In robotics and automation, a control loop is a non-terminating loop that regulates the state of a system.
Here is one example of a control loop: a thermostat in a room.
When you set the temperature, that's telling the thermostat about your desired state. The actual room temperature is the current state. The thermostat acts to bring the current state closer to the desired state, by turning equipment on or off.In Kubernetes, controllers are control loops that watch the state of your cluster, then make or request changes where needed. Each controller tries to move the current cluster state closer to the desired state.
A controller tracks at least one Kubernetes resource type. These objects have a spec field that represents the desired state. The controller(s) for that resource are responsible for making the current state come closer to that desired state.
The controller might carry the action out itself; more commonly, in Kubernetes, a controller will send messages to the API server that have useful side effects. You'll see examples of this below.
Control via API server
The Job controller is an example of a Kubernetes built-in controller. Built-in controllers manage state by interacting with the cluster API server.
Job is a Kubernetes resource that runs a Pod, or perhaps several Pods, to carry out a task and then stop.
(Once scheduled, Pod objects become part of the desired state for a kubelet).
When the Job controller sees a new task it makes sure that, somewhere in your cluster, the kubelets on a set of Nodes are running the right number of Pods to get the work done. The Job controller does not run any Pods or containers itself. Instead, the Job controller tells the API server to create or remove Pods. Other components in the control plane act on the new information (there are new Pods to schedule and run), and eventually the work is done.
After you create a new Job, the desired state is for that Job to be completed. The Job controller makes the current state for that Job be nearer to your desired state: creating Pods that do the work you wanted for that Job, so that the Job is closer to completion.
Controllers also update the objects that configure them.
For example: once the work is done for a Job, the Job controller
updates that Job object to mark it
(This is a bit like how some thermostats turn a light off to indicate that your room is now at the temperature you set).
By contrast with Job, some controllers need to make changes to things outside of your cluster.
For example, if you use a control loop to make sure there are enough Nodes in your cluster, then that controller needs something outside the current cluster to set up new Nodes when needed.
Controllers that interact with external state find their desired state from the API server, then communicate directly with an external system to bring the current state closer in line.
(There actually is a controller that horizontally scales the nodes in your cluster. See Cluster autoscaling).
The important point here is that the controller makes some change to bring about your desired state, and then reports current state back to your cluster's API server. Other control loops can observe that reported data and take their own actions.
In the thermostat example, if the room is very cold then a different controller might also turn on a frost protection heater. With Kubernetes clusters, the control plane indirectly works with IP address management tools, storage services, cloud provider APIS, and other services by extending Kubernetes to implement that.
Desired versus current state
Kubernetes takes a cloud-native view of systems, and is able to handle constant change.
Your cluster could be changing at any point as work happens and control loops automatically fix failures. This means that, potentially, your cluster never reaches a stable state.
As long as the controllers for your cluster are running and able to make useful changes, it doesn't matter if the overall state is or is not stable.
As a tenet of its design, Kubernetes uses lots of controllers that each manage a particular aspect of cluster state. Most commonly, a particular control loop (controller) uses one kind of resource as its desired state, and has a different kind of resource that it manages to make that desired state happen. For example, a controller for Jobs tracks Job objects (to discover new work) and Pod objects (to run the Jobs, and then to see when the work is finished). In this case something else creates the Jobs, whereas the Job controller creates Pods.
It's useful to have simple controllers rather than one, monolithic set of control loops that are interlinked. Controllers can fail, so Kubernetes is designed to allow for that.
There can be several controllers that create or update the same kind of object. Behind the scenes, Kubernetes controllers make sure that they only pay attention to the resources linked to their controlling resource.
For example, you can have Deployments and Jobs; these both create Pods. The Job controller does not delete the Pods that your Deployment created, because there is information (labels) the controllers can use to tell those Pods apart.
Ways of running controllers
Kubernetes comes with a set of built-in controllers that run inside the kube-controller-manager. These built-in controllers provide important core behaviors.
The Deployment controller and Job controller are examples of controllers that come as part of Kubernetes itself ("built-in" controllers). Kubernetes lets you run a resilient control plane, so that if any of the built-in controllers were to fail, another part of the control plane will take over the work.
You can find controllers that run outside the control plane, to extend Kubernetes. Or, if you want, you can write a new controller yourself. You can run your own controller as a set of Pods, or externally to Kubernetes. What fits best will depend on what that particular controller does.
- Read about the Kubernetes control plane
- Discover some of the basic Kubernetes objects
- Learn more about the Kubernetes API
- If you want to write your own controller, see Extension Patterns in Extending Kubernetes.