☸️ Cloud Native Resource Monitoring App on K8s! 🐋

🏛️ System Architecture

🎬 Demo

🔍 Key Features:

📊 Real-time System Monitoring

🛠 Kubernetes, Docker, and AWS Integration

🌐 Cloud-native Architecture

📈 Performance Metrics Visualization

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/Ameya-2003/SysMonitor

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

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

🤝 Contributions:

Contributions to this repository are welcome! Feel free to fork the repository and submit pull requests with your enhancements or new projects.

✍ Contact Information

If you have any questions or suggestions regarding the solutions in this repository, you can reach out to me Via Mail.

🙏 Thank you for your interest!