queue        controllers.Queue
	cfg          config.RepairConfig
	events       kclient.EventRecorder
	repairedPods map[types.NamespacedName]types.UID
}

func NewRepairController(client kube.Client, cfg config.RepairConfig) (*Controller, error) {
	c := &Controller{
		cfg:          cfg,
		client:       client,
		events:       kclient.NewEventRecorder(client, "cni-repair"),

1. By installing a separate, very basic "CNI plugin" binary onto the node to forward low-level pod lifecycle events (CmdAdd/CmdDel/etc) from whatever node-level CNI subsystem is in use to this node agent for processing via socket.
1. By running as a node-level daemonset that:

- listens for these UDS events from the CNI plugin (which fire when new pods are spawned in an ambient-enabled namespace), and adds those pods to the ambient mesh.

	assert.NoError(t, err)

	// wait for an update events
	// total 3 update at before unlabel point: 1. init ns reconcile 2. ns label reconcile 3. pod annotation update
	mt.Assert(EventTotals.Name(), map[string]string{"type": "update"}, monitortest.AtLeast(4))

	waitForMockCalls()

	assertPodNotAnnotated(t, client, pod)

	// patch a test label to emulate a POD update event

		return nil
	}

	// If a file we are watching has a change event, yield and let caller check validity
	select {
	case <-watcher.Events:
		// Something changed, and we must yield
		return nil
	case err := <-watcher.Errors:
		// We had a watch error - that's no good
		return err
	case <-ctx.Done():
		return ctx.Err()
	default:

        {
          "datasource": {
            "type": "prometheus",
            "uid": "${datasource}"
          },
          "expr": "max(pilot_k8s_cfg_events{type=\"VirtualService\", event=\"add\"}) - (max(pilot_k8s_cfg_events{type=\"VirtualService\", event=\"delete\"}) or max(up * 0))",
          "format": "time_series",
          "intervalFactor": 1,
          "refId": "A"
        }
      ],

      appropriateness of using or redistributing the Work and assume any
      risks associated with Your exercise of permissions under this License.

   8. Limitation of Liability. In no event and under no legal theory,
      whether in tort (including negligence), contract, or otherwise,
      unless required by applicable law (such as deliberate and grossly

As a result, they do not go through `PushContext`, and changes do not trigger a `PushContext` recomputation. Instead, the current state is incrementally computed based on events from `ServiceDiscovery`.

#### Conclusion

Overall, the high level config ingestion flow:

```mermaid
graph TD
    sd(Service Discovery)
    cs(ConfigStore)
    ep(Endpoints)
    pc(PushContext)

      appropriateness of using or redistributing the Work and assume any
      risks associated with Your exercise of permissions under this License.

   8. Limitation of Liability. In no event and under no legal theory,
      whether in tort (including negligence), contract, or otherwise,
      unless required by applicable law (such as deliberate and grossly

      appropriateness of using or redistributing the Work and assume any
      risks associated with Your exercise of permissions under this License.

   8. Limitation of Liability. In no event and under no legal theory,
      whether in tort (including negligence), contract, or otherwise,
      unless required by applicable law (such as deliberate and grossly

      appropriateness of using or redistributing the Work and assume any
      risks associated with Your exercise of permissions under this License.

   8. Limitation of Liability. In no event and under no legal theory,
      whether in tort (including negligence), contract, or otherwise,
      unless required by applicable law (such as deliberate and grossly