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coming up with all the new updates: kubectl rollout resume deployment/nginx-deployment The output is similar to this: deployment.apps/nginx-deployment resumed Watch the status of the rollout until it's done. kubectl get rs -w The output is similar to this: NAME DESIRED CURRENT READY AGE nginx-2142116321 2 2 2 2m nginx-3926361531 2 2 0 6s nginx-3926361531 2 2 1 18s nginx-2142116321 1 2 2 2m• • • •

nginx-2142116321 1 2 2 2m nginx-3926361531 3 2 1 18s nginx-3926361531 3 2 1 18s nginx-2142116321 1 1 1 2m nginx-3926361531 3 3 1 18s nginx-3926361531 3 3 2 19s nginx-2142116321 0 1 1 2m nginx-2142116321 0 1 1 2m nginx-2142116321 0 0 0 2m nginx-3926361531 3 3 3 20s Get the status of the latest rollout: kubectl get rs The output is similar to this: NAME DESIRED CURRENT READY AGE nginx-2142116321 0 0 0 2m nginx-3926361531 3 3 3 28s Note: You cannot rollback a paused Deployment until you resume it. Deployment status A Deployment enters various states during its lifecycle. It can be progressing while rolling out a new ReplicaSet, it can be complete , or it can

fail to progress . Progressing Deployment Kubernetes marks a Deployment as progressing when one of the following tasks is performed: The Deployment creates a new ReplicaSet. The Deployment is scaling up its newest ReplicaSet. The Deployment is scaling down its older ReplicaSet(s). New Pods become ready or available (ready for at least MinReadySeconds ). When the rollout becomes “progressing”, the Deployment controller adds a condition with the following attributes to the Deployment's .status.conditions : type: Progressing status: "True" reason: NewReplicaSetCreated | reason: FoundNewReplicaSet | reason: ReplicaSetUpdated You can monitor the progress for a Deployment by using kubectl rollout status . Complete Deployment Kubernetes marks a Deployment as complete when it has the following characteristics: All of the replicas associated with the Deployment have been updated to the latest version you've specified, meaning any updates you've requested have been completed. All of the repl

icas associated with the Deployment are available.• • • • • • • • •

No old replicas for the Deployment are running. When the rollout becomes “complete”, the Deployment controller sets a condition with the following attributes to the Deployment's .status.conditions : type: Progressing status: "True" reason: NewReplicaSetAvailable This Progressing condition will retain a status value of "True" until a new rollout is initiated. The condition holds even when availability of replicas changes (which does instead affect the Available condition). You can check if a Deployment has completed by using kubectl rollout status . If the rollout completed successfully, kubectl rollout status returns a zero exit code. kubectl rollout status deployment/nginx-deployment The output is similar to this: Waiting for rollout to finish: 2 of 3 updated replicas are available... deployment "nginx-deployment" successfully rolled out and the exit status from kubectl rollout is 0 (success): echo $? 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: Progressing status: "False" reason: ProgressDeadlineExceeded This condition can also fail early and is then set to status value of "False" due to reasons as ReplicaSetCreateError . Also, the deadline is not taken into account anymore once the Deployment rollout completes. See the Kubernetes API conventions for more information on status conditions. Note: Kubernetes takes no action on a stalled Deployment other than to report a status condition with reason: ProgressDeadlineExceeded . Higher level orchestrators can take advantage of it and act accordingly, for example, rollback the Deployment to its previous version. Note: If you pause a Deployment rollout, Kubernetes does not check progress against your specified deadline. You can safely pause a Deployment rollout in the middle of a rollout and resume without triggeri

ng 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 sa

tisfy 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 us

ing 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 deadlin

and the exit status from kubectl rollout is 1 (indicating an error): echo $? 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 othe

r 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 ap

propriate 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 , .spe

c.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 str

ategy 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 immediatel

(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 scal

ed 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 : Max Unavailable Max Surge Hybrid 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.min

ReadySeconds is an optional field that specifies the minimum number of seconds for which a newly created Pod should be ready without any of its containers crashing, for it to be considered available. This defaults to 0 (the Pod will be considered available as soon as it is ready). To learn more about when a Pod is considered ready, see Container Probes . Revision History Limit A Deployment's revision history is stored in the ReplicaSets it controls. .spec.revisionHistoryLimit is an optional field that specifies the number of old ReplicaSets to retain to allow rollback. These old ReplicaSets consume resources in etcd and crowd the output of kubectl get rs . The configuration of each Deployment revision is stored in its ReplicaSets; therefore, once an old ReplicaSet is deleted, you lose the ability to rollback to that revision of Deployment. By default, 10 old ReplicaSets will be kept, however its ideal value depends on the frequency and stability of new Deployments. More specifically,

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

Paused .spec.paused is an optional boolean field for pausing and resuming a Deployment. The only difference between a paused Deployment and one that is not paused, is that any changes into the PodTemplateSpec of the paused Deployment will not trigger new rollouts as long as it is paused. A Deployment is not paused by default when it is created. What's next Learn more about Pods . Run a stateless application using a Deployment . Read the Deployment to understand the Deployment API. Read about PodDisruptionBudget and how you can use it to manage application availability during disruptions. Use kubectl to create a Deployment . 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 numb

er 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 Owner

Reference or the OwnerReference is not a Controller and it matches a ReplicaSet's selector, it will be immediately acquired by said ReplicaSet. When to use a ReplicaSet A ReplicaSet ensures that a specified number of pod replicas are running at any given time. However, a Deployment is a higher-level concept that manages ReplicaSets and provides declarative updates to Pods along with a lot of other useful features. Therefore, we recommend using Deployments instead of directly using ReplicaSets, unless you require custom update orchestration or don't require updates at all.• • • •

This actually means that you may never need to manipulate ReplicaSet objects: use a Deployment instead, and define your application in the spec section. Example controllers/frontend.yaml apiVersion : apps/v1 kind: ReplicaSet metadata : name : frontend labels : app: guestbook tier: frontend spec: # modify replicas according to your case replicas : 3 selector : matchLabels : tier: frontend template : metadata : labels : tier: frontend spec: containers : - name : php-redis image : gcr.io/google_samples/gb-frontend:v3 Saving this manifest into frontend.yaml and submitting it to a Kubernetes cluster will create the defined ReplicaSet and the Pods that it manages. kubectl apply -f https://kubernetes.io/examples/controllers/frontend.yaml You can then get the current ReplicaSets deployed: kubectl get rs And see the frontend one you created: NAME DESIRED CURRENT READY AGE frontend 3 3 3

6s You can also check on the state of the ReplicaSet: kubectl describe rs/frontend And you will see output similar to: Name: frontend Namespace: default Selector: tier=frontend Labels: app=guestboo

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

or the Pods brought up: kubectl get pods You should see Pod information similar to: NAME READY STATUS RESTARTS AGE frontend-b2zdv 1/1 Running 0 6m36s frontend-vcmts 1/1 Running 0 6m36s frontend-wtsmm 1/1 Running 0 6m36s You can also verify that the owner reference of these pods is set to the frontend ReplicaSet. To do this, get the yaml of one of the Pods running: kubectl get pods frontend-b2zdv -o yaml The output will look similar to this, with the frontend ReplicaSet's info set in the metadata's ownerReferences field: apiVersion : v1 kind: Pod metadata : creationTimestamp : "2020-02-12T07:06:16Z" generateName : frontend- labels : tier: frontend name : frontend-b2zdv namespace : default ownerReferences : - apiVersion : apps/v1 blockOwnerDeletion : tru

controller : true kind: ReplicaSet name : frontend uid: f391f6db-bb9b-4c09-ae74-6a1f77f3d5cf ... Non-Template Pod acquisitions While you can create bare Pods with no problems, it is strongly recommended to make sure that the bare Pods do not have labels which match the selector of one of your ReplicaSets. The reason for this is because a ReplicaSet is not limited to owning Pods specified by its template-- it can acquire other Pods in the manner specified in the previous sections. Take the previous frontend ReplicaSet example, and the Pods specified in the following manifest: pods/pod-rs.yaml apiVersion : v1 kind: Pod metadata : name : pod1 labels : tier: frontend spec: containers : - name : hello1 image : gcr.io/google-samples/hello-app:2.0 --- apiVersion : v1 kind: Pod metadata : name : pod2 labels : tier: frontend spec: containers : - name : hello2 image : gcr.io/google-samples/hello-app:1.0 As those Pods do not have a Controller (or any

object) as their owner reference and match the selector of the frontend ReplicaSet, they will immediately be acquired by it. Suppose you create the Pods after the frontend ReplicaSet has been deployed and has set up its initial Pod replicas to fulfill its replica count requirement: kubectl apply -f https://kubernetes.io/examples/pods/pod-rs.yaml The new Pods will be acquired by the ReplicaSet, and then immediately terminated as the ReplicaSet would be over its desired count

Fetching the Pods: kubectl get pods The output shows that the new Pods are either already terminated, or in the process of being terminated: NAME READY STATUS RESTARTS AGE frontend-b2zdv 1/1 Running 0 10m frontend-vcmts 1/1 Running 0 10m frontend-wtsmm 1/1 Running 0 10m pod1 0/1 Terminating 0 1s pod2 0/1 Terminating 0 1s If you create the Pods first: kubectl apply -f https://kubernetes.io/examples/pods/pod-rs.yaml And then create the ReplicaSet however: kubectl apply -f https://kubernetes.io/examples/controllers/frontend.yaml You shall see that the ReplicaSet has acquired the Pods and has only created new ones according to its spec until the number of its new Pods and the original matches its desired count. As fetching the Pods: kubectl get pods Will reveal in its output: NAME READY STATUS RESTARTS AGE frontend-hmmj2 1/1 Ru

nning 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 contr

ollers, 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 Po

ds, 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 choose

s 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 t

o 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.1. 2. 3. 4.

Users should avoid updating the annotation frequently, such as updating it based on a metric value, because doing so will generate a significant number of pod updates on the apiserver. Example Use Case The different pods of an application could have different utilization levels. On scale down, the application may prefer to remove the pods with lower utilization. To avoid frequently updating the pods, the application should update controller.kubernetes.io/pod-deletion-cost once before issuing a scale down (setting the annotation to a value proportional to pod utilization level). This works if the application itself controls the down scaling; for example, the driver pod of a Spark deployment. ReplicaSet as a Horizontal Pod Autoscaler Target A ReplicaSet can also be a target for Horizontal Pod Autoscalers (HPA) . That is, a ReplicaSet can be auto-scaled by an HPA. Here is an example HPA targeting the ReplicaSet we created in the previous example. controllers/hpa-rs.yaml apiVersion : au

toscaling/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 Dep

loyments 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 lif

etime that is tied to a machine lifetime: the Pod needs to be running on the machine before other Pods start, and are safe to terminate when the machine is otherwise ready to be rebooted/shutdown. ReplicationController ReplicaSets are the successors to ReplicationControllers . The two serve the same purpose, and behave similarly, except that a ReplicationController does not support set-based selector requirements as described in the labels user guide . As such, ReplicaSets are preferred over ReplicationControllers What's next Learn about Pods . Learn about Deployments . Run a Stateless Application Using a Deployment , which relies on ReplicaSets to work. ReplicaSet is a top-level resource in the Kubernetes REST API. Read the ReplicaSet object definition to understand the API for replica sets. Read about PodDisruptionBudget and how you can use it to manage application availability during disruptions. 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, auto

mated 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 terminati

on 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 us

es 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 .sp

ec.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 t

hat 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 DomainService (ns/ name)StatefulSet (ns/name)StatefulSet Domain Pod DNSPod Hostname cluster.localdefault/ nginxdefault/web nginx.default.svc.cluster.localweb- {0..N-1}.nginx.default.svc.cluster.localweb- {0..N-1} cluster.loc

alfoo/ nginxfoo/web nginx.foo.svc.cluster.localweb- {0..N-1}.nginx.foo.svc.cluster.localweb- {0..N-1} kube.localfoo/ nginxfoo/web nginx.foo.svc.kube.local web-{0..N-1}.nginx.foo.svc.kube.localweb- {0..N-1} Note: Cluster Domain will be set to cluster.local unless otherwise configured . Stable Storage For each VolumeClaimTemplate entry defined in a StatefulSet, each Pod receives one PersistentVolumeClaim. In the nginx example above, each Pod receives a single PersistentVolume with a StorageClass of my-storage-class and 1 GiB of provisioned storage. If no StorageClass is specified, then the default StorageClass will be used. When a Pod is (re)scheduled onto a node, its volumeMounts mount the PersistentVolumes associated with its PersistentVolume Claims. Note that, the PersistentVolumes associated with the Pods' PersistentVolume Claims are not deleted when the Pods, or StatefulSet are deleted. This must be done manually.•

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

ing 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 part

ition 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 a

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 w

hen 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 th

e 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, depen

ding 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 Follow an example of deploying a stateful application . Follow an example of deploying Cassandra with Stateful Sets . Follow an example of running a replicated stateful application . Learn how to scale a StatefulSet . Learn what's involved when you delete a StatefulSet . Learn how to configure a Pod to use a volume for storage . Learn how to configure a Pod to use a PersistentVolume for storage . StatefulSet is a top-level resource in the Kubernetes REST API. Read the StatefulSet object definition to understand the API for stateful sets. Read about PodDisruptionBudget and how you can use it to manage application availability during disruptions. 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: controllers/daemonset.yaml apiVersion : apps/v1 kind: DaemonSet metadata : name : fluentd-elasticsearch namespace : kube-system labels : k8s-app : fluentd-logging• • ◦

◦ ◦ ◦ ◦ ◦ ◦ • • • •

spec: selector : matchLabels : name : fluentd-elasticsearch template : metadata : labels : name : fluentd-elasticsearch spec: tolerations : # these tolerations are to have the daemonset runnable on control plane nodes # remove them if your control plane nodes should not run pods - key: node-role.kubernetes.io/control-plane operator : Exists effect : NoSchedule - key: node-role.kubernetes.io/master operator : Exists effect : NoSchedule containers : - name : fluentd-elasticsearch image : quay.io/fluentd_elasticsearch/fluentd:v2.5.2 resources : limits : memory : 200Mi requests : cpu: 100m memory : 200Mi volumeMounts : - name : varlog mountPath : /var/log # it may be desirable to set a high priority class to ensure that a DaemonSet Pod # preempts running Pods # priori

tyClassName: 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 . matchExpress

ions - 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: Tolerations for DaemonSet pods Toleration key Effect Details node.kubernetes.io/ not-readyNoExecuteDaemonSet Pods can be scheduled onto nodes that are not healthy or ready to accept Pods. Any DaemonSet Pods running on such nodes will not b

e evicted. node.kubernetes.io/ unreachableNoExecuteDaemonSet Pods can be scheduled onto nodes that are unreachable from the node controller. Any DaemonSet Pods running on such nodes will not be evicted. node.kubernetes.io/ disk-pressureNoScheduleDaemonSet Pods can be scheduled onto nodes with disk pressure issues. node.kubernetes.io/ memory-pressureNoScheduleDaemonSet Pods can be scheduled onto nodes with memory pressure issues. node.kubernetes.io/pid- pressureNoScheduleDaemonSet Pods can be scheduled onto nodes with process pressure issues. node.kubernetes.io/ unschedulableNoScheduleDaemonSet Pods can be scheduled onto nodes that are unschedulable. node.kubernetes.io/ network-unavailableNoScheduleOnly added for DaemonSet Pods that request host networking , i.e., Pods having spec.hostNetwork: true . Such DaemonSet Pods can be scheduled onto nodes with unavailable network. You can add your own tolerations to the Pods of a DaemonSet as well, by defining these in the Pod template of the D

aemonSet. 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 situatio

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 ad

d 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 numb

er of replicas and rolling out updates are more important than controlling exactly which host the Pod runs on. Use a DaemonSet when it is important that a copy of a Pod always run on all or certain hosts, if the DaemonSet provides node-level functionality that allows other Pods to run correctly on that particular node. For example, network plugins often include a component that runs as a DaemonSet. The DaemonSet component makes sure that the node where it's running has working cluster networking. What's next Learn about Pods . Learn about static Pods , which are useful for running Kubernetes control plane components. Find out how to use DaemonSets Perform a rolling update on a DaemonSet Perform a rollback on a DaemonSet (for example, if a roll out didn't work how you expected). Understand how Kubernetes assigns Pods to Nodes . Learn about device plugins and add ons , which often run as DaemonSets. DaemonSet is a top-level resource in the Kubernetes REST API. Read the DaemonSet obje

ct definition to understand the API for daemon sets. 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. controllers/job.yaml 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 thi

s command: kubectl apply -f https://kubernetes.io/examples/controllers/job.yaml The output is similar to this: job.batch/pi created Check on the status of the Job with kubectl : kubectl describe job pi kubectl get job pi -o yaml 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 =json

path ='{.items [*].metadata.name}' ) echo $pods The output is similar to this: pi-5rwd7 Here, the selector is the same as the selector for the Job. The --output=jsonpath option specifies an expression with the name from each Pod in the returned list. View the standard output of one of the pods: kubectl logs $pods Another way to view the logs of a Job: kubectl logs jobs/pi The output is similar to this

3.1415926535897932384626433832795028841971693993751058209749445923078164062862089986 280348253421170679821480865132823066470938446095505822317253594081284811174502841027 019385211055596446229489549303819644288109756659334461284756482337867831652712019091 456485669234603486104543266482133936072602491412737245870066063155881748815209209628 292540917153643678925903600113305305488204665213841469519415116094330572703657595919 530921861173819326117931051185480744623799627495673518857527248912279381830119491298 336733624406566430860213949463952247371907021798609437027705392171762931767523846748 184676694051320005681271452635608277857713427577896091736371787214684409012249534301 465495853710507922796892589235420199561121290219608640344181598136297747713099605187 072113499999983729780499510597317328160963185950244594553469083026425223082533446850 352619311881710100031378387528865875332083814206171776691473035982534904287554687311 59562863882353787593751957781857780532171226806613001927876611195

9092164201989380952 572010654858632788659361533818279682303019520353018529689957736225994138912497217752 834791315155748572424541506959508295331168617278558890750983817546374649393192550604 009277016711390098488240128583616035637076601047101819429555961989467678374494482553 797747268471040475346462080466842590694912933136770289891521047521620569660240580381 501935112533824300355876402474964732639141992726042699227967823547816360093417216412 199245863150302861829745557067498385054945885869269956909272107975093029553211653449 872027559602364806654991198818347977535663698074265425278625518184175746728909777727 938000816470600161452491921732172147723501414419735685481613611573525521334757418494 684385233239073941433345477624168625189835694855620992192221842725502542568876717904 946016534668049886272327917860857843838279679766814541009538837863609506800642251252 051173929848960841284886269456042419652850222106611863067442786220391949450471237137 869609563643719172874677646575739624138908658

326459958133904780275901 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 la

bels (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 indepe