* so far, runner group parameter is only set for runners with org scope
* now set group for enterprise runners as well
* removed null check for org scope as either org or enterprise will be set
* if a new runner pod was just scheduled to start up right before a
registration expired, it will not get a new registration token and go in
an infinite update loop (until #341) kicks in
* if registzration tokens got updated a little bit before they actually
expired, just starting up pods will way more likely get a working token
* if a runner pod starts up with an invalid token, it will go in an
infinite retry loop, appearing as RUNNING from the outside
* normally, this error situation is detected because no corresponding
runner objects exists in GitHub and the pod will get removed after
registration timeout
* if the GitHub runner object already existed before - e.g. because a
finalizer was not properly run as part of a partial Kubernetes crash,
the runner will always stay in a running mode, even updating the
registration token will not kill the problematic pod
* introducing RunnerOffline exception that can be handled in runner
controller and replicaset controller
* as runners are offline when a pod is completed and marked for restart,
only do additional restart checks if no restart was already decided,
making code a bit cleaner and saving GitHub API calls after each job
completion
* so far, only push events would trigger the DockerHub login step
* hence, attempts to release would fail because of a permission problem (tested locally)
* adding OR condition to also login in case a release got published
* the reconciliation loop is often much faster than the runner startup,
so changing runner not found related messages to debug and also add the
possibility that the runner just needs more time
* as pointed out in #281 the currently used image for the
kube-rbac-proxy - gcr.io/kubebuilder/kube-rbac-proxy:v0.4.1" - does not
have an ARM64 image
* hence, trying to use the standard deployment manifest / helm char will
fail on ARM64 systems
* replaced image with quay.io/brancz/kube-rbac-proxy:v0.8.0 which is the
latest version from the upstream maintainer
(https://github.com/brancz/kube-rbac-proxy/blob/master/Makefile#L13)
* successfully tested on both AMD64 and ARM64 clusters
* fixes#281
* errors.Is compares all members of a struct to return true which never
happened
* switched to type check instead of exact value check
* notRegistered was using double negation in if statement which lead to
unregistering runners after the registration timeout
* ... otherwise it will take 40 seconds (until a node is detected as unreachable) + 5 minutes (until pods are evicted from unreachable/crashed nodes)
* pods stuck in "Terminating" status on unreachable nodes will only be freed once #307 gets merged
* if a k8s node becomes unresponsive, the kube controller will soft
delete all pods after the eviction time (default 5 mins)
* as long as the node stays unresponsive, the pod will never leave the
last status and hence the runner controller will assume that everything
is fine with the pod and will not try to create new pods
* this can result in a situation where a horizontal autoscaler thinks
that none of its runners are currently busy and will not schedule any
further runners / pods, resulting in a broken runner deployment until the
runnerreplicaset is deleted or the node comes back online
* introducing a pod deletion timeout (1 minute) after which the runner
controller will try to reboot the runner and create a pod on a working
node
* use forceful deletion and requeue for pods that have been stuck for
more than one minute in terminating state
* gracefully handling race conditions if pod gets finally forcefully deleted within
* when setting a GitHub Enterprise server URL without a namespace, an error occurs: "error: the server doesn't have a resource type "controller-manager"
* setting default namespace "actions-runner-system" makes the example work out of the box
* ensure that minReplicas <= desiredReplicas <= maxReplicas no matter what
* before this change, if the number of runners was much larger than the max number, the applied scale down factor might still result in a desired value > maxReplicas
* if for resource constraints in the cluster, runners would be permanently restarted, the number of runners could go up more than the reverse scale down factor until the next reconciliation round, resulting in a situation where the number of runners climbs up even though it should actually go down
* by checking whether the desiredReplicas is always <= maxReplicas, infinite scaling up loops can be prevented
