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You can constrain a {{< glossary\_tooltip text="Pod" term\_id="pod" >}} so that it is \_restricted\_ to run on particular {{< glossary\_tooltip text="node(s)" term\_id="node" >}}, or to \_prefer\_ to run on particular nodes. There are several ways to do this and the recommended approaches all use [label selectors](/docs/concepts/overview/working-with-objects/labels/) to facilitate the selection. Often, you do not need to set any such constraints; the {{< glossary\_tooltip text="scheduler" term\_id="kube-scheduler" >}} will automatically do a reasonable placement (for example, spreading your Pods across nodes so as not place Pods on a node with insufficient free resources). However, there are some circumstances where you may want to control which node the Pod deploys to, for example, to ensure that a Pod ends up on a node with an SSD attached to it, or to co-locate Pods from two different services that communicate a lot into the same availability zone. You can use any of the following methods to choose where Kubernetes schedules specific Pods: - [nodeSelector](#nodeselector) field matching against [node labels](#built-in-node-labels) - [Affinity and anti-affinity](#affinity-and-anti-affinity) - [nodeName](#nodename) field - [Pod topology spread constraints](#pod-topology-spread-constraints) ## Node labels {#built-in-node-labels} Like many other Kubernetes objects, nodes have [labels](/docs/concepts/overview/working-with-objects/labels/). You can [attach labels manually](/docs/tasks/configure-pod-container/assign-pods-nodes/#add-a-label-to-a-node). Kubernetes also populates a [standard set of labels](/docs/reference/node/node-labels/) on all nodes in a cluster. {{}} The value of these labels is cloud provider specific and is not guaranteed to be reliable. For example, the value of `kubernetes.io/hostname` may be the same as the node name in some environments and a different value in other environments. {{}} ### Node isolation/restriction Adding labels to nodes allows you to target Pods for scheduling on specific nodes or groups of nodes. You can use this functionality to ensure that specific Pods only run on nodes with certain isolation, security, or regulatory properties. If you use labels for node isolation, choose label keys that the {{}} cannot modify. This prevents a compromised node from setting those labels on itself so that the scheduler schedules workloads onto the compromised node. The [`NodeRestriction` admission plugin](/docs/reference/access-authn-authz/admission-controllers/#noderestriction) prevents the kubelet from setting or modifying labels with a `node-restriction.kubernetes.io/` prefix. To make use of that label prefix for node isolation: 1. Ensure you are using the [Node authorizer](/docs/reference/access-authn-authz/node/) and have \_enabled\_ the `NodeRestriction` admission plugin. 2. Add labels with the `node-restriction.kubernetes.io/` prefix to your nodes, and use those labels in your [node selectors](#nodeselector). For example, `example.com.node-restriction.kubernetes.io/fips=true` or `example.com.node-restriction.kubernetes.io/pci-dss=true`. ## nodeSelector `nodeSelector` is the simplest recommended form of node selection constraint. You can add the `nodeSelector` field to your Pod specification and specify the [node labels](#built-in-node-labels) you want the target node to have. Kubernetes only schedules the Pod onto nodes that have each of the labels you specify. See [Assign Pods to Nodes](/docs/tasks/configure-pod-container/assign-pods-nodes) for more information. ## Affinity and anti-affinity `nodeSelector` is the simplest way to constrain Pods to nodes with specific labels. Affinity and anti-affinity expand the types of constraints you can define. Some of the benefits of affinity and anti-affinity include: - The affinity/anti-affinity language is more expressive. `nodeSelector` only selects nodes with all the specified labels. Affinity/anti-affinity gives you more control over the selection logic. - You can indicate that a rule is \*soft\* or \*preferred\*, so that the scheduler still schedules the Pod even if it can't find a matching node. - You can constrain a Pod using labels on other Pods running on the node (or other topological domain), instead of just node labels, which allows you to define rules for which Pods can be co-located on a node. The affinity feature consists of two types of affinity: - \*Node affinity\* functions like the `nodeSelector` field but is more expressive and allows
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/assign-pod-node.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/assign-pod-node.md
|
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on the node (or other topological domain), instead of just node labels, which allows you to define rules for which Pods can be co-located on a node. The affinity feature consists of two types of affinity: - \*Node affinity\* functions like the `nodeSelector` field but is more expressive and allows you to specify soft rules. - \*Inter-pod affinity/anti-affinity\* allows you to constrain Pods against labels on other Pods. ### Node affinity Node affinity is conceptually similar to `nodeSelector`, allowing you to constrain which nodes your Pod can be scheduled on based on node labels. There are two types of node affinity: - `requiredDuringSchedulingIgnoredDuringExecution`: The scheduler can't schedule the Pod unless the rule is met. This functions like `nodeSelector`, but with a more expressive syntax. - `preferredDuringSchedulingIgnoredDuringExecution`: The scheduler tries to find a node that meets the rule. If a matching node is not available, the scheduler still schedules the Pod. {{}} In the preceding types, `IgnoredDuringExecution` means that if the node labels change after Kubernetes schedules the Pod, the Pod continues to run. {{}} You can specify node affinities using the `.spec.affinity.nodeAffinity` field in your Pod spec. For example, consider the following Pod spec: {{% code\_sample file="pods/pod-with-node-affinity.yaml" %}} In this example, the following rules apply: - The node \*must\* have a label with the key `topology.kubernetes.io/zone` and the value of that label \*must\* be either `antarctica-east1` or `antarctica-west1`. - The node \*preferably\* has a label with the key `another-node-label-key` and the value `another-node-label-value`. You can use the `operator` field to specify a logical operator for Kubernetes to use when interpreting the rules. You can use `In`, `NotIn`, `Exists`, `DoesNotExist`, `Gt` and `Lt`. Read [Operators](#operators) to learn more about how these work. `NotIn` and `DoesNotExist` allow you to define node anti-affinity behavior. Alternatively, you can use [node taints](/docs/concepts/scheduling-eviction/taint-and-toleration/) to repel Pods from specific nodes. {{}} If you specify both `nodeSelector` and `nodeAffinity`, \*both\* must be satisfied for the Pod to be scheduled onto a node. If you specify multiple terms in `nodeSelectorTerms` associated with `nodeAffinity` types, then the Pod can be scheduled onto a node if one of the specified terms can be satisfied (terms are ORed). If you specify multiple expressions in a single `matchExpressions` field associated with a term in `nodeSelectorTerms`, then the Pod can be scheduled onto a node only if all the expressions are satisfied (expressions are ANDed). {{}} See [Assign Pods to Nodes using Node Affinity](/docs/tasks/configure-pod-container/assign-pods-nodes-using-node-affinity/) for more information. #### Node affinity weight You can specify a `weight` between 1 and 100 for each instance of the `preferredDuringSchedulingIgnoredDuringExecution` affinity type. When the scheduler finds nodes that meet all the other scheduling requirements of the Pod, the scheduler iterates through every preferred rule that the node satisfies and adds the value of the `weight` for that expression to a sum. The final sum is added to the score of other priority functions for the node. Nodes with the highest total score are prioritized when the scheduler makes a scheduling decision for the Pod. For example, consider the following Pod spec: {{% code\_sample file="pods/pod-with-affinity-preferred-weight.yaml" %}} If there are two possible nodes that match the `preferredDuringSchedulingIgnoredDuringExecution` rule, one with the `label-1:key-1` label and another with the `label-2:key-2` label, the scheduler considers the `weight` of each node and adds the weight to the other scores for that node, and schedules the Pod onto the node with the highest final score. {{}} If you want Kubernetes to successfully schedule the Pods in this example, you must have existing nodes with the `kubernetes.io/os=linux` label. {{}} #### Node affinity per scheduling profile {{< feature-state for\_k8s\_version="v1.20" state="beta" >}} When configuring multiple [scheduling profiles](/docs/reference/scheduling/config/#multiple-profiles), you
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/assign-pod-node.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/assign-pod-node.md
|
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|
the Pod onto the node with the highest final score. {{}} If you want Kubernetes to successfully schedule the Pods in this example, you must have existing nodes with the `kubernetes.io/os=linux` label. {{}} #### Node affinity per scheduling profile {{< feature-state for\_k8s\_version="v1.20" state="beta" >}} When configuring multiple [scheduling profiles](/docs/reference/scheduling/config/#multiple-profiles), you can associate a profile with a node affinity, which is useful if a profile only applies to a specific set of nodes. To do so, add an `addedAffinity` to the `args` field of the [`NodeAffinity` plugin](/docs/reference/scheduling/config/#scheduling-plugins) in the [scheduler configuration](/docs/reference/scheduling/config/). For example: ```yaml apiVersion: kubescheduler.config.k8s.io/v1 kind: KubeSchedulerConfiguration profiles: - schedulerName: default-scheduler - schedulerName: foo-scheduler pluginConfig: - name: NodeAffinity args: addedAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: - matchExpressions: - key: scheduler-profile operator: In values: - foo ``` The `addedAffinity` is applied to all Pods that set `.spec.schedulerName` to `foo-scheduler`, in addition to the NodeAffinity specified in the PodSpec. That is, in order to match the Pod, nodes need to satisfy `addedAffinity` and the Pod's `.spec.NodeAffinity`. Since the `addedAffinity` is not visible to end users, its behavior might be unexpected to them. Use node labels that have a clear correlation to the scheduler profile name. {{< note >}} The DaemonSet controller, which [creates Pods for DaemonSets](/docs/concepts/workloads/controllers/daemonset/#how-daemon-pods-are-scheduled), does not support scheduling profiles. When the DaemonSet controller creates Pods, the default Kubernetes scheduler places those Pods and honors any `nodeAffinity` rules in the DaemonSet controller. {{< /note >}} ### Inter-pod affinity and anti-affinity Inter-pod affinity and anti-affinity allow you to constrain which nodes your Pods can be scheduled on based on the labels of Pods already running on that node, instead of the node labels. #### Types of Inter-pod Affinity and Anti-affinity Inter-pod affinity and anti-affinity take the form "this Pod should (or, in the case of anti-affinity, should not) run in an X if that X is already running one or more Pods that meet rule Y", where X is a topology domain like node, rack, cloud provider zone or region, or similar and Y is the rule Kubernetes tries to satisfy. You express these rules (Y) as [label selectors](/docs/concepts/overview/working-with-objects/labels/#label-selectors) with an optional associated list of namespaces. Pods are namespaced objects in Kubernetes, so Pod labels also implicitly have namespaces. Any label selectors for Pod labels should specify the namespaces in which Kubernetes should look for those labels. You express the topology domain (X) using a `topologyKey`, which is the key for the node label that the system uses to denote the domain. For examples, see [Well-Known Labels, Annotations and Taints](/docs/reference/labels-annotations-taints/). {{< note >}} Inter-pod affinity and anti-affinity require substantial amounts of processing which can slow down scheduling in large clusters significantly. We do not recommend using them in clusters larger than several hundred nodes. {{}} {{< note >}} Pod anti-affinity requires nodes to be consistently labeled, in other words, every node in the cluster must have an appropriate label matching `topologyKey`. If some or all nodes are missing the specified `topologyKey` label, it can lead to unintended behavior. {{}} Similar to [node affinity](#node-affinity) are two types of Pod affinity and anti-affinity as follows: - `requiredDuringSchedulingIgnoredDuringExecution` - `preferredDuringSchedulingIgnoredDuringExecution` For example, you could use `requiredDuringSchedulingIgnoredDuringExecution` affinity to tell the scheduler to co-locate Pods of two services in the same cloud provider zone because they communicate with each other a lot. Similarly, you could use `preferredDuringSchedulingIgnoredDuringExecution` anti-affinity to spread Pods from a service across multiple cloud provider zones. To use inter-pod affinity, use the `affinity.podAffinity` field in the Pod spec. For inter-pod anti-affinity, use the `affinity.podAntiAffinity` field in the Pod spec. #### Scheduling Behavior When scheduling a new Pod, the Kubernetes scheduler evaluates the Pod's affinity/anti-affinity rules in
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/assign-pod-node.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/assign-pod-node.md
|
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spread Pods from a service across multiple cloud provider zones. To use inter-pod affinity, use the `affinity.podAffinity` field in the Pod spec. For inter-pod anti-affinity, use the `affinity.podAntiAffinity` field in the Pod spec. #### Scheduling Behavior When scheduling a new Pod, the Kubernetes scheduler evaluates the Pod's affinity/anti-affinity rules in the context of the current cluster state: 1. Hard Constraints (Node Filtering): - `podAffinity.requiredDuringSchedulingIgnoredDuringExecution` and `podAntiAffinity.requiredDuringSchedulingIgnoredDuringExecution`: - The scheduler ensures the new Pod is assigned to nodes that satisfy these required affinity and anti-affinity rules based on existing Pods. 2. Soft Constraints (Scoring): - `podAffinity.preferredDuringSchedulingIgnoredDuringExecution` and `podAntiAffinity.preferredDuringSchedulingIgnoredDuringExecution`: - The scheduler scores nodes based on how well they meet these preferred affinity and anti-affinity rules to optimize Pod placement. 3. Ignored Fields: - Existing Pods' `podAffinity.preferredDuringSchedulingIgnoredDuringExecution`: - These preferred affinity rules are not considered during the scheduling decision for new Pods. - Existing Pods' `podAntiAffinity.preferredDuringSchedulingIgnoredDuringExecution`: - Similarly, preferred anti-affinity rules of existing Pods are ignored during scheduling. #### Scheduling a Group of Pods with Inter-pod Affinity to Themselves If the current Pod being scheduled is the first in a series that have affinity to themselves, it is allowed to be scheduled if it passes all other affinity checks. This is determined by verifying that no other Pod in the cluster matches the namespace and selector of this Pod, that the Pod matches its own terms, and the chosen node matches all requested topologies. This ensures that there will not be a deadlock even if all the Pods have inter-pod affinity specified. #### Pod Affinity Example {#an-example-of-a-pod-that-uses-pod-affinity} Consider the following Pod spec: {{% code\_sample file="pods/pod-with-pod-affinity.yaml" %}} This example defines one Pod affinity rule and one Pod anti-affinity rule. The Pod affinity rule uses the "hard" `requiredDuringSchedulingIgnoredDuringExecution`, while the anti-affinity rule uses the "soft" `preferredDuringSchedulingIgnoredDuringExecution`. The affinity rule specifies that the scheduler is allowed to place the example Pod on a node only if that node belongs to a specific [zone](/docs/concepts/scheduling-eviction/topology-spread-constraints/) where other Pods have been labeled with `security=S1`. For instance, if we have a cluster with a designated zone, let's call it "Zone V," consisting of nodes labeled with `topology.kubernetes.io/zone=V`, the scheduler can assign the Pod to any node within Zone V, as long as there is at least one Pod within Zone V already labeled with `security=S1`. Conversely, if there are no Pods with `security=S1` labels in Zone V, the scheduler will not assign the example Pod to any node in that zone. The anti-affinity rule specifies that the scheduler should try to avoid scheduling the Pod on a node if that node belongs to a specific [zone](/docs/concepts/scheduling-eviction/topology-spread-constraints/) where other Pods have been labeled with `security=S2`. For instance, if we have a cluster with a designated zone, let's call it "Zone R," consisting of nodes labeled with `topology.kubernetes.io/zone=R`, the scheduler should avoid assigning the Pod to any node within Zone R, as long as there is at least one Pod within Zone R already labeled with `security=S2`. Conversely, the anti-affinity rule does not impact scheduling into Zone R if there are no Pods with `security=S2` labels. To get yourself more familiar with the examples of Pod affinity and anti-affinity, refer to the [design proposal](https://git.k8s.io/design-proposals-archive/scheduling/podaffinity.md). You can use the `In`, `NotIn`, `Exists` and `DoesNotExist` values in the `operator` field for Pod affinity and anti-affinity. Read [Operators](#operators) to learn more about how these work. In principle, the `topologyKey` can be any allowed label key with the following exceptions for performance and security reasons: - For Pod affinity and anti-affinity, an empty `topologyKey` field is not allowed in both `requiredDuringSchedulingIgnoredDuringExecution` and `preferredDuringSchedulingIgnoredDuringExecution`. - For `requiredDuringSchedulingIgnoredDuringExecution` Pod anti-affinity rules, the admission controller `LimitPodHardAntiAffinityTopology` limits `topologyKey`
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/assign-pod-node.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/assign-pod-node.md
|
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] | 0.161423
|
work. In principle, the `topologyKey` can be any allowed label key with the following exceptions for performance and security reasons: - For Pod affinity and anti-affinity, an empty `topologyKey` field is not allowed in both `requiredDuringSchedulingIgnoredDuringExecution` and `preferredDuringSchedulingIgnoredDuringExecution`. - For `requiredDuringSchedulingIgnoredDuringExecution` Pod anti-affinity rules, the admission controller `LimitPodHardAntiAffinityTopology` limits `topologyKey` to `kubernetes.io/hostname`. You can modify or disable the admission controller if you want to allow custom topologies. In addition to `labelSelector` and `topologyKey`, you can optionally specify a list of namespaces which the `labelSelector` should match against using the `namespaces` field at the same level as `labelSelector` and `topologyKey`. If omitted or empty, `namespaces` defaults to the namespace of the Pod where the affinity/anti-affinity definition appears. #### Namespace Selector {{< feature-state for\_k8s\_version="v1.24" state="stable" >}} You can also select matching namespaces using `namespaceSelector`, which is a label query over the set of namespaces. The affinity term is applied to namespaces selected by both `namespaceSelector` and the `namespaces` field. Note that an empty `namespaceSelector` ({}) matches all namespaces, while a null or empty `namespaces` list and null `namespaceSelector` matches the namespace of the Pod where the rule is defined. #### matchLabelKeys {{< feature-state feature\_gate\_name="MatchLabelKeysInPodAffinity" >}} {{< note >}} The `matchLabelKeys` field is a beta-level field and is enabled by default in Kubernetes {{< skew currentVersion >}}. When you want to disable it, you have to disable it explicitly via the `MatchLabelKeysInPodAffinity` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/). {{< /note >}} Kubernetes includes an optional `matchLabelKeys` field for Pod affinity or anti-affinity. The field specifies keys for the labels that should match with the incoming Pod's labels, when satisfying the Pod (anti)affinity. The keys are used to look up values from the Pod labels; those key-value labels are combined (using `AND`) with the match restrictions defined using the `labelSelector` field. The combined filtering selects the set of existing Pods that will be taken into Pod (anti)affinity calculation. {{< caution >}} It's not recommended to use `matchLabelKeys` with labels that might be updated directly on pods. Even if you edit the pod's label that is specified at `matchLabelKeys` \*\*directly\*\*, (that is, not via a deployment), kube-apiserver doesn't reflect the label update onto the merged `labelSelector`. {{< /caution >}} A common use case is to use `matchLabelKeys` with `pod-template-hash` (set on Pods managed as part of a Deployment, where the value is unique for each revision). Using `pod-template-hash` in `matchLabelKeys` allows you to target the Pods that belong to the same revision as the incoming Pod, so that a rolling upgrade won't break affinity. ```yaml apiVersion: apps/v1 kind: Deployment metadata: name: application-server ... spec: template: spec: affinity: podAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: app operator: In values: - database topologyKey: topology.kubernetes.io/zone # Only Pods from a given rollout are taken into consideration when calculating pod affinity. # If you update the Deployment, the replacement Pods follow their own affinity rules # (if there are any defined in the new Pod template) matchLabelKeys: - pod-template-hash ``` #### mismatchLabelKeys {{< feature-state feature\_gate\_name="MatchLabelKeysInPodAffinity" >}} {{< note >}} The `mismatchLabelKeys` field is a beta-level field and is enabled by default in Kubernetes {{< skew currentVersion >}}. When you want to disable it, you have to disable it explicitly via the `MatchLabelKeysInPodAffinity` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/). {{< /note >}} Kubernetes includes an optional `mismatchLabelKeys` field for Pod affinity or anti-affinity. The field specifies keys for the labels that should not match with the incoming Pod's labels, when satisfying the Pod (anti)affinity. {{< caution >}} It's not recommended to use `mismatchLabelKeys` with labels that might be updated directly on pods. Even if you edit the pod's label that is specified at `mismatchLabelKeys` \*\*directly\*\*, (that is, not
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/assign-pod-node.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/assign-pod-node.md
|
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] | 0.117271
|
the labels that should not match with the incoming Pod's labels, when satisfying the Pod (anti)affinity. {{< caution >}} It's not recommended to use `mismatchLabelKeys` with labels that might be updated directly on pods. Even if you edit the pod's label that is specified at `mismatchLabelKeys` \*\*directly\*\*, (that is, not via a deployment), kube-apiserver doesn't reflect the label update onto the merged `labelSelector`. {{< /caution >}} One example use case is to ensure Pods go to the topology domain (node, zone, etc) where only Pods from the same tenant or team are scheduled in. In other words, you want to avoid running Pods from two different tenants on the same topology domain at the same time. ```yaml apiVersion: v1 kind: Pod metadata: labels: # Assume that all relevant Pods have a "tenant" label set tenant: tenant-a ... spec: affinity: podAffinity: requiredDuringSchedulingIgnoredDuringExecution: # ensure that Pods associated with this tenant land on the correct node pool - matchLabelKeys: - tenant labelSelector: {} topologyKey: node-pool podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: # ensure that Pods associated with this tenant can't schedule to nodes used for another tenant - mismatchLabelKeys: - tenant # whatever the value of the "tenant" label for this Pod, prevent # scheduling to nodes in any pool where any Pod from a different # tenant is running. labelSelector: # We have to have the labelSelector which selects only Pods with the tenant label, # otherwise this Pod would have anti-affinity against Pods from daemonsets as well, for example, # which aren't supposed to have the tenant label. matchExpressions: - key: tenant operator: Exists topologyKey: node-pool ``` #### More practical use-cases Inter-pod affinity and anti-affinity can be even more useful when they are used with higher level collections such as ReplicaSets, StatefulSets, Deployments, etc. These rules allow you to configure that a set of workloads should be co-located in the same defined topology; for example, preferring to place two related Pods onto the same node. For example: imagine a three-node cluster. You use the cluster to run a web application and also an in-memory cache (such as Redis). For this example, also assume that latency between the web application and the memory cache should be as low as is practical. You could use inter-pod affinity and anti-affinity to co-locate the web servers with the cache as much as possible. In the following example Deployment for the Redis cache, the replicas get the label `app=store`. The `podAntiAffinity` rule tells the scheduler to avoid placing multiple replicas with the `app=store` label on a single node. This creates each cache in a separate node. ```yaml apiVersion: apps/v1 kind: Deployment metadata: name: redis-cache spec: selector: matchLabels: app: store replicas: 3 template: metadata: labels: app: store spec: affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: app operator: In values: - store topologyKey: "kubernetes.io/hostname" containers: - name: redis-server image: redis:3.2-alpine ``` The following example Deployment for the web servers creates replicas with the label `app=web-store`. The Pod affinity rule tells the scheduler to place each replica on a node that has a Pod with the label `app=store`. The Pod anti-affinity rule tells the scheduler never to place multiple `app=web-store` servers on a single node. ```yaml apiVersion: apps/v1 kind: Deployment metadata: name: web-server spec: selector: matchLabels: app: web-store replicas: 3 template: metadata: labels: app: web-store spec: affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: app operator: In values: - web-store topologyKey: "kubernetes.io/hostname" podAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: app operator: In values: - store topologyKey: "kubernetes.io/hostname" containers: - name: web-app image: nginx:1.16-alpine ``` Creating the two preceding Deployments results in the following cluster layout, where each web server is co-located
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/assign-pod-node.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/assign-pod-node.md
|
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] | 0.095209
|
matchExpressions: - key: app operator: In values: - web-store topologyKey: "kubernetes.io/hostname" podAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: app operator: In values: - store topologyKey: "kubernetes.io/hostname" containers: - name: web-app image: nginx:1.16-alpine ``` Creating the two preceding Deployments results in the following cluster layout, where each web server is co-located with a cache, on three separate nodes. | node-1 | node-2 | node-3 | | :-----------: | :-----------: | :-----------: | | \*webserver-1\* | \*webserver-2\* | \*webserver-3\* | | \*cache-1\* | \*cache-2\* | \*cache-3\* | The overall effect is that each cache instance is likely to be accessed by a single client that is running on the same node. This approach aims to minimize both skew (imbalanced load) and latency. You might have other reasons to use Pod anti-affinity. See the [ZooKeeper tutorial](/docs/tutorials/stateful-application/zookeeper/#tolerating-node-failure) for an example of a StatefulSet configured with anti-affinity for high availability, using the same technique as this example. ## nodeName `nodeName` is a more direct form of node selection than affinity or `nodeSelector`. `nodeName` is a field in the Pod spec. If the `nodeName` field is not empty, the scheduler ignores the Pod and the kubelet on the named node tries to place the Pod on that node. Using `nodeName` overrules using `nodeSelector` or affinity and anti-affinity rules. Some of the limitations of using `nodeName` to select nodes are: - If the named node does not exist, the Pod will not run, and in some cases may be automatically deleted. - If the named node does not have the resources to accommodate the Pod, the Pod will fail and its reason will indicate why, for example OutOfmemory or OutOfcpu. - Node names in cloud environments are not always predictable or stable. {{< warning >}} `nodeName` is intended for use by custom schedulers or advanced use cases where you need to bypass any configured schedulers. Bypassing the schedulers might lead to failed Pods if the assigned Nodes get oversubscribed. You can use [node affinity](#node-affinity) or the [`nodeSelector` field](#nodeselector) to assign a Pod to a specific Node without bypassing the schedulers. {{}} Here is an example of a Pod spec using the `nodeName` field: ```yaml apiVersion: v1 kind: Pod metadata: name: nginx spec: containers: - name: nginx image: nginx nodeName: kube-01 ``` The above Pod will only run on the node `kube-01`. ## nominatedNodeName {{< feature-state feature\_gate\_name="NominatedNodeNameForExpectation" >}} `nominatedNodeName` can be used for external components to nominate node for a pending pod. This nomination is best effort: it might be ignored if the scheduler determines the pod cannot go to a nominated node. Also, this field can be (over)written by the scheduler: - If the scheduler finds a node to nominate via the preemption. - If the scheduler decides where the pod is going, and move it to the binding cycle. - Note that, in this case, `nominatedNodeName` is put only when the pod has to go through `WaitOnPermit` or `PreBind` extension points. Here is an example of a Pod status using the `nominatedNodeName` field: ```yaml apiVersion: v1 kind: Pod metadata: name: nginx ... status: nominatedNodeName: kube-01 ``` ## Pod topology spread constraints You can use \_topology spread constraints\_ to control how {{< glossary\_tooltip text="Pods" term\_id="Pod" >}} are spread across your cluster among failure-domains such as regions, zones, nodes, or among any other topology domains that you define. You might do this to improve performance, expected availability, or overall utilization. Read [Pod topology spread constraints](/docs/concepts/scheduling-eviction/topology-spread-constraints/) to learn more about how these work. ## Pod topology labels {{< feature-state feature\_gate\_name="PodTopologyLabelsAdmission" >}} Pods inherit the topology labels (`topology.kubernetes.io/zone` and `topology.kubernetes.io/region`) from their assigned Node if those labels are present. These
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/assign-pod-node.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/assign-pod-node.md
|
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|
You might do this to improve performance, expected availability, or overall utilization. Read [Pod topology spread constraints](/docs/concepts/scheduling-eviction/topology-spread-constraints/) to learn more about how these work. ## Pod topology labels {{< feature-state feature\_gate\_name="PodTopologyLabelsAdmission" >}} Pods inherit the topology labels (`topology.kubernetes.io/zone` and `topology.kubernetes.io/region`) from their assigned Node if those labels are present. These labels can then be utilized via the Downward API to provide the workload with node topology awareness. Here is an example of a Pod using downward API for it's zone and region: ```yaml apiVersion: v1 kind: Pod metadata: name: pod-with-topology-labels spec: containers: - name: app image: alpine command: ["sh", "-c", "env"] env: - name: MY\_ZONE valueFrom: fieldRef: fieldPath: metadata.labels['topology.kubernetes.io/zone'] - name: MY\_REGION valueFrom: fieldRef: fieldPath: metadata.labels['topology.kubernetes.io/region'] ``` ## Operators The following are all the logical operators that you can use in the `operator` field for `nodeAffinity` and `podAffinity` mentioned above. | Operator | Behavior | | :------------: | :-------------: | | `In` | The label value is present in the supplied set of strings | | `NotIn` | The label value is not contained in the supplied set of strings | | `Exists` | A label with this key exists on the object | | `DoesNotExist` | No label with this key exists on the object | The following operators can only be used with `nodeAffinity`. | Operator | Behavior | | :------------: | :-------------: | | `Gt` | The field value will be parsed as an integer, and that integer is greater than the integer that results from parsing the value of a label named by this selector | | `Lt` | The field value will be parsed as an integer, and that integer is less than the integer that results from parsing the value of a label named by this selector | {{}} `Gt` and `Lt` operators will not work with non-integer values. If the given value doesn't parse as an integer, the Pod will fail to get scheduled. Also, `Gt` and `Lt` are not available for `podAffinity`. {{}} ## {{% heading "whatsnext" %}} - Read more about [taints and tolerations](/docs/concepts/scheduling-eviction/taint-and-toleration/). - Read the design docs for [node affinity](https://git.k8s.io/design-proposals-archive/scheduling/nodeaffinity.md) and for [inter-pod affinity/anti-affinity](https://git.k8s.io/design-proposals-archive/scheduling/podaffinity.md). - Learn about how the [topology manager](/docs/tasks/administer-cluster/topology-manager/) takes part in node-level resource allocation decisions. - Learn how to use [nodeSelector](/docs/tasks/configure-pod-container/assign-pods-nodes/). - Learn how to use [affinity and anti-affinity](/docs/tasks/configure-pod-container/assign-pods-nodes-using-node-affinity/).
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/assign-pod-node.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/assign-pod-node.md
|
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] | 0.155918
|
{{< feature-state for\_k8s\_version="v1.19" state="stable" >}} The \_scheduling framework\_ is a pluggable architecture for the Kubernetes scheduler. It consists of a set of "plugin" APIs that are compiled directly into the scheduler. These APIs allow most scheduling features to be implemented as plugins, while keeping the scheduling "core" lightweight and maintainable. Refer to the [design proposal of the scheduling framework][kep] for more technical information on the design of the framework. [kep]: https://github.com/kubernetes/enhancements/blob/master/keps/sig-scheduling/624-scheduling-framework/README.md ## Framework workflow The Scheduling Framework defines a few extension points. Scheduler plugins register to be invoked at one or more extension points. Some of these plugins can change the scheduling decisions and some are informational only. Each attempt to schedule one Pod is split into two phases, the \*\*scheduling cycle\*\* and the \*\*binding cycle\*\*. ### Scheduling cycle & binding cycle The scheduling cycle selects a node for the Pod, and the binding cycle applies that decision to the cluster. Together, a scheduling cycle and binding cycle are referred to as a "scheduling context". Scheduling cycles are run serially, while binding cycles may run concurrently. A scheduling or binding cycle can be aborted if the Pod is determined to be unschedulable or if there is an internal error. The Pod will be returned to the queue and retried. ## Interfaces The following picture shows the scheduling context of a Pod and the interfaces that the scheduling framework exposes. One plugin may implement multiple interfaces to perform more complex or stateful tasks. Some interfaces match the scheduler extension points which can be configured through [Scheduler Configuration](/docs/reference/scheduling/config/#extension-points). {{< figure src="/images/docs/scheduling-framework-extensions.png" title="Scheduling framework extension points" class="diagram-large">}} ### PreEnqueue {#pre-enqueue} These plugins are called prior to adding Pods to the internal active queue, where Pods are marked as ready for scheduling. Only when all PreEnqueue plugins return `Success`, the Pod is allowed to enter the active queue. Otherwise, it's placed in the internal unschedulable Pods list, and doesn't get an `Unschedulable` condition. For more details about how internal scheduler queues work, read [Scheduling queue in kube-scheduler](https://github.com/kubernetes/community/blob/f03b6d5692bd979f07dd472e7b6836b2dad0fd9b/contributors/devel/sig-scheduling/scheduler\_queues.md). ### EnqueueExtension EnqueueExtension is the interface where the plugin can control whether to retry scheduling of Pods rejected by this plugin, based on changes in the cluster. Plugins that implement PreEnqueue, PreFilter, Filter, Reserve or Permit should implement this interface. ### QueueingHint {{< feature-state feature\_gate\_name="SchedulerQueueingHints" >}} QueueingHint is a callback function for deciding whether a Pod can be requeued to the active queue or backoff queue. It's executed every time a certain kind of event or change happens in the cluster. When the QueueingHint finds that the event might make the Pod schedulable, the Pod is put into the active queue or the backoff queue so that the scheduler will retry the scheduling of the Pod. ### QueueSort {#queue-sort} These plugins are used to sort Pods in the scheduling queue. A queue sort plugin essentially provides a `Less(Pod1, Pod2)` function. Only one queue sort plugin may be enabled at a time. ### PreFilter {#pre-filter} These plugins are used to pre-process info about the Pod, or to check certain conditions that the cluster or the Pod must meet. If a PreFilter plugin returns an error, the scheduling cycle is aborted. ### Filter These plugins are used to filter out nodes that cannot run the Pod. For each node, the scheduler will call filter plugins in their configured order. If any filter plugin marks the node as infeasible, the remaining plugins will not be called for that node. Nodes may be evaluated concurrently. ### PostFilter {#post-filter} These plugins are called after the Filter phase, but only when no feasible nodes were found for the pod. Plugins are called in
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/scheduling-framework.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/scheduling-framework.md
|
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] | 0.251967
|
If any filter plugin marks the node as infeasible, the remaining plugins will not be called for that node. Nodes may be evaluated concurrently. ### PostFilter {#post-filter} These plugins are called after the Filter phase, but only when no feasible nodes were found for the pod. Plugins are called in their configured order. If any postFilter plugin marks the node as `Schedulable`, the remaining plugins will not be called. A typical PostFilter implementation is preemption, which tries to make the pod schedulable by preempting other Pods. ### PreScore {#pre-score} These plugins are used to perform "pre-scoring" work, which generates a sharable state for Score plugins to use. If a PreScore plugin returns an error, the scheduling cycle is aborted. ### Score {#scoring} These plugins are used to rank nodes that have passed the filtering phase. The scheduler will call each scoring plugin for each node. There will be a well defined range of integers representing the minimum and maximum scores. After the [NormalizeScore](#normalize-scoring) phase, the scheduler will combine node scores from all plugins according to the configured plugin weights. #### Capacity scoring {#scoring-capacity} {{< feature-state feature\_gate\_name="StorageCapacityScoring" >}} The feature gate `VolumeCapacityPriority` was used in v1.32 to support storage that are statically provisioned. Starting from v1.33, the new feature gate `StorageCapacityScoring` replaces the old `VolumeCapacityPriority` gate with added support to dynamically provisioned storage. When `StorageCapacityScoring` is enabled, the VolumeBinding plugin in the kube-scheduler is extended to score Nodes based on the storage capacity on each of them. This feature is applicable to CSI volumes that supported [Storage Capacity](/docs/concepts/storage/storage-capacity/), including local storage backed by a CSI driver. ### NormalizeScore {#normalize-scoring} These plugins are used to modify scores before the scheduler computes a final ranking of Nodes. A plugin that registers for this extension point will be called with the [Score](#scoring) results from the same plugin. This is called once per plugin per scheduling cycle. For example, suppose a plugin `BlinkingLightScorer` ranks Nodes based on how many blinking lights they have. ```go func ScoreNode(\_ \*v1.pod, n \*v1.Node) (int, error) { return getBlinkingLightCount(n) } ``` However, the maximum count of blinking lights may be small compared to `NodeScoreMax`. To fix this, `BlinkingLightScorer` should also register for this extension point. ```go func NormalizeScores(scores map[string]int) { highest := 0 for \_, score := range scores { highest = max(highest, score) } for node, score := range scores { scores[node] = score\*NodeScoreMax/highest } } ``` If any NormalizeScore plugin returns an error, the scheduling cycle is aborted. {{< note >}} Plugins wishing to perform "pre-reserve" work should use the NormalizeScore extension point. {{< /note >}} ### Reserve {#reserve} A plugin that implements the Reserve interface has two methods, namely `Reserve` and `Unreserve`, that back two informational scheduling phases called Reserve and Unreserve, respectively. Plugins which maintain runtime state (aka "stateful plugins") should use these phases to be notified by the scheduler when resources on a node are being reserved and unreserved for a given Pod. The Reserve phase happens before the scheduler actually binds a Pod to its designated node. It exists to prevent race conditions while the scheduler waits for the bind to succeed. The `Reserve` method of each Reserve plugin may succeed or fail; if one `Reserve` method call fails, subsequent plugins are not executed and the Reserve phase is considered to have failed. If the `Reserve` method of all plugins succeed, the Reserve phase is considered to be successful and the rest of the scheduling cycle and the binding cycle are executed. The Unreserve phase is triggered if the Reserve phase or a later phase fails. When this happens, the `Unreserve` method of \*\*all\*\*
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/scheduling-framework.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/scheduling-framework.md
|
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] | 0.177835
|
the `Reserve` method of all plugins succeed, the Reserve phase is considered to be successful and the rest of the scheduling cycle and the binding cycle are executed. The Unreserve phase is triggered if the Reserve phase or a later phase fails. When this happens, the `Unreserve` method of \*\*all\*\* Reserve plugins will be executed in the reverse order of `Reserve` method calls. This phase exists to clean up the state associated with the reserved Pod. {{< caution >}} The implementation of the `Unreserve` method in Reserve plugins must be idempotent and may not fail. {{< /caution >}} ### Permit \_Permit\_ plugins are invoked at the end of the scheduling cycle for each Pod, to prevent or delay the binding to the candidate node. A permit plugin can do one of the three things: 1. \*\*approve\*\* \ Once all Permit plugins approve a Pod, it is sent for binding. 1. \*\*deny\*\* \ If any Permit plugin denies a Pod, it is returned to the scheduling queue. This will trigger the Unreserve phase in [Reserve plugins](#reserve). 1. \*\*wait\*\* (with a timeout) \ If a Permit plugin returns "wait", then the Pod is kept in an internal "waiting" Pods list, and the binding cycle of this Pod starts but directly blocks until it gets approved. If a timeout occurs, \*\*wait\*\* becomes \*\*deny\*\* and the Pod is returned to the scheduling queue, triggering the Unreserve phase in [Reserve plugins](#reserve). {{< note >}} While any plugin can access the list of "waiting" Pods and approve them (see [`FrameworkHandle`](https://git.k8s.io/enhancements/keps/sig-scheduling/624-scheduling-framework#frameworkhandle)), we expect only the permit plugins to approve binding of reserved Pods that are in "waiting" state. Once a Pod is approved, it is sent to the [PreBind](#pre-bind) phase. {{< /note >}} ### PreBind {#pre-bind} These plugins are used to perform any work required before a Pod is bound. For example, a pre-bind plugin may provision a network volume and mount it on the target node before allowing the Pod to run there. If any PreBind plugin returns an error, the Pod is [rejected](#reserve) and returned to the scheduling queue. ### Bind These plugins are used to bind a Pod to a Node. Bind plugins will not be called until all PreBind plugins have completed. Each bind plugin is called in the configured order. A bind plugin may choose whether or not to handle the given Pod. If a bind plugin chooses to handle a Pod, \*\*the remaining bind plugins are skipped\*\*. ### PostBind {#post-bind} This is an informational interface. Post-bind plugins are called after a Pod is successfully bound. This is the end of a binding cycle, and can be used to clean up associated resources. ## Plugin API There are two steps to the plugin API. First, plugins must register and get configured, then they use the extension point interfaces. Extension point interfaces have the following form. ```go type Plugin interface { Name() string } type QueueSortPlugin interface { Plugin Less(\*v1.pod, \*v1.pod) bool } type PreFilterPlugin interface { Plugin PreFilter(context.Context, \*framework.CycleState, \*v1.pod) error } // ... ``` ## Plugin configuration You can enable or disable plugins in the scheduler configuration. If you are using Kubernetes v1.18 or later, most scheduling [plugins](/docs/reference/scheduling/config/#scheduling-plugins) are in use and enabled by default. In addition to default plugins, you can also implement your own scheduling plugins and get them configured along with default plugins. You can visit [scheduler-plugins](https://github.com/kubernetes-sigs/scheduler-plugins) for more details. If you are using Kubernetes v1.18 or later, you can configure a set of plugins as a scheduler profile and then define multiple profiles to fit various kinds of workload. Learn more at [multiple profiles](/docs/reference/scheduling/config/#multiple-profiles).
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/scheduling-framework.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/scheduling-framework.md
|
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] | 0.163442
|
configured along with default plugins. You can visit [scheduler-plugins](https://github.com/kubernetes-sigs/scheduler-plugins) for more details. If you are using Kubernetes v1.18 or later, you can configure a set of plugins as a scheduler profile and then define multiple profiles to fit various kinds of workload. Learn more at [multiple profiles](/docs/reference/scheduling/config/#multiple-profiles).
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/scheduling-framework.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/scheduling-framework.md
|
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] | 0.138498
|
{{< feature-state for\_k8s\_version="v1.30" state="stable" >}} Pods were considered ready for scheduling once created. Kubernetes scheduler does its due diligence to find nodes to place all pending Pods. However, in a real-world case, some Pods may stay in a "miss-essential-resources" state for a long period. These Pods actually churn the scheduler (and downstream integrators like Cluster AutoScaler) in an unnecessary manner. By specifying/removing a Pod's `.spec.schedulingGates`, you can control when a Pod is ready to be considered for scheduling. ## Configuring Pod schedulingGates The `schedulingGates` field contains a list of strings, and each string literal is perceived as a criteria that Pod should be satisfied before considered schedulable. This field can be initialized only when a Pod is created (either by the client, or mutated during admission). After creation, each schedulingGate can be removed in arbitrary order, but addition of a new scheduling gate is disallowed. {{< figure src="/docs/images/podSchedulingGates.svg" alt="pod-scheduling-gates-diagram" caption="Figure. Pod SchedulingGates" class="diagram-large" link="https://mermaid.live/edit#pako:eNplkktTwyAUhf8KgzuHWpukaYszutGlK3caFxQuCVMCGSDVTKf\_XfKyPlhxz4HDB9wT5lYAptgHFuBRsdKxenFMClMYFIdfUdRYgbiD6ItJTEbR8wpEq5UpUfnDTf-5cbPoJjcbXdcaE61RVJIiqJvQ\_Y30D-OCt-t3tFjcR5wZayiVnIGmkv4NiEfX9jijKTmmRH5jf0sRugOP0HyHUc1m6KGMFP27cM28fwSJDluPpNKaXqVJzmFNfHD2APRKSjnNFx9KhIpmzSfhVls3eHdTRrwG8QnxKfEZUUNeYTDBNbiaKRF\_5dSfX-BQQQ0FpnEqQLJWhwIX5hyXsjbYl85wTINrgeC2EZd\_xFQy7b\_VJ6GCdd-itkxALE84dE3fAqXyIUZya6Qqe711OspVCI2ny2Vv35QqVO3-htt66ZWomAvVcZcv8yTfsiSFfJOydZoKvl\_ttjLJVlJsblcJw-czwQ0zr9ZeqGDgeR77b2jD8xdtjtDn" >}} ## Usage example To mark a Pod not-ready for scheduling, you can create it with one or more scheduling gates like this: {{% code\_sample file="pods/pod-with-scheduling-gates.yaml" %}} After the Pod's creation, you can check its state using: ```bash kubectl get pod test-pod ``` The output reveals it's in `SchedulingGated` state: ```none NAME READY STATUS RESTARTS AGE test-pod 0/1 SchedulingGated 0 7s ``` You can also check its `schedulingGates` field by running: ```bash kubectl get pod test-pod -o jsonpath='{.spec.schedulingGates}' ``` The output is: ```none [{"name":"example.com/foo"},{"name":"example.com/bar"}] ``` To inform scheduler this Pod is ready for scheduling, you can remove its `schedulingGates` entirely by reapplying a modified manifest: {{% code\_sample file="pods/pod-without-scheduling-gates.yaml" %}} You can check if the `schedulingGates` is cleared by running: ```bash kubectl get pod test-pod -o jsonpath='{.spec.schedulingGates}' ``` The output is expected to be empty. And you can check its latest status by running: ```bash kubectl get pod test-pod -o wide ``` Given the test-pod doesn't request any CPU/memory resources, it's expected that this Pod's state get transited from previous `SchedulingGated` to `Running`: ```none NAME READY STATUS RESTARTS AGE IP NODE test-pod 1/1 Running 0 15s 10.0.0.4 node-2 ``` ## Observability The metric `scheduler\_pending\_pods` comes with a new label `"gated"` to distinguish whether a Pod has been tried scheduling but claimed as unschedulable, or explicitly marked as not ready for scheduling. You can use `scheduler\_pending\_pods{queue="gated"}` to check the metric result. ## Mutable Pod scheduling directives You can mutate scheduling directives of Pods while they have scheduling gates, with certain constraints. At a high level, you can only tighten the scheduling directives of a Pod. In other words, the updated directives would cause the Pods to only be able to be scheduled on a subset of the nodes that it would previously match. More concretely, the rules for updating a Pod's scheduling directives are as follows: 1. For `.spec.nodeSelector`, only additions are allowed. If absent, it will be allowed to be set. 2. For `spec.affinity.nodeAffinity`, if nil, then setting anything is allowed. 3. If `NodeSelectorTerms` was empty, it will be allowed to be set. If not empty, then only additions of `NodeSelectorRequirements` to `matchExpressions` or `fieldExpressions` are allowed, and no changes to existing `matchExpressions` and `fieldExpressions` will be allowed. This is because the terms in `.requiredDuringSchedulingIgnoredDuringExecution.NodeSelectorTerms`, are ORed while the expressions in `nodeSelectorTerms[].matchExpressions` and `nodeSelectorTerms[].fieldExpressions` are ANDed. 4. For `.preferredDuringSchedulingIgnoredDuringExecution`, all updates are allowed. This is because preferred terms are not authoritative, and so policy controllers don't validate those terms. ## {{% heading "whatsnext" %}} \* Read the [PodSchedulingReadiness KEP](https://github.com/kubernetes/enhancements/blob/master/keps/sig-scheduling/3521-pod-scheduling-readiness) for more details
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/pod-scheduling-readiness.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/pod-scheduling-readiness.md
|
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] | 0.183612
|
terms are not authoritative, and so policy controllers don't validate those terms. ## {{% heading "whatsnext" %}} \* Read the [PodSchedulingReadiness KEP](https://github.com/kubernetes/enhancements/blob/master/keps/sig-scheduling/3521-pod-scheduling-readiness) for more details
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/pod-scheduling-readiness.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/pod-scheduling-readiness.md
|
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] | 0.148923
|
{{}} The {{}} monitors resources like memory, disk space, and filesystem inodes on your cluster's nodes. When one or more of these resources reach specific consumption levels, the kubelet can proactively fail one or more pods on the node to reclaim resources and prevent starvation. During a node-pressure eviction, the kubelet sets the [phase](/docs/concepts/workloads/pods/pod-lifecycle/#pod-phase) for the selected pods to `Failed`, and terminates the Pod. Node-pressure eviction is not the same as [API-initiated eviction](/docs/concepts/scheduling-eviction/api-eviction/). The kubelet does not respect your configured {{}} or the pod's `terminationGracePeriodSeconds`. If you use [soft eviction thresholds](#soft-eviction-thresholds), the kubelet respects your configured `eviction-max-pod-grace-period`. If you use [hard eviction thresholds](#hard-eviction-thresholds), the kubelet uses a `0s` grace period (immediate shutdown) for termination. ## Self healing behavior The kubelet attempts to [reclaim node-level resources](#reclaim-node-resources) before it terminates end-user pods. For example, it removes unused container images when disk resources are starved. If the pods are managed by a {{< glossary\_tooltip text="workload" term\_id="workload" >}} management object (such as {{< glossary\_tooltip text="StatefulSet" term\_id="statefulset" >}} or {{< glossary\_tooltip text="Deployment" term\_id="deployment" >}}) that replaces failed pods, the control plane (`kube-controller-manager`) creates new pods in place of the evicted pods. ### Self healing for static pods If you are running a [static pod](/docs/concepts/workloads/pods/#static-pods) on a node that is under resource pressure, the kubelet may evict that static Pod. The kubelet then tries to create a replacement, because static Pods always represent an intent to run a Pod on that node. The kubelet takes the \_priority\_ of the static pod into account when creating a replacement. If the static pod manifest specifies a low priority, and there are higher-priority Pods defined within the cluster's control plane, and the node is under resource pressure, the kubelet may not be able to make room for that static pod. The kubelet continues to attempt to run all static pods even when there is resource pressure on a node. ## Eviction signals and thresholds The kubelet uses various parameters to make eviction decisions, like the following: - Eviction signals - Eviction thresholds - Monitoring intervals ### Eviction signals {#eviction-signals} Eviction signals are the current state of a particular resource at a specific point in time. The kubelet uses eviction signals to make eviction decisions by comparing the signals to eviction thresholds, which are the minimum amount of the resource that should be available on the node. The kubelet uses the following eviction signals: | Eviction Signal | Description | Linux Only | |--------------------------|---------------------------------------------------------------------------------------|------------| | `memory.available` | `memory.available` := `node.status.capacity[memory]` - `node.stats.memory.workingSet` | | | `nodefs.available` | `nodefs.available` := `node.stats.fs.available` | | | `nodefs.inodesFree` | `nodefs.inodesFree` := `node.stats.fs.inodesFree` | • | | `imagefs.available` | `imagefs.available` := `node.stats.runtime.imagefs.available` | | | `imagefs.inodesFree` | `imagefs.inodesFree` := `node.stats.runtime.imagefs.inodesFree` | • | | `containerfs.available` | `containerfs.available` := `node.stats.runtime.containerfs.available` | | | `containerfs.inodesFree` | `containerfs.inodesFree` := `node.stats.runtime.containerfs.inodesFree` | • | | `pid.available` | `pid.available` := `node.stats.rlimit.maxpid` - `node.stats.rlimit.curproc` | • | In this table, the \*\*Description\*\* column shows how kubelet gets the value of the signal. Each signal supports either a percentage or a literal value. The kubelet calculates the percentage value relative to the total capacity associated with the signal. #### Memory signals On Linux nodes, the value for `memory.available` is derived from the cgroupfs instead of tools like `free -m`. This is important because `free -m` does not work in a container, and if users use the [node allocatable](/docs/tasks/administer-cluster/reserve-compute-resources/#node-allocatable) feature, out of resource decisions are made local to the end user Pod part of the cgroup hierarchy as well as the root node. This [script](/examples/admin/resource/memory-available.sh) or [cgroupv2 script](/examples/admin/resource/memory-available-cgroupv2.sh) reproduces the same set of steps that the kubelet performs to calculate `memory.available`. The kubelet
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/node-pressure-eviction.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/node-pressure-eviction.md
|
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] | 0.192978
|
if users use the [node allocatable](/docs/tasks/administer-cluster/reserve-compute-resources/#node-allocatable) feature, out of resource decisions are made local to the end user Pod part of the cgroup hierarchy as well as the root node. This [script](/examples/admin/resource/memory-available.sh) or [cgroupv2 script](/examples/admin/resource/memory-available-cgroupv2.sh) reproduces the same set of steps that the kubelet performs to calculate `memory.available`. The kubelet excludes inactive\_file (the number of bytes of file-backed memory on the inactive LRU list) from its calculation, as it assumes that memory is reclaimable under pressure. On Windows nodes, the value for `memory.available` is derived from the node's global memory commit levels (queried through the [`GetPerformanceInfo()`](https://learn.microsoft.com/windows/win32/api/psapi/nf-psapi-getperformanceinfo) system call) by subtracting the node's global [`CommitTotal`](https://learn.microsoft.com/windows/win32/api/psapi/ns-psapi-performance\_information) from the node's [`CommitLimit`](https://learn.microsoft.com/windows/win32/api/psapi/ns-psapi-performance\_information). Please note that `CommitLimit` can change if the node's page-file size changes! #### Filesystem signals The kubelet recognizes three specific filesystem identifiers that can be used with eviction signals (`.inodesFree` or `.available`): 1. `nodefs`: The node's main filesystem, used for local disk volumes, emptyDir volumes not backed by memory, log storage, ephemeral storage, and more. For example, `nodefs` contains `/var/lib/kubelet`. 1. `imagefs`: An optional filesystem that container runtimes can use to store container images (which are the read-only layers) and container writable layers. 1. `containerfs`: An optional filesystem that container runtime can use to store the writeable layers. Similar to the main filesystem (see `nodefs`), it's used to store local disk volumes, emptyDir volumes not backed by memory, log storage, and ephemeral storage, except for the container images. When `containerfs` is used, the `imagefs` filesystem can be split to only store images (read-only layers) and nothing else. {{}} {{< feature-state feature\_gate\_name="KubeletSeparateDiskGC" >}} The \_split image filesystem\_ feature, which enables support for the `containerfs` filesystem, adds several new eviction signals, thresholds and metrics. To use `containerfs`, the Kubernetes release v{{< skew currentVersion >}} requires the `KubeletSeparateDiskGC` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/) to be enabled. Currently, only CRI-O (v1.29 or higher) offers the `containerfs` filesystem support. {{}} As such, kubelet generally allows three options for container filesystems: - Everything is on the single `nodefs`, also referred to as "rootfs" or simply "root", and there is no dedicated image filesystem. - Container storage (see `nodefs`) is on a dedicated disk, and `imagefs` (writable and read-only layers) is separate from the root filesystem. This is often referred to as "split disk" (or "separate disk") filesystem. - Container filesystem `containerfs` (same as `nodefs` plus writable layers) is on root and the container images (read-only layers) are stored on separate `imagefs`. This is often referred to as "split image" filesystem. The kubelet will attempt to auto-discover these filesystems with their current configuration directly from the underlying container runtime and will ignore other local node filesystems. The kubelet does not support other container filesystems or storage configurations, and it does not currently support multiple filesystems for images and containers. ### Deprecated kubelet garbage collection features Some kubelet garbage collection features are deprecated in favor of eviction: | Existing Flag | Rationale | | ------------- | --------- | | `--maximum-dead-containers` | deprecated once old logs are stored outside of container's context | | `--maximum-dead-containers-per-container` | deprecated once old logs are stored outside of container's context | | `--minimum-container-ttl-duration` | deprecated once old logs are stored outside of container's context | ### Eviction thresholds You can specify custom eviction thresholds for the kubelet to use when it makes eviction decisions. You can configure [soft](#soft-eviction-thresholds) and [hard](#hard-eviction-thresholds) eviction thresholds. Eviction thresholds have the form `[eviction-signal][operator][quantity]`, where: - `eviction-signal` is the [eviction signal](#eviction-signals) to use. - `operator` is the [relational operator](https://en.wikipedia.org/wiki/Relational\_operator#Standard\_relational\_operators) you want, such as `<` (less than). - `quantity` is the eviction threshold amount, such as `1Gi`. The value of `quantity` must match the quantity
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/node-pressure-eviction.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/node-pressure-eviction.md
|
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] | 0.179435
|
and [hard](#hard-eviction-thresholds) eviction thresholds. Eviction thresholds have the form `[eviction-signal][operator][quantity]`, where: - `eviction-signal` is the [eviction signal](#eviction-signals) to use. - `operator` is the [relational operator](https://en.wikipedia.org/wiki/Relational\_operator#Standard\_relational\_operators) you want, such as `<` (less than). - `quantity` is the eviction threshold amount, such as `1Gi`. The value of `quantity` must match the quantity representation used by Kubernetes. You can use either literal values or percentages (`%`). For example, if a node has 10GiB of total memory and you want trigger eviction if the available memory falls below 1GiB, you can define the eviction threshold as either `memory.available<10%` or `memory.available<1Gi` (you cannot use both). #### Soft eviction thresholds {#soft-eviction-thresholds} A soft eviction threshold pairs an eviction threshold with a required administrator-specified grace period. The kubelet does not evict pods until the grace period is exceeded. The kubelet returns an error on startup if you do not specify a grace period. You can specify both a soft eviction threshold grace period and a maximum allowed pod termination grace period for kubelet to use during evictions. If you specify a maximum allowed grace period and the soft eviction threshold is met, the kubelet uses the lesser of the two grace periods. If you do not specify a maximum allowed grace period, the kubelet kills evicted pods immediately without graceful termination. You can use the following flags to configure soft eviction thresholds: - `eviction-soft`: A set of eviction thresholds like `memory.available<1.5Gi` that can trigger pod eviction if held over the specified grace period. - `eviction-soft-grace-period`: A set of eviction grace periods like `memory.available=1m30s` that define how long a soft eviction threshold must hold before triggering a Pod eviction. - `eviction-max-pod-grace-period`: The maximum allowed grace period (in seconds) to use when terminating pods in response to a soft eviction threshold being met. #### Hard eviction thresholds {#hard-eviction-thresholds} A hard eviction threshold has no grace period. When a hard eviction threshold is met, the kubelet kills pods immediately without graceful termination to reclaim the starved resource. You can use the `eviction-hard` flag to configure a set of hard eviction thresholds like `memory.available<1Gi`. The kubelet has the following default hard eviction thresholds: - `memory.available<100Mi` (Linux nodes) - `memory.available<500Mi` (Windows nodes) - `nodefs.available<10%` - `imagefs.available<15%` - `nodefs.inodesFree<5%` (Linux nodes) - `imagefs.inodesFree<5%` (Linux nodes) These default values of hard eviction thresholds will only be set if none of the parameters is changed. If you change the value of any parameter, then the values of other parameters will not be inherited as the default values and will be set to zero. In order to provide custom values, you should provide all the thresholds respectively. You can also set the kubelet config MergeDefaultEvictionSettings to true in the kubelet configuration file. If set to true and any parameter is changed, then the other parameters will inherit their default values instead of 0. The `containerfs.available` and `containerfs.inodesFree` (Linux nodes) default eviction thresholds will be set as follows: - If a single filesystem is used for everything, then `containerfs` thresholds are set the same as `nodefs`. - If separate filesystems are configured for both images and containers, then `containerfs` thresholds are set the same as `imagefs`. Setting custom overrides for thresholds related to `containersfs` is currently not supported, and a warning will be issued if an attempt to do so is made; any provided custom values will, as such, be ignored. ## Eviction monitoring interval The kubelet evaluates eviction thresholds based on its configured `housekeeping-interval`, which defaults to `10s`. ## Node conditions {#node-conditions} The kubelet reports [node conditions](/docs/concepts/architecture/nodes/#condition) to reflect that the node is under pressure because hard or soft eviction threshold is met, independent of configured
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/node-pressure-eviction.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/node-pressure-eviction.md
|
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] | 0.147714
|
as such, be ignored. ## Eviction monitoring interval The kubelet evaluates eviction thresholds based on its configured `housekeeping-interval`, which defaults to `10s`. ## Node conditions {#node-conditions} The kubelet reports [node conditions](/docs/concepts/architecture/nodes/#condition) to reflect that the node is under pressure because hard or soft eviction threshold is met, independent of configured grace periods. The kubelet maps eviction signals to node conditions as follows: | Node Condition | Eviction Signal | Description | |-------------------|---------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------| | `MemoryPressure` | `memory.available` | Available memory on the node has satisfied an eviction threshold | | `DiskPressure` | `nodefs.available`, `nodefs.inodesFree`, `imagefs.available`, `imagefs.inodesFree`, `containerfs.available`, or `containerfs.inodesFree` | Available disk space and inodes on either the node's root filesystem, image filesystem, or container filesystem has satisfied an eviction threshold | | `PIDPressure` | `pid.available` | Available processes identifiers on the (Linux) node has fallen below an eviction threshold | The control plane also [maps](/docs/concepts/scheduling-eviction/taint-and-toleration/#taint-nodes-by-condition) these node conditions to taints. The kubelet updates the node conditions based on the configured `--node-status-update-frequency`, which defaults to `10s`. ### Node condition oscillation In some cases, nodes oscillate above and below soft eviction thresholds without holding for the defined grace periods. This causes the reported node condition to constantly switch between `true` and `false`, leading to bad eviction decisions. To protect against oscillation, you can use the `eviction-pressure-transition-period` flag, which controls how long the kubelet must wait before transitioning a node condition to a different state. The transition period has a default value of `5m`. ### Reclaiming node level resources {#reclaim-node-resources} The kubelet tries to reclaim node-level resources before it evicts end-user pods. When a `DiskPressure` node condition is reported, the kubelet reclaims node-level resources based on the filesystems on the node. #### Without `imagefs` or `containerfs` If the node only has a `nodefs` filesystem that meets eviction thresholds, the kubelet frees up disk space in the following order: 1. Garbage collect dead pods and containers. 1. Delete unused images. #### With `imagefs` If the node has a dedicated `imagefs` filesystem for container runtimes to use, the kubelet does the following: - If the `nodefs` filesystem meets the eviction thresholds, the kubelet garbage collects dead pods and containers. - If the `imagefs` filesystem meets the eviction thresholds, the kubelet deletes all unused images. #### With `imagefs` and `containerfs` If the node has a dedicated `containerfs` alongside the `imagefs` filesystem configured for the container runtimes to use, then kubelet will attempt to reclaim resources as follows: - If the `containerfs` filesystem meets the eviction thresholds, the kubelet garbage collects dead pods and containers. - If the `imagefs` filesystem meets the eviction thresholds, the kubelet deletes all unused images. ### Pod selection for kubelet eviction If the kubelet's attempts to reclaim node-level resources don't bring the eviction signal below the threshold, the kubelet begins to evict end-user pods. The kubelet uses the following parameters to determine the pod eviction order: 1. Whether the pod's resource usage exceeds requests 1. [Pod Priority](/docs/concepts/scheduling-eviction/pod-priority-preemption/) 1. The pod's resource usage relative to requests As a result, kubelet ranks and evicts pods in the following order: 1. `BestEffort` or `Burstable` pods where the usage exceeds requests. These pods are evicted based on their Priority and then by how much their usage level exceeds the request. 1. `Guaranteed` pods and `Burstable` pods where the usage is less than requests are evicted last, based on their Priority. {{}} The kubelet does not use the pod's [QoS class](/docs/concepts/workloads/pods/pod-qos/) to determine the eviction order. You can use the QoS class to estimate the most likely pod eviction order when reclaiming resources like memory. QoS classification does not apply to EphemeralStorage requests, so the above scenario
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/node-pressure-eviction.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/node-pressure-eviction.md
|
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|
based on their Priority. {{}} The kubelet does not use the pod's [QoS class](/docs/concepts/workloads/pods/pod-qos/) to determine the eviction order. You can use the QoS class to estimate the most likely pod eviction order when reclaiming resources like memory. QoS classification does not apply to EphemeralStorage requests, so the above scenario will not apply if the node is, for example, under `DiskPressure`. {{}} `Guaranteed` pods are guaranteed only when requests and limits are specified for all the containers and they are equal. These pods will never be evicted because of another pod's resource consumption. If a system daemon (such as `kubelet` and `journald`) is consuming more resources than were reserved via `system-reserved` or `kube-reserved` allocations, and the node only has `Guaranteed` or `Burstable` pods using less resources than requests left on it, then the kubelet must choose to evict one of these pods to preserve node stability and to limit the impact of resource starvation on other pods. In this case, it will choose to evict pods of lowest Priority first. If you are running a [static pod](/docs/concepts/workloads/pods/#static-pods) and want to avoid having it evicted under resource pressure, set the `priority` field for that Pod directly. Static pods do not support the `priorityClassName` field. When the kubelet evicts pods in response to inode or process ID starvation, it uses the Pods' relative priority to determine the eviction order, because inodes and PIDs have no requests. The kubelet sorts pods differently based on whether the node has a dedicated `imagefs` or `containerfs` filesystem: #### Without `imagefs` or `containerfs` (`nodefs` and `imagefs` use the same filesystem) {#without-imagefs} - If `nodefs` triggers evictions, the kubelet sorts pods based on their total disk usage (`local volumes + logs and a writable layer of all containers`). #### With `imagefs` (`nodefs` and `imagefs` filesystems are separate) {#with-imagefs} - If `nodefs` triggers evictions, the kubelet sorts pods based on `nodefs` usage (`local volumes + logs of all containers`). - If `imagefs` triggers evictions, the kubelet sorts pods based on the writable layer usage of all containers. #### With `imagesfs` and `containerfs` (`imagefs` and `containerfs` have been split) {#with-containersfs} - If `containerfs` triggers evictions, the kubelet sorts pods based on `containerfs` usage (`local volumes + logs and a writable layer of all containers`). - If `imagefs` triggers evictions, the kubelet sorts pods based on the `storage of images` rank, which represents the disk usage of a given image. ### Minimum eviction reclaim {{}} As of Kubernetes v{{< skew currentVersion >}}, you cannot set a custom value for the `containerfs.available` metric. The configuration for this specific metric will be set automatically to reflect values set for either the `nodefs` or `imagefs`, depending on the configuration. {{}} In some cases, pod eviction only reclaims a small amount of the starved resource. This can lead to the kubelet repeatedly hitting the configured eviction thresholds and triggering multiple evictions. You can use the `--eviction-minimum-reclaim` flag or a [kubelet config file](/docs/tasks/administer-cluster/kubelet-config-file/) to configure a minimum reclaim amount for each resource. When the kubelet notices that a resource is starved, it continues to reclaim that resource until it reclaims the quantity you specify. For example, the following configuration sets minimum reclaim amounts: ```yaml apiVersion: kubelet.config.k8s.io/v1beta1 kind: KubeletConfiguration evictionHard: memory.available: "500Mi" nodefs.available: "1Gi" imagefs.available: "100Gi" evictionMinimumReclaim: memory.available: "0Mi" nodefs.available: "500Mi" imagefs.available: "2Gi" ``` In this example, if the `nodefs.available` signal meets the eviction threshold, the kubelet reclaims the resource until the signal reaches the threshold of 1GiB, and then continues to reclaim the minimum amount of 500MiB, until the available nodefs storage value reaches 1.5GiB. Similarly, the kubelet tries to reclaim the `imagefs` resource until the
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/node-pressure-eviction.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/node-pressure-eviction.md
|
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|
if the `nodefs.available` signal meets the eviction threshold, the kubelet reclaims the resource until the signal reaches the threshold of 1GiB, and then continues to reclaim the minimum amount of 500MiB, until the available nodefs storage value reaches 1.5GiB. Similarly, the kubelet tries to reclaim the `imagefs` resource until the `imagefs.available` value reaches `102Gi`, representing 102 GiB of available container image storage. If the amount of storage that the kubelet could reclaim is less than 2GiB, the kubelet doesn't reclaim anything. The default `eviction-minimum-reclaim` is `0` for all resources. ## Node out of memory behavior If the node experiences an \_out of memory\_ (OOM) event prior to the kubelet being able to reclaim memory, the node depends on the [oom\_killer](https://lwn.net/Articles/391222/) to respond. The kubelet sets an `oom\_score\_adj` value for each container based on the QoS for the pod. | Quality of Service | `oom\_score\_adj` | |--------------------|-----------------------------------------------------------------------------------| | `Guaranteed` | -997 | | `BestEffort` | 1000 | | `Burstable` | \_min(max(2, 1000 - (1000 × memoryRequestBytes) / machineMemoryCapacityBytes), 999)\_ | {{}} The kubelet also sets an `oom\_score\_adj` value of `-997` for any containers in Pods that have `system-node-critical` {{}}. {{}} If the kubelet can't reclaim memory before a node experiences OOM, the `oom\_killer` calculates an `oom\_score` based on the percentage of memory it's using on the node, and then adds the `oom\_score\_adj` to get an effective `oom\_score` for each container. It then kills the container with the highest score. This means that containers in low QoS pods that consume a large amount of memory relative to their scheduling requests are killed first. Unlike pod eviction, if a container is OOM killed, the kubelet can restart it based on its `restartPolicy`. ## Good practices {#node-pressure-eviction-good-practices} The following sections describe good practice for eviction configuration. ### Schedulable resources and eviction policies When you configure the kubelet with an eviction policy, you should make sure that the scheduler will not schedule pods if they will trigger eviction because they immediately induce memory pressure. Consider the following scenario: - Node memory capacity: 10GiB - Operator wants to reserve 10% of memory capacity for system daemons (kernel, `kubelet`, etc.) - Operator wants to evict Pods at 95% memory utilization to reduce incidence of system OOM. For this to work, the kubelet is launched as follows: ```none --eviction-hard=memory.available<500Mi --system-reserved=memory=1.5Gi ``` In this configuration, the `--system-reserved` flag reserves 1.5GiB of memory for the system, which is `10% of the total memory + the eviction threshold amount`. The node can reach the eviction threshold if a pod is using more than its request, or if the system is using more than 1GiB of memory, which makes the `memory.available` signal fall below 500MiB and triggers the threshold. ### DaemonSets and node-pressure eviction {#daemonset} Pod priority is a major factor in making eviction decisions. If you do not want the kubelet to evict pods that belong to a DaemonSet, give those pods a high enough priority by specifying a suitable `priorityClassName` in the pod spec. You can also use a lower priority, or the default, to only allow pods from that DaemonSet to run when there are enough resources. ## Known issues The following sections describe known issues related to out of resource handling. ### kubelet may not observe memory pressure right away By default, the kubelet polls cAdvisor to collect memory usage stats at a regular interval. If memory usage increases within that window rapidly, the kubelet may not observe `MemoryPressure` fast enough, and the OOM killer will still be invoked. You can use the `--kernel-memcg-notification` flag to enable the `memcg` notification API on the kubelet to get notified immediately
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/node-pressure-eviction.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/node-pressure-eviction.md
|
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] | 0.156521
|
memory usage stats at a regular interval. If memory usage increases within that window rapidly, the kubelet may not observe `MemoryPressure` fast enough, and the OOM killer will still be invoked. You can use the `--kernel-memcg-notification` flag to enable the `memcg` notification API on the kubelet to get notified immediately when a threshold is crossed. If you are not trying to achieve extreme utilization, but a sensible measure of overcommit, a viable workaround for this issue is to use the `--kube-reserved` and `--system-reserved` flags to allocate memory for the system. ### active\_file memory is not considered as available memory On Linux, the kernel tracks the number of bytes of file-backed memory on active least recently used (LRU) list as the `active\_file` statistic. The kubelet treats `active\_file` memory areas as not reclaimable. For workloads that make intensive use of block-backed local storage, including ephemeral local storage, kernel-level caches of file and block data means that many recently accessed cache pages are likely to be counted as `active\_file`. If enough of these kernel block buffers are on the active LRU list, the kubelet is liable to observe this as high resource use and taint the node as experiencing memory pressure - triggering pod eviction. For more details, see [https://github.com/kubernetes/kubernetes/issues/43916](https://github.com/kubernetes/kubernetes/issues/43916) You can work around that behavior by setting the memory limit and memory request the same for containers likely to perform intensive I/O activity. You will need to estimate or measure an optimal memory limit value for that container. ## {{% heading "whatsnext" %}} - Learn about [API-initiated Eviction](/docs/concepts/scheduling-eviction/api-eviction/) - Learn about [Pod Priority and Preemption](/docs/concepts/scheduling-eviction/pod-priority-preemption/) - Learn about [PodDisruptionBudgets](/docs/tasks/run-application/configure-pdb/) - Learn about [Quality of Service](/docs/tasks/configure-pod-container/quality-service-pod/) (QoS) - Check out the [Eviction API](/docs/reference/generated/kubernetes-api/{{}}/#create-eviction-pod-v1-core)
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/node-pressure-eviction.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/node-pressure-eviction.md
|
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] | 0.160825
|
{{< feature-state for\_k8s\_version="v1.14" state="stable" >}} [Pods](/docs/concepts/workloads/pods/) can have \_priority\_. Priority indicates the importance of a Pod relative to other Pods. If a Pod cannot be scheduled, the scheduler tries to preempt (evict) lower priority Pods to make scheduling of the pending Pod possible. {{< warning >}} In a cluster where not all users are trusted, a malicious user could create Pods at the highest possible priorities, causing other Pods to be evicted/not get scheduled. An administrator can use ResourceQuota to prevent users from creating pods at high priorities. See [limit Priority Class consumption by default](/docs/concepts/policy/resource-quotas/#limit-priority-class-consumption-by-default) for details. {{< /warning >}} ## How to use priority and preemption To use priority and preemption: 1. Add one or more [PriorityClasses](#priorityclass). 1. Create Pods with[`priorityClassName`](#pod-priority) set to one of the added PriorityClasses. Of course you do not need to create the Pods directly; normally you would add `priorityClassName` to the Pod template of a collection object like a Deployment. Keep reading for more information about these steps. {{< note >}} Kubernetes already ships with two PriorityClasses: `system-cluster-critical` and `system-node-critical`. These are common classes and are used to [ensure that critical components are always scheduled first](/docs/tasks/administer-cluster/guaranteed-scheduling-critical-addon-pods/). {{< /note >}} ## PriorityClass A PriorityClass is a non-namespaced object that defines a mapping from a priority class name to the integer value of the priority. The name is specified in the `name` field of the PriorityClass object's metadata. The value is specified in the required `value` field. The higher the value, the higher the priority. The name of a PriorityClass object must be a valid [DNS subdomain name](/docs/concepts/overview/working-with-objects/names#dns-subdomain-names), and it cannot be prefixed with `system-`. A PriorityClass object can have any 32-bit integer value smaller than or equal to 1 billion. This means that the range of values for a PriorityClass object is from -2147483648 to 1000000000 inclusive. Larger numbers are reserved for built-in PriorityClasses that represent critical system Pods. A cluster admin should create one PriorityClass object for each such mapping that they want. PriorityClass also has two optional fields: `globalDefault` and `description`. The `globalDefault` field indicates that the value of this PriorityClass should be used for Pods without a `priorityClassName`. Only one PriorityClass with `globalDefault` set to true can exist in the system. If there is no PriorityClass with `globalDefault` set, the priority of Pods with no `priorityClassName` is zero. The `description` field is an arbitrary string. It is meant to tell users of the cluster when they should use this PriorityClass. ### Notes about PodPriority and existing clusters - If you upgrade an existing cluster without this feature, the priority of your existing Pods is effectively zero. - Addition of a PriorityClass with `globalDefault` set to `true` does not change the priorities of existing Pods. The value of such a PriorityClass is used only for Pods created after the PriorityClass is added. - If you delete a PriorityClass, existing Pods that use the name of the deleted PriorityClass remain unchanged, but you cannot create more Pods that use the name of the deleted PriorityClass. ### Example PriorityClass ```yaml apiVersion: scheduling.k8s.io/v1 kind: PriorityClass metadata: name: high-priority value: 1000000 globalDefault: false description: "This priority class should be used for XYZ service pods only." ``` ## Non-preempting PriorityClass {#non-preempting-priority-class} {{< feature-state for\_k8s\_version="v1.24" state="stable" >}} Pods with `preemptionPolicy: Never` will be placed in the scheduling queue ahead of lower-priority pods, but they cannot preempt other pods. A non-preempting pod waiting to be scheduled will stay in the scheduling queue, until sufficient resources are free, and it can be scheduled. Non-preempting pods, like other pods, are subject to scheduler back-off. This means that if the scheduler tries these
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/pod-priority-preemption.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/pod-priority-preemption.md
|
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|
of lower-priority pods, but they cannot preempt other pods. A non-preempting pod waiting to be scheduled will stay in the scheduling queue, until sufficient resources are free, and it can be scheduled. Non-preempting pods, like other pods, are subject to scheduler back-off. This means that if the scheduler tries these pods and they cannot be scheduled, they will be retried with lower frequency, allowing other pods with lower priority to be scheduled before them. Non-preempting pods may still be preempted by other, high-priority pods. `preemptionPolicy` defaults to `PreemptLowerPriority`, which will allow pods of that PriorityClass to preempt lower-priority pods (as is existing default behavior). If `preemptionPolicy` is set to `Never`, pods in that PriorityClass will be non-preempting. An example use case is for data science workloads. A user may submit a job that they want to be prioritized above other workloads, but do not wish to discard existing work by preempting running pods. The high priority job with `preemptionPolicy: Never` will be scheduled ahead of other queued pods, as soon as sufficient cluster resources "naturally" become free. ### Example Non-preempting PriorityClass ```yaml apiVersion: scheduling.k8s.io/v1 kind: PriorityClass metadata: name: high-priority-nonpreempting value: 1000000 preemptionPolicy: Never globalDefault: false description: "This priority class will not cause other pods to be preempted." ``` ## Pod priority After you have one or more PriorityClasses, you can create Pods that specify one of those PriorityClass names in their specifications. The priority admission controller uses the `priorityClassName` field and populates the integer value of the priority. If the priority class is not found, the Pod is rejected. The following YAML is an example of a Pod configuration that uses the PriorityClass created in the preceding example. The priority admission controller checks the specification and resolves the priority of the Pod to 1000000. ```yaml apiVersion: v1 kind: Pod metadata: name: nginx labels: env: test spec: containers: - name: nginx image: nginx imagePullPolicy: IfNotPresent priorityClassName: high-priority ``` ### Effect of Pod priority on scheduling order When Pod priority is enabled, the scheduler orders pending Pods by their priority and a pending Pod is placed ahead of other pending Pods with lower priority in the scheduling queue. As a result, the higher priority Pod may be scheduled sooner than Pods with lower priority if its scheduling requirements are met. If such Pod cannot be scheduled, the scheduler will continue and try to schedule other lower priority Pods. ## Preemption When Pods are created, they go to a queue and wait to be scheduled. The scheduler picks a Pod from the queue and tries to schedule it on a Node. If no Node is found that satisfies all the specified requirements of the Pod, preemption logic is triggered for the pending Pod. Let's call the pending Pod P. Preemption logic tries to find a Node where removal of one or more Pods with lower priority than P would enable P to be scheduled on that Node. If such a Node is found, one or more lower priority Pods get evicted from the Node. After the Pods are gone, P can be scheduled on the Node. ### User exposed information When Pod P preempts one or more Pods on Node N, `nominatedNodeName` field of Pod P's status is set to the name of Node N. This field helps the scheduler track resources reserved for Pod P and also gives users information about preemptions in their clusters. Please note that Pod P is not necessarily scheduled to the "nominated Node". The scheduler always tries the "nominated Node" before iterating over any other nodes. After victim Pods are preempted, they get their graceful termination period.
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/pod-priority-preemption.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/pod-priority-preemption.md
|
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|
for Pod P and also gives users information about preemptions in their clusters. Please note that Pod P is not necessarily scheduled to the "nominated Node". The scheduler always tries the "nominated Node" before iterating over any other nodes. After victim Pods are preempted, they get their graceful termination period. If another node becomes available while scheduler is waiting for the victim Pods to terminate, scheduler may use the other node to schedule Pod P. As a result `nominatedNodeName` and `nodeName` of Pod spec are not always the same. Also, if the scheduler preempts Pods on Node N, but then a higher priority Pod than Pod P arrives, the scheduler may give Node N to the new higher priority Pod. In such a case, scheduler clears `nominatedNodeName` of Pod P. By doing this, scheduler makes Pod P eligible to preempt Pods on another Node. ### Limitations of preemption #### Graceful termination of preemption victims When Pods are preempted, the victims get their [graceful termination period](/docs/concepts/workloads/pods/pod-lifecycle/#pod-termination). They have that much time to finish their work and exit. If they don't, they are killed. This graceful termination period creates a time gap between the point that the scheduler preempts Pods and the time when the pending Pod (P) can be scheduled on the Node (N). In the meantime, the scheduler keeps scheduling other pending Pods. As victims exit or get terminated, the scheduler tries to schedule Pods in the pending queue. Therefore, there is usually a time gap between the point that scheduler preempts victims and the time that Pod P is scheduled. In order to minimize this gap, one can set graceful termination period of lower priority Pods to zero or a small number. #### PodDisruptionBudget is supported, but not guaranteed A [PodDisruptionBudget](/docs/concepts/workloads/pods/disruptions/) (PDB) allows application owners to limit the number of Pods of a replicated application that are down simultaneously from voluntary disruptions. Kubernetes supports PDB when preempting Pods, but respecting PDB is best effort. The scheduler tries to find victims whose PDB are not violated by preemption, but if no such victims are found, preemption will still happen, and lower priority Pods will be removed despite their PDBs being violated. #### Inter-Pod affinity on lower-priority Pods A Node is considered for preemption only when the answer to this question is yes: "If all the Pods with lower priority than the pending Pod are removed from the Node, can the pending Pod be scheduled on the Node?" {{< note >}} Preemption does not necessarily remove all lower-priority Pods. If the pending Pod can be scheduled by removing fewer than all lower-priority Pods, then only a portion of the lower-priority Pods are removed. Even so, the answer to the preceding question must be yes. If the answer is no, the Node is not considered for preemption. {{< /note >}} If a pending Pod has inter-pod {{< glossary\_tooltip text="affinity" term\_id="affinity" >}} to one or more of the lower-priority Pods on the Node, the inter-Pod affinity rule cannot be satisfied in the absence of those lower-priority Pods. In this case, the scheduler does not preempt any Pods on the Node. Instead, it looks for another Node. The scheduler might find a suitable Node or it might not. There is no guarantee that the pending Pod can be scheduled. Our recommended solution for this problem is to create inter-Pod affinity only towards equal or higher priority Pods. #### Cross node preemption Suppose a Node N is being considered for preemption so that a pending Pod P can be scheduled on N. P might become feasible on N only if a Pod on another Node
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/pod-priority-preemption.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/pod-priority-preemption.md
|
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] | 0.162495
|
problem is to create inter-Pod affinity only towards equal or higher priority Pods. #### Cross node preemption Suppose a Node N is being considered for preemption so that a pending Pod P can be scheduled on N. P might become feasible on N only if a Pod on another Node is preempted. Here's an example: \* Pod P is being considered for Node N. \* Pod Q is running on another Node in the same Zone as Node N. \* Pod P has Zone-wide anti-affinity with Pod Q (`topologyKey: topology.kubernetes.io/zone`). \* There are no other cases of anti-affinity between Pod P and other Pods in the Zone. \* In order to schedule Pod P on Node N, Pod Q can be preempted, but scheduler does not perform cross-node preemption. So, Pod P will be deemed unschedulable on Node N. If Pod Q were removed from its Node, the Pod anti-affinity violation would be gone, and Pod P could possibly be scheduled on Node N. We may consider adding cross Node preemption in future versions if there is enough demand and if we find an algorithm with reasonable performance. ## Troubleshooting Pod priority and preemption can have unwanted side effects. Here are some examples of potential problems and ways to deal with them. ### Pods are preempted unnecessarily Preemption removes existing Pods from a cluster under resource pressure to make room for higher priority pending Pods. If you give high priorities to certain Pods by mistake, these unintentionally high priority Pods may cause preemption in your cluster. Pod priority is specified by setting the `priorityClassName` field in the Pod's specification. The integer value for priority is then resolved and populated to the `priority` field of `podSpec`. To address the problem, you can change the `priorityClassName` for those Pods to use lower priority classes, or leave that field empty. An empty `priorityClassName` is resolved to zero by default. When a Pod is preempted, there will be events recorded for the preempted Pod. Preemption should happen only when a cluster does not have enough resources for a Pod. In such cases, preemption happens only when the priority of the pending Pod (preemptor) is higher than the victim Pods. Preemption must not happen when there is no pending Pod, or when the pending Pods have equal or lower priority than the victims. If preemption happens in such scenarios, please file an issue. ### Pods are preempted, but the preemptor is not scheduled When pods are preempted, they receive their requested graceful termination period, which is by default 30 seconds. If the victim Pods do not terminate within this period, they are forcibly terminated. Once all the victims go away, the preemptor Pod can be scheduled. While the preemptor Pod is waiting for the victims to go away, a higher priority Pod may be created that fits on the same Node. In this case, the scheduler will schedule the higher priority Pod instead of the preemptor. This is expected behavior: the Pod with the higher priority should take the place of a Pod with a lower priority. ### Higher priority Pods are preempted before lower priority pods The scheduler tries to find nodes that can run a pending Pod. If no node is found, the scheduler tries to remove Pods with lower priority from an arbitrary node in order to make room for the pending pod. If a node with low priority Pods is not feasible to run the pending Pod, the scheduler may choose another node with higher priority Pods (compared to the Pods on the other node) for preemption. The victims must still
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/pod-priority-preemption.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/pod-priority-preemption.md
|
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|
arbitrary node in order to make room for the pending pod. If a node with low priority Pods is not feasible to run the pending Pod, the scheduler may choose another node with higher priority Pods (compared to the Pods on the other node) for preemption. The victims must still have lower priority than the preemptor Pod. When there are multiple nodes available for preemption, the scheduler tries to choose the node with a set of Pods with lowest priority. However, if such Pods have PodDisruptionBudget that would be violated if they are preempted then the scheduler may choose another node with higher priority Pods. When multiple nodes exist for preemption and none of the above scenarios apply, the scheduler chooses a node with the lowest priority. ## Interactions between Pod priority and quality of service {#interactions-of-pod-priority-and-qos} Pod priority and {{< glossary\_tooltip text="QoS class" term\_id="qos-class" >}} are two orthogonal features with few interactions and no default restrictions on setting the priority of a Pod based on its QoS classes. The scheduler's preemption logic does not consider QoS when choosing preemption targets. Preemption considers Pod priority and attempts to choose a set of targets with the lowest priority. Higher-priority Pods are considered for preemption only if the removal of the lowest priority Pods is not sufficient to allow the scheduler to schedule the preemptor Pod, or if the lowest priority Pods are protected by `PodDisruptionBudget`. The kubelet uses Priority to determine pod order for [node-pressure eviction](/docs/concepts/scheduling-eviction/node-pressure-eviction/). You can use the QoS class to estimate the order in which pods are most likely to get evicted. The kubelet ranks pods for eviction based on the following factors: 1. Whether the starved resource usage exceeds requests 1. Pod Priority 1. Amount of resource usage relative to requests See [Pod selection for kubelet eviction](/docs/concepts/scheduling-eviction/node-pressure-eviction/#pod-selection-for-kubelet-eviction) for more details. kubelet node-pressure eviction does not evict Pods when their usage does not exceed their requests. If a Pod with lower priority is not exceeding its requests, it won't be evicted. Another Pod with higher priority that exceeds its requests may be evicted. ## {{% heading "whatsnext" %}} \* Read about using ResourceQuotas in connection with PriorityClasses: [limit Priority Class consumption by default](/docs/concepts/policy/resource-quotas/#limit-priority-class-consumption-by-default) \* Learn about [Pod Disruption](/docs/concepts/workloads/pods/disruptions/) \* Learn about [API-initiated Eviction](/docs/concepts/scheduling-eviction/api-eviction/) \* Learn about [Node-pressure Eviction](/docs/concepts/scheduling-eviction/node-pressure-eviction/)
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/pod-priority-preemption.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/pod-priority-preemption.md
|
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{{< feature-state feature\_gate\_name="GangScheduling" >}} Gang scheduling ensures that a group of Pods are scheduled on an "all-or-nothing" basis. If the cluster cannot accommodate the entire group (or a defined minimum number of Pods), none of the Pods are bound to a node. This feature depends on the [Workload API](/docs/concepts/workloads/workload-api/). Ensure the [`GenericWorkload`](/docs/reference/command-line-tools-reference/feature-gates/#GenericWorkload) feature gate and the `scheduling.k8s.io/v1alpha1` {{< glossary\_tooltip text="API group" term\_id="api-group" >}} are enabled in the cluster. ## How it works When the `GangScheduling` plugin is enabled, the scheduler alters the lifecycle for Pods belonging to a `gang` [pod group policy](/docs/concepts/workloads/workload-api/policies/) within a [Workload](/docs/concepts/workloads/workload-api/). The process follows these steps independently for each pod group and its replica key: 1. The scheduler holds Pods in the `PreEnqueue` phase until: \* The referenced Workload object is created. \* The referenced pod group exists in a Workload. \* The number of Pods that have been created for the specific group is at least equal to the `minCount`. Pods do not enter the active scheduling queue until all of these conditions are met. 2. Once the quorum is met, the scheduler attempts to find placements for all Pods in the group. All assigned Pods wait at the `WaitOnPermit` gate during this process. Note that in the Alpha phase of this feature, finding a placement is based on pod-by-pod scheduling, rather than a single-cycle approach. 3. If the scheduler finds valid placements for at least `minCount` Pods, it allows all of them to be bound to their assigned nodes. If it cannot find placements for the entire group within a fixed timeout of 5 minutes, none of the Pods are scheduled. Instead, they are moved to the unschedulable queue to wait for cluster resources to free up, allowing other workloads to be scheduled in the meantime. ## {{% heading "whatsnext" %}} \* Learn about the [Workload API](/docs/concepts/workloads/workload-api/). \* See how to [reference a Workload](/docs/concepts/workloads/pods/workload-reference/) in a Pod.
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/gang-scheduling.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/gang-scheduling.md
|
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{{< feature-state feature\_gate\_name="DynamicResourceAllocation" >}} This page describes \_dynamic resource allocation (DRA)\_ in Kubernetes. ## About DRA {#about-dra} {{< glossary\_definition prepend="DRA is" term\_id="dra" length="all" >}} Allocating resources with DRA is a similar experience to [dynamic volume provisioning](/docs/concepts/storage/dynamic-provisioning/), in which you use PersistentVolumeClaims to claim storage capacity from storage classes and request the claimed capacity in your Pods. ### Benefits of DRA {#dra-benefits} DRA provides a flexible way to categorize, request, and use devices in your cluster. Using DRA provides benefits like the following: \* \*\*Flexible device filtering\*\*: use common expression language (CEL) to perform fine-grained filtering for specific device attributes. \* \*\*Device sharing\*\*: share the same resource with multiple containers or Pods by referencing the corresponding resource claim. \* \*\*Centralized device categorization\*\*: device drivers and cluster admins can use device classes to provide app operators with hardware categories that are optimized for various use cases. For example, you can create a cost-optimized device class for general-purpose workloads, and a high-performance device class for critical jobs. \* \*\*Simplified Pod requests\*\*: with DRA, app operators don't need to specify device quantities in Pod resource requests. Instead, the Pod references a resource claim, and the device configuration in that claim applies to the Pod. These benefits provide significant improvements in the device allocation workflow when compared to [device plugins](/docs/concepts/extend-kubernetes/compute-storage-net/device-plugins/), which require per-container device requests, don't support device sharing, and don't support expression-based device filtering. ### Types of DRA users {#dra-user-types} The workflow of using DRA to allocate devices involves the following types of users: \* \*\*Device owner\*\*: responsible for devices. Device owners might be commercial vendors, the cluster operator, or another entity. To use DRA, devices must have DRA-compatible drivers that do the following: \* Create ResourceSlices that provide Kubernetes with information about nodes and resources. \* Update ResourceSlices when resource capacity in the cluster changes. \* Optionally, create DeviceClasses that workload operators can use to claim devices. \* \*\*Cluster admin\*\*: responsible for configuring clusters and nodes, attaching devices, installing drivers, and similar tasks. To use DRA, cluster admins do the following: \* Attach devices to nodes. \* Install device drivers that support DRA. \* Optionally, create DeviceClasses that workload operators can use to claim devices. \* \*\*Workload operator\*\*: responsible for deploying and managing workloads in the cluster. To use DRA to allocate devices to Pods, workload operators do the following: \* Create ResourceClaims or ResourceClaimTemplates to request specific configurations within DeviceClasses. \* Deploy workloads that use specific ResourceClaims or ResourceClaimTemplates. ## DRA terminology {#terminology} DRA uses the following Kubernetes API kinds to provide the core allocation functionality. All of these API kinds are included in the `resource.k8s.io/v1` {{< glossary\_tooltip text="API group" term\_id="api-group" >}}. DeviceClass : Defines a category of devices that can be claimed and how to select specific device attributes in claims. The DeviceClass parameters can match zero or more devices in ResourceSlices. To claim devices from a DeviceClass, ResourceClaims select specific device attributes. ResourceClaim : Describes a request for access to attached resources, such as devices, in the cluster. ResourceClaims provide Pods with access to a specific resource. ResourceClaims can be created by workload operators or generated by Kubernetes based on a ResourceClaimTemplate. ResourceClaimTemplate : Defines a template that Kubernetes uses to create per-Pod ResourceClaims for a workload. ResourceClaimTemplates provide Pods with access to separate, similar resources. Each ResourceClaim that Kubernetes generates from the template is bound to a specific Pod. When the Pod terminates, Kubernetes deletes the corresponding ResourceClaim. ResourceSlice : Represents one or more resources that are attached to nodes, such as devices. Drivers create and manage ResourceSlices in the cluster. When a ResourceClaim is created and used in
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/dynamic-resource-allocation.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/dynamic-resource-allocation.md
|
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|
generates from the template is bound to a specific Pod. When the Pod terminates, Kubernetes deletes the corresponding ResourceClaim. ResourceSlice : Represents one or more resources that are attached to nodes, such as devices. Drivers create and manage ResourceSlices in the cluster. When a ResourceClaim is created and used in a Pod, Kubernetes uses ResourceSlices to find nodes that have access to the claimed resources. Kubernetes allocates resources to the ResourceClaim and schedules the Pod onto a node that can access the resources. ### DeviceClass {#deviceclass} A DeviceClass lets cluster admins or device drivers define categories of devices in the cluster. DeviceClasses tell operators what devices they can request and how they can request those devices. You can use [common expression language (CEL)](https://cel.dev) to select devices based on specific attributes. A ResourceClaim that references the DeviceClass can then request specific configurations within the DeviceClass. To create a DeviceClass, see [Set Up DRA in a Cluster](/docs/tasks/configure-pod-container/assign-resources/set-up-dra-cluster). ### ResourceClaims and ResourceClaimTemplates {#resourceclaims-templates} A ResourceClaim defines the resources that a workload needs. Every ResourceClaim has \_requests\_ that reference a DeviceClass and select devices from that DeviceClass. ResourceClaims can also use \_selectors\_ to filter for devices that meet specific requirements, and can use \_constraints\_ to limit the devices that can satisfy a request. ResourceClaims can be created by workload operators or can be generated by Kubernetes based on a ResourceClaimTemplate. A ResourceClaimTemplate defines a template that Kubernetes can use to auto-generate ResourceClaims for Pods. #### Use cases for ResourceClaims and ResourceClaimTemplates {#when-to-use-rc-rct} The method that you use depends on your requirements, as follows: \* \*\*ResourceClaim\*\*: you want multiple Pods to share access to specific devices. You manually manage the lifecycle of ResourceClaims that you create. \* \*\*ResourceClaimTemplate\*\*: you want Pods to have independent access to separate, similarly-configured devices. Kubernetes generates ResourceClaims from the specification in the ResourceClaimTemplate. The lifetime of each generated ResourceClaim is bound to the lifetime of the corresponding Pod. When you define a workload, you can use {{< glossary\_tooltip term\_id="cel" text="Common Expression Language (CEL)" >}} to filter for specific device attributes or capacity. The available parameters for filtering depend on the device and the drivers. If you directly reference a specific ResourceClaim in a Pod, that ResourceClaim must already exist in the same namespace as the Pod. If the ResourceClaim doesn't exist in the namespace, the Pod won't schedule. This behavior is similar to how a PersistentVolumeClaim must exist in the same namespace as a Pod that references it. You can reference an auto-generated ResourceClaim in a Pod, but this isn't recommended because auto-generated ResourceClaims are bound to the lifetime of the Pod that triggered the generation. To learn how to claim resources using one of these methods, see [Allocate Devices to Workloads with DRA](/docs/tasks/configure-pod-container/assign-resources/allocate-devices-dra/). #### Prioritized list {#prioritized-list} {{< feature-state feature\_gate\_name="DRAPrioritizedList" >}} You can provide a prioritized list of subrequests for requests in a ResourceClaim or ResourceClaimTemplate. The scheduler will then select the first subrequest that can be allocated. This allows users to specify alternative devices that can be used by the workload if the primary choice is not available. In the example below, the ResourceClaimTemplate requested a device with the color black and the size large. If a device with those attributes is not available, the pod cannot be scheduled. With the prioritized list feature, a second alternative can be specified, which requests two devices with the color white and size small. The large black device will be allocated if it is available. If it is not, but two small white devices are available, the pod will still be able to run. ```yaml apiVersion: resource.k8s.io/v1 kind: ResourceClaimTemplate metadata: name:
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/dynamic-resource-allocation.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/dynamic-resource-allocation.md
|
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] | 0.201168
|
be specified, which requests two devices with the color white and size small. The large black device will be allocated if it is available. If it is not, but two small white devices are available, the pod will still be able to run. ```yaml apiVersion: resource.k8s.io/v1 kind: ResourceClaimTemplate metadata: name: prioritized-list-claim-template spec: spec: devices: requests: - name: req-0 firstAvailable: - name: large-black deviceClassName: resource.example.com selectors: - cel: expression: |- device.attributes["resource-driver.example.com"].color == "black" && device.attributes["resource-driver.example.com"].size == "large" - name: small-white deviceClassName: resource.example.com selectors: - cel: expression: |- device.attributes["resource-driver.example.com"].color == "white" && device.attributes["resource-driver.example.com"].size == "small" count: 2 ``` If the pod is eligible for multiple nodes in the cluster, the scheduler will use the index of chosen subrequests from any prioritized lists as one of the inputs when it scores each node. So nodes that can allocate devices requested in a higher ranked subrequest are more likely to be chosen than nodes that can only allocate devices for lower ranked subrequests. The decision is made on a per-Pod basis, so if the Pod is a member of a ReplicaSet or similar grouping, you cannot rely on all the members of the group having the same subrequest chosen. Your workload must be able to accommodate this. Prioritized lists is a \*beta feature\* and is enabled by default with the `DRAPrioritizedList` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/) in the kube-apiserver and kube-scheduler. ### ResourceSlice {#resourceslice} Each ResourceSlice represents one or more {{< glossary\_tooltip term\_id="device" text="devices" >}} in a pool. The pool is managed by a device driver, which creates and manages ResourceSlices. The resources in a pool might be represented by a single ResourceSlice or span multiple ResourceSlices. ResourceSlices provide useful information to device users and to the scheduler, and are crucial for dynamic resource allocation. Every ResourceSlice must include the following information: \* \*\*Resource pool\*\*: a group of one or more resources that the driver manages. The pool can span more than one ResourceSlice. Changes to the resources in a pool must be propagated across all of the ResourceSlices in that pool. The device driver that manages the pool is responsible for ensuring that this propagation happens. \* \*\*Devices\*\*: devices in the managed pool. A ResourceSlice can list every device in a pool or a subset of the devices in a pool. The ResourceSlice defines device information like attributes, versions, and capacity. Device users can select devices for allocation by filtering for device information in ResourceClaims or in DeviceClasses. \* \*\*Nodes\*\*: the nodes that can access the resources. Drivers can choose which nodes can access the resources, whether that's all of the nodes in the cluster, a single named node, or nodes that have specific node labels. Drivers use a {{< glossary\_tooltip text="controller" term\_id="controller" >}} to reconcile ResourceSlices in the cluster with the information that the driver has to publish. This controller overwrites any manual changes, such as cluster users creating or modifying ResourceSlices. Consider the following example ResourceSlice: ```yaml apiVersion: resource.k8s.io/v1 kind: ResourceSlice metadata: name: cat-slice spec: driver: "resource-driver.example.com" pool: generation: 1 name: "black-cat-pool" resourceSliceCount: 1 # The allNodes field defines whether any node in the cluster can access the device. allNodes: true devices: - name: "large-black-cat" attributes: color: string: "black" size: string: "large" cat: bool: true ``` This ResourceSlice is managed by the `resource-driver.example.com` driver in the `black-cat-pool` pool. The `allNodes: true` field indicates that any node in the cluster can access the devices. There's one device in the ResourceSlice, named `large-black-cat`, with the following attributes: \* `color`: `black` \* `size`: `large` \* `cat`: `true` A DeviceClass could select this ResourceSlice by using these attributes, and a ResourceClaim could filter for specific devices in that
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/dynamic-resource-allocation.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/dynamic-resource-allocation.md
|
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|
any node in the cluster can access the devices. There's one device in the ResourceSlice, named `large-black-cat`, with the following attributes: \* `color`: `black` \* `size`: `large` \* `cat`: `true` A DeviceClass could select this ResourceSlice by using these attributes, and a ResourceClaim could filter for specific devices in that DeviceClass. ## How resource allocation with DRA works {#how-it-works} The following sections describe the workflow for the various [types of DRA users](#dra-user-types) and for the Kubernetes system during dynamic resource allocation. ### Workflow for users {#user-workflow} 1. \*\*Driver creation\*\*: device owners or third-party entities create drivers that can create and manage ResourceSlices in the cluster. These drivers optionally also create DeviceClasses that define a category of devices and how to request them. 1. \*\*Cluster configuration\*\*: cluster admins create clusters, attach devices to nodes, and install the DRA device drivers. Cluster admins optionally create DeviceClasses that define categories of devices and how to request them. 1. \*\*Resource claims\*\*: workload operators create ResourceClaimTemplates or ResourceClaims that request specific device configurations within a DeviceClass. In the same step, workload operators modify their Kubernetes manifests to request those ResourceClaimTemplates or ResourceClaims. ### Workflow for Kubernetes {#kubernetes-workflow} 1. \*\*ResourceSlice creation\*\*: drivers in the cluster create ResourceSlices that represent one or more devices in a managed pool of similar devices. 1. \*\*Workload creation\*\*: the cluster control plane checks new workloads for references to ResourceClaimTemplates or to specific ResourceClaims. \* If the workload uses a ResourceClaimTemplate, a controller named the `resourceclaim-controller` generates ResourceClaims for every Pod in the workload. \* If the workload uses a specific ResourceClaim, Kubernetes checks whether that ResourceClaim exists in the cluster. If the ResourceClaim doesn't exist, the Pods won't deploy. 1. \*\*ResourceSlice filtering\*\*: for every Pod, Kubernetes checks the ResourceSlices in the cluster to find a device that satisfies all of the following criteria: \* The nodes that can access the resources are eligible to run the Pod. \* The ResourceSlice has unallocated resources that match the requirements of the Pod's ResourceClaim. 1. \*\*Resource allocation\*\*: after finding an eligible ResourceSlice for a Pod's ResourceClaim, the Kubernetes scheduler updates the ResourceClaim with the allocation details. 1. \*\*Pod scheduling\*\*: when resource allocation is complete, the scheduler places the Pod on a node that can access the allocated resource. The device driver and the kubelet on that node configure the device and the Pod's access to the device. ## Observability of dynamic resources {#observability-dynamic-resources} You can check the status of dynamically allocated resources by using any of the following methods: \* [kubelet device metrics](#monitoring-resources) \* [ResourceClaim status](#resourceclaim-device-status) \* [Device health monitoring](#device-health-monitoring) ### kubelet device metrics {#monitoring-resources} The `PodResourcesLister` kubelet gRPC service lets you monitor in-use devices. The `DynamicResource` message provides information that's specific to dynamic resource allocation, such as the device name and the claim name. For details, see [Monitoring device plugin resources](/docs/concepts/extend-kubernetes/compute-storage-net/device-plugins/#monitoring-device-plugin-resources). ### ResourceClaim device status {#resourceclaim-device-status} {{< feature-state feature\_gate\_name="DRAResourceClaimDeviceStatus" >}} DRA drivers can report driver-specific [device status](/docs/concepts/overview/working-with-objects/#object-spec-and-status) data for each allocated device in the `status.devices` field of a ResourceClaim. For example, the driver might list the IP addresses that are assigned to a network interface device. The accuracy of the information that a driver adds to a ResourceClaim `status.devices` field depends on the driver. Evaluate drivers to decide whether you can rely on this field as the only source of device information. If you disable the `DRAResourceClaimDeviceStatus` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/), the `status.devices` field automatically gets cleared when storing the ResourceClaim. A ResourceClaim device status is supported when it is possible, from a DRA driver, to update an existing ResourceClaim where the `status.devices` field is set. For details about the `status.devices` field, see the {{<
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/dynamic-resource-allocation.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/dynamic-resource-allocation.md
|
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] | 0.197489
|
you disable the `DRAResourceClaimDeviceStatus` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/), the `status.devices` field automatically gets cleared when storing the ResourceClaim. A ResourceClaim device status is supported when it is possible, from a DRA driver, to update an existing ResourceClaim where the `status.devices` field is set. For details about the `status.devices` field, see the {{< api-reference page="workload-resources/resource-claim-v1beta1" anchor="ResourceClaimStatus" text="ResourceClaim" >}} API reference. ### Device Health Monitoring {#device-health-monitoring} {{< feature-state feature\_gate\_name="ResourceHealthStatus" >}} As an alpha feature, Kubernetes provides a mechanism for monitoring and reporting the health of dynamically allocated infrastructure resources. For stateful applications running on specialized hardware, it is critical to know when a device has failed or become unhealthy. It is also helpful to find out if the device recovers. To enable this functionality, the `ResourceHealthStatus` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/resource-health-status/) must be enabled, and the DRA driver must implement the `DRAResourceHealth` gRPC service. When a DRA driver detects that an allocated device has become unhealthy, it reports this status back to the kubelet. This health information is then exposed directly in the Pod's status. The kubelet populates the `allocatedResourcesStatus` field in the status of each container, detailing the health of each device assigned to that container. This provides crucial visibility for users and controllers to react to hardware failures. For a Pod that is failing, you can inspect this status to determine if the failure was related to an unhealthy device. ## Pre-scheduled Pods When you - or another API client - create a Pod with `spec.nodeName` already set, the scheduler gets bypassed. If some ResourceClaim needed by that Pod does not exist yet, is not allocated or not reserved for the Pod, then the kubelet will fail to run the Pod and re-check periodically because those requirements might still get fulfilled later. Such a situation can also arise when support for dynamic resource allocation was not enabled in the scheduler at the time when the Pod got scheduled (version skew, configuration, feature gate, etc.). kube-controller-manager detects this and tries to make the Pod runnable by reserving the required ResourceClaims. However, this only works if those were allocated by the scheduler for some other pod. It is better to avoid bypassing the scheduler because a Pod that is assigned to a node blocks normal resources (RAM, CPU) that then cannot be used for other Pods while the Pod is stuck. To make a Pod run on a specific node while still going through the normal scheduling flow, create the Pod with a node selector that exactly matches the desired node: ```yaml apiVersion: v1 kind: Pod metadata: name: pod-with-cats spec: nodeSelector: kubernetes.io/hostname: name-of-the-intended-node ... ``` You may also be able to mutate the incoming Pod, at admission time, to unset the `.spec.nodeName` field and to use a node selector instead. ## DRA beta features {#beta-features} The following sections describe DRA features that are available in the Beta [feature stage](/docs/reference/command-line-tools-reference/feature-gates/#feature-stages). For more information, see [Set up DRA in the cluster](/docs/tasks/configure-pod-container/assign-resources/set-up-dra-cluster/). ### Admin access {#admin-access} {{< feature-state feature\_gate\_name="DRAAdminAccess" >}} You can mark a request in a ResourceClaim or ResourceClaimTemplate as having privileged features for maintenance and troubleshooting tasks. A request with admin access grants access to in-use devices and may enable additional permissions when making the device available in a container: ```yaml apiVersion: resource.k8s.io/v1 kind: ResourceClaimTemplate metadata: name: large-black-cat-claim-template spec: spec: devices: requests: - name: req-0 exactly: deviceClassName: resource.example.com allocationMode: All adminAccess: true ``` If this feature is disabled, the `adminAccess` field will be removed automatically when creating such a ResourceClaim. Admin access is a privileged mode and should not be granted to regular users in multi-tenant clusters. Starting with Kubernetes v1.33, only users authorized to create ResourceClaim or
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/dynamic-resource-allocation.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/dynamic-resource-allocation.md
|
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] | 0.180396
|
allocationMode: All adminAccess: true ``` If this feature is disabled, the `adminAccess` field will be removed automatically when creating such a ResourceClaim. Admin access is a privileged mode and should not be granted to regular users in multi-tenant clusters. Starting with Kubernetes v1.33, only users authorized to create ResourceClaim or ResourceClaimTemplate objects in namespaces labeled with `resource.k8s.io/admin-access: "true"` (case-sensitive) can use the `adminAccess` field. This ensures that non-admin users cannot misuse the feature. Starting with Kubernetes v1.34, this label has been updated to `resource.kubernetes.io/admin-access: "true"`. ## DRA alpha features {#alpha-features} The following sections describe DRA features that are available in the Alpha [feature stage](/docs/reference/command-line-tools-reference/feature-gates/#feature-stages). They depend on enabling feature gates and may depend on additional {{< glossary\_tooltip text="API groups" term\_id="api-group" >}}. For more information, see [Set up DRA in the cluster](/docs/tasks/configure-pod-container/assign-resources/set-up-dra-cluster/). ### Extended resource allocation by DRA {#extended-resource} {{< feature-state feature\_gate\_name="DRAExtendedResource" >}} You can provide an extended resource name for a DeviceClass. The scheduler will then select the devices matching the class for the extended resource requests. This allows users to continue using extended resource requests in a pod to request either extended resources provided by device plugin, or DRA devices. The same extended resource can be provided either by device plugin, or DRA on one single cluster node. The same extended resource can be provided by device plugin on some nodes, and DRA on other nodes in the same cluster. In the example below, the DeviceClass is given an extendedResourceName `example.com/gpu`. If a pod requested for the extended resource `example.com/gpu: 2`, it can be scheduled to a node with two or more devices matching the DeviceClass. ```yaml apiVersion: resource.k8s.io/v1 kind: DeviceClass metadata: name: gpu.example.com spec: selectors: - cel: expression: device.driver == 'gpu.example.com' && device.attributes['gpu.example.com'].type == 'gpu' extendedResourceName: example.com/gpu ``` In addition, users can use a special extended resource to allocate devices without having to explicitly create a ResourceClaim. Using the extended resource name prefix `deviceclass.resource.kubernetes.io/` and the DeviceClass name. This works for any DeviceClass, even if it does not specify an extended resource name. The resulting ResourceClaim will contain a request for an `ExactCount` of the specified number of devices of that DeviceClass. Extended resource allocation by DRA is an \*alpha feature\* and only enabled when the `DRAExtendedResource` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/) is enabled in the kube-apiserver, kube-scheduler, and kubelet. ### Partitionable devices {#partitionable-devices} {{< feature-state feature\_gate\_name="DRAPartitionableDevices" >}} Devices represented in DRA don't necessarily have to be a single unit connected to a single machine, but can also be a logical device comprised of multiple devices connected to multiple machines. These devices might consume overlapping resources of the underlying phyical devices, meaning that when one logical device is allocated other devices will no longer be available. In the ResourceSlice API, this is represented as a list of named CounterSets, each of which contains a set of named counters. The counters represent the resources available on the physical device that are used by the logical devices advertised through DRA. Logical devices can specify the ConsumesCounters list. Each entry contains a reference to a CounterSet and a set of named counters with the amounts they will consume. So for a device to be allocatable, the referenced counter sets must have sufficient quantity for the counters referenced by the device. CounterSets must be specified in separate ResourceSlices from devices. Devices can consume counters from any CounterSet defined in the same resource pool as the device. Here is an example of two devices, each consuming 6Gi of memory from the a shared counter with 8Gi of memory. Thus, only one of the devices can be allocated at any point in time. The scheduler handles this
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/dynamic-resource-allocation.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/dynamic-resource-allocation.md
|
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] | 0.089307
|
any CounterSet defined in the same resource pool as the device. Here is an example of two devices, each consuming 6Gi of memory from the a shared counter with 8Gi of memory. Thus, only one of the devices can be allocated at any point in time. The scheduler handles this and it is transparent to the consumer as the ResourceClaim API is not affected. ```yaml kind: ResourceSlice apiVersion: resource.k8s.io/v1 metadata: name: resourceslice-with-countersets spec: nodeName: worker-1 pool: name: pool generation: 1 resourceSliceCount: 2 driver: dra.example.com sharedCounters: - name: gpu-1-counters counters: memory: value: 8Gi --- kind: ResourceSlice apiVersion: resource.k8s.io/v1 metadata: name: resourceslice-with-devices spec: nodeName: worker-1 pool: name: pool generation: 1 resourceSliceCount: 2 driver: dra.example.com devices: - name: device-1 consumesCounters: - counterSet: gpu-1-counters counters: memory: value: 6Gi - name: device-2 consumesCounters: - counterSet: gpu-1-counters counters: memory: value: 6Gi ``` Partitionable devices is an \*alpha feature\* and only enabled when the `DRAPartitionableDevices` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/) is enabled in the kube-apiserver and kube-scheduler. ## Consumable capacity {{< feature-state feature\_gate\_name="DRAConsumableCapacity" >}} The consumable capacity feature allows the same devices to be consumed by multiple independent ResourceClaims, with the Kubernetes scheduler managing how much of the device's capacity is used up by each claim. This is analogous to how Pods can share the resources on a Node; ResourceClaims can share the resources on a Device. The device driver can set `allowMultipleAllocations` field added in `.spec.devices` of `ResourceSlice` to allow allocating that device to multiple independent ResourceClaims or to multiple requests within a ResourceClaim. Users can set `capacity` field added in `spec.devices.requests` of `ResourceClaim` to specify the device resource requirements for each allocation. For the device that allows multiple allocations, the requested capacity is drawn from — or consumed from — its total capacity, a concept known as \*\*consumable capacity\*\*. Then, the scheduler ensures that the aggregate consumed capacity across all claims does not exceed the device’s overall capacity. Furthermore, driver authors can use the `requestPolicy` constraints on individual device capacities to control how those capacities are consumed. For example, the driver author can specify that a given capacity is only consumed in increments of 1Gi. Here is an example of a network device which allows multiple allocations and contains a consumable bandwidth capacity. ```yaml kind: ResourceSlice apiVersion: resource.k8s.io/v1 metadata: name: resourceslice spec: nodeName: worker-1 pool: name: pool generation: 1 resourceSliceCount: 1 driver: dra.example.com devices: - name: eth1 allowMultipleAllocations: true attributes: name: string: "eth1" capacity: bandwidth: requestPolicy: default: "1M" validRange: min: "1M" step: "8" value: "10G" ``` The consumable capacity can be requested as shown in the below example. ```yaml apiVersion: resource.k8s.io/v1 kind: ResourceClaimTemplate metadata: name: bandwidth-claim-template spec: spec: devices: requests: - name: req-0 exactly: deviceClassName: resource.example.com capacity: requests: bandwidth: 1G ``` The allocation result will include the consumed capacity and the identifier of the share. ```yaml apiVersion: resource.k8s.io/v1 kind: ResourceClaim ... status: allocation: devices: results: - consumedCapacity: bandwidth: 1G device: eth1 shareID: "a671734a-e8e5-11e4-8fde-42010af09327" ``` In this example, a multiply-allocatable device was chosen. However, any `resource.example.com` device with at least the requested 1G bandwidth could have met the requirement. If a non-multiply-allocatable device were chosen, the allocation would have resulted in the entire device. To force the use of a only multiply-allocatable devices, you can use the CEL criteria `device.allowMultipleAllocations == true`. ### Device taints and tolerations {#device-taints-and-tolerations} {{< feature-state feature\_gate\_name="DRADeviceTaints" >}} Device taints are similar to node taints: a taint has a string key, a string value, and an effect. The effect is applied to the ResourceClaim which is using a tainted device and to all Pods referencing that ResourceClaim. The "NoSchedule" effect prevents scheduling those Pods. Tainted devices are ignored when trying to allocate a ResourceClaim because
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/dynamic-resource-allocation.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/dynamic-resource-allocation.md
|
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] | 0.120609
|
a taint has a string key, a string value, and an effect. The effect is applied to the ResourceClaim which is using a tainted device and to all Pods referencing that ResourceClaim. The "NoSchedule" effect prevents scheduling those Pods. Tainted devices are ignored when trying to allocate a ResourceClaim because using them would prevent scheduling of Pods. The "NoExecute" effect implies "NoSchedule" and in addition causes eviction of all Pods which have been scheduled already. This eviction is implemented in the device taint eviction controller in kube-controller-manager by deleting affected Pods. The "None" effect is ignored by the scheduler and eviction controller. DRA drivers can use it to communicate exceptions to admins or other controllers, like for example degraded health of a device. Admins can also use it to do dry-runs of pod eviction in DeviceTaintRules (more on that below). ResourceClaims can tolerate taints. If a taint is tolerated, its effect does not apply. An empty toleration matches all taints. A toleration can be limited to certain effects and/or match certain key/value pairs. A toleration can check that a certain key exists, regardless which value it has, or it can check for specific values of a key. For more information on this matching see the [node taint concepts](/docs/concepts/scheduling-eviction/taint-and-toleration#concepts). Eviction can be delayed by tolerating a taint for a certain duration. That delay starts at the time when a taint gets added to a device, which is recorded in a field of the taint. Taints apply as described above also to ResourceClaims allocating "all" devices on a node. All devices must be untainted or all of their taints must be tolerated. Allocating a device with admin access (described [above](#admin-access)) is not exempt either. An admin using that mode must explicitly tolerate all taints to access tainted devices. Device taints and tolerations is an \*alpha feature\* and only enabled when the `DRADeviceTaints` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/) is enabled in the kube-apiserver, kube-controller-manager and kube-scheduler. To use DeviceTaintRules, the `resource.k8s.io/v1alpha3` API version must be enabled. You can add taints to devices in the following ways, by using the DeviceTaintRule API kind. #### Taints set by the driver A DRA driver can add taints to the device information that it publishes in ResourceSlices. Consult the documentation of a DRA driver to learn whether the driver uses taints and what their keys and values are. #### Taints set by an admin {{< feature-state feature\_gate\_name="DRADeviceTaintRules" >}} An admin or a control plane component can taint devices without having to tell the DRA driver to include taints in its device information in ResourceSlices. They do that by creating DeviceTaintRules. Each DeviceTaintRule adds one taint to devices which match the device selector. Without such a selector, no devices are tainted. This makes it harder to accidentally evict all pods using ResourceClaims when leaving out the selector by mistake. Devices can be selected by giving the name of a DeviceClass, driver, pool, and/or device. The DeviceClass selects all devices that are selected by the selectors in that DeviceClass. With just the driver name, an admin can taint all devices managed by that driver, for example while doing some kind of maintenance of that driver across the entire cluster. Adding a pool name can limit the taint to a single node, if the driver manages node-local devices. Finally, adding the device name can select one specific device. The device name and pool name can also be used alone, if desired. For example, drivers for node-local devices are encouraged to use the node name as their pool name. Then tainting with that pool name automatically taints all devices on a node. Drivers might
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/dynamic-resource-allocation.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/dynamic-resource-allocation.md
|
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] | 0.204937
|
can select one specific device. The device name and pool name can also be used alone, if desired. For example, drivers for node-local devices are encouraged to use the node name as their pool name. Then tainting with that pool name automatically taints all devices on a node. Drivers might use stable names like "gpu-0" that hide which specific device is currently assigned to that name. To support tainting a specific hardware instance, CEL selectors can be used in a DeviceTaintRule to match a vendor-specific unique ID attribute, if the driver supports one for its hardware. The taint applies as long as the DeviceTaintRule exists. It can be modified and and removed at any time. Here is one example of a DeviceTaintRule for a fictional DRA driver: ```yaml apiVersion: resource.k8s.io/v1alpha3 kind: DeviceTaintRule metadata: name: example spec: # The entire hardware installation for this # particular driver is broken. # Evict all pods and don't schedule new ones. deviceSelector: driver: dra.example.com taint: key: dra.example.com/unhealthy value: Broken effect: NoExecute ``` The apiserver automatically tracks when this taint was created and the eviction controller adds a condition with some information: ``` kubectl describe devicetaintrules ``` ``` Name: example ... Spec: Device Selector: Driver: dra.example.com Taint: Effect: NoExecute Key: dra.example.com/unhealthy Time Added: 2025-11-05T18:15:37Z Value: Broken Status: Conditions: Last Transition Time: 2025-11-05T18:15:37Z Message: 1 pod evicted since starting the controller. Observed Generation: 1 Reason: Completed Status: False Type: EvictionInProgress Events: ``` Pods get evicted by deleting them. Usually this happens very quickly, except when a toleration for the taint delays it for a certain period or when there are very many pods which need to be evicted. When it takes longer, the message provides information about the current status: 2 pods need to be evicted in 2 different namespaces. 1 pod evicted since starting the controller. The condition can be used to check whether an eviction is currently active: kubectl wait --for=condition=EvictionInProgress=false DeviceTaintRule/example Beware of the potential race between scheduler and controller observing the new taint at different times, which can lead to pods still being scheduled at a time when the controller thinks that there are none which need to be evicted and thus sets this condition to `False`. In practice, this race is made very unlikely by updating the status only after an intentional delay of a few seconds. For `effect: None`, the message provides information about the number of affected devices, how many of those are allocated, and how many pods would be evicted if the effect was `NoExecute`. This can be used to do a dry-run before actually triggering eviction: - Create a DeviceTaintRule with the desired selectors and `effect: None`. - Review the message: 3 published devices selected. 1 allocated device selected. 1 pod would be evicted in 1 namespace if the effect was NoExecute. This information will not be updated again. Recreate the DeviceTaintRule to trigger an update. Published devices are those listed in ResourceSlices. Tainting them prevents allocation for new pods. Only allocated devices cause eviction of the pods using them. - Edit the DeviceTaintRule and change the effect into `NoExecute`. ### Device Binding Conditions {#device-binding-conditions} {{< feature-state feature\_gate\_name="DRADeviceBindingConditions" >}} Device Binding Conditions allow the Kubernetes scheduler to delay Pod binding until external resources, such as fabric-attached GPUs or reprogrammable FPGAs, are confirmed to be ready. This waiting behavior is implemented in the [PreBind phase](/docs/concepts/scheduling-eviction/scheduling-framework/#pre-bind) of the scheduling framework. During this phase, the scheduler checks whether all required device conditions are satisfied before proceeding with binding. This improves scheduling reliability by avoiding premature binding and enables coordination with external device controllers. To use this feature, device drivers (typically
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/dynamic-resource-allocation.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/dynamic-resource-allocation.md
|
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] | 0.129279
|
behavior is implemented in the [PreBind phase](/docs/concepts/scheduling-eviction/scheduling-framework/#pre-bind) of the scheduling framework. During this phase, the scheduler checks whether all required device conditions are satisfied before proceeding with binding. This improves scheduling reliability by avoiding premature binding and enables coordination with external device controllers. To use this feature, device drivers (typically managed by driver owners) must publish the following fields in the `Device` section of a `ResourceSlice`. Cluster administrators must enable the `DRADeviceBindingConditions` and `DRAResourceClaimDeviceStatus` feature gates for the scheduler to honor these fields. - `bindingConditions`: A list of condition types that must be set to True in the status.conditions field of the associated ResourceClaim before the Pod can be bound. These typically represent readiness signals such as "DeviceAttached" or "DeviceInitialized". - `bindingFailureConditions`: A list of condition types that, if set to True in status.conditions field of the associated ResourceClaim, indicate a failure state. If any of these conditions are True, the scheduler will abort binding and reschedule the Pod. - `bindsToNode`: if set to `true`, the scheduler records the selected node name in the `status.allocation.nodeSelector` field of the ResourceClaim. This does not affect the Pod's `spec.nodeSelector`. Instead, it sets a node selector inside the ResourceClaim, which external controllers can use to perform node-specific operations such as device attachment or preparation. All condition types listed in bindingConditions and bindingFailureConditions are evaluated from the `status.conditions` field of the ResourceClaim. External controllers are responsible for updating these conditions using standard Kubernetes condition semantics (`type`, `status`, `reason`, `message`, `lastTransitionTime`). The scheduler waits up to \*\*600 seconds\*\* (default) for all `bindingConditions` to become `True`. If the timeout is reached or any `bindingFailureConditions` are `True`, the scheduler clears the allocation and reschedules the Pod. This timeout duration is configurable by the user through `KubeSchedulerConfiguration`. ```yaml apiVersion: resource.k8s.io/v1 kind: ResourceSlice metadata: name: gpu-slice spec: driver: dra.example.com nodeSelector: nodeSelectorTerms: - matchExpressions: - key: accelerator-type operator: In values: - "high-performance" pool: name: gpu-pool generation: 1 resourceSliceCount: 1 devices: - name: gpu-1 attributes: vendor: string: "example" model: string: "example-gpu" bindsToNode: true bindingConditions: - dra.example.com/is-prepared bindingFailureConditions: - dra.example.com/preparing-failed ``` This example ResourceSlice has the following properties: - The ResourceSlice targets nodes labeled with `accelerator-type=high-performance`, so that the scheduler uses only a specific set of eligible nodes. - The scheduler selects one node from the selected group (for example, `node-3`) and sets the `status.allocation.nodeSelector` field in the ResourceClaim to that node name. - The `dra.example.com/is-prepared` binding condition indicates that the device `gpu-1` must be prepared (the `is-prepared` condition has a status of `True`) before binding. - If the `gpu-1` device preparation fails (the `preparing-failed` condition has a status of `True`), the scheduler aborts binding. - The scheduler waits up to 600 seconds (default) for the device to become ready. - External controllers can use the node selector in the ResourceClaim to perform node-specific setup on the selected node. An example of configuring this timeout in `KubeSchedulerConfiguration` is given below: ```yaml apiVersion: kubescheduler.config.k8s.io/v1 kind: KubeSchedulerConfiguration profiles: - schedulerName: default-scheduler pluginConfig: - name: DynamicResources args: apiVersion: kubescheduler.config.k8s.io/v1 kind: DynamicResourcesArgs bindingTimeout: 60s ``` ## {{% heading "whatsnext" %}} - [Set Up DRA in a Cluster](/docs/tasks/configure-pod-container/assign-resources/set-up-dra-cluster/) - [Allocate devices to workloads using DRA](/docs/tasks/configure-pod-container/assign-resources/allocate-devices-dra/) - For more information on the design, see the [Dynamic Resource Allocation with Structured Parameters](https://github.com/kubernetes/enhancements/tree/master/keps/sig-node/4381-dra-structured-parameters) KEP.
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/dynamic-resource-allocation.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/dynamic-resource-allocation.md
|
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] | 0.189836
|
{{< feature-state for\_k8s\_version="v1.24" state="stable" >}} When you run a Pod on a Node, the Pod itself takes an amount of system resources. These resources are additional to the resources needed to run the container(s) inside the Pod. In Kubernetes, \_Pod Overhead\_ is a way to account for the resources consumed by the Pod infrastructure on top of the container requests & limits. In Kubernetes, the Pod's overhead is set at [admission](/docs/reference/access-authn-authz/extensible-admission-controllers/#what-are-admission-webhooks) time according to the overhead associated with the Pod's [RuntimeClass](/docs/concepts/containers/runtime-class/). A pod's overhead is considered in addition to the sum of container resource requests when scheduling a Pod. Similarly, the kubelet will include the Pod overhead when sizing the Pod cgroup, and when carrying out Pod eviction ranking. ## Configuring Pod overhead {#set-up} You need to make sure a `RuntimeClass` is utilized which defines the `overhead` field. ## Usage example To work with Pod overhead, you need a RuntimeClass that defines the `overhead` field. As an example, you could use the following RuntimeClass definition with a virtualization container runtime (in this example, Kata Containers combined with the Firecracker virtual machine monitor) that uses around 120MiB per Pod for the virtual machine and the guest OS: ```yaml # You need to change this example to match the actual runtime name, and per-Pod # resource overhead, that the container runtime is adding in your cluster. apiVersion: node.k8s.io/v1 kind: RuntimeClass metadata: name: kata-fc handler: kata-fc overhead: podFixed: memory: "120Mi" cpu: "250m" ``` Workloads which are created which specify the `kata-fc` RuntimeClass handler will take the memory and cpu overheads into account for resource quota calculations, node scheduling, as well as Pod cgroup sizing. Consider running the given example workload, test-pod: ```yaml apiVersion: v1 kind: Pod metadata: name: test-pod spec: runtimeClassName: kata-fc containers: - name: busybox-ctr image: busybox:1.28 stdin: true tty: true resources: limits: cpu: 500m memory: 100Mi - name: nginx-ctr image: nginx resources: limits: cpu: 1500m memory: 100Mi ``` {{< note >}} If only `limits` are specified in the pod definition, kubelet will deduce `requests` from those limits and set them to be the same as the defined `limits`. {{< /note >}} At admission time the RuntimeClass [admission controller](/docs/reference/access-authn-authz/admission-controllers/) updates the workload's PodSpec to include the `overhead` as described in the RuntimeClass. If the PodSpec already has this field defined, the Pod will be rejected. In the given example, since only the RuntimeClass name is specified, the admission controller mutates the Pod to include an `overhead`. After the RuntimeClass admission controller has made modifications, you can check the updated Pod overhead value: ```bash kubectl get pod test-pod -o jsonpath='{.spec.overhead}' ``` The output is: ``` map[cpu:250m memory:120Mi] ``` If a [ResourceQuota](/docs/concepts/policy/resource-quotas/) is defined, the sum of container requests as well as the `overhead` field are counted. When the kube-scheduler is deciding which node should run a new Pod, the scheduler considers that Pod's `overhead` as well as the sum of container requests for that Pod. For this example, the scheduler adds the requests and the overhead, then looks for a node that has 2.25 CPU and 320 MiB of memory available. Once a Pod is scheduled to a node, the kubelet on that node creates a new {{< glossary\_tooltip text="cgroup" term\_id="cgroup" >}} for the Pod. It is within this pod that the underlying container runtime will create containers. If the resource has a limit defined for each container (Guaranteed QoS or Burstable QoS with limits defined), the kubelet will set an upper limit for the pod cgroup associated with that resource (cpu.cfs\_quota\_us for CPU and memory.limit\_in\_bytes memory). This upper limit is based on the sum of the container limits plus the `overhead` defined
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/pod-overhead.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/pod-overhead.md
|
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|
limit defined for each container (Guaranteed QoS or Burstable QoS with limits defined), the kubelet will set an upper limit for the pod cgroup associated with that resource (cpu.cfs\_quota\_us for CPU and memory.limit\_in\_bytes memory). This upper limit is based on the sum of the container limits plus the `overhead` defined in the PodSpec. For CPU, if the Pod is Guaranteed or Burstable QoS, the kubelet will set `cpu.shares` based on the sum of container requests plus the `overhead` defined in the PodSpec. Looking at our example, verify the container requests for the workload: ```bash kubectl get pod test-pod -o jsonpath='{.spec.containers[\*].resources.limits}' ``` The total container requests are 2000m CPU and 200MiB of memory: ``` map[cpu: 500m memory:100Mi] map[cpu:1500m memory:100Mi] ``` Check this against what is observed by the node: ```bash kubectl describe node | grep test-pod -B2 ``` The output shows requests for 2250m CPU, and for 320MiB of memory. The requests include Pod overhead: ``` Namespace Name CPU Requests CPU Limits Memory Requests Memory Limits AGE --------- ---- ------------ ---------- --------------- ------------- --- default test-pod 2250m (56%) 2250m (56%) 320Mi (1%) 320Mi (1%) 36m ``` ## Verify Pod cgroup limits Check the Pod's memory cgroups on the node where the workload is running. In the following example, [`crictl`](https://github.com/kubernetes-sigs/cri-tools/blob/master/docs/crictl.md) is used on the node, which provides a CLI for CRI-compatible container runtimes. This is an advanced example to show Pod overhead behavior, and it is not expected that users should need to check cgroups directly on the node. First, on the particular node, determine the Pod identifier: ```bash # Run this on the node where the Pod is scheduled POD\_ID="$(sudo crictl pods --name test-pod -q)" ``` From this, you can determine the cgroup path for the Pod: ```bash # Run this on the node where the Pod is scheduled sudo crictl inspectp -o=json $POD\_ID | grep cgroupsPath ``` The resulting cgroup path includes the Pod's `pause` container. The Pod level cgroup is one directory above. ``` "cgroupsPath": "/kubepods/podd7f4b509-cf94-4951-9417-d1087c92a5b2/7ccf55aee35dd16aca4189c952d83487297f3cd760f1bbf09620e206e7d0c27a" ``` In this specific case, the pod cgroup path is `kubepods/podd7f4b509-cf94-4951-9417-d1087c92a5b2`. Verify the Pod level cgroup setting for memory: ```bash # Run this on the node where the Pod is scheduled. # Also, change the name of the cgroup to match the cgroup allocated for your pod. cat /sys/fs/cgroup/memory/kubepods/podd7f4b509-cf94-4951-9417-d1087c92a5b2/memory.limit\_in\_bytes ``` This is 320 MiB, as expected: ``` 335544320 ``` ### Observability Some `kube\_pod\_overhead\_\*` metrics are available in [kube-state-metrics](https://github.com/kubernetes/kube-state-metrics) to help identify when Pod overhead is being utilized and to help observe stability of workloads running with a defined overhead. ## {{% heading "whatsnext" %}} \* Learn more about [RuntimeClass](/docs/concepts/containers/runtime-class/) \* Read the [PodOverhead Design](https://github.com/kubernetes/enhancements/tree/master/keps/sig-node/688-pod-overhead) enhancement proposal for extra context
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/pod-overhead.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/pod-overhead.md
|
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] | 0.16094
|
{{< feature-state for\_k8s\_version="v1.14" state="beta" >}} [kube-scheduler](/docs/concepts/scheduling-eviction/kube-scheduler/#kube-scheduler) is the Kubernetes default scheduler. It is responsible for placement of Pods on Nodes in a cluster. Nodes in a cluster that meet the scheduling requirements of a Pod are called \_feasible\_ Nodes for the Pod. The scheduler finds feasible Nodes for a Pod and then runs a set of functions to score the feasible Nodes, picking a Node with the highest score among the feasible ones to run the Pod. The scheduler then notifies the API server about this decision in a process called \_Binding\_. This page explains performance tuning optimizations that are relevant for large Kubernetes clusters. In large clusters, you can tune the scheduler's behaviour balancing scheduling outcomes between latency (new Pods are placed quickly) and accuracy (the scheduler rarely makes poor placement decisions). You configure this tuning setting via kube-scheduler setting `percentageOfNodesToScore`. This KubeSchedulerConfiguration setting determines a threshold for scheduling nodes in your cluster. ### Setting the threshold The `percentageOfNodesToScore` option accepts whole numeric values between 0 and 100. The value 0 is a special number which indicates that the kube-scheduler should use its compiled-in default. If you set `percentageOfNodesToScore` above 100, kube-scheduler acts as if you had set a value of 100. To change the value, edit the [kube-scheduler configuration file](/docs/reference/config-api/kube-scheduler-config.v1/) and then restart the scheduler. In many cases, the configuration file can be found at `/etc/kubernetes/config/kube-scheduler.yaml`. After you have made this change, you can run ```bash kubectl get pods -n kube-system | grep kube-scheduler ``` to verify that the kube-scheduler component is healthy. ## Node scoring threshold {#percentage-of-nodes-to-score} To improve scheduling performance, the kube-scheduler can stop looking for feasible nodes once it has found enough of them. In large clusters, this saves time compared to a naive approach that would consider every node. You specify a threshold for how many nodes are enough, as a whole number percentage of all the nodes in your cluster. The kube-scheduler converts this into an integer number of nodes. During scheduling, if the kube-scheduler has identified enough feasible nodes to exceed the configured percentage, the kube-scheduler stops searching for more feasible nodes and moves on to the [scoring phase](/docs/concepts/scheduling-eviction/kube-scheduler/#kube-scheduler-implementation). [How the scheduler iterates over Nodes](#how-the-scheduler-iterates-over-nodes) describes the process in detail. ### Default threshold If you don't specify a threshold, Kubernetes calculates a figure using a linear formula that yields 50% for a 100-node cluster and yields 10% for a 5000-node cluster. The lower bound for the automatic value is 5%. This means that the kube-scheduler always scores at least 5% of your cluster no matter how large the cluster is, unless you have explicitly set `percentageOfNodesToScore` to be smaller than 5. If you want the scheduler to score all nodes in your cluster, set `percentageOfNodesToScore` to 100. ## Example Below is an example configuration that sets `percentageOfNodesToScore` to 50%. ```yaml apiVersion: kubescheduler.config.k8s.io/v1alpha1 kind: KubeSchedulerConfiguration algorithmSource: provider: DefaultProvider ... percentageOfNodesToScore: 50 ``` ## Tuning percentageOfNodesToScore `percentageOfNodesToScore` must be a value between 1 and 100 with the default value being calculated based on the cluster size. There is also a hardcoded minimum value of 100 nodes. {{< note >}}In clusters with less than 100 feasible nodes, the scheduler still checks all the nodes because there are not enough feasible nodes to stop the scheduler's search early. In a small cluster, if you set a low value for `percentageOfNodesToScore`, your change will have no or little effect, for a similar reason. If your cluster has several hundred Nodes or fewer, leave this configuration option at its default value. Making changes is unlikely to improve the scheduler's performance significantly. {{< /note >}} An important detail to
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/scheduler-perf-tuning.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/scheduler-perf-tuning.md
|
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] | 0.216641
|
low value for `percentageOfNodesToScore`, your change will have no or little effect, for a similar reason. If your cluster has several hundred Nodes or fewer, leave this configuration option at its default value. Making changes is unlikely to improve the scheduler's performance significantly. {{< /note >}} An important detail to consider when setting this value is that when a smaller number of nodes in a cluster are checked for feasibility, some nodes are not sent to be scored for a given Pod. As a result, a Node which could possibly score a higher value for running the given Pod might not even be passed to the scoring phase. This would result in a less than ideal placement of the Pod. You should avoid setting `percentageOfNodesToScore` very low so that kube-scheduler does not make frequent, poor Pod placement decisions. Avoid setting the percentage to anything below 10%, unless the scheduler's throughput is critical for your application and the score of nodes is not important. In other words, you prefer to run the Pod on any Node as long as it is feasible. ## How the scheduler iterates over Nodes This section is intended for those who want to understand the internal details of this feature. In order to give all the Nodes in a cluster a fair chance of being considered for running Pods, the scheduler iterates over the nodes in a round robin fashion. You can imagine that Nodes are in an array. The scheduler starts from the start of the array and checks feasibility of the nodes until it finds enough Nodes as specified by `percentageOfNodesToScore`. For the next Pod, the scheduler continues from the point in the Node array that it stopped at when checking feasibility of Nodes for the previous Pod. If Nodes are in multiple zones, the scheduler iterates over Nodes in various zones to ensure that Nodes from different zones are considered in the feasibility checks. As an example, consider six nodes in two zones: ``` Zone 1: Node 1, Node 2, Node 3, Node 4 Zone 2: Node 5, Node 6 ``` The Scheduler evaluates feasibility of the nodes in this order: ``` Node 1, Node 5, Node 2, Node 6, Node 3, Node 4 ``` After going over all the Nodes, it goes back to Node 1. ## Enabling Opportunistic Batching {{< feature-state feature\_gate\_name="OpportunisticBatching" >}} When scheduling large workloads, pod definitions are typically identical and require the scheduler to perform the same operations over and over again. The [Opportunistic Batching](/docs/reference/command-line-tools-reference/feature-gates/#OpportunisticBatching) feature allows the scheduler to reuse the filtering and scoring results between scheduling cycles which greatly speeds up the scheduling process. Basically, this feature works like: 1. The scheduler schedules pod-1 and caches the scheduling result. 1. The scheduler schedules pod-2, 3, ... with the cached results. 1. The cache expires after 0.5 second. The scheduler schedules the next pod which builds a new cache. Pods with equivalent scheduling constraints have to come to the scheduling cycle back to back. When the scheduler schedules a pod with different constraints, the cache is not used, but replaced with a new one. We apply this batching scheduling to specific pods that: 1. Don't have inter pod affinity/anti-affinity 1. Don't have tpology spread constraints 1. Don't have DRA (i.e., don't have any Resource Claims) 1. Scheduled exclusively on nodes (i.e., placing more than one pods on one node invalidates the cache) Also, to enable this feature, the scheduler configuration needs to: 1. Disable [default topology spread](/docs/concepts/scheduling-eviction/topology-spread-constraints/#internal-default-constraints) (set empty) 1. Disable [DRAExtendedResource](/docs/reference/command-line-tools-reference/feature-gates/#DRAExtendedResource) feature. 1. Set `IgnorePreferredTermsOfExistingPods` of [InterPodAffinityArgs](/docs/reference/config-api/kube-scheduler-config.v1/#kubescheduler-config-k8s-io-v1-InterPodAffinityArgs) to `true` to make the batching more efficient Note
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/scheduler-perf-tuning.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/scheduler-perf-tuning.md
|
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] | 0.136588
|
exclusively on nodes (i.e., placing more than one pods on one node invalidates the cache) Also, to enable this feature, the scheduler configuration needs to: 1. Disable [default topology spread](/docs/concepts/scheduling-eviction/topology-spread-constraints/#internal-default-constraints) (set empty) 1. Disable [DRAExtendedResource](/docs/reference/command-line-tools-reference/feature-gates/#DRAExtendedResource) feature. 1. Set `IgnorePreferredTermsOfExistingPods` of [InterPodAffinityArgs](/docs/reference/config-api/kube-scheduler-config.v1/#kubescheduler-config-k8s-io-v1-InterPodAffinityArgs) to `true` to make the batching more efficient Note that whenever: 1. Exisiting pods use pod affinity constraints that match any of the scheduled pods' labels, the feature may bring no benefit 1. Custom plugins are used, they need to implement the Signature extension point The restrictions and conditions are expected to evolve in furutre releases. ## {{% heading "whatsnext" %}} \* Check the [kube-scheduler configuration reference (v1)](/docs/reference/config-api/kube-scheduler-config.v1/)
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/scheduler-perf-tuning.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/scheduler-perf-tuning.md
|
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] | 0.124101
|
In the [scheduling-plugin](/docs/reference/scheduling/config/#scheduling-plugins) `NodeResourcesFit` of kube-scheduler, there are two scoring strategies that support the bin packing of resources: `MostAllocated` and `RequestedToCapacityRatio`. ## Enabling bin packing using MostAllocated strategy The `MostAllocated` strategy scores the nodes based on the utilization of resources, favoring the ones with higher allocation. For each resource type, you can set a weight to modify its influence in the node score. To set the `MostAllocated` strategy for the `NodeResourcesFit` plugin, use a [scheduler configuration](/docs/reference/scheduling/config) similar to the following: ```yaml apiVersion: kubescheduler.config.k8s.io/v1 kind: KubeSchedulerConfiguration profiles: - pluginConfig: - args: scoringStrategy: resources: - name: cpu weight: 1 - name: memory weight: 1 - name: intel.com/foo weight: 3 - name: intel.com/bar weight: 3 type: MostAllocated name: NodeResourcesFit ``` To learn more about other parameters and their default configuration, see the API documentation for [`NodeResourcesFitArgs`](/docs/reference/config-api/kube-scheduler-config.v1/#kubescheduler-config-k8s-io-v1-NodeResourcesFitArgs). ## Enabling bin packing using RequestedToCapacityRatio The `RequestedToCapacityRatio` strategy allows the users to specify the resources along with weights for each resource to score nodes based on the request to capacity ratio. This allows users to bin pack extended resources by using appropriate parameters to improve the utilization of scarce resources in large clusters. It favors nodes according to a configured function of the allocated resources. The behavior of the `RequestedToCapacityRatio` in the `NodeResourcesFit` score function can be controlled by the [scoringStrategy](/docs/reference/config-api/kube-scheduler-config.v1/#kubescheduler-config-k8s-io-v1-ScoringStrategy) field. Within the `scoringStrategy` field, you can configure two parameters: `requestedToCapacityRatio` and `resources`. The `shape` in the `requestedToCapacityRatio` parameter allows the user to tune the function as least requested or most requested based on `utilization` and `score` values. The `resources` parameter comprises both the `name` of the resource to be considered during scoring and its corresponding `weight`, which specifies the weight of each resource. Below is an example configuration that sets the bin packing behavior for extended resources `intel.com/foo` and `intel.com/bar` using the `requestedToCapacityRatio` field. ```yaml apiVersion: kubescheduler.config.k8s.io/v1 kind: KubeSchedulerConfiguration profiles: - pluginConfig: - args: scoringStrategy: resources: - name: intel.com/foo weight: 3 - name: intel.com/bar weight: 3 requestedToCapacityRatio: shape: - utilization: 0 score: 0 - utilization: 100 score: 10 type: RequestedToCapacityRatio name: NodeResourcesFit ``` Referencing the `KubeSchedulerConfiguration` file with the kube-scheduler flag `--config=/path/to/config/file` will pass the configuration to the scheduler. To learn more about other parameters and their default configuration, see the API documentation for [`NodeResourcesFitArgs`](/docs/reference/config-api/kube-scheduler-config.v1/#kubescheduler-config-k8s-io-v1-NodeResourcesFitArgs). ### Tuning the score function `shape` is used to specify the behavior of the `RequestedToCapacityRatio` function. ```yaml shape: - utilization: 0 score: 0 - utilization: 100 score: 10 ``` The above arguments give the node a `score` of 0 if `utilization` is 0% and 10 for `utilization` 100%, thus enabling bin packing behavior. To enable least requested the score value must be reversed as follows. ```yaml shape: - utilization: 0 score: 10 - utilization: 100 score: 0 ``` `resources` is an optional parameter which defaults to: ```yaml resources: - name: cpu weight: 1 - name: memory weight: 1 ``` It can be used to add extended resources as follows: ```yaml resources: - name: intel.com/foo weight: 5 - name: cpu weight: 3 - name: memory weight: 1 ``` The `weight` parameter is optional and is set to 1 if not specified. Also, the `weight` cannot be set to a negative value. ### Node scoring for capacity allocation This section is intended for those who want to understand the internal details of this feature. Below is an example of how the node score is calculated for a given set of values. Requested resources: ``` intel.com/foo : 2 memory: 256MB cpu: 2 ``` Resource weights: ``` intel.com/foo : 5 memory: 1 cpu: 3 ``` FunctionShapePoint {{0, 0}, {100, 10}} Node 1 spec: ``` Available: intel.com/foo: 4 memory: 1 GB cpu: 8 Used:
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/resource-bin-packing.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/resource-bin-packing.md
|
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] | 0.141571
|
node score is calculated for a given set of values. Requested resources: ``` intel.com/foo : 2 memory: 256MB cpu: 2 ``` Resource weights: ``` intel.com/foo : 5 memory: 1 cpu: 3 ``` FunctionShapePoint {{0, 0}, {100, 10}} Node 1 spec: ``` Available: intel.com/foo: 4 memory: 1 GB cpu: 8 Used: intel.com/foo: 1 memory: 256MB cpu: 1 ``` Node score: ``` intel.com/foo = resourceScoringFunction((2+1),4) = (100 - ((4-3)\*100/4)) = (100 - 25) = 75 # requested + used = 75% \* available = rawScoringFunction(75) = 7 # floor(75/10) memory = resourceScoringFunction((256+256),1024) = (100 -((1024-512)\*100/1024)) = 50 # requested + used = 50% \* available = rawScoringFunction(50) = 5 # floor(50/10) cpu = resourceScoringFunction((2+1),8) = (100 -((8-3)\*100/8)) = 37.5 # requested + used = 37.5% \* available = rawScoringFunction(37.5) = 3 # floor(37.5/10) NodeScore = ((7 \* 5) + (5 \* 1) + (3 \* 3)) / (5 + 1 + 3) = 5 ``` Node 2 spec: ``` Available: intel.com/foo: 8 memory: 1GB cpu: 8 Used: intel.com/foo: 2 memory: 512MB cpu: 6 ``` Node score: ``` intel.com/foo = resourceScoringFunction((2+2),8) = (100 - ((8-4)\*100/8) = (100 - 50) = 50 = rawScoringFunction(50) = 5 memory = resourceScoringFunction((256+512),1024) = (100 -((1024-768)\*100/1024)) = 75 = rawScoringFunction(75) = 7 cpu = resourceScoringFunction((2+6),8) = (100 -((8-8)\*100/8)) = 100 = rawScoringFunction(100) = 10 NodeScore = ((5 \* 5) + (7 \* 1) + (10 \* 3)) / (5 + 1 + 3) = 7 ``` ## {{% heading "whatsnext" %}} - Read more about the [scheduling framework](/docs/concepts/scheduling-eviction/scheduling-framework/) - Read more about [scheduler configuration](/docs/reference/scheduling/config/)
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/resource-bin-packing.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/resource-bin-packing.md
|
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|
{{< glossary\_definition term\_id="api-eviction" length="short" >}} You can request eviction by calling the Eviction API directly, or programmatically using a client of the {{}}, like the `kubectl drain` command. This creates an `Eviction` object, which causes the API server to terminate the Pod. API-initiated evictions respect your configured [`PodDisruptionBudgets`](/docs/tasks/run-application/configure-pdb/) and [`terminationGracePeriodSeconds`](/docs/concepts/workloads/pods/pod-lifecycle#pod-termination). Using the API to create an Eviction object for a Pod is like performing a policy-controlled [`DELETE` operation](/docs/reference/kubernetes-api/workload-resources/pod-v1/#delete-delete-a-pod) on the Pod. ## Calling the Eviction API You can use a [Kubernetes language client](/docs/tasks/administer-cluster/access-cluster-api/#programmatic-access-to-the-api) to access the Kubernetes API and create an `Eviction` object. To do this, you POST the attempted operation, similar to the following example: {{< tabs name="Eviction\_example" >}} {{% tab name="policy/v1" %}} {{< note >}} `policy/v1` Eviction is available in v1.22+. Use `policy/v1beta1` with prior releases. {{< /note >}} ```json { "apiVersion": "policy/v1", "kind": "Eviction", "metadata": { "name": "quux", "namespace": "default" } } ``` {{% /tab %}} {{% tab name="policy/v1beta1" %}} {{< note >}} Deprecated in v1.22 in favor of `policy/v1` {{< /note >}} ```json { "apiVersion": "policy/v1beta1", "kind": "Eviction", "metadata": { "name": "quux", "namespace": "default" } } ``` {{% /tab %}} {{< /tabs >}} Alternatively, you can attempt an eviction operation by accessing the API using `curl` or `wget`, similar to the following example: ```bash curl -v -H 'Content-type: application/json' https://your-cluster-api-endpoint.example/api/v1/namespaces/default/pods/quux/eviction -d @eviction.json ``` ## How API-initiated eviction works When you request an eviction using the API, the API server performs admission checks and responds in one of the following ways: \* `200 OK`: the eviction is allowed, the `Eviction` subresource is created, and the Pod is deleted, similar to sending a `DELETE` request to the Pod URL. \* `429 Too Many Requests`: the eviction is not currently allowed because of the configured {{}}. You may be able to attempt the eviction again later. You might also see this response because of API rate limiting. \* `500 Internal Server Error`: the eviction is not allowed because there is a misconfiguration, like if multiple PodDisruptionBudgets reference the same Pod. If the Pod you want to evict isn't part of a workload that has a PodDisruptionBudget, the API server always returns `200 OK` and allows the eviction. If the API server allows the eviction, the Pod is deleted as follows: 1. The `Pod` resource in the API server is updated with a deletion timestamp, after which the API server considers the `Pod` resource to be terminated. The `Pod` resource is also marked with the configured grace period. 1. The {{}} on the node where the local Pod is running notices that the `Pod` resource is marked for termination and starts to gracefully shut down the local Pod. 1. While the kubelet is shutting the Pod down, the control plane removes the Pod from {{}} objects. As a result, controllers no longer consider the Pod as a valid object. 1. After the grace period for the Pod expires, the kubelet forcefully terminates the local Pod. 1. The kubelet tells the API server to remove the `Pod` resource. 1. The API server deletes the `Pod` resource. ## Troubleshooting stuck evictions In some cases, your applications may enter a broken state, where the Eviction API will only return `429` or `500` responses until you intervene. This can happen if, for example, a ReplicaSet creates pods for your application but new pods do not enter a `Ready` state. You may also notice this behavior in cases where the last evicted Pod had a long termination grace period. If you notice stuck evictions, try one of the following solutions: \* Abort or pause the automated operation causing the issue. Investigate the stuck application before
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/api-eviction.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/api-eviction.md
|
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] | 0.100753
|
not enter a `Ready` state. You may also notice this behavior in cases where the last evicted Pod had a long termination grace period. If you notice stuck evictions, try one of the following solutions: \* Abort or pause the automated operation causing the issue. Investigate the stuck application before you restart the operation. \* Wait a while, then directly delete the Pod from your cluster control plane instead of using the Eviction API. ## {{% heading "whatsnext" %}} \* Learn how to protect your applications with a [Pod Disruption Budget](/docs/tasks/run-application/configure-pdb/). \* Learn about [Node-pressure Eviction](/docs/concepts/scheduling-eviction/node-pressure-eviction/). \* Learn about [Pod Priority and Preemption](/docs/concepts/scheduling-eviction/pod-priority-preemption/).
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/api-eviction.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/api-eviction.md
|
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] | 0.108774
|
[\_Node affinity\_](/docs/concepts/scheduling-eviction/assign-pod-node/#affinity-and-anti-affinity) is a property of {{< glossary\_tooltip text="Pods" term\_id="pod" >}} that \*attracts\* them to a set of {{< glossary\_tooltip text="nodes" term\_id="node" >}} (either as a preference or a hard requirement). \_Taints\_ are the opposite -- they allow a node to repel a set of pods. \_Tolerations\_ are applied to pods. Tolerations allow the scheduler to schedule pods with matching taints. Tolerations allow scheduling but don't guarantee scheduling: the scheduler also [evaluates other parameters](/docs/concepts/scheduling-eviction/pod-priority-preemption/) as part of its function. Taints and tolerations work together to ensure that pods are not scheduled onto inappropriate nodes. One or more taints are applied to a node; this marks that the node should not accept any pods that do not tolerate the taints. ## Concepts You add a taint to a node using [kubectl taint](/docs/reference/generated/kubectl/kubectl-commands#taint). For example, ```shell kubectl taint nodes node1 key1=value1:NoSchedule ``` places a taint on node `node1`. The taint has key `key1`, value `value1`, and taint effect `NoSchedule`. This means that no pod will be able to schedule onto `node1` unless it has a matching toleration. To remove the taint added by the command above, you can run: ```shell kubectl taint nodes node1 key1=value1:NoSchedule- ``` You specify a toleration for a pod in the PodSpec. Both of the following tolerations "match" the taint created by the `kubectl taint` line above, and thus a pod with either toleration would be able to schedule onto `node1`: ```yaml tolerations: - key: "key1" operator: "Equal" value: "value1" effect: "NoSchedule" ``` ```yaml tolerations: - key: "key1" operator: "Exists" effect: "NoSchedule" ``` The default Kubernetes scheduler takes taints and tolerations into account when selecting a node to run a particular Pod. However, if you manually specify the `.spec.nodeName` for a Pod, that action bypasses the scheduler; the Pod is then bound onto the node where you assigned it, even if there are `NoSchedule` taints on that node that you selected. If this happens and the node also has a `NoExecute` taint set, the kubelet will eject the Pod unless there is an appropriate tolerance set. Here's an example of a pod that has some tolerations defined: {{% code\_sample file="pods/pod-with-toleration.yaml" %}} The default value for `operator` is `Equal`. A toleration "matches" a taint if the keys are the same and the effects are the same, and: \* the `operator` is `Exists` (in which case no `value` should be specified), or \* the `operator` is `Equal` and the values should be equal. {{< note >}} There are two special cases: If the `key` is empty, then the `operator` must be `Exists`, which matches all keys and values. Note that the `effect` still needs to be matched at the same time. An empty `effect` matches all effects with key `key1`. {{< /note >}} The above example used the `effect` of `NoSchedule`. Alternatively, you can use the `effect` of `PreferNoSchedule`. The allowed values for the `effect` field are: `NoExecute` : This affects pods that are already running on the node as follows: \* Pods that do not tolerate the taint are evicted immediately \* Pods that tolerate the taint without specifying `tolerationSeconds` in their toleration specification remain bound forever \* Pods that tolerate the taint with a specified `tolerationSeconds` remain bound for the specified amount of time. After that time elapses, the node lifecycle controller evicts the Pods from the node. `NoSchedule` : No new Pods will be scheduled on the tainted node unless they have a matching toleration. Pods currently running on the node are \*\*not\*\* evicted. `PreferNoSchedule` : `PreferNoSchedule` is a "preference" or "soft" version of `NoSchedule`. The control plane will \*try\* to avoid placing a Pod that does
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/taint-and-toleration.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/taint-and-toleration.md
|
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] | 0.194007
|
`NoSchedule` : No new Pods will be scheduled on the tainted node unless they have a matching toleration. Pods currently running on the node are \*\*not\*\* evicted. `PreferNoSchedule` : `PreferNoSchedule` is a "preference" or "soft" version of `NoSchedule`. The control plane will \*try\* to avoid placing a Pod that does not tolerate the taint on the node, but it is not guaranteed. You can put multiple taints on the same node and multiple tolerations on the same pod. The way Kubernetes processes multiple taints and tolerations is like a filter: start with all of a node's taints, then ignore the ones for which the pod has a matching toleration; the remaining un-ignored taints have the indicated effects on the pod. In particular, \* if there is at least one un-ignored taint with effect `NoSchedule` then Kubernetes will not schedule the pod onto that node \* if there is no un-ignored taint with effect `NoSchedule` but there is at least one un-ignored taint with effect `PreferNoSchedule` then Kubernetes will \*try\* to not schedule the pod onto the node \* if there is at least one un-ignored taint with effect `NoExecute` then the pod will be evicted from the node (if it is already running on the node), and will not be scheduled onto the node (if it is not yet running on the node). For example, imagine you taint a node like this ```shell kubectl taint nodes node1 key1=value1:NoSchedule kubectl taint nodes node1 key1=value1:NoExecute kubectl taint nodes node1 key2=value2:NoSchedule ``` And a pod has two tolerations: ```yaml tolerations: - key: "key1" operator: "Equal" value: "value1" effect: "NoSchedule" - key: "key1" operator: "Equal" value: "value1" effect: "NoExecute" ``` In this case, the pod will not be able to schedule onto the node, because there is no toleration matching the third taint. But it will be able to continue running if it is already running on the node when the taint is added, because the third taint is the only one of the three that is not tolerated by the pod. Normally, if a taint with effect `NoExecute` is added to a node, then any pods that do not tolerate the taint will be evicted immediately, and pods that do tolerate the taint will never be evicted. However, a toleration with `NoExecute` effect can specify an optional `tolerationSeconds` field that dictates how long the pod will stay bound to the node after the taint is added. For example, ```yaml tolerations: - key: "key1" operator: "Equal" value: "value1" effect: "NoExecute" tolerationSeconds: 3600 ``` means that if this pod is running and a matching taint is added to the node, then the pod will stay bound to the node for 3600 seconds, and then be evicted. If the taint is removed before that time, the pod will not be evicted. ## Numeric comparison operators {#numeric-comparison-operators} {{< feature-state feature\_gate\_name="TaintTolerationComparisonOperators" >}} In addition to `Equal` and `Exists`, you can use numeric comparison operators (`Gt` and `Lt`) to match taints with integer values. This is useful for threshold-based scheduling, such as matching nodes by reliability level or SLA tier. \* `Gt` matches when the toleration value is greater than the taint value. \* `Lt` matches when the toleration value is less than the taint value. For numeric operators, both the toleration and taint values must be valid integers. If either value cannot be parsed as an integer, the toleration does not match. {{< note >}} When you create a Pod that uses `Gt` or `Lt` tolerations operators, the API server validates that the toleration values are valid integers. Taint values on nodes are not validated at node registration
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/taint-and-toleration.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/taint-and-toleration.md
|
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] | 0.13988
|
If either value cannot be parsed as an integer, the toleration does not match. {{< note >}} When you create a Pod that uses `Gt` or `Lt` tolerations operators, the API server validates that the toleration values are valid integers. Taint values on nodes are not validated at node registration time. If a node has a non-numeric taint value (for example, `servicelevel.organization.example/agreed-service-level=high:NoSchedule`), pods with numeric comparison operators will not match that taint and cannot schedule on that node. {{< /note >}} For example, if nodes are tainted with a value representing a service level agreement (SLA): ```shell kubectl taint nodes node1 servicelevel.organization.example/agreed-service-level=950:NoSchedule ``` A pod can tolerate nodes with SLA greater than 900: {{% code\_sample file="pods/pod-with-numeric-toleration.yaml" %}} This toleration matches the taint on `node1` because `950 > 900` (the taint value is greater than the toleration value for the `Gt` operator). Similarly, you can use the `Lt` operator to match taints where the taint value is less than the toleration value: ```yaml tolerations: - key: "servicelevel.organization.example/agreed-service-level" operator: "Lt" value: "1000" effect: "NoSchedule" ``` {{< note >}} When using numeric comparison operators: \* Both the toleration and taint values must be valid signed 64-bit integers (zero leading numbers (e.g., "0550") are not allowed). \* If a value cannot be parsed as an integer, the toleration does not match. \* Numeric operators work with all taint effects: `NoSchedule`, `PreferNoSchedule`, and `NoExecute`. \* For `PreferNoSchedule` with numeric operators: if a pod's toleration doesn't satisfy the numeric comparison (e.g., toleration value < taint value when using `Gt`), the scheduler gives the node a lower priority but may still schedule there if no better options exist. {{< /note >}} {{< warning >}} Before disabling the `TaintTolerationComparisonOperators` feature gate: \* You should identify all workloads using the `Gt` or `Lt` operators to avoid controller hot-loops. \* Update all workload controller templates to use `Equal` or `Exists` operators instead \* Delete any pending pods that use `Gt` or `Lt` operators \* Monitor the `apiserver\_request\_total` metric for spikes in validation errors {{< /warning >}} ## Example Use Cases Taints and tolerations are a flexible way to steer pods \*away\* from nodes or evict pods that shouldn't be running. A few of the use cases are \* \*\*Dedicated Nodes\*\*: If you want to dedicate a set of nodes for exclusive use by a particular set of users, you can add a taint to those nodes (say, `kubectl taint nodes nodename dedicated=groupName:NoSchedule`) and then add a corresponding toleration to their pods (this would be done most easily by writing a custom [admission controller](/docs/reference/access-authn-authz/admission-controllers/)). The pods with the tolerations will then be allowed to use the tainted (dedicated) nodes as well as any other nodes in the cluster. If you want to dedicate the nodes to them \*and\* ensure they \*only\* use the dedicated nodes, then you should additionally add a label similar to the taint to the same set of nodes (e.g. `dedicated=groupName`), and the admission controller should additionally add a node affinity to require that the pods can only schedule onto nodes labeled with `dedicated=groupName`. \* \*\*Nodes with Special Hardware\*\*: In a cluster where a small subset of nodes have specialized hardware (for example GPUs), it is desirable to keep pods that don't need the specialized hardware off of those nodes, thus leaving room for later-arriving pods that do need the specialized hardware. This can be done by tainting the nodes that have the specialized hardware (e.g. `kubectl taint nodes nodename special=true:NoSchedule` or `kubectl taint nodes nodename special=true:PreferNoSchedule`) and adding a corresponding toleration to pods that use the special hardware. As in the dedicated nodes use case, it is
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/taint-and-toleration.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/taint-and-toleration.md
|
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|
need the specialized hardware. This can be done by tainting the nodes that have the specialized hardware (e.g. `kubectl taint nodes nodename special=true:NoSchedule` or `kubectl taint nodes nodename special=true:PreferNoSchedule`) and adding a corresponding toleration to pods that use the special hardware. As in the dedicated nodes use case, it is probably easiest to apply the tolerations using a custom [admission controller](/docs/reference/access-authn-authz/admission-controllers/). For example, it is recommended to use [Extended Resources](/docs/concepts/configuration/manage-resources-containers/#extended-resources) to represent the special hardware, taint your special hardware nodes with the extended resource name and run the [ExtendedResourceToleration](/docs/reference/access-authn-authz/admission-controllers/#extendedresourcetoleration) admission controller. Now, because the nodes are tainted, no pods without the toleration will schedule on them. But when you submit a pod that requests the extended resource, the `ExtendedResourceToleration` admission controller will automatically add the correct toleration to the pod and that pod will schedule on the special hardware nodes. This will make sure that these special hardware nodes are dedicated for pods requesting such hardware and you don't have to manually add tolerations to your pods. \* \*\*Taint based Evictions\*\*: A per-pod-configurable eviction behavior when there are node problems, which is described in the next section. ## Taint based Evictions {{< feature-state for\_k8s\_version="v1.18" state="stable" >}} The node controller automatically taints a Node when certain conditions are true. The following taints are built in: \* `node.kubernetes.io/not-ready`: Node is not ready. This corresponds to the NodeCondition `Ready` being "`False`". \* `node.kubernetes.io/unreachable`: Node is unreachable from the node controller. This corresponds to the NodeCondition `Ready` being "`Unknown`". \* `node.kubernetes.io/memory-pressure`: Node has memory pressure. \* `node.kubernetes.io/disk-pressure`: Node has disk pressure. \* `node.kubernetes.io/pid-pressure`: Node has PID pressure. \* `node.kubernetes.io/network-unavailable`: Node's network is unavailable. \* `node.kubernetes.io/unschedulable`: Node is unschedulable. \* `node.cloudprovider.kubernetes.io/uninitialized`: When the kubelet is started with an "external" cloud provider, this taint is set on a node to mark it as unusable. After a controller from the cloud-controller-manager initializes this node, the kubelet removes this taint. In case a node is to be drained, the node controller or the kubelet adds relevant taints with `NoExecute` effect. This effect is added by default for the `node.kubernetes.io/not-ready` and `node.kubernetes.io/unreachable` taints. If the fault condition returns to normal, the kubelet or node controller can remove the relevant taint(s). In some cases when the node is unreachable, the API server is unable to communicate with the kubelet on the node. The decision to delete the pods cannot be communicated to the kubelet until communication with the API server is re-established. In the meantime, the pods that are scheduled for deletion may continue to run on the partitioned node. {{< note >}} The control plane limits the rate of adding new taints to nodes. This rate limiting manages the number of evictions that are triggered when many nodes become unreachable at once (for example: if there is a network disruption). {{< /note >}} You can specify `tolerationSeconds` for a Pod to define how long that Pod stays bound to a failing or unresponsive Node. For example, you might want to keep an application with a lot of local state bound to node for a long time in the event of network partition, hoping that the partition will recover and thus the pod eviction can be avoided. The toleration you set for that Pod might look like: ```yaml tolerations: - key: "node.kubernetes.io/unreachable" operator: "Exists" effect: "NoExecute" tolerationSeconds: 6000 ``` {{< note >}} Kubernetes automatically adds a toleration for `node.kubernetes.io/not-ready` and `node.kubernetes.io/unreachable` with `tolerationSeconds=300`, unless you, or a controller, set those tolerations explicitly. These automatically-added tolerations mean that Pods remain bound to Nodes for 5 minutes after one of these problems is detected. {{< /note >}} [DaemonSet](/docs/concepts/workloads/controllers/daemonset/) pods are created with
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/taint-and-toleration.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/taint-and-toleration.md
|
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] | 0.175589
|
note >}} Kubernetes automatically adds a toleration for `node.kubernetes.io/not-ready` and `node.kubernetes.io/unreachable` with `tolerationSeconds=300`, unless you, or a controller, set those tolerations explicitly. These automatically-added tolerations mean that Pods remain bound to Nodes for 5 minutes after one of these problems is detected. {{< /note >}} [DaemonSet](/docs/concepts/workloads/controllers/daemonset/) pods are created with `NoExecute` tolerations for the following taints with no `tolerationSeconds`: \* `node.kubernetes.io/unreachable` \* `node.kubernetes.io/not-ready` This ensures that DaemonSet pods are never evicted due to these problems. {{< note >}} The node controller was responsible for adding taints to nodes and evicting pods. But after 1.29, the taint-based eviction implementation has been moved out of node controller into a separate, and independent component called taint-eviction-controller. Users can optionally disable taint-based eviction by setting `--controllers=-taint-eviction-controller` in kube-controller-manager. {{< /note >}} ## Taint Nodes by Condition The control plane, using the node {{}}, automatically creates taints with a `NoSchedule` effect for [node conditions](/docs/concepts/scheduling-eviction/node-pressure-eviction/#node-conditions). The scheduler checks taints, not node conditions, when it makes scheduling decisions. This ensures that node conditions don't directly affect scheduling. For example, if the `DiskPressure` node condition is active, the control plane adds the `node.kubernetes.io/disk-pressure` taint and does not schedule new pods onto the affected node. If the `MemoryPressure` node condition is active, the control plane adds the `node.kubernetes.io/memory-pressure` taint. You can ignore node conditions for newly created pods by adding the corresponding Pod tolerations. The control plane also adds the `node.kubernetes.io/memory-pressure` toleration on pods that have a {{< glossary\_tooltip text="QoS class" term\_id="qos-class" >}} other than `BestEffort`. This is because Kubernetes treats pods in the `Guaranteed` or `Burstable` QoS classes (even pods with no memory request set) as if they are able to cope with memory pressure, while new `BestEffort` pods are not scheduled onto the affected node. The DaemonSet controller automatically adds the following `NoSchedule` tolerations to all daemons, to prevent DaemonSets from breaking. \* `node.kubernetes.io/memory-pressure` \* `node.kubernetes.io/disk-pressure` \* `node.kubernetes.io/pid-pressure` (1.14 or later) \* `node.kubernetes.io/unschedulable` (1.10 or later) \* `node.kubernetes.io/network-unavailable` (\*host network only\*) Adding these tolerations ensures backward compatibility. You can also add arbitrary tolerations to DaemonSets. ## Device taints and tolerations Instead of tainting entire nodes, administrators can also [taint individual devices](/docs/concepts/scheduling-eviction/dynamic-resource-allocation#device-taints-and-tolerations) when the cluster uses [dynamic resource allocation](/docs/concepts/scheduling-eviction/dynamic-resource-allocation) to manage special hardware. The advantage is that tainting can be targeted towards exactly the hardware that is faulty or needs maintenance. Tolerations are also supported and can be specified when requesting devices. Like taints they apply to all pods which share the same allocated device. ## {{% heading "whatsnext" %}} \* Read about [Node-pressure Eviction](/docs/concepts/scheduling-eviction/node-pressure-eviction/) and how you can configure it \* Read about [Pod Priority](/docs/concepts/scheduling-eviction/pod-priority-preemption/) \* Read about [device taints and tolerations](/docs/concepts/scheduling-eviction/dynamic-resource-allocation#device-taints-and-tolerations)
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/taint-and-toleration.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/taint-and-toleration.md
|
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] | 0.143609
|
You can use \_topology spread constraints\_ to control how {{< glossary\_tooltip text="Pods" term\_id="Pod" >}} are spread across your cluster among failure-domains such as regions, zones, nodes, and other user-defined topology domains. This can help to achieve high availability as well as efficient resource utilization. You can set [cluster-level constraints](#cluster-level-default-constraints) as a default, or configure topology spread constraints for individual workloads. ## Motivation Imagine that you have a cluster of up to twenty nodes, and you want to run a {{< glossary\_tooltip text="workload" term\_id="workload" >}} that automatically scales how many replicas it uses. There could be as few as two Pods or as many as fifteen. When there are only two Pods, you'd prefer not to have both of those Pods run on the same node: you would run the risk that a single node failure takes your workload offline. In addition to this basic usage, there are some advanced usage examples that enable your workloads to benefit on high availability and cluster utilization. As you scale up and run more Pods, a different concern becomes important. Imagine that you have three nodes running five Pods each. The nodes have enough capacity to run that many replicas; however, the clients that interact with this workload are split across three different datacenters (or infrastructure zones). Now you have less concern about a single node failure, but you notice that latency is higher than you'd like, and you are paying for network costs associated with sending network traffic between the different zones. You decide that under normal operation you'd prefer to have a similar number of replicas [scheduled](/docs/concepts/scheduling-eviction/) into each infrastructure zone, and you'd like the cluster to self-heal in the case that there is a problem. Pod topology spread constraints offer you a declarative way to configure that. ## `topologySpreadConstraints` field The Pod API includes a field, `spec.topologySpreadConstraints`. The usage of this field looks like the following: ```yaml --- apiVersion: v1 kind: Pod metadata: name: example-pod spec: # Configure a topology spread constraint topologySpreadConstraints: - maxSkew: minDomains: # optional topologyKey: whenUnsatisfiable: labelSelector: matchLabelKeys: # optional; beta since v1.27 nodeAffinityPolicy: [Honor|Ignore] # optional; beta since v1.26 nodeTaintsPolicy: [Honor|Ignore] # optional; beta since v1.26 ### other Pod fields go here ``` {{< note >}} There can only be one `topologySpreadConstraint` for a given `topologyKey` and `whenUnsatisfiable` value. For example, if you have defined a `topologySpreadConstraint` that uses the `topologyKey` "kubernetes.io/hostname" and `whenUnsatisfiable` value "DoNotSchedule", you can only add another `topologySpreadConstraint` for the `topologyKey` "kubernetes.io/hostname" if you use a different `whenUnsatisfiable` value. {{< /note >}} You can read more about this field by running `kubectl explain Pod.spec.topologySpreadConstraints` or refer to the [scheduling](/docs/reference/kubernetes-api/workload-resources/pod-v1/#scheduling) section of the API reference for Pod. ### Spread constraint definition You can define one or multiple `topologySpreadConstraints` entries to instruct the kube-scheduler how to place each incoming Pod in relation to the existing Pods across your cluster. Those fields are: - \*\*maxSkew\*\* describes the degree to which Pods may be unevenly distributed. You must specify this field and the number must be greater than zero. Its semantics differ according to the value of `whenUnsatisfiable`: - if you select `whenUnsatisfiable: DoNotSchedule`, then `maxSkew` defines the maximum permitted difference between the number of matching pods in the target topology and the \_global minimum\_ (the minimum number of matching pods in an eligible domain or zero if the number of eligible domains is less than MinDomains). For example, if you have 3 zones with 2, 2 and 1 matching pods respectively, `MaxSkew` is set to 1 then the global minimum is 1. - if you select `whenUnsatisfiable: ScheduleAnyway`, the scheduler gives higher precedence to topologies
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/topology-spread-constraints.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/topology-spread-constraints.md
|
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] | 0.088223
|
zero if the number of eligible domains is less than MinDomains). For example, if you have 3 zones with 2, 2 and 1 matching pods respectively, `MaxSkew` is set to 1 then the global minimum is 1. - if you select `whenUnsatisfiable: ScheduleAnyway`, the scheduler gives higher precedence to topologies that would help reduce the skew. - \*\*minDomains\*\* indicates a minimum number of eligible domains. This field is optional. A domain is a particular instance of a topology. An eligible domain is a domain whose nodes match the node selector. {{< note >}} Before Kubernetes v1.30, the `minDomains` field was only available if the `MinDomainsInPodTopologySpread` [feature gate](/docs/reference/command-line-tools-reference/feature-gates-removed/) was enabled (default since v1.28). In older Kubernetes clusters it might be explicitly disabled or the field might not be available. {{< /note >}} - The value of `minDomains` must be greater than 0, when specified. You can only specify `minDomains` in conjunction with `whenUnsatisfiable: DoNotSchedule`. - When the number of eligible domains with match topology keys is less than `minDomains`, Pod topology spread treats global minimum as 0, and then the calculation of `skew` is performed. The global minimum is the minimum number of matching Pods in an eligible domain, or zero if the number of eligible domains is less than `minDomains`. - When the number of eligible domains with matching topology keys equals or is greater than `minDomains`, this value has no effect on scheduling. - If you do not specify `minDomains`, the constraint behaves as if `minDomains` is 1. - \*\*topologyKey\*\* is the key of [node labels](#node-labels). Nodes that have a label with this key and identical values are considered to be in the same topology. We call each instance of a topology (in other words, a pair) a domain. The scheduler will try to put a balanced number of pods into each domain. Also, we define an eligible domain as a domain whose nodes meet the requirements of nodeAffinityPolicy and nodeTaintsPolicy. - \*\*whenUnsatisfiable\*\* indicates how to deal with a Pod if it doesn't satisfy the spread constraint: - `DoNotSchedule` (default) tells the scheduler not to schedule it. - `ScheduleAnyway` tells the scheduler to still schedule it while prioritizing nodes that minimize the skew. - \*\*labelSelector\*\* is used to find matching Pods. Pods that match this label selector are counted to determine the number of Pods in their corresponding topology domain. See [Label Selectors](/docs/concepts/overview/working-with-objects/labels/#label-selectors) for more details. - \*\*matchLabelKeys\*\* is a list of pod label keys to select the group of pods over which the spreading skew will be calculated. At a pod creation, the kube-apiserver uses those keys to lookup values from the incoming pod labels, and those key-value labels will be merged with any existing `labelSelector`. The same key is forbidden to exist in both `matchLabelKeys` and `labelSelector`. `matchLabelKeys` cannot be set when `labelSelector` isn't set. Keys that don't exist in the pod labels will be ignored. A null or empty list means only match against the `labelSelector`. {{< caution >}} It's not recommended to use `matchLabelKeys` with labels that might be updated directly on pods. Even if you edit the pod's label that is specified at `matchLabelKeys` \*\*directly\*\*, (that is, you edit the Pod and not a Deployment), kube-apiserver doesn't reflect the label update onto the merged `labelSelector`. {{< /caution >}} With `matchLabelKeys`, you don't need to update the `pod.spec` between different revisions. The controller/operator just needs to set different values to the same label key for different revisions. For example, if you are configuring a Deployment, you can use the label keyed with [pod-template-hash](/docs/concepts/workloads/controllers/deployment/#pod-template-hash-label), which is added automatically by the Deployment controller, to distinguish between different
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/topology-spread-constraints.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/topology-spread-constraints.md
|
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update the `pod.spec` between different revisions. The controller/operator just needs to set different values to the same label key for different revisions. For example, if you are configuring a Deployment, you can use the label keyed with [pod-template-hash](/docs/concepts/workloads/controllers/deployment/#pod-template-hash-label), which is added automatically by the Deployment controller, to distinguish between different revisions in a single Deployment. ```yaml topologySpreadConstraints: - maxSkew: 1 topologyKey: kubernetes.io/hostname whenUnsatisfiable: DoNotSchedule labelSelector: matchLabels: app: foo matchLabelKeys: - pod-template-hash ``` {{< note >}} The `matchLabelKeys` field is a beta-level field and enabled by default in 1.27. You can disable it by disabling the `MatchLabelKeysInPodTopologySpread` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/). Before v1.34, `matchLabelKeys` was handled implicitly. Since v1.34, key-value labels corresponding to `matchLabelKeys` are explicitly merged into `labelSelector`. You can disable it and revert to the previous behavior by disabling the `MatchLabelKeysInPodTopologySpreadSelectorMerge` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/) of kube-apiserver. {{< /note >}} - \*\*nodeAffinityPolicy\*\* indicates how we will treat Pod's nodeAffinity/nodeSelector when calculating pod topology spread skew. Options are: - Honor: only nodes matching nodeAffinity/nodeSelector are included in the calculations. - Ignore: nodeAffinity/nodeSelector are ignored. All nodes are included in the calculations. If this value is null, the behavior is equivalent to the Honor policy. {{< note >}} The `nodeAffinityPolicy` became beta in 1.26 and graduated to GA in 1.33. It's enabled by default in beta, you can disable it by disabling the `NodeInclusionPolicyInPodTopologySpread` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/). {{< /note >}} - \*\*nodeTaintsPolicy\*\* indicates how we will treat node taints when calculating pod topology spread skew. Options are: - Honor: nodes without taints, along with tainted nodes for which the incoming pod has a toleration, are included. - Ignore: node taints are ignored. All nodes are included. If this value is null, the behavior is equivalent to the Ignore policy. {{< note >}} The `nodeTaintsPolicy` became beta in 1.26 and graduated to GA in 1.33. It's enabled by default in beta, you can disable it by disabling the `NodeInclusionPolicyInPodTopologySpread` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/). {{< /note >}} When a Pod defines more than one `topologySpreadConstraint`, those constraints are combined using a logical AND operation: the kube-scheduler looks for a node for the incoming Pod that satisfies all the configured constraints. ### Node labels Topology spread constraints rely on node labels to identify the topology domain(s) that each {{< glossary\_tooltip text="node" term\_id="node" >}} is in. For example, a node might have labels: ```yaml region: us-east-1 zone: us-east-1a ``` {{< note >}} For brevity, this example doesn't use the [well-known](/docs/reference/labels-annotations-taints/) label keys `topology.kubernetes.io/zone` and `topology.kubernetes.io/region`. However, those registered label keys are nonetheless recommended rather than the private (unqualified) label keys `region` and `zone` that are used here. You can't make a reliable assumption about the meaning of a private label key between different contexts. {{< /note >}} Suppose you have a 4-node cluster with the following labels: ``` NAME STATUS ROLES AGE VERSION LABELS node1 Ready 4m26s v1.16.0 node=node1,zone=zoneA node2 Ready 3m58s v1.16.0 node=node2,zone=zoneA node3 Ready 3m17s v1.16.0 node=node3,zone=zoneB node4 Ready 2m43s v1.16.0 node=node4,zone=zoneB ``` Then the cluster is logically viewed as below: {{}} graph TB subgraph "zoneB" n3(Node3) n4(Node4) end subgraph "zoneA" n1(Node1) n2(Node2) end classDef plain fill:#ddd,stroke:#fff,stroke-width:4px,color:#000; classDef k8s fill:#326ce5,stroke:#fff,stroke-width:4px,color:#fff; classDef cluster fill:#fff,stroke:#bbb,stroke-width:2px,color:#326ce5; class n1,n2,n3,n4 k8s; class zoneA,zoneB cluster; {{< /mermaid >}} ## Consistency You should set the same Pod topology spread constraints on all pods in a group. Usually, if you are using a workload controller such as a Deployment, the pod template takes care of this for you. If you mix different spread constraints then Kubernetes follows the API definition of the field; however, the behavior is more likely to become confusing and troubleshooting is less straightforward. You need a mechanism to ensure that all the nodes in
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/topology-spread-constraints.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/topology-spread-constraints.md
|
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Deployment, the pod template takes care of this for you. If you mix different spread constraints then Kubernetes follows the API definition of the field; however, the behavior is more likely to become confusing and troubleshooting is less straightforward. You need a mechanism to ensure that all the nodes in a topology domain (such as a cloud provider region) are labeled consistently. To avoid you needing to manually label nodes, most clusters automatically populate well-known labels such as `kubernetes.io/hostname`. Check whether your cluster supports this. ## Topology spread constraint examples ### Example: one topology spread constraint {#example-one-topologyspreadconstraint} Suppose you have a 4-node cluster where 3 Pods labeled `foo: bar` are located in node1, node2 and node3 respectively: {{}} graph BT subgraph "zoneB" p3(Pod) --> n3(Node3) n4(Node4) end subgraph "zoneA" p1(Pod) --> n1(Node1) p2(Pod) --> n2(Node2) end classDef plain fill:#ddd,stroke:#fff,stroke-width:4px,color:#000; classDef k8s fill:#326ce5,stroke:#fff,stroke-width:4px,color:#fff; classDef cluster fill:#fff,stroke:#bbb,stroke-width:2px,color:#326ce5; class n1,n2,n3,n4,p1,p2,p3 k8s; class zoneA,zoneB cluster; {{< /mermaid >}} If you want an incoming Pod to be evenly spread with existing Pods across zones, you can use a manifest similar to: {{% code\_sample file="pods/topology-spread-constraints/one-constraint.yaml" %}} From that manifest, `topologyKey: zone` implies the even distribution will only be applied to nodes that are labeled `zone: ` (nodes that don't have a `zone` label are skipped). The field `whenUnsatisfiable: DoNotSchedule` tells the scheduler to let the incoming Pod stay pending if the scheduler can't find a way to satisfy the constraint. If the scheduler placed this incoming Pod into zone `A`, the distribution of Pods would become `[3, 1]`. That means the actual skew is then 2 (calculated as `3 - 1`), which violates `maxSkew: 1`. To satisfy the constraints and context for this example, the incoming Pod can only be placed onto a node in zone `B`: {{}} graph BT subgraph "zoneB" p3(Pod) --> n3(Node3) p4(mypod) --> n4(Node4) end subgraph "zoneA" p1(Pod) --> n1(Node1) p2(Pod) --> n2(Node2) end classDef plain fill:#ddd,stroke:#fff,stroke-width:4px,color:#000; classDef k8s fill:#326ce5,stroke:#fff,stroke-width:4px,color:#fff; classDef cluster fill:#fff,stroke:#bbb,stroke-width:2px,color:#326ce5; class n1,n2,n3,n4,p1,p2,p3 k8s; class p4 plain; class zoneA,zoneB cluster; {{< /mermaid >}} OR {{}} graph BT subgraph "zoneB" p3(Pod) --> n3(Node3) p4(mypod) --> n3 n4(Node4) end subgraph "zoneA" p1(Pod) --> n1(Node1) p2(Pod) --> n2(Node2) end classDef plain fill:#ddd,stroke:#fff,stroke-width:4px,color:#000; classDef k8s fill:#326ce5,stroke:#fff,stroke-width:4px,color:#fff; classDef cluster fill:#fff,stroke:#bbb,stroke-width:2px,color:#326ce5; class n1,n2,n3,n4,p1,p2,p3 k8s; class p4 plain; class zoneA,zoneB cluster; {{< /mermaid >}} You can tweak the Pod spec to meet various kinds of requirements: - Change `maxSkew` to a bigger value - such as `2` - so that the incoming Pod can be placed into zone `A` as well. - Change `topologyKey` to `node` so as to distribute the Pods evenly across nodes instead of zones. In the above example, if `maxSkew` remains `1`, the incoming Pod can only be placed onto the node `node4`. - Change `whenUnsatisfiable: DoNotSchedule` to `whenUnsatisfiable: ScheduleAnyway` to ensure the incoming Pod to be always schedulable (suppose other scheduling APIs are satisfied). However, it's preferred to be placed into the topology domain which has fewer matching Pods. (Be aware that this preference is jointly normalized with other internal scheduling priorities such as resource usage ratio). ### Example: multiple topology spread constraints {#example-multiple-topologyspreadconstraints} This builds upon the previous example. Suppose you have a 4-node cluster where 3 existing Pods labeled `foo: bar` are located on node1, node2 and node3 respectively: {{}} graph BT subgraph "zoneB" p3(Pod) --> n3(Node3) n4(Node4) end subgraph "zoneA" p1(Pod) --> n1(Node1) p2(Pod) --> n2(Node2) end classDef plain fill:#ddd,stroke:#fff,stroke-width:4px,color:#000; classDef k8s fill:#326ce5,stroke:#fff,stroke-width:4px,color:#fff; classDef cluster fill:#fff,stroke:#bbb,stroke-width:2px,color:#326ce5; class n1,n2,n3,n4,p1,p2,p3 k8s; class p4 plain; class zoneA,zoneB cluster; {{< /mermaid >}} You can combine two topology spread constraints to control the spread of Pods both by node and by zone: {{% code\_sample
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/topology-spread-constraints.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/topology-spread-constraints.md
|
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] | 0.068246
|
"zoneA" p1(Pod) --> n1(Node1) p2(Pod) --> n2(Node2) end classDef plain fill:#ddd,stroke:#fff,stroke-width:4px,color:#000; classDef k8s fill:#326ce5,stroke:#fff,stroke-width:4px,color:#fff; classDef cluster fill:#fff,stroke:#bbb,stroke-width:2px,color:#326ce5; class n1,n2,n3,n4,p1,p2,p3 k8s; class p4 plain; class zoneA,zoneB cluster; {{< /mermaid >}} You can combine two topology spread constraints to control the spread of Pods both by node and by zone: {{% code\_sample file="pods/topology-spread-constraints/two-constraints.yaml" %}} In this case, to match the first constraint, the incoming Pod can only be placed onto nodes in zone `B`; while in terms of the second constraint, the incoming Pod can only be scheduled to the node `node4`. The scheduler only considers options that satisfy all defined constraints, so the only valid placement is onto node `node4`. ### Example: conflicting topology spread constraints {#example-conflicting-topologyspreadconstraints} Multiple constraints can lead to conflicts. Suppose you have a 3-node cluster across 2 zones: {{}} graph BT subgraph "zoneB" p4(Pod) --> n3(Node3) p5(Pod) --> n3 end subgraph "zoneA" p1(Pod) --> n1(Node1) p2(Pod) --> n1 p3(Pod) --> n2(Node2) end classDef plain fill:#ddd,stroke:#fff,stroke-width:4px,color:#000; classDef k8s fill:#326ce5,stroke:#fff,stroke-width:4px,color:#fff; classDef cluster fill:#fff,stroke:#bbb,stroke-width:2px,color:#326ce5; class n1,n2,n3,n4,p1,p2,p3,p4,p5 k8s; class zoneA,zoneB cluster; {{< /mermaid >}} If you were to apply [`two-constraints.yaml`](https://raw.githubusercontent.com/kubernetes/website/main/content/en/examples/pods/topology-spread-constraints/two-constraints.yaml) (the manifest from the previous example) to \*\*this\*\* cluster, you would see that the Pod `mypod` stays in the `Pending` state. This happens because: to satisfy the first constraint, the Pod `mypod` can only be placed into zone `B`; while in terms of the second constraint, the Pod `mypod` can only schedule to node `node2`. The intersection of the two constraints returns an empty set, and the scheduler cannot place the Pod. To overcome this situation, you can either increase the value of `maxSkew` or modify one of the constraints to use `whenUnsatisfiable: ScheduleAnyway`. Depending on circumstances, you might also decide to delete an existing Pod manually - for example, if you are troubleshooting why a bug-fix rollout is not making progress. #### Interaction with node affinity and node selectors The scheduler will skip the non-matching nodes from the skew calculations if the incoming Pod has `spec.nodeSelector` or `spec.affinity.nodeAffinity` defined. ### Example: topology spread constraints with node affinity {#example-topologyspreadconstraints-with-nodeaffinity} Suppose you have a 5-node cluster ranging across zones A to C: {{}} graph BT subgraph "zoneB" p3(Pod) --> n3(Node3) n4(Node4) end subgraph "zoneA" p1(Pod) --> n1(Node1) p2(Pod) --> n2(Node2) end classDef plain fill:#ddd,stroke:#fff,stroke-width:4px,color:#000; classDef k8s fill:#326ce5,stroke:#fff,stroke-width:4px,color:#fff; classDef cluster fill:#fff,stroke:#bbb,stroke-width:2px,color:#326ce5; class n1,n2,n3,n4,p1,p2,p3 k8s; class p4 plain; class zoneA,zoneB cluster; {{< /mermaid >}} {{}} graph BT subgraph "zoneC" n5(Node5) end classDef plain fill:#ddd,stroke:#fff,stroke-width:4px,color:#000; classDef k8s fill:#326ce5,stroke:#fff,stroke-width:4px,color:#fff; classDef cluster fill:#fff,stroke:#bbb,stroke-width:2px,color:#326ce5; class n5 k8s; class zoneC cluster; {{< /mermaid >}} and you know that zone `C` must be excluded. In this case, you can compose a manifest as below, so that Pod `mypod` will be placed into zone `B` instead of zone `C`. Similarly, Kubernetes also respects `spec.nodeSelector`. {{% code\_sample file="pods/topology-spread-constraints/one-constraint-with-nodeaffinity.yaml" %}} ## Implicit conventions There are some implicit conventions worth noting here: - Only the Pods holding the same namespace as the incoming Pod can be matching candidates. - The scheduler only considers nodes that have all `topologySpreadConstraints[\*].topologyKey` present at the same time. Nodes missing any of these `topologyKeys` are bypassed. This implies that: 1. any Pods located on those bypassed nodes do not impact `maxSkew` calculation - in the above [example](#example-conflicting-topologyspreadconstraints), suppose the node `node1` does not have a label "zone", then the 2 Pods will be disregarded, hence the incoming Pod will be scheduled into zone `A`. 2. the incoming Pod has no chances to be scheduled onto this kind of nodes - in the above example, suppose a node `node5` has the \*\*mistyped\*\* label `zone-typo: zoneC` (and no `zone` label set). After node `node5` joins the cluster, it will be bypassed
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/topology-spread-constraints.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/topology-spread-constraints.md
|
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|
be scheduled into zone `A`. 2. the incoming Pod has no chances to be scheduled onto this kind of nodes - in the above example, suppose a node `node5` has the \*\*mistyped\*\* label `zone-typo: zoneC` (and no `zone` label set). After node `node5` joins the cluster, it will be bypassed and Pods for this workload aren't scheduled there. - Be aware of what will happen if the incoming Pod's `topologySpreadConstraints[\*].labelSelector` doesn't match its own labels. In the above example, if you remove the incoming Pod's labels, it can still be placed onto nodes in zone `B`, since the constraints are still satisfied. However, after that placement, the degree of imbalance of the cluster remains unchanged - it's still zone `A` having 2 Pods labeled as `foo: bar`, and zone `B` having 1 Pod labeled as `foo: bar`. If this is not what you expect, update the workload's `topologySpreadConstraints[\*].labelSelector` to match the labels in the pod template. ## Cluster-level default constraints It is possible to set default topology spread constraints for a cluster. Default topology spread constraints are applied to a Pod if, and only if: - It doesn't define any constraints in its `.spec.topologySpreadConstraints`. - It belongs to a Service, ReplicaSet, StatefulSet or ReplicationController. Default constraints can be set as part of the `PodTopologySpread` plugin arguments in a [scheduling profile](/docs/reference/scheduling/config/#profiles). The constraints are specified with the same [API above](#topologyspreadconstraints-field), except that `labelSelector` must be empty. The selectors are calculated from the Services, ReplicaSets, StatefulSets or ReplicationControllers that the Pod belongs to. An example configuration might look like follows: ```yaml apiVersion: kubescheduler.config.k8s.io/v1 kind: KubeSchedulerConfiguration profiles: - schedulerName: default-scheduler pluginConfig: - name: PodTopologySpread args: defaultConstraints: - maxSkew: 1 topologyKey: topology.kubernetes.io/zone whenUnsatisfiable: ScheduleAnyway defaultingType: List ``` ### Built-in default constraints {#internal-default-constraints} {{< feature-state for\_k8s\_version="v1.24" state="stable" >}} If you don't configure any cluster-level default constraints for pod topology spreading, then kube-scheduler acts as if you specified the following default topology constraints: ```yaml defaultConstraints: - maxSkew: 3 topologyKey: "kubernetes.io/hostname" whenUnsatisfiable: ScheduleAnyway - maxSkew: 5 topologyKey: "topology.kubernetes.io/zone" whenUnsatisfiable: ScheduleAnyway ``` Also, the legacy `SelectorSpread` plugin, which provides an equivalent behavior, is disabled by default. {{< note >}} The `PodTopologySpread` plugin does not score the nodes that don't have the topology keys specified in the spreading constraints. This might result in a different default behavior compared to the legacy `SelectorSpread` plugin when using the default topology constraints. If your nodes are not expected to have \*\*both\*\* `kubernetes.io/hostname` and `topology.kubernetes.io/zone` labels set, define your own constraints instead of using the Kubernetes defaults. {{< /note >}} If you don't want to use the default Pod spreading constraints for your cluster, you can disable those defaults by setting `defaultingType` to `List` and leaving empty `defaultConstraints` in the `PodTopologySpread` plugin configuration: ```yaml apiVersion: kubescheduler.config.k8s.io/v1 kind: KubeSchedulerConfiguration profiles: - schedulerName: default-scheduler pluginConfig: - name: PodTopologySpread args: defaultConstraints: [] defaultingType: List ``` ## Comparison with podAffinity and podAntiAffinity {#comparison-with-podaffinity-podantiaffinity} In Kubernetes, [inter-Pod affinity and anti-affinity](/docs/concepts/scheduling-eviction/assign-pod-node/#inter-pod-affinity-and-anti-affinity) control how Pods are scheduled in relation to one another - either more packed or more scattered. `podAffinity` : attracts Pods; you can try to pack any number of Pods into qualifying topology domain(s). `podAntiAffinity` : repels Pods. If you set this to `requiredDuringSchedulingIgnoredDuringExecution` mode then only a single Pod can be scheduled into a single topology domain; if you choose `preferredDuringSchedulingIgnoredDuringExecution` then you lose the ability to enforce the constraint. For finer control, you can specify topology spread constraints to distribute Pods across different topology domains - to achieve either high availability or cost-saving. This can also help on rolling update workloads and scaling out replicas smoothly. For more context, see the [Motivation](https://github.com/kubernetes/enhancements/tree/master/keps/sig-scheduling/895-pod-topology-spread#motivation) section of the enhancement
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/topology-spread-constraints.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/topology-spread-constraints.md
|
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|
enforce the constraint. For finer control, you can specify topology spread constraints to distribute Pods across different topology domains - to achieve either high availability or cost-saving. This can also help on rolling update workloads and scaling out replicas smoothly. For more context, see the [Motivation](https://github.com/kubernetes/enhancements/tree/master/keps/sig-scheduling/895-pod-topology-spread#motivation) section of the enhancement proposal about Pod topology spread constraints. ## Known limitations - There's no guarantee that the constraints remain satisfied when Pods are removed. For example, scaling down a Deployment may result in imbalanced Pods distribution. You can use a tool such as the [Descheduler](https://github.com/kubernetes-sigs/descheduler) to rebalance the Pods distribution. - Pods matched on tainted nodes are respected. See [Issue 80921](https://github.com/kubernetes/kubernetes/issues/80921). - The scheduler doesn't have prior knowledge of all the zones or other topology domains that a cluster has. They are determined from the existing nodes in the cluster. This could lead to a problem in autoscaled clusters, when a node pool (or node group) is scaled to zero nodes, and you're expecting the cluster to scale up, because, in this case, those topology domains won't be considered until there is at least one node in them. You can work around this by using a Node autoscaler that is aware of Pod topology spread constraints and is also aware of the overall set of topology domains. - Pods that don't match their own labelSelector create "ghost pods". If a pod's labels don't match the `labelSelector` in its topology spread constraint, the pod won't count itself in spread calculations. This means: - Multiple such pods can just accumulate on the same topology (until matching pods are newly created/deleted) because those pod's schedule don't change a spreading calculation result. - The spreading constraint works in an unintended way, most likely not matching your expectations Ensure your pod's labels match the `labelSelector` in your spread constraints. Typically, a pod should match its own topology spread constraint selector. ## {{% heading "whatsnext" %}} - The blog article [Introducing PodTopologySpread](/blog/2020/05/introducing-podtopologyspread/) explains `maxSkew` in some detail, as well as covering some advanced usage examples. - Read the [scheduling](/docs/reference/kubernetes-api/workload-resources/pod-v1/#scheduling) section of the API reference for Pod.
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/topology-spread-constraints.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/topology-spread-constraints.md
|
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|
In Kubernetes, \_scheduling\_ refers to making sure that {{< glossary\_tooltip text="Pods" term\_id="pod" >}} are matched to {{< glossary\_tooltip text="Nodes" term\_id="node" >}} so that {{< glossary\_tooltip term\_id="kubelet" >}} can run them. ## Scheduling overview {#scheduling} A scheduler watches for newly created Pods that have no Node assigned. For every Pod that the scheduler discovers, the scheduler becomes responsible for finding the best Node for that Pod to run on. The scheduler reaches this placement decision taking into account the scheduling principles described below. If you want to understand why Pods are placed onto a particular Node, or if you're planning to implement a custom scheduler yourself, this page will help you learn about scheduling. ## kube-scheduler [kube-scheduler](/docs/reference/command-line-tools-reference/kube-scheduler/) is the default scheduler for Kubernetes and runs as part of the {{< glossary\_tooltip text="control plane" term\_id="control-plane" >}}. kube-scheduler is designed so that, if you want and need to, you can write your own scheduling component and use that instead. Kube-scheduler selects an optimal node to run newly created or not yet scheduled (unscheduled) pods. Since containers in pods - and pods themselves - can have different requirements, the scheduler filters out any nodes that don't meet a Pod's specific scheduling needs. Alternatively, the API lets you specify a node for a Pod when you create it, but this is unusual and is only done in special cases. In a cluster, Nodes that meet the scheduling requirements for a Pod are called \_feasible\_ nodes. If none of the nodes are suitable, the pod remains unscheduled until the scheduler is able to place it. The scheduler finds feasible Nodes for a Pod and then runs a set of functions to score the feasible Nodes and picks a Node with the highest score among the feasible ones to run the Pod. The scheduler then notifies the API server about this decision in a process called \_binding\_. Factors that need to be taken into account for scheduling decisions include individual and collective resource requirements, hardware / software / policy constraints, affinity and anti-affinity specifications, data locality, inter-workload interference, and so on. ### Node selection in kube-scheduler {#kube-scheduler-implementation} kube-scheduler selects a node for the pod in a 2-step operation: 1. Filtering 1. Scoring The \_filtering\_ step finds the set of Nodes where it's feasible to schedule the Pod. For example, the PodFitsResources filter checks whether a candidate Node has enough available resources to meet a Pod's specific resource requests. After this step, the node list contains any suitable Nodes; often, there will be more than one. If the list is empty, that Pod isn't (yet) schedulable. In the \_scoring\_ step, the scheduler ranks the remaining nodes to choose the most suitable Pod placement. The scheduler assigns a score to each Node that survived filtering, basing this score on the active scoring rules. Finally, kube-scheduler assigns the Pod to the Node with the highest ranking. If there is more than one node with equal scores, kube-scheduler selects one of these at random. There are two supported ways to configure the filtering and scoring behavior of the scheduler: 1. [Scheduling Policies](/docs/reference/scheduling/policies) allow you to configure \_Predicates\_ for filtering and \_Priorities\_ for scoring. 1. [Scheduling Profiles](/docs/reference/scheduling/config/#profiles) allow you to configure Plugins that implement different scheduling stages, including: `QueueSort`, `Filter`, `Score`, `Bind`, `Reserve`, `Permit`, and others. You can also configure the kube-scheduler to run different profiles. ## {{% heading "whatsnext" %}} \* Read about [scheduler performance tuning](/docs/concepts/scheduling-eviction/scheduler-perf-tuning/) \* Read about [Pod topology spread constraints](/docs/concepts/scheduling-eviction/topology-spread-constraints/) \* Read the [reference documentation](/docs/reference/command-line-tools-reference/kube-scheduler/) for kube-scheduler \* Read the [kube-scheduler config (v1)](/docs/reference/config-api/kube-scheduler-config.v1/) reference \* Learn about [configuring multiple schedulers](/docs/tasks/extend-kubernetes/configure-multiple-schedulers/) \* Learn about [topology management policies](/docs/tasks/administer-cluster/topology-manager/) \* Learn
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/kube-scheduler.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/kube-scheduler.md
|
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|
to run different profiles. ## {{% heading "whatsnext" %}} \* Read about [scheduler performance tuning](/docs/concepts/scheduling-eviction/scheduler-perf-tuning/) \* Read about [Pod topology spread constraints](/docs/concepts/scheduling-eviction/topology-spread-constraints/) \* Read the [reference documentation](/docs/reference/command-line-tools-reference/kube-scheduler/) for kube-scheduler \* Read the [kube-scheduler config (v1)](/docs/reference/config-api/kube-scheduler-config.v1/) reference \* Learn about [configuring multiple schedulers](/docs/tasks/extend-kubernetes/configure-multiple-schedulers/) \* Learn about [topology management policies](/docs/tasks/administer-cluster/topology-manager/) \* Learn about [Pod Overhead](/docs/concepts/scheduling-eviction/pod-overhead/) \* Learn about scheduling of Pods that use volumes in: \* [Volume Topology Support](/docs/concepts/storage/storage-classes/#volume-binding-mode) \* [Storage Capacity Tracking](/docs/concepts/storage/storage-capacity/) \* [Node-specific Volume Limits](/docs/concepts/storage/storage-limits/)
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/kube-scheduler.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/kube-scheduler.md
|
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] | 0.111926
|
In Kubernetes, scheduling refers to making sure that {{}} are matched to {{}} so that the {{}} can run them. Preemption is the process of terminating Pods with lower {{}} so that Pods with higher Priority can schedule on Nodes. Eviction is the process of terminating one or more Pods on Nodes. ## Scheduling \* [Kubernetes Scheduler](/docs/concepts/scheduling-eviction/kube-scheduler/) \* [Assigning Pods to Nodes](/docs/concepts/scheduling-eviction/assign-pod-node/) \* [Pod Overhead](/docs/concepts/scheduling-eviction/pod-overhead/) \* [Pod Topology Spread Constraints](/docs/concepts/scheduling-eviction/topology-spread-constraints/) \* [Taints and Tolerations](/docs/concepts/scheduling-eviction/taint-and-toleration/) \* [Scheduling Framework](/docs/concepts/scheduling-eviction/scheduling-framework) \* [Dynamic Resource Allocation](/docs/concepts/scheduling-eviction/dynamic-resource-allocation) \* [Scheduler Performance Tuning](/docs/concepts/scheduling-eviction/scheduler-perf-tuning/) \* [Resource Bin Packing for Extended Resources](/docs/concepts/scheduling-eviction/resource-bin-packing/) \* [Pod Scheduling Readiness](/docs/concepts/scheduling-eviction/pod-scheduling-readiness/) \* [Gang Scheduling](/docs/concepts/scheduling-eviction/gang-scheduling/) \* [Descheduler](https://github.com/kubernetes-sigs/descheduler#descheduler-for-kubernetes) \* [Node Declared Features](/docs/concepts/scheduling-eviction/node-declared-features/) ## Pod Disruption {{}} \* [Pod Priority and Preemption](/docs/concepts/scheduling-eviction/pod-priority-preemption/) \* [Node-pressure Eviction](/docs/concepts/scheduling-eviction/node-pressure-eviction/) \* [API-initiated Eviction](/docs/concepts/scheduling-eviction/api-eviction/)
| null |
./vendor/kubernetes/content/en/docs/concepts/scheduling-eviction/_index.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/scheduling-eviction/_index.md
|
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] | 0.200773
|
This document describes \_ephemeral volumes\_ in Kubernetes. Familiarity with [volumes](/docs/concepts/storage/volumes/) is suggested, in particular PersistentVolumeClaim and PersistentVolume. Some applications need additional storage but don't care whether that data is stored persistently across restarts. For example, caching services are often limited by memory size and can move infrequently used data into storage that is slower than memory with little impact on overall performance. Other applications expect some read-only input data to be present in files, like configuration data or secret keys. \_Ephemeral volumes\_ are designed for these use cases. Because volumes follow the Pod's lifetime and get created and deleted along with the Pod, Pods can be stopped and restarted without being limited to where some persistent volume is available. Ephemeral volumes are specified \_inline\_ in the Pod spec, which simplifies application deployment and management. ### Types of ephemeral volumes Kubernetes supports several different kinds of ephemeral volumes for different purposes: - [emptyDir](/docs/concepts/storage/volumes/#emptydir): empty at Pod startup, with storage coming locally from the kubelet base directory (usually the root disk) or RAM - [configMap](/docs/concepts/storage/volumes/#configmap), [downwardAPI](/docs/concepts/storage/volumes/#downwardapi), [secret](/docs/concepts/storage/volumes/#secret): inject different kinds of Kubernetes data into a Pod - [image](/docs/concepts/storage/volumes/#image): allows mounting container image files or artifacts, directly to a Pod. - [CSI ephemeral volumes](#csi-ephemeral-volumes): similar to the previous volume kinds, but provided by special {{< glossary\_tooltip text="CSI" term\_id="csi" >}} drivers which specifically [support this feature](https://kubernetes-csi.github.io/docs/ephemeral-local-volumes.html) - [generic ephemeral volumes](#generic-ephemeral-volumes), which can be provided by all storage drivers that also support persistent volumes `emptyDir`, `configMap`, `downwardAPI`, `secret` are provided as [local ephemeral storage](/docs/concepts/storage/ephemeral-storage/). They are managed by kubelet on each node. CSI ephemeral volumes \*must\* be provided by third-party CSI storage drivers. Generic ephemeral volumes \*can\* be provided by third-party CSI storage drivers, but also by any other storage driver that supports dynamic provisioning. Some CSI drivers are written specifically for CSI ephemeral volumes and do not support dynamic provisioning: those then cannot be used for generic ephemeral volumes. The advantage of using third-party drivers is that they can offer functionality that Kubernetes itself does not support, for example storage with different performance characteristics than the disk that is managed by kubelet, or injecting different data. ### CSI ephemeral volumes {{< feature-state for\_k8s\_version="v1.25" state="stable" >}} {{< note >}} CSI ephemeral volumes are only supported by a subset of CSI drivers. The Kubernetes CSI [Drivers list](https://kubernetes-csi.github.io/docs/drivers.html) shows which drivers support ephemeral volumes. {{< /note >}} Conceptually, CSI ephemeral volumes are similar to `configMap`, `downwardAPI` and `secret` volume types: the storage is managed locally on each node and is created together with other local resources after a Pod has been scheduled onto a node. Kubernetes has no concept of rescheduling Pods anymore at this stage. Volume creation has to be unlikely to fail, otherwise Pod startup gets stuck. In particular, [storage capacity aware Pod scheduling](/docs/concepts/storage/storage-capacity/) is \*not\* supported for these volumes. They are currently also not covered by the storage resource usage limits of a Pod, because that is something that kubelet can only enforce for storage that it manages itself. Here's an example manifest for a Pod that uses CSI ephemeral storage: ```yaml kind: Pod apiVersion: v1 metadata: name: my-csi-app spec: containers: - name: my-frontend image: busybox:1.28 volumeMounts: - mountPath: "/data" name: my-csi-inline-vol command: [ "sleep", "1000000" ] volumes: - name: my-csi-inline-vol csi: driver: inline.storage.kubernetes.io volumeAttributes: foo: bar ``` The `volumeAttributes` determine what volume is prepared by the driver. These attributes are specific to each driver and not standardized. See the documentation of each CSI driver for further instructions. ### CSI driver restrictions CSI ephemeral volumes allow users to provide `volumeAttributes` directly to the CSI driver as part of the Pod spec. A CSI driver
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/ephemeral-volumes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/ephemeral-volumes.md
|
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] | 0.169992
|
prepared by the driver. These attributes are specific to each driver and not standardized. See the documentation of each CSI driver for further instructions. ### CSI driver restrictions CSI ephemeral volumes allow users to provide `volumeAttributes` directly to the CSI driver as part of the Pod spec. A CSI driver allowing `volumeAttributes` that are typically restricted to administrators is NOT suitable for use in an inline ephemeral volume. For example, parameters that are normally defined in the StorageClass should not be exposed to users through the use of inline ephemeral volumes. Cluster administrators who need to restrict the CSI drivers that are allowed to be used as inline volumes within a Pod spec may do so by: - Removing `Ephemeral` from `volumeLifecycleModes` in the CSIDriver spec, which prevents the driver from being used as an inline ephemeral volume. - Using an [admission webhook](/docs/reference/access-authn-authz/extensible-admission-controllers/) to restrict how this driver is used. ### Generic ephemeral volumes {{< feature-state for\_k8s\_version="v1.23" state="stable" >}} Generic ephemeral volumes are similar to `emptyDir` volumes in the sense that they provide a per-pod directory for scratch data that is usually empty after provisioning. But they may also have additional features: - Storage can be local or network-attached. - Volumes can have a fixed size that Pods are not able to exceed. - Volumes may have some initial data, depending on the driver and parameters. - Typical operations on volumes are supported assuming that the driver supports them, including [snapshotting](/docs/concepts/storage/volume-snapshots/), [cloning](/docs/concepts/storage/volume-pvc-datasource/), [resizing](/docs/concepts/storage/persistent-volumes/#expanding-persistent-volumes-claims), and [storage capacity tracking](/docs/concepts/storage/storage-capacity/). Example: ```yaml kind: Pod apiVersion: v1 metadata: name: my-app spec: containers: - name: my-frontend image: busybox:1.28 volumeMounts: - mountPath: "/scratch" name: scratch-volume command: [ "sleep", "1000000" ] volumes: - name: scratch-volume ephemeral: volumeClaimTemplate: metadata: labels: type: my-frontend-volume spec: accessModes: [ "ReadWriteOnce" ] storageClassName: "scratch-storage-class" resources: requests: storage: 1Gi ``` ### Lifecycle and PersistentVolumeClaim The key design idea is that the [parameters for a volume claim](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#ephemeralvolumesource-v1-core) are allowed inside a volume source of the Pod. Labels, annotations and the whole set of fields for a PersistentVolumeClaim are supported. When such a Pod gets created, the ephemeral volume controller then creates an actual PersistentVolumeClaim object in the same namespace as the Pod and ensures that the PersistentVolumeClaim gets deleted when the Pod gets deleted. That triggers volume binding and/or provisioning, either immediately if the {{< glossary\_tooltip text="StorageClass" term\_id="storage-class" >}} uses immediate volume binding or when the Pod is tentatively scheduled onto a node (`WaitForFirstConsumer` volume binding mode). The latter is recommended for generic ephemeral volumes because then the scheduler is free to choose a suitable node for the Pod. With immediate binding, the scheduler is forced to select a node that has access to the volume once it is available. In terms of [resource ownership](/docs/concepts/architecture/garbage-collection/#owners-dependents), a Pod that has generic ephemeral storage is the owner of the PersistentVolumeClaim(s) that provide that ephemeral storage. When the Pod is deleted, the Kubernetes garbage collector deletes the PVC, which then usually triggers deletion of the volume because the default reclaim policy of storage classes is to delete volumes. You can create quasi-ephemeral local storage using a StorageClass with a reclaim policy of `retain`: the storage outlives the Pod, and in this case you need to ensure that volume clean up happens separately. While these PVCs exist, they can be used like any other PVC. In particular, they can be referenced as data source in volume cloning or snapshotting. The PVC object also holds the current status of the volume. ### PersistentVolumeClaim naming Naming of the automatically created PVCs is deterministic: the name is a combination of the Pod name and volume name, with a hyphen
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/ephemeral-volumes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/ephemeral-volumes.md
|
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|
particular, they can be referenced as data source in volume cloning or snapshotting. The PVC object also holds the current status of the volume. ### PersistentVolumeClaim naming Naming of the automatically created PVCs is deterministic: the name is a combination of the Pod name and volume name, with a hyphen (`-`) in the middle. In the example above, the PVC name will be `my-app-scratch-volume`. This deterministic naming makes it easier to interact with the PVC because one does not have to search for it once the Pod name and volume name are known. The deterministic naming also introduces a potential conflict between different Pods (a Pod "pod-a" with volume "scratch" and another Pod with name "pod" and volume "a-scratch" both end up with the same PVC name "pod-a-scratch") and between Pods and manually created PVCs. Such conflicts are detected: a PVC is only used for an ephemeral volume if it was created for the Pod. This check is based on the ownership relationship. An existing PVC is not overwritten or modified. But this does not resolve the conflict because without the right PVC, the Pod cannot start. {{< caution >}} Take care when naming Pods and volumes inside the same namespace, so that these conflicts can't occur. {{< /caution >}} ### Security Using generic ephemeral volumes allows users to create PVCs indirectly if they can create Pods, even if they do not have permission to create PVCs directly. Cluster administrators must be aware of this. If this does not fit their security model, they should use an [admission webhook](/docs/reference/access-authn-authz/extensible-admission-controllers/) that rejects objects like Pods that have a generic ephemeral volume. The normal [namespace quota for PVCs](/docs/concepts/policy/resource-quotas/#storage-resource-quota) still applies, so even if users are allowed to use this new mechanism, they cannot use it to circumvent other policies. ## {{% heading "whatsnext" %}} ### Ephemeral volumes managed by kubelet See [local ephemeral storage](/docs/concepts/storage/ephemeral-storage/). ### CSI ephemeral volumes - For more information on the design, see the [Ephemeral Inline CSI volumes KEP](https://github.com/kubernetes/enhancements/blob/ad6021b3d61a49040a3f835e12c8bb5424db2bbb/keps/sig-storage/20190122-csi-inline-volumes.md). - For more information on further development of this feature, see the [enhancement tracking issue #596](https://github.com/kubernetes/enhancements/issues/596). ### Generic ephemeral volumes - For more information on the design, see the [Generic ephemeral inline volumes KEP](https://github.com/kubernetes/enhancements/blob/master/keps/sig-storage/1698-generic-ephemeral-volumes/README.md).
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/ephemeral-volumes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/ephemeral-volumes.md
|
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|
Storage capacity is limited and may vary depending on the node on which a pod runs: network-attached storage might not be accessible by all nodes, or storage is local to a node to begin with. {{< feature-state for\_k8s\_version="v1.24" state="stable" >}} This page describes how Kubernetes keeps track of storage capacity and how the scheduler uses that information to [schedule Pods](/docs/concepts/scheduling-eviction/) onto nodes that have access to enough storage capacity for the remaining missing volumes. Without storage capacity tracking, the scheduler may choose a node that doesn't have enough capacity to provision a volume and multiple scheduling retries will be needed. ## {{% heading "prerequisites" %}} Kubernetes v{{< skew currentVersion >}} includes cluster-level API support for storage capacity tracking. To use this you must also be using a CSI driver that supports capacity tracking. Consult the documentation for the CSI drivers that you use to find out whether this support is available and, if so, how to use it. If you are not running Kubernetes v{{< skew currentVersion >}}, check the documentation for that version of Kubernetes. ## API There are two API extensions for this feature: - [CSIStorageCapacity](/docs/reference/kubernetes-api/config-and-storage-resources/csi-storage-capacity-v1/) objects: these get produced by a CSI driver in the namespace where the driver is installed. Each object contains capacity information for one storage class and defines which nodes have access to that storage. - [The `CSIDriverSpec.StorageCapacity` field](/docs/reference/kubernetes-api/config-and-storage-resources/csi-driver-v1/#CSIDriverSpec): when set to `true`, the Kubernetes scheduler will consider storage capacity for volumes that use the CSI driver. ## Scheduling Storage capacity information is used by the Kubernetes scheduler if: - a Pod uses a volume that has not been created yet, - that volume uses a {{< glossary\_tooltip text="StorageClass" term\_id="storage-class" >}} which references a CSI driver and uses `WaitForFirstConsumer` [volume binding mode](/docs/concepts/storage/storage-classes/#volume-binding-mode), and - the `CSIDriver` object for the driver has `StorageCapacity` set to true. In that case, the scheduler only considers nodes for the Pod which have enough storage available to them. This check is very simplistic and only compares the size of the volume against the capacity listed in `CSIStorageCapacity` objects with a topology that includes the node. For volumes with `Immediate` volume binding mode, the storage driver decides where to create the volume, independently of Pods that will use the volume. The scheduler then schedules Pods onto nodes where the volume is available after the volume has been created. For [CSI ephemeral volumes](/docs/concepts/storage/ephemeral-volumes/#csi-ephemeral-volumes), scheduling always happens without considering storage capacity. This is based on the assumption that this volume type is only used by special CSI drivers which are local to a node and do not need significant resources there. ## Rescheduling When a node has been selected for a Pod with `WaitForFirstConsumer` volumes, that decision is still tentative. The next step is that the CSI storage driver gets asked to create the volume with a hint that the volume is supposed to be available on the selected node. Because Kubernetes might have chosen a node based on out-dated capacity information, it is possible that the volume cannot really be created. The node selection is then reset and the Kubernetes scheduler tries again to find a node for the Pod. ## Limitations Storage capacity tracking increases the chance that scheduling works on the first try, but cannot guarantee this because the scheduler has to decide based on potentially out-dated information. Usually, the same retry mechanism as for scheduling without any storage capacity information handles scheduling failures. One situation where scheduling can fail permanently is when a Pod uses multiple volumes: one volume might have been created already in a topology segment which then does not have enough capacity left for
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/storage-capacity.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/storage-capacity.md
|
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-0.0409780815243721,
-0.02323758788406849
] | 0.112731
|
Usually, the same retry mechanism as for scheduling without any storage capacity information handles scheduling failures. One situation where scheduling can fail permanently is when a Pod uses multiple volumes: one volume might have been created already in a topology segment which then does not have enough capacity left for another volume. Manual intervention is necessary to recover from this, for example by increasing capacity or deleting the volume that was already created. ## {{% heading "whatsnext" %}} - For more information on the design, see the [Storage Capacity Constraints for Pod Scheduling KEP](https://github.com/kubernetes/enhancements/blob/master/keps/sig-storage/1472-storage-capacity-tracking/README.md).
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/storage-capacity.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/storage-capacity.md
|
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] | 0.134958
|
This page provides an storage overview specific to the Windows operating system. ## Persistent storage {#storage} Windows has a layered filesystem driver to mount container layers and create a copy filesystem based on NTFS. All file paths in the container are resolved only within the context of that container. \* With Docker, volume mounts can only target a directory in the container, and not an individual file. This limitation does not apply to containerd. \* Volume mounts cannot project files or directories back to the host filesystem. \* Read-only filesystems are not supported because write access is always required for the Windows registry and SAM database. However, read-only volumes are supported. \* Volume user-masks and permissions are not available. Because the SAM is not shared between the host & container, there's no mapping between them. All permissions are resolved within the context of the container. As a result, the following storage functionality is not supported on Windows nodes: \* Volume subpath mounts: only the entire volume can be mounted in a Windows container \* Subpath volume mounting for Secrets \* Host mount projection \* Read-only root filesystem (mapped volumes still support `readOnly`) \* Block device mapping \* Memory as the storage medium (for example, `emptyDir.medium` set to `Memory`) \* File system features like uid/gid; per-user Linux filesystem permissions \* Setting [secret permissions with DefaultMode](/docs/tasks/inject-data-application/distribute-credentials-secure/#set-posix-permissions-for-secret-keys) (due to UID/GID dependency) \* NFS based storage/volume support \* Expanding the mounted volume (resizefs) Kubernetes {{< glossary\_tooltip text="volumes" term\_id="volume" >}} enable complex applications, with data persistence and Pod volume sharing requirements, to be deployed on Kubernetes. Management of persistent volumes associated with a specific storage back-end or protocol includes actions such as provisioning/de-provisioning/resizing of volumes, attaching/detaching a volume to/from a Kubernetes node and mounting/dismounting a volume to/from individual containers in a pod that needs to persist data. Volume management components are shipped as Kubernetes volume [plugin](/docs/concepts/storage/volumes/#volume-types). The following broad classes of Kubernetes volume plugins are supported on Windows: \* [`FlexVolume plugins`](/docs/concepts/storage/volumes/#flexvolume) \* Please note that FlexVolumes have been deprecated as of 1.23 \* [`CSI Plugins`](/docs/concepts/storage/volumes/#csi) ##### In-tree volume plugins The following in-tree plugins support persistent storage on Windows nodes: \* [`azureFile`](/docs/concepts/storage/volumes/#azurefile) \* [`vsphereVolume`](/docs/concepts/storage/volumes/#vspherevolume)
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/windows-storage.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/windows-storage.md
|
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|
This document describes \_projected volumes\_ in Kubernetes. Familiarity with [volumes](/docs/concepts/storage/volumes/) is suggested. ## Introduction A `projected` volume maps several existing volume sources into the same directory. Currently, the following types of volume sources can be projected: \* [`secret`](/docs/concepts/storage/volumes/#secret) \* [`downwardAPI`](/docs/concepts/storage/volumes/#downwardapi) \* [`configMap`](/docs/concepts/storage/volumes/#configmap) \* [`serviceAccountToken`](#serviceaccounttoken) \* [`clusterTrustBundle`](#clustertrustbundle) \* [`podCertificate`](#podcertificate) All sources are required to be in the same namespace as the Pod. For more details, see the [all-in-one volume](https://git.k8s.io/design-proposals-archive/node/all-in-one-volume.md) design document. ### Example configuration with a secret, a downwardAPI, and a configMap {#example-configuration-secret-downwardapi-configmap} {{% code\_sample file="pods/storage/projected-secret-downwardapi-configmap.yaml" %}} ### Example configuration: secrets with a non-default permission mode set {#example-configuration-secrets-nondefault-permission-mode} {{% code\_sample file="pods/storage/projected-secrets-nondefault-permission-mode.yaml" %}} Each projected volume source is listed in the spec under `sources`. The parameters are nearly the same with two exceptions: \* For secrets, the `secretName` field has been changed to `name` to be consistent with ConfigMap naming. \* The `defaultMode` can only be specified at the projected level and not for each volume source. However, as illustrated above, you can explicitly set the `mode` for each individual projection. ## serviceAccountToken projected volumes {#serviceaccounttoken} You can inject the token for the current [service account](/docs/reference/access-authn-authz/authentication/#service-account-tokens) into a Pod at a specified path. For example: {{% code\_sample file="pods/storage/projected-service-account-token.yaml" %}} The example Pod has a projected volume containing the injected service account token. Containers in this Pod can use that token to access the Kubernetes API server, authenticating with the identity of [the pod's ServiceAccount](/docs/tasks/configure-pod-container/configure-service-account/). The `audience` field contains the intended audience of the token. A recipient of the token must identify itself with an identifier specified in the audience of the token, and otherwise should reject the token. This field is optional and it defaults to the identifier of the API server. The `expirationSeconds` is the expected duration of validity of the service account token. It defaults to 1 hour and must be at least 10 minutes (600 seconds). An administrator can also limit its maximum value by specifying the `--service-account-max-token-expiration` option for the API server. The `path` field specifies a relative path to the mount point of the projected volume. {{< note >}} A container using a projected volume source as a [`subPath`](/docs/concepts/storage/volumes/#using-subpath) volume mount will not receive updates for those volume sources. {{< /note >}} ## clusterTrustBundle projected volumes {#clustertrustbundle} {{< feature-state feature\_gate\_name="ClusterTrustBundleProjection" >}} {{< note >}} To use this feature in Kubernetes {{< skew currentVersion >}}, you must enable support for ClusterTrustBundle objects with the `ClusterTrustBundle` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/) and `--runtime-config=certificates.k8s.io/v1beta1/clustertrustbundles=true` kube-apiserver flag, then enable the `ClusterTrustBundleProjection` feature gate. {{< /note >}} The `clusterTrustBundle` projected volume source injects the contents of one or more [ClusterTrustBundle](/docs/reference/access-authn-authz/certificate-signing-requests#cluster-trust-bundles) objects as an automatically-updating file in the container filesystem. ClusterTrustBundles can be selected either by [name](/docs/reference/access-authn-authz/certificate-signing-requests#ctb-signer-unlinked) or by [signer name](/docs/reference/access-authn-authz/certificate-signing-requests#ctb-signer-linked). To select by name, use the `name` field to designate a single ClusterTrustBundle object. To select by signer name, use the `signerName` field (and optionally the `labelSelector` field) to designate a set of ClusterTrustBundle objects that use the given signer name. If `labelSelector` is not present, then all ClusterTrustBundles for that signer are selected. The kubelet deduplicates the certificates in the selected ClusterTrustBundle objects, normalizes the PEM representations (discarding comments and headers), reorders the certificates, and writes them into the file named by `path`. As the set of selected ClusterTrustBundles or their content changes, kubelet keeps the file up-to-date. By default, the kubelet will prevent the pod from starting if the named ClusterTrustBundle is not found, or if `signerName` / `labelSelector` do not match any ClusterTrustBundles. If this behavior is not what you want, then set the `optional` field to `true`, and the pod will start up with an empty file at `path`. {{% code\_sample
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/projected-volumes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/projected-volumes.md
|
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] | 0.101463
|
the pod from starting if the named ClusterTrustBundle is not found, or if `signerName` / `labelSelector` do not match any ClusterTrustBundles. If this behavior is not what you want, then set the `optional` field to `true`, and the pod will start up with an empty file at `path`. {{% code\_sample file="pods/storage/projected-clustertrustbundle.yaml" %}} ## podCertificate projected volumes {#podcertificate} {{< feature-state feature\_gate\_name="PodCertificateRequest" >}} {{< note >}} In Kubernetes {{< skew currentVersion >}}, you must enable support for Pod Certificates using the `PodCertificateRequest` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/) and the `--runtime-config=certificates.k8s.io/v1beta1/podcertificaterequests=true` kube-apiserver flag. {{< /note >}} The `podCertificate` projected volumes source securely provisions a private key and X.509 certificate chain for pod to use as client or server credentials. Kubelet will then handle refreshing the private key and certificate chain when they get close to expiration. The application just has to make sure that it reloads the file promptly when it changes, with a mechanism like `inotify` or polling. Each `podCertificate` projection supports the following configuration fields: \* `signerName`: The [signer](/docs/reference/access-authn-authz/certificate-signing-requests#signers) you want to issue the certificate. Note that signers may have their own access requirements, and may refuse to issue certificates to your pod. \* `keyType`: The type of private key that should be generated. Valid values are `ED25519`, `ECDSAP256`, `ECDSAP384`, `ECDSAP521`, `RSA3072`, and `RSA4096`. \* `maxExpirationSeconds`: The maximum lifetime you will accept for the certificate issued to the pod. If not set, will be defaulted to `86400` (24 hours). Must be at least `3600` (1 hour), and at most `7862400` (91 days). Kubernetes built-in signers are restricted to a max lifetime of `86400` (1 day). The signer is allowed to issue a certificate with a lifetime shorter than what you've specified. \* `credentialBundlePath`: Relative path within the projection where the credential bundle should be written. The credential bundle is a PEM-formatted file, where the first block is a "PRIVATE KEY" block that contains a PKCS#8-serialized private key, and the remaining blocks are "CERTIFICATE" blocks that comprise the certificate chain (leaf certificate and any intermediates). \* `keyPath` and `certificateChainPath`: Separate paths where Kubelet should write \*just\* the private key or certificate chain. \* `userAnnotations`: a map that allows you to pass additional information to the signer implementation. It is copied verbatim into the `spec.unverifiedUserAnnotations` field of the [PodCertificateRequest](docs/reference/access-authn-authz/certificate-signing-requests#pod-certificate-requests) objects that Kubelet creates. Entries are subject to the same validation as object metadata annotations, with the addition that all keys must be domain-prefixed. No restrictions are placed on values, except an overall size limitation on the entire field. Other than these basic validations, the API server does not conduct any extra validations. The signer implementations should be very careful when consuming this data. Signers must not inherently trust this data without first performing the appropriate verification steps. Signers should document the keys and values they support. Signers should deny requests that contain keys they do not recognize. {{< note >}} Most applications should prefer using `credentialBundlePath` unless they need the key and certificates in separate files for compatibility reasons. Kubelet uses an atomic writing strategy based on symlinks to make sure that when you open the files it projects, you read either the old content or the new content. However, if you read the key and certificate chain from separate files, Kubelet may rotate the credentials after your first read and before your second read, resulting in your application loading a mismatched key and certificate. {{< /note >}} {{% code\_sample file="pods/storage/projected-podcertificate.yaml" %}} ## SecurityContext interactions The [proposal](https://git.k8s.io/enhancements/keps/sig-storage/2451-service-account-token-volumes#proposal) for file permission handling in projected service account volume enhancement introduced the projected files having the correct owner permissions set. ### Linux In Linux pods that have a projected
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/projected-volumes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/projected-volumes.md
|
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] | 0.08002
|
in your application loading a mismatched key and certificate. {{< /note >}} {{% code\_sample file="pods/storage/projected-podcertificate.yaml" %}} ## SecurityContext interactions The [proposal](https://git.k8s.io/enhancements/keps/sig-storage/2451-service-account-token-volumes#proposal) for file permission handling in projected service account volume enhancement introduced the projected files having the correct owner permissions set. ### Linux In Linux pods that have a projected volume and `RunAsUser` set in the Pod [`SecurityContext`](/docs/reference/kubernetes-api/workload-resources/pod-v1/#security-context), the projected files have the correct ownership set including container user ownership. When all containers in a pod have the same `runAsUser` set in their [`PodSecurityContext`](/docs/reference/kubernetes-api/workload-resources/pod-v1/#security-context) or container [`SecurityContext`](/docs/reference/kubernetes-api/workload-resources/pod-v1/#security-context-1), then the kubelet ensures that the contents of the `serviceAccountToken` volume are owned by that user, and the token file has its permission mode set to `0600`. {{< note >}} {{< glossary\_tooltip text="Ephemeral containers" term\_id="ephemeral-container" >}} added to a Pod after it is created do \*not\* change volume permissions that were set when the pod was created. If a Pod's `serviceAccountToken` volume permissions were set to `0600` because all other containers in the Pod have the same `runAsUser`, ephemeral containers must use the same `runAsUser` to be able to read the token. {{< /note >}} ### Windows In Windows pods that have a projected volume and `RunAsUsername` set in the Pod `SecurityContext`, the ownership is not enforced due to the way user accounts are managed in Windows. Windows stores and manages local user and group accounts in a database file called Security Account Manager (SAM). Each container maintains its own instance of the SAM database, to which the host has no visibility into while the container is running. Windows containers are designed to run the user mode portion of the OS in isolation from the host, hence the maintenance of a virtual SAM database. As a result, the kubelet running on the host does not have the ability to dynamically configure host file ownership for virtualized container accounts. It is recommended that if files on the host machine are to be shared with the container then they should be placed into their own volume mount outside of `C:\`. By default, the projected files will have the following ownership as shown for an example projected volume file: ```powershell PS C:\> Get-Acl C:\var\run\secrets\kubernetes.io\serviceaccount\..2021\_08\_31\_22\_22\_18.318230061\ca.crt | Format-List Path : Microsoft.PowerShell.Core\FileSystem::C:\var\run\secrets\kubernetes.io\serviceaccount\..2021\_08\_31\_22\_22\_18.318230061\ca.crt Owner : BUILTIN\Administrators Group : NT AUTHORITY\SYSTEM Access : NT AUTHORITY\SYSTEM Allow FullControl BUILTIN\Administrators Allow FullControl BUILTIN\Users Allow ReadAndExecute, Synchronize Audit : Sddl : O:BAG:SYD:AI(A;ID;FA;;;SY)(A;ID;FA;;;BA)(A;ID;0x1200a9;;;BU) ``` This implies all administrator users like `ContainerAdministrator` will have read, write and execute access while, non-administrator users will have read and execute access. {{< note >}} In general, granting the container access to the host is discouraged as it can open the door for potential security exploits. Creating a Windows Pod with `RunAsUser` in it's `SecurityContext` will result in the Pod being stuck at `ContainerCreating` forever. So it is advised to not use the Linux only `RunAsUser` option with Windows Pods. {{< /note >}}
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/projected-volumes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/projected-volumes.md
|
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|
In Kubernetes, a \_VolumeSnapshot\_ represents a snapshot of a volume on a storage system. This document assumes that you are already familiar with Kubernetes [persistent volumes](/docs/concepts/storage/persistent-volumes/). ## Introduction Similar to how API resources `PersistentVolume` and `PersistentVolumeClaim` are used to provision volumes for users and administrators, `VolumeSnapshotContent` and `VolumeSnapshot` API resources are provided to create volume snapshots for users and administrators. A `VolumeSnapshotContent` is a snapshot taken from a volume in the cluster that has been provisioned by an administrator. It is a resource in the cluster just like a PersistentVolume is a cluster resource. A `VolumeSnapshot` is a request for snapshot of a volume by a user. It is similar to a PersistentVolumeClaim. `VolumeSnapshotClass` allows you to specify different attributes belonging to a `VolumeSnapshot`. These attributes may differ among snapshots taken from the same volume on the storage system and therefore cannot be expressed by using the same `StorageClass` of a `PersistentVolumeClaim`. Volume snapshots provide Kubernetes users with a standardized way to copy a volume's contents at a particular point in time without creating an entirely new volume. This functionality enables, for example, database administrators to backup databases before performing edit or delete modifications. Users need to be aware of the following when using this feature: - API Objects `VolumeSnapshot`, `VolumeSnapshotContent`, and `VolumeSnapshotClass` are {{< glossary\_tooltip term\_id="CustomResourceDefinition" text="CRDs" >}}, not part of the core API. - `VolumeSnapshot` support is only available for CSI drivers. - As part of the deployment process of `VolumeSnapshot`, the Kubernetes team provides a snapshot controller to be deployed into the control plane, and a sidecar helper container called csi-snapshotter to be deployed together with the CSI driver. The snapshot controller watches `VolumeSnapshot` and `VolumeSnapshotContent` objects and is responsible for the creation and deletion of `VolumeSnapshotContent` object. The sidecar csi-snapshotter watches `VolumeSnapshotContent` objects and triggers `CreateSnapshot` and `DeleteSnapshot` operations against a CSI endpoint. - There is also a validating webhook server which provides tightened validation on snapshot objects. This should be installed by the Kubernetes distros along with the snapshot controller and CRDs, not CSI drivers. It should be installed in all Kubernetes clusters that has the snapshot feature enabled. - CSI drivers may or may not have implemented the volume snapshot functionality. The CSI drivers that have provided support for volume snapshot will likely use the csi-snapshotter. See [CSI Driver documentation](https://kubernetes-csi.github.io/docs/) for details. - The CRDs and snapshot controller installations are the responsibility of the Kubernetes distribution. For advanced use cases, such as creating group snapshots of multiple volumes, see the external [CSI Volume Group Snapshot documentation](https://kubernetes-csi.github.io/docs/group-snapshot-restore-feature.html). ## Lifecycle of a volume snapshot and volume snapshot content `VolumeSnapshotContents` are resources in the cluster. `VolumeSnapshots` are requests for those resources. The interaction between `VolumeSnapshotContents` and `VolumeSnapshots` follow this lifecycle: ### Provisioning Volume Snapshot There are two ways snapshots may be provisioned: pre-provisioned or dynamically provisioned. #### Pre-provisioned {#static} A cluster administrator creates a number of `VolumeSnapshotContents`. They carry the details of the real volume snapshot on the storage system which is available for use by cluster users. They exist in the Kubernetes API and are available for consumption. #### Dynamic Instead of using a pre-existing snapshot, you can request that a snapshot to be dynamically taken from a PersistentVolumeClaim. The [VolumeSnapshotClass](/docs/concepts/storage/volume-snapshot-classes/) specifies storage provider-specific parameters to use when taking a snapshot. ### Binding The snapshot controller handles the binding of a `VolumeSnapshot` object with an appropriate `VolumeSnapshotContent` object, in both pre-provisioned and dynamically provisioned scenarios. The binding is a one-to-one mapping. In the case of pre-provisioned binding, the VolumeSnapshot will remain unbound until the requested VolumeSnapshotContent object is created. ### Persistent Volume Claim as Snapshot Source
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/volume-snapshots.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/volume-snapshots.md
|
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] | 0.134857
|
handles the binding of a `VolumeSnapshot` object with an appropriate `VolumeSnapshotContent` object, in both pre-provisioned and dynamically provisioned scenarios. The binding is a one-to-one mapping. In the case of pre-provisioned binding, the VolumeSnapshot will remain unbound until the requested VolumeSnapshotContent object is created. ### Persistent Volume Claim as Snapshot Source Protection The purpose of this protection is to ensure that in-use {{< glossary\_tooltip text="PersistentVolumeClaim" term\_id="persistent-volume-claim" >}} API objects are not removed from the system while a snapshot is being taken from it (as this may result in data loss). While a snapshot is being taken of a PersistentVolumeClaim, that PersistentVolumeClaim is in-use. If you delete a PersistentVolumeClaim API object in active use as a snapshot source, the PersistentVolumeClaim object is not removed immediately. Instead, removal of the PersistentVolumeClaim object is postponed until the snapshot is readyToUse or aborted. ### Delete Deletion is triggered by deleting the `VolumeSnapshot` object, and the `DeletionPolicy` will be followed. If the `DeletionPolicy` is `Delete`, then the underlying storage snapshot will be deleted along with the `VolumeSnapshotContent` object. If the `DeletionPolicy` is `Retain`, then both the underlying snapshot and `VolumeSnapshotContent` remain. ## VolumeSnapshots Each VolumeSnapshot contains a spec and a status. ```yaml apiVersion: snapshot.storage.k8s.io/v1 kind: VolumeSnapshot metadata: name: new-snapshot-test spec: volumeSnapshotClassName: csi-hostpath-snapclass source: persistentVolumeClaimName: pvc-test ``` `persistentVolumeClaimName` is the name of the PersistentVolumeClaim data source for the snapshot. This field is required for dynamically provisioning a snapshot. A volume snapshot can request a particular class by specifying the name of a [VolumeSnapshotClass](/docs/concepts/storage/volume-snapshot-classes/) using the attribute `volumeSnapshotClassName`. If nothing is set, then the default class is used if available. For pre-provisioned snapshots, you need to specify a `volumeSnapshotContentName` as the source for the snapshot as shown in the following example. The `volumeSnapshotContentName` source field is required for pre-provisioned snapshots. ```yaml apiVersion: snapshot.storage.k8s.io/v1 kind: VolumeSnapshot metadata: name: test-snapshot spec: source: volumeSnapshotContentName: test-content ``` ## Volume Snapshot Contents Each VolumeSnapshotContent contains a spec and status. In dynamic provisioning, the snapshot common controller creates `VolumeSnapshotContent` objects. Here is an example: ```yaml apiVersion: snapshot.storage.k8s.io/v1 kind: VolumeSnapshotContent metadata: name: snapcontent-72d9a349-aacd-42d2-a240-d775650d2455 spec: deletionPolicy: Delete driver: hostpath.csi.k8s.io source: volumeHandle: ee0cfb94-f8d4-11e9-b2d8-0242ac110002 sourceVolumeMode: Filesystem volumeSnapshotClassName: csi-hostpath-snapclass volumeSnapshotRef: name: new-snapshot-test namespace: default uid: 72d9a349-aacd-42d2-a240-d775650d2455 ``` `volumeHandle` is the unique identifier of the volume created on the storage backend and returned by the CSI driver during the volume creation. This field is required for dynamically provisioning a snapshot. It specifies the volume source of the snapshot. For pre-provisioned snapshots, you (as cluster administrator) are responsible for creating the `VolumeSnapshotContent` object as follows. ```yaml apiVersion: snapshot.storage.k8s.io/v1 kind: VolumeSnapshotContent metadata: name: new-snapshot-content-test spec: deletionPolicy: Delete driver: hostpath.csi.k8s.io source: snapshotHandle: 7bdd0de3-aaeb-11e8-9aae-0242ac110002 sourceVolumeMode: Filesystem volumeSnapshotRef: name: new-snapshot-test namespace: default ``` `snapshotHandle` is the unique identifier of the volume snapshot created on the storage backend. This field is required for the pre-provisioned snapshots. It specifies the CSI snapshot id on the storage system that this `VolumeSnapshotContent` represents. `sourceVolumeMode` is the mode of the volume whose snapshot is taken. The value of the `sourceVolumeMode` field can be either `Filesystem` or `Block`. If the source volume mode is not specified, Kubernetes treats the snapshot as if the source volume's mode is unknown. `volumeSnapshotRef` is the reference of the corresponding `VolumeSnapshot`. Note that when the `VolumeSnapshotContent` is being created as a pre-provisioned snapshot, the `VolumeSnapshot` referenced in `volumeSnapshotRef` might not exist yet. ## Converting the volume mode of a Snapshot {#convert-volume-mode} If the `VolumeSnapshots` API installed on your cluster supports the `sourceVolumeMode` field, then the API has the capability to prevent unauthorized users from converting the mode of a volume. To check if your cluster has capability for this feature, run the following command: ```yaml $
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/volume-snapshots.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/volume-snapshots.md
|
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|
mode of a Snapshot {#convert-volume-mode} If the `VolumeSnapshots` API installed on your cluster supports the `sourceVolumeMode` field, then the API has the capability to prevent unauthorized users from converting the mode of a volume. To check if your cluster has capability for this feature, run the following command: ```yaml $ kubectl get crd volumesnapshotcontent -o yaml ``` If you want to allow users to create a `PersistentVolumeClaim` from an existing `VolumeSnapshot`, but with a different volume mode than the source, the annotation `snapshot.storage.kubernetes.io/allow-volume-mode-change: "true"`needs to be added to the `VolumeSnapshotContent` that corresponds to the `VolumeSnapshot`. For pre-provisioned snapshots, `spec.sourceVolumeMode` needs to be populated by the cluster administrator. An example `VolumeSnapshotContent` resource with this feature enabled would look like: ```yaml apiVersion: snapshot.storage.k8s.io/v1 kind: VolumeSnapshotContent metadata: name: new-snapshot-content-test annotations: - snapshot.storage.kubernetes.io/allow-volume-mode-change: "true" spec: deletionPolicy: Delete driver: hostpath.csi.k8s.io source: snapshotHandle: 7bdd0de3-aaeb-11e8-9aae-0242ac110002 sourceVolumeMode: Filesystem volumeSnapshotRef: name: new-snapshot-test namespace: default ``` ## Provisioning Volumes from Snapshots You can provision a new volume, pre-populated with data from a snapshot, by using the \_dataSource\_ field in the `PersistentVolumeClaim` object. For more details, see [Volume Snapshot and Restore Volume from Snapshot](/docs/concepts/storage/persistent-volumes/#volume-snapshot-and-restore-volume-from-snapshot-support).
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/volume-snapshots.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/volume-snapshots.md
|
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|
This document describes the concept of cloning existing CSI Volumes in Kubernetes. Familiarity with [Volumes](/docs/concepts/storage/volumes) is suggested. ## Introduction The {{< glossary\_tooltip text="CSI" term\_id="csi" >}} Volume Cloning feature adds support for specifying existing {{< glossary\_tooltip text="PVC" term\_id="persistent-volume-claim" >}}s in the `dataSource` field to indicate a user would like to clone a {{< glossary\_tooltip term\_id="volume" >}}. A Clone is defined as a duplicate of an existing Kubernetes Volume that can be consumed as any standard Volume would be. The only difference is that upon provisioning, rather than creating a "new" empty Volume, the back end device creates an exact duplicate of the specified Volume. The implementation of cloning, from the perspective of the Kubernetes API, adds the ability to specify an existing PVC as a dataSource during new PVC creation. The source PVC must be bound and available (not in use). Users need to be aware of the following when using this feature: \* Cloning support (`VolumePVCDataSource`) is only available for CSI drivers. \* Cloning support is only available for dynamic provisioners. \* CSI drivers may or may not have implemented the volume cloning functionality. \* You can only clone a PVC when it exists in the same namespace as the destination PVC (source and destination must be in the same namespace). \* Cloning is supported with a different Storage Class. - Destination volume can be the same or a different storage class as the source. - Default storage class can be used and storageClassName omitted in the spec. \* Cloning can only be performed between two volumes that use the same VolumeMode setting (if you request a block mode volume, the source MUST also be block mode) ## Provisioning Clones are provisioned like any other PVC with the exception of adding a dataSource that references an existing PVC in the same namespace. ```yaml apiVersion: v1 kind: PersistentVolumeClaim metadata: name: clone-of-pvc-1 namespace: myns spec: accessModes: - ReadWriteOnce storageClassName: cloning resources: requests: storage: 5Gi dataSource: kind: PersistentVolumeClaim name: pvc-1 ``` {{< note >}} You must specify a capacity value for `spec.resources.requests.storage`, and the value you specify must be the same or larger than the capacity of the source volume. {{< /note >}} The result is a new PVC with the name `clone-of-pvc-1` that has the exact same content as the specified source `pvc-1`. ## Usage Upon availability of the new PVC, the cloned PVC is consumed the same as other PVC. It's also expected at this point that the newly created PVC is an independent object. It can be consumed, cloned, snapshotted, or deleted independently and without consideration for it's original dataSource PVC. This also implies that the source is not linked in any way to the newly created clone, it may also be modified or deleted without affecting the newly created clone.
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/volume-pvc-datasource.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/volume-pvc-datasource.md
|
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] | 0.071162
|
This document describes the concept of a StorageClass in Kubernetes. Familiarity with [volumes](/docs/concepts/storage/volumes/) and [persistent volumes](/docs/concepts/storage/persistent-volumes) is suggested. A StorageClass provides a way for administrators to describe the \_classes\_ of storage they offer. Different classes might map to quality-of-service levels, or to backup policies, or to arbitrary policies determined by the cluster administrators. Kubernetes itself is unopinionated about what classes represent. The Kubernetes concept of a storage class is similar to “profiles” in some other storage system designs. ## StorageClass objects Each StorageClass contains the fields `provisioner`, `parameters`, and `reclaimPolicy`, which are used when a PersistentVolume belonging to the class needs to be dynamically provisioned to satisfy a PersistentVolumeClaim (PVC). The name of a StorageClass object is significant, and is how users can request a particular class. Administrators set the name and other parameters of a class when first creating StorageClass objects. As an administrator, you can specify a default StorageClass that applies to any PVCs that don't request a specific class. For more details, see the [PersistentVolumeClaim concept](/docs/concepts/storage/persistent-volumes/#persistentvolumeclaims). Here's an example of a StorageClass: {{% code\_sample file="storage/storageclass-low-latency.yaml" %}} ## Default StorageClass You can mark a StorageClass as the default for your cluster. For instructions on setting the default StorageClass, see [Change the default StorageClass](/docs/tasks/administer-cluster/change-default-storage-class/). When a PVC does not specify a `storageClassName`, the default StorageClass is used. If you set the [`storageclass.kubernetes.io/is-default-class`](/docs/reference/labels-annotations-taints/#storageclass-kubernetes-io-is-default-class) annotation to true on more than one StorageClass in your cluster, and you then create a PersistentVolumeClaim with no `storageClassName` set, Kubernetes uses the most recently created default StorageClass. {{< note >}} You should try to only have one StorageClass in your cluster that is marked as the default. The reason that Kubernetes allows you to have multiple default StorageClasses is to allow for seamless migration. {{< /note >}} You can create a PersistentVolumeClaim without specifying a `storageClassName` for the new PVC, and you can do so even when no default StorageClass exists in your cluster. In this case, the new PVC creates as you defined it, and the `storageClassName` of that PVC remains unset until a default becomes available. You can have a cluster without any default StorageClass. If you don't mark any StorageClass as default (and one hasn't been set for you by, for example, a cloud provider), then Kubernetes cannot apply that defaulting for PersistentVolumeClaims that need it. If or when a default StorageClass becomes available, the control plane identifies any existing PVCs without `storageClassName`. For the PVCs that either have an empty value for `storageClassName` or do not have this key, the control plane then updates those PVCs to set `storageClassName` to match the new default StorageClass. If you have an existing PVC where the `storageClassName` is `""`, and you configure a default StorageClass, then this PVC will not get updated. In order to keep binding to PVs with `storageClassName` set to `""` (while a default StorageClass is present), you need to set the `storageClassName` of the associated PVC to `""`. ## Provisioner Each StorageClass has a provisioner that determines what volume plugin is used for provisioning PVs. This field must be specified. | Volume Plugin | Internal Provisioner | Config Example | | :------------------- | :------------------: | :-----------------------------------: | | AzureFile | ✓ | [Azure File](#azure-file) | | CephFS | - | - | | FC | - | - | | FlexVolume | - | - | | iSCSI | - | - | | Local | - | [Local](#local) | | NFS | - | [NFS](#nfs) | | PortworxVolume | ✓ | [Portworx Volume](#portworx-volume) | | RBD | - | [Ceph RBD](#ceph-rbd) | | VsphereVolume | ✓ | [vSphere](#vsphere) |
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/storage-classes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/storage-classes.md
|
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|
FlexVolume | - | - | | iSCSI | - | - | | Local | - | [Local](#local) | | NFS | - | [NFS](#nfs) | | PortworxVolume | ✓ | [Portworx Volume](#portworx-volume) | | RBD | - | [Ceph RBD](#ceph-rbd) | | VsphereVolume | ✓ | [vSphere](#vsphere) | You are not restricted to specifying the "internal" provisioners listed here (whose names are prefixed with "kubernetes.io" and shipped alongside Kubernetes). You can also run and specify external provisioners, which are independent programs that follow a [specification](https://git.k8s.io/design-proposals-archive/storage/volume-provisioning.md) defined by Kubernetes. Authors of external provisioners have full discretion over where their code lives, how the provisioner is shipped, how it needs to be run, what volume plugin it uses (including Flex), etc. The repository [kubernetes-sigs/sig-storage-lib-external-provisioner](https://github.com/kubernetes-sigs/sig-storage-lib-external-provisioner) houses a library for writing external provisioners that implements the bulk of the specification. Some external provisioners are listed under the repository [kubernetes-sigs/sig-storage-lib-external-provisioner](https://github.com/kubernetes-sigs/sig-storage-lib-external-provisioner). For example, NFS doesn't provide an internal provisioner, but an external provisioner can be used. There are also cases when 3rd party storage vendors provide their own external provisioner. ## Reclaim policy PersistentVolumes that are dynamically created by a StorageClass will have the [reclaim policy](/docs/concepts/storage/persistent-volumes/#reclaiming) specified in the `reclaimPolicy` field of the class, which can be either `Delete` or `Retain`. If no `reclaimPolicy` is specified when a StorageClass object is created, it will default to `Delete`. PersistentVolumes that are created manually and managed via a StorageClass will have whatever reclaim policy they were assigned at creation. ## Volume expansion {#allow-volume-expansion} PersistentVolumes can be configured to be expandable. This allows you to resize the volume by editing the corresponding PVC object, requesting a new larger amount of storage. The following types of volumes support volume expansion, when the underlying StorageClass has the field `allowVolumeExpansion` set to true. {{< table caption = "Table of Volume types and the version of Kubernetes they require" >}} | Volume type | Required Kubernetes version for volume expansion | | :------------------- | :----------------------------------------------- | | Azure File | 1.11 | | CSI | 1.24 | | FlexVolume | 1.13 | | Portworx | 1.11 | | rbd | 1.11 | {{< /table >}} {{< note >}} You can only use the volume expansion feature to grow a Volume, not to shrink it. {{< /note >}} ## Mount options PersistentVolumes that are dynamically created by a StorageClass will have the mount options specified in the `mountOptions` field of the class. If the volume plugin does not support mount options but mount options are specified, provisioning will fail. Mount options are \*\*not\*\* validated on either the class or PV. If a mount option is invalid, the PV mount fails. ## Volume binding mode The `volumeBindingMode` field controls when [volume binding and dynamic provisioning](/docs/concepts/storage/persistent-volumes/#provisioning) should occur. When unset, `Immediate` mode is used by default. The `Immediate` mode indicates that volume binding and dynamic provisioning occurs once the PersistentVolumeClaim is created. For storage backends that are topology-constrained and not globally accessible from all Nodes in the cluster, PersistentVolumes will be bound or provisioned without knowledge of the Pod's scheduling requirements. This may result in unschedulable Pods. A cluster administrator can address this issue by specifying the `WaitForFirstConsumer` mode which will delay the binding and provisioning of a PersistentVolume until a Pod using the PersistentVolumeClaim is created. PersistentVolumes will be selected or provisioned conforming to the topology that is specified by the Pod's scheduling constraints. These include, but are not limited to, [resource requirements](/docs/concepts/configuration/manage-resources-containers/), [node selectors](/docs/concepts/scheduling-eviction/assign-pod-node/#nodeselector), [pod affinity and anti-affinity](/docs/concepts/scheduling-eviction/assign-pod-node/#affinity-and-anti-affinity), and [taints and tolerations](/docs/concepts/scheduling-eviction/taint-and-toleration). The following plugins support `WaitForFirstConsumer` with dynamic provisioning: - CSI volumes, provided that the specific CSI driver supports this The
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/storage-classes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/storage-classes.md
|
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|
to the topology that is specified by the Pod's scheduling constraints. These include, but are not limited to, [resource requirements](/docs/concepts/configuration/manage-resources-containers/), [node selectors](/docs/concepts/scheduling-eviction/assign-pod-node/#nodeselector), [pod affinity and anti-affinity](/docs/concepts/scheduling-eviction/assign-pod-node/#affinity-and-anti-affinity), and [taints and tolerations](/docs/concepts/scheduling-eviction/taint-and-toleration). The following plugins support `WaitForFirstConsumer` with dynamic provisioning: - CSI volumes, provided that the specific CSI driver supports this The following plugins support `WaitForFirstConsumer` with pre-created PersistentVolume binding: - CSI volumes, provided that the specific CSI driver supports this - [`local`](#local) {{< note >}} If you choose to use `WaitForFirstConsumer`, do not use `nodeName` in the Pod spec to specify node affinity. If `nodeName` is used in this case, the scheduler will be bypassed and PVC will remain in `pending` state. Instead, you can use node selector for `kubernetes.io/hostname`: {{< /note >}} {{% code\_sample language="yaml" file="storage/storageclass/pod-volume-binding.yaml" %}} ## Allowed topologies When a cluster operator specifies the `WaitForFirstConsumer` volume binding mode, it is no longer necessary to restrict provisioning to specific topologies in most situations. However, if still required, `allowedTopologies` can be specified. This example demonstrates how to restrict the topology of provisioned volumes to specific zones and should be used as a replacement for the `zone` and `zones` parameters for the supported plugins. {{% code\_sample language="yaml" file="storage/storageclass/storageclass-topology.yaml" %}} ## Parameters StorageClasses have parameters that describe volumes belonging to the storage class. Different parameters may be accepted depending on the `provisioner`. When a parameter is omitted, some default is used. There can be at most 512 parameters defined for a StorageClass. The total length of the parameters object including its keys and values cannot exceed 256 KiB. ### AWS EBS Kubernetes {{< skew currentVersion >}} does not include a `awsElasticBlockStore` volume type. The AWSElasticBlockStore in-tree storage driver was deprecated in the Kubernetes v1.19 release and then removed entirely in the v1.27 release. The Kubernetes project suggests that you use the [AWS EBS](https://github.com/kubernetes-sigs/aws-ebs-csi-driver) out-of-tree storage driver instead. Here is an example StorageClass for the AWS EBS CSI driver: {{% code\_sample language="yaml" file="storage/storageclass/storageclass-aws-ebs.yaml" %}} `tagSpecification`: Tags with this prefix are applied to dynamically provisioned EBS volumes. ### AWS EFS To configure AWS EFS storage, you can use the out-of-tree [AWS\_EFS\_CSI\_DRIVER](https://github.com/kubernetes-sigs/aws-efs-csi-driver). {{% code\_sample language="yaml" file="storage/storageclass/storageclass-aws-efs.yaml" %}} - `provisioningMode`: The type of volume to be provisioned by Amazon EFS. Currently, only access point based provisioning is supported (`efs-ap`). - `fileSystemId`: The file system under which the access point is created. - `directoryPerms`: The directory permissions of the root directory created by the access point. For more details, refer to the [AWS\_EFS\_CSI\_Driver Dynamic Provisioning](https://github.com/kubernetes-sigs/aws-efs-csi-driver/blob/master/examples/kubernetes/dynamic\_provisioning/README.md) documentation. ### NFS To configure NFS storage, you can use the in-tree driver or the [NFS CSI driver for Kubernetes](https://github.com/kubernetes-csi/csi-driver-nfs#readme) (recommended). {{% code\_sample language="yaml" file="storage/storageclass/storageclass-nfs.yaml" %}} - `server`: Server is the hostname or IP address of the NFS server. - `path`: Path that is exported by the NFS server. - `readOnly`: A flag indicating whether the storage will be mounted as read only (default false). Kubernetes doesn't include an internal NFS provisioner. You need to use an external provisioner to create a StorageClass for NFS. Here are some examples: - [NFS Ganesha server and external provisioner](https://github.com/kubernetes-sigs/nfs-ganesha-server-and-external-provisioner) - [NFS subdir external provisioner](https://github.com/kubernetes-sigs/nfs-subdir-external-provisioner) ### vSphere There are two types of provisioners for vSphere storage classes: - [CSI provisioner](#vsphere-provisioner-csi): `csi.vsphere.vmware.com` - [vCP provisioner](#vcp-provisioner): `kubernetes.io/vsphere-volume` In-tree provisioners are [deprecated](/blog/2019/12/09/kubernetes-1-17-feature-csi-migration-beta/#why-are-we-migrating-in-tree-plugins-to-csi). For more information on the CSI provisioner, see [Kubernetes vSphere CSI Driver](https://vsphere-csi-driver.sigs.k8s.io/) and [vSphereVolume CSI migration](/docs/concepts/storage/volumes/#vsphere-csi-migration). #### CSI Provisioner {#vsphere-provisioner-csi} The vSphere CSI StorageClass provisioner works with Tanzu Kubernetes clusters. For an example, refer to the [vSphere CSI repository](https://github.com/kubernetes-sigs/vsphere-csi-driver/blob/master/example/vanilla-k8s-RWM-filesystem-volumes/example-sc.yaml). #### vCP Provisioner The following examples use the VMware Cloud Provider (vCP) StorageClass provisioner. 1. Create a StorageClass with a user specified disk format. ```yaml apiVersion: storage.k8s.io/v1 kind:
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/storage-classes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/storage-classes.md
|
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] | 0.138686
|
CSI Provisioner {#vsphere-provisioner-csi} The vSphere CSI StorageClass provisioner works with Tanzu Kubernetes clusters. For an example, refer to the [vSphere CSI repository](https://github.com/kubernetes-sigs/vsphere-csi-driver/blob/master/example/vanilla-k8s-RWM-filesystem-volumes/example-sc.yaml). #### vCP Provisioner The following examples use the VMware Cloud Provider (vCP) StorageClass provisioner. 1. Create a StorageClass with a user specified disk format. ```yaml apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: fast provisioner: kubernetes.io/vsphere-volume parameters: diskformat: zeroedthick ``` `diskformat`: `thin`, `zeroedthick` and `eagerzeroedthick`. Default: `"thin"`. 2. Create a StorageClass with a disk format on a user specified datastore. ```yaml apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: fast provisioner: kubernetes.io/vsphere-volume parameters: diskformat: zeroedthick datastore: VSANDatastore ``` `datastore`: The user can also specify the datastore in the StorageClass. The volume will be created on the datastore specified in the StorageClass, which in this case is `VSANDatastore`. This field is optional. If the datastore is not specified, then the volume will be created on the datastore specified in the vSphere config file used to initialize the vSphere Cloud Provider. 3. Storage Policy Management inside kubernetes - Using existing vCenter SPBM policy One of the most important features of vSphere for Storage Management is policy based Management. Storage Policy Based Management (SPBM) is a storage policy framework that provides a single unified control plane across a broad range of data services and storage solutions. SPBM enables vSphere administrators to overcome upfront storage provisioning challenges, such as capacity planning, differentiated service levels and managing capacity headroom. The SPBM policies can be specified in the StorageClass using the `storagePolicyName` parameter. - Virtual SAN policy support inside Kubernetes Vsphere Infrastructure (VI) Admins will have the ability to specify custom Virtual SAN Storage Capabilities during dynamic volume provisioning. You can now define storage requirements, such as performance and availability, in the form of storage capabilities during dynamic volume provisioning. The storage capability requirements are converted into a Virtual SAN policy which are then pushed down to the Virtual SAN layer when a persistent volume (virtual disk) is being created. The virtual disk is distributed across the Virtual SAN datastore to meet the requirements. You can see [Storage Policy Based Management for dynamic provisioning of volumes](https://github.com/vmware-archive/vsphere-storage-for-kubernetes/blob/fa4c8b8ad46a85b6555d715dd9d27ff69839df53/documentation/policy-based-mgmt.md) for more details on how to use storage policies for persistent volumes management. ### Ceph RBD (deprecated) {#ceph-rbd} {{< note >}} {{< feature-state state="deprecated" for\_k8s\_version="v1.28" >}} This internal provisioner of Ceph RBD is deprecated. Please use [CephFS RBD CSI driver](https://github.com/ceph/ceph-csi). {{< /note >}} {{% code\_sample language="yaml" file="storage/storageclass/storageclass-ceph-rbd.yaml" %}} - `monitors`: Ceph monitors, comma delimited. This parameter is required. - `adminId`: Ceph client ID that is capable of creating images in the pool. Default is "admin". - `adminSecretName`: Secret Name for `adminId`. This parameter is required. The provided secret must have type "kubernetes.io/rbd". - `adminSecretNamespace`: The namespace for `adminSecretName`. Default is "default". - `pool`: Ceph RBD pool. Default is "rbd". - `userId`: Ceph client ID that is used to map the RBD image. Default is the same as `adminId`. - `userSecretName`: The name of Ceph Secret for `userId` to map RBD image. It must exist in the same namespace as PVCs. This parameter is required. The provided secret must have type "kubernetes.io/rbd", for example created in this way: ```shell kubectl create secret generic ceph-secret --type="kubernetes.io/rbd" \ --from-literal=key='QVFEQ1pMdFhPUnQrSmhBQUFYaERWNHJsZ3BsMmNjcDR6RFZST0E9PQ==' \ --namespace=kube-system ``` - `userSecretNamespace`: The namespace for `userSecretName`. - `fsType`: fsType that is supported by kubernetes. Default: `"ext4"`. - `imageFormat`: Ceph RBD image format, "1" or "2". Default is "2". - `imageFeatures`: This parameter is optional and should only be used if you set `imageFormat` to "2". Currently supported features are `layering` only. Default is "", and no features are turned on. ### Azure Disk Kubernetes {{< skew currentVersion >}} does not include a `azureDisk`
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/storage-classes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/storage-classes.md
|
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|
or "2". Default is "2". - `imageFeatures`: This parameter is optional and should only be used if you set `imageFormat` to "2". Currently supported features are `layering` only. Default is "", and no features are turned on. ### Azure Disk Kubernetes {{< skew currentVersion >}} does not include a `azureDisk` volume type. The `azureDisk` in-tree storage driver was deprecated in the Kubernetes v1.19 release and then removed entirely in the v1.27 release. The Kubernetes project suggests that you use the [Azure Disk](https://github.com/kubernetes-sigs/azuredisk-csi-driver) third party storage driver instead. ### Azure File (deprecated) {#azure-file} {{% code\_sample language="yaml" file="storage/storageclass/storageclass-azure-file.yaml" %}} - `skuName`: Azure storage account SKU tier. Default is empty. - `location`: Azure storage account location. Default is empty. - `storageAccount`: Azure storage account name. Default is empty. If a storage account is not provided, all storage accounts associated with the resource group are searched to find one that matches `skuName` and `location`. If a storage account is provided, it must reside in the same resource group as the cluster, and `skuName` and `location` are ignored. - `secretNamespace`: the namespace of the secret that contains the Azure Storage Account Name and Key. Default is the same as the Pod. - `secretName`: the name of the secret that contains the Azure Storage Account Name and Key. Default is `azure-storage-account--secret` - `readOnly`: a flag indicating whether the storage will be mounted as read only. Defaults to false which means a read/write mount. This setting will impact the `ReadOnly` setting in VolumeMounts as well. During storage provisioning, a secret named by `secretName` is created for the mounting credentials. If the cluster has enabled both [RBAC](/docs/reference/access-authn-authz/rbac/) and [Controller Roles](/docs/reference/access-authn-authz/rbac/#controller-roles), add the `create` permission of resource `secret` for clusterrole `system:controller:persistent-volume-binder`. In a multi-tenancy context, it is strongly recommended to set the value for `secretNamespace` explicitly, otherwise the storage account credentials may be read by other users. ### Portworx volume (deprecated) {#portworx-volume} {{% code\_sample language="yaml" file="storage/storageclass/storageclass-portworx-volume.yaml" %}} - `fs`: filesystem to be laid out: `none/xfs/ext4` (default: `ext4`). - `block\_size`: block size in Kbytes (default: `32`). - `repl`: number of synchronous replicas to be provided in the form of replication factor `1..3` (default: `1`) A string is expected here i.e. `"1"` and not `1`. - `priority\_io`: determines whether the volume will be created from higher performance or a lower priority storage `high/medium/low` (default: `low`). - `snap\_interval`: clock/time interval in minutes for when to trigger snapshots. Snapshots are incremental based on difference with the prior snapshot, 0 disables snaps (default: `0`). A string is expected here i.e. `"70"` and not `70`. - `aggregation\_level`: specifies the number of chunks the volume would be distributed into, 0 indicates a non-aggregated volume (default: `0`). A string is expected here i.e. `"0"` and not `0` - `ephemeral`: specifies whether the volume should be cleaned-up after unmount or should be persistent. `emptyDir` use case can set this value to true and `persistent volumes` use case such as for databases like Cassandra should set to false, `true/false` (default `false`). A string is expected here i.e. `"true"` and not `true`. ### Local {{% code\_sample language="yaml" file="storage/storageclass/storageclass-local.yaml" %}} Local volumes do not support dynamic provisioning in Kubernetes {{< skew currentVersion >}}; however a StorageClass should still be created to delay volume binding until a Pod is actually scheduled to the appropriate node. This is specified by the `WaitForFirstConsumer` volume binding mode. Delaying volume binding allows the scheduler to consider all of a Pod's scheduling constraints when choosing an appropriate PersistentVolume for a PersistentVolumeClaim.
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/storage-classes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/storage-classes.md
|
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] | 0.026628
|
binding mode. Delaying volume binding allows the scheduler to consider all of a Pod's scheduling constraints when choosing an appropriate PersistentVolume for a PersistentVolumeClaim.
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/storage-classes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/storage-classes.md
|
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] | 0.122275
|
This document describes the concept of VolumeSnapshotClass in Kubernetes. Familiarity with [volume snapshots](/docs/concepts/storage/volume-snapshots/) and [storage classes](/docs/concepts/storage/storage-classes) is suggested. ## Introduction Just like StorageClass provides a way for administrators to describe the "classes" of storage they offer when provisioning a volume, VolumeSnapshotClass provides a way to describe the "classes" of storage when provisioning a volume snapshot. ## The VolumeSnapshotClass Resource Each VolumeSnapshotClass contains the fields `driver`, `deletionPolicy`, and `parameters`, which are used when a VolumeSnapshot belonging to the class needs to be dynamically provisioned. The name of a VolumeSnapshotClass object is significant, and is how users can request a particular class. Administrators set the name and other parameters of a class when first creating VolumeSnapshotClass objects, and the objects cannot be updated once they are created. {{< note >}} Installation of the CRDs is the responsibility of the Kubernetes distribution. Without the required CRDs present, the creation of a VolumeSnapshotClass fails. {{< /note >}} ```yaml apiVersion: snapshot.storage.k8s.io/v1 kind: VolumeSnapshotClass metadata: name: csi-hostpath-snapclass driver: hostpath.csi.k8s.io deletionPolicy: Delete parameters: ``` Administrators can specify a default VolumeSnapshotClass for VolumeSnapshots that don't request any particular class to bind to by adding the `snapshot.storage.kubernetes.io/is-default-class: "true"` annotation: ```yaml apiVersion: snapshot.storage.k8s.io/v1 kind: VolumeSnapshotClass metadata: name: csi-hostpath-snapclass annotations: snapshot.storage.kubernetes.io/is-default-class: "true" driver: hostpath.csi.k8s.io deletionPolicy: Delete parameters: ``` If multiple CSI drivers exist, a default VolumeSnapshotClass can be specified for each of them. ### VolumeSnapshotClass dependencies When you create a VolumeSnapshot without specifying a VolumeSnapshotClass, Kubernetes automatically selects a default VolumeSnapshotClass that has a CSI driver matching the CSI driver of the PVC’s StorageClass. This behavior allows multiple default VolumeSnapshotClass objects to coexist in a cluster, as long as each one is associated with a unique CSI driver. Always ensure that there is only one default VolumeSnapshotClass for each CSI driver. If multiple default VolumeSnapshotClass objects are created using the same CSI driver, a VolumeSnapshot creation will fail because Kubernetes cannot determine which one to use. ### Driver Volume snapshot classes have a driver that determines what CSI volume plugin is used for provisioning VolumeSnapshots. This field must be specified. ### DeletionPolicy Volume snapshot classes have a [deletionPolicy](/docs/concepts/storage/volume-snapshots/#delete). It enables you to configure what happens to a VolumeSnapshotContent when the VolumeSnapshot object it is bound to is to be deleted. The deletionPolicy of a volume snapshot class can either be `Retain` or `Delete`. This field must be specified. If the deletionPolicy is `Delete`, then the underlying storage snapshot will be deleted along with the VolumeSnapshotContent object. If the deletionPolicy is `Retain`, then both the underlying snapshot and VolumeSnapshotContent remain. ## Parameters Volume snapshot classes have parameters that describe volume snapshots belonging to the volume snapshot class. Different parameters may be accepted depending on the `driver`.
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/volume-snapshot-classes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/volume-snapshot-classes.md
|
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] | 0.09558
|
This document describes \_persistent volumes\_ in Kubernetes. Familiarity with [volumes](/docs/concepts/storage/volumes/), [StorageClasses](/docs/concepts/storage/storage-classes/) and [VolumeAttributesClasses](/docs/concepts/storage/volume-attributes-classes/) is suggested. ## Introduction Managing storage is a distinct problem from managing compute instances. The PersistentVolume subsystem provides an API for users and administrators that abstracts details of how storage is provided from how it is consumed. To do this, we introduce two new API resources: PersistentVolume and PersistentVolumeClaim. A \_PersistentVolume\_ (PV) is a piece of storage in the cluster that has been provisioned by an administrator or dynamically provisioned using [Storage Classes](/docs/concepts/storage/storage-classes/). It is a resource in the cluster just like a node is a cluster resource. PVs are volume plugins like Volumes, but have a lifecycle independent of any individual Pod that uses the PV. This API object captures the details of the implementation of the storage, be that NFS, iSCSI, or a cloud-provider-specific storage system. A \_PersistentVolumeClaim\_ (PVC) is a request for storage by a user. It is similar to a Pod. Pods consume node resources and PVCs consume PV resources. Pods can request specific levels of resources (CPU and Memory). Claims can request specific size and access modes (e.g., they can be mounted ReadWriteOnce, ReadOnlyMany, ReadWriteMany, or ReadWriteOncePod, see [AccessModes](#access-modes)). While PersistentVolumeClaims allow a user to consume abstract storage resources, it is common that users need PersistentVolumes with varying properties, such as performance, for different problems. Cluster administrators need to be able to offer a variety of PersistentVolumes that differ in more ways than size and access modes, without exposing users to the details of how those volumes are implemented. For these needs, there is the \_StorageClass\_ resource. See the [detailed walkthrough with working examples](/docs/tasks/configure-pod-container/configure-persistent-volume-storage/). ## Lifecycle of a volume and claim PVs are resources in the cluster. PVCs are requests for those resources and also act as claim checks to the resource. The interaction between PVs and PVCs follows this lifecycle: ### Provisioning There are two ways PVs may be provisioned: statically or dynamically. #### Static A cluster administrator creates a number of PVs. They carry the details of the real storage, which is available for use by cluster users. They exist in the Kubernetes API and are available for consumption. #### Dynamic When none of the static PVs the administrator created match a user's PersistentVolumeClaim, the cluster may try to dynamically provision a volume specially for the PVC. This provisioning is based on StorageClasses: the PVC must request a [storage class](/docs/concepts/storage/storage-classes/) and the administrator must have created and configured that class for dynamic provisioning to occur. Claims that request the class `""` effectively disable dynamic provisioning for themselves. To enable dynamic storage provisioning based on storage class, the cluster administrator needs to enable the `DefaultStorageClass` [admission controller](/docs/reference/access-authn-authz/admission-controllers/#defaultstorageclass) on the API server. This can be done, for example, by ensuring that `DefaultStorageClass` is among the comma-delimited, ordered list of values for the `--enable-admission-plugins` flag of the API server component. For more information on API server command-line flags, check [kube-apiserver](/docs/reference/command-line-tools-reference/kube-apiserver/) documentation. ### Binding A user creates, or in the case of dynamic provisioning, has already created, a PersistentVolumeClaim with a specific amount of storage requested and with certain access modes. A control loop in the control plane watches for new PVCs, finds a matching PV (if possible), and binds them together. If a PV was dynamically provisioned for a new PVC, the loop will always bind that PV to the PVC. Otherwise, the user will always get at least what they asked for, but the volume may be in excess of what was requested. Once bound, PersistentVolumeClaim binds are exclusive, regardless of how they were bound. A PVC to PV binding is a
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/persistent-volumes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/persistent-volumes.md
|
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] | 0.165763
|
will always bind that PV to the PVC. Otherwise, the user will always get at least what they asked for, but the volume may be in excess of what was requested. Once bound, PersistentVolumeClaim binds are exclusive, regardless of how they were bound. A PVC to PV binding is a one-to-one mapping, using a ClaimRef which is a bi-directional binding between the PersistentVolume and the PersistentVolumeClaim. Claims will remain unbound indefinitely if a matching volume does not exist. Claims will be bound as matching volumes become available. For example, a cluster provisioned with many 50Gi PVs would not match a PVC requesting 100Gi. The PVC can be bound when a 100Gi PV is added to the cluster. ### Using Pods use claims as volumes. The cluster inspects the claim to find the bound volume and mounts that volume for a Pod. For volumes that support multiple access modes, the user specifies which mode is desired when using their claim as a volume in a Pod. Once a user has a claim and that claim is bound, the bound PV belongs to the user for as long as they need it. Users schedule Pods and access their claimed PVs by including a `persistentVolumeClaim` section in a Pod's `volumes` block. See [Claims As Volumes](#claims-as-volumes) for more details on this. ### Storage Object in Use Protection The purpose of the Storage Object in Use Protection feature is to ensure that PersistentVolumeClaims (PVCs) in active use by a Pod and PersistentVolume (PVs) that are bound to PVCs are not removed from the system, as this may result in data loss. {{< note >}} PVC is in active use by a Pod when a Pod object exists that is using the PVC. {{< /note >}} If a user deletes a PVC in active use by a Pod, the PVC is not removed immediately. PVC removal is postponed until the PVC is no longer actively used by any Pods. Also, if an admin deletes a PV that is bound to a PVC, the PV is not removed immediately. PV removal is postponed until the PV is no longer bound to a PVC. You can see that a PVC is protected when the PVC's status is `Terminating` and the `Finalizers` list includes `kubernetes.io/pvc-protection`: ```shell kubectl describe pvc hostpath Name: hostpath Namespace: default StorageClass: example-hostpath Status: Terminating Volume: Labels: Annotations: volume.beta.kubernetes.io/storage-class=example-hostpath volume.beta.kubernetes.io/storage-provisioner=example.com/hostpath Finalizers: [kubernetes.io/pvc-protection] ... ``` You can see that a PV is protected when the PV's status is `Terminating` and the `Finalizers` list includes `kubernetes.io/pv-protection` too: ```shell kubectl describe pv task-pv-volume Name: task-pv-volume Labels: type=local Annotations: Finalizers: [kubernetes.io/pv-protection] StorageClass: standard Status: Terminating Claim: Reclaim Policy: Delete Access Modes: RWO Capacity: 1Gi Message: Source: Type: HostPath (bare host directory volume) Path: /tmp/data HostPathType: Events: ``` ### Reclaiming When a user is done with their volume, they can delete the PVC objects from the API that allows reclamation of the resource. The reclaim policy for a PersistentVolume tells the cluster what to do with the volume after it has been released of its claim. Currently, volumes can either be Retained, Recycled, or Deleted. #### Retain The `Retain` reclaim policy allows for manual reclamation of the resource. When the PersistentVolumeClaim is deleted, the PersistentVolume still exists and the volume is considered "released". But it is not yet available for another claim because the previous claimant's data remains on the volume. An administrator can manually reclaim the volume with the following steps. 1. Delete the PersistentVolume. The associated storage asset in external infrastructure still exists after the PV is deleted. 1. Manually clean up the data on the associated storage asset
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/persistent-volumes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/persistent-volumes.md
|
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] | 0.054419
|
claim because the previous claimant's data remains on the volume. An administrator can manually reclaim the volume with the following steps. 1. Delete the PersistentVolume. The associated storage asset in external infrastructure still exists after the PV is deleted. 1. Manually clean up the data on the associated storage asset accordingly. 1. Manually delete the associated storage asset. If you want to reuse the same storage asset, create a new PersistentVolume with the same storage asset definition. #### Delete For volume plugins that support the `Delete` reclaim policy, deletion removes both the PersistentVolume object from Kubernetes, as well as the associated storage asset in the external infrastructure. Volumes that were dynamically provisioned inherit the [reclaim policy of their StorageClass](#reclaim-policy), which defaults to `Delete`. The administrator should configure the StorageClass according to users' expectations; otherwise, the PV must be edited or patched after it is created. See [Change the Reclaim Policy of a PersistentVolume](/docs/tasks/administer-cluster/change-pv-reclaim-policy/). #### Recycle {{< warning >}} The `Recycle` reclaim policy is deprecated. Instead, the recommended approach is to use dynamic provisioning. {{< /warning >}} If supported by the underlying volume plugin, the `Recycle` reclaim policy performs a basic scrub (`rm -rf /thevolume/\*`) on the volume and makes it available again for a new claim. However, an administrator can configure a custom recycler Pod template using the Kubernetes controller manager command line arguments as described in the [reference](/docs/reference/command-line-tools-reference/kube-controller-manager/). The custom recycler Pod template must contain a `volumes` specification, as shown in the example below: ```yaml apiVersion: v1 kind: Pod metadata: name: pv-recycler namespace: default spec: restartPolicy: Never volumes: - name: vol hostPath: path: /any/path/it/will/be/replaced containers: - name: pv-recycler image: "registry.k8s.io/busybox" command: ["/bin/sh", "-c", "test -e /scrub && rm -rf /scrub/..?\* /scrub/.[!.]\* /scrub/\* && test -z \"$(ls -A /scrub)\" || exit 1"] volumeMounts: - name: vol mountPath: /scrub ``` However, the particular path specified in the custom recycler Pod template in the `volumes` part is replaced with the particular path of the volume that is being recycled. ### PersistentVolume deletion protection finalizer {{< feature-state feature\_gate\_name="HonorPVReclaimPolicy" >}} Finalizers can be added on a PersistentVolume to ensure that PersistentVolumes having `Delete` reclaim policy are deleted only after the backing storage are deleted. The finalizer `external-provisioner.volume.kubernetes.io/finalizer`(introduced in v1.31) is added to both dynamically provisioned and statically provisioned CSI volumes. The finalizer `kubernetes.io/pv-controller`(introduced in v1.31) is added to dynamically provisioned in-tree plugin volumes and skipped for statically provisioned in-tree plugin volumes. The following is an example of dynamically provisioned in-tree plugin volume: ```shell kubectl describe pv pvc-74a498d6-3929-47e8-8c02-078c1ece4d78 Name: pvc-74a498d6-3929-47e8-8c02-078c1ece4d78 Labels: Annotations: kubernetes.io/createdby: vsphere-volume-dynamic-provisioner pv.kubernetes.io/bound-by-controller: yes pv.kubernetes.io/provisioned-by: kubernetes.io/vsphere-volume Finalizers: [kubernetes.io/pv-protection kubernetes.io/pv-controller] StorageClass: vcp-sc Status: Bound Claim: default/vcp-pvc-1 Reclaim Policy: Delete Access Modes: RWO VolumeMode: Filesystem Capacity: 1Gi Node Affinity: Message: Source: Type: vSphereVolume (a Persistent Disk resource in vSphere) VolumePath: [vsanDatastore] d49c4a62-166f-ce12-c464-020077ba5d46/kubernetes-dynamic-pvc-74a498d6-3929-47e8-8c02-078c1ece4d78.vmdk FSType: ext4 StoragePolicyName: vSAN Default Storage Policy Events: ``` The finalizer `external-provisioner.volume.kubernetes.io/finalizer` is added for CSI volumes. The following is an example: ```shell Name: pvc-2f0bab97-85a8-4552-8044-eb8be45cf48d Labels: Annotations: pv.kubernetes.io/provisioned-by: csi.vsphere.vmware.com Finalizers: [kubernetes.io/pv-protection external-provisioner.volume.kubernetes.io/finalizer] StorageClass: fast Status: Bound Claim: demo-app/nginx-logs Reclaim Policy: Delete Access Modes: RWO VolumeMode: Filesystem Capacity: 200Mi Node Affinity: Message: Source: Type: CSI (a Container Storage Interface (CSI) volume source) Driver: csi.vsphere.vmware.com FSType: ext4 VolumeHandle: 44830fa8-79b4-406b-8b58-621ba25353fd ReadOnly: false VolumeAttributes: storage.kubernetes.io/csiProvisionerIdentity=1648442357185-8081-csi.vsphere.vmware.com type=vSphere CNS Block Volume Events: ``` When the `CSIMigration{provider}` feature flag is enabled for a specific in-tree volume plugin, the `kubernetes.io/pv-controller` finalizer is replaced by the `external-provisioner.volume.kubernetes.io/finalizer` finalizer. The finalizers ensure that the PV object is removed only after the volume is deleted from the storage backend provided the reclaim policy of the PV is `Delete`. This also ensures that the volume is deleted from storage backend irrespective of the order of
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/persistent-volumes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/persistent-volumes.md
|
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] | 0.012039
|
is replaced by the `external-provisioner.volume.kubernetes.io/finalizer` finalizer. The finalizers ensure that the PV object is removed only after the volume is deleted from the storage backend provided the reclaim policy of the PV is `Delete`. This also ensures that the volume is deleted from storage backend irrespective of the order of deletion of PV and PVC. ### Reserving a PersistentVolume The control plane can [bind PersistentVolumeClaims to matching PersistentVolumes](#binding) in the cluster. However, if you want a PVC to bind to a specific PV, you need to pre-bind them. By specifying a PersistentVolume in a PersistentVolumeClaim, you declare a binding between that specific PV and PVC. If the PersistentVolume exists and has not reserved PersistentVolumeClaims through its `claimRef` field, then the PersistentVolume and PersistentVolumeClaim will be bound. The binding happens regardless of some volume matching criteria, including node affinity. The control plane still checks that [storage class](/docs/concepts/storage/storage-classes/), access modes, and requested storage size are valid. ```yaml apiVersion: v1 kind: PersistentVolumeClaim metadata: name: foo-pvc namespace: foo spec: storageClassName: "" # Empty string must be explicitly set otherwise default StorageClass will be set volumeName: foo-pv ... ``` This method does not guarantee any binding privileges to the PersistentVolume. If other PersistentVolumeClaims could use the PV that you specify, you first need to reserve that storage volume. Specify the relevant PersistentVolumeClaim in the `claimRef` field of the PV so that other PVCs can not bind to it. ```yaml apiVersion: v1 kind: PersistentVolume metadata: name: foo-pv spec: storageClassName: "" claimRef: name: foo-pvc namespace: foo ... ``` This is useful if you want to consume PersistentVolumes that have their `persistentVolumeReclaimPolicy` set to `Retain`, including cases where you are reusing an existing PV. ### Expanding Persistent Volumes Claims {{< feature-state for\_k8s\_version="v1.24" state="stable" >}} Support for expanding PersistentVolumeClaims (PVCs) is enabled by default. You can expand the following types of volumes: \* {{< glossary\_tooltip text="csi" term\_id="csi" >}} (including some CSI migrated volme types) \* flexVolume (deprecated) \* portworxVolume (deprecated) You can only expand a PVC if its storage class's `allowVolumeExpansion` field is set to true. ```yaml apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: example-vol-default provisioner: vendor-name.example/magicstorage parameters: resturl: "http://192.168.10.100:8080" restuser: "" secretNamespace: "" secretName: "" allowVolumeExpansion: true ``` To request a larger volume for a PVC, edit the PVC object and specify a larger size. This triggers expansion of the volume that backs the underlying PersistentVolume. A new PersistentVolume is never created to satisfy the claim. Instead, an existing volume is resized. {{< warning >}} Directly editing the size of a PersistentVolume can prevent an automatic resize of that volume. If you edit the capacity of a PersistentVolume, and then edit the `.spec` of a matching PersistentVolumeClaim to make the size of the PersistentVolumeClaim match the PersistentVolume, then no storage resize happens. The Kubernetes control plane will see that the desired state of both resources matches, conclude that the backing volume size has been manually increased and that no resize is necessary. {{< /warning >}} #### CSI Volume expansion {{< feature-state for\_k8s\_version="v1.24" state="stable" >}} Support for expanding CSI volumes is enabled by default but it also requires a specific CSI driver to support volume expansion. Refer to documentation of the specific CSI driver for more information. #### Resizing a volume containing a file system You can only resize volumes containing a file system if the file system is XFS, Ext3, or Ext4. When a volume contains a file system, the file system is only resized when a new Pod is using the PersistentVolumeClaim in `ReadWrite` mode. File system expansion is either done when a Pod is starting up or when a Pod is running and the underlying file system
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/persistent-volumes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/persistent-volumes.md
|
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|
Ext3, or Ext4. When a volume contains a file system, the file system is only resized when a new Pod is using the PersistentVolumeClaim in `ReadWrite` mode. File system expansion is either done when a Pod is starting up or when a Pod is running and the underlying file system supports online expansion. FlexVolumes (deprecated since Kubernetes v1.23) allow resize if the driver is configured with the `RequiresFSResize` capability to `true`. The FlexVolume can be resized on Pod restart. #### Resizing an in-use PersistentVolumeClaim {{< feature-state for\_k8s\_version="v1.24" state="stable" >}} In this case, you don't need to delete and recreate a Pod or deployment that is using an existing PVC. Any in-use PVC automatically becomes available to its Pod as soon as its file system has been expanded. This feature has no effect on PVCs that are not in use by a Pod or deployment. You must create a Pod that uses the PVC before the expansion can complete. Similar to other volume types - FlexVolume volumes can also be expanded when in-use by a Pod. {{< note >}} FlexVolume resize is possible only when the underlying driver supports resize. {{< /note >}} #### Recovering from Failure when Expanding Volumes If a user specifies a new size that is too big to be satisfied by underlying storage system, expansion of PVC will be continuously retried until user or cluster administrator takes some action. This can be undesirable and hence Kubernetes provides following methods of recovering from such failures. {{< tabs name="recovery\_methods" >}} {{% tab name="Manually with Cluster Administrator access" %}} If expanding underlying storage fails, the cluster administrator can manually recover the Persistent Volume Claim (PVC) state and cancel the resize requests. Otherwise, the resize requests are continuously retried by the controller without administrator intervention. 1. Mark the PersistentVolume(PV) that is bound to the PersistentVolumeClaim(PVC) with `Retain` reclaim policy. 2. Delete the PVC. Since PV has `Retain` reclaim policy - we will not lose any data when we recreate the PVC. 3. Delete the `claimRef` entry from PV specs, so as new PVC can bind to it. This should make the PV `Available`. 4. Re-create the PVC with smaller size than PV and set `volumeName` field of the PVC to the name of the PV. This should bind new PVC to existing PV. 5. Don't forget to restore the reclaim policy of the PV. {{% /tab %}} {{% tab name="By requesting expansion to smaller size" %}} If expansion has failed for a PVC, you can retry expansion with a smaller size than the previously requested value. To request a new expansion attempt with a smaller proposed size, edit `.spec.resources` for that PVC and choose a value that is less than the value you previously tried. This is useful if expansion to a higher value did not succeed because of capacity constraint. If that has happened, or you suspect that it might have, you can retry expansion by specifying a size that is within the capacity limits of underlying storage provider. You can monitor status of resize operation by watching `.status.allocatedResourceStatuses` and events on the PVC. Note that, although you can specify a lower amount of storage than what was requested previously, the new value must still be higher than `.status.capacity`. Kubernetes does not support shrinking a PVC to less than its current size. {{% /tab %}} {{% /tabs %}} ## Types of Persistent Volumes PersistentVolume types are implemented as plugins. Kubernetes currently supports the following plugins: \* [`csi`](/docs/concepts/storage/volumes/#csi) - Container Storage Interface (CSI) \* [`fc`](/docs/concepts/storage/volumes/#fc) - Fibre Channel (FC) storage \* [`hostPath`](/docs/concepts/storage/volumes/#hostpath) - HostPath volume (for single node testing only; WILL
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/persistent-volumes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/persistent-volumes.md
|
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|
current size. {{% /tab %}} {{% /tabs %}} ## Types of Persistent Volumes PersistentVolume types are implemented as plugins. Kubernetes currently supports the following plugins: \* [`csi`](/docs/concepts/storage/volumes/#csi) - Container Storage Interface (CSI) \* [`fc`](/docs/concepts/storage/volumes/#fc) - Fibre Channel (FC) storage \* [`hostPath`](/docs/concepts/storage/volumes/#hostpath) - HostPath volume (for single node testing only; WILL NOT WORK in a multi-node cluster; consider using `local` volume instead) \* [`iscsi`](/docs/concepts/storage/volumes/#iscsi) - iSCSI (SCSI over IP) storage \* [`local`](/docs/concepts/storage/volumes/#local) - local storage devices mounted on nodes. \* [`nfs`](/docs/concepts/storage/volumes/#nfs) - Network File System (NFS) storage The following types of PersistentVolume are deprecated but still available. If you are using these volume types except for `flexVolume`, `cephfs` and `rbd`, please install corresponding CSI drivers. \* [`awsElasticBlockStore`](/docs/concepts/storage/volumes/#awselasticblockstore) - AWS Elastic Block Store (EBS) (\*\*migration on by default\*\* starting v1.23) \* [`azureDisk`](/docs/concepts/storage/volumes/#azuredisk) - Azure Disk (\*\*migration on by default\*\* starting v1.23) \* [`azureFile`](/docs/concepts/storage/volumes/#azurefile) - Azure File (\*\*migration on by default\*\* starting v1.24) \* [`cinder`](/docs/concepts/storage/volumes/#cinder) - Cinder (OpenStack block storage) (\*\*migration on by default\*\* starting v1.21) \* [`flexVolume`](/docs/concepts/storage/volumes/#flexvolume) - FlexVolume (\*\*deprecated\*\* starting v1.23, no migration plan and no plan to remove support) \* [`gcePersistentDisk`](/docs/concepts/storage/volumes/#gcePersistentDisk) - GCE Persistent Disk (\*\*migration on by default\*\* starting v1.23) \* [`portworxVolume`](/docs/concepts/storage/volumes/#portworxvolume) - Portworx volume (\*\*migration on by default\*\* starting v1.31) \* [`vsphereVolume`](/docs/concepts/storage/volumes/#vspherevolume) - vSphere VMDK volume (\*\*migration on by default\*\* starting v1.25) Older versions of Kubernetes also supported the following in-tree PersistentVolume types: \* [`cephfs`](/docs/concepts/storage/volumes/#cephfs) (\*\*not available\*\* starting v1.31) \* `flocker` - Flocker storage. (\*\*not available\*\* starting v1.25) \* `glusterfs` - GlusterFS storage. (\*\*not available\*\* starting v1.26) \* `photonPersistentDisk` - Photon controller persistent disk. (\*\*not available\*\* starting v1.15) \* `quobyte` - Quobyte volume. (\*\*not available\*\* starting v1.25) \* [`rbd`](/docs/concepts/storage/volumes/#rbd) - Rados Block Device (RBD) volume (\*\*not available\*\* starting v1.31) \* `scaleIO` - ScaleIO volume. (\*\*not available\*\* starting v1.21) \* `storageos` - StorageOS volume. (\*\*not available\*\* starting v1.25) ## Persistent Volumes Each PV contains a spec and status, which is the specification and status of the volume. The name of a PersistentVolume object must be a valid [DNS subdomain name](/docs/concepts/overview/working-with-objects/names#dns-subdomain-names). ```yaml apiVersion: v1 kind: PersistentVolume metadata: name: pv0003 spec: capacity: storage: 5Gi volumeMode: Filesystem accessModes: - ReadWriteOnce persistentVolumeReclaimPolicy: Recycle storageClassName: slow mountOptions: - hard - nfsvers=4.1 nfs: path: /tmp server: 172.17.0.2 ``` {{< note >}} Helper programs relating to the volume type may be required for consumption of a PersistentVolume within a cluster. In this example, the PersistentVolume is of type NFS and the helper program /sbin/mount.nfs is required to support the mounting of NFS filesystems. {{< /note >}} ### Capacity Generally, a PV will have a specific storage capacity. This is set using the PV's `capacity` attribute which is a {{< glossary\_tooltip term\_id="quantity" >}} value. Currently, storage size is the only resource that can be set or requested. Future attributes may include IOPS, throughput, etc. ### Volume Mode {{< feature-state for\_k8s\_version="v1.18" state="stable" >}} Kubernetes supports two `volumeModes` of PersistentVolumes: `Filesystem` and `Block`. `volumeMode` is an optional API parameter. `Filesystem` is the default mode used when `volumeMode` parameter is omitted. A volume with `volumeMode: Filesystem` is \*mounted\* into Pods into a directory. If the volume is backed by a block device and the device is empty, Kubernetes creates a filesystem on the device before mounting it for the first time. You can set the value of `volumeMode` to `Block` to use a volume as a raw block device. Such volume is presented into a Pod as a block device, without any filesystem on it. This mode is useful to provide a Pod the fastest possible way to access a volume, without any filesystem layer between the Pod and the volume. On the other hand, the application running in the
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/persistent-volumes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/persistent-volumes.md
|
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] | 0.15837
|
volume is presented into a Pod as a block device, without any filesystem on it. This mode is useful to provide a Pod the fastest possible way to access a volume, without any filesystem layer between the Pod and the volume. On the other hand, the application running in the Pod must know how to handle a raw block device. See [Raw Block Volume Support](#raw-block-volume-support) for an example on how to use a volume with `volumeMode: Block` in a Pod. ### Access Modes A PersistentVolume can be mounted on a host in any way supported by the resource provider. As shown in the table below, providers will have different capabilities and each PV's access modes are set to the specific modes supported by that particular volume. For example, NFS can support multiple read/write clients, but a specific NFS PV might be exported on the server as read-only. Each PV gets its own set of access modes describing that specific PV's capabilities. The access modes are: `ReadWriteOnce` : the volume can be mounted as read-write by a single node. ReadWriteOnce access mode still can allow multiple pods to access (read from or write to) that volume when the pods are running on the same node. For single pod access, please see ReadWriteOncePod. `ReadOnlyMany` : the volume can be mounted as read-only by many nodes. `ReadWriteMany` : the volume can be mounted as read-write by many nodes. `ReadWriteOncePod` : {{< feature-state for\_k8s\_version="v1.29" state="stable" >}} the volume can be mounted as read-write by a single Pod. Use ReadWriteOncePod access mode if you want to ensure that only one pod across the whole cluster can read that PVC or write to it. {{< note >}} The `ReadWriteOncePod` access mode is only supported for {{< glossary\_tooltip text="CSI" term\_id="csi" >}} volumes and Kubernetes version 1.22+. To use this feature you will need to update the following [CSI sidecars](https://kubernetes-csi.github.io/docs/sidecar-containers.html) to these versions or greater: \* [csi-provisioner:v3.0.0+](https://github.com/kubernetes-csi/external-provisioner/releases/tag/v3.0.0) \* [csi-attacher:v3.3.0+](https://github.com/kubernetes-csi/external-attacher/releases/tag/v3.3.0) \* [csi-resizer:v1.3.0+](https://github.com/kubernetes-csi/external-resizer/releases/tag/v1.3.0) {{< /note >}} In the CLI, the access modes are abbreviated to: \* RWO - ReadWriteOnce \* ROX - ReadOnlyMany \* RWX - ReadWriteMany \* RWOP - ReadWriteOncePod {{< note >}} Kubernetes uses volume access modes to match PersistentVolumeClaims and PersistentVolumes. In some cases, the volume access modes also constrain where the PersistentVolume can be mounted. Volume access modes do \*\*not\*\* enforce write protection once the storage has been mounted. Even if the access modes are specified as ReadWriteOnce, ReadOnlyMany, or ReadWriteMany, they don't set any constraints on the volume. For example, even if a PersistentVolume is created as ReadOnlyMany, it is no guarantee that it will be read-only. If the access modes are specified as ReadWriteOncePod, the volume is constrained and can be mounted on only a single Pod. {{< /note >}} > \_\_Important!\_\_ A volume can only be mounted using one access mode at a time, > even if it supports many. | Volume Plugin | ReadWriteOnce | ReadOnlyMany | ReadWriteMany | ReadWriteOncePod | | :--- | :---: | :---: | :---: | - | | AzureFile | ✓ | ✓ | ✓ | - | | CephFS | ✓ | ✓ | ✓ | - | | CSI | depends on the driver | depends on the driver | depends on the driver | depends on the driver | | FC | ✓ | ✓ | - | - | | FlexVolume | ✓ | ✓ | depends on the driver | - | | HostPath | ✓ | - | - | - | | iSCSI | ✓ | ✓ | - | - | | NFS | ✓ | ✓ | ✓
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/persistent-volumes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/persistent-volumes.md
|
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] | 0.16746
|
✓ | - | - | | FlexVolume | ✓ | ✓ | depends on the driver | - | | HostPath | ✓ | - | - | - | | iSCSI | ✓ | ✓ | - | - | | NFS | ✓ | ✓ | ✓ | - | | RBD | ✓ | ✓ | - | - | | VsphereVolume | ✓ | - | - (works when Pods are collocated) | - | | PortworxVolume | ✓ | - | ✓ | - | - | ### Class A PV can have a class, which is specified by setting the `storageClassName` attribute to the name of a [StorageClass](/docs/concepts/storage/storage-classes/). A PV of a particular class can only be bound to PVCs requesting that class. A PV with no `storageClassName` has no class and can only be bound to PVCs that request no particular class. In the past, the annotation `volume.beta.kubernetes.io/storage-class` was used instead of the `storageClassName` attribute. This annotation is still working; however, it will become fully deprecated in a future Kubernetes release. ### Reclaim Policy Current reclaim policies are: \* Retain -- manual reclamation \* Recycle -- basic scrub (`rm -rf /thevolume/\*`) \* Delete -- delete the volume For Kubernetes {{< skew currentVersion >}}, only `nfs` and `hostPath` volume types support recycling. ### Mount Options A Kubernetes administrator can specify additional mount options for when a Persistent Volume is mounted on a node. {{< note >}} Not all Persistent Volume types support mount options. {{< /note >}} The following volume types support mount options: \* `csi` (including CSI migrated volume types) \* `iscsi` \* `nfs` Mount options are not validated. If a mount option is invalid, the mount fails. In the past, the annotation `volume.beta.kubernetes.io/mount-options` was used instead of the `mountOptions` attribute. This annotation is still working; however, it will become fully deprecated in a future Kubernetes release. ### Node Affinity {{< note >}} For most volume types, you do not need to set this field. You need to explicitly set this for [local](/docs/concepts/storage/volumes/#local) volumes. {{< /note >}} A PV can specify node affinity to define constraints that limit what nodes this volume can be accessed from. Pods that use a PV will only be scheduled to nodes that are selected by the node affinity. To specify node affinity, set `nodeAffinity` in the `.spec` of a PV. The [PersistentVolume](/docs/reference/kubernetes-api/config-and-storage-resources/persistent-volume-v1/#PersistentVolumeSpec) API reference has more details on this field. #### Updates to node affinity {{< feature-state feature\_gate\_name="MutablePVNodeAffinity" >}} If the `MutablePVNodeAffinity` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/) is enabled in your cluster, the `.spec.nodeAffinity` field of a PersistentVolume is mutable. This allows cluster administrators or external storage controller to update the node affinity of a PersistentVolume when the data is migrated, without interrupting the running pods. When updating the node affinity, you should ensure that the new node affinity still matches the nodes where the volume is currently in use. For the pods violating the new affinity, if the pod is already running, it may continue to run. But Kubernetes does not support this configuration. You should terminate the violating pods soon. Due to in memory caching, the pods created after the update may still be scheduled according to the old node affinity for a short period of time. To use this feature, you should enable the `MutablePVNodeAffinity` feature gate on the following components: - `kube-apiserver` - `kubelet` ### Phase A PersistentVolume will be in one of the following phases: `Available` : a free resource that is not yet bound to a claim `Bound` : the volume is bound to a claim `Released` : the claim has been deleted,
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/persistent-volumes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/persistent-volumes.md
|
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] | 0.11622
|
gate on the following components: - `kube-apiserver` - `kubelet` ### Phase A PersistentVolume will be in one of the following phases: `Available` : a free resource that is not yet bound to a claim `Bound` : the volume is bound to a claim `Released` : the claim has been deleted, but the associated storage resource is not yet reclaimed by the cluster `Failed` : the volume has failed its (automated) reclamation You can see the name of the PVC bound to the PV using `kubectl describe persistentvolume `. #### Phase transition timestamp {{< feature-state feature\_gate\_name="PersistentVolumeLastPhaseTransitionTime" >}} The `.status` field for a PersistentVolume can include an alpha `lastPhaseTransitionTime` field. This field records the timestamp of when the volume last transitioned its phase. For newly created volumes the phase is set to `Pending` and `lastPhaseTransitionTime` is set to the current time. ## PersistentVolumeClaims Each PVC contains a spec and status, which is the specification and status of the claim. The name of a PersistentVolumeClaim object must be a valid [DNS subdomain name](/docs/concepts/overview/working-with-objects/names#dns-subdomain-names). ```yaml apiVersion: v1 kind: PersistentVolumeClaim metadata: name: myclaim spec: accessModes: - ReadWriteOnce volumeMode: Filesystem resources: requests: storage: 8Gi storageClassName: slow selector: matchLabels: release: "stable" matchExpressions: - {key: environment, operator: In, values: [dev]} ``` ### Access Modes Claims use [the same conventions as volumes](#access-modes) when requesting storage with specific access modes. ### Volume Modes Claims use [the same convention as volumes](#volume-mode) to indicate the consumption of the volume as either a filesystem or block device. ### Volume Name Claims can use the `volumeName` field to explicitly bind to a specific PersistentVolume. You can also leave `volumeName` unset, indicating that you'd like Kubernetes to set up a new PersistentVolume that matches the claim. If the specified PV is already bound to another PVC, the binding will be stuck in a pending state. ### Resources Claims, like Pods, can request specific quantities of a resource. In this case, the request is for storage. The same [resource model](https://git.k8s.io/design-proposals-archive/scheduling/resources.md) applies to both volumes and claims. {{< note >}} For `Filesystem` volumes, the storage request refers to the "outer" volume size (i.e. the allocated size from the storage backend). This means that the writeable size may be slightly lower for providers that build a filesystem on top of a block device, due to filesystem overhead. This is especially visible with XFS, where many metadata features are enabled by default. {{< /note >}} ### Selector Claims can specify a [label selector](/docs/concepts/overview/working-with-objects/labels/#label-selectors) to further filter the set of volumes. Only the volumes whose labels match the selector can be bound to the claim. The selector can consist of two fields: \* `matchLabels` - the volume must have a label with this value \* `matchExpressions` - a list of requirements made by specifying key, list of values, and operator that relates the key and values. Valid operators include `In`, `NotIn`, `Exists`, and `DoesNotExist`. All of the requirements, from both `matchLabels` and `matchExpressions`, are ANDed together – they must all be satisfied in order to match. ### Class A claim can request a particular class by specifying the name of a [StorageClass](/docs/concepts/storage/storage-classes/) using the attribute `storageClassName`. Only PVs of the requested class, ones with the same `storageClassName` as the PVC, can be bound to the PVC. PVCs don't necessarily have to request a class. A PVC with its `storageClassName` set equal to `""` is always interpreted to be requesting a PV with no class, so it can only be bound to PVs with no class (no annotation or one set equal to `""`). A PVC with no `storageClassName` is not quite the same and is treated differently by the cluster,
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/persistent-volumes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/persistent-volumes.md
|
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] | 0.144253
|
equal to `""` is always interpreted to be requesting a PV with no class, so it can only be bound to PVs with no class (no annotation or one set equal to `""`). A PVC with no `storageClassName` is not quite the same and is treated differently by the cluster, depending on whether the [`DefaultStorageClass` admission plugin](/docs/reference/access-authn-authz/admission-controllers/#defaultstorageclass) is turned on. \* If the admission plugin is turned on, the administrator may specify a default StorageClass. All PVCs that have no `storageClassName` can be bound only to PVs of that default. Specifying a default StorageClass is done by setting the annotation `storageclass.kubernetes.io/is-default-class` equal to `true` in a StorageClass object. If the administrator does not specify a default, the cluster responds to PVC creation as if the admission plugin were turned off. If more than one default StorageClass is specified, the newest default is used when the PVC is dynamically provisioned. \* If the admission plugin is turned off, there is no notion of a default StorageClass. All PVCs that have `storageClassName` set to `""` can be bound only to PVs that have `storageClassName` also set to `""`. However, PVCs with missing `storageClassName` can be updated later once default StorageClass becomes available. If the PVC gets updated it will no longer bind to PVs that have `storageClassName` also set to `""`. See [retroactive default StorageClass assignment](#retroactive-default-storageclass-assignment) for more details. Depending on installation method, a default StorageClass may be deployed to a Kubernetes cluster by addon manager during installation. When a PVC specifies a `selector` in addition to requesting a StorageClass, the requirements are ANDed together: only a PV of the requested class and with the requested labels may be bound to the PVC. {{< note >}} Currently, a PVC with a non-empty `selector` can't have a PV dynamically provisioned for it. {{< /note >}} In the past, the annotation `volume.beta.kubernetes.io/storage-class` was used instead of `storageClassName` attribute. This annotation is still working; however, it won't be supported in a future Kubernetes release. #### Retroactive default StorageClass assignment {{< feature-state for\_k8s\_version="v1.28" state="stable" >}} You can create a PersistentVolumeClaim without specifying a `storageClassName` for the new PVC, and you can do so even when no default StorageClass exists in your cluster. In this case, the new PVC creates as you defined it, and the `storageClassName` of that PVC remains unset until default becomes available. When a default StorageClass becomes available, the control plane identifies any existing PVCs without `storageClassName`. For the PVCs that either have an empty value for `storageClassName` or do not have this key, the control plane then updates those PVCs to set `storageClassName` to match the new default StorageClass. If you have an existing PVC where the `storageClassName` is `""`, and you configure a default StorageClass, then this PVC will not get updated. In order to keep binding to PVs with `storageClassName` set to `""` (while a default StorageClass is present), you need to set the `storageClassName` of the associated PVC to `""`. This behavior helps administrators change default StorageClass by removing the old one first and then creating or setting another one. This brief window while there is no default causes PVCs without `storageClassName` created at that time to not have any default, but due to the retroactive default StorageClass assignment this way of changing defaults is safe. ## Claims As Volumes Pods access storage by using the claim as a volume. Claims must exist in the same namespace as the Pod using the claim. The cluster finds the claim in the Pod's namespace and uses it to get the PersistentVolume backing the claim. The volume is then mounted to the
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/persistent-volumes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/persistent-volumes.md
|
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0.014208225533366203
] | 0.065113
|
Volumes Pods access storage by using the claim as a volume. Claims must exist in the same namespace as the Pod using the claim. The cluster finds the claim in the Pod's namespace and uses it to get the PersistentVolume backing the claim. The volume is then mounted to the host and into the Pod. ```yaml apiVersion: v1 kind: Pod metadata: name: mypod spec: containers: - name: myfrontend image: nginx volumeMounts: - mountPath: "/var/www/html" name: mypd volumes: - name: mypd persistentVolumeClaim: claimName: myclaim ``` ### A Note on Namespaces PersistentVolumes binds are exclusive, and since PersistentVolumeClaims are namespaced objects, mounting claims with "Many" modes (`ROX`, `RWX`) is only possible within one namespace. ### PersistentVolumes typed `hostPath` A `hostPath` PersistentVolume uses a file or directory on the Node to emulate network-attached storage. See [an example of `hostPath` typed volume](/docs/tasks/configure-pod-container/configure-persistent-volume-storage/#create-a-persistentvolume). ## Raw Block Volume Support {{< feature-state for\_k8s\_version="v1.18" state="stable" >}} The following volume plugins support raw block volumes, including dynamic provisioning where applicable: \* CSI (including some CSI migrated volume types) \* FC (Fibre Channel) \* iSCSI \* Local volume ### PersistentVolume using a Raw Block Volume {#persistent-volume-using-a-raw-block-volume} ```yaml apiVersion: v1 kind: PersistentVolume metadata: name: block-pv spec: capacity: storage: 10Gi accessModes: - ReadWriteOnce volumeMode: Block persistentVolumeReclaimPolicy: Retain fc: targetWWNs: ["50060e801049cfd1"] lun: 0 readOnly: false ``` ### PersistentVolumeClaim requesting a Raw Block Volume {#persistent-volume-claim-requesting-a-raw-block-volume} ```yaml apiVersion: v1 kind: PersistentVolumeClaim metadata: name: block-pvc spec: accessModes: - ReadWriteOnce volumeMode: Block resources: requests: storage: 10Gi ``` ### Pod specification adding Raw Block Device path in container ```yaml apiVersion: v1 kind: Pod metadata: name: pod-with-block-volume spec: containers: - name: fc-container image: fedora:26 command: ["/bin/sh", "-c"] args: [ "tail -f /dev/null" ] volumeDevices: - name: data devicePath: /dev/xvda volumes: - name: data persistentVolumeClaim: claimName: block-pvc ``` {{< note >}} When adding a raw block device for a Pod, you specify the device path in the container instead of a mount path. {{< /note >}} ### Binding Block Volumes If a user requests a raw block volume by indicating this using the `volumeMode` field in the PersistentVolumeClaim spec, the binding rules differ slightly from previous releases that didn't consider this mode as part of the spec. Listed is a table of possible combinations the user and admin might specify for requesting a raw block device. The table indicates if the volume will be bound or not given the combinations: Volume binding matrix for statically provisioned volumes: | PV volumeMode | PVC volumeMode | Result | | --------------|:---------------:| ----------------:| | unspecified | unspecified | BIND | | unspecified | Block | NO BIND | | unspecified | Filesystem | BIND | | Block | unspecified | NO BIND | | Block | Block | BIND | | Block | Filesystem | NO BIND | | Filesystem | Filesystem | BIND | | Filesystem | Block | NO BIND | | Filesystem | unspecified | BIND | {{< note >}} Only statically provisioned volumes are supported for alpha release. Administrators should take care to consider these values when working with raw block devices. {{< /note >}} ## Volume Snapshot and Restore Volume from Snapshot Support {{< feature-state for\_k8s\_version="v1.20" state="stable" >}} Volume snapshots only support the out-of-tree CSI volume plugins. For details, see [Volume Snapshots](/docs/concepts/storage/volume-snapshots/). In-tree volume plugins are deprecated. You can read about the deprecated volume plugins in the [Volume Plugin FAQ](https://github.com/kubernetes/community/blob/master/sig-storage/volume-plugin-faq.md). ### Create a PersistentVolumeClaim from a Volume Snapshot {#create-persistent-volume-claim-from-volume-snapshot} ```yaml apiVersion: v1 kind: PersistentVolumeClaim metadata: name: restore-pvc spec: storageClassName: csi-hostpath-sc dataSource: name: new-snapshot-test kind: VolumeSnapshot apiGroup: snapshot.storage.k8s.io accessModes: - ReadWriteOnce resources: requests: storage: 10Gi ``` ## Volume Cloning [Volume Cloning](/docs/concepts/storage/volume-pvc-datasource/) only available for CSI volume plugins. ### Create PersistentVolumeClaim
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/persistent-volumes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/persistent-volumes.md
|
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|
FAQ](https://github.com/kubernetes/community/blob/master/sig-storage/volume-plugin-faq.md). ### Create a PersistentVolumeClaim from a Volume Snapshot {#create-persistent-volume-claim-from-volume-snapshot} ```yaml apiVersion: v1 kind: PersistentVolumeClaim metadata: name: restore-pvc spec: storageClassName: csi-hostpath-sc dataSource: name: new-snapshot-test kind: VolumeSnapshot apiGroup: snapshot.storage.k8s.io accessModes: - ReadWriteOnce resources: requests: storage: 10Gi ``` ## Volume Cloning [Volume Cloning](/docs/concepts/storage/volume-pvc-datasource/) only available for CSI volume plugins. ### Create PersistentVolumeClaim from an existing PVC {#create-persistent-volume-claim-from-an-existing-pvc} ```yaml apiVersion: v1 kind: PersistentVolumeClaim metadata: name: cloned-pvc spec: storageClassName: my-csi-plugin dataSource: name: existing-src-pvc-name kind: PersistentVolumeClaim accessModes: - ReadWriteOnce resources: requests: storage: 10Gi ``` ## Volume populators and data sources {{< feature-state for\_k8s\_version="v1.24" state="beta" >}} Kubernetes supports custom volume populators. To use custom volume populators, you must enable the `AnyVolumeDataSource` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/) for the kube-apiserver and kube-controller-manager. Volume populators take advantage of a PVC spec field called `dataSourceRef`. Unlike the `dataSource` field, which can only contain either a reference to another PersistentVolumeClaim or to a VolumeSnapshot, the `dataSourceRef` field can contain a reference to any object in the same namespace, except for core objects other than PVCs. For clusters that have the feature gate enabled, use of the `dataSourceRef` is preferred over `dataSource`. ## Cross namespace data sources {{< feature-state for\_k8s\_version="v1.26" state="alpha" >}} Kubernetes supports cross namespace volume data sources. To use cross namespace volume data sources, you must enable the `AnyVolumeDataSource` and `CrossNamespaceVolumeDataSource` [feature gates](/docs/reference/command-line-tools-reference/feature-gates/) for the kube-apiserver and kube-controller-manager. Also, you must enable the `CrossNamespaceVolumeDataSource` feature gate for the csi-provisioner. Enabling the `CrossNamespaceVolumeDataSource` feature gate allows you to specify a namespace in the dataSourceRef field. {{< note >}} When you specify a namespace for a volume data source, Kubernetes checks for a ReferenceGrant in the other namespace before accepting the reference. ReferenceGrant is part of the `gateway.networking.k8s.io` extension APIs. See [ReferenceGrant](https://gateway-api.sigs.k8s.io/api-types/referencegrant/) in the Gateway API documentation for details. This means that you must extend your Kubernetes cluster with at least ReferenceGrant from the Gateway API before you can use this mechanism. {{< /note >}} ## Data source references The `dataSourceRef` field behaves almost the same as the `dataSource` field. If one is specified while the other is not, the API server will give both fields the same value. Neither field can be changed after creation, and attempting to specify different values for the two fields will result in a validation error. Therefore the two fields will always have the same contents. There are two differences between the `dataSourceRef` field and the `dataSource` field that users should be aware of: \* The `dataSource` field ignores invalid values (as if the field was blank) while the `dataSourceRef` field never ignores values and will cause an error if an invalid value is used. Invalid values are any core object (objects with no apiGroup) except for PVCs. \* The `dataSourceRef` field may contain different types of objects, while the `dataSource` field only allows PVCs and VolumeSnapshots. When the `CrossNamespaceVolumeDataSource` feature is enabled, there are additional differences: \* The `dataSource` field only allows local objects, while the `dataSourceRef` field allows objects in any namespaces. \* When namespace is specified, `dataSource` and `dataSourceRef` are not synced. Users should always use `dataSourceRef` on clusters that have the feature gate enabled, and fall back to `dataSource` on clusters that do not. It is not necessary to look at both fields under any circumstance. The duplicated values with slightly different semantics exist only for backwards compatibility. In particular, a mixture of older and newer controllers are able to interoperate because the fields are the same. ### Using volume populators Volume populators are {{< glossary\_tooltip text="controllers" term\_id="controller" >}} that can create non-empty volumes, where the contents of the volume are determined by a Custom Resource. Users create a populated volume by referring to
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/persistent-volumes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/persistent-volumes.md
|
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] | 0.086027
|
and newer controllers are able to interoperate because the fields are the same. ### Using volume populators Volume populators are {{< glossary\_tooltip text="controllers" term\_id="controller" >}} that can create non-empty volumes, where the contents of the volume are determined by a Custom Resource. Users create a populated volume by referring to a Custom Resource using the `dataSourceRef` field: ```yaml apiVersion: v1 kind: PersistentVolumeClaim metadata: name: populated-pvc spec: dataSourceRef: name: example-name kind: ExampleDataSource apiGroup: example.storage.k8s.io accessModes: - ReadWriteOnce resources: requests: storage: 10Gi ``` Because volume populators are external components, attempts to create a PVC that uses one can fail if not all the correct components are installed. External controllers should generate events on the PVC to provide feedback on the status of the creation, including warnings if the PVC cannot be created due to some missing component. You can install the alpha [volume data source validator](https://github.com/kubernetes-csi/volume-data-source-validator) controller into your cluster. That controller generates warning Events on a PVC in the case that no populator is registered to handle that kind of data source. When a suitable populator is installed for a PVC, it's the responsibility of that populator controller to report Events that relate to volume creation and issues during the process. ### Using a cross-namespace volume data source {{< feature-state for\_k8s\_version="v1.26" state="alpha" >}} Create a ReferenceGrant to allow the namespace owner to accept the reference. You define a populated volume by specifying a cross namespace volume data source using the `dataSourceRef` field. You must already have a valid ReferenceGrant in the source namespace: ```yaml apiVersion: gateway.networking.k8s.io/v1beta1 kind: ReferenceGrant metadata: name: allow-ns1-pvc namespace: default spec: from: - group: "" kind: PersistentVolumeClaim namespace: ns1 to: - group: snapshot.storage.k8s.io kind: VolumeSnapshot name: new-snapshot-demo ``` ```yaml apiVersion: v1 kind: PersistentVolumeClaim metadata: name: foo-pvc namespace: ns1 spec: storageClassName: example accessModes: - ReadWriteOnce resources: requests: storage: 1Gi dataSourceRef: apiGroup: snapshot.storage.k8s.io kind: VolumeSnapshot name: new-snapshot-demo namespace: default volumeMode: Filesystem ``` ## Writing Portable Configuration If you're writing configuration templates or examples that run on a wide range of clusters and need persistent storage, it is recommended that you use the following pattern: - Include PersistentVolumeClaim objects in your bundle of config (alongside Deployments, ConfigMaps, etc). - Do not include PersistentVolume objects in the config, since the user instantiating the config may not have permission to create PersistentVolumes. - Give the user the option of providing a storage class name when instantiating the template. - If the user provides a storage class name, put that value into the `persistentVolumeClaim.storageClassName` field. This will cause the PVC to match the right storage class if the cluster has StorageClasses enabled by the admin. - If the user does not provide a storage class name, leave the `persistentVolumeClaim.storageClassName` field as nil. This will cause a PV to be automatically provisioned for the user with the default StorageClass in the cluster. Many cluster environments have a default StorageClass installed, or administrators can create their own default StorageClass. - In your tooling, watch for PVCs that are not getting bound after some time and surface this to the user, as this may indicate that the cluster has no dynamic storage support (in which case the user should create a matching PV) or the cluster has no storage system (in which case the user cannot deploy config requiring PVCs). ## {{% heading "whatsnext" %}} \* Learn more about [Creating a PersistentVolume](/docs/tasks/configure-pod-container/configure-persistent-volume-storage/#create-a-persistentvolume). \* Learn more about [Creating a PersistentVolumeClaim](/docs/tasks/configure-pod-container/configure-persistent-volume-storage/#create-a-persistentvolumeclaim). \* Read the [Persistent Storage design document](https://git.k8s.io/design-proposals-archive/storage/persistent-storage.md). ### API references {#reference} Read about the APIs described in this page: \* [`PersistentVolume`](/docs/reference/kubernetes-api/config-and-storage-resources/persistent-volume-v1/) \* [`PersistentVolumeClaim`](/docs/reference/kubernetes-api/config-and-storage-resources/persistent-volume-claim-v1/)
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/persistent-volumes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/persistent-volumes.md
|
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] | 0.002893
|
more about [Creating a PersistentVolume](/docs/tasks/configure-pod-container/configure-persistent-volume-storage/#create-a-persistentvolume). \* Learn more about [Creating a PersistentVolumeClaim](/docs/tasks/configure-pod-container/configure-persistent-volume-storage/#create-a-persistentvolumeclaim). \* Read the [Persistent Storage design document](https://git.k8s.io/design-proposals-archive/storage/persistent-storage.md). ### API references {#reference} Read about the APIs described in this page: \* [`PersistentVolume`](/docs/reference/kubernetes-api/config-and-storage-resources/persistent-volume-v1/) \* [`PersistentVolumeClaim`](/docs/reference/kubernetes-api/config-and-storage-resources/persistent-volume-claim-v1/)
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/persistent-volumes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/persistent-volumes.md
|
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] | 0.119035
|
Nodes have local ephemeral storage, backed by locally-attached writeable devices or, sometimes, by RAM. "Ephemeral" means that there is no long-term guarantee about durability. Pods use ephemeral local storage for scratch space, caching, and for logs. The kubelet can provide scratch space to Pods using local ephemeral storage to mount [`emptyDir`](/docs/concepts/storage/volumes/#emptydir) {{< glossary\_tooltip term\_id="volume" text="volumes" >}} into containers. The kubelet also uses this kind of storage to hold [node-level container logs](/docs/concepts/cluster-administration/logging/#logging-at-the-node-level), container images, and the writable layers of running containers. {{< caution >}} If a node fails, the data in its ephemeral storage can be lost. Your applications cannot expect any performance SLAs (disk IOPS for example) from local ephemeral storage. {{< /caution >}} {{< note >}} To make the resource quota work on ephemeral-storage, two things need to be done: \* An admin sets the resource quota for ephemeral-storage in a namespace. \* A user needs to specify limits for the ephemeral-storage resource in the Pod spec. If the user doesn't specify the ephemeral-storage resource limit in the Pod spec, the resource quota is not enforced on ephemeral-storage. {{< /note >}} Kubernetes lets you track, reserve and limit the amount of ephemeral local storage a Pod can consume. ## Configurations for local ephemeral storage {#configurations} Kubernetes supports two ways to configure local ephemeral storage on a node: {{< tabs name="local\_storage\_configurations" >}} {{% tab name="Single filesystem" %}} In this configuration, you place all different kinds of ephemeral local data (`emptyDir` volumes, writeable layers, container images, logs) into one filesystem. The most effective way to configure the kubelet means dedicating this filesystem to Kubernetes (kubelet) data. The kubelet also writes [node-level container logs](/docs/concepts/cluster-administration/logging/#logging-at-the-node-level) and treats these similarly to ephemeral local storage. The kubelet writes logs to files inside its configured log directory (`/var/log` by default); and has a base directory for other locally stored data (`/var/lib/kubelet` by default). Typically, both `/var/lib/kubelet` and `/var/log` are on the system root filesystem, and the kubelet is designed with that layout in mind. Your node can have as many other filesystems, not used for Kubernetes, as you like. {{% /tab %}} {{% tab name="Two filesystems" %}} You have a filesystem on the node that you're using for ephemeral data that comes from running Pods: logs, and `emptyDir` volumes. You can use this filesystem for other data (for example: system logs not related to Kubernetes); it can even be the root filesystem. The kubelet also writes [node-level container logs](/docs/concepts/cluster-administration/logging/#logging-at-the-node-level) into the first filesystem, and treats these similarly to ephemeral local storage. You also use a separate filesystem, backed by a different logical storage device. In this configuration, the directory where you tell the kubelet to place container image layers and writeable layers is on this second filesystem. The first filesystem does not hold any image layers or writeable layers. Your node can have as many other filesystems, not used for Kubernetes, as you like. {{% /tab %}} {{< /tabs >}} The kubelet can measure how much local storage it is using. It does this provided that you have set up the node using one of the supported configurations for local ephemeral storage. If you have a different configuration, then the kubelet does not apply resource limits for ephemeral local storage. {{< note >}} The kubelet tracks `tmpfs` emptyDir volumes as container memory use, rather than as local ephemeral storage. {{< /note >}} {{< note >}} The kubelet will only track the root filesystem for ephemeral storage. OS layouts that mount a separate disk to `/var/lib/kubelet` or `/var/lib/containers` will not report ephemeral storage correctly. {{< /note >}} ## Setting requests and limits for local ephemeral storage {#requests-limits}
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/ephemeral-storage.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/ephemeral-storage.md
|
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] | 0.190095
|
local ephemeral storage. {{< /note >}} {{< note >}} The kubelet will only track the root filesystem for ephemeral storage. OS layouts that mount a separate disk to `/var/lib/kubelet` or `/var/lib/containers` will not report ephemeral storage correctly. {{< /note >}} ## Setting requests and limits for local ephemeral storage {#requests-limits} You can specify `ephemeral-storage` for managing local ephemeral storage. Each container of a Pod can specify either or both of the following: \* `spec.containers[].resources.limits.ephemeral-storage` \* `spec.containers[].resources.requests.ephemeral-storage` Limits and requests for `ephemeral-storage` are measured in byte quantities. You can express storage as a plain integer or as a fixed-point number using one of these suffixes: E, P, T, G, M, k. You can also use the power-of-two equivalents: Ei, Pi, Ti, Gi, Mi, Ki. For example, the following quantities all represent roughly the same value: - `128974848` - `129e6` - `129M` - `123Mi` Pay attention to the case of the suffixes. If you request `400m` of ephemeral-storage, this is a request for 0.4 bytes. Someone who types that probably meant to ask for 400 mebibytes (`400Mi`) or 400 megabytes (`400M`). In the following example, the Pod has two containers. Each container has a request of 2GiB of local ephemeral storage. Each container has a limit of 4GiB of local ephemeral storage. Therefore, the Pod has a request of 4GiB of local ephemeral storage, and a limit of 8GiB of local ephemeral storage. 500Mi of that limit could be consumed by the `emptyDir` volume. ```yaml apiVersion: v1 kind: Pod metadata: name: frontend spec: containers: - name: app image: images.my-company.example/app:v4 resources: requests: ephemeral-storage: "2Gi" limits: ephemeral-storage: "4Gi" volumeMounts: - name: ephemeral mountPath: "/tmp" - name: log-aggregator image: images.my-company.example/log-aggregator:v6 resources: requests: ephemeral-storage: "2Gi" limits: ephemeral-storage: "4Gi" volumeMounts: - name: ephemeral mountPath: "/tmp" volumes: - name: ephemeral emptyDir: sizeLimit: 500Mi ``` ## How Pods with ephemeral-storage requests are scheduled When you create a Pod, the Kubernetes scheduler selects a node for the Pod to run on. Each node has a maximum amount of local ephemeral storage it can provide for Pods. For more information, see [Node Allocatable](/docs/tasks/administer-cluster/reserve-compute-resources/#node-allocatable). The scheduler ensures that the sum of the resource requests of the scheduled containers is less than the capacity of the node. ## Ephemeral storage consumption management {#resource-emphemeralstorage-consumption} If the kubelet is managing local ephemeral storage as a resource, then the kubelet measures storage use in: - `emptyDir` volumes, except \_tmpfs\_ `emptyDir` volumes - directories holding node-level logs - writeable container layers If a Pod is using more ephemeral storage than you allow it to, the kubelet sets an eviction signal that triggers Pod eviction. For container-level isolation, if a container's writable layer and log usage exceeds its storage limit, the kubelet marks the Pod for eviction. For pod-level isolation the kubelet works out an overall Pod storage limit by summing the limits for the containers in that Pod. In this case, if the sum of the local ephemeral storage usage from all containers and also the Pod's `emptyDir` volumes exceeds the overall Pod storage limit, then the kubelet also marks the Pod for eviction. {{< caution >}} If the kubelet is not measuring local ephemeral storage, then a Pod that exceeds its local storage limit will not be evicted for breaching local storage resource limits. However, if the filesystem space for writeable container layers, node-level logs, or `emptyDir` volumes falls low, the node {{< glossary\_tooltip text="taints" term\_id="taint" >}} itself as short on local storage and this taint triggers eviction for any Pods that don't specifically tolerate the taint. See the supported [configurations](#configurations) for ephemeral local storage. {{< /caution >}} The kubelet supports different ways to measure Pod
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/ephemeral-storage.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/ephemeral-storage.md
|
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|
`emptyDir` volumes falls low, the node {{< glossary\_tooltip text="taints" term\_id="taint" >}} itself as short on local storage and this taint triggers eviction for any Pods that don't specifically tolerate the taint. See the supported [configurations](#configurations) for ephemeral local storage. {{< /caution >}} The kubelet supports different ways to measure Pod storage use: {{< tabs name="resource-emphemeralstorage-measurement" >}} {{% tab name="Periodic scanning" %}} The kubelet performs regular, scheduled checks that scan each `emptyDir` volume, container log directory, and writeable container layer. The scan measures how much space is used. {{< note >}} In this mode, the kubelet does not track open file descriptors for deleted files. If you (or a container) create a file inside an `emptyDir` volume, something then opens that file, and you delete the file while it is still open, then the inode for the deleted file stays until you close that file but the kubelet does not categorize the space as in use. {{< /note >}} {{% /tab %}} {{% tab name="Filesystem project quota" %}} {{< feature-state feature\_gate\_name="LocalStorageCapacityIsolationFSQuotaMonitoring" >}} Project quotas are an operating-system level feature for managing storage use on filesystems. With Kubernetes, you can enable project quotas for monitoring storage use. Make sure that the filesystem backing the `emptyDir` volumes, on the node, provides project quota support. For example, XFS and ext4fs offer project quotas. {{< note >}} Project quotas let you monitor storage use; they do not enforce limits. {{< /note >}} Kubernetes uses project IDs starting from `1048576`. The IDs in use are registered in `/etc/projects` and `/etc/projid`. If project IDs in this range are used for other purposes on the system, those project IDs must be registered in `/etc/projects` and `/etc/projid` so that Kubernetes does not use them. Quotas are faster and more accurate than directory scanning. When a directory is assigned to a project, all files created under a directory are created in that project, and the kernel merely has to keep track of how many blocks are in use by files in that project. If a file is created and deleted, but has an open file descriptor, it continues to consume space. Quota tracking records that space accurately whereas directory scans overlook the storage used by deleted files. To use quotas to track a pod's resource usage, the pod must be in a user namespace. Within user namespaces, the kernel restricts changes to projectIDs on the filesystem, ensuring the reliability of storage metrics calculated by quotas. If you want to use project quotas, you should: \* Enable the `LocalStorageCapacityIsolationFSQuotaMonitoring=true` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/) using the `featureGates` field in the [kubelet configuration](/docs/reference/config-api/kubelet-config.v1beta1/). \* Ensure the `UserNamespacesSupport` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/) is enabled, and that the kernel, CRI implementation and OCI runtime support user namespaces. \* Ensure that the root filesystem (or optional runtime filesystem) has project quotas enabled. All XFS filesystems support project quotas. For ext4 filesystems, you need to enable the project quota tracking feature while the filesystem is not mounted. ```bash # For ext4, with /dev/block-device not mounted sudo tune2fs -O project -Q prjquota /dev/block-device ``` \* Ensure that the root filesystem (or optional runtime filesystem) is mounted with project quotas enabled. For both XFS and ext4fs, the mount option is named `prjquota`. If you don't want to use project quotas, you should: \* Disable the `LocalStorageCapacityIsolationFSQuotaMonitoring` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/) using the `featureGates` field in the [kubelet configuration](/docs/reference/config-api/kubelet-config.v1beta1/). {{% /tab %}} {{< /tabs >}} ## {{% heading "whatsnext" %}} \* Read about [project quotas](https://www.linux.org/docs/man8/xfs\_quota.html) in XFS
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/ephemeral-storage.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/ephemeral-storage.md
|
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] | 0.170057
|
the [kubelet configuration](/docs/reference/config-api/kubelet-config.v1beta1/). {{% /tab %}} {{< /tabs >}} ## {{% heading "whatsnext" %}} \* Read about [project quotas](https://www.linux.org/docs/man8/xfs\_quota.html) in XFS
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/ephemeral-storage.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/ephemeral-storage.md
|
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] | 0.089498
|
This page describes the maximum number of volumes that can be attached to a Node for various cloud providers. Cloud providers like Google, Amazon, and Microsoft typically have a limit on how many volumes can be attached to a Node. It is important for Kubernetes to respect those limits. Otherwise, Pods scheduled on a Node could get stuck waiting for volumes to attach. ## Kubernetes default limits The Kubernetes scheduler has default limits on the number of volumes that can be attached to a Node: | Cloud service | Maximum volumes per Node | | --- | --- | | [Amazon Elastic Block Store (EBS)](https://aws.amazon.com/ebs/) | 39 | | [Google Persistent Disk](https://cloud.google.com/persistent-disk/) | 16 | | [Microsoft Azure Disk Storage](https://azure.microsoft.com/en-us/services/storage/main-disks/) | 16 | ## Dynamic volume limits {{< feature-state state="stable" for\_k8s\_version="v1.17" >}} Dynamic volume limits are supported for following volume types. - Amazon EBS - Google Persistent Disk - Azure Disk - CSI For volumes managed by in-tree volume plugins, Kubernetes automatically determines the Node type and enforces the appropriate maximum number of volumes for the node. For example: \* On [Google Compute Engine](https://cloud.google.com/compute/), up to 127 volumes can be attached to a node, [depending on the node type](https://cloud.google.com/compute/docs/disks/#pdnumberlimits). \* For Amazon EBS disks on M5,C5,R5,T3 and Z1D instance types, Kubernetes allows only 25 volumes to be attached to a Node. For other instance types on [Amazon Elastic Compute Cloud (EC2)](https://aws.amazon.com/ec2/), Kubernetes allows 39 volumes to be attached to a Node. \* On Azure, up to 64 disks can be attached to a node, depending on the node type. For more details, refer to [Sizes for virtual machines in Azure](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes). \* If a CSI storage driver advertises a maximum number of volumes for a Node (using `NodeGetInfo`), the {{< glossary\_tooltip text="kube-scheduler" term\_id="kube-scheduler" >}} honors that limit. Refer to the [CSI specifications](https://github.com/container-storage-interface/spec/blob/master/spec.md#nodegetinfo) for details. \* For volumes managed by in-tree plugins that have been migrated to a CSI driver, the maximum number of volumes will be the one reported by the CSI driver. ### Mutable CSI Node Allocatable Count {{< feature-state feature\_gate\_name="MutableCSINodeAllocatableCount" >}} CSI drivers can dynamically adjust the maximum number of volumes that can be attached to a Node at runtime. This enhances scheduling accuracy and reduces pod scheduling failures due to changes in resource availability. To use this feature, you must enable the `MutableCSINodeAllocatableCount` feature gate on the following components: - `kube-apiserver` - `kubelet` #### Periodic Updates When enabled, CSI drivers can request periodic updates to their volume limits by setting the `nodeAllocatableUpdatePeriodSeconds` field in the `CSIDriver` specification. For example: ```yaml apiVersion: storage.k8s.io/v1 kind: CSIDriver metadata: name: hostpath.csi.k8s.io spec: nodeAllocatableUpdatePeriodSeconds: 60 ``` Kubelet will periodically call the corresponding CSI driver’s `NodeGetInfo` endpoint to refresh the maximum number of attachable volumes, using the interval specified in `nodeAllocatableUpdatePeriodSeconds`. The minimum allowed value for this field is 10 seconds. If a volume attachment operation fails with a `ResourceExhausted` error (gRPC code 8), Kubernetes triggers an immediate update to the allocatable volume count for that Node. Additionally, kubelet marks affected pods as Failed, allowing their controllers to handle recreation. This prevents pods from getting stuck indefinitely in the `ContainerCreating` state. ### Preventing Pod placement without CSI driver {{< feature-state feature\_gate\_name="VolumeLimitScaling" >}} If `VolumeLimitScaling` [feature gate](/docs/reference/command-line-tools-reference/feature-gates#VolumeLimitScaling) is enabled and a CSI driver has corresponding `CSIDriver` object installed, then scheduler will prevent pod placement to nodes that do not yet have CSI driver installed. This limitation only applies to pods that require corresponding CSI volume.
| null |
./vendor/kubernetes/content/en/docs/concepts/storage/storage-limits.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/concepts/storage/storage-limits.md
|
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|
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