Building a Multi-Tier Flask App with Gunicorn, Nginx, PostgreSQL, and Systemd Using Ansible

Rahat Naqvi — Sun, 04 May 2025 03:11:31 +0000

In this blog, we’ll walk through how to automate the deployment of a production-grade, multi-tier Flask application using Ansible. This deployment stack uses Gunicorn to serve the Flask app, Nginx as a reverse proxy, PostgreSQL as the backend database, and Systemd to manage services — all orchestrated through Ansible playbooks for repeatability and automation.

Project Overview

The core idea behind this project is to take a typical Flask app and elevate it into a real-world, scalable deployment using industry-standard components. By automating every step using Ansible, we eliminate manual setup, reduce configuration drift, and create a consistent deployment pipeline — all from scratch on a local or remote Ubuntu machine.

Stack Components

Ansible: Handles orchestration and automation
Flask: Lightweight Python web framework
Gunicorn: WSGI HTTP server for running Flask
Nginx: Acts as a reverse proxy and static file server
PostgreSQL: Relational database for application storage
Systemd: Ensures services like Gunicorn and PostgreSQL are managed and restarted if needed

Objectives

Automate the full-stack deployment of a Flask application
Use Ansible to manage packages, copy files, create users, and configure services
Set up a proper separation between the app layer (Flask/Gunicorn), proxy layer (Nginx), and database (PostgreSQL)
Configure Systemd services to ensure auto-start and reliability
Learn practical Ansible skills for infrastructure as code (IaC)

Workflow

Clone the repository and enter the ansible directory.
Set up the inventory file to target your localhost or remote machine.
Customize application and database settings in the Ansible playbook if needed.
Run the playbook using ansible-playbook, which installs dependencies, configures Nginx, sets up the database, and deploys the app.
Access the app in your browser and validate that all services are running using systemctl.

Project Structure

multi-tier-flask-app/
├── app/                   # Flask app code
├── ansible/
│   ├── inventory.ini      # Target host(s)
│   ├── playbook.yml       # Main Ansible automation
│   └── templates/         # Jinja2 templates for configs
└── README.md

Requirements in my case

Ubuntu system with Python 3.8+ and Ansible installed
Sudo access to install and manage system services
Internet access for package installation

This hands-on project is perfect for DevOps beginners and intermediate users who want to solidify their understanding of Ansible, Linux services, and full-stack deployment patterns. It bridges the gap between development and operations by combining automation with real-world application deployment.

How I Built a Flask-Celery-Redis Stack with Docker

Rahat Naqvi — Fri, 02 May 2025 03:33:44 +0000

I recently built a complete Flask-Celery-Redis stack using Docker and Docker Compose. This setup is widely used in real-world web applications where background task processing is essential — for example, sending emails, processing uploads, or long-running API calls.

Stack Overview

Flask: Python web framework for handling HTTP requests.
Celery: A distributed task queue to run background jobs.
Redis: An in-memory data store used by Celery as a message broker.
Docker: Containerized all services and orchestrated them using Docker Compose.

What I Built
I created a small Flask app with a route that triggers a background task — a simple sleep function to simulate long processing. Celery picks up the task and processes it asynchronously, and Redis handles the message queue.

Folder Structure

flask-celery-redis/
├── app/
│   ├── __init__.py
│   ├── tasks.py
│   └── views.py
├── requirements.txt
├── docker-compose.yml
├── Dockerfile

What I Learned

How to decouple long-running tasks from the main app flow using Celery.
How to define and connect multi-container environments using Docker Compose.
How Redis serves as a reliable and fast broker for Celery.
Importance of defining persistent volumes and networking in Docker Compose.

Challenges I Faced

Task not executing? I learned the hard way that Celery must run in a separate container and explicitly point to Redis in the correct Docker network format (e.g., redis://redis:6379/0).
Flask not communicating with Celery? Fixed it by ensuring both services were on the same Docker Compose network.

Code & Demo
I founf the full source code on GitHub:
👉 GitHub Repository

Final Thoughts
This project gave me real-world insight into scalable architecture and asynchronous processing. It’s a foundation I now use in other Dockerized applications like Django, monitoring stacks, and even DevSecOps pipelines.

If you found this helpful or want to collaborate, connect with me on LinkedIn or check out more projects on my GitHub.

DEV Community: Rahat Naqvi

Building a Multi-Tier Flask App with Gunicorn, Nginx, PostgreSQL, and Systemd Using Ansible

How I Built a Flask-Celery-Redis Stack with Docker