fabrico-implementing-kubernetes

name	fabrico-implementing-kubernetes
description	Kubernetes deployment patterns, Helm charts, and cluster management. Use when deploying applications to K8s, designing workload configurations, implementing scaling strategies, or managing cluster resources.

Kubernetes Patterns

When to Use

• Deploying applications to Kubernetes
• Designing Deployment, StatefulSet, or Job configurations
• Implementing auto-scaling (HPA, VPA, KEDA)
• Creating or modifying Helm charts
• Setting up ingress, networking, and service mesh
• Configuring resource requests, limits, and QoS

Project Detection

Check which Kubernetes tooling the project uses:

• helm/ or Chart.yaml → Helm charts
• kustomize/ or kustomization.yaml → Kustomize
• k8s/ or kubernetes/ with *.yaml → Raw manifests
• skaffold.yaml → Skaffold for local dev
• argocd/ or Application resources → ArgoCD GitOps
• flux-system/ or Kustomization CRD → Flux GitOps

Use the context7 MCP server (tools mcp__context7__*) to look up Kubernetes API versions and syntax.

Workload Type Decision

Workload Type	Use When
Deployment	Stateless apps, web servers, APIs
StatefulSet	Databases, stateful apps needing stable identity
DaemonSet	Node-level agents (logging, monitoring)
Job	One-time tasks, batch processing
CronJob	Scheduled recurring tasks

Deployment Configuration

Resource Management

resources:
  requests:    # Scheduler uses for placement
    memory: "256Mi"
    cpu: "100m"
  limits:      # Kubelet enforces these
    memory: "512Mi"
    cpu: "500m"

Rules:

• Always set requests (required for scheduling)
• Set memory limits to prevent OOM impact on node
• CPU limits optional (can cause throttling)
• Request:Limit ratio of 1:2 is good starting point

QoS Classes

Class	Condition	Eviction Priority
Guaranteed	requests == limits (all containers)	Last to evict
Burstable	requests < limits	Medium
BestEffort	No requests or limits	First to evict

Rule: Production workloads should be Guaranteed or Burstable, never BestEffort.

Probes Configuration

livenessProbe:      # Restarts container if fails
  httpGet:
    path: /healthz
    port: 8080
  initialDelaySeconds: 15
  periodSeconds: 10
  failureThreshold: 3

readinessProbe:     # Removes from Service if fails
  httpGet:
    path: /ready
    port: 8080
  initialDelaySeconds: 5
  periodSeconds: 5
  failureThreshold: 3

startupProbe:       # Delays liveness until startup complete
  httpGet:
    path: /healthz
    port: 8080
  failureThreshold: 30
  periodSeconds: 10

Rules:

• Always configure readinessProbe (graceful traffic handling)
• Use startupProbe for slow-starting apps (instead of long initialDelaySeconds)
• livenessProbe should check app health, not dependencies

Pod Disruption Budget

apiVersion: policy/v1
kind: PodDisruptionBudget
metadata:
  name: api-pdb
spec:
  minAvailable: 2        # OR maxUnavailable: 1
  selector:
    matchLabels:
      app: api

Rule: Always create PDB for production workloads to ensure availability during node drains.

Pod Anti-Affinity

affinity:
  podAntiAffinity:
    preferredDuringSchedulingIgnoredDuringExecution:
      - weight: 100
        podAffinityTerm:
          labelSelector:
            matchLabels:
              app: api
          topologyKey: kubernetes.io/hostname

Rule: Spread replicas across nodes/zones for high availability.

Scaling Strategies

Horizontal Pod Autoscaler (HPA)

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: api-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: api
  minReplicas: 2
  maxReplicas: 10
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 70
  behavior:
    scaleDown:
      stabilizationWindowSeconds: 300  # Prevent flapping

Scaling Decision Matrix

Scaling Type	Use When	Tool
CPU-based	General compute workloads	HPA
Memory-based	Memory-intensive apps	HPA
Custom metrics	Queue depth, request rate	HPA + Prometheus Adapter
Event-driven	Message queues, scheduled jobs	KEDA
Vertical	Right-sizing requests/limits	VPA

Helm Chart Structure

mychart/
├── Chart.yaml          # Chart metadata
├── values.yaml         # Default values
├── values-dev.yaml     # Environment overrides
├── values-prod.yaml
├── templates/
│   ├── _helpers.tpl    # Template helpers
│   ├── deployment.yaml
│   ├── service.yaml
│   ├── ingress.yaml
│   ├── hpa.yaml
│   ├── pdb.yaml
│   └── configmap.yaml
└── charts/             # Dependencies

Helm Best Practices

# values.yaml - use structured defaults
replicaCount: 2

image:
  repository: myapp
  tag: ""  # Override in CI, not here
  pullPolicy: IfNotPresent

resources:
  requests:
    memory: "256Mi"
    cpu: "100m"
  limits:
    memory: "512Mi"

# Enable/disable optional components
autoscaling:
  enabled: true
  minReplicas: 2
  maxReplicas: 10

Rules:

• Don't hardcode image tags in values.yaml (set in CI)
• Use {{ include "mychart.fullname" . }} for resource names
• Provide sensible defaults, override per environment

Ingress Configuration

Ingress Class Decision

Ingress Controller	Use When
nginx-ingress	General purpose, widely supported
AWS ALB	AWS-native, integrated with WAF/ACM
Traefik	Simple setup, automatic HTTPS
Istio Gateway	Service mesh already in use

Ingress Example (nginx)

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: api-ingress
  annotations:
    nginx.ingress.kubernetes.io/ssl-redirect: "true"
    cert-manager.io/cluster-issuer: "letsencrypt-prod"
spec:
  ingressClassName: nginx
  tls:
    - hosts:
        - api.example.com
      secretName: api-tls
  rules:
    - host: api.example.com
      http:
        paths:
          - path: /
            pathType: Prefix
            backend:
              service:
                name: api
                port:
                  number: 80

Security Context

securityContext:
  runAsNonRoot: true
  runAsUser: 1000
  runAsGroup: 1000
  fsGroup: 1000
  seccompProfile:
    type: RuntimeDefault

containers:
  - name: app
    securityContext:
      allowPrivilegeEscalation: false
      readOnlyRootFilesystem: true
      capabilities:
        drop:
          - ALL

Rule: Always run as non-root with minimal capabilities in production.

Process

1. Discover context → Check existing K8s manifests, Helm charts, Kustomize
2. Choose workload type → Deployment, StatefulSet, Job based on requirements
3. Configure resources → Set requests/limits based on profiling or estimates
4. Add probes → Configure readiness, liveness, and startup probes
5. Enable scaling → Add HPA/KEDA based on scaling requirements
6. Add resilience → PDB, pod anti-affinity, topology spread
7. Configure security → Security context, network policies
8. Validate → kubectl apply --dry-run=server, helm template

Checklist

• Resource requests and limits defined
• Readiness and liveness probes configured
• PodDisruptionBudget created for production
• Pod anti-affinity or topology spread configured
• HPA configured for variable workloads
• Security context with non-root user
• Image pull policy appropriate (Never use latest in prod)
• Labels consistent (app, version, environment)
• Namespace isolation per environment

Anti-Patterns

Don't	Do
Use latest image tag	Pin specific versions or SHA
Skip resource requests	Always set requests for scheduling
Single replica in production	Minimum 2 replicas with PDB
Run as root	Use non-root user with minimal caps
Missing readiness probe	Configure probes for graceful traffic
kubectl apply in production	GitOps with ArgoCD/Flux
Hardcode values in manifests	Use Helm values or Kustomize overlays
Ignore pod eviction	Set PDB to maintain availability

Related Skills

• fabrico-implementing-observability - For K8s monitoring and logging setup
• fabrico-implementing-ci-cd - For K8s deployment pipelines
• fabrico-managing-secrets - For K8s secret management patterns
• fabrico-implementing-terraform-modules - For provisioning K8s clusters