10 Best Practices for Managing Kubernetes at Scale
We will discuss some of the best practices for managing distributed workloads on Kubernetes clusters to simplify deploying and managing containers.
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Join For FreeAs 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.
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