2328 Kubernetes Deployment 36af8b48

Kubernetes Deployment Guide

Complete guide for deploying Skill Seekers on Kubernetes.

Table of Contents

• Prerequisites
• Quick Start with Helm
• Manual Deployment
• Configuration
• Scaling
• High Availability
• Monitoring
• Ingress & Load Balancing
• Storage
• Security
• Troubleshooting

Prerequisites

1. Kubernetes Cluster

Minimum requirements:

• Kubernetes v1.21+
• kubectl configured
• 2 nodes (minimum)
• 4 CPU cores total
• 8 GB RAM total

Cloud providers:

• AWS: EKS (Elastic Kubernetes Service)
• GCP: GKE (Google Kubernetes Engine)
• Azure: AKS (Azure Kubernetes Service)
• Local: Minikube, kind, k3s

2. Required Tools

# kubectl
curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl"
sudo install -o root -g root -m 0755 kubectl /usr/local/bin/kubectl

# Helm 3
curl https://raw.githubusercontent.com/helm/helm/main/scripts/get-helm-3 | bash

# Verify installations
kubectl version --client
helm version

3. Cluster Access

# Verify cluster connection
kubectl cluster-info
kubectl get nodes

# Create namespace
kubectl create namespace skillseekers
kubectl config set-context --current --namespace=skillseekers

Quick Start with Helm

1. Install with Default Values

# Add Helm repository (when available)
helm repo add skillseekers https://charts.skillseekers.io
helm repo update

# Install release
helm install skillseekers skillseekers/skillseekers \
  --namespace skillseekers \
  --create-namespace

# Or install from local chart
helm install skillseekers ./helm/skillseekers \
  --namespace skillseekers \
  --create-namespace

2. Install with Custom Values

# Create values file
cat > values-prod.yaml <<EOF
replicaCount: 3

secrets:
  anthropicApiKey: "sk-ant-..."
  githubToken: "ghp_..."
  openaiApiKey: "sk-..."

resources:
  limits:
    cpu: 2000m
    memory: 4Gi
  requests:
    cpu: 1000m
    memory: 2Gi

ingress:
  enabled: true
  className: nginx
  hosts:
    - host: api.skillseekers.example.com
      paths:
        - path: /
          pathType: Prefix
  tls:
    - secretName: skillseekers-tls
      hosts:
        - api.skillseekers.example.com

autoscaling:
  enabled: true
  minReplicas: 2
  maxReplicas: 10
  targetCPUUtilizationPercentage: 70
EOF

# Install with custom values
helm install skillseekers ./helm/skillseekers \
  --namespace skillseekers \
  --create-namespace \
  --values values-prod.yaml

3. Helm Commands

# List releases
helm list -n skillseekers

# Get status
helm status skillseekers -n skillseekers

# Upgrade release
helm upgrade skillseekers ./helm/skillseekers \
  --namespace skillseekers \
  --values values-prod.yaml

# Rollback
helm rollback skillseekers 1 -n skillseekers

# Uninstall
helm uninstall skillseekers -n skillseekers

Manual Deployment

1. Secrets

Create secrets for API keys:

# secrets.yaml
apiVersion: v1
kind: Secret
metadata:
  name: skillseekers-secrets
  namespace: skillseekers
type: Opaque
stringData:
  ANTHROPIC_API_KEY: "sk-ant-..."
  GITHUB_TOKEN: "ghp_..."
  OPENAI_API_KEY: "sk-..."
  VOYAGE_API_KEY: "..."

kubectl apply -f secrets.yaml

2. ConfigMap

# configmap.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: skillseekers-config
  namespace: skillseekers
data:
  MCP_TRANSPORT: "http"
  MCP_PORT: "8765"
  LOG_LEVEL: "INFO"
  CACHE_TTL: "86400"

kubectl apply -f configmap.yaml

3. Deployment

# deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: skillseekers-mcp
  namespace: skillseekers
  labels:
    app: skillseekers
    component: mcp-server
spec:
  replicas: 3
  selector:
    matchLabels:
      app: skillseekers
      component: mcp-server
  template:
    metadata:
      labels:
        app: skillseekers
        component: mcp-server
    spec:
      containers:
      - name: mcp-server
        image: skillseekers:2.9.0
        imagePullPolicy: IfNotPresent
        ports:
        - containerPort: 8765
          name: http
          protocol: TCP
        env:
        - name: MCP_TRANSPORT
          valueFrom:
            configMapKeyRef:
              name: skillseekers-config
              key: MCP_TRANSPORT
        - name: MCP_PORT
          valueFrom:
            configMapKeyRef:
              name: skillseekers-config
              key: MCP_PORT
        - name: ANTHROPIC_API_KEY
          valueFrom:
            secretKeyRef:
              name: skillseekers-secrets
              key: ANTHROPIC_API_KEY
        - name: GITHUB_TOKEN
          valueFrom:
            secretKeyRef:
              name: skillseekers-secrets
              key: GITHUB_TOKEN
        resources:
          requests:
            cpu: 1000m
            memory: 2Gi
          limits:
            cpu: 2000m
            memory: 4Gi
        livenessProbe:
          httpGet:
            path: /health
            port: 8765
          initialDelaySeconds: 30
          periodSeconds: 10
          timeoutSeconds: 5
          failureThreshold: 3
        readinessProbe:
          httpGet:
            path: /health
            port: 8765
          initialDelaySeconds: 10
          periodSeconds: 5
          timeoutSeconds: 3
          failureThreshold: 2
        volumeMounts:
        - name: data
          mountPath: /app/data
        - name: cache
          mountPath: /app/cache
      volumes:
      - name: data
        persistentVolumeClaim:
          claimName: skillseekers-data
      - name: cache
        emptyDir: {}

kubectl apply -f deployment.yaml

4. Service

# service.yaml
apiVersion: v1
kind: Service
metadata:
  name: skillseekers-mcp
  namespace: skillseekers
  labels:
    app: skillseekers
    component: mcp-server
spec:
  type: ClusterIP
  ports:
  - port: 8765
    targetPort: 8765
    protocol: TCP
    name: http
  selector:
    app: skillseekers
    component: mcp-server

kubectl apply -f service.yaml

5. Verify Deployment

# Check pods
kubectl get pods -n skillseekers

# Check services
kubectl get svc -n skillseekers

# Check logs
kubectl logs -n skillseekers -l app=skillseekers --tail=100 -f

# Port forward for testing
kubectl port-forward -n skillseekers svc/skillseekers-mcp 8765:8765

# Test endpoint
curl http://localhost:8765/health

Configuration

1. Resource Requests & Limits

resources:
  requests:
    cpu: 500m      # Guaranteed CPU
    memory: 1Gi    # Guaranteed memory
  limits:
    cpu: 2000m     # Maximum CPU
    memory: 4Gi    # Maximum memory

2. Environment Variables

env:
# From ConfigMap
- name: LOG_LEVEL
  valueFrom:
    configMapKeyRef:
      name: skillseekers-config
      key: LOG_LEVEL

# From Secret
- name: ANTHROPIC_API_KEY
  valueFrom:
    secretKeyRef:
      name: skillseekers-secrets
      key: ANTHROPIC_API_KEY

# Direct value
- name: MCP_TRANSPORT
  value: "http"

3. Multi-Environment Setup

# Development
helm install skillseekers-dev ./helm/skillseekers \
  --namespace skillseekers-dev \
  --values values-dev.yaml

# Staging
helm install skillseekers-staging ./helm/skillseekers \
  --namespace skillseekers-staging \
  --values values-staging.yaml

# Production
helm install skillseekers-prod ./helm/skillseekers \
  --namespace skillseekers-prod \
  --values values-prod.yaml

Scaling

1. Manual Scaling

# Scale deployment
kubectl scale deployment skillseekers-mcp -n skillseekers --replicas=5

# Verify
kubectl get pods -n skillseekers

2. Horizontal Pod Autoscaler (HPA)

# hpa.yaml
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: skillseekers-mcp
  namespace: skillseekers
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: skillseekers-mcp
  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
  behavior:
    scaleDown:
      stabilizationWindowSeconds: 300
      policies:
      - type: Percent
        value: 50
        periodSeconds: 60
    scaleUp:
      stabilizationWindowSeconds: 0
      policies:
      - type: Percent
        value: 100
        periodSeconds: 15
      - type: Pods
        value: 2
        periodSeconds: 15
      selectPolicy: Max

kubectl apply -f hpa.yaml

# Monitor autoscaling
kubectl get hpa -n skillseekers --watch

3. Vertical Pod Autoscaler (VPA)

# vpa.yaml
apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
  name: skillseekers-mcp
  namespace: skillseekers
spec:
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: skillseekers-mcp
  updatePolicy:
    updateMode: "Auto"
  resourcePolicy:
    containerPolicies:
    - containerName: mcp-server
      minAllowed:
        cpu: 500m
        memory: 1Gi
      maxAllowed:
        cpu: 4000m
        memory: 8Gi

High Availability

1. Pod Disruption Budget

# pdb.yaml
apiVersion: policy/v1
kind: PodDisruptionBudget
metadata:
  name: skillseekers-mcp
  namespace: skillseekers
spec:
  minAvailable: 2
  selector:
    matchLabels:
      app: skillseekers
      component: mcp-server

2. Pod Anti-Affinity

spec:
  affinity:
    podAntiAffinity:
      preferredDuringSchedulingIgnoredDuringExecution:
      - weight: 100
        podAffinityTerm:
          labelSelector:
            matchExpressions:
            - key: app
              operator: In
              values:
              - skillseekers
          topologyKey: kubernetes.io/hostname

3. Node Affinity

spec:
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
        - matchExpressions:
          - key: node-role
            operator: In
            values:
            - worker
      preferredDuringSchedulingIgnoredDuringExecution:
      - weight: 1
        preference:
          matchExpressions:
          - key: node-type
            operator: In
            values:
            - high-cpu

4. Multi-Zone Deployment

spec:
  topologySpreadConstraints:
  - maxSkew: 1
    topologyKey: topology.kubernetes.io/zone
    whenUnsatisfiable: DoNotSchedule
    labelSelector:
      matchLabels:
        app: skillseekers

Monitoring

1. Prometheus Metrics

# servicemonitor.yaml
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: skillseekers-mcp
  namespace: skillseekers
spec:
  selector:
    matchLabels:
      app: skillseekers
  endpoints:
  - port: metrics
    interval: 30s
    path: /metrics

2. Grafana Dashboard

# Import dashboard
kubectl apply -f grafana/dashboard.json

3. Logging with Fluentd

# fluentd-configmap.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: fluentd-config
data:
  fluent.conf: |
    <source>
      @type tail
      path /var/log/containers/skillseekers*.log
      pos_file /var/log/fluentd-skillseekers.pos
      tag kubernetes.*
      format json
    </source>
    <match **>
      @type elasticsearch
      host elasticsearch
      port 9200
    </match>

Ingress & Load Balancing

1. Nginx Ingress

# ingress.yaml
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: skillseekers
  namespace: skillseekers
  annotations:
    kubernetes.io/ingress.class: nginx
    cert-manager.io/cluster-issuer: letsencrypt-prod
    nginx.ingress.kubernetes.io/rate-limit: "100"
    nginx.ingress.kubernetes.io/ssl-redirect: "true"
spec:
  tls:
  - hosts:
    - api.skillseekers.example.com
    secretName: skillseekers-tls
  rules:
  - host: api.skillseekers.example.com
    http:
      paths:
      - path: /
        pathType: Prefix
        backend:
          service:
            name: skillseekers-mcp
            port:
              number: 8765

2. TLS with cert-manager

# Install cert-manager
kubectl apply -f https://github.com/cert-manager/cert-manager/releases/download/v1.13.0/cert-manager.yaml

# Create ClusterIssuer
cat <<EOF | kubectl apply -f -
apiVersion: cert-manager.io/v1
kind: ClusterIssuer
metadata:
  name: letsencrypt-prod
spec:
  acme:
    server: https://acme-v02.api.letsencrypt.org/directory
    email: admin@example.com
    privateKeySecretRef:
      name: letsencrypt-prod
    solvers:
    - http01:
        ingress:
          class: nginx
EOF

Storage

1. Persistent Volume

# pv.yaml
apiVersion: v1
kind: PersistentVolume
metadata:
  name: skillseekers-data
spec:
  capacity:
    storage: 50Gi
  accessModes:
  - ReadWriteOnce
  persistentVolumeReclaimPolicy: Retain
  storageClassName: standard
  hostPath:
    path: /mnt/skillseekers-data

2. Persistent Volume Claim

# pvc.yaml
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: skillseekers-data
  namespace: skillseekers
spec:
  accessModes:
  - ReadWriteOnce
  resources:
    requests:
      storage: 50Gi
  storageClassName: standard

3. StatefulSet (for stateful workloads)

apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: skillseekers-cache
spec:
  serviceName: skillseekers-cache
  replicas: 3
  volumeClaimTemplates:
  - metadata:
      name: data
    spec:
      accessModes: [ "ReadWriteOnce" ]
      resources:
        requests:
          storage: 10Gi

Security

1. Network Policies

# networkpolicy.yaml
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: skillseekers-mcp
  namespace: skillseekers
spec:
  podSelector:
    matchLabels:
      app: skillseekers
  policyTypes:
  - Ingress
  - Egress
  ingress:
  - from:
    - namespaceSelector:
        matchLabels:
          name: skillseekers
    ports:
    - protocol: TCP
      port: 8765
  egress:
  - to:
    - namespaceSelector: {}
    ports:
    - protocol: TCP
      port: 443  # HTTPS
    - protocol: TCP
      port: 80   # HTTP

2. Pod Security Policy

# psp.yaml
apiVersion: policy/v1beta1
kind: PodSecurityPolicy
metadata:
  name: skillseekers-restricted
spec:
  privileged: false
  allowPrivilegeEscalation: false
  requiredDropCapabilities:
  - ALL
  volumes:
  - 'configMap'
  - 'emptyDir'
  - 'projected'
  - 'secret'
  - 'persistentVolumeClaim'
  runAsUser:
    rule: 'MustRunAsNonRoot'
  seLinux:
    rule: 'RunAsAny'
  fsGroup:
    rule: 'RunAsAny'

3. RBAC

# rbac.yaml
apiVersion: v1
kind: ServiceAccount
metadata:
  name: skillseekers
  namespace: skillseekers
---
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  name: skillseekers
  namespace: skillseekers
rules:
- apiGroups: [""]
  resources: ["configmaps", "secrets"]
  verbs: ["get", "list"]
---
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: skillseekers
  namespace: skillseekers
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: Role
  name: skillseekers
subjects:
- kind: ServiceAccount
  name: skillseekers
  namespace: skillseekers

Troubleshooting

Common Issues

1. Pods Not Starting

# Check pod status
kubectl get pods -n skillseekers

# Describe pod
kubectl describe pod <pod-name> -n skillseekers

# Check events
kubectl get events -n skillseekers --sort-by='.lastTimestamp'

# Check logs
kubectl logs <pod-name> -n skillseekers

2. Image Pull Errors

# Check image pull secrets
kubectl get secrets -n skillseekers

# Create image pull secret
kubectl create secret docker-registry regcred \
  --docker-server=registry.example.com \
  --docker-username=user \
  --docker-password=password \
  -n skillseekers

# Use in pod spec
spec:
  imagePullSecrets:
  - name: regcred

3. Resource Constraints

# Check node resources
kubectl top nodes

# Check pod resources
kubectl top pods -n skillseekers

# Increase resources
kubectl edit deployment skillseekers-mcp -n skillseekers

4. Service Not Accessible

# Check service
kubectl get svc -n skillseekers
kubectl describe svc skillseekers-mcp -n skillseekers

# Check endpoints
kubectl get endpoints -n skillseekers

# Port forward
kubectl port-forward svc/skillseekers-mcp 8765:8765 -n skillseekers

Debug Commands

# Execute command in pod
kubectl exec -it <pod-name> -n skillseekers -- /bin/bash

# Copy files from pod
kubectl cp skillseekers/<pod-name>:/app/data ./data

# Check pod networking
kubectl exec <pod-name> -n skillseekers -- nslookup google.com

# View full pod spec
kubectl get pod <pod-name> -n skillseekers -o yaml

# Restart deployment
kubectl rollout restart deployment skillseekers-mcp -n skillseekers

Best Practices

1. Always set resource requests and limits
2. Use namespaces for environment separation
3. Enable autoscaling for variable workloads
4. Implement health checks (liveness & readiness)
5. Use Secrets for sensitive data
6. Enable monitoring and logging
7. Implement Pod Disruption Budgets for HA
8. Use RBAC for access control
9. Enable Network Policies
10. Regular backup of persistent volumes

Next Steps

• Review PRODUCTION_DEPLOYMENT.md for general guidelines
• See DOCKER_DEPLOYMENT.md for container-specific details
• Check TROUBLESHOOTING.md for common issues

Need help? Open an issue on GitHub.