Yash Sonawane

Posted on Feb 24

🚀 Building a Cloud-Native Microservices Voting System on AWS using Kubernetes (Kind) & Argo CD

#aws #devops #kubernetes #microservices

👨‍💻 Author: Yash Sonawane
🔗 GitHub Repository: https://github.com/yashsonawane25/k8s-microservices-vote-system
📍 Pune, India

📌 Introduction

Modern applications are built using microservices and deployed using container orchestration platforms like Kubernetes. To gain deep hands-on experience with production-style DevOps workflows, I built and deployed a cloud-native voting system on an AWS EC2 instance using:

Kubernetes (Kind)
Docker
Argo CD
Prometheus
Grafana
Redis
PostgreSQL

Instead of using managed Kubernetes services like EKS, I manually configured the cluster using Kind to understand Kubernetes internals, networking, service exposure, and Git-driven deployments.

This article explains the architecture, implementation, monitoring setup, and key learnings from this project.

🏗 System Architecture

🔎 Architecture Overview

The application follows a distributed microservices model:

🧩 Components

Python Frontend – Allows users to vote between two options
Redis – Temporarily stores incoming votes
.NET Worker Service – Processes votes asynchronously
PostgreSQL Database – Stores persistent vote data
Node.js Result Service – Displays live voting results

🔁 Request Flow

User submits vote via frontend
Vote is stored in Redis
Worker consumes vote and writes to PostgreSQL
Result service reads from PostgreSQL and displays updated results
Kubernetes manages scaling and availability
Argo CD syncs application state from GitHub

This architecture simulates real-world distributed systems used in production environments.

☁️ Infrastructure Setup on AWS

Step 1 – Launch EC2 Instance

Ubuntu-based EC2 instance
Configured Security Groups
Installed Docker

sudo apt update
sudo apt install docker.io -y

Step 2 – Install Kind & Create Kubernetes Cluster

Kind allows running Kubernetes clusters inside Docker containers.

curl -Lo ./kind https://kind.sigs.k8s.io/dl/latest/kind-linux-amd64
chmod +x ./kind
sudo mv kind /usr/local/bin/

kind create cluster

Verified cluster setup:

kubectl get nodes

🚀 Deploying Microservices to Kubernetes

Created Kubernetes manifests for:

Deployments
Services
ConfigMaps
Persistent Volumes

Applied configuration:

kubectl apply -f k8s/

Verified pod status:

kubectl get pods

🔄 Continuous Deployment using Argo CD

Installed Argo CD inside the cluster:

kubectl create namespace argocd
kubectl apply -n argocd -f https://raw.githubusercontent.com/argoproj/argo-cd/stable/manifests/install.yaml

Connected the GitHub repository to Argo CD.

Enabled:

Automated Sync
Self-Healing
Version-controlled deployments

Now every Git push updates the Kubernetes cluster automatically.

📊 Observability & Monitoring

Production systems require deep visibility into performance and health.

🔎 Prometheus Metrics

Prometheus collects:

Pod metrics
Node metrics
CPU usage
Memory utilization
Service health

📈 Grafana Dashboards

Grafana provides real-time visualization for:

Cluster health
Resource consumption
Performance monitoring
Troubleshooting bottlenecks

🗄 Database & Persistence

PostgreSQL is configured with persistent volumes to ensure data durability even if pods restart.

This ensures:

Data integrity
Fault tolerance
Reliable state management

🎯 Key DevOps Concepts Applied

✔ Kubernetes cluster provisioning from scratch
✔ Microservices architecture deployment
✔ Container orchestration
✔ Automated Git-based deployments
✔ Monitoring & observability
✔ Self-healing workloads
✔ Persistent storage management

🧠 Challenges Faced & Learnings

During this project, I encountered and resolved:

Image pull issues
Service exposure and NodePort configuration
Container networking challenges
Monitoring configuration tuning
Kubernetes resource debugging

This hands-on debugging strengthened my real-world DevOps troubleshooting skills.

💼 Why This Project Matters

This project replicates a production-style cloud-native deployment pipeline without relying on managed Kubernetes services.

It demonstrates:

Infrastructure setup on cloud VMs
Container orchestration
Automated application delivery
Observability stack integration
Scalable microservices design

🔗 Source Code

👉 https://github.com/yashsonawane25/k8s-microservices-vote-system

🚀 Open to Opportunities

I am actively seeking DevOps / Cloud Internship opportunities where I can apply Kubernetes, CI/CD, monitoring, and automation skills in production environments.

If you're hiring or collaborating, let's connect.