docker --version

import time
import random

def consume_memory():
    data = []
    while True:
        # Simulate memory usage by appending large lists
        data.append([random.random() for _ in range(1000000)])
        # Pause briefly to keep the CPU usage high
        time.sleep(0.1)
        # Release some memory to avoid crashes (optional)
        if len(data) > 50:  
            data.pop(0)

def consume_cpu():
    while True:
        # Simulate high CPU usage by performing computations
        _ = [x ** 2 for x in range(1000000)]

if __name__ == "__main__":
    consume_memory()
    consume_cpu()

# Use a lightweight Python base image
FROM python:3.9-slim

# Set the working directory
WORKDIR /app

# Copy the Python script into the container
COPY high_resource_usage.py .

# Set the command to run the Python script
CMD ["python", "high_resource_usage.py"]

# Build the Docker image
docker build -t USERNAME/high-resource-usage-app:latest .

# Push the image to the registry
docker push USERNAME/high-resource-usage-app:latest

apiVersion: apps/v1
kind: Deployment
metadata:
  name: high-resource-deployment
  labels:
    app: high-resource
spec:
  replicas: 2  # Number of replicas for load testing
  selector:
    matchLabels:
      app: high-resource
  template:
    metadata:
      labels:
        app: high-resource
    spec:
      containers:
      - name: high-resource
        image: USERNAME/high-resource-usage-app:latest
        imagePullPolicy: Always
        resources:
          limits:
            memory: "512Mi"
            cpu: "500m"
          requests:
            memory: "256Mi"
            cpu: "250m"

kubectl apply -f high-resource-deployment.yaml

apiVersion: v1
kind: Service
metadata:
  name: high-resource-service
spec:
  selector:
    app: high-resource
  ports:
    - protocol: TCP
      port: 80
      targetPort: 8080  # Adjust if needed to match the container's exposed port
  type: ClusterIP  # Use LoadBalancer if external access is required

kubectl apply -f high-resource-service.yaml