shohail-DeV/Cloud-Native-Resource-Monitoring

A web application using Flask to collect and display real-time infrastructure health metrics of the CPU & memory . Also containerized the application with Docker for efficient scaling and deployed it on an Amazon EKS cluster using Kubernetes for management across multiple instances.

Cloud Native Resource Monitoring Python App on K8s!

Skillst Acquired in:

1. Running a Python App locally .
2. Docker and containerization of a Python application .
   • Creating Dockerfile
   • Building DockerImage
   • Running Docker Container
   • Docker Commands
3. Creation of ECR repository using Python Boto3 and pushing Docker Image to ECR .
4. Kubernetes and development of EKS cluster and nodegroups .
5. Kubernetes deployments and services using Python .

1: Creating a Python Flask application

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

Create an ECR repository using Python:

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

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="568373317874.dkr.ecr.us-east-1.amazonaws.com/my-cloud-native-repo: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 25 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