread, gevent, ASGI) for a specific workload

Core Concepts

Pre-fork Worker Model

Gunicorn runs an arbiter (master) process that manages a pool of worker processes. The arbiter listens on sockets and distributes connections to workers. Workers handle requests independently — a worker crash affects only its in-flight requests.

Configuration Priority Chain

Gunicorn reads configuration from five sources, increasing priority:

1. Environment variables (per-setting support)
2. Framework-specific config (Paste Deploy)
3. Python config file (gunicorn.conf.py, auto-loaded from working directory)
4. GUNICORN_CMD_ARGS environment variable
5. Command-line arguments (highest priority)

Print fully resolved config: gunicorn --print-config APP_MODULE Validate and exit: gunicorn --check-config APP_MODULE

Worker Types

Worker	Concurrency	Keep-Alive	Best For
sync (default)	1 request/worker	No	CPU-bound apps behind a buffering proxy
gthread	Thread pool	Yes	Mixed workloads, moderate concurrency
gevent	Greenlets	Yes	I/O-bound, WebSockets, streaming
asgi	AsyncIO	Yes	FastAPI, Starlette, Quart (async frameworks)
tornado	Tornado IOLoop	Yes	Native Tornado applications

Installation / Setup

# Quick install
pip install gunicorn

# With async extras
pip install gunicorn[gevent,setproctitle]

# In a virtual environment (recommended)
python -m venv venv && source venv/bin/activate
pip install gunicorn

# Docker
docker pull ghcr.io/benoitc/gunicorn:latest

Usage Examples

Basic WSGI (Flask)

gunicorn app:app --workers 4

Django

gunicorn myproject.wsgi --workers 4

FastAPI (ASGI)

gunicorn main:app --worker-class asgi --workers 4

With a Configuration File

Create gunicorn.conf.py:

import multiprocessing

bind = "0.0.0.0:8000"
workers = multiprocessing.cpu_count() * 2 + 1
accesslog = "-"
errorlog = "-"
loglevel = "info"

Run (auto-loads gunicorn.conf.py from current directory):

gunicorn app:app

Worker Count Formula

Start with 2 * CPU_CORES + 1 workers, adjust under load. Use TTIN/TTOU signals to scale at runtime.

Advanced Topics

Configuration Settings: Full reference of all Gunicorn settings with defaults and descriptions → Configuration Settings

Deployment Patterns: Nginx proxy, systemd socket activation, Docker, PROXY protocol, process managers → Deployment Patterns

Workers and Customization: Worker type selection, ASGI worker details, Dirty Arbiters for heavy workloads, custom applications → Workers and Customization

Operations: Signal handling, binary upgrades, logging, statsD instrumentation, FAQ → Operations