Nikola JokicandCopilot App
7833438dd7
The scaleset listener no longer dissects the message it polls. It owns session management, polling and acking, and hands the whole message to a single Scale call, so acquiring jobs and recording metrics move to the only component that still reads them. That handover is what makes the work parallelisable. The listener used to replay a message one API call at a time, in a fixed order: every job started patch, then every job completed, then the scale patch. The job events touch distinct EphemeralRunners and carry no ordering between them, so they now run across a bounded worker pool, with the worker that patches the EphemeralRunnerSet running alongside them. The one ordering that does matter is kept. deleteIdleEphemeralRunners skips a runner only once it carries a job request ID, so a patch that lowers the replica count could offer up a runner that just picked up a job if it were published while job started patches were still in flight. The scaling worker therefore waits for the event workers on a scale down, and only then. A patch that scales up or holds cannot delete anything. Kubernetes has no bulk write: get, create, update, patch and delete are single-resource verbs, and deletecollection is the only collection-scoped mutating verb there is, so N events cannot be collapsed into fewer requests. Server side apply is a PATCH on one object URI and does not change that. Issuing the N patches concurrently over the one HTTP/2 connection is the available win; the alternative is writing fewer objects, which trades N cheap independent writes for one contended, watch-amplifying, size-bounded write. The pool size is configurable through listenerConfig.scaler.workers, alongside the existing qps and burst, and defaults to 10: two calls per event keeps a full pool well inside the default QPS budget. Statistics are now cached by the scaler. The listener stopped tracking them, and a long poll that times out carries no message at all, so without the cache an idle scale set would stop converging. 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.