KubernetesJobNotStarting

KubernetesJobNotStarting #

Job {{ $labels.namespace }}/{{ $labels.job_name }} did not start for 10 minutes

Alert Rule
alert: KubernetesJobNotStarting
annotations:
  description: |-
    Job {{ $labels.namespace }}/{{ $labels.job_name }} did not start for 10 minutes
      VALUE = {{ $value }}
      LABELS = {{ $labels }}    
  runbook: https://srerun.github.io/prometheus-alerts/runbooks/kubestate-exporter/kubernetesjobnotstarting/
  summary: Kubernetes Job not starting ({{ $labels.namespace }}/{{ $labels.job_name
    }})
expr: kube_job_status_active == 0 and kube_job_status_failed == 0 and kube_job_status_succeeded
  == 0 and (time() - kube_job_status_start_time) > 600
for: 0m
labels:
  severity: warning

Meaning #

The KubernetesJobNotStarting alert is triggered when a Kubernetes job has not started for 10 minutes. This alert is critical as it indicates that a job that is supposed to be running is not executing, which can lead to delays, data loss, or other issues depending on the job’s purpose.

Impact #

The impact of this alert can be significant, depending on the job’s responsibility. Some possible consequences include:

  • Delays in data processing or reporting
  • Incomplete or missing data
  • Inability to perform critical tasks or functions
  • Unavailability of dependent services or applications
  • Increased latency or errors in dependent systems

Diagnosis #

To diagnose the issue, follow these steps:

  1. Check the Kubernetes job’s status using kubectl describe job <job_name> -n <namespace>
  2. Verify that the job’s configuration is correct, including the container image, command, and arguments
  3. Check the job’s pod logs for errors or warnings using kubectl logs <job_pod_name> -n <namespace>
  4. Verify that the job’s dependencies, such as services or other pods, are available and running correctly
  5. Check the cluster’s resource utilization, such as CPU, memory, and disk usage, to ensure that the job is not being starved of resources

Mitigation #

To mitigate the issue, follow these steps:

  1. Check the job’s configuration and update it if necessary to ensure that it is correct and valid
  2. Verify that the job’s dependencies are available and running correctly
  3. Increase the job’s resource allocation, such as CPU or memory, if necessary
  4. Check the cluster’s resource utilization and adjust resource allocation or node count as needed
  5. Implement retry mechanisms or timeouts to ensure that the job is retried if it fails to start
  6. Consider implementing a fallback or backup job to ensure that critical tasks are executed even if the primary job fails to start
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