---
title: 控制器
content_template: templates/concept
weight: 30
---

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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.
-->

在机器人技术和自动化中，控制环是一个控制系统状态的不终止的循环。

这是一个控制环的例子：房间里的温度自动调节器。

当你设置了温度，告诉了温度自动调节器你的*期望状态*。房间的实际温度是*当前状态*。通过对设备的开关控制，温度自动调节器让其当前状态接近期望状态。

{{< glossary_definition term_id="controller" length="short">}}

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<!--
## Controller pattern

A controller tracks at least one Kubernetes resource type.
These [objects](/docs/concepts/overview/working-with-objects/kubernetes-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
{{< glossary_tooltip text="API server" term_id="kube-apiserver" >}} that have
useful side effects. You'll see examples of this below.

{{< comment >}}
Some built-in controllers, such as the namespace controller, act on objects
that do not have a spec. For simplicity, this page omits explaining that
detail.
{{< /comment >}}
-->
## 控制器模式 {#controller-pattern}

一个控制器至少追踪一种类型的 Kubernetes 资源。这些[对象](/docs/concepts/overview/working-with-objects/kubernetes-objects/)有一个代表期望状态的指定字段。正对这种资源的控制器就是要使他的当前状态接近与期望状态。

控制器可能会自行执行操作；在 Kubernetes 中更常见的是一个控制器会发送信息给 {{< glossary_tooltip text="API 服务器" term_id="kube-apiserver" >}}，这会有副作用。看下面这个例子。

{{< comment >}}
一些内置的控制器，比如命名空间控制器，针对没有指定命名空间的对象。为了简单起见，这篇文章没有详细介绍这些细节。
{{< /comment >}}

<!--
### Control via API server

The {{< glossary_tooltip term_id="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
{{< glossary_tooltip term_id="pod" >}}, or perhaps several Pods, to carry out
a task and then stop.

(Once [scheduled](/docs/concepts/scheduling/), 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
{{< glossary_tooltip text="control plane" term_id="control-plane" >}}
act on the new information (there are new Pods to schedule and run),
and eventually the work is done.
-->

### 通过 API 服务器来控制 {#control-via-API-server}

{{< glossary_tooltip term_id="job" >}} 控制器是一个 Kubernetes 内置控制器的例子。内置控制器通过和集群 API 服务器交互来管理状态。

Job 是一种 Kubernetes 资源，它运行一个 {{< glossary_tooltip term_id="pod" >}}，或者可能是多个 Pod，来执行一个任务然后停止。

（一旦[被调度了](/docs/concepts/scheduling/)），对 kubelet 来说 Pod 对象就会变成了期望状态的一部分。

在集群中，当 Job 控制器拿到新任务时，它会保证一组 Node 节点上的 kubelet 可以运行正确数量的 Pod 来完成工作。
Job 控制器不会自己运行任何的 Pod 或者容器。Job 控制器是通知 API 服务器来创建或者移除 Pod。
{{< glossary_tooltip text="控制平面" term_id="control-plane" >}}中的其它组件根据新的消息而反应（调度新的 Pod 并且运行它）并且最终完成工作。

<!--
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 `Finished`.

(This is a bit like how some thermostats turn a light off to
indicate that your room is now at the temperature you set).
-->

创建新 Job 后，所期望的状态就是完成这个 Job。Job 控制器会让 Job 的当前状态不断接近期望状态：创建为 Job 要完成工作所需要的 Pod，使 Job 的状态接近完成。

控制器也会更新配置对象。例如：一旦 Job 的工作完成了，Job 控制器会更新 Job 对象的状态为 `Finished`。

（这有点像温度自动调节器关闭了一个灯，以此来告诉你房间的温度现在到你设定的值了）。

<!--
### Direct control

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 {{< glossary_tooltip text="Nodes" term_id="node" >}}
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](/docs/tasks/administer-cluster/cluster-management/#cluster-autoscaling)).
-->

### 直接控制 {#direct-control}

相比 Job 控制器，有些控制器需要对集群外的一些东西进行修改。

例如，如果你使用一个控制环来保证集群中有足够的{{< glossary_tooltip text="节点" term_id="node" >}}，那么控制就需要当前集群外的一些服务在需要时创建新节点。

和外部状态交互的控制器从 API 服务器获取到它想要的状态，然后直接和外部系统进行通信并使当前状态更接近期望状态。

（实际上有一个控制器可以水平地扩展集群中的节点。请看[集群自动扩缩容](/docs/tasks/administer-cluster/cluster-management/#cluster-autoscaling)）。

<!--
## Desired versus current state {#desired-vs-current}

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.
-->

## 期望状态与当前状态 {#desired-vs-current}

Kubernetes 采用了系统的云原生视图，并且可以处理持续的变化。

在任务执行时，集群随时都可能被修改，并且控制环会自动的修复故障。这意味着很可能集群永远不会达到稳定状态。

只要集群中控制器的在运行并且进行有效的修改，整体状态的稳定与否是无关紧要的。
<!--
## Design

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.

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.

For example: a controller for Jobs tracks Job objects (to discover
new work) and Pod object (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.

{{< note >}}
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 ({{< glossary_tooltip term_id="label" text="labels" >}})
the controllers can use to tell those Pods apart.
{{< /note >}}
-->
## 设计 {#design}

作为设计的一个原则，Kubernetes 使用了很多控制器，每个控制器管理集群状态的一个特定方面。最常见的一个特定的控制器使用一种类型的资源作为它的期望状态，控制器管理控制另外一种类型的资源向它的期望状态发展。

使用简单的控制器而不是一组相互连接的单体控制环是很有用的。控制器会失败，所以 Kubernetes 的设计是考虑到了这一点。

例如：为 Job 追踪 Job 对象（发现新工作）和 Pod 对象（运行 Job，并且等工作完成）的控制器。在本例中，其它东西创建作业，而作业控制器创建 Pod。

{{< note >}}
可以有多个控制器来创建或者更新相同类型的对象。在这之后，Kubernetes 控制器确保他们只关心和它们控制资源相关联的资源。

例如，你可以有 Deployments 和 Jobs；它们都可以创建 Pod。Job 控制器不删除 Deployment 创建的 Pod，因为有信息({{< glossary_tooltip term_id="label" text="标签" >}})让控制器可以区分这些 Pod。
{{< /note >}}

<!--
## Ways of running controllers {#running-controllers}

Kubernetes comes with a set of built-in controllers that run inside
the {{< glossary_tooltip term_id="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](/docs/concepts/#kubernetes-control-plane)
* Discover some of the basic [Kubernetes objects](/docs/concepts/#kubernetes-objects)
* Learn more about the [Kubernetes API](/docs/concepts/overview/kubernetes-api/)
* If you want to write your own controller, see [Extension Patterns](/docs/concepts/extend-kubernetes/extend-cluster/#extension-patterns) in Extending Kubernetes.
-->

## 运行控制器的方式 {#running-controllers}

Kubernetes 自带有一组内置的控制器，运行在 {{< glossary_tooltip term_id="kube-controller-manager" >}} 内。这些内置的控制器提供了重要的核心功能。

Deployment 控制器和 Job 控制器是 Kubernetes 内置控制器的典型例子。Kubernetes 运行一个弹性的控制平面，所以如果任意内置控制器失败了，控制平面的另外一部分会接替它的工作。

你会发现控制平面外面运行的控制器，扩展了 Kubernetes 的能力。或者，如果你愿意，你也可以写一个新控制器。你可以以一组 Pod 来运行你的控制器，或者运行在 Kubernetes 外面。什么是最合适的控制器，这将取决于特定控制器的功能。

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* 请阅读 [Kubernetes 控制平面](/docs/concepts/#kubernetes-control-plane)
* 了解一些基本的 [Kubernetes 对象](/docs/concepts/#kubernetes-objects)
* 学习更多的 [Kubernetes API](/docs/concepts/overview/kubernetes-api/)
* 如果你想写自己的控制器，请看 Kubernetes 的[扩展模式](/docs/concepts/extend-kubernetes/extend-cluster/#extension-patterns)。
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