Home

Beginner-Friendly DevOps Project Explanation

Goal:

To build a simple web app, deploy it in the cloud, and monitor it in real-time using DevOps tools.

Step 1: Build a Simple Web App

We started with a Python Flask app that shows a simple message like "Hello from DevOps!".

Why Flask?

1. It's a lightweight Python web framework — perfect for beginners.
2. Easy to write and test.
3. Simple to extend with monitoring tools.

🐳Step 2: Containerize the App with Docker

Instead of running the app manually, we wrapped it into a Docker container.

Why Docker?

• Makes the app portable: “Runs the same anywhere.”
• Simplifies deployment (no need to install Python, Flask, etc., on each server).
• Easy to manage multiple apps as containers.

We wrote a Dockerfile to: Use a Python base image. Install Flask and monitoring tools.

Start the app automatically when the container runs.

🔭 Step 3: Expose Metrics for Monitoring

We wanted to track app usage — like how many times it’s visited.

📌 How? We added a special URL /metrics using prometheus_flask_exporter.

📌 Why /metrics?

1. It gives live information like:

• How many requests your app received
• What type (GET/POST)
• What status codes (200/404/etc.)

2. These are machine-readable numbers, perfect for monitoring.

Step 4: Deploy Everything on an Azure VM

We needed a server to host our app, so we used an Azure Virtual Machine (VM).

📌 Why Azure VM?

• It’s like a computer in the cloud.
• You control it like your own system.
• Easy to install Docker and run multiple containers.

📈 Step 5: Add Prometheus to Collect Metrics

We used Prometheus to collect data from the app automatically.

📌 Why Prometheus?

• It pulls data from /metrics every few seconds.
• Stores all the data in a time-series database.
• Helps us track performance over time.

🛠️ We configured prometheus.yml to tell Prometheus:

“Hey, go check on this container called webapp every 15 seconds at port 5000.”

📊 Step 6: Visualize the Data with Grafana

Seeing numbers is fine… but visual dashboards make it easier!

📌 Why Grafana?

• Turns raw metrics into charts and graphs.
• Helps identify issues quickly.
• Lets you customize dashboards for your app.

🛠️ We connected Grafana to Prometheus and built a dashboard showing:

• Request rates
• Errors (if any)
• Traffic over time

🔁 Step 7: Automate with GitHub Actions (CI/CD)

Manually deploying containers every time is tiring. 📌 Why GitHub Actions?

• It automates the process when you push code to GitHub.
• It builds the Docker image, pushes it to Docker Hub, and deploys it to the Azure VM via SSH.

🔐 We used GitHub Secrets to keep:

• Docker credentials
• SSH key to login to the Azure VM

🔗 Step 8: Connect Everything with Docker Network

Prometheus couldn’t “talk” to Flask app until we connected both to a custom Docker network (devops-net).

📌 Why Docker Network? Allows containers to communicate using their names. For example, Prometheus can reach webapp:5000 because they’re on the same network.

✅ Final Result:

You have a working system where:

• A Flask app is deployed in Docker on Azure.
• Prometheus collects its metrics.
• Grafana visualizes them.
• GitHub Actions automates deployment when code is updated.