15 Essential Kubernetes Best Practices Every Engineer Needs

Kubernetes best practices are essential for running reliable, secure, and scalable container platforms in real production environments. They help teams avoid common operational issues while ensuring Kubernetes clusters remain stable, efficient, and easy to maintain as workloads grow.

Whether you’re preparing for DevOps or Kubernetes interviews or managing live production clusters, understanding these best practices shows that you can work confidently with cloud-native systems. It demonstrates that you know how to operate Kubernetes beyond simple deployments and apply it in real-world scenarios.

This topic frequently comes up in Kubernetes and cloud-native interviews because it tests practical skills, not just theory. Interviewers want to assess your knowledge of production operations, security hardening, resource management, and operational excellence. Being able to explain and apply Kubernetes best practices shows that you can manage clusters reliably at scale and make informed engineering decisions.

What Interviewers Are Really Looking For

When asked about Kubernetes best practices, interviewers want to assess:

Your understanding of resource requests and limits
Knowledge of security hardening and RBAC
Experience with health checks and self-healing
Familiarity with deployment strategies and rollback procedures
Understanding of monitoring and observability
Practical experience with production-ready configurations

Your answer should demonstrate that you think beyond getting pods running, you understand how to operate Kubernetes clusters reliably at scale with proper governance and security.

Core Kubernetes Best Practices Principles

Kubernetes best practices revolve around reliability, security, efficiency, and maintainability. Implementing Kubernetes best practices correctly ensures your clusters remain stable, secure, and cost-effective in production environments.

Key principles include:

Resource management: Set appropriate requests and limits for predictable performance
Security first: Implement RBAC, network policies, and pod security standards
High availability: Design for failures with replicas and anti-affinity rules
Observability: Implement comprehensive logging, metrics, and tracing
Declarative configuration: Use GitOps and version control for all manifests

Essential Kubernetes Best Practices

1. Set Resource Requests and Limits

Resource management is fundamental to Kubernetes best practices and cluster stability.

Why resource management matters:

Requests: Guaranteed resources for scheduling decisions
Limits: Maximum resources to prevent runaway containers
Without proper settings: Pods can starve each other, nodes can crash

Resource configuration:

apiVersion: v1
kind: Pod
metadata:
  name: resource-demo
spec:
  containers:
  - name: app
    image: nginx
    resources:
      requests:
        memory: "128Mi"
        cpu: "250m"
      limits:
        memory: "256Mi"
        cpu: "500m"

Best practices for resources:

Always set requests: Ensures proper scheduling
Set limits carefully: Prevents resource monopolization
CPU limits can throttle: Consider not setting CPU limits for latency-sensitive apps
Memory limits are strict: Pods are OOMKilled if exceeded
Use Vertical Pod Autoscaler: Automatically adjusts requests based on usage

Resource sizing guidelines:

Application Type	Memory Request	CPU Request	Notes
Stateless web app	256Mi-512Mi	100m-250m	Scale horizontally
API service	512Mi-1Gi	250m-500m	Monitor P95 latency
Background worker	256Mi-1Gi	100m-500m	Can tolerate throttling
Database	2Gi-8Gi	500m-2000m	Needs consistent performance
Cache (Redis)	1Gi-4Gi	250m-1000m	Memory-intensive

2. Implement Health Checks

Health checks are critical Kubernetes best practices for self-healing and zero-downtime deployments.

Three types of probes:

Liveness Probe:

Detects if container is alive
Restarts container if fails
Use for deadlock detection

Readiness Probe:

Determines if pod can serve traffic
Removes from service endpoints if fails
Use during startup and maintenance

Startup Probe:

Allows slow-starting containers extra time
Delays liveness/readiness checks
Use for legacy applications

Health check configuration:

apiVersion: v1
kind: Pod
metadata:
  name: healthcheck-demo
spec:
  containers:
  - name: app
    image: myapp:1.0
    ports:
    - containerPort: 8080
    
    # Startup probe for slow-starting apps
    startupProbe:
      httpGet:
        path: /healthz
        port: 8080
      failureThreshold: 30
      periodSeconds: 10
    
    # Liveness probe detects deadlocks
    livenessProbe:
      httpGet:
        path: /healthz
        port: 8080
      initialDelaySeconds: 30
      periodSeconds: 10
      timeoutSeconds: 5
      failureThreshold: 3
    
    # Readiness probe for traffic management
    readinessProbe:
      httpGet:
        path: /ready
        port: 8080
      initialDelaySeconds: 5
      periodSeconds: 5
      timeoutSeconds: 3
      failureThreshold: 3

Health check best practices:

Use different endpoints: /healthz for liveness, /ready for readiness
Keep probes lightweight: Should respond quickly (< 1 second)
Set appropriate timeouts: Balance between false positives and detection speed
Don’t check dependencies in liveness: Only check if the app itself is working
Check dependencies in readiness: Database, cache availability

3. Use Namespaces for Isolation

Namespaces are essential Kubernetes best practices for multi-tenant environments and organizational separation.

Why namespaces matter:

Logical isolation: Separate environments, teams, applications
Resource quotas: Limit resources per namespace
Access control: RBAC policies per namespace
Network policies: Isolate traffic between namespaces

Namespace organization patterns:

By Environment:

# Production, staging, development
production
staging
development

By Team:

# Team-based separation
team-platform
team-data
team-frontend

By Application:

# Application-based separation
app-payment
app-inventory
app-notifications

Namespace configuration with resource quotas:

apiVersion: v1
kind: Namespace
metadata:
  name: production
  labels:
    environment: production
---
apiVersion: v1
kind: ResourceQuota
metadata:
  name: production-quota
  namespace: production
spec:
  hard:
    requests.cpu: "100"
    requests.memory: 200Gi
    limits.cpu: "200"
    limits.memory: 400Gi
    pods: "100"
    services.loadbalancers: "5"

Namespace best practices:

Use meaningful names: Clear purpose identification
Apply resource quotas: Prevent resource monopolization
Implement network policies: Control inter-namespace communication
Set default limits: LimitRange for default pod resources
Avoid default namespace: Always use named namespaces in production

4. Implement RBAC and Security

Security is paramount in Kubernetes best practices for production clusters.

RBAC components:

ServiceAccount: Identity for pods
Role/ClusterRole: Permissions definition
RoleBinding/ClusterRoleBinding: Assigns roles to subjects

Principle of least privilege example:

# ServiceAccount for application
apiVersion: v1
kind: ServiceAccount
metadata:
  name: myapp-sa
  namespace: production
---
# Role with minimal permissions
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  name: myapp-role
  namespace: production
rules:
- apiGroups: [""]
  resources: ["configmaps", "secrets"]
  verbs: ["get", "list"]
- apiGroups: [""]
  resources: ["pods"]
  verbs: ["get", "list", "watch"]
---
# RoleBinding connects ServiceAccount to Role
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: myapp-binding
  namespace: production
subjects:
- kind: ServiceAccount
  name: myapp-sa
  namespace: production
roleRef:
  kind: Role
  name: myapp-role
  apiGroup: rbac.authorization.k8s.io

Security best practices:

Never use default ServiceAccount: Create dedicated service accounts
Apply Pod Security Standards: Enforce baseline/restricted policies
Enable audit logging: Track all API server access
Use network policies: Restrict pod-to-pod communication
Scan images regularly: Use Trivy, Snyk, or similar tools
Rotate credentials: Automate secret rotation
Disable anonymous auth: Require authentication for all access

Pod Security Standards:

apiVersion: v1
kind: Namespace
metadata:
  name: production
  labels:
    # Enforce restricted security standard
    pod-security.kubernetes.io/enforce: restricted
    pod-security.kubernetes.io/audit: restricted
    pod-security.kubernetes.io/warn: restricted

5. Use ConfigMaps and Secrets Properly

Configuration management is critical in Kubernetes best practices for maintainable applications.

ConfigMaps for non-sensitive data:

apiVersion: v1
kind: ConfigMap
metadata:
  name: app-config
  namespace: production
data:
  app.properties: |
    server.port=8080
    log.level=INFO
    feature.enabled=true
  database.url: "postgres://db.example.com:5432/mydb"

Secrets for sensitive data:

apiVersion: v1
kind: Secret
metadata:
  name: app-secrets
  namespace: production
type: Opaque
stringData:
  database.password: "mySecurePassword123"
  api.key: "sk-1234567890abcdef"

Using ConfigMaps and Secrets in pods:

apiVersion: v1
kind: Pod
metadata:
  name: myapp
spec:
  containers:
  - name: app
    image: myapp:1.0
    # Environment variables from ConfigMap
    env:
    - name: APP_PORT
      valueFrom:
        configMapKeyRef:
          name: app-config
          key: server.port
    # Environment variables from Secret
    - name: DB_PASSWORD
      valueFrom:
        secretKeyRef:
          name: app-secrets
          key: database.password
    # Mount ConfigMap as volume
    volumeMounts:
    - name: config
      mountPath: /etc/config
  volumes:
  - name: config
    configMap:
      name: app-config

Configuration best practices:

Use ConfigMaps for configuration: Never hardcode in images
Store secrets securely: Use Secrets, not ConfigMaps for sensitive data
Consider external secret management: Vault, AWS Secrets Manager, Azure Key Vault
Use immutable ConfigMaps/Secrets: Add immutable: true for better performance
Version your configurations: Track changes in Git
Avoid excessive secrets: Don’t create one secret per value

6. Implement Proper Labels and Annotations

Labels and annotations are essential Kubernetes best practices for organization and automation.

Recommended label schema:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: myapp
  namespace: production
  labels:
    # Kubernetes recommended labels
    app.kubernetes.io/name: myapp
    app.kubernetes.io/instance: myapp-production
    app.kubernetes.io/version: "1.2.3"
    app.kubernetes.io/component: backend
    app.kubernetes.io/part-of: ecommerce-platform
    app.kubernetes.io/managed-by: helm
    
    # Custom organizational labels
    team: platform
    environment: production
    cost-center: engineering
  
  annotations:
    # Metadata for automation
    deployment.kubernetes.io/revision: "5"
    prometheus.io/scrape: "true"
    prometheus.io/port: "8080"
    prometheus.io/path: "/metrics"

Label best practices:

Use consistent schema: Follow Kubernetes recommended labels
Label everything: Deployments, pods, services, namespaces
Use for selection: Labels enable powerful queries
Keep labels stable: Don’t change frequently
Use annotations for metadata: Non-identifying information

Label selectors for monitoring:

# Query by application
kubectl get pods -l app.kubernetes.io/name=myapp

# Query by environment
kubectl get pods -l environment=production

# Query by team
kubectl get all -l team=platform --all-namespaces

7. Use Deployments with Proper Strategies

Deployment strategies are critical Kubernetes best practices for zero-downtime updates.

Deployment with rolling update:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: myapp
spec:
  replicas: 5
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxUnavailable: 1    # Max pods unavailable during update
      maxSurge: 1          # Max extra pods during update
  
  selector:
    matchLabels:
      app: myapp
  
  template:
    metadata:
      labels:
        app: myapp
        version: "1.2.3"
    spec:
      containers:
      - name: app
        image: myapp:1.2.3
        ports:
        - containerPort: 8080
        
        # Graceful shutdown
        lifecycle:
          preStop:
            exec:
              command: ["/bin/sh", "-c", "sleep 15"]
        
        # Fast health checks during rollout
        readinessProbe:
          httpGet:
            path: /ready
            port: 8080
          periodSeconds: 2
          failureThreshold: 3

Deployment strategies comparison:

Strategy	Use Case	Downtime	Risk	Complexity
RollingUpdate	Standard deployments	None	Low	Low
Recreate	Stateful apps, dev/test	Yes	Low	Very Low
Blue-Green	Critical services	None	Low	Medium
Canary	High-risk changes	None	Very Low	High

Deployment best practices:

Always use Deployments: Not bare pods or ReplicaSets
Set appropriate replicas: At least 3 for production
Configure rolling update: Balance speed and stability
Use Pod Disruption Budgets: Prevent too many pods down simultaneously
Implement graceful shutdown: Handle SIGTERM properly
Test rollbacks: Practice rollback procedures

Pod Disruption Budget:

apiVersion: policy/v1
kind: PodDisruptionBudget
metadata:
  name: myapp-pdb
spec:
  minAvailable: 2
  selector:
    matchLabels:
      app: myapp

8. Implement Network Policies

Network policies are essential Kubernetes best practices for security and compliance.

Why network policies matter:

Default behavior: All pods can communicate with all pods
Security risk: Compromised pod can attack others
Compliance requirement: Many standards require network segmentation

Deny-all baseline policy:

apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: deny-all
  namespace: production
spec:
  podSelector: {}
  policyTypes:
  - Ingress
  - Egress

Allow specific traffic:

apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: api-network-policy
  namespace: production
spec:
  podSelector:
    matchLabels:
      app: api
  policyTypes:
  - Ingress
  - Egress
  
  # Allow incoming traffic
  ingress:
  - from:
    # Only from frontend pods
    - podSelector:
        matchLabels:
          app: frontend
    ports:
    - protocol: TCP
      port: 8080
  
  # Allow outgoing traffic
  egress:
  - to:
    # Only to database
    - podSelector:
        matchLabels:
          app: database
    ports:
    - protocol: TCP
      port: 5432
  - to:
    # DNS resolution
    - namespaceSelector:
        matchLabels:
          name: kube-system
    ports:
    - protocol: UDP
      port: 53

Network policy best practices:

Start with deny-all: Default deny, explicitly allow
Test before enforcing: Use audit mode if available
Allow DNS: Most pods need DNS resolution
Document policies: Complex policies need documentation
Use namespace selectors: For cross-namespace communication
Monitor blocked traffic: Track denied connections

9. Use Horizontal Pod Autoscaling

Autoscaling is a key component of Kubernetes best practices for efficiency and reliability.

HPA based on CPU:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: myapp-hpa
  namespace: production
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: myapp
  
  minReplicas: 3
  maxReplicas: 20
  
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  
  behavior:
    scaleDown:
      stabilizationWindowSeconds: 300
      policies:
      - type: Percent
        value: 50
        periodSeconds: 60
    scaleUp:
      stabilizationWindowSeconds: 0
      policies:
      - type: Percent
        value: 100
        periodSeconds: 15

HPA with custom metrics:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: myapp-custom-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: myapp
  minReplicas: 3
  maxReplicas: 50
  metrics:
  # CPU metric
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  # Custom application metric
  - type: Pods
    pods:
      metric:
        name: http_requests_per_second
      target:
        type: AverageValue
        averageValue: "1000"

Autoscaling best practices:

Set meaningful min/max: Protect against over-scaling
Use multiple metrics: CPU, memory, custom metrics
Configure scale-down stabilization: Prevent flapping
Monitor scaling events: Track why scaling happened
Test scaling behavior: Simulate load to verify
Ensure resource requests set: HPA requires requests

10. Implement Proper Logging and Monitoring

Observability is fundamental to Kubernetes best practices for production operations.

Logging strategy:

Application logs to stdout/stderr:

apiVersion: v1
kind: Pod
metadata:
  name: myapp
spec:
  containers:
  - name: app
    image: myapp:1.0
    # Application logs to stdout
    command: ["./app"]
    # Logs are collected automatically by node-level agents

Centralized logging architecture:

Application Pods
  ↓ stdout/stderr
DaemonSet (Fluent Bit / Fluentd)
  ↓
Log Aggregation (Elasticsearch / Loki / CloudWatch)
  ↓
Visualization (Kibana / Grafana)

Monitoring with Prometheus:

apiVersion: v1
kind: Pod
metadata:
  name: myapp
  annotations:
    # Prometheus scraping configuration
    prometheus.io/scrape: "true"
    prometheus.io/port: "8080"
    prometheus.io/path: "/metrics"
spec:
  containers:
  - name: app
    image: myapp:1.0
    ports:
    - name: metrics
      containerPort: 8080

Observability best practices:

Use structured logging: JSON format for easier parsing
Include correlation IDs: Track requests across services
Expose metrics endpoint: Prometheus-compatible format
Set up dashboards: Grafana for key metrics
Configure alerts: Critical metrics trigger notifications
Implement distributed tracing: Jaeger, Zipkin, or similar
Monitor control plane: Track API server, etcd, scheduler

Key metrics to monitor:

Category	Metrics	Alerts
Node	CPU, memory, disk usage	> 80% utilization
Pod	Restart count, crash loops	> 5 restarts/hour
Application	Request rate, error rate, latency	Error rate > 5%
Resources	CPU throttling, OOMKills	Any OOMKills
Control Plane	API server latency, etcd health	Latency > 1s

11. Use Persistent Storage Properly

Storage management is critical in Kubernetes best practices for stateful applications.

PersistentVolumeClaim for stateful workloads:

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: database-pvc
  namespace: production
spec:
  accessModes:
  - ReadWriteOnce
  storageClassName: fast-ssd
  resources:
    requests:
      storage: 100Gi
---
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: database
spec:
  serviceName: database
  replicas: 3
  selector:
    matchLabels:
      app: database
  template:
    metadata:
      labels:
        app: database
    spec:
      containers:
      - name: postgres
        image: postgres:14
        volumeMounts:
        - name: data
          mountPath: /var/lib/postgresql/data
  volumeClaimTemplates:
  - metadata:
      name: data
    spec:
      accessModes: ["ReadWriteOnce"]
      storageClassName: fast-ssd
      resources:
        requests:
          storage: 100Gi

Storage best practices:

Use StatefulSets for stateful apps: Not Deployments
Choose appropriate storage class: SSD for databases, standard for logs
Set appropriate access modes: RWO, ROX, or RWX based on needs
Implement backup strategies: Regular volume snapshots
Monitor storage usage: Alert on high utilization
Plan for growth: Resize PVCs or use auto-expansion

12. Implement GitOps

GitOps is a modern Kubernetes best practice for declarative infrastructure management.

GitOps principles:

Git as single source of truth: All manifests in version control
Automated synchronization: Tools watch Git and apply changes
Pull-based deployment: Cluster pulls changes, not push from CI/CD
Declarative configuration: Describe desired state, not steps

GitOps workflow:

Developer → Git Repository → ArgoCD/Flux → Kubernetes Cluster
              ↓
          Code Review
              ↓
          Merge to main
              ↓
      Automatic Deployment

GitOps best practices:

Separate app and infra repos: Different change frequencies
Use environment branches: main for prod, staging branch, etc.
Implement automatic sync: With manual approval for production
Enable auto-healing: Automatically fix drift
Use Kustomize or Helm: Template management
Encrypt secrets in Git: Use Sealed Secrets or SOPS

13. Set Up Disaster Recovery

Disaster recovery planning is essential in Kubernetes best practices for production resilience.

Backup strategy:

What to backup:

etcd cluster data (critical)
Persistent volumes
Cluster configuration
Application manifests

Backup tools:

Velero for cluster backups
Volume snapshot controllers
etcd snapshot utilities

DR best practices:

Automate backups: Daily etcd snapshots minimum
Test restore procedures: Regular DR drills
Store backups off-cluster: Different region/cloud
Document recovery steps: Clear runbooks
Set RTO/RPO targets: Know acceptable downtime/data loss
Use multi-region clusters: For critical applications

14. Optimize Resource Costs

Cost optimization is an important aspect of Kubernetes best practices.

Cost optimization strategies:

Right-size resources:

Use VPA recommendations
Monitor actual usage vs requests
Adjust based on P95 usage patterns

Use appropriate node types:

Spot instances for non-critical workloads
Reserved instances for stable workloads
Burstable instances for variable loads

Implement cluster autoscaling:

# Cluster Autoscaler configuration
spec:
  minNodes: 3
  maxNodes: 50
  scaleDownUtilizationThreshold: 0.5
  scaleDownDelayAfterAdd: 10m

Cost optimization best practices:

Set namespace resource quotas: Prevent runaway costs
Use node affinity: Pack pods efficiently
Enable cluster autoscaler: Scale down unused nodes
Monitor resource waste: Identify over-provisioned pods
Use Karpenter: Advanced node provisioning for AWS
Implement showback/chargeback: Track costs per team/app

15. Maintain Cluster Hygiene

Ongoing maintenance is crucial in Kubernetes best practices for long-term stability.

Regular maintenance tasks:

Upgrade Kubernetes regularly:

Stay within 2-3 minor versions of latest
Test upgrades in non-production first
Follow upgrade documentation carefully
Backup before upgrades

Clean up unused resources:

# Find unused ConfigMaps
kubectl get configmaps --all-namespaces

# Find unused Secrets
kubectl get secrets --all-namespaces

# Find completed jobs
kubectl get jobs --field-selector status.successful=1

# Find evicted/failed pods
kubectl get pods --all-namespaces --field-selector status.phase=Failed

Maintenance best practices:

Schedule regular upgrades: Quarterly or semi-annual
Clean up completed jobs: Use TTL controllers
Remove unused images: Reduce node storage pressure
Rotate certificates: Automate certificate management
Patch security vulnerabilities: Timely node OS updates
Review and remove deprecated APIs: Before upgrades
Conduct capacity planning: Quarterly reviews

How This Connects to Container Platforms

Once you’ve mastered Kubernetes best practices, you’ll deploy your workloads efficiently. Understanding ECS vs EKS differences helps you decide when Kubernetes complexity is worth it.

For the containers themselves, follow Docker image best practices to ensure your images are optimized for Kubernetes deployment.

When designing your infrastructure, apply multi-account AWS environment principles to manage multiple Kubernetes clusters across accounts.

Example Interview Answer

Here’s how to confidently answer “What are Kubernetes best practices?” in an interview:

“Kubernetes best practices span several critical areas that I always implement in production.

Resource Management: I always set resource requests and limits on every container. Requests ensure proper scheduling, limits prevent resource monopolization. I use VPA to tune these over time based on actual usage.

Health Checks: I implement all three probe types—startup for slow apps, liveness to detect deadlocks, and readiness to manage traffic. The key is keeping probes lightweight and using different endpoints for different purposes.

Security: I follow least privilege with dedicated ServiceAccounts, implement RBAC, enforce Pod Security Standards, and use network policies to segment traffic. I never use the default ServiceAccount in production.

High Availability: I run at least 3 replicas for critical services, configure Pod Disruption Budgets, use anti-affinity rules, and spread across availability zones.

Observability: Applications log to stdout in structured format, expose Prometheus metrics, and we use distributed tracing. We have dashboards for golden signals—latency, traffic, errors, and saturation.

Configuration: I use ConfigMaps for configuration, Secrets for sensitive data, and prefer external secret management like Vault for highly sensitive credentials.

GitOps: All manifests are in Git, ArgoCD automatically syncs changes, and we never kubectl apply directly in production. This gives us audit trails, easy rollbacks, and disaster recovery.

Deployment Strategy: I use rolling updates with appropriate maxUnavailable and maxSurge settings, implement graceful shutdown with preStop hooks, and maintain comprehensive rollback procedures.

In my experience, teams that skip these best practices face production incidents around resource contention, security breaches, or deployment failures that proper practices would prevent.”

This answer demonstrates comprehensive understanding and practical production experience.

Common Mistakes to Avoid

🚫 No resource requests/limits: Causes unpredictable performance and cluster instability

🚫 Using default ServiceAccount: Security vulnerability, violates least privilege

🚫 No health checks: Prevents self-healing and causes downtime during deployments

🚫 Running as root: Major security risk, unnecessary for most applications

🚫 No network policies: Allows lateral movement in security breaches

🚫 Single replica in production: No high availability, downtime during updates

🚫 kubectl apply to production: No audit trail, difficult rollbacks, human error

🚫 Not monitoring: Can’t detect or diagnose issues quickly

🚫 Hardcoding configuration: Makes environment promotion difficult and error-prone

🚫 Ignoring upgrades: Fall behind on security patches and features

Each mistake represents a gap in production readiness and operational maturity.

Kubernetes Best Practices Checklist

Security

Dedicated ServiceAccounts for applications
RBAC configured with least privilege
Pod Security Standards enforced
Network policies implemented
Container images scanned for vulnerabilities
Secrets encrypted at rest
No root containers in production

Reliability

Resource requests and limits set
Health probes configured (liveness, readiness, startup)
Minimum 3 replicas for critical services
Pod Disruption Budgets defined
Anti-affinity rules for spreading
Graceful shutdown implemented

Configuration

All configuration in ConfigMaps/Secrets
No hardcoded values in images
Environment-specific configurations separated
External secret management for sensitive data
Immutable ConfigMaps for critical config

Observability

Structured logging to stdout/stderr
Metrics endpoint exposed
Distributed tracing implemented
Dashboards for key metrics
Alerts configured for critical conditions
Log aggregation configured

Operations

GitOps workflow implemented
All manifests in version control
Automated testing of manifests
Deployment strategies defined
Rollback procedures documented
Regular backup and DR testing
Cluster upgrade schedule maintained

Key Takeaways

Kubernetes best practices start with resource management: Set requests and limits always
Security is non-negotiable: RBAC, network policies, and PSS are mandatory
Health checks enable self-healing: Implement all three probe types properly
Use namespaces for isolation: Separate by environment, team, or application
GitOps is the modern standard: All changes through Git, automated sync
Observability requires three pillars: Logs, metrics, and traces
High availability needs multiple replicas: Minimum 3 for production services
Configuration belongs in ConfigMaps: Never hardcode in container images
Network policies are essential: Default deny, explicitly allow
Regular maintenance prevents issues: Upgrades, cleanup, and capacity planning

Additional Resources

For official Kubernetes guidance, review:

This comprehensive guide to Kubernetes best practices will help you confidently answer interview questions and operate production-grade Kubernetes clusters.