Cloud Native Resource Monitoring Python App on K8s!

Things you will Learn 🤯

1. Python and How to create Monitoring Application in Python using Flask and psutil.
2. How to run a Python App locally.
3. Learn Docker and How to containerize a Python application.
   • Creating Dockerfile.
   • Building DockerImage.
   • Running Docker Container.
   • Docker Commands.
4. Create ECR repository using Python 'Boto3' and pushing Docker Image to ECR.
5. Learn Kubernetes and Create EKS cluster and Nodegroups.
6. Create Kubernetes Deployments and Services using Python.

Architecture Diagram 📌

Prerequisites 📋

(Things to have before starting the projects)

• AWS Account.
• Programmatic access and AWS configured with CLI.
• Python3 Installed.
• Docker and Kubectl installed.
• Code editor (Vscode)

✨Let’s Start the Project ✨

Part 1: Deploying the Flask application locally

Step 1: Clone the code

Clone the code from the repository:

git clone https://github.com/Daoud-Hussain/Cloud-Native-Monitoring.git

Step 2: Install dependencies

The application uses the psutil and Flask, Plotly, boto3 libraries. Install them using pip:

pip3 install -r requirements.txt

Step 3: 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.

Part 2: Dockerizing the Flask application

Step 1: Create a Dockerfile

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"]

Step 2: Build the Docker image

To build the Docker image, execute the following command:

docker build -t <image_name> .

Step 3: Run the Docker container

To 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.

Part 3: Pushing the Docker image to ECR

Step 1: Create an ECR repository

You can create an ECR repository programmatically using Python Boto3. Boto3 is the name of the Python SDK for AWS. It allows you to directly create, update, and delete AWS resources from your Python scripts. You can do it manually using Amazon ECR service.

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)

Step 2: Push the Docker image to ECR

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

docker push <ecr_repo_uri>:<tag>

Part 4: Creating an EKS cluster and deploying the app using Python

Step 1: Create an EKS cluster

Create an EKS cluster and add node group

Step 2: Create a node group

Create a node group in the EKS cluster.

Step 3: Create deployment and service

In Kubernetes, a Deployment manages the creation and scaling of pods, ensuring the desired number are running, while a Service exposes the deployed pods to external or internal network traffic, enabling communication between components.

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",
                        image="public.ecr.aws/r3n6z2g2/my_monitoring_app_image:latest",
                        ports=[client.V1ContainerPort(container_port=5000)]
                    )
                ]
            )
        )
    )
)

# 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 26 with your image Uri.

• 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 -n default (to check the pods)

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

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

Output 📌

Contributing 🖇️

Pull requests are welcome for any changes.

Author 🙋‍♂

• Daoud Hussain on LinkedIn.
• You Can also check out my Medium for articles on DevOps Tools and Technologies.️