application-latency-Issues.yml

#Issue
#Your notification-service application is facing latency issues due to a backlog of processing messages. 
#The service is designed to handle incoming notifications, but as traffic increases, 
#the existing single-instance deployment is unable to process messages in real-time, leading to delays in notifications being sent out.

#Solution
#To address the latency issue, you can implement Horizontal Pod Autoscaler (HPA) 
#along with message queue integration to dynamically scale the notification-service 
#based on the number of pending messages in the queue.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: notification-service
spec:
  replicas: 1  # Start with one pod; HPA will scale this up as needed
  selector:
    matchLabels:
      app: notification
  template:
    metadata:
      labels:
        app: notification
    spec:
      containers:
      - name: notification
        image: notification-service:latest
        ports:
        - containerPort: 8080
        env:
        - name: QUEUE_URL
          value: "https://your-message-queue-url"  # Example environment variable for queue
        resources:
          requests:
            memory: "128Mi"
            cpu: "100m"
          limits:
            memory: "256Mi"
            cpu: "500m"

---
apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
metadata:
  name: notification-service-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: notification-service
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: External
    external:
      metric:
        name: queue_length  # This metric should be defined in your monitoring solution
      target:
        type: AverageValue
        averageValue: "10"  # Scale when the average queue length exceeds 10


#Explanation
#Deployment: Manages the notification-service, with initial resource requests and limits defined. 
#The environment variable QUEUE_URL is used to connect to the message queue.
#HorizontalPodAutoscaler (HPA):
#scaleTargetRef: Specifies the target deployment for scaling.
#minReplicas: Starts with at least one pod.
#maxReplicas: Can scale up to ten pods based on the defined metrics.
#metrics: Monitors an external metric (e.g., queue length) to determine when to scale up or down.