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Kubernetes runs your {{< glossary\_tooltip text="workload" term\_id="workload" >}} by placing containers into Pods to run on \_Nodes\_. A node may be a virtual or physical machine, depending on the cluster. Each node is managed by the {{< glossary\_tooltip text="control plane" term\_id="control-plane" >}} and contains the services necessary to run {{< glossary\_tooltip text="Pods" term\_id="pod" >}}. Typically you have several nodes in a cluster; in a learning or resource-limited environment, you might have only one node. The [components](/docs/concepts/architecture/#node-components) on a node include the {{< glossary\_tooltip text="kubelet" term\_id="kubelet" >}}, a {{< glossary\_tooltip text="container runtime" term\_id="container-runtime" >}}, and the {{< glossary\_tooltip text="kube-proxy" term\_id="kube-proxy" >}}. ## Management There are two main ways to have Nodes added to the {{< glossary\_tooltip text="API server" term\_id="kube-apiserver" >}}: 1. The kubelet on a node self-registers to the control plane 2. You (or another human user) manually add a Node object After you create a Node {{< glossary\_tooltip text="object" term\_id="object" >}}, or the kubelet on a node self-registers, the control plane checks whether the new Node object is valid. For example, if you try to create a Node from the following JSON manifest: ```json { "kind": "Node", "apiVersion": "v1", "metadata": { "name": "10.240.79.157", "labels": { "name": "my-first-k8s-node" } } } ``` Kubernetes creates a Node object internally (the representation). Kubernetes checks that a kubelet has registered to the API server that matches the `metadata.name` field of the Node. If the node is healthy (i.e. all necessary services are running), then it is eligible to run a Pod. Otherwise, that node is ignored for any cluster activity until it becomes healthy. {{< note >}} Kubernetes keeps the object for the invalid Node and continues checking to see whether it becomes healthy. You, or a {{< glossary\_tooltip term\_id="controller" text="controller">}}, must explicitly delete the Node object to stop that health checking. {{< /note >}} The name of a Node object must be a valid [DNS subdomain name](/docs/concepts/overview/working-with-objects/names#dns-subdomain-names). ### Node name uniqueness The [name](/docs/concepts/overview/working-with-objects/names#names) identifies a Node. Two Nodes cannot have the same name at the same time. Kubernetes also assumes that a resource with the same name is the same object. In the case of a Node, it is implicitly assumed that an instance using the same name will have the same state (e.g. network settings, root disk contents) and attributes like node labels. This may lead to inconsistencies if an instance was modified without changing its name. If the Node needs to be replaced or updated significantly, the existing Node object needs to be removed from API server first and re-added after the update. ### Self-registration of Nodes When the kubelet flag `--register-node` is true (the default), the kubelet will attempt to register itself with the API server. This is the preferred pattern, used by most distros. For self-registration, the kubelet is started with the following options: - `--kubeconfig` - Path to credentials to authenticate itself to the API server. - `--cloud-provider` - How to talk to a {{< glossary\_tooltip text="cloud provider" term\_id="cloud-provider" >}} to read metadata about itself. - `--register-node` - Automatically register with the API server. - `--register-with-taints` - Register the node with the given list of {{< glossary\_tooltip text="taints" term\_id="taint" >}} (comma separated `=:`). No-op if `register-node` is false. - `--node-ip` - Optional comma-separated list of the IP addresses for the node. You can only specify a single address for each address family. For example, in a single-stack IPv4 cluster, you set this value to be the IPv4 address that the kubelet should use for the node. See [configure IPv4/IPv6 dual stack](/docs/concepts/services-networking/dual-stack/#configure-ipv4-ipv6-dual-stack) for details of running a dual-stack cluster. If you don't provide this argument, the kubelet uses the
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/architecture/nodes.md
|
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for each address family. For example, in a single-stack IPv4 cluster, you set this value to be the IPv4 address that the kubelet should use for the node. See [configure IPv4/IPv6 dual stack](/docs/concepts/services-networking/dual-stack/#configure-ipv4-ipv6-dual-stack) for details of running a dual-stack cluster. If you don't provide this argument, the kubelet uses the node's default IPv4 address, if any; if the node has no IPv4 addresses then the kubelet uses the node's default IPv6 address. - `--node-labels` - {{< glossary\_tooltip text="Labels" term\_id="label" >}} to add when registering the node in the cluster (see label restrictions enforced by the [NodeRestriction admission plugin](/docs/reference/access-authn-authz/admission-controllers/#noderestriction)). - `--node-status-update-frequency` - Specifies how often kubelet posts its node status to the API server. When the [Node authorization mode](/docs/reference/access-authn-authz/node/) and [NodeRestriction admission plugin](/docs/reference/access-authn-authz/admission-controllers/#noderestriction) are enabled, kubelets are only authorized to create/modify their own Node resource. {{< note >}} As mentioned in the [Node name uniqueness](#node-name-uniqueness) section, when Node configuration needs to be updated, it is a good practice to re-register the node with the API server. For example, if the kubelet is being restarted with a new set of `--node-labels`, but the same Node name is used, the change will not take effect, as labels are only set (or modified) upon Node registration with the API server. Pods already scheduled on the Node may misbehave or cause issues if the Node configuration will be changed on kubelet restart. For example, an already running Pod may be tainted against the new labels assigned to the Node, while other Pods, that are incompatible with that Pod will be scheduled based on this new label. Node re-registration ensures all Pods will be drained and properly re-scheduled. {{< /note >}} ### Manual Node administration You can create and modify Node objects using {{< glossary\_tooltip text="kubectl" term\_id="kubectl" >}}. When you want to create Node objects manually, set the kubelet flag `--register-node=false`. You can modify Node objects regardless of the setting of `--register-node`. For example, you can set labels on an existing Node or mark it unschedulable. You can set optional node role(s) for nodes by adding one or more `node-role.kubernetes.io/: ` labels to the node where characters of `` are limited by the [syntax](/docs/concepts/overview/working-with-objects/labels/#syntax-and-character-set) rules for labels. Kubernetes ignores the label value for node roles; by convention, you can set it to the same string you used for the node role in the label key. You can use labels on Nodes in conjunction with node selectors on Pods to control scheduling. For example, you can constrain a Pod to only be eligible to run on a subset of the available nodes. Marking a node as unschedulable prevents the scheduler from placing new pods onto that Node but does not affect existing Pods on the Node. This is useful as a preparatory step before a node reboot or other maintenance. To mark a Node unschedulable, run: ```shell kubectl cordon $NODENAME ``` See [Safely Drain a Node](/docs/tasks/administer-cluster/safely-drain-node/) for more details. {{< note >}} Pods that are part of a {{< glossary\_tooltip term\_id="daemonset" >}} tolerate being run on an unschedulable Node. DaemonSets typically provide node-local services that should run on the Node even if it is being drained of workload applications. {{< /note >}} ## Node status A Node's status contains the following information: \* [Addresses](/docs/reference/node/node-status/#addresses) \* [Conditions](/docs/reference/node/node-status/#condition) \* [Capacity and Allocatable](/docs/reference/node/node-status/#capacity) \* [Info](/docs/reference/node/node-status/#info) You can use `kubectl` to view a Node's status and other details: ```shell kubectl describe node ``` See [Node Status](/docs/reference/node/node-status/) for more details. ## Node heartbeats Heartbeats, sent by Kubernetes nodes, help your cluster determine the availability of each node, and to take action when failures are detected. For nodes there are two forms of
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/architecture/nodes.md
|
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] | 0.117302
|
view a Node's status and other details: ```shell kubectl describe node ``` See [Node Status](/docs/reference/node/node-status/) for more details. ## Node heartbeats Heartbeats, sent by Kubernetes nodes, help your cluster determine the availability of each node, and to take action when failures are detected. For nodes there are two forms of heartbeats: \* Updates to the [`.status`](/docs/reference/node/node-status/) of a Node. \* [Lease](/docs/concepts/architecture/leases/) objects within the `kube-node-lease` {{< glossary\_tooltip term\_id="namespace" text="namespace">}}. Each Node has an associated Lease object. ## Node controller The node {{< glossary\_tooltip text="controller" term\_id="controller" >}} is a Kubernetes control plane component that manages various aspects of nodes. The node controller has multiple roles in a node's life. The first is assigning a CIDR block to the node when it is registered (if CIDR assignment is turned on). The second is keeping the node controller's internal list of nodes up to date with the cloud provider's list of available machines. When running in a cloud environment and whenever a node is unhealthy, the node controller asks the cloud provider if the VM for that node is still available. If not, the node controller deletes the node from its list of nodes. The third is monitoring the nodes' health. The node controller is responsible for: - In the case that a node becomes unreachable, updating the `Ready` condition in the Node's `.status` field. In this case the node controller sets the `Ready` condition to `Unknown`. - If a node remains unreachable: triggering [API-initiated eviction](/docs/concepts/scheduling-eviction/api-eviction/) for all of the Pods on the unreachable node. By default, the node controller waits 5 minutes between marking the node as `Unknown` and submitting the first eviction request. By default, the node controller checks the state of each node every 5 seconds. This period can be configured using the `--node-monitor-period` flag on the `kube-controller-manager` component. ### Rate limits on eviction In most cases, the node controller limits the eviction rate to `--node-eviction-rate` (default 0.1) per second, meaning it won't evict pods from more than 1 node per 10 seconds. The node eviction behavior changes when a node in a given availability zone becomes unhealthy. The node controller checks what percentage of nodes in the zone are unhealthy (the `Ready` condition is `Unknown` or `False`) at the same time: - If the fraction of unhealthy nodes is at least `--unhealthy-zone-threshold` (default 0.55), then the eviction rate is reduced. - If the cluster is small (i.e. has less than or equal to `--large-cluster-size-threshold` nodes - default 50), then evictions are stopped. - Otherwise, the eviction rate is reduced to `--secondary-node-eviction-rate` (default 0.01) per second. The reason these policies are implemented per availability zone is because one availability zone might become partitioned from the control plane while the others remain connected. If your cluster does not span multiple cloud provider availability zones, then the eviction mechanism does not take per-zone unavailability into account. A key reason for spreading your nodes across availability zones is so that the workload can be shifted to healthy zones when one entire zone goes down. Therefore, if all nodes in a zone are unhealthy, then the node controller evicts at the normal rate of `--node-eviction-rate`. The corner case is when all zones are completely unhealthy (none of the nodes in the cluster are healthy). In such a case, the node controller assumes that there is some problem with connectivity between the control plane and the nodes, and doesn't perform any evictions. (If there has been an outage and some nodes reappear, the node controller does evict pods from the remaining nodes that are unhealthy or unreachable). The node controller is also responsible for evicting
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/architecture/nodes.md
|
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] | 0.150148
|
is some problem with connectivity between the control plane and the nodes, and doesn't perform any evictions. (If there has been an outage and some nodes reappear, the node controller does evict pods from the remaining nodes that are unhealthy or unreachable). The node controller is also responsible for evicting pods running on nodes with `NoExecute` taints, unless those pods tolerate that taint. The node controller also adds {{< glossary\_tooltip text="taints" term\_id="taint" >}} corresponding to node problems like node unreachable or not ready. This means that the scheduler won't place Pods onto unhealthy nodes. ## Resource capacity tracking {#node-capacity} Node objects track information about the Node's resource capacity: for example, the amount of memory available and the number of CPUs. Nodes that [self register](#self-registration-of-nodes) report their capacity during registration. If you [manually](#manual-node-administration) add a Node, then you need to set the node's capacity information when you add it. The Kubernetes {{< glossary\_tooltip text="scheduler" term\_id="kube-scheduler" >}} ensures that there are enough resources for all the Pods on a Node. The scheduler checks that the sum of the requests of containers on the node is no greater than the node's capacity. That sum of requests includes all containers managed by the kubelet, but excludes any containers started directly by the container runtime, and also excludes any processes running outside of the kubelet's control. {{< note >}} If you want to explicitly reserve resources for non-Pod processes, see [reserve resources for system daemons](/docs/tasks/administer-cluster/reserve-compute-resources/#system-reserved). {{< /note >}} ## Node topology {{< feature-state feature\_gate\_name="TopologyManager" >}} If you have enabled the `TopologyManager` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/), then the kubelet can use topology hints when making resource assignment decisions. See [Control Topology Management Policies on a Node](/docs/tasks/administer-cluster/topology-manager/) for more information. ## {{% heading "whatsnext" %}} Learn more about the following: \* [Components](/docs/concepts/architecture/#node-components) that make up a node. \* [API definition for Node](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#node-v1-core). \* [Node](https://git.k8s.io/design-proposals-archive/architecture/architecture.md#the-kubernetes-node) section of the architecture design document. \* [Graceful/non-graceful node shutdown](/docs/concepts/cluster-administration/node-shutdown/). \* [Node autoscaling](/docs/concepts/cluster-administration/node-autoscaling/) to manage the number and size of nodes in your cluster. \* [Taints and Tolerations](/docs/concepts/scheduling-eviction/taint-and-toleration/). \* [Node Resource Managers](/docs/concepts/policy/node-resource-managers/). \* [Resource Management for Windows nodes](/docs/concepts/configuration/windows-resource-management/).
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/architecture/nodes.md
|
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] | 0.146607
|
In robotics and automation, a \_control loop\_ is a non-terminating loop that regulates the state of a system. Here is one example of a control loop: a thermostat in a room. When you set the temperature, that's telling the thermostat about your \*desired state\*. The actual room temperature is the \*current state\*. The thermostat acts to bring the current state closer to the desired state, by turning equipment on or off. {{< glossary\_definition term\_id="controller" length="short">}} ## Controller pattern A controller tracks at least one Kubernetes resource type. These {{< glossary\_tooltip text="objects" term\_id="object" >}} have a spec field that represents the desired state. The controller(s) for that resource are responsible for making the current state come closer to that desired state. The controller might carry the action out itself; more commonly, in Kubernetes, a controller will send messages to the {{< glossary\_tooltip text="API server" term\_id="kube-apiserver" >}} that have useful side effects. You'll see examples of this below. {{< comment >}} Some built-in controllers, such as the namespace controller, act on objects that do not have a spec. For simplicity, this page omits explaining that detail. {{< /comment >}} ### Control via API server The {{< glossary\_tooltip term\_id="job" >}} controller is an example of a Kubernetes built-in controller. Built-in controllers manage state by interacting with the cluster API server. Job is a Kubernetes resource that runs a {{< glossary\_tooltip term\_id="pod" >}}, or perhaps several Pods, to carry out a task and then stop. (Once [scheduled](/docs/concepts/scheduling-eviction/), Pod objects become part of the desired state for a kubelet). When the Job controller sees a new task it makes sure that, somewhere in your cluster, the kubelets on a set of Nodes are running the right number of Pods to get the work done. The Job controller does not run any Pods or containers itself. Instead, the Job controller tells the API server to create or remove Pods. Other components in the {{< glossary\_tooltip text="control plane" term\_id="control-plane" >}} act on the new information (there are new Pods to schedule and run), and eventually the work is done. After you create a new Job, the desired state is for that Job to be completed. The Job controller makes the current state for that Job be nearer to your desired state: creating Pods that do the work you wanted for that Job, so that the Job is closer to completion. Controllers also update the objects that configure them. For example: once the work is done for a Job, the Job controller updates that Job object to mark it `Finished`. (This is a bit like how some thermostats turn a light off to indicate that your room is now at the temperature you set). ### Direct control In contrast with Job, some controllers need to make changes to things outside of your cluster. For example, if you use a control loop to make sure there are enough {{< glossary\_tooltip text="Nodes" term\_id="node" >}} in your cluster, then that controller needs something outside the current cluster to set up new Nodes when needed. Controllers that interact with external state find their desired state from the API server, then communicate directly with an external system to bring the current state closer in line. (There actually is a [controller](https://github.com/kubernetes/autoscaler/) that horizontally scales the nodes in your cluster.) The important point here is that the controller makes some changes to bring about your desired state, and then reports the current state back to your cluster's API server. Other control loops can observe that reported data and take their own actions. In the thermostat example, if the room is very cold then a different
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/architecture/controller.md
|
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that the controller makes some changes to bring about your desired state, and then reports the current state back to your cluster's API server. Other control loops can observe that reported data and take their own actions. In the thermostat example, if the room is very cold then a different controller might also turn on a frost protection heater. With Kubernetes clusters, the control plane indirectly works with IP address management tools, storage services, cloud provider APIs, and other services by [extending Kubernetes](/docs/concepts/extend-kubernetes/) to implement that. ## Desired versus current state {#desired-vs-current} Kubernetes takes a cloud-native view of systems, and is able to handle constant change. Your cluster could be changing at any point as work happens and control loops automatically fix failures. This means that, potentially, your cluster never reaches a stable state. As long as the controllers for your cluster are running and able to make useful changes, it doesn't matter if the overall state is stable or not. ## Design As a tenet of its design, Kubernetes uses lots of controllers that each manage a particular aspect of cluster state. Most commonly, a particular control loop (controller) uses one kind of resource as its desired state, and has a different kind of resource that it manages to make that desired state happen. For example, a controller for Jobs tracks Job objects (to discover new work) and Pod objects (to run the Jobs, and then to see when the work is finished). In this case something else creates the Jobs, whereas the Job controller creates Pods. It's useful to have simple controllers rather than one, monolithic set of control loops that are interlinked. Controllers can fail, so Kubernetes is designed to allow for that. {{< note >}} There can be several controllers that create or update the same kind of object. Behind the scenes, Kubernetes controllers make sure that they only pay attention to the resources linked to their controlling resource. For example, you can have Deployments and Jobs; these both create Pods. The Job controller does not delete the Pods that your Deployment created, because there is information ({{< glossary\_tooltip term\_id="label" text="labels" >}}) the controllers can use to tell those Pods apart. {{< /note >}} ## Ways of running controllers {#running-controllers} Kubernetes comes with a set of built-in controllers that run inside the {{< glossary\_tooltip term\_id="kube-controller-manager" >}}. These built-in controllers provide important core behaviors. The Deployment controller and Job controller are examples of controllers that come as part of Kubernetes itself ("built-in" controllers). Kubernetes lets you run a resilient control plane, so that if any of the built-in controllers were to fail, another part of the control plane will take over the work. You can find controllers that run outside the control plane, to extend Kubernetes. Or, if you want, you can write a new controller yourself. You can run your own controller as a set of Pods, or externally to Kubernetes. What fits best will depend on what that particular controller does. ## {{% heading "whatsnext" %}} \* Read about the [Kubernetes control plane](/docs/concepts/architecture/#control-plane-components) \* Discover some of the basic [Kubernetes objects](/docs/concepts/overview/working-with-objects/) \* Learn more about the [Kubernetes API](/docs/concepts/overview/kubernetes-api/) \* If you want to write your own controller, see [Kubernetes extension patterns](/docs/concepts/extend-kubernetes/#extension-patterns) and the [sample-controller](https://github.com/kubernetes/sample-controller) repository.
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/architecture/controller.md
|
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|
{{}} This allows the clean up of resources like the following: \* [Terminated pods](/docs/concepts/workloads/pods/pod-lifecycle/#pod-garbage-collection) \* [Completed Jobs](/docs/concepts/workloads/controllers/ttlafterfinished/) \* [Objects without owner references](#owners-dependents) \* [Unused containers and container images](#containers-images) \* [Dynamically provisioned PersistentVolumes with a StorageClass reclaim policy of Delete](/docs/concepts/storage/persistent-volumes/#delete) \* [Stale or expired CertificateSigningRequests (CSRs)](/docs/reference/access-authn-authz/certificate-signing-requests/#request-signing-process) \* {{}} deleted in the following scenarios: \* On a cloud when the cluster uses a [cloud controller manager](/docs/concepts/architecture/cloud-controller/) \* On-premises when the cluster uses an addon similar to a cloud controller manager \* [Node Lease objects](/docs/concepts/architecture/nodes/#heartbeats) ## Owners and dependents {#owners-dependents} Many objects in Kubernetes link to each other through [\*owner references\*](/docs/concepts/overview/working-with-objects/owners-dependents/). Owner references tell the control plane which objects are dependent on others. Kubernetes uses owner references to give the control plane, and other API clients, the opportunity to clean up related resources before deleting an object. In most cases, Kubernetes manages owner references automatically. Ownership is different from the [labels and selectors](/docs/concepts/overview/working-with-objects/labels/) mechanism that some resources also use. For example, consider a {{}} that creates `EndpointSlice` objects. The Service uses \*labels\* to allow the control plane to determine which `EndpointSlice` objects are used for that Service. In addition to the labels, each `EndpointSlice` that is managed on behalf of a Service has an owner reference. Owner references help different parts of Kubernetes avoid interfering with objects they don’t control. {{< note >}} Cross-namespace owner references are disallowed by design. Namespaced dependents can specify cluster-scoped or namespaced owners. A namespaced owner \*\*must\*\* exist in the same namespace as the dependent. If it does not, the owner reference is treated as absent, and the dependent is subject to deletion once all owners are verified absent. Cluster-scoped dependents can only specify cluster-scoped owners. In v1.20+, if a cluster-scoped dependent specifies a namespaced kind as an owner, it is treated as having an unresolvable owner reference, and is not able to be garbage collected. In v1.20+, if the garbage collector detects an invalid cross-namespace `ownerReference`, or a cluster-scoped dependent with an `ownerReference` referencing a namespaced kind, a warning Event with a reason of `OwnerRefInvalidNamespace` and an `involvedObject` of the invalid dependent is reported. You can check for that kind of Event by running `kubectl get events -A --field-selector=reason=OwnerRefInvalidNamespace`. {{< /note >}} ## Cascading deletion {#cascading-deletion} Kubernetes checks for and deletes objects that no longer have owner references, like the pods left behind when you delete a ReplicaSet. When you delete an object, you can control whether Kubernetes deletes the object's dependents automatically, in a process called \*cascading deletion\*. There are two types of cascading deletion, as follows: \* Foreground cascading deletion \* Background cascading deletion You can also control how and when garbage collection deletes resources that have owner references using Kubernetes {{}}. ### Foreground cascading deletion {#foreground-deletion} In foreground cascading deletion, the owner object you're deleting first enters a \*deletion in progress\* state. In this state, the following happens to the owner object: \* The Kubernetes API server sets the object's `metadata.deletionTimestamp` field to the time the object was marked for deletion. \* The Kubernetes API server also sets the `metadata.finalizers` field to `foregroundDeletion`. \* The object remains visible through the Kubernetes API until the deletion process is complete. After the owner object enters the \*deletion in progress\* state, the controller deletes dependents it knows about. After deleting all the dependent objects it knows about, the controller deletes the owner object. At this point, the object is no longer visible in the Kubernetes API. During foreground cascading deletion, the only dependents that block owner deletion are those that have the `ownerReference.blockOwnerDeletion=true` field and are in the garbage collection controller cache. The garbage collection controller cache
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/architecture/garbage-collection.md
|
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the controller deletes the owner object. At this point, the object is no longer visible in the Kubernetes API. During foreground cascading deletion, the only dependents that block owner deletion are those that have the `ownerReference.blockOwnerDeletion=true` field and are in the garbage collection controller cache. The garbage collection controller cache may not contain objects whose resource type cannot be listed / watched successfully, or objects that are created concurrent with deletion of an owner object. See [Use foreground cascading deletion](/docs/tasks/administer-cluster/use-cascading-deletion/#use-foreground-cascading-deletion) to learn more. ### Background cascading deletion {#background-deletion} In background cascading deletion, the Kubernetes API server deletes the owner object immediately and the garbage collector controller (custom or default) cleans up the dependent objects in the background. If a finalizer exists, it ensures that objects are not deleted until all necessary clean-up tasks are completed. By default, Kubernetes uses background cascading deletion unless you manually use foreground deletion or choose to orphan the dependent objects. See [Use background cascading deletion](/docs/tasks/administer-cluster/use-cascading-deletion/#use-background-cascading-deletion) to learn more. ### Orphaned dependents When Kubernetes deletes an owner object, the dependents left behind are called \*orphan\* objects. By default, Kubernetes deletes dependent objects. To learn how to override this behaviour, see [Delete owner objects and orphan dependents](/docs/tasks/administer-cluster/use-cascading-deletion/#set-orphan-deletion-policy). ## Garbage collection of unused containers and images {#containers-images} The {{}} performs garbage collection on unused images every five minutes and on unused containers every minute. You should avoid using external garbage collection tools, as these can break the kubelet behavior and remove containers that should exist. To configure options for unused container and image garbage collection, tune the kubelet using a [configuration file](/docs/tasks/administer-cluster/kubelet-config-file/) and change the parameters related to garbage collection using the [`KubeletConfiguration`](/docs/reference/config-api/kubelet-config.v1beta1/) resource type. ### Container image lifecycle Kubernetes manages the lifecycle of all images through its \*image manager\*, which is part of the kubelet, with the cooperation of {{< glossary\_tooltip text="cadvisor" term\_id="cadvisor" >}}. The kubelet considers the following disk usage limits when making garbage collection decisions: \* `HighThresholdPercent` \* `LowThresholdPercent` Disk usage above the configured `HighThresholdPercent` value triggers garbage collection, which deletes images in order based on the last time they were used, starting with the oldest first. The kubelet deletes images until disk usage reaches the `LowThresholdPercent` value. #### Garbage collection for unused container images {#image-maximum-age-gc} You can specify the maximum time a local image can be unused for, regardless of disk usage. This is a kubelet setting that you configure for each node. To configure the setting, you need to set a value for the `imageMaximumGCAge` field in the kubelet configuration file. The value is specified as a Kubernetes {{< glossary\_tooltip text="duration" term\_id="duration" >}}. See [duration](/docs/reference/glossary/?all=true#term-duration) in the glossary for more details. For example, you can set the configuration field to `12h45m`, which means 12 hours and 45 minutes. {{< note >}} This feature does not track image usage across kubelet restarts. If the kubelet is restarted, the tracked image age is reset, causing the kubelet to wait the full `imageMaximumGCAge` duration before qualifying images for garbage collection based on image age. {{< /note>}} ### Container garbage collection {#container-image-garbage-collection} The kubelet garbage collects unused containers based on the following variables, which you can define: \* `MinAge`: the minimum age at which the kubelet can garbage collect a container. Disable by setting to `0`. \* `MaxPerPodContainer`: the maximum number of dead containers each Pod can have. Disable by setting to less than `0`. \* `MaxContainers`: the maximum number of dead containers the cluster can have. Disable by setting to less than `0`. In addition to these variables, the kubelet garbage collects unidentified and deleted containers, typically starting with the oldest first. `MaxPerPodContainer` and `MaxContainers` may
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/architecture/garbage-collection.md
|
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] | 0.10022
|
have. Disable by setting to less than `0`. \* `MaxContainers`: the maximum number of dead containers the cluster can have. Disable by setting to less than `0`. In addition to these variables, the kubelet garbage collects unidentified and deleted containers, typically starting with the oldest first. `MaxPerPodContainer` and `MaxContainers` may potentially conflict with each other in situations where retaining the maximum number of containers per Pod (`MaxPerPodContainer`) would go outside the allowable total of global dead containers (`MaxContainers`). In this situation, the kubelet adjusts `MaxPerPodContainer` to address the conflict. A worst-case scenario would be to downgrade `MaxPerPodContainer` to `1` and evict the oldest containers. Additionally, containers owned by pods that have been deleted are removed once they are older than `MinAge`. {{}} The kubelet only garbage collects the containers it manages. {{}} ## Configuring garbage collection {#configuring-gc} You can tune garbage collection of resources by configuring options specific to the controllers managing those resources. The following pages show you how to configure garbage collection: \* [Configuring cascading deletion of Kubernetes objects](/docs/tasks/administer-cluster/use-cascading-deletion/) \* [Configuring cleanup of finished Jobs](/docs/concepts/workloads/controllers/ttlafterfinished/) ## {{% heading "whatsnext" %}} \* Learn more about [ownership of Kubernetes objects](/docs/concepts/overview/working-with-objects/owners-dependents/). \* Learn more about Kubernetes [finalizers](/docs/concepts/overview/working-with-objects/finalizers/). \* Learn about the [TTL controller](/docs/concepts/workloads/controllers/ttlafterfinished/) that cleans up finished Jobs.
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/architecture/garbage-collection.md
|
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|
Distributed systems often have a need for \_leases\_, which provide a mechanism to lock shared resources and coordinate activity between members of a set. In Kubernetes, the lease concept is represented by [Lease](/docs/reference/kubernetes-api/cluster-resources/lease-v1/) objects in the `coordination.k8s.io` {{< glossary\_tooltip text="API Group" term\_id="api-group" >}}, which are used for system-critical capabilities such as node heartbeats and component-level leader election. ## Node heartbeats {#node-heart-beats} Kubernetes uses the Lease API to communicate kubelet node heartbeats to the Kubernetes API server. For every `Node` , there is a `Lease` object with a matching name in the `kube-node-lease` namespace. Under the hood, every kubelet heartbeat is an \*\*update\*\* request to this `Lease` object, updating the `spec.renewTime` field for the Lease. The Kubernetes control plane uses the time stamp of this field to determine the availability of this `Node`. See [Node Lease objects](/docs/concepts/architecture/nodes/#node-heartbeats) for more details. ## Leader election Kubernetes also uses Leases to ensure only one instance of a component is running at any given time. This is used by control plane components like `kube-controller-manager` and `kube-scheduler` in HA configurations, where only one instance of the component should be actively running while the other instances are on stand-by. Read [coordinated leader election](/docs/concepts/cluster-administration/coordinated-leader-election) to learn about how Kubernetes builds on the Lease API to select which component instance acts as leader. ## API server identity {{< feature-state feature\_gate\_name="APIServerIdentity" >}} Starting in Kubernetes v1.26, each `kube-apiserver` uses the Lease API to publish its identity to the rest of the system. While not particularly useful on its own, this provides a mechanism for clients to discover how many instances of `kube-apiserver` are operating the Kubernetes control plane. Existence of kube-apiserver leases enables future capabilities that may require coordination between each kube-apiserver. You can inspect Leases owned by each kube-apiserver by checking for lease objects in the `kube-system` namespace with the name `apiserver-`. Alternatively you can use the label selector `apiserver.kubernetes.io/identity=kube-apiserver`: ```shell kubectl -n kube-system get lease -l apiserver.kubernetes.io/identity=kube-apiserver ``` ``` NAME HOLDER AGE apiserver-07a5ea9b9b072c4a5f3d1c3702 apiserver-07a5ea9b9b072c4a5f3d1c3702\_0c8914f7-0f35-440e-8676-7844977d3a05 5m33s apiserver-7be9e061c59d368b3ddaf1376e apiserver-7be9e061c59d368b3ddaf1376e\_84f2a85d-37c1-4b14-b6b9-603e62e4896f 4m23s apiserver-1dfef752bcb36637d2763d1868 apiserver-1dfef752bcb36637d2763d1868\_c5ffa286-8a9a-45d4-91e7-61118ed58d2e 4m43s ``` The SHA256 hash used in the lease name is based on the OS hostname as seen by that API server. Each kube-apiserver should be configured to use a hostname that is unique within the cluster. New instances of kube-apiserver that use the same hostname will take over existing Leases using a new holder identity, as opposed to instantiating new Lease objects. You can check the hostname used by kube-apiserver by checking the value of the `kubernetes.io/hostname` label: ```shell kubectl -n kube-system get lease apiserver-07a5ea9b9b072c4a5f3d1c3702 -o yaml ``` ```yaml apiVersion: coordination.k8s.io/v1 kind: Lease metadata: creationTimestamp: "2023-07-02T13:16:48Z" labels: apiserver.kubernetes.io/identity: kube-apiserver kubernetes.io/hostname: master-1 name: apiserver-07a5ea9b9b072c4a5f3d1c3702 namespace: kube-system resourceVersion: "334899" uid: 90870ab5-1ba9-4523-b215-e4d4e662acb1 spec: holderIdentity: apiserver-07a5ea9b9b072c4a5f3d1c3702\_0c8914f7-0f35-440e-8676-7844977d3a05 leaseDurationSeconds: 3600 renewTime: "2023-07-04T21:58:48.065888Z" ``` Expired leases from kube-apiservers that no longer exist are garbage collected by new kube-apiservers after 1 hour. You can disable API server identity leases by disabling the `APIServerIdentity` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/). ## Workloads {#custom-workload} Your own workload can define its own use of Leases. For example, you might run a custom {{< glossary\_tooltip term\_id="controller" text="controller" >}} where a primary or leader member performs operations that its peers do not. You define a Lease so that the controller replicas can select or elect a leader, using the Kubernetes API for coordination. If you do use a Lease, it's a good practice to define a name for the Lease that is obviously linked to the product or component. For example, if you have a component named Example Foo, use a Lease named `example-foo`. If a cluster operator or another end user could deploy multiple instances of a component, select a name prefix and
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/architecture/leases.md
|
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] | 0.25342
|
define a name for the Lease that is obviously linked to the product or component. For example, if you have a component named Example Foo, use a Lease named `example-foo`. If a cluster operator or another end user could deploy multiple instances of a component, select a name prefix and pick a mechanism (such as hash of the name of the Deployment) to avoid name collisions for the Leases. You can use another approach so long as it achieves the same outcome: different software products do not conflict with one another.
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/architecture/leases.md
|
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] | 0.062441
|
On Linux, {{< glossary\_tooltip text="control groups" term\_id="cgroup" >}} constrain resources that are allocated to processes. The {{< glossary\_tooltip text="kubelet" term\_id="kubelet" >}} and the underlying container runtime need to interface with cgroups to enforce [resource management for pods and containers](/docs/concepts/configuration/manage-resources-containers/) which includes cpu/memory requests and limits for containerized workloads. There are two versions of cgroups in Linux: cgroup v1 and cgroup v2. cgroup v2 is the new generation of the `cgroup` API. ## What is cgroup v2? {#cgroup-v2} {{< feature-state for\_k8s\_version="v1.25" state="stable" >}} cgroup v2 is the next version of the Linux `cgroup` API. cgroup v2 provides a unified control system with enhanced resource management capabilities. cgroup v2 offers several improvements over cgroup v1, such as the following: - Single unified hierarchy design in API - Safer sub-tree delegation to containers - Newer features like [Pressure Stall Information](https://www.kernel.org/doc/html/latest/accounting/psi.html) - Enhanced resource allocation management and isolation across multiple resources - Unified accounting for different types of memory allocations (network memory, kernel memory, etc) - Accounting for non-immediate resource changes such as page cache write backs Some Kubernetes features exclusively use cgroup v2 for enhanced resource management and isolation. For example, the [MemoryQoS](/docs/concepts/workloads/pods/pod-qos/#memory-qos-with-cgroup-v2) feature improves memory QoS and relies on cgroup v2 primitives. ## Using cgroup v2 {#using-cgroupv2} The recommended way to use cgroup v2 is to use a Linux distribution that enables and uses cgroup v2 by default. To check if your distribution uses cgroup v2, refer to [Identify cgroup version on Linux nodes](#check-cgroup-version). ### Requirements cgroup v2 has the following requirements: \* OS distribution enables cgroup v2 \* Linux Kernel version is 5.8 or later \* Container runtime supports cgroup v2. For example: \* [containerd](https://containerd.io/) v1.4 and later \* [cri-o](https://cri-o.io/) v1.20 and later \* The kubelet and the container runtime are configured to use the [systemd cgroup driver](/docs/setup/production-environment/container-runtimes#systemd-cgroup-driver) ### Linux Distribution cgroup v2 support For a list of Linux distributions that use cgroup v2, refer to the [cgroup v2 documentation](https://github.com/opencontainers/runc/blob/main/docs/cgroup-v2.md) \* Container Optimized OS (since M97) \* Ubuntu (since 21.10, 22.04+ recommended) \* Debian GNU/Linux (since Debian 11 bullseye) \* Fedora (since 31) \* Arch Linux (since April 2021) \* RHEL and RHEL-like distributions (since 9) To check if your distribution is using cgroup v2, refer to your distribution's documentation or follow the instructions in [Identify the cgroup version on Linux nodes](#check-cgroup-version). You can also enable cgroup v2 manually on your Linux distribution by modifying the kernel cmdline boot arguments. If your distribution uses GRUB, `systemd.unified\_cgroup\_hierarchy=1` should be added in `GRUB\_CMDLINE\_LINUX` under `/etc/default/grub`, followed by `sudo update-grub`. However, the recommended approach is to use a distribution that already enables cgroup v2 by default. ### Migrating to cgroup v2 {#migrating-cgroupv2} To migrate to cgroup v2, ensure that you meet the [requirements](#requirements), then upgrade to a kernel version that enables cgroup v2 by default. The kubelet automatically detects that the OS is running on cgroup v2 and performs accordingly with no additional configuration required. There should not be any noticeable difference in the user experience when switching to cgroup v2, unless users are accessing the cgroup file system directly, either on the node or from within the containers. cgroup v2 uses a different API than cgroup v1, so if there are any applications that directly access the cgroup file system, they need to be updated to newer versions that support cgroup v2. For example: \* Some third-party monitoring and security agents may depend on the cgroup filesystem. Update these agents to versions that support cgroup v2. \* If you run [cAdvisor](https://github.com/google/cadvisor) as a stand-alone DaemonSet for monitoring pods and containers, update it to v0.43.0 or later. \* If you deploy Java applications, prefer
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/architecture/cgroups.md
|
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|
example: \* Some third-party monitoring and security agents may depend on the cgroup filesystem. Update these agents to versions that support cgroup v2. \* If you run [cAdvisor](https://github.com/google/cadvisor) as a stand-alone DaemonSet for monitoring pods and containers, update it to v0.43.0 or later. \* If you deploy Java applications, prefer to use versions which fully support cgroup v2: \* [OpenJDK / HotSpot](https://bugs.openjdk.org/browse/JDK-8230305): jdk8u372, 11.0.16, 15 and later \* [IBM Semeru Runtimes](https://www.ibm.com/support/pages/apar/IJ46681): 8.0.382.0, 11.0.20.0, 17.0.8.0, and later \* [IBM Java](https://www.ibm.com/support/pages/apar/IJ46681): 8.0.8.6 and later \* If you are using the [uber-go/automaxprocs](https://github.com/uber-go/automaxprocs) package, make sure the version you use is v1.5.1 or higher. ## Identify the cgroup version on Linux Nodes {#check-cgroup-version} The cgroup version depends on the Linux distribution being used and the default cgroup version configured on the OS. To check which cgroup version your distribution uses, run the `stat -fc %T /sys/fs/cgroup/` command on the node: ```shell stat -fc %T /sys/fs/cgroup/ ``` For cgroup v2, the output is `cgroup2fs`. For cgroup v1, the output is `tmpfs.` ## Deprecation of cgroup v1 {{< feature-state for\_k8s\_version="v1.35" state="deprecated" >}} Kubernetes has deprecated cgroup v1. Removal will follow [Kubernetes deprecation policy](/docs/reference/using-api/deprecation-policy/). Kubelet will no longer start on a cgroup v1 node by default. To disable this setting a cluster admin should set `failCgroupV1` to false in the [kubelet configuration file](/docs/tasks/administer-cluster/kubelet-config-file/). ## {{% heading "whatsnext" %}} - Learn more about [cgroups](https://man7.org/linux/man-pages/man7/cgroups.7.html) - Learn more about [container runtime](/docs/concepts/architecture/cri) - Learn more about [cgroup drivers](/docs/setup/production-environment/container-runtimes#cgroup-drivers)
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/architecture/cgroups.md
|
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|
A Kubernetes cluster consists of a control plane plus a set of worker machines, called nodes, that run containerized applications. Every cluster needs at least one worker node in order to run Pods. The worker node(s) host the Pods that are the components of the application workload. The control plane manages the worker nodes and the Pods in the cluster. In production environments, the control plane usually runs across multiple computers and a cluster usually runs multiple nodes, providing fault-tolerance and high availability. This document outlines the various components you need to have for a complete and working Kubernetes cluster. {{< figure src="/images/docs/kubernetes-cluster-architecture.svg" alt="The control plane (kube-apiserver, etcd, kube-controller-manager, kube-scheduler) and several nodes. Each node is running a kubelet and kube-proxy." caption="Figure 1. Kubernetes cluster components." class="diagram-large" >}} {{< details summary="About this architecture" >}} The diagram in Figure 1 presents an example reference architecture for a Kubernetes cluster. The actual distribution of components can vary based on specific cluster setups and requirements. In the diagram, each node runs the [`kube-proxy`](#kube-proxy) component. You need a network proxy component on each node to ensure that the {{< glossary\_tooltip text="Service" term\_id="service">}} API and associated behaviors are available on your cluster network. However, some network plugins provide their own, third party implementation of proxying. When you use that kind of network plugin, the node does not need to run `kube-proxy`. {{< /details >}} ## Control plane components The control plane's components make global decisions about the cluster (for example, scheduling), as well as detecting and responding to cluster events (for example, starting up a new {{< glossary\_tooltip text="pod" term\_id="pod">}} when a Deployment's `{{< glossary\_tooltip text="replicas" term\_id="replica" >}}` field is unsatisfied). Control plane components can be run on any machine in the cluster. However, for simplicity, setup scripts typically start all control plane components on the same machine, and do not run user containers on this machine. See [Creating Highly Available clusters with kubeadm](/docs/setup/production-environment/tools/kubeadm/high-availability/) for an example control plane setup that runs across multiple machines. ### kube-apiserver {{< glossary\_definition term\_id="kube-apiserver" length="all" >}} ### etcd {{< glossary\_definition term\_id="etcd" length="all" >}} ### kube-scheduler {{< glossary\_definition term\_id="kube-scheduler" length="all" >}} ### kube-controller-manager {{< glossary\_definition term\_id="kube-controller-manager" length="all" >}} There are many different types of controllers. Some examples of them are: - Node controller: Responsible for noticing and responding when nodes go down. - Job controller: Watches for Job objects that represent one-off tasks, then creates Pods to run those tasks to completion. - EndpointSlice controller: Populates EndpointSlice objects (to provide a link between Services and Pods). - ServiceAccount controller: Create default ServiceAccounts for new namespaces. The above is not an exhaustive list. ### cloud-controller-manager {{< glossary\_definition term\_id="cloud-controller-manager" length="short" >}} The cloud-controller-manager only runs controllers that are specific to your cloud provider. If you are running Kubernetes on your own premises, or in a learning environment inside your own PC, the cluster does not have a cloud controller manager. As with the kube-controller-manager, the cloud-controller-manager combines several logically independent control loops into a single binary that you run as a single process. You can scale horizontally (run more than one copy) to improve performance or to help tolerate failures. The following controllers can have cloud provider dependencies: - Node controller: For checking the cloud provider to determine if a node has been deleted in the cloud after it stops responding - Route controller: For setting up routes in the underlying cloud infrastructure - Service controller: For creating, updating and deleting cloud provider load balancers --- ## Node components Node components run on every node, maintaining running pods and providing the Kubernetes runtime environment. ### kubelet {{< glossary\_definition term\_id="kubelet" length="all" >}} ###
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/architecture/_index.md
|
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Route controller: For setting up routes in the underlying cloud infrastructure - Service controller: For creating, updating and deleting cloud provider load balancers --- ## Node components Node components run on every node, maintaining running pods and providing the Kubernetes runtime environment. ### kubelet {{< glossary\_definition term\_id="kubelet" length="all" >}} ### kube-proxy (optional) {#kube-proxy} {{< glossary\_definition term\_id="kube-proxy" length="all" >}} If you use a [network plugin](#network-plugins) that implements packet forwarding for Services by itself, and providing equivalent behavior to kube-proxy, then you do not need to run kube-proxy on the nodes in your cluster. ### Container runtime {{< glossary\_definition term\_id="container-runtime" length="all" >}} ## Addons Addons use Kubernetes resources ({{< glossary\_tooltip term\_id="daemonset" >}}, {{< glossary\_tooltip term\_id="deployment" >}}, etc) to implement cluster features. Because these are providing cluster-level features, namespaced resources for addons belong within the `kube-system` namespace. Selected addons are described below; for an extended list of available addons, please see [Addons](/docs/concepts/cluster-administration/addons/). ### DNS While the other addons are not strictly required, all Kubernetes clusters should have [cluster DNS](/docs/concepts/services-networking/dns-pod-service/), as many examples rely on it. Cluster DNS is a DNS server, in addition to the other DNS server(s) in your environment, which serves DNS records for Kubernetes services. Containers started by Kubernetes automatically include this DNS server in their DNS searches. ### Web UI (Dashboard) [Dashboard](/docs/tasks/access-application-cluster/web-ui-dashboard/) is a general purpose, web-based UI for Kubernetes clusters. It allows users to manage and troubleshoot applications running in the cluster, as well as the cluster itself. ### Container resource monitoring [Container Resource Monitoring](/docs/tasks/debug/debug-cluster/resource-usage-monitoring/) records generic time-series metrics about containers in a central database, and provides a UI for browsing that data. ### Cluster-level Logging A [cluster-level logging](/docs/concepts/cluster-administration/logging/) mechanism is responsible for saving container logs to a central log store with a search/browsing interface. ### Network plugins [Network plugins](/docs/concepts/extend-kubernetes/compute-storage-net/network-plugins) are software components that implement the container network interface (CNI) specification. They are responsible for allocating IP addresses to pods and enabling them to communicate with each other within the cluster. ## Architecture variations While the core components of Kubernetes remain consistent, the way they are deployed and managed can vary. Understanding these variations is crucial for designing and maintaining Kubernetes clusters that meet specific operational needs. ### Control plane deployment options The control plane components can be deployed in several ways: Traditional deployment : Control plane components run directly on dedicated machines or VMs, often managed as systemd services. Static Pods : Control plane components are deployed as static Pods, managed by the kubelet on specific nodes. This is a common approach used by tools like kubeadm. Self-hosted : The control plane runs as Pods within the Kubernetes cluster itself, managed by Deployments and StatefulSets or other Kubernetes primitives. Managed Kubernetes services : Cloud providers often abstract away the control plane, managing its components as part of their service offering. ### Workload placement considerations The placement of workloads, including the control plane components, can vary based on cluster size, performance requirements, and operational policies: - In smaller or development clusters, control plane components and user workloads might run on the same nodes. - Larger production clusters often dedicate specific nodes to control plane components, separating them from user workloads. - Some organizations run critical add-ons or monitoring tools on control plane nodes. ### Cluster management tools Tools like kubeadm, kops, and Kubespray offer different approaches to deploying and managing clusters, each with its own method of component layout and management. ### Customization and extensibility Kubernetes architecture allows for significant customization: - Custom schedulers can be deployed to work alongside the default Kubernetes scheduler or to replace it entirely. - API servers can be extended with CustomResourceDefinitions and
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/architecture/_index.md
|
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] | 0.076347
|
and managing clusters, each with its own method of component layout and management. ### Customization and extensibility Kubernetes architecture allows for significant customization: - Custom schedulers can be deployed to work alongside the default Kubernetes scheduler or to replace it entirely. - API servers can be extended with CustomResourceDefinitions and API Aggregation. - Cloud providers can integrate deeply with Kubernetes using the cloud-controller-manager. The flexibility of Kubernetes architecture allows organizations to tailor their clusters to specific needs, balancing factors such as operational complexity, performance, and management overhead. ## {{% heading "whatsnext" %}} Learn more about the following: - [Nodes](/docs/concepts/architecture/nodes/) and [their communication](/docs/concepts/architecture/control-plane-node-communication/) with the control plane. - Kubernetes [controllers](/docs/concepts/architecture/controller/). - [kube-scheduler](/docs/concepts/scheduling-eviction/kube-scheduler/) which is the default scheduler for Kubernetes. - Etcd's official [documentation](https://etcd.io/docs/). - Several [container runtimes](/docs/setup/production-environment/container-runtimes/) in Kubernetes. - Integrating with cloud providers using [cloud-controller-manager](/docs/concepts/architecture/cloud-controller/). - [kubectl](/docs/reference/generated/kubectl/kubectl-commands) commands.
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/architecture/_index.md
|
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|
Operators are software extensions to Kubernetes that make use of [custom resources](/docs/concepts/extend-kubernetes/api-extension/custom-resources/) to manage applications and their components. Operators follow Kubernetes principles, notably the [control loop](/docs/concepts/architecture/controller). ## Motivation The \_operator pattern\_ aims to capture the key aim of a human operator who is managing a service or set of services. Human operators who look after specific applications and services have deep knowledge of how the system ought to behave, how to deploy it, and how to react if there are problems. People who run workloads on Kubernetes often like to use automation to take care of repeatable tasks. The operator pattern captures how you can write code to automate a task beyond what Kubernetes itself provides. ## Operators in Kubernetes Kubernetes is designed for automation. Out of the box, you get lots of built-in automation from the core of Kubernetes. You can use Kubernetes to automate deploying and running workloads, \*and\* you can automate how Kubernetes does that. Kubernetes' {{< glossary\_tooltip text="operator pattern" term\_id="operator-pattern" >}} concept lets you extend the cluster's behaviour without modifying the code of Kubernetes itself by linking {{< glossary\_tooltip text="controllers" term\_id="controller" >}} to one or more custom resources. Operators are clients of the Kubernetes API that act as controllers for a [Custom Resource](/docs/concepts/extend-kubernetes/api-extension/custom-resources/). ## An example operator {#example} Some of the things that you can use an operator to automate include: \* deploying an application on demand \* taking and restoring backups of that application's state \* handling upgrades of the application code alongside related changes such as database schemas or extra configuration settings \* publishing a Service to applications that don't support Kubernetes APIs to discover them \* simulating failure in all or part of your cluster to test its resilience \* choosing a leader for a distributed application without an internal member election process What might an operator look like in more detail? Here's an example: 1. A custom resource named SampleDB, that you can configure into the cluster. 2. A Deployment that makes sure a Pod is running that contains the controller part of the operator. 3. A container image of the operator code. 4. Controller code that queries the control plane to find out what SampleDB resources are configured. 5. The core of the operator is code to tell the API server how to make reality match the configured resources. \* If you add a new SampleDB, the operator sets up PersistentVolumeClaims to provide durable database storage, a StatefulSet to run SampleDB and a Job to handle initial configuration. \* If you delete it, the operator takes a snapshot, then makes sure that the StatefulSet and Volumes are also removed. 6. The operator also manages regular database backups. For each SampleDB resource, the operator determines when to create a Pod that can connect to the database and take backups. These Pods would rely on a ConfigMap and / or a Secret that has database connection details and credentials. 7. Because the operator aims to provide robust automation for the resource it manages, there would be additional supporting code. For this example, code checks to see if the database is running an old version and, if so, creates Job objects that upgrade it for you. ## Deploying operators The most common way to deploy an operator is to add the Custom Resource Definition and its associated Controller to your cluster. The Controller will normally run outside of the {{< glossary\_tooltip text="control plane" term\_id="control-plane" >}}, much as you would run any containerized application. For example, you can run the controller in your cluster as a Deployment. ## Using an operator {#using-operators} Once you have
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/extend-kubernetes/operator.md
|
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|
and its associated Controller to your cluster. The Controller will normally run outside of the {{< glossary\_tooltip text="control plane" term\_id="control-plane" >}}, much as you would run any containerized application. For example, you can run the controller in your cluster as a Deployment. ## Using an operator {#using-operators} Once you have an operator deployed, you'd use it by adding, modifying or deleting the kind of resource that the operator uses. Following the above example, you would set up a Deployment for the operator itself, and then: ```shell kubectl get SampleDB # find configured databases kubectl edit SampleDB/example-database # manually change some settings ``` …and that's it! The operator will take care of applying the changes as well as keeping the existing service in good shape. ## Writing your own operator {#writing-operator} If there isn't an operator in the ecosystem that implements the behavior you want, you can code your own. You also implement an operator (that is, a Controller) using any language / runtime that can act as a [client for the Kubernetes API](/docs/reference/using-api/client-libraries/). Following are a few libraries and tools you can use to write your own cloud native operator. {{% thirdparty-content %}} \* [Charmed Operator Framework](https://juju.is/) \* [Java Operator SDK](https://github.com/operator-framework/java-operator-sdk) \* [Kopf](https://github.com/nolar/kopf) (Kubernetes Operator Pythonic Framework) \* [kube-rs](https://kube.rs/) (Rust) \* [kubebuilder](https://book.kubebuilder.io/) \* [KubeOps](https://dotnet.github.io/dotnet-operator-sdk/) (.NET operator SDK) \* [Mast](https://docs.ansi.services/mast/user\_guide/operator/) \* [Metacontroller](https://metacontroller.github.io/metacontroller/intro.html) along with WebHooks that you implement yourself \* [Operator Framework](https://operatorframework.io) \* [shell-operator](https://github.com/flant/shell-operator) ## {{% heading "whatsnext" %}} \* Read the {{< glossary\_tooltip text="CNCF" term\_id="cncf" >}} [Operator White Paper](https://github.com/cncf/tag-app-delivery/blob/163962c4b1cd70d085107fc579e3e04c2e14d59c/operator-wg/whitepaper/Operator-WhitePaper\_v1-0.md). \* Learn more about [Custom Resources](/docs/concepts/extend-kubernetes/api-extension/custom-resources/) \* Find ready-made operators on [OperatorHub.io](https://operatorhub.io/) to suit your use case \* [Publish](https://operatorhub.io/) your operator for other people to use \* Read [CoreOS' original article](https://web.archive.org/web/20170129131616/https://coreos.com/blog/introducing-operators.html) that introduced the operator pattern (this is an archived version of the original article). \* Read an [article](https://cloud.google.com/blog/products/containers-kubernetes/best-practices-for-building-kubernetes-operators-and-stateful-apps) from Google Cloud about best practices for building operators
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/extend-kubernetes/operator.md
|
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Kubernetes is highly configurable and extensible. As a result, there is rarely a need to fork or submit patches to the Kubernetes project code. This guide describes the options for customizing a Kubernetes cluster. It is aimed at {{< glossary\_tooltip text="cluster operators" term\_id="cluster-operator" >}} who want to understand how to adapt their Kubernetes cluster to the needs of their work environment. Developers who are prospective {{< glossary\_tooltip text="Platform Developers" term\_id="platform-developer" >}} or Kubernetes Project {{< glossary\_tooltip text="Contributors" term\_id="contributor" >}} will also find it useful as an introduction to what extension points and patterns exist, and their trade-offs and limitations. Customization approaches can be broadly divided into [configuration](#configuration), which only involves changing command line arguments, local configuration files, or API resources; and [extensions](#extensions), which involve running additional programs, additional network services, or both. This document is primarily about \_extensions\_. ## Configuration \*Configuration files\* and \*command arguments\* are documented in the [Reference](/docs/reference/) section of the online documentation, with a page for each binary: \* [`kube-apiserver`](/docs/reference/command-line-tools-reference/kube-apiserver/) \* [`kube-controller-manager`](/docs/reference/command-line-tools-reference/kube-controller-manager/) \* [`kube-scheduler`](/docs/reference/command-line-tools-reference/kube-scheduler/) \* [`kubelet`](/docs/reference/command-line-tools-reference/kubelet/) \* [`kube-proxy`](/docs/reference/command-line-tools-reference/kube-proxy/) Command arguments and configuration files may not always be changeable in a hosted Kubernetes service or a distribution with managed installation. When they are changeable, they are usually only changeable by the cluster operator. Also, they are subject to change in future Kubernetes versions, and setting them may require restarting processes. For those reasons, they should be used only when there are no other options. Built-in \*policy APIs\*, such as [ResourceQuota](/docs/concepts/policy/resource-quotas/), [NetworkPolicy](/docs/concepts/services-networking/network-policies/) and Role-based Access Control ([RBAC](/docs/reference/access-authn-authz/rbac/)), are built-in Kubernetes APIs that provide declaratively configured policy settings. APIs are typically usable even with hosted Kubernetes services and with managed Kubernetes installations. The built-in policy APIs follow the same conventions as other Kubernetes resources such as Pods. When you use a policy APIs that is [stable](/docs/reference/using-api/#api-versioning), you benefit from a [defined support policy](/docs/reference/using-api/deprecation-policy/) like other Kubernetes APIs. For these reasons, policy APIs are recommended over \*configuration files\* and \*command arguments\* where suitable. ## Extensions Extensions are software components that extend and deeply integrate with Kubernetes. They adapt it to support new types and new kinds of hardware. Many cluster administrators use a hosted or distribution instance of Kubernetes. These clusters come with extensions pre-installed. As a result, most Kubernetes users will not need to install extensions and even fewer users will need to author new ones. ### Extension patterns Kubernetes is designed to be automated by writing client programs. Any program that reads and/or writes to the Kubernetes API can provide useful automation. \*Automation\* can run on the cluster or off it. By following the guidance in this doc you can write highly available and robust automation. Automation generally works with any Kubernetes cluster, including hosted clusters and managed installations. There is a specific pattern for writing client programs that work well with Kubernetes called the {{< glossary\_tooltip term\_id="controller" text="controller" >}} pattern. Controllers typically read an object's `.spec`, possibly do things, and then update the object's `.status`. A controller is a client of the Kubernetes API. When Kubernetes is the client and calls out to a remote service, Kubernetes calls this a \*webhook\*. The remote service is called a \*webhook backend\*. As with custom controllers, webhooks do add a point of failure. {{< note >}} Outside of Kubernetes, the term “webhook” typically refers to a mechanism for asynchronous notifications, where the webhook call serves as a one-way notification to another system or component. In the Kubernetes ecosystem, even synchronous HTTP callouts are often described as “webhooks”. {{< /note >}} In the webhook model, Kubernetes makes a network request to a remote service. With the alternative \*binary Plugin\* model, Kubernetes executes
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|
the webhook call serves as a one-way notification to another system or component. In the Kubernetes ecosystem, even synchronous HTTP callouts are often described as “webhooks”. {{< /note >}} In the webhook model, Kubernetes makes a network request to a remote service. With the alternative \*binary Plugin\* model, Kubernetes executes a binary (program). Binary plugins are used by the kubelet (for example, [CSI storage plugins](https://kubernetes-csi.github.io/docs/) and [CNI network plugins](/docs/concepts/extend-kubernetes/compute-storage-net/network-plugins/)), and by kubectl (see [Extend kubectl with plugins](/docs/tasks/extend-kubectl/kubectl-plugins/)). ### Extension points This diagram shows the extension points in a Kubernetes cluster and the clients that access it. {{< figure src="/docs/concepts/extend-kubernetes/extension-points.png" alt="Symbolic representation of seven numbered extension points for Kubernetes" class="diagram-large" caption="Kubernetes extension points" >}} #### Key to the figure 1. Users often interact with the Kubernetes API using `kubectl`. [Plugins](#client-extensions) customise the behaviour of clients. There are generic extensions that can apply to different clients, as well as specific ways to extend `kubectl`. 1. The API server handles all requests. Several types of extension points in the API server allow authenticating requests, or blocking them based on their content, editing content, and handling deletion. These are described in the [API Access Extensions](#api-access-extensions) section. 1. The API server serves various kinds of \*resources\*. \*Built-in resource kinds\*, such as `pods`, are defined by the Kubernetes project and can't be changed. Read [API extensions](#api-extensions) to learn about extending the Kubernetes API. 1. The Kubernetes scheduler [decides](/docs/concepts/scheduling-eviction/assign-pod-node/) which nodes to place pods on. There are several ways to extend scheduling, which are described in the [Scheduling extensions](#scheduling-extensions) section. 1. Much of the behavior of Kubernetes is implemented by programs called {{< glossary\_tooltip term\_id="controller" text="controllers" >}}, that are clients of the API server. Controllers are often used in conjunction with custom resources. Read [combining new APIs with automation](#combining-new-apis-with-automation) and [Changing built-in resources](#changing-built-in-resources) to learn more. 1. The kubelet runs on servers (nodes), and helps pods appear like virtual servers with their own IPs on the cluster network. [Network Plugins](#network-plugins) allow for different implementations of pod networking. 1. You can use [Device Plugins](#device-plugins) to integrate custom hardware or other special node-local facilities, and make these available to Pods running in your cluster. The kubelet includes support for working with device plugins. The kubelet also mounts and unmounts {{< glossary\_tooltip text="volume" term\_id="volume" >}} for pods and their containers. You can use [Storage Plugins](#storage-plugins) to add support for new kinds of storage and other volume types. #### Extension point choice flowchart {#extension-flowchart} If you are unsure where to start, this flowchart can help. Note that some solutions may involve several types of extensions. {{< figure src="/docs/concepts/extend-kubernetes/flowchart.svg" alt="Flowchart with questions about use cases and guidance for implementers. Green circles indicate yes; red circles indicate no." class="diagram-large" caption="Flowchart guide to select an extension approach" >}} --- ## Client extensions Plugins for kubectl are separate binaries that add or replace the behavior of specific subcommands. The `kubectl` tool can also integrate with [credential plugins](/docs/reference/access-authn-authz/authentication/#client-go-credential-plugins) These extensions only affect a individual user's local environment, and so cannot enforce site-wide policies. If you want to extend the `kubectl` tool, read [Extend kubectl with plugins](/docs/tasks/extend-kubectl/kubectl-plugins/). ## API extensions ### Custom resource definitions Consider adding a \_Custom Resource\_ to Kubernetes if you want to define new controllers, application configuration objects or other declarative APIs, and to manage them using Kubernetes tools, such as `kubectl`. For more about Custom Resources, see the [Custom Resources](/docs/concepts/extend-kubernetes/api-extension/custom-resources/) concept guide. ### API aggregation layer You can use Kubernetes' [API Aggregation Layer](/docs/concepts/extend-kubernetes/api-extension/apiserver-aggregation/) to integrate the Kubernetes API with additional services such as for [metrics](/docs/tasks/debug/debug-cluster/resource-metrics-pipeline/). ### Combining new APIs with automation A combination of a custom resource API and a control loop is
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|
about Custom Resources, see the [Custom Resources](/docs/concepts/extend-kubernetes/api-extension/custom-resources/) concept guide. ### API aggregation layer You can use Kubernetes' [API Aggregation Layer](/docs/concepts/extend-kubernetes/api-extension/apiserver-aggregation/) to integrate the Kubernetes API with additional services such as for [metrics](/docs/tasks/debug/debug-cluster/resource-metrics-pipeline/). ### Combining new APIs with automation A combination of a custom resource API and a control loop is called the {{< glossary\_tooltip term\_id="controller" text="controllers" >}} pattern. If your controller takes the place of a human operator deploying infrastructure based on a desired state, then the controller may also be following the {{< glossary\_tooltip text="operator pattern" term\_id="operator-pattern" >}}. The Operator pattern is used to manage specific applications; usually, these are applications that maintain state and require care in how they are managed. You can also make your own custom APIs and control loops that manage other resources, such as storage, or to define policies (such as an access control restriction). ### Changing built-in resources When you extend the Kubernetes API by adding custom resources, the added resources always fall into a new API Groups. You cannot replace or change existing API groups. Adding an API does not directly let you affect the behavior of existing APIs (such as Pods), whereas \_API Access Extensions\_ do. ## API access extensions When a request reaches the Kubernetes API Server, it is first \_authenticated\_, then \_authorized\_, and is then subject to various types of \_admission control\_ (some requests are in fact not authenticated, and get special treatment). See [Controlling Access to the Kubernetes API](/docs/concepts/security/controlling-access/) for more on this flow. Each of the steps in the Kubernetes authentication / authorization flow offers extension points. ### Authentication [Authentication](/docs/reference/access-authn-authz/authentication/) maps headers or certificates in all requests to a username for the client making the request. Kubernetes has several built-in authentication methods that it supports. It can also sit behind an authenticating proxy, and it can send a token from an `Authorization:` header to a remote service for verification (an [authentication webhook](/docs/reference/access-authn-authz/authentication/#webhook-token-authentication)) if those don't meet your needs. ### Authorization [Authorization](/docs/reference/access-authn-authz/authorization/) determines whether specific users can read, write, and do other operations on API resources. It works at the level of whole resources -- it doesn't discriminate based on arbitrary object fields. If the built-in authorization options don't meet your needs, an [authorization webhook](/docs/reference/access-authn-authz/webhook/) allows calling out to custom code that makes an authorization decision. ### Dynamic admission control After a request is authorized, if it is a write operation, it also goes through [Admission Control](/docs/reference/access-authn-authz/admission-controllers/) steps. In addition to the built-in steps, there are several extensions: \* The [Image Policy webhook](/docs/reference/access-authn-authz/admission-controllers/#imagepolicywebhook) restricts what images can be run in containers. \* To make arbitrary admission control decisions, a general [Admission webhook](/docs/reference/access-authn-authz/extensible-admission-controllers/#admission-webhooks) can be used. Admission webhooks can reject creations or updates. Some admission webhooks modify the incoming request data before it is handled further by Kubernetes. ## Infrastructure extensions ### Device plugins \_Device plugins\_ allow a node to discover new Node resources (in addition to the builtin ones like cpu and memory) via a [Device Plugin](/docs/concepts/extend-kubernetes/compute-storage-net/device-plugins/). ### Storage plugins {{< glossary\_tooltip text="Container Storage Interface" term\_id="csi" >}} (CSI) plugins provide a way to extend Kubernetes with supports for new kinds of volumes. The volumes can be backed by durable external storage, or provide ephemeral storage, or they might offer a read-only interface to information using a filesystem paradigm. Kubernetes also includes support for [FlexVolume](/docs/concepts/storage/volumes/#flexvolume) plugins, which are deprecated since Kubernetes v1.23 (in favour of CSI). FlexVolume plugins allow users to mount volume types that aren't natively supported by Kubernetes. When you run a Pod that relies on FlexVolume storage, the kubelet calls a binary plugin to mount the volume. The archived [FlexVolume](https://git.k8s.io/design-proposals-archive/storage/flexvolume-deployment.md) design proposal has more detail on
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/extend-kubernetes/_index.md
|
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|
since Kubernetes v1.23 (in favour of CSI). FlexVolume plugins allow users to mount volume types that aren't natively supported by Kubernetes. When you run a Pod that relies on FlexVolume storage, the kubelet calls a binary plugin to mount the volume. The archived [FlexVolume](https://git.k8s.io/design-proposals-archive/storage/flexvolume-deployment.md) design proposal has more detail on this approach. The [Kubernetes Volume Plugin FAQ for Storage Vendors](https://github.com/kubernetes/community/blob/master/sig-storage/volume-plugin-faq.md#kubernetes-volume-plugin-faq-for-storage-vendors) includes general information on storage plugins. ### Network plugins Your Kubernetes cluster needs a \_network plugin\_ in order to have a working Pod network and to support other aspects of the Kubernetes network model. [Network Plugins](/docs/concepts/extend-kubernetes/compute-storage-net/network-plugins/) allow Kubernetes to work with different networking topologies and technologies. ### Kubelet image credential provider plugins {{< feature-state for\_k8s\_version="v1.26" state="stable" >}} Kubelet image credential providers are plugins for the kubelet to dynamically retrieve image registry credentials. The credentials are then used when pulling images from container image registries that match the configuration. The plugins can communicate with external services or use local files to obtain credentials. This way, the kubelet does not need to have static credentials for each registry, and can support various authentication methods and protocols. For plugin configuration details, see [Configure a kubelet image credential provider](/docs/tasks/administer-cluster/kubelet-credential-provider/). ## Scheduling extensions The scheduler is a special type of controller that watches pods, and assigns pods to nodes. The default scheduler can be replaced entirely, while continuing to use other Kubernetes components, or [multiple schedulers](/docs/tasks/extend-kubernetes/configure-multiple-schedulers/) can run at the same time. This is a significant undertaking, and almost all Kubernetes users find they do not need to modify the scheduler. You can control which [scheduling plugins](/docs/reference/scheduling/config/#scheduling-plugins) are active, or associate sets of plugins with different named [scheduler profiles](/docs/reference/scheduling/config/#multiple-profiles). You can also write your own plugin that integrates with one or more of the kube-scheduler's [extension points](/docs/concepts/scheduling-eviction/scheduling-framework/#extension-points). Finally, the built in `kube-scheduler` component supports a [webhook](https://git.k8s.io/design-proposals-archive/scheduling/scheduler\_extender.md) that permits a remote HTTP backend (scheduler extension) to filter and / or prioritize the nodes that the kube-scheduler chooses for a pod. {{< note >}} You can only affect node filtering and node prioritization with a scheduler extender webhook; other extension points are not available through the webhook integration. {{< /note >}} ## {{% heading "whatsnext" %}} \* Learn more about infrastructure extensions \* [Device Plugins](/docs/concepts/extend-kubernetes/compute-storage-net/device-plugins/) \* [Network Plugins](/docs/concepts/extend-kubernetes/compute-storage-net/network-plugins/) \* CSI [storage plugins](https://kubernetes-csi.github.io/docs/) \* Learn about [kubectl plugins](/docs/tasks/extend-kubectl/kubectl-plugins/) \* Learn more about [Custom Resources](/docs/concepts/extend-kubernetes/api-extension/custom-resources/) \* Learn more about [Extension API Servers](/docs/concepts/extend-kubernetes/api-extension/apiserver-aggregation/) \* Learn about [Dynamic admission control](/docs/reference/access-authn-authz/extensible-admission-controllers/) \* Learn about the [Operator pattern](/docs/concepts/extend-kubernetes/operator/)
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/extend-kubernetes/_index.md
|
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] | 0.12408
|
Kubernetes (version 1.3 through to the latest {{< skew latestVersion >}}, and likely onwards) lets you use [Container Network Interface](https://github.com/containernetworking/cni) (CNI) plugins for cluster networking. You must use a CNI plugin that is compatible with your cluster and that suits your needs. Different plugins are available (both open- and closed- source) in the wider Kubernetes ecosystem. A CNI plugin is required to implement the [Kubernetes network model](/docs/concepts/services-networking/#the-kubernetes-network-model). You must use a CNI plugin that is compatible with the [v0.4.0](https://github.com/containernetworking/cni/blob/spec-v0.4.0/SPEC.md) or later releases of the CNI specification. The Kubernetes project recommends using a plugin that is compatible with the [v1.0.0](https://github.com/containernetworking/cni/blob/spec-v1.0.0/SPEC.md) CNI specification (plugins can be compatible with multiple spec versions). ## Installation A Container Runtime, in the networking context, is a daemon on a node configured to provide CRI Services for kubelet. In particular, the Container Runtime must be configured to load the CNI plugins required to implement the Kubernetes network model. {{< note >}} Prior to Kubernetes 1.24, the CNI plugins could also be managed by the kubelet using the `cni-bin-dir` and `network-plugin` command-line parameters. These command-line parameters were removed in Kubernetes 1.24, with management of the CNI no longer in scope for kubelet. See [Troubleshooting CNI plugin-related errors](/docs/tasks/administer-cluster/migrating-from-dockershim/troubleshooting-cni-plugin-related-errors/) if you are facing issues following the removal of dockershim. {{< /note >}} For specific information about how a Container Runtime manages the CNI plugins, see the documentation for that Container Runtime, for example: - [containerd](https://github.com/containerd/containerd/blob/main/script/setup/install-cni) - [CRI-O](https://github.com/cri-o/cri-o/blob/main/contrib/cni/README.md) For specific information about how to install and manage a CNI plugin, see the documentation for that plugin or [networking provider](/docs/concepts/cluster-administration/networking/#how-to-implement-the-kubernetes-network-model). ## Network Plugin Requirements ### Loopback CNI In addition to the CNI plugin installed on the nodes for implementing the Kubernetes network model, Kubernetes also requires the container runtimes to provide a loopback interface `lo`, which is used for each sandbox (pod sandboxes, vm sandboxes, ...). Implementing the loopback interface can be accomplished by re-using the [CNI loopback plugin.](https://github.com/containernetworking/plugins/blob/master/plugins/main/loopback/loopback.go) or by developing your own code to achieve this (see [this example from CRI-O](https://github.com/cri-o/ocicni/blob/release-1.24/pkg/ocicni/util\_linux.go#L91)). ### Support hostPort The CNI networking plugin supports `hostPort`. You can use the official [portmap](https://github.com/containernetworking/plugins/tree/master/plugins/meta/portmap) plugin offered by the CNI plugin team or use your own plugin with portMapping functionality. If you want to enable `hostPort` support, you must specify `portMappings capability` in your `cni-conf-dir`. For example: ```json { "name": "k8s-pod-network", "cniVersion": "0.4.0", "plugins": [ { "type": "calico", "log\_level": "info", "datastore\_type": "kubernetes", "nodename": "127.0.0.1", "ipam": { "type": "host-local", "subnet": "usePodCidr" }, "policy": { "type": "k8s" }, "kubernetes": { "kubeconfig": "/etc/cni/net.d/calico-kubeconfig" } }, { "type": "portmap", "capabilities": {"portMappings": true}, "externalSetMarkChain": "KUBE-MARK-MASQ" } ] } ``` ### Support traffic shaping \*\*Experimental Feature\*\* The CNI networking plugin also supports pod ingress and egress traffic shaping. You can use the official [bandwidth](https://github.com/containernetworking/plugins/tree/master/plugins/meta/bandwidth) plugin offered by the CNI plugin team or use your own plugin with bandwidth control functionality. If you want to enable traffic shaping support, you must add the `bandwidth` plugin to your CNI configuration file (default `/etc/cni/net.d`) and ensure that the binary is included in your CNI bin dir (default `/opt/cni/bin`). ```json { "name": "k8s-pod-network", "cniVersion": "0.4.0", "plugins": [ { "type": "calico", "log\_level": "info", "datastore\_type": "kubernetes", "nodename": "127.0.0.1", "ipam": { "type": "host-local", "subnet": "usePodCidr" }, "policy": { "type": "k8s" }, "kubernetes": { "kubeconfig": "/etc/cni/net.d/calico-kubeconfig" } }, { "type": "bandwidth", "capabilities": {"bandwidth": true} } ] } ``` Now you can add the `kubernetes.io/ingress-bandwidth` and `kubernetes.io/egress-bandwidth` annotations to your Pod. For example: ```yaml apiVersion: v1 kind: Pod metadata: annotations: kubernetes.io/ingress-bandwidth: 1M kubernetes.io/egress-bandwidth: 1M ... ``` ## {{% heading "whatsnext" %}} - Learn more about [Cluster Networking](/docs/concepts/cluster-administration/networking/) - Learn more about [Network Policies](/docs/concepts/services-networking/network-policies/) - Learn about the [Troubleshooting CNI plugin-related errors](/docs/tasks/administer-cluster/migrating-from-dockershim/troubleshooting-cni-plugin-related-errors/)
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/extend-kubernetes/compute-storage-net/network-plugins.md
|
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] | 0.138923
|
`kubernetes.io/ingress-bandwidth` and `kubernetes.io/egress-bandwidth` annotations to your Pod. For example: ```yaml apiVersion: v1 kind: Pod metadata: annotations: kubernetes.io/ingress-bandwidth: 1M kubernetes.io/egress-bandwidth: 1M ... ``` ## {{% heading "whatsnext" %}} - Learn more about [Cluster Networking](/docs/concepts/cluster-administration/networking/) - Learn more about [Network Policies](/docs/concepts/services-networking/network-policies/) - Learn about the [Troubleshooting CNI plugin-related errors](/docs/tasks/administer-cluster/migrating-from-dockershim/troubleshooting-cni-plugin-related-errors/)
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/extend-kubernetes/compute-storage-net/network-plugins.md
|
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] | 0.191907
|
{{< feature-state for\_k8s\_version="v1.26" state="stable" >}} Kubernetes provides a device plugin framework that you can use to advertise system hardware resources to the {{< glossary\_tooltip term\_id="kubelet" >}}. Instead of customizing the code for Kubernetes itself, vendors can implement a device plugin that you deploy either manually or as a {{< glossary\_tooltip term\_id="daemonset" >}}. The targeted devices include GPUs, high-performance NICs, FPGAs, InfiniBand adapters, and other similar computing resources that may require vendor specific initialization and setup. ## Device plugin registration The kubelet exports a `Registration` gRPC service: ```gRPC service Registration { rpc Register(RegisterRequest) returns (Empty) {} } ``` A device plugin can register itself with the kubelet through this gRPC service. During the registration, the device plugin needs to send: \* The name of its Unix socket. \* The Device Plugin API version against which it was built. \* The `ResourceName` it wants to advertise. Here `ResourceName` needs to follow the [extended resource naming scheme](/docs/concepts/configuration/manage-resources-containers/#extended-resources) as `vendor-domain/resourcetype`. (For example, an NVIDIA GPU is advertised as `nvidia.com/gpu`.) Following a successful registration, the device plugin sends the kubelet the list of devices it manages, and the kubelet is then in charge of advertising those resources to the API server as part of the kubelet node status update. For example, after a device plugin registers `hardware-vendor.example/foo` with the kubelet and reports two healthy devices on a node, the node status is updated to advertise that the node has 2 "Foo" devices installed and available. Then, users can request devices as part of a Pod specification (see [`container`](/docs/reference/kubernetes-api/workload-resources/pod-v1/#Container)). Requesting extended resources is similar to how you manage requests and limits for other resources, with the following differences: \* Extended resources are only supported as integer resources and cannot be overcommitted. \* Devices cannot be shared between containers. ### Example {#example-pod} Suppose a Kubernetes cluster is running a device plugin that advertises resource `hardware-vendor.example/foo` on certain nodes. Here is an example of a pod requesting this resource to run a demo workload: ```yaml --- apiVersion: v1 kind: Pod metadata: name: demo-pod spec: containers: - name: demo-container-1 image: registry.k8s.io/pause:3.8 resources: limits: hardware-vendor.example/foo: 2 # # This Pod needs 2 of the hardware-vendor.example/foo devices # and can only schedule onto a Node that's able to satisfy # that need. # # If the Node has more than 2 of those devices available, the # remainder would be available for other Pods to use. ``` ## Device plugin implementation The general workflow of a device plugin includes the following steps: 1. Initialization. During this phase, the device plugin performs vendor-specific initialization and setup to make sure the devices are in a ready state. 1. The plugin starts a gRPC service, with a Unix socket under the host path `/var/lib/kubelet/device-plugins/`, that implements the following interfaces: ```gRPC service DevicePlugin { // GetDevicePluginOptions returns options to be communicated with Device Manager. rpc GetDevicePluginOptions(Empty) returns (DevicePluginOptions) {} // ListAndWatch returns a stream of List of Devices // Whenever a Device state change or a Device disappears, ListAndWatch // returns the new list rpc ListAndWatch(Empty) returns (stream ListAndWatchResponse) {} // Allocate is called during container creation so that the Device // Plugin can run device specific operations and instruct Kubelet // of the steps to make the Device available in the container rpc Allocate(AllocateRequest) returns (AllocateResponse) {} // GetPreferredAllocation returns a preferred set of devices to allocate // from a list of available ones. The resulting preferred allocation is not // guaranteed to be the allocation ultimately performed by the // devicemanager. It is only designed to help the devicemanager make a more // informed allocation decision when possible. rpc GetPreferredAllocation(PreferredAllocationRequest) returns (PreferredAllocationResponse) {}
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/extend-kubernetes/compute-storage-net/device-plugins.md
|
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] | 0.181273
|
devices to allocate // from a list of available ones. The resulting preferred allocation is not // guaranteed to be the allocation ultimately performed by the // devicemanager. It is only designed to help the devicemanager make a more // informed allocation decision when possible. rpc GetPreferredAllocation(PreferredAllocationRequest) returns (PreferredAllocationResponse) {} // PreStartContainer is called, if indicated by Device Plugin during registration phase, // before each container start. Device plugin can run device specific operations // such as resetting the device before making devices available to the container. rpc PreStartContainer(PreStartContainerRequest) returns (PreStartContainerResponse) {} } ``` {{< note >}} Plugins are not required to provide useful implementations for `GetPreferredAllocation()` or `PreStartContainer()`. Flags indicating the availability of these calls, if any, should be set in the `DevicePluginOptions` message sent back by a call to `GetDevicePluginOptions()`. The `kubelet` will always call `GetDevicePluginOptions()` to see which optional functions are available, before calling any of them directly. {{< /note >}} 1. The plugin registers itself with the kubelet through the Unix socket at host path `/var/lib/kubelet/device-plugins/kubelet.sock`. {{< note >}} The ordering of the workflow is important. A plugin MUST start serving gRPC service before registering itself with kubelet for successful registration. {{< /note >}} 1. After successfully registering itself, the device plugin runs in serving mode, during which it keeps monitoring device health and reports back to the kubelet upon any device state changes. It is also responsible for serving `Allocate` gRPC requests. During `Allocate`, the device plugin may do device-specific preparation; for example, GPU cleanup or QRNG initialization. If the operations succeed, the device plugin returns an `AllocateResponse` that contains container runtime configurations for accessing the allocated devices. The kubelet passes this information to the container runtime. An `AllocateResponse` contains zero or more `ContainerAllocateResponse` objects. In these, the device plugin defines modifications that must be made to a container's definition to provide access to the device. These modifications include: \* [Annotations](/docs/concepts/overview/working-with-objects/annotations/) \* device nodes \* environment variables \* mounts \* fully-qualified CDI device names {{< note >}} The processing of the fully-qualified CDI device names by the Device Manager requires that the `DevicePluginCDIDevices` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/) is enabled for both the kubelet and the kube-apiserver. This was added as an alpha feature in Kubernetes v1.28, graduated to beta in v1.29 and to GA in v1.31. {{< /note >}} ### Handling kubelet restarts A device plugin is expected to detect kubelet restarts and re-register itself with the new kubelet instance. A new kubelet instance deletes all the existing Unix sockets under `/var/lib/kubelet/device-plugins` when it starts. A device plugin can monitor the deletion of its Unix socket and re-register itself upon such an event. ### Device plugin and unhealthy devices There are cases when devices fail or are shut down. The responsibility of the Device Plugin in this case is to notify the kubelet about the situation using the `ListAndWatchResponse` API. Once a device is marked as unhealthy, the kubelet will decrease the allocatable count for this resource on the Node to reflect how many devices can be used for scheduling new pods. Capacity count for the resource will not change. Pods that were assigned to the failed devices will continue be assigned to this device. It is typical that code relying on the device will start failing and Pod may get into Failed phase if `restartPolicy` for the Pod was not `Always` or enter the crash loop otherwise. Before Kubernetes v1.31, the way to know whether or not a Pod is associated with the failed device is to use the [PodResources API](#monitoring-device-plugin-resources). {{< feature-state feature\_gate\_name="ResourceHealthStatus" >}} By enabling the feature gate `ResourceHealthStatus`, the field `allocatedResourcesStatus` will be
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/extend-kubernetes/compute-storage-net/device-plugins.md
|
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] | 0.044112
|
the Pod was not `Always` or enter the crash loop otherwise. Before Kubernetes v1.31, the way to know whether or not a Pod is associated with the failed device is to use the [PodResources API](#monitoring-device-plugin-resources). {{< feature-state feature\_gate\_name="ResourceHealthStatus" >}} By enabling the feature gate `ResourceHealthStatus`, the field `allocatedResourcesStatus` will be added to each container status, within the `.status` for each Pod. The `allocatedResourcesStatus` field reports health information for each device assigned to the container. For a failed Pod, or where you suspect a fault, you can use this status to understand whether the Pod behavior may be associated with device failure. For example, if an accelerator is reporting an over-temperature event, the `allocatedResourcesStatus` field may be able to report this. ## Device plugin deployment You can deploy a device plugin as a DaemonSet, as a package for your node's operating system, or manually. The canonical directory `/var/lib/kubelet/device-plugins` requires privileged access, so a device plugin must run in a privileged security context. If you're deploying a device plugin as a DaemonSet, `/var/lib/kubelet/device-plugins` must be mounted as a {{< glossary\_tooltip term\_id="volume" >}} in the plugin's [PodSpec](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#podspec-v1-core). If you choose the DaemonSet approach you can rely on Kubernetes to: place the device plugin's Pod onto Nodes, to restart the daemon Pod after failure, and to help automate upgrades. ## API compatibility Previously, the versioning scheme required the Device Plugin's API version to match exactly the Kubelet's version. Since the graduation of this feature to Beta in v1.12 this is no longer a hard requirement. The API is versioned and has been stable since Beta graduation of this feature. Because of this, kubelet upgrades should be seamless but there still may be changes in the API before stabilization making upgrades not guaranteed to be non-breaking. {{< note >}} Although the Device Manager component of Kubernetes is a generally available feature, the \_device plugin API\_ is not stable. For information on the device plugin API and version compatibility, read [Device Plugin API versions](/docs/reference/node/device-plugin-api-versions/). {{< /note >}} As a project, Kubernetes recommends that device plugin developers: \* Watch for Device Plugin API changes in the future releases. \* Support multiple versions of the device plugin API for backward/forward compatibility. To run device plugins on nodes that need to be upgraded to a Kubernetes release with a newer device plugin API version, upgrade your device plugins to support both versions before upgrading these nodes. Taking that approach will ensure the continuous functioning of the device allocations during the upgrade. ## Monitoring device plugin resources {{< feature-state for\_k8s\_version="v1.28" state="stable" >}} In order to monitor resources provided by device plugins, monitoring agents need to be able to discover the set of devices that are in-use on the node and obtain metadata to describe which container the metric should be associated with. [Prometheus](https://prometheus.io/) metrics exposed by device monitoring agents should follow the [Kubernetes Instrumentation Guidelines](https://github.com/kubernetes/community/blob/master/contributors/devel/sig-instrumentation/metric-instrumentation.md), identifying containers using `pod`, `namespace`, and `container` prometheus labels. The kubelet provides a gRPC service to enable discovery of in-use devices, and to provide metadata for these devices: ```gRPC // PodResourcesLister is a service provided by the kubelet that provides information about the // node resources consumed by pods and containers on the node service PodResourcesLister { rpc List(ListPodResourcesRequest) returns (ListPodResourcesResponse) {} rpc GetAllocatableResources(AllocatableResourcesRequest) returns (AllocatableResourcesResponse) {} rpc Get(GetPodResourcesRequest) returns (GetPodResourcesResponse) {} } ``` ### `List` gRPC endpoint {#grpc-endpoint-list} The `List` endpoint provides information on resources of running pods, with details such as the id of exclusively allocated CPUs, device id as it was reported by device plugins and id of the NUMA node where these devices are allocated. Also, for NUMA-based
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/extend-kubernetes/compute-storage-net/device-plugins.md
|
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] | 0.154204
|
} ``` ### `List` gRPC endpoint {#grpc-endpoint-list} The `List` endpoint provides information on resources of running pods, with details such as the id of exclusively allocated CPUs, device id as it was reported by device plugins and id of the NUMA node where these devices are allocated. Also, for NUMA-based machines, it contains the information about memory and hugepages reserved for a container. Starting from Kubernetes v1.27, the `List` endpoint can provide information on resources of running pods allocated in `ResourceClaims` by the `DynamicResourceAllocation` API. Starting from Kubernetes v1.34, this feature is enabled by default. To disable, `kubelet` must be started with the following flags: ``` --feature-gates=KubeletPodResourcesDynamicResources=false ``` ```gRPC // ListPodResourcesResponse is the response returned by List function message ListPodResourcesResponse { repeated PodResources pod\_resources = 1; } // PodResources contains information about the node resources assigned to a pod message PodResources { string name = 1; string namespace = 2; repeated ContainerResources containers = 3; } // ContainerResources contains information about the resources assigned to a container message ContainerResources { string name = 1; repeated ContainerDevices devices = 2; repeated int64 cpu\_ids = 3; repeated ContainerMemory memory = 4; repeated DynamicResource dynamic\_resources = 5; } // ContainerMemory contains information about memory and hugepages assigned to a container message ContainerMemory { string memory\_type = 1; uint64 size = 2; TopologyInfo topology = 3; } // Topology describes hardware topology of the resource message TopologyInfo { repeated NUMANode nodes = 1; } // NUMA representation of NUMA node message NUMANode { int64 ID = 1; } // ContainerDevices contains information about the devices assigned to a container message ContainerDevices { string resource\_name = 1; repeated string device\_ids = 2; TopologyInfo topology = 3; } // DynamicResource contains information about the devices assigned to a container by Dynamic Resource Allocation message DynamicResource { string class\_name = 1; string claim\_name = 2; string claim\_namespace = 3; repeated ClaimResource claim\_resources = 4; } // ClaimResource contains per-plugin resource information message ClaimResource { repeated CDIDevice cdi\_devices = 1 [(gogoproto.customname) = "CDIDevices"]; } // CDIDevice specifies a CDI device information message CDIDevice { // Fully qualified CDI device name // for example: vendor.com/gpu=gpudevice1 // see more details in the CDI specification: // https://github.com/container-orchestrated-devices/container-device-interface/blob/main/SPEC.md string name = 1; } ``` {{< note >}} cpu\_ids in the `ContainerResources` in the `List` endpoint correspond to exclusive CPUs allocated to a particular container. If the goal is to evaluate CPUs that belong to the shared pool, the `List` endpoint needs to be used in conjunction with the `GetAllocatableResources` endpoint as explained below: 1. Call `GetAllocatableResources` to get a list of all the allocatable CPUs 2. Call `GetCpuIds` on all `ContainerResources` in the system 3. Subtract out all of the CPUs from the `GetCpuIds` calls from the `GetAllocatableResources` call {{< /note >}} ### `GetAllocatableResources` gRPC endpoint {#grpc-endpoint-getallocatableresources} {{< feature-state state="stable" for\_k8s\_version="v1.28" >}} GetAllocatableResources provides information on resources initially available on the worker node. It provides more information than kubelet exports to APIServer. {{< note >}} `GetAllocatableResources` should only be used to evaluate [allocatable](/docs/tasks/administer-cluster/reserve-compute-resources/#node-allocatable) resources on a node. If the goal is to evaluate free/unallocated resources it should be used in conjunction with the List() endpoint. The result obtained by `GetAllocatableResources` would remain the same unless the underlying resources exposed to kubelet change. This happens rarely but when it does (for example: hotplug/hotunplug, device health changes), client is expected to call `GetAllocatableResources` endpoint. However, calling `GetAllocatableResources` endpoint is not sufficient in case of cpu and/or memory update and Kubelet needs to be restarted to reflect the correct resource capacity and allocatable. {{< /note >}} ```gRPC // AllocatableResourcesResponses contains information about all the devices
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/extend-kubernetes/compute-storage-net/device-plugins.md
|
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] | 0.263646
|
device health changes), client is expected to call `GetAllocatableResources` endpoint. However, calling `GetAllocatableResources` endpoint is not sufficient in case of cpu and/or memory update and Kubelet needs to be restarted to reflect the correct resource capacity and allocatable. {{< /note >}} ```gRPC // AllocatableResourcesResponses contains information about all the devices known by the kubelet message AllocatableResourcesResponse { repeated ContainerDevices devices = 1; repeated int64 cpu\_ids = 2; repeated ContainerMemory memory = 3; } ``` `ContainerDevices` do expose the topology information declaring to which NUMA cells the device is affine. The NUMA cells are identified using a opaque integer ID, which value is consistent to what device plugins report [when they register themselves to the kubelet](/docs/concepts/extend-kubernetes/compute-storage-net/device-plugins/#device-plugin-integration-with-the-topology-manager). The gRPC service is served over a unix socket at `/var/lib/kubelet/pod-resources/kubelet.sock`. Monitoring agents for device plugin resources can be deployed as a daemon, or as a DaemonSet. The canonical directory `/var/lib/kubelet/pod-resources` requires privileged access, so monitoring agents must run in a privileged security context. If a device monitoring agent is running as a DaemonSet, `/var/lib/kubelet/pod-resources` must be mounted as a {{< glossary\_tooltip term\_id="volume" >}} in the device monitoring agent's [PodSpec](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#podspec-v1-core). {{< note >}} When accessing the `/var/lib/kubelet/pod-resources/kubelet.sock` from DaemonSet or any other app deployed as a container on the host, which is mounting socket as a volume, it is a good practice to mount directory `/var/lib/kubelet/pod-resources/` instead of the `/var/lib/kubelet/pod-resources/kubelet.sock`. This will ensure that after kubelet restart, container will be able to re-connect to this socket. Container mounts are managed by inode referencing the socket or directory, depending on what was mounted. When kubelet restarts, socket is deleted and a new socket is created, while directory stays untouched. So the original inode for the socket become unusable. Inode to directory will continue working. {{< /note >}} ### `Get` gRPC endpoint {#grpc-endpoint-get} {{< feature-state state="beta" for\_k8s\_version="v1.34" >}} The `Get` endpoint provides information on resources of a running Pod. It exposes information similar to those described in the `List` endpoint. The `Get` endpoint requires `PodName` and `PodNamespace` of the running Pod. ```gRPC // GetPodResourcesRequest contains information about the pod message GetPodResourcesRequest { string pod\_name = 1; string pod\_namespace = 2; } ``` To disable this feature, you must start your kubelet services with the following flag: ``` --feature-gates=KubeletPodResourcesGet=false ``` The `Get` endpoint can provide Pod information related to dynamic resources allocated by the dynamic resource allocation API. Starting from Kubernetes v1.34, this feature is enabled by default. To disable, `kubelet` must be started with the following flags: ``` --feature-gates=KubeletPodResourcesDynamicResources=false ``` ## Device plugin integration with the Topology Manager {{< feature-state for\_k8s\_version="v1.27" state="stable" >}} The Topology Manager is a Kubelet component that allows resources to be co-ordinated in a Topology aligned manner. In order to do this, the Device Plugin API was extended to include a `TopologyInfo` struct. ```gRPC message TopologyInfo { repeated NUMANode nodes = 1; } message NUMANode { int64 ID = 1; } ``` Device Plugins that wish to leverage the Topology Manager can send back a populated TopologyInfo struct as part of the device registration, along with the device IDs and the health of the device. The device manager will then use this information to consult with the Topology Manager and make resource assignment decisions. `TopologyInfo` supports setting a `nodes` field to either `nil` or a list of NUMA nodes. This allows the Device Plugin to advertise a device that spans multiple NUMA nodes. Setting `TopologyInfo` to `nil` or providing an empty list of NUMA nodes for a given device indicates that the Device Plugin does not have a NUMA affinity preference for that device. An example `TopologyInfo` struct populated for
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/extend-kubernetes/compute-storage-net/device-plugins.md
|
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|
allows the Device Plugin to advertise a device that spans multiple NUMA nodes. Setting `TopologyInfo` to `nil` or providing an empty list of NUMA nodes for a given device indicates that the Device Plugin does not have a NUMA affinity preference for that device. An example `TopologyInfo` struct populated for a device by a Device Plugin: ``` pluginapi.Device{ID: "25102017", Health: pluginapi.Healthy, Topology:&pluginapi.TopologyInfo{Nodes: []\*pluginapi.NUMANode{&pluginapi.NUMANode{ID: 0,},}}} ``` ## Device plugin examples {#examples} {{% thirdparty-content %}} Here are some examples of device plugin implementations: \* [Akri](https://github.com/project-akri/akri), which lets you easily expose heterogeneous leaf devices (such as IP cameras and USB devices). \* The [AMD GPU device plugin](https://github.com/ROCm/k8s-device-plugin) \* The [generic device plugin](https://github.com/squat/generic-device-plugin) for generic Linux devices and USB devices \* The [HAMi](https://github.com/Project-HAMi/HAMi) for heterogeneous AI computing virtualization middleware (for example, NVIDIA, Cambricon, Hygon, Iluvatar, MThreads, Ascend, Metax) \* The [Intel device plugins](https://github.com/intel/intel-device-plugins-for-kubernetes) for Intel GPU, FPGA, QAT, VPU, SGX, DSA, DLB and IAA devices \* The [KubeVirt device plugins](https://github.com/kubevirt/kubernetes-device-plugins) for hardware-assisted virtualization \* The [NVIDIA GPU device plugin](https://github.com/NVIDIA/k8s-device-plugin), NVIDIA's official device plugin to expose NVIDIA GPUs and monitor GPU health \* The [NVIDIA GPU device plugin for Container-Optimized OS](https://github.com/GoogleCloudPlatform/container-engine-accelerators/tree/master/cmd/nvidia\_gpu) \* The [RDMA device plugin](https://github.com/hustcat/k8s-rdma-device-plugin) \* The [SocketCAN device plugin](https://github.com/collabora/k8s-socketcan) \* The [Solarflare device plugin](https://github.com/vikaschoudhary16/sfc-device-plugin) \* The [SR-IOV Network device plugin](https://github.com/intel/sriov-network-device-plugin) \* The [Xilinx FPGA device plugins](https://github.com/Xilinx/FPGA\_as\_a\_Service/tree/master/k8s-device-plugin) for Xilinx FPGA devices ## {{% heading "whatsnext" %}} \* Learn about [scheduling GPU resources](/docs/tasks/manage-gpus/scheduling-gpus/) using device plugins \* Learn about [advertising extended resources](/docs/tasks/administer-cluster/extended-resource-node/) on a node \* Learn about the [Topology Manager](/docs/tasks/administer-cluster/topology-manager/) \* Read about using [hardware acceleration for TLS ingress](/blog/2019/04/24/hardware-accelerated-ssl/tls-termination-in-ingress-controllers-using-kubernetes-device-plugins-and-runtimeclass/) with Kubernetes \* Read more about [Extended Resource allocation by DRA](/docs/concepts/scheduling-eviction/dynamic-resource-allocation/#extended-resource)
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/extend-kubernetes/compute-storage-net/device-plugins.md
|
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] | 0.24888
|
This section covers extensions to your cluster that do not come as part as Kubernetes itself. You can use these extensions to enhance the nodes in your cluster, or to provide the network fabric that links Pods together. \* [CSI](/docs/concepts/storage/volumes/#csi) and [FlexVolume](/docs/concepts/storage/volumes/#flexvolume) storage plugins {{< glossary\_tooltip text="Container Storage Interface" term\_id="csi" >}} (CSI) plugins provide a way to extend Kubernetes with supports for new kinds of volumes. The volumes can be backed by durable external storage, or provide ephemeral storage, or they might offer a read-only interface to information using a filesystem paradigm. Kubernetes also includes support for [FlexVolume](/docs/concepts/storage/volumes/#flexvolume) plugins, which are deprecated since Kubernetes v1.23 (in favour of CSI). FlexVolume plugins allow users to mount volume types that aren't natively supported by Kubernetes. When you run a Pod that relies on FlexVolume storage, the kubelet calls a binary plugin to mount the volume. The archived [FlexVolume](https://git.k8s.io/design-proposals-archive/storage/flexvolume-deployment.md) design proposal has more detail on this approach. The [Kubernetes Volume Plugin FAQ for Storage Vendors](https://github.com/kubernetes/community/blob/master/sig-storage/volume-plugin-faq.md#kubernetes-volume-plugin-faq-for-storage-vendors) includes general information on storage plugins. \* [Device plugins](/docs/concepts/extend-kubernetes/compute-storage-net/device-plugins/) Device plugins allow a node to discover new Node facilities (in addition to the built-in node resources such as `cpu` and `memory`), and provide these custom node-local facilities to Pods that request them. \* [Network plugins](/docs/concepts/extend-kubernetes/compute-storage-net/network-plugins/) Network plugins allow Kubernetes to work with different networking topologies and technologies. Your Kubernetes cluster needs a \_network plugin\_ in order to have a working Pod network and to support other aspects of the Kubernetes network model. Kubernetes {{< skew currentVersion >}} is compatible with {{< glossary\_tooltip text="CNI" term\_id="cni" >}} network plugins.
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/extend-kubernetes/compute-storage-net/_index.md
|
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|
\*Custom resources\* are extensions of the Kubernetes API. This page discusses when to add a custom resource to your Kubernetes cluster and when to use a standalone service. It describes the two methods for adding custom resources and how to choose between them. ## Custom resources A \*resource\* is an endpoint in the [Kubernetes API](/docs/concepts/overview/kubernetes-api/) that stores a collection of {{< glossary\_tooltip text="API objects" term\_id="object" >}} of a certain kind; for example, the built-in \*pods\* resource contains a collection of Pod objects. A \*custom resource\* is an extension of the Kubernetes API that is not necessarily available in a default Kubernetes installation. It represents a customization of a particular Kubernetes installation. However, many core Kubernetes functions are now built using custom resources, making Kubernetes more modular. Custom resources can appear and disappear in a running cluster through dynamic registration, and cluster admins can update custom resources independently of the cluster itself. Once a custom resource is installed, users can create and access its objects using {{< glossary\_tooltip text="kubectl" term\_id="kubectl" >}}, just as they do for built-in resources like \*Pods\*. ## Custom controllers On their own, custom resources let you store and retrieve structured data. When you combine a custom resource with a \*custom controller\*, custom resources provide a true \_declarative API\_. The Kubernetes [declarative API](/docs/concepts/overview/kubernetes-api/) enforces a separation of responsibilities. You declare the desired state of your resource. The Kubernetes controller keeps the current state of Kubernetes objects in sync with your declared desired state. This is in contrast to an imperative API, where you \*instruct\* a server what to do. You can deploy and update a custom controller on a running cluster, independently of the cluster's lifecycle. Custom controllers can work with any kind of resource, but they are especially effective when combined with custom resources. The [Operator pattern](/docs/concepts/extend-kubernetes/operator/) combines custom resources and custom controllers. You can use custom controllers to encode domain knowledge for specific applications into an extension of the Kubernetes API. ## Should I add a custom resource to my Kubernetes cluster? When creating a new API, consider whether to [aggregate your API with the Kubernetes cluster APIs](/docs/concepts/extend-kubernetes/api-extension/apiserver-aggregation/) or let your API stand alone. | Consider API aggregation if: | Prefer a stand-alone API if: | | ---------------------------- | ---------------------------- | | Your API is [Declarative](#declarative-apis). | Your API does not fit the [Declarative](#declarative-apis) model. | | You want your new types to be readable and writable using `kubectl`.| `kubectl` support is not required | | You want to view your new types in a Kubernetes UI, such as dashboard, alongside built-in types. | Kubernetes UI support is not required. | | You are developing a new API. | You already have a program that serves your API and works well. | | You are willing to accept the format restriction that Kubernetes puts on REST resource paths, such as API Groups and Namespaces. (See the [API Overview](/docs/concepts/overview/kubernetes-api/).) | You need to have specific REST paths to be compatible with an already defined REST API. | | Your resources are naturally scoped to a cluster or namespaces of a cluster. | Cluster or namespace scoped resources are a poor fit; you need control over the specifics of resource paths. | | You want to reuse [Kubernetes API support features](#common-features). | You don't need those features. | ### Declarative APIs In a Declarative API, typically: - Your API consists of a relatively small number of relatively small objects (resources). - The objects define configuration of applications or infrastructure. - The objects are updated relatively infrequently. - Humans often need to read and write the objects. - The
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/extend-kubernetes/api-extension/custom-resources.md
|
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] | 0.290114
|
### Declarative APIs In a Declarative API, typically: - Your API consists of a relatively small number of relatively small objects (resources). - The objects define configuration of applications or infrastructure. - The objects are updated relatively infrequently. - Humans often need to read and write the objects. - The main operations on the objects are CRUD-y (creating, reading, updating and deleting). - Transactions across objects are not required: the API represents a desired state, not an exact state. Imperative APIs are not declarative. Signs that your API might not be declarative include: - The client says "do this", and then gets a synchronous response back when it is done. - The client says "do this", and then gets an operation ID back, and has to check a separate Operation object to determine completion of the request. - You talk about Remote Procedure Calls (RPCs). - Directly storing large amounts of data; for example, > a few kB per object, or > 1000s of objects. - High bandwidth access (10s of requests per second sustained) needed. - Store end-user data (such as images, PII, etc.) or other large-scale data processed by applications. - The natural operations on the objects are not CRUD-y. - The API is not easily modeled as objects. - You chose to represent pending operations with an operation ID or an operation object. ## Should I use a ConfigMap or a custom resource? Use a ConfigMap if any of the following apply: \* There is an existing, well-documented configuration file format, such as a `mysql.cnf` or `pom.xml`. \* You want to put the entire configuration into one key of a ConfigMap. \* The main use of the configuration file is for a program running in a Pod on your cluster to consume the file to configure itself. \* Consumers of the file prefer to consume via file in a Pod or environment variable in a pod, rather than the Kubernetes API. \* You want to perform rolling updates via Deployment, etc., when the file is updated. {{< note >}} Use a {{< glossary\_tooltip text="Secret" term\_id="secret" >}} for sensitive data, which is similar to a ConfigMap but more secure. {{< /note >}} Use a custom resource (CRD or Aggregated API) if most of the following apply: \* You want to use Kubernetes client libraries and CLIs to create and update the new resource. \* You want top-level support from `kubectl`; for example, `kubectl get my-object object-name`. \* You want to build new automation that watches for updates on the new object, and then CRUD other objects, or vice versa. \* You want to write automation that handles updates to the object. \* You want to use Kubernetes API conventions like `.spec`, `.status`, and `.metadata`. \* You want the object to be an abstraction over a collection of controlled resources, or a summarization of other resources. ## Adding custom resources Kubernetes provides two ways to add custom resources to your cluster: - CRDs are simple and can be created without any programming. - [API Aggregation](/docs/concepts/extend-kubernetes/api-extension/apiserver-aggregation/) requires programming, but allows more control over API behaviors like how data is stored and conversion between API versions. Kubernetes provides these two options to meet the needs of different users, so that neither ease of use nor flexibility is compromised. Aggregated APIs are subordinate API servers that sit behind the primary API server, which acts as a proxy. This arrangement is called [API Aggregation](/docs/concepts/extend-kubernetes/api-extension/apiserver-aggregation/)(AA). To users, the Kubernetes API appears extended. CRDs allow users to create new types of resources without adding another API server. You do not need to understand API Aggregation
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/extend-kubernetes/api-extension/custom-resources.md
|
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|
subordinate API servers that sit behind the primary API server, which acts as a proxy. This arrangement is called [API Aggregation](/docs/concepts/extend-kubernetes/api-extension/apiserver-aggregation/)(AA). To users, the Kubernetes API appears extended. CRDs allow users to create new types of resources without adding another API server. You do not need to understand API Aggregation to use CRDs. Regardless of how they are installed, the new resources are referred to as Custom Resources to distinguish them from built-in Kubernetes resources (like pods). {{< note >}} Avoid using a Custom Resource as data storage for application, end user, or monitoring data: architecture designs that store application data within the Kubernetes API typically represent a design that is too closely coupled. Architecturally, [cloud native](https://www.cncf.io/about/faq/#what-is-cloud-native) application architectures favor loose coupling between components. If part of your workload requires a backing service for its routine operation, run that backing service as a component or consume it as an external service. This way, your workload does not rely on the Kubernetes API for its normal operation. {{< /note >}} ## CustomResourceDefinitions The [CustomResourceDefinition](/docs/tasks/extend-kubernetes/custom-resources/custom-resource-definitions/) API resource allows you to define custom resources. Defining a CRD object creates a new custom resource with a name and schema that you specify. The Kubernetes API serves and handles the storage of your custom resource. The name of the CRD object itself must be a valid [DNS subdomain name](/docs/concepts/overview/working-with-objects/names#dns-subdomain-names) derived from the defined resource name and its API group; see [how to create a CRD](/docs/tasks/extend-kubernetes/custom-resources/custom-resource-definitions#create-a-customresourcedefinition) for more details. Further, the name of an object whose kind/resource is defined by a CRD must also be a valid DNS subdomain name. This frees you from writing your own API server to handle the custom resource, but the generic nature of the implementation means you have less flexibility than with [API server aggregation](#api-server-aggregation). Refer to the [custom controller example](https://github.com/kubernetes/sample-controller) for an example of how to register a new custom resource, work with instances of your new resource type, and use a controller to handle events. ## API server aggregation Usually, each resource in the Kubernetes API requires code that handles REST requests and manages persistent storage of objects. The main Kubernetes API server handles built-in resources like \*pods\* and \*services\*, and can also generically handle custom resources through [CRDs](#customresourcedefinitions). The [aggregation layer](/docs/concepts/extend-kubernetes/api-extension/apiserver-aggregation/) allows you to provide specialized implementations for your custom resources by writing and deploying your own API server. The main API server delegates requests to your API server for the custom resources that you handle, making them available to all of its clients. ## Choosing a method for adding custom resources CRDs are easier to use. Aggregated APIs are more flexible. Choose the method that best meets your needs. Typically, CRDs are a good fit if: \* You have a handful of fields \* You are using the resource within your company, or as part of a small open-source project (as opposed to a commercial product) ### Comparing ease of use CRDs are easier to create than Aggregated APIs. | CRDs | Aggregated API | | --------------------------- | -------------- | | Do not require programming. Users can choose any language for a CRD controller. | Requires programming and building binary and image. | | No additional service to run; CRDs are handled by API server. | An additional service to create and that could fail. | | No ongoing support once the CRD is created. Any bug fixes are picked up as part of normal Kubernetes Master upgrades. | May need to periodically pickup bug fixes from upstream and rebuild and update the Aggregated API server. | | No need to handle multiple versions of your
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/extend-kubernetes/api-extension/custom-resources.md
|
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] | 0.124303
|
| No ongoing support once the CRD is created. Any bug fixes are picked up as part of normal Kubernetes Master upgrades. | May need to periodically pickup bug fixes from upstream and rebuild and update the Aggregated API server. | | No need to handle multiple versions of your API; for example, when you control the client for this resource, you can upgrade it in sync with the API. | You need to handle multiple versions of your API; for example, when developing an extension to share with the world. | ### Advanced features and flexibility Aggregated APIs offer more advanced API features and customization of other features; for example, the storage layer. | Feature | Description | CRDs | Aggregated API | | ------- | ----------- | ---- | -------------- | | Validation | Help users prevent errors and allow you to evolve your API independently of your clients. These features are most useful when there are many clients who can't all update at the same time. | Yes. Most validation can be specified in the CRD using [OpenAPI v3.0 validation](/docs/tasks/extend-kubernetes/custom-resources/custom-resource-definitions/#validation). [CRDValidationRatcheting](/docs/tasks/extend-kubernetes/custom-resources/custom-resource-definitions/#validation-ratcheting) feature gate allows failing validations specified using OpenAPI also can be ignored if the failing part of the resource was unchanged. Any other validations supported by addition of a [Validating Webhook](/docs/reference/access-authn-authz/admission-controllers/#validatingadmissionwebhook-alpha-in-1-8-beta-in-1-9). | Yes, arbitrary validation checks | | Defaulting | See above | Yes, either via [OpenAPI v3.0 validation](/docs/tasks/extend-kubernetes/custom-resources/custom-resource-definitions/#defaulting) `default` keyword (GA in 1.17), or via a [Mutating Webhook](/docs/reference/access-authn-authz/admission-controllers/#mutatingadmissionwebhook) (though this will not be run when reading from etcd for old objects). | Yes | | Multi-versioning | Allows serving the same object through two API versions. Can help ease API changes like renaming fields. Less important if you control your client versions. | [Yes](/docs/tasks/extend-kubernetes/custom-resources/custom-resource-definition-versioning) | Yes | | Custom Storage | If you need storage with a different performance mode (for example, a time-series database instead of key-value store) or isolation for security (for example, encryption of sensitive information, etc.) | No | Yes | | Custom Business Logic | Perform arbitrary checks or actions when creating, reading, updating or deleting an object | Yes, using [Webhooks](/docs/reference/access-authn-authz/extensible-admission-controllers/#admission-webhooks). | Yes | | Scale Subresource | Allows systems like HorizontalPodAutoscaler and PodDisruptionBudget interact with your new resource | [Yes](/docs/tasks/extend-kubernetes/custom-resources/custom-resource-definitions/#scale-subresource) | Yes | | Status Subresource | Allows fine-grained access control where user writes the spec section and the controller writes the status section. Allows incrementing object Generation on custom resource data mutation (requires separate spec and status sections in the resource) | [Yes](/docs/tasks/extend-kubernetes/custom-resources/custom-resource-definitions/#status-subresource) | Yes | | Other Subresources | Add operations other than CRUD, such as "logs" or "exec". | No | Yes | | strategic-merge-patch | The new endpoints support PATCH with `Content-Type: application/strategic-merge-patch+json`. Useful for updating objects that may be modified both locally, and by the server. For more information, see ["Update API Objects in Place Using kubectl patch"](/docs/tasks/manage-kubernetes-objects/update-api-object-kubectl-patch/) | No | Yes | | Protocol Buffers | The new resource supports clients that want to use Protocol Buffers | No | Yes | | OpenAPI Schema | Is there an OpenAPI (swagger) schema for the types that can be dynamically fetched from the server? Is the user protected from misspelling field names by ensuring only allowed fields are set? Are types enforced (in other words, don't put an `int` in a `string` field?) | Yes, based on the [OpenAPI v3.0 validation](/docs/tasks/extend-kubernetes/custom-resources/custom-resource-definitions/#validation) schema (GA in 1.16). | Yes | | Instance Name | Does this extension mechanism impose any constraints on the names of objects whose kind/resource is defined this way? | Yes, such an object's name must be a valid DNS subdomain name.
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/extend-kubernetes/api-extension/custom-resources.md
|
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|
| Yes, based on the [OpenAPI v3.0 validation](/docs/tasks/extend-kubernetes/custom-resources/custom-resource-definitions/#validation) schema (GA in 1.16). | Yes | | Instance Name | Does this extension mechanism impose any constraints on the names of objects whose kind/resource is defined this way? | Yes, such an object's name must be a valid DNS subdomain name. | No | ### Common Features When you create a custom resource, either via a CRD or an AA, you get many features for your API, compared to implementing it outside the Kubernetes platform: | Feature | What it does | | ------- | ------------ | | CRUD | The new endpoints support CRUD basic operations via HTTP and `kubectl` | | Watch | The new endpoints support Kubernetes Watch operations via HTTP | | Discovery | Clients like `kubectl` and dashboard automatically offer list, display, and field edit operations on your resources | | json-patch | The new endpoints support PATCH with `Content-Type: application/json-patch+json` | | merge-patch | The new endpoints support PATCH with `Content-Type: application/merge-patch+json` | | HTTPS | The new endpoints uses HTTPS | | Built-in Authentication | Access to the extension uses the core API server (aggregation layer) for authentication | | Built-in Authorization | Access to the extension can reuse the authorization used by the core API server; for example, RBAC. | | Finalizers | Block deletion of extension resources until external cleanup happens. | | Admission Webhooks | Set default values and validate extension resources during any create/update/delete operation. | | UI/CLI Display | Kubectl, dashboard can display extension resources. | | Unset versus Empty | Clients can distinguish unset fields from zero-valued fields. | | Client Libraries Generation | Kubernetes provides generic client libraries, as well as tools to generate type-specific client libraries. | | Labels and annotations | Common metadata across objects that tools know how to edit for core and custom resources. | ## Preparing to install a custom resource There are several points to be aware of before adding a custom resource to your cluster. ### Third party code and new points of failure While creating a CRD does not automatically add any new points of failure (for example, by causing third party code to run on your API server), packages (for example, Charts) or other installation bundles often include CRDs as well as a Deployment of third-party code that implements the business logic for a new custom resource. Installing an Aggregated API server always involves running a new Deployment. ### Storage Custom resources consume storage space in the same way that ConfigMaps do. Creating too many custom resources may overload your API server's storage space. Custom resources are placed into storage based upon the the current storage version of the resource, defined in the CRD spec. Any update to a custom resource will use the currently defined storage version to store the resource. All other versions either need to have all the fields of that version or define conversions to work properly. Aggregated API servers may use the same storage as the main API server, in which case the same warning applies. ### Authentication, authorization, and auditing CRDs always use the same authentication, authorization, and audit logging as the built-in resources of your API server. If you use RBAC for authorization, most RBAC roles will not grant access to the new resources (except the cluster-admin role or any role created with wildcard rules). You'll need to explicitly grant access to the new resources. CRDs and Aggregated APIs often come bundled with new role definitions for the types they add. Aggregated API servers may or may not use the same
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/extend-kubernetes/api-extension/custom-resources.md
|
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] | 0.124033
|
the new resources (except the cluster-admin role or any role created with wildcard rules). You'll need to explicitly grant access to the new resources. CRDs and Aggregated APIs often come bundled with new role definitions for the types they add. Aggregated API servers may or may not use the same authentication, authorization, and auditing as the primary API server. ## Accessing a custom resource Kubernetes [client libraries](/docs/reference/using-api/client-libraries/) can be used to access custom resources. Not all client libraries support custom resources. The \_Go\_ and \_Python\_ client libraries do. When you add a custom resource, you can access it using: - `kubectl` - The Kubernetes dynamic client. - A REST client that you write. - A client generated using [Kubernetes client generation tools](https://github.com/kubernetes/code-generator) (generating one is an advanced undertaking, but some projects may provide a client along with the CRD or AA). ## Custom resource field selectors [Field Selectors](/docs/concepts/overview/working-with-objects/field-selectors/) let clients select custom resources based on the value of one or more resource fields. All custom resources support the `metadata.name` and `metadata.namespace` field selectors. Fields declared in a {{< glossary\_tooltip term\_id="CustomResourceDefinition" text="CustomResourceDefinition" >}} may also be used with field selectors when included in the `spec.versions[\*].selectableFields` field of the {{< glossary\_tooltip term\_id="CustomResourceDefinition" text="CustomResourceDefinition" >}}. ### Selectable fields for custom resources {#crd-selectable-fields} {{< feature-state feature\_gate\_name="CustomResourceFieldSelectors" >}} The `spec.versions[\*].selectableFields` field of a {{< glossary\_tooltip term\_id="CustomResourceDefinition" text="CustomResourceDefinition" >}} may be used to declare which other fields in a custom resource may be used in field selectors. The following example adds the `.spec.color` and `.spec.size` fields as selectable fields. {{% code\_sample file="customresourcedefinition/shirt-resource-definition.yaml" %}} Field selectors can then be used to get only resources with a `color` of `blue`: ```shell kubectl get shirts.stable.example.com --field-selector spec.color=blue ``` The output should be: ``` NAME COLOR SIZE example1 blue S example2 blue M ``` ## {{% heading "whatsnext" %}} \* Learn how to [Extend the Kubernetes API with the aggregation layer](/docs/concepts/extend-kubernetes/api-extension/apiserver-aggregation/). \* Learn how to [Extend the Kubernetes API with CustomResourceDefinition](/docs/tasks/extend-kubernetes/custom-resources/custom-resource-definitions/).
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/extend-kubernetes/api-extension/custom-resources.md
|
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|
The aggregation layer allows Kubernetes to be extended with additional APIs, beyond what is offered by the core Kubernetes APIs. The additional APIs can either be ready-made solutions such as a [metrics server](https://github.com/kubernetes-sigs/metrics-server), or APIs that you develop yourself. The aggregation layer is different from [Custom Resource Definitions](/docs/concepts/extend-kubernetes/api-extension/custom-resources/), which are a way to make the {{< glossary\_tooltip term\_id="kube-apiserver" text="kube-apiserver" >}} recognise new kinds of object. ## Aggregation layer The aggregation layer runs in-process with the kube-apiserver. Until an extension resource is registered, the aggregation layer will do nothing. To register an API, you add an \_APIService\_ object, which "claims" the URL path in the Kubernetes API. At that point, the aggregation layer will proxy anything sent to that API path (e.g. `/apis/myextension.mycompany.io/v1/…`) to the registered APIService. The most common way to implement the APIService is to run an \*extension API server\* in Pod(s) that run in your cluster. If you're using the extension API server to manage resources in your cluster, the extension API server (also written as "extension-apiserver") is typically paired with one or more {{< glossary\_tooltip text="controllers" term\_id="controller" >}}. The apiserver-builder library provides a skeleton for both extension API servers and the associated controller(s). ### Response latency Extension API servers should have low latency networking to and from the kube-apiserver. Discovery requests are required to round-trip from the kube-apiserver in five seconds or less. If your extension API server cannot achieve that latency requirement, consider making changes that let you meet it. ## {{% heading "whatsnext" %}} \* To get the aggregator working in your environment, [configure the aggregation layer](/docs/tasks/extend-kubernetes/configure-aggregation-layer/). \* Then, [setup an extension api-server](/docs/tasks/extend-kubernetes/setup-extension-api-server/) to work with the aggregation layer. \* Read about [APIService](/docs/reference/kubernetes-api/cluster-resources/api-service-v1/) in the API reference \* Learn about [Declarative Validation Concepts](/docs/reference/using-api/declarative-validation.md), an internal mechanism for defining validation rules that in the future will help support validation for extension API server development. Alternatively: learn how to [extend the Kubernetes API using Custom Resource Definitions](/docs/tasks/extend-kubernetes/custom-resources/custom-resource-definitions/).
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/extend-kubernetes/api-extension/apiserver-aggregation.md
|
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] | 0.171218
|
Custom resources are extensions of the Kubernetes API. Kubernetes provides two ways to add custom resources to your cluster: - The [CustomResourceDefinition](/docs/concepts/extend-kubernetes/api-extension/custom-resources/) (CRD) mechanism allows you to declaratively define a new custom API with an API group, kind, and schema that you specify. The Kubernetes control plane serves and handles the storage of your custom resource. CRDs allow you to create new types of resources for your cluster without writing and running a custom API server. - The [aggregation layer](/docs/concepts/extend-kubernetes/api-extension/apiserver-aggregation/) sits behind the primary API server, which acts as a proxy. This arrangement is called API Aggregation (AA), which allows you to provide specialized implementations for your custom resources by writing and deploying your own API server. The main API server delegates requests to your API server for the custom APIs that you specify, making them available to all of its clients.
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/extend-kubernetes/api-extension/_index.md
|
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A container image represents binary data that encapsulates an application and all its software dependencies. Container images are executable software bundles that can run standalone and that make very well-defined assumptions about their runtime environment. You typically create a container image of your application and push it to a registry before referring to it in a {{< glossary\_tooltip text="Pod" term\_id="pod" >}}. This page provides an outline of the container image concept. {{< note >}} If you are looking for the container images for a Kubernetes release (such as v{{< skew latestVersion >}}, the latest minor release), visit [Download Kubernetes](https://kubernetes.io/releases/download/). {{< /note >}} ## Image names Container images are usually given a name such as `pause`, `example/mycontainer`, or `kube-apiserver`. Images can also include a registry hostname; for example: `fictional.registry.example/imagename`, and possibly a port number as well; for example: `fictional.registry.example:10443/imagename`. If you don't specify a registry hostname, Kubernetes assumes that you mean the [Docker public registry](https://hub.docker.com/). You can change this behavior by setting a default image registry in the [container runtime](/docs/setup/production-environment/container-runtimes/) configuration. After the image name part you can add a \_tag\_ or \_digest\_ (in the same way you would when using with commands like `docker` or `podman`). Tags let you identify different versions of the same series of images. Digests are a unique identifier for a specific version of an image. Digests are hashes of the image's content, and are immutable. Tags can be moved to point to different images, but digests are fixed. Image tags consist of lowercase and uppercase letters, digits, underscores (`\_`), periods (`.`), and dashes (`-`). A tag can be up to 128 characters long, and must conform to the following regex pattern: `[a-zA-Z0-9\_][a-zA-Z0-9.\_-]{0,127}`. You can read more about it and find the validation regex in the [OCI Distribution Specification](https://github.com/opencontainers/distribution-spec/blob/master/spec.md#workflow-categories). If you don't specify a tag, Kubernetes assumes you mean the tag `latest`. Image digests consists of a hash algorithm (such as `sha256`) and a hash value. For example: `sha256:1ff6c18fbef2045af6b9c16bf034cc421a29027b800e4f9b68ae9b1cb3e9ae07`. You can find more information about the digest format in the [OCI Image Specification](https://github.com/opencontainers/image-spec/blob/master/descriptor.md#digests). Some image name examples that Kubernetes can use are: - `busybox` — Image name only, no tag or digest. Kubernetes will use the Docker public registry and latest tag. Equivalent to `docker.io/library/busybox:latest`. - `busybox:1.32.0` — Image name with tag. Kubernetes will use the Docker public registry. Equivalent to `docker.io/library/busybox:1.32.0`. - `registry.k8s.io/pause:latest` — Image name with a custom registry and latest tag. - `registry.k8s.io/pause:3.5` — Image name with a custom registry and non-latest tag. - `registry.k8s.io/pause@sha256:1ff6c18fbef2045af6b9c16bf034cc421a29027b800e4f9b68ae9b1cb3e9ae07` — Image name with digest. - `registry.k8s.io/pause:3.5@sha256:1ff6c18fbef2045af6b9c16bf034cc421a29027b800e4f9b68ae9b1cb3e9ae07` — Image name with tag and digest. Only the digest will be used for pulling. ## Updating images When you first create a {{< glossary\_tooltip text="Deployment" term\_id="deployment" >}}, {{< glossary\_tooltip text="StatefulSet" term\_id="statefulset" >}}, Pod, or other object that includes a PodTemplate, and a pull policy was not explicitly specified, then by default the pull policy of all containers in that Pod will be set to `IfNotPresent`. This policy causes the {{< glossary\_tooltip text="kubelet" term\_id="kubelet" >}} to skip pulling an image if it already exists. ### Image pull policy The `imagePullPolicy` for a container and the tag of the image both affect \_when\_ the [kubelet](/docs/reference/command-line-tools-reference/kubelet/) attempts to pull (download) the specified image. Here's a list of the values you can set for `imagePullPolicy` and the effects these values have: `IfNotPresent` : the image is pulled only if it is not already present locally. `Always` : every time the kubelet launches a container, the kubelet queries the container image registry to resolve the name to an image [digest](https://docs.docker.com/engine/reference/commandline/pull/#pull-an-image-by-digest-immutable-identifier). If the kubelet has a container image with that exact digest cached locally, the kubelet
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/containers/images.md
|
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] | 0.147546
|
image is pulled only if it is not already present locally. `Always` : every time the kubelet launches a container, the kubelet queries the container image registry to resolve the name to an image [digest](https://docs.docker.com/engine/reference/commandline/pull/#pull-an-image-by-digest-immutable-identifier). If the kubelet has a container image with that exact digest cached locally, the kubelet uses its cached image; otherwise, the kubelet pulls the image with the resolved digest, and uses that image to launch the container. `Never` : the kubelet does not try fetching the image. If the image is somehow already present locally, the kubelet attempts to start the container; otherwise, startup fails. See [pre-pulled images](#pre-pulled-images) for more details. The caching semantics of the underlying image provider make even `imagePullPolicy: Always` efficient, as long as the registry is reliably accessible. Your container runtime can notice that the image layers already exist on the node so that they don't need to be downloaded again. {{< note >}} You should avoid using the `:latest` tag when deploying containers in production as it is harder to track which version of the image is running and more difficult to roll back properly. Instead, specify a meaningful tag such as `v1.42.0` and/or a digest. {{< /note >}} To make sure the Pod always uses the same version of a container image, you can specify the image's digest; replace `:` with `@` (for example, `image@sha256:45b23dee08af5e43a7fea6c4cf9c25ccf269ee113168c19722f87876677c5cb2`). When using image tags, if the image registry were to change the code that the tag on that image represents, you might end up with a mix of Pods running the old and new code. An image digest uniquely identifies a specific version of the image, so Kubernetes runs the same code every time it starts a container with that image name and digest specified. Specifying an image by digest pins the code that you run so that a change at the registry cannot lead to that mix of versions. There are third-party [admission controllers](/docs/reference/access-authn-authz/admission-controllers/) that mutate Pods (and PodTemplates) when they are created, so that the running workload is defined based on an image digest rather than a tag. That might be useful if you want to make sure that your entire workload is running the same code no matter what tag changes happen at the registry. #### Default image pull policy {#imagepullpolicy-defaulting} When you (or a controller) submit a new Pod to the API server, your cluster sets the `imagePullPolicy` field when specific conditions are met: - if you omit the `imagePullPolicy` field, and you specify the digest for the container image, the `imagePullPolicy` is automatically set to `IfNotPresent`. - if you omit the `imagePullPolicy` field, and the tag for the container image is `:latest`, `imagePullPolicy` is automatically set to `Always`. - if you omit the `imagePullPolicy` field, and you don't specify the tag for the container image, `imagePullPolicy` is automatically set to `Always`. - if you omit the `imagePullPolicy` field, and you specify a tag for the container image that isn't `:latest`, the `imagePullPolicy` is automatically set to `IfNotPresent`. {{< note >}} The value of `imagePullPolicy` of the container is always set when the object is first \_created\_, and is not updated if the image's tag or digest later changes. For example, if you create a Deployment with an image whose tag is \_not\_ `:latest`, and later update that Deployment's image to a `:latest` tag, the `imagePullPolicy` field will \_not\_ change to `Always`. You must manually change the pull policy of any object after its initial creation. {{< /note >}} #### Required image pull If you would like to always force a pull, you can do one of the following: -
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/containers/images.md
|
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] | 0.048842
|
to a `:latest` tag, the `imagePullPolicy` field will \_not\_ change to `Always`. You must manually change the pull policy of any object after its initial creation. {{< /note >}} #### Required image pull If you would like to always force a pull, you can do one of the following: - Set the `imagePullPolicy` of the container to `Always`. - Omit the `imagePullPolicy` and use `:latest` as the tag for the image to use; Kubernetes will set the policy to `Always` when you submit the Pod. - Omit the `imagePullPolicy` and the tag for the image to use; Kubernetes will set the policy to `Always` when you submit the Pod. - Enable the [AlwaysPullImages](/docs/reference/access-authn-authz/admission-controllers/#alwayspullimages) admission controller. ### ImagePullBackOff When a kubelet starts creating containers for a Pod using a container runtime, it might be possible the container is in [Waiting](/docs/concepts/workloads/pods/pod-lifecycle/#container-state-waiting) state because of `ImagePullBackOff`. The status `ImagePullBackOff` means that a container could not start because Kubernetes could not pull a container image (for reasons such as invalid image name, or pulling from a private registry without `imagePullSecret`). The `BackOff` part indicates that Kubernetes will keep trying to pull the image, with an increasing back-off delay. Kubernetes raises the delay between each attempt until it reaches a compiled-in limit, which is 300 seconds (5 minutes). ### Image pull per runtime class {{< feature-state feature\_gate\_name="RuntimeClassInImageCriApi" >}} Kubernetes includes alpha support for performing image pulls based on the RuntimeClass of a Pod. If you enable the `RuntimeClassInImageCriApi` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/), the kubelet references container images by a tuple of image name and runtime handler rather than just the image name or digest. Your {{< glossary\_tooltip text="container runtime" term\_id="container-runtime" >}} may adapt its behavior based on the selected runtime handler. Pulling images based on runtime class is useful for VM-based containers, such as Windows Hyper-V containers. ## Serial and parallel image pulls By default, the kubelet pulls images serially. In other words, the kubelet sends only one image pull request to the image service at a time. Other image pull requests have to wait until the one being processed is complete. Nodes make image pull decisions in isolation. Even when you use serialized image pulls, two different nodes can pull the same image in parallel. If you would like to enable parallel image pulls, you can set the field `serializeImagePulls` to false in the [kubelet configuration](/docs/reference/config-api/kubelet-config.v1beta1/). With `serializeImagePulls` set to false, image pull requests will be sent to the image service immediately, and multiple images will be pulled at the same time. When enabling parallel image pulls, ensure that the image service of your container runtime can handle parallel image pulls. The kubelet never pulls multiple images in parallel on behalf of one Pod. For example, if you have a Pod that has an init container and an application container, the image pulls for the two containers will not be parallelized. However, if you have two Pods that use different images, and the parallel image pull feature is enabled, the kubelet will pull the images in parallel on behalf of the two different Pods. ### Maximum parallel image pulls {{< feature-state for\_k8s\_version="v1.35" state="stable" >}} When `serializeImagePulls` is set to false, the kubelet defaults to no limit on the maximum number of images being pulled at the same time. If you would like to limit the number of parallel image pulls, you can set the field `maxParallelImagePulls` in the kubelet configuration. With `maxParallelImagePulls` set to \_n\_, only \_n\_ images can be pulled at the same time, and any image pull beyond \_n\_ will have to wait until at least one ongoing image pull is complete. Limiting the
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/containers/images.md
|
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|
of parallel image pulls, you can set the field `maxParallelImagePulls` in the kubelet configuration. With `maxParallelImagePulls` set to \_n\_, only \_n\_ images can be pulled at the same time, and any image pull beyond \_n\_ will have to wait until at least one ongoing image pull is complete. Limiting the number of parallel image pulls prevents image pulling from consuming too much network bandwidth or disk I/O, when parallel image pulling is enabled. You can set `maxParallelImagePulls` to a positive number that is greater than or equal to 1. If you set `maxParallelImagePulls` to be greater than or equal to 2, you must set `serializeImagePulls` to false. The kubelet will fail to start with an invalid `maxParallelImagePulls` setting. ## Multi-architecture images with image indexes As well as providing binary images, a container registry can also serve a [container image index](https://github.com/opencontainers/image-spec/blob/master/image-index.md). An image index can point to multiple [image manifests](https://github.com/opencontainers/image-spec/blob/master/manifest.md) for architecture-specific versions of a container. The idea is that you can have a name for an image (for example: `pause`, `example/mycontainer`, `kube-apiserver`) and allow different systems to fetch the right binary image for the machine architecture they are using. The Kubernetes project typically creates container images for its releases with names that include the suffix `-$(ARCH)`. For backward compatibility, generate older images with suffixes. For instance, an image named as `pause` would be a multi-architecture image containing manifests for all supported architectures, while `pause-amd64` would be a backward-compatible version for older configurations, or for YAML files with hardcoded image names containing suffixes. ## Using a private registry Private registries may require authentication to be able to discover and/or pull images from them. Credentials can be provided in several ways: - [Specifying `imagePullSecrets` when you define a Pod](#specifying-imagepullsecrets-on-a-pod) Only Pods which provide their own keys can access the private registry. - [Configuring Nodes to Authenticate to a Private Registry](#configuring-nodes-to-authenticate-to-a-private-registry) - All Pods can read any configured private registries. - Requires node configuration by cluster administrator. - Using a \_kubelet credential provider\_ plugin to [dynamically fetch credentials for private registries](#kubelet-credential-provider) The kubelet can be configured to use credential provider exec plugin for the respective private registry. - [Pre-pulled Images](#pre-pulled-images) - All Pods can use any images cached on a node. - Requires root access to all nodes to set up. - Vendor-specific or local extensions If you're using a custom node configuration, you (or your cloud provider) can implement your mechanism for authenticating the node to the container registry. These options are explained in more detail below. ### Specifying `imagePullSecrets` on a Pod {{< note >}} This is the recommended approach to run containers based on images in private registries. {{< /note >}} Kubernetes supports specifying container image registry keys on a Pod. All `imagePullSecrets` must be Secrets that exist in the same {{< glossary\_tooltip term\_id="namespace" >}} as the Pod. These Secrets must be of type `kubernetes.io/dockercfg` or `kubernetes.io/dockerconfigjson`. ### Configuring nodes to authenticate to a private registry Specific instructions for setting credentials depends on the container runtime and registry you chose to use. You should refer to your solution's documentation for the most accurate information. For an example of configuring a private container image registry, see the [Pull an Image from a Private Registry](/docs/tasks/configure-pod-container/pull-image-private-registry) task. That example uses a private registry in Docker Hub. ### Kubelet credential provider for authenticated image pulls {#kubelet-credential-provider} You can configure the kubelet to invoke a plugin binary to dynamically fetch registry credentials for a container image. This is the most robust and versatile way to fetch credentials for private registries, but also requires kubelet-level configuration to enable. This technique can be especially useful for running
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/containers/images.md
|
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pulls {#kubelet-credential-provider} You can configure the kubelet to invoke a plugin binary to dynamically fetch registry credentials for a container image. This is the most robust and versatile way to fetch credentials for private registries, but also requires kubelet-level configuration to enable. This technique can be especially useful for running {{< glossary\_tooltip term\_id="static-pod" text="static Pods" >}} that require container images hosted in a private registry. Using a {{< glossary\_tooltip term\_id="service-account" >}} or a {{< glossary\_tooltip term\_id="secret" >}} to provide private registry credentials is not possible in the specification of a static Pod, because it \_cannot\_ have references to other API resources in its specification. See [Configure a kubelet image credential provider](/docs/tasks/administer-cluster/kubelet-credential-provider/) for more details. ### Interpretation of config.json {#config-json} The interpretation of `config.json` varies between the original Docker implementation and the Kubernetes interpretation. In Docker, the `auths` keys can only specify root URLs, whereas Kubernetes allows glob URLs as well as prefix-matched paths. The only limitation is that glob patterns (`\*`) have to include the dot (`.`) for each subdomain. The amount of matched subdomains has to be equal to the amount of glob patterns (`\*.`), for example: - `\*.kubernetes.io` will \*not\* match `kubernetes.io`, but will match `abc.kubernetes.io`. - `\*.\*.kubernetes.io` will \*not\* match `abc.kubernetes.io`, but will match `abc.def.kubernetes.io`. - `prefix.\*.io` will match `prefix.kubernetes.io`. - `\*-good.kubernetes.io` will match `prefix-good.kubernetes.io`. This means that a `config.json` like this is valid: ```json { "auths": { "my-registry.example/images": { "auth": "…" }, "\*.my-registry.example/images": { "auth": "…" } } } ``` Image pull operations pass the credentials to the CRI container runtime for every valid pattern. For example, the following container image names would match successfully: - `my-registry.example/images` - `my-registry.example/images/my-image` - `my-registry.example/images/another-image` - `sub.my-registry.example/images/my-image` However, these container image names would \*not\* match: - `a.sub.my-registry.example/images/my-image` - `a.b.sub.my-registry.example/images/my-image` The kubelet performs image pulls sequentially for every found credential. This means that multiple entries in `config.json` for different paths are possible, too: ```json { "auths": { "my-registry.example/images": { "auth": "…" }, "my-registry.example/images/subpath": { "auth": "…" } } } ``` If now a container specifies an image `my-registry.example/images/subpath/my-image` to be pulled, then the kubelet will try to download it using both authentication sources if one of them fails. ### Pre-pulled images {{< note >}} This approach is suitable if you can control node configuration. It will not work reliably if your cloud provider manages nodes and replaces them automatically. {{< /note >}} By default, the kubelet tries to pull each image from the specified registry. However, if the `imagePullPolicy` property of the container is set to `IfNotPresent` or `Never`, then a local image is used (preferentially or exclusively, respectively). If you want to rely on pre-pulled images as a substitute for registry authentication, you must ensure all nodes in the cluster have the same pre-pulled images. This can be used to preload certain images for speed or as an alternative to authenticating to a private registry. Similar to the usage of the [kubelet credential provider](#kubelet-credential-provider), pre-pulled images are also suitable for launching {{< glossary\_tooltip text="static Pods" term\_id="static-pod" >}} that depend on images hosted in a private registry. {{< note >}} {{< feature-state feature\_gate\_name="KubeletEnsureSecretPulledImages" >}} Access to pre-pulled images may be authorized according to [image pull credential verification](#ensureimagepullcredentialverification). {{< /note >}} ### Ensure image pull credential verification {#ensureimagepullcredentialverification} {{< feature-state feature\_gate\_name="KubeletEnsureSecretPulledImages" >}} If the `KubeletEnsureSecretPulledImages` feature gate is enabled for your cluster, Kubernetes will validate image credentials for every image that requires credentials to be pulled, even if that image is already present on the node. This validation ensures that images in a Pod request which have not been successfully pulled with the provided credentials must re-pull the images from the registry.
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/containers/images.md
|
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Kubernetes will validate image credentials for every image that requires credentials to be pulled, even if that image is already present on the node. This validation ensures that images in a Pod request which have not been successfully pulled with the provided credentials must re-pull the images from the registry. Additionally, image pulls that re-use the same credentials which previously resulted in a successful image pull will not need to re-pull from the registry and are instead validated locally without accessing the registry (provided the image is available locally). This is controlled by the`imagePullCredentialsVerificationPolicy` field in the [Kubelet configuration](/docs/reference/config-api/kubelet-config.v1beta1/#kubelet-config-k8s-io-v1beta1-ImagePullCredentialsVerificationPolicy). This configuration controls when image pull credentials must be verified if the image is already present on the node: \* `NeverVerify`: Mimics the behavior of having this feature gate disabled. If the image is present locally, image pull credentials are not verified. \* `NeverVerifyPreloadedImages`: Images pulled outside the kubelet are not verified, but all other images will have their credentials verified. This is the default behavior. \* `NeverVerifyAllowListedImages`: Images pulled outside the kubelet and mentioned within the `preloadedImagesVerificationAllowlist` specified in the kubelet config are not verified. \* `AlwaysVerify`: All images will have their credentials verified before they can be used. This verification applies to [pre-pulled images](#pre-pulled-images), images pulled using node-wide secrets, and images pulled using Pod-level secrets. {{< note >}} In the case of credential rotation, the credentials previously used to pull the image will continue to verify without the need to access the registry. New or rotated credentials will require the image to be re-pulled from the registry. {{< /note >}} #### Enabling `KubeletEnsureSecretPulledImages` for the first time When the `KubeletEnsureSecretPulledImages` gets enabled for the first time, either by a kubelet upgrade or by explicitly enabling the feature, if a kubelet is able to access any images at that time, these will all be considered pre-pulled. This happens because in this case the kubelet has no records about the images being pulled. The kubelet will only be able to start making image pull records as any image gets pulled for the first time. If this is a concern, it is advised to clean up nodes of all images that should not be considered pre-pulled before enabling the feature. Note that removing the directory holding the image pulled records will have the same effect on kubelet restart, particularly the images currently cached in the nodes by the container runtime will all be considered pre-pulled. ### Creating a Secret with a Docker config You need to know the username, registry password and client email address for authenticating to the registry, as well as its hostname. Run the following command, substituting placeholders with the appropriate values: ```shell kubectl create secret docker-registry \ --docker-server= \ --docker-username= \ --docker-password= \ --docker-email= ``` If you already have a Docker credentials file then, rather than using the above command, you can import the credentials file as a Kubernetes {{< glossary\_tooltip text="Secret" term\_id="secret" >}}. [Create a Secret based on existing Docker credentials](/docs/tasks/configure-pod-container/pull-image-private-registry/#registry-secret-existing-credentials) explains how to set this up. This is particularly useful if you are using multiple private container registries, as `kubectl create secret docker-registry` creates a Secret that only works with a single private registry. {{< note >}} Pods can only reference image pull secrets in their own namespace, so this process needs to be done one time per namespace. {{< /note >}} #### Referring to `imagePullSecrets` on a Pod Now, you can create pods which reference that secret by adding the `imagePullSecrets` section to a Pod definition. Each item in the `imagePullSecrets` array can only reference one Secret in the same namespace. For example: ```shell cat
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/containers/images.md
|
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] | 0.060425
|
time per namespace. {{< /note >}} #### Referring to `imagePullSecrets` on a Pod Now, you can create pods which reference that secret by adding the `imagePullSecrets` section to a Pod definition. Each item in the `imagePullSecrets` array can only reference one Secret in the same namespace. For example: ```shell cat < pod.yaml apiVersion: v1 kind: Pod metadata: name: foo namespace: awesomeapps spec: containers: - name: foo image: janedoe/awesomeapp:v1 imagePullSecrets: - name: myregistrykey EOF cat <> ./kustomization.yaml resources: - pod.yaml EOF ``` This needs to be done for each Pod that is using a private registry. However, you can automate this process by specifying the `imagePullSecrets` section in a [ServiceAccount](/docs/tasks/configure-pod-container/configure-service-account/) resource. See [Add ImagePullSecrets to a Service Account](/docs/tasks/configure-pod-container/configure-service-account/#add-imagepullsecrets-to-a-service-account) for detailed instructions. You can use this in conjunction with a per-node `.docker/config.json`. The credentials will be merged. ### Use cases There are a number of solutions for configuring private registries. Here are some common use cases and suggested solutions. 1. Cluster running only non-proprietary (e.g. open-source) images. No need to hide images. - Use public images from a public registry - No configuration required. - Some cloud providers automatically cache or mirror public images, which improves availability and reduces the time to pull images. 1. Cluster running some proprietary images which should be hidden to those outside the company, but visible to all cluster users. - Use a hosted private registry - Manual configuration may be required on the nodes that need to access to private registry. - Or, run an internal private registry behind your firewall with open read access. - No Kubernetes configuration is required. - Use a hosted container image registry service that controls image access - It will work better with Node autoscaling than manual node configuration. - Or, on a cluster where changing the node configuration is inconvenient, use `imagePullSecrets`. 1. Cluster with proprietary images, a few of which require stricter access control. - Ensure [AlwaysPullImages admission controller](/docs/reference/access-authn-authz/admission-controllers/#alwayspullimages) is active. Otherwise, all Pods potentially have access to all images. - Move sensitive data into a Secret resource, instead of packaging it in an image. 1. A multi-tenant cluster where each tenant needs own private registry. - Ensure [AlwaysPullImages admission controller](/docs/reference/access-authn-authz/admission-controllers/#alwayspullimages) is active. Otherwise, all Pods of all tenants potentially have access to all images. - Run a private registry with authorization required. - Generate registry credentials for each tenant, store into a Secret, and propagate the Secret to every tenant namespace. - The tenant then adds that Secret to `imagePullSecrets` of each namespace. If you need access to multiple registries, you can create one Secret per registry. ## Legacy built-in kubelet credential provider In older versions of Kubernetes, the kubelet had a direct integration with cloud provider credentials. This provided the ability to dynamically fetch credentials for image registries. There were three built-in implementations of the kubelet credential provider integration: ACR (Azure Container Registry), ECR (Elastic Container Registry), and GCR (Google Container Registry). Starting with version 1.26 of Kubernetes, the legacy mechanism has been removed, so you would need to either: - configure a kubelet image credential provider on each node; or - specify image pull credentials using `imagePullSecrets` and at least one Secret. ## {{% heading "whatsnext" %}} \* Read the [OCI Image Manifest Specification](https://github.com/opencontainers/image-spec/blob/main/manifest.md). \* Learn about [container image garbage collection](/docs/concepts/architecture/garbage-collection/#container-image-garbage-collection). \* Learn more about [pulling an Image from a Private Registry](/docs/tasks/configure-pod-container/pull-image-private-registry).
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/containers/images.md
|
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] | 0.121544
|
[pulling an Image from a Private Registry](/docs/tasks/configure-pod-container/pull-image-private-registry).
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/containers/images.md
|
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|
{{< feature-state for\_k8s\_version="v1.20" state="stable" >}} This page describes the RuntimeClass resource and runtime selection mechanism. RuntimeClass is a feature for selecting the container runtime configuration. The container runtime configuration is used to run a Pod's containers. ## Motivation You can set a different RuntimeClass between different Pods to provide a balance of performance versus security. For example, if part of your workload deserves a high level of information security assurance, you might choose to schedule those Pods so that they run in a container runtime that uses hardware virtualization. You'd then benefit from the extra isolation of the alternative runtime, at the expense of some additional overhead. You can also use RuntimeClass to run different Pods with the same container runtime but with different settings. ## Setup 1. Configure the CRI implementation on nodes (runtime dependent) 2. Create the corresponding RuntimeClass resources ### 1. Configure the CRI implementation on nodes The configurations available through RuntimeClass are Container Runtime Interface (CRI) implementation dependent. See the corresponding documentation ([below](#cri-configuration)) for your CRI implementation for how to configure. {{< note >}} RuntimeClass assumes a homogeneous node configuration across the cluster by default (which means that all nodes are configured the same way with respect to container runtimes). To support heterogeneous node configurations, see [Scheduling](#scheduling) below. {{< /note >}} The configurations have a corresponding `handler` name, referenced by the RuntimeClass. The handler must be a valid [DNS label name](/docs/concepts/overview/working-with-objects/names/#dns-label-names). ### 2. Create the corresponding RuntimeClass resources The configurations setup in step 1 should each have an associated `handler` name, which identifies the configuration. For each handler, create a corresponding RuntimeClass object. The RuntimeClass resource currently only has 2 significant fields: the RuntimeClass name (`metadata.name`) and the handler (`handler`). The object definition looks like this: ```yaml # RuntimeClass is defined in the node.k8s.io API group apiVersion: node.k8s.io/v1 kind: RuntimeClass metadata: # The name the RuntimeClass will be referenced by. # RuntimeClass is a non-namespaced resource. name: myclass # The name of the corresponding CRI configuration handler: myconfiguration ``` The name of a RuntimeClass object must be a valid [DNS subdomain name](/docs/concepts/overview/working-with-objects/names#dns-subdomain-names). {{< note >}} It is recommended that RuntimeClass write operations (create/update/patch/delete) be restricted to the cluster administrator. This is typically the default. See [Authorization Overview](/docs/reference/access-authn-authz/authorization/) for more details. {{< /note >}} ## Usage Once RuntimeClasses are configured for the cluster, you can specify a `runtimeClassName` in the Pod spec to use it. For example: ```yaml apiVersion: v1 kind: Pod metadata: name: mypod spec: runtimeClassName: myclass # ... ``` This will instruct the kubelet to use the named RuntimeClass to run this pod. If the named RuntimeClass does not exist, or the CRI cannot run the corresponding handler, the pod will enter the `Failed` terminal [phase](/docs/concepts/workloads/pods/pod-lifecycle/#pod-phase). Look for a corresponding [event](/docs/tasks/debug/debug-application/debug-running-pod/) for an error message. If no `runtimeClassName` is specified, the default RuntimeHandler will be used, which is equivalent to the behavior when the RuntimeClass feature is disabled. ### CRI Configuration For more details on setting up CRI runtimes, see [CRI installation](/docs/setup/production-environment/container-runtimes/). #### {{< glossary\_tooltip term\_id="containerd" >}} Runtime handlers are configured through containerd's configuration at `/etc/containerd/config.toml`. Valid handlers are configured under the runtimes section: ``` [plugins."io.containerd.grpc.v1.cri".containerd.runtimes.${HANDLER\_NAME}] ``` See containerd's [config documentation](https://github.com/containerd/containerd/blob/main/docs/cri/config.md) for more details: #### {{< glossary\_tooltip term\_id="cri-o" >}} Runtime handlers are configured through CRI-O's configuration at `/etc/crio/crio.conf`. Valid handlers are configured under the [crio.runtime table](https://github.com/cri-o/cri-o/blob/master/docs/crio.conf.5.md#crioruntime-table): ``` [crio.runtime.runtimes.${HANDLER\_NAME}] runtime\_path = "${PATH\_TO\_BINARY}" ``` See CRI-O's [config documentation](https://github.com/cri-o/cri-o/blob/master/docs/crio.conf.5.md) for more details. ## Scheduling {{< feature-state for\_k8s\_version="v1.16" state="beta" >}} By specifying the `scheduling` field for a RuntimeClass, you can set constraints to ensure that Pods running with this RuntimeClass are scheduled to nodes that support it. If `scheduling`
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/containers/runtime-class.md
|
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|
table](https://github.com/cri-o/cri-o/blob/master/docs/crio.conf.5.md#crioruntime-table): ``` [crio.runtime.runtimes.${HANDLER\_NAME}] runtime\_path = "${PATH\_TO\_BINARY}" ``` See CRI-O's [config documentation](https://github.com/cri-o/cri-o/blob/master/docs/crio.conf.5.md) for more details. ## Scheduling {{< feature-state for\_k8s\_version="v1.16" state="beta" >}} By specifying the `scheduling` field for a RuntimeClass, you can set constraints to ensure that Pods running with this RuntimeClass are scheduled to nodes that support it. If `scheduling` is not set, this RuntimeClass is assumed to be supported by all nodes. To ensure pods land on nodes supporting a specific RuntimeClass, that set of nodes should have a common label which is then selected by the `runtimeclass.scheduling.nodeSelector` field. The RuntimeClass's nodeSelector is merged with the pod's nodeSelector in admission, effectively taking the intersection of the set of nodes selected by each. If there is a conflict, the pod will be rejected. If the supported nodes are tainted to prevent other RuntimeClass pods from running on the node, you can add `tolerations` to the RuntimeClass. As with the `nodeSelector`, the tolerations are merged with the pod's tolerations in admission, effectively taking the union of the set of nodes tolerated by each. To learn more about configuring the node selector and tolerations, see [Assigning Pods to Nodes](/docs/concepts/scheduling-eviction/assign-pod-node/). ### Pod Overhead {{< feature-state for\_k8s\_version="v1.24" state="stable" >}} You can specify \_overhead\_ resources that are associated with running a Pod. Declaring overhead allows the cluster (including the scheduler) to account for it when making decisions about Pods and resources. Pod overhead is defined in RuntimeClass through the `overhead` field. Through the use of this field, you can specify the overhead of running pods utilizing this RuntimeClass and ensure these overheads are accounted for in Kubernetes. ## {{% heading "whatsnext" %}} - [RuntimeClass Design](https://github.com/kubernetes/enhancements/blob/master/keps/sig-node/585-runtime-class/README.md) - [RuntimeClass Scheduling Design](https://github.com/kubernetes/enhancements/blob/master/keps/sig-node/585-runtime-class/README.md#runtimeclass-scheduling) - Read about the [Pod Overhead](/docs/concepts/scheduling-eviction/pod-overhead/) concept - [PodOverhead Feature Design](https://github.com/kubernetes/enhancements/tree/master/keps/sig-node/688-pod-overhead)
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/containers/runtime-class.md
|
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] | 0.252191
|
This page describes the resources available to Containers in the Container environment. ## Container environment The Kubernetes Container environment provides several important resources to Containers: \* A filesystem, which is a combination of an [image](/docs/concepts/containers/images/) and one or more [volumes](/docs/concepts/storage/volumes/). \* Information about the Container itself. \* Information about other objects in the cluster. ### Container information The \*hostname\* of a Container is the name of the Pod in which the Container is running. It is available through the `hostname` command or the [`gethostname`](https://man7.org/linux/man-pages/man2/gethostname.2.html) function call in libc. The Pod name and namespace are available as environment variables through the [downward API](/docs/tasks/inject-data-application/downward-api-volume-expose-pod-information/). User defined environment variables from the Pod definition are also available to the Container, as are any environment variables specified statically in the container image. ### Cluster information A list of all services that were running when a Container was created is available to that Container as environment variables. This list is limited to services within the same namespace as the new Container's Pod and Kubernetes control plane services. For a service named \*foo\* that maps to a Container named \*bar\*, the following variables are defined: ```shell FOO\_SERVICE\_HOST= FOO\_SERVICE\_PORT= ``` Services have dedicated IP addresses and are available to the Container via DNS, if [DNS addon](https://releases.k8s.io/v{{< skew currentPatchVersion >}}/cluster/addons/dns/) is enabled. ## {{% heading "whatsnext" %}} \* Learn more about [Container lifecycle hooks](/docs/concepts/containers/container-lifecycle-hooks/). \* Get hands-on experience [attaching handlers to Container lifecycle events](/docs/tasks/configure-pod-container/attach-handler-lifecycle-event/).
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/containers/container-environment.md
|
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|
This page describes how kubelet managed Containers can use the Container lifecycle hook framework to run code triggered by events during their management lifecycle. ## Overview Analogous to many programming language frameworks that have component lifecycle hooks, such as Angular, Kubernetes provides Containers with lifecycle hooks. The hooks enable Containers to be aware of events in their management lifecycle and run code implemented in a handler when the corresponding lifecycle hook is executed. ## Container hooks There are two hooks that are exposed to Containers: `PostStart` This hook is executed immediately after a container is created. It is unspecified whether the hook will execute before or after the container ENTRYPOINT. No parameters are passed to the handler. `PreStop` This hook is called immediately before a container is terminated due to an API request or management event such as a liveness/startup probe failure, preemption, resource contention and others. A call to the `PreStop` hook fails if the container is already in a terminated or completed state and the hook must complete before the TERM signal to stop the container can be sent. The Pod's termination grace period countdown begins before the `PreStop` hook is executed, so regardless of the outcome of the handler, the container will eventually terminate within the Pod's termination grace period. No parameters are passed to the handler. A more detailed description of the termination behavior can be found in [Termination of Pods](/docs/concepts/workloads/pods/pod-lifecycle/#pod-termination). `StopSignal` The StopSignal lifecycle can be used to define a stop signal which would be sent to the container when it is stopped. If you set this, it overrides any `STOPSIGNAL` instruction defined within the container image. A more detailed description of termination behaviour with custom stop signals can be found in [Stop Signals](/docs/concepts/workloads/pods/pod-lifecycle/#pod-termination-stop-signals). ### Hook handler implementations Containers can access a hook by implementing and registering a handler for that hook. There are three types of hook handlers that can be implemented for Containers: \* Exec - Executes a specific command, such as `pre-stop.sh`, inside the cgroups and namespaces of the Container. Resources consumed by the command are counted against the Container. \* HTTP - Executes an HTTP request against a specific endpoint on the Container. \* Sleep - Pauses the container for a specified duration. ### Hook handler execution When a Container lifecycle management hook is called, the Kubernetes management system executes the handler according to the hook action, `httpGet`, `tcpSocket` ([deprecated](/docs/reference/generated/kubernetes-api/v1.31/#lifecyclehandler-v1-core)) and `sleep` are executed by the kubelet process, and `exec` is executed in the container. The `PostStart` hook handler call is initiated when a container is created, meaning the container ENTRYPOINT and the `PostStart` hook are triggered simultaneously. (This means it generally doesn't make sense to use an HTTP hook for `PostStart`, since there is no guarantee that the container's process will have fully started up when the hook runs.) If the `PostStart` hook takes too long to execute or if it hangs, it can prevent the container from transitioning to a `running` state. `PreStop` hooks are not executed asynchronously from the signal to stop the Container; the hook must complete its execution before the TERM signal can be sent. If a `PreStop` hook hangs during execution, the Pod's phase will be `Terminating` and remain there until the Pod is killed after its `terminationGracePeriodSeconds` expires. This grace period applies to the total time it takes for both the `PreStop` hook to execute and for the Container to stop normally. If, for example, `terminationGracePeriodSeconds` is 60, and the hook takes 55 seconds to complete, and the Container takes 10 seconds to stop normally after receiving the signal, then the Container will be
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/containers/container-lifecycle-hooks.md
|
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] | 0.160096
|
total time it takes for both the `PreStop` hook to execute and for the Container to stop normally. If, for example, `terminationGracePeriodSeconds` is 60, and the hook takes 55 seconds to complete, and the Container takes 10 seconds to stop normally after receiving the signal, then the Container will be killed before it can stop normally, since `terminationGracePeriodSeconds` is less than the total time (55+10) it takes for these two things to happen. If either a `PostStart` or `PreStop` hook fails, it kills the Container. Users should make their hook handlers as lightweight as possible. There are cases, however, when long running commands make sense, such as when saving state prior to stopping a Container. ### Hook delivery guarantees Hook delivery is intended to be \*at least once\*, which means that a hook may be called multiple times for any given event, such as for `PostStart` or `PreStop`. It is up to the hook implementation to handle this correctly. Generally, only single deliveries are made. If, for example, an HTTP hook receiver is down and is unable to take traffic, there is no attempt to resend. In some rare cases, however, double delivery may occur. For instance, if a kubelet restarts in the middle of sending a hook, the hook might be resent after the kubelet comes back up. ### Debugging Hook handlers The logs for a Hook handler are not exposed in Pod events. If a handler fails for some reason, it broadcasts an event. For `PostStart`, this is the `FailedPostStartHook` event, and for `PreStop`, this is the `FailedPreStopHook` event. To generate a failed `FailedPostStartHook` event yourself, modify the [lifecycle-events.yaml](https://k8s.io/examples/pods/lifecycle-events.yaml) file to change the postStart command to "badcommand" and apply it. Here is some example output of the resulting events you see from running `kubectl describe pod lifecycle-demo`: ``` Events: Type Reason Age From Message ---- ------ ---- ---- ------- Normal Scheduled 7s default-scheduler Successfully assigned default/lifecycle-demo to ip-XXX-XXX-XX-XX.us-east-2... Normal Pulled 6s kubelet Successfully pulled image "nginx" in 229.604315ms Normal Pulling 4s (x2 over 6s) kubelet Pulling image "nginx" Normal Created 4s (x2 over 5s) kubelet Created container lifecycle-demo-container Normal Started 4s (x2 over 5s) kubelet Started container lifecycle-demo-container Warning FailedPostStartHook 4s (x2 over 5s) kubelet Exec lifecycle hook ([badcommand]) for Container "lifecycle-demo-container" in Pod "lifecycle-demo\_default(30229739-9651-4e5a-9a32-a8f1688862db)" failed - error: command 'badcommand' exited with 126: , message: "OCI runtime exec failed: exec failed: container\_linux.go:380: starting container process caused: exec: \"badcommand\": executable file not found in $PATH: unknown\r\n" Normal Killing 4s (x2 over 5s) kubelet FailedPostStartHook Normal Pulled 4s kubelet Successfully pulled image "nginx" in 215.66395ms Warning BackOff 2s (x2 over 3s) kubelet Back-off restarting failed container ``` ## {{% heading "whatsnext" %}} \* Learn more about the [Container environment](/docs/concepts/containers/container-environment/). \* Get hands-on experience [attaching handlers to Container lifecycle events](/docs/tasks/configure-pod-container/attach-handler-lifecycle-event/).
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/containers/container-lifecycle-hooks.md
|
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] | 0.06515
|
This page will discuss containers and container images, as well as their use in operations and solution development. The word \_container\_ is an overloaded term. Whenever you use the word, check whether your audience uses the same definition. Each container that you run is repeatable; the standardization from having dependencies included means that you get the same behavior wherever you run it. Containers decouple applications from the underlying host infrastructure. This makes deployment easier in different cloud or OS environments. Each {{< glossary\_tooltip text="node" term\_id="node" >}} in a Kubernetes cluster runs the containers that form the [Pods](/docs/concepts/workloads/pods/) assigned to that node. Containers in a Pod are co-located and co-scheduled to run on the same node. ## Container images A [container image](/docs/concepts/containers/images/) is a ready-to-run software package containing everything needed to run an application: the code and any runtime it requires, application and system libraries, and default values for any essential settings. Containers are intended to be stateless and [immutable](https://glossary.cncf.io/immutable-infrastructure/): you should not change the code of a container that is already running. If you have a containerized application and want to make changes, the correct process is to build a new image that includes the change, then recreate the container to start from the updated image. ## Container runtimes {{< glossary\_definition term\_id="container-runtime" length="all" >}} Usually, you can allow your cluster to pick the default container runtime for a Pod. If you need to use more than one container runtime in your cluster, you can specify the [RuntimeClass](/docs/concepts/containers/runtime-class/) for a Pod to make sure that Kubernetes runs those containers using a particular container runtime. You can also use RuntimeClass to run different Pods with the same container runtime but with different settings.
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/containers/_index.md
|
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|
The CRI is a plugin interface which enables the kubelet to use a wide variety of container runtimes, without having a need to recompile the cluster components. You need a working {{}} on each Node in your cluster, so that the {{< glossary\_tooltip text="kubelet" term\_id="kubelet" >}} can launch {{< glossary\_tooltip text="Pods" term\_id="pod" >}} and their containers. {{< glossary\_definition prepend="The Container Runtime Interface (CRI) is" term\_id="cri" length="all" >}} ## The API {#api} {{< feature-state for\_k8s\_version="v1.23" state="stable" >}} The kubelet acts as a client when connecting to the container runtime via gRPC. The runtime and image service endpoints have to be available in the container runtime, which can be configured separately within the kubelet by using the `--container-runtime-endpoint` [command line flag](/docs/reference/command-line-tools-reference/kubelet/). For Kubernetes v1.26 and later, the kubelet requires that the container runtime supports the `v1` CRI API. If a container runtime does not support the `v1` API, the kubelet will not register the node. ## Upgrading When upgrading the Kubernetes version on a node, the kubelet restarts. If the container runtime does not support the `v1` CRI API, the kubelet will fail to register and report an error. If a gRPC re-dial is required because the container runtime has been upgraded, the runtime must support the `v1` CRI API for the connection to succeed. This might require a restart of the kubelet after the container runtime is correctly configured. ## {{% heading "whatsnext" %}} - Learn more about the CRI [protocol definition](https://github.com/kubernetes/cri-api/blob/v0.33.1/pkg/apis/runtime/v1/api.proto)
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/containers/cri.md
|
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|
Windows applications constitute a large portion of the services and applications that run in many organizations. [Windows containers](https://aka.ms/windowscontainers) provide a way to encapsulate processes and package dependencies, making it easier to use DevOps practices and follow cloud native patterns for Windows applications. Organizations with investments in Windows-based applications and Linux-based applications don't have to look for separate orchestrators to manage their workloads, leading to increased operational efficiencies across their deployments, regardless of operating system. ## Windows nodes in Kubernetes To enable the orchestration of Windows containers in Kubernetes, include Windows nodes in your existing Linux cluster. Scheduling Windows containers in {{< glossary\_tooltip text="Pods" term\_id="pod" >}} on Kubernetes is similar to scheduling Linux-based containers. In order to run Windows containers, your Kubernetes cluster must include multiple operating systems. While you can only run the {{< glossary\_tooltip text="control plane" term\_id="control-plane" >}} on Linux, you can deploy worker nodes running either Windows or Linux. Windows {{< glossary\_tooltip text="nodes" term\_id="node" >}} are [supported](#windows-os-version-support) provided that the operating system is Windows Server 2019 or Windows Server 2022. This document uses the term \*Windows containers\* to mean Windows containers with process isolation. Kubernetes does not support running Windows containers with [Hyper-V isolation](https://docs.microsoft.com/en-us/virtualization/windowscontainers/manage-containers/hyperv-container). ## Compatibility and limitations {#limitations} Some node features are only available if you use a specific [container runtime](#container-runtime); others are not available on Windows nodes, including: \* HugePages: not supported for Windows containers \* Privileged containers: not supported for Windows containers. [HostProcess Containers](/docs/tasks/configure-pod-container/create-hostprocess-pod/) offer similar functionality. \* TerminationGracePeriod: requires containerD Not all features of shared namespaces are supported. See [API compatibility](#api) for more details. See [Windows OS version compatibility](#windows-os-version-support) for details on the Windows versions that Kubernetes is tested against. From an API and kubectl perspective, Windows containers behave in much the same way as Linux-based containers. However, there are some notable differences in key functionality which are outlined in this section. ### Comparison with Linux {#compatibility-linux-similarities} Key Kubernetes elements work the same way in Windows as they do in Linux. This section refers to several key workload abstractions and how they map to Windows. \* [Pods](/docs/concepts/workloads/pods/) A Pod is the basic building block of Kubernetes–the smallest and simplest unit in the Kubernetes object model that you create or deploy. You may not deploy Windows and Linux containers in the same Pod. All containers in a Pod are scheduled onto a single Node where each Node represents a specific platform and architecture. The following Pod capabilities, properties and events are supported with Windows containers: \* Single or multiple containers per Pod with process isolation and volume sharing \* Pod `status` fields \* Readiness, liveness, and startup probes \* postStart & preStop container lifecycle hooks \* ConfigMap, Secrets: as environment variables or volumes \* `emptyDir` volumes \* Named pipe host mounts \* Resource limits \* OS field: The `.spec.os.name` field should be set to `windows` to indicate that the current Pod uses Windows containers. If you set the `.spec.os.name` field to `windows`, you must not set the following fields in the `.spec` of that Pod: \* `spec.hostPID` \* `spec.hostIPC` \* `spec.securityContext.seLinuxOptions` \* `spec.securityContext.seccompProfile` \* `spec.securityContext.fsGroup` \* `spec.securityContext.fsGroupChangePolicy` \* `spec.securityContext.sysctls` \* `spec.shareProcessNamespace` \* `spec.securityContext.runAsUser` \* `spec.securityContext.runAsGroup` \* `spec.securityContext.supplementalGroups` \* `spec.containers[\*].securityContext.seLinuxOptions` \* `spec.containers[\*].securityContext.seccompProfile` \* `spec.containers[\*].securityContext.capabilities` \* `spec.containers[\*].securityContext.readOnlyRootFilesystem` \* `spec.containers[\*].securityContext.privileged` \* `spec.containers[\*].securityContext.allowPrivilegeEscalation` \* `spec.containers[\*].securityContext.procMount` \* `spec.containers[\*].securityContext.runAsUser` \* `spec.containers[\*].securityContext.runAsGroup` In the above list, wildcards (`\*`) indicate all elements in a list. For example, `spec.containers[\*].securityContext` refers to the SecurityContext object for all containers. If any of these fields is specified, the Pod will not be admitted by the API server. \* [Workload resources](/docs/concepts/workloads/controllers/) including: \* ReplicaSet \* Deployment \* StatefulSet \* DaemonSet \* Job \* CronJob \* ReplicationController \* {{<
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/windows/intro.md
|
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] | 0.156244
|
a list. For example, `spec.containers[\*].securityContext` refers to the SecurityContext object for all containers. If any of these fields is specified, the Pod will not be admitted by the API server. \* [Workload resources](/docs/concepts/workloads/controllers/) including: \* ReplicaSet \* Deployment \* StatefulSet \* DaemonSet \* Job \* CronJob \* ReplicationController \* {{< glossary\_tooltip text="Services" term\_id="service" >}} See [Load balancing and Services](/docs/concepts/services-networking/windows-networking/#load-balancing-and-services) for more details. Pods, workload resources, and Services are critical elements to managing Windows workloads on Kubernetes. However, on their own they are not enough to enable the proper lifecycle management of Windows workloads in a dynamic cloud native environment. \* `kubectl exec` \* Pod and container metrics \* {{< glossary\_tooltip text="Horizontal pod autoscaling" term\_id="horizontal-pod-autoscaler" >}} \* {{< glossary\_tooltip text="Resource quotas" term\_id="resource-quota" >}} \* Scheduler preemption ### Command line options for the kubelet {#kubelet-compatibility} Some kubelet command line options behave differently on Windows, as described below: \* The `--windows-priorityclass` lets you set the scheduling priority of the kubelet process (see [CPU resource management](/docs/concepts/configuration/windows-resource-management/#resource-management-cpu)) \* The `--kube-reserved`, `--system-reserved` , and `--eviction-hard` flags update [NodeAllocatable](/docs/tasks/administer-cluster/reserve-compute-resources/#node-allocatable) \* Eviction by using `--enforce-node-allocable` is not implemented \* When running on a Windows node the kubelet does not have memory or CPU restrictions. `--kube-reserved` and `--system-reserved` only subtract from `NodeAllocatable` and do not guarantee resource provided for workloads. See [Resource Management for Windows nodes](/docs/concepts/configuration/windows-resource-management/#resource-reservation) for more information. \* The `PIDPressure` Condition is not implemented \* The kubelet does not take OOM eviction actions ### API compatibility {#api} There are subtle differences in the way the Kubernetes APIs work for Windows due to the OS and container runtime. Some workload properties were designed for Linux, and fail to run on Windows. At a high level, these OS concepts are different: \* Identity - Linux uses userID (UID) and groupID (GID) which are represented as integer types. User and group names are not canonical - they are just an alias in `/etc/groups` or `/etc/passwd` back to UID+GID. Windows uses a larger binary [security identifier](https://docs.microsoft.com/en-us/windows/security/identity-protection/access-control/security-identifiers) (SID) which is stored in the Windows Security Access Manager (SAM) database. This database is not shared between the host and containers, or between containers. \* File permissions - Windows uses an access control list based on (SIDs), whereas POSIX systems such as Linux use a bitmask based on object permissions and UID+GID, plus \_optional\_ access control lists. \* File paths - the convention on Windows is to use `\` instead of `/`. The Go IO libraries typically accept both and just make it work, but when you're setting a path or command line that's interpreted inside a container, `\` may be needed. \* Signals - Windows interactive apps handle termination differently, and can implement one or more of these: \* A UI thread handles well-defined messages including `WM\_CLOSE`. \* Console apps handle Ctrl-C or Ctrl-break using a Control Handler. \* Services register a Service Control Handler function that can accept `SERVICE\_CONTROL\_STOP` control codes. Container exit codes follow the same convention where 0 is success, and nonzero is failure. The specific error codes may differ across Windows and Linux. However, exit codes passed from the Kubernetes components (kubelet, kube-proxy) are unchanged. #### Field compatibility for container specifications {#compatibility-v1-pod-spec-containers} The following list documents differences between how Pod container specifications work between Windows and Linux: \* Huge pages are not implemented in the Windows container runtime, and are not available. They require [asserting a user privilege](https://docs.microsoft.com/en-us/windows/desktop/Memory/large-page-support) that's not configurable for containers. \* `requests.cpu` and `requests.memory` - requests are subtracted from node available resources, so they can be used to avoid overprovisioning a node. However, they cannot be used to guarantee resources in an overprovisioned node. They should
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/windows/intro.md
|
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] | 0.200106
|
are not available. They require [asserting a user privilege](https://docs.microsoft.com/en-us/windows/desktop/Memory/large-page-support) that's not configurable for containers. \* `requests.cpu` and `requests.memory` - requests are subtracted from node available resources, so they can be used to avoid overprovisioning a node. However, they cannot be used to guarantee resources in an overprovisioned node. They should be applied to all containers as a best practice if the operator wants to avoid overprovisioning entirely. \* `securityContext.allowPrivilegeEscalation` - not possible on Windows; none of the capabilities are hooked up \* `securityContext.capabilities` - POSIX capabilities are not implemented on Windows \* `securityContext.privileged` - Windows doesn't support privileged containers, use [HostProcess Containers](/docs/tasks/configure-pod-container/create-hostprocess-pod/) instead \* `securityContext.procMount` - Windows doesn't have a `/proc` filesystem \* `securityContext.readOnlyRootFilesystem` - not possible on Windows; write access is required for registry & system processes to run inside the container \* `securityContext.runAsGroup` - not possible on Windows as there is no GID support \* `securityContext.runAsNonRoot` - this setting will prevent containers from running as `ContainerAdministrator` which is the closest equivalent to a root user on Windows. \* `securityContext.runAsUser` - use [`runAsUserName`](/docs/tasks/configure-pod-container/configure-runasusername) instead \* `securityContext.seLinuxOptions` - not possible on Windows as SELinux is Linux-specific \* `terminationMessagePath` - this has some limitations in that Windows doesn't support mapping single files. The default value is `/dev/termination-log`, which does work because it does not exist on Windows by default. #### Field compatibility for Pod specifications {#compatibility-v1-pod} The following list documents differences between how Pod specifications work between Windows and Linux: \* `hostIPC` and `hostpid` - host namespace sharing is not possible on Windows \* `hostNetwork` - host networking is not possible on Windows \* `dnsPolicy` - setting the Pod `dnsPolicy` to `ClusterFirstWithHostNet` is not supported on Windows because host networking is not provided. Pods always run with a container network. \* `podSecurityContext` [see below](#compatibility-v1-pod-spec-containers-securitycontext) \* `shareProcessNamespace` - this is a beta feature, and depends on Linux namespaces which are not implemented on Windows. Windows cannot share process namespaces or the container's root filesystem. Only the network can be shared. \* `terminationGracePeriodSeconds` - this is not fully implemented in Docker on Windows, see the [GitHub issue](https://github.com/moby/moby/issues/25982). The behavior today is that the ENTRYPOINT process is sent CTRL\_SHUTDOWN\_EVENT, then Windows waits 5 seconds by default, and finally shuts down all processes using the normal Windows shutdown behavior. The 5 second default is actually in the Windows registry [inside the container](https://github.com/moby/moby/issues/25982#issuecomment-426441183), so it can be overridden when the container is built. \* `volumeDevices` - this is a beta feature, and is not implemented on Windows. Windows cannot attach raw block devices to pods. \* `volumes` \* If you define an `emptyDir` volume, you cannot set its volume source to `memory`. \* You cannot enable `mountPropagation` for volume mounts as this is not supported on Windows. #### Host network access {#compatibility-v1-pod-sec-containers-hostnetwork} Kubernetes v1.26 to v1.32 included alpha support for running Windows Pods in the host's network namespace. Kubernetes v{{< skew currentVersion >}} does \*\*not\*\* include the `WindowsHostNetwork` feature gate or support for running Windows Pods in the host's network namespace. #### Field compatibility for Pod security context {#compatibility-v1-pod-spec-containers-securitycontext} Only the `securityContext.runAsNonRoot` and `securityContext.windowsOptions` from the Pod [`securityContext`](/docs/reference/kubernetes-api/workload-resources/pod-v1/#security-context) fields work on Windows. ## Node problem detector The node problem detector (see [Monitor Node Health](/docs/tasks/debug/debug-cluster/monitor-node-health/)) has preliminary support for Windows. For more information, visit the project's [GitHub page](https://github.com/kubernetes/node-problem-detector#windows). ## Pause container In a Kubernetes Pod, an infrastructure or “pause” container is first created to host the container. In Linux, the cgroups and namespaces that make up a pod need a process to maintain their continued existence; the pause process provides this. Containers that belong to the same pod, including infrastructure and worker containers, share a common network
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/windows/intro.md
|
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|
or “pause” container is first created to host the container. In Linux, the cgroups and namespaces that make up a pod need a process to maintain their continued existence; the pause process provides this. Containers that belong to the same pod, including infrastructure and worker containers, share a common network endpoint (same IPv4 and / or IPv6 address, same network port spaces). Kubernetes uses pause containers to allow for worker containers crashing or restarting without losing any of the networking configuration. Kubernetes maintains a multi-architecture image that includes support for Windows. For Kubernetes v{{< skew currentPatchVersion >}} the recommended pause image is `registry.k8s.io/pause:3.6`. The [source code](https://github.com/kubernetes/kubernetes/tree/master/build/pause) is available on GitHub. Microsoft maintains a different multi-architecture image, with Linux and Windows amd64 support, that you can find as `mcr.microsoft.com/oss/kubernetes/pause:3.6`. This image is built from the same source as the Kubernetes maintained image but all of the Windows binaries are [authenticode signed](https://docs.microsoft.com/en-us/windows-hardware/drivers/install/authenticode) by Microsoft. The Kubernetes project recommends using the Microsoft maintained image if you are deploying to a production or production-like environment that requires signed binaries. ## Container runtimes {#container-runtime} You need to install a {{< glossary\_tooltip text="container runtime" term\_id="container-runtime" >}} into each node in the cluster so that Pods can run there. The following container runtimes work with Windows: {{% thirdparty-content %}} ### ContainerD {{< feature-state for\_k8s\_version="v1.20" state="stable" >}} You can use {{< glossary\_tooltip term\_id="containerd" text="ContainerD" >}} 1.4.0+ as the container runtime for Kubernetes nodes that run Windows. Learn how to [install ContainerD on a Windows node](/docs/setup/production-environment/container-runtimes/#containerd). {{< note >}} There is a [known limitation](/docs/tasks/configure-pod-container/configure-gmsa/#gmsa-limitations) when using GMSA with containerd to access Windows network shares, which requires a kernel patch. {{< /note >}} ### Mirantis Container Runtime {#mcr} [Mirantis Container Runtime](https://docs.mirantis.com/mcr/25.0/overview.html) (MCR) is available as a container runtime for all Windows Server 2019 and later versions. See [Install MCR on Windows Servers](https://docs.mirantis.com/mcr/25.0/install/mcr-windows.html) for more information. ## Windows OS version compatibility {#windows-os-version-support} On Windows nodes, strict compatibility rules apply where the host OS version must match the container base image OS version. Only Windows containers with a container operating system of Windows Server 2019 are fully supported. For Kubernetes v{{< skew currentVersion >}}, operating system compatibility for Windows nodes (and Pods) is as follows: Windows Server LTSC release : Windows Server 2019 : Windows Server 2022 Windows Server SAC release : Windows Server version 20H2 The Kubernetes [version-skew policy](/docs/setup/release/version-skew-policy/) also applies. ## Hardware recommendations and considerations {#windows-hardware-recommendations} {{% thirdparty-content %}} {{< note >}} The following hardware specifications outlined here should be regarded as sensible default values. They are not intended to represent minimum requirements or specific recommendations for production environments. Depending on the requirements for your workload these values may need to be adjusted. {{< /note >}} - 64-bit processor 4 CPU cores or more, capable of supporting virtualization - 8GB or more of RAM - 50GB or more of free disk space Refer to [Hardware requirements for Windows Server Microsoft documentation](https://learn.microsoft.com/en-us/windows-server/get-started/hardware-requirements) for the most up-to-date information on minimum hardware requirements. For guidance on deciding on resources for production worker nodes refer to [Production worker nodes Kubernetes documentation](/docs/setup/production-environment/#production-worker-nodes). To optimize system resources, if a graphical user interface is not required, it may be preferable to use a Windows Server OS installation that excludes the [Windows Desktop Experience](https://learn.microsoft.com/en-us/windows-server/get-started/install-options-server-core-desktop-experience) installation option, as this configuration typically frees up more system resources. In assessing disk space for Windows worker nodes, take note that Windows container images are typically larger than Linux container images, with container image sizes ranging from [300MB to over 10GB](https://techcommunity.microsoft.com/t5/containers/nano-server-x-server-core-x-server-which-base-image-is-the-right/ba-p/2835785) for a single image. Additionally, take note that the `C:` drive in Windows containers represents a virtual free size of 20GB by
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/windows/intro.md
|
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] | 0.224902
|
space for Windows worker nodes, take note that Windows container images are typically larger than Linux container images, with container image sizes ranging from [300MB to over 10GB](https://techcommunity.microsoft.com/t5/containers/nano-server-x-server-core-x-server-which-base-image-is-the-right/ba-p/2835785) for a single image. Additionally, take note that the `C:` drive in Windows containers represents a virtual free size of 20GB by default, which is not the actual consumed space, but rather the disk size for which a single container can grow to occupy when using local storage on the host. See [Containers on Windows - Container Storage Documentation](https://learn.microsoft.com/en-us/virtualization/windowscontainers/manage-containers/container-storage#storage-limits) for more detail. ## Getting help and troubleshooting {#troubleshooting} Your main source of help for troubleshooting your Kubernetes cluster should start with the [Troubleshooting](/docs/tasks/debug/) page. Some additional, Windows-specific troubleshooting help is included in this section. Logs are an important element of troubleshooting issues in Kubernetes. Make sure to include them any time you seek troubleshooting assistance from other contributors. Follow the instructions in the SIG Windows [contributing guide on gathering logs](https://github.com/kubernetes/community/blob/master/sig-windows/CONTRIBUTING.md#gathering-logs). ### Reporting issues and feature requests If you have what looks like a bug, or you would like to make a feature request, please follow the [SIG Windows contributing guide](https://github.com/kubernetes/community/blob/master/sig-windows/CONTRIBUTING.md#reporting-issues-and-feature-requests) to create a new issue. You should first search the list of issues in case it was reported previously and comment with your experience on the issue and add additional logs. SIG Windows channel on the Kubernetes Slack is also a great avenue to get some initial support and troubleshooting ideas prior to creating a ticket. ### Validating the Windows cluster operability The Kubernetes project provides a \_Windows Operational Readiness\_ specification, accompanied by a structured test suite. This suite is split into two sets of tests, core and extended, each containing categories aimed at testing specific areas. It can be used to validate all the functionalities of a Windows and hybrid system (mixed with Linux nodes) with full coverage. To set up the project on a newly created cluster, refer to the instructions in the [project guide](https://github.com/kubernetes-sigs/windows-operational-readiness/blob/main/README.md). ## Deployment tools The kubeadm tool helps you to deploy a Kubernetes cluster, providing the control plane to manage the cluster it, and nodes to run your workloads. The Kubernetes [cluster API](https://cluster-api.sigs.k8s.io/) project also provides means to automate deployment of Windows nodes. ## Windows distribution channels For a detailed explanation of Windows distribution channels see the [Microsoft documentation](https://docs.microsoft.com/en-us/windows-server/get-started-19/servicing-channels-19). Information on the different Windows Server servicing channels including their support models can be found at [Windows Server servicing channels](https://docs.microsoft.com/en-us/windows-server/get-started/servicing-channels-comparison).
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/windows/intro.md
|
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] | 0.118907
|
This page provides a walkthrough for some steps you can follow to run Windows containers using Kubernetes. The page also highlights some Windows specific functionality within Kubernetes. It is important to note that creating and deploying services and workloads on Kubernetes behaves in much the same way for Linux and Windows containers. The [kubectl commands](/docs/reference/kubectl/) to interface with the cluster are identical. The examples in this page are provided to jumpstart your experience with Windows containers. ## Objectives Configure an example deployment to run Windows containers on a Windows node. ## {{% heading "prerequisites" %}} You should already have access to a Kubernetes cluster that includes a worker node running Windows Server. ## Getting Started: Deploying a Windows workload The example YAML file below deploys a simple webserver application running inside a Windows container. Create a manifest named `win-webserver.yaml` with the contents below: ```yaml --- apiVersion: v1 kind: Service metadata: name: win-webserver labels: app: win-webserver spec: ports: # the port that this service should serve on - port: 80 targetPort: 80 selector: app: win-webserver type: NodePort --- apiVersion: apps/v1 kind: Deployment metadata: labels: app: win-webserver name: win-webserver spec: replicas: 2 selector: matchLabels: app: win-webserver template: metadata: labels: app: win-webserver name: win-webserver spec: containers: - name: windowswebserver image: mcr.microsoft.com/windows/servercore:ltsc2019 command: - powershell.exe - -command - "<#code used from https://gist.github.com/19WAS85/5424431#> ; $$listener = New-Object System.Net.HttpListener ; $$listener.Prefixes.Add('http://\*:80/') ; $$listener.Start() ; $$callerCounts = @{} ; Write-Host('Listening at http://\*:80/') ; while ($$listener.IsListening) { ;$$context = $$listener.GetContext() ;$$requestUrl = $$context.Request.Url ;$$clientIP = $$context.Request.RemoteEndPoint.Address ;$$response = $$context.Response ;Write-Host '' ;Write-Host('> {0}' -f $$requestUrl) ; ;$$count = 1 ;$$k=$$callerCounts.Get\_Item($$clientIP) ;if ($$k -ne $$null) { $$count += $$k } ;$$callerCounts.Set\_Item($$clientIP, $$count) ;$$ip=(Get-NetAdapter | Get-NetIpAddress); $$header=' # Windows Container Web Server ' ;$$callerCountsString='' ;$$callerCounts.Keys | % { $$callerCountsString+=' IP {0} callerCount {1} ' -f $$ip[1].IPAddress,$$callerCounts.Item($$\_) } ;$$footer=' ' ;$$content='{0}{1}{2}' -f $$header,$$callerCountsString,$$footer ;Write-Output $$content ;$$buffer = [System.Text.Encoding]::UTF8.GetBytes($$content) ;$$response.ContentLength64 = $$buffer.Length ;$$response.OutputStream.Write($$buffer, 0, $$buffer.Length) ;$$response.Close() ;$$responseStatus = $$response.StatusCode ;Write-Host('< {0}' -f $$responseStatus) } ; " nodeSelector: kubernetes.io/os: windows ``` {{< note >}} Port mapping is also supported, but for simplicity this example exposes port 80 of the container directly to the Service. {{< /note >}} 1. Check that all nodes are healthy: ```bash kubectl get nodes ``` 1. Deploy the service and watch for pod updates: ```bash kubectl apply -f win-webserver.yaml kubectl get pods -o wide -w ``` When the service is deployed correctly both Pods are marked as Ready. To exit the watch command, press Ctrl+C. 1. Check that the deployment succeeded. To verify: \* Several pods listed from the Linux control plane node, use `kubectl get pods` \* Node-to-pod communication across the network, `curl` port 80 of your pod IPs from the Linux control plane node to check for a web server response \* Pod-to-pod communication, ping between pods (and across hosts, if you have more than one Windows node) using `kubectl exec` \* Service-to-pod communication, `curl` the virtual service IP (seen under `kubectl get services`) from the Linux control plane node and from individual pods \* Service discovery, `curl` the service name with the Kubernetes [default DNS suffix](/docs/concepts/services-networking/dns-pod-service/#services) \* Inbound connectivity, `curl` the NodePort from the Linux control plane node or machines outside of the cluster \* Outbound connectivity, `curl` external IPs from inside the pod using `kubectl exec` {{< note >}} Windows container hosts are not able to access the IP of services scheduled on them due to current platform limitations of the Windows networking stack. Only Windows pods are able to access service IPs. {{< /note >}} ## Observability ### Capturing logs from workloads Logs are an important element of observability; they enable users to gain
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/windows/user-guide.md
|
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|
to access the IP of services scheduled on them due to current platform limitations of the Windows networking stack. Only Windows pods are able to access service IPs. {{< /note >}} ## Observability ### Capturing logs from workloads Logs are an important element of observability; they enable users to gain insights into the operational aspect of workloads and are a key ingredient to troubleshooting issues. Because Windows containers and workloads inside Windows containers behave differently from Linux containers, users had a hard time collecting logs, limiting operational visibility. Windows workloads for example are usually configured to log to ETW (Event Tracing for Windows) or push entries to the application event log. [LogMonitor](https://github.com/microsoft/windows-container-tools/tree/master/LogMonitor), an open source tool by Microsoft, is the recommended way to monitor configured log sources inside a Windows container. LogMonitor supports monitoring event logs, ETW providers, and custom application logs, piping them to STDOUT for consumption by `kubectl logs `. Follow the instructions in the LogMonitor GitHub page to copy its binaries and configuration files to all your containers and add the necessary entrypoints for LogMonitor to push your logs to STDOUT. ## Configuring container user ### Using configurable Container usernames Windows containers can be configured to run their entrypoints and processes with different usernames than the image defaults. Learn more about it [here](/docs/tasks/configure-pod-container/configure-runasusername/). ### Managing Workload Identity with Group Managed Service Accounts Windows container workloads can be configured to use Group Managed Service Accounts (GMSA). Group Managed Service Accounts are a specific type of Active Directory account that provide automatic password management, simplified service principal name (SPN) management, and the ability to delegate the management to other administrators across multiple servers. Containers configured with a GMSA can access external Active Directory Domain resources while carrying the identity configured with the GMSA. Learn more about configuring and using GMSA for Windows containers [here](/docs/tasks/configure-pod-container/configure-gmsa/). ## Taints and tolerations Users need to use some combination of {{}} and node selectors in order to schedule Linux and Windows workloads to their respective OS-specific nodes. The recommended approach is outlined below, with one of its main goals being that this approach should not break compatibility for existing Linux workloads. You can (and should) set `.spec.os.name` for each Pod, to indicate the operating system that the containers in that Pod are designed for. For Pods that run Linux containers, set `.spec.os.name` to `linux`. For Pods that run Windows containers, set `.spec.os.name` to `windows`. {{< note >}} If you are running a version of Kubernetes older than 1.24, you may need to enable the `IdentifyPodOS` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/) to be able to set a value for `.spec.pod.os`. {{< /note >}} The scheduler does not use the value of `.spec.os.name` when assigning Pods to nodes. You should use normal Kubernetes mechanisms for [assigning pods to nodes](/docs/concepts/scheduling-eviction/assign-pod-node/) to ensure that the control plane for your cluster places pods onto nodes that are running the appropriate operating system. The `.spec.os.name` value has no effect on the scheduling of the Windows pods, so taints and tolerations (or node selectors) are still required to ensure that the Windows pods land onto appropriate Windows nodes. ### Ensuring OS-specific workloads land on the appropriate container host Users can ensure Windows containers can be scheduled on the appropriate host using taints and tolerations. All Kubernetes nodes running Kubernetes {{< skew currentVersion >}} have the following default labels: \* kubernetes.io/os = [windows|linux] \* kubernetes.io/arch = [amd64|arm64|...] If a Pod specification does not specify a `nodeSelector` such as `"kubernetes.io/os": windows`, it is possible the Pod can be scheduled on any host, Windows or Linux. This can be problematic since a Windows container can only run
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/windows/user-guide.md
|
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|
the following default labels: \* kubernetes.io/os = [windows|linux] \* kubernetes.io/arch = [amd64|arm64|...] If a Pod specification does not specify a `nodeSelector` such as `"kubernetes.io/os": windows`, it is possible the Pod can be scheduled on any host, Windows or Linux. This can be problematic since a Windows container can only run on Windows and a Linux container can only run on Linux. The best practice for Kubernetes {{< skew currentVersion >}} is to use a `nodeSelector`. However, in many cases users have a pre-existing large number of deployments for Linux containers, as well as an ecosystem of off-the-shelf configurations, such as community Helm charts, and programmatic Pod generation cases, such as with operators. In those situations, you may be hesitant to make the configuration change to add `nodeSelector` fields to all Pods and Pod templates. The alternative is to use taints. Because the kubelet can set taints during registration, it could easily be modified to automatically add a taint when running on Windows only. For example: `--register-with-taints='os=windows:NoSchedule'` By adding a taint to all Windows nodes, nothing will be scheduled on them (that includes existing Linux Pods). In order for a Windows Pod to be scheduled on a Windows node, it would need both the `nodeSelector` and the appropriate matching toleration to choose Windows. ```yaml nodeSelector: kubernetes.io/os: windows node.kubernetes.io/windows-build: '10.0.17763' tolerations: - key: "os" operator: "Equal" value: "windows" effect: "NoSchedule" ``` ### Handling multiple Windows versions in the same cluster The Windows Server version used by each pod must match that of the node. If you want to use multiple Windows Server versions in the same cluster, then you should set additional node labels and `nodeSelector` fields. Kubernetes automatically adds a label, [`node.kubernetes.io/windows-build`](/docs/reference/labels-annotations-taints/#nodekubernetesiowindows-build) to simplify this. This label reflects the Windows major, minor, and build number that need to match for compatibility. Here are values used for each Windows Server version: | Product Name | Version | |--------------------------------------|------------------------| | Windows Server 2019 | 10.0.17763 | | Windows Server 2022 | 10.0.20348 | ### Simplifying with RuntimeClass [RuntimeClass] can be used to simplify the process of using taints and tolerations. A cluster administrator can create a `RuntimeClass` object which is used to encapsulate these taints and tolerations. 1. Save this file to `runtimeClasses.yml`. It includes the appropriate `nodeSelector` for the Windows OS, architecture, and version. ```yaml --- apiVersion: node.k8s.io/v1 kind: RuntimeClass metadata: name: windows-2019 handler: example-container-runtime-handler scheduling: nodeSelector: kubernetes.io/os: 'windows' kubernetes.io/arch: 'amd64' node.kubernetes.io/windows-build: '10.0.17763' tolerations: - effect: NoSchedule key: os operator: Equal value: "windows" ``` 1. Run `kubectl create -f runtimeClasses.yml` using as a cluster administrator 1. Add `runtimeClassName: windows-2019` as appropriate to Pod specs For example: ```yaml --- apiVersion: apps/v1 kind: Deployment metadata: name: iis-2019 labels: app: iis-2019 spec: replicas: 1 template: metadata: name: iis-2019 labels: app: iis-2019 spec: runtimeClassName: windows-2019 containers: - name: iis image: mcr.microsoft.com/windows/servercore/iis:windowsservercore-ltsc2019 resources: limits: cpu: 1 memory: 800Mi requests: cpu: .1 memory: 300Mi ports: - containerPort: 80 selector: matchLabels: app: iis-2019 --- apiVersion: v1 kind: Service metadata: name: iis spec: type: LoadBalancer ports: - protocol: TCP port: 80 selector: app: iis-2019 ``` [RuntimeClass]: /docs/concepts/containers/runtime-class/
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/windows/user-guide.md
|
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] | 0.128436
|
Kubernetes supports worker {{< glossary\_tooltip text="nodes" term\_id="node" >}} running either Linux or Microsoft Windows. {{% thirdparty-content single="true" %}} The CNCF and its parent the Linux Foundation take a vendor-neutral approach towards compatibility. It is possible to join your [Windows server](https://www.microsoft.com/en-us/windows-server) as a worker node to a Kubernetes cluster. You can [install and set up kubectl on Windows](/docs/tasks/tools/install-kubectl-windows/) no matter what operating system you use within your cluster. If you are using Windows nodes, you can read: \* [Networking On Windows](/docs/concepts/services-networking/windows-networking/) \* [Windows Storage In Kubernetes](/docs/concepts/storage/windows-storage/) \* [Resource Management for Windows Nodes](/docs/concepts/configuration/windows-resource-management/) \* [Configure RunAsUserName for Windows Pods and Containers](/docs/tasks/configure-pod-container/configure-runasusername/) \* [Create A Windows HostProcess Pod](/docs/tasks/configure-pod-container/create-hostprocess-pod/) \* [Configure Group Managed Service Accounts for Windows Pods and Containers](/docs/tasks/configure-pod-container/configure-gmsa/) \* [Security For Windows Nodes](/docs/concepts/security/windows-security/) \* [Windows Debugging Tips](/docs/tasks/debug/debug-cluster/windows/) \* [Guide for Scheduling Windows Containers in Kubernetes](/docs/concepts/windows/user-guide) or, for an overview, read:
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/windows/_index.md
|
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] | 0.145183
|
The core of Kubernetes' {{< glossary\_tooltip text="control plane" term\_id="control-plane" >}} is the {{< glossary\_tooltip text="API server" term\_id="kube-apiserver" >}}. The API server exposes an HTTP API that lets end users, different parts of your cluster, and external components communicate with one another. The Kubernetes API lets you query and manipulate the state of API objects in Kubernetes (for example: Pods, Namespaces, ConfigMaps, and Events). Most operations can be performed through the [kubectl](/docs/reference/kubectl/) command-line interface or other command-line tools, such as [kubeadm](/docs/reference/setup-tools/kubeadm/), which in turn use the API. However, you can also access the API directly using REST calls. Kubernetes provides a set of [client libraries](/docs/reference/using-api/client-libraries/) for those looking to write applications using the Kubernetes API. Each Kubernetes cluster publishes the specification of the APIs that the cluster serves. There are two mechanisms that Kubernetes uses to publish these API specifications; both are useful to enable automatic interoperability. For example, the `kubectl` tool fetches and caches the API specification for enabling command-line completion and other features. The two supported mechanisms are as follows: - [The Discovery API](#discovery-api) provides information about the Kubernetes APIs: API names, resources, versions, and supported operations. This is a Kubernetes specific term as it is a separate API from the Kubernetes OpenAPI. It is intended to be a brief summary of the available resources and it does not detail specific schema for the resources. For reference about resource schemas, please refer to the OpenAPI document. - The [Kubernetes OpenAPI Document](#openapi-interface-definition) provides (full) [OpenAPI v2.0 and 3.0 schemas](https://www.openapis.org/) for all Kubernetes API endpoints. The OpenAPI v3 is the preferred method for accessing OpenAPI as it provides a more comprehensive and accurate view of the API. It includes all the available API paths, as well as all resources consumed and produced for every operations on every endpoints. It also includes any extensibility components that a cluster supports. The data is a complete specification and is significantly larger than that from the Discovery API. ## Discovery API Kubernetes publishes a list of all group versions and resources supported via the Discovery API. This includes the following for each resource: - Name - Cluster or namespaced scope - Endpoint URL and supported verbs - Alternative names - Group, version, kind The API is available in both aggregated and unaggregated form. The aggregated discovery serves two endpoints, while the unaggregated discovery serves a separate endpoint for each group version. ### Aggregated discovery {{< feature-state feature\_gate\_name="AggregatedDiscoveryEndpoint" >}} Kubernetes offers stable support for \_aggregated discovery\_, publishing all resources supported by a cluster through two endpoints (`/api` and `/apis`). Requesting this endpoint drastically reduces the number of requests sent to fetch the discovery data from the cluster. You can access the data by requesting the respective endpoints with an `Accept` header indicating the aggregated discovery resource: `Accept: application/json;v=v2;g=apidiscovery.k8s.io;as=APIGroupDiscoveryList`. Without indicating the resource type using the `Accept` header, the default response for the `/api` and `/apis` endpoint is an unaggregated discovery document. The [discovery document](https://github.com/kubernetes/kubernetes/blob/release-{{< skew currentVersion >}}/api/discovery/aggregated\_v2.json) for the built-in resources can be found in the Kubernetes GitHub repository. This Github document can be used as a reference of the base set of the available resources if a Kubernetes cluster is not available to query. The endpoint also supports ETag and protobuf encoding. ### Unaggregated discovery Without discovery aggregation, discovery is published in levels, with the root endpoints publishing discovery information for downstream documents. A list of all group versions supported by a cluster is published at the `/api` and `/apis` endpoints. Example: ``` { "kind": "APIGroupList", "apiVersion": "v1", "groups": [ { "name": "apiregistration.k8s.io", "versions": [ { "groupVersion": "apiregistration.k8s.io/v1", "version": "v1" } ], "preferredVersion": { "groupVersion":
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/kubernetes-api.md
|
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] | 0.19977
|
root endpoints publishing discovery information for downstream documents. A list of all group versions supported by a cluster is published at the `/api` and `/apis` endpoints. Example: ``` { "kind": "APIGroupList", "apiVersion": "v1", "groups": [ { "name": "apiregistration.k8s.io", "versions": [ { "groupVersion": "apiregistration.k8s.io/v1", "version": "v1" } ], "preferredVersion": { "groupVersion": "apiregistration.k8s.io/v1", "version": "v1" } }, { "name": "apps", "versions": [ { "groupVersion": "apps/v1", "version": "v1" } ], "preferredVersion": { "groupVersion": "apps/v1", "version": "v1" } }, ... } ``` Additional requests are needed to obtain the discovery document for each group version at `/apis//` (for example: `/apis/rbac.authorization.k8s.io/v1alpha1`), which advertises the list of resources served under a particular group version. These endpoints are used by kubectl to fetch the list of resources supported by a cluster. ## OpenAPI interface definition For details about the OpenAPI specifications, see the [OpenAPI documentation](https://www.openapis.org/). Kubernetes serves both OpenAPI v2.0 and OpenAPI v3.0. OpenAPI v3 is the preferred method of accessing the OpenAPI because it offers a more comprehensive (lossless) representation of Kubernetes resources. Due to limitations of OpenAPI version 2, certain fields are dropped from the published OpenAPI including but not limited to `default`, `nullable`, `oneOf`. ### OpenAPI V2 The Kubernetes API server serves an aggregated OpenAPI v2 spec via the `/openapi/v2` endpoint. You can request the response format using request headers as follows: Valid request header values for OpenAPI v2 queries | Header | Possible values | Notes | | --- | --- | --- | | `Accept-Encoding` | `gzip` | *not supplying this header is also acceptable* | | `Accept` | `application/com.github.proto-openapi.spec.v2@v1.0+protobuf` | *mainly for intra-cluster use* | | `application/json` | *default* | | `*` | *serves* `application/json` | {{< warning >}} The validation rules published as part of OpenAPI schemas may not be complete, and usually aren't. Additional validation occurs within the API server. If you want precise and complete verification, a `kubectl apply --dry-run=server` runs all the applicable validation (and also activates admission-time checks). {{< /warning >}} ### OpenAPI V3 {{< feature-state feature\_gate\_name="OpenAPIV3" >}} Kubernetes supports publishing a description of its APIs as OpenAPI v3. A discovery endpoint `/openapi/v3` is provided to see a list of all group/versions available. This endpoint only returns JSON. These group/versions are provided in the following format: ```yaml { "paths": { ..., "api/v1": { "serverRelativeURL": "/openapi/v3/api/v1?hash=CC0E9BFD992D8C59AEC98A1E2336F899E8318D3CF4C68944C3DEC640AF5AB52D864AC50DAA8D145B3494F75FA3CFF939FCBDDA431DAD3CA79738B297795818CF" }, "apis/admissionregistration.k8s.io/v1": { "serverRelativeURL": "/openapi/v3/apis/admissionregistration.k8s.io/v1?hash=E19CC93A116982CE5422FC42B590A8AFAD92CDE9AE4D59B5CAAD568F083AD07946E6CB5817531680BCE6E215C16973CD39003B0425F3477CFD854E89A9DB6597" }, .... } } ``` The relative URLs are pointing to immutable OpenAPI descriptions, in order to improve client-side caching. The proper HTTP caching headers are also set by the API server for that purpose (`Expires` to 1 year in the future, and `Cache-Control` to `immutable`). When an obsolete URL is used, the API server returns a redirect to the newest URL. The Kubernetes API server publishes an OpenAPI v3 spec per Kubernetes group version at the `/openapi/v3/apis//?hash=` endpoint. Refer to the table below for accepted request headers. Valid request header values for OpenAPI v3 queries | Header | Possible values | Notes | | --- | --- | --- | | `Accept-Encoding` | `gzip` | *not supplying this header is also acceptable* | | `Accept` | `application/com.github.proto-openapi.spec.v3@v1.0+protobuf` | *mainly for intra-cluster use* | | `application/json` | *default* | | `*` | *serves* `application/json` | A Golang implementation to fetch the OpenAPI V3 is provided in the package [`k8s.io/client-go/openapi3`](https://pkg.go.dev/k8s.io/client-go/openapi3). Kubernetes {{< skew currentVersion >}} publishes OpenAPI v2.0 and v3.0; there are no plans to support 3.1 in the near future. ### Protobuf serialization Kubernetes implements an alternative Protobuf based serialization format that is primarily intended for intra-cluster communication. For more information about this format, see the [Kubernetes Protobuf serialization](https://git.k8s.io/design-proposals-archive/api-machinery/protobuf.md) design proposal and the Interface Definition Language
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/kubernetes-api.md
|
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|
v2.0 and v3.0; there are no plans to support 3.1 in the near future. ### Protobuf serialization Kubernetes implements an alternative Protobuf based serialization format that is primarily intended for intra-cluster communication. For more information about this format, see the [Kubernetes Protobuf serialization](https://git.k8s.io/design-proposals-archive/api-machinery/protobuf.md) design proposal and the Interface Definition Language (IDL) files for each schema located in the Go packages that define the API objects. ## Persistence Kubernetes stores the serialized state of objects by writing them into {{< glossary\_tooltip term\_id="etcd" >}}. ## API groups and versioning To make it easier to eliminate fields or restructure resource representations, Kubernetes supports multiple API versions, each at a different API path, such as `/api/v1` or `/apis/rbac.authorization.k8s.io/v1alpha1`. Versioning is done at the API level rather than at the resource or field level to ensure that the API presents a clear, consistent view of system resources and behavior, and to enable controlling access to end-of-life and/or experimental APIs. To make it easier to evolve and to extend its API, Kubernetes implements [API groups](/docs/reference/using-api/#api-groups) that can be [enabled or disabled](/docs/reference/using-api/#enabling-or-disabling). API resources are distinguished by their API group, resource type, namespace (for namespaced resources), and name. The API server handles the conversion between API versions transparently: all the different versions are actually representations of the same persisted data. The API server may serve the same underlying data through multiple API versions. For example, suppose there are two API versions, `v1` and `v1beta1`, for the same resource. If you originally created an object using the `v1beta1` version of its API, you can later read, update, or delete that object using either the `v1beta1` or the `v1` API version, until the `v1beta1` version is deprecated and removed. At that point you can continue accessing and modifying the object using the `v1` API. ### API changes Any system that is successful needs to grow and change as new use cases emerge or existing ones change. Therefore, Kubernetes has designed the Kubernetes API to continuously change and grow. The Kubernetes project aims to \_not\_ break compatibility with existing clients, and to maintain that compatibility for a length of time so that other projects have an opportunity to adapt. In general, new API resources and new resource fields can be added often and frequently. Elimination of resources or fields requires following the [API deprecation policy](/docs/reference/using-api/deprecation-policy/). Kubernetes makes a strong commitment to maintain compatibility for official Kubernetes APIs once they reach general availability (GA), typically at API version `v1`. Additionally, Kubernetes maintains compatibility with data persisted via \_beta\_ API versions of official Kubernetes APIs, and ensures that data can be converted and accessed via GA API versions when the feature goes stable. If you adopt a beta API version, you will need to transition to a subsequent beta or stable API version once the API graduates. The best time to do this is while the beta API is in its deprecation period, since objects are simultaneously accessible via both API versions. Once the beta API completes its deprecation period and is no longer served, the replacement API version must be used. {{< note >}} Although Kubernetes also aims to maintain compatibility for \_alpha\_ APIs versions, in some circumstances this is not possible. If you use any alpha API versions, check the release notes for Kubernetes when upgrading your cluster, in case the API did change in incompatible ways that require deleting all existing alpha objects prior to upgrade. {{< /note >}} Refer to [API versions reference](/docs/reference/using-api/#api-versioning) for more details on the API version level definitions. ## API Extension The Kubernetes API can be extended in one of two ways: 1. [Custom
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/kubernetes-api.md
|
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|
the API did change in incompatible ways that require deleting all existing alpha objects prior to upgrade. {{< /note >}} Refer to [API versions reference](/docs/reference/using-api/#api-versioning) for more details on the API version level definitions. ## API Extension The Kubernetes API can be extended in one of two ways: 1. [Custom resources](/docs/concepts/extend-kubernetes/api-extension/custom-resources/) let you declaratively define how the API server should provide your chosen resource API. 1. You can also extend the Kubernetes API by implementing an [aggregation layer](/docs/concepts/extend-kubernetes/api-extension/apiserver-aggregation/). ## {{% heading "whatsnext" %}} - Learn how to extend the Kubernetes API by adding your own [CustomResourceDefinition](/docs/tasks/extend-kubernetes/custom-resources/custom-resource-definitions/). - [Controlling Access To The Kubernetes API](/docs/concepts/security/controlling-access/) describes how the cluster manages authentication and authorization for API access. - Learn about API endpoints, resource types and samples by reading [API Reference](/docs/reference/kubernetes-api/). - Learn about what constitutes a compatible change, and how to change the API, from [API changes](https://git.k8s.io/community/contributors/devel/sig-architecture/api\_changes.md#readme).
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/kubernetes-api.md
|
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|
This page provides a high-level overview of the essential components that make up a Kubernetes cluster. {{< figure src="/images/docs/components-of-kubernetes.svg" alt="Components of Kubernetes" caption="The components of a Kubernetes cluster" class="diagram-large" clicktozoom="true" >}} ## Core Components A Kubernetes cluster consists of a control plane and one or more worker nodes. Here's a brief overview of the main components: ### Control Plane Components Manage the overall state of the cluster: [kube-apiserver](/docs/concepts/architecture/#kube-apiserver) : The core component server that exposes the Kubernetes HTTP API. [etcd](/docs/concepts/architecture/#etcd) : Consistent and highly-available key value store for all API server data. [kube-scheduler](/docs/concepts/architecture/#kube-scheduler) : Looks for Pods not yet bound to a node, and assigns each Pod to a suitable node. [kube-controller-manager](/docs/concepts/architecture/#kube-controller-manager) : Runs {{< glossary\_tooltip text="controllers" term\_id="controller" >}} to implement Kubernetes API behavior. [cloud-controller-manager](/docs/concepts/architecture/#cloud-controller-manager) (optional) : Integrates with underlying cloud provider(s). ### Node Components Run on every node, maintaining running pods and providing the Kubernetes runtime environment: [kubelet](/docs/concepts/architecture/#kubelet) : Ensures that Pods are running, including their containers. [kube-proxy](/docs/concepts/architecture/#kube-proxy) (optional) : Maintains network rules on nodes to implement {{< glossary\_tooltip text="Services" term\_id="service" >}}. [Container runtime](/docs/concepts/architecture/#container-runtime) : Software responsible for running containers. Read [Container Runtimes](/docs/setup/production-environment/container-runtimes/) to learn more. {{% thirdparty-content single="true" %}} Your cluster may require additional software on each node; for example, you might also run [systemd](https://systemd.io/) on a Linux node to supervise local components. ## Addons Addons extend the functionality of Kubernetes. A few important examples include: [DNS](/docs/concepts/architecture/#dns) : For cluster-wide DNS resolution. [Web UI](/docs/concepts/architecture/#web-ui-dashboard) (Dashboard) : For cluster management via a web interface. [Container Resource Monitoring](/docs/concepts/architecture/#container-resource-monitoring) : For collecting and storing container metrics. [Cluster-level Logging](/docs/concepts/architecture/#cluster-level-logging) : For saving container logs to a central log store. ## Flexibility in Architecture Kubernetes allows for flexibility in how these components are deployed and managed. The architecture can be adapted to various needs, from small development environments to large-scale production deployments. For more detailed information about each component and various ways to configure your cluster architecture, see the [Cluster Architecture](/docs/concepts/architecture/) page.
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/components.md
|
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This page is an overview of Kubernetes. The name Kubernetes originates from Greek, meaning helmsman or pilot. K8s as an abbreviation results from counting the eight letters between the "K" and the "s". Google open sourced the Kubernetes project in 2014. Kubernetes combines [over 15 years of Google's experience](/blog/2015/04/borg-predecessor-to-kubernetes/) running production workloads at scale with best-of-breed ideas and practices from the community. ## Why you need Kubernetes and what it can do {#why-you-need-kubernetes-and-what-can-it-do} Containers are a good way to bundle and run your applications. In a production environment, you need to manage the containers that run the applications and ensure that there is no downtime. For example, if a container goes down, another container needs to start. Wouldn't it be easier if this behavior was handled by a system? That's how Kubernetes comes to the rescue! Kubernetes provides you with a framework to run distributed systems resiliently. It takes care of scaling and failover for your application, provides deployment patterns, and more. For example: Kubernetes can easily manage a canary deployment for your system. Kubernetes provides you with: \* \*\*Service discovery and load balancing\*\* Kubernetes can expose a container using a DNS name or their own IP address. If traffic to a container is high, Kubernetes is able to load balance and distribute the network traffic so that the deployment is stable. \* \*\*Storage orchestration\*\* Kubernetes allows you to automatically mount a storage system of your choice, such as local storage, public cloud providers, and more. \* \*\*Automated rollouts and rollbacks\*\* You can describe the desired state for your deployed containers using Kubernetes, and it can change the actual state to the desired state at a controlled rate. For example, you can automate Kubernetes to create new containers for your deployment, remove existing containers and adopt all their resources to the new container. \* \*\*Automatic bin packing\*\* You provide Kubernetes with a cluster of nodes that it can use to run containerized tasks. You tell Kubernetes how much CPU and memory (RAM) each container needs. Kubernetes can fit containers onto your nodes to make the best use of your resources. \* \*\*Self-healing\*\* Kubernetes restarts containers that fail, replaces containers, kills containers that don't respond to your user-defined health check, and doesn't advertise them to clients until they are ready to serve. \* \*\*Secret and configuration management\*\* Kubernetes lets you store and manage sensitive information, such as passwords, OAuth tokens, and SSH keys. You can deploy and update secrets and application configuration without rebuilding your container images, and without exposing secrets in your stack configuration. \* \*\*Batch execution\*\* In addition to services, Kubernetes can manage your batch and CI workloads, replacing containers that fail, if desired. \* \*\*Horizontal scaling\*\* Scale your application up and down with a simple command, with a UI, or automatically based on CPU usage. \* \*\*IPv4/IPv6 dual-stack\*\* Allocation of IPv4 and IPv6 addresses to Pods and Services \* \*\*Designed for extensibility\*\* Add features to your Kubernetes cluster without changing upstream source code. ## What Kubernetes is not Kubernetes is not a traditional, all-inclusive PaaS (Platform as a Service) system. Since Kubernetes operates at the container level rather than at the hardware level, it provides some generally applicable features common to PaaS offerings, such as deployment, scaling, load balancing, and lets users integrate their logging, monitoring, and alerting solutions. However, Kubernetes is not monolithic, and these default solutions are optional and pluggable. Kubernetes provides the building blocks for building developer platforms, but preserves user choice and flexibility where it is important. Kubernetes: \* Does not limit the types of applications supported. Kubernetes aims to support an extremely diverse
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/_index.md
|
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solutions. However, Kubernetes is not monolithic, and these default solutions are optional and pluggable. Kubernetes provides the building blocks for building developer platforms, but preserves user choice and flexibility where it is important. Kubernetes: \* Does not limit the types of applications supported. Kubernetes aims to support an extremely diverse variety of workloads, including stateless, stateful, and data-processing workloads. If an application can run in a container, it should run great on Kubernetes. \* Does not deploy source code and does not build your application. Continuous Integration, Delivery, and Deployment (CI/CD) workflows are determined by organization cultures and preferences as well as technical requirements. \* Does not provide application-level services, such as middleware (for example, message buses), data-processing frameworks (for example, Spark), databases (for example, MySQL), caches, nor cluster storage systems (for example, Ceph) as built-in services. Such components can run on Kubernetes, and/or can be accessed by applications running on Kubernetes through portable mechanisms, such as the [Open Service Broker](https://openservicebrokerapi.org/). \* Does not dictate logging, monitoring, or alerting solutions. It provides some integrations as proof of concept, and mechanisms to collect and export metrics. \* Does not provide nor mandate a configuration language/system (for example, Jsonnet). It provides a declarative API that may be targeted by arbitrary forms of declarative specifications. \* Does not provide nor adopt any comprehensive machine configuration, maintenance, management, or self-healing systems. \* Additionally, Kubernetes is not a mere orchestration system. In fact, it eliminates the need for orchestration. The technical definition of orchestration is execution of a defined workflow: first do A, then B, then C. In contrast, Kubernetes comprises a set of independent, composable control processes that continuously drive the current state towards the provided desired state. It shouldn't matter how you get from A to C. Centralized control is also not required. This results in a system that is easier to use and more powerful, robust, resilient, and extensible. ## Historical context for Kubernetes {#going-back-in-time} Let's take a look at why Kubernetes is so useful by going back in time.  \*\*Traditional deployment era:\*\* Early on, organizations ran applications on physical servers. There was no way to define resource boundaries for applications in a physical server, and this caused resource allocation issues. For example, if multiple applications run on a physical server, there can be instances where one application would take up most of the resources, and as a result, the other applications would underperform. A solution for this would be to run each application on a different physical server. But this did not scale as resources were underutilized, and it was expensive for organizations to maintain many physical servers. \*\*Virtualized deployment era:\*\* As a solution, virtualization was introduced. It allows you to run multiple Virtual Machines (VMs) on a single physical server's CPU. Virtualization allows applications to be isolated between VMs and provides a level of security as the information of one application cannot be freely accessed by another application. Virtualization allows better utilization of resources in a physical server and allows better scalability because an application can be added or updated easily, reduces hardware costs, and much more. With virtualization you can present a set of physical resources as a cluster of disposable virtual machines. Each VM is a full machine running all the components, including its own operating system, on top of the virtualized hardware. \*\*Container deployment era:\*\* Containers are similar to VMs, but they have relaxed isolation properties to share the Operating System (OS) among the applications. Therefore, containers are considered lightweight. Similar to a VM, a container has its own filesystem, share of CPU, memory, process
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/_index.md
|
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] | 0.238087
|
system, on top of the virtualized hardware. \*\*Container deployment era:\*\* Containers are similar to VMs, but they have relaxed isolation properties to share the Operating System (OS) among the applications. Therefore, containers are considered lightweight. Similar to a VM, a container has its own filesystem, share of CPU, memory, process space, and more. As they are decoupled from the underlying infrastructure, they are portable across clouds and OS distributions. Containers have become popular because they provide extra benefits, such as: \* Agile application creation and deployment: increased ease and efficiency of container image creation compared to VM image use. \* Continuous development, integration, and deployment: provides reliable and frequent container image build and deployment with quick and efficient rollbacks (due to image immutability). \* Dev and Ops separation of concerns: create application container images at build/release time rather than deployment time, thereby decoupling applications from infrastructure. \* Observability: not only surfaces OS-level information and metrics, but also application health and other signals. \* Environmental consistency across development, testing, and production: runs the same on a laptop as it does in the cloud. \* Cloud and OS distribution portability: runs on Ubuntu, RHEL, CoreOS, on-premises, on major public clouds, and anywhere else. \* Application-centric management: raises the level of abstraction from running an OS on virtual hardware to running an application on an OS using logical resources. \* Loosely coupled, distributed, elastic, liberated micro-services: applications are broken into smaller, independent pieces and can be deployed and managed dynamically – not a monolithic stack running on one big single-purpose machine. \* Resource isolation: predictable application performance. \* Resource utilization: high efficiency and density. ## {{% heading "whatsnext" %}} \* Take a look at the [Kubernetes Components](/docs/concepts/overview/components/) \* Take a look at the [The Kubernetes API](/docs/concepts/overview/kubernetes-api/) \* Take a look at the [Cluster Architecture](/docs/concepts/architecture/) \* Ready to [Get Started](/docs/setup/)?
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/_index.md
|
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|
Each {{< glossary\_tooltip text="object" term\_id="object" >}} in your cluster has a [\_Name\_](#names) that is unique for that type of resource. Every Kubernetes object also has a [\_UID\_](#uids) that is unique across your whole cluster. For example, you can only have one Pod named `myapp-1234` within the same [namespace](/docs/concepts/overview/working-with-objects/namespaces/), but you can have one Pod and one Deployment that are each named `myapp-1234`. For non-unique user-provided attributes, Kubernetes provides [labels](/docs/concepts/overview/working-with-objects/labels/) and [annotations](/docs/concepts/overview/working-with-objects/annotations/). ## Names {{< glossary\_definition term\_id="name" length="all" >}} \*\*Names must be unique across all [API versions](/docs/concepts/overview/kubernetes-api/#api-groups-and-versioning) of the same resource. API resources are distinguished by their API group, resource type, namespace (for namespaced resources), and name. In other words, API version is irrelevant in this context.\*\* {{< note >}} In cases when objects represent a physical entity, like a Node representing a physical host, when the host is re-created under the same name without deleting and re-creating the Node, Kubernetes treats the new host as the old one, which may lead to inconsistencies. {{< /note >}} The server may generate a name when `generateName` is provided instead of `name` in a resource create request. When `generateName` is used, the provided value is used as a name prefix, which server appends a generated suffix to. Even though the name is generated, it may conflict with existing names resulting in an HTTP 409 response. This became far less likely to happen in Kubernetes v1.31 and later, since the server will make up to 8 attempts to generate a unique name before returning an HTTP 409 response. Below are four types of commonly used name constraints for resources. ### DNS Subdomain Names Most resource types require a name that can be used as a DNS subdomain name as defined in [RFC 1123](https://tools.ietf.org/html/rfc1123). This means the name must: - contain no more than 253 characters - contain only lowercase alphanumeric characters, '-' or '.' - start with an alphanumeric character - end with an alphanumeric character ### RFC 1123 Label Names {#dns-label-names} Some resource types require their names to follow the DNS label standard as defined in [RFC 1123](https://tools.ietf.org/html/rfc1123). This means the name must: - contain at most 63 characters - contain only lowercase alphanumeric characters or '-' - start with an alphabetic character - end with an alphanumeric character {{< note >}} When the `RelaxedServiceNameValidation` feature gate is enabled, Service object names are allowed to start with a digit. {{< /note >}} ### RFC 1035 Label Names Some resource types require their names to follow the DNS label standard as defined in [RFC 1035](https://tools.ietf.org/html/rfc1035). This means the name must: - contain at most 63 characters - contain only lowercase alphanumeric characters or '-' - start with an alphabetic character - end with an alphanumeric character {{< note >}} While RFC 1123 technically allows labels to start with digits, the current Kubernetes implementation requires both RFC 1035 and RFC 1123 labels to start with an alphabetic character. The exception is when the `RelaxedServiceNameValidation` feature gate is enabled for Service objects, which allows Service names to start with digits. {{< /note >}} ### Path Segment Names Some resource types require their names to be able to be safely encoded as a path segment. In other words, the name may not be "." or ".." and the name may not contain "/" or "%". Here's an example manifest for a Pod named `nginx-demo`. ```yaml apiVersion: v1 kind: Pod metadata: name: nginx-demo spec: containers: - name: nginx image: nginx:1.14.2 ports: - containerPort: 80 ``` {{< note >}} Some resource types have additional restrictions on their names. {{< /note >}} ## UIDs {{< glossary\_definition term\_id="uid" length="all" >}} Kubernetes
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/working-with-objects/names.md
|
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|
example manifest for a Pod named `nginx-demo`. ```yaml apiVersion: v1 kind: Pod metadata: name: nginx-demo spec: containers: - name: nginx image: nginx:1.14.2 ports: - containerPort: 80 ``` {{< note >}} Some resource types have additional restrictions on their names. {{< /note >}} ## UIDs {{< glossary\_definition term\_id="uid" length="all" >}} Kubernetes UIDs are universally unique identifiers (also known as UUIDs). UUIDs are standardized as ISO/IEC 9834-8 and as ITU-T X.667. ## {{% heading "whatsnext" %}} \* Read about [labels](/docs/concepts/overview/working-with-objects/labels/) and [annotations](/docs/concepts/overview/working-with-objects/annotations/) in Kubernetes. \* See the [Identifiers and Names in Kubernetes](https://git.k8s.io/design-proposals-archive/architecture/identifiers.md) design document.
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/working-with-objects/names.md
|
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] | 0.160364
|
\_Field selectors\_ let you select Kubernetes {{< glossary\_tooltip text="objects" term\_id="object" >}} based on the value of one or more resource fields. Here are some examples of field selector queries: \* `metadata.name=my-service` \* `metadata.namespace!=default` \* `status.phase=Pending` This `kubectl` command selects all Pods for which the value of the [`status.phase`](/docs/concepts/workloads/pods/pod-lifecycle/#pod-phase) field is `Running`: ```shell kubectl get pods --field-selector status.phase=Running ``` {{< note >}} Field selectors are essentially resource \*filters\*. By default, no selectors/filters are applied, meaning that all resources of the specified type are selected. This makes the `kubectl` queries `kubectl get pods` and `kubectl get pods --field-selector ""` equivalent. {{< /note >}} ## Supported fields Supported field selectors vary by Kubernetes resource type. All resource types support the `metadata.name` and `metadata.namespace` fields. Using unsupported field selectors produces an error. For example: ```shell kubectl get ingress --field-selector foo.bar=baz ``` ``` Error from server (BadRequest): Unable to find "ingresses" that match label selector "", field selector "foo.bar=baz": "foo.bar" is not a known field selector: only "metadata.name", "metadata.namespace" ``` ### List of supported fields | Kind | Fields | | ------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Pod | `spec.nodeName` `spec.restartPolicy` `spec.schedulerName` `spec.serviceAccountName` `spec.hostNetwork` `status.phase` `status.podIP` `status.podIPs` `status.nominatedNodeName` | | Event | `involvedObject.kind` `involvedObject.namespace` `involvedObject.name` `involvedObject.uid` `involvedObject.apiVersion` `involvedObject.resourceVersion` `involvedObject.fieldPath` `reason` `reportingComponent` `source` `type` | | Secret | `type` | | Namespace | `status.phase` | | ReplicaSet | `status.replicas` | | ReplicationController | `status.replicas` | | Job | `status.successful` | | Node | `spec.unschedulable` | | CertificateSigningRequest | `spec.signerName` | ### Custom resources fields All custom resource types support the `metadata.name` and `metadata.namespace` fields. Additionally, the `spec.versions[\*].selectableFields` field of a {{< glossary\_tooltip term\_id="CustomResourceDefinition" text="CustomResourceDefinition" >}} declares which other fields in a custom resource may be used in field selectors. See [selectable fields for custom resources](/docs/tasks/extend-kubernetes/custom-resources/custom-resource-definitions/#crd-selectable-fields) for more information about how to use field selectors with CustomResourceDefinitions. ## Supported operators You can use the `=`, `==`, and `!=` operators with field selectors (`=` and `==` mean the same thing). This `kubectl` command, for example, selects all Kubernetes Services that aren't in the `default` namespace: ```shell kubectl get services --all-namespaces --field-selector metadata.namespace!=default ``` {{< note >}} [Set-based operators](/docs/concepts/overview/working-with-objects/labels/#set-based-requirement) (`in`, `notin`, `exists`) are not supported for field selectors. {{< /note >}} ## Chained selectors As with [label](/docs/concepts/overview/working-with-objects/labels) and other selectors, field selectors can be chained together as a comma-separated list. This `kubectl` command selects all Pods for which the `status.phase` does not equal `Running` and the `spec.restartPolicy` field equals `Always`: ```shell kubectl get pods --field-selector=status.phase!=Running,spec.restartPolicy=Always ``` ## Multiple resource types You can use field selectors across multiple resource types. This `kubectl` command selects all Statefulsets and Services that are not in the `default` namespace: ```shell kubectl get statefulsets,services --all-namespaces --field-selector metadata.namespace!=default ```
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/working-with-objects/field-selectors.md
|
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|
{{}} You can use finalizers to control {{}} of {{< glossary\_tooltip text="objects" term\_id="object" >}} by alerting {{}} to perform specific cleanup tasks before deleting the target resource. Finalizers don't usually specify the code to execute. Instead, they are typically lists of keys on a specific resource similar to annotations. Kubernetes specifies some finalizers automatically, but you can also specify your own. ## How finalizers work When you create a resource using a manifest file, you can specify finalizers in the `metadata.finalizers` field. When you attempt to delete the resource, the API server handling the delete request notices the values in the `finalizers` field and does the following: \* Modifies the object to add a `metadata.deletionTimestamp` field with the time you started the deletion. \* Prevents the object from being removed until all items are removed from its `metadata.finalizers` field \* Returns a `202` status code (HTTP "Accepted") The controller managing that finalizer notices the update to the object setting the `metadata.deletionTimestamp`, indicating deletion of the object has been requested. The controller then attempts to satisfy the requirements of the finalizers specified for that resource. Each time a finalizer condition is satisfied, the controller removes that key from the resource's `finalizers` field. When the `finalizers` field is emptied, an object with a `deletionTimestamp` field set is automatically deleted. You can also use finalizers to prevent deletion of unmanaged resources. A common example of a finalizer is `kubernetes.io/pv-protection`, which prevents accidental deletion of `PersistentVolume` objects. When a `PersistentVolume` object is in use by a Pod, Kubernetes adds the `pv-protection` finalizer. If you try to delete the `PersistentVolume`, it enters a `Terminating` status, but the controller can't delete it because the finalizer exists. When the Pod stops using the `PersistentVolume`, Kubernetes clears the `pv-protection` finalizer, and the controller deletes the volume. {{}} \* When you `DELETE` an object, Kubernetes adds the deletion timestamp for that object and then immediately starts to restrict changes to the `.metadata.finalizers` field for the object that is now pending deletion. You can remove existing finalizers (deleting an entry from the `finalizers` list) but you cannot add a new finalizer. You also cannot modify the `deletionTimestamp` for an object once it is set. \* After the deletion is requested, you can not resurrect this object. The only way is to delete it and make a new similar object. {{}} {{}} Custom finalizer names \*\*must\*\* be publicly qualified finalizer names, such as `example.com/finalizer-name`. Kubernetes enforces this format; the API server rejects writes to objects where the change does not use qualified finalizer names for any custom finalizer. {{}} ## Owner references, labels, and finalizers {#owners-labels-finalizers} Like {{}}, [owner references](/docs/concepts/overview/working-with-objects/owners-dependents/) describe the relationships between objects in Kubernetes, but are used for a different purpose. When a {{}} manages objects like Pods, it uses labels to track changes to groups of related objects. For example, when a {{}} creates one or more Pods, the Job controller applies labels to those pods and tracks changes to any Pods in the cluster with the same label. The Job controller also adds \*owner references\* to those Pods, pointing at the Job that created the Pods. If you delete the Job while these Pods are running, Kubernetes uses the owner references (not labels) to determine which Pods in the cluster need cleanup. Kubernetes also processes finalizers when it identifies owner references on a resource targeted for deletion. In some situations, finalizers can block the deletion of dependent objects, which can cause the targeted owner object to remain for longer than expected without being fully deleted. In these situations, you should check finalizers and owner references on
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/working-with-objects/finalizers.md
|
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] | 0.096675
|
when it identifies owner references on a resource targeted for deletion. In some situations, finalizers can block the deletion of dependent objects, which can cause the targeted owner object to remain for longer than expected without being fully deleted. In these situations, you should check finalizers and owner references on the target owner and dependent objects to troubleshoot the cause. {{}} In cases where objects are stuck in a deleting state, avoid manually removing finalizers to allow deletion to continue. Finalizers are usually added to resources for a reason, so forcefully removing them can lead to issues in your cluster. This should only be done when the purpose of the finalizer is understood and is accomplished in another way (for example, manually cleaning up some dependent object). {{}} ## {{% heading "whatsnext" %}} \* Read [Using Finalizers to Control Deletion](/blog/2021/05/14/using-finalizers-to-control-deletion/) on the Kubernetes blog.
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/working-with-objects/finalizers.md
|
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] | 0.121315
|
In Kubernetes, \_namespaces\_ provide a mechanism for isolating groups of resources within a single cluster. Names of resources need to be unique within a namespace, but not across namespaces. Namespace-based scoping is applicable only for namespaced {{< glossary\_tooltip text="objects" term\_id="object" >}} \_(e.g. Deployments, Services, etc.)\_ and not for cluster-wide objects \_(e.g. StorageClass, Nodes, PersistentVolumes, etc.)\_. ## When to Use Multiple Namespaces Namespaces are intended for use in environments with many users spread across multiple teams, or projects. For clusters with a few to tens of users, you should not need to create or think about namespaces at all. Start using namespaces when you need the features they provide. Namespaces provide a scope for names. Names of resources need to be unique within a namespace, but not across namespaces. Namespaces cannot be nested inside one another and each Kubernetes resource can only be in one namespace. Namespaces are a way to divide cluster resources between multiple users (via [resource quota](/docs/concepts/policy/resource-quotas/)). It is not necessary to use multiple namespaces to separate slightly different resources, such as different versions of the same software: use {{< glossary\_tooltip text="labels" term\_id="label" >}} to distinguish resources within the same namespace. {{< note >}} For a production cluster, consider \_not\_ using the `default` namespace. Instead, make other namespaces and use those. {{< /note >}} ## Initial namespaces Kubernetes starts with four initial namespaces: `default` : Kubernetes includes this namespace so that you can start using your new cluster without first creating a namespace. `kube-node-lease` : This namespace holds [Lease](/docs/concepts/architecture/leases/) objects associated with each node. Node leases allow the kubelet to send [heartbeats](/docs/concepts/architecture/nodes/#node-heartbeats) so that the control plane can detect node failure. `kube-public` : This namespace is readable by \*all\* clients (including those not authenticated). This namespace is mostly reserved for cluster usage, in case that some resources should be visible and readable publicly throughout the whole cluster. The public aspect of this namespace is only a convention, not a requirement. `kube-system` : The namespace for objects created by the Kubernetes system. ## Working with Namespaces Creation and deletion of namespaces are described in the [Admin Guide documentation for namespaces](/docs/tasks/administer-cluster/namespaces). {{< note >}} Avoid creating namespaces with the prefix `kube-`, since it is reserved for Kubernetes system namespaces. {{< /note >}} ### Viewing namespaces You can list the current namespaces in a cluster using: ```shell kubectl get namespace ``` ``` NAME STATUS AGE default Active 1d kube-node-lease Active 1d kube-public Active 1d kube-system Active 1d ``` ### Setting the namespace for a request To set the namespace for a current request, use the `--namespace` flag. For example: ```shell kubectl run nginx --image=nginx --namespace= kubectl get pods --namespace= ``` ### Setting the namespace preference You can permanently save the namespace for all subsequent kubectl commands in that context. ```shell kubectl config set-context --current --namespace= # Validate it kubectl config view --minify | grep namespace: ``` ## Namespaces and DNS When you create a [Service](/docs/concepts/services-networking/service/), it creates a corresponding [DNS entry](/docs/concepts/services-networking/dns-pod-service/). This entry is of the form `..svc.cluster.local`, which means that if a container only uses ``, it will resolve to the service which is local to a namespace. This is useful for using the same configuration across multiple namespaces such as Development, Staging and Production. If you want to reach across namespaces, you need to use the fully qualified domain name (FQDN). As a result, all namespace names must be valid [RFC 1123 DNS labels](/docs/concepts/overview/working-with-objects/names/#dns-label-names). {{< warning >}} By creating namespaces with the same name as [public top-level domains](https://data.iana.org/TLD/tlds-alpha-by-domain.txt), Services in these namespaces can have short DNS names that overlap with public DNS records. Workloads from any namespace performing a
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/working-with-objects/namespaces.md
|
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|
name (FQDN). As a result, all namespace names must be valid [RFC 1123 DNS labels](/docs/concepts/overview/working-with-objects/names/#dns-label-names). {{< warning >}} By creating namespaces with the same name as [public top-level domains](https://data.iana.org/TLD/tlds-alpha-by-domain.txt), Services in these namespaces can have short DNS names that overlap with public DNS records. Workloads from any namespace performing a DNS lookup without a [trailing dot](https://datatracker.ietf.org/doc/html/rfc1034#page-8) will be redirected to those services, taking precedence over public DNS. To mitigate this, limit privileges for creating namespaces to trusted users. If required, you could additionally configure third-party security controls, such as [admission webhooks](/docs/reference/access-authn-authz/extensible-admission-controllers/), to block creating any namespace with the name of [public TLDs](https://data.iana.org/TLD/tlds-alpha-by-domain.txt). {{< /warning >}} ## Not all objects are in a namespace Most Kubernetes resources (e.g. pods, services, replication controllers, and others) are in some namespaces. However namespace resources are not themselves in a namespace. And low-level resources, such as [nodes](/docs/concepts/architecture/nodes/) and [persistentVolumes](/docs/concepts/storage/persistent-volumes/), are not in any namespace. To see which Kubernetes resources are and aren't in a namespace: ```shell # In a namespace kubectl api-resources --namespaced=true # Not in a namespace kubectl api-resources --namespaced=false ``` ## Automatic labelling {{< feature-state for\_k8s\_version="1.22" state="stable" >}} The Kubernetes control plane sets an immutable {{< glossary\_tooltip text="label" term\_id="label" >}} `kubernetes.io/metadata.name` on all namespaces. The value of the label is the namespace name. ## {{% heading "whatsnext" %}} \* Learn more about [creating a new namespace](/docs/tasks/administer-cluster/namespaces/#creating-a-new-namespace). \* Learn more about [deleting a namespace](/docs/tasks/administer-cluster/namespaces/#deleting-a-namespace).
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/working-with-objects/namespaces.md
|
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You can visualize and manage Kubernetes objects with more tools than kubectl and the dashboard. A common set of labels allows tools to work interoperably, describing objects in a common manner that all tools can understand. In addition to supporting tooling, the recommended labels describe applications in a way that can be queried. The metadata is organized around the concept of an \_application\_. Kubernetes is not a platform as a service (PaaS) and doesn't have or enforce a formal notion of an application. Instead, applications are informal and described with metadata. The definition of what an application contains is loose. {{< note >}} These are recommended labels. They make it easier to manage applications but aren't required for any core tooling. {{< /note >}} Shared labels and annotations share a common prefix: `app.kubernetes.io`. Labels without a prefix are private to users. The shared prefix ensures that shared labels do not interfere with custom user labels. ## Labels In order to take full advantage of using these labels, they should be applied on every resource object. | Key | Description | Example | Type | | ----------------------------------- | --------------------- | -------- | ---- | | `app.kubernetes.io/name` | The name of the application | `mysql` | string | | `app.kubernetes.io/instance` | A unique name identifying the instance of an application | `mysql-abcxyz` | string | | `app.kubernetes.io/version` | The current version of the application (e.g., a [SemVer 1.0](https://semver.org/spec/v1.0.0.html), revision hash, etc.) | `5.7.21` | string | | `app.kubernetes.io/component` | The component within the architecture | `database` | string | | `app.kubernetes.io/part-of` | The name of a higher level application this one is part of | `wordpress` | string | | `app.kubernetes.io/managed-by` | The tool being used to manage the operation of an application | `Helm` | string | To illustrate these labels in action, consider the following {{< glossary\_tooltip text="StatefulSet" term\_id="statefulset" >}} object: ```yaml # This is an excerpt apiVersion: apps/v1 kind: StatefulSet metadata: labels: app.kubernetes.io/name: mysql app.kubernetes.io/instance: mysql-abcxyz app.kubernetes.io/version: "5.7.21" app.kubernetes.io/component: database app.kubernetes.io/part-of: wordpress app.kubernetes.io/managed-by: Helm ``` ## Applications And Instances Of Applications An application can be installed one or more times into a Kubernetes cluster and, in some cases, the same namespace. For example, WordPress can be installed more than once where different websites are different installations of WordPress. The name of an application and the instance name are recorded separately. For example, WordPress has a `app.kubernetes.io/name` of `wordpress` while it has an instance name, represented as `app.kubernetes.io/instance` with a value of `wordpress-abcxyz`. This enables the application and instance of the application to be identifiable. Every instance of an application must have a unique name. ## Examples To illustrate different ways to use these labels the following examples have varying complexity. ### A Simple Stateless Service Consider the case for a simple stateless service deployed using `Deployment` and `Service` objects. The following two snippets represent how the labels could be used in their simplest form. The `Deployment` is used to oversee the pods running the application itself. ```yaml apiVersion: apps/v1 kind: Deployment metadata: labels: app.kubernetes.io/name: myservice app.kubernetes.io/instance: myservice-abcxyz ... ``` The `Service` is used to expose the application. ```yaml apiVersion: v1 kind: Service metadata: labels: app.kubernetes.io/name: myservice app.kubernetes.io/instance: myservice-abcxyz ... ``` ### Web Application With A Database Consider a slightly more complicated application: a web application (WordPress) using a database (MySQL), installed using Helm. The following snippets illustrate the start of objects used to deploy this application. The start to the following `Deployment` is used for WordPress: ```yaml apiVersion: apps/v1 kind: Deployment metadata: labels: app.kubernetes.io/name: wordpress app.kubernetes.io/instance: wordpress-abcxyz app.kubernetes.io/version: "4.9.4" app.kubernetes.io/managed-by: Helm app.kubernetes.io/component: server app.kubernetes.io/part-of: wordpress ... ``` The `Service`
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/working-with-objects/common-labels.md
|
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] | 0.211962
|
(MySQL), installed using Helm. The following snippets illustrate the start of objects used to deploy this application. The start to the following `Deployment` is used for WordPress: ```yaml apiVersion: apps/v1 kind: Deployment metadata: labels: app.kubernetes.io/name: wordpress app.kubernetes.io/instance: wordpress-abcxyz app.kubernetes.io/version: "4.9.4" app.kubernetes.io/managed-by: Helm app.kubernetes.io/component: server app.kubernetes.io/part-of: wordpress ... ``` The `Service` is used to expose WordPress: ```yaml apiVersion: v1 kind: Service metadata: labels: app.kubernetes.io/name: wordpress app.kubernetes.io/instance: wordpress-abcxyz app.kubernetes.io/version: "4.9.4" app.kubernetes.io/managed-by: Helm app.kubernetes.io/component: server app.kubernetes.io/part-of: wordpress ... ``` MySQL is exposed as a `StatefulSet` with metadata for both it and the larger application it belongs to: ```yaml apiVersion: apps/v1 kind: StatefulSet metadata: labels: app.kubernetes.io/name: mysql app.kubernetes.io/instance: mysql-abcxyz app.kubernetes.io/version: "5.7.21" app.kubernetes.io/managed-by: Helm app.kubernetes.io/component: database app.kubernetes.io/part-of: wordpress ... ``` The `Service` is used to expose MySQL as part of WordPress: ```yaml apiVersion: v1 kind: Service metadata: labels: app.kubernetes.io/name: mysql app.kubernetes.io/instance: mysql-abcxyz app.kubernetes.io/version: "5.7.21" app.kubernetes.io/managed-by: Helm app.kubernetes.io/component: database app.kubernetes.io/part-of: wordpress ... ``` With the MySQL `StatefulSet` and `Service` you'll notice information about both MySQL and WordPress, the broader application, are included.
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/working-with-objects/common-labels.md
|
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] | 0.135973
|
You can use Kubernetes annotations to attach arbitrary non-identifying metadata to {{< glossary\_tooltip text="objects" term\_id="object" >}}. Clients such as tools and libraries can retrieve this metadata. ## Attaching metadata to objects You can use either labels or annotations to attach metadata to Kubernetes objects. Labels can be used to select objects and to find collections of objects that satisfy certain conditions. In contrast, annotations are not used to identify and select objects. The metadata in an annotation can be small or large, structured or unstructured, and can include characters not permitted by labels. It is possible to use labels as well as annotations in the metadata of the same object. Annotations, like labels, are key/value maps: ```json "metadata": { "annotations": { "key1" : "value1", "key2" : "value2" } } ``` {{}} The keys and the values in the map must be strings. In other words, you cannot use numeric, boolean, list or other types for either the keys or the values. {{}} Here are some examples of information that could be recorded in annotations: \* Fields managed by a declarative configuration layer. Attaching these fields as annotations distinguishes them from default values set by clients or servers, and from auto-generated fields and fields set by auto-sizing or auto-scaling systems. \* Build, release, or image information like timestamps, release IDs, git branch, PR numbers, image hashes, and registry address. \* Pointers to logging, monitoring, analytics, or audit repositories. \* Client library or tool information that can be used for debugging purposes: for example, name, version, and build information. \* User or tool/system provenance information, such as URLs of related objects from other ecosystem components. \* Lightweight rollout tool metadata: for example, config or checkpoints. \* Phone or pager numbers of persons responsible, or directory entries that specify where that information can be found, such as a team web site. \* Directives from the end-user to the implementations to modify behavior or engage non-standard features. Instead of using annotations, you could store this type of information in an external database or directory, but that would make it much harder to produce shared client libraries and tools for deployment, management, introspection, and the like. ## Syntax and character set \_Annotations\_ are key/value pairs. Valid annotation keys have two segments: an optional prefix and name, separated by a slash (`/`). The name segment is required and must be 63 characters or less, beginning and ending with an alphanumeric character (`[a-z0-9A-Z]`) with dashes (`-`), underscores (`\_`), dots (`.`), and alphanumerics between. The prefix is optional. If specified, the prefix must be a DNS subdomain: a series of DNS labels separated by dots (`.`), not longer than 253 characters in total, followed by a slash (`/`). If the prefix is omitted, the annotation Key is presumed to be private to the user. Automated system components (e.g. `kube-scheduler`, `kube-controller-manager`, `kube-apiserver`, `kubectl`, or other third-party automation) which add annotations to end-user objects must specify a prefix. The `kubernetes.io/` and `k8s.io/` prefixes are reserved for Kubernetes core components. For example, here's a manifest for a Pod that has the annotation `imageregistry: https://hub.docker.com/` : ```yaml apiVersion: v1 kind: Pod metadata: name: annotations-demo annotations: imageregistry: "https://hub.docker.com/" spec: containers: - name: nginx image: nginx:1.14.2 ports: - containerPort: 80 ``` ## {{% heading "whatsnext" %}} - Learn more about [Labels and Selectors](/docs/concepts/overview/working-with-objects/labels/). - Find [Well-known labels, Annotations and Taints](/docs/reference/labels-annotations-taints/)
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/working-with-objects/annotations.md
|
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] | 0.112435
|
Find [Well-known labels, Annotations and Taints](/docs/reference/labels-annotations-taints/)
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/working-with-objects/annotations.md
|
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0.06862149387598038,
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] | 0.20047
|
The Kubernetes API server stores objects, relying on an etcd-compatible backing store (often, the backing storage is etcd itself). Each object is serialized using a particular version of that API type; for example, the v1 representation of a ConfigMap. Kubernetes uses the term \_storage version\_ to describe how an object is stored in your cluster. The Kubernetes API also relies on automatic conversion; for example, if you have a HorizontalPodAutoscaler, then you can interact with that HorizontalPodAutoscaler using any mix of the v1 and v2 versions of the HorizontalPodAutoscaler API. Kubernetes is responsible for converting each API call so that clients do not see what version is actually serialized. For cluster administrators, object storage version is an important concept to understand since it is what links the API representation of the object to the actual encoding in the storage backend. This can be important for when the underlying binary encodings of the object matter, such as for encryption at rest, or API deprecation. The same API may have multiple storage versions that the API Server can then convert to an object schema. A single object that is part of that resource must only have one storage version at any time. This means that the API Server is aware of the binary encodings of the objects and is able to convert between all the stored versions to the API Representation of the object dynamically. The version of an object is separate from the storage version entirely. For example, a `v1alpha1` and `v1beta1` API Object for the same Resource will be encoded the same in storage as long as the storage version has not been updated between the two objects. ## Storage version to resource mapping Every resource will have 1 active storage version at any point in time, meaning that any write to an object will store the object at that storage version. The storage version can be updated however, making it so that objects can be stored at differing versions. One object will only be stored at one storage version at any time. Reads from the API Server will convert the stored data to the API representation of the object. This makes it so that old storage versions can sit indefinitely as long as no updates occur to the object. Writes, on the other hand, will convert the stored object to the new representation upon update. ## Storage versions for custom resources {#CustomResourceDefinition-storage-version} [Custom resources](/docs/concepts/extend-kubernetes/api-extension/custom-resources/#storage) are defined dynamically, and as such differ from built in Kubernetes types with their storage version. Builtin objects generally have their storage encoding defined separately from their API types, where the stored object acts as a hub and the specific version of the resource does not matter apart from being a field in the object schema. However, for custom resources, a certain version of the resource must be set as the storage version. The schema defined by that specific version of the custom resource will be used as the encoding of the resource in the storage layer. See the [advanced CRD featureset](/docs/concepts/extend-kubernetes/api-extension/custom-resources/#advanced-features-and-flexibility) for more detailed information on the API setup and versioning. For example see this CustomResourceDefinition for \_crontabs\_: ```yaml apiVersion: apiextensions.k8s.io/v1 kind: CustomResourceDefinition metadata: name: crontabs.example.com spec: group: example.com # list of versions supported by this CustomResourceDefinition versions: - name: v1beta1 # Each version can be enabled/disabled by Served flag. served: true # One and only one version must be marked as the storage version. storage: true schema: openAPIV3Schema: type: object properties: host: type: string port: type: string - name: v1 served: true storage: false schema: openAPIV3Schema: type: object properties: host: type: string port:
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/working-with-objects/storage-version.md
|
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|
can be enabled/disabled by Served flag. served: true # One and only one version must be marked as the storage version. storage: true schema: openAPIV3Schema: type: object properties: host: type: string port: type: string - name: v1 served: true storage: false schema: openAPIV3Schema: type: object properties: host: type: string port: type: string time: type: string conversion: strategy: None scope: Namespaced names: plural: crontabs singular: crontab kind: CronTab shortNames: - ct ``` The `v1beta1` API definition is used as the storage version, meaning that any updates or creation of `crontabs` will be stored with the object schema of the `v1beta1` api. In this case it actually would mean that the `v1` API object would never be able to store the `time` field since it is not part of the storage definition. This schema is used in the storage layer as the binary encoding of the object itself. Trying to set two versions as the stored version at the same time is considered invalid, since that would mean that two data schemes would be considered valid ways to store the objects at the same time. Upon modification of the version that is used for storage, that version of the API will be used to store any new or update CRs. Watching or getting the object will have the object be in use but will just convert the object from the old storage version and not affect the object. Only updating or creating will have an effect and use the newly defined storage version. ## How storage versions are relevant to encryption at rest There are tools to [encrypt the at rest storage](/docs/tasks/administer-cluster/kms-provider/) of a cluster, especially for cluster secrets. This adds an additional layer of protection for data exfiltration since the actual stored data in the cluster is encrypted. This means that the API Server is actually decrypting the data as it retrieves them from storage. The APIServer must have the key for that storage version in order to decode the object properly. The storage version in this case is more than just the binary encoding of the object. As long as what is stored can be somehow converted into the API object, it can be used as a storage version. ## Migrating to a different storage version Multiple storage versions for a single resource can pose problems for cluster administrators. A cluster administrator may not remove old versions of an API for CRDs which may be unsupported until they are sure that all objects are no longer using the storege version associated with it. With a large number of objects and an opaque view into which ones are new and which ones still are backed by old storage versions, it makes it difficult to tell when a version can be safely removed. If a version is removed prematurely, it can mean being unable to read the object entirely. Another important issue is the use of encryption keys as defined in the section above. Since a resource must be actively in use to update the storage version, when a key rotation is done, both the old encryption key and the new encryption key must remain in use until the administrator is sure all objects have been written to at least once. This poses both security risks and usability issues, since a key cannot be fully removed from use until then. See [storage version migration](/docs/tasks/manage-kubernetes-objects/storage-version-migration) on examples of how to run a migration to ensure that all objects are using a newer storage version without manual intervention.
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/working-with-objects/storage-version.md
|
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] | 0.020963
|
cannot be fully removed from use until then. See [storage version migration](/docs/tasks/manage-kubernetes-objects/storage-version-migration) on examples of how to run a migration to ensure that all objects are using a newer storage version without manual intervention.
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/working-with-objects/storage-version.md
|
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|
In Kubernetes, some {{< glossary\_tooltip text="objects" term\_id="object" >}} are \*owners\* of other objects. For example, a {{}} is the owner of a set of Pods. These owned objects are \*dependents\* of their owner. Ownership is different from the [labels and selectors](/docs/concepts/overview/working-with-objects/labels/) mechanism that some resources also use. For example, consider a Service that creates `EndpointSlice` objects. The Service uses {{}} to allow the control plane to determine which `EndpointSlice` objects are used for that Service. In addition to the labels, each `EndpointSlice` that is managed on behalf of a Service has an owner reference. Owner references help different parts of Kubernetes avoid interfering with objects they don’t control. ## Owner references in object specifications Dependent objects have a `metadata.ownerReferences` field that references their owner object. A valid owner reference consists of the object name and a {{}} within the same {{}} as the dependent object. Kubernetes sets the value of this field automatically for objects that are dependents of other objects like ReplicaSets, DaemonSets, Deployments, Jobs and CronJobs, and ReplicationControllers. You can also configure these relationships manually by changing the value of this field. However, you usually don't need to and can allow Kubernetes to automatically manage the relationships. Dependent objects also have an `ownerReferences.blockOwnerDeletion` field that takes a boolean value and controls whether specific dependents can block garbage collection from deleting their owner object. Kubernetes automatically sets this field to `true` if a {{}} (for example, the Deployment controller) sets the value of the `metadata.ownerReferences` field. You can also set the value of the `blockOwnerDeletion` field manually to control which dependents block garbage collection. A Kubernetes admission controller controls user access to change this field for dependent resources, based on the delete permissions of the owner. This control prevents unauthorized users from delaying owner object deletion. {{< note >}} Cross-namespace owner references are disallowed by design. Namespaced dependents can specify cluster-scoped or namespaced owners. A namespaced owner \*\*must\*\* exist in the same namespace as the dependent. If it does not, the owner reference is treated as absent, and the dependent is subject to deletion once all owners are verified absent. Cluster-scoped dependents can only specify cluster-scoped owners. In v1.20+, if a cluster-scoped dependent specifies a namespaced kind as an owner, it is treated as having an unresolvable owner reference, and is not able to be garbage collected. In v1.20+, if the garbage collector detects an invalid cross-namespace `ownerReference`, or a cluster-scoped dependent with an `ownerReference` referencing a namespaced kind, a warning Event with a reason of `OwnerRefInvalidNamespace` and an `involvedObject` of the invalid dependent is reported. You can check for that kind of Event by running `kubectl get events -A --field-selector=reason=OwnerRefInvalidNamespace`. {{< /note >}} ## Ownership and finalizers When you tell Kubernetes to delete a resource, the API server allows the managing controller to process any [finalizer rules](/docs/concepts/overview/working-with-objects/finalizers/) for the resource. {{}} prevent accidental deletion of resources your cluster may still need to function correctly. For example, if you try to delete a [PersistentVolume](/docs/concepts/storage/persistent-volumes/) that is still in use by a Pod, the deletion does not happen immediately because the `PersistentVolume` has the `kubernetes.io/pv-protection` finalizer on it. Instead, the [volume](/docs/concepts/storage/volumes/) remains in the `Terminating` status until Kubernetes clears the finalizer, which only happens after the `PersistentVolume` is no longer bound to a Pod. Kubernetes also adds finalizers to an owner resource when you use either [foreground or orphan cascading deletion](/docs/concepts/architecture/garbage-collection/#cascading-deletion). In foreground deletion, it adds the `foreground` finalizer so that the controller must delete dependent resources that also have `ownerReferences.blockOwnerDeletion=true` before it deletes the owner. If you specify an orphan deletion policy, Kubernetes adds the `orphan` finalizer so that
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/working-with-objects/owners-dependents.md
|
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|
owner resource when you use either [foreground or orphan cascading deletion](/docs/concepts/architecture/garbage-collection/#cascading-deletion). In foreground deletion, it adds the `foreground` finalizer so that the controller must delete dependent resources that also have `ownerReferences.blockOwnerDeletion=true` before it deletes the owner. If you specify an orphan deletion policy, Kubernetes adds the `orphan` finalizer so that the controller ignores dependent resources after it deletes the owner object. ## {{% heading "whatsnext" %}} \* Learn more about [Kubernetes finalizers](/docs/concepts/overview/working-with-objects/finalizers/). \* Learn about [garbage collection](/docs/concepts/architecture/garbage-collection). \* Read the API reference for [object metadata](/docs/reference/kubernetes-api/common-definitions/object-meta/#System).
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/working-with-objects/owners-dependents.md
|
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] | 0.109722
|
\_Labels\_ are key/value pairs that are attached to {{< glossary\_tooltip text="objects" term\_id="object" >}} such as Pods. Labels are intended to be used to specify identifying attributes of objects that are meaningful and relevant to users, but do not directly imply semantics to the core system. Labels can be used to organize and to select subsets of objects. Labels can be attached to objects at creation time and subsequently added and modified at any time. Each object can have a set of key/value labels defined. Each Key must be unique for a given object. ```json "metadata": { "labels": { "key1" : "value1", "key2" : "value2" } } ``` Labels allow for efficient queries and watches and are ideal for use in UIs and CLIs. Non-identifying information should be recorded using [annotations](/docs/concepts/overview/working-with-objects/annotations/). ## Motivation Labels enable users to map their own organizational structures onto system objects in a loosely coupled fashion, without requiring clients to store these mappings. Service deployments and batch processing pipelines are often multi-dimensional entities (e.g., multiple partitions or deployments, multiple release tracks, multiple tiers, multiple micro-services per tier). Management often requires cross-cutting operations, which breaks encapsulation of strictly hierarchical representations, especially rigid hierarchies determined by the infrastructure rather than by users. Example labels: \* `"release" : "stable"`, `"release" : "canary"` \* `"environment" : "dev"`, `"environment" : "qa"`, `"environment" : "production"` \* `"tier" : "frontend"`, `"tier" : "backend"`, `"tier" : "cache"` \* `"partition" : "customerA"`, `"partition" : "customerB"` \* `"track" : "daily"`, `"track" : "weekly"` These are examples of [commonly used labels](/docs/concepts/overview/working-with-objects/common-labels/); you are free to develop your own conventions. Keep in mind that label Key must be unique for a given object. ## Syntax and character set \_Labels\_ are key/value pairs. Valid label keys have two segments: an optional prefix and name, separated by a slash (`/`). The name segment is required and must be 63 characters or less, beginning and ending with an alphanumeric character (`[a-z0-9A-Z]`) with dashes (`-`), underscores (`\_`), dots (`.`), and alphanumerics between. The prefix is optional. If specified, the prefix must be a DNS subdomain: a series of DNS labels separated by dots (`.`), not longer than 253 characters in total, followed by a slash (`/`). If the prefix is omitted, the label Key is presumed to be private to the user. Automated system components (e.g. `kube-scheduler`, `kube-controller-manager`, `kube-apiserver`, `kubectl`, or other third-party automation) which add labels to end-user objects must specify a prefix. The `kubernetes.io/` and `k8s.io/` prefixes are [reserved](/docs/reference/labels-annotations-taints/) for Kubernetes core components. Valid label value: \* must be 63 characters or less (can be empty), \* unless empty, must begin and end with an alphanumeric character (`[a-z0-9A-Z]`), \* could contain dashes (`-`), underscores (`\_`), dots (`.`), and alphanumerics between. For example, here's a manifest for a Pod that has two labels `environment: production` and `app: nginx`: ```yaml apiVersion: v1 kind: Pod metadata: name: label-demo labels: environment: production app: nginx spec: containers: - name: nginx image: nginx:1.14.2 ports: - containerPort: 80 ``` ## Label selectors Unlike [names and UIDs](/docs/concepts/overview/working-with-objects/names/), labels do not provide uniqueness. In general, we expect many objects to carry the same label(s). Via a \_label selector\_, the client/user can identify a set of objects. The label selector is the core grouping primitive in Kubernetes. The API currently supports two types of selectors: \_equality-based\_ and \_set-based\_. A label selector can be made of multiple \_requirements\_ which are comma-separated. In the case of multiple requirements, all must be satisfied so the comma separator acts as a logical \_AND\_ (`&&`) operator. The semantics of empty or non-specified selectors are dependent on the context, and API types that use selectors should document
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/working-with-objects/labels.md
|
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|
can be made of multiple \_requirements\_ which are comma-separated. In the case of multiple requirements, all must be satisfied so the comma separator acts as a logical \_AND\_ (`&&`) operator. The semantics of empty or non-specified selectors are dependent on the context, and API types that use selectors should document the validity and meaning of them. {{< note >}} For some API types, such as ReplicaSets, the label selectors of two instances must not overlap within a namespace, or the controller can see that as conflicting instructions and fail to determine how many replicas should be present. {{< /note >}} {{< caution >}} For both equality-based and set-based conditions there is no logical \_OR\_ (`||`) operator. Ensure your filter statements are structured accordingly. {{< /caution >}} ### \_Equality-based\_ requirement \_Equality-\_ or \_inequality-based\_ requirements allow filtering by label keys and values. Matching objects must satisfy all of the specified label constraints, though they may have additional labels as well. Three kinds of operators are admitted `=`,`==`,`!=`. The first two represent \_equality\_ (and are synonyms), while the latter represents \_inequality\_. For example: ``` environment = production tier != frontend ``` The former selects all resources with key equal to `environment` and value equal to `production`. The latter selects all resources with key equal to `tier` and value distinct from `frontend`, and all resources with no labels with the `tier` key. One could filter for resources in `production` excluding `frontend` using the comma operator: `environment=production,tier!=frontend` One usage scenario for equality-based label requirement is for Pods to specify node selection criteria. For example, the sample Pod below selects nodes where the `accelerator` label exists and is set to `nvidia-tesla-p100`. ```yaml apiVersion: v1 kind: Pod metadata: name: cuda-test spec: containers: - name: cuda-test image: "registry.k8s.io/cuda-vector-add:v0.1" resources: limits: nvidia.com/gpu: 1 nodeSelector: accelerator: nvidia-tesla-p100 ``` ### \_Set-based\_ requirement \_Set-based\_ label requirements allow filtering keys according to a set of values. Three kinds of operators are supported: `in`,`notin` and `exists` (only the key identifier). For example: ``` environment in (production, qa) tier notin (frontend, backend) partition !partition ``` - The first example selects all resources with key equal to `environment` and value equal to `production` or `qa`. - The second example selects all resources with key equal to `tier` and values other than `frontend` and `backend`, and all resources with no labels with the `tier` key. - The third example selects all resources including a label with key `partition`; no values are checked. - The fourth example selects all resources without a label with key `partition`; no values are checked. Similarly the comma separator acts as an \_AND\_ operator. So filtering resources with a `partition` key (no matter the value) and with `environment` different than `qa` can be achieved using `partition,environment notin (qa)`. The \_set-based\_ label selector is a general form of equality since `environment=production` is equivalent to `environment in (production)`; similarly for `!=` and `notin`. \_Set-based\_ requirements can be mixed with \_equality-based\_ requirements. For example: `partition in (customerA, customerB),environment!=qa`. ## API ### LIST and WATCH filtering For \*\*list\*\* and \*\*watch\*\* operations, you can specify label selectors to filter the sets of objects returned; you specify the filter using a query parameter. (To learn in detail about watches in Kubernetes, read [efficient detection of changes](/docs/reference/using-api/api-concepts/#efficient-detection-of-changes)). Both requirements are permitted (presented here as they would appear in a URL query string): \* \_equality-based\_ requirements: `?labelSelector=environment%3Dproduction,tier%3Dfrontend` \* \_set-based\_ requirements: `?labelSelector=environment+in+%28production%2Cqa%29%2Ctier+in+%28frontend%29` Both label selector styles can be used to list or watch resources via a REST client. For example, targeting `apiserver` with `kubectl` and using \_equality-based\_ one may write: ```shell kubectl get pods -l environment=production,tier=frontend ``` or using \_set-based\_ requirements: ```shell
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/working-with-objects/labels.md
|
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] | 0.081878
|
query string): \* \_equality-based\_ requirements: `?labelSelector=environment%3Dproduction,tier%3Dfrontend` \* \_set-based\_ requirements: `?labelSelector=environment+in+%28production%2Cqa%29%2Ctier+in+%28frontend%29` Both label selector styles can be used to list or watch resources via a REST client. For example, targeting `apiserver` with `kubectl` and using \_equality-based\_ one may write: ```shell kubectl get pods -l environment=production,tier=frontend ``` or using \_set-based\_ requirements: ```shell kubectl get pods -l 'environment in (production),tier in (frontend)' ``` As already mentioned \_set-based\_ requirements are more expressive. For instance, they can implement the \_OR\_ operator on values: ```shell kubectl get pods -l 'environment in (production, qa)' ``` or restricting negative matching via \_notin\_ operator: ```shell kubectl get pods -l 'environment,environment notin (frontend)' ``` ### Set references in API objects Some Kubernetes objects, such as [`services`](/docs/concepts/services-networking/service/) and [`replicationcontrollers`](/docs/concepts/workloads/controllers/replicationcontroller/), also use label selectors to specify sets of other resources, such as [pods](/docs/concepts/workloads/pods/). #### Service and ReplicationController The set of pods that a `service` targets is defined with a label selector. Similarly, the population of pods that a `replicationcontroller` should manage is also defined with a label selector. Label selectors for both objects are defined in `json` or `yaml` files using maps, and only \_equality-based\_ requirement selectors are supported: ```json "selector": { "component" : "redis", } ``` or ```yaml selector: component: redis ``` This selector (respectively in `json` or `yaml` format) is equivalent to `component=redis` or `component in (redis)`. #### Resources that support set-based requirements Newer resources, such as [`Job`](/docs/concepts/workloads/controllers/job/), [`Deployment`](/docs/concepts/workloads/controllers/deployment/), [`ReplicaSet`](/docs/concepts/workloads/controllers/replicaset/), and [`DaemonSet`](/docs/concepts/workloads/controllers/daemonset/), support \_set-based\_ requirements as well. ```yaml selector: matchLabels: component: redis matchExpressions: - { key: tier, operator: In, values: [cache] } - { key: environment, operator: NotIn, values: [dev] } ``` `matchLabels` is a map of `{key,value}` pairs. A single `{key,value}` in the `matchLabels` map is equivalent to an element of `matchExpressions`, whose `key` field is "key", the `operator` is "In", and the `values` array contains only "value". `matchExpressions` is a list of pod selector requirements. Valid operators include In, NotIn, Exists, and DoesNotExist. The values set must be non-empty in the case of In and NotIn. All of the requirements, from both `matchLabels` and `matchExpressions` are ANDed together -- they must all be satisfied in order to match. #### Selecting sets of nodes One use case for selecting over labels is to constrain the set of nodes onto which a pod can schedule. See the documentation on [node selection](/docs/concepts/scheduling-eviction/assign-pod-node/) for more information. ## Using labels effectively You can apply a single label to any resources, but this is not always the best practice. There are many scenarios where multiple labels should be used to distinguish resource sets from one another. For instance, different applications would use different values for the `app` label, but a multi-tier application, such as the [guestbook example](https://github.com/kubernetes/examples/tree/master/web/guestbook/), would additionally need to distinguish each tier. The frontend could carry the following labels: ```yaml labels: app: guestbook tier: frontend ``` while the Redis master and replica would have different `tier` labels, and perhaps even an additional `role` label: ```yaml labels: app: guestbook tier: backend role: master ``` and ```yaml labels: app: guestbook tier: backend role: replica ``` The labels allow for slicing and dicing the resources along any dimension specified by a label: ```shell kubectl apply -f examples/guestbook/all-in-one/guestbook-all-in-one.yaml kubectl get pods -Lapp -Ltier -Lrole ``` ```none NAME READY STATUS RESTARTS AGE APP TIER ROLE guestbook-fe-4nlpb 1/1 Running 0 1m guestbook frontend guestbook-fe-ght6d 1/1 Running 0 1m guestbook frontend guestbook-fe-jpy62 1/1 Running 0 1m guestbook frontend guestbook-redis-master-5pg3b 1/1 Running 0 1m guestbook backend master guestbook-redis-replica-2q2yf 1/1 Running 0 1m guestbook backend replica guestbook-redis-replica-qgazl 1/1 Running 0 1m guestbook backend replica my-nginx-divi2 1/1 Running 0 29m nginx my-nginx-o0ef1 1/1 Running 0 29m nginx ``` ```shell kubectl get pods
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/working-with-objects/labels.md
|
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|
guestbook frontend guestbook-fe-jpy62 1/1 Running 0 1m guestbook frontend guestbook-redis-master-5pg3b 1/1 Running 0 1m guestbook backend master guestbook-redis-replica-2q2yf 1/1 Running 0 1m guestbook backend replica guestbook-redis-replica-qgazl 1/1 Running 0 1m guestbook backend replica my-nginx-divi2 1/1 Running 0 29m nginx my-nginx-o0ef1 1/1 Running 0 29m nginx ``` ```shell kubectl get pods -lapp=guestbook,role=replica ``` ```none NAME READY STATUS RESTARTS AGE guestbook-redis-replica-2q2yf 1/1 Running 0 3m guestbook-redis-replica-qgazl 1/1 Running 0 3m ``` ## Updating labels Sometimes you may want to relabel existing pods and other resources before creating new resources. This can be done with `kubectl label`. For example, if you want to label all your NGINX Pods as frontend tier, run: ```shell kubectl label pods -l app=nginx tier=fe ``` ```none pod/my-nginx-2035384211-j5fhi labeled pod/my-nginx-2035384211-u2c7e labeled pod/my-nginx-2035384211-u3t6x labeled ``` This first filters all pods with the label "app=nginx", and then labels them with the "tier=fe". To see the pods you labeled, run: ```shell kubectl get pods -l app=nginx -L tier ``` ```none NAME READY STATUS RESTARTS AGE TIER my-nginx-2035384211-j5fhi 1/1 Running 0 23m fe my-nginx-2035384211-u2c7e 1/1 Running 0 23m fe my-nginx-2035384211-u3t6x 1/1 Running 0 23m fe ``` This outputs all "app=nginx" pods, with an additional label column of pods' tier (specified with `-L` or `--label-columns`). For more information, please see [kubectl label](/docs/reference/generated/kubectl/kubectl-commands/#label). ## {{% heading "whatsnext" %}} - Learn how to [add a label to a node](/docs/tasks/configure-pod-container/assign-pods-nodes/#add-a-label-to-a-node) - Find [Well-known labels, Annotations and Taints](/docs/reference/labels-annotations-taints/) - See [Recommended labels](/docs/concepts/overview/working-with-objects/common-labels/) - [Enforce Pod Security Standards with Namespace Labels](/docs/tasks/configure-pod-container/enforce-standards-namespace-labels/) - Read a blog on [Writing a Controller for Pod Labels](/blog/2021/06/21/writing-a-controller-for-pod-labels/)
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/working-with-objects/labels.md
|
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|
The `kubectl` command-line tool supports several different ways to create and manage Kubernetes {{< glossary\_tooltip text="objects" term\_id="object" >}}. This document provides an overview of the different approaches. Read the [Kubectl book](https://kubectl.docs.kubernetes.io) for details of managing objects by Kubectl. ## Management techniques {{< warning >}} A Kubernetes object should be managed using only one technique. Mixing and matching techniques for the same object results in undefined behavior. {{< /warning >}} | Management technique | Operates on |Recommended environment | Supported writers | Learning curve | |----------------------------------|----------------------|------------------------|--------------------|----------------| | Imperative commands | Live objects | Development projects | 1+ | Lowest | | Imperative object configuration | Individual files | Production projects | 1 | Moderate | | Declarative object configuration | Directories of files | Production projects | 1+ | Highest | ## Imperative commands When using imperative commands, a user operates directly on live objects in a cluster. The user provides operations to the `kubectl` command as arguments or flags. This is the recommended way to get started or to run a one-off task in a cluster. Because this technique operates directly on live objects, it provides no history of previous configurations. ### Examples Run an instance of the nginx container by creating a Deployment object: ```sh kubectl create deployment nginx --image nginx ``` ### Trade-offs Advantages compared to object configuration: - Commands are expressed as a single action word. - Commands require only a single step to make changes to the cluster. Disadvantages compared to object configuration: - Commands do not integrate with change review processes. - Commands do not provide an audit trail associated with changes. - Commands do not provide a source of records except for what is live. - Commands do not provide a template for creating new objects. ## Imperative object configuration In imperative object configuration, the kubectl command specifies the operation (create, replace, etc.), optional flags and at least one file name. The file specified must contain a full definition of the object in YAML or JSON format. See the [API reference](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/) for more details on object definitions. {{< warning >}} The imperative `replace` command replaces the existing spec with the newly provided one, dropping all changes to the object missing from the configuration file. This approach should not be used with resource types whose specs are updated independently of the configuration file. Services of type `LoadBalancer`, for example, have their `externalIPs` field updated independently from the configuration by the cluster. {{< /warning >}} ### Examples Create the objects defined in a configuration file: ```sh kubectl create -f nginx.yaml ``` Delete the objects defined in two configuration files: ```sh kubectl delete -f nginx.yaml -f redis.yaml ``` Update the objects defined in a configuration file by overwriting the live configuration: ```sh kubectl replace -f nginx.yaml ``` ### Trade-offs Advantages compared to imperative commands: - Object configuration can be stored in a source control system such as Git. - Object configuration can integrate with processes such as reviewing changes before push and audit trails. - Object configuration provides a template for creating new objects. Disadvantages compared to imperative commands: - Object configuration requires basic understanding of the object schema. - Object configuration requires the additional step of writing a YAML file. Advantages compared to declarative object configuration: - Imperative object configuration behavior is simpler and easier to understand. - As of Kubernetes version 1.5, imperative object configuration is more mature. Disadvantages compared to declarative object configuration: - Imperative object configuration works best on files, not directories. - Updates to live objects must be reflected in configuration files, or they will be lost during the
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/working-with-objects/object-management.md
|
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|
and easier to understand. - As of Kubernetes version 1.5, imperative object configuration is more mature. Disadvantages compared to declarative object configuration: - Imperative object configuration works best on files, not directories. - Updates to live objects must be reflected in configuration files, or they will be lost during the next replacement. ## Declarative object configuration When using declarative object configuration, a user operates on object configuration files stored locally, however the user does not define the operations to be taken on the files. Create, update, and delete operations are automatically detected per-object by `kubectl`. This enables working on directories, where different operations might be needed for different objects. {{< note >}} Declarative object configuration retains changes made by other writers, even if the changes are not merged back to the object configuration file. This is possible by using the `patch` API operation to write only observed differences, instead of using the `replace` API operation to replace the entire object configuration. {{< /note >}} ### Examples Process all object configuration files in the `configs` directory, and create or patch the live objects. You can first `diff` to see what changes are going to be made, and then apply: ```sh kubectl diff -f configs/ kubectl apply -f configs/ ``` Recursively process directories: ```sh kubectl diff -R -f configs/ kubectl apply -R -f configs/ ``` ### Trade-offs Advantages compared to imperative object configuration: - Changes made directly to live objects are retained, even if they are not merged back into the configuration files. - Declarative object configuration has better support for operating on directories and automatically detecting operation types (create, patch, delete) per-object. Disadvantages compared to imperative object configuration: - Declarative object configuration is harder to debug and understand results when they are unexpected. - Partial updates using diffs create complex merge and patch operations. ## {{% heading "whatsnext" %}} - [Managing Kubernetes Objects Using Imperative Commands](/docs/tasks/manage-kubernetes-objects/imperative-command/) - [Imperative Management of Kubernetes Objects Using Configuration Files](/docs/tasks/manage-kubernetes-objects/imperative-config/) - [Declarative Management of Kubernetes Objects Using Configuration Files](/docs/tasks/manage-kubernetes-objects/declarative-config/) - [Declarative Management of Kubernetes Objects Using Kustomize](/docs/tasks/manage-kubernetes-objects/kustomization/) - [Kubectl Command Reference](/docs/reference/generated/kubectl/kubectl-commands/) - [Kubectl Book](https://kubectl.docs.kubernetes.io) - [Kubernetes API Reference](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/)
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/working-with-objects/object-management.md
|
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] | 0.140906
|
This page explains how Kubernetes objects are represented in the Kubernetes API, and how you can express them in `.yaml` format. ## Understanding Kubernetes objects {#kubernetes-objects} \*Kubernetes objects\* are persistent entities in the Kubernetes system. Kubernetes uses these entities to represent the state of your cluster. Specifically, they can describe: \* What containerized applications are running (and on which nodes) \* The resources available to those applications \* The policies around how those applications behave, such as restart policies, upgrades, and fault-tolerance A Kubernetes object is a "record of intent"--once you create the object, the Kubernetes system will constantly work to ensure that the object exists. By creating an object, you're effectively telling the Kubernetes system what you want your cluster's workload to look like; this is your cluster's \*desired state\*. To work with Kubernetes objects—whether to create, modify, or delete them—you'll need to use the [Kubernetes API](/docs/concepts/overview/kubernetes-api/). When you use the `kubectl` command-line interface, for example, the CLI makes the necessary Kubernetes API calls for you. You can also use the Kubernetes API directly in your own programs using one of the [Client Libraries](/docs/reference/using-api/client-libraries/). ### Object spec and status Almost every Kubernetes object includes two nested object fields that govern the object's configuration: the object \*`spec`\* and the object \*`status`\*. For objects that have a `spec`, you have to set this when you create the object, providing a description of the characteristics you want the resource to have: its \_desired state\_. The `status` describes the \_current state\_ of the object, supplied and updated by the Kubernetes system and its components. The Kubernetes {{< glossary\_tooltip text="control plane" term\_id="control-plane" >}} continually and actively manages every object's actual state to match the desired state you supplied. For example: in Kubernetes, a Deployment is an object that can represent an application running on your cluster. When you create the Deployment, you might set the Deployment `spec` to specify that you want three replicas of the application to be running. The Kubernetes system reads the Deployment spec and starts three instances of your desired application--updating the status to match your spec. If any of those instances should fail (a status change), the Kubernetes system responds to the difference between spec and status by making a correction--in this case, starting a replacement instance. For more information on the object spec, status, and metadata, see the [Kubernetes API Conventions](https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md). ### Describing a Kubernetes object When you create an object in Kubernetes, you must provide the object spec that describes its desired state, as well as some basic information about the object (such as a name). When you use the Kubernetes API to create the object (either directly or via `kubectl`), that API request must include that information as JSON in the request body. Most often, you provide the information to `kubectl` in a file known as a \_manifest\_. By convention, manifests are YAML (you could also use JSON format). Tools such as `kubectl` convert the information from a manifest into JSON or another supported serialization format when making the API request over HTTP. Here's an example manifest that shows the required fields and object spec for a Kubernetes Deployment: {{% code\_sample file="application/deployment.yaml" %}} One way to create a Deployment using a manifest file like the one above is to use the [`kubectl apply`](/docs/reference/generated/kubectl/kubectl-commands#apply) command in the `kubectl` command-line interface, passing the `.yaml` file as an argument. Here's an example: ```shell kubectl apply -f https://k8s.io/examples/application/deployment.yaml ``` The output is similar to this: ``` deployment.apps/nginx-deployment created ``` ### Required fields In the manifest (YAML or JSON file) for the Kubernetes object you want to create, you'll need to
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/working-with-objects/_index.md
|
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|
`kubectl` command-line interface, passing the `.yaml` file as an argument. Here's an example: ```shell kubectl apply -f https://k8s.io/examples/application/deployment.yaml ``` The output is similar to this: ``` deployment.apps/nginx-deployment created ``` ### Required fields In the manifest (YAML or JSON file) for the Kubernetes object you want to create, you'll need to set values for the following fields: \* `apiVersion` - Which version of the Kubernetes API you're using to create this object \* `kind` - What kind of object you want to create \* `metadata` - Data that helps uniquely identify the object, including a `name` string, `UID`, and optional `namespace` \* `spec` - What state you desire for the object The precise format of the object `spec` is different for every Kubernetes object, and contains nested fields specific to that object. The [Kubernetes API Reference](/docs/reference/kubernetes-api/) can help you find the spec format for all of the objects you can create using Kubernetes. For example, see the [`spec` field](/docs/reference/kubernetes-api/workload-resources/pod-v1/#PodSpec) for the Pod API reference. For each Pod, the `.spec` field specifies the pod and its desired state (such as the container image name for each container within that pod). Another example of an object specification is the [`spec` field](/docs/reference/kubernetes-api/workload-resources/stateful-set-v1/#StatefulSetSpec) for the StatefulSet API. For StatefulSet, the `.spec` field specifies the StatefulSet and its desired state. Within the `.spec` of a StatefulSet is a [template](/docs/concepts/workloads/pods/#pod-templates) for Pod objects. That template describes Pods that the StatefulSet controller will create in order to satisfy the StatefulSet specification. Different kinds of objects can also have different `.status`; again, the API reference pages detail the structure of that `.status` field, and its content for each different type of object. See [Kubernetes Configuration Best Practices](/blog/2025/11/25/configuration-good-practices/) for additional information on writing YAML configuration files. ## Server side field validation Starting with Kubernetes v1.25, the API server offers server side [field validation](/docs/reference/using-api/api-concepts/#field-validation) that detects unrecognized or duplicate fields in an object. It provides all the functionality of `kubectl --validate` on the server side. The `kubectl` tool uses the `--validate` flag to set the level of field validation. It accepts the values `ignore`, `warn`, and `strict` while also accepting the values `true` (equivalent to `strict`) and `false` (equivalent to `ignore`). The default validation setting for `kubectl` is `--validate=true`. `Strict` : Strict field validation, errors on validation failure `Warn` : Field validation is performed, but errors are exposed as warnings rather than failing the request `Ignore` : No server side field validation is performed When `kubectl` cannot connect to an API server that supports field validation it will fall back to using client-side validation. Kubernetes 1.27 and later versions always offer field validation; older Kubernetes releases might not. If your cluster is older than v1.27, check the documentation for your version of Kubernetes. ## {{% heading "whatsnext" %}} If you're new to Kubernetes, read more about the following: \* [Pods](/docs/concepts/workloads/pods/) which are the most important basic Kubernetes objects. \* [Deployment](/docs/concepts/workloads/controllers/deployment/) objects. \* [Controllers](/docs/concepts/architecture/controller/) in Kubernetes. \* [kubectl](/docs/reference/kubectl/) and [kubectl commands](/docs/reference/generated/kubectl/kubectl-commands). [Kubernetes Object Management](/docs/concepts/overview/working-with-objects/object-management/) explains how to use `kubectl` to manage objects. You might need to [install kubectl](/docs/tasks/tools/#kubectl) if you don't already have it available. To learn about the Kubernetes API in general, visit: \* [Kubernetes API overview](/docs/reference/using-api/) To learn about objects in Kubernetes in more depth, read other pages in this section:
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/overview/working-with-objects/_index.md
|
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|
By default, containers run with unbounded [compute resources](/docs/concepts/configuration/manage-resources-containers/) on a Kubernetes cluster. Using Kubernetes [resource quotas](/docs/concepts/policy/resource-quotas/), administrators (also termed \_cluster operators\_) can restrict consumption and creation of cluster resources (such as CPU time, memory, and persistent storage) within a specified {{< glossary\_tooltip text="namespace" term\_id="namespace" >}}. Within a namespace, a {{< glossary\_tooltip text="Pod" term\_id="Pod" >}} can consume as much CPU and memory as is allowed by the ResourceQuotas that apply to that namespace. As a cluster operator, or as a namespace-level administrator, you might also be concerned about making sure that a single object cannot monopolize all available resources within a namespace. A LimitRange is a policy to constrain the resource allocations (limits and requests) that you can specify for each applicable object kind (such as Pod or {{< glossary\_tooltip text="PersistentVolumeClaim" term\_id="persistent-volume-claim" >}}) in a namespace. A \_LimitRange\_ provides constraints that can: - Enforce minimum and maximum compute resources usage per Pod or Container in a namespace. - Enforce minimum and maximum storage request per {{< glossary\_tooltip text="PersistentVolumeClaim" term\_id="persistent-volume-claim" >}} in a namespace. - Enforce a ratio between request and limit for a resource in a namespace. - Set default request/limit for compute resources in a namespace and automatically inject them to Containers at runtime. Kubernetes constrains resource allocations to Pods in a particular namespace whenever there is at least one LimitRange object in that namespace. The name of a LimitRange object must be a valid [DNS subdomain name](/docs/concepts/overview/working-with-objects/names#dns-subdomain-names). ## Constraints on resource limits and requests - The administrator creates a LimitRange in a namespace. - Users create (or try to create) objects in that namespace, such as Pods or PersistentVolumeClaims. - First, the LimitRange admission controller applies default request and limit values for all Pods (and their containers) that do not set compute resource requirements. - Second, the LimitRange tracks usage to ensure it does not exceed resource minimum, maximum and ratio defined in any LimitRange present in the namespace. - If you attempt to create or update an object (Pod or PersistentVolumeClaim) that violates a LimitRange constraint, your request to the API server will fail with an HTTP status code `403 Forbidden` and a message explaining the constraint that has been violated. - If you add a LimitRange in a namespace that applies to compute-related resources such as `cpu` and `memory`, you must specify requests or limits for those values. Otherwise, the system may reject Pod creation. - LimitRange validations occur only at Pod admission stage, not on running Pods. If you add or modify a LimitRange, the Pods that already exist in that namespace continue unchanged. - If two or more LimitRange objects exist in the namespace, it is not deterministic which default value will be applied. ## LimitRange and admission checks for Pods A LimitRange does \*\*not\*\* check the consistency of the default values it applies. This means that a default value for the \_limit\_ that is set by LimitRange may be less than the \_request\_ value specified for the container in the spec that a client submits to the API server. If that happens, the final Pod will not be schedulable. For example, you define a LimitRange with below manifest: {{< note >}} The following examples operate within the default namespace of your cluster, as the namespace parameter is undefined and the LimitRange scope is limited to the namespace level. This implies that any references or operations within these examples will interact with elements within the default namespace of your cluster. You can override the operating namespace by configuring namespace in the `metadata.namespace` field. {{< /note >}} {{% code\_sample file="concepts/policy/limit-range/problematic-limit-range.yaml" %}} along with a Pod that
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/policy/limit-range.md
|
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|
to the namespace level. This implies that any references or operations within these examples will interact with elements within the default namespace of your cluster. You can override the operating namespace by configuring namespace in the `metadata.namespace` field. {{< /note >}} {{% code\_sample file="concepts/policy/limit-range/problematic-limit-range.yaml" %}} along with a Pod that declares a CPU resource request of `700m`, but not a limit: {{% code\_sample file="concepts/policy/limit-range/example-conflict-with-limitrange-cpu.yaml" %}} then that Pod will not be scheduled, failing with an error similar to: ``` Pod "example-conflict-with-limitrange-cpu" is invalid: spec.containers[0].resources.requests: Invalid value: "700m": must be less than or equal to cpu limit ``` If you set both `request` and `limit`, then that new Pod will be scheduled successfully even with the same LimitRange in place: {{% code\_sample file="concepts/policy/limit-range/example-no-conflict-with-limitrange-cpu.yaml" %}} ## Example resource constraints Examples of policies that could be created using LimitRange are: - In a 2 node cluster with a capacity of 8 GiB RAM and 16 cores, constrain Pods in a namespace to request 100m of CPU with a max limit of 500m for CPU and request 200Mi for Memory with a max limit of 600Mi for Memory. - Define default CPU limit and request to 150m and memory default request to 300Mi for Containers started with no cpu and memory requests in their specs. In the case where the total limits of the namespace is less than the sum of the limits of the Pods/Containers, there may be contention for resources. In this case, the Containers or Pods will not be created. Neither contention nor changes to a LimitRange will affect already created resources. ## {{% heading "whatsnext" %}} For examples on using limits, see: - [how to configure minimum and maximum CPU constraints per namespace](/docs/tasks/administer-cluster/manage-resources/cpu-constraint-namespace/). - [how to configure minimum and maximum Memory constraints per namespace](/docs/tasks/administer-cluster/manage-resources/memory-constraint-namespace/). - [how to configure default CPU Requests and Limits per namespace](/docs/tasks/administer-cluster/manage-resources/cpu-default-namespace/). - [how to configure default Memory Requests and Limits per namespace](/docs/tasks/administer-cluster/manage-resources/memory-default-namespace/). - [how to configure minimum and maximum Storage consumption per namespace](/docs/tasks/administer-cluster/limit-storage-consumption/#limitrange-to-limit-requests-for-storage). - a [detailed example on configuring quota per namespace](/docs/tasks/administer-cluster/manage-resources/quota-memory-cpu-namespace/). Refer to the [LimitRanger design document](https://git.k8s.io/design-proposals-archive/resource-management/admission\_control\_limit\_range.md) for context and historical information.
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/policy/limit-range.md
|
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|
When several users or teams share a cluster with a fixed number of nodes, there is a concern that one team could use more than its fair share of resources. \_Resource quotas\_ are a tool for administrators to address this concern. A resource quota, defined by a ResourceQuota object, provides constraints that limit aggregate resource consumption per {{< glossary\_tooltip text="namespace" term\_id="namespace" >}}. A ResourceQuota can also limit the [quantity of objects that can be created in a namespace](#quota-on-object-count) by API kind, as well as the total amount of {{< glossary\_tooltip text="infrastructure resources" term\_id="infrastructure-resource" >}} that may be consumed by API objects found in that namespace. {{< caution >}} Neither contention nor changes to quota will affect already created resources. {{< /caution >}} ## How Kubernetes ResourceQuotas work ResourceQuotas work like this: - Different teams work in different namespaces. This separation can be enforced with [RBAC](/docs/reference/access-authn-authz/rbac/) or any other [authorization](/docs/reference/access-authn-authz/authorization/) mechanism. - A cluster administrator creates at least one ResourceQuota for each namespace. - To make sure the enforcement stays enforced, the cluster administrator should also restrict access to delete or update that ResourceQuota; for example, by defining a [ValidatingAdmissionPolicy](/docs/reference/access-authn-authz/validating-admission-policy/). - Users create resources (pods, services, etc.) in the namespace, and the quota system tracks usage to ensure it does not exceed hard resource limits defined in a ResourceQuota. You can apply a [scope](#quota-scopes) to a ResourceQuota to limit where it applies, - If creating or updating a resource violates a quota constraint, the control plane rejects that request with HTTP status code `403 Forbidden`. The error includes a message explaining the constraint that would have been violated. - If quotas are enabled in a namespace for {{< glossary\_tooltip text="resource" term\_id="infrastructure-resource" >}} such as `cpu` and `memory`, users must specify requests or limits for those values when they define a Pod; otherwise, the quota system may reject pod creation. The resource quota [walkthrough](/docs/tasks/administer-cluster/manage-resources/quota-memory-cpu-namespace/) shows an example of how to avoid this problem. {{< note >}} \* You can define a [LimitRange](/docs/concepts/policy/limit-range/) to force defaults on pods that make no compute resource requirements (so that users don't have to remember to do that). {{< /note >}} You often do not create Pods directly; for example, you more usually create a [workload management](/docs/concepts/workloads/controllers/) object such as a {{< glossary\_tooltip term\_id="deployment" >}}. If you create a Deployment that tries to use more resources than are available, the creation of the Deployment (or other workload management object) \*\*succeeds\*\*, but the Deployment may not be able to get all of the Pods it manages to exist. In that case you can check the status of the Deployment, for example with `kubectl describe`, to see what has happened. - For `cpu` and `memory` resources, ResourceQuotas enforce that \*\*every\*\* (new) pod in that namespace sets a limit for that resource. If you enforce a resource quota in a namespace for either `cpu` or `memory`, you and other clients, \*\*must\*\* specify either `requests` or `limits` for that resource, for every new Pod you submit. If you don't, the control plane may reject admission for that Pod. - For other resources: ResourceQuota works and will ignore pods in the namespace without setting a limit or request for that resource. It means that you can create a new pod without limit/request for ephemeral storage if the resource quota limits the ephemeral storage of this namespace. You can use a [LimitRange](/docs/concepts/policy/limit-range/) to automatically set a default request for these resources. The name of a ResourceQuota object must be a valid [DNS subdomain name](/docs/concepts/overview/working-with-objects/names#dns-subdomain-names). Examples of policies that could be created using namespaces and quotas are: - In a cluster with a capacity
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/policy/resource-quotas.md
|
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] | 0.105629
|
storage of this namespace. You can use a [LimitRange](/docs/concepts/policy/limit-range/) to automatically set a default request for these resources. The name of a ResourceQuota object must be a valid [DNS subdomain name](/docs/concepts/overview/working-with-objects/names#dns-subdomain-names). Examples of policies that could be created using namespaces and quotas are: - In a cluster with a capacity of 32 GiB RAM, and 16 cores, let team A use 20 GiB and 10 cores, let B use 10GiB and 4 cores, and hold 2GiB and 2 cores in reserve for future allocation. - Limit the "testing" namespace to using 1 core and 1GiB RAM. Let the "production" namespace use any amount. In the case where the total capacity of the cluster is less than the sum of the quotas of the namespaces, there may be contention for resources. This is handled on a first-come-first-served basis. ## Enabling Resource Quota ResourceQuota support is enabled by default for many Kubernetes distributions. It is enabled when the {{< glossary\_tooltip text="API server" term\_id="kube-apiserver" >}} `--enable-admission-plugins=` flag has `ResourceQuota` as one of its arguments. A resource quota is enforced in a particular namespace when there is a ResourceQuota in that namespace. ## Types of resource quota The ResourceQuota mechanism lets you enforce different kinds of limits. This section describes the types of limit that you can enforce. ### Quota for infrastructure resources {#compute-resource-quota} You can limit the total sum of [compute resources](/docs/concepts/configuration/manage-resources-containers/) that can be requested in a given namespace. The following resource types are supported: | Resource Name | Description | | ------------- | ----------- | | `limits.cpu` | Across all pods in a non-terminal state, the sum of CPU limits cannot exceed this value. | | `limits.memory` | Across all pods in a non-terminal state, the sum of memory limits cannot exceed this value. | | `requests.cpu` | Across all pods in a non-terminal state, the sum of CPU requests cannot exceed this value. | | `requests.memory` | Across all pods in a non-terminal state, the sum of memory requests cannot exceed this value. | | `hugepages-` | Across all pods in a non-terminal state, the number of huge page requests of the specified size cannot exceed this value. | | `cpu` | Same as `requests.cpu` | | `memory` | Same as `requests.memory` | ### Quota for extended resources In addition to the resources mentioned above, in release 1.10, quota support for [extended resources](/docs/concepts/configuration/manage-resources-containers/#extended-resources) is added. As overcommit is not allowed for extended resources, it makes no sense to specify both `requests` and `limits` for the same extended resource in a quota. So for extended resources, only quota items with prefix `requests.` are allowed. Take the GPU resource as an example, if the resource name is `nvidia.com/gpu`, and you want to limit the total number of GPUs requested in a namespace to 4, you can define a quota as follows: \* `requests.nvidia.com/gpu: 4` See [Viewing and Setting Quotas](#viewing-and-setting-quotas) for more details. ### Quota for DRA resource claims DRA (Dynamic Resource Allocation) resource claims can request DRA resources by device class. For an example device class named `examplegpu`, you want to limit the total number of GPUs requested in a namespace to 4, you can define a quota as follows: \* `examplegpu.deviceclass.resource.k8s.io/devices: 4` When [Extended Resource allocation by DRA](/docs/concepts/scheduling-eviction/dynamic-resource-allocation/#extended-resource) is enabled, the same device class named `examplegpu` can be requested via extended resource either explicitly when the device class's ExtendedResourceName field is given, say, `example.com/gpu`, then you can define a quota as follows: \* `requests.example.com/gpu: 4` or implicitly using the derived extended resource name from device class name `examplegpu`, you can define a quota as follows: \* `requests.deviceclass.resource.kubernetes.io/examplegpu: 4` All devices requested
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/policy/resource-quotas.md
|
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|
resource either explicitly when the device class's ExtendedResourceName field is given, say, `example.com/gpu`, then you can define a quota as follows: \* `requests.example.com/gpu: 4` or implicitly using the derived extended resource name from device class name `examplegpu`, you can define a quota as follows: \* `requests.deviceclass.resource.kubernetes.io/examplegpu: 4` All devices requested from resource claims or extended resources are counted towards all three quotas listed above. The extended resource quota e.g. `requests.example.com/gpu: 4`, also counts the devices provided by device plugin. See [Viewing and Setting Quotas](#viewing-and-setting-quotas) for more details. ### Quota for storage You can limit the total sum of [storage](/docs/concepts/storage/persistent-volumes/) for volumes that can be requested in a given namespace. In addition, you can limit consumption of storage resources based on associated [StorageClass](/docs/concepts/storage/storage-classes/). | Resource Name | Description | | ------------- | ----------- | | `requests.storage` | Across all persistent volume claims, the sum of storage requests cannot exceed this value. | | `persistentvolumeclaims` | The total number of [PersistentVolumeClaims](/docs/concepts/storage/persistent-volumes/#persistentvolumeclaims) that can exist in the namespace. | | `.storageclass.storage.k8s.io/requests.storage` | Across all persistent volume claims associated with the ``, the sum of storage requests cannot exceed this value. | | `.storageclass.storage.k8s.io/persistentvolumeclaims` | Across all persistent volume claims associated with the ``, the total number of [persistent volume claims](/docs/concepts/storage/persistent-volumes/#persistentvolumeclaims) that can exist in the namespace. | For example, if you want to quota storage with `gold` StorageClass separate from a `bronze` StorageClass, you can define a quota as follows: \* `gold.storageclass.storage.k8s.io/requests.storage: 500Gi` \* `bronze.storageclass.storage.k8s.io/requests.storage: 100Gi` #### Quota for local ephemeral storage {{< feature-state for\_k8s\_version="v1.8" state="alpha" >}} | Resource Name | Description | | ------------- | ----------- | | `requests.ephemeral-storage` | Across all pods in the namespace, the sum of local ephemeral storage requests cannot exceed this value. | | `limits.ephemeral-storage` | Across all pods in the namespace, the sum of local ephemeral storage limits cannot exceed this value. | | `ephemeral-storage` | Same as `requests.ephemeral-storage`. | {{< note >}} When using a CRI container runtime, container logs will count against the ephemeral storage quota. This can result in the unexpected eviction of pods that have exhausted their storage quotas. Refer to [Logging Architecture](/docs/concepts/cluster-administration/logging/) for details. {{< /note >}} ### Quota on object count You can set quota for \*the total number of one particular {{< glossary\_tooltip text="resource" term\_id="api-resource" >}} kind\* in the Kubernetes API, using the following syntax: \* `count/.` for resources from non-core API groups \* `count/` for resources from the core API group For example, the PodTemplate API is in the core API group and so if you want to limit the number of PodTemplate objects in a namespace, you use `count/podtemplates`. These types of quotas are useful to protect against exhaustion of control plane storage. For example, you may want to limit the number of Secrets in a server given their large size. Too many Secrets in a cluster can actually prevent servers and controllers from starting. You can set a quota for Jobs to protect against a poorly configured CronJob. CronJobs that create too many Jobs in a namespace can lead to a denial of service. If you define a quota this way, it applies to Kubernetes' APIs that are part of the API server, and to any custom resources backed by a CustomResourceDefinition. For example, to create a quota on a `widgets` custom resource in the `example.com` API group, use `count/widgets.example.com`. If you use [API aggregation](/docs/concepts/extend-kubernetes/api-extension/apiserver-aggregation/) to add additional, custom APIs that are not defined as CustomResourceDefinitions, the core Kubernetes control plane does not enforce quota for the aggregated API. The extension API server is expected to provide quota enforcement if that's appropriate for the custom API.
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/policy/resource-quotas.md
|
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] | 0.088768
|
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