Comprehensive list of K8s ecosystem errors

DevOps Diaries

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IN TODAY'S EDITION

We will discuss

All comprehensive list of potential errors and issues categorized by areas of the Kubernetes ecosystem.

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USE CASE

List of possible errors of the Kubernetes ecosystem

Resolving issues in Kubernetes can be systematic and efficient if you follow a structured approach. So, in this article, we will try to discuss different objects and associated possible errors and we will also develop a progressive and right directional approach to figure out core reason of causes.

1. Pod and Container Issues

• CrashLoopBackOff: Pod is crashing repeatedly due to incorrect configurations or application failures.

• ImagePullBackOff: Kubernetes cannot pull the specified image from the container registry.

• ErrImagePull: Error pulling the image (due to incorrect image name, tag, or authentication issues).

• OOMKilled: Pod is terminated because it exceeds its memory limits.

• ContainerCreating: Pod is stuck in ContainerCreating state due to volume, network, or image pull issues.

• Completed Pods: Pods are stuck in Completed state due to job completion but not being cleaned up.

• Terminating Pods: Pods are stuck in Terminating state, often caused by finalizer misconfigurations or unresponsive cleanup.

• PodEviction: Pods are evicted from nodes due to resource pressure or taints and tolerations mismatch.

2. Deployment and ReplicaSet Issues

• ReplicaSet Misalignment: Desired replicas don’t match actual replicas due to scheduling failures or insufficient resources.

• FailedScheduling: Pods fail to schedule due to resource constraints, taints, or node affinity/anti-affinity rules.

• Rolling Update Issues: New deployments cause downtime due to misconfigured readiness/liveness probes or missing maxUnavailable/maxSurge settings.

• HPA (Horizontal Pod Autoscaler) Issues:

  • HPA not scaling due to missing metrics (e.g., CPU/Memory not exposed).

  • Incorrect resource requests/limits affecting scaling decisions.

3. Networking Issues

• DNS Resolution Failures: Pods cannot resolve service names due to CoreDNS misconfigurations or failures.

• Connection Refused: Services are not accessible due to misconfigured or absent network policies.

• Service Not Reachable: Issues with ClusterIP, NodePort, or LoadBalancer services caused by configuration errors or cloud provider limitations.

• NetworkPolicy Blocking Traffic: Improperly defined NetworkPolicies block desired ingress/egress traffic.

• Kube-proxy Issues: Problems with service networking due to kube-proxy misconfigurations.

4. Storage Issues

• VolumeMount Failure: Persistent Volumes (PVs) fail to attach or mount to Pods.

• PVC Pending: Persistent Volume Claims (PVCs) remain in a Pending state due to insufficient or unavailable storage.

• Data Loss: Misconfigured StatefulSets or volume binding lead to data loss when Pods are rescheduled.

5. Control Plane Issues

• API Server Unreachable: Issues due to high load, misconfiguration, or certificate problems.

• etcd Performance Degradation: etcd cluster has high latency or data corruption, affecting the overall cluster performance.

• Controller Manager Issues: Failures in applying desired state due to misconfigurations or bugs.

• Scheduler Failures: Scheduler fails to allocate Pods to nodes due to configuration or resource exhaustion.

6. Authentication and Authorization Issues

• RBAC Denied Errors: Users or Pods cannot perform actions due to missing or misconfigured Role-Based Access Control (RBAC) permissions.

• Service Account Issues: Missing or misconfigured Service Accounts cause authentication failures for workloads.

• Webhook Authentication Errors: External authenticators fail due to connectivity or misconfigurations.

7. Node Issues

• Node Not Ready: Nodes are marked NotReady due to kubelet, disk pressure, or network issues.

• DiskPressure or MemoryPressure: Nodes face resource exhaustion, evicting Pods or causing degraded performance.

• Uncordoned Nodes: Nodes remain cordoned after maintenance or upgrades, causing scheduling failures.

• Kubelet Failures: Kubelet crashes or becomes unresponsive, affecting Pods on the node.

8. Ingress and Load Balancer Issues

• Ingress Not Routing: Misconfigured Ingress resources or absent Ingress controllers.

• SSL/TLS Errors: Missing or improperly configured certificates for HTTPS traffic.

• LoadBalancer Not Created: Cloud Load Balancer fails to provision due to provider-specific limitations or misconfigurations.

• Backend Service Unhealthy: Services backing the Ingress are not passing health checks.

9. Configuration Issues

• Environment Variables Missing: Pods crash due to missing or incorrectly set environment variables.

• Secret and ConfigMap Issues: Secrets or ConfigMaps are not mounted correctly, causing application errors.

• Improper Resource Requests and Limits: Over or under-provisioned resource requests causing scheduling or runtime failures.

10. Logging and Monitoring Issues

• Log Aggregation Issues: Centralized logging tools (e.g., Fluentd, Logstash) fail to collect or forward logs.

• Metrics Missing: Metrics server or Prometheus is misconfigured, leading to missing metrics for scaling or monitoring.

• Application Debugging Issues: Lack of application-specific logs due to inadequate logging configurations.

11. Cluster Upgrades and Maintenance Issues

• Version Skew: Components (e.g., kube-apiserver, kubelet) running incompatible versions after upgrades.

• Downtime During Upgrade: Misconfigured workloads cause downtime during control plane or node upgrades.

• Custom CRD Breakages: Breaking changes in custom resource definitions (CRDs) due to API deprecations.

12. Scaling and Performance Issues

• Node Resource Exhaustion: Nodes run out of CPU, memory, or disk, causing degraded performance or Pod evictions.

• Cluster Autoscaler Failures: Cluster Autoscaler fails to provision nodes due to cloud provider limitations or incorrect configurations.

• Throttling in API Server: High API request volume causes API server throttling.

13. Debugging and Observability Challenges

• Lack of Metrics or Logs: Missing metrics/logs make debugging difficult.

• Event Overwrites: Kubernetes events have a short retention window, leading to lost debugging context.

• Misinterpreting Liveness/Readiness Probes: Incorrectly configured probes cause unnecessary Pod restarts.

Proactive Prevention Tips:

1. Use Resource Requests and Limits: Avoid resource exhaustion.

2. Regularly Audit Configurations: Check RBAC, network policies, and resource quotas.

3. Monitor Cluster Health: Set up robust logging and monitoring.

4. Plan Upgrades Carefully: Test upgrades in staging before production.

5. Test Workloads Thoroughly: Verify configurations and readiness/liveness probes.

Each issue can be addressed with specific debugging techniques or preventive measures. Let me know if you'd like deeper insights into any specific error or its resolution!