project-31-cloud-native-monitoring

Cloud-Native-Monitoring-App

Deploying an App built with Python using Flask and psutil on ECR and Kubernete

This is a monitoring app built with python, and it would be contanerized with docker and deployed to EkS

🛡️ 2026 DevSecOps Enhancements (What You Will Learn)

This project's programmatic deployment methodology via Boto3 and Python has been formalized into a DevSecOps approach:

1. IAM Least Privilege & STS: Python scripts (like ecr.py and eks.py) that interact directly with AWS APIs require strict IAM boundary enforcement. They must be executed utilizing short-lived AWS STS credentials or Kubernetes IRSA (IAM Roles for Service Accounts) to prevent long-lived credential leakage.
2. Container OS Hardening: The Dockerfile has been optimized. To comply with modern standards, the internal Flask server should be run utilizing an unprivileged user context, mitigating the blast radius if the psutil or Flask dependencies ever suffer from a zero-day exploit.

Prerequisites

• Learn Docker and How to containerize a Python application
• Creating Dockerfile
• Building DockerImage
• Running Docker Container
• Docker Commands
• Create ECR repository using Python Boto3 and pushing Docker Image to ECR
• Learn Kubernetes and Create EKS cluster and Nodegroups
• Create Kubernetes Deployments and Services using Python!

STEP 1 - Installations of Services on your WorkStation

• Install AWS CLI, then Go to your aws account and get your secret keys and configure the workspace aws configure
• Install python on your workstation and a python extention in vscode
• The application uses the psutil and Flask, Plotly, boto3 libraries. Install them using pip pip3 install -r requirements.txt
• Install dependencies psutil pip3 install psutil and flask pip install flask
• Install python for ECR SDK pip install boto3
• Install kubernetes, add the K8S python dependencies client library pip install kubernetes the extenstion of kubernetes in vscode
• Install the docker extention in vscode

Step 2: Run the application

To run the application, navigate to the root directory of the project and execute the following command:

$ python3 app.py

This will start the Flask server on localhost:5000. Navigate to http://localhost:5000/ on your browser to access the application.

Step 3: Dockerizing the Flask application

• Create a Dockerfile in the root directory of the project with the following contents:

# Use the official Python image as the base image
FROM python:3.9-slim-buster

# Set the working directory in the container
WORKDIR /app

# Copy the requirements file to the working directory
COPY requirements.txt .

RUN pip3 install --no-cache-dir -r requirements.txt

# Copy the application code to the working directory
COPY . .

# Set the environment variables for the Flask app
ENV FLASK_RUN_HOST=0.0.0.0

# Expose the port on which the Flask app will run
EXPOSE 5000

# Start the Flask app when the container is run
CMD ["flask", "run"]

• Build the Docker image, execute the following command:

$ docker build -t <image_name> .

• Run the Docker container, execute the following command:

$ docker run -p 5000:5000 <image_name>

This will start the Flask server in a Docker container on localhost:5000. Navigate to http://localhost:5000/ on your browser to access the application.

Step 4 - Pushing the Docker image to ECR

• Create an ECR repository using Python in a folder ecr.py:
• Configure the ECR repository to your workspace to enable a push, you will find the process in console view push commands

import boto3

# Create an ECR client
ecr_client = boto3.client('ecr')

# Create a new ECR repository
repository_name = 'my-ecr-repo'
response = ecr_client.create_repository(repositoryName=repository_name)

# Print the repository URI
repository_uri = response['repository']['repositoryUri']
print(repository_uri)

Then run this python3 ecr.py

• Setup password and credentials for ECR

# Replace <AWS_ACCOUNT_ID> with your AWS account ID
aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin <AWS_ACCOUNT_ID>.dkr.ecr.us-east-1.amazonaws.com

• Push the Docker image to ECR using the push commands on the console:

 $ docker push <ecr_repo_uri>:<tag>

Step 5 - Creating an EKS cluster and deploying the app using Python**

• Create an EKS cluster cloud-native-cluster and add node group in aws console

• Create a node group nodes in the EKS cluster.

• Create deployment and service in a folder eks.py

from kubernetes import client, config

# Load Kubernetes configuration
config.load_kube_config()

# Create a Kubernetes API client
api_client = client.ApiClient()

# Define the deployment
deployment = client.V1Deployment(
    metadata=client.V1ObjectMeta(name="my-flask-app"),
    spec=client.V1DeploymentSpec(
        replicas=1,
        selector=client.V1LabelSelector(
            match_labels={"app": "my-flask-app"}
        ),
        template=client.V1PodTemplateSpec(
            metadata=client.V1ObjectMeta(
                labels={"app": "my-flask-app"}
            ),
            spec=client.V1PodSpec(
                containers=[
                    client.V1Container(
                        name="my-flask-container",
# Replace <AWS_ACCOUNT_ID> with your AWS account ID
                        image="<AWS_ACCOUNT_ID>.dkr.ecr.us-east-1.amazonaws.com/<IMAGE_NAME>:latest",
                        ports=[client.V1ContainerPort(container_port=5000)]
                    )
                ]
            )
        )
    )
)

# This is an automation to run deployment and svc using python
# Create the deployment
api_instance = client.AppsV1Api(api_client)
api_instance.create_namespaced_deployment(
    namespace="default",
    body=deployment
)

# Define the service
service = client.V1Service(
    metadata=client.V1ObjectMeta(name="my-flask-service"),
    spec=client.V1ServiceSpec(
        selector={"app": "my-flask-app"},
        ports=[client.V1ServicePort(port=5000)]
    )
)

# Create the service
api_instance = client.CoreV1Api(api_client)
api_instance.create_namespaced_service(
    namespace="default",
    body=service
)

make sure to edit the name of the image on line 25 with your image Url.

To run the K8s commands for deployment and service instead of adding the python script you create deployment.yml and service.ymluse these commands kubectl apply -f deployment.yml and kubectl apply -f service.yml

• Configure the aws EKS to your work space

aws eks update-kubeconfig --name cloud-native-cluster

• Once you run this file by running “python3 eks.py” deployment and service will be created.
• Check by running following commands:

kubectl get deployment -n default (check deployments)
kubectl get service -n default (check service)
kubectl get pods <name of pod> -n default (to check the pods)

#edit images created if u made errors
kubectl edit deployment my-flask-app -n default 

#this will pull down the editted image
kubectl get pod -n default -w

Once your pod is up and running, run the port-forward to expose the service

kubectl port-forward service/<service_name> 5000:5000

Hit the Star! ⭐

If you are planning to use this repo for learning, please hit the star. Thanks!

Author by DevCloud Ninjas