KubernetesApiClientErrors

KubernetesApiClientErrors #

Kubernetes API client is experiencing high error rate

Alert Rule
alert: KubernetesApiClientErrors
annotations:
  description: |-
    Kubernetes API client is experiencing high error rate
      VALUE = {{ $value }}
      LABELS = {{ $labels }}    
  runbook: https://srerun.github.io/prometheus-alerts/runbooks/kubestate-exporter/kubernetesapiclienterrors/
  summary: Kubernetes API client errors (instance {{ $labels.instance }})
expr: (sum(rate(rest_client_requests_total{code=~"(4|5).."}[1m])) by (instance, job)
  / sum(rate(rest_client_requests_total[1m])) by (instance, job)) * 100 > 1
for: 2m
labels:
  severity: critical

Meaning #

The KubernetesApiClientErrors alert is triggered when the rate of API client errors exceeds 1% of the total API requests made to the Kubernetes API server within a 1-minute window. This alert indicates that the Kubernetes API client is experiencing a high error rate, which can lead to issues with cluster management and resource utilization.

Impact #

  • Delayed or failed deployments of applications and services
  • Increased latency and errors in cluster management operations
  • Reduced reliability and availability of cluster resources
  • Potential security risks due to unmanaged resources and untimely alerts

Diagnosis #

To diagnose the root cause of the issue, follow these steps:

  1. Investigate the API client logs to identify the error codes and messages.
  2. Check the Kubernetes API server logs for any issues or errors.
  3. Verify that the API client is correctly configured and authenticated.
  4. Check for any network connectivity issues between the API client and the API server.
  5. Review the cluster resource utilization and verify that there are no issues with resource contention.

Mitigation #

To mitigate the issue, follow these steps:

  1. Restart the API client to reset the connection and retry failed requests.
  2. Verify that the API client is correctly configured and authenticated.
  3. Check for any software updates or patches for the API client and apply them if necessary.
  4. Implement retries and exponential backoff mechanisms in the API client to handle temporary errors.
  5. Consider increasing the timeouts and limits for API requests to reduce the error rate.
  6. If the issue persists, consider rolling back to a previous version of the API client or seeking assistance from the Kubernetes support team.
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