---
approvers:
- bprashanth
- janetkuo
title: Replication Controller
---

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NOTE: A [`Deployment`](/docs/concepts/workloads/controllers/deployment/) that configures a [`ReplicaSet`](/docs/concepts/workloads/controllers/replicaset/) is now the recommended way to set up replication.

A _ReplicationController_ ensures that a specified number of pod replicas are running at any one
time. In other words, a ReplicationController makes sure that a pod or a homogeneous set of pods is
always up and available.

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## How a ReplicationController Works

If there are too many pods, the ReplicationController terminates the extra pods. If there are too few, the
ReplicationController starts more pods. Unlike manually created pods, the pods maintained by a
ReplicationController are automatically replaced if they fail, are deleted, or are terminated.
For example, your pods are re-created on a node after disruptive maintenance such as a kernel upgrade.
For this reason, you should use a ReplicationController even if your application requires
only a single pod. A ReplicationController is similar to a process supervisor,
but instead of supervising individual processes on a single node, the ReplicationController supervises multiple pods
across multiple nodes.

ReplicationController is often abbreviated to "rc" or "rcs" in discussion, and as a shortcut in
kubectl commands.

A simple case is to create one ReplicationController object to reliably run one instance of
a Pod indefinitely.  A more complex use case is to run several identical replicas of a replicated
service, such as web servers.

## Running an example ReplicationController

This example ReplicationController config runs three copies of the nginx web server.

{% include code.html language="yaml" file="replication.yaml" ghlink="/docs/concepts/workloads/controllers/replication.yaml" %}

Run the example job by downloading the example file and then running this command:

```shell
$ kubectl create -f ./replication.yaml
replicationcontroller "nginx" created
```

Check on the status of the ReplicationController using this command:

```shell
$ kubectl describe replicationcontrollers/nginx
Name:        nginx
Namespace:   default
Image(s):    nginx
Selector:    app=nginx
Labels:      app=nginx
Replicas:    3 current / 3 desired
Pods Status: 0 Running / 3 Waiting / 0 Succeeded / 0 Failed
Events:
  FirstSeen       LastSeen     Count    From                        SubobjectPath    Type      Reason              Message
  ---------       --------     -----    ----                        -------------    ----      ------              -------
  20s             20s          1        {replication-controller }                    Normal    SuccessfulCreate    Created pod: nginx-qrm3m
  20s             20s          1        {replication-controller }                    Normal    SuccessfulCreate    Created pod: nginx-3ntk0
  20s             20s          1        {replication-controller }                    Normal    SuccessfulCreate    Created pod: nginx-4ok8v
```

Here, three pods are created, but none is running yet, perhaps because the image is being pulled.
A little later, the same command may show:

```shell
Pods Status:    3 Running / 0 Waiting / 0 Succeeded / 0 Failed
```

To list all the pods that belong to the ReplicationController in a machine readable form, you can use a command like this:

```shell
$ pods=$(kubectl get pods --selector=app=nginx --output=jsonpath={.items..metadata.name})
echo $pods
nginx-3ntk0 nginx-4ok8v nginx-qrm3m
```

Here, the selector is the same as the selector for the ReplicationController (seen in the
`kubectl describe` output, and in a different form in `replication.yaml`.  The `--output=jsonpath` option
specifies an expression that just gets the name from each pod in the returned list.


## Writing a ReplicationController Spec

As with all other Kubernetes config, a ReplicationController needs `apiVersion`, `kind`, and `metadata` fields.  For
general information about working with config files, see [here](/docs/user-guide/simple-yaml/),
[here](/docs/user-guide/configuring-containers/), and [here](/docs/concepts/tools/kubectl/object-management-overview/).

A ReplicationController also needs a [`.spec` section](https://git.k8s.io/community/contributors/devel/api-conventions.md#spec-and-status).

### Pod Template

The `.spec.template` is the only required field of the `.spec`.

The `.spec.template` is a [pod template](/docs/concepts/workloads/pods/pod-overview/#pod-templates). It has exactly the same schema as a [pod](/docs/concepts/workloads/pods/pod/), except it is nested and does not have an `apiVersion` or `kind`.

In addition to required fields for a Pod, a pod template in a ReplicationController must specify appropriate
labels and an appropriate restart policy. For labels, make sure not to overlap with other controllers. See [pod selector](#pod-selector).

Only a [`.spec.template.spec.restartPolicy`](/docs/concepts/workloads/pods/pod-lifecycle/) equal to `Always` is allowed, which is the default
if not specified.

For local container restarts, ReplicationControllers delegate to an agent on the node,
for example the [Kubelet](/docs/admin/kubelet/) or Docker.

### Labels on the ReplicationController

The ReplicationController can itself have labels (`.metadata.labels`).  Typically, you
would set these the same as the `.spec.template.metadata.labels`; if `.metadata.labels` is not specified
then it defaults to  `.spec.template.metadata.labels`.  However, they are allowed to be
different, and the `.metadata.labels` do not affect the behavior of the ReplicationController.

### Pod Selector

The `.spec.selector` field is a [label selector](/docs/user-guide/labels/#label-selectors). A ReplicationController
manages all the pods with labels that match the selector. It does not distinguish
between pods that it created or deleted and pods that another person or process created or
deleted. This allows the ReplicationController to be replaced without affecting the running pods.

If specified, the `.spec.template.metadata.labels` must be equal to the `.spec.selector`, or it will
be rejected by the API.  If `.spec.selector` is unspecified, it will be defaulted to
`.spec.template.metadata.labels`.

Also you should not normally create any pods whose labels match this selector, either directly, with 
another ReplicationController, or with another controller such as Job. If you do so, the
ReplicationController thinks that it created the other pods.  Kubernetes does not stop you
from doing this.

If you do end up with multiple controllers that have overlapping selectors, you
will have to manage the deletion yourself (see [below](#working-with-replicationcontrollers)).

### Multiple Replicas

You can specify how many pods should run concurrently by setting `.spec.replicas` to the number
of pods you would like to have running concurrently.  The number running at any time may be higher
or lower, such as if the replicas was just increased or decreased, or if a pod is gracefully
shutdown, and a replacement starts early.

If you do not specify `.spec.replicas`, then it defaults to 1.

## Working with ReplicationControllers

### Deleting a ReplicationController and its Pods

To delete a ReplicationController and all its pods, use [`kubectl
delete`](/docs/user-guide/kubectl/{{page.version}}/#delete).  Kubectl will scale the ReplicationController to zero and wait
for it to delete each pod before deleting the ReplicationController itself.  If this kubectl
command is interrupted, it can be restarted.

When using the REST API or go client library, you need to do the steps explicitly (scale replicas to
0, wait for pod deletions, then delete the ReplicationController).

### Deleting just a ReplicationController

You can delete a ReplicationController without affecting any of its pods.

Using kubectl, specify the `--cascade=false` option to [`kubectl delete`](/docs/user-guide/kubectl/{{page.version}}/#delete).

When using the REST API or go client library, simply delete the ReplicationController object.

Once the original is deleted, you can create a new ReplicationController to replace it.  As long
as the old and new `.spec.selector` are the same, then the new one will adopt the old pods.
However, it will not make any effort to make existing pods match a new, different pod template.
To update pods to a new spec in a controlled way, use a [rolling update](#rolling-updates).

### Isolating pods from a ReplicationController

Pods may be removed from a ReplicationController's target set by changing their labels. This technique may be used to remove pods from service for debugging, data recovery, etc. Pods that are removed in this way will be replaced automatically (assuming that the number of replicas is not also changed).

## Common usage patterns

### Rescheduling

As mentioned above, whether you have 1 pod you want to keep running, or 1000, a ReplicationController will ensure that the specified number of pods exists, even in the event of node failure or pod termination (for example, due to an action by another control agent).

### Scaling

The ReplicationController makes it easy to scale the number of replicas up or down, either manually or by an auto-scaling control agent, by simply updating the `replicas` field.

### Rolling updates

The ReplicationController is designed to facilitate rolling updates to a service by replacing pods one-by-one.

As explained in [#1353](http://issue.k8s.io/1353), the recommended approach is to create a new ReplicationController with 1 replica, scale the new (+1) and old (-1) controllers one by one, and then delete the old controller after it reaches 0 replicas. This predictably updates the set of pods regardless of unexpected failures.

Ideally, the rolling update controller would take application readiness into account, and would ensure that a sufficient number of pods were productively serving at any given time.

The two ReplicationControllers would need to create pods with at least one differentiating label, such as the image tag of the primary container of the pod, since it is typically image updates that motivate rolling updates.

Rolling update is implemented in the client tool
[`kubectl rolling-update`](/docs/user-guide/kubectl/{{page.version}}/#rolling-update). Visit [`kubectl rolling-update` task](/docs/tasks/run-application/rolling-update-replication-controller/) for more concrete examples.

### Multiple release tracks

In addition to running multiple releases of an application while a rolling update is in progress, it's common to run multiple releases for an extended period of time, or even continuously, using multiple release tracks. The tracks would be differentiated by labels.

For instance, a service might target all pods with `tier in (frontend), environment in (prod)`.  Now say you have 10 replicated pods that make up this tier.  But you want to be able to 'canary' a new version of this component.  You could set up a ReplicationController with `replicas` set to 9 for the bulk of the replicas, with labels `tier=frontend, environment=prod, track=stable`, and another ReplicationController with `replicas` set to 1 for the canary, with labels `tier=frontend, environment=prod, track=canary`.  Now the service is covering both the canary and non-canary pods.  But you can mess with the ReplicationControllers separately to test things out, monitor the results, etc.

### Using ReplicationControllers with Services

Multiple ReplicationControllers can sit behind a single service, so that, for example, some traffic
goes to the old version, and some goes to the new version.

A ReplicationController will never terminate on its own, but it isn't expected to be as long-lived as services. Services may be composed of pods controlled by multiple ReplicationControllers, and it is expected that many ReplicationControllers may be created and destroyed over the lifetime of a service (for instance, to perform an update of pods that run the service). Both services themselves and their clients should remain oblivious to the ReplicationControllers that maintain the pods of the services.

## Writing programs for Replication

Pods created by a ReplicationController are intended to be fungible and semantically identical, though their configurations may become heterogeneous over time. This is an obvious fit for replicated stateless servers, but ReplicationControllers can also be used to maintain availability of master-elected, sharded, and worker-pool applications. Such applications should use dynamic work assignment mechanisms, such as the [etcd lock module](https://coreos.com/docs/distributed-configuration/etcd-modules/) or [RabbitMQ work queues](https://www.rabbitmq.com/tutorials/tutorial-two-python.html), as opposed to static/one-time customization of the configuration of each pod, which is considered an anti-pattern. Any pod customization performed, such as vertical auto-sizing of resources (for example, cpu or memory), should be performed by another online controller process, not unlike the ReplicationController itself.

## Responsibilities of the ReplicationController

The ReplicationController simply ensures that the desired number of pods matches its label selector and are operational. Currently, only terminated pods are excluded from its count. In the future, [readiness](http://issue.k8s.io/620) and other information available from the system may be taken into account, we may add more controls over the replacement policy, and we plan to emit events that could be used by external clients to implement arbitrarily sophisticated replacement and/or scale-down policies.

The ReplicationController is forever constrained to this narrow responsibility. It itself will not perform readiness nor liveness probes. Rather than performing auto-scaling, it is intended to be controlled by an external auto-scaler (as discussed in [#492](http://issue.k8s.io/492)), which would change its `replicas` field. We will not add scheduling policies (for example, [spreading](http://issue.k8s.io/367#issuecomment-48428019)) to the ReplicationController. Nor should it verify that the pods controlled match the currently specified template, as that would obstruct auto-sizing and other automated processes. Similarly, completion deadlines, ordering dependencies, configuration expansion, and other features belong elsewhere. We even plan to factor out the mechanism for bulk pod creation ([#170](http://issue.k8s.io/170)).

The ReplicationController is intended to be a composable building-block primitive. We expect higher-level APIs and/or tools to be built on top of it and other complementary primitives for user convenience in the future. The "macro" operations currently supported by kubectl (run, stop, scale, rolling-update) are proof-of-concept examples of this. For instance, we could imagine something like [Asgard](http://techblog.netflix.com/2012/06/asgard-web-based-cloud-management-and.html) managing ReplicationControllers, auto-scalers, services, scheduling policies, canaries, etc.


## API Object

Replication controller is a top-level resource in the Kubernetes REST API. More details about the
API object can be found at: [ReplicationController API
object](/docs/api-reference/{{page.version}}/#replicationcontroller-v1-core).

## Alternatives to ReplicationController

### ReplicaSet

[`ReplicaSet`](/docs/concepts/workloads/controllers/replicaset/) is the next-generation ReplicationController that supports the new [set-based label selector](/docs/user-guide/labels/#set-based-requirement).
It’s mainly used by [`Deployment`](/docs/concepts/workloads/controllers/deployment/) as a mechanism to orchestrate pod creation, deletion and updates.
Note that we recommend using Deployments instead of directly using Replica Sets, unless you require custom update orchestration or don’t require updates at all.


### Deployment (Recommended)

[`Deployment`](/docs/concepts/workloads/controllers/deployment/) is a higher-level API object that updates its underlying Replica Sets and their Pods
in a similar fashion as `kubectl rolling-update`. Deployments are recommended if you want this rolling update functionality,
because unlike `kubectl rolling-update`, they are declarative, server-side, and have additional features.

### Bare Pods

Unlike in the case where a user directly created pods, a ReplicationController replaces pods that are deleted or terminated for any reason, such as in the case of node failure or disruptive node maintenance, such as a kernel upgrade. For this reason, we recommend that you use a ReplicationController even if your application requires only a single pod. Think of it similarly to a process supervisor, only it supervises multiple pods across multiple nodes instead of individual processes on a single node.  A ReplicationController delegates local container restarts to some agent on the node (for example, Kubelet or Docker).

### Job

Use a [`Job`](/docs/concepts/jobs/run-to-completion-finite-workloads/) instead of a ReplicationController for pods that are expected to terminate on their own
(that is, batch jobs).

### DaemonSet

Use a [`DaemonSet`](/docs/concepts/workloads/controllers/daemonset/) instead of a ReplicationController for pods that provide a
machine-level function, such as machine monitoring or machine logging.  These pods have a lifetime that is tied
to a machine lifetime: the pod needs to be running on the machine before other pods start, and are
safe to terminate when the machine is otherwise ready to be rebooted/shutdown.

## For more information

Read [Run Stateless AP Replication Controller](/docs/tutorials/stateless-application/run-stateless-ap-replication-controller/).

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