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Let the listener own the EphemeralRunner Running phase transition (#4646)

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Nikola Jokic
2026-09-15 13:46:40 +02:00
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docs/gha-runner-scale-set-controller/samples/grafana-dashboard/README.md
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@@ -66,7 +66,7 @@ The dashboard includes the following metrics:
| Running Jobs | The number of runners that are currently processing jobs. |
| Failed Runners | The total number of ephemeral runners that have failed to properly start. This may require reviewing the custom resource and logs to identify and resolve the root causes. Common causes include resource issues and failure to pull the required image. |
| Listeners | The number of listeners currently running and attempting to manage jobs for the scale set. This should match the number of scale sets deployed. |
| Pending Runners | The total number of ephemeral runners that ARC has requested and is waiting for Kubernetes to provide in a running state. If the Kubernetes API server is responsive, this will typically match the number of runner pods that are in a pending state. This number includes requests for runner pods that have not yet been scheduled. When this number is higher than the number of runner pods in a pending state, it can indicate performance issues. |
| Pending Runners | The total number of ephemeral runners that have not been assigned a job. This covers runners that Kubernetes has not finished starting as well as runners that are registered and idle waiting for work, so with a non-zero `minRunners` it does not fall to zero. On its own it is therefore not a signal of scheduling trouble; compare it against the number of runner pods actually in a pending state, and treat a persistent excess of pending runners over pending pods as the indicator of performance issues. |
| Registered Runners | The total number of ephemeral runners that have been successfully registered. |
| Active Runners | The total number of runners that are active and either available or processing jobs. |
| Out of Memory | The number of containers that have been terminated by the OOMKiller. This can indicate that the requests/ limits for one or more pods on the node were configured improperly, allowing pods to request more memory than the node had available. |