Workload Management
Kubernetes provides several built-in APIs for declarative management of your
workloads
and the components of those workloads.
Ultimately, your applications run as containers inside
Pods; however, managing individual
Pods would be a lot of effort. For example, if a Pod fails, you probably want to
run a new Pod to replace it. Kubernetes can do that for you.
You use the Kubernetes API to create a workload
object that represents a higher abstraction level
than a Pod, and then the Kubernetes
control plane automatically manages
Pod objects on your behalf, based on the specification for the workload object you defined.
The built-in APIs for managing workloads are:
Deployment (and, indirectly, ReplicaSet),
the most common way to run an application on your cluster.
Deployment is a good fit for managing a stateless application workload on your cluster, where
any Pod in the Deployment is interchangeable and can be replaced if needed.
(Deployments are a replacement for the legacy
ReplicationController API).
A StatefulSet lets you
manage one or more Pods – all running the same application code – where the Pods rely
on having a distinct identity. This is different from a Deployment where the Pods are
expected to be interchangeable.
The most common use for a StatefulSet is to be able to make a link between its Pods and
their persistent storage. For example, you can run a StatefulSet that associates each Pod
with a PersistentVolume. If one of the Pods
in the StatefulSet fails, Kubernetes makes a replacement Pod that is connected to the
same PersistentVolume.
A DaemonSet defines Pods that provide
facilities that are local to a specific node;
for example, a driver that lets containers on that node access a storage system. You use a DaemonSet
when the driver, or other node-level service, has to run on the node where it's useful.
Each Pod in a DaemonSet performs a role similar to a system daemon on a classic Unix / POSIX
server.
A DaemonSet might be fundamental to the operation of your cluster,
such as a plugin to let that node access
cluster networking,
it might help you to manage the node,
or it could provide less essential facilities that enhance the container platform you are running.
You can run DaemonSets (and their pods) across every node in your cluster, or across just a subset (for example,
only install the GPU accelerator driver on nodes that have a GPU installed).
You can use a Job and / or
a CronJob to
define tasks that run to completion and then stop. A Job represents a one-off task,
whereas each CronJob repeats according to a schedule.
Other topics in this section:
1 - Deployments
A Deployment manages a set of Pods to run an application workload, usually one that doesn't maintain state.
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:
Creating a Deployment
The following is an example of a Deployment. It creates a ReplicaSet to bring up three nginx Pods:
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. This name will become the basis for the ReplicaSets
and Pods which are created later. See Writing a Deployment Spec
for more details.
The Deployment creates a ReplicaSet that creates three replicated Pods, indicated by the .spec.replicas field.
The .spec.selector field defines how the created ReplicaSet 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]-[HASH]. This name will become the basis for the Pods
which are created.
The HASH string is the same as the pod-template-hash label on the ReplicaSet.
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=75675f5897
nginx-deployment-75675f5897-kzszj 1/1 Running 0 18s app=nginx,pod-template-hash=75675f5897
nginx-deployment-75675f5897-qqcnn 1/1 Running 0 18s app=nginx,pod-template-hash=75675f5897
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
where deployment/nginx-deployment indicates the Deployment,
nginx indicates the Container the update will take place and
nginx:1.16.1 indicates the new image and its tag.
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...
or
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.
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:
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 (adding the replica count from
nginx-deployment-1564180365 and nginx-deployment-2035384211) is 3, and the number of
new replicas (from nginx-deployment-3066724191) is 1.
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.
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 set up 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.
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:
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:
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.
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:
The output is similar to this:
NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE
nginx 3 3 3 3 1m
Get the rollout status:
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:
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.
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:
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: Progressingstatus: "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: Progressingstatus: "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):
0
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: Progressingstatus: "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):
1
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.
When the control plane creates new Pods for a Deployment, the .metadata.name of the
Deployment is part of the basis for naming those Pods. The name of a Deployment must be a valid
DNS subdomain
value, but this can produce unexpected results for the Pod hostnames. For best compatibility,
the name should follow the more restrictive rules for a
DNS label.
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.
Here are some Rolling Update Deployment examples that use the maxUnavailable and maxSurge:
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
strategy:
type: RollingUpdate
rollingUpdate:
maxUnavailable: 1
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
strategy:
type: RollingUpdate
rollingUpdate:
maxSurge: 1
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
strategy:
type: RollingUpdate
rollingUpdate:
maxSurge: 1
maxUnavailable: 1
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
2 - ReplicaSet
A ReplicaSet's purpose is to maintain a stable set of replica Pods running at any given time. Usually, you define a Deployment and let that Deployment manage ReplicaSets automatically.
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
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: us-docker.pkg.dev/google-samples/containers/gke/gb-frontend:v5
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:
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: <none>
Replicas: 3 current / 3 desired
Pods Status: 3 Running / 0 Waiting / 0 Succeeded / 0 Failed
Pod Template:
Labels: tier=frontend
Containers:
php-redis:
Image: us-docker.pkg.dev/google-samples/containers/gke/gb-frontend:v5
Port: <none>
Host Port: <none>
Environment: <none>
Mounts: <none>
Volumes: <none>
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal SuccessfulCreate 13s replicaset-controller Created pod: frontend-gbgfx
Normal SuccessfulCreate 13s replicaset-controller Created pod: frontend-rwz57
Normal SuccessfulCreate 13s replicaset-controller Created pod: frontend-wkl7w
And lastly you can check for the Pods brought up:
You should see Pod information similar to:
NAME READY STATUS RESTARTS AGE
frontend-gbgfx 1/1 Running 0 10m
frontend-rwz57 1/1 Running 0 10m
frontend-wkl7w 1/1 Running 0 10m
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-gbgfx -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: "2024-02-28T22:30:44Z"
generateName: frontend-
labels:
tier: frontend
name: frontend-gbgfx
namespace: default
ownerReferences:
- apiVersion: apps/v1
blockOwnerDeletion: true
controller: true
kind: ReplicaSet
name: frontend
uid: e129deca-f864-481b-bb16-b27abfd92292
...
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:
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:
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:
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-homogeneous 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.
When the control plane creates new Pods for a ReplicaSet, the .metadata.name of the
ReplicaSet is part of the basis for naming those Pods. The name of a ReplicaSet must be a valid
DNS subdomain
value, but this can produce unexpected results for the Pod hostnames. For best compatibility,
the name should follow the more restrictive rules for a
DNS label.
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 [-2147483648, 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.
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 such as Kubelet.
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
3 - StatefulSets
A StatefulSet runs a group of Pods, and maintains a sticky identity for each of those Pods. This is useful for managing applications that need persistent storage or a stable, unique network identity.
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 its 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: registry.k8s.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
Note: This example uses the ReadWriteOnce access mode, for simplicity. For
production use, the Kubernetes project recommends using the ReadWriteOncePod
access mode instead.
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 label.
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 v1.25 [stable]
.spec.minReadySeconds is an optional field that specifies the minimum number of seconds for which a newly
created Pod should be running and ready without any of its containers crashing, for it to be considered available.
This is used to check progression of a rollout when using a Rolling Update strategy.
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 consists 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, that is unique over the Set. By default,
pods will be assigned ordinals from 0 up through N-1. The StatefulSet controller
will also add a pod label with this index: apps.kubernetes.io/pod-index.
Start ordinal
FEATURE STATE: Kubernetes v1.27 [beta]
.spec.ordinals is an optional field that allows you to configure the integer
ordinals assigned to each Pod. It defaults to nil. You must enable the
StatefulSetStartOrdinal
feature gate to
use this field. Once enabled, you can configure the following options:
.spec.ordinals.start: If the .spec.ordinals.start field is set, Pods will
be assigned ordinals from .spec.ordinals.start up through
.spec.ordinals.start + .spec.replicas - 1.
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} |
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.
Pod index label
FEATURE STATE: Kubernetes v1.28 [beta]
When the StatefulSet controller creates a Pod,
the new Pod is labelled with apps.kubernetes.io/pod-index. The value of this label is the ordinal index of
the Pod. This label allows you to route traffic to a particular pod index, filter logs/metrics
using the pod index label, and more. Note the feature gate PodIndexLabel must be enabled for this
feature, and it is enabled by default.
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 updates 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 v1.27 [beta]
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
on the API server and the controller manager 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
others 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
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 defines Pods that provide node-local facilities. These might be fundamental to the operation of your cluster, such as a networking helper tool, or be part of an add-on.
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:
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
# it may be desirable to set a high priority class to ensure that a DaemonSet Pod
# preempts running Pods
# priorityClassName: important
terminationGracePeriodSeconds: 30
volumes:
- name: varlog
hostPath:
path: /var/log
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
A DaemonSet can be used to ensure that all eligible nodes run a copy of a Pod.
The DaemonSet controller creates a Pod for each eligible node and adds the
spec.affinity.nodeAffinity field of the Pod to match the target host. After
the Pod is created, the default scheduler typically takes over and then binds
the Pod to the target host by setting the .spec.nodeName field. If the new
Pod cannot fit on the node, the default scheduler may preempt (evict) some of
the existing Pods based on the
priority
of the new Pod.
Note: If it's important that the DaemonSet pod run on each node, it's often desirable
to set the
.spec.template.spec.priorityClassName of the DaemonSet to a
PriorityClass
with a higher priority to ensure that this eviction occurs.
The user can specify a different scheduler for the Pods of the DaemonSet, by
setting the .spec.template.spec.schedulerName field of the DaemonSet.
The original node affinity specified at the
.spec.template.spec.affinity.nodeAffinity field (if specified) is taken into
consideration by the DaemonSet controller when evaluating the eligible nodes,
but is replaced on the created Pod with the node affinity that matches the name
of the eligible node.
nodeAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchFields:
- key: metadata.name
operator: In
values:
- target-host-name
Taints and tolerations
The DaemonSet controller automatically adds a set of tolerations to DaemonSet Pods:
You can add your own tolerations to the Pods of a DaemonSet as well, by
defining these in the Pod template of the DaemonSet.
Because the DaemonSet controller sets the
node.kubernetes.io/unschedulable:NoSchedule toleration automatically,
Kubernetes can run DaemonSet Pods on nodes that are marked as unschedulable.
If you use a DaemonSet to provide an important node-level function, such as
cluster networking, it is
helpful that Kubernetes places DaemonSet Pods on nodes before they are ready.
For example, without that special toleration, you could end up in a deadlock
situation where the node is not marked as ready because the network plugin is
not running there, and at the same time the network plugin is not running on
that node because the node is not yet ready.
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.
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
5 - Jobs
Jobs represent one-off tasks that run to completion and then stop.
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.
apiVersion: batch/v1
kind: Job
metadata:
name: pi
spec:
template:
spec:
containers:
- name: pi
image: perl:5.34.0
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:
Name: pi
Namespace: default
Selector: batch.kubernetes.io/controller-uid=c9948307-e56d-4b5d-8302-ae2d7b7da67c
Labels: batch.kubernetes.io/controller-uid=c9948307-e56d-4b5d-8302-ae2d7b7da67c
batch.kubernetes.io/job-name=pi
...
Annotations: batch.kubernetes.io/job-tracking: ""
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: batch.kubernetes.io/controller-uid=c9948307-e56d-4b5d-8302-ae2d7b7da67c
batch.kubernetes.io/job-name=pi
Containers:
pi:
Image: perl:5.34.0
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 21s job-controller Created pod: pi-xf9p4
Normal Completed 18s job-controller Job completed
apiVersion: batch/v1
kind: Job
metadata:
annotations: batch.kubernetes.io/job-tracking: ""
...
creationTimestamp: "2022-11-10T17:53:53Z"
generation: 1
labels:
batch.kubernetes.io/controller-uid: 863452e6-270d-420e-9b94-53a54146c223
batch.kubernetes.io/job-name: pi
name: pi
namespace: default
resourceVersion: "4751"
uid: 204fb678-040b-497f-9266-35ffa8716d14
spec:
backoffLimit: 4
completionMode: NonIndexed
completions: 1
parallelism: 1
selector:
matchLabels:
batch.kubernetes.io/controller-uid: 863452e6-270d-420e-9b94-53a54146c223
suspend: false
template:
metadata:
creationTimestamp: null
labels:
batch.kubernetes.io/controller-uid: 863452e6-270d-420e-9b94-53a54146c223
batch.kubernetes.io/job-name: pi
spec:
containers:
- command:
- perl
- -Mbignum=bpi
- -wle
- print bpi(2000)
image: perl:5.34.0
imagePullPolicy: IfNotPresent
name: pi
resources: {}
terminationMessagePath: /dev/termination-log
terminationMessagePolicy: File
dnsPolicy: ClusterFirst
restartPolicy: Never
schedulerName: default-scheduler
securityContext: {}
terminationGracePeriodSeconds: 30
status:
active: 1
ready: 0
startTime: "2022-11-10T17:53:57Z"
uncountedTerminatedPods: {}
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=batch.kubernetes.io/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:
Another way to view the logs of a Job:
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.
When the control plane creates new Pods for a Job, the .metadata.name of the
Job is part of the basis for naming those Pods. The name of a Job must be a valid
DNS subdomain
value, but this can produce unexpected results for the Pod hostnames. For best compatibility,
the name should follow the more restrictive rules for a
DNS label.
Even when the name is a DNS subdomain, the name must be no longer than 63
characters.
A Job also needs a .spec section.
Job Labels
Job labels will have batch.kubernetes.io/ prefix for job-name and controller-uid.
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 four mechanisms:
- The Pod annotation
batch.kubernetes.io/job-completion-index. - The Pod label
batch.kubernetes.io/job-completion-index (for v1.28 and later). Note
the feature gate PodIndexLabel must be enabled to use this label, and it is enabled
by default. - 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. For more information about
how to configure this, see Job with Pod-to-Pod Communication. - From the containerized 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: Although rare, more than one Pod could be started for the same index (due to various reasons such as node failures,
kubelet restarts, or Pod evictions). In this case, only the first Pod that completes successfully will
count towards the completion count and update the status of the Job. The other Pods that are running
or completed for the same index will be deleted by the Job controller once they are detected.
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.
By default, each pod failure is counted towards the .spec.backoffLimit limit,
see pod backoff failure policy. However, you can
customize handling of pod failures by setting the Job's pod failure policy.
Additionally, you can choose to count the pod failures independently for each
index of an Indexed Job by setting the .spec.backoffLimitPerIndex field
(for more information, see backoff limit per index).
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.
When the feature gates
PodDisruptionConditions and JobPodFailurePolicy are both enabled,
and the .spec.podFailurePolicy field is set, the Job controller does not consider a terminating
Pod (a pod that has a .metadata.deletionTimestamp field set) as a failure until that Pod is
terminal (its .status.phase is Failed or Succeeded). However, the Job controller
creates a replacement Pod as soon as the termination becomes apparent. Once the
pod terminates, the Job controller evaluates .backoffLimit and .podFailurePolicy
for the relevant Job, taking this now-terminated Pod into consideration.
If either of these requirements is not satisfied, the Job controller counts
a terminating Pod as an immediate failure, even if that Pod later terminates
with phase: "Succeeded".
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 number of retries is calculated in two ways:
- The number of Pods with
.status.phase = "Failed". - When using
restartPolicy = "OnFailure", the number of retries in all the
containers of Pods with .status.phase equal to Pending or Running.
If either of the calculations reaches the .spec.backoffLimit, the Job is
considered failed.
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.
Backoff limit per index
FEATURE STATE: Kubernetes v1.29 [beta]
Note: You can only configure the backoff limit per index for an
Indexed Job, if you
have the
JobBackoffLimitPerIndex feature gate
enabled in your cluster.
When you run an indexed Job, you can choose to handle retries
for pod failures independently for each index. To do so, set the
.spec.backoffLimitPerIndex to specify the maximal number of pod failures
per index.
When the per-index backoff limit is exceeded for an index, Kubernetes considers the index as failed and adds it to the
.status.failedIndexes field. The succeeded indexes, those with a successfully
executed pods, are recorded in the .status.completedIndexes field, regardless of whether you set
the backoffLimitPerIndex field.
Note that a failing index does not interrupt execution of other indexes.
Once all indexes finish for a Job where you specified a backoff limit per index,
if at least one of those indexes did fail, the Job controller marks the overall
Job as failed, by setting the Failed condition in the status. The Job gets
marked as failed even if some, potentially nearly all, of the indexes were
processed successfully.
You can additionally limit the maximal number of indexes marked failed by
setting the .spec.maxFailedIndexes field.
When the number of failed indexes exceeds the maxFailedIndexes field, the
Job controller triggers termination of all remaining running Pods for that Job.
Once all pods are terminated, the entire Job is marked failed by the Job
controller, by setting the Failed condition in the Job status.
Here is an example manifest for a Job that defines a backoffLimitPerIndex:
apiVersion: batch/v1
kind: Job
metadata:
name: job-backoff-limit-per-index-example
spec:
completions: 10
parallelism: 3
completionMode: Indexed # required for the feature
backoffLimitPerIndex: 1 # maximal number of failures per index
maxFailedIndexes: 5 # maximal number of failed indexes before terminating the Job execution
template:
spec:
restartPolicy: Never # required for the feature
containers:
- name: example
image: python
command: # The jobs fails as there is at least one failed index
# (all even indexes fail in here), yet all indexes
# are executed as maxFailedIndexes is not exceeded.
- python3
- -c
- |
import os, sys
print("Hello world")
if int(os.environ.get("JOB_COMPLETION_INDEX")) % 2 == 0:
sys.exit(1)
In the example above, the Job controller allows for one restart for each
of the indexes. When the total number of failed indexes exceeds 5, then
the entire Job is terminated.
Once the job is finished, the Job status looks as follows:
kubectl get -o yaml job job-backoff-limit-per-index-example
status:
completedIndexes: 1,3,5,7,9
failedIndexes: 0,2,4,6,8
succeeded: 5 # 1 succeeded pod for each of 5 succeeded indexes
failed: 10 # 2 failed pods (1 retry) for each of 5 failed indexes
conditions:
- message: Job has failed indexes
reason: FailedIndexes
status: "True"
type: Failed
Additionally, you may want to use the per-index backoff along with a
pod failure policy. When using
per-index backoff, there is a new FailIndex action available which allows you to
avoid unnecessary retries within an index.
Pod failure policy
FEATURE STATE: Kubernetes v1.26 [beta]
Note: You can only configure a Pod failure policy for a Job if you have the
JobPodFailurePolicy feature gate
enabled in your cluster. Additionally, it is recommended
to enable the
PodDisruptionConditions feature gate in order to be able to detect and handle
Pod disruption conditions in the Pod failure policy (see also:
Pod disruption conditions).
Both feature gates are available in Kubernetes 1.30.
A Pod failure policy, defined with the .spec.podFailurePolicy field, enables
your cluster to handle Pod failures based on the container exit codes and the
Pod conditions.
In some situations, you may want to have a better control when handling Pod
failures than the control provided by the Pod backoff failure policy,
which is based on the Job's .spec.backoffLimit. These are some examples of use cases:
- To optimize costs of running workloads by avoiding unnecessary Pod restarts,
you can terminate a Job as soon as one of its Pods fails with an exit code
indicating a software bug.
- To guarantee that your Job finishes even if there are disruptions, you can
ignore Pod failures caused by disruptions (such as preemption,
API-initiated eviction
or taint-based eviction) so
that they don't count towards the
.spec.backoffLimit limit of retries.
You can configure a Pod failure policy, in the .spec.podFailurePolicy field,
to meet the above use cases. This policy can handle Pod failures based on the
container exit codes and the Pod conditions.
Here is a manifest for a Job that defines a podFailurePolicy:
apiVersion: batch/v1
kind: Job
metadata:
name: job-pod-failure-policy-example
spec:
completions: 12
parallelism: 3
template:
spec:
restartPolicy: Never
containers:
- name: main
image: docker.io/library/bash:5
command: ["bash"] # example command simulating a bug which triggers the FailJob action
args:
- -c
- echo "Hello world!" && sleep 5 && exit 42
backoffLimit: 6
podFailurePolicy:
rules:
- action: FailJob
onExitCodes:
containerName: main # optional
operator: In # one of: In, NotIn
values: [42]
- action: Ignore # one of: Ignore, FailJob, Count
onPodConditions:
- type: DisruptionTarget # indicates Pod disruption
In the example above, the first rule of the Pod failure policy specifies that
the Job should be marked failed if the main container fails with the 42 exit
code. The following are the rules for the main container specifically:
- an exit code of 0 means that the container succeeded
- an exit code of 42 means that the entire Job failed
- any other exit code represents that the container failed, and hence the entire
Pod. The Pod will be re-created if the total number of restarts is
below
backoffLimit. If the backoffLimit is reached the entire Job failed.
Note: Because the Pod template specifies a restartPolicy: Never,
the kubelet does not restart the main container in that particular Pod.
The second rule of the Pod failure policy, specifying the Ignore action for
failed Pods with condition DisruptionTarget excludes Pod disruptions from
being counted towards the .spec.backoffLimit limit of retries.
Note: If the Job failed, either by the Pod failure policy or Pod backoff
failure policy, and the Job is running multiple Pods, Kubernetes terminates all
the Pods in that Job that are still Pending or Running.
These are some requirements and semantics of the API:
- if you want to use a
.spec.podFailurePolicy field for a Job, you must
also define that Job's pod template with .spec.restartPolicy set to Never. - the Pod failure policy rules you specify under
spec.podFailurePolicy.rules
are evaluated in order. Once a rule matches a Pod failure, the remaining rules
are ignored. When no rule matches the Pod failure, the default
handling applies. - you may want to restrict a rule to a specific container by specifying its name
in
spec.podFailurePolicy.rules[*].onExitCodes.containerName. When not specified the rule
applies to all containers. When specified, it should match one the container
or initContainer names in the Pod template. - you may specify the action taken when a Pod failure policy is matched by
spec.podFailurePolicy.rules[*].action. Possible values are:FailJob: use to indicate that the Pod's job should be marked as Failed and
all running Pods should be terminated.Ignore: use to indicate that the counter towards the .spec.backoffLimit
should not be incremented and a replacement Pod should be created.Count: use to indicate that the Pod should be handled in the default way.
The counter towards the .spec.backoffLimit should be incremented.FailIndex: use this action along with backoff limit per index
to avoid unnecessary retries within the index of a failed pod.
Note: When you use a
podFailurePolicy, the job controller only matches Pods in the
Failed phase. Pods with a deletion timestamp that are not in a terminal phase
(
Failed or
Succeeded) are considered still terminating. This implies that
terminating pods retain a
tracking finalizer
until they reach a terminal phase.
Since Kubernetes 1.27, Kubelet transitions deleted pods to a terminal phase
(see:
Pod Phase). This
ensures that deleted pods have their finalizers removed by the Job controller.
Note: Starting with Kubernetes v1.28, when Pod failure policy is used, the Job controller recreates
terminating Pods only once these Pods reach the terminal
Failed phase. This behavior is similar
to
podReplacementPolicy: Failed. For more information, see
Pod replacement policy.
Success policy
FEATURE STATE: Kubernetes v1.30 [alpha]
Note: You can only configure a success policy for an Indexed Job if you have the
JobSuccessPolicy feature gate
enabled in your cluster.
When creating an Indexed Job, you can define when a Job can be declared as succeeded using a .spec.successPolicy,
based on the pods that succeeded.
By default, a Job succeeds when the number of succeeded Pods equals .spec.completions.
These are some situations where you might want additional control for declaring a Job succeeded:
- When running simulations with different parameters,
you might not need all the simulations to succeed for the overall Job to be successful.
- When following a leader-worker pattern, only the success of the leader determines the success or
failure of a Job. Examples of this are frameworks like MPI and PyTorch etc.
You can configure a success policy, in the .spec.successPolicy field,
to meet the above use cases. This policy can handle Job success based on the
succeeded pods. After the Job meets the success policy, the job controller terminates the lingering Pods.
A success policy is defined by rules. Each rule can take one of the following forms:
- When you specify the
succeededIndexes only,
once all indexes specified in the succeededIndexes succeed, the job controller marks the Job as succeeded.
The succeededIndexes must be a list of intervals between 0 and .spec.completions-1. - When you specify the
succeededCount only,
once the number of succeeded indexes reaches the succeededCount, the job controller marks the Job as succeeded. - When you specify both
succeededIndexes and succeededCount,
once the number of succeeded indexes from the subset of indexes specified in the succeededIndexes reaches the succeededCount,
the job controller marks the Job as succeeded.
Note that when you specify multiple rules in the .spec.succeessPolicy.rules,
the job controller evaluates the rules in order. Once the Job meets a rule, the job controller ignores remaining rules.
Here is a manifest for a Job with successPolicy:
apiVersion: batch/v1
kind: Job
spec:
parallelism: 10
completions: 10
completionMode: Indexed # Required for the success policy
successPolicy:
rules:
- succeededIndexes: 0,2-3
succeededCount: 1
template:
spec:
containers:
- name: main
image: python
command: # Provided that at least one of the Pods with 0, 2, and 3 indexes has succeeded,
# the overall Job is a success.
- python3
- -c
- |
import os, sys
if os.environ.get("JOB_COMPLETION_INDEX") == "2":
sys.exit(0)
else:
sys.exit(1)
In the example above, both succeededIndexes and succeededCount have been specified.
Therefore, the job controller will mark the Job as succeeded and terminate the lingering Pods
when either of the specified indexes, 0, 2, or 3, succeed.
The Job that meets the success policy gets the SuccessCriteriaMet condition.
After the removal of the lingering Pods is issued, the Job gets the Complete condition.
Note that the succeededIndexes is represented as intervals separated by a hyphen.
The number are listed in represented by the first and last element of the series, separated by a hyphen.
Note: When you specify both a success policy and some terminating policies such as .spec.backoffLimit and .spec.podFailurePolicy,
once the Job meets either policy, the job controller respects the terminating policy and ignores the success policy.
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:5.34.0
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:5.34.0
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 process 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, versus a single Job object for all work items.
One Job per work item creates some overhead for the user and for the system to manage
large numbers of Job objects.
A single Job for all work items is better for large numbers of items.
- Number of Pods created equals number of work items, versus each Pod can process multiple work items.
When the number of Pods equals the number of work items, the Pods typically
requires less modification to existing code and containers. Having each Pod
process multiple work items is better for large numbers of items.
- 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.
- When the Job is associated with a
headless Service,
you can enable the Pods within a Job to communicate with each other to
collaborate in a computation.
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.
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.
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.
When you suspend a Job, any running Pods that don't have a status of Completed
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:
...
You can also toggle Job suspension by patching the Job using the command line.
Suspend an active Job:
kubectl patch job/myjob --type=strategic --patch '{"spec":{"suspend":true}}'
Resume a suspended Job:
kubectl patch job/myjob --type=strategic --patch '{"spec":{"suspend":false}}'
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 v1.27 [stable]
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, annotations and scheduling gates.
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:
batch.kubernetes.io/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 batch.kubernetes.io/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:
batch.kubernetes.io/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 v1.26 [stable]
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.
Elastic Indexed Jobs
FEATURE STATE: Kubernetes v1.27 [beta]
You can scale Indexed Jobs up or down by mutating both .spec.parallelism
and .spec.completions together such that .spec.parallelism == .spec.completions.
When the ElasticIndexedJobfeature gate
on the API server
is disabled, .spec.completions is immutable.
Use cases for elastic Indexed Jobs include batch workloads which require
scaling an indexed Job, such as MPI, Horovord, Ray, and PyTorch training jobs.
Delayed creation of replacement pods
FEATURE STATE: Kubernetes v1.29 [beta]
Note: You can only set
podReplacementPolicy on Jobs if you enable the
JobPodReplacementPolicy
feature gate
(enabled by default).
By default, the Job controller recreates Pods as soon they either fail or are terminating (have a deletion timestamp).
This means that, at a given time, when some of the Pods are terminating, the number of running Pods for a Job
can be greater than parallelism or greater than one Pod per index (if you are using an Indexed Job).
You may choose to create replacement Pods only when the terminating Pod is fully terminal (has status.phase: Failed).
To do this, set the .spec.podReplacementPolicy: Failed.
The default replacement policy depends on whether the Job has a podFailurePolicy set.
With no Pod failure policy defined for a Job, omitting the podReplacementPolicy field selects the
TerminatingOrFailed replacement policy:
the control plane creates replacement Pods immediately upon Pod deletion
(as soon as the control plane sees that a Pod for this Job has deletionTimestamp set).
For Jobs with a Pod failure policy set, the default podReplacementPolicy is Failed, and no other
value is permitted.
See Pod failure policy to learn more about Pod failure policies for Jobs.
kind: Job
metadata:
name: new
...
spec:
podReplacementPolicy: Failed
...
Provided your cluster has the feature gate enabled, you can inspect the .status.terminating field of a Job.
The value of the field is the number of Pods owned by the Job that are currently terminating.
kubectl get jobs/myjob -o yaml
apiVersion: batch/v1
kind: Job
# .metadata and .spec omitted
status:
terminating: 3 # three Pods are terminating and have not yet reached the Failed phase
Delegation of managing a Job object to external controller
FEATURE STATE: Kubernetes v1.30 [alpha]
Note: You can only set the
managedBy field on Jobs if you enable the
JobManagedBy
feature gate
(disabled by default).
This feature allows you to disable the built-in Job controller, for a specific
Job, and delegate reconciliation of the Job to an external controller.
You indicate the controller that reconciles the Job by setting a custom value
for the spec.managedBy field - any value
other than kubernetes.io/job-controller. The value of the field is immutable.
Note: When using this feature, make sure the controller indicated by the field is
installed, otherwise the Job may not be reconciled at all.
Note:When developing an external Job controller be aware that your controller needs
to operate in a fashion conformant with the definitions of the API spec and
status fields of the Job object.
Please review these in detail in the Job API.
We also recommend that you run the e2e conformance tests for the Job object to
verify your implementation.
Finally, when developing an external Job controller make sure it does not use the
batch.kubernetes.io/job-tracking finalizer, reserved for the built-in controller.
Warning: If you are considering to disable the JobManagedBy feature gate, or to
downgrade the cluster to a version without the feature gate enabled, check if
there are jobs with a custom value of the spec.managedBy field. If there
are such jobs, there is a risk that they might be reconciled by two controllers
after the operation: the built-in Job controller and the external controller
indicated by the field value.
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:
- 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. - Practice how to configure handling of retriable and non-retriable pod failures
using
podFailurePolicy, based on the step-by-step examples.
6 - Automatic Cleanup for Finished Jobs
A time-to-live mechanism to clean up old Jobs that have finished execution.
FEATURE STATE: Kubernetes v1.23 [stable]
When your Job has finished, it's useful to keep that Job in the API (and not immediately delete the Job)
so that you can tell whether the Job succeeded or failed.
Kubernetes' TTL-after-finished controller provides a
TTL (time to live) mechanism to limit the lifetime of Job objects that
have finished execution.
Cleanup for finished Jobs
The TTL-after-finished controller is only supported for Jobs. You can use this mechanism 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 assumes that a Job is eligible to be cleaned up
TTL seconds after the Job has finished. The timer starts once the
status condition of the Job changes to show that the Job is either Complete or Failed; once the TTL has
expired, that Job becomes eligible for
cascading removal. 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.
Kubernetes honors object lifecycle guarantees on the Job, such as waiting for
finalizers.
You can set the TTL seconds 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.
- Manually set this field of existing, already finished Jobs, so that they become eligible
for cleanup.
- 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. For this case, the webhook needs
to detect changes to the
.status of the Job and only set a TTL when the Job
is being marked as completed. - Write your own controller to manage the cleanup TTL for Jobs that match a particular
selector-selector.
Caveats
Updating TTL for finished Jobs
You can modify the TTL period, e.g. .spec.ttlSecondsAfterFinished field of Jobs,
after the job is created or has finished. If you extend the TTL period after the
existing ttlSecondsAfterFinished period has expired, Kubernetes doesn't guarantee
to retain that Job, even if an update to extend the TTL returns a successful API
response.
Time skew
Because the 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 your cluster, which may cause the control plane 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
A CronJob starts one-time Jobs on a repeating schedule.
FEATURE STATE: Kubernetes v1.21 [stable]
A CronJob creates Jobs on a repeating schedule.
CronJob is meant for performing regular scheduled actions such as backups, report generation,
and so on. One CronJob object is like one line of a crontab (cron table) file on a
Unix system. It runs a Job periodically on a given schedule, written in
Cron format.
CronJobs have limitations and idiosyncrasies.
For example, in certain circumstances, a single CronJob can create multiple concurrent Jobs. See the limitations below.
When the control plane creates new Jobs and (indirectly) Pods for a CronJob, the .metadata.name
of the CronJob is part of the basis for naming those Pods. The name of a CronJob must be a valid
DNS subdomain
value, but this can produce unexpected results for the Pod hostnames. For best compatibility,
the name should follow the more restrictive rules for a
DNS label.
Even when the name is a DNS subdomain, the name must be no longer than 52
characters. This is because the CronJob controller will automatically append
11 characters to the name you provide and there is a constraint that the
length of a Job name is no more than 63 characters.
Example
This example CronJob manifest prints the current time and a hello message every minute:
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).
Writing a CronJob spec
Schedule syntax
The .spec.schedule field is required. The value of that field follows the Cron 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)
# │ │ │ │ │ OR sun, mon, tue, wed, thu, fri, sat
# │ │ │ │ │
# │ │ │ │ │
# * * * * *
For example, 0 0 13 * 5 states that the task must be started every Friday at midnight, as well as on the 13th of each month at midnight.
The format also includes extended "Vixie cron" step values. As explained in the
FreeBSD manual:
Step values can be used in conjunction with ranges. Following a range
with /<number> specifies skips of the number's value through the
range. For example, 0-23/2 can be used in the hours field to specify
command execution every other hour (the alternative in the V7 standard is
0,2,4,6,8,10,12,14,16,18,20,22). Steps are also permitted after an
asterisk, so if you want to say "every two hours", just use */2.
Note: A question mark (?) in the schedule has the same meaning as an asterisk *, that is,
it stands for any of available value for a given field.
Other than the standard syntax, some macros like @monthly can also be used:
| 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 * * * * |
To generate CronJob schedule expressions, you can also use web tools like crontab.guru.
Job template
The .spec.jobTemplate defines a template for the Jobs that the CronJob creates, and it is required.
It has exactly the same schema as a Job, except that
it is nested and does not have an apiVersion or kind.
You can specify common metadata for the templated Jobs, such as
labels or
annotations.
For information about writing a Job .spec, see Writing a Job Spec.
Deadline for delayed Job start
The .spec.startingDeadlineSeconds field is optional.
This field defines a deadline (in whole seconds) for starting the Job, if that Job misses its scheduled time
for any reason.
After missing the deadline, the CronJob skips that instance of the Job (future occurrences are still scheduled).
For example, if you have a backup Job that runs twice a day, you might allow it to start up to 8 hours late,
but no later, because a backup taken any later wouldn't be useful: you would instead prefer to wait for
the next scheduled run.
For Jobs that miss their configured deadline, Kubernetes treats them as failed Jobs.
If you don't specify startingDeadlineSeconds for a CronJob, the Job occurrences have no deadline.
If the .spec.startingDeadlineSeconds field is set (not null), the CronJob
controller measures the time between when a Job is expected to be created and
now. If the difference is higher than that limit, it will skip this execution.
For example, if it is set to 200, it allows a Job to be created for up to 200
seconds after the actual schedule.
Concurrency policy
The .spec.concurrencyPolicy field is also optional.
It specifies how to treat concurrent executions of a Job that is created by this CronJob.
The spec may specify only one of the following concurrency policies:
Allow (default): The CronJob allows concurrently running JobsForbid: The CronJob does not allow concurrent runs; if it is time for a new Job run and the
previous Job run hasn't finished yet, the CronJob skips the new Job run. Also note that when the
previous Job run finishes, .spec.startingDeadlineSeconds is still taken into account and may
result in a new Job run.Replace: If it is time for a new Job run and the previous Job run hasn't finished yet, the
CronJob replaces the currently running Job run with a new Job run
Note that concurrency policy only applies to the Jobs created by the same CronJob.
If there are multiple CronJobs, their respective Jobs are always allowed to run concurrently.
Schedule suspension
You can suspend execution of Jobs for a CronJob, by setting the optional .spec.suspend field
to true. The field defaults to false.
This setting does not affect Jobs that the CronJob has already started.
If you do set that field to true, all subsequent executions are suspended (they remain
scheduled, but the CronJob controller does not start the Jobs to run the tasks) until
you unsuspend the CronJob.
Caution: Executions that are suspended during their scheduled time count as missed Jobs.
When
.spec.suspend changes from
true to
false on an existing CronJob without a
starting deadline, the missed Jobs are scheduled immediately.
Jobs history limits
The .spec.successfulJobsHistoryLimit and .spec.failedJobsHistoryLimit fields specify
how many completed and failed Jobs should be kept. Both fields are optional.
.spec.successfulJobsHistoryLimit: This field specifies the number of successful finished
jobs to keep. The default value is 3. Setting this field to 0 will not keep any successful jobs.
.spec.failedJobsHistoryLimit: This field specifies the number of failed finished jobs to keep.
The default value is 1. Setting this field to 0 will not keep any failed jobs.
For another way to clean up Jobs automatically, see
Clean up finished Jobs automatically.
Time zones
FEATURE STATE: Kubernetes v1.27 [stable]
For CronJobs with no time zone specified, the kube-controller-manager
interprets schedules relative to its local time zone.
You can specify a time zone for a CronJob by setting .spec.timeZone to the name
of a valid time zone.
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
Unsupported TimeZone specification
Specifying a timezone using CRON_TZ or TZ variables inside .spec.schedule
is not officially supported (and never has been).
Starting with Kubernetes 1.29 if you try to set a schedule that includes TZ or CRON_TZ
timezone specification, Kubernetes will fail to create the resource with a validation
error.
Updates to CronJobs already using TZ or CRON_TZ will continue to report a
warning to the client.
Modifying a CronJob
By design, a CronJob contains a template for new Jobs.
If you modify an existing CronJob, the changes you make will apply to new Jobs that
start to run after your modification is complete. Jobs (and their Pods) that have already
started continue to run without changes.
That is, the CronJob does not update existing Jobs, even if those remain running.
Job creation
A CronJob creates a Job object approximately once per execution time of its schedule.
The scheduling is approximate because there
are certain circumstances where two Jobs might be created, or no Job might be created.
Kubernetes tries to avoid those situations, but does not completely prevent them. Therefore,
the Jobs that you define 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 (i.e., 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.
What's next
- Learn about Pods and
Jobs, two concepts
that CronJobs rely upon.
- Read about the detailed 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.
CronJob is part of the Kubernetes REST API.
Read the
CronJob
API reference for more details.
8 - ReplicationController
Legacy API for managing workloads that can scale horizontally. Superseded by the Deployment and ReplicaSet APIs.
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.
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 Manifest
As with all other Kubernetes config, a ReplicationController needs apiVersion, kind, and metadata fields.
When the control plane creates new Pods for a ReplicationController, the .metadata.name of the
ReplicationController is part of the basis for naming those Pods. The name of a ReplicationController must be a valid
DNS subdomain
value, but this can produce unexpected results for the Pod hostnames. For best compatibility,
the name should follow the more restrictive rules for a
DNS label.
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.
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, such as the kubelet.
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.
