Kubernetes Session 3

A time-to-live mechanism to clean up old Jobs that have finished execution.

ReplicaSet

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

How a ReplicaSet works

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

A ReplicaSet is linked to its Pods via the Pods' 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 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

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

You can then get the current ReplicaSets deployed:

And see the frontend one you created:

You can also check on the state of the ReplicaSet:

And you will see output similar to:

And lastly you can check for the Pods brought up:

You should see Pod information similar to:

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:

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

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:

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:

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:

If you create the Pods first:

And then create the ReplicaSet however:

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:

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

Writing a ReplicaSet manifest

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

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 value, but this can produce unexpected results for the Pod hostnames. For best compatibility, the name should follow the more restrictive rules for a .

A ReplicaSet also needs a .

Pod Template

The .spec.template is a 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 field, .spec.template.spec.restartPolicy, the only allowed value is Always, which is the default.

Pod Selector

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

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 . The 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:

Deleting just a ReplicaSet

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

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

Pod deletion cost

FEATURE STATE: Kubernetes v1.22 [beta]

Using the annotation, users can set a preference regarding which pods to remove first when downscaling a ReplicaSet.

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

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

This feature is beta and enabled by default. You can disable it using the 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 . 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.

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.

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

ReplicationController

Note: A that configures a is now the recommended way to set up replication.

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

How a ReplicationController Works

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

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

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

Running an example ReplicationController

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

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

The output is similar to this:

Check on the status of the ReplicationController using this command:

The output is similar to this:

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:

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

The output is similar to this:

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 value, but this can produce unexpected results for the Pod hostnames. For best compatibility, the name should follow the more restrictive rules for a .

For general information about working with configuration files, see .

A ReplicationController also needs a .

Pod Template

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

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

Only a 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 .

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 . 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 ).

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 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 , 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 .

When using the REST API or , 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 .

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

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 managing ReplicationControllers, auto-scalers, services, scheduling policies, canaries, etc.

Deployments

A Deployment provides declarative updates for and .

You describe a desired state in a Deployment, and the Deployment 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:

. The ReplicaSet creates Pods in the background. Check the status of the rollout to see if it succeeds or not.
by updating the PodTemplateSpec of the Deployment. A new ReplicaSet is created and the Deployment manages moving the Pods from the old ReplicaSet to the new one at a controlled rate. Each new ReplicaSet updates the revision of the Deployment.
if the current state of the Deployment is not stable. Each rollback updates the revision of the Deployment.

Creating a Deployment

Before creating a Deployment define an for a container.

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

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 for more details.
The Deployment creates a ReplicaSet that creates three replicated Pods, indicated by the .spec.replicas 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:
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:
When you inspect the Deployments in your cluster, the following fields are displayed:

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.
or use the following command:
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.

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:
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:

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:
The output is similar to this:
The rollout gets stuck. You can verify it by checking the rollout status:
The output is similar to this:

Checking Rollout History of a Deployment

Follow the steps given below to check the rollout history:

First, check the revisions of this Deployment:
The output is similar to this:
CHANGE-CAUSE is copied from the Deployment annotation kubernetes.io/change-cause to its revisions upon creation. You can specify theCHANGE-CAUSE message by:

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:
The output is similar to this:
Alternatively, you can rollback to a specific revision by specifying it with --to-revision:
The output is similar to this:
For more details about rollout related commands, read .

Scaling a Deployment

You can scale a Deployment by using the following command:

The output is similar to this:

Assuming 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.

The output is similar to this:

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, =3, and =2.

Ensure that the 10 replicas in your Deployment are running.
The output is similar to this:
You update to a new image which happens to be unresolvable from inside the cluster.
The output is similar to this:

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:

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

The output is similar to this:

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:
Get the rollout status:
The output is similar to this:

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

Deployment status

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

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).

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

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

Complete Deployment

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

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

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

type: Progressing
status: "True"
reason: NewReplicaSetAvailable

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

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

The output is similar to this:

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

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

One way you can detect this condition is to specify a deadline parameter in your Deployment spec: (). .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:

The output is similar to this:

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 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:

The output is similar to this:

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

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

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).

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.

The output is similar to this:

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

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 .

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 , configuring containers, and 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 value, but this can produce unexpected results for the Pod hostnames. For best compatibility, the name should follow the more restrictive rules for a .

A Deployment also needs a .

Pod Template

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

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

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

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

Selector

.spec.selector is a required field that specifies a 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 .

Rolling Update Deployment

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

Max Unavailable

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

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

Max Surge

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

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

Progress Deadline Seconds

.spec.progressDeadlineSeconds is an optional field that specifies the number of seconds you want to wait for your Deployment to progress before the system reports back that the Deployment has - 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 .

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.

StatefulSets

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

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

Like a , 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.

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. or may be better suited to your stateless needs.

Limitations

The storage for a given Pod must either be provisioned by a 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.

Components

The example below demonstrates the components of a StatefulSet.

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

The name of a StatefulSet object must be a valid .

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 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 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 .

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 , 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.

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 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 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 section, you are responsible for creating the 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

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

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

Deployment and Scaling Guarantees

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

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

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 , 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 .

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:

OnDeleteWhen 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.RollingUpdateThe 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 ), 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 enabled.

Forced rollback

When using with the default (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 , 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 on the API server and the controller manager to use this field. Once enabled, there are two policies you can configure for each StatefulSet:

whenDeletedconfigures the volume retention behavior that applies when the StatefulSet is deletedwhenScaledconfigures 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.

DeleteThe 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.

The StatefulSet adds to its PVCs, which are then deleted by the 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 (or any similar API for horizontal scaling) is managing scaling for a Statefulset, don't set .spec.replicas. Instead, allow the Kubernetes to manage the .spec.replicas field automatically.

DaemonSet

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

Some typical uses of a DaemonSet are:

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

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

Writing a DaemonSet Spec

Create a DaemonSet

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

Create a DaemonSet based on the YAML file:

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 and .

The name of a DaemonSet object must be a valid .

A DaemonSet also needs a section.

Pod Template

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

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

A Pod Template in a DaemonSet must have a 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 .

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

How Daemon Pods are scheduled

A DaemonSet ensures 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 of the new Pod.

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.

Taints and tolerations

The DaemonSet controller automatically adds a set of to DaemonSet Pods:

Toleration key

Effect

Details

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 , 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.

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 on a DaemonSet.

Jobs

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

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

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

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

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.

You can run the example with this command:

The output is similar to this:

Check on the status of the Job with kubectl:

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:

The output is similar to this:

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:

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 value, but this can produce unexpected results for the Pod hostnames. For best compatibility, the name should follow the more restrictive rules for a . Even when the name is a DNS subdomain, the name must be no longer than 63 characters.

A Job also needs a .

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 . It has exactly the same schema as a , 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 ) and an appropriate restart policy.

Only a 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 .

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.

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 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.

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

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 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 . However, you can customize handling of pod failures by setting the Job's .

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 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.

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 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: ). Both feature gates are available in Kubernetes 1.27.

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 , 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 , or -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:

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

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.

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 until they reach a terminal phase. Since Kubernetes 1.27, Kubelet transitions deleted pods to a terminal phase (see: ). This ensures that deleted pods have their finalizers removed by the Job controller.

Job termination and cleanup

When a Job completes, no more Pods are created, but the Pods are 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:

Note that both the Job spec and the 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 , 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 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:

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 eventually 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 and to place a cap on the amount of resources that a particular namespace can consume.

Job patterns

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

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

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

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

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

Pattern

Single Job object

Fewer pods than work items?

Use app unmodified?

When you specify completions with .spec.completions, each Pod created by the Job controller has an identical . 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 . 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:

You can also toggle Job suspension by patching the Job using the command line.

Suspend an active Job:

Resume a suspended Job:

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

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:

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 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 .

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:

The output is similar to this:

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.

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]Note: The control plane doesn't track Jobs using finalizers, if the Jobs were created when the feature gate JobTrackingWithFinalizers was disabled, even after you upgrade the control plane to 1.26.

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.

Jobs created before upgrading to Kubernetes 1.26 or before the feature gate JobTrackingWithFinalizers is enabled are tracked without the use of Pod finalizers. The Job updates the status counters for succeeded and failed Pods based only on the Pods that exist in the cluster. The contol plane can lose track of the progress of the Job if Pods are deleted from the cluster.

You can determine if the control plane is tracking a Job using Pod finalizers by checking if the Job has the annotation batch.kubernetes.io/job-tracking. You should not manually add or remove this annotation from Jobs. Instead, you can recreate the Jobs to ensure they are tracked using Pod finalizers.

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 ElasticIndexedJob on the 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.

AWS S3

Amazon S3 has various features you can use to organize and manage your data in ways that support specific use cases, enable cost efficiencies, enforce security, and meet compliance requirements. Data is stored as objects within resources called “buckets”, and a single object can be up to 5 terabytes in size. S3 features include capabilities to append metadata tags to objects, move and store data across the S3 Storage Classes, configure and enforce data access controls, secure data against unauthorized users, run big data analytics, monitor data at the object and bucket levels, and view storage usage and activity trends across your organization. Objects can be accessed through S3 Access Points or directly through the bucket hostname.

Storage management and monitoring

Amazon S3’s flat, non-hierarchical structure and various management features are helping customers of all sizes and industries organize their data in ways that are valuable to their businesses and teams. All objects are stored in S3 buckets and can be organized with shared names called prefixes. You can also append up to 10 key-value pairs called S3 object tags to each object, which can be created, updated, and deleted throughout an object’s lifecycle. To keep track of objects and their respective tags, buckets, and prefixes, you can use an S3 Inventory report that lists your stored objects within an S3 bucket or with a specific prefix, and their respective metadata and encryption status. S3 Inventory can be configured to generate reports on a daily or a weekly basis.

Storage management

With S3 bucket names, prefixes, object tags, and S3 Inventory, you have a range of ways to categorize and report on your data, and subsequently can configure other S3 features to take action. Whether you store thousands of objects or a billion, makes it simple to manage your data in Amazon S3 at any scale. With S3 Batch Operations, you can copy objects between buckets, replace object tag sets, modify access controls, and restore archived objects from S3 Glacier Flexible Retrieval and S3 Glacier Deep Archive storage classes, with a single S3 API request or a few steps in the S3 console. You can also use S3 Batch Operations to run AWS Lambda functions across your objects to run custom business logic, such as processing data or transcoding image files. To get started, specify a list of target objects by using an S3 Inventory report or by providing a custom list, and then select the desired operation from a pre-populated menu. When an S3 Batch Operation request is done, you'll receive a notification and a completion report of all changes made. Learn more about S3 Batch Operations by .

Amazon S3 also supports features that help maintain data version control, prevent accidental deletions, and replicate data to the same or a different AWS Region. With S3 Versioning, you can preserve, retrieve, and restore every version of an object stored in Amazon S3, which allows you to recover from unintended user actions and application failures. To prevent accidental deletions, enable Multi-Factor Authentication (MFA) Delete on an S3 bucket. If you try to delete an object stored in an MFA Delete-enabled bucket, it will require two forms of authentication: your AWS account credentials and the concatenation of a valid serial number, a space, and the six-digit code displayed on an approved authentication device, like a hardware key fob or a Universal 2nd Factor (U2F) security key.

With , you can replicate objects (and their respective metadata and object tags) to one or more destination buckets into the same or different AWS Regions for reduced latency, compliance, security, disaster recovery, and other use cases. You can configure to replicate objects from a source S3 bucket to one or more destination buckets in different AWS Regions. replicates objects between buckets in the same AWS Region. While live replication like CRR and SRR automatically replicates newly uploaded objects as they are written to your bucket, allows you to replicate existing objects. You can use S3 Batch Replication to backfill a newly created bucket with existing objects, retry objects that were previously unable to replicate, migrate data across accounts, or add new buckets to your data lake. helps you meet compliance requirements for data replication by providing an SLA and visibility into replication times.

To access replicated data sets in S3 buckets across AWS Regions and accounts, use Amazon to create a single global endpoint for your applications and clients to use regardless of their location. This global endpoint allows you to build multi-Region applications with the same simple architecture you would use in a single Region, and then to run those applications anywhere in the world. Amazon S3 Multi-Region Access Points can accelerate performance by up to 60% when accessing data sets that are replicated across multiple AWS Regions and accounts. Based on AWS Global Accelerator, S3 Multi-Region Access Points consider factors like network congestion and the location of the requesting application to dynamically route your requests over the AWS network to the lowest latency copy of your data. Using , you can failover between your replicated data sets across AWS Regions, allowing you to shift your S3 data request traffic to an alternate AWS Region within minutes.

You can also enforce write-once-read-many (WORM) policies with . This S3 management feature blocks object version deletion during a customer-defined retention period so that you can enforce retention policies as an added layer of data protection or to meet compliance obligations. You can migrate workloads from existing WORM systems into Amazon S3, and configure S3 Object Lock at the object- and bucket-levels to prevent object version deletions prior to a pre-defined Retain Until Date or Legal Hold Date. Objects with S3 Object Lock retain WORM protection, even if they are moved to different storage classes with an S3 Lifecycle policy. To track what objects have S3 Object Lock, you can refer to an S3 Inventory report that includes the WORM status of objects. S3 Object Lock can be configured in one of two modes. When deployed in Governance mode, AWS accounts with specific IAM permissions are able to remove S3 Object Lock from objects. If you require stronger immutability in order to comply with regulations, you can use Compliance Mode. In Compliance Mode, the protection cannot be removed by any user, including the root account.

Storage monitoring

In addition to these management capabilities, use Amazon S3 features and other AWS services to monitor and control your S3 resources. Apply tags to S3 buckets to allocate costs across multiple business dimensions (such as cost centers, application names, or owners), then use AWS Cost Allocation Reports to view the usage and costs aggregated by the bucket tags. You can also use Amazon CloudWatch to track the operational health of your AWS resources and configure billing alerts for estimated charges that reach a user-defined threshold. Use AWS CloudTrail to track and report on bucket- and object-level activities, and configure S3 Event Notifications to trigger workflows and alerts or invoke AWS Lambda when a specific change is made to your S3 resources. S3 Event Notifications automatically transcodes media files as they’re uploaded to S3, processes data files as they become available, and synchronizes objects with other data stores. Additionally, you can verify integrity of data transferred to and from Amazon S3, and can access the checksum information at any time using the GetObjectAttributes S3 API or an S3 Inventory report. You can choose from four supported checksum algorithms (SHA-1, SHA-256, CRC32, or CRC32C) for data integrity checking on your upload and download requests depending on your application needs.

Learn more about and

Storage analytics and insights

S3 Storage Lens

delivers organization-wide visibility into object storage usage, activity trends, and makes actionable recommendations to improve cost-efficiency and apply data protection best practices. S3 Storage Lens is the first cloud storage analytics solution to provide a single view of object storage usage and activity across hundreds, or even thousands, of accounts in an organization, with drill-downs to generate insights at the account, bucket, or even prefix level. Drawing from more than 14 years of experience helping customers optimize their storage, S3 Storage Lens analyzes organization-wide metrics to deliver contextual recommendations to find ways to reduce storage costs and apply best practices on data protection. To learn more, visit the

S3 Storage Class Analysis

Amazon S3 Storage Class Analysis analyzes storage access patterns to help you decide when to transition the right data to the right storage class. This Amazon S3 feature observes data access patterns to help you determine when to transition less frequently accessed storage to a lower-cost storage class. You can use the results to help improve your S3 Lifecycle policies. You can configure storage class analysis to analyze all the objects in a bucket. Or, you can configure filters to group objects together for analysis by common prefix, by object tags, or by both prefix and tags. To learn more, visit the .

Storage classes

With Amazon S3, you can store data across a range of different S3 storage classes purpose-built for specific use cases and access patterns: S3 Intelligent-Tiering, S3 Standard, S3 Standard-Infrequent Access (S3 Standard-IA), S3 One Zone-Infrequent Access (S3 One Zone-IA), S3 Glacier Instant Retrieval, S3 Glacier Flexible Retrieval, S3 Glacier Deep Archive, and S3 Outposts.

Every S3 storage class supports a specific data access level at corresponding costs or geographic location.

For data with changing, unknown, or unpredictable access patterns, such as data lakes, analytics, or new applications, use S3 Intelligent-Tiering, which automatically optimizes your storage costs. S3 Intelligent-Tiering automatically moves your data between three low latency access tiers optimized for frequent, infrequent, and rare access. When subsets of objects become archived over time, you can activate the archive access tier designed for asynchronous access.

For more predictable access patterns, you can store mission-critical production data in S3 Standard for frequent access, save costs by storing infrequently accessed data in S3 Standard-IA or S3 One Zone-IA, and archive data at the lowest costs in the archival storage classes — S3 Glacier Instant Retrieval, S3 Glacier Flexible Retrieval, and S3 Glacier Deep Archive. You can use S3 Storage Class Analysis to monitor access patterns across objects to discover data that should be moved to lower-cost storage classes. Then you can use this information to configure an S3 Lifecycle policy that makes the data transfer. S3 Lifecycle policies can also be used to expire objects at the end of their lifecycles.

If you have data residency requirements that can’t be met by an existing AWS Region, you can use the S3 Outposts storage class to store your S3 data on premises using S3 on Outposts.

Learn more by visiting , , and

Access management and security

Access management

To protect your data in Amazon S3, by default, users only have access to the S3 resources they create. You can grant access to other users by using one or a combination of the following access management features: AWS Identity and Access Management (IAM) to create users and manage their respective access; Access Control Lists (ACLs) to make individual objects accessible to authorized users; bucket policies to configure permissions for all objects within a single S3 bucket; to simplify managing data access to shared data sets by creating access points with names and permissions specific to each application or sets of applications; and Query String Authentication to grant time-limited access to others with temporary URLs. Amazon S3 also supports Audit Logs that list the requests made against your S3 resources for complete visibility into who is accessing what data.

Security

Amazon S3 offers flexible security features to block unauthorized users from accessing your data. Use VPC endpoints to connect to S3 resources from your Amazon Virtual Private Cloud (Amazon VPC) and from on-premises. Amazon S3 encrypts all new data uploads to any bucket (as of January 5, 2023). Amazon S3 supports both server-side encryption (with three key management options) and client-side encryption for data uploads. Use S3 Inventory to check the encryption status of your S3 objects (see for more information on S3 Inventory). is a set of security controls that ensures S3 buckets and objects do not have public access. With a few clicks in the Amazon S3 Management Console, you can apply the S3 Block Public Access settings to all buckets within your AWS account or to specific S3 buckets. Once the settings are applied to an AWS account, any existing or new buckets and objects associated with that account inherit the settings that prevent public access. S3 Block Public Access settings override other S3 access permissions, making it easy for the account administrator to enforce a “no public access” policy regardless of how an object is added, how a bucket is created, or if there are existing access permissions. S3 Block Public Access controls are auditable, provide a further layer of control, and use AWS Trusted Advisor bucket permission checks, AWS CloudTrail logs, and Amazon CloudWatch alarms. You should enable Block Public Access for all accounts and buckets that you do not want publicly accessible.

is a feature that disables Access Control Lists (ACLs), changing ownership for all objects to the bucket owner and simplifying access management for data stored in S3. When you configure the S3 Object Ownership Bucket owner enforced setting, ACLs will no longer affect permissions for your bucket and the objects in it. All access control will be defined using resource-based policies, user policies, or some combination of these. Before you disable ACLs, review your bucket and object ACLs. To identify Amazon S3 requests that required ACLs for authorization, you can use the aclRequired field in or .

Using S3 Access Points that are restricted to a Virtual Private Cloud (VPC), you can easily firewall your S3 data within your private network. Additionally, you can use AWS Service Control Policies to require that any new S3 Access Point in your organization is restricted to VPC-only access.

is a feature that helps you simplify permissions management as you set, verify, and refine policies for your S3 buckets and access points. Access Analyzer for S3 monitors your existing bucket access policies to verify that they provide only the required access to your S3 resources. Access Analyzer for S3 evaluates your bucket access policies so that you can swiftly remediate any buckets with access that isn't required. When reviewing results that show potentially shared access to a bucket, you can Block Public Access to the bucket with a single click in the S3 console. For auditing purposes, you can download Access Analyzer for S3 findings as a CSV report. Additionally, the S3 console reports security warnings, errors, and suggestions from IAM Access Analyzer as you author your S3 policies. The console automatically runs more than 100 policy checks to validate your policies. These checks save you time, guide you to resolve errors, and help you apply security best practices.

IAM makes it easier for you to analyze access and reduce permissions to achieve least privilege by providing the timestamp when a user or role last used S3 and the associated actions. Use this “last accessed” information to analyze S3 access, identify unused permissions, and remove them confidently. To learn more see .

You can use to discover and protect sensitive data stored in Amazon S3. Macie automatically gathers a complete S3 inventory and continually evaluates every bucket to alert on any publicly accessible buckets, unencrypted buckets, or buckets shared or replicated with AWS accounts outside of your organization. Then, Macie applies machine learning and pattern matching techniques to the buckets you select to identify and alert you to sensitive data, such as personally identifiable information (PII). As security findings are generated, they are pushed out to the Amazon CloudWatch Events, making it easy to integrate with existing workflow systems and to trigger automated remediation with services like AWS Step Functions to take action like closing a public bucket or adding resource tags.

provides private connectivity between Amazon S3 and on-premises. You can provision interface VPC endpoints for S3 in your VPC to connect your on-premises applications directly with S3 over AWS Direct Connect or AWS VPN. Requests to interface VPC endpoints for S3 are automatically routed to S3 over the Amazon network. You can set security groups and configure VPC endpoint policies for your interface VPC endpoints for additional access controls.

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Data processing

S3 Object Lambda

With you can add your own code to S3 GET, HEAD, and LIST requests to modify and process data as it is returned to an application. You can use custom code to modify the data returned by standard S3 GET requests to filter rows, dynamically resize images, redact confidential data, and much more. You can also use S3 Object Lambda to modify the output of S3 LIST requests to create a custom view of objects in a bucket and S3 HEAD requests to modify object metadata like object name and size. Powered by AWS Lambda functions, your code runs on infrastructure that is fully managed by AWS, eliminating the need to create and store derivative copies of your data or to run expensive proxies, all with no changes required to applications.

S3 Object Lambda uses AWS Lambda functions to automatically process the output of a standard S3 GET, HEAD, or LIST request. AWS Lambda is a serverless compute service that runs customer-defined code without requiring management of underlying compute resources. With just a few clicks in the AWS Management Console, you can configure a Lambda function and attach it to a S3 Object Lambda Access Point. From that point forward, S3 will automatically call your Lambda function to process any data retrieved through the S3 Object Lambda Access Point, returning a transformed result back to the application. You can author and execute your own custom Lambda functions, tailoring S3 Object Lambda’s data transformation to your specific use case.

Query in place

Amazon S3 has a built-in feature and complementary services that query data without needing to copy and load it into a separate analytics platform or data warehouse. This means you can run big data analytics directly on your data stored in Amazon S3. S3 Select is an S3 feature designed to increase query performance by up to 400%, and reduce querying costs as much as 80%. It works by retrieving a subset of an object’s data (using simple SQL expressions) instead of the entire object, which can be up to 5 terabytes in size.

Amazon S3 is also compatible with AWS analytics services Amazon Athena and Amazon Redshift Spectrum. Amazon Athena queries your data in Amazon S3 without needing to extract and load it into a separate service or platform. It uses standard SQL expressions to analyze your data, delivers results within seconds, and is commonly used for ad hoc data discovery. Amazon Redshift Spectrum also runs SQL queries directly against data at rest in Amazon S3, and is more appropriate for complex queries and large data sets (up to exabytes). Because Amazon Athena and Amazon Redshift share a common data catalog and data formats, you can use them both against the same data sets in Amazon S3.

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Data transfer

AWS provides a portfolio of data transfer services to provide the right solution for any data migration project. The level of connectivity is a major factor in data migration, and AWS has offerings that can address your hybrid cloud storage, online data transfer, and offline data transfer needs.

Hybrid cloud storage: is a hybrid cloud storage service that lets you seamlessly connect and extend your on-premises applications to AWS Storage. Customers use Storage Gateway to seamlessly replace tape libraries with cloud storage, provide cloud storage-backed file shares, or create a low-latency cache to access data in AWS for on-premises applications.

Online data transfer: makes it easy and efficient to transfer hundreds of terabytes and millions of files into Amazon S3, up to 10x faster than open-source tools. DataSync automatically handles or eliminates many manual tasks, including scripting copy jobs, scheduling and monitoring transfers, validating data, and optimizing network utilization. Additionally, you can use AWS DataSync to copy objects between a bucket on S3 on Outposts and a bucket stored in an AWS Region. The provides fully managed, simple, and seamless file transfer to Amazon S3 using SFTP, FTPS, and FTP. enables fast transfers of files over long distances between your client and your Amazon S3 bucket.

Offline data transfer / little or no connectivity: The is purpose-built for use in edge locations where network capacity is constrained or nonexistent and provides storage and computing capabilities in harsh environments. is the smallest, ultra-portable, rugged, and secure device in the AWS Snow Family offering edge computing, data storage, and data transfer on-the-go, in austere environments with little or no connectivity. The service uses ruggedized, portable storage and edge computing devices for data collection, processing, and migration. Customers can ship the physical Snowball device for offline data migration to AWS. is an exabyte-scale data transfer service used to move massive volumes of data to the cloud, including video libraries, image repositories, or even a complete data center migration.

Customers can also work with third-party providers from the to deploy hybrid storage architectures, integrate Amazon S3 into existing applications and workflows, and transfer data to and from AWS.

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Data exchange

accelerates time to insight with direct access to data providers' Amazon S3 data. AWS Data Exchange for Amazon S3 helps you easily find, subscribe to, and use third-party data files for storage cost optimization, simplified data licensing management, and more. It is intended for subscribers who want to easily use third-party data files for data analysis with AWS services without needing to create or manage data copies. It is also helpful for data providers who want to offer in-place access to data hosted in their Amazon S3 buckets.

Once data subscribers are entitled to an AWS Data Exchange for Amazon S3 dataset, they can start data analysis without having to set up their own S3 buckets, copy data files into those S3 buckets, or pay associated storage fees. Data analysis can be done with AWS services such as Amazon Athena, Amazon SageMaker Feature Store, or Amazon EMR. Subscribers access the same S3 objects that the data provider maintains and are therefore always using the most up-to-date data available, without additional engineering or operational work. Data providers can easily set up AWS Data Exchange for Amazon S3 on top of their existing S3 buckets to share direct access to an entire S3 bucket or specific prefixes and S3 objects. After setup, AWS Data Exchange automatically manages subscriptions, entitlements, billing, and payment.

Performance

Amazon S3 provides industry leading performance for cloud object storage. Amazon S3 supports parallel requests, which means you can scale your S3 performance by the factor of your compute cluster, without making any customizations to your application. Performance scales per prefix, so you can use as many prefixes as you need in parallel to achieve the required throughput. There are no limits to the number of prefixes. Amazon S3 performance supports at least 3,500 requests per second to add data and 5,500 requests per second to retrieve data. Each S3 prefix can support these request rates, making it simple to increase performance significantly.

To achieve this S3 request rate performance you do not need to randomize object prefixes to achieve faster performance. That means you can use logical or sequential naming patterns in S3 object naming without any performance implications. Refer to the and for the most current information about performance optimization for Amazon S3.

Consistency

Amazon S3 delivers strong read-after-write consistency automatically for all applications, without changes to performance or availability, without sacrificing regional isolation for applications, and at no additional cost. With , S3 simplifies the migration of on-premises analytics workloads by removing the need to make changes to applications, and reduces costs by removing the need for extra infrastructure to provide strong consistency.

Any request for S3 storage is strongly consistent. After a successful write of a new object or an overwrite of an existing object, any subsequent read request immediately receives the latest version of the object. S3 also provides strong consistency for list operations, so after a write, you can immediately perform a listing of the objects in a bucket with any changes reflected.

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Amazon RDS for PostgreSQL

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AWS

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hashtagUses of AWS

hashtagPay-As-You-Go

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SeMA Deployment Architecture

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Linux for DevOps

Recommended Application HW Sizing

Kubernetes Configmaps

Benefits

Easy, managed deployments

Fast, predictable storage

Backup and recovery

High availability and read replicas

Monitoring and metrics

Isolation and security

What is AWS?

Uses of AWS

Pay-As-You-Go

Advantages of AWS

1) Flexibility

2) Cost-effectiveness

3) Scalability/Elasticity

4) Security

History of AWS

SeMA Logical deployment architecture

SeMA physical Deployment Architecture

Linux History

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Linux

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Docker

Live code screening

Recommended Application HW Sizing

Kubernetes PersistentVolumes (PVs) and PersistentVolumeClaims (PVCs)

The Difference Between PVs and PVCs in Kubernetes

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Easy, managed deployments

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Isolation and security

What is AWS?

Uses of AWS

Pay-As-You-Go

Advantages of AWS

1) Flexibility

2) Cost-effectiveness

3) Scalability/Elasticity

4) Security

History of AWS

SeMA Logical deployment architecture

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