Zero-Downtime Migration to Kubernetes

Overview

An established e-commerce platform was running on legacy infrastructure that couldn't handle traffic spikes during peak sales events. They needed to modernize without disrupting their 24/7 business operations.

Industry: E-commerce Platform Company Size: 100-200 employees Timeline: 4 months Technologies: Kubernetes, Helm, AWS EKS, Terraform, GitOps (Flux), Prometheus

The Challenge

The company's infrastructure was showing its age:

Technical Challenges

• Legacy VM-based infrastructure difficult to scale
• Manual scaling during traffic spikes (Black Friday, sales events)
• Long deployment times (45+ minutes)
• Inconsistent environments between dev, staging, and production
• Resource inefficiency - servers running at 15% utilization
• No auto-healing - outages required manual intervention
• Scaling limitations - couldn't handle 10x traffic spikes

Business Impact

• Site slowdowns during peak events (lost revenue)
• Failed scaling during Black Friday 2024 (4 hours of degraded service)
• $250k in lost sales during one incident
• Customer complaints about performance
• Engineering team spending 30% of time on infrastructure firefighting

The CTO's directive: "We need infrastructure that scales automatically and doesn't go down during our busiest days."

The Solution

I designed and implemented a comprehensive migration to Kubernetes on AWS EKS with zero downtime:

1. Architecture Design

Created a modern, scalable architecture:

• Multi-AZ AWS EKS cluster for high availability
• Horizontal Pod Autoscaling for automatic capacity
• GitOps workflows for deployments
• Service mesh for traffic management
• Centralized logging and monitoring

2. Gradual Migration Strategy

Avoided "big bang" migration with phased approach:

Phase 1: Non-critical services (2 weeks)

• Image processing service
• Email notification service
• Reporting service

Phase 2: API services (4 weeks)

• Product catalog API
• User management API
• Order management API

Phase 3: Frontend and critical services (6 weeks)

• Web application
• Checkout service
• Payment processing

Phase 4: Database migration (2 weeks)

• RDS with read replicas
• Redis clusters
• Elasticsearch

3. Infrastructure as Code

Managed all infrastructure with Terraform:

module "eks" {
  source = "terraform-aws-modules/eks/aws"

  cluster_name    = "production-eks"
  cluster_version = "1.28"

  vpc_id     = module.vpc.vpc_id
  subnet_ids = module.vpc.private_subnets

  eks_managed_node_groups = {
    general = {
      desired_size = 3
      min_size     = 3
      max_size     = 10

      instance_types = ["t3.large"]
      capacity_type  = "SPOT"
    }

    critical = {
      desired_size = 2
      min_size     = 2
      max_size     = 5

      instance_types = ["t3.xlarge"]
      capacity_type  = "ON_DEMAND"

      labels = {
        workload = "critical"
      }

      taints = [{
        key    = "critical"
        value  = "true"
        effect = "NoSchedule"
      }]
    }
  }
}

4. Helm Charts for Applications

Standardized deployments with Helm:

# values.yaml for web application
replicaCount: 3

image:
  repository: ecr.aws/company/webapp
  tag: "{{ .Values.version }}"

autoscaling:
  enabled: true
  minReplicas: 3
  maxReplicas: 50
  targetCPUUtilizationPercentage: 70

resources:
  requests:
    cpu: 500m
    memory: 512Mi
  limits:
    cpu: 1000m
    memory: 1Gi

ingress:
  enabled: true
  className: alb
  annotations:
    alb.ingress.kubernetes.io/scheme: internet-facing
    alb.ingress.kubernetes.io/target-type: ip

5. GitOps with Flux

Implemented automated deployments:

apiVersion: kustomize.toolkit.fluxcd.io/v1
kind: Kustomization
metadata:
  name: webapp-production
  namespace: flux-system
spec:
  interval: 5m
  path: ./kubernetes/production
  prune: true
  sourceRef:
    kind: GitRepository
    name: webapp
  healthChecks:
    - apiVersion: apps/v1
      kind: Deployment
      name: webapp
      namespace: production

6. Auto-Scaling Configuration

Configured both horizontal and vertical scaling:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: webapp-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: webapp
  minReplicas: 3
  maxReplicas: 50
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80
  behavior:
    scaleUp:
      stabilizationWindowSeconds: 60
      policies:
      - type: Percent
        value: 50
        periodSeconds: 60
    scaleDown:
      stabilizationWindowSeconds: 300
      policies:
      - type: Percent
        value: 10
        periodSeconds: 60

7. Monitoring & Observability

Deployed comprehensive monitoring stack:

• Prometheus for metrics collection
• Grafana for visualization
• Alert Manager for incident alerts
• Fluent Bit for log aggregation
• Jaeger for distributed tracing

8. Traffic Migration

Used weighted routing for gradual traffic shift:

1. Deploy new Kubernetes services alongside legacy
2. Route 10% traffic to new infrastructure
3. Monitor for issues
4. Gradually increase to 100%
5. Decommission legacy infrastructure

The Results

Uptime & Reliability

• Uptime: 99.7% → 99.99%
• Mean time to recovery: 45 minutes → 3 minutes
• Zero downtime during migration
• Auto-healing resolved 95% of incidents automatically

Performance

• Response time: 800ms → 250ms (69% improvement)
• Time to scale: 15 minutes → 30 seconds
• Deployment time: 45 minutes → 5 minutes
• Handled 10x traffic spike during Black Friday without issues

Business Impact

• Black Friday 2025: Zero incidents, handled record traffic
• Revenue impact: $0 lost to infrastructure issues (vs $250k previous year)
• Customer satisfaction: Up 35%
• Engineering productivity: 30% more time on features vs firefighting

Cost Efficiency

Despite more powerful infrastructure:

• Monthly infrastructure costs: $45k → $38k (15% reduction)
• Resource utilization: 15% → 65%
• Spot instances: Saved $12k/month
• Auto-scaling: Prevented over-provisioning

Technologies Used

Core Infrastructure

• Kubernetes: AWS EKS 1.28
• Infrastructure as Code: Terraform
• Container Registry: Amazon ECR
• Load Balancing: AWS ALB Ingress Controller

Deployment & GitOps

• GitOps: Flux CD
• Package Management: Helm
• CI/CD: GitHub Actions
• Secret Management: External Secrets Operator + AWS Secrets Manager

Monitoring & Observability

• Metrics: Prometheus, Grafana
• Logging: Fluent Bit, CloudWatch Logs
• Tracing: Jaeger
• Alerting: Alert Manager, PagerDuty

Databases & Storage

• Databases: Amazon RDS (PostgreSQL)
• Caching: Redis on Kubernetes
• Search: Elasticsearch on Kubernetes
• Object Storage: S3

Implementation Timeline

Month 1: Foundation

• EKS cluster setup
• Networking and security
• Monitoring stack deployment
• CI/CD pipeline

Month 2: Initial Services

• Migrated 5 non-critical services
• Established migration patterns
• Team training
• Documentation

Month 3: Core Services

• Migrated API services
• Load testing and optimization
• Disaster recovery testing
• Performance tuning

Month 4: Critical Services & Cutover

• Migrated frontend and checkout
• Gradual traffic migration
• Final cutover
• Legacy decommissioning

Key Success Factors

1. Gradual migration - No big bang, reduced risk
2. Comprehensive testing - Load testing before production
3. Parallel running - Old and new infrastructure side-by-side
4. Strong monitoring - Caught issues before customers noticed
5. Team training - Engineers comfortable with Kubernetes before migration
6. GitOps - Reliable, auditable deployments
7. Auto-scaling - Handled unpredictable traffic patterns

Client Testimonial

"The migration was seamless. We went from dreading Black Friday to confidently handling 10x normal traffic. The auto-scaling just worked. Our engineering team is shipping features 3x faster now." - VP of Engineering

"For the first time in our company's history, we had zero infrastructure incidents during our biggest sale. That's a direct result of this migration." - CTO

Long-Term Benefits

6 Months Post-Migration

• Scaled to support 50% user growth without infrastructure changes
• Deployed 150+ production changes with zero downtime
• Reduced infrastructure team from 5 to 3 (automation)
• Engineering team grew 40% without proportional infrastructure burden
• Site reliability is now a competitive advantage

Developer Experience

• Local development: Mimics production with Minikube
• Preview environments: Automatic per pull request
• Self-service deployments: Engineers deploy independently
• Faster feedback loops: Minutes instead of hours

Lessons Learned

1. Start small - Migrate non-critical services first
2. Monitor everything - Can't manage what you don't measure
3. Test at scale - Load testing revealed critical issues
4. Train the team - Success depends on team expertise
5. GitOps is essential - Declarative deployments are game-changing
6. Auto-scaling works - When properly configured
7. Cost optimization matters - Kubernetes can be cheaper than VMs

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