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Workload Resources

1: Deployments
2: ReplicaSet
3: StatefulSets
4: DaemonSet
5: Jobs
6: Automatic Clean-up for Finished Jobs
7: CronJob
8: ReplicationController

1 - Deployments

A Deployment provides declarative updates for Pods and ReplicaSets.

You describe a desired state in a Deployment, and the Deployment Controller changes the actual state to the desired state at a controlled rate. You can define Deployments to create new ReplicaSets, or to remove existing Deployments and adopt all their resources with new Deployments.

Note: Do not manage ReplicaSets owned by a Deployment. Consider opening an issue in the main Kubernetes repository if your use case is not covered below.

Use Case

The following are typical use cases for Deployments:

Create a Deployment to rollout a ReplicaSet. The ReplicaSet creates Pods in the background. Check the status of the rollout to see if it succeeds or not.
Declare the new state of the Pods by updating the PodTemplateSpec of the Deployment. A new ReplicaSet is created and the Deployment manages moving the Pods from the old ReplicaSet to the new one at a controlled rate. Each new ReplicaSet updates the revision of the Deployment.
Rollback to an earlier Deployment revision if the current state of the Deployment is not stable. Each rollback updates the revision of the Deployment.
Scale up the Deployment to facilitate more load.
Pause the rollout of a Deployment to apply multiple fixes to its PodTemplateSpec and then resume it to start a new rollout.
Use the status of the Deployment as an indicator that a rollout has stuck.
Clean up older ReplicaSets that you don't need anymore.

Creating a Deployment

The following is an example of a Deployment. It creates a ReplicaSet to bring up three nginx Pods:

controllers/nginx-deployment.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
  labels:
    app: nginx
spec:
  replicas: 3
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx:1.14.2
        ports:
        - containerPort: 80

In this example:

A Deployment named nginx-deployment is created, indicated by the .metadata.name field.
The Deployment creates three replicated Pods, indicated by the .spec.replicas field.
The .spec.selector field defines how the Deployment finds which Pods to manage. In this case, you select a label that is defined in the Pod template (app: nginx). However, more sophisticated selection rules are possible, as long as the Pod template itself satisfies the rule.

Note: The .spec.selector.matchLabels field is a map of {key,value} pairs. A single {key,value} in the matchLabels map is equivalent to an element of matchExpressions, whose key field is "key", the operator is "In", and the values array contains only "value". All of the requirements, from both matchLabels and matchExpressions, must be satisfied in order to match.
The template field contains the following sub-fields:
- The Pods are labeled app: nginxusing the .metadata.labels field.
- The Pod template's specification, or .template.spec field, indicates that the Pods run one container, nginx, which runs the nginx Docker Hub image at version 1.14.2.
- Create one container and name it nginx using the .spec.template.spec.containers[0].name field.

Before you begin, make sure your Kubernetes cluster is up and running. Follow the steps given below to create the above Deployment:

Create the Deployment by running the following command:

kubectl apply -f https://k8s.io/examples/controllers/nginx-deployment.yaml

Run kubectl get deployments to check if the Deployment was created.

If the Deployment is still being created, the output is similar to the following:
```
NAME               READY   UP-TO-DATE   AVAILABLE   AGE
nginx-deployment   0/3     0            0           1s
```
When you inspect the Deployments in your cluster, the following fields are displayed:
- NAME lists the names of the Deployments in the namespace.
- READY displays how many replicas of the application are available to your users. It follows the pattern ready/desired.
- UP-TO-DATE displays the number of replicas that have been updated to achieve the desired state.
- AVAILABLE displays how many replicas of the application are available to your users.
- AGE displays the amount of time that the application has been running.
Notice how the number of desired replicas is 3 according to .spec.replicas field.

To see the Deployment rollout status, run kubectl rollout status deployment/nginx-deployment.

The output is similar to:

Waiting for rollout to finish: 2 out of 3 new replicas have been updated...
deployment "nginx-deployment" successfully rolled out

Run the kubectl get deployments again a few seconds later. The output is similar to this:
```
NAME               READY   UP-TO-DATE   AVAILABLE   AGE
nginx-deployment   3/3     3            3           18s
```
Notice that the Deployment has created all three replicas, and all replicas are up-to-date (they contain the latest Pod template) and available.
To see the ReplicaSet (rs) created by the Deployment, run kubectl get rs. The output is similar to this:
```
NAME                          DESIRED   CURRENT   READY   AGE
nginx-deployment-75675f5897   3         3         3       18s
```
ReplicaSet output shows the following fields:
- NAME lists the names of the ReplicaSets in the namespace.
- DESIRED displays the desired number of replicas of the application, which you define when you create the Deployment. This is the desired state.
- CURRENT displays how many replicas are currently running.
- READY displays how many replicas of the application are available to your users.
- AGE displays the amount of time that the application has been running.
Notice that the name of the ReplicaSet is always formatted as [DEPLOYMENT-NAME]-[RANDOM-STRING]. The random string is randomly generated and uses the pod-template-hash as a seed.

To see the labels automatically generated for each Pod, run kubectl get pods --show-labels. The output is similar to:

NAME                                READY     STATUS    RESTARTS   AGE       LABELS
nginx-deployment-75675f5897-7ci7o   1/1       Running   0          18s       app=nginx,pod-template-hash=3123191453
nginx-deployment-75675f5897-kzszj   1/1       Running   0          18s       app=nginx,pod-template-hash=3123191453
nginx-deployment-75675f5897-qqcnn   1/1       Running   0          18s       app=nginx,pod-template-hash=3123191453

The created ReplicaSet ensures that there are three nginx Pods.

Note:

You must specify an appropriate selector and Pod template labels in a Deployment (in this case, app: nginx).

Do not overlap labels or selectors with other controllers (including other Deployments and StatefulSets). Kubernetes doesn't stop you from overlapping, and if multiple controllers have overlapping selectors those controllers might conflict and behave unexpectedly.

Pod-template-hash label

Caution: Do not change this label.

The pod-template-hash label is added by the Deployment controller to every ReplicaSet that a Deployment creates or adopts.

This label ensures that child ReplicaSets of a Deployment do not overlap. It is generated by hashing the PodTemplate of the ReplicaSet and using the resulting hash as the label value that is added to the ReplicaSet selector, Pod template labels, and in any existing Pods that the ReplicaSet might have.

Updating a Deployment

Note: A Deployment's rollout is triggered if and only if the Deployment's Pod template (that is, .spec.template) is changed, for example if the labels or container images of the template are updated. Other updates, such as scaling the Deployment, do not trigger a rollout.

Follow the steps given below to update your Deployment:

Let's update the nginx Pods to use the nginx:1.16.1 image instead of the nginx:1.14.2 image.
```
kubectl set image deployment.v1.apps/nginx-deployment nginx=nginx:1.16.1
```
or use the following command:
```
kubectl set image deployment/nginx-deployment nginx=nginx:1.16.1
```
The output is similar to:
```
deployment.apps/nginx-deployment image updated
```
Alternatively, you can edit the Deployment and change .spec.template.spec.containers[0].image from nginx:1.14.2 to nginx:1.16.1:
```
kubectl edit deployment/nginx-deployment
```
The output is similar to:
```
deployment.apps/nginx-deployment edited
```

To see the rollout status, run:

kubectl rollout status deployment/nginx-deployment

The output is similar to this:

Waiting for rollout to finish: 2 out of 3 new replicas have been updated...

deployment "nginx-deployment" successfully rolled out

Get more details on your updated Deployment:

After the rollout succeeds, you can view the Deployment by running kubectl get deployments. The output is similar to this:
```
NAME               READY   UP-TO-DATE   AVAILABLE   AGE
nginx-deployment   3/3     3            3           36s
```
Run kubectl get rs to see that the Deployment updated the Pods by creating a new ReplicaSet and scaling it up to 3 replicas, as well as scaling down the old ReplicaSet to 0 replicas.
```
kubectl get rs
```
The output is similar to this:
```
NAME                          DESIRED   CURRENT   READY   AGE
nginx-deployment-1564180365   3         3         3       6s
nginx-deployment-2035384211   0         0         0       36s
```
Running get pods should now show only the new Pods:
```
kubectl get pods
```
The output is similar to this:
```
NAME                                READY     STATUS    RESTARTS   AGE
nginx-deployment-1564180365-khku8   1/1       Running   0          14s
nginx-deployment-1564180365-nacti   1/1       Running   0          14s
nginx-deployment-1564180365-z9gth   1/1       Running   0          14s
```
Next time you want to update these Pods, you only need to update the Deployment's Pod template again.

Deployment ensures that only a certain number of Pods are down while they are being updated. By default, it ensures that at least 75% of the desired number of Pods are up (25% max unavailable).

Deployment also ensures that only a certain number of Pods are created above the desired number of Pods. By default, it ensures that at most 125% of the desired number of Pods are up (25% max surge).

For example, if you look at the above Deployment closely, you will see that it first creates a new Pod, then deletes an old Pod, and creates another new one. It does not kill old Pods until a sufficient number of new Pods have come up, and does not create new Pods until a sufficient number of old Pods have been killed. It makes sure that at least 3 Pods are available and that at max 4 Pods in total are available. In case of a Deployment with 4 replicas, the number of Pods would be between 3 and 5.

Get details of your Deployment:

kubectl describe deployments

The output is similar to this:

Name:                   nginx-deployment
Namespace:              default
CreationTimestamp:      Thu, 30 Nov 2017 10:56:25 +0000
Labels:                 app=nginx
Annotations:            deployment.kubernetes.io/revision=2
Selector:               app=nginx
Replicas:               3 desired | 3 updated | 3 total | 3 available | 0 unavailable
StrategyType:           RollingUpdate
MinReadySeconds:        0
RollingUpdateStrategy:  25% max unavailable, 25% max surge
Pod Template:
  Labels:  app=nginx
   Containers:
    nginx:
      Image:        nginx:1.16.1
      Port:         80/TCP
      Environment:  <none>
      Mounts:       <none>
    Volumes:        <none>
  Conditions:
    Type           Status  Reason
    ----           ------  ------
    Available      True    MinimumReplicasAvailable
    Progressing    True    NewReplicaSetAvailable
  OldReplicaSets:  <none>
  NewReplicaSet:   nginx-deployment-1564180365 (3/3 replicas created)
  Events:
    Type    Reason             Age   From                   Message
    ----    ------             ----  ----                   -------
    Normal  ScalingReplicaSet  2m    deployment-controller  Scaled up replica set nginx-deployment-2035384211 to 3
    Normal  ScalingReplicaSet  24s   deployment-controller  Scaled up replica set nginx-deployment-1564180365 to 1
    Normal  ScalingReplicaSet  22s   deployment-controller  Scaled down replica set nginx-deployment-2035384211 to 2
    Normal  ScalingReplicaSet  22s   deployment-controller  Scaled up replica set nginx-deployment-1564180365 to 2
    Normal  ScalingReplicaSet  19s   deployment-controller  Scaled down replica set nginx-deployment-2035384211 to 1
    Normal  ScalingReplicaSet  19s   deployment-controller  Scaled up replica set nginx-deployment-1564180365 to 3
    Normal  ScalingReplicaSet  14s   deployment-controller  Scaled down replica set nginx-deployment-2035384211 to 0

Here you see that when you first created the Deployment, it created a ReplicaSet (nginx-deployment-2035384211) and scaled it up to 3 replicas directly. When you updated the Deployment, it created a new ReplicaSet (nginx-deployment-1564180365) and scaled it up to 1 and waited for it to come up. Then it scaled down the old ReplicaSet to 2 and scaled up the new ReplicaSet to 2 so that at least 3 Pods were available and at most 4 Pods were created at all times. It then continued scaling up and down the new and the old ReplicaSet, with the same rolling update strategy. Finally, you'll have 3 available replicas in the new ReplicaSet, and the old ReplicaSet is scaled down to 0.

Note: Kubernetes doesn't count terminating Pods when calculating the number of availableReplicas, which must be between replicas - maxUnavailable and replicas + maxSurge. As a result, you might notice that there are more Pods than expected during a rollout, and that the total resources consumed by the Deployment is more than replicas + maxSurge until the terminationGracePeriodSeconds of the terminating Pods expires.

Rollover (aka multiple updates in-flight)

Each time a new Deployment is observed by the Deployment controller, a ReplicaSet is created to bring up the desired Pods. If the Deployment is updated, the existing ReplicaSet that controls Pods whose labels match .spec.selector but whose template does not match .spec.template are scaled down. Eventually, the new ReplicaSet is scaled to .spec.replicas and all old ReplicaSets is scaled to 0.

If you update a Deployment while an existing rollout is in progress, the Deployment creates a new ReplicaSet as per the update and start scaling that up, and rolls over the ReplicaSet that it was scaling up previously -- it will add it to its list of old ReplicaSets and start scaling it down.

For example, suppose you create a Deployment to create 5 replicas of nginx:1.14.2, but then update the Deployment to create 5 replicas of nginx:1.16.1, when only 3 replicas of nginx:1.14.2 had been created. In that case, the Deployment immediately starts killing the 3 nginx:1.14.2 Pods that it had created, and starts creating nginx:1.16.1 Pods. It does not wait for the 5 replicas of nginx:1.14.2 to be created before changing course.

Label selector updates

It is generally discouraged to make label selector updates and it is suggested to plan your selectors up front. In any case, if you need to perform a label selector update, exercise great caution and make sure you have grasped all of the implications.

Note: In API version apps/v1, a Deployment's label selector is immutable after it gets created.

Selector additions require the Pod template labels in the Deployment spec to be updated with the new label too, otherwise a validation error is returned. This change is a non-overlapping one, meaning that the new selector does not select ReplicaSets and Pods created with the old selector, resulting in orphaning all old ReplicaSets and creating a new ReplicaSet.
Selector updates changes the existing value in a selector key -- result in the same behavior as additions.
Selector removals removes an existing key from the Deployment selector -- do not require any changes in the Pod template labels. Existing ReplicaSets are not orphaned, and a new ReplicaSet is not created, but note that the removed label still exists in any existing Pods and ReplicaSets.

Rolling Back a Deployment

Sometimes, you may want to rollback a Deployment; for example, when the Deployment is not stable, such as crash looping. By default, all of the Deployment's rollout history is kept in the system so that you can rollback anytime you want (you can change that by modifying revision history limit).

Note: A Deployment's revision is created when a Deployment's rollout is triggered. This means that the new revision is created if and only if the Deployment's Pod template (.spec.template) is changed, for example if you update the labels or container images of the template. Other updates, such as scaling the Deployment, do not create a Deployment revision, so that you can facilitate simultaneous manual- or auto-scaling. This means that when you roll back to an earlier revision, only the Deployment's Pod template part is rolled back.

Suppose that you made a typo while updating the Deployment, by putting the image name as nginx:1.161 instead of nginx:1.16.1:
```
kubectl set image deployment/nginx-deployment nginx=nginx:1.161 
```
The output is similar to this:
```
deployment.apps/nginx-deployment image updated
```

The rollout gets stuck. You can verify it by checking the rollout status:

kubectl rollout status deployment/nginx-deployment

The output is similar to this:

Waiting for rollout to finish: 1 out of 3 new replicas have been updated...

Press Ctrl-C to stop the above rollout status watch. For more information on stuck rollouts, read more here.

You see that the number of old replicas (nginx-deployment-1564180365 and nginx-deployment-2035384211) is 2, and new replicas (nginx-deployment-3066724191) is 1.

kubectl get rs

The output is similar to this:

NAME                          DESIRED   CURRENT   READY   AGE
nginx-deployment-1564180365   3         3         3       25s
nginx-deployment-2035384211   0         0         0       36s
nginx-deployment-3066724191   1         1         0       6s

Looking at the Pods created, you see that 1 Pod created by new ReplicaSet is stuck in an image pull loop.

kubectl get pods

The output is similar to this:

NAME                                READY     STATUS             RESTARTS   AGE
nginx-deployment-1564180365-70iae   1/1       Running            0          25s
nginx-deployment-1564180365-jbqqo   1/1       Running            0          25s
nginx-deployment-1564180365-hysrc   1/1       Running            0          25s
nginx-deployment-3066724191-08mng   0/1       ImagePullBackOff   0          6s

Note: The Deployment controller stops the bad rollout automatically, and stops scaling up the new ReplicaSet. This depends on the rollingUpdate parameters (maxUnavailable specifically) that you have specified. Kubernetes by default sets the value to 25%.

Get the description of the Deployment:

kubectl describe deployment

The output is similar to this:

Name:           nginx-deployment
Namespace:      default
CreationTimestamp:  Tue, 15 Mar 2016 14:48:04 -0700
Labels:         app=nginx
Selector:       app=nginx
Replicas:       3 desired | 1 updated | 4 total | 3 available | 1 unavailable
StrategyType:       RollingUpdate
MinReadySeconds:    0
RollingUpdateStrategy:  25% max unavailable, 25% max surge
Pod Template:
  Labels:  app=nginx
  Containers:
   nginx:
    Image:        nginx:1.161
    Port:         80/TCP
    Host Port:    0/TCP
    Environment:  <none>
    Mounts:       <none>
  Volumes:        <none>
Conditions:
  Type           Status  Reason
  ----           ------  ------
  Available      True    MinimumReplicasAvailable
  Progressing    True    ReplicaSetUpdated
OldReplicaSets:     nginx-deployment-1564180365 (3/3 replicas created)
NewReplicaSet:      nginx-deployment-3066724191 (1/1 replicas created)
Events:
  FirstSeen LastSeen    Count   From                    SubObjectPath   Type        Reason              Message
  --------- --------    -----   ----                    -------------   --------    ------              -------
  1m        1m          1       {deployment-controller }                Normal      ScalingReplicaSet   Scaled up replica set nginx-deployment-2035384211 to 3
  22s       22s         1       {deployment-controller }                Normal      ScalingReplicaSet   Scaled up replica set nginx-deployment-1564180365 to 1
  22s       22s         1       {deployment-controller }                Normal      ScalingReplicaSet   Scaled down replica set nginx-deployment-2035384211 to 2
  22s       22s         1       {deployment-controller }                Normal      ScalingReplicaSet   Scaled up replica set nginx-deployment-1564180365 to 2
  21s       21s         1       {deployment-controller }                Normal      ScalingReplicaSet   Scaled down replica set nginx-deployment-2035384211 to 1
  21s       21s         1       {deployment-controller }                Normal      ScalingReplicaSet   Scaled up replica set nginx-deployment-1564180365 to 3
  13s       13s         1       {deployment-controller }                Normal      ScalingReplicaSet   Scaled down replica set nginx-deployment-2035384211 to 0
  13s       13s         1       {deployment-controller }                Normal      ScalingReplicaSet   Scaled up replica set nginx-deployment-3066724191 to 1

To fix this, you need to rollback to a previous revision of Deployment that is stable.

Checking Rollout History of a Deployment

Follow the steps given below to check the rollout history:

First, check the revisions of this Deployment:
```
kubectl rollout history deployment/nginx-deployment
```
The output is similar to this:
```
deployments "nginx-deployment"
REVISION    CHANGE-CAUSE
1           kubectl apply --filename=https://k8s.io/examples/controllers/nginx-deployment.yaml
2           kubectl set image deployment/nginx-deployment nginx=nginx:1.16.1
3           kubectl set image deployment/nginx-deployment nginx=nginx:1.161
```
CHANGE-CAUSE is copied from the Deployment annotation kubernetes.io/change-cause to its revisions upon creation. You can specify theCHANGE-CAUSE message by:
- Annotating the Deployment with kubectl annotate deployment/nginx-deployment kubernetes.io/change-cause="image updated to 1.16.1"
- Manually editing the manifest of the resource.

To see the details of each revision, run:

kubectl rollout history deployment/nginx-deployment --revision=2

The output is similar to this:

deployments "nginx-deployment" revision 2
  Labels:       app=nginx
          pod-template-hash=1159050644
  Annotations:  kubernetes.io/change-cause=kubectl set image deployment/nginx-deployment nginx=nginx:1.16.1
  Containers:
   nginx:
    Image:      nginx:1.16.1
    Port:       80/TCP
     QoS Tier:
        cpu:      BestEffort
        memory:   BestEffort
    Environment Variables:      <none>
  No volumes.

Rolling Back to a Previous Revision

Follow the steps given below to rollback the Deployment from the current version to the previous version, which is version 2.

Now you've decided to undo the current rollout and rollback to the previous revision:
```
kubectl rollout undo deployment/nginx-deployment
```
The output is similar to this:
```
deployment.apps/nginx-deployment rolled back
```
Alternatively, you can rollback to a specific revision by specifying it with --to-revision:
```
kubectl rollout undo deployment/nginx-deployment --to-revision=2
```
The output is similar to this:
```
deployment.apps/nginx-deployment rolled back
```
For more details about rollout related commands, read kubectl rollout.

The Deployment is now rolled back to a previous stable revision. As you can see, a DeploymentRollback event for rolling back to revision 2 is generated from Deployment controller.

Check if the rollback was successful and the Deployment is running as expected, run:

kubectl get deployment nginx-deployment

The output is similar to this:

NAME               READY   UP-TO-DATE   AVAILABLE   AGE
nginx-deployment   3/3     3            3           30m

Get the description of the Deployment:

kubectl describe deployment nginx-deployment

The output is similar to this:

Name:                   nginx-deployment
Namespace:              default
CreationTimestamp:      Sun, 02 Sep 2018 18:17:55 -0500
Labels:                 app=nginx
Annotations:            deployment.kubernetes.io/revision=4
                        kubernetes.io/change-cause=kubectl set image deployment/nginx-deployment nginx=nginx:1.16.1
Selector:               app=nginx
Replicas:               3 desired | 3 updated | 3 total | 3 available | 0 unavailable
StrategyType:           RollingUpdate
MinReadySeconds:        0
RollingUpdateStrategy:  25% max unavailable, 25% max surge
Pod Template:
  Labels:  app=nginx
  Containers:
   nginx:
    Image:        nginx:1.16.1
    Port:         80/TCP
    Host Port:    0/TCP
    Environment:  <none>
    Mounts:       <none>
  Volumes:        <none>
Conditions:
  Type           Status  Reason
  ----           ------  ------
  Available      True    MinimumReplicasAvailable
  Progressing    True    NewReplicaSetAvailable
OldReplicaSets:  <none>
NewReplicaSet:   nginx-deployment-c4747d96c (3/3 replicas created)
Events:
  Type    Reason              Age   From                   Message
  ----    ------              ----  ----                   -------
  Normal  ScalingReplicaSet   12m   deployment-controller  Scaled up replica set nginx-deployment-75675f5897 to 3
  Normal  ScalingReplicaSet   11m   deployment-controller  Scaled up replica set nginx-deployment-c4747d96c to 1
  Normal  ScalingReplicaSet   11m   deployment-controller  Scaled down replica set nginx-deployment-75675f5897 to 2
  Normal  ScalingReplicaSet   11m   deployment-controller  Scaled up replica set nginx-deployment-c4747d96c to 2
  Normal  ScalingReplicaSet   11m   deployment-controller  Scaled down replica set nginx-deployment-75675f5897 to 1
  Normal  ScalingReplicaSet   11m   deployment-controller  Scaled up replica set nginx-deployment-c4747d96c to 3
  Normal  ScalingReplicaSet   11m   deployment-controller  Scaled down replica set nginx-deployment-75675f5897 to 0
  Normal  ScalingReplicaSet   11m   deployment-controller  Scaled up replica set nginx-deployment-595696685f to 1
  Normal  DeploymentRollback  15s   deployment-controller  Rolled back deployment "nginx-deployment" to revision 2
  Normal  ScalingReplicaSet   15s   deployment-controller  Scaled down replica set nginx-deployment-595696685f to 0

Scaling a Deployment

You can scale a Deployment by using the following command:

kubectl scale deployment/nginx-deployment --replicas=10

The output is similar to this:

deployment.apps/nginx-deployment scaled

Assuming horizontal Pod autoscaling is enabled in your cluster, you can setup an autoscaler for your Deployment and choose the minimum and maximum number of Pods you want to run based on the CPU utilization of your existing Pods.

kubectl autoscale deployment/nginx-deployment --min=10 --max=15 --cpu-percent=80

The output is similar to this:

deployment.apps/nginx-deployment scaled

Proportional scaling

RollingUpdate Deployments support running multiple versions of an application at the same time. When you or an autoscaler scales a RollingUpdate Deployment that is in the middle of a rollout (either in progress or paused), the Deployment controller balances the additional replicas in the existing active ReplicaSets (ReplicaSets with Pods) in order to mitigate risk. This is called proportional scaling.

For example, you are running a Deployment with 10 replicas, maxSurge=3, and maxUnavailable=2.

Ensure that the 10 replicas in your Deployment are running.

kubectl get deploy

The output is similar to this:

NAME                 DESIRED   CURRENT   UP-TO-DATE   AVAILABLE   AGE
nginx-deployment     10        10        10           10          50s

You update to a new image which happens to be unresolvable from inside the cluster.

kubectl set image deployment/nginx-deployment nginx=nginx:sometag

The output is similar to this:

deployment.apps/nginx-deployment image updated

The image update starts a new rollout with ReplicaSet nginx-deployment-1989198191, but it's blocked due to the maxUnavailable requirement that you mentioned above. Check out the rollout status:

kubectl get rs

The output is similar to this:

NAME                          DESIRED   CURRENT   READY     AGE
nginx-deployment-1989198191   5         5         0         9s
nginx-deployment-618515232    8         8         8         1m

Then a new scaling request for the Deployment comes along. The autoscaler increments the Deployment replicas to 15. The Deployment controller needs to decide where to add these new 5 replicas. If you weren't using proportional scaling, all 5 of them would be added in the new ReplicaSet. With proportional scaling, you spread the additional replicas across all ReplicaSets. Bigger proportions go to the ReplicaSets with the most replicas and lower proportions go to ReplicaSets with less replicas. Any leftovers are added to the ReplicaSet with the most replicas. ReplicaSets with zero replicas are not scaled up.

In our example above, 3 replicas are added to the old ReplicaSet and 2 replicas are added to the new ReplicaSet. The rollout process should eventually move all replicas to the new ReplicaSet, assuming the new replicas become healthy. To confirm this, run:

kubectl get deploy

The output is similar to this:

NAME                 DESIRED   CURRENT   UP-TO-DATE   AVAILABLE   AGE
nginx-deployment     15        18        7            8           7m

The rollout status confirms how the replicas were added to each ReplicaSet.

kubectl get rs

The output is similar to this:

NAME                          DESIRED   CURRENT   READY     AGE
nginx-deployment-1989198191   7         7         0         7m
nginx-deployment-618515232    11        11        11        7m

Pausing and Resuming a rollout of a Deployment

When you update a Deployment, or plan to, you can pause rollouts for that Deployment before you trigger one or more updates. When you're ready to apply those changes, you resume rollouts for the Deployment. This approach allows you to apply multiple fixes in between pausing and resuming without triggering unnecessary rollouts.

For example, with a Deployment that was created:

Get the Deployment details:

kubectl get deploy

The output is similar to this:

NAME      DESIRED   CURRENT   UP-TO-DATE   AVAILABLE   AGE
nginx     3         3         3            3           1m

Get the rollout status:

kubectl get rs

The output is similar to this:

NAME               DESIRED   CURRENT   READY     AGE
nginx-2142116321   3         3         3         1m

Pause by running the following command:

kubectl rollout pause deployment/nginx-deployment

The output is similar to this:

deployment.apps/nginx-deployment paused

Then update the image of the Deployment:

kubectl set image deployment/nginx-deployment nginx=nginx:1.16.1

The output is similar to this:

deployment.apps/nginx-deployment image updated

Notice that no new rollout started:

kubectl rollout history deployment/nginx-deployment

The output is similar to this:

deployments "nginx"
REVISION  CHANGE-CAUSE
1   <none>

Get the rollout status to verify that the existing ReplicaSet has not changed:

kubectl get rs

The output is similar to this:

NAME               DESIRED   CURRENT   READY     AGE
nginx-2142116321   3         3         3         2m

You can make as many updates as you wish, for example, update the resources that will be used:
```
kubectl set resources deployment/nginx-deployment -c=nginx --limits=cpu=200m,memory=512Mi
```
The output is similar to this:
```
deployment.apps/nginx-deployment resource requirements updated
```
The initial state of the Deployment prior to pausing its rollout will continue its function, but new updates to the Deployment will not have any effect as long as the Deployment rollout is paused.
Eventually, resume the Deployment rollout and observe a new ReplicaSet coming up with all the new updates:
```
kubectl rollout resume deployment/nginx-deployment
```
The output is similar to this:
```
deployment.apps/nginx-deployment resumed
```

Watch the status of the rollout until it's done.

kubectl get rs -w

The output is similar to this:

NAME               DESIRED   CURRENT   READY     AGE
nginx-2142116321   2         2         2         2m
nginx-3926361531   2         2         0         6s
nginx-3926361531   2         2         1         18s
nginx-2142116321   1         2         2         2m
nginx-2142116321   1         2         2         2m
nginx-3926361531   3         2         1         18s
nginx-3926361531   3         2         1         18s
nginx-2142116321   1         1         1         2m
nginx-3926361531   3         3         1         18s
nginx-3926361531   3         3         2         19s
nginx-2142116321   0         1         1         2m
nginx-2142116321   0         1         1         2m
nginx-2142116321   0         0         0         2m
nginx-3926361531   3         3         3         20s

Get the status of the latest rollout:

kubectl get rs

The output is similar to this:

NAME               DESIRED   CURRENT   READY     AGE
nginx-2142116321   0         0         0         2m
nginx-3926361531   3         3         3         28s

Note: You cannot rollback a paused Deployment until you resume it.

Deployment status

A Deployment enters various states during its lifecycle. It can be progressing while rolling out a new ReplicaSet, it can be complete, or it can fail to progress.

Progressing Deployment

Kubernetes marks a Deployment as progressing when one of the following tasks is performed:

The Deployment creates a new ReplicaSet.
The Deployment is scaling up its newest ReplicaSet.
The Deployment is scaling down its older ReplicaSet(s).
New Pods become ready or available (ready for at least MinReadySeconds).

When the rollout becomes “progressing”, the Deployment controller adds a condition with the following attributes to the Deployment's .status.conditions:

type: Progressing
status: "True"
reason: NewReplicaSetCreated | reason: FoundNewReplicaSet | reason: ReplicaSetUpdated

You can monitor the progress for a Deployment by using kubectl rollout status.

Complete Deployment

Kubernetes marks a Deployment as complete when it has the following characteristics:

All of the replicas associated with the Deployment have been updated to the latest version you've specified, meaning any updates you've requested have been completed.
All of the replicas associated with the Deployment are available.
No old replicas for the Deployment are running.

When the rollout becomes “complete”, the Deployment controller sets a condition with the following attributes to the Deployment's .status.conditions:

type: Progressing
status: "True"
reason: NewReplicaSetAvailable

This Progressing condition will retain a status value of "True" until a new rollout is initiated. The condition holds even when availability of replicas changes (which does instead affect the Available condition).

You can check if a Deployment has completed by using kubectl rollout status. If the rollout completed successfully, kubectl rollout status returns a zero exit code.

kubectl rollout status deployment/nginx-deployment

The output is similar to this:

Waiting for rollout to finish: 2 of 3 updated replicas are available...
deployment "nginx-deployment" successfully rolled out

and the exit status from kubectl rollout is 0 (success):

echo $?

Failed Deployment

Your Deployment may get stuck trying to deploy its newest ReplicaSet without ever completing. This can occur due to some of the following factors:

Insufficient quota
Readiness probe failures
Image pull errors
Insufficient permissions
Limit ranges
Application runtime misconfiguration

One way you can detect this condition is to specify a deadline parameter in your Deployment spec: (.spec.progressDeadlineSeconds). .spec.progressDeadlineSeconds denotes the number of seconds the Deployment controller waits before indicating (in the Deployment status) that the Deployment progress has stalled.

The following kubectl command sets the spec with progressDeadlineSeconds to make the controller report lack of progress of a rollout for a Deployment after 10 minutes:

kubectl patch deployment/nginx-deployment -p '{"spec":{"progressDeadlineSeconds":600}}'

The output is similar to this:

deployment.apps/nginx-deployment patched

Once the deadline has been exceeded, the Deployment controller adds a DeploymentCondition with the following attributes to the Deployment's .status.conditions:

type: Progressing
status: "False"
reason: ProgressDeadlineExceeded

This condition can also fail early and is then set to status value of "False" due to reasons as ReplicaSetCreateError. Also, the deadline is not taken into account anymore once the Deployment rollout completes.

See the Kubernetes API conventions for more information on status conditions.

Note: Kubernetes takes no action on a stalled Deployment other than to report a status condition with reason: ProgressDeadlineExceeded. Higher level orchestrators can take advantage of it and act accordingly, for example, rollback the Deployment to its previous version.

Note: If you pause a Deployment rollout, Kubernetes does not check progress against your specified deadline. You can safely pause a Deployment rollout in the middle of a rollout and resume without triggering the condition for exceeding the deadline.

You may experience transient errors with your Deployments, either due to a low timeout that you have set or due to any other kind of error that can be treated as transient. For example, let's suppose you have insufficient quota. If you describe the Deployment you will notice the following section:

kubectl describe deployment nginx-deployment

The output is similar to this:

<...>
Conditions:
  Type            Status  Reason
  ----            ------  ------
  Available       True    MinimumReplicasAvailable
  Progressing     True    ReplicaSetUpdated
  ReplicaFailure  True    FailedCreate
<...>

If you run kubectl get deployment nginx-deployment -o yaml, the Deployment status is similar to this:

status:
  availableReplicas: 2
  conditions:
  - lastTransitionTime: 2016-10-04T12:25:39Z
    lastUpdateTime: 2016-10-04T12:25:39Z
    message: Replica set "nginx-deployment-4262182780" is progressing.
    reason: ReplicaSetUpdated
    status: "True"
    type: Progressing
  - lastTransitionTime: 2016-10-04T12:25:42Z
    lastUpdateTime: 2016-10-04T12:25:42Z
    message: Deployment has minimum availability.
    reason: MinimumReplicasAvailable
    status: "True"
    type: Available
  - lastTransitionTime: 2016-10-04T12:25:39Z
    lastUpdateTime: 2016-10-04T12:25:39Z
    message: 'Error creating: pods "nginx-deployment-4262182780-" is forbidden: exceeded quota:
      object-counts, requested: pods=1, used: pods=3, limited: pods=2'
    reason: FailedCreate
    status: "True"
    type: ReplicaFailure
  observedGeneration: 3
  replicas: 2
  unavailableReplicas: 2

Eventually, once the Deployment progress deadline is exceeded, Kubernetes updates the status and the reason for the Progressing condition:

Conditions:
  Type            Status  Reason
  ----            ------  ------
  Available       True    MinimumReplicasAvailable
  Progressing     False   ProgressDeadlineExceeded
  ReplicaFailure  True    FailedCreate

You can address an issue of insufficient quota by scaling down your Deployment, by scaling down other controllers you may be running, or by increasing quota in your namespace. If you satisfy the quota conditions and the Deployment controller then completes the Deployment rollout, you'll see the Deployment's status update with a successful condition (status: "True" and reason: NewReplicaSetAvailable).

Conditions:
  Type          Status  Reason
  ----          ------  ------
  Available     True    MinimumReplicasAvailable
  Progressing   True    NewReplicaSetAvailable

type: Available with status: "True" means that your Deployment has minimum availability. Minimum availability is dictated by the parameters specified in the deployment strategy. type: Progressing with status: "True" means that your Deployment is either in the middle of a rollout and it is progressing or that it has successfully completed its progress and the minimum required new replicas are available (see the Reason of the condition for the particulars - in our case reason: NewReplicaSetAvailable means that the Deployment is complete).

You can check if a Deployment has failed to progress by using kubectl rollout status. kubectl rollout status returns a non-zero exit code if the Deployment has exceeded the progression deadline.

kubectl rollout status deployment/nginx-deployment

The output is similar to this:

Waiting for rollout to finish: 2 out of 3 new replicas have been updated...
error: deployment "nginx" exceeded its progress deadline

and the exit status from kubectl rollout is 1 (indicating an error):

echo $?

Operating on a failed deployment

All actions that apply to a complete Deployment also apply to a failed Deployment. You can scale it up/down, roll back to a previous revision, or even pause it if you need to apply multiple tweaks in the Deployment Pod template.

Clean up Policy

You can set .spec.revisionHistoryLimit field in a Deployment to specify how many old ReplicaSets for this Deployment you want to retain. The rest will be garbage-collected in the background. By default, it is 10.

Note: Explicitly setting this field to 0, will result in cleaning up all the history of your Deployment thus that Deployment will not be able to roll back.

Canary Deployment

If you want to roll out releases to a subset of users or servers using the Deployment, you can create multiple Deployments, one for each release, following the canary pattern described in managing resources.

Writing a Deployment Spec

As with all other Kubernetes configs, a Deployment needs .apiVersion, .kind, and .metadata fields. For general information about working with config files, see deploying applications, configuring containers, and using kubectl to manage resources documents. The name of a Deployment object must be a valid DNS subdomain name.

A Deployment also needs a .spec section.

Pod Template

The .spec.template and .spec.selector are the only required fields of the .spec.

The .spec.template is a Pod template. It has exactly the same schema as a 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 Deployment must specify appropriate labels and an appropriate restart policy. For labels, make sure not to overlap with other controllers. See selector.

Only a .spec.template.spec.restartPolicy equal to Always is allowed, which is the default if not specified.

Replicas

.spec.replicas is an optional field that specifies the number of desired Pods. It defaults to 1.

Should you manually scale a Deployment, example via kubectl scale deployment deployment --replicas=X, and then you update that Deployment based on a manifest (for example: by running kubectl apply -f deployment.yaml), then applying that manifest overwrites the manual scaling that you previously did.

If a HorizontalPodAutoscaler (or any similar API for horizontal scaling) is managing scaling for a Deployment, don't set .spec.replicas.

Instead, allow the Kubernetes control plane to manage the .spec.replicas field automatically.

Selector

.spec.selector is a required field that specifies a label selector for the Pods targeted by this Deployment.

.spec.selector must match .spec.template.metadata.labels, or it will be rejected by the API.

In API version apps/v1, .spec.selector and .metadata.labels do not default to .spec.template.metadata.labels if not set. So they must be set explicitly. Also note that .spec.selector is immutable after creation of the Deployment in apps/v1.

A Deployment may terminate Pods whose labels match the selector if their template is different from .spec.template or if the total number of such Pods exceeds .spec.replicas. It brings up new Pods with .spec.template if the number of Pods is less than the desired number.

Note: You should not create other Pods whose labels match this selector, either directly, by creating another Deployment, or by creating another controller such as a ReplicaSet or a ReplicationController. If you do so, the first Deployment thinks that it created these other Pods. Kubernetes does not stop you from doing this.

If you have multiple controllers that have overlapping selectors, the controllers will fight with each other and won't behave correctly.

Strategy

.spec.strategy specifies the strategy used to replace old Pods by new ones. .spec.strategy.type can be "Recreate" or "RollingUpdate". "RollingUpdate" is the default value.

Recreate Deployment

All existing Pods are killed before new ones are created when .spec.strategy.type==Recreate.

Note: This will only guarantee Pod termination previous to creation for upgrades. If you upgrade a Deployment, all Pods of the old revision will be terminated immediately. Successful removal is awaited before any Pod of the new revision is created. If you manually delete a Pod, the lifecycle is controlled by the ReplicaSet and the replacement will be created immediately (even if the old Pod is still in a Terminating state). If you need an "at most" guarantee for your Pods, you should consider using a StatefulSet.

Rolling Update Deployment

The Deployment updates Pods in a rolling update fashion when .spec.strategy.type==RollingUpdate. You can specify maxUnavailable and maxSurge to control the rolling update process.

Max Unavailable

.spec.strategy.rollingUpdate.maxUnavailable is an optional field that specifies the maximum number of Pods that can be unavailable during the update process. The value can be an absolute number (for example, 5) or a percentage of desired Pods (for example, 10%). The absolute number is calculated from percentage by rounding down. The value cannot be 0 if .spec.strategy.rollingUpdate.maxSurge is 0. The default value is 25%.

For example, when this value is set to 30%, the old ReplicaSet can be scaled down to 70% of desired Pods immediately when the rolling update starts. Once new Pods are ready, old ReplicaSet can be scaled down further, followed by scaling up the new ReplicaSet, ensuring that the total number of Pods available at all times during the update is at least 70% of the desired Pods.

Max Surge

.spec.strategy.rollingUpdate.maxSurge is an optional field that specifies the maximum number of Pods that can be created over the desired number of Pods. The value can be an absolute number (for example, 5) or a percentage of desired Pods (for example, 10%). The value cannot be 0 if MaxUnavailable is 0. The absolute number is calculated from the percentage by rounding up. The default value is 25%.

For example, when this value is set to 30%, the new ReplicaSet can be scaled up immediately when the rolling update starts, such that the total number of old and new Pods does not exceed 130% of desired Pods. Once old Pods have been killed, the new ReplicaSet can be scaled up further, ensuring that the total number of Pods running at any time during the update is at most 130% of desired Pods.

Progress Deadline Seconds

.spec.progressDeadlineSeconds is an optional field that specifies the number of seconds you want to wait for your Deployment to progress before the system reports back that the Deployment has failed progressing - surfaced as a condition with type: Progressing, status: "False". and reason: ProgressDeadlineExceeded in the status of the resource. The Deployment controller will keep retrying the Deployment. This defaults to 600. In the future, once automatic rollback will be implemented, the Deployment controller will roll back a Deployment as soon as it observes such a condition.

If specified, this field needs to be greater than .spec.minReadySeconds.

Min Ready Seconds

.spec.minReadySeconds is an optional field that specifies the minimum number of seconds for which a newly created Pod should be ready without any of its containers crashing, for it to be considered available. This defaults to 0 (the Pod will be considered available as soon as it is ready). To learn more about when a Pod is considered ready, see Container Probes.

Revision History Limit

A Deployment's revision history is stored in the ReplicaSets it controls.

.spec.revisionHistoryLimit is an optional field that specifies the number of old ReplicaSets to retain to allow rollback. These old ReplicaSets consume resources in etcd and crowd the output of kubectl get rs. The configuration of each Deployment revision is stored in its ReplicaSets; therefore, once an old ReplicaSet is deleted, you lose the ability to rollback to that revision of Deployment. By default, 10 old ReplicaSets will be kept, however its ideal value depends on the frequency and stability of new Deployments.

More specifically, setting this field to zero means that all old ReplicaSets with 0 replicas will be cleaned up. In this case, a new Deployment rollout cannot be undone, since its revision history is cleaned up.

Paused

.spec.paused is an optional boolean field for pausing and resuming a Deployment. The only difference between a paused Deployment and one that is not paused, is that any changes into the PodTemplateSpec of the paused Deployment will not trigger new rollouts as long as it is paused. A Deployment is not paused by default when it is created.

What's next

Learn about Pods.
Run a Stateless Application Using a Deployment.
Deployment is a top-level resource in the Kubernetes REST API. Read the Deployment object definition to understand the API for deployments.
Read about PodDisruptionBudget and how you can use it to manage application availability during disruptions.

2 - ReplicaSet

A ReplicaSet's purpose is to maintain a stable set of replica Pods running at any given time. As such, it is often used to guarantee the availability of a specified number of identical Pods.

How a ReplicaSet works

A ReplicaSet is defined with fields, including a selector that specifies how to identify Pods it can acquire, a number of replicas indicating how many Pods it should be maintaining, and a pod template specifying the data of new Pods it should create to meet the number of replicas criteria. A ReplicaSet then fulfills its purpose by creating and deleting Pods as needed to reach the desired number. When a ReplicaSet needs to create new Pods, it uses its Pod template.

A ReplicaSet is linked to its Pods via the Pods' metadata.ownerReferences field, which specifies what resource the current object is owned by. All Pods acquired by a ReplicaSet have their owning ReplicaSet's identifying information within their ownerReferences field. It's through this link that the ReplicaSet knows of the state of the Pods it is maintaining and plans accordingly.

A ReplicaSet identifies new Pods to acquire by using its selector. If there is a Pod that has no OwnerReference or the OwnerReference is not a Controller and it matches a ReplicaSet's selector, it will be immediately acquired by said ReplicaSet.

When to use a ReplicaSet

A ReplicaSet ensures that a specified number of pod replicas are running at any given time. However, a Deployment is a higher-level concept that manages ReplicaSets and provides declarative updates to Pods along with a lot of other useful features. Therefore, we recommend using Deployments instead of directly using ReplicaSets, unless you require custom update orchestration or don't require updates at all.

This actually means that you may never need to manipulate ReplicaSet objects: use a Deployment instead, and define your application in the spec section.

Example

controllers/frontend.yaml

apiVersion: apps/v1
kind: ReplicaSet
metadata:
  name: frontend
  labels:
    app: guestbook
    tier: frontend
spec:
  # modify replicas according to your case
  replicas: 3
  selector:
    matchLabels:
      tier: frontend
  template:
    metadata:
      labels:
        tier: frontend
    spec:
      containers:
      - name: php-redis
        image: gcr.io/google_samples/gb-frontend:v3

Saving this manifest into frontend.yaml and submitting it to a Kubernetes cluster will create the defined ReplicaSet and the Pods that it manages.

kubectl apply -f https://kubernetes.io/examples/controllers/frontend.yaml

You can then get the current ReplicaSets deployed:

kubectl get rs

And see the frontend one you created:

NAME       DESIRED   CURRENT   READY   AGE
frontend   3         3         3       6s

You can also check on the state of the ReplicaSet:

kubectl describe rs/frontend

And you will see output similar to:

Name:         frontend
Namespace:    default
Selector:     tier=frontend
Labels:       app=guestbook
              tier=frontend
Annotations:  kubectl.kubernetes.io/last-applied-configuration:
                {"apiVersion":"apps/v1","kind":"ReplicaSet","metadata":{"annotations":{},"labels":{"app":"guestbook","tier":"frontend"},"name":"frontend",...
Replicas:     3 current / 3 desired
Pods Status:  3 Running / 0 Waiting / 0 Succeeded / 0 Failed
Pod Template:
  Labels:  tier=frontend
  Containers:
   php-redis:
    Image:        gcr.io/google_samples/gb-frontend:v3
    Port:         <none>
    Host Port:    <none>
    Environment:  <none>
    Mounts:       <none>
  Volumes:        <none>
Events:
  Type    Reason            Age   From                   Message
  ----    ------            ----  ----                   -------
  Normal  SuccessfulCreate  117s  replicaset-controller  Created pod: frontend-wtsmm
  Normal  SuccessfulCreate  116s  replicaset-controller  Created pod: frontend-b2zdv
  Normal  SuccessfulCreate  116s  replicaset-controller  Created pod: frontend-vcmts

And lastly you can check for the Pods brought up:

kubectl get pods

You should see Pod information similar to:

NAME             READY   STATUS    RESTARTS   AGE
frontend-b2zdv   1/1     Running   0          6m36s
frontend-vcmts   1/1     Running   0          6m36s
frontend-wtsmm   1/1     Running   0          6m36s

You can also verify that the owner reference of these pods is set to the frontend ReplicaSet. To do this, get the yaml of one of the Pods running:

kubectl get pods frontend-b2zdv -o yaml

The output will look similar to this, with the frontend ReplicaSet's info set in the metadata's ownerReferences field:

apiVersion: v1
kind: Pod
metadata:
  creationTimestamp: "2020-02-12T07:06:16Z"
  generateName: frontend-
  labels:
    tier: frontend
  name: frontend-b2zdv
  namespace: default
  ownerReferences:
  - apiVersion: apps/v1
    blockOwnerDeletion: true
    controller: true
    kind: ReplicaSet
    name: frontend
    uid: f391f6db-bb9b-4c09-ae74-6a1f77f3d5cf
...

Non-Template Pod acquisitions

While you can create bare Pods with no problems, it is strongly recommended to make sure that the bare Pods do not have labels which match the selector of one of your ReplicaSets. The reason for this is because a ReplicaSet is not limited to owning Pods specified by its template-- it can acquire other Pods in the manner specified in the previous sections.

Take the previous frontend ReplicaSet example, and the Pods specified in the following manifest:

pods/pod-rs.yaml

apiVersion: v1
kind: Pod
metadata:
  name: pod1
  labels:
    tier: frontend
spec:
  containers:
  - name: hello1
    image: gcr.io/google-samples/hello-app:2.0

---

apiVersion: v1
kind: Pod
metadata:
  name: pod2
  labels:
    tier: frontend
spec:
  containers:
  - name: hello2
    image: gcr.io/google-samples/hello-app:1.0

As those Pods do not have a Controller (or any object) as their owner reference and match the selector of the frontend ReplicaSet, they will immediately be acquired by it.

Suppose you create the Pods after the frontend ReplicaSet has been deployed and has set up its initial Pod replicas to fulfill its replica count requirement:

kubectl apply -f https://kubernetes.io/examples/pods/pod-rs.yaml

The new Pods will be acquired by the ReplicaSet, and then immediately terminated as the ReplicaSet would be over its desired count.

Fetching the Pods:

kubectl get pods

The output shows that the new Pods are either already terminated, or in the process of being terminated:

NAME             READY   STATUS        RESTARTS   AGE
frontend-b2zdv   1/1     Running       0          10m
frontend-vcmts   1/1     Running       0          10m
frontend-wtsmm   1/1     Running       0          10m
pod1             0/1     Terminating   0          1s
pod2             0/1     Terminating   0          1s

If you create the Pods first:

kubectl apply -f https://kubernetes.io/examples/pods/pod-rs.yaml

And then create the ReplicaSet however:

kubectl apply -f https://kubernetes.io/examples/controllers/frontend.yaml

You shall see that the ReplicaSet has acquired the Pods and has only created new ones according to its spec until the number of its new Pods and the original matches its desired count. As fetching the Pods:

kubectl get pods

Will reveal in its output:

NAME             READY   STATUS    RESTARTS   AGE
frontend-hmmj2   1/1     Running   0          9s
pod1             1/1     Running   0          36s
pod2             1/1     Running   0          36s

In this manner, a ReplicaSet can own a non-homogenous set of Pods

Writing a ReplicaSet manifest

As with all other Kubernetes API objects, a ReplicaSet needs the apiVersion, kind, and metadata fields. For ReplicaSets, the kind is always a ReplicaSet.

The name of a ReplicaSet object must be a valid DNS subdomain name.

A ReplicaSet also needs a .spec section.

Pod Template

The .spec.template is a pod template which is also required to have labels in place. In our frontend.yaml example we had one label: tier: frontend. Be careful not to overlap with the selectors of other controllers, lest they try to adopt this Pod.

For the template's restart policy field, .spec.template.spec.restartPolicy, the only allowed value is Always, which is the default.

Pod Selector

The .spec.selector field is a label selector. As discussed earlier these are the labels used to identify potential Pods to acquire. In our frontend.yaml example, the selector was:

matchLabels:
  tier: frontend

In the ReplicaSet, .spec.template.metadata.labels must match spec.selector, or it will be rejected by the API.

Note: For 2 ReplicaSets specifying the same .spec.selector but different .spec.template.metadata.labels and .spec.template.spec fields, each ReplicaSet ignores the Pods created by the other ReplicaSet.

Replicas

You can specify how many Pods should run concurrently by setting .spec.replicas. The ReplicaSet will create/delete its Pods to match this number.

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

Working with ReplicaSets

Deleting a ReplicaSet and its Pods

To delete a ReplicaSet and all of its Pods, use kubectl delete. The Garbage collector automatically deletes all of the dependent Pods by default.

When using the REST API or the client-go library, you must set propagationPolicy to Background or Foreground in the -d option. For example:

kubectl proxy --port=8080
curl -X DELETE  'localhost:8080/apis/apps/v1/namespaces/default/replicasets/frontend' \
> -d '{"kind":"DeleteOptions","apiVersion":"v1","propagationPolicy":"Foreground"}' \
> -H "Content-Type: application/json"

Deleting just a ReplicaSet

You can delete a ReplicaSet without affecting any of its Pods using kubectl delete with the --cascade=orphan option. When using the REST API or the client-go library, you must set propagationPolicy to Orphan. For example:

kubectl proxy --port=8080
curl -X DELETE  'localhost:8080/apis/apps/v1/namespaces/default/replicasets/frontend' \
> -d '{"kind":"DeleteOptions","apiVersion":"v1","propagationPolicy":"Orphan"}' \
> -H "Content-Type: application/json"

Once the original is deleted, you can create a new ReplicaSet 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 Deployment, as ReplicaSets do not support a rolling update directly.

Isolating Pods from a ReplicaSet

You can remove Pods from a ReplicaSet 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).

Scaling a ReplicaSet

A ReplicaSet can be easily scaled up or down by simply updating the .spec.replicas field. The ReplicaSet controller ensures that a desired number of Pods with a matching label selector are available and operational.

When scaling down, the ReplicaSet controller chooses which pods to delete by sorting the available pods to prioritize scaling down pods based on the following general algorithm:

Pending (and unschedulable) pods are scaled down first
If controller.kubernetes.io/pod-deletion-cost annotation is set, then the pod with the lower value will come first.
Pods on nodes with more replicas come before pods on nodes with fewer replicas.
If the pods' creation times differ, the pod that was created more recently comes before the older pod (the creation times are bucketed on an integer log scale when the LogarithmicScaleDown feature gate is enabled)

If all of the above match, then selection is random.

Pod deletion cost

FEATURE STATE: Kubernetes v1.22 [beta]

Using the controller.kubernetes.io/pod-deletion-cost annotation, users can set a preference regarding which pods to remove first when downscaling a ReplicaSet.

The annotation should be set on the pod, the range is [-2147483647, 2147483647]. It represents the cost of deleting a pod compared to other pods belonging to the same ReplicaSet. Pods with lower deletion cost are preferred to be deleted before pods with higher deletion cost.

The implicit value for this annotation for pods that don't set it is 0; negative values are permitted. Invalid values will be rejected by the API server.

This feature is beta and enabled by default. You can disable it using the feature gate PodDeletionCost in both kube-apiserver and kube-controller-manager.

Note:

This is honored on a best-effort basis, so it does not offer any guarantees on pod deletion order.
Users should avoid updating the annotation frequently, such as updating it based on a metric value, because doing so will generate a significant number of pod updates on the apiserver.

Example Use Case

The different pods of an application could have different utilization levels. On scale down, the application may prefer to remove the pods with lower utilization. To avoid frequently updating the pods, the application should update controller.kubernetes.io/pod-deletion-cost once before issuing a scale down (setting the annotation to a value proportional to pod utilization level). This works if the application itself controls the down scaling; for example, the driver pod of a Spark deployment.

ReplicaSet as a Horizontal Pod Autoscaler Target

A ReplicaSet can also be a target for Horizontal Pod Autoscalers (HPA). That is, a ReplicaSet can be auto-scaled by an HPA. Here is an example HPA targeting the ReplicaSet we created in the previous example.

controllers/hpa-rs.yaml

apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
metadata:
  name: frontend-scaler
spec:
  scaleTargetRef:
    kind: ReplicaSet
    name: frontend
  minReplicas: 3
  maxReplicas: 10
  targetCPUUtilizationPercentage: 50

Saving this manifest into hpa-rs.yaml and submitting it to a Kubernetes cluster should create the defined HPA that autoscales the target ReplicaSet depending on the CPU usage of the replicated Pods.

kubectl apply -f https://k8s.io/examples/controllers/hpa-rs.yaml

Alternatively, you can use the kubectl autoscale command to accomplish the same (and it's easier!)

kubectl autoscale rs frontend --max=10 --min=3 --cpu-percent=50

Alternatives to ReplicaSet

Deployment (recommended)

Deployment is an object which can own ReplicaSets and update them and their Pods via declarative, server-side rolling updates. While ReplicaSets can be used independently, today they're mainly used by Deployments as a mechanism to orchestrate Pod creation, deletion and updates. When you use Deployments you don't have to worry about managing the ReplicaSets that they create. Deployments own and manage their ReplicaSets. As such, it is recommended to use Deployments when you want ReplicaSets.

Bare Pods

Unlike the case where a user directly created Pods, a ReplicaSet 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 ReplicaSet 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 ReplicaSet delegates local container restarts to some agent on the node (for example, Kubelet or Docker).

Job

Use a Job instead of a ReplicaSet for Pods that are expected to terminate on their own (that is, batch jobs).

DaemonSet

Use a DaemonSet instead of a ReplicaSet 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.

ReplicationController

ReplicaSets are the successors to ReplicationControllers. The two serve the same purpose, and behave similarly, except that a ReplicationController does not support set-based selector requirements as described in the labels user guide. As such, ReplicaSets are preferred over ReplicationControllers

What's next

Learn about Pods.
Learn about Deployments.
Run a Stateless Application Using a Deployment, which relies on ReplicaSets to work.
ReplicaSet is a top-level resource in the Kubernetes REST API. Read the ReplicaSet object definition to understand the API for replica sets.
Read about PodDisruptionBudget and how you can use it to manage application availability during disruptions.

3 - StatefulSets

StatefulSet is the workload API object used to manage stateful applications.

Manages the deployment and scaling of a set of Pods, and provides guarantees about the ordering and uniqueness of these Pods.

Like a Deployment, a StatefulSet manages Pods that are based on an identical container spec. Unlike a Deployment, a StatefulSet maintains a sticky identity for each of their Pods. These pods are created from the same spec, but are not interchangeable: each has a persistent identifier that it maintains across any rescheduling.

If you want to use storage volumes to provide persistence for your workload, you can use a StatefulSet as part of the solution. Although individual Pods in a StatefulSet are susceptible to failure, the persistent Pod identifiers make it easier to match existing volumes to the new Pods that replace any that have failed.

Using StatefulSets

StatefulSets are valuable for applications that require one or more of the following.

Stable, unique network identifiers.
Stable, persistent storage.
Ordered, graceful deployment and scaling.
Ordered, automated rolling updates.

In the above, stable is synonymous with persistence across Pod (re)scheduling. If an application doesn't require any stable identifiers or ordered deployment, deletion, or scaling, you should deploy your application using a workload object that provides a set of stateless replicas. Deployment or ReplicaSet may be better suited to your stateless needs.

Limitations

The storage for a given Pod must either be provisioned by a PersistentVolume Provisioner based on the requested storage class, or pre-provisioned by an admin.
Deleting and/or scaling a StatefulSet down will not delete the volumes associated with the StatefulSet. This is done to ensure data safety, which is generally more valuable than an automatic purge of all related StatefulSet resources.
StatefulSets currently require a Headless Service to be responsible for the network identity of the Pods. You are responsible for creating this Service.
StatefulSets do not provide any guarantees on the termination of pods when a StatefulSet is deleted. To achieve ordered and graceful termination of the pods in the StatefulSet, it is possible to scale the StatefulSet down to 0 prior to deletion.
When using Rolling Updates with the default Pod Management Policy (OrderedReady), it's possible to get into a broken state that requires manual intervention to repair.

Components

The example below demonstrates the components of a StatefulSet.

apiVersion: v1
kind: Service
metadata:
  name: nginx
  labels:
    app: nginx
spec:
  ports:
  - port: 80
    name: web
  clusterIP: None
  selector:
    app: nginx
---
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: web
spec:
  selector:
    matchLabels:
      app: nginx # has to match .spec.template.metadata.labels
  serviceName: "nginx"
  replicas: 3 # by default is 1
  minReadySeconds: 10 # by default is 0
  template:
    metadata:
      labels:
        app: nginx # has to match .spec.selector.matchLabels
    spec:
      terminationGracePeriodSeconds: 10
      containers:
      - name: nginx
        image: k8s.gcr.io/nginx-slim:0.8
        ports:
        - containerPort: 80
          name: web
        volumeMounts:
        - name: www
          mountPath: /usr/share/nginx/html
  volumeClaimTemplates:
  - metadata:
      name: www
    spec:
      accessModes: [ "ReadWriteOnce" ]
      storageClassName: "my-storage-class"
      resources:
        requests:
          storage: 1Gi

In the above example:

A Headless Service, named nginx, is used to control the network domain.
The StatefulSet, named web, has a Spec that indicates that 3 replicas of the nginx container will be launched in unique Pods.
The volumeClaimTemplates will provide stable storage using PersistentVolumes provisioned by a PersistentVolume Provisioner.

The name of a StatefulSet object must be a valid DNS subdomain name.

Pod Selector

You must set the .spec.selector field of a StatefulSet to match the labels of its .spec.template.metadata.labels. Failing to specify a matching Pod Selector will result in a validation error during StatefulSet creation.

Volume Claim Templates

You can set the .spec.volumeClaimTemplates which can provide stable storage using PersistentVolumes provisioned by a PersistentVolume Provisioner.

Minimum ready seconds

FEATURE STATE: Kubernetes v1.23 [beta]

.spec.minReadySeconds is an optional field that specifies the minimum number of seconds for which a newly created Pod should be ready without any of its containers crashing, for it to be considered available. Please note that this feature is beta and enabled by default. Please opt out by unsetting the StatefulSetMinReadySeconds flag, if you don't want this feature to be enabled. This field defaults to 0 (the Pod will be considered available as soon as it is ready). To learn more about when a Pod is considered ready, see Container Probes.

Pod Identity

StatefulSet Pods have a unique identity that is comprised of an ordinal, a stable network identity, and stable storage. The identity sticks to the Pod, regardless of which node it's (re)scheduled on.

Ordinal Index

For a StatefulSet with N replicas, each Pod in the StatefulSet will be assigned an integer ordinal, from 0 up through N-1, that is unique over the Set.

Stable Network ID

Each Pod in a StatefulSet derives its hostname from the name of the StatefulSet and the ordinal of the Pod. The pattern for the constructed hostname is $(statefulset name)-$(ordinal). The example above will create three Pods named web-0,web-1,web-2. A StatefulSet can use a Headless Service to control the domain of its Pods. The domain managed by this Service takes the form: $(service name).$(namespace).svc.cluster.local, where "cluster.local" is the cluster domain. As each Pod is created, it gets a matching DNS subdomain, taking the form: $(podname).$(governing service domain), where the governing service is defined by the serviceName field on the StatefulSet.

Depending on how DNS is configured in your cluster, you may not be able to look up the DNS name for a newly-run Pod immediately. This behavior can occur when other clients in the cluster have already sent queries for the hostname of the Pod before it was created. Negative caching (normal in DNS) means that the results of previous failed lookups are remembered and reused, even after the Pod is running, for at least a few seconds.

If you need to discover Pods promptly after they are created, you have a few options:

Query the Kubernetes API directly (for example, using a watch) rather than relying on DNS lookups.
Decrease the time of caching in your Kubernetes DNS provider (typically this means editing the config map for CoreDNS, which currently caches for 30 seconds).

As mentioned in the limitations section, you are responsible for creating the Headless Service responsible for the network identity of the pods.

Here are some examples of choices for Cluster Domain, Service name, StatefulSet name, and how that affects the DNS names for the StatefulSet's Pods.

Cluster Domain	Service (ns/name)	StatefulSet (ns/name)	StatefulSet Domain	Pod DNS	Pod Hostname
cluster.local	default/nginx	default/web	nginx.default.svc.cluster.local	web-{0..N-1}.nginx.default.svc.cluster.local	web-{0..N-1}
cluster.local	foo/nginx	foo/web	nginx.foo.svc.cluster.local	web-{0..N-1}.nginx.foo.svc.cluster.local	web-{0..N-1}
kube.local	foo/nginx	foo/web	nginx.foo.svc.kube.local	web-{0..N-1}.nginx.foo.svc.kube.local	web-{0..N-1}

Note: Cluster Domain will be set to cluster.local unless otherwise configured.

Stable Storage

For each VolumeClaimTemplate entry defined in a StatefulSet, each Pod receives one PersistentVolumeClaim. In the nginx example above, each Pod receives a single PersistentVolume with a StorageClass of my-storage-class and 1 Gib of provisioned storage. If no StorageClass is specified, then the default StorageClass will be used. When a Pod is (re)scheduled onto a node, its volumeMounts mount the PersistentVolumes associated with its PersistentVolume Claims. Note that, the PersistentVolumes associated with the Pods' PersistentVolume Claims are not deleted when the Pods, or StatefulSet are deleted. This must be done manually.

Pod Name Label

When the StatefulSet Controller creates a Pod, it adds a label, statefulset.kubernetes.io/pod-name, that is set to the name of the Pod. This label allows you to attach a Service to a specific Pod in the StatefulSet.

Deployment and Scaling Guarantees

For a StatefulSet with N replicas, when Pods are being deployed, they are created sequentially, in order from {0..N-1}.
When Pods are being deleted, they are terminated in reverse order, from {N-1..0}.
Before a scaling operation is applied to a Pod, all of its predecessors must be Running and Ready.
Before a Pod is terminated, all of its successors must be completely shutdown.

The StatefulSet should not specify a pod.Spec.TerminationGracePeriodSeconds of 0. This practice is unsafe and strongly discouraged. For further explanation, please refer to force deleting StatefulSet Pods.

When the nginx example above is created, three Pods will be deployed in the order web-0, web-1, web-2. web-1 will not be deployed before web-0 is Running and Ready, and web-2 will not be deployed until web-1 is Running and Ready. If web-0 should fail, after web-1 is Running and Ready, but before web-2 is launched, web-2 will not be launched until web-0 is successfully relaunched and becomes Running and Ready.

If a user were to scale the deployed example by patching the StatefulSet such that replicas=1, web-2 would be terminated first. web-1 would not be terminated until web-2 is fully shutdown and deleted. If web-0 were to fail after web-2 has been terminated and is completely shutdown, but prior to web-1's termination, web-1 would not be terminated until web-0 is Running and Ready.

Pod Management Policies

StatefulSet allows you to relax its ordering guarantees while preserving its uniqueness and identity guarantees via its .spec.podManagementPolicy field.

OrderedReady Pod Management

OrderedReady pod management is the default for StatefulSets. It implements the behavior described above.

Parallel Pod Management

Parallel pod management tells the StatefulSet controller to launch or terminate all Pods in parallel, and to not wait for Pods to become Running and Ready or completely terminated prior to launching or terminating another Pod. This option only affects the behavior for scaling operations. Updates are not affected.

Update strategies

A StatefulSet's .spec.updateStrategy field allows you to configure and disable automated rolling updates for containers, labels, resource request/limits, and annotations for the Pods in a StatefulSet. There are two possible values:

OnDelete: When a StatefulSet's .spec.updateStrategy.type is set to OnDelete, the StatefulSet controller will not automatically update the Pods in a StatefulSet. Users must manually delete Pods to cause the controller to create new Pods that reflect modifications made to a StatefulSet's .spec.template.
RollingUpdate: The RollingUpdate update strategy implements automated, rolling update for the Pods in a StatefulSet. This is the default update strategy.

Rolling Updates

When a StatefulSet's .spec.updateStrategy.type is set to RollingUpdate, the StatefulSet controller will delete and recreate each Pod in the StatefulSet. It will proceed in the same order as Pod termination (from the largest ordinal to the smallest), updating each Pod one at a time.

The Kubernetes control plane waits until an updated Pod is Running and Ready prior to updating its predecessor. If you have set .spec.minReadySeconds (see Minimum Ready Seconds), the control plane additionally waits that amount of time after the Pod turns ready, before moving on.

Partitioned rolling updates

The RollingUpdate update strategy can be partitioned, by specifying a .spec.updateStrategy.rollingUpdate.partition. If a partition is specified, all Pods with an ordinal that is greater than or equal to the partition will be updated when the StatefulSet's .spec.template is updated. All Pods with an ordinal that is less than the partition will not be updated, and, even if they are deleted, they will be recreated at the previous version. If a StatefulSet's .spec.updateStrategy.rollingUpdate.partition is greater than its .spec.replicas, updates to its .spec.template will not be propagated to its Pods. In most cases you will not need to use a partition, but they are useful if you want to stage an update, roll out a canary, or perform a phased roll out.

Maximum unavailable Pods

FEATURE STATE: Kubernetes v1.24 [alpha]

You can control the maximum number of Pods that can be unavailable during an update by specifying the .spec.updateStrategy.rollingUpdate.maxUnavailable field. The value can be an absolute number (for example, 5) or a percentage of desired Pods (for example, 10%). Absolute number is calculated from the percentage value by rounding it up. This field cannot be 0. The default setting is 1.

This field applies to all Pods in the range 0 to replicas - 1. If there is any unavailable Pod in the range 0 to replicas - 1, it will be counted towards maxUnavailable.

Note: The maxUnavailable field is in Alpha stage and it is honored only by API servers that are running with the MaxUnavailableStatefulSet feature gate enabled.

Forced rollback

When using Rolling Updates with the default Pod Management Policy (OrderedReady), it's possible to get into a broken state that requires manual intervention to repair.

If you update the Pod template to a configuration that never becomes Running and Ready (for example, due to a bad binary or application-level configuration error), StatefulSet will stop the rollout and wait.

In this state, it's not enough to revert the Pod template to a good configuration. Due to a known issue, StatefulSet will continue to wait for the broken Pod to become Ready (which never happens) before it will attempt to revert it back to the working configuration.

After reverting the template, you must also delete any Pods that StatefulSet had already attempted to run with the bad configuration. StatefulSet will then begin to recreate the Pods using the reverted template.

PersistentVolumeClaim retention

FEATURE STATE: Kubernetes v1.23 [alpha]

The optional .spec.persistentVolumeClaimRetentionPolicy field controls if and how PVCs are deleted during the lifecycle of a StatefulSet. You must enable the StatefulSetAutoDeletePVC feature gate to use this field. Once enabled, there are two policies you can configure for each StatefulSet:

whenDeleted: configures the volume retention behavior that applies when the StatefulSet is deleted
whenScaled: configures the volume retention behavior that applies when the replica count of the StatefulSet is reduced; for example, when scaling down the set.

For each policy that you can configure, you can set the value to either Delete or Retain.

Delete: The PVCs created from the StatefulSet volumeClaimTemplate are deleted for each Pod affected by the policy. With the whenDeleted policy all PVCs from the volumeClaimTemplate are deleted after their Pods have been deleted. With the whenScaled policy, only PVCs corresponding to Pod replicas being scaled down are deleted, after their Pods have been deleted.
Retain (default): PVCs from the volumeClaimTemplate are not affected when their Pod is deleted. This is the behavior before this new feature.

Bear in mind that these policies only apply when Pods are being removed due to the StatefulSet being deleted or scaled down. For example, if a Pod associated with a StatefulSet fails due to node failure, and the control plane creates a replacement Pod, the StatefulSet retains the existing PVC. The existing volume is unaffected, and the cluster will attach it to the node where the new Pod is about to launch.

The default for policies is Retain, matching the StatefulSet behavior before this new feature.

Here is an example policy.

apiVersion: apps/v1
kind: StatefulSet
...
spec:
  persistentVolumeClaimRetentionPolicy:
    whenDeleted: Retain
    whenScaled: Delete
...

The StatefulSet controller adds owner references to its PVCs, which are then deleted by the garbage collector after the Pod is terminated. This enables the Pod to cleanly unmount all volumes before the PVCs are deleted (and before the backing PV and volume are deleted, depending on the retain policy). When you set the whenDeleted policy to Delete, an owner reference to the StatefulSet instance is placed on all PVCs associated with that StatefulSet.

The whenScaled policy must delete PVCs only when a Pod is scaled down, and not when a Pod is deleted for another reason. When reconciling, the StatefulSet controller compares its desired replica count to the actual Pods present on the cluster. Any StatefulSet Pod whose id greater than the replica count is condemned and marked for deletion. If the whenScaled policy is Delete, the condemned Pods are first set as owners to the associated StatefulSet template PVCs, before the Pod is deleted. This causes the PVCs to be garbage collected after only the condemned Pods have terminated.

This means that if the controller crashes and restarts, no Pod will be deleted before its owner reference has been updated appropriate to the policy. If a condemned Pod is force-deleted while the controller is down, the owner reference may or may not have been set up, depending on when the controller crashed. It may take several reconcile loops to update the owner references, so some condemned Pods may have set up owner references and other may not. For this reason we recommend waiting for the controller to come back up, which will verify owner references before terminating Pods. If that is not possible, the operator should verify the owner references on PVCs to ensure the expected objects are deleted when Pods are force-deleted.

Replicas

.spec.replicas is an optional field that specifies the number of desired Pods. It defaults to 1.

Should you manually scale a deployment, example via kubectl scale statefulset statefulset --replicas=X, and then you update that StatefulSet based on a manifest (for example: by running kubectl apply -f statefulset.yaml), then applying that manifest overwrites the manual scaling that you previously did.

If a HorizontalPodAutoscaler (or any similar API for horizontal scaling) is managing scaling for a Statefulset, don't set .spec.replicas. Instead, allow the Kubernetes control plane to manage the .spec.replicas field automatically.

What's next

Learn about Pods.
Find out how to use StatefulSets
- Follow an example of deploying a stateful application.
- Follow an example of deploying Cassandra with Stateful Sets.
- Follow an example of running a replicated stateful application.
- Learn how to scale a StatefulSet.
- Learn what's involved when you delete a StatefulSet.
- Learn how to configure a Pod to use a volume for storage.
- Learn how to configure a Pod to use a PersistentVolume for storage.
StatefulSet is a top-level resource in the Kubernetes REST API. Read the StatefulSet object definition to understand the API for stateful sets.
Read about PodDisruptionBudget and how you can use it to manage application availability during disruptions.

4 - DaemonSet

A DaemonSet ensures that all (or some) Nodes run a copy of a Pod. As nodes are added to the cluster, Pods are added to them. As nodes are removed from the cluster, those Pods are garbage collected. Deleting a DaemonSet will clean up the Pods it created.

Some typical uses of a DaemonSet are:

running a cluster storage daemon on every node
running a logs collection daemon on every node
running a node monitoring daemon on every node

In a simple case, one DaemonSet, covering all nodes, would be used for each type of daemon. A more complex setup might use multiple DaemonSets for a single type of daemon, but with different flags and/or different memory and cpu requests for different hardware types.

Writing a DaemonSet Spec

Create a DaemonSet

You can describe a DaemonSet in a YAML file. For example, the daemonset.yaml file below describes a DaemonSet that runs the fluentd-elasticsearch Docker image:

controllers/daemonset.yaml

apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: fluentd-elasticsearch
  namespace: kube-system
  labels:
    k8s-app: fluentd-logging
spec:
  selector:
    matchLabels:
      name: fluentd-elasticsearch
  template:
    metadata:
      labels:
        name: fluentd-elasticsearch
    spec:
      tolerations:
      # these tolerations are to have the daemonset runnable on control plane nodes
      # remove them if your control plane nodes should not run pods
      - key: node-role.kubernetes.io/control-plane
        operator: Exists
        effect: NoSchedule
      - key: node-role.kubernetes.io/master
        operator: Exists
        effect: NoSchedule
      containers:
      - name: fluentd-elasticsearch
        image: quay.io/fluentd_elasticsearch/fluentd:v2.5.2
        resources:
          limits:
            memory: 200Mi
          requests:
            cpu: 100m
            memory: 200Mi
        volumeMounts:
        - name: varlog
          mountPath: /var/log
        - name: varlibdockercontainers
          mountPath: /var/lib/docker/containers
          readOnly: true
      terminationGracePeriodSeconds: 30
      volumes:
      - name: varlog
        hostPath:
          path: /var/log
      - name: varlibdockercontainers
        hostPath:
          path: /var/lib/docker/containers

Create a DaemonSet based on the YAML file:

kubectl apply -f https://k8s.io/examples/controllers/daemonset.yaml

Required Fields

As with all other Kubernetes config, a DaemonSet needs apiVersion, kind, and metadata fields. For general information about working with config files, see running stateless applications and object management using kubectl.

The name of a DaemonSet object must be a valid DNS subdomain name.

A DaemonSet also needs a .spec section.

Pod Template

The .spec.template is one of the required fields in .spec.

The .spec.template is a pod template. It has exactly the same schema as a 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 DaemonSet has to specify appropriate labels (see pod selector).

A Pod Template in a DaemonSet must have a RestartPolicy equal to Always, or be unspecified, which defaults to Always.

Pod Selector

The .spec.selector field is a pod selector. It works the same as the .spec.selector of a Job.

You must specify a pod selector that matches the labels of the .spec.template. Also, once a DaemonSet is created, its .spec.selector can not be mutated. Mutating the pod selector can lead to the unintentional orphaning of Pods, and it was found to be confusing to users.

The .spec.selector is an object consisting of two fields:

matchLabels - works the same as the .spec.selector of a ReplicationController.
matchExpressions - allows to build more sophisticated selectors by specifying key, list of values and an operator that relates the key and values.

When the two are specified the result is ANDed.

The .spec.selector must match the .spec.template.metadata.labels. Config with these two not matching will be rejected by the API.

Running Pods on select Nodes

If you specify a .spec.template.spec.nodeSelector, then the DaemonSet controller will create Pods on nodes which match that node selector. Likewise if you specify a .spec.template.spec.affinity, then DaemonSet controller will create Pods on nodes which match that node affinity. If you do not specify either, then the DaemonSet controller will create Pods on all nodes.

How Daemon Pods are scheduled

Scheduled by default scheduler

FEATURE STATE: Kubernetes 1.17 [stable]

A DaemonSet ensures that all eligible nodes run a copy of a Pod. Normally, the node that a Pod runs on is selected by the Kubernetes scheduler. However, DaemonSet pods are created and scheduled by the DaemonSet controller instead. That introduces the following issues:

Inconsistent Pod behavior: Normal Pods waiting to be scheduled are created and in Pending state, but DaemonSet pods are not created in Pending state. This is confusing to the user.
Pod preemption is handled by default scheduler. When preemption is enabled, the DaemonSet controller will make scheduling decisions without considering pod priority and preemption.

ScheduleDaemonSetPods allows you to schedule DaemonSets using the default scheduler instead of the DaemonSet controller, by adding the NodeAffinity term to the DaemonSet pods, instead of the .spec.nodeName term. The default scheduler is then used to bind the pod to the target host. If node affinity of the DaemonSet pod already exists, it is replaced (the original node affinity was taken into account before selecting the target host). The DaemonSet controller only performs these operations when creating or modifying DaemonSet pods, and no changes are made to the spec.template of the DaemonSet.

nodeAffinity:
  requiredDuringSchedulingIgnoredDuringExecution:
    nodeSelectorTerms:
    - matchFields:
      - key: metadata.name
        operator: In
        values:
        - target-host-name

In addition, node.kubernetes.io/unschedulable:NoSchedule toleration is added automatically to DaemonSet Pods. The default scheduler ignores unschedulable Nodes when scheduling DaemonSet Pods.

Taints and Tolerations

Although Daemon Pods respect taints and tolerations, the following tolerations are added to DaemonSet Pods automatically according to the related features.

Toleration Key	Effect	Version	Description
`node.kubernetes.io/not-ready`	NoExecute	1.13+	DaemonSet pods will not be evicted when there are node problems such as a network partition.
`node.kubernetes.io/unreachable`	NoExecute	1.13+	DaemonSet pods will not be evicted when there are node problems such as a network partition.
`node.kubernetes.io/disk-pressure`	NoSchedule	1.8+	DaemonSet pods tolerate disk-pressure attributes by default scheduler.
`node.kubernetes.io/memory-pressure`	NoSchedule	1.8+	DaemonSet pods tolerate memory-pressure attributes by default scheduler.
`node.kubernetes.io/unschedulable`	NoSchedule	1.12+	DaemonSet pods tolerate unschedulable attributes by default scheduler.
`node.kubernetes.io/network-unavailable`	NoSchedule	1.12+	DaemonSet pods, who uses host network, tolerate network-unavailable attributes by default scheduler.

Communicating with Daemon Pods

Some possible patterns for communicating with Pods in a DaemonSet are:

Push: Pods in the DaemonSet are configured to send updates to another service, such as a stats database. They do not have clients.
NodeIP and Known Port: Pods in the DaemonSet can use a hostPort, so that the pods are reachable via the node IPs. Clients know the list of node IPs somehow, and know the port by convention.
DNS: Create a headless service with the same pod selector, and then discover DaemonSets using the endpoints resource or retrieve multiple A records from DNS.
Service: Create a service with the same Pod selector, and use the service to reach a daemon on a random node. (No way to reach specific node.)

Updating a DaemonSet

If node labels are changed, the DaemonSet will promptly add Pods to newly matching nodes and delete Pods from newly not-matching nodes.

You can modify the Pods that a DaemonSet creates. However, Pods do not allow all fields to be updated. Also, the DaemonSet controller will use the original template the next time a node (even with the same name) is created.

You can delete a DaemonSet. If you specify --cascade=orphan with kubectl, then the Pods will be left on the nodes. If you subsequently create a new DaemonSet with the same selector, the new DaemonSet adopts the existing Pods. If any Pods need replacing the DaemonSet replaces them according to its updateStrategy.

You can perform a rolling update on a DaemonSet.

Alternatives to DaemonSet

Init scripts

It is certainly possible to run daemon processes by directly starting them on a node (e.g. using init, upstartd, or systemd). This is perfectly fine. However, there are several advantages to running such processes via a DaemonSet:

Ability to monitor and manage logs for daemons in the same way as applications.
Same config language and tools (e.g. Pod templates, kubectl) for daemons and applications.
Running daemons in containers with resource limits increases isolation between daemons from app containers. However, this can also be accomplished by running the daemons in a container but not in a Pod (e.g. start directly via Docker).

Bare Pods

It is possible to create Pods directly which specify a particular node to run on. However, a DaemonSet 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, you should use a DaemonSet rather than creating individual Pods.

Static Pods

It is possible to create Pods by writing a file to a certain directory watched by Kubelet. These are called static pods. Unlike DaemonSet, static Pods cannot be managed with kubectl or other Kubernetes API clients. Static Pods do not depend on the apiserver, making them useful in cluster bootstrapping cases. Also, static Pods may be deprecated in the future.

Deployments

DaemonSets are similar to Deployments in that they both create Pods, and those Pods have processes which are not expected to terminate (e.g. web servers, storage servers).

Use a Deployment for stateless services, like frontends, where scaling up and down the number of replicas and rolling out updates are more important than controlling exactly which host the Pod runs on. Use a DaemonSet when it is important that a copy of a Pod always run on all or certain hosts, if the DaemonSet provides node-level functionality that allows other Pods to run correctly on that particular node.

For example, network plugins often include a component that runs as a DaemonSet. The DaemonSet component makes sure that the node where it's running has working cluster networking.

What's next

Learn about Pods.
- Learn about static Pods, which are useful for running Kubernetes control plane components.
Find out how to use DaemonSets
- Perform a rolling update on a DaemonSet
- Perform a rollback on a DaemonSet (for example, if a roll out didn't work how you expected).
Understand how Kubernetes assigns Pods to Nodes.
Learn about device plugins and add ons, which often run as DaemonSets.
DaemonSet is a top-level resource in the Kubernetes REST API. Read the DaemonSet object definition to understand the API for daemon sets.

5 - Jobs

A Job creates one or more Pods and will continue to retry execution of the Pods until a specified number of them successfully terminate. As pods successfully complete, the Job tracks the successful completions. When a specified number of successful completions is reached, the task (ie, Job) is complete. Deleting a Job will clean up the Pods it created. Suspending a Job will delete its active Pods until the Job is resumed again.

A simple case is to create one Job object in order to reliably run one Pod to completion. The Job object will start a new Pod if the first Pod fails or is deleted (for example due to a node hardware failure or a node reboot).

You can also use a Job to run multiple Pods in parallel.

If you want to run a Job (either a single task, or several in parallel) on a schedule, see CronJob.

Running an example Job

Here is an example Job config. It computes π to 2000 places and prints it out. It takes around 10s to complete.

controllers/job.yaml

apiVersion: batch/v1
kind: Job
metadata:
  name: pi
spec:
  template:
    spec:
      containers:
      - name: pi
        image: perl
        command: ["perl",  "-Mbignum=bpi", "-wle", "print bpi(2000)"]
      restartPolicy: Never
  backoffLimit: 4

You can run the example with this command:

kubectl apply -f https://kubernetes.io/examples/controllers/job.yaml

The output is similar to this:

job.batch/pi created

Check on the status of the Job with kubectl:

kubectl describe jobs/pi

The output is similar to this:

Name:           pi
Namespace:      default
Selector:       controller-uid=c9948307-e56d-4b5d-8302-ae2d7b7da67c
Labels:         controller-uid=c9948307-e56d-4b5d-8302-ae2d7b7da67c
                job-name=pi
Annotations:    kubectl.kubernetes.io/last-applied-configuration:
                  {"apiVersion":"batch/v1","kind":"Job","metadata":{"annotations":{},"name":"pi","namespace":"default"},"spec":{"backoffLimit":4,"template":...
Parallelism:    1
Completions:    1
Start Time:     Mon, 02 Dec 2019 15:20:11 +0200
Completed At:   Mon, 02 Dec 2019 15:21:16 +0200
Duration:       65s
Pods Statuses:  0 Running / 1 Succeeded / 0 Failed
Pod Template:
  Labels:  controller-uid=c9948307-e56d-4b5d-8302-ae2d7b7da67c
           job-name=pi
  Containers:
   pi:
    Image:      perl
    Port:       <none>
    Host Port:  <none>
    Command:
      perl
      -Mbignum=bpi
      -wle
      print bpi(2000)
    Environment:  <none>
    Mounts:       <none>
  Volumes:        <none>
Events:
  Type    Reason            Age   From            Message
  ----    ------            ----  ----            -------
  Normal  SuccessfulCreate  14m   job-controller  Created pod: pi-5rwd7

To view completed Pods of a Job, use kubectl get pods.

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

pods=$(kubectl get pods --selector=job-name=pi --output=jsonpath='{.items[*].metadata.name}')
echo $pods

The output is similar to this:

pi-5rwd7

Here, the selector is the same as the selector for the Job. The --output=jsonpath option specifies an expression with the name from each Pod in the returned list.

View the standard output of one of the pods:

kubectl logs $pods

The output is similar to this:

3.1415926535897932384626433832795028841971693993751058209749445923078164062862089986280348253421170679821480865132823066470938446095505822317253594081284811174502841027019385211055596446229489549303819644288109756659334461284756482337867831652712019091456485669234603486104543266482133936072602491412737245870066063155881748815209209628292540917153643678925903600113305305488204665213841469519415116094330572703657595919530921861173819326117931051185480744623799627495673518857527248912279381830119491298336733624406566430860213949463952247371907021798609437027705392171762931767523846748184676694051320005681271452635608277857713427577896091736371787214684409012249534301465495853710507922796892589235420199561121290219608640344181598136297747713099605187072113499999983729780499510597317328160963185950244594553469083026425223082533446850352619311881710100031378387528865875332083814206171776691473035982534904287554687311595628638823537875937519577818577805321712268066130019278766111959092164201989380952572010654858632788659361533818279682303019520353018529689957736225994138912497217752834791315155748572424541506959508295331168617278558890750983817546374649393192550604009277016711390098488240128583616035637076601047101819429555961989467678374494482553797747268471040475346462080466842590694912933136770289891521047521620569660240580381501935112533824300355876402474964732639141992726042699227967823547816360093417216412199245863150302861829745557067498385054945885869269956909272107975093029553211653449872027559602364806654991198818347977535663698074265425278625518184175746728909777727938000816470600161452491921732172147723501414419735685481613611573525521334757418494684385233239073941433345477624168625189835694855620992192221842725502542568876717904946016534668049886272327917860857843838279679766814541009538837863609506800642251252051173929848960841284886269456042419652850222106611863067442786220391949450471237137869609563643719172874677646575739624138908658326459958133904780275901

Writing a Job spec

As with all other Kubernetes config, a Job needs apiVersion, kind, and metadata fields. Its name must be a valid DNS subdomain name.

A Job also needs a .spec section.

Pod Template

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

The .spec.template is a pod template. It has exactly the same schema as a 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 Job must specify appropriate labels (see pod selector) and an appropriate restart policy.

Only a RestartPolicy equal to Never or OnFailure is allowed.

Pod selector

The .spec.selector field is optional. In almost all cases you should not specify it. See section specifying your own pod selector.

Parallel execution for Jobs

There are three main types of task suitable to run as a Job:

Non-parallel Jobs
- normally, only one Pod is started, unless the Pod fails.
- the Job is complete as soon as its Pod terminates successfully.
Parallel Jobs with a fixed completion count:
- specify a non-zero positive value for .spec.completions.
- the Job represents the overall task, and is complete when there are .spec.completions successful Pods.
- when using .spec.completionMode="Indexed", each Pod gets a different index in the range 0 to .spec.completions-1.
Parallel Jobs with a work queue:
- do not specify .spec.completions, default to .spec.parallelism.
- the Pods must coordinate amongst themselves or an external service to determine what each should work on. For example, a Pod might fetch a batch of up to N items from the work queue.
- each Pod is independently capable of determining whether or not all its peers are done, and thus that the entire Job is done.
- when any Pod from the Job terminates with success, no new Pods are created.
- once at least one Pod has terminated with success and all Pods are terminated, then the Job is completed with success.
- once any Pod has exited with success, no other Pod should still be doing any work for this task or writing any output. They should all be in the process of exiting.

For a non-parallel Job, you can leave both .spec.completions and .spec.parallelism unset. When both are unset, both are defaulted to 1.

For a fixed completion count Job, you should set .spec.completions to the number of completions needed. You can set .spec.parallelism, or leave it unset and it will default to 1.

For a work queue Job, you must leave .spec.completions unset, and set .spec.parallelism to a non-negative integer.

For more information about how to make use of the different types of job, see the job patterns section.

Controlling parallelism

The requested parallelism (.spec.parallelism) can be set to any non-negative value. If it is unspecified, it defaults to 1. If it is specified as 0, then the Job is effectively paused until it is increased.

Actual parallelism (number of pods running at any instant) may be more or less than requested parallelism, for a variety of reasons:

For fixed completion count Jobs, the actual number of pods running in parallel will not exceed the number of remaining completions. Higher values of .spec.parallelism are effectively ignored.
For work queue Jobs, no new Pods are started after any Pod has succeeded -- remaining Pods are allowed to complete, however.
If the Job Controller has not had time to react.
If the Job controller failed to create Pods for any reason (lack of ResourceQuota, lack of permission, etc.), then there may be fewer pods than requested.
The Job controller may throttle new Pod creation due to excessive previous pod failures in the same Job.
When a Pod is gracefully shut down, it takes time to stop.

Completion mode

FEATURE STATE: Kubernetes v1.24 [stable]

Jobs with fixed completion count - that is, jobs that have non null .spec.completions - can have a completion mode that is specified in .spec.completionMode:

NonIndexed (default): the Job is considered complete when there have been .spec.completions successfully completed Pods. In other words, each Pod completion is homologous to each other. Note that Jobs that have null .spec.completions are implicitly NonIndexed.
Indexed: the Pods of a Job get an associated completion index from 0 to .spec.completions-1. The index is available through three mechanisms:
- The Pod annotation batch.kubernetes.io/job-completion-index.
- As part of the Pod hostname, following the pattern $(job-name)-$(index). When you use an Indexed Job in combination with a Service, Pods within the Job can use the deterministic hostnames to address each other via DNS.
- From the containarized task, in the environment variable JOB_COMPLETION_INDEX.
The Job is considered complete when there is one successfully completed Pod for each index. For more information about how to use this mode, see Indexed Job for Parallel Processing with Static Work Assignment. Note that, although rare, more than one Pod could be started for the same index, but only one of them will count towards the completion count.

Handling Pod and container failures

A container in a Pod may fail for a number of reasons, such as because the process in it exited with a non-zero exit code, or the container was killed for exceeding a memory limit, etc. If this happens, and the .spec.template.spec.restartPolicy = "OnFailure", then the Pod stays on the node, but the container is re-run. Therefore, your program needs to handle the case when it is restarted locally, or else specify .spec.template.spec.restartPolicy = "Never". See pod lifecycle for more information on restartPolicy.

An entire Pod can also fail, for a number of reasons, such as when the pod is kicked off the node (node is upgraded, rebooted, deleted, etc.), or if a container of the Pod fails and the .spec.template.spec.restartPolicy = "Never". When a Pod fails, then the Job controller starts a new Pod. This means that your application needs to handle the case when it is restarted in a new pod. In particular, it needs to handle temporary files, locks, incomplete output and the like caused by previous runs.

Note that even if you specify .spec.parallelism = 1 and .spec.completions = 1 and .spec.template.spec.restartPolicy = "Never", the same program may sometimes be started twice.

If you do specify .spec.parallelism and .spec.completions both greater than 1, then there may be multiple pods running at once. Therefore, your pods must also be tolerant of concurrency.

Pod backoff failure policy

There are situations where you want to fail a Job after some amount of retries due to a logical error in configuration etc. To do so, set .spec.backoffLimit to specify the number of retries before considering a Job as failed. The back-off limit is set by default to 6. Failed Pods associated with the Job are recreated by the Job controller with an exponential back-off delay (10s, 20s, 40s ...) capped at six minutes. The back-off count is reset when a Job's Pod is deleted or successful without any other Pods for the Job failing around that time.

Note: If your job has restartPolicy = "OnFailure", keep in mind that your Pod running the Job will be terminated once the job backoff limit has been reached. This can make debugging the Job's executable more difficult. We suggest setting restartPolicy = "Never" when debugging the Job or using a logging system to ensure output from failed Jobs is not lost inadvertently.

Job termination and cleanup

When a Job completes, no more Pods are created, but the Pods are usually not deleted either. Keeping them around allows you to still view the logs of completed pods to check for errors, warnings, or other diagnostic output. The job object also remains after it is completed so that you can view its status. It is up to the user to delete old jobs after noting their status. Delete the job with kubectl (e.g. kubectl delete jobs/pi or kubectl delete -f ./job.yaml). When you delete the job using kubectl, all the pods it created are deleted too.

By default, a Job will run uninterrupted unless a Pod fails (restartPolicy=Never) or a Container exits in error (restartPolicy=OnFailure), at which point the Job defers to the .spec.backoffLimit described above. Once .spec.backoffLimit has been reached the Job will be marked as failed and any running Pods will be terminated.

Another way to terminate a Job is by setting an active deadline. Do this by setting the .spec.activeDeadlineSeconds field of the Job to a number of seconds. The activeDeadlineSeconds applies to the duration of the job, no matter how many Pods are created. Once a Job reaches activeDeadlineSeconds, all of its running Pods are terminated and the Job status will become type: Failed with reason: DeadlineExceeded.

Note that a Job's .spec.activeDeadlineSeconds takes precedence over its .spec.backoffLimit. Therefore, a Job that is retrying one or more failed Pods will not deploy additional Pods once it reaches the time limit specified by activeDeadlineSeconds, even if the backoffLimit is not yet reached.

Example:

apiVersion: batch/v1
kind: Job
metadata:
  name: pi-with-timeout
spec:
  backoffLimit: 5
  activeDeadlineSeconds: 100
  template:
    spec:
      containers:
      - name: pi
        image: perl
        command: ["perl",  "-Mbignum=bpi", "-wle", "print bpi(2000)"]
      restartPolicy: Never

Note that both the Job spec and the Pod template spec within the Job have an activeDeadlineSeconds field. Ensure that you set this field at the proper level.

Keep in mind that the restartPolicy applies to the Pod, and not to the Job itself: there is no automatic Job restart once the Job status is type: Failed. That is, the Job termination mechanisms activated with .spec.activeDeadlineSeconds and .spec.backoffLimit result in a permanent Job failure that requires manual intervention to resolve.

Clean up finished jobs automatically

Finished Jobs are usually no longer needed in the system. Keeping them around in the system will put pressure on the API server. If the Jobs are managed directly by a higher level controller, such as CronJobs, the Jobs can be cleaned up by CronJobs based on the specified capacity-based cleanup policy.

TTL mechanism for finished Jobs

FEATURE STATE: Kubernetes v1.23 [stable]

Another way to clean up finished Jobs (either Complete or Failed) automatically is to use a TTL mechanism provided by a TTL controller for finished resources, by specifying the .spec.ttlSecondsAfterFinished field of the Job.

When the TTL controller cleans up the Job, it will delete the Job cascadingly, i.e. delete its dependent objects, such as Pods, together with the Job. Note that when the Job is deleted, its lifecycle guarantees, such as finalizers, will be honored.

For example:

apiVersion: batch/v1
kind: Job
metadata:
  name: pi-with-ttl
spec:
  ttlSecondsAfterFinished: 100
  template:
    spec:
      containers:
      - name: pi
        image: perl
        command: ["perl",  "-Mbignum=bpi", "-wle", "print bpi(2000)"]
      restartPolicy: Never

The Job pi-with-ttl will be eligible to be automatically deleted, 100 seconds after it finishes.

If the field is set to 0, the Job will be eligible to be automatically deleted immediately after it finishes. If the field is unset, this Job won't be cleaned up by the TTL controller after it finishes.

Note:

It is recommended to set ttlSecondsAfterFinished field because unmanaged jobs (Jobs that you created directly, and not indirectly through other workload APIs such as CronJob) have a default deletion policy of orphanDependents causing Pods created by an unmanaged Job to be left around after that Job is fully deleted. Even though the control plane eventually garbage collects the Pods from a deleted Job after they either fail or complete, sometimes those lingering pods may cause cluster performance degradation or in worst case cause the cluster to go offline due to this degradation.

You can use LimitRanges and ResourceQuotas to place a cap on the amount of resources that a particular namespace can consume.

Job patterns

The Job object can be used to support reliable parallel execution of Pods. The Job object is not designed to support closely-communicating parallel processes, as commonly found in scientific computing. It does support parallel processing of a set of independent but related work items. These might be emails to be sent, frames to be rendered, files to be transcoded, ranges of keys in a NoSQL database to scan, and so on.

In a complex system, there may be multiple different sets of work items. Here we are just considering one set of work items that the user wants to manage together — a batch job.

There are several different patterns for parallel computation, each with strengths and weaknesses. The tradeoffs are:

One Job object for each work item, vs. a single Job object for all work items. The latter is better for large numbers of work items. The former creates some overhead for the user and for the system to manage large numbers of Job objects.
Number of pods created equals number of work items, vs. each Pod can process multiple work items. The former typically requires less modification to existing code and containers. The latter is better for large numbers of work items, for similar reasons to the previous bullet.
Several approaches use a work queue. This requires running a queue service, and modifications to the existing program or container to make it use the work queue. Other approaches are easier to adapt to an existing containerised application.

The tradeoffs are summarized here, with columns 2 to 4 corresponding to the above tradeoffs. The pattern names are also links to examples and more detailed description.

Pattern	Single Job object	Fewer pods than work items?	Use app unmodified?
Queue with Pod Per Work Item	✓		sometimes
Queue with Variable Pod Count	✓	✓
Indexed Job with Static Work Assignment	✓		✓
Job Template Expansion			✓

When you specify completions with .spec.completions, each Pod created by the Job controller has an identical spec. This means that all pods for a task will have the same command line and the same image, the same volumes, and (almost) the same environment variables. These patterns are different ways to arrange for pods to work on different things.

This table shows the required settings for .spec.parallelism and .spec.completions for each of the patterns. Here, W is the number of work items.

Pattern	`.spec.completions`	`.spec.parallelism`
Queue with Pod Per Work Item	W	any
Queue with Variable Pod Count	null	any
Indexed Job with Static Work Assignment	W	any
Job Template Expansion	1	should be 1

Advanced usage

Suspending a Job

FEATURE STATE: Kubernetes v1.24 [stable]

When a Job is created, the Job controller will immediately begin creating Pods to satisfy the Job's requirements and will continue to do so until the Job is complete. However, you may want to temporarily suspend a Job's execution and resume it later, or start Jobs in suspended state and have a custom controller decide later when to start them.

To suspend a Job, you can update the .spec.suspend field of the Job to true; later, when you want to resume it again, update it to false. Creating a Job with .spec.suspend set to true will create it in the suspended state.

When a Job is resumed from suspension, its .status.startTime field will be reset to the current time. This means that the .spec.activeDeadlineSeconds timer will be stopped and reset when a Job is suspended and resumed.

Remember that suspending a Job will delete all active Pods. When the Job is suspended, your Pods will be terminated with a SIGTERM signal. The Pod's graceful termination period will be honored and your Pod must handle this signal in this period. This may involve saving progress for later or undoing changes. Pods terminated this way will not count towards the Job's completions count.

An example Job definition in the suspended state can be like so:

kubectl get job myjob -o yaml

apiVersion: batch/v1
kind: Job
metadata:
  name: myjob
spec:
  suspend: true
  parallelism: 1
  completions: 5
  template:
    spec:
      ...

The Job's status can be used to determine if a Job is suspended or has been suspended in the past:

kubectl get jobs/myjob -o yaml

apiVersion: batch/v1
kind: Job
# .metadata and .spec omitted
status:
  conditions:
  - lastProbeTime: "2021-02-05T13:14:33Z"
    lastTransitionTime: "2021-02-05T13:14:33Z"
    status: "True"
    type: Suspended
  startTime: "2021-02-05T13:13:48Z"

The Job condition of type "Suspended" with status "True" means the Job is suspended; the lastTransitionTime field can be used to determine how long the Job has been suspended for. If the status of that condition is "False", then the Job was previously suspended and is now running. If such a condition does not exist in the Job's status, the Job has never been stopped.

Events are also created when the Job is suspended and resumed:

kubectl describe jobs/myjob

Name:           myjob
...
Events:
  Type    Reason            Age   From            Message
  ----    ------            ----  ----            -------
  Normal  SuccessfulCreate  12m   job-controller  Created pod: myjob-hlrpl
  Normal  SuccessfulDelete  11m   job-controller  Deleted pod: myjob-hlrpl
  Normal  Suspended         11m   job-controller  Job suspended
  Normal  SuccessfulCreate  3s    job-controller  Created pod: myjob-jvb44
  Normal  Resumed           3s    job-controller  Job resumed

The last four events, particularly the "Suspended" and "Resumed" events, are directly a result of toggling the .spec.suspend field. In the time between these two events, we see that no Pods were created, but Pod creation restarted as soon as the Job was resumed.

Mutable Scheduling Directives

FEATURE STATE: Kubernetes v1.23 [beta]

Note: In order to use this behavior, you must enable the JobMutableNodeSchedulingDirectives feature gate on the API server. It is enabled by default.

In most cases a parallel job will want the pods to run with constraints, like all in the same zone, or all either on GPU model x or y but not a mix of both.

The suspend field is the first step towards achieving those semantics. Suspend allows a custom queue controller to decide when a job should start; However, once a job is unsuspended, a custom queue controller has no influence on where the pods of a job will actually land.

This feature allows updating a Job's scheduling directives before it starts, which gives custom queue controllers the ability to influence pod placement while at the same time offloading actual pod-to-node assignment to kube-scheduler. This is allowed only for suspended Jobs that have never been unsuspended before.

The fields in a Job's pod template that can be updated are node affinity, node selector, tolerations, labels and annotations.

Specifying your own Pod selector

Normally, when you create a Job object, you do not specify .spec.selector. The system defaulting logic adds this field when the Job is created. It picks a selector value that will not overlap with any other jobs.

However, in some cases, you might need to override this automatically set selector. To do this, you can specify the .spec.selector of the Job.

Be very careful when doing this. If you specify a label selector which is not unique to the pods of that Job, and which matches unrelated Pods, then pods of the unrelated job may be deleted, or this Job may count other Pods as completing it, or one or both Jobs may refuse to create Pods or run to completion. If a non-unique selector is chosen, then other controllers (e.g. ReplicationController) and their Pods may behave in unpredictable ways too. Kubernetes will not stop you from making a mistake when specifying .spec.selector.

Here is an example of a case when you might want to use this feature.

Say Job old is already running. You want existing Pods to keep running, but you want the rest of the Pods it creates to use a different pod template and for the Job to have a new name. You cannot update the Job because these fields are not updatable. Therefore, you delete Job old but leave its pods running, using kubectl delete jobs/old --cascade=orphan. Before deleting it, you make a note of what selector it uses:

kubectl get job old -o yaml

The output is similar to this:

kind: Job
metadata:
  name: old
  ...
spec:
  selector:
    matchLabels:
      controller-uid: a8f3d00d-c6d2-11e5-9f87-42010af00002
  ...

Then you create a new Job with name new and you explicitly specify the same selector. Since the existing Pods have label controller-uid=a8f3d00d-c6d2-11e5-9f87-42010af00002, they are controlled by Job new as well.

You need to specify manualSelector: true in the new Job since you are not using the selector that the system normally generates for you automatically.

kind: Job
metadata:
  name: new
  ...
spec:
  manualSelector: true
  selector:
    matchLabels:
      controller-uid: a8f3d00d-c6d2-11e5-9f87-42010af00002
  ...

The new Job itself will have a different uid from a8f3d00d-c6d2-11e5-9f87-42010af00002. Setting manualSelector: true tells the system that you know what you are doing and to allow this mismatch.

Job tracking with finalizers

FEATURE STATE: Kubernetes v1.23 [beta]

Note:

In order to use this behavior, you must enable the JobTrackingWithFinalizers feature gate on the API server and the controller manager. It is enabled by default.

When enabled, the control plane tracks new Jobs using the behavior described below. Jobs created before the feature was enabled are unaffected. As a user, the only difference you would see is that the control plane tracking of Job completion is more accurate.

When this feature isn't enabled, the Job Controller relies on counting the Pods that exist in the cluster to track the Job status, that is, to keep the counters for succeeded and failed Pods. However, Pods can be removed for a number of reasons, including:

The garbage collector that removes orphan Pods when a Node goes down.
The garbage collector that removes finished Pods (in Succeeded or Failed phase) after a threshold.
Human intervention to delete Pods belonging to a Job.
An external controller (not provided as part of Kubernetes) that removes or replaces Pods.

If you enable the JobTrackingWithFinalizers feature for your cluster, the control plane keeps track of the Pods that belong to any Job and notices if any such Pod is removed from the API server. To do that, the Job controller creates Pods with the finalizer batch.kubernetes.io/job-tracking. The controller removes the finalizer only after the Pod has been accounted for in the Job status, allowing the Pod to be removed by other controllers or users.

The Job controller uses the new algorithm for new Jobs only. Jobs created before the feature is enabled are unaffected. You can determine if the Job controller is tracking a Job using Pod finalizers by checking if the Job has the annotation batch.kubernetes.io/job-tracking. You should not manually add or remove this annotation from Jobs.

Alternatives

Bare Pods

When the node that a Pod is running on reboots or fails, the pod is terminated and will not be restarted. However, a Job will create new Pods to replace terminated ones. For this reason, we recommend that you use a Job rather than a bare Pod, even if your application requires only a single Pod.

Replication Controller

Jobs are complementary to Replication Controllers. A Replication Controller manages Pods which are not expected to terminate (e.g. web servers), and a Job manages Pods that are expected to terminate (e.g. batch tasks).

As discussed in Pod Lifecycle, Job is only appropriate for pods with RestartPolicy equal to OnFailure or Never. (Note: If RestartPolicy is not set, the default value is Always.)

Single Job starts controller Pod

Another pattern is for a single Job to create a Pod which then creates other Pods, acting as a sort of custom controller for those Pods. This allows the most flexibility, but may be somewhat complicated to get started with and offers less integration with Kubernetes.

One example of this pattern would be a Job which starts a Pod which runs a script that in turn starts a Spark master controller (see spark example), runs a spark driver, and then cleans up.

An advantage of this approach is that the overall process gets the completion guarantee of a Job object, but maintains complete control over what Pods are created and how work is assigned to them.

What's next

Learn about Pods.
Read about different ways of running Jobs:
- Coarse Parallel Processing Using a Work Queue
- Fine Parallel Processing Using a Work Queue
- Use an indexed Job for parallel processing with static work assignment (beta)
- Create multiple Jobs based on a template: Parallel Processing using Expansions
Follow the links within Clean up finished jobs automatically to learn more about how your cluster can clean up completed and / or failed tasks.
Job is part of the Kubernetes REST API. Read the Job object definition to understand the API for jobs.
Read about CronJob, which you can use to define a series of Jobs that will run based on a schedule, similar to the UNIX tool cron.

6 - Automatic Clean-up for Finished Jobs

FEATURE STATE: Kubernetes v1.23 [stable]

TTL-after-finished controller provides a TTL (time to live) mechanism to limit the lifetime of resource objects that have finished execution. TTL controller only handles Jobs.

TTL-after-finished Controller

The TTL-after-finished controller is only supported for Jobs. A cluster operator can use this feature to clean up finished Jobs (either Complete or Failed) automatically by specifying the .spec.ttlSecondsAfterFinished field of a Job, as in this example. The TTL-after-finished controller will assume that a job is eligible to be cleaned up TTL seconds after the job has finished, in other words, when the TTL has expired. When the TTL-after-finished controller cleans up a job, it will delete it cascadingly, that is to say it will delete its dependent objects together with it. Note that when the job is deleted, its lifecycle guarantees, such as finalizers, will be honored.

The TTL seconds can be set at any time. Here are some examples for setting the .spec.ttlSecondsAfterFinished field of a Job:

Specify this field in the job manifest, so that a Job can be cleaned up automatically some time after it finishes.
Set this field of existing, already finished jobs, to adopt this new feature.
Use a mutating admission webhook to set this field dynamically at job creation time. Cluster administrators can use this to enforce a TTL policy for finished jobs.
Use a mutating admission webhook to set this field dynamically after the job has finished, and choose different TTL values based on job status, labels, etc.

Caveat

Updating TTL Seconds

Note that the TTL period, e.g. .spec.ttlSecondsAfterFinished field of Jobs, can be modified after the job is created or has finished. However, once the Job becomes eligible to be deleted (when the TTL has expired), the system won't guarantee that the Jobs will be kept, even if an update to extend the TTL returns a successful API response.

Time Skew

Because TTL-after-finished controller uses timestamps stored in the Kubernetes jobs to determine whether the TTL has expired or not, this feature is sensitive to time skew in the cluster, which may cause TTL-after-finish controller to clean up job objects at the wrong time.

Clocks aren't always correct, but the difference should be very small. Please be aware of this risk when setting a non-zero TTL.

What's next

7 - CronJob

FEATURE STATE: Kubernetes v1.21 [stable]

A CronJob creates Jobs on a repeating schedule.

One CronJob object is like one line of a crontab (cron table) file. It runs a job periodically on a given schedule, written in Cron format.

Caution:

All CronJob schedule: times are based on the timezone of the kube-controller-manager.

If your control plane runs the kube-controller-manager in Pods or bare containers, the timezone set for the kube-controller-manager container determines the timezone that the cron job controller uses.

Caution:

The v1 CronJob API does not officially support setting timezone as explained above.

Setting variables such as CRON_TZ or TZ is not officially supported by the Kubernetes project. CRON_TZ or TZ is an implementation detail of the internal library being used for parsing and calculating the next Job creation time. Any usage of it is not recommended in a production cluster.

When creating the manifest for a CronJob resource, make sure the name you provide is a valid DNS subdomain name. The name must be no longer than 52 characters. This is because the CronJob controller will automatically append 11 characters to the job name provided and there is a constraint that the maximum length of a Job name is no more than 63 characters.

CronJob

CronJobs are meant for performing regular scheduled actions such as backups, report generation, and so on. Each of those tasks should be configured to recur indefinitely (for example: once a day / week / month); you can define the point in time within that interval when the job should start.

Example

This example CronJob manifest prints the current time and a hello message every minute:

application/job/cronjob.yaml

apiVersion: batch/v1
kind: CronJob
metadata:
  name: hello
spec:
  schedule: "* * * * *"
  jobTemplate:
    spec:
      template:
        spec:
          containers:
          - name: hello
            image: busybox:1.28
            imagePullPolicy: IfNotPresent
            command:
            - /bin/sh
            - -c
            - date; echo Hello from the Kubernetes cluster
          restartPolicy: OnFailure

(Running Automated Tasks with a CronJob takes you through this example in more detail).

Cron schedule syntax

# ┌───────────── minute (0 - 59)
# │ ┌───────────── hour (0 - 23)
# │ │ ┌───────────── day of the month (1 - 31)
# │ │ │ ┌───────────── month (1 - 12)
# │ │ │ │ ┌───────────── day of the week (0 - 6) (Sunday to Saturday;
# │ │ │ │ │                                   7 is also Sunday on some systems)
# │ │ │ │ │                                   OR sun, mon, tue, wed, thu, fri, sat
# │ │ │ │ │
# * * * * *

Entry	Description	Equivalent to
@yearly (or @annually)	Run once a year at midnight of 1 January	0 0 1 1 *
@monthly	Run once a month at midnight of the first day of the month	0 0 1 * *
@weekly	Run once a week at midnight on Sunday morning	0 0 * * 0
@daily (or @midnight)	Run once a day at midnight	0 0 * * *
@hourly	Run once an hour at the beginning of the hour	0 * * * *

For example, the line below states that the task must be started every Friday at midnight, as well as on the 13th of each month at midnight:

0 0 13 * 5

To generate CronJob schedule expressions, you can also use web tools like crontab.guru.

Time zones

For CronJobs with no time zone specified, the kube-controller-manager interprets schedules relative to its local time zone.

FEATURE STATE: Kubernetes v1.24 [alpha]

If you enable the CronJobTimeZone feature gate, you can specify a time zone for a CronJob (if you don't enable that feature gate, or if you are using a version of Kubernetes that does not have experimental time zone support, all CronJobs in your cluster have an unspecified timezone).

When you have the feature enabled, you can set spec.timeZone to the name of a valid time zone name. For example, setting spec.timeZone: "Etc/UTC" instructs Kubernetes to interpret the schedule relative to Coordinated Universal Time.

A time zone database from the Go standard library is included in the binaries and used as a fallback in case an external database is not available on the system.

CronJob limitations

A cron job creates a job object about once per execution time of its schedule. We say "about" because there are certain circumstances where two jobs might be created, or no job might be created. We attempt to make these rare, but do not completely prevent them. Therefore, jobs should be idempotent.

If startingDeadlineSeconds is set to a large value or left unset (the default) and if concurrencyPolicy is set to Allow, the jobs will always run at least once.

Caution: If startingDeadlineSeconds is set to a value less than 10 seconds, the CronJob may not be scheduled. This is because the CronJob controller checks things every 10 seconds.

For every CronJob, the CronJob Controller checks how many schedules it missed in the duration from its last scheduled time until now. If there are more than 100 missed schedules, then it does not start the job and logs the error

Cannot determine if job needs to be started. Too many missed start time (> 100). Set or decrease .spec.startingDeadlineSeconds or check clock skew.

It is important to note that if the startingDeadlineSeconds field is set (not nil), the controller counts how many missed jobs occurred from the value of startingDeadlineSeconds until now rather than from the last scheduled time until now. For example, if startingDeadlineSeconds is 200, the controller counts how many missed jobs occurred in the last 200 seconds.

A CronJob is counted as missed if it has failed to be created at its scheduled time. For example, If concurrencyPolicy is set to Forbid and a CronJob was attempted to be scheduled when there was a previous schedule still running, then it would count as missed.

For example, suppose a CronJob is set to schedule a new Job every one minute beginning at 08:30:00, and its startingDeadlineSeconds field is not set. If the CronJob controller happens to be down from 08:29:00 to 10:21:00, the job will not start as the number of missed jobs which missed their schedule is greater than 100.

To illustrate this concept further, suppose a CronJob is set to schedule a new Job every one minute beginning at 08:30:00, and its startingDeadlineSeconds is set to 200 seconds. If the CronJob controller happens to be down for the same period as the previous example (08:29:00 to 10:21:00,) the Job will still start at 10:22:00. This happens as the controller now checks how many missed schedules happened in the last 200 seconds (ie, 3 missed schedules), rather than from the last scheduled time until now.

The CronJob is only responsible for creating Jobs that match its schedule, and the Job in turn is responsible for the management of the Pods it represents.

Controller version

Starting with Kubernetes v1.21 the second version of the CronJob controller is the default implementation. To disable the default CronJob controller and use the original CronJob controller instead, one pass the CronJobControllerV2 feature gate flag to the kube-controller-manager, and set this flag to false. For example:

--feature-gates="CronJobControllerV2=false"

What's next

Learn about Pods and Jobs, two concepts that CronJobs rely upon.
Read about the format of CronJob .spec.schedule fields.
For instructions on creating and working with CronJobs, and for an example of a CronJob manifest, see Running automated tasks with CronJobs.
For instructions to clean up failed or completed jobs automatically, see Clean up Jobs automatically
CronJob is part of the Kubernetes REST API. Read the CronJob object definition to understand the API for Kubernetes cron jobs.

8 - ReplicationController

Note: A Deployment that configures a 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.

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" 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.

controllers/replication.yaml

apiVersion: v1
kind: ReplicationController
metadata:
  name: nginx
spec:
  replicas: 3
  selector:
    app: nginx
  template:
    metadata:
      name: nginx
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx
        ports:
        - containerPort: 80

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

kubectl apply -f https://k8s.io/examples/controllers/replication.yaml

The output is similar to this:

replicationcontroller/nginx created

Check on the status of the ReplicationController using this command:

kubectl describe replicationcontrollers/nginx

The output is similar to this:

Name:        nginx
Namespace:   default
Selector:    app=nginx
Labels:      app=nginx
Annotations:    <none>
Replicas:    3 current / 3 desired
Pods Status: 0 Running / 3 Waiting / 0 Succeeded / 0 Failed
Pod Template:
  Labels:       app=nginx
  Containers:
   nginx:
    Image:              nginx
    Port:               80/TCP
    Environment:        <none>
    Mounts:             <none>
  Volumes:              <none>
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:

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:

pods=$(kubectl get pods --selector=app=nginx --output=jsonpath={.items..metadata.name})
echo $pods

The output is similar to this:

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 with 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. The name of a ReplicationController object must be a valid DNS subdomain name. For general information about working with configuration files, see object management.

A ReplicationController also needs a .spec section.

Pod Template

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

The .spec.template is a pod template. It has exactly the same schema as a 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.

Only a .spec.template.spec.restartPolicy 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 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. 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).

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 were 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. 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 client library, you need to do the steps explicitly (scale replicas to 0, wait for pod deletions, then delete the ReplicationController).

Deleting only a ReplicationController

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

Using kubectl, specify the --cascade=orphan option to kubectl delete.

When using the REST API or client library, you can 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.

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 and data recovery. 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 enables scaling the number of replicas up or down, either manually or by an auto-scaling control agent, by 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, 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.

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 RabbitMQ work queues, 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 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 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), which would change its replicas field. We will not add scheduling policies (for example, spreading) 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).

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, scale) are proof-of-concept examples of this. For instance, we could imagine something like Asgard 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.

Alternatives to ReplicationController

ReplicaSet

ReplicaSet is the next-generation ReplicationController that supports the new set-based label selector. It's mainly used by 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 is a higher-level API object that updates its underlying Replica Sets and their Pods. Deployments are recommended if you want the rolling update functionality, because 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 instead of a ReplicationController for pods that are expected to terminate on their own (that is, batch jobs).

DaemonSet

Use a 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.

What's next

Learn about Pods.
Learn about Deployment, the replacement for ReplicationController.
ReplicationController is part of the Kubernetes REST API. Read the ReplicationController object definition to understand the API for replication controllers.