Nikola JokicandCopilot App
dd1e877f9e
The scale-up suppression read Status.FinishedRunnerCleanupPatchID off the EphemeralRunnerSet that Reconcile fetched through the manager's cached client. That marker is written by the cleanup reconcile, and it is the runner deletions performed by that same reconcile which trigger the next one, so the follow-up reconcile is regularly served from an informer cache that has not yet observed the controller's own status write. The marker read as 0, the guard did not fire, and the controller created a replacement runner for a job that had already finished. That is the exact spurious scale up the guard exists to prevent, so it is a correctness problem in a cluster and not only a flaky test. Make the decision from authoritative state. A cached hit still short circuits, because nothing ever clears the marker, so a hit cannot be a false positive. Only a miss falls through to an uncached Get via a new APIReader, which SetupWithManager fills in from mgr.GetAPIReader() so no construction site can forget it. The extra read is confined to scale-up decisions, where the controller is about to issue creates anyway. The window was transient: updateStatus copies the marker from the in-memory object and patches with MergeFrom, so a stale reconcile produces no diff for that field and cannot clobber the recorded value. The envtest spec that caught this only fails under CI load, so the regression is pinned down directly instead. TestScaleUpServicedByFinished RunnerCleanup drives the decision with a lagging cached client and an API reader that already has the write, and asserts the suppression still holds. Pointing the read back at the cached client fails that case and only that case. Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>
Actions Runner Controller (ARC)
About
Actions Runner Controller (ARC) is a Kubernetes operator that orchestrates and scales self-hosted runners for GitHub Actions.
With ARC, you can create runner scale sets that automatically scale based on the number of workflows running in your repository, organization, or enterprise. Because controlled runners can be ephemeral and based on containers, new runner instances can scale up or down rapidly and cleanly. For more information about autoscaling, see "Autoscaling with self-hosted runners."
You can set up ARC on Kubernetes using Helm, then create and run a workflow that uses runner scale sets. For more information about runner scale sets, see "Deploying runner scale sets with Actions Runner Controller."
People
Actions Runner Controller (ARC) is an open-source project currently developed and maintained in collaboration with the GitHub Actions team, external maintainers @mumoshu and @toast-gear, various contributors, and the awesome community.
If you think the project is awesome and is adding value to your business, please consider directly sponsoring community maintainers and individual contributors via GitHub Sponsors.
If you are already the employer of one of the contributors, sponsoring via GitHub Sponsors might not be an option. Just support them by other means!
See the sponsorship dashboard for the former and the current sponsors.
Getting Started
To give ARC a try with just a handful of commands, please refer to the Quickstart guide.
For an overview of ARC, please refer to About ARC.
With the introduction of autoscaling runner scale sets, the existing autoscaling modes are now legacy. The legacy modes have certain use cases and will continue to be maintained by the community only.
For further information on what is supported by GitHub and what's managed by the community, please refer to this announcement discussion.
Documentation
ARC documentation is available on docs.github.com.
Legacy documentation
The following documentation is for the legacy autoscaling modes that continue to be maintained by the community:
- Quickstart guide
- About ARC
- Installing ARC
- Authenticating to the GitHub API
- Deploying ARC runners
- Adding ARC runners to a repository, organization, or enterprise
- Automatically scaling runners
- Using custom volumes
- Using ARC runners in a workflow
- Managing access with runner groups
- Configuring Windows runners
- Using ARC across organizations
- Using entrypoint features
- Deploying alternative runners
- Monitoring and troubleshooting
Contributing
We welcome contributions from the community. For more details on contributing to the project (including requirements), please refer to "Getting Started with Contributing."
Troubleshooting
We are very happy to help you with any issues you have. Please refer to the "Troubleshooting" section for common issues.