Kubernetes Cheatsheet

Scaling

Use this Kubernetes reference while you build software engineering projects, review code for technical interview prep, or polish examples for a software engineer resume.

Manual Scaling

kubectl scale deployment api --replicas=5
kubectl scale statefulset postgres --replicas=3

# scale multiple at once
kubectl scale deployment api worker --replicas=4

# conditional scale (only if current count matches)
kubectl scale deployment api --replicas=10 --current-replicas=5

HorizontalPodAutoscaler (HPA)

Automatically adjusts replicas based on metrics. Requires metrics-server installed.

CPU/Memory HPA (autoscaling/v2)

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: api-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: api
  minReplicas: 2
  maxReplicas: 20
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70    # % of CPU request
  - type: Resource
    resource:
      name: memory
      target:
        type: AverageValue
        averageValue: 200Mi

Custom / External Metrics HPA

  metrics:
  - type: Pods
    pods:
      metric:
        name: http_requests_per_second
      target:
        type: AverageValue
        averageValue: "500"
  - type: External
    external:
      metric:
        name: sqs_queue_depth
        selector:
          matchLabels:
            queue: orders
      target:
        type: AverageValue
        averageValue: "100"

HPA Behavior (scale speed)

spec:
  behavior:
    scaleUp:
      stabilizationWindowSeconds: 60
      policies:
      - type: Pods
        value: 4
        periodSeconds: 60
    scaleDown:
      stabilizationWindowSeconds: 300    # wait 5 min before scaling down
      policies:
      - type: Percent
        value: 10
        periodSeconds: 60

kubectl get hpa
kubectl describe hpa api-hpa     # current metrics, target, last scale event
kubectl top pods                 # live CPU/memory (needs metrics-server)

VerticalPodAutoscaler (VPA)

Adjusts CPU/memory requests on existing Pods. Requires the VPA addon.

apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
  name: api-vpa
spec:
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: api
  updatePolicy:
    updateMode: "Auto"    # Auto | Recreate | Initial | Off
  resourcePolicy:
    containerPolicies:
    - containerName: api
      minAllowed:
        cpu: 100m
        memory: 128Mi
      maxAllowed:
        cpu: 2
        memory: 2Gi

Do not use HPA (CPU/memory) and VPA (Auto mode) together on the same Deployment — they conflict. Use VPA Off mode to just get recommendations, or use KEDA + VPA.

Cluster Autoscaler

Adds/removes Nodes when Pods are unschedulable or Nodes are underutilized. Configured at the Node Group / cloud provider level, not per-workload.

# check autoscaler logs
kubectl -n kube-system logs deployment/cluster-autoscaler

# annotate node to prevent scale-down
kubectl annotate node my-node cluster-autoscaler.kubernetes.io/scale-down-disabled=true

KEDA (Kubernetes Event-Driven Autoscaling)

Scale to zero and scale from external event sources (queues, cron, Prometheus, etc.):

apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: worker-scaledobject
spec:
  scaleTargetRef:
    name: worker
  minReplicaCount: 0          # can scale to zero
  maxReplicaCount: 50
  pollingInterval: 15
  cooldownPeriod: 60
  triggers:
  - type: aws-sqs-queue
    metadata:
      queueURL: https://sqs.us-east-1.amazonaws.com/123456/orders
      queueLength: "10"
      awsRegion: us-east-1

Node Affinity and Taints (control where Pods land)

Node Selector (simple)

spec:
  nodeSelector:
    kubernetes.io/arch: arm64

Node Affinity (expressive)

spec:
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
        - matchExpressions:
          - key: node-type
            operator: In
            values: [gpu]
      preferredDuringSchedulingIgnoredDuringExecution:
      - weight: 1
        preference:
          matchExpressions:
          - key: zone
            operator: In
            values: [us-east-1a]

Taints and Tolerations

# taint a node (no Pods without toleration)
kubectl taint nodes gpu-node gpu=true:NoSchedule

# remove taint
kubectl taint nodes gpu-node gpu=true:NoSchedule-

spec:
  tolerations:
  - key: gpu
    operator: Equal
    value: "true"
    effect: NoSchedule

Pod Anti-Affinity (spread across zones)

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

Topology Spread Constraints

Distribute Pods evenly across zones/nodes:

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