Python Web Deployment | Heroku, AWS, and Docker in Practice

Introduction

“Development is done—time to deploy”

Deployment is the process of shipping your application to real users.

1. Deployment prep

requirements.txt

# Generate pinned dependencies
pip freeze > requirements.txt

Flask==2.3.0
gunicorn==20.1.0
python-dotenv==1.0.0

Environment variables (.env)

# .env
SECRET_KEY=your-secret-key
DATABASE_URL=postgresql://user:pass@localhost/db
DEBUG=False

# app.py
from dotenv import load_dotenv
import os
load_dotenv()
app.config['SECRET_KEY'] = os.getenv('SECRET_KEY')
app.config['DEBUG'] = os.getenv('DEBUG', 'False') == 'True'

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2. Gunicorn

Running with Gunicorn

# Install
pip install gunicorn
# Run
gunicorn app:app
# Workers and bind address
gunicorn -w 4 -b 0.0.0.0:8000 app:app

gunicorn.conf.py

bind = "0.0.0.0:8000"
workers = 4
worker_class = "sync"
timeout = 30
keepalive = 2

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3. Docker deployment

Dockerfile

FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
EXPOSE 8000
CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:8000", "app:app"]

docker-compose.yml

version: '3.8'
services:
  web:
    build: .
    ports:
      - "8000:8000"
    environment:
      - DATABASE_URL=postgresql://user:pass@db:5432/mydb
    depends_on:
      - db
  
  db:
    image: postgres:15
    environment:
      - POSTGRES_USER=user
      - POSTGRES_PASSWORD=pass
      - POSTGRES_DB=mydb
    volumes:
      - postgres_data:/var/lib/postgresql/data
volumes:
  postgres_data:

# Start stack
docker-compose up -d

---

4. Heroku deployment

Procfile

web: gunicorn app:app

runtime.txt

python-3.11.0

Deploy commands

# After installing Heroku CLI
heroku login
heroku create myapp
# Set config
heroku config:set SECRET_KEY=your-secret-key
# Deploy
git push heroku main
# Logs
heroku logs --tail

---

5. AWS deployment (EC2)

EC2 setup

# SSH into server
ssh -i key.pem ubuntu@ec2-instance
# Install Python tooling
sudo apt update
sudo apt install python3-pip python3-venv
# Clone project
git clone https://github.com/user/myapp.git
cd myapp
# Virtual environment
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
# Run Gunicorn
gunicorn -w 4 -b 0.0.0.0:8000 app:app

Nginx configuration

# /etc/nginx/sites-available/myapp
server {
    listen 80;
    server_name example.com;
    
    location / {
        proxy_pass http://127.0.0.1:8000;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
    }
}

# Enable site
sudo ln -s /etc/nginx/sites-available/myapp /etc/nginx/sites-enabled/
sudo nginx -t
sudo systemctl restart nginx

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6. Practical tips

Deployment checklist

# Environment variables
# SECRET_KEY, DATABASE_URL, etc.
# DEBUG = False
app.config['DEBUG'] = False
# ALLOWED_HOSTS (Django)
ALLOWED_HOSTS = ['yourdomain.com']
# Static files
python manage.py collectstatic
# Database migrations
python manage.py migrate
# HTTPS
# Let's Encrypt, Cloudflare, etc.

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Going deeper

Example: Gunicorn + systemd (minimal production pattern)

Assume the app is exposed as myapp.wsgi:app. This example binds to 127.0.0.1:8000 (not socket activation). /etc/systemd/system/myapp.service:

[Unit]
Description=My Gunicorn App
After=network.target
[Service]
User=www-data
Group=www-data
WorkingDirectory=/var/www/myapp
Environment="PATH=/var/www/myapp/venv/bin"
EnvironmentFile=/etc/myapp.env
ExecStart=/var/www/myapp/venv/bin/gunicorn -w 4 -b 127.0.0.1:8000 myapp.wsgi:app
Restart=always
[Install]
WantedBy=multi-user.target

After deployment: sudo systemctl daemon-reload && sudo systemctl enable --now myapp.

Common mistakes

• Running with DEBUG=True in production and leaking secrets or tracebacks.
• Hard-coding virtualenv paths so deploy scripts break on the server.
• Relying on load balancers without health checks, so dead processes go unnoticed.

Safety notes

• Keep secrets in environment variables or a secret manager—never commit them to Git.
• Re-validate ALLOWED_HOSTS, CORS, and CSRF under your production profile.

How teams usually operate

• Put Nginx or Caddy in front for TLS termination.
• Ship logs to journald and onward to CloudWatch, ELK, or similar.
• For containers, prefer read-only root filesystems and non-root users.

Options at a glance

Approach	Strengths
PaaS (Heroku, etc.)	Simple operations
VM + systemd	Control vs. cost tradeoff
Kubernetes	Large scale, many services

Further reading

• Gunicorn settings
• Django deployment checklist
• Flask deployment guide

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Summary

Key takeaways

1. Gunicorn: production WSGI server
2. Docker: reproducible environments
3. Heroku: fast path to hosted apps
4. AWS: flexible, often cost-effective at scale
5. Nginx: reverse proxy in front of app workers

Next steps

• Pandas for data analysis
• Web scraping (future topics in the series)

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Related posts

• [Python environment setup | Install on Windows & Mac](/en/blog/python-series-01-environment-setup/
• [Python basics: variables, operators, control flow](/en/blog/python-series-02-basic-syntax/

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자주 묻는 질문 (FAQ)

Q. 이 내용을 실무에서 언제 쓰나요?

A. Deploy Python web apps: requirements.txt, .env, Gunicorn, Docker and Compose, Heroku, AWS EC2 with Nginx, systemd, and a… 실무에서는 위 본문의 예제와 선택 가이드를 참고해 적용하면 됩니다.

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A. 각 글 하단의 이전 글 또는 관련 글 링크를 따라가면 순서대로 배울 수 있습니다. Python 시리즈 목차에서 전체 흐름을 확인할 수 있습니다.

Q. 더 깊이 공부하려면?

A. cppreference와 해당 라이브러리 공식 문서를 참고하세요. 글 말미의 참고 자료 링크도 활용하면 좋습니다.

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같이 보면 좋은 글 (내부 링크)

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• [Django Basics](/en/blog/python-series-12-django-basics/
• [Docker Compose Tutorial for Beginners](/en/blog/docker-compose-tutorial/
• [C++ DevContainer & Docker Guide — Standardize Builds,](/en/blog/cpp-series-40-3-docker-cpp/
• [Kubernetes Deployment with minikube | Node.js API](/en/blog/kubernetes-deployment-strategies/

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