CI/CD Pipeline Automation

Streamlining development workflows with automated CI/CD pipelines

Overview
#

The CI/CD Pipeline Automation project demonstrates how to build a complete automated workflow that handles testing, building, security scanning, and deployment of applications. This project showcases DevOps best practices and infrastructure-as-code principles.

Problem Statement
#

Manual deployment processes are error-prone, time-consuming, and inconsistent. This project automates the entire software delivery pipeline to enable:

Fast, reliable deployments
Consistent build environments
Automated testing and quality gates
Security scanning before deployment
Rollback capabilities
Deployment notifications and monitoring

Technologies Used
#

GitHub Actions - CI/CD workflow automation
Docker - Containerization of applications
Kubernetes - Container orchestration
Helm - Kubernetes package management
Terraform - Infrastructure as Code
SonarQube - Code quality and security analysis
Trivy - Container vulnerability scanning
Prometheus & Grafana - Monitoring and alerting
ArgoCD - GitOps continuous deployment

Pipeline Stages
#

graph LR
    A[Code Push] --> B[Lint & Test]
    B --> C{Tests Pass?}
    C -->|Yes| D[Build Docker Image]
    C -->|No| Z[Notify Failure]
    D --> E[Security Scan]
    E --> F{Vulnerabilities?}
    F -->|None/Critical=0| G[Push to Registry]
    F -->|Critical Found| Z
    G --> H[Deploy to Staging]
    H --> I[Integration Tests]
    I --> J{Tests Pass?}
    J -->|Yes| K[Deploy to Production]
    J -->|No| Z
    K --> L[Health Checks]
    L --> M[Notify Success]

GitHub Actions Workflow
#

name: CI/CD Pipeline

on:
  push:
    branches: [main, develop]
  pull_request:
    branches: [main]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      
      - name: Set up Node.js
        uses: actions/setup-node@v4
        with:
          node-version: '20'
          cache: 'npm'
      
      - name: Install dependencies
        run: npm ci
      
      - name: Run linter
        run: npm run lint
      
      - name: Run tests
        run: npm test -- --coverage
      
      - name: Upload coverage
        uses: codecov/codecov-action@v3

  build:
    needs: test
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      
      - name: Build Docker image
        run: docker build -t myapp:${{ github.sha }} .
      
      - name: Run Trivy vulnerability scanner
        uses: aquasecurity/trivy-action@master
        with:
          image-ref: 'myapp:${{ github.sha }}'
          format: 'table'
          exit-code: '1'
          ignore-unfixed: true
          severity: 'CRITICAL'
      
      - name: Push to registry
        run: |
          docker tag myapp:${{ github.sha }} registry.example.com/myapp:${{ github.sha }}
          docker push registry.example.com/myapp:${{ github.sha }}

  deploy:
    needs: build
    if: github.ref == 'refs/heads/main'
    runs-on: ubuntu-latest
    steps:
      - name: Deploy to Kubernetes
        uses: azure/setup-kubectl@v3
      
      - name: Update deployment
        run: |
          kubectl set image deployment/myapp \
            myapp=registry.example.com/myapp:${{ github.sha }} \
            -n production
          
          kubectl rollout status deployment/myapp -n production --timeout=300s

Infrastructure as Code
#

Terraform Configuration
#

resource "aws_eks_cluster" "main" {
  name     = "production-cluster"
  role_arn = aws_iam_role.eks_cluster.arn

  vpc_config {
    subnet_ids = aws_subnet.private[*].id
  }
}

resource "helm_release" "application" {
  name       = "myapp"
  repository = "https://charts.example.com"
  chart      = "myapp"
  namespace  = "production"

  set {
    name  = "image.tag"
    value = var.app_version
  }
}

Kubernetes Deployment
#

apiVersion: apps/v1
kind: Deployment
metadata:
  name: myapp
  namespace: production
spec:
  replicas: 3
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 1
      maxUnavailable: 0
  template:
    spec:
      containers:
        - name: myapp
          image: registry.example.com/myapp:latest
          resources:
            requests:
              memory: "256Mi"
              cpu: "250m"
            limits:
              memory: "512Mi"
              cpu: "500m"
          livenessProbe:
            httpGet:
              path: /health
              port: 8080
            initialDelaySeconds: 30
            periodSeconds: 10
          readinessProbe:
            httpGet:
              path: /ready
              port: 8080
            initialDelaySeconds: 5
            periodSeconds: 5

Getting Started
#

Prerequisites
#

GitHub repository
Docker installed locally
kubectl configured for your cluster
AWS/GCP/Azure account (for cloud resources)

Local Development
#

# Clone the repository
git clone https://github.com/jahruhn/cicd-pipeline.git
cd cicd-pipeline

# Start local Kubernetes (Docker Desktop or kind)
kubectl cluster-info

# Deploy locally
helm install myapp ./charts/myapp \
  --namespace development \
  --create-namespace

# Port-forward to access locally
kubectl port-forward svc/myapp 8080:80 -n development

Set Up GitHub Actions
#

Add repository secrets:

AWS_ACCESS_KEY_ID
AWS_SECRET_ACCESS_KEY
DOCKER_REGISTRY_PASSWORD
KUBE_CONFIG_DATA

Push to main branch to trigger pipeline

Monitoring & Observability
#

Prometheus Metrics
#

apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: myapp
spec:
  selector:
    matchLabels:
      app: myapp
  endpoints:
    - port: metrics
      interval: 15s

Grafana Dashboard
#

Key metrics tracked:

Deployment frequency
Deployment success rate
Mean time to recovery (MTTR)
Lead time for changes
Application performance (latency, throughput, errors)

Challenges & Learnings
#

Secrets Management: Initially stored secrets in GitHub Actions, but migrated to HashiCorp Vault for better security, auditability, and rotation policies.

Rollback Strategy: Implemented automatic rollback when health checks fail after deployment. Learned the importance of database migration strategies that support both forward and backward compatibility during rollbacks.

Pipeline Performance: Optimized pipeline execution time from 25 minutes to 8 minutes by:

Implementing job parallelization
Caching dependencies
Using faster runners
Conditional job execution

Best Practices Implemented
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✅ Immutable Deployments: Each build creates a new Docker image with unique tag
✅ Infrastructure as Code: All infrastructure defined in version-controlled files
✅ Environment Parity: Local, staging, and production environments are identical
✅ Automated Rollbacks: Health check failures trigger automatic rollback
✅ Security First: Vulnerability scanning before deployment
✅ Observability: Comprehensive logging, metrics, and tracing
✅ Documentation: Runbooks for common operational tasks

Future Enhancements
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Implement canary deployments
Add chaos engineering tests
Multi-region deployment support
GitOps with ArgoCD (instead of push-based deploys)
Cost monitoring and optimization
Automated performance testing in pipeline

Links
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Source Code: GitHub Repository
Documentation: Pipeline Docs
Terraform Modules: Infrastructure Code

💡 Note: This is a sample project demonstrating DevOps skills. Replace with your actual CI/CD implementation and update all links.

Overview#

Problem Statement#

Technologies Used#

Pipeline Stages#

GitHub Actions Workflow#

Infrastructure as Code#

Terraform Configuration#

Kubernetes Deployment#

Getting Started#

Prerequisites#

Local Development#

Set Up GitHub Actions#

Monitoring & Observability#

Prometheus Metrics#

Grafana Dashboard#

Challenges & Learnings#

Best Practices Implemented#

Future Enhancements#

Links#