🚀 Building a Production-Ready Movie Ticketing Platform: A Complete DevOps Journey

From Manual Deployments to Automated CI/CD: How I Built and Deployed BookMyShow Clone on AWS EKS with Zero Downtime

---

🎯 Introduction

Hey there! 👋

Have you ever wondered what it takes to deploy a real-world application with enterprise-grade DevOps practices? Not just a simple "hello world" container, but a complete, production-ready system with automated testing, security scanning, auto-scaling, and real-time monitoring?

That's exactly what I set out to build, and in this comprehensive guide, I'll take you through my journey of deploying a BookMyShow clone - a movie ticketing platform - using modern DevOps tools and practices.

What You'll Learn

By the end of this post, you'll understand:

• ✅ How to build a complete CI/CD pipeline from scratch

• ✅ Deploying applications to AWS EKS (Kubernetes)

• ✅ Implementing automated security scanning and code quality checks

• ✅ Setting up real-time monitoring with Prometheus and Grafana

• ✅ Achieving zero-downtime deployments with Kubernetes

• ✅ Real-world challenges and solutions from production deployment

Why This Project?

In today's cloud-native world, knowing how to deploy applications manually isn't enough. Companies need:

• Fast deployments (multiple times per day)

• Reliable systems (99.9%+ uptime)

• Secure infrastructure (automated vulnerability scanning)

• Observable applications (real-time metrics and alerts)

This project demonstrates all of these in action.

---

🎬 The Challenge

The Problem Statement

Imagine you're tasked with modernizing a legacy movie ticketing application. The current state:

Before DevOps:

• 😓 Manual deployments taking 2-3 hours

• 🐛 High failure rate (~30-40% failed deployments)

• 🔍 No visibility into application health

• 😱 Security vulnerabilities discovered only after incidents

• ⏰ Deployments only once a week (fear of breaking things)

• 📉 No auto-scaling (server crashes during peak times)

Business Impact:

• Lost revenue during downtime

• Slow feature delivery

• Security risks

• Poor user experience during peak loads

The Goal

Transform this into a modern, cloud-native application with:

• ⚡ 12-minute deployments (instead of 2+ hours)

• 🎯 95%+ success rate in deployments

• 📊 Real-time monitoring and alerting

• 🔒 Automated security scanning

• 🚀 Multiple deployments per day

• 📈 Auto-scaling based on load

---

💡 Solution Overview

The Technology Stack

I chose a modern, battle-tested stack:

High-Level Architecture

┌─────────────────────────────────────────────────┐
│              DEVELOPER WORKFLOW                  │
│                                                  │
│  Developer → Git Push → GitHub → Webhook        │
└─────────────────────────────────────────────────┘
                        ↓
┌─────────────────────────────────────────────────┐
│              JENKINS CI/CD PIPELINE              │
│                                                  │
│  Checkout → SonarQube → Build → Test → Trivy    │
│     ↓                                            │
│  Docker Build → Push → Deploy to EKS → Notify   │
└─────────────────────────────────────────────────┘
                        ↓
┌─────────────────────────────────────────────────┐
│              AWS EKS CLUSTER                     │
│                                                  │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐     │
│  │  Pod 1   │  │  Pod 2   │  │  Pod 3   │     │
│  │  Node.js │  │  Node.js │  │  Node.js │     │
│  └──────────┘  └──────────┘  └──────────┘     │
│        ↓              ↓              ↓          │
│  ┌────────────────────────────────────────┐    │
│  │        LoadBalancer (AWS ELB)          │    │
│  └────────────────────────────────────────┘    │
└─────────────────────────────────────────────────┘
                        ↓
                  [End Users]
                        ↑
                   Monitor with
┌─────────────────────────────────────────────────┐
│         PROMETHEUS + GRAFANA                     │
│                                                  │
│  Real-time Metrics → Alerts → Dashboards        │
└─────────────────────────────────────────────────┘

The Game Plan

The implementation was divided into 4 phases:

1. Phase 1: Infrastructure Setup (AWS, EKS, Servers)

2. Phase 2: CI/CD Pipeline (Jenkins, SonarQube, Docker)

3. Phase 3: Kubernetes Deployment (EKS, LoadBalancer)

4. Phase 4: Monitoring (Prometheus, Grafana)

Let's dive into each phase! 🏊‍♂️

---

🏗️ Phase 1: Infrastructure Setup

Setting Up AWS Infrastructure

First Decision: Which AWS Services?

After evaluating options, I chose:

• Amazon EKS for Kubernetes (managed control plane = less operational overhead)

• EC2 t3.medium for worker nodes (cost-effective for our workload)

• Classic Load Balancer for traffic distribution

• EBS gp3 volumes for storage (better price/performance than gp2)

Infrastructure Components

1. BMS Server (Development Server)

Instance: EC2 t2.large
RAM: 8 GB
vCPU: 2
Storage: 28 GB
Purpose: Jenkins, Docker, Build tools

This server runs:

• Jenkins CI/CD server

• Docker for building images

• AWS CLI, kubectl, eksctl

• Development tools

2. EKS Cluster

Control Plane: Managed by AWS
Worker Nodes: 3x t3.medium
Kubernetes Version: 1.30
Regions: us-east-1a, us-east-1b

3. Monitoring Server

Instance: EC2 t2.medium
RAM: 4 GB
vCPU: 2
Storage: 20 GB
Purpose: Prometheus, Grafana, Node Exporter

Step-by-Step Infrastructure Creation

1. IAM User Setup

Why not use root account? Security best practice. If credentials are compromised, impact is limited.

Created IAM user eks-admin with these policies:

• AmazonEC2FullAccess

• AmazonEKSClusterPolicy

• AmazonEKSWorkerNodePolicy

• IAMFullAccess

• Custom EKS policy for cluster operations

{
    "Version": "2012-10-17",
    "Statement": [{
        "Effect": "Allow",
        "Action": "eks:*",
        "Resource": "*"
    }]
}

Generated Access Keys and stored them securely (never commit to Git!).

2. Installing Essential Tools

Connected to BMS Server and installed:

AWS CLI:

curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip"
unzip awscliv2.zip
sudo ./aws/install
aws configure  # Entered access keys here

kubectl (Kubernetes CLI):

curl -o kubectl https://amazon-eks.s3.us-west-2.amazonaws.com/1.19.6/2021-01-05/bin/linux/amd64/kubectl
chmod +x ./kubectl
sudo mv ./kubectl /usr/local/bin
kubectl version --client

eksctl (EKS Management Tool):

curl --silent --location "https://github.com/weaveworks/eksctl/releases/latest/download/eksctl_$(uname -s)_amd64.tar.gz" | tar xz -C /tmp
sudo mv /tmp/eksctl /usr/local/bin
eksctl version

3. Creating EKS Cluster

This was the most exciting part! Creating a production Kubernetes cluster with one command:

Step A: Create Control Plane (10 minutes)

eksctl create cluster \
  --name=bms-eks \
  --region=us-east-1 \
  --zones=us-east-1a,us-east-1b \
  --version=1.30 \
  --without-nodegroup

While waiting, I grabbed coffee ☕ and watched AWS create:

• VPC with public/private subnets

• Internet Gateway

• Route tables

• Security groups

• EKS control plane

Step B: Associate OIDC Provider (2 minutes)

eksctl utils associate-iam-oidc-provider \
    --region us-east-1 \
    --cluster bms-eks \
    --approve

Why OIDC? This enables IAM Roles for Service Accounts (IRSA). It allows Kubernetes pods to securely access AWS services without storing credentials. Game-changer for security!

Step C: Create Worker Nodes (10 minutes)

eksctl create nodegroup \
  --cluster=bms-eks \
  --region=us-east-1 \
  --name=worker-nodes \
  --node-type=t3.medium \
  --nodes=3 \
  --nodes-min=2 \
  --nodes-max=4 \
  --node-volume-size=20 \
  --ssh-access \
  --ssh-public-key=YourKeyName \
  --managed \
  --asg-access \
  --external-dns-access \
  --full-ecr-access \
  --appmesh-access \
  --alb-ingress-access

Verification:

kubectl get nodes

NAME                             STATUS   ROLES    AGE
ip-192-168-20-52.ec2.internal    Ready    <none>   5m
ip-192-168-34-87.ec2.internal    Ready    <none>   5m
ip-192-168-35-132.ec2.internal   Ready    <none>   5m

✅ Success! Three healthy nodes running.

Security Configuration

Configured security groups to allow:

• Port 22: SSH access

• Port 80/443: HTTP/HTTPS traffic

• Port 3000-10000: Application ports

• Port 8080: Jenkins

• Port 9000: SonarQube

• Port 9090: Prometheus

• Port 3000: Grafana

Pro Tip: In production, restrict SSH to specific IPs only!

---

🔄 Phase 2: CI/CD Pipeline

The Jenkins Setup

Installing Jenkins

Created an installation script:

#!/bin/bash
# Install Java 17 (Jenkins requirement)
sudo apt install openjdk-17-jre-headless -y

# Add Jenkins repository
sudo wget -O /usr/share/keyrings/jenkins-keyring.asc \
  https://pkg.jenkins.io/debian-stable/jenkins.io-2023.key

echo deb [signed-by=/usr/share/keyrings/jenkins-keyring.asc] \
  https://pkg.jenkins.io/debian-stable binary/ | sudo tee \
  /etc/apt/sources.list.d/jenkins.list > /dev/null

# Install Jenkins
sudo apt-get update
sudo apt-get install jenkins -y

Executed: chmod +x jenkins.sh && ./jenkins.sh

Accessed Jenkins:

• URL: http://server-ip:8080

• Retrieved initial password: sudo cat /var/lib/jenkins/secrets/initialAdminPassword

• Installed recommended plugins

• Created admin user

Installing Docker

Jenkins needs Docker to build container images:

#!/bin/bash
sudo apt-get update
sudo apt-get install -y ca-certificates curl

# Add Docker GPG key and repository
sudo install -m 0755 -d /etc/apt/keyrings
sudo curl -fsSL https://download.docker.com/linux/ubuntu/gpg \
  -o /etc/apt/keyrings/docker.asc
sudo chmod a+r /etc/apt/keyrings/docker.asc

echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] \
  https://download.docker.com/linux/ubuntu \
  $(. /etc/os-release && echo "$VERSION_CODENAME") stable" | \
  sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

# Install Docker
sudo apt-get update
sudo apt-get install -y docker-ce docker-ce-cli containerd.io

Fix permissions:

sudo chmod 666 /var/run/docker.sock
docker --version  # Verify
docker login -u your-dockerhub-username  # Login to Docker Hub

Security Scanning with Trivy

Trivy scans Docker images for vulnerabilities:

#!/bin/bash
sudo apt-get install wget apt-transport-https gnupg -y
wget -qO - https://aquasecurity.github.io/trivy-repo/deb/public.key | \
  gpg --dearmor | sudo tee /usr/share/keyrings/trivy.gpg > /dev/null
echo "deb [signed-by=/usr/share/keyrings/trivy.gpg] \
  https://aquasecurity.github.io/trivy-repo/deb generic main" | \
  sudo tee -a /etc/apt/sources.list.d/trivy.list
sudo apt-get update
sudo apt-get install trivy -y

Code Quality with SonarQube

Deployed SonarQube as a Docker container:

docker run -d --name sonar -p 9000:9000 sonarqube:lts-community

Setup:

1. Accessed http://server-ip:9000

2. Default login: admin/admin

3. Changed password

4. Generated authentication token for Jenkins

Jenkins Configuration

Essential Plugins Installed

Navigated to: Manage Jenkins → Plugins → Available

Installed:

• ✅ Eclipse Temurin Installer (Java)

• ✅ SonarQube Scanner

• ✅ NodeJS

• ✅ Docker (multiple plugins)

• ✅ Kubernetes (CLI, Client API, Credentials)

• ✅ Pipeline Stage View

• ✅ Email Extension Template

• ✅ Prometheus Metrics

Tool Configuration

Java JDK:

• Name: jdk17

• Version: JDK 17.0.x

• Install automatically: ✓

Node.js:

• Name: node23

• Version: NodeJS 23.x

• Install automatically: ✓

SonarQube Scanner:

• Name: sonar-scanner

• Install automatically: ✓

Docker:

• Name: docker

• Install automatically: ✓

SonarQube Integration

In SonarQube:

1. Generated token: Administration → Security → Users → Tokens

2. Copied token (looks like: squ_xxxxxxxxxxxxx)

In Jenkins:

1. Manage Jenkins → System → SonarQube Servers

2. Added server:

   • Name: sonar-server

   • URL: http://server-ip:9000

   • Authentication token: Created credential with token

Email Notifications Setup

This was crucial for team notifications!

Generated Gmail App Password:

1. Google Account → Security → 2-Step Verification

2. App Passwords → Create for Jenkins

3. Copied 16-character password

Configured in Jenkins:

Extended Email:

• SMTP Server: smtp.gmail.com

• Port: 465

• Credentials: Username + App Password

• Use SSL: ✓

• Content Type: HTML

Standard Email:

• Same SMTP settings

• Added default recipients

• Configured triggers: Always, Failure, Success

Tested by sending test email - worked perfectly! ✉️

The Complete Jenkins Pipeline

Here's the heart of the automation - the Jenkinsfile:

pipeline {
    agent any

    tools {
        jdk 'jdk17'
        nodejs 'node23'
    }

    environment {
        SCANNER_HOME = tool 'sonar-scanner'
        DOCKER_IMAGE = 'your-dockerhub-username/bms:latest'
        EKS_CLUSTER_NAME = 'kastro-eks'
        AWS_REGION = 'us-east-1'
    }

    stages {
        stage('Clean Workspace') {
            steps {
                cleanWs()
                echo '🧹 Workspace cleaned'
            }
        }

        stage('Checkout from Git') {
            steps {
                git branch: 'main', 
                    url: 'https://github.com/your-repo/Book-My-Show.git'
                sh 'ls -la'
                echo '✅ Code checked out successfully'
            }
        }

        stage('SonarQube Analysis') {
            steps {
                withSonarQubeEnv('sonar-server') {
                    sh '''
                    echo "🔍 Starting code analysis..."
                    $SCANNER_HOME/bin/sonar-scanner \
                        -Dsonar.projectName=BMS \
                        -Dsonar.projectKey=BMS
                    '''
                }
                echo '✅ SonarQube analysis completed'
            }
        }

        stage('Quality Gate') {
            steps {
                script {
                    echo '🚦 Waiting for quality gate...'
                    waitForQualityGate abortPipeline: false, 
                                       credentialsId: 'Sonar-token'
                }
                echo '✅ Quality gate passed'
            }
        }

        stage('Install Dependencies') {
            steps {
                sh '''
                cd bookmyshow-app
                echo "📦 Installing npm packages..."
                if [ -f package.json ]; then
                    rm -rf node_modules package-lock.json
                    npm install
                    echo "✅ Dependencies installed"
                else
                    echo "❌ Error: package.json not found!"
                    exit 1
                fi
                '''
            }
        }

        stage('Trivy FS Scan') {
            steps {
                sh '''
                echo "🔒 Running Trivy security scan..."
                trivy fs . > trivyfs.txt
                echo "✅ Security scan completed"
                '''
            }
        }

        stage('Docker Build & Push') {
            steps {
                script {
                    withDockerRegistry(credentialsId: 'docker', toolName: 'docker') {
                        sh '''
                        echo "🐳 Building Docker image..."
                        docker build --no-cache -t $DOCKER_IMAGE \
                          -f bookmyshow-app/Dockerfile bookmyshow-app

                        echo "📤 Pushing to Docker Hub..."
                        docker push $DOCKER_IMAGE
                        echo "✅ Image pushed successfully"
                        '''
                    }
                }
            }
        }

        stage('Deploy to EKS Cluster') {
            steps {
                script {
                    sh '''
                    echo "☸️  Deploying to Kubernetes..."

                    # Configure kubectl
                    aws eks update-kubeconfig \
                      --name $EKS_CLUSTER_NAME \
                      --region $AWS_REGION

                    # Apply configurations
                    kubectl apply -f deployment.yml
                    kubectl apply -f service.yml

                    echo "⏳ Waiting for rollout..."
                    kubectl rollout status deployment/bookmyshow-deployment

                    echo "🎉 Deployment successful!"

                    # Show status
                    echo "\n📊 Current Status:"
                    kubectl get pods
                    kubectl get svc
                    '''
                }
            }
        }
    }

    post {
        always {
            emailext (
                attachLog: true,
                subject: "${currentBuild.result}: ${env.JOB_NAME} #${env.BUILD_NUMBER}",
                body: """
                <html>
                <body style="font-family: Arial, sans-serif;">
                    <h2 style="color: ${currentBuild.result == 'SUCCESS' ? 'green' : 'red'};">
                        Build ${currentBuild.result}
                    </h2>
                    <p><strong>Project:</strong> ${env.JOB_NAME}</p>
                    <p><strong>Build Number:</strong> ${env.BUILD_NUMBER}</p>
                    <p><strong>Duration:</strong> ${currentBuild.durationString}</p>
                    <p><strong>Build URL:</strong> 
                       <a href="${env.BUILD_URL}">${env.BUILD_URL}</a>
                    </p>
                    <hr>
                    <p>Check attached logs for details.</p>
                </body>
                </html>
                """,
                to: 'your-email@gmail.com',
                mimeType: 'text/html',
                attachmentsPattern: 'trivyfs.txt'
            )
        }
        success {
            echo '🎉 Pipeline completed successfully!'
        }
        failure {
            echo '❌ Pipeline failed. Check logs for details.'
        }
    }
}

Configuring Jenkins for Kubernetes

Critical step: Jenkins needs AWS credentials to access EKS.

# Switch to jenkins user
sudo -su jenkins

# Configure AWS
aws configure
# Entered: Access Key, Secret Key, Region

# Verify
aws sts get-caller-identity

# Update kubeconfig
aws eks update-kubeconfig --name kastro-eks --region us-east-1

# Test kubectl
kubectl get nodes

# Exit jenkins user
exit

# Restart Jenkins
sudo systemctl restart jenkins

Why this matters: Jenkins runs as the jenkins user, not as root or your user. All AWS/kubectl configurations must be done as jenkins user.

---

☸️ Phase 3: Kubernetes Deployment

Creating Kubernetes Manifests

Deployment Configuration

apiVersion: apps/v1
kind: Deployment
metadata:
  name: bookmyshow-deployment
  labels:
    app: bookmyshow
spec:
  replicas: 3  # High availability
  selector:
    matchLabels:
      app: bookmyshow
  strategy:
    type: RollingUpdate  # Zero-downtime updates
    rollingUpdate:
      maxSurge: 1
      maxUnavailable: 0
  template:
    metadata:
      labels:
        app: bookmyshow
      annotations:
        prometheus.io/scrape: "true"
        prometheus.io/port: "3000"
    spec:
      containers:
      - name: bookmyshow
        image: your-dockerhub-username/bms:latest
        ports:
        - containerPort: 3000
          name: http
        resources:
          requests:
            memory: "256Mi"
            cpu: "250m"
          limits:
            memory: "512Mi"
            cpu: "500m"
        livenessProbe:
          httpGet:
            path: /
            port: 3000
          initialDelaySeconds: 30
          periodSeconds: 10
          timeoutSeconds: 5
          failureThreshold: 3
        readinessProbe:
          httpGet:
            path: /
            port: 3000
          initialDelaySeconds: 10
          periodSeconds: 5
          timeoutSeconds: 3
          failureThreshold: 3
        env:
        - name: NODE_ENV
          value: "production"
        - name: PORT
          value: "3000"

Key Features:

• 3 replicas: High availability across nodes

• Rolling updates: Zero downtime during deployments

• Resource limits: Prevents resource hogging

• Health checks: Auto-restart unhealthy pods

• Prometheus annotations: Metrics scraping

Service Configuration (LoadBalancer)

apiVersion: v1
kind: Service
metadata:
  name: bms-service
  labels:
    app: bookmyshow
spec:
  type: LoadBalancer  # AWS ELB provisioned automatically
  selector:
    app: bookmyshow
  ports:
    - protocol: TCP
      port: 80
      targetPort: 3000
      name: http
  sessionAffinity: ClientIP  # Sticky sessions

What happens: When applied, AWS automatically creates a Classic Load Balancer and configures it to route traffic to our pods!

Deployment in Action

# Apply configurations
kubectl apply -f deployment.yml
kubectl apply -f service.yml

# Watch deployment progress
kubectl get pods -w

# Output:
NAME                                   READY   STATUS    RESTARTS
bookmyshow-deployment-7d4c8b9f-8xk2p  1/1     Running   0
bookmyshow-deployment-7d4c8b9f-9lm4q  1/1     Running   0
bookmyshow-deployment-7d4c8b9f-x7n9r  1/1     Running   0

# Check service
kubectl get svc

# Output:
NAME          TYPE           EXTERNAL-IP
bms-service   LoadBalancer   a5b2f875...elb.amazonaws.com

Moment of truth: Accessing the LoadBalancer URL... 🤞

curl http://a5b2f875...elb.amazonaws.com

It works! 🎉 Application is live and accessible!

Auto-Scaling Configuration

Added Horizontal Pod Autoscaler:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: bms-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: bookmyshow-deployment
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80

Result: Application automatically scales from 2 to 10 pods based on CPU/memory usage!

---

📊 Phase 4: Monitoring & Observability

Why Monitoring Matters

Without monitoring, you're flying blind. You need to know:

• Is the application healthy?

• Are users experiencing slow responses?

• Is memory leaking?

• When should you scale?

Prometheus Setup

Launched a separate monitoring server (t2.medium) and installed Prometheus.

Created prometheus user:

sudo useradd --system --no-create-home --shell /bin/false prometheus

Downloaded and installed:

wget https://github.com/prometheus/prometheus/releases/download/v2.47.1/prometheus-2.47.1.linux-amd64.tar.gz
tar -xvf prometheus-2.47.1.linux-amd64.tar.gz

sudo mkdir -p /data /etc/prometheus
cd prometheus-2.47.1.linux-amd64/
sudo mv prometheus promtool /usr/local/bin/
sudo mv consoles/ console_libraries/ /etc/prometheus/
sudo mv prometheus.yml /etc/prometheus/

sudo chown -R prometheus:prometheus /etc/prometheus/ /data/

Created systemd service:

[Unit]
Description=Prometheus
Wants=network-online.target
After=network-online.target

[Service]
User=prometheus
Group=prometheus
Type=simple
Restart=on-failure
RestartSec=5s
ExecStart=/usr/local/bin/prometheus \
  --config.file=/etc/prometheus/prometheus.yml \
  --storage.tsdb.path=/data \
  --web.console.templates=/etc/prometheus/consoles \
  --web.console.libraries=/etc/prometheus/console_libraries \
  --web.listen-address=0.0.0.0:9090 \
  --web.enable-lifecycle

[Install]
WantedBy=multi-user.target

Started Prometheus:

sudo systemctl enable prometheus
sudo systemctl start prometheus
sudo systemctl status prometheus

Accessed: http://monitoring-server-ip:9090 ✅

Node Exporter for System Metrics

sudo useradd --system --no-create-home --shell /bin/false node_exporter
wget https://github.com/prometheus/node_exporter/releases/download/v1.6.1/node_exporter-1.6.1.linux-amd64.tar.gz
tar -xvf node_exporter-1.6.1.linux-amd64.tar.gz
sudo mv node_exporter-1.6.1.linux-amd64/node_exporter /usr/local/bin/

Created service and started:

sudo systemctl enable node_exporter
sudo systemctl start node_exporter

Configuring Scrape Targets

Edited /etc/prometheus/prometheus.yml:

scrape_configs:
  - job_name: 'prometheus'
    static_configs:
      - targets: ['localhost:9090']

  - job_name: 'node_exporter'
    static_configs:
      - targets: ['monitoring-server-ip:9100']

  - job_name: 'jenkins'
    metrics_path: '/prometheus'
    static_configs:
      - targets: ['jenkins-server-ip:8080']

  - job_name: 'kubernetes-pods'
    kubernetes_sd_configs:
      - role: pod

Reloaded configuration:

curl -X POST http://localhost:9090/-/reload

Verified targets: All showing as UP! 🟢

Grafana Setup

Installed Grafana:

# Add GPG key
wget -q -O - https://packages.grafana.com/gpg.key | sudo apt-key add -

# Add repository
echo "deb https://packages.grafana.com/oss/deb stable main" | \
  sudo tee -a /etc/apt/sources.list.d/grafana.list

# Install
sudo apt-get update
sudo apt-get install grafana -y

# Start service
sudo systemctl enable grafana-server
sudo systemctl start grafana-server

Accessed: http://monitoring-server-ip:3000

• Default credentials: admin/admin

• Changed password on first login

Setting Up Dashboards

Added Prometheus Data Source:

1. Configuration → Data Sources → Add data source

2. Selected Prometheus

3. URL: http://localhost:9090

4. Clicked "Save & Test" ✅

Imported Pre-built Dashboards:

Dashboard 1: Node Exporter Full (ID: 1860)

• Comprehensive system metrics

• CPU, Memory, Disk, Network

• Beautiful visualizations

Dashboard 2: Jenkins Performance (ID: 9964)

• Build statistics

• Success/failure rates

• Queue metrics

• Executor usage

The Result:

Real-time visibility into:

• 📊 System resource usage

• 🏗️ Build performance

• ☸️ Kubernetes cluster health

• 📈 Application metrics

Now I could see everything happening in real-time! No more guessing.

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💪 Challenges & Solutions

Every project has challenges. Here were mine and how I solved them:

Challenge 1: LoadBalancer Not Accessible

The Problem:

After deploying to Kubernetes, I got the LoadBalancer DNS from kubectl get svc, but when I tried to access it... nothing. Timeout errors.

kubectl get svc
NAME          TYPE           EXTERNAL-IP
bms-service   LoadBalancer   a5b2f875...elb.amazonaws.com

curl http://a5b2f875...elb.amazonaws.com
# curl: (7) Failed to connect to a5b2f875...elb.amazonaws.com port 80: Connection timed out

The Investigation:

I spent 2 hours debugging:

1. ✅ Pods were running fine

2. ✅ Service was created

3. ✅ LoadBalancer was provisioned in AWS

4. ❌ But couldn't access the application

The Root Cause:

Security group! The automatically created LoadBalancer security group wasn't allowing inbound traffic on port 80.

The Solution:

# Found the LoadBalancer security group
aws elbv2 describe-load-balancers \
  --region us-east-1 | grep SecurityGroups

# Added inbound rule for port 80
aws ec2 authorize-security-group-ingress \
    --group-id sg-xxxxx \
    --protocol tcp \
    --port 80 \
    --cidr 0.0.0.0/0 \
    --region us-east-1

Result: Application immediately accessible! 🎉

Lesson Learned: Always verify security groups after creating AWS resources. Don't assume they're configured correctly.

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Challenge 2: Jenkins Can't Access EKS

The Problem:

Pipeline failed at the deployment stage:

error: You must be logged in to the server (Unauthorized)

The Root Cause:

I had configured AWS CLI and kubectl as my user, but Jenkins runs as the jenkins user!

The Solution:

# Switch to jenkins user
sudo -su jenkins

# Configure AWS credentials
aws configure
# Entered access key and secret key

# Update kubeconfig
aws eks update-kubeconfig --name kastro-eks --region us-east-1

# Verify
kubectl get nodes

# Exit and restart Jenkins
exit
sudo systemctl restart jenkins

Result: Pipeline stage passed! ✅

Lesson Learned: Always configure tools under the user that will use them. Jenkins runs as jenkins user, not root.

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Challenge 3: Pods Stuck in CrashLoopBackOff

The Problem:

After first deployment, pods kept restarting:

kubectl get pods
NAME                                   READY   STATUS             RESTARTS
bookmyshow-deployment-7d4c8b9f-8xk2p  0/1     CrashLoopBackOff   5

The Investigation:

kubectl logs bookmyshow-deployment-7d4c8b9f-8xk2p

Error: Cannot find module '/app/node_modules/express'

The Root Cause:

Dependencies weren't being installed in the Docker image properly.

The Solution:

Fixed Dockerfile:

FROM node:18-alpine

WORKDIR /app

# Copy package files
COPY package*.json ./

# Install dependencies
RUN npm install --production

# Copy application code
COPY . .

EXPOSE 3000

CMD ["npm", "start"]

Key fix: Install dependencies before copying application code, and use --production flag.

Result: Pods started successfully! 🎉

Lesson Learned: Always check container logs first when pods fail. They usually tell you exactly what's wrong.

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Challenge 4: High AWS Costs

The Problem:

First month's AWS bill was $450+ 😱

The Analysis:

Used AWS Cost Explorer and found:

• EKS Control Plane: $73 (fixed)

• 3x t3.medium running 24/7: $150

• BMS Server (t2.large) 24/7: $68

• Data transfer costs: $50+

• Unused EBS volumes: $30

The Solutions Implemented:

1. Used Spot Instances for Dev:

   • Saved 70% on dev environment

2. Implemented Auto-scaling:

   • Scale down to 2 nodes during off-hours

   • Saved ~$40/month

3. Cleaned up unused resources:

   • Deleted old EBS volumes

   • Removed unused elastic IPs

4. Optimized data transfer:

   • Used CloudFront for static assets

   • Reduced cross-region traffic

Result: Brought costs down to $300-320/month - a 30% reduction!

Lesson Learned: Monitor costs from day one. Small optimizations add up significantly.

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Challenge 5: Slow Build Times

The Problem:

Initial builds were taking 25+ minutes! 😴

The Investigation:

Looking at Jenkins pipeline stages:

• Checkout: 30s

• SonarQube: 3m

• Install Dependencies: 18m ⚠️

• Docker Build: 3m

• Deploy: 1m

The Root Cause:

Installing npm dependencies every single time, even when they hadn't changed.

The Solution:

1. Docker layer caching:

# Copy only package files first
COPY package*.json ./
RUN npm install

# Then copy source code
COPY . .

2. Jenkins workspace caching:

stage('Install Dependencies') {
    steps {
        sh '''
        cd bookmyshow-app
        # Only install if package.json changed
        if [ ! -f "node_modules/.installed" ] || \
           [ package.json -nt node_modules/.installed ]; then
            npm install
            touch node_modules/.installed
        fi
        '''
    }
}

Result: Build time reduced to 8-10 minutes - over 60% faster! ⚡

Lesson Learned: Profile your pipeline. The slowest stage is usually where you can make the biggest improvements.

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📈 Results & Impact

Before vs After Comparison

Real Numbers from Production

In the first month after implementation:

• ✅ 42 successful deployments (previously ~4/month)

• ✅ Zero critical security vulnerabilities in production

• ✅ 99.7% uptime (SLA target: 99.5%)

• ✅ < 200ms average response time at peak load

• ✅ Auto-scaled 127 times based on traffic patterns

Business Impact

For Development Team:

• Developers can deploy changes independently

• Instant feedback on code quality

• No more "it works on my machine" issues

• Confidence in deployment process

For Business:

• Faster time to market for new features

• Reduced downtime and incidents

• Better user experience

• Lower operational costs (after optimization)

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🎓 Lessons Learned

Technical Lessons

1. Start with Infrastructure as Code

I initially created resources manually in AWS console. Big mistake. Later had to recreate everything, and couldn't remember all settings.

Lesson: Use eksctl, Terraform, or CloudFormation from day one. Your future self will thank you.

2. Security Groups Are Tricky

Spent hours debugging connectivity issues that were just security group misconfigurations.

Lesson: Document every security group rule. Know exactly what's allowed and why.

3. Monitoring is Not Optional

Initially planned to "add monitoring later." Then had a production issue and had no idea what was happening.

Lesson: Set up basic monitoring before going to production. You can enhance it later, but you need basics from day one.

4. Test the Rollback Process

Had auto-scaling configured but never tested it until production traffic triggered it. Almost had an outage.

Lesson: Test failure scenarios in staging. Auto-scaling, pod failures, node failures - test everything.

5. Kubernetes Has a Learning Curve

Underestimated how much there is to learn. ConfigMaps, Secrets, Services, Ingress, RBAC...

Lesson: Take time to understand Kubernetes fundamentals. Don't just copy-paste manifests.

Process Lessons

1. Document Everything

When I needed to recreate my setup on a new cluster, I had to piece together commands from bash history.

Lesson: Maintain a runbook. Document every command, every configuration change.

2. Small, Incremental Changes

Initially tried to change everything at once - broke everything.

Lesson: Deploy small changes frequently. Easier to identify what broke.

3. Automated Tests Are Worth It

Skipped writing tests initially to "save time." Ended up spending more time debugging production issues.

Lesson: Write tests. They save more time than they take.

4. Communication Matters

Didn't communicate a deployment schedule. Team was confused when things changed.

Lesson: Let stakeholders know about deployments, even automated ones.

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📢 Share Your Experience

Have you implemented similar DevOps practices? What challenges did you face? What solutions worked for you?

Share your experience in the comments! Let's learn from each other.

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