10 Best Practices for Managing Kubernetes at Scale

As organizations use microservices and cloud-native architectures, Kubernetes is becoming the norm for container orchestration. As much as Kubernetes simplifies deploying and managing containers, workloads at scale make life complex, and robust practices are necessary. 

In this article, I will cover technical strategies and best practices for workload management at scale in Kubernetes.

Knowing Challenges in Scaling Kubernetes

Scaling out in Kubernetes entails overcoming obstacles such as:

• Cluster resource scheduling. Optimized CPU, memory, and disk usage across nodes.
• Network complexity. Consistent service-to-service communications in big, distributed environments.
• Fault and scalability. Handling availability during failures and during a scale-out/scale-in scenario.
• Operational overheads. Removing repetitive operations such as scaling, monitoring, and balancing loads.
• Security at scale. Role-based access controls (RBAC), secrets, and network policies in big clusters.

In this article, I will go through examples of overcoming such obstacles with a combination of native Kubernetes capabilities and complementary tools.

Capabilities and Tools

1. Efficient Scheduling of Cluster Resources

Performance at scale is determined directly by the distribution of resources at scale. There are a variety of capabilities in Kubernetes for optimized use of resources:

Requests and Limits

Declaring CPU and memory requests and limits will cause fair distribution of resources and will not permit noising neighbors to consume all resources.

apiVersion: v1

kind: Pod

metadata:

  name: resource-demo

spec:

  containers:

    - name: app

      image: nginx

      resources:

        requests:

          memory: "128Mi"

          cpu: "500m"

        limits:

          memory: "256Mi"

          cpu: "1"

Best practices:

• Use quota for enforcing at the namespace level.
• Periodically analyze usage with kubectl top and make any required tweaks to limits.

Cluster Autoscaler

The autoscaler scales your cluster’s node count dynamically according to workload demand.

kubectl apply -f https://github.com/kubernetes/autoscaler/releases/download/cluster-autoscaler-<version>/cluster-autoscaler.yaml

Best practices:

• Label your autoscaler operations appropriately for your nodes.
• Scale behavior monitor to avert over-provisioning.

2. Horizontal and Vertical Pod Autoscaling

Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA) autoscaling capabilities are native in Kubernetes, but service meshes like Istio and Linkerd make and simplify inter-service communications easier and more efficient.

Horizontal Pod Autoscaler (HPA)

HPA scales replicas of pods according to CPU, memory, or custom metrics.

Example: CPU usage for autoscaling

apiVersion: autoscaling/v2

kind: HorizontalPodAutoscaler

metadata:

  name: hpa-example

spec:

  scaleTargetRef:

    apiVersion: apps/v1

    kind: Deployment

    name: web-app

  minReplicas: 2

  maxReplicas: 10

  metrics:

    - type: Resource

      resource:

        name: cpu

        target:

          type: Utilization

          averageUtilization: 50

Vertical Pod Autoscaler (VPA)

Vertical Pod Autoscaler scales a run-time request and limit of a pod.

kubectl apply -f https://github.com/kubernetes/autoscaler/releases/download/vertical-pod-autoscaler-<version>/vpa.yaml

3. Optimizing Networking at Scale

Service Mesh

Service meshes like Istio and Linkerd make and simplify inter-service communications easier and more efficient through the abstraction of service loads, retries, and encryption concerns.

Example: Istio VirtualService for routing traffic

apiVersion: networking.istio.io/v1beta1

kind: VirtualService

metadata:

  name: example-route

spec:

  hosts:

    - example.com

  http:

    - route:

        - destination:

            host: service-v1

          weight: 80

        - destination:

            host: service-v2

          weight: 20

Network Policies

Use network policies for constraining traffic between pods for enhanced security.

apiVersion: networking.k8s.io/v1

kind: NetworkPolicy

metadata:

  name: restrict-traffic

spec:

  podSelector:

    matchLabels:

      app: web-app

  policyTypes:

    - Ingress

  ingress:

    - from:

        - podSelector:

            matchLabels:

              role: backend

4. Observability Enhancement

Observability is critical in controlling Kubernetes at a larger level. Use tools like Prometheus, Grafana, and Jaeger for metrics, logs, and tracing.

Prometheus Metrics

Use Prometheus annotations for scrapping pod metrics.

apiVersion: v1

kind: Pod

metadata:

  annotations:

    prometheus.io/scrape: "true"

    prometheus.io/port: "8080"

  labels:

    app: monitored-app

spec:

  containers:

    - name: app

      image: monitored-app:latest

      ports:

        - containerPort: 8080

5. Building Resilience

Pod Disruption Budgets (PDB)

Use PDBs for maintaining a minimum availability of pods during maintenance and upgrades.

apiVersion: policy/v1

kind: PodDisruptionBudget

metadata:

  name: pdb-example

spec:

  minAvailable: 2

  selector:

    matchLabels:

      app: web-app

Rolling Updates

Roll out updates in phases in a manner that does not cause any downtime.

kubectl set image deployment/web-app web-app=web-app:v2 --record

kubectl rollout status deployment/web-app

6. Securing Kubernetes at Scale

RBAC Configuration

Use RBAC for constraining the privileges of the user and app.

apiVersion: rbac.authorization.k8s.io/v1

kind: Role

metadata:

  namespace: default

  name: pod-reader

rules:

  - apiGroups: [""]

    resources: ["pods"]

    verbs: ["get", "list", "watch"]

Secrets Management

Utilize Kubernetes Secrets for secure management of sensitive information. Use Kubernetes Secrets to manage sensitive information securely.

apiVersion: v1

kind: Secret

metadata:

  name: db-credentials

type: Opaque

data:

  username: dXNlcg==

  password: cGFzc3dvcmQ=

7. GitOps for Automation

Utilize GitOps with tools such as ArgoCD and Flux. Version and store Kubernetes manifest in Git repos and have clusters auto-synced with them.

8. Testing at Scale

Mock out high-scale workloads with tools such as K6 and Locust. Verify configuration, resource assignments, and scaling in testing environments.

9. Handling Storage at Scale

Dynamic Persistent Volume Provisioning

Storage for applications is dynamically provisioned with automation.

apiVersion: storage.k8s.io/v1

kind: StorageClass

metadata:

  name: fast-storage

provisioner: kubernetes.io/aws-ebs

parameters:

  type: gp2

  fsType: ext4

10. Optimizing CI/CD Pipelines for Kubernetes

Build and Push a Docker Image

Streamline creating and publishing container images with CI/CD tools such as Jenkins, GitHub Actions, and GitLab CI.

Conclusion

To scale Kubernetes, one must have a combination of efficient use of resources, automation, observability, and strong security processes in place. By taking full use of Kubernetes-native capabilities and combining them with complementary tools, your workloads can be high-performance, secure, and resilient at any scale.