Avoid These 10 Common Kubernetes Mistakes to Boost Reliability

Introduction

Kubernetes is a powerful platform for managing automatically scalable, highly available cloud‑native applications, but many users repeatedly make avoidable mistakes. This guide examines the most common errors and offers practical tips to prevent them.

Not Setting Resource Requests

Omitting or under‑specifying CPU requests causes nodes to become overloaded, leading to CPU throttling, increased latency, and timeouts. Examples of problematic configurations include:

BestEffort resources: {} CPU set too low

resources:
  requests:
    cpu: "1m"

Burstable (risk of OOMKill)

resources:
  requests:
    memory: "128Mi"
    cpu: "500m"
  limits:
    memory: "256Mi"
    cpu: 2

Guaranteed

resources:
  requests:
    memory: "128Mi"
    cpu: 2
  limits:
    memory: "128Mi"
    cpu: 2

Use metrics-server to observe current pod CPU and memory usage:

kubectl top pods
kubectl top pods --containers
kubectl top nodes

For historical trends and alerts, integrate Prometheus, DataDog, or similar systems, and consider the VerticalPodAutoscaler to automatically adjust requests and limits.

Omitting Health Checks

Health probes are essential for service reliability. Kubernetes provides three probe types:

Liveness Check – verifies that a container is still running.

Readiness Check – indicates when a container is ready to receive traffic.

Startup Probe – determines when a container has successfully started.

Using the :latest Tag

Deploying images tagged :latest in production makes version tracking and rollbacks difficult. Pin specific image versions instead.

Over‑Privileged Containers

Granting containers excessive privileges (e.g., running a Docker daemon inside a container) introduces security risks. Avoid assigning CAP_SYS_ADMIN and limit host filesystem access.

Lack of Monitoring and Logging

Insufficient observability hampers troubleshooting. Deploy tools such as Prometheus, Grafana, Fluentd, and Jaeger to collect metrics, logs, and traces, enabling deeper insight into cluster health.

Using the Default Namespace for All Objects

Relying solely on the default namespace reduces isolation and makes resource management harder. Create dedicated namespaces per project, team, or application to improve organization and access control.

Missing Security Configurations

Secure your cluster by configuring authentication, RBAC, network policies, and storage protections. Use role‑based access control to limit permissions based on user roles (e.g., admin vs. operator).

Missing PodDisruptionBudget

Define a PodDisruptionBudget to guarantee a minimum number of pods remain available during node upgrades or failures.

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

Pod Self‑Protection (Anti‑Affinity)

Specify podAntiAffinity rules to spread replicas across nodes, preventing simultaneous loss of all pods.

affinity:
  podAntiAffinity:
    requiredDuringSchedulingIgnoredDuringExecution:
    - labelSelector:
        matchExpressions:
        - key: "app"
          operator: In
          values:
          - db
      topologyKey: "kubernetes.io/hostname"

Per‑Service Load Balancing

Exposing many services as type: LoadBalancer can be costly. Prefer a shared external load balancer via type: NodePort combined with an ingress controller (e.g., nginx‑ingress, Traefik, Istio). Internal services should communicate via ClusterIP and built‑in DNS.

Do not use public DNS/IPs for internal services to avoid latency and cost issues.

Unaware Cluster Autoscaling

External autoscalers that ignore pod scheduling constraints (resource requests, affinity, taints) may fail to add nodes when needed, leaving pods pending. Ensure autoscaling logic respects these constraints and the topology of persistent volumes.

Conclusion

While Kubernetes simplifies container orchestration, avoiding the outlined mistakes—proper resource specification, health checks, security hardening, observability, namespace hygiene, and informed autoscaling—significantly improves stability, performance, and security of cloud‑native deployments.