Sock Shop : A Microservice Demo Application

The application is the user-facing part of an online shop that sells socks. It is intended to aid the demonstration and testing of microservice and cloud native technologies.

It is built using Spring Boot, Go kit and Node.js and is packaged in Docker containers.

You can read more about the application design.

Deployment Platforms

The deploy folder contains scripts and instructions to provision the application onto your favourite platform.

Please let us know if there is a platform that you would like to see supported.

AWS EKS Deployment

This project demonstrates the deployment of the Sock Shop Microservices Application on AWS EKS (Elastic Kubernetes Service). The application is deployed using Kubernetes manifests and leverages AWS cloud-native services.

1. Set Up EKS Cluster

aws eks create-cluster --name sock-shop-cluster --region us-east-1 \
  --kubernetes-version 1.24 \
  --role-arn arn:aws:iam::YOUR_ACCOUNT_ID:role/EKS-Cluster-Role \
  --resources-vpc-config subnetIds=subnet-XXXXX,subnet-XXXXX,securityGroupIds=sg-XXXXX

2. Deploy Sock Shop Microservices

git clone https://github.com/microservices-demo/microservices-demo.git
cd microservices-demo/deploy/kubernetes
kubectl apply -f complete-demo.yaml

3. Deploy Monitoring (Prometheus & Grafana)

kubectl create namespace monitoring
helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm install prometheus prometheus-community/kube-prometheus-stack -n monitoring

4. Configure Horizontal Pod Autoscaling (HPA)

Configured Horizontal Pod Autoscaling (HPA) for each deployment, specifying the following parameters for all services:

• minReplicas: 1
• maxReplicas: 10
• targetCPUUtilizationPercentage: 50%

apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
metadata:
  name: front-end
  namespace: sock-shop
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: front-end
    
minReplicas: 1
maxReplicas: 10
targetCPUUtilizationPercentage: 50

Apply it using:

kubectl apply -f hpa.yaml

5. Load Testing with Locust

Deploy a load testing application using Locust:

kubectl create namespace loadtest
kubectl apply -f load-test.yaml

Monitor HPA behavior using:

kubectl get hpa -w

Screenshot

Visualizing the application

Use Weave Scope or Weave Cloud to visualize the application once it's running in the selected target platform.

Results

• Successfully Deployed on AWS EKS: Set up and configured an EKS cluster to run the Sock Shop microservices.
• Implemented Monitoring: Integrated Prometheus and Grafana to track key performance metrics and resource utilization.
• Configured Auto-Scaling: Used Kubernetes Horizontal Pod Autoscaler (HPA) to dynamically adjust pod counts based on CPU utilization.
• Load Testing & Optimization: Conducted stress testing with Locust to evaluate application performance and resource scaling.
• Identified Resource-Intensive Services: Determined that 'front-end', 'cart', and 'order' services consumed the most resources, allowing for better optimization strategies.
• Scaling Observations: The HPA successfully adjusted the number of pods based on real-time CPU utilization.
• Performance: The system maintained stability under increased load, and unnecessary resource consumption was avoided during low-traffic periods.
• Resilience: Liveness and readiness probes ensured fault tolerance and prevented requests from being sent to unhealthy pods.

Demo Video

Watch Deployment and Scaling in Action

Conclusion

This project demonstrates a scalable, reliable, and cloud-native deployment of a microservices-based e-commerce application on AWS EKS. By integrating monitoring, autoscaling, and load testing, we ensure high availability and efficient resource utilization.